Download Effects of climate change on Arctic arthropod assemblages and

Effects of climate change on Arctic arthropod
assemblages and distribution
PhD thesis
Rikke Reisner Hansen
Academic advisors: Main supervisor Toke Thomas Høye and co-supervisor Signe Normand
Submitted 29/08/2016
Data sheet
Title:
Effects of climate change on Arctic arthropod assemblages and
distribution
Author University:
Aarhus University
Publisher:
Aarhus University – Denmark
URL: www.au.dk
Supervisors:
Assessment committee:
Arctic arthropods, climate change, community composition,
distribution, diversity, life history traits, monitoring, species
richness, spatial variation, temporal variation
Date of publication:
August 2016
Please cite as:
Hansen, R. R. (2016) Effects of climate change on Arctic
arthropod assemblages and distribution. PhD thesis, Aarhus
University, Denmark, 144 pp.
Keywords:
Number of pages:
144
PREFACE………………………………………………………………………………………..5
LIST OF PAPERS……………………………………………………………………………….6
ACKNOWLEDGEMENTS……………………………………………………………………...7
SUMMARY……………………………………………………………………………………...8
RESUMÉ (Danish summary)…………………………………………………………………....9
SYNOPSIS……………………………………………………………………………………....10
Introduction……………………………………………………………………………………...10
Study sites and approaches……………………………………………………………………...11
Arctic arthropod community composition…………………………………………………….....13
Potential climate change effects on arthropod composition…………………………………….15
Arctic arthropod responses to climate change…………………………………………………..16
Future recommendations and perspectives……………………………………………………...20
References………………………………………………………………………………………..21
PAPER I:
High spatial variation in terrestrial arthropod species diversity and composition near the
Greenland ice cap……………………………………………………………………………….27
PAPER II:
Meter scale variation in shrub dominance and soil moisture structure Arctic arthropod
communities……………………………………………………………………………………..37
PAPER III:
Diversity and composition changes along a continentality gradient in the Arctic……………..55
PAPER IV:
Synchronous seasonal dynamics in composition, richness and turnover of arthropod
assemblages across the Arctic…………………………………………………………………..97
PAPER V:
Habitat-specific effects of climate change on a low-mobility Arctic spider species…………...122
PAPER VI:
High-Arctic butterflies become smaller with rising temperatures……………………………..132
PAPER VII:
Phenology of high-Arctic butterflies and their floral resources: Species-specific responses to
climate change………………………………………………………………………………….136
As if some little Arctic flower,
Upon the polar hem,
Went wandering down the latitudes,
Until it puzzled came
To continents of summer,
To firmaments of sun,
To strange, bright crowds of flowers,
And birds of foreign tongue!
I say, as if this little flower
To Eden wandered in –
What then? Why, nothing, only
Your inference therefrom!
-
Emily Dickinson 1860
PREFACE
This thesis is the realization of three years’ work at the Department of Bioscience, Aarhus
University, during which I have been stationed mostly at the Arctic Research Center, but also at
the section for Biodiversity and conservation, Kalø. The fieldwork, which formed a large part of
the thesis, was conducted in Nuuk, South-west Greenland during the summer of 2013.
The thesis was supervised by Toke Thomas Høye with Signe Normand as co-supervisor,
and I was also fortunate to be a part of a larger scientific collaboration with researchers from
Canada, USA and Norway.
The thesis is comprised of two main parts. An introduction of the general conceptual
framework, preceding a summary of the results of the scientific papers, included in the thesis.
Part two consists of seven papers. Paper I and II have been published in Polar Biology and PeerJ
respectively. Paper III is in preparation for BMC Ecology and paper IV in preparation for
Ecography. Paper V – VII have been published in Polar Biology, Biology Letters and Current
Zoology, respectively.
In this synopsis, I will first describe the patterns of global climate change and its
amplification in the Arctic, which will set the scene for the main subject of my thesis; Overall
impacts of climate change at the species level and at the community level. I will present some of
the recent research in this field and contrast it to my research on diversity and compositional
changes in Arctic arthropods. Life history traits such as phenology, reproduction and body size
have been heavily impacted by climate change. I will present some of the results from recent
research in this field and relate changes in life history traits to changes in distributional patterns
(i.e. species occurrences). The Arctic is a large heterogeneous biome, and some of the results
may therefor only be applicable in a narrow geographic context. I have attempted to distinguish
between the patterns that can be generalized into an overall Arctic context and the patterns that
are unique for Greenland. Finally, I round up the synopsis with a conclusion and finishing
comments of future recommendations based on the findings of my PhD.
LIST OF PAPERS
PAPER I: Hansen RR, Hansen OLP, Bowden JJ, Normand S, Bay C, Sørensen JG, and Høye
TT. 2016a. High spatial variation in terrestrial arthropod species diversity and composition near
the Greenland ice cap. Polar Biology:1-10 DOI: 10.1007/s00300-016-1893-2.
PAPER II: Hansen RR, Hansen OLP, Bowden JJ, Treier UA, Normand S, and Høye TT. 2016b.
Meter scale variation in shrub dominance and soil moisture structure Arctic arthropod
communities. Peerj 4:e2224 DOI: 10.7717/peerj.2224.
PAPER III: Hansen OLP, Hansen RR, Bowden JJ, Normand S, Treier U and Høye TT. In prep.
Diversity and composition changes along a continentality gradient in the Arctic. BMC Ecology.
PAPER IV: Hansen RR, Asmus A, Bowden JJ, Loboda S, Iler AM, CaraDonna PJ, Hansen OLP,
Normand S, Loonen MJJE, Schmidt NM, Aastrup P and Høye TT. In prep. Synchronous
seasonal dynamics in composition, richness and turnover of arthropod assemblages across the
Arctic. Ecography.
PAPER V: Bowden JJ, Hansen RR, Olsen K, and Høye TT. 2015b. Habitat-specific effects of
climate change on a low-mobility Arctic spider species. Polar Biology 38:559-568 DOI:
10.1007/s00300-014-1622-7.
PAPER VI: Bowden JJ, Eskildsen A, Hansen RR, Olsen K, Kurle CM, and Høye TT. 2015a.
High-Arctic butterflies become smaller with rising temperatures. Biology Letters 11 DOI:
10.1098/rsbl.2015.0574.
PAPER VII: Høye TT, Eskildsen A, Hansen RR, Bowden JJ, Schmidt NM, and Kissling WD.
2014. Phenology of high-arctic butterflies and their floral resources: Species-specific responses
to climate change. Current Zoology 60:243-251
ACKNOWLEDGEMENTS
The past three years work, now culminating with the production of this thesis, has been exciting,
but also challenging years and I could not have achieved this without the support of family,
friends and colleagues.
Some of the challenges, I had help overcoming with tremendous support from my
supervisor Toke Thomas Høye and co-supervisor Signe Normand. Both offered me sound
scientific guidance during all phases of my PhD, and helped me to understand all aspects of the
publication process. I would particularly like to thank Toke Thomas Høye for enthusiastically
believing in my results, and encouraging me to pursue my goals. I am grateful to both for
showing me that pursuing a scientific career does not need to come at the expense of family, but
that career and family can act in concert and strengthen the outcome in the end.
My colleagues both at Kalø and Arctic Research Center as well as colleagues visiting
from abroad, have made the years fly bye, not only by offering scientific sparring at a qualified
and interdisciplinary level, but particularly by adding pleasant company to the journey.
I kindly thank the Arctic Research Center for providing funding and housing throughout
the full course of my PhD. I would also like to thank the Natural History Museum in Aarhus,
who harbored me during the sorting and identification process and provided materials, room and
enjoyable company.
I owe very special gratitude to my son Daq Lindhardt Reisner who has endured my
absence, short holidays and parental mood swings with a smile and an amazing attitude. This
could not have gone as smoothly without the help of my mom Karin Bursche Leth and the BuskVeje family, who all have provided my son with a second home, in my absence. Finally, I would
like to thank my partner Lars Nygaard Nielsen for bearing with me in the final stages and
showing me unconditional encouragement and loving support.
Rikke Reisner Hansen
August 2016
SUMMARY
With this thesis I provide new insight into how climate change can be expected to affect different
aspects of future arthropod diversity (i.e. composition, diversity and life history traits), through
the deciphering of past and contemporary distribution patterns. I identify important drivers of
local and regional variation in community and population differences of Arctic terrestrial
arthropods. The focus is primarily on Greenland, yet the results are discussed in a wider
geographical context. We show that Arctic arthropod assemblages varies over very short
distances and that patterns in composition and diversity are dictated by local to landscape scale
habitat features (Paper I and II). Large geographical features, such as fjords, glaciers and
mountain ranges fragments species pools and creates distinct site specific arthropod communities
(Paper I, II and III). These findings are in sharp contrast to the conception of the Arctic as a
species poor and homogenous environment. While heterogeneity of the habitat sampled and the
overall size of the study area are variables shown to be positively correlated with species
diversity, less attention has been given to the temporal accumulation of species within and
between years. We demonstrate a non-uniform seasonal development of Arctic arthropod
community composition throughout the season along with peaking richness patterns during parts
of the season, where conditions are optimal. These patterns are repeated across multiple Arctic
sites, spanning a large climatic gradient, but with significant inter-annual and habitat variation
(Paper IV). Changes in important life history traits (i.e. body size and phenology) are features
that will eventually cause species to alter their occurrences, and ultimately whole community
compositions. Species specific investigations revealed that body size variation as a response to
climate change, may be masked by local habitat heterogeneity and potentially depend on trophic
level of the study organism (Paper V and VI). Inter-annual body size variations of a high Arctic
crab spider differ significantly between the habitats, as well as, between males and females
(Paper V). Body size of high arctic butterfly is significantly decreasing as a response to
increasing temperatures over nearly 20 years (Paper VI). Several studies have demonstrated
decoupling of ecological events, such as flight time in butterflies and the peak abundance of
floral resources. One of our studies (Paper VII), investigating phenological responses to recent
warming for two Arctic butterfly species, show a trend towards earlier flight time for one of the
species (Boloria Chariclea) and not the other (Colias hecla). The study furthermore
demonstrates contrasting species specific relations with abiotic variables and confirms the need
for species specific studies of phenological responses.
RESUMÉ
Med denne afhandling, bidrager jeg med viden om hvordan klimaændringer kan forventes at
påvirke forskellige aspekter af fremtidig diversitet af leddyr (i.e. artssammensætning, diversitet
og livshistorietræk), via en udredning af fortidige og nutidige udbredelsesmønstre. Jeg
identificerer de vigtige drivkræfter bag lokal og regional variation i samfunds- og
populationsmæssige forskelle i landlevende Arktiske leddyr. Fokus er primært på Grønland men
resultaterne diskuteres i en bredere geografisk kontekst. Vi viser at leddyrs samfund varierer over
korte afstande og at mønstre i sammensætning og diversitet er dikteret af forskelle i habitater på
lokal til landskabs skala (Artikel I og II). Store geografiske barrierer, såsom fjorde, gletsjere og
bjergkæder hjælper til at fragmentere artspuljer og skabe lokalt specifikke artssammensætninger
(Artikel I, II og III). Disse resultater står i skarp kontrast til den gængse opfattelse af Arktis som
et artsfattigt og homogent terræn. Heterogeniteten af et område, samt størrelsen af arealet som
indsamles på, har vist sig at være positivt korreleret med artsdiversitet, mindre opmærksomhed
er imidlertid blevet tildelt sammenhængen mellem tidsmæssig akkumulering af arter indenfor og
årerne imellem. Vi viser at artsammensætninger af Arktiske leddyr ikke udvikler sig ens gennem
sæsonen, men at der er perioder hvor artsrigdom topper og hvor der sker få ændringer i
artssammensætning fra en uge til næste. Disse mønstre gentager sig over flere lokaliteter i Arktis,
som tilsammen spænder over store klimatiske gradienter (Artikel IV). Ændringer i vigtige
livshistorietræk (e.g. kropsstørrelser of fænologi) gør at arter ændrer forekomster og vil i sidste
ende ændre artssammensætninger for hele leddyrssamfund. Artsspecifikke undersøgelser viser at
variation i kropsstørrelser muligvis maskeres af forskelle i habitater og kan afhænge af trofisk
niveau (Artikel V og VI). År til år variation i kropsstørrelser hos en høj-Arktisk krabbe edderkop
er signifikant forskellige imellem habitater såvel som mellem hanner og hunner (Artikel V).
Størrelsen af en Arktisk sommerfugl er blevet signifikant mindre gennem de seneste næsten 20
år, som et respons på stigende temperature (Paper VI). En del studier har påvist en afkobling
mellem økologiske events, såsom flyvetid af sommerfugle og optimal tilgængelighed af deres
føderessourcer. Et af vores studier (Artikel VII), som undersøger fænologiske responser til
seneste tids opvarmning hos to arter af Arktiske sommerfugle, demonstrerer tendens til tidligere
flyvetid hos den ene art (Boloria chariclea) og ikke den anden (Colias hecla). Studiet viser
derudover nogle kontrasterende artsspecifikke sammenhæng med abiotiske variable og
understreger behovet for artsspecifikke studier af fænologi.
SYNOPSIS
Introduction
Documenting the spatial and temporal distribution of species is essential to grasp the
consequences a changing climate has for biodiversity (Heller & Zavaleta 2009). Arthropods as
well as plants are central for many food chains and make up the vast majority of global and
Arctic biodiversity (Hodkinson 2013). With arthropods being sensitive to abiotic conditions
(Danks 2004) and while they provide an array of ecosystem services, a deeper knowledge of
their ecology, diversity and distribution is crucial to understand how to conserve Arctic
biodiversity as a whole. However, basic knowledge regarding all aspects of Arctic arthropods is
still scant and lacking far behind that of plants and vertebrates.
The Arctic is often referred to as a homogenous and biodiversity low region (Hodkinson
2013), yet it is home to more than 2200 species of insects and spiders (Danks 1981),
approximately 800 of which live in Greenland (Böcher et al. 2015). Greenland covers an area of
2.166.086 km2, with an area of 410.449 km2 free of ice, and is divided into high- low- and subArctic regions (Greenland Statbank 2015). The North American Arctic spans an area nearly four
times the size of the ice-free Greenland and houses, in comparison, approximately 1650
terrestrial arthropod species (Danks 1991). Within Greenland, the inland icecap, fjord and
topographical features provide large barriers for species to disperse (Normand et al. 2013;
Hansen et al. 2016a). A large difference in species composition and diversity is therefore found
between the latitudinal and longitudinal gradients. These gradients and the large variation in e.g.
topography and environmental variables, renders Greenland a very interesting geographic focal
point for studying distribution patterns and the effects of climate change.
Global warming is unequivocal and during the 20th century the globally averaged
combined land and ocean surface temperature has increased by approximately 0.85. Most of this
over the course of the past couple of decades (Ipcc 2014b). Climate change across the globe is
however, not uniform due to polar amplification and feedback mechanisms (figure 1). Polar
amplification refers to the phenomenon that warming or cooling trends are strongest over high
latitudes, particularly over the Arctic. This is due to poleward atmospheric heat transfer, oceanic
oscillation dynamics (Manabe 1969) and a number of positive feedback mechanisms, including
diminishing snow and sea ice cover (Screen & Simmonds 2010), as well as permafrost melt
(Kellogg 1983). Melting of the permafrost forms part of a feedback mechanism, contributing to
the release of large quantities of methane encapsulated in the frozen soils as massive carbon
stocks. Permafrost dynamics are not directly being addressed in this thesis, but play an indirect
role in the altering of tundra habitats (Woo & Young 2006; Myers-Smith et al. 2011; Perreault et
al. 2015). Due to the complex and heterogeneous patterns of the climate system, there is vast
geographic variation even within the Arctic. Predicting climate change patterns at a scale
relevant to organisms with narrow range distributions, like arthropods, therefore requires detailed
levels of ground-truthing. I intend to show how the balance between detailed investigations and
broader scope approaches can supplement each other and add to pan Arctic generalizations about
climate change effects on arthropod community composition.
Figure 1: Change in average surface temperature (a) and change in average precipitation (b) based on multi-model mean
projections for 2081–2100 relative to 1986–2005 under two different scenarios. Least case (left) and worst case (right)
(Ipcc 2014c).
Study sites and approaches
Inferring contemporary patterns in species composition onto future prediction of distributional
changes, can partly be achieved through time for space substitution (Pickett 1989). The large
variation in environmental variables within Greenland makes for an ideal region for these
inferences. Even though the results of the studies in this thesis all pertains to Greenland as the
geographic focus of the research, some of the mechanisms responsible for the patterns that I
present in this thesis, can be generalized to the Arctic as a whole. For instance, the Greenlandic
ice cap with its glacier arms forms a dispersal barrier for species with limited dispersal capacity
(i.e. arthropods and plants) (Normand et al. 2013; Hansen et al. 2016a) and affects distributional
patterns. However, general dispersal barriers are not unique for Greenland as large parts of the
Arctic is comprised of large water bodies, glaciers, mountain ranges, all limiting dispersal and
area of suitable habitats for species (CAFF 2013). Hydrology and vegetation changes, as well as,
increasing temperatures are phenomena occurring across the Arctic, yet, the direction and
magnitude is not uniform (Woo & Young 2006; Ipcc 2014a; Myers-Smith et al. 2015; Wrona et
al. 2016). The Godthåbsfjord area, South-west Greenland covers a large variety in climatic and
topographic gradients and serves as a model area to study responses to these variations. It
stretches from the coast to the inland ice sheet and branches out, covering a large heterogenous
area (Figure 2). The mouth of the fjord is characterized by a coastal climate with relatively high
precipitation and narrow annual temperature range. As we move further in the fjord, towards a
more continental climate, the gap between winter and summer temperatures widens and humidity
decreases. Apart from the more direct influences of climate variability within the fjord system,
the fjord also represents a large gradient in habitat variability. This generates a large variety in
living conditions for a large suite of organisms. Topographic variation occurs both along the
fjord systems, but also within each location and serves as proxy temperature measures.
Figure 2: The two main study sites in Greenland. The Zackenberg valley has been depicted with a white circle
The second focus area is at the Zackenberg research station in North-east Greenland. In a
global change context, Zackenberg research station is situated ideally at the border between highand low Arctic (Figure 2). This region is predicted to undergo the largest climatic changes
worldwide (ACIA 2004) and is unaffected by direct human influence. The Zackenberg Basic
program is part of the most extensive ecosystem monitoring program in the Arctic; the
Greenland Ecosystem Monitoring program (GEM) (Albon et al. 2014). The geographical
locations are Nuuk (SW Greenland) and Zackenberg (NE Greenland). Arthropods have been
collected as contribution to GEM in Nuuk, and Zackenberg, since 2007 and 1996 respectively.
The Zackenberg Basic Monitoring Program has been in operation since 1996 with standardized
collection of arthropods via a gridded pitfall trapping regime. The overall aim of the biological
monitoring program (Biobasis) is to establish long and simultaneous data series on all trophic
levels in a low Arctic ecosystem. The biological program includes a large number of parameters
related to terrestrial plants, arthropods, birds and mammal dynamics, and lake ecology. During
the past 20 years where Zackenberg basic has been operating, the temperature in the region has
increased significantly (Bowden et al. 2015).
Arctic arthropod community composition
Global change is altering the landscape, making new land available for farming, mining and
other extractive industries. These activities are predicted to affect large land masses (CAFF
2013), hereby increasing the possibility that rare species will go extinct. In order to foresee how
the change in land-use will affect the inhabiting arthropod species, we first need to identify areas
of conservation concern and understand how the abiotic environment shapes arthropod species
composition. Species distribution patterns vary across multiple spatial scales (Willis & Whittaker
2002), but with four overall amplitudes relevant to arthropods. I) Global scale: Species diversity
generally decreases toward the poles (Gaston 2000), with the Arctic as a region relatively low in
biodiversity. II) Regional scale: Within the Arctic itself, large climatic gradients divide the
Arctic into high, low and sub-Arctic regions, which coupled with large scale dispersal barriers
(i.e. inland ice cap), create a variety of living conditions. III) Local scale: Large habitat
variability and dispersal barriers, creates variations in species composition. A handful of studies
have now shown pronounced compositional changes at regional to local scales within groups of
arthropods (Bowden & Buddle 2010; Rich et al. 2013; Sikes et al. 2013). IV) Micro scale.
Variation in habitats, operating at relatively minute scales (a few meters), can furthermore cause
species to cluster, increasing the potential of species extinction during periods of disturbance.
One major obstacle in identifying how the environment will change future patterns in Arctic
arthropod species distributions is limited data on contemporary arthropod species distribution.
In paper I we addressed some of the paucities in Arctic arthropod literature, investigating
basic distributional patterns. We examined the degree of spatial variation in species diversity and
assemblage structure among five habitat types at two sites of similar abiotic conditions and plant
species composition. We discovered distinct differences in arthropod composition and diversity
between the two sites and habitats, even though compositions were largely driven by the same,
spatially homogenous abiotic variables (soil moisture and temperature). The study demonstrated
large turnover, even over much smaller spatial scales than expected and thus highlighted the
importance of the heterogeneity of Arctic landscapes on local to landscape scale distributional
patterns of Arctic arthropods.
One of ecology’s most general rules states that the size of an area sampled is positively
correlated with the number of species detected (Connor & McCoy 1979; Lomolino 2000). This
is referred to as the species-area relationship. This rule holds true in the Arctic, where a recent
study demonstrated how short-term sampling of multiple habitats, compared to long term
sampling in fewer habitats, resulted in drastic increases in the number of species detected (Sikes
et al. 2013). The temporal pendant of species-area relationship: species-time relationship, states
how observing an area over temporal increments accumulates species (Preston 1960). However,
studies also show that there are seasonal windows, where conditions are optimal, resulting in a
peak in species numbers (White & Gilchrist 2007; Xu et al. 2015). This suggests that sampling
efforts, aimed at capturing the total species pool, could be prioritized to match these windows of
optimal conditions. To date, these temporal patterns have received very little, if any, attention in
Arctic arthropod research.
In paper IV, we used the Zackenberg family and species level arthropod data, coupled
with family level data from three other Arctic sites spanning a large climatic gradient, to explore
the seasonal development of Arctic arthropod communities across multiple sites, habitats and
taxa. We were able to show how the seasonal patterns of Arctic arthropod composition and
richness developed synchronously across a large Arctic gradient at both the species and family
level. There was a tendency towards earlier peak in richness at the sub-Arctic sites, but across all
sites one week of carefully planned sampling, represented a large proportion of the total season
richness. There was, however, large inter-annual variation for the sites with fewer yearly
replications, as compared to Zackenberg with 18 years of data. Results also showed large
variation between the habitats. This led us to the conclusion that short-term sampling over
multiple habitats and years can detect significant proportions of the full arthropod community
that a site holds through a full season.
Potential climate change effects on arthropod communities
The warming of the Arctic region holds dramatic consequences for terrestrial biodiversity
(Callaghan et al. 2005; Post et al. 2009) and carries both direct and indirect consequences for
Arctic arthropods. Some of these effects are summarized in figure 3. With arthropods being
ectothermic, they are directly influenced by changing temperatures (figure 3, pathway 1). This
makes them particularly susceptible to the extremes caused by temperature inclinations, which is
thought to impact species composition and diversity heavily (Danks 2004). The forecasted
change in precipitation, combined with higher temperatures, glacier melt, and permafrost thaw,
alters soil moisture content (Rawlins et al. 2010; Perreault et al. 2015) causing dramatic shifts in
plant species compositions (Myers-Smith et al. 2011; Elmendorf et al. 2012; Wrona et al. 2016),
as well as thermocast erosion, again influencing the hydrological regime (Perreault et al. 2015)
(figure 3, pathway 2, 3, 4 and 5). All of which leads increased plant productivity and indirectly
effects arthropod composition and diversity (figure 3, pathway 6) (Hodkinson 2013). The
hydrological response, however, is not unidirectional - while some areas are becoming wetter
and others are drying out (Zona et al. 2009), highly dependent upon permafrost dynamics (Woo
& Young 2006). Altered vegetation structure creates a change in surface temperatures, with
shading in the summer and snow accumulation in the winter (Blok et al. 2010), impacting the
soil dwelling micro arthropods that are important detritivores and food sources for larger
arthropods (figure 3, pathway 7 and 8). Changes in surface covers will also undoubtedly alter
both micro and macro habitats as structures in the tundra habitats are rearranged (Lawrence &
Swenson 2011). At subsurface level, for instance, rhizosphere processes and mycorrhizal
associations are influenced by shifts in vegetation (Lindahl & Tunlid 2015) (figure 3, pathway
9). The current observed shrub expansions, tree-line advances and species shifts is reducing local
surface albedo (figure 3, pathway 10), exacerbating regional warming through a change in the
reflecting snow and ice cover (Wenxin et al. 2013). The transition from tundra to shrubland
promotes various additional feedback mechanisms, caused by increased evapotranspiration,
resulting in further water loss (figure 3, pathway 11). The idiosyncrasies of these dynamics
across the Arctic (Myers-Smith et al. 2015) necessitates multiple site comparisons of habitat and
species responses to climate change and a deeper understanding of the interplay between biotic
and abiotic responses.
Figure 3: Conceptual diagram of the some of the direct and indirect effects of climate change on arthropod community
composition. The direct effect of warming is indicated by a dashed line
Arthropod responses to climate change
There are three ways arthropod species can respond to the direct and indirect effects of a
changing climate. Either through range shifts, defined as the change in geographic distributions
over time (Lenoir & Svenning 2013), phenological advancement of life history events (Høye &
Forchhammer 2008; Høye et al. 2014), and finally through direct alteration of body sizes in
organisms adapting to the abrupt changes in physical conditions (Gardner et al. 2011). The three
types of species responses to climate change, affect species at the individual and population
level, eventually resulting in altered species occurrences, which ultimately sums up to changes in
whole arthropod communities. The first four papers of this thesis addresses the potential climate
change effects on arthropod communities, while the last three papers examine the species
specific effects on life history traits, such as body size and phenology.
-
Arthropod species compositional changes to habitat and climatic gradients
The alteration of habitats due to shrub expansion into open tundra and changing wetland
hydrology, are likely to affect habitat availability for many organisms through changes in
species’ distributions, diversity and composition. Terrestrial arthropods in particular, are
associated with specific habitat types and likely respond strongly to habitat changes in the Arctic
(Bowden & Buddle 2010; Rich et al. 2013; Sikes et al. 2013; Sweet et al. 2014; Hansen et al.
2016b). Paper II used data collected in the 2013 field campaign to the Godthåbsfjord area, were
we sampled transitions in shrub dominance and soil moisture between three different habitats
(fen, dwarf shrub heath, and tall shrub tundra) at three different sites. We provided evidence that
arthropod diversity and composition vary with soil moisture and vegetation height over very
small spatial scales (10–20 m), and discussed the implications of changing tundra habitats to the
associated biodiversity. We also identified a handful of species significantly associated with the
three different habitats and found that most of the species in the study displayed some form of
habitat affinity, matching previous reports (Böcher et al. 2015). Papers I and II both disputed the
homogeneity of Arctic arthropod composition and firmly documented the variation in arthropod
communities over very small spatial scales.
Range shifts in species-changing occurrences (during their pursuit of optimal conditions)
affects whole species compositions, and is a well-documented response to climate change
(Lenoir & Svenning 2015). Range shifts include changes in latitude and elevation, and are
therefore directly linked to increasing temperatures, as species track their thermal niches. This
implies species movements limited to northward or upward shift. However, species composition
is highly affected by other factors acting out at the horizontal, and to some extent, local level,
such as land use changes (Eskildsen 2015), topographic variation (Rovzar et al. 2016) and soil
moisture variation (Le Roux et al. 2013). These gradients have been shown to vary greatly, even
at the meter-scale (Suvanto et al. 2014), consequently broadening the need for further inclusion
of gradients in range shift studies.
Studies of local climatic gradients can provide us with valuable information on
distributional shifts (Lenoir & Svenning 2015), as variations in microclimate can be controlled
by topography. Thus, species responses to local topographic variation may help in predicting
how compositions are likely to change in the future. Coastal mountainous regions are strongly
influenced by continentality (Hodkinson 2005) due to significant differences in thermal capacity
between ocean and land. Such differences likely contribute to the structuring of arthropod
assemblages. In paper III, we explored the large gradient in continentality represented by the
Godthåbsfjord fjord system and tested whether species diversity and composition changed in
response to continentality and micro-climatic conditions. We furthermore tested whether the two
groups of arthropods used in the study (beetles and spiders), responded in synchrony to
continentality and microclimate. We found that species composition changed significantly along
the fjord and that diversity increased with continentality. Spiders and beetles did not, however,
show a similar response to the two gradients, as spider diversity responded more to changes in
local topography and beetles to continentality. Difference in dispersal capacity between the two
groups is likely a contributing factor to this observed pattern, highlighting the need to include
functional traits in future studies. A resulting conclusion of this study was that adding details, in
terms of multiple environmental gradients and taxonomic groups, increases our understanding of
how species assemblages may diversify as a response to climate change.
-
Changes to life history traits
Body size is a particularly important trait to the life history of species and has been deemed the
third universal response to climate change, alongside shifts in phenology and range (Gardner et
al. 2011). In terrestrial arthropods, body size is primarily influenced by food and temperature
(Chown & Gaston 2010) and changes in body size affect both fecundity and mass (Bowden &
Buddle 2012), as well as overall fitness (Jakob et al. 1996). Snowmelt has proven to be a
particularly important predictor of phenology and body size variation (Høye et al. 2009; Høye et
al. 2013) and is occurring significantly earlier in the Arctic each year. The timing of snowmelt is
modulated by local habitat (Stow et al. 2004), with later snowmelt in low-lying habitats with
more or less pronounced shrubby vegetation.
Paper number V explores the effect local habitat has on body size changes. Using
individuals of a high Arctic crab spider, collected through the GEM program, we examined how
reproductive traits (e.g. body size) and sex ratios have changed over the years with increasing
temperatures and earlier snowmelt. The individuals were collected in mesic and arid dwarf shrub
heath, and the distinctness of the habitats presented the opportunity to study exactly how the
habitat modifies the impact of snowmelt on body size change. Body size variation was
significantly related to the timing of snowmelt, and differed significantly between the sexes and
between habitats, with the spiders in the mesic habitat showing a stronger response to later
snowmelt. This study illustrates how local variation in habitats may conceal climate change
effects, highlighting the need to include multiple habitats to the study of species-specific
responses to climate change.
Two hypotheses describe how warmer temperatures drive body size changes in
ectothermic species. First, the metabolic rates of arthropods increase with warmer temperatures
(Gillooly et al. 2001; Gardner et al. 2011). A mismatch may then arise between the increase in
metabolic rate and the resources an organism needs to accrue in order to compensate for energy
losses related to increased metabolic costs, thus resulting in smaller body size. In contrast, the
longer season associated with rising temperatures allows organisms to obtain more resources
during the growth season, resulting in larger body sizes (Høye et al. 2009). These two responses
may therefor depend upon trophic level and act in concert (Sheridan & Bickford 2011).
In paper number VI, we studied the response of body size to increasing temperatures in
two species of butterflies: Colias hecla and Boloria chariclea also collected at Zackenberg as
part of the GEM program. We found that wing length decreased in both species in response to
warmer summer temperatures, likely caused by increasing metabolism. A previous study on wolf
spiders, collected from the same traps, contrasted these findings, as wolf spiders were increasing
in body size in response to earlier snowmelt (Høye et al. 2009). Though the study organisms did
not respond to the same abiotic variables, the variables are linked to increasing season length.
Hence, the two studies corroborate the theory that the contrasting responses depend on trophic
level, and thereby energy level of food-resources. If the diet of an organism is energy rich, a
longer active season is beneficial. However, for the opposite scenario, a longer active season is
costly, and reduces important aspects of fitness related traits, such as body size.
Phenology is a key indicator of species responses to climate change, particularly in polar
regions (Parmesan 2006). In recent years, a growing number of studies have documented shift in
phenology in a wide array of taxa (Roy & Sparks 2000; Kutz et al. 2005; Høye & Forchhammer
2008; Høye et al. 2014). The changes reported encompass earlier adult emergence and expanded
flight season of insects, earlier breeding activity and arrival of migrant birds, as well as earlier
flowering of plants (Walther et al. 2002). Set astride the direct influences on the life history of
individual species, climate changes also carry the potential to disrupt both inter- and intraspecific interactions (Høye et al. 2013). Decoupling of ecological events, such as mismatches
between resources and consumer activity, are some of the more severe examples and pose
imminent threats to the persistence of overall ecosystem functioning (Walther et al. 2002; Post et
al. 2009).
In paper VII, we demonstrated a trend towards earlier flight season in the most abundant
butterfly species (Boloria chariclea) in the Zackenberg valley. The timing of onset, peak and end
of the flight season in B. chariclea were closely related to snowmelt, July temperatures, and
dynamics between snowmelt and increased temperature. The duration of the butterfly flight
season was significantly positively correlated to the temporal overlap with floral resources, and
shows how the aforementioned decoupling of ecological events is already manifesting.
Future recommendations and perspectives
Results presented in this thesis show that Arctic arthropod species respond to very local changes
in habitat parameters in terms of compositional changes as well as life history traits (e.g. body
size and phenology). Understanding how species respond to local changes in the environment
can help us to recognize how species distribute over larger spatial gradients. In order to achieve
this, however, we first need to test the generality of mechanisms driving the local patterns. Many
studies already point to idiosyncratic site responses, potentially caused by uneven temporal
coverage, unmeasured local variation, or low statistical power (Myers-Smith et al. 2011;
Oberbauer et al. 2013). We therefore need to assess whether this is an artifact of unstandardized
sampling protocols or true site specific characteristics. Hence, spatial upscaling of long-term
ecosystem-level monitoring programs, such as Zackenberg and Nuuk basic, can help avoid the
caveats of unstandardized studies.
These ecosystem monitoring programs are costly, and to fill all the gaps and represent all
compartments of Arctic ecosystems is a daunting task. Additional short-term studies may work
as a supplement to current data compilation. Through spatial upscaling of standardized sampling
designs of gradient studies mimicking future ecological changes, we can distinguish
idiosyncrasies from general mechanisms. With a synchronous seasonal development and
aggregation of arthropod species, we are able to upscale compositional studies to cover larger
spatial extents with so-called “snapshot” gradient studies. This kind of sampling will provide
large-scale insights to the spatial variation in species assemblages and the driving mechanisms
behind, as well as standardize sampling procedures, for more reliable pan-Arctic inferences. The
Circumpolar Biodiversity Monitoring Program (CBMP) is an international network of scientists,
government agencies, Indigenous organizations and conservation groups working together to
harmonize and integrate efforts to monitor the Arctic's living resources (CAFF 2013). This
“network of networks” facilitates the interplay between full scale monitoring programs and
snapshot studies and is a valuable platform in future studies
Most of the papers presented in this thesis hint at responses acting out at a highly detailed
and local level. Responses that may go undetected if investigated at more coarse spatial
resolutions. Hence the devil is in the details, but detailed investigations of general mechanisms
are applicable in wider contexts. The results of this thesis show that the drastic increase in
temperatures during the last couple of decades, and the following rearranging of structural
components in the Arctic tundra, holds dire implications for arthropod diversity. With
consequences encompassing decoupling of important life history events, changes to reproduction
and overall fitness with resulting changes in species compositions and diversity reported over
multiple taxa. Arthropods are globally the most diverse group of organisms, even in the Arctic.
Hence, community- and species-level responses to climate change will reveal overall threats to
other aspects of biodiversity. Moreover, arthropod responses are acute due to their ectothermic
lifestyle, mobility, and short generation time, rendering them the “canary in the coal mine” for
climate change implications at multiple trophic levels. It is clear that arthropod studies are
increasingly important and should, in my objective opinion, be granted detailed consideration in
future research of climate change effects on Arctic terrestrial environments.
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Polar Biol
DOI 10.1007/s00300-016-1893-2
ORIGINAL PAPER
High spatial variation in terrestrial arthropod species diversity
and composition near the Greenland ice cap
Rikke Reisner Hansen1 • Oskar Liset Pryds Hansen1 • Joseph James Bowden1 •
Signe Normand2 • Christian Bay3 • Jesper Givskov Sørensen2 • Toke Thomas Høye1,4,5
Received: 17 August 2015 / Revised: 11 January 2016 / Accepted: 11 January 2016
Ó Springer-Verlag Berlin Heidelberg 2016
Abstract Arthropods form a major part of the terrestrial
species diversity in the Arctic, and are particularly sensitive
to temporal changes in the abiotic environment. It is assumed
that most Arctic arthropods are habitat generalists and that
their diversity patterns exhibit low spatial variation. The
empirical basis for this assumption, however, is weak. We
examine the degree of spatial variation in species diversity
and assemblage structure among five habitat types at two
sites of similar abiotic conditions and plant species composition in southwest Greenland, using standardized field collection methods for spiders, beetles and butterflies. We
employed non-metric multidimensional scaling, species
richness estimation, community dissimilarity and indicator
species analysis to test for local (within site)- and regional
(between site)-scale differences in arthropod communities.
To identify specific drivers of local arthropod assemblages,
we used a combination of ordination techniques and linear
regression. Species richness and the species pool differed
between sites, with the latter indicating high species turnover. Local-scale assemblage patterns were related to soil
moisture and temperature. We conclude that Arctic arthropod species assemblages vary substantially over short distances due to local soil characteristics, while regional
variation in the species pool is likely influenced by geographic barriers, i.e., inland ice sheet, glaciers, mountains
and large water bodies. In order to predict future changes to
Arctic arthropod diversity, further efforts are needed to
disentangle contemporary drivers of diversity at multiple
spatial scales.
Keywords Araneae Biodiversity Climate change Coleoptera Habitat heterogeneity Lepidoptera
Introduction
Electronic supplementary material The online version of this
article (doi:10.1007/s00300-016-1893-2) contains supplementary
material, which is available to authorized users.
& Rikke Reisner Hansen
[email protected]
1
Arctic Research Centre, Aarhus University, Ny Munkegade
114, Bldg. 1540, 8000 Aarhus C, Denmark
2
Department of Bioscience, Aarhus, Aarhus University, Ny
Munkegade 116, Bldg. 1540, 8000 Aarhus C, Denmark
3
Department of Bioscience, Roskilde, Aarhus University,
Frederiksborgvej 399, 4000 Roskilde, Denmark
4
Aarhus Institute of Advanced Studies, Aarhus University,
Høegh-Guldbergs Gade 6B, Bldg. 1630, 8000 Aarhus C,
Denmark
5
Department of Bioscience, Kalø, Aarhus University,
Grenåvej 14, 8410 Rønde, Denmark
Climate change is causing rapid alterations to Arctic habitats (Myers-Smith et al. 2011; Elmendorf et al. 2012; Schmidt et al. 2012), and for many taxonomic groups like
arthropods, we are largely unable to identify biodiversity
hotspots and prioritize conservation efforts in a changing
Arctic environment (CAFF 2013). The climatic changes are
furthermore causing glaciers and the Greenland ice cap to
retreat and expose new land, which is already stimulating
industrial interests and activities. Such activities will affect
large areas, and to identify areas of conservation concern,
we have to understand how specific aspects of the abiotic
environment shapes Arctic biodiversity (CAFF 2013).
Species assemblages vary across spatial scales (Willis
and Whittaker 2002) with species diversity generally
decreasing toward the poles (Gaston 2000). This also
applies within the Arctic where large climatic gradients
123
Polar Biol
divide the Arctic into high, low and sub-Arctic regions,
creating a variety of living conditions. While much of the
Arctic may be perceived as a collection of homogeneous
landscapes inhabited by generalist species, and rather
depauperate in diversity, some studies have shown pronounced compositional changes at regional to local scales
within groups of arthropods (Bowden and Buddle 2010;
Rich et al. 2013). If we incorrectly assume little or no
species turnover at small scales, however, we risk the
extinction of local and rare species in connection with
industrial activities and climate change. Species distributions can furthermore be clustered within microhabitats at
very fine spatial scales, increasing the probability of significant species loss during periods of disturbance (Sander
and Wardell-Johnson 2011). In the Arctic, arthropods are
of particular interest as this group comprises a large part of
the Arctic biodiversity (Høye and Sikes 2013). Collectively, the number of Arctic arthropod species greatly
exceeds that of all other non-microbial eukaryotic species
groups combined, including plants and vertebrates (Hodkinson 2013). To provide adequate, reliable predictions for
conservation and survey planning, we need to understand
how arthropods are distributed and to identify the environmental factors which are driving variation in arthropod
assemblages.
Turnover patterns can be affected by a multitude of
factors. Peninsula effects can serve as a filter to species
dispersal creating a change in species pools over small
distances (Simpson 1964). The Arctic in general and
Greenland in particular is dominated by fjords, glaciers,
large water bodies and the inland ice cap that can create
important dispersal barriers. Species composition and
diversity varies with habitat heterogeneity such that a very
heterogeneous habitat has more suitable living conditions
for more species with a larger range of functional traits
(Tews et al. 2004; Kostylev et al. 2005; Brind’Amour et al.
2011). Small-scale topographic and abiotic changes can
further create distinct microhabitats supporting certain
species pools (Suvanto et al. 2014). For arthropods, this is
particularly relevant as they represent a great variety of life
history, behavior and morphological and physiological
adaptations (Wise 1993; Danks 2004). A recent study on
spider diversity at Toolik Lake, Alaska, documented high
species turnover just by sampling different habitats at a
local scale (Sikes et al. 2013), highlighting the importance
of investigating patterns of arthropod diversity at a higher
spatial resolution.
Habitat heterogeneity and complexity is often referred to
as the variation in vegetation structure (Imhoff et al. 1997;
Venn and Kotze 2014). However, vegetation structure
integrates a larger suite of variables, such as solar radiation
and soil characteristics. By separating these factors confounded within vegetation structure, we may better
123
understand the drivers of changes in arthropod communities. A large body of the literature supports variation in
vegetation structure as a strong driver of arthropod communities at regional to landscape scales (Halaj et al. 2000;
Jimenez-Valverde and Lobo 2007; Bowden and Buddle
2010; Rich et al. 2013). Vegetation structure affects multiple aspects of microclimatic conditions (e.g., temperature
and moisture regimes) to which arthropods are highly
sensitive (Rypstra 1986; Rushton and Eyre 1992; Bonte
et al. 2002). Yet, only a few studies have studied the
specific environmental variables structuring Arctic arthropod communities (Bowden and Buddle 2010; Rich et al.
2013).
The goal of this study was to investigate how the
composition of Arctic arthropod assemblages changes at a
local and regional scale by examining species richness,
composition and species turnover within and between two
sites of similar vegetation composition and abiotic conditions. Specifically, we were interested in answering two
questions: (1) Do species assemblages and turnover rates
change at local and regional scales in low Arctic Greenland? (2) Which environmental parameters predict local
arthropod assemblages in areas of low structural complexity? We chose three arthropod taxa representing different functional groups as our focal study organisms:
butterflies (pollinators that are herbivorous in all life
stages), spiders (generalist predators and strongly influenced by structural elements for web building, foraging
etc.) and beetles (including both herbivores, fungivores,
detritivores and predators).
Materials and methods
Study area and data
The study was carried out in West Greenland at the remote
area of Isua, situated approximately 100 km northeast of the
capital Nuuk. The location was selected with the additional
aim to obtain baseline biodiversity data due to plans of
mining the region’s large iron deposits. The two study sites
selected, referred to as site A (65°270 N, 50°220 W) and site B
(65°220 N, 50°460 W), respectively, are situated approximately 32 km apart. Site A is located close (*2 km) to the
ice edge at an approximate elevation of 600 m.a.s.l, and site
B is further away (*34 km) at an approximate elevation of
700 m.a.s.l (Fig. 1). Both sites had similar dwarf shrub cover
and comparable plant communities. The vegetation of the
two study sites could be divided into the following five
habitat types: fen, snow-bed, herb slope, exposed ridge and
dry heath. The dominant plant species (occurring in more
than 30 out of the 80 plots selected) found at each site were
Vaccinium uliginosum, Empetrum nigrum, Carex bigelowii
Polar Biol
Fig. 1 Map of the study area in
southwest Greenland. Site A
(white) at 65°270 N, 50°220 W
and site B (black) at 65°220 N,
50°460 W are situated
approximately 32 km apart. The
figure includes an inset map of
the entire Greenland with the
capital of Greenland (Nuuk)
depicted with a diamond-shaped
dot, and the study area indicated
with a black dot
Ice sheet
Site A
Site B
Nuuk
10 km
and Salix herbacea. Additional information on plant species
is given in Online Resource 1. In each habitat category at
each site, a 40 9 40 m plot group, comprising 16 different
10 9 10 m plots was established. In total, 160 plots (two
sites* five habitat types * 16 plots in each plot group) were
established.
Arthropods were collected using yellow pitfall traps
([ = 10 cm). Two pitfall traps separated by half a meter
were dug into the ground with the rim of the pitfall flush to the
soil surface in each plot (320 pitfall traps in total). The color
of the pitfalls was chosen to catch flying as well as surfaceactive arthropods (Høye et al. 2014). At site B, the traps were
active between July 28 and August 1, 2013; at site A the traps
were active from August 4 to August 8, 2013, resulting in a
total of 3200 pitfall trap days. Because the sites were very
remote and access to each site was only possible with chartered helicopter, we opted to sample intensely across a high
number of plots to optimize our collection of most arthropods in the relatively short time frame. Vegetation cover of
shrubs, herbs, graminoids and bare ground was estimated in a
circle with a 5 m radius from the center of each plot as percent cover and were classified in six categories as not present
or in equal-sized percentage ranges of: 1–20 %, 21–40 %,
41–60 %, 61–80 % and 81–100 %. Vegetation height was
recorded as prevailing height in 50 % of the circle and
classified in four groups (0–5 cm, 6–10 cm, 11–15 cm and
16–20 cm). Presence of plant species were registered in the
5-m circle. Slope was measured in vertical meters between
the highest and lowest point of the plot. Aspect was recorded
using a Garmin S30 and classified to nearest cardinal
direction (north, south, east, and west). Soil moisture (%
volumetric water content) and soil temperature (with one
decimal accuracy) was measured consistently in five places
within a 1 m radius from the center of each plot (center,
north, south, east and west) using a soil moisture sensor and a
standard digital cooking thermometer. All spiders, beetles
and butterflies were identified to the species level based on
genital and morphological characters using a WildÒ M5A
stereo microscope. Spiders were identified using the available literature through The World Spider Catalog (World
Spider Catalog 2016) as well as Spiders of North America
(Paquin and Dupérré 2003). Beetles were identified using
both Scandinavian and North American literature (Lindroth
1985, 1986; Böcher 1988) and consulting the collection at
the Natural History Museum, Aarhus. All specimens were
identified by the corresponding author. Specimens are preserved in 75 % ethanol at the Natural History Museum,
Aarhus. The dataset with habitat information, sampling
periods and coordinates is available in Online Resource
2 and through Global Biodoversity Information Facility
(GBIF) (http://dx.doi.org/doi:10.15468/hblhkl). Not all
juveniles or larvae could be assigned to species, so only adult
specimens were included in the data analysis.
Data analysis
We ran species richness estimations and ordinations on
both plant and arthropod community matrices to compare
patterns of diversity and composition for both groups.
Specifically, we wanted to examine whether a potentially
lower arthropod diversity closer to the ice edge could be an
effect of the plant communities being at an earlier
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successional stage. All statistical analyses were conducted
in R, version 3.1.0 (R Development Core Team 2015).
Species diversity
Species diversity was rarefied and extrapolated for investigation across sites and habitats based on Hill numbers
(q = 0; species richness, q = 1; Shannon diversity, q = 2;
Simpson diversity) and standardized by sample coverage
(Chao and Jost 2012; Chao et al. 2014) using the iNEXT
package (Hsieh et al. 2014). Prior to extrapolation, both
samples were approximating 100 % coverage, so they were
both compared at 100 % coverage, minimizing extrapolation. We tested whether the results were significantly different across sites with a Student’s t test. A Venn diagram
was created to visually interpret the difference in species
pools between the two sites. To investigate how turnover
rates differed between sites, we used the function ‘betadisper’ in the ‘vegan’ package (Oksanen et al. 2015),
followed by ANOVA. This test assesses the variability in
average distances from the group centroid among individual sampling units and is recommended as a method to
measure beta diversity (Anderson et al. 2006). For this
analysis, we employed the Chao dissimilarity index which
is robust even with under-sampled data, containing many
rare species (Chao et al. 2005). We then used ANOVA to
test the difference between the sampling units. This was
followed by a partitioning of beta diversity into nestedness
and turnover with the use of the package ‘betapart’
(Baselga and Orme 2012). This function calculates three
measures of beta diversity: overall beta diversity (bSOR),
spatial turnover rates (bSIM) and nestedness (bNES). The
latter two define how much of the overall beta diversity is
attributed to pure spatial turnover rates and how much is
due to nestedness based on the Sørensen index (Baselga
2010). We compared partitioning of beta diversity between
the two sites and within sites among habitats, to assess how
the actual species turnover differs at the various levels.
Species composition
Non-metric multidimensional scaling (NMDS) (Legendre
and Legendre 1998) was conducted using the function
metaMDS (K = 2, iterations = 100) based on the Bray–
Curtis dissimilarity index. Ordinations were performed
using the ‘vegan’ package in R (Oksanen et al. 2015) which
uses multiple random start configurations to find a
stable global solution for the ordination and transforms
data to reduce the influence of highly abundant species.
Because of the relatively short sampling periods, we were
concerned about undersampling. Therefore, we investigated several combinations of the data when running
NMDS. This led us to the conclusion that both arthropods
123
and plant species were better represented by amalgamating
the 16 plots of 10 9 10 m into four clusters of four
neighboring plots of 20 9 20 m. Two arthropod plots
contained no specimens belonging to the three investigated
groups, and these plots were omitted from all ordination
analyses. To further counteract a potential effect of
undersampling, we reran the ordinations on arthropod
abundance data without singletons (species represented by
one individual). Site and habitat effects in the composition
of the arthropod assemblages were analyzed with analysis
of dissimilarities, using the function ‘adonis’ in the ‘vegan’
package. Analysis of dissimilarities is analogous to distance-based redundancy analysis and is recommended as a
robust method for permutational multivariate analysis of
variance with community ecology data (McArdle and
Anderson 2001).
Environmental correlation with composition
The importance of local (within site) predictor variables for
structuring of arthropods at each site was determined by
vectors fitted to ordinations. To match the structure of the
amalgamated arthropod matrix, all values of predictor
variables were averaged over the four plots and standardized to a mean of zero and a standard deviation of one to
enable a meaningful interpretation of the results. To allow
for a more objective interpretation of the ordination results
and as a basis for choosing variables in the later model
selection, the continuous abiotic variables were overlaid on
the NMDS plot as vectors, using the ‘envfit’ function in the
‘vegan’ package (Oksanen et al. 2015). The direction and
length of each vector indicates the direction of the gradient
and the strength of the correlation, respectively. Only the
significant variables were included in further analyses. All
tested variables can be found in Online Resource 3. We
chose not to include plant species composition as a predictor as we were interested in testing the specific drivers
of arthropod distributions and plant species composition
may confound such factors as vegetation structure, soil
moisture and soil temperature.
To assure that the difference in arthropod composition
and species diversity at the two sites was not caused by
differences in environmental conditions, we tested for
difference in means of the significant measured variables
with t tests. To account for covariation and colinearity
between variables not accounted for by the ‘envfit’ analysis, we employed a generalized linear model (GLM) to fit
the scores of the two ordination axes. This technique has
been applied previously to spatially predict vegetation
gradients along a climatic gradient (Muenchow et al.
2013). Using this technique, we tested for an interaction
between habitat and the significant variables from the
‘envfit’ analysis, and an interaction was only included in
Polar Biol
the final model if significant. We explored different possibilities for nonlinear responses using both correlation
analysis and a generalized additive model and found no
indication of nonlinearity between any of the predictor
variables and the extracted ordination axes. Model
parameters were chosen based on the significance of variables from the vector fitting described above. Backward
selection was then employed until the simplest GLM
explaining the largest portion of the variance was obtained.
Finally, we ran a species indicator analysis to assess the
strength and statistical significance of the relationship
between species occurrence/abundance and the specific
habitats. For this purpose, we used the function ‘multipatt’
in the R package ‘indicspecies’ (Cáceres and Legendre
2009).
Results
A total of 868 adult individuals, constituting 36 species and
14 families were identified within the three preselected
orders: Lepidoptera (178 individuals, two families, two
species), Coleoptera (66 individuals, six families, seven
species) and Araneae (628 individuals, six families, 27
species). Among these was a spider species from the family
Linyphiidae that was previously unknown from Greenland
[Pelecopsis mengei (Simon, 1884), 7 individuals] and a
weevil with only a few previous records from Greenland
[Rutidosoma globulus (Herbst, 1795), 22 individuals]
(Böcher 2015; Marusik 2015). This latter finding was
restricted to two habitats (herb slope and snow-bed) at site
A (Table 1).
There was no significant difference in the means of the
environmental predictor variables between the two sites
except for percent herb cover (Online Resource 3). Soil
moisture content was highly variable within sites and
vegetation height was very similar between sites (Online
Resource 3).
Plant species assembly at site B was a subset of species
represented at site A, i.e., strict nestedness (Online
Resource 6), but for arthropods, the species pool at each
site consisted partly of species restricted to either site A or
B as well as shared species (Fig. 3b). There was a nearsignificant difference in turnover rates between the sites
(F1,36 = 2.98, p = 0.09), and a significant difference
between habitats within site A (F4,13 = 4.00, p = 0.03) but
not at site B (F4,15 = 1.52, p = 0.24), indicating higher
between habitat variation at site A. For plant species, there
was no significant difference in turnover rates between or
within site. Pure spatial turnover between sites was equally
influential as nestedness on overall beta diversity
(bSOR = 0.37, bSIM = 0.19 and bNES = 0.18). Pure spatial
turnover, however, was high between the habitats within
sites, whereas nestedness contributed little to turnover (site
A: bSOR = 0.72, bSIM = 0.66 and bNES = 0.06; site B:
bSOR = 0.62, bSIM = 0.55 and bNES = 0.08).
Species composition
Visual inspection of the NMDS ordination (runs = 100,
stress = 0.18) revealed segregation at the site level as
arthropod composition in similar habitats differed between
sites (Fig. 4a). For plants (runs = 4, stress = 0.19), the
habitats at the different sites resembled each other more than
the arthropods (Online Resource 7). Results of the analysis
of dissimilarities (Adonis) showed that arthropod communities differed significantly between sites (F1,37 = 8,54,
p \ 0.0001) and between the habitats at the different sites
(F4,33 = 5.05, p \ 0.0001). Composition of arthropod
communities varied by site, and the analysis of predictor
variables were performed at site level to identify the specific
drivers of the variation in within-site arthropod assemblages.
The plant communities also differed significantly between
sites (F1,39 = 18.30, p \ 0.0001) and between the habitats
within site (F4,35 = 5.05, p \ 0.0001).
Environmental correlation with composition
Species diversity
Species richness (q = 0) increased significantly from site
A to site B (t = -6.3, p \ 0.0001), but Shannon (q = 1)
and Simpson (q = 2) diversity did not differ between sites
(Fig. 2). This was exactly opposite for extrapolated plant
species richness as both species richness and Shannon and
Simpsons diversity were significantly higher at site A
(Online Resource 4). Habitat-level species richness for
arthropods was also significantly higher at site B for herb
slope, ridge, heath and snow-bed (p \ 0.05), while fen
habitats did not differ significantly (Fig. 3a). Estimated
plant species richness at habitat level was higher than or
similar at site A compared to site B (Online Resource 5).
At site A, four significant variables were selected by vector
fitting: soil moisture, soil temperature, slope and aspect. At
site B, the variables soil moisture, soil temperature, bare
ground and vegetation height were significant (Online
Resource 3, Fig. 4b, c). These variables formed the basis
for the model selection. Only soil moisture and soil temperature significantly predicted species composition at both
sites, and at site B, vegetation height was also included in
the final model (Table 2). There were no significant
interactions between the variables, and interaction terms
were not included in the final model.
Species indicator analyses selected eight species as
significant for site A (p \ 0.05). Two species were
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Polar Biol
Table 1 Overview of the abundance of identified arthropods and the habitats they were collected from
Site
A
Order
Family
Species
Araneae
Dictynidae
Dictyna major (Menge, 1869)
Araneae
Dictynidae
Emblyna borealis (O.P. Cambridge, 1877)
Araneae
Hahniidae
Hahnia glacialis (Sørensen, 1898)
Araneae
Linyphiidae
Agyneta nigriceps (Simon, 1884)
Araneae
Linyphiidae
Agyneta subtilis
Araneae
Linyphiidae
Ceratinella ornatula (Crosby & Bishop,
1925)
Araneae
Linyphiidae
Collinsia holmgreni (Thorell, 1871)
2
Araneae
Linyphiidae
Collinsia spetsbergensis (Thorell, 1871)
3
Araneae
Linyphiidae
Erigone arctica (White, 1852)
Araneae
Linyphiidae
Erigone tirolensis (L. Koch, 1872)
Araneae
Linyphiidae
Erigone whymperi (O.P. Cambridge, 1877)
Araneae
Linyphiidae
Islandiana princeps (Brændegård, 1932)
Araneae
Linyphiidae
Mecynargus borealis (Jackson, 1930)
Araneae
Linyphiidae
Mecynargus morulus (O.P. Cambridge,
1873)
Araneae
Linyphiidae
Oreoneta frigida (Thorell, 1872)
Araneae
Linyphiidae
Pelecopsis mengei (Simon, 1884)
Araneae
Linyphiidae
Sciastes extremus (Holm, 1967)
Araneae
Linyphiidae
Semljicola obtusus (Emerton, 1914)
Araneae
Linyphiidae
Typhocrestus pygmaeus (Sorensen, 1898)
Fen
B
1
Ridge
22
Herb
Heath
Snow
2
Fen
Ridge
2
Herb
Heath
Snow
1
1
4
1
14
1
2
1
1
22
1
3
9
1
31
2
1
1
3
1
4
1
1
2
1
2
2
7
1
1
1
1
1
1
Araneae
Linyphiidae
Walckenaeria clavicornis (Emerton, 1882)
1
2
Araneae
Lycosidae
Arctosa insignita (Thorell, 1872)
64
7
11
Araneae
Lycosidae
Pardosa furcifera (Thorell, 1875)
7
2
Araneae
Lycosidae
Pardosa glacialis (Thorell, 1872)
1
2
1
Araneae
Lycosidae
Pardosa groenlandica (Thorell, 1872)
1
1
28
Araneae
Lycosidae
Pardosa hyperborea (Thorell, 1872)
Araneae
Philodromidae
Thanatus arcticus (Thorell, 1872)
Araneae
Theridiidae
Enoplognatha intrepida (Sorensen, 1898)
Coleoptera
Byrrhidae
Byrrhus fasciatus (Forster, 1771)
Coleoptera
Carabidae
Bembidion grapii (Gyllenhal, 1827)
Coleoptera
Carabidae
Patrobus septentrionis (Dejean, 1821)
Coleoptera
Coccinellidae
Coccinella transversoguttata (Falderman,
1835)
Coleoptera
Curculionidae
Rutidosoma globulus (Herbst, 1795)
Coleoptera
Dytiscidae
Hydroporus morio (Aubé, 1838)
Coleoptera
Staphylinidae
Mycetoporus nigrans (Mäklin, 1853)
3
Lepidoptera
Nymphalidae
Boloria chariclea (Schneider, 1794)
10
Lepidoptera
Pieridae
Colias hecla (Lefèbvre, 1836)
6
4
3
5
1
4
23
9
92
3
15
36
82
1
2
22
3
9
1
2
2
5
6
1
3
1
3
5
1
2
1
2
1
8
1
2
6
5
3
7
6
2
13
1
1
1
26
1
1
2
1
18
13
94
The table is sorted by order, then family and then species
significantly associated with fens at site A: the water beetle
Hydroporus morio (Aubé, 1838) and the wolf spider Pardosa furcifera (Thorell, 1875). Two species associated with
heath habitats: the wolf spider Pardosa groenlandica
(Thorell, 1872) and the ladybug Coccinella transversoguttata (Falderman, 1835) and three species were associated with ridges: the mesh weaver Dictyna major
(Menge, 1869), the philodromid spider Thanatus arcticus
(Thorell, 1872) and the ground beetle Bembidion grapii
(Gyllenhal, 1827). A rare weevil, R. globulus (Herbst,
123
1795), was found in both snow-bed and herb slope habitats.
For site B, eight species were also selected as significant
indicator species (p \ 0.05). The comb-tailed spider Hahnia glacialis (Sørensen, 1898) and the wolf spider Pardosa
hyperborea (Thorell, 1872) were associated with heath
habitats, R. globulus (Herbst, 1795) and the sheet web
spider P. mengei (Simon, 1884) were associated with herb
slopes, the sheet web spider Erigone arctica was found in
fen and snow-bed habitats, C. transversoguttata and P.
groenlandica were found in heath and ridge habitats and
Polar Biol
50
a
fen
ridge
herb slope
snow bed
heath
40
30
20
NMDS2
Effective species numbers
**
10
0
Species richness
Shannon diversity
Simpson diversity
Fig. 2 Diversity profiles, characterized by effective number of
species (± Standard Error) for Hill number order q = 0 (species
richness), q = 1 (Shannon diversity) and q = 2 (Simpson diversity),
at site A (white) and site B (black) extrapolated to a common base
coverage of 100 %. Significance (p \ 0.05) of differences between
sites is indicated with asterisk
NMDS1
b
c
NMDS2
Soil temperature
a
60
Aspect
*
50
Soil moisture
NMDS2
Slope
Soil temperature
Bare ground
Soil moisture
Vegetation height
40
*
*
30
*
20
10
0
Fen
Ridge
Herb
Snow
Heath
b
NMDS1
NMDS1
Fig. 4 a Arthropod NMDS plots for site A (white) and B (black).
Each dot represents the species composition in a specific plot sampled
in one of five habitat types (fen, snow-bed, herb slope, exposed ridge
and dry heath). Arthropod NMDS plot for site A (b) and site B
(c) with the significant predictors from the envfit analysis overlaid as
vectors. The direction and length of each vector indicates the
direction of the gradient and the strength (p \ 0.05) of the correlation,
respectively
the wolf spider Arctosa insignita (Thorell, 1872) was
associated with all habitats except wind-exposed ridges.
Discussion
Fig. 3 a Species richness (? Standard Error) for Hill number q = 0
within habitats extrapolated to a common base coverage of 1. Site A
and site B are represented by white and black colors, respectively.
Significance (p \ 0.05) of differences between sites for each habitat
type is indicated with asterisk. b Venn diagrams based on percentages
of species unique to site A (white), species unique to site B (black)
and species shared among site A and site B (gray)
We found clear regional-scale differences in the species
richness and composition of arthropod communities at two
remote sites in low Arctic Greenland with the site closest to
the ice edge containing the least species. This difference
was evident in most of the five habitat types sampled at the
two sites and fits poorly with the common perception of the
Arctic as a homogenous biome (CAFF 2013). These findings correspond well with the findings of Sikes et al.
(2013), where sampling habitats adjacent to two long-term
experimental research sites (LTER) yielded 24 spider
species that were not found at LTER. Two of these were
new state records (Sikes et al. 2013). Plant species richness
123
Polar Biol
Table 2 Summary of the non-metric multidimensional scaling
(NMDS) analysis
Model
Parameter
Site A, MDS1
Intercept
F1,16 = 33.74
Soil temperature
Estimate
p
0.83 ± 0.15
\0.0001
-0.06 ± 0.01
\0.0001
0.62 ± 0.27
\0.0001
R2Adj = 0.66
p \ 0.0001
Site A, MDS2
Intercept
F2,15 = 65.54
Soil moisture
R2Adj
= 0.30
p = 0.027
Soil temperature
Site B, MDS1
Intercept
F3,16 = 21.17
Soil moisture
R2Adj = 0.76
Soil temperature
-0.0053 ± 0.0017
0.009
-0.034 ± 0.016
0.05
-0.0013 ± 0.35
\0.0001
0.0058 ± 0.0022
0.02
0.094 ± 0.022
\0.0001
0.006
p \ 0.0001
Vegetation height
0.19 ± 0.061
Site B, MDS2
Intercept
0.37 ± 0.12
0.005
F2,28 = 11.73
Vegetation height
-0.23 ± 0.067
0.003
R2Adj = 0.36
p = 0.003
The table presents results of the linear regression models of the
extracted NMDS ordination axes 1 and 2 from each site in separate
rows. Parameters are chosen based on an environmental fitting
analysis
displayed the opposite pattern and suggests that the difference in arthropod communities was not due to an earlier
succession stage at the site closest to the ice sheet. The
partitioning of beta diversity suggests that some of the
turnover between the sites is due to a subset of species at
site A, but there is also some degree of pure spatial turnover, also reflected by the Venn diagram. This serves as an
indication that the arthropod community at site A is not
solely a result of source–sink dynamics between the two
sites, but also different species pools. A distinct difference
between the two sites is the effective land area that forms
the basis for species recruitment to each site. The inland ice
sheet and a large glacier reduce the vegetated area in the
surroundings of site A. A peninsula effect at site A generated by the reduced immigration potential provides a
likely explanation to our observed differences in turnover
and diversity (Simpson 1964). In other words, the surroundings at the two sites have similar abiotic conditions,
but very different proportions of suitable habitat. The relative abundance of species in the samples affects diversity
measures, particularly when the common species are
habitat generalists (Chao et al. 2005). Wolf spiders are
highly abundant and make up more than 50 % of the
individuals in the samples. When adding equal weight to
common and uncommon species in the samples (q = 0),
we found a significantly higher species richness at site B
than at site A, but when adding more and more weight to
123
the more common species (q = 1 and 2), the coveragebased difference in diversity disappeared. Wolf spiders are
well adapted to open tundra habitats and are good dispersers, so when a group (i.e., wolf spiders) is highly
abundant and well adapted to the prevailing habitat type,
they are likely to be more successful in establishing new
populations than less abundant groups of ballooning spider
species that require specific habitat features to establish
(Bonte et al. 2003). Wolf spiders then occur in high
numbers at both sites and thus dominate the diversity
measures. Butterflies and wolf spiders are both active dispersers, yet the butterflies are mostly restricted to site B.
The active flight of butterflies allow for more controlled
habitat selection compared to the random ballooning of
wolf spiders.
At the local scale, i.e., between habitats within sites, we
found significant species turnover as well as significant
indicator species for each habitat. This indicates that a
number of arthropod species exhibit strong habitat fidelity
driven by different microclimatic conditions (i.e. soil
moisture and temperature) specific to each habitat. Our
results show that soil moisture and soil temperature are
important factors in determining arthropod species patterns
at the local scale. Plant species composition has previously
been shown to influence local arthropod species composition more than soil characteristics (Schaffers et al. 2008).
Plant species composition is comprised of several factors,
such as soil properties, vegetation structure and solar
radiation, and might, as effect, mask specific predictor
variables. The species found in this study are comprised of
groups with a high amount of generalist predatory species.
Among the beetles, only a few are herbivores, and these are
believed to be polyphagous (Böcher 2015). The food
preferences of the two butterfly species are unknown in
Greenland, but in other regions they feed on multiple plant
species in the larval stage (Karlsholt et al. 2015). We thus
have no reason to believe that any herbivore–plant interactions cause a loss of descriptive power in the models.
Furthermore, it is generally believed that plant–arthropod
associations in the Arctic are expressions of the plantspecific abiotic microenvironment and not arthropod–plant
relationships (Coulson et al. 2003).
Shifts in precipitation regimes are inherent components of
climate change (Zona et al. 2009; Umbanhowar et al. 2013),
and the resultant spatial variation in soil moisture is likely to
be an underestimated factor in the Arctic (Le Roux et al.
2013). Plant species compositions may not reflect sudden or
very local changes in hydrology, and studies have demonstrated how the rate of plant compositional responses to
climate change depend heavily on habitat type and latitude
and responds faster at the northern latitudes (Daniels et al.
2011; Schmidt et al. 2012). Arctic arthropods are sensitive to
rapid changes in the abiotic environment at the population
Polar Biol
(Bowden et al. 2013, 2015; Høye et al. 2014) and community
level (Høye and Forchhammer 2008a, b). Hence, changes in
the composition of Arctic ecological communities in
response to climate change may be more rapidly detected in
arthropods than in plants.
The scale of observation is important for identifying
areas of high species diversity (Willis and Whittaker 2002),
and the ability to detect rare species is highly dependent on
the spatial resolution and extent of the survey (Crawley and
Harral 2001; Lennon et al. 2001). In this study, we have
shown that species pools of Arctic arthropods can change
over relatively short distances even in areas of low species
diversity and that local conditions can influence turnover
rates. Future studies investigate the detailed habitat
requirements of individual arthropod species, e.g., by
determining indicator species for different habitat types. A
quantification of key functional traits like dispersal ability
will further aid efforts to identify areas of particular conservation concern and will facilitate a more accurate prediction of species vulnerability to resource development
and climate change. In this study, we found two species
with none or very few previous records from Greenland
among the 36 species identified. This highlights the limited
existing knowledge on the occurrence of individual species
of arthropods in Greenland. Our study documents that
diversity patterns of arctic arthropods are more complex
than previously thought with considerable local and
regional spatial variation.
Acknowledgments We gratefully acknowledge logistical support
from the Arctic Research Centre (ARC), Aarhus University. This
work is a contribution to the Arctic Science Partnership (ASP) aspnet.org. We would also like to thank the Natural History Museum,
Aarhus for use of laboratory and equipment during the identification
process. TTH was supported by a Jens Christian Skou fellowship at
the Aarhus Institute of Advanced Studies, and SN was supported by
the Villum foundation’s Young Investigator Programme
(VKR023456).
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Meter scale variation in shrub dominance
and soil moisture structure Arctic
arthropod communities
Rikke Reisner Hansen1,2, Oskar Liset Pryds Hansen1,2,
Joseph J. Bowden1, Urs A. Treier1,3,4, Signe Normand1,3,4
and Toke Høye1,2,5
1
Arctic Research Centre, Department of Bioscience, Aarhus University, Aarhus C, Denmark
Department of Bioscience, Kalø, Aarhus University, Rønde, Denmark
3
Ecoinformatics & Biodiversity, Department of Bioscience, Aarhus University, Aarhus, Denmark
4
Swiss Federal Institute for Forest, Snow and Landscape Research, Birmensdorf, Switzerland
5
Aarhus Institute of Advanced Studies, Aarhus University, Aarhus, Denmark
2
ABSTRACT
Submitted 5 May 2016
Accepted 15 June 2016
Published 14 July 2016
Corresponding author
Rikke Reisner Hansen,
[email protected]
Academic editor
Dezene Huber
Additional Information and
Declarations can be found on
page 13
DOI 10.7717/peerj.2224
Copyright
2016 Hansen et al.
Distributed under
Creative Commons CC-BY 4.0
The Arctic is warming at twice the rate of the rest of the world. This impacts Arctic
species both directly, through increased temperatures, and indirectly, through
structural changes in their habitats. Species are expected to exhibit idiosyncratic
responses to structural change, which calls for detailed investigations at the species
and community level. Here, we investigate how arthropod assemblages of spiders
and beetles respond to variation in habitat structure at small spatial scales. We
sampled transitions in shrub dominance and soil moisture between three different
habitats (fen, dwarf shrub heath, and tall shrub tundra) at three different sites along
a fjord gradient in southwest Greenland, using yellow pitfall cups. We identified
2,547 individuals belonging to 47 species. We used species richness estimation,
indicator species analysis and latent variable modeling to examine differences in
arthropod community structure in response to habitat variation at local (within site)
and regional scales (between sites). We estimated species responses to the
environment by fitting species-specific generalized linear models with
environmental covariates. Species assemblages were segregated at the habitat and site
level. Each habitat hosted significant indicator species, and species richness and
diversity were significantly lower in fen habitats. Assemblage patterns were
significantly linked to changes in soil moisture and vegetation height, as well as
geographic location. We show that meter-scale variation among habitats affects
arthropod community structure, supporting the notion that the Arctic tundra is a
heterogeneous environment. To gain sufficient insight into temporal biodiversity
change, we require studies of species distributions detailing species habitat
preferences.
Subjects Biodiversity, Biogeography, Conservation Biology, Ecology, Entomology
Keywords Coleoptera, Araneae, Environmental gradients, Biodiversity, Habitat suitability
INTRODUCTION
Understanding the factors that structure ecological communities on continental, regional
and local scales provides the basis for understanding how global changes might affect
How to cite this article Hansen et al. (2016), Meter scale variation in shrub dominance and soil moisture structure Arctic arthropod
communities. PeerJ 4:e2224; DOI 10.7717/peerj.2224
species composition and biodiversity (Vellend et al., 2013; Dornelas et al., 2014).
Climate change is happening at an accelerated pace in the Arctic (Callaghan et al., 2004;
IPCC, 2014), and altered moisture regimes and shrub expansion are two of the most
prominent habitat-altering phenomena caused by these changes (Rouse et al., 1997;
Tape, Sturm & Racine, 2006; Myers-Smith et al., 2011; Elmendorf et al., 2012). Shrub
expansion and altered moisture regimes represent considerable consequences of climate
change to the Arctic tundra; altering unique habitats such as open heath, wetlands and
grasslands (ACIA, 2004). Firstly, warming in the Arctic has led to accelerated plant growth,
particular for woody plants, causing a shift towards greater shrub cover, and a northward
migration of the tree line (Callaghan, Tweedie & Webber, 2011), increased biomass
(Epstein et al., 2012), and changes in plant species composition (Walker et al., 2012). These
trends are expected to continue during future climate change (Normand et al., 2013;
Pearson et al., 2013). Secondly, a changing Arctic climate with changes in precipitation,
glacial melt, and permafrost degradation may alter the spatial extent of wetlands (Avis,
Weaver & Meissner, 2011). In areas with continuous permafrost, top soils become wetter
due to the impermeable strata that prevent infiltration and percolation (Woo & Young,
2006). Some areas with discontinuous permafrost, however, become drier, due to
increased net evapotranspiration and increased drainage due to permafrost thaw (Zona
et al., 2009; Perreault et al., 2015). The long term persistence of Arctic wetlands is debated,
but climate change projections and field studies indicate deterioration and ultimate
destruction of Arctic wetlands (Woo & Young, 2006). These habitat changes, both
shrubification and wetland deterioration, will trigger several feedback loops within the
climate system (Chapin et al., 2005) and may have profound effects on ecosystems
(Post et al., 2009). In order to understand how these habitat changes, affect Arctic
biodiversity, we need to adequately describe how Arctic species composition responds to
environmental changes.
The alteration of habitats, due to e.g., shrub expansion into open tundra and changing
wetland hydrology, are likely to affect habitat availability for many organisms, through
changes in species’ distributions, diversity, and composition. Terrestrial arthropods
(e.g. insects and spiders) in particular, are associated with specific habitat types and likely
respond strongly to habitat changes in the Arctic (Bowden & Buddle, 2010; Rich, Gough &
Boelman, 2013). Arthropods have long been recognized as valuable indicators of changing
environments because of their relatively short lifecycles and their physiology being directly
driven by the external environment (ectothermic). Studies of the impacts of habitat
changes upon Arctic arthropod communities are, however, only beginning to emerge
(Bowden & Buddle, 2010; Rich, Gough & Boelman, 2013; Sikes, Draney & Fleshman, 2013;
Sweet et al., 2014; Hansen et al., 2016). In spite of the common conception of the Arctic as
a species-poor and relatively homogenous environment, studies have shown that
arthropod assemblages vary substantially over short distances (Hansen et al., 2016), with
species responding to local and regional climatic gradients (O. L. P. Hanser et al., 2013,
unpublished data). Arthropod communities are expected to change in response to the
direct effects of increasing temperatures and prolonged growing seasons (Høye et al., 2013;
Høye et al., 2014), but also indirectly through changes in soil moisture and vegetation
Hansen et al. (2016), PeerJ, DOI 10.7717/peerj.2224
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structure (Bowden & Buddle, 2010; Hansen et al., 2016), changes to snowmelt dynamics
(Høye et al., 2009; Bowden et al., 2015b), and shrub expansion (Rich, Gough & Boelman,
2013). Several studies indicate direct effects of temperature change on arthropods (Post
et al., 2009; Høye et al., 2013; Bowden et al., 2015a), but we do not yet fully comprehend
the distribution of, or habitat requirements for, the majority of Arctic arthropod species.
Arctic and alpine tundra areas are vast, and the knowledge of geographical variation
associated with recent environmental and ecosystem change is limited. In this study,
we explore the influence of moisture regime and habitat structure on the composition
and diversity of Arctic arthropod communities, and investigate the site specific effects of
the drivers of change. We propose the following hypothesis: Arctic arthropod assemblages
and diversity vary with soil moisture and vegetation height at very small spatial scales
(10–20 m). Specifically, we compare beetle and spider communities sampled in different
habitats (fen, dwarf shrub heath, and tall shrub tundra) at three sites along a large scale
gradient. We expect to find distinct arthropod communities in each habitat, and that
abundances of groups like wolf spiders, and other active hunters, will be lower in the tall
shrub tundra compared to open habitats.
METHODS
Study area and sampling design
Arthropods were sampled with uncovered pitfall traps from the 29th of June to the 23rd of
July 2013 at three sites (1, 2, and 3) along the Godthaabsfjord in West Greenland (Fig. 1).
Site 1 was situated at the mouth of the fjord and thus characterized by a coastal climate
with relatively high precipitation, narrow annual temperature range, and topographic
variation (app. 0–300 m.a.s.l.). The shrub community at site 1 was dominated by dwarf
shrubs and a very sparse cover of tall shrub species such as Salix glauca (Lange (family:
Salicidae)). Site 2 was low lying and flat, and characterized by a mosaic of low shrub
vegetation (< 50 cm), dominated by S. glauca, mixed with Betula nana (Lange (family:
Betulacae)), Vaccinium uliginosum (L., (family: Ericacae)), Rhododendron groenlandicum
(Oeder (family: Ericacae)), and Empetrum nigrum (Lange (family: Ericacae)). Site 3 was
characterized by a continental climate and pronounced topographic variation (app. 0–600
m.a.s.l.) with well-defined tall shrub patches dominated by high growth of S. glauca
and Alnus crispa (Aiton (family: Betulacae)) (> 50 cm). These patches were mainly located
at south facing slopes below 100 m.a.s.l. All dwarf shrub species at site 2 were also
present at site 3.
Moisture transitions (fen-heath) were sampled at sites 1 and 2, while transitions in
vegetation height and cover of tall shrubs (heath-shrub) were sampled at sites 2 and 3.
Four fen-heath plots were established, two at site 1 and two at site 2. Each fen-heath plot
consisted of two sub-plots placed 10 m apart. Each sub-plot was situated exactly 5 m from
a distinct fen-heath transition zone (Fig. 2). Twelve heath-shrub plots were established
at site 2 and site 3 (six at each site). Each heath-shrub plot consisted of two sub-plots
20 m apart; one located at the center of a patch of tall shrubs and one in the adjacent open
dwarf shrub heath. Each sub-plot was delineated by a circle with a 5 m radius. At the
center of each sub-plot, two yellow pitfall traps (9 cm diameter) were placed 50 cm
Hansen et al. (2016), PeerJ, DOI 10.7717/peerj.2224
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A
Site 3
Site 2
Site 1
B
Nuuk
50
0
Km
Figure 1 Map of the study area. (A) The Godthåbsfjord area, South-West Greenland (64 11′N, 51 44′W),
showing the three study sites (1, 2 and 3) depicted with a circle and the capital Nuuk depicted with a
diamond. (B) Greenland with the study area framed in a square (Loecher & Ropkins, 2015). Mapdata:
© Google 2016.
apart (Fig. 2). The traps were dug down such that the rim was flush with the surface and
filled one third with a soap water solution. There was no overflow due to rainwater
accumulation during sampling. The color of the pitfalls was chosen to catch flying as well
as surface-active arthropods (Høye et al., 2014). Pitfall traps were emptied twice, once
halfway through and once at the end of the sampling period. Samples were stored
separately.
The following structural and environmental parameters were measured in each
sub-plot: (i) percent cover of shrubs, herbs, graminoids and bare ground in six categories:
0, 1–20, 21–40, 41–60, 61–80, and 81–100%, (ii) height (to the nearest 5 cm) of the
vegetation height with the highest coverage in the sub-plot, (iii) presence of plant species,
(iv) slope in vertical meters between the highest and lowest point of the sub-plot,
(v) aspect, recorded using a handheld GPS and classified to nearest cardinal direction
(North, South, East, and West), (vi) pH, measured directly with a soil pH measurement
kit, model HI 99121, (vii) soil type at 15 cm depth was recorded as humus or sand.
Specimens and data
All spiders and beetles were sorted from the samples and the adult specimens were
identified (by RRH) to species based on morphological characters using a Wild M5A
stereo microscope. Not all juveniles could be assigned to species, so only adult specimens
were included in the analysis. Spiders were identified using the available literature through
The World Spider Catalog (2016) and Spiders of North America (Paquin & Dupérré, 2003).
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A
Fen
B
Shrub
10 m
20 m
5m radius
5m radius
Heath
Heath
Figure 2 Sampling design. Conceptual figure of the sampling design showing fen transects in the (A)
and shrub transects in the (B).
Beetles were identified using both Scandinavian and North American literature (Lindroth,
1985; Lindroth, 1986; Böcher, 1988) and by consulting the collection at the Natural History
Museum Aarhus, Denmark. Specimens are preserved in 75% ethanol at the Natural
History Museum Aarhus. The dataset is available through the Global Biodiversity
Information Facility (http://doi.org/10.15468/li6jkm).
Data analysis
The mean and standard error were calculated for significant environmental variables
across all habitats at each site. We ran a correlation analysis of all potential variables, based
on Pearson’s correlation coefficient, and tested whether the uncorrelated variables differed
significantly between sites and habitats with a MANOVA. To counteract effects of
potential-under sampling, all analyses were carried out excluding singletons. All analyses
were carried out in R version 3.2.2.
Species diversity
Species diversity was rarefied and extrapolated for investigation across habitats based on
Hill numbers (q = 0; species richness, q = 1; Shannon diversity, q = 2; Simpson diversity)
and standardized by sample coverage (Chao & Jost, 2012; Chao et al., 2014) using the
iNEXT R-package (TC Hsieh, KH Ma & A Chao, 2014. unpublished data). We extrapolated
to double the reference sample of the habitat with the smallest sample coverage (shrub).
Samples were compared at base-coverage, estimated as a minimum of Ca and Cb, where
Ca is maximum coverage at reference sample size and Cb is minimum coverage at two
times reference sample size. iNEXT computes bootstrap confidence bands around the
sampling curves, facilitating the comparisons of diversity across multiple assemblages.
We then visually assessed if diversity measures differed significantly between habitats.
We ran a species indicator analysis to assess the strength and statistical significance of
the relationship between species abundances and the specific habitats. We used the
function ‘multipatt’ in the R package ‘indicspecies’ (De Cáceres, Legendre & Moretti,
2010). This analysis provides a specificity value ‘A’(0–1), which indicates the probability of
a certain species occurring in a certain habitat as well as a sensitivity value ‘B’(0–1), which
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indicates how many of the plots belonging to a certain habitat the target species is located
in. Significance (p < 0.05) is assessed based on the A and B values (De Cáceres & Legendre,
2009). In addition to significance testing, we opted to describe habitat preferences more
broadly by assigning all species with an A value for a given habitat larger than 0.8 and
a B value larger than 0.1 to that specific habitat. In this way, the importance of the
sensitivity value is down weighted.
Species composition
Traditional methods to visually investigate how arthropod species composition varies
between habitats, such as non-metric multidimensional scaling (NMDS), have been
shown to confound trends in location with changes in dispersion, leading to potentially
misleading results (Warton, Wright & Wang, 2012). To avoid these issues while still
enabling visualization, we employed latent variable modelling through the R package
‘boral’ (Hui, 2016). Latent variable modelling is a Bayesian model-based approach that
explains community composition through a set of underlying latent variables to account
for residual correlation, for example due to biotic interaction. This method offers the
possibility to adjust the distribution family to e.g., negative binomial distribution which
better accounts for over-dispersion in count data. Thus, it accounts for the increasing
mean-variance relationship without confounding location with dispersion (Hui et al.,
2015). Three “types” of models may be fitted: (1) With covariates and no latent
variables, boral fits independent response General Linear Models (GLMs) such that the
columns of y are assumed to be independent; (2) With no covariates, boral fits a pure
latent variable model (Rabe-Hesketh, Skrondal & Pickles, 2004) to perform model-based
unconstrained ordination (Hui et al., 2015); (3) With covariates and latent variables, boral
fits correlated response GLMs, with latent variables.
Sub-plots placed in dwarf shrub heath could potentially differ depending on the
transition examined (fen-heath or shrub-heath). Therefore, we created latent variable
plots for both plants and arthropods to visually assess if the heath sub-plots in the fenheath and shrub-heath plots groups were distinguishable. In the latent variable plot for
plant species composition, heath sub-plots were not segregated (Fig. S1) and all heath
plots were hereafter treated as one category.
We modelled arthropod species’ distributions with two latent variables to enable
visualization comparable to a two dimensional NMDS. From the latent variable model,
we extracted the posterior median values of the latent variables which we used as
coordinates on ordination axes to represent species composition at plot level (Hui et al.,
2015). We then tested the difference in local species composition based on these
coordinates between the paired samples (fen-heath or shrub-heath) for each transect
using paired t-tests.
To test the significance of and, interactions between, the environmental variables and
site, we used a multivariate extension of GLMs using the function ‘manyglm’ in the
package ‘mvabund’ (Wang et al., 2012). This recently developed method offers the
possibility to model distributions based on count data by assuming a negative binomial
distribution. Vegetation height and graminoid cover have higher resolutions compared to
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the classifications ‘fen’ and ‘shrub’ as these are measured on a continuous scale. We used
vegetation height as a proxy for shrub treatment effects and cover of graminoids as a proxy
for soil moisture. The gradients in these variables are representative of the moisture
transition of fen-heath plot groups and the shrub dominance transition of the shrubheath plot groups (Fig. S2). We tested for main effects of all the un-correlated variables,
selected by the Pearson correlation analyses, and for an interaction between the variables
and site. The model assumptions of mean-variance and log-linearity were examined with
residual vs. fit plots and a normal quantile plot, and no transformations were needed.
RESULTS
A total of 2,547 individuals, constituting 45 species and 13 families were identified within
the two orders: Araneae (2,223 individuals, seven families, 37 species) and Coleoptera
(324 individuals, 6 families, 8 species). We found a species of sheet web spider (Wabasso
cacuminatus (Millidge, 1984)) not previously known from Greenland, represented by one
individual. One species (Pelecopsis mengei, (Simon, 1884)), represented in our samples by
three individuals, remained unknown from Greenland until recently (Marusik, 2015;
Hansen et al., 2016) (Table 1).
Extrapolated species richness (q = 0) did not differ significantly between habitats due to
overlapping confidence intervals but there was a trend towards higher species richness in
heath sub-plots, lower in shrub sub-plots, and lowest in fen sub-plots (Fig. 3). The same
pattern was observed for Shannon diversity (q = 1) as well as for Simpson diversity (q = 2),
however both these indices differed significantly between habitats, with the highest
diversity in the shrub sub-plots, intermediate in the heath sub-plots, and lowest in the fen
sub-plots (Fig. 3).
The three species significantly (p < 0.05) associated with fen habitats were all sheet web
spiders. Erigone whymperi (O.P. Cambridge, 1877), Mecynargus paetulus (O.P. Cambridge,
1875), and Wabasso quaestio (Chamberlin, 1948). Just one species, the ladybird Coccinella
transversoguttata (Faldermann, 1835), was significantly associated with heath habitats.
Shrub habitats housed six significantly associated species: the comb-footed spider
Ohlertidion lundbecki (Sørensen, 1894), and five species of sheet web spiders: Dismodicus
decemoculatus (Emerton, 1852), Improphantes complicates (Emerton, 1882), Pocadicnemis
americana (Millidge, 1984), Semljicola obtusus (Emerton, 1914), Sisicus apertus (Holm,
1939) (Table 1).
The latent variable plots showed that the plant species composition of the shrub subplots overlapped with the composition of the heath plots (Fig. S1), but vegetation height
was significantly different (Table 2). The plant species composition of the fen plots was
different from both the heath and shrub sub-plots (Fig. S1). Arthropod species
composition was segregated both at site and habitat level, but the distribution of sub-plots
in the latent variable arthropod plot indicated interaction between site and treatment
(Fig. 4).
Vegetation height in the shrub sub-plots at site 2 was significantly lower than that at
site 3 (F1,21 = 13.46, p = 0.001), and the overall cover of graminoids was significantly lower
at the fen sub-plots at site 1, compared to the fen sub-plots at site 2 (F1,29 = 0.21,
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Table 1 Arthropod species. List of arthropod species sampled and their abundance in three habitats; fen, dwarf shrub heath, and tall shrub tundra
at three sites along the Nuuk fiord in Western Greenland. The last column shows the results of a species indicator analysis (for details see main text).
Species were assigned to one of the three habitats if A (specificity value) > 0.8 and B (sensitivity value) > 0.1. The table is sorted by order, family, and
species, respectively.
Order
Family
Species
Abundance
Fen
Araneae
Heath
Habitat
Shrub
Dictynidae
Dictyna major (Menge, 1869)
Gnaphosidae
Haplodrassus signifer (C.L. Koch, 1839)
1
Hahniidae
Hahnia glacialis (Sørensen, 1898)
1
7
1
No classification
Linyphiidae
Agyneta jacksoni (Simon, 1884)
3
8
1
No classification
Agyneta nigripes (Brændegård, 1937)
2
3
1
2
1
Bathyphantes simillimus (L. Koch, 1879)
Dismodicus decemoculatus (Emerton, 1852)
No classification
No classification
Fen and heath
1
No classification
10
Shrub*
Erigone arctica (White, 1852)
6
Fen
Erigone psycrophila (Thorell, 1871)
1
No classification
Erigone whymperi (O.P. Cambridge, 1877)
8
Fen*
Hilaira herniosa (Thorell, 1875)
1
Hybauchenidium gibbosum (Sørensen, 1898)
Hypsosinga groenlandica (Simon, 1889)
2
Improphantes complicatus (Emerton, 1882)
5
3
Heath and shrub
2
4
Heath and shrub
2
8
Shrub*
1
No classification
1
Heath and shrub
Lepthyphantes turbatrix (O.P. Cambridge, 1877)
Mecynargus borealis (Jackson, 1930)
4
Mecynargus morulus (O.P. Cambridge, 1873)
2
Mecynargus paetulus (O.P. Cambridge, 1875)
No classification
Heath
Fen*
33
Oreonetides vaginatus (Thorell, 1872)
1
No classification
Pelecopsis mengei (Simon, 1884)
2
1
Heath and shrub
Pocadicnemis americana (Millidge, 1976)
6
18
Shrub*
Sciastes extremus (Holm, 1967)
1
Scotinotylus sacer (Crosby, 1929)
Semljicola obtusus (Emerton, 1914)
3
Sisicus apertus (Holm, 1939)
Tiso aestivus (L. Koch, 1872)
1
Wabasso cacuminatus (Millidge, 1984)
No classification
5
Shrub
6
15
Shrub*
1
3
Shrub*
31
1
Heath
1
Wabasso quaestio (Chamberlin, 1948)
12
Walckenaeria karpinskii (O.P. Cambridge, 1873)
6
No classification
Fen*
21
Fen and heath*
17
Heath
Thomisidae
Xysticus durus (Sørensen, 1898)
Lycosidae
Arctosa insignita (Thorell, 1872)
17
29
2
Fen and heath*
Pardosa furcifera (Thorell, 1875)
524
552
257
No classification
Pardosa groenlandica (Thorell, 1872)
17
23
8
No classification
Pardosa hyperborea (Thorell, 1872)
6
347
140
Heath and shrub*
Philodromidae
Thanatus arcticus (Thorell, 1872)
2
10
Theridiidae
Robertus fuscus (Emerton, 1894)
1
No classification
Ohlertidion lundbecki (Sørensen, 1898)
2
Shrub
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Fen and heath
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Table 1 (continued ).
Order
Family
Species
Abundance
Fen
Coleoptera
Habitat
Heath
Shrub
Byrrhidae
Byrrhus fasciatus (Forster, 1771)
1
11
Carabidae
Patrobus septentrionis (Dejean, 1821)
50
17
23
Fen and shrub*
Coccinellidae
Coccinella transversoguttata (Falderman, 1835)
51
2
Heath*
Cryptophagidae
Caenoscelis ferruginea (Sahlberg, 1820)
38
2
Heath and shrub
Curculionidae
Otiorynchus arcticus (O. Fabricius, 1780)
1
20
1
Heath
Otiorynchus nodosus (Müller, 1764)
18
66
19
No classification
Staphylinidae
Heath
Mycetoporus nigrans (Mäklin, 1853)
2
No classification
Quedius fellmanni (Zetterstedt, 1838)
2
No classification
Note:
* Indicates Significance, p < 0.05.
Shannon diversity
Simpson diversity
Species richness
4.0
7
30
3.5
Estimates
6
3.0
25
5
Habitat
Fen
Heath
Shrub
2.5
4
20
2.0
3
Figure 3 Diversity profiles. Species richness, Shannon diversity and Simpson diversity coloured by
habitat. Error bars represent 95 percent confidence intervals.
Table 2 Environmental variables. Mean (± S.E) of the environmental variables included in GLM’s and
latent variable models, showing the difference between sites and treatments. Graminoid cover was
measured in six categories: 0, 1–20, 21–40, 41–60, 61–80, and 81–100%. Vegetation height was measured
(classified to the nearest 5 cm) of the vegetation height with the highest coverage in the sub-plot.
Site
Habitat
Vegetation height
(height classes)
Graminoid
(% cover)
pH
Site 1
Heath
2.6 (0.2)
15 (5)
5.8 (0.1)
Fen
2.5 (0.2)
55 (6.3)
5.5 (0.2)
Heath
2.4 (0.2)
18.6 (3.7)
6.4 (0.1)
Site 2
Site 3
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Fen
2.3 (0.3)
75 (6.3)
6.5 (0.04)
Shrub
7.5 (1.2)
10.3 (3.5)
6.2 (0.3)
Heath
3.2 (0.4)
12.7 (11.4)
6.2 (0.2)
Shrub
28.5 (4.1)
4 (1.9)
6.5 (0.04)
9/18
LV2
Shrub
Heath
Fen
Site 1
Site 2
Site 3
LV1
Figure 4 Latent variable plot for arthropod species composition. Species distribution plot of the best
fitted latent variable model showing the mean of the latent variable with a negative binomial distribution. Ellipses represent 95 percent confidence intervals around the centroids of each habitat.
Table 3 Table of deviance. Results of the multivariate generalized linear model, including all variables
tested, along with residual degrees of freedom, degrees of freedom, deviance and p-value.
Parameter
Residual degrees
of freedom
Degrees of
freedom
Deviance
p-value
Intercept
55
Vegetation height (height class)
54
1
117.9
0.001
Graminoid cover (% cover)
53
1
93.2
0.001
pH
52
1
43.0
0.389
Site
50
2
296.2
0.001
Vegetation height: site
48
2
35.0
0.639
Graminoid cover: site
46
2
103.8
0.003
pH: site
44
2
40.8
0.568
p = 0.049). The pH levels were nearly significantly different between the shrub sub-plots
from site 2 to site 3 (F1,21 = 4.17, p = 0.054) and highly significantly different between
the fen sub-plots from site 1 to site 2 (F1,29 = 66.01, p < 0.0001). pH did not differ between
fen and heath (F1,29 = 0.69, p = 0.41) or shrub and heath (F1,21 = 0.50, p = 0.49). Cover
of graminoids was significantly lower for heath sub-plots compared to fen sub-plots
(F1,29 = 74.54, p < 0.0001). Vegetation height differed significantly between shrub and
heath habitats (F1,21 = 26.04, p < 0.0001), with lower vegetation height in the heath
sub-plots compared to shrub sub-plots (Table 2; Fig. S2).
Arthropod species composition differed significantly due to different moisture regimes
and different height classes. pH levels were not a significant driver of arthropod
communities, nor was there a significant interaction between site and the levels of pH.
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Table 4 T-test of local transitions. Paired t-test of the local transitions in soil moisture and shrub
dominance. LV1 and LV2 represent the first and second coordinate of the latent variable.
Model
Residual degrees
of freedom
Estimates
t
p
Fen transect site 1 LV1
7
-0.86
-5.32
0.001
Fen transect site 1 LV2
7
-0.43
-4.78
0.002
Fen transect site 2 LV1
7
-1.70
-0.26
0.13
Fen transect site 2 LV2
7
-0.37
-3.21
0.02
Shrub transect site 2 LV1
5
-0.72
-3.90
0.01
Shrub transect site 2 LV2
5
-0.35
-3.10
0.03
Shrub transect site 3 LV1
5
-1.16
-5.50
0.003
Shrub transect site 3 LV2
5
-0.62
-3.28
0.02
There was a significant interaction between cover of graminoid species and site, but
no significant interaction was detected between height class and site (Table 3). Arthropod
species composition differed significantly between the local fen-heath transitions, but for
site 2 only; one latent variable axis differed significantly between fen-heath transitions. The
local shrub-heath transects differed significantly for both axes and both sites (Table 4).
DISCUSSION
Although Arctic tundra is often perceived as a relative homogenous biome, it consists of a
wide range of habitat types due to strong environmental transitions occurring over short
spatial scales. In this study, we have demonstrated clear effects of vegetation height and
soil moisture on diversity and composition of spiders and beetles in low Arctic Greenland.
This effect is evident across distances of 10–20 m. Fen, heath, and shrub vegetation hosted
distinct arthropod communities differing in both composition and diversity. While
previous studies have emphasized the importance of vegetation structure as predictors
of Arctic arthropod communities (Bowden & Buddle, 2010; Rich, Gough & Boelman, 2013;
Sweet et al., 2014), it has not been demonstrated that such effects are visible at the
scale of meters.
Existing literature generally agrees with the habitat classifications we have assigned the
species in this study. According to existing descriptions of habitat preferences, the
wetland species we find in this study are found strictly in wet open habitats, whereas both
shrub and heathland species mostly have a more general distribution (Leech, University
of Alberta & Department of Entomology, 1966; Böcher, 2015; Marusik, 2015), indicating a
higher degree of habitat specialization in the fens. The sheet web spider, Erigone arctica,
was significantly linked to wet fen habitats in an alpine study site in West Greenland
(Hansen et al., 2016), and in this study, E. arctica was also linked to fen plots, further
suggesting habitat specialization. We found the lowest diversity in the fens, which are
spatially limited, compared to much more wide spread heathland habitats. Both tall shrub
tundra and dwarf shrub heath are comprised of different habitats with open patches,
moist areas, and varying vegetation structure. Such variation in habitat structure likely
leads to higher diversity compared to the fen habitats, which are rather homogenous.
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This particular study area is characterized as low Arctic with discontinuous permafrost
unaffected by glacial meltwater. Models predict that this area will experience increased
evapotranspiration and precipitation (Rawlins et al., 2010). Increased drainage due to
permafrost melt coupled with evapotranspiration is likely to lead to wetland deterioration.
Shrubification has been forecasted to be most pronounced at the boundary between high
and low Arctic where permafrost is melting and in areas where soil moisture is greatest
(Myers-Smith et al., 2015). In the Godthåbsfjord, it is therefore likely that shrub expansion
will be most notable in the fens and snow-beds. With shrubification (Myers-Smith et al.,
2011; Elmendorf et al., 2012), as well as, increased land use such as forestry and agriculture
(ACIA, 2004), wetland habitats are at risk (CAFF, 2013). Our results suggest that wetland
deterioration and shrubification will strongly affect arthropod communities, and may
compromise the living conditions of individual specialized species.
We found an interaction between site and graminoid cover, suggesting that the fens
differ between sites. Wetlands with coastal proximity are known to be impacted by salt
influx from the sea (Woo & Young, 2006). This is supported by the slightly higher pH at
site 1 compared to site 2, but does not explain differences in arthropod composition in
the fens between the coastal (site 1) and intermediate site (site 2), as pH was not
significant in the multivariate GLM. Even though plant species composition showed clear
segregation of wet and dry plots, conditions may be drier at the intermediate site than
at the coastal site, where summer precipitation is higher. Plant species composition
reflects an integration of seasonal variation in soil moisture conditions (Daniels et al.,
2011) such that they may not reflect sudden soil moisture changes. The variation in
moisture regime only partially explained arthropod species composition at the
intermediate site and supports the idea of drier conditions at the intermediate site
affecting arthropod species composition differently.
We expected the effect of vegetation height to be less pronounced at the intermediate
site due to the patchy structure of the shrubs and overall lower vegetation; yet, we did not
find an interaction between site and treatment. We studied mostly mobile predatory
species. The few herbivores like the weevils: Otiorynchus arcticus (O. Fabricius, 1780) and
Otiorynchus nodosus (Müller, 1764) are mostly found in open heath plots with low
vegetation. It is conceivable that even a small change in vegetation height has an effect on
the surface active predatory species, because vegetation height may also affect the
composition and abundance of prey items. The web builders, like sheet web spiders,
require some amount of vegetation structure to form webs, but even low shrubs provide
structure and shelter. Rich, Gough & Boelman (2013) found that overall arthropod
abundance and species richness increased in shrub plots in Arctic Alaska, but suggested
that for groups like wolf spiders and other active hunters, full shrub encroachment of
open habitats could be detrimental. Our results show that abundances of these groups are
lower in shrub sub-plots and support this notion.
CONCLUSION
We have established a baseline of species occurrence in relation to transition in soil
moisture and shrub dominance which will facilitate future assessment of changes in Arctic
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arthropod communities, where these transitions in habitat structure are likely to change.
The variation in community composition at the scale of meters was surprising and
suggests drastic changes in arthropod species composition given continued shrubification
and wetland deterioration. We found that the strength of the environmental predictor
variables varied among sites. Understanding the sources of such site variation is an
important topic for future studies. Two important steps are needed to further the
knowledge of arthropod responses to changing habitats. Primarily, we need information
on species occurrence across multiple taxa and multiple environmental gradients.
Habitat preferences of species are needed to determine the effects that climate change will
have in Arctic ecosystems. Spiders and butterflies have proven useful for detection of rapid
environmental change due to climate change (Høye et al., 2014; Bowden et al., 2015a;
Bowden et al., 2015b) and may serve as important indicator taxa in future studies.
Secondly, we need further studies of spatial variability and change in environmental
gradients like soil moisture.
ACKNOWLEDGEMENTS
This work is a contribution to the Arctic Science Partnership (ASP: http://asp-net.org).
We would also like to thank the Natural History Museum Aarhus for use of laboratory
and equipment during the identification process.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
Toke Thomas Høye was supported by a Jens Christian Skou fellowship at the Aarhus
Institute of Advanced Studies and Signe Normand was supported by the Villum
foundation’s Young Investigator Programme (VKR023456). Logistical support was
provided by the Arctic Research Centre (ARC), Aarhus University. The funders had no
role in study design, data collection and analysis, decision to publish, or preparation of the
manuscript.
Grant Disclosures
The following grant information was disclosed by the authors:
Jens Christian Skou Fellowship at the Aarhus Institute of Advanced Studies.
Villum foundation’s Young Investigator Programme: VKR023456.
Arctic Research Centre (ARC), Aarhus University.
Competing Interests
The authors declare there are no competing interests.
Author Contributions
Rikke Reisner Hansen conceived and designed the experiments, performed the
experiments, analyzed the data, wrote the paper, prepared figures and/or tables,
reviewed drafts of the paper.
Hansen et al. (2016), PeerJ, DOI 10.7717/peerj.2224
13/18
Oskar Liset Pryds Hansen performed the experiments, contributed reagents/materials/
analysis tools, reviewed drafts of the paper, contributed substantially to the design of the
experiments, contributed to data interpretation and approved the final version.
Joseph J. Bowden contributed reagents/materials/analysis tools, reviewed drafts of the
paper, contributed substantially to data interpretation and approved the final version.
Urs A. Treier reviewed drafts of the paper, contributed to the fieldwork, data
interpretation and approved the final version.
Signe Normand reviewed drafts of the paper, contributed to the fieldwork, data
interpretation and approved the final version.
Toke Høye conceived and designed the experiments, reviewed drafts of the paper,
contributed substantially to the fieldwork, data interpretation and approved the final
version.
Data Deposition
The following information was supplied regarding data availability:
Global Biodiversity Information Facility:
http://www.gbif.org/dataset/59d25ddf-355d-47d7-b8a1-c1e2819014c7
DOI: 10.15468/li6jkm
Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/
10.7717/peerj.2224#supplemental-information.
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Title
Diversity and composition changes along a continentality gradient in the
Arctic
Authors
1. Oskar L. P. Hansen1,2,3
a. [email protected]
b. Corresponding author
2. Rikke R. Hansen1,2
.
[email protected]
3. Joseph J. Bowden1,3
.
[email protected]
4. Signe Normand4
.
[email protected]
5. Urs Treier1,4
.
[email protected]
6. Toke T. Høye1,2,5
.
1
[email protected]
Department of Bioscience - Arctic Research Centre, Aarhus University, Ny Munkegade 114,
bldg. 1540, DK-8000 Aarhus C
2
Department of Bioscience - Biodiversity and Conservation, Aarhus University, Grenåvej 14,
Kalø, DK-8410 Rønde
3
Natural History Museum Aarhus, Wilhelm Meyers Allé 210, Universitetsparken, DK-8000
Aarhus C
4
Department of Bioscience - Ecoinformatics and Biodiversity, Aarhus University, Ny
Munkegade 116, bldg. 1540, DK-800 Aarhus C
5
Aarhus Institute of Advanced Studies, Aarhus University, Høegh-Guldbergs Gade 6B, bldg.
1630, DK-8000 Aarhus C
Abstract
Background: Studies using climatic gradients can be valuable in order to predict range shifts
triggered by climate change. These studies and their impact on species abundances, however, are
unevenly distributed among the ecoregions of the world and are generally scarce in the Arctic.
We tested whether species diversity and composition changed in response to change in
continentality and micro-climatic gradients in the Arctic. We also tested the similarity of
diversity and composition patterns along the gradient among the two dominant groups of ground
dwelling arthropods, spiders (Araneae) and beetles (Coleoptera). The study was conducted at the
Godthaabsfjord (64° 10'N 51° 45'W), west Greenland during the summer 2013. Three sites were
selected along the fjord to represent a continentality gradient. At each site three micro climates,
defined as north-facing, south-facing and horizontal plots, were selected at which pitfall trapping
methods were used to collect ground-dwelling arthropods. In this study we will first quantify the
compositional and diversity changes with increasing continentality and with increasing solar
radiation. For each of the gradients we will assess if spider (Araneae) and beetle (Coleoptera)
community composition are varying in the same way along these climatic gradients.
Results: In total, 53 species (43 spider species, 10 beetle species) of 107 species known in
Greenland were identified from 4,181 individuals. Species diversity increased towards
continental sites, while species composition changed significantly along the gradient. Along the
micro climatic gradient species diversity increased towards warmer habitats for beetles, while
spider diversity peaked at horizontal plots. Nor were the best explanatory variables the same for
both taxa, as beetles were best correlated to continentality, while changes in spider communities
were best explained by local topography.
Conclusions: The diversity and community composition does change along increasing
continentality and solar radiation. However, the community composition for the dominant wolf
spiders, were markedly different from other groups. Also, species diversity profiles showed
contrasting patterns along the two gradients. This study emphasizes the need to include multiple
gradients and multiple taxa to understand the complexity of local and regional responses to
climate.
Keywords
Continentality, Micro climate, Biodiversity, Arctic, Climate, Araneae, Coleoptera, Indicator taxa
Background
Climatic gradients are important to understand in order to predict range shifts triggered by
climate change (Lenoir & Svenning 2015). Still, changes in composition and diversity of species
along climatic gradients (e.g., elevation) are not well documented in Arctic arthropods and plants
(But see, Bowden & Buddle, 2010; Bruun et al., 2006). Studies on contemporary range shifts
have typically focused on uni-variate range shifts, such as latitude or altitude. These clines may
represent narrow gradients that do not consider east- to west-ward range shifts. The effects of
local gradients have not been considered along with regional structuring of species and
communities (Lennoir & Svenning 2015).
Climatic gradients structure community composition of species across a large range of spatial
scales, from micro climates varying within a few meters, depending on topography, a few
hundred altitudinal meters on a mountain side to continental distances of hundreds or thousands
kilometres (Hodkinson 2005; Whittaker 1960). Therefore, climatic gradients are important to
include in a broader framework for understanding the distribution of species. In coastal
mountainous areas, continentality is a strong gradient due to the large differences in thermal
capacity between the ocean and land. It is, however, an infrequently considered gradient that
changes in temperature lapse rate along elevation (Hodkinson 2005). It could, therefore, also
strongly affect arthropod species composition and diversity.
Continentality is a pronounced climatic gradient in Northern ecosystems (e.g. Hein et al. 2014a;
Hein, et al. 2014b), which can be described by three physical properties: diurnal temperature
range, proportion of heat shared by interface substances and the depth or height to which heat is
transported (Driscoll & Fong 1992). Continental areas are thus more susceptible to temperature
fluctuations than coastal areas, due to their physical properties. Apart from the physical
properties, Löffler & Finch (2005), proposed a set of additional covariates along the
continentality gradient, which includes increased solar radiation, less precipitation, higher
maximum temperature, less soil moisture, and lower wind velocity in continental areas. These
geophysical properties do not only affect arthropods directly, but could also cause indirect effects
on arthropods through plant growth or other secondary biotic interactions. With increased
primary production and more stable weather, a continental site could be expected to hold greater
species diversity than coastal sites.
Snow dynamics (i.e. snow cover and snowmelt timing) and shrub cover (Myers-Smith et al.
2011; Löffler & Finch 2005) are both linked to temperature and wind, and therefore change with
elevation and continentality. Snowmelt timing determines the onset of the summer season for
Arctic arthropods and plants (Høye et al. 2007), and is therefore an important driver of plant and
arthropod community composition (Kreyling et al. 2012; Dollery et al. 2006).
Studies of climatic gradients and their impact on communities of species are unevenly distributed
among ecoregions and highly desirable for the Arctic in order to assess current patterns, and
predict future changes in range shifts for populations and communities (Lenoir & Svenning
2015; Meltofte 2013). Greenland, in particular, poses a huge potential for knowledge gain in
climate related range shifts through the exploration of climatic gradients, as historical sampling
is scarce or non-existent (Jensen & Christensen 2003). Furthermore, the diversity of arthropod
communities in the Arctic have traditionally been seen as more simple than they are (Meltofte
2013), yet arthropod species compositions have been shown to be heterogeneous and vary in
diversity and composition over short distances (Hansen et al 2016).
Differences in arthropod communities between coastal and continental areas around Greenland
have been registered based on scattered, non-systematic sampling and anecdotal evidence
(Jensen & Christensen 2003). While this has increased our knowledges on the distributions of a
few species, data on arthropod community composition is lacking for most regions in Greenland
(e.g. Böcher et al 2015; Høye et al. 2013; Meltofte 2013).
A single taxon may not act as a suitable surrogate measure for the diversity and compositional
responses of all other taxa along climatic gradients, as individual taxa may respond differently
(Axmacher et al. 2011). This is especially true at small spatial scales, where the variability in the
environment does not correspond to the same variability in species richness in all taxa (Ricketts
et al. 2002; Giorgini et al. 2015). At larger scales, however, a single taxon may be a reasonable
predictor of another (Mikusinski et al. 2001; Giorgini et al. 2015). It is however not clear if all
taxa will vary in similar ways along climatic gradients or if some taxa respond differently than
others.
This study addresses the following questions 1) is species diversity increasing and does species
composition change with increasing continentality and with increasing solar radiation (from
north to south facing slopes)? 2) are spiders (Araneae) and beetle (Coleoptera) community
composition varying in the same way along these climatic gradients?
Results
In total, 4,181 adult individuals of spiders and beetles were collected and identified from 96
plots, comprising 43 spider species and 10 beetle species, table S1. The wolf spiders (Lycosidae)
with 3,114 individuals and four species were the most numerous and widely distributed family,
leaving 527 and 540 individuals, belonging to the beetles (Coleoptera) and other spiders
(Araneae excl. Lycosidae), respectively (table S1). Wolf spiders were present in all 96 plots,
while other spiders and beetles were present in 91 and 88 plots, respectively. Hence the analysis
of spiders was done separately with and without wolf spiders.
Species diversity
Species richness increased significantly from the coastal site, 1, to the intermediate and
continental sites, 2 and 3, for all groups combined (figure 1a). Shannon diversity increased
significantly between site 1 and 3, but when up weighing abundant species (Simpson diversity)
this increase along the continentality gradient disappeared. For beetles, the greatest increase in
diversity occurred between the intermediate site, 2, and the continental site, 3, closest to the head
of the fjord. For spiders, the increase primarily occurred closer to the mouth of the fjord, between
site 1 and 2 (figure 1a).
North-facing plots were significantly less species rich than horizontal plots along the local
climatic gradients (figure 1b), while no difference was found for Simpson and Shannon diversity.
For beetles no differences were detected between any of the micro-climates. The horizontal plots
yielded a significantly higher species richness of spiders than both the south- and north-facing
plots. For spiders, excluding the wolf spiders, the confidence interval of north-facing plots
spanned the confidence intervals of the two other groups and was therefore excluded to optimize
the reference coverage for the remaining groups and thus increase estimate precision.
Composition
The three sites separated in ordination space (figure 2) along the continentality gradient for all
combinations of taxonomic groups. It is also clear that the micro-climates tended to vary along
the same trajectory as continentality, such that communities from cold microclimates at the
continental site are similar to the communities from warmer microclimates at the coastal sites.
The fitted environmental variables revealed that slope and continentality separated the
communities in a perpendicular direction from each other consistently for all taxonomic subsets.
Species composition of spiders, beetles, and both groups combined, differed significantly
between sites and between micro-climates (table 1). We found a significant interaction term
between site and microclimate for all taxonomic subsets except for the beetles (table 1). Some of
the abundant species differed significantly between micro-climates and/or sites (table S2). All
significant species showed increased activity-abundance from the coastal site 1 to the continental
site 3 and from North- to South-facing plots (table S3). The structuring of sites was best
explained by continentality and slope (table 2). The interpretation is highly sensitive to the
selection of taxonomic group; however wolf spiders dictated the overall picture of community
structure. For beetles and spiders, excluding wolf spiders, continentality alone was the best
explanatory variable. Topography did not add explanatory power when continentality was
included (table 2). Point-biserial correlation coefficients were calculated between species
abundances and sites, 19 of 53 species were significantly correlated with one or two sites in the
fjord (table S4). The highest number of indicator species were found at the most continental site
with 11 of the species associated with that climate. Pardosa groenlandica, Erigone whymperi,
and Mecynargus paetulus were significant indicators for the most oceanic site. For the most
continental site 11 species were significant indicators, among those Pardosa hyperborea,
Coccinella transversoguttata, Pocadicnemis americana and Caenoscelis ferruginea had a pointbiserial correlation above 0.450. Pocadicnemis americana, who was almost exclusively found on
south-facing plots, was in combination with Pardosa groenlandica and P. hyperborea highly
correlated with the warm south facing plots (point-biserial correlation 0.360, pval = 0.010).
Interestingly Pardosa hyperborea in combination with Mycetoporus nigrans were suggested as
the best indicators for north facing plots (point-biserial correlation 0.376, pval = 0.005), however
P. hyperborea was not significant alone contrary to M. nigrans (point-biserial correlation 0.422,
pval = 0.002).
Discussion
Species diversity increased significantly along the continentality gradient and along the local
climate gradient. Species composition also varied significantly along the two gradients. The
changes were, however, not consistent among taxonomic groups, nor were the variables
predicted to have the most explanatory power for species composition the same among the
taxonomic groups.
Even though the general pattern, when assessing the extreme endpoints of the gradients were
similar, beetles and spiders did not respond similarly along the gradients. While beetle diversity
decreased with temperature, i.e. highest at south-facing plots and lowest at north-facing plots,
spider diversity was estimated to be most diverse at horizontal plots. This difference could be
caused by scale, topographic context, other covariables or a combination.
Species diversity
While spiders and beetles both had significantly higher species diversity at the continental site
relative to the coastal site, there was a discrepancy at the intermediate site, where beetle diversity
did not increase, contrary to the spiders (figure 1a). This discrepancy is not visible in the model
combining all taxonomic groups, indicating that patterns of diversity of specific taxonomic or
functional groups can be masked by more diverse groups. Further the differences between
spiders and beetles could be better understood in an analysis of species diversity by functional
traits.
Composition
We found continentality to be a strong gradient for structuring of arthropod community
composition. The main driver of the other spiders and beetles lies within the continentality
gradient, which clearly separates the sites.
Taxonomic differences
If the taxonomic groups varied similarly along the climatic gradients, it would be possible to
predict both abundances and species richness based on information about a selected indicator
group. This has been done in several studies covering plants, birds, butterflies and beetles
(Axmacher et al. 2011; Giorgini et al. 2015; Ricketts et al. 2002; Mikusinski et al. 2001); the
conclusions, however, have been inconsistent. Ricketts et al. (2002), for example, suggest that
differences in habitat preferences between arthropod orders makes it difficult to assume indicator
taxa at small scales. Also, Giorgini et al. (2015) find that species richness is more reliably
estimated at coarser scales. While the overall patterns of species diversity and community
composition between the two groups at the extremes in this study correspond, idiosyncrasies
exists along both the continentality and micro-climatic gradients that may not be apparent in
simple correlations between taxonomic groups. Thus the scale in this study is too fine to imply
consistent changes among taxonomic groups, as scale matters in the ability to predict diversity of
one group from another (Giorgini et al. 2015).
Topographic context
The sourrounding topography may also interact with the local topography. The topography on
larger scale creates different living conditions for otherwise equal micro-climates. e.g. some
south facing plots on the continental site, situated on a mountain-side, are not only differenct
from other sites, but also from other south-facing plots at the same site, suggesting that
topographic context could enhance or decrease the effect of a slope at a small scale. When the
contiguous area becomes smaller the very local ability to avoid extinctions and to be a subject for
immigration decreases, creating an area in which virtually all populations would be sink
populations and only maintained by immigration from adjacent habitats (Hanski 1998; Eriksson
et al. 2002). This could also create a more mixed community that is not entirely defined by the
micro-climate, but subject to an edge effect between habitats (Ries & Sisk 2004).
Covariables
The interaction term between the two gradients for arthropod communities in this study may be
partly explained by micro-climate- and continentality- specific variation in snowmelt timing. On
larger scales snowmelt is driven by radiative fluxes and energy advection, which varies greatly
(Mioduszewski et al. 2014; Mioduszewski et al. 2015). Prevailing wind and wind speed on the
other hand controls snow accumulation at smaller scales (Pomeroy et al. 1997). At smaller
scales, snowmelt timing is also highly variable in space. Snow accumulation in itself may not
have any short term impact on arthropod survival or community composition (Convey et al.
2014; Legault & Weis 2013). Slope is an important predictor of wolf spider composition and is
somewhat orthogonal to continentality (figure 2). While slope could be a proxy for soil moisture,
as meso-topography is well correlated with soil moisture (Le Roux et al. 2013) it is not, however,
possible to disentangle the ridges and depressions in this study. Horizontal plots could thus
include both the highest and lowest soil moisture. Other methods using digital elevation models
have shown not to produce good predictions of soil moisture (Le Roux et al. 2013).
These results indicate that future studies, investigating broad scale variation in species diversity
and composition, should include continentality, as well as, local topography. The results also
show that the diversity of taxonomic groups respond at different stages to climatic gradients,
though overall trends are similar. In future research, we recommend that a broad variety of taxa
should be included, whenever possible to understand such idiosyncratic responses.
Conclusions
Both the continentality and micro-climate affected the species diversity and species composition.
2) The two arthropod orders, beetles and spiders, did not respond completely synchronously to
either of the two gradients, albeit, the overall pattern was the same.
This study emphasizes the need for future studies to include multiple gradients and multiple taxa
to better understand the complexity of climatic responses in what has been thought of as simple
systems. This study also shows how two gradients can interact and even though having different
scales and different covariables. This may also suggest temperature to be the dominant variable
for some, but not all species.
Methods
This study was conducted at Godthaabsfjorden (64° 10'N 51° 45'W), west Greenland (figure 3)
between June 27 and July 20, 2013. Three sites were selected along the fjord from the mouth to
the head. The resulting continentality gradient stretched approximately 60 km in total with 30 km
between each site. Microclimates at each site were categorized as North facing, South facing or
horizontal so as to represent a local micro-climatic gradient of solar radiation defined by slope
and aspect. Horizontal plots did not have a slope greater than 11°, while north and south were
greater than 11° and within 70° from geographic north/south. From the 186 plots sampled, 96
plots with equal, concurrent sampling effort (figure S1) and situated below 60 m.a.s.l. were
chosen within the aforementioned climate gradient criteria. The plots (table 3) were distributed
slightly unevenly among the gradient categories with 30, 50 and 16 on site 1, 2 and 3
respectively and of which 37, 11 and 48 categorized as south, north and horizontal plots. Each
plot was sampled using yellow pitfall traps (H: 81 mm, ⌀: 100 mm) for 16 to 20 (mean 17.4)
days. Samples from plots extending more than 20 days and plots with a total sampling duration
less than 14 days were excluded, in order to keep the sampling effort at a comparable level
(figure S1). At each plot, two pitfall traps were set at a distance of about 2 meters from each
other.
Species Identification
Spiders were identified using literature available through The World Spider Catalog (World
Spider Catalog 2014). Beetles were identified using various Scandinavian and North American
literature (i.e., Böcher 1988; Lindroth et al. 1985; Lindroth et al. 1986; Palm 1966)⁠ and through
consultation of the collection at the Natural History Museum Aarhus, Denmark. All individuals
were identified to the lowest possible taxonomic level, however only adult individuals identified
to species level were used in the analysis. Specimens are preserved in 75% vol. ethanol at the
Natural History Museum, Aarhus.
Data analysis
Species Diversity Analysis
Data analysis was conducted using R version 3.2.3 (2015-12-10) (R Core Team 2015). Diversity
for each micro-climate at each site was rarefied and extrapolated, using abundance data with the
iNEXT R-package (Hsieh et al. 2014; Chao et al. 2014). Initially all groups (each local microclimate for each site, n = 9, and local micro-climates pooled together, n = 3) were compared, but
due to low sample-sizes (table 3) and to reduce the within group variation, it was not feasible to
compare other than the best replicated set of groups for the two gradients (i.e. site 2 for microclimates and south facing plots for continentality).
Hill numbers, which represent species richness, shannon diversity, weighting species by relative
frequency and simpson diversity, weighting dominant species (Chao et al. 2014), were used to
analyze species diversity. The units of the Hill numbers are effective number of species that is the
number of equally abundant species equivalent to a diversity measure (Chao et al. 2014).
Maximum extrapolation ratio, r, (i.e the ratio of extrapolated sample size and reference sample
size) was set to r = 2, as recommended by Chao et al. (2014). Each model was compared at a
common base coverage that was set to Cbase = min{Ca, Cb}, where Ca = max{C(n1), C(n2), ...,
C(nk)} and Cb = min{C(rn1), C(rn2), ..., C(rnk)}, where n1, n2, ..., nk is the reference sample sizes
for the groups. In this way, at least one group was not extrapolated and thus optimizing the
precision and contrasts of the estimates. If a group confidence interval at Cbase spanned the two
other groups, within a given order, this group was removed and the analysis rerun, to increase
contrast among the remaining groups.
Composition analysis
To assess species composition along the two gradients and among taxonomic groups, four nonmetric multidimensional scaling (NMDS) ordinations were run using the vegan package version
2.3-5 (Oksanen et al. 2015). For each analysis a maximum of 5000 random starts were chosen to
find the global minimum stress solution. Spearman rank correlation between community matrix
and the gradients were used to check that Bray-Curtis distance was the most appropriate
dissimilarity index to be used in the distance matrix. Monte Carlo simulations with randomized
data (n = 25) of ten runs each at maximum 20 random starts and visual inspection of scree plots
were used to choose the optimal number of dimensions and insure that stress levels by the data
were not higher than what could be generated from randomized data. Ellipsoids describing the
standard error of the average of scores for each site were added to assess the differences among
sites in ordination space. Continuous environmental variables were fitted to each plot as vectors
to describe the ordination space and included slope, topography, continentality, species richness
and abundance. Slope was assessed as the field-measure: number of vertical meters per 10
horizontal meters. Topography was calculated as a continuous index to quantify the combined
information of slope and aspect (equation 1 and 2). For plots with aspect towards north the index
would generate a negative value, and for plots with aspect towards south a positive value
dependent on the steepness of the slope. Horizontal plots received values near zero.
To test for differences in communities between sites and habitats a model approach was used
(Warton et al. 2014). This was done for two reasons. First, more common and widely used
PERMANOVA and related distance-metric tests are not well-suited, being either too liberal or
too conservative in their conclusions for unbalanced data sets and requires similar variability in
all species for non-confounding interpretation of dispersion and displacement (Warton et al.
2012; Anderson & Walsh 2013). The null hypothesis stating no difference between sites and
habitats was tested using the manyglm function in the R-package mvabund (Wang et al. 2012).
This function fitted a generalized linear model for high dimensional data with a negative
binomial error distribution. Analysis of deviance was assessed using likelihood-ratio-test and
species specific contributions were assessed using p-values adjusted for multiple testing. The
model assumptions of mean-variance and log-linearity were controlled through residual vs. fit
plots. No patterns were found to indicate violations of the model assumptions.
Point-biserial correlation coefficients (i.e Pearson correlation between species abundance and a
binary vector for site membership) were used to identify indicator species for each site or
combination of sites following De Cáceres & Legendre (2009) and De Cáceres et al. (2010).
Spearman rank correlations between community dissimilarity matrices and environmental
variables (Clarke & Ainsworth 1993) were used to assess the set of environmental variables that
best explained the data. Full documentation of the analysis can be found in the appendix.
Declarations
Authors' contributions
OLPH, RRH and TTH conceived the idea and planned the field work. OLPH, RRH, TTH, SN
and UT carried out the fieldwork. OLPH, RRH and JJB identified the species. OLPH carried out
the analyses and wrote the first draft. Everyone contributed to the interpretation of results and
preparation of the final version of the paper.
Acknowledgements
We would like to acknowledge the Natural History Museum, Aarhus for laboratory space and
curation of specimens.
Funding
Availability of data and material
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Figures
Figure 1
Estimated effective number of species (± 95% confidence interval) calculated for
species richness, shannon diversity, and simpson diversity, along two gradients: (a)
Continentality for south-facing plots and (b) local temperature gradient (north,
horizontal and south facing plots) at the intermediate site, 2. (a) Continentality: For
spiders and beetles the species richness increases along the gradient, but the
increase happens in different parts of the fjord. Spider species diversity increases
between the coastal and intermediate sites, 1 and 2, while the increase for beetles
happens between the intermediate and continental sites, 2 and 3. (b) The local
micro-climatic gradient suggest that spiders have the highest species richness on
horizontal plots.
1
Figure 2
2
3
4
5
6
7
8
9
Two dimensional non-metric multidimensional scaling (NMDS) ordination plots,
based on Bray-Curtis distances. For each ordination a subset is shown for each site.
Centroids (± SE) for sites and covariates are printed in the rightmost panel of each
row. (a) represent all species combined together, while (b) represent subsets of
taxonomic groups. Wolf spiders heavily dominate the overall structure (top and
third row), while the remaining spiders and beetles are structured along the
continentality gradient. Stress spiders and beetles: 14, beetles: 18, wolf spiders:
12, spiders excl wolf spiders: 14
10
11
12
Tables
13
Table 3
14
Number of plots that fulfilled the selection criteria for each category.
North Horizontal South
15
Site 1, Coastal
2
15
13
Site 2, Intermediate
6
31
13
Site 3, Continental
3
2
11
16
Table 1
17
18
19
20
Analysis of deviance tables. Testing the hypothesis that the community between
sites and habitats is not different (based on glm for each species, see table S2).
The test was run for all data combined and for subsets of taxonomic groups. All but
the interaction term for beetles were significant at α = 0.05.
Res.Df Df.diff
Dev Pr(>Dev)
Spiders and Beetles
(Intercept)
95
site
93
2 408.128
0.001
micro climate
91
2 275.343
0.001
site:micro climate
87
4 156.992
0.001
Beetles
(Intercept)
87
site
85
2 146.752
0.001
micro climate
83
2
64.594
0.002
site:micro climate
79
4
23.934
0.229
Spiders
(Intercept)
95
site
93
2 254.544
0.001
micro climate
91
2 213.367
0.001
site:micro climate
87
4 134.112
0.001
Wolf spiders
(Intercept)
95
site
93
2
68.789
0.001
micro climate
91
2
57.797
0.001
site:micro climate
87
4
60.099
0.001
Spiders excl. wolf spiders
21
(Intercept)
90
site
88
2 184.903
0.001
micro climate
86
2 156.411
0.001
site:micro climate
82
4
0.001
75.452
22
Table 2
23
24
Spearman rank correlations between community dissimilarities and the best
combination of environmental variables. Based on bioenv in vegan R-package.
Variables
size correlation
Spiders and Beetles
topography
1
0.354
continentality slope
2
0.444
continentality topography slope
3
0.443
continentality
1
0.310
continentality topography
2
0.286
continentality topography slope
3
0.246
topography
1
0.352
continentalityslope
2
0.421
continentality topography slope
3
0.426
continentality
1
0.103
continentality slope
2
0.104
continentality topography slope
3
0.089
Beetles
Wolf spiders
Spiders excl. wolfspiders
25
26
Equations
27
Eq 1
28
29
30
Eq 2
31
Supplementary material
32
Figure S1
33
Samples used in the analysis. Dots denotes start and endpoints for samples.
34
35
36
37
Table S1
Occurrences of species and number of individuals in the nine different combinations of site (Coastal,
Intermediate and Continental) and local climatic gradient (horizontal, north or south).
Coastal
Horizont
al
Beetles
Ground
beetles
Nort
h
Intermediate
Sout
h
Horizont
al
Nort
h
Continental
Sout
h
Horizont
al
Nort
h
Sout
h
Tota
l
Bembidion
grapii
0
0
0
0
0
0
0
0
1
1
39
0
3
32
0
6
3
1
3
87
Coccinella
transversogutta
ta
0
1
1
13
4
12
3
7
44
85
Nephus
redtenbacheri
0
0
0
0
0
0
0
0
2
2
Dorytomus
imbecillus
0
0
0
1
0
0
0
0
0
1
Otiorhynchus
arcticus
3
0
13
0
0
0
5
0
21
42
Otiorhynchus
nodosus
10
1
12
100
21
18
3
8
11
184
Byrrhus
fasciatus
2
0
3
8
0
11
0
2
5
31
Caenoscelis
ferruginea
0
0
0
0
0
0
3
0
79
82
Mycetoporus
nigrans
0
1
0
0
1
1
0
7
2
12
Arctosa
insignita
6
0
10
45
3
10
0
0
0
74
Pardosa
furcifera
438
0
33
802
36
230
29
40
8
161
6
25
7
21
5
2
24
1
5
12
102
6
0
39
64
104
291
20
56
742
132
2
0
0
0
0
0
0
0
0
1
1
Patrobus
septentrionis
Ladybirdbeetl
es
Weevils
Other beetles
Spiders
Wolf spiders
Pardosa
groenlandica
Pardosa
hyperborea
Theridiidae
Enoplognatha
intrepida
Ohlertidion
lundbecki
0
0
0
0
0
0
0
0
1
1
Robertus fuscus
0
0
0
1
0
0
0
0
0
1
Dictyna major
0
0
0
0
0
1
0
0
0
1
Emblyna
borealis
0
0
0
1
0
1
0
0
0
2
Hahnia glacialis
1
0
10
0
0
17
1
12
16
57
Haplodrassus
signifer
0
0
3
1
0
0
0
1
0
5
Hypsosinga
groenlandica
0
0
0
30
0
3
0
0
11
44
Thanatus
arcticus
2
0
2
7
1
7
0
0
16
35
Xysticus durus
0
0
2
6
1
7
0
0
11
27
Agyneta
allosubtilis
0
0
0
2
0
0
0
0
0
2
Agyneta
jacksoni
4
0
6
14
0
2
0
1
4
31
Agyneta
nigripes
0
0
0
1
0
0
0
0
0
1
Agyneta subtilis
0
0
0
4
0
0
0
0
0
4
Bathyphantes
eumenis
0
0
0
1
0
0
0
0
0
1
Dismodicus
decemoculatus
0
0
0
11
0
1
0
11
2
25
Erigone arctica
6
0
0
0
0
0
0
0
0
6
Erigone
psychrophila
1
0
0
0
0
0
0
0
0
1
Erigone
whymperi
7
0
0
1
0
0
0
0
0
8
Hilaira herniosa
0
0
0
0
1
0
0
0
0
1
Hybauchenidiu
m gibbosum
0
0
0
0
0
0
1
2
4
7
Improphantes
0
0
2
2
1
2
0
1
1
9
Other spiders
Linyphiidae
complicatus
Islandiana
princeps
0
0
0
0
1
0
0
0
0
1
Lepthyphantes
turbatrix
0
0
0
1
0
0
0
0
1
2
Mecynargus
borealis
2
0
1
4
0
0
0
0
0
7
Mecynargus
morulus
0
0
4
4
1
3
0
0
0
12
Mecynargus
paetulus
31
0
0
4
0
0
0
1
0
36
Oreonetides
vaginatus
0
0
0
1
0
1
0
0
0
2
Pelecopsis
mengei
0
0
0
3
0
2
0
0
0
5
Pocadicnemis
americana
0
0
1
1
0
12
0
0
41
55
Sciastes
extremus
0
0
0
1
0
0
0
0
0
1
Semljicola
obtusus
4
3
4
19
0
11
0
0
0
41
Sisicus apertus
0
0
0
3
0
1
0
0
1
5
Tiso aestivus
0
0
0
1
6
6
0
0
11
24
Wabasso
cacuminatus
0
0
0
0
0
0
0
4
0
4
10
0
0
5
0
0
0
0
0
15
Walckenaeria
castanea
0
0
0
0
0
0
0
0
1
1
Walckenaeria
clavicornis
0
3
0
2
0
0
0
0
0
5
Walckenaeria
karpinskii
8
2
9
26
2
5
0
0
2
54
605
18
179
1227
185
685
69
159
105
4
418
1
Wabasso
quaestio
Total
38
39
Table S2
40
Analysis of deviance table for each species. Pr(>Dev) and deviance in brackets is reported
41
42
for each species.
Species
Stat
p-val
Pardosa groenlandica
0.298
0.039
Erigone whymperi
0.258
0.046
Mecynargus paetulus
0.238
0.039
Otiorhynchus nodosus
0.382
0.003
Arctosa insignita
0.363
0.001
Pardosa hyperborea
0.662
0.001
Coccinella transversoguttata
0.507
0.001
Pocadicnemis americana
0.467
0.001
Caenoscelis ferruginea
0.461
0.001
Hybauchenidium gibbosum
0.413
0.001
Hahnia glacialis
0.389
0.001
Thanatus arcticus
0.362
0.003
Otiorhynchus arcticus
0.346
0.004
Mycetoporus nigrans
0.337
0.001
Tiso aestivus
0.282
0.025
Dismodicus decemoculatus
0.270
0.027
Pardosa furcifera
0.350
0.005
Walckenaeria karpinskii
0.273
0.039
0.264
0.043
Site 1
Site 2
Site 3
Site 1 & 2
Site 2 & 3
Hypsosinga groenlandica
43
44
Table S3
45
46
Univariate abundance estimates from the manyglm.
(Inter
cept)
sit
e2
sit
e3
habita
tNorth
habitatH
orisontal
site2:hab
itatNorth
site3:hab
itatNorth
site2:habita
tHorisontal
site3:habita
tHorisontal
0
0
0
0
0
0
0
0
0
Bembidio
n grapii
14.82
0
12
.4
2
0
0
0
-12.42
0
-12.42
Patrobus
septentri
onis
-1.47
0.
69
0.
17
-13.35
2.42
-0.69
13.55
-1.62
-0.72
Otiorhyn
chus
arcticus
0
14
.8
2
0.
65
-14.82
-1.61
14.82
-0.65
1.61
1.88
Otiorhyn
chus
nodosus
-0.08
0.
41
0.
08
-0.61
-0.33
1.54
1.59
1.17
0.73
Dorytom
us
imbecillu
s
14.82
0
0
0
0
0
0
11.38
0
Coccinell
a
transvers
oguttata
-2.56
2.
48
3.
95
1.87
-12.25
-2.2
-2.41
11.46
11.27
Nephus
redtenba
cheri
14.82
0
13
.1
1
0
0
0
-13.11
0
-13.11
Byrrhus
fasciatus
-1.47
1.
3
0.
68
-13.35
-0.55
-1.3
13.73
-0.64
-13.48
Caenosce
lis
ferrugine
a
14.82
0
16
.7
9
0
0
0
-16.79
0
-1.57
Gen.Var
Beetles
Ground
beetles
Weevils
Ladybir
dbeetles
Other
beetles
Mycetopo
rus
nigrans
14.82
12
.2
5
13
.1
1
14.12
0
-13.35
-11.57
-12.25
-13.11
Emblyna
borealis
14.82
12
.2
5
0
0
0
-12.25
0
-0.87
0
Dictyna
major
14.82
12
.2
5
0
0
0
-12.25
0
-12.25
0
Hahnia
glacialis
-0.26
0.
53
0.
64
-14.55
-2.45
-0.53
15.56
-12.64
1.38
Haplodra
ssus
signifer
-1.47
13
.3
5
13
.3
5
-13.35
-13.35
13.35
27.07
24.73
13.35
Hypsosin
ga
groenlan
dica
14.82
13
.3
5
14
.8
2
0
0
-13.35
-14.82
1.43
-14.82
Thanatus
arcticus
-1.87
1.
25
2.
25
-12.94
-0.14
11.77
-2.25
-0.73
-15.05
Xysticus
durus
-1.87
1.
25
1.
87
-12.94
-12.94
11.77
-1.87
11.92
-1.87
Arctosa
insignita
-0.26
0
14
.5
5
-14.55
-0.65
14.12
14.55
1.29
0.65
Pardosa
furcifera
0.93
1.
94
1.
25
-15.75
2.44
14.67
18.66
-2.06
0.55
Pardosa
groenlan
dica
0.48
0.
13
0.
39
0.77
0.03
-2.48
-0.35
-2.47
-0.81
Pardosa
hyperbor
ea
1.1
2.
01
3.
11
-15.91
-2.01
15.66
14.63
-0.37
0.11
Spiders
Other
spiders
Wolf
spiders
Theridii
dae
Enoplogn
atha
intrepida
14.82
0
12
.4
2
0
0
0
-12.42
0
-12.42
Robertus
fuscus
14.82
0
0
0
0
0
0
11.38
0
Ohlertidi
on
lundbecki
14.82
0
12
.4
2
0
0
0
-12.42
0
-12.42
Agyneta
allosubtili
s
14.82
0
0
0
0
0
0
12.07
0
Agyneta
jacksoni
-0.77
1.
1
0.
24
-14.04
-0.55
1.1
13.96
1.63
-13.26
Agyneta
nigripes
14.82
0
0
0
0
0
0
11.38
0
Agyneta
subtilis
14.82
0
0
0
0
0
0
12.77
0
Bathypha
ntes
eumenis
14.82
0
0
0
0
0
0
11.38
0
Dismodic
us
decemoc
ulatus
14.82
12
.2
5
13
.1
1
0
0
-12.25
3
1.53
-13.11
Erigone
arctica
14.82
0
0
0
13.9
0
0
-13.9
-13.9
Erigone
psychrop
hila
14.82
0
0
0
12.11
0
0
-12.11
-12.11
Erigone
whymper
i
14.82
0
0
0
14.05
0
0
-2.67
-14.05
Hilaira
herniosa
14.82
0
0
0
0
13.02
0
0
0
Hybauch
enidium
gibbosu
m
14.82
0
13
.8
0
0
0
0.61
0
0.32
Improph
antes
-1.87
0
0.
-12.94
-12.94
13.02
14.24
12.07
0.53
Linyphii
dae
complicat
us
53
Islandian
a
princeps
14.82
0
0
0
0
13.02
0
0
0
Lepthyph
antes
turbatrix
14.82
0
12
.4
2
0
0
0
-12.42
11.38
-12.42
Mecynarg
us
borealis
-2.56
12
.2
5
12
.2
5
-12.25
0.55
12.25
12.25
12.22
-0.55
Mecynarg
us
morulus
-1.18
0.
29
13
.6
4
-13.64
-13.64
13.31
13.64
13.06
13.64
Mecynarg
us
paetulus
14.82
0
0
0
15.54
0
13.72
-2.77
-15.54
Oreoneti
des
vaginatu
s
14.82
12
.2
5
0
0
0
-12.25
0
-0.87
0
Pelecopsi
s mengei
14.82
12
.9
4
0
0
0
-12.94
0
-0.46
0
Pocadicn
emis
american
a
-2.56
2.
48
3.
88
-12.25
-12.25
-2.48
-3.88
8.9
-3.88
Sciastes
extremus
14.82
0
0
0
0
0
0
11.38
0
Semljicol
a
obtusus
-1.18
1.
01
13
.6
4
1.58
-0.14
-16.23
-1.58
-0.18
0.14
Sisicus
apertus
14.82
12
.2
5
12
.4
2
0
0
-12.25
-12.42
0.23
-12.42
Tiso
aestivus
14.82
14
.0
4
14
.8
2
0
0
0.77
-14.82
-2.66
-14.82
Wabasso
cacumina
tus
14.82
0
0
0
0
0
15.1
0
0
Wabasso
quaestio
14.82
0
0
0
14.41
0
0
-1.42
-14.41
Walckena
eria
castanea
14.82
0
12
.4
2
0
0
0
-12.42
0
-12.42
Walckena
eria
clavicorni
s
14.82
0
0
15.22
0
-15.22
-15.22
12.07
0
Walckena
eria
karpinskii
-0.37
0.
59
1.
34
0.37
-0.26
-0.51
-13.48
1.04
-12.85
47
48
Table S4
49
50
Point-biserial correlations between species abundance and stations (Association function: r.g.).
Selected number of species: 19.
Species
Stat
p-val
Pardosa groenlandica
0.298
0.039
Erigone whymperi
0.258
0.046
Mecynargus paetulus
0.238
0.039
Otiorhynchus nodosus
0.382
0.003
Arctosa insignita
0.363
0.001
Pardosa hyperborea
0.662
0.001
Coccinella transversoguttata
0.507
0.001
Pocadicnemis americana
0.467
0.001
Caenoscelis ferruginea
0.461
0.001
Hybauchenidium gibbosum
0.413
0.001
Hahnia glacialis
0.389
0.001
Thanatus arcticus
0.362
0.003
Otiorhynchus arcticus
0.346
0.004
Mycetoporus nigrans
0.337
0.001
Site 1
Site 2
Site 3
Tiso aestivus
0.282
0.025
Dismodicus decemoculatus
0.27
0.027
Pardosa furcifera
0.35
0.005
Walckenaeria karpinskii
0.273
0.039
0.264
0.043
Site 1 & 2
Site 2 & 3
Hypsosinga groenlandica
51
52
Table S5
53
54
familyname
genusnam
e
speciesname
authorname
wdescriptio
n
wingr
ef
wdevelopm
ent
flying
Carabidae
Nebria
rufescens
(Ström,
1768)
Fully
developed,
but never
observed
flying (cf
Lindroth
1945a and
Lindroth et
al 1974).
In
Scandinavi
an
specimens
the flight
muscles
may be
reduced (cf
Gislén &
Brinck,
1950). cf
Larson and
Gigja
(1959)
mentioned
a few
icelandic
individuals
with
strikingly
better
developed
wings than
in most
speciemens
2
Full
no
.
Carabidae
Patrobus
septentrionis
Dejan, 1821
Hind wings
always fully
developed,
and
spontaneou
s flight has
been
observed in
northern
Norway (cf
Lindroth
1945a)
2
Full
yes
Carabidae
Bembidion
grapii
Gyllenhal,
1827
Polymorphi
c hind
wings. Four
types from
fully
developed
to quite
rudimentar
y, scale like
(cf Lindroth
1945a,
1949,
1957,
1958,
1963a,
1979;
Lindroth et
al 1973).
High
spatial
variation in
Greenland
cf Lindroth
(1959) and
table 3.
2
Full/reduce
d
yes/n
o
Carabidae
Trichocellu
s
cognatus
(Gyllenhal,
1827)
Hind wings
always well
developed
and at
least in
europe
flight is
frequent
(cf Lindroth
1945a, Den
Boer et al
1980)
2
Full
yes
Dytiscidae
Hyrdoporu
s
morio
Aubé, 1838
Hind wings
always fully
developed
and able to
2
Full
yes
fly.
Dytiscidae
Colymbete
s
dolabratus
(Paykull,
1798)
Hind wings
constantly
full and
functional,
and the
species has
frequently
been
observed
flying
2
Full
yes
Gyrinidae
Gyrinus
opacus
Sahlberg,
1819
The hinds
wings are
always fully
developed
and
functional
2
Full
yes
Hydrophilida
e
Helophorus
brevipalpis
Bedel, 1881
The hind
wings are
always fully
developed,
and the
species is
an active
flyer (cf
BalfourBrowne,
1958),
frequently
found in
wind drift
material on
seashores
in Denmark
(cf. M
Hansen
1986)
2
Full
yes
Staphylinida
e
Omalium
excavatum
Stephens,
1834
The hind
wings are
fully
developed
and no
doubt
functional
(cf.
Lindroth et
al 1973)
2
Full
yes
Staphylinida
e
Xylodromu
s
concinnus
(Marsham,
1802)
No
information
2
NA
NA
Staphylinida
e
Euaestethu
s
laeviusculus
Mannerheim
, 1844
No
information
1
NA
NA
Staphylinida
e
Lathrobium
fulvipenne
Gravenhorst
, 1806
In Iceland
(and
Greenland)
the hind
wings are
rudimentar
y, but a
macroptero
us form is
known
from the
European
Continent
(cf.
Lindroth et
al 1973)
2
Full/reduce
d
yes/n
o
Staphylinida
e
Quedius
mesomelinus
(Marsham,
1802)
The hind
wings are
slightly
reduced
and
according
to Lindroth
et al
(1973)
possibly
nonfunctio
nal.
2
reduced
no
Staphylinida
e
Quedius
fellmanni
(Zetterstedt
, 1838)
Wings
reduced
and not fit
for flight
(cf
Smetana
1964)
2
reduced
no
Staphylinida
e
Mycetopor
us
nigrans
Mäklin,
1853
No
information
1
NA
NA
Staphylinida
e
Micralymm
a
marinum
(Ström,
1783)
The hind
wings are
always
entirely
reduced
2
reduced
no
Staphylinida
e
Micralymm
a
brevilingue
Schiödte,
1845
Hind wings
always
entirely
reduced
2
reduced
no
Staphylinida
e
Gnypeta
cavicollis
Sahlberg,
1880
The hind
wings are
fully
developed.
2
Full
NA
Staphylinida
Atheta
groenlandica
Mahler,
The hind
2
Full
yes
e
1988
wings are
always fully
developed
and no
functional
doubt.
Staphylinida
e
Atheta
islandica
(Kraatz,
1856)
The hind
wings are
full and
functional
(Lindroth
et al 1973)
2
Full
yes
Staphylinida
e
Atheta
hyperborea
Brudin,
1940
Hind wings
fully
developed
2
Full
NA
Staphylinida
e
Atheta
vestita
(Gravenhor
st, 1806)
The hind
wings are
fully
developed
and
probably
functional
(cf Lindroth
et al 1973)
2
Full
yes
Byrrhidae
Simplocari
a
metallica
(Sturm,
1807)
The hind
wings are
always fully
developed.
2
Full
NA
Byrrhidae
Simplocari
a
elongata
Sahlberg,
1903
The hind
wings are
fully
developed.
2
Full
NA
Byrrhidae
Arctobyrrh
us
subcanus
(LeConte,
1878)
At least in
greenland
the hind
wings are
so short
that flight
is
impossible
2
reduced
no
Byrrhidae
Byrrhus
fasciatus
Forster,
1771
The hind
wings are
fully
developed
and the
species
regularly
flies in
sunny
wearher (cf
Lindroth
2
Full
yes
1931 and
Lindroth et
al 1973).
Cryptophagi
dae
Caenosceli
s
ferruginea
(Sahlberg,
1820)
Wings fully
developed
2
Full
NA
Coccinellidae
Nephus
redtenbacheri
(Mulsant,
1846)
Wings fully
developed
and
functional
2
Full
yes
Coccinellidae
Coccinella
transversogutt
ata
Falderman,
1835
The hind
wings are
always fully
developed
and the
species are
quite often
seen flying
short
distances,
especially
when
disturbed
2
Full
yes
Latridiidae
Latridius
minutus
(Linnaeus,
1767)
Hind wings
are fully
developed
and
functional
2
Full
yes
Latridiidae
Latridius
transversus
(Olivier,
1790)
No
information
1
NA
NA
Latridiidae
Corticaria
rubripes
Mannerheim
, 1844
Hind wings
are always
fully
developed
2
Full
NA
Chrysomelid
ae
Phratora
polaris
(Sparre
Scheider,
1886)
No
information
1
NA
NA
Curculionida
e
Otiorhynch
us
arcticus
(O.
Fabricius,
1780)
The hind
wings are
always
rudimentar
y
2
rudimentar
y
no
Curculionida
e
Otiorhynch
us
nodosus
(Müller,
1764)
Always
wingless
2
Wingless
no
Curculionida
e
Hypera
diversipunctat
a
(Schrank,
1798)
In
greenland
wings
highly
reduced
2
reduced
no
Curculionida
e
Dorytomus
imbecillus
Faust, 1882
No
information
2
NA
NA
Curculionida
e
Rutidosom
a
globulus
(Herbst,
1795)
The
hindwings
are
rudimentar
y
2
rudimentar
y
no
Hydrophilida
e
Cercyon
obsoletus
(Gyllenhal,
1808)
No
information
2
NA
NA
Staphylinida
e
Euphaleru
m
sorbi
(Gyllenhal,
1810)
No
information
2
NA
NA
Staphylinida
e
Ocalea
cf. picata
(Stephens,
1832)
No
information
2
NA
NA
Staphylinida
e
Othius
angustus
Stephens,
1833
No
information
2
NA
NA
Staphylinida
e
Philonthus
cf. cepholates
(Gravenhor
st, 1802)
Macroptero
us.
2
Full
NA
Staphylinida
e
Philonthus
politus
(Linnaeus,
1758)
Macroptero
us and
often flying
(cf Lindroth
et al 1973)
2
Full
yes
Buprestidae
Melanophil
a
acuminata
(De Geer,
1774)
No
information
2
NA
NA
Dermestidae
Anthrenus
museorum
(Linnaeus,
1758)
No
information
2
NA
NA
Dermestidae
Attagenus
pellio
(Linnaeus,
1758)
No
information
2
NA
NA
Dermestidae
Dermestes
lardarius
(Linnaeus,
1758)
No
information
2
NA
NA
Dermestidae
Reesa
vespulae
(Milliron,
1939)
No
information
2
NA
NA
Lyctidae
Lyctus
brunneus
(Stephens,
1830)
No
information
2
NA
NA
Anobiidae
Ernobius
mollis
(Linnaeus,
1758)
No
information
2
NA
NA
Anobiidae
Anobium
punctatum
(De Geer,
1774)
No
information
2
NA
NA
Ptinidae
Ptinus
fur
(Linnaeus,
1758)
No
information
2
NA
NA
Ptinidae
Ptinus
tectus
Boieldieu,
No
2
NA
NA
1856
information
Ptinidae
Ptinus
raptor
Sturm,
1837
No
information
2
NA
NA
Trogossitida
e
Tenebroide
s
mauritanicus
(Linnaeus,
1758)
No
information
2
NA
NA
Malachiidae
Malachius
aeneus
(Linnaeus,
1758)
No
information
2
NA
NA
Cucujidae
Oryzaephil
us
surinamensis
(Linnaeus,
1758)
No
information
2
NA
NA
Cryptophagi
dae
Cryptopha
gus
acutangulus
Gyllenhal,
1827
No
information
2
NA
NA
Cryptophagi
dae
Cryptopha
gus
lapponicus
Gyllenhal,
1827
No
information
2
NA
NA
Latridiidae
Corticaria
serrata
(Paykull,
1798)
No
information
2
NA
NA
Latridiidae
Dienerella
filum
(Aubé,
1850)
No
information
2
NA
NA
Latridiidae
Thes
bergrothi
(Reitter,
1880)
No
information
2
NA
NA
Tenebrionida
e
Tribolium
confusum
Jacquelin du
Val, 1863
No
information
1
NA
NA
Tenebrionida
e
Tribolium
destructor
Uyttenboog
art, 1934
No
information
2
NA
NA
Tenebrionida
e
Tenebrio
obscurus
Fabricius,
1792
No
information
2
NA
NA
Cerambycida
e
Callidium
violaceum
(Linnaeus,
1758)
No
information
2
NA
NA
Cerambycida
e
Gracilia
minuta
(Fabricius,
1781)
No
information
2
NA
NA
Cerambycida
e
Molorchus
minor
(Linnaeus,
1758)
No
information
2
NA
NA
Cerambycida
e
Phymatode
s
testaceus
(Linnaeus,
1758)
No
information
2
NA
NA
Cerambycida
e
Pogonoche
rus
fasciculatus
(De Geer,
1775)
No
information
2
NA
NA
Cerambycida
e
Tetropium
castaneum
(Linnaeus,
1758)
No
information
2
NA
NA
Cerambycida
e
Xylotrechu
s
colonus
(Fabricius,
1775)
No
information
2
NA
NA
55
Curculionida
e
Otiorhynch
us
sulcatus
(Fabricius,
1775)
No
information
1
NA
NA
Scolytidae
Pityogenes
chalcographus
(Linnaeus,
1761)
No
information
2
NA
NA
Synchronous seasonal dynamics in composition,
richness and turnover of arthropod assemblages
across the Arctic
Rikke Reisner Hansena*, Ashley Asmusb, Joseph James Bowdena, Sarah Lobodac, Amy M. Ilerd,
Paul J. CaraDonnad, Oskar Liset Pryds Hansena,f, Signe Normanda,g, Maarten J.J.E. Loonene,
Niels Martin Schmidta,h, Peter Aastrupa,h, Toke Thomas Høyea,i
a. Arctic Research Centre, Department of Bioscience, Aarhus University, Aarhus, Denmark
b. Department of Biology, University of Texas, Arlington, USA
c. Mc. Gill University, Montréal, Canada
d. Aarhus Institute of Advanced Studies, Aarhus University, Aarhus, Denmark
e. Arctic Centre, University of Groningen, The Netherlands
f. Natural history museum of Aarhus, Aarhus, Denmark
g. Ecoinformatics & Biodiversity, Department of Bioscience, Aarhus
University, Aarhus, Denmark
h. Arctic Ecosystem Ecology, Department of Bioscience, Roskilde, Denmark
i. Department of Bioscience, Kalø, Aarhus University, Rønde, Denmark
* Corresponding author: Rikke Reisner Hansen, E-mail: [email protected]
Abstract
Tracking changes in community composition in response to environmental change, requires
knowledge on species distributions in space and time. For Arctic Arthropods this knowledge is
accumulating slowly due to the prohibitive costs of field work and species identification.
Accordingly, it is important to determine to which extent the timing and duration of sampling
affects detectability of species. With a solid base in a monitoring scheme incorporating nearly 20
years of consecutive weekly arthropod sampling in North-Greenland, we studied how sampling
time and duration of sampling, affected the distribution patterns of Arctic arthropods at family
and species level resolutions. Through multivariate models, turnover measures, and species
richness, we assessed the seasonal window where arthropod diversity was stable and compared
the results to similar data from three other sites in the Arctic, spanning a large geographic and
climatic. To further document how this seasonal window could be expected to deviate in
response to climate change, we used environmental fitting analysis to correlate findings with
environmental variables. Arthropod compositions displayed a similar multivariate pattern across
sites, habitats taxonomic resolution and years. However, compositions differed significantly
between habitats as well as sampling dates, and there was an interaction between sampling dates
and habitats for most sites. Increasing temperatures and advancement in timing of snowmelt
extended the seasonal window where richness peaked, and this also varied slightly among sites.
The recommendations made here are a useful tool for accruing large scale data on distributional
patterns.
Introduction
Global biodiversity loss is occurring at an alarming rate, due to global change and increasing
human influence (Butchart et al. 2010). Monitoring of biodiversity, as well as, gaining a full
comprehension of the spatial and temporal distribution of species are among the top priorities in
ecology (Heller & Zavaleta 2009). Recent accelerating climate change, which alters habitats and
compromises species living conditions, increases relevance further (Elmendorf et al. 2012). In
spite of increasing focus, sufficient information about the distribution of species richness in large
regions, such as the Arctic, remains sparse for many biological groups (CAFF 2013; Hodkinson
2013). Many ecological studies seek to track changes in species diversity and composition
throughout the season and over multiple years (Høye & Forchhammer 2008; Spitzer & Jaroš
2009; Bowden et al. 2015), in order to adequately describe the changes in communities brought
on by environmental disturbances, such as climate change (Magurran et al. 2010). Although
sampling all taxa at multiple sites through a full season of activity is preferable, it may be a
daunting task, due to logistical constraints and limited resources available. This predicament,
however, raises an interesting question. What fraction of the community is ignored by not
covering full seasons?
Numerous studies on species-area relationships have shown strong positive relationships
between species richness and turnover and the area or the heterogeneity of the habitat sampled
(Connor & McCoy 1979; Lomolino 2000; Hansen et al. 2016a; Hansen et al. 2016b; Vandvik et
al. 2016). The temporal pendant of species area relationship, the species–time relationship (Adler
& Lauenroth 2003; White & Gilchrist 2007; McGlinn & Palmer 2009), describes the similarly
positive relationship between species richness and the length of time a site is studied (Preston
1960), but has not yet been studied as intensively. Temporal species accumulation operates at
multiple time scales. Firstly species accumulates intraannually as seasons and conditions shifts.
Secondly, variability in weather patterns between years accumulates species over decades or
even centuries. Thirdly, there is the evolutionary change in extinction and speciation rates,
operating over centuries and millennia (Preston 1960; Magurran et al. 2010; Boggs 2016).
Therefore the seasonal development of species is expected to change across years. Moreover,
changes over time in a community may occur as the observed accumulation of richness as more
species are added throughout the time frame, but also from turnover events where one species
replaces another, making both community composition and richness important measures (Yen et
al. 2016). Most past and contemporary studies of species-time relationships refer to the
interannual variation of species aggregating over the course of multiple years.
A few studies have indicated that lowering the temporal extent and resolution of
sampling may have limited impact on species compositions and diversity (Nally et al. 2004; Xu
et al. 2015). It is, however, unknown to what spatial and taxonomic extent the generality of these
patterns can be inferred. Furthermore, when combining spatial and temporal patterns, the
influence of increasing the spatial extent exceeds that of temporal increments on species richness
(Erös & Schmera 2010). One way of capturing the seasonal window where local colonization
and extinction patterns are low, is to look for statistical regularity and aggregation in the
observed compositional patterns (White & Gilchrist 2007). Considering this, the rate of species
accumulation might not be constant over the span of a season, and specific subsets in time may
capture significant proportions of the community.
Multiple studies of Arctic distributional patterns have disputed the common conception
of the Arctic as a rather homogenous landscape and that heterogeneity between habitats cause
great diversification (Bowden & Buddle 2010; Normand et al. 2013; Sikes et al. 2013; Ernst et
al. 2016; Hansen et al. 2016a), yet little focus has been given to the temporal variation in
arthropod compositions across the active season in Arctic regions. While species in the Arctic
are under strong seasonal impacts, the region offers a powerful model system for studying
changes over time. Hence temporal studies of Arctic communities can prove useful for
overcoming the challenges brought on by sampling large or multiple areas. Moreover, the Arctic
region spans large climatic gradients, rendering results of temporal studies applicable at the
global scale.
Invertebrates have long been recognized as good indicators of changing communities due
to their small body sizes, relatively short generation time and ectothermic lifestyle. Terrestrial
arthropods furthermore play an important role in the Arctic food webs as they represent more
than one third of all terrestrial organisms, including plants and fungi (CAFF 2013). Since timing
of snowmelt and increasing summer temperatures have been identified as important drivers of
the active season length of Arctic arthropods (Høye & Forchhammer 2008), they are likely to be
important factors in controlling seasonal dynamics of Arctic arthropod communities.
In this study, we evaluate the effect of sampling time and duration on arthropod diversity,
turnover and composition. More specifically, we study the seasonal development across multiple
sites, taxa and habitats. By including both family- and species level data, we verify whether the
patterns are replicated at lower taxonomic resolutions. We investigate the difference in species
composition and diversity between habitats and discuss this in relation to temporal variation in
species composition and species richness. We propose an optimal timeframe where communities
are stable and furthermore suggest a tradeoff between sampling more habitats and shorter
timeframes for answering questions regarding compositional changes. An optimal sampling
window (a) maximizes community diversity; (b) is characteristic of whole-season community
composition; and (c) is robust to effects of inter-annual weather variability and long-term climate
change. The goal of this study was to determine the “optimal” sampling window (date and
duration of sampling) for arthropod communities across a wide array of terrestrial arctic
locations and habitat types. We divided this goal into two objectives:
Objective 1: We describe seasonal dynamics in community composition and compare
indices of arthropod community composition, stability and species richness for
sampling windows that vary in date and duration across multiple Arctic sites and
habitat types;
Objective 2: We evaluate the effects of inter-annual weather variability and long-term
climate trends on community assembly and community indices for each sampling
window within sites and habitat types.
Methods
Data
We compiled datasets entailing three different regions within the Arctic (Svalbard, Greenland
and Alaska). The Greenland datasets are comprised of two datasets collected as part of
Greenland ecosystem Monitoring (GEM). One consists of 18 consecutive years (1996 – 2014)
(samples from 2010 were lost in transit from Greenland) of weekly pitfall trap samples from
Zackenberg valley, North-east Greenland (74º28’ N, 20º34’ W). The Nuuk dataset, consists of
weekly pitfall trap samples (2008 – 2010) in Kobbefjord, South-west Greenland (64°07’N /
51°21'W). Both datasets have been identified to family level and furthermore butterflies, spiders
and muscid flies have been identified to the species level for Zackenberg. The Alaskan dataset,
sampled at Toolik lake at (68°38' N, 149°35' W) holds two consecutive years (2010 and 2011) of
weekly pitfall trap sampling, and has been identified to family level (Rich et al. 2013). From
Svalbard (78°55' N, 11°55' W), pitfall sampling occurred from 2009 to 2011 and has been
identified to the lowest possible taxonomic level (Table 1). For each dataset, we selected the
weeks where all habitats had been equally sampled across all years and standardized to counts
per trap per week. The week numbers were calculated from day of year.
Analysis of site characteristics
We examined the distribution of the four regions in relation to climatic gradients within the
Arctic using Principal Component Analysis (PCA). Data for five climatic variables were
extracted from the worldclim data set (Hijmans et al. 2005): annual mean temperature, mean
temperature of the warmest quarter, precipitation of the warmest quarter, precipitation of the
coldest quarter, and minimum temperature of the coldest month. PCA was conducted with
climate data within the sub- to high Arctic and the most common climate (75-percentile) within
Greenland, subarctic, low arctic, and high arctic was delineated. The four regions were plotted
according to their climatic conditions with the multidimensional climate space.
Arthropod community composition and seasonal assembly
To visually identify the time frame of the season where arthropod species composition was most
stable, we first modeled each dataset through latent variable modelling. Latent variable
modelling is a Bayesian model-based approach that explains community composition through a
set of underlying latent variables to account for residual correlation, for example due to biotic
interaction. This method offers the possibility to adjust the distribution family to account for
over-dispersion in count data via negative binomial distribution. Thus, it also accounts for the
increasing mean-variance relationship without confounding location with dispersion (Hui et al.
2015). Three “types" of models may be fitted: 1) With covariates and no latent variables, boral
fits independent response GLMs such that the columns of y are assumed to be independent; 2)
With no covariates, boral fits a pure latent variable model (Rabe-Hesketh et al. 2004) to perform
model-based unconstrained ordination (Hui et al. 2015); 3) With covariates and latent variables,
boral fits correlated response GLMs, with latent variables.
We created latent variable models at site levels for each dataset with two latent variables.
to visualize how the arthropod communities were distributed. This method is comparable to a
two dimensional non metric multidimensional scaling (NMDS) plot. From the latent variable
model, we extracted the posterior median values of the latent variables which we used as
coordinates on ordination axes to represent family level composition at plot level (Hui et al.
2015). We drew convex ellipses around posterior median values belonging to each habitat for
each dataset based on 95 percent confidence limits of the yearly averages. For this purpose, we
used the function ‘ordiellipse’ in the r package ‘Vegan’ (Oksanen et al. 2016). We repeated this
analysis at species level for the Zackenberg dataset.
We then tested whether the seasonal development in arthropod composition differed
significantly between habitats through a multivariate extension of General Linear Models
(GLMs), using the function ‘manyglm’ in the package ‘mvabund’ (Wang et al. 2012). This
recently developed method offers the possibility to model distributions based on count data by
assuming a negative binomial distribution. We tested for main effects of week and habitats and
for an interaction between the two terms. We then repeated the latent variable modeling at
habitat level for all datasets with a significant interaction term, and calculated convex ellipsoids
around posterior median values belonging to each week based on the standard error of the yearly
averages. We extracted the centroid of each ellipsoid, which we plotted on top of the latent
variable plot. At habitat level, we tested if the seasonal development differed significantly
between years, by testing for an interaction between time of season (week) and year.
Sampling window – based arthropod species richness and turnover
Multivariate methods for measuring beta diversity have been shown robust compared to
classical methods. This is due to the dependency upon gamma diversity in classical methods,
such that when sampling effort increases, so does beta diversity along with gamma diversity
(Bennett & Gilbert 2016). We used the centroids extracted from the latent variable models for
each habitat from the Zackenberg family level dataset. While the distance to the group centroid is
a common way of measuring spatial beta diversity (Anderson et al. 2006), it describes the yearly
variations in beta diversity within the weeks and not the unidirectional, temporal drift in
multivariate space from one week to the next. We used the distance between week to week
centroids as a measure of temporal turnover, and used the average area of the two ellipsoids as
error bars to represent the interannual variation. We calculated species richness for each week
and each habitat across all years. To identify the timeframe and length of timeframe in which the
highest number of species were represented, we calculated species richness for each combination
of timing and duration; four week sampling at nine different points, three week sampling at ten
different points, two week sampling at eleven different points of the season and one week
sampling at 12 different points. We explored how what proportion of total season species
richness was captures for each of the sampling strategies.
Climatic variability and the effect on seasonal development
We used the Zackenberg family level dataset to analyze the sensitivity of diversity to
climatic variables. This dataset is the only one, where environmental variables were measured
every year. Timing of snowmelt has been shown to occur significantly earlier, as well as average
may-august temperatures to become significantly warmer (Bowden et al. 2015). Timing of
snowmelt has furthermore proven a significant predictor of season length for arthropods (Høye et
al. 2014). To identify if the different seasonal development between years was climate related,
we ran a multivariate correlation analysis with the function ‘envfit’ in the ‘vegan’ package. We
used the posterior median values of the latent variables as response variables and timing of
snowmelt, average may-august temperature, year and week as predictor variables. We examined
how the changing climate has affected seasonal development of arthropod assemblages by
dividing the Zackenberg family level dataset into years of early and late snowmelt, as well as,
warm and cold years. Early snowmelt years were categorized as years where the average day of
year for snowmelt lay below the average day of year for all years. Similarly, warm years where
categorized as years where the average summer (may august) temperature lay above the average
across years. Following these divisions, we calculated species richness for each category.
Results
Analysis of site characteristics
The investigated sites were distributed across the climatic space of the PCA. Two sites were
distributed within the high Arctic climate (Ny Aalesund and Zackenberg) and two sites in the
low Arctic (Nuuk and Toolik) (Fig. 1). The first two axis of the PCA explained 87% of the
variation and was mainly correlated with precipitation of the warmest quarter (1. Axis) and
temperature of the warmest quarter (2. axis). Toolik have warmer summers then the other sites
and Nuuk, Ny Aalesund and Zackenberg is distributed along a gradient from wet to dryer
conditions.
Arthropod community composition and seasonal assembly
Arthropod compositions differed significantly between all of the investigated habitats at
each site and there was a significant effect of week (Table 2). The moisture gradient was a
driving component across sites. At Zackenberg, there was a clear distinction between arthropod
communities, in the wet habitats and arthropod communities in the dry and mesic habitats. For
Nuuk habitats, the biggest distinction was found between the shrub covered habitat and the wet,
with the dry habitat as an intermediate (Fig. 2). However, the interaction between seasonal
development and habitat was not significant for Toolik and Ny Aalesund, even though arthropod
compositions differed significantly between both habitats and week (Table 2).
The weekly development through the season, displayed a characteristic u-shaped pattern
for all the habitats at all the sites. The arthropod compositions were more similar towards the
beginning and the end of the season. There was a change in multivariate space from week to
week throughout the season, yet, the communities seemed to stabilize during mid-season with
less distance between the weekly centroids (Fig. 3).
Sampling window – based on arthropod species richness and turnover
At Zackenberg, the weeks of significantly (non-overlapping error bands) highest richness
where between week 29 until week 33 for the dry habitat. The mesic habitat varied from week to
week and richness never reached a plateau. In the wet habitat, richness increased until week 29
and stabilized before dropping in week 32. Turnover patterns were opposite to richness and
stabilized at the lowest point between week 29 and 30, until increasing again between week 32
and 33. Turnover in the mesic habitat did not reach a stable low point until between week 32 and
33. In Nuuk, there was inter annual variability both in species richness and turnover, but species
richness in the shrubs and the dry habitat peaked from week 29 to 32. The wet habitat had
significantly higher species richness in week 32 and it was overall lower than in the shrub and
dry habitat. Turnover did not differ significantly in any of the habitats between any of the weeks
for Nuuk. Species richness at Toolik was significantly highest in week 25 to 30 and declined
drastically in week 31. Turnover was lowest between weeks 26 to 28. In Svalbard, species
richness was significantly highest in week 29 with a maximum of 13 taxa present, and turnover
did not change significantly between any of the weeks (Fig. 4).
Analysis of sampling strategy in Zackenberg mirrored the weeks of highest richness and
turnover and revealed that sampling either week in a one-week sampling strategy between weeks
28-32, yielded 69-72 percent of the taxa present when sampling a full season. Four weeks of
sampling that time frame yielded 79-81 percent of the full season taxa detected. The optimal
timespan for one week sampling strategy in Nuuk was starting week 28 and 29 which detected
55-57 percent of the full season taxonomic richness, whereas four weeks of sampling with the
same starting weeks presented 64-69 percent. In Toolik, the optimal starting week was between
week 26 and 28 for one week sampling duration, which detected 59-61 percent of the taxa
present throughout a full season and four week in with the same starting weeks yielded 75-77
percent. In Ny-Aalesund, there was no significant difference between sampling duration (Fig.
5).
The effect of climatic variability on seasonal development
Temperatures had a significant effect on arthropod compositions in the dry and mesic
habitat at Zackenberg and year was highly correlated with temperature. In the wet habitat, there
was no significant effect of snowmelt, temperature and year (Fig. 6). The average day of year for
snowmelt across all years was day 152. For six years (2004, 2005, 2009, 2010, 2011 and 2013)
the average snowmelt date lay below this threshold, and these were categorized as early
snowmelt years. Average may-august temperature was 2.23 °C and the years 1996 – 2002 and
2014 had an average may-august temperature below this threshold. Taxonomic richness was
higher and peaked earlier for the warm and early snowmelt years (Fig. 7).
Discussion
Across sites, habitats, and taxonomic levels, arthropod compositions displayed similar trends in
seasonal development with less seasonal drift during the peak season weeks (i.e. weeks with low
turnover). In present study one week of carefully planned sampling at Zackenberg research
station, represented up to 72 percent of richness detected through a full season of sampling.
Zackenberg monitoring program presents us with a unique chance of studying temporal
trends as it has been operating for 20 years and has collected data on arthropods with weekly
intervals throughout the active season. A long term monitoring program at ecosystem level is
quite unique and can answer questions entailing interaction dynamics through trophic mismatch
and cascades (Mortensen et al. 2014). Additional short term sampling schemes with high spatial
resolution across multiple sites could help fill gaps in our knowledge of species distributions and
add information to the model predictions of climate change effects on biodiversity. This could be
partly accomplished through citizen science schemes as well as cross disciplinary cooperation
among researches, where full seasonal commitments may not be an option, but simultaneous
multiple year and site collections are. With a strong standardized sampling design available,
people without scientific training can carry out fieldwork and will be capable of generating data,
which will help biologists answer questions regarding species distributions. Utilizing local
communities is a valuable way of accruing large amounts of data otherwise unattainable. It is
important to note that we are not trying to limit the temporal extent, nor resolution of long term
monitoring schemes. These recommendations are solely for studies of distributional patterns and
inferences on life history traits, species interactions and other species specific responses, require
longer timeframes to answer. We are still dependent on long term sampling strategies spanning
full seasons with higher detailed resolution levels. However, these findings have merits in
addition to the high resolution monitoring schemes.
Climate changes are altering habitats (Myers-Smith et al. 2011; Elmendorf et al. 2012)
and expanding the active season for arthropods (Høye & Forchhammer 2008), affecting
arthropod compositions across the Arctic (Bowden & Buddle 2010; Hansen et al. 2016b). Time
and duration of peak richness may therefor change with a changing climate. Our results show
that increasing temperatures does not cause shifts in timing of peak richness, but increases
species richness and extends the time frame, where species richness is significantly highest.
Sampling strategies have previously been studied in other ecosystems and for other groups of
organisms with the intent of describing effect of lower sampling extent and intensity on diversity
patterns. For instance temporal aggregation of species had a substantial effect on the species-time
relationship of rodent populations in Arizona (White & Gilchrist 2007). A study of ciliated
protozoa in the Yellow sea, north China, a sampling regime with one third the amount of original
sampling, recovered > 75% information of the total seasonal variability and > 90 percent of
protozoan ciliate species present (Xu et al. 2015). Compared to our findings of little effect of
changing climates on timing of optimal sampling window, as well as, the similar u-shaped
pattern across a large climatic gradient, super generalizations to other ecosystems and regions is
possible.
The study indicates that short term sampling procedures comes with some considerations.
The peak in species richness varies between sites with earlier seasonal peaks at the lower
latitudes. As a consequence, sampling one week requires careful consideration of the geographic
location of the study. Interaction between seasonal development and habitat coupled with
different temporal development in species richness underlines not only a need to sample multiple
habitats, but also a detailed consideration towards habitat characteristics, such as soil moisture
and vegetation structures. For instance, wet and mesic habitats display the lowest and latest
peaks in species richness. These results mirror a previous study from the Godthåbsfjord area in
Nuuk, where diversity and species richness where overall lowest in the fens (Hansen et al.
2016b). The year to year variation in turnover for some sites (Nuuk and Ny Aalesund), which is
less pronounced for species richness, indicates that abundances vary substantially between years.
However, multiple inter annual short term samplings will help counterbalance the year-to-year
variability. Shorter sampling windows are more sensitive to stochastic events, such as less
optimal weather and it is therefore always desirable to sample the longest term possible.
This study is to our knowledge the first to answer questions regarding sampling time and
duration of Arctic arthropods, and may prove valuable in the planning of logistically challenging
field work (Post & Høye 2013). In conclusion, should compromises be made between spatial and
temporal sampling resolution and extent, we believe that an increase of spatial, at the cost of
temporal intra annual extent and resolution, is preferable. In combination with the long term
monitoring programs, these recommendations will go a long way in mapping species
distributions, as well as, responses to climate change.
Acknowledgements
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1
Table 1
2
Overview of sites: The asterisk indicates that the dataset is used for supportive analyses and the
3
results can be found in supplement material
4
5
6
7
8
9
10
11
12
13
Dataset
Position
Years
Sampling
intensity
Taxonomic
resolution
Habitats
1996 – 2014
Selected
sampling
period
(weeks)
24:35
Zackenberg
(Greenland)
Nuuk
(Greenland)
Ny
Aalesund
(Svalbard)
Toolik lake
74º28’ N,
20º34’ W
64°07’N /
51°21'W
78°55' N,
11°55' W
Weekly
Family level
2008 – 2014
25:39
Weekly
Family level
2009 – 2011
27:33
Two-day
interval
2010 – 2011
21:30
Weekly
Family,
super family
and order
Family level
Dry,mesic
and wet
Dry, wet
and shrub
Mesic and
dry
68°38' N,
149°35' W
Zackenberg* 74º28’ N,
20º34’ W
1996 – 2014
24:35
Weekly
Species level
(butterflies,
spiders and
muscid flies)
Open and
shrub
Dry, mesic
and wet
14
Table 2
15
Table of deviance. Table showing results of the multivariate GLM, testing the difference
16
between habitat and weeks, as well as the interaction between them at family and species level.
17
The Zackenberg family level interaction between year and week at habitat level is also shown.
Dataset
Variable
Zackenberg
Week
Habitat
Week:Habitat
Week
Habitat
Week:Habitat
Week
Habitat
Week:Habitat
Week
Habitat
Week:Habitat
Week
Habitat
Week:Habitat
Year
Week
Year:Week
Year
Week
Year:Week
Year
Week
Year:Week
Nuuk
Ny Aalesund
Toolik lake
Zackenberg
species level
Zackenberg wet
Zackenberg mesic
Zackenberg dry
18
19
20
21
22
23
Degrees of
freedom
1
2
2
1
2
2
1
1
1
1
1
1
1
2
2
1
1
1
1
1
1
1
1
1
Deviance
P
753
2848.1
554.7
493.2
761.7
186.1
133.6
130.8
23.9
508.1
244.8
91.6
511.2
2194.7
97.5
198
382.2
65.9
233.5
640.9
93.2
406
321.3
130.3
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.190
0.010
<0.001
0.265
<0.001
<0.001
<0.001
<0.001
<0.001
0.020
<0.001
<0.001
0.010
<0.001
<0.001
0.010
24
Figure 1
25
Climate space. Plot of the principal component analyses with the following variables
26
downloaded from http://www.worldclim.org/bioclim: annual mean temperature, minimum
27
temperature of the coldest month, mean temperature of the warmest quarter, precipitation of the
28
warmest quarter and precipitation of the coldest quarter. Climate data is plotted with grey colors
29
and the sites in blue. Dashed lines delineate the 75-percentile of the climatic conditions within
30
high-, low- and sub-Arctic, respectively.
31
32
33
34
Figure 2
35
Site level latent variable plots. Latent variable plots displaying how arthropod communities are
36
distributed along two latent variables. Ellipsoids show the 95 percent confidence bands around
37
habitats.
38
39
40
41
42
Figure 3
43
Seasonal variation in arthropod community composition. Latent variable plots showing the latent
44
variable models divided onto habitats. The division was only made if there was a significant
45
interaction between habitat and week. The points below are centroids of the standard error of the
46
weekly centroids.
47
48
49
50
51
Figure 4
52
Richness and turnover plots. Family level richness and turnover plots showing species richness,
53
as well as, distance between centroids for the weekly average of across years. Both indices are
54
shown with standard errors of interannual variation.
55
56
57
58
59
60
61
62
63
64
65
66
Figure 5
67
68
69
70
71
Proportion of total species richness. Line graph showing how much of the total species richness
is detected with four different sampling durations (one week, two week, three week and four
week). The week number on the x-axis shows starting week and the y-axis shows the proportion
of the total species richness detected within a year. Error bars are standard error of the
interannual variation.
72
73
74
75
76
77
78
79
Figure 6
80
81
Correlation with environmental variables. Biplots showing only significant (p<0.05) variables
from the environmental fitting analysis
82
83
84
85
86
87
88
89
90
91
92
93
94
95
Figure 7
96
A) Climatic variability and effect on species richness. Seasonal development of species richness
97
divided into years with late (blue colors) versus early (red colors) snowmelt and cold (blue
98
colors) versus warm (red colors) years. Error bars represents standard error of the weekly
99
average across years.
100
101
102
103
Supplements
104
Fig S1: Species level latent variable models for Zackenberg data. The top figure shows all
105
habitats and the three figures below shows the seasonal weekly development divided onto
106
habitats
107
Polar Biol (2015) 38:559–568
DOI 10.1007/s00300-014-1622-7
ORIGINAL PAPER
Habitat-specific effects of climate change on a low-mobility Arctic
spider species
Joseph J. Bowden • Rikke R. Hansen
Kent Olsen • Toke T. Høye
•
Received: 10 June 2014 / Revised: 8 October 2014 / Accepted: 12 November 2014 / Published online: 2 December 2014
Springer-Verlag Berlin Heidelberg 2014
Abstract Terrestrial ecosystems are heterogeneous habitat mosaics of varying vegetation types that are differentially affected by climate change. Arctic plant
communities, for example, are changing faster in moist
habitats than in dry habitats and abiotic changes like
snowmelt vary locally among habitats. Such differences
between habitats may influence the effects of climate
changes on animals and this could be especially true in
low-mobility species. Suitable model systems to test this
idea, however, are rare. We examined how proxies of
reproductive success (body size, juvenile/female ratios)
and sex ratios have changed in low-mobility crab spiders
collected systematically over a 17-year period (1996–2012)
from two distinct habitats (mesic and arid dwarf shrub
heath) at Zackenberg in northeast Greenland. We identified
all adults in the collection to confirm that they represented
just one species (Xysticus deichmanni Sørensen) based on
morphology. This provided a unique opportunity to measure recruitment potential because we could assume that all
juvenile crab spiders belonged to that species. We determined sex, stage, and size of all specimens (n = 2,115).
Body size variation was significantly related to the timing
of snowmelt and differed significantly between the sexes
and habitats with the spiders in the mesic habitat showing a
stronger temporal response to later snowmelt. Juvenile/
female ratios also differed significantly between habitats;
as did the overall abundance of individuals. We found
significant main effects of snowmelt and habitat on sex
ratio with the proportion of females decreasing significantly in response to later snowmelt only in the mesic sites.
Effects of climate change may be masked by habitat differences and have implications for future range changes of
species at both small and large spatial extents. We recommend that local habitat differences are included in
analyses of species responses to climate change.
Electronic supplementary material The online version of this
article (doi:10.1007/s00300-014-1622-7) contains supplementary
material, which is available to authorized users.
Keywords High Arctic Arthropod Zackenberg Thomisidae Mesic heath Dry heath
J. J. Bowden (&) R. R. Hansen T. T. Høye
Arctic Research Centre, Aarhus University, C.F. Møllers Allé 8,
Bldg. 1110, 8000 Århus C, Denmark
e-mail: [email protected]
Introduction
K. Olsen
Natural History Museum Aarhus, Wilhelm Meyers Allé 210,
8000 Århus C, Denmark
T. T. Høye
Aarhus Institute of Advanced Studies, Aarhus University,
Høegh-Guldbergs Gade 6B, Bldg. 1632, 8000 Århus C, Denmark
T. T. Høye
Department of Bioscience, Kalø, Aarhus University, Grenåvej
14, 8410 Rønde, Denmark
Terrestrial ecosystems are heterogeneous mosaics of ecoregions at a global scale (Olson et al. 2001) and vegetation
types at regional scales (Walker et al. 2005). Similarly,
there is great heterogeneity in regional (Rühlund et al.
2013) and local changes to temperature and soil moisture
across regions like the Arctic tundra. Specifically, some
areas are becoming wetter, due to permafrost melt and
some dryer, due to increased net evapotranspiration (Zona
et al. 2009). Recent climate change has been occurring at
an accelerated rate in the Arctic with substantial ecological
123
560
repercussions (ACIA 2005; Post et al. 2009; Høye et al.
2013); however, local habitat may modulate these changes,
for example, snowmelt occurs later and vegetation is typically higher in wet low-lying areas (Stow et al. 2004).
Studies and syntheses of Arctic plant communities have
revealed that species’ responses to climate change are
idiosyncratic (Myers-Smith et al. 2011; Elmendorf et al.
2012), but generally these changes (species composition
and/or local abundance) have been more pronounced in wet
tundra habitats (Elmendorf et al. 2012; Schmidt et al.
2012a). Local effects to individuals are highly dependent
upon small-scale variation in temperature and moisture
regime, and climate-induced shifts can thus have significant impacts upon populations and communities over small
spatial scales (Kudo and Hirao 2006; Høye et al. 2007a,
2013). The biological effects of climate change occur
in situ and act upon traits that dictate whether individuals
in a population will be able to adapt to a changing environment or disperse to another more suitable habitat
(Bourne et al. 2014).
Body size is a particularly important trait to the life
history (e.g., reproductive success), physiology (e.g., thermal limits) and ecology (e.g., resource acquisition) of
individuals in a population (Chown and Gaston 2010). In
terrestrial arthropods, body size is primarily influenced by
food availability and temperature (Chown and Gaston
2010) and is often positively correlated to fecundity, mass
(e.g., Bowden and Buddle 2012) and overall fitness (Jakob
et al. 1996). In the Arctic, however, snowmelt has proven
to be a particularly important predictor of phenology and
body size variation (e.g., Høye et al. 2009, 2013). Like
insects (Stillwell et al. 2010), female spiders are frequently
known to exhibit higher phenotypic plasticity in body size
than males in response to environmental gradients (Uhl
et al. 2004).
Previous studies have shown that variation in body size
is a good indicator of sensitivity to variation in growth
season length in wolf spiders (Lycosidae) (Høye et al.
2009; Høye and Hammel 2010; Bowden et al. 2013). It is,
however, still unclear whether cohort-specific changes in
body size are reflected in more direct measures of fitness
and whether similar responses are found in other taxa and
in different habitats. The crab spiders (Thomisidae) are
closely related to wolf spiders, but represent an ecologically distinct group of spiders, which comprises species of
non-web building, sit-and-wait predators. While it is clear
that the timing of snowmelt significantly affects body size
variation in the Arctic wolf spider [Pardosa glacialis
(Thorell)] (Høye et al. 2009), the crab spiders are less
mobile, meaning that as juveniles they possess a low propensity for dispersal (Morse 1992) and as adults that they
123
Polar Biol (2015) 38:559–568
remain on the ground below or adjacent to vegetation
(Leech 1996) due to their slow-moving sit-and-wait lifestyle. Crab spiders, like other arthropods with low mobility
(e.g., Thomas 2000; Thomas et al. 2001; Warren et al.
2001), are therefore likely more strongly influenced by
local scale differences in habitat. Additionally, snowmeltinduced variation over time or between habitats in the
Arctic could directly (e.g., sex-specific responses to climate
change/increased juvenile mortality) or indirectly (e.g.,
alterations to female body size) influence sex ratios,
fecundity and/or subsequent recruitment. Since females
command significantly greater reproductive requirements,
they frequently display greater responses in body size to
gradients in resource availability (e.g., Høye et al. 2009;
Høye and Hammel 2010).
Terrestrial arthropods comprise a substantial portion of
the Arctic’s terrestrial biomass and species diversity (Callaghan et al. 2004). Spiders serve as a central component to
Arctic terrestrial food webs (Hodkinson and Coulson 2004;
Roslin et al. 2013), serving as food for many birds and
small mammals, but also as top predators of mesofauna and
are well represented in diversity and abundance on the
Arctic tundra (e.g., Bowden and Buddle 2010). Long-term
data on Arctic arthropods is scarce; Zackenberg, Greenland, however, represents a long-term monitoring program
that has been collecting terrestrial arthropods and environmental data for nearly 20 years in the high Arctic. Two
of the habitat types being monitored are mesic and arid
heath in which the mesic habitat undergoes substantially
later snowmelt on average (Høye and Forchhammer 2008).
Previous literature suggests that our focal species, Xysticus
deichmanni Sørensen, can be found more frequently in arid
tundra habitats (Leech 1996; Wyant et al. 2011). Our
objective was to determine how habitat-specific snowmelt
has influenced inter-annual variation in body size, recruitment and sex ratio in a species with low mobility (X. deichmanni). We predicted that body size would decrease
significantly with later snowmelt due to the shorter season
with which to obtain resources. We also predicted that
there would be a significant difference between the two
habitats with smaller body size in the mesic habitat due to
the substantially shorter snow-free period, resulting in a
significant snowmelt–habitat interaction. Furthermore,
because of their relatively higher costs of reproduction, we
predicted that females would exhibit a stronger response to
later snowmelt than males. We also predicted that there
would be significant main effects of snowmelt and habitat,
as well as, a significant interaction between snowmelt and
habitat on recruitment (indexed by proportion of juveniles
to adult females) and sex ratios (indexed by proportion of
females to males).
Polar Biol (2015) 38:559–568
Materials and methods
Sampling and specimens
Arthropods were collected weekly during the summer
months (June, July and August) each year and sorted to the
family level as part of the Zackenberg Basic Monitoring
Program (Schmidt et al. 2012b) at Zackenberg; located in
northeast Greenland (74280 N, 20340 W, Fig. 1). Specimens are preserved in 70 % ethanol and currently housed
at the Natural History Museum in Aarhus, Denmark. We
identified all specimens of Thomisidae to the species level
and confirmed that X. deichmanni is the only crab spider
species in the collection. It is distinguishable based on
morphology from other such species in the genus (Dondale
and Redner 1978). Y. Marusik had also previously identified numerous specimens from the collection from 2002
and confirmed that all individuals examined were X. deichmanni. The family is represented by just two known
species in Greenland, Xysticus durus (Sørensen) in the
south and X. deichmanni in the northeast (Marusik et al.
2006). X. deichmanni is a ground-dwelling species distributed broadly across the Arctic from Alaska across
Canada and into northern Greenland (Leech 1996). We
only used individuals collected from plots 3, 4, 5 and 7 as
561
per the Monitoring Program (see Høye and Forchhammer
2008); representing each two mesic and two arid sites,
respectively, over a 17-year period from 1996 to 2012
(2,010 samples were unfortunately not available). Over this
period, average summer (June–August) temperature was
4.63 ± 0.22 C (±SE) as recorded 2 m above the surface
by a centrally located meteorological station. The mesic
and arid sites are dominated by similar vegetation cover,
largely made up of Dryas octopetala, Salix arctica, lichen
and grasses with Cassiope tetragona in the mesic sites and
lie approximately 500 m apart from one another (Høye and
Forchhammer 2008). The most distinct difference between
the two habitat types is the moisture regime (mesic versus
arid) and the related timing of snowmelt. Specifically,
snowmelt occurs earlier and at a more consistent date,
inter-annually, in the two arid sites. Similar mesic sites in
the area average 34.85 ± 1.90 % (±SE) water by volume
while similar dry sites average 18.85 ± 0.95 % water by
volume at a depth of 5 cm (Sigsgaard et al. 2014). The
Zackenberg Basic Monitoring Program has been in operation since 1996 with standardized collection of arthropods
via a gridded pitfall trapping regime (Schmidt et al. 2012b).
Each plot consists of a 10 m 9 20 m grid utilizing 8
(1996–2006) or 4 (2007–present) pitfall traps. Pitfalls at
Zackenberg consist of 10-cm-diameter yellow cups and are
Fig. 1 Map of study region
with inset orthophoto indicating
the two (3, 4) mesic and two (5,
7) arid sites sampled at
Zackenberg (74280 N,
20340 W) in northeast
Greenland between 1996 and
2012
123
562
Polar Biol (2015) 38:559–568
open during the activity season when plots are snow free
(mid-June) until snow begins to fall again (early September). To obtain body size, we measured the carapace width
of all specimens using a Wild M5A stereo microscope
with an ocular micrometer.
Statistical analysis and data
Snowmelt (DOY)
Although temperature is important to the growth and survival of arthropods, timing of snowmelt is a particularly
good predictor of body size in wolf spiders (Høye et al.
2009) and phenology of terrestrial arthropods at Zackenberg (Høye et al. 2007b; Høye and Forchhammer 2008).
We used habitat-specific snowmelt date, calculated as the
day of year (DOY) from January 1st, when \50 % of the
plot about the respective traps was covered with snow.
While snowmelt between the two habitats is highly correlated (r = 0.81, Pearson), it occurs much later in the mesic
habitat, which results in higher soil moisture. Furthermore,
we included a habitat effect as the inter-annual variation is
over twice that of the arid sites (Fig. 2). We also included
sex as a factor due to the influence of sexual size dimorphism in the species (Dondale and Redner 1978).
We used general linear models weighted by sampling
error in R (R Development Core Team 2013) to determine
how annual mean adult body size of X. deichmanni changed over 17 years. Weights were based on the reciprocal of
standard deviation so as to up-weight the years with the
smallest errors; singletons and doubletons were removed
from the analyses (one female from 1999 and two females
from 2008). The terms snowmelt and habitat were included
in every full model. We also ran within-habitat models to
test for habitat-specific effects of snowmelt. We tested how
sex ratios and juvenile/female ratios (also determined to fit
Gaussian error distributions) differed through time and
between the two habitats in response to snowmelt using
linear models. We expressed juvenile/female ratio [juveniles/(juveniles ? females)] and sex ratio [females/(females ? males)] as proportions (Wilson and Hardy 2002);
providing us with a standardized index that allowed us to
compare potential changes in recruitment (Clark et al.
2004) and sex ratio with snowmelt and between the two
habitat types. We also tested how the total abundance
(measured as activity density) differed between the habitats
and with snowmelt. We recognize that the pitfall collection
method is biased toward mature males searching for mates
and our index of sex ratio underestimates the actual proportion of females in the populations; similarly, we opted
to represent recruitment by the proportion of juveniles to
females due to the large number of males collected. We
used all specimens available from the four sites for our
analyses, but due to the influence that sample size may
have on proportions in some years with relatively low
sample size, we conducted tests at higher restrictions (at
least 10, 15, 20 individuals owing to the response) and our
results proved to be robust. We included interactions in the
initial models, but only retained them if they were significant and all responses were log-transformed to improve
linearity and confirmed to meet assumptions of normality
and homoscedasticity.
Results
190
We identified a total of 2,115 (1,259 adult) individuals
belonging to the species X. deichmanni for our analyses
(Table S1). Overall, 2 times more males and 1.7 times
more females were collected in the arid habitat than the
mesic habitat.
180
Body size and abundance
Year vs Arid.Snow.melt
Year vs Mesic.Snow.melt
Year vs ChirArid.1
Year vs ChirMesic.1
170
160
150
140
130
1996
1998
2000
2002
2004
2006
2008
2010
2012
Year
Fig. 2 Difference in inter-annual snowmelt date between mesic and
arid habitats from 1996 to 2012 at Zackenberg, Greenland. Snowmelt
date, measured as day of year (DOY) from the 1st day of each annum
for arid (solid line) and mesic (dashed line) habitats
123
Body size (Fig. 3a) varied greatly over the sampling
period with snowmelt and sex both contributing significantly to the model describing body size variation
(Table 1). There was no significant effect of inter-annual
snowmelt in the arid habitat, but the individuals from the
mesic habitat were negatively influenced by later snowmelt (Table 1). We also noted no significant (p = 0.123)
effect of snowmelt, controlling for sex, on body size using
a global averaged snowmelt with individuals averaged
over both habitats (F2,29 = 117.8, R2Adj = 0.88). Females
were significantly larger than males, body size decreased
with later snowmelt dates and mesic sites yielded significantly larger individuals than their arid counterparts
(Table 1). Females and males were 0.072 ± 0.019
Polar Biol (2015) 38:559–568
a
563
Table 1 Results of weighted linear regression models (weighted by
1/standard deviation/sex/habitat/year) and parameters of body size
variation in adult Xysticus deichmanni from Zackenberg, Greenland
2.6
Carapace Width (mm)
2.5
Model
2.4
p
Full
Intercept
1.0 ± 4.5E-2
\0.001
Sex (male)
-9.0E-2 ± 5.0E-3
\0.001
2.2
R2Adj = 0.85
p B 0.001
Snowmelt
Habitat (mesic)
-1.0E-3 ± 3.0E-4
3.0E-2 ± 8.0E-3
0.002
\0.001
Arid
Intercept
9.1E-1 ± 5.7E-2
\0.001
2.1
F2,28 = 150.5
Sex (male)
-8.5E-2 ± 5.0E-2
\0.001
R2Adj = 0.91
Snowmelt
-4.0E-4 ± 4.0E-4
0.32
2.3
p B 0.001
1996
1998
2000
2002
2004
2006
2008
2010
2012
Year
Juveniles/(Juveniles + Females)
Estimate
F3,59 = 113.6
2.0
b
Parameter
1.0
1.1 ± 7.5E-2
\0.001
Sex (male)
-9.4E-2 ± 8.0E-3
\0.001
Snowmelt
-1.0E-3 ± 5.0E-4
0.01
Mesic
Intercept
F2,29 = 69.22
R2Adj = 0.81
p B 0.001
0.8
0.6
Male
Intercept
9.1E-1 ± 5.0E-2
\0.001
F2,29 = 4.88
Habitat (mesic)
2.5E-2 ± 8.0E-3
0.005
R2Adj
Snowmelt
-9.0E-4 ± 3.0E-4
0.009
1.0 ± 7.8E-2
\0.001
= 0.20
p = 0.01
Female
Intercept
0.4
F2,29 = 3.68
R2Adj = 0.15
Habitat (mesic)
Snowmelt
0.2
p = 0.04
0.0
Specimens were collected from four sites (two arid, two mesic) and
totaling 1,259 individuals collected over a 17-year period
1996
1998
2000
2002
2004
2006
2008
2010
3.5E-2 ± 1.3E-2
-1.0E-3 ± 5.0E-4
0.01
0.06
2012
Year
Fig. 3 Inter-annual variation in body size and juvenile/female ratios
between mesic and arid habitats at Zackenberg, Greenland. a Body
size of male (open) and female (closed) Xysticus deichmanni from
1996 to 2012 collected in arid (triangles) and mesic (circles) heath
sites in Zackenberg, Greenland. Body size was measured as carapace
width in millimeters to the nearest hundredth of a millimeter. Error
bars represent standard errors from each yearly mean. b Variation in
juvenile/female ratios, measured as proportion of juveniles in the
population, of Xysticus deichmanni from 1996 to 2012 collected from
arid (solid line) and mesic (dashed line) habitats in Zackenberg,
Greenland. No juveniles were collected in 1998 at the mesic sites, and
no samples were available for 2010
(Fig. 4a) and 0.034 ± 0.009 mm (Fig. 4b) larger (±SE)
in the mesic sites, respectively, and differed significantly
between habitats. Surprisingly, while females exhibited
higher variation intra-annually and between habitats, only
males responded significantly to inter-annual variation in
snowmelt date (Table 1). The total abundance of X. deichmanni was significantly (p = 0.005) lower in the mesic
sites (F2,29 = 13.7, R2Adj = 0.45; Fig. 4c), despite a significantly later average snowmelt (F1,30 = 38.06,
R2Adj = 0.54, p \ 0.001). Total abundance did not vary
significantly with inter-annual snowmelt (p = 0.77).
Recruitment index
Juvenile/female ratios varied greatly over the sampling
period (Fig. 3b) and were significantly lower in the mesic
habitat (Table 2; Fig. 4d). Juvenile/female ratios did not
vary significantly with snowmelt within the mesic habitat,
but did decrease significantly with later snowmelt within the
arid habitat (Table 2). Significantly more (F1,30 = 29.1,
p B 0.001) juveniles were captured from the arid habitat
than the mesic habitat (1.23 ± 0.18 and 0.27 ± 0.05 individuals per trap day per annum ± SE, respectively). No
juveniles were collected during the year 1998 at the mesic
sites.
Sex ratios
There were significant effects of snowmelt and habitat on
female/male ratios overall revealing a higher proportion of
females in the mesic site. Although there was an overall higher
proportion of females/males in the mesic habitat, the proportion
of females decreased significantly in years of later snowmelt
(Table 3; Fig. 5). Sex ratio did not change significantly with
snowmelt in the arid habitat, but there was no significant
interaction between snowmelt and habitat (p = 0.07).
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564
Polar Biol (2015) 38:559–568
p = 0.01
2.4
2.3
2.2
2.1
2.5
p = 0.005
2.4
2.3
2.2
2.1
2.0
2.0
Mesic
Mesic
Arid
c
Arid
d
0.12
Abundance (per trap day)
Male carapace width (mm)
b
2.5
p = < 0.001
0.10
0.08
0.06
0.04
0.02
0.00
Mesic
Juveniles/(Juveniles + Females)
Female carapace width (mm)
a
1.0
p = 0.04
0.8
0.6
0.4
0.2
0.0
Arid
Mesic
Arid
Fig. 4 Bar graphs showing significant differences in body size,
juvenile/female ratios and abundance between mesic and arid habitats
at Zackenberg, Greenland. a female and b male body size (measured
as carapace width in millimeters) c abundance (averaged to
individuals per trap day per annum) of all individuals and d juvenile/female ratios (proportion of juveniles in the population) between
the two habitats. Error bars represent standard errors. p values are
based on full models including snowmelt and habitat
Table 2 Results of full and habitat-specific linear regression models
with parameters of juvenile/female ratios in Xysticus deichmanni
from Zackenberg, Greenland
Table 3 Results of full and habitat-specific linear regression models
with parameters of sex ratios in Xysticus deichmanni from Zackenberg, Greenland
Model
Parameter
p
Model
Parameter
Full
Intercept
-8.8E-2 ± 1.2
0.49
Full
Intercept
F2,28 = 3.52
Habitat (mesic)
-4.3E-1 ± 2E-1
0.04
F2,29 = 3.35
Habitat (mesic)
R2Adj = 0.14
Snowmelt
0.65
R2Adj = 0.13
Snowmelt
Estimate
4.0E-3 ± 8.0E-3
p = 0.04
Estimate
p
8.7E-1 ± 5.7E-1
0.008
1.3E-1 ± 5.0E-2
0.02
-4.3E-3 ± 2.0E-3
0.05
p = 0.05
Arid
Intercept
F1,14 = 6.40
Snowmelt
1.6 ± 7.4E–1
0.05
Arid
Intercept
5.3E-2 ± 4.5E-1
0.91
-1.3E-2 ± 5.0E-3
0.02
F1,14 = 0.17
Snowmelt
1.0E-3 ± 3.0E-3
0.69
R2Adj = 0.26
R2Adj B 0.01
p = 0.02
p = 0.69
Mesic
Intercept
F1,13 = 0.69
Snowmelt
-2.6 ± 2.3
1.2E-2 ± 1.4E-2
0.29
Mesic
Intercept
0.42
F1,14 = 6.43
Snowmelt
1.4 ± 4.5E-1
0.007
-6.8E-3 ± 2.7E-3
0.025
R2Adj B 0.01
R2Adj = 0.26
p = 0.42
p = 0.025
Specimens were collected from four sites (two arid, two mesic) collected over a 17-year period
Specimens were collected from four sites (two arid, two mesic) collected over a 17-year period
Discussion
Local, habitat-specific variation in species’ phenological
responses to climate change is particularly important in
Arctic and alpine ecosystems (e.g., Kudo and Hirao 2006;
123
Høye et al. 2013). In these systems, snowmelt effectively
opens the activity/growth season for many species of animals and plants. The snow-free period determines the
amount of resources that can be accumulated during the
Polar Biol (2015) 38:559–568
565
Females/(Females + Males)
0.6
0.5
0.4
0.3
0.2
0.1
0.0
130
140
150
160
170
180
190
Snowmelt (DOY)
Fig. 5 Linear relationships between sex ratio (measured as proportion of females in population) and habitat-specific snowmelt date.
Annual sex ratios in response to habitat-specific snowmelt in the
mesic (closed circles) and arid (open circles) habitats. Data represent
proportions of females in the adult population collected using pitfall
traps between 1996 and 2012 at Zackenberg, Greenland
growing season. Snow is unevenly distributed across the
landscape and the habitat-specific timing of snowmelt
influences both incident radiation and subsequent soil
moisture levels to create habitats of varying aridity. Here,
we have shown that such variation in the abiotic environment also affects fitness-related traits like body size and
juvenile/female ratios, as well as sex ratios in a species
with low mobility. Importantly, we identified effects of
climate when accounting for habitat variation which was
not detectable in models not taking local habitat variation
into account.
Previously, Høye et al. (2009) have shown that date of
snowmelt significantly affects average annual body size in
the Arctic wolf spider, such that years with a longer snowfree period are associated with a larger mean body size.
Conceivably, the additional time enables individuals to
obtain more resources and achieve a larger adult size. Our
finding is in contrast to Høye et al. (2009) who showed that
the highly mobile wolf spiders seem less affected by habitat-specific snowmelt although they did identify plot differences in males and juveniles. This emphasizes the
importance of species-specific responses to climate change,
such that low-mobility species like X. deichmanni are
likely more affected by landscape scale habitat variation.
Similar to Høye et al. (2009), we suggest that, since
females are driven to obtain a large body size under
fecundity selection (Stillwell et al. 2010), both the cost
associated with reproduction and environmental constraints
impose strong limits upon final body size in the Arctic. We
did not, however, detect a significant response in female
body size to inter-annual variation in snowmelt as
expected, but females did exhibit higher intra-annual variation in size as compared to males (Fig. 3a). Size variation
in males is also affected by resource limitation and thought
to be primarily under sexual selection (Stillwell et al.
2010). Small size in males, therefore, could be advantageous despite strong environmental constraints on size
because early maturation may ensure mating success.
Hence, any size variation in individual males is likely
affected by resource availability and modulated by the
balance between the benefits of early maturation versus
large size in the competition for mates.
The larger body size and smaller juvenile/female ratio in
the mesic habitat are somewhat surprising and suggests
habitat differences are very important in this species.
Habitat differences in the species may arise through a
combination of mechanisms to achieve optimal growth and
fitness such as delayed maturation, higher resource availability in mesic sites and possibly, migration of specific-size
individuals to preferable sites. A delayed maturation is quite
possible given the delay in season onset due to the significantly later average snowmelt in the mesic habitat. Later
snowmelt does, however, mean that maturation occurs
during a warmer part of the growing season, where
resources may be more abundant (Høye and Forchhammer
2008). Moreover, the variance in snowmelt date is over two
times greater in the mesic sites than the arid sites. Such
unpredictable timing of snowmelt compounded with the
significantly shorter snow-free period could lead to significant alterations in life history strategy (e.g., delaying
maturation to maximize reproductive output or general
reductions in reproductive fitness). Still, there could be
higher resource availability in the mesic sites leading to the
significantly larger body size detected in these sites. Indeed,
mesic habitats likely support a higher abundance of small
flies that require an aquatic or semi-aquatic life stage and
may constitute a sizeable portion of the spider’s diet (Leech
1996). We have witnessed substantial pulses of some
groups in the mesic habitat. For example, the Chironomidae
are on average higher in abundance in the mesic sites
(0.52 ± 0.17 individuals per trap day per annum) than in
the arid sites (0.34 ± 0.07 individuals per trap day per
annum) although, not significantly. Lastly, it is possible that
large individuals preferably migrate to mesic habitats simply to avoid desiccation stress. Indeed, species vary greatly
in their desiccation tolerance and distribute themselves
accordingly over fine spatial scales (e.g., Devito et al.
2004), but surface area to volume ratio decreases with
increasing size; making larger individuals less susceptible
to desiccation. It is unlikely that there is a great degree of
movement between the populations as spiders in the genus
Xysticus are slow-moving ground dwellers that do not show
a high propensity for aerial dispersal (Morse 1992; Larrivée
and Buddle 2011) or movement as adults (Leech 1996).
123
566
Contrary to our prediction, we found significantly larger
body sizes in the mesic habitat, yet there was a significantly
lower abundance of crab spiders at these sites. Similarly,
Wyant et al. (2011) found substantial differences in the
abundance of X. deichmanni between dry and moist tundra
(89 and five individuals, respectively) over a three year
sampling period in Alaska, adding support to our designation of sites as being arid or mesic. Furthermore, as early
as 1966, Leech observed that X. deichmanni ‘‘…is a
member of the arid Arctic faunal element.’’ This, compounded with evidence from sex ratios and juvenile/female
ratios suggests that mesic sites serve better for resource
acquisition (potentially with the cost of delayed maturation/reproduction) and that the arid habitat represents better
sites for mating and reproduction; especially in years of
late snowmelt.
While larger size in arthropods in theory provides the
necessary resources for offspring production, trade-offs in
reproductive effort may prevent the production of many fit
offspring (Bowden and Buddle 2012) that can successfully
be recruited into the population. Similarly, Puzin et al. (2011)
found higher average egg-sac mass (reproductive output) in
two species of wolf spider when collected from their
respective optimal habitats. They also detected a trade-off
between egg size–number in one species, such that more
smaller progeny were produced in the optimal habitat while
fewer large progeny were produced in the sub-optimal habitat. This may help explain the higher juvenile/female ratios
detected in the arid tundra habitat where females were significantly smaller. Moreover, the mesic habitat could pose a
higher risk of juvenile mortality due to predation, parasitism
or disease, yet, increased juvenile mortality could come
about either through juvenile susceptibility to overwintering
(Martyniuk and Wise 1985) or simply through increased
growth rate with high food availability (Higgins and Rankin
2001). Furthermore, Benton and Uetz (1986) have shown
that survival of juveniles from arid habitats is higher than
juveniles from moist habitats in a tropical spider at a broad
array of humidity treatments, suggesting that they possess a
stronger tolerance to desiccation stress. Whether differences
between habitats are primarily driven by time constraints
related to timing of snowmelt, differences in resource
availability or susceptibility to desiccation stress should be
tested experimentally.
The significant decrease in female/male ratios may
explain why larger females in the mesic sites do not translate into higher abundance of juveniles (and overall individuals) in these sites. Fewer females in the population in
relation to later snowmelts in the mesic sites, due to, for
example, sexual mismatch or delayed maturation could
negatively affect population dynamics (Miller-Rushing
et al. 2010) in the mesic habitat. Although our assessment of
the proportion of females in the population is male biased
123
Polar Biol (2015) 38:559–568
(given the collection technique), the collection methods
among habitats and over time are standardized. Like many
others with Arctic and/or alpine distributions (e.g., Schmoller 1970; Høye et al. 2009), X. deichmanni seems to
require multiple years to mature (J.J.B. personal observation), so lagged effects may also play a role in the population dynamics of these species (e.g., Høye et al. 2009) and
this should be investigated further with an experimental
approach. Here, we have shown the value of long-term
arthropod collections to reveal how habitat can mediate the
effects of climate change on species and that it may have
particularly strong effects upon low-mobility species in the
Arctic. We predicted that body size, recruitment (measured
as proportion of juveniles in the population) and sex ratios
in a low-mobility spider would be significantly affected by
temporal variation in snowmelt, habitat and their interaction. We also predicted that females would exhibit a
stronger response in body size to snowmelt than males. We
found that body size varied significantly with snowmelt and
between the habitats with a stronger effect of snowmelt in
the mesic habitat, but females did not show a stronger
response than males. We found that our index of recruitment was significantly higher in the arid tundra and varied
significantly with snowmelt in this habitat. Lastly, there
were significant main effects of habitat and snowmelt on
sex ratio with the proportion of females in the mesic habitat
responding negatively to later snowmelt, but did not find
any significant interactions. These relatively contemporary
effects may be intensified by future habitat-specific climate
changes. While increased warming and net solar radiation
could allow for more efficient incubation of eggs and
incident heat, for example, increased alteration to the tundra
(e.g., shrubification), related to an increase in soil moisture
(Zhang et al. 2013) may exacerbate the effects of habitatspecific climate change on ground-dwelling terrestrial
arthropods in the Arctic whose physiology is directly controlled by the abiotic environment.
Acknowledgments Samples from Zackenberg, Northeast Greenland, were provided by BioBasis, the Department of Bioscience,
Aarhus University, Denmark, and Natural History Museum Aarhus,
Denmark. We would also like to acknowledge Climate Basis for
access to environmental data.
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Global change biology
rsbl.royalsocietypublishing.org
Research
Cite this article: Bowden JJ, Eskildsen A,
Hansen RR, Olsen K, Kurle CM, Høye TT. 2015
High-Arctic butterflies become smaller with
rising temperatures. Biol. Lett. 11: 20150574.
http://dx.doi.org/10.1098/rsbl.2015.0574
Received: 3 July 2015
Accepted: 9 September 2015
Subject Areas:
ecology
High-Arctic butterflies become smaller
with rising temperatures
Joseph J. Bowden1, Anne Eskildsen2,4, Rikke R. Hansen1,4, Kent Olsen2,5,
Carolyn M. Kurle6 and Toke T. Høye1,3,4
1
Arctic Research Centre, 2Department of Bioscience, and 3Aarhus Institute of Advanced Studies,
Aarhus University, 8000 Aarhus, Denmark
4
Department of Bioscience, Kalø, Aarhus University, 8410 Rønde, Denmark
5
Natural History Museum Aarhus, 8000 Aarhus, Denmark
6
Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
The response of body size to increasing temperature constitutes a universal
response to climate change that could strongly affect terrestrial ectotherms,
but the magnitude and direction of such responses remain unknown in most
species. The metabolic cost of increased temperature could reduce body size
but long growing seasons could also increase body size as was recently
shown in an Arctic spider species. Here, we present the longest known time
series on body size variation in two High-Arctic butterfly species: Boloria chariclea and Colias hecla. We measured wing length of nearly 4500 individuals
collected annually between 1996 and 2013 from Zackenberg, Greenland and
found that wing length significantly decreased at a similar rate in both species
in response to warmer summers. Body size is strongly related to dispersal
capacity and fecundity and our results suggest that these Arctic species could
face severe challenges in response to ongoing rapid climate change.
Keywords:
Lepidoptera, insect, terrestrial arthropod
1. Introduction
Author for correspondence:
Joseph J. Bowden
e-mail: [email protected]
Electronic supplementary material is available
at http://dx.doi.org/10.1098/rsbl.2015.0574 or
via http://rsbl.royalsocietypublishing.org.
Body size change is regarded as a third universal species response to climate
change along with shifts in phenology and range [1,2]. Body size is a key trait
related to the life history of individuals with implications for reproductive success
[3,4] and dispersal capacity [5,6]. In ectothermic species, like arthropods, body
size is especially influenced by the abiotic environment. Two patterns in ecology
summarize the general pattern of ectothermic body size variation in response to
temperature change. The adaptation of Bergmann’s rule to ectotherms describes
latitudinal or thermal variation in size, such that larger individuals occur at higher
latitudes and in colder environments [7]. Similarly, the specific pattern of
higher temperatures resulting in smaller adult size of ectotherms, deemed the
temperature–size rule, has been commonly found via experiments on numerous
taxa [8–10]. While there are several proposed ways that temperature may influence final adult body size (including effects on food limitation and predation
rates), the role of metabolism is central [1,9].
Although both Bergmann’s rule and the temperature–size rule predict larger
individuals in colder environments, the opposite pattern also occurs [7,9]. Hence,
two hypotheses detail how external temperatures may drive size responses to climate change in arthropods in seasonal environments. First, the metabolic rates of
arthropods increase with warmer temperatures [1,11]. Therefore, in warmer
environments, organisms become smaller if they cannot offset energy losses related
to increased metabolic costs during growth. Conversely, rising temperatures in seasonal environments associated with longer growing seasons may allow arthropods
to obtain more resources and grow larger [2,12]. These two mechanisms may act in
concert, moderating the outcomes of each or the effect of a longer growing season
could depend upon trophic level [2]. Although feeding rates of herbivores may
& 2015 The Author(s) Published by the Royal Society. All rights reserved.
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(a) 170
2. Material and methods
(b)
2
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increase to some peak [13], extended seasons could also mean
lower plant-food quality, especially during late season [14].
Temperature and timing of snowmelt define the activity
and growing season for numerous Arctic taxa and influence
life histories (e.g. [15,16]). Here, we present the longest time
series of body size variation within arthropod (i.e. butterflies)
species at high latitude. We tested which of the hypotheses
was better supported by our data using snowmelt as a
proxy for season length and temperature as a proxy for the
metabolism hypothesis and predicted that previous year’s
temperature would best explain variation in body size as it
covers the majority of the larval growing period.
snowmelt (DOY)
160
150
140
Biol. Lett. 11: 20150574
130
0
(b) Data analysis
We used current year’s May–June (tempt) and previous year’s
May–August (tempt21) temperatures to encompass the larval
growing season. Current and previous years’ snowmelt date
(snowt and snowt21) were used as proxies for season length. We
used linear mixed-models with a Gaussian error distribution to
determine which predictor(s) best explained variation in wing
length using annual averages (years as replicates) for each sex in
each species. We composed an a priori list of candidate models
based upon what we currently know about the development of
these species (electronic supplementary material, table S1) and the
best model was determined by model selection using the Akaike
information criterion corrected for small sample size (AICc). We
repeated the analyses using individuals as replicates to test for
random effects of day of year (DOY) and plot using log-likelihood
ratio tests. We tested for plot variation using Tukey’s honestly significant difference (HSD). Differences in body size between plots
were tested in subsequent models using individuals as replicates
when there was a significant plot effect. Normality was assessed
using q–q-plots. Statistical tests were conducted using the R
environment for statistical computing [20].
3. Results
Timing of snowmelt is occurring significantly earlier (figure 1a)
and average temperatures over the activity period of caterpillars have increased significantly (figure 1b,c) during the
study period. While some of the variables were significantly
–1
–2
–3
(c)
4
average tempt–1 (°C)
Butterfly specimens and climate (snowmelt and temperature) data
were collected in northeast Greenland (748280 N, 208340 W) from
1996 to 2013 as part of the Zackenberg Basic Monitoring Programme [17]. See the electronic supplementary material (detailed
Material and methods) and references therein for more details on
climate data collection and plant community characteristics.
Arthropods were collected weekly during the activity season
each year in one window trap plot and six pitfall trap plots in an
area less than 1 km2, but the peak flight period for adult butterflies
occurs mid–late July depending upon snowmelt and temperature
[18]. The butterfly community at Zackenberg has four species but
is dominated by the Arctic fritillary, Boloria chariclea Schneider and
the northern clouded yellow, Colias hecla Lefèbvre. We measured
all 3629 (1934 males and 1695 females) B. chariclea and 847 (531
males and 316 females) C. hecla collected. Both of these species
most probably have a 2 year generation time at our site [19] with
adult size depending on resources accrued during their larval
stages. We determined sex, and measured the length of one forewing from thoracic attachment to apex of the wing to the nearest
0.01 mm21 using a Diesellaw 150 mm digital caliper.
average tempt (°C)
(a) Study area and data
3
2
1
1995
2000
2005
year
2010
2015
Figure 1. (a) Timing of snowmelt (F1,16 ¼ 8.92, estimate ¼ 21.05,
R2Adj ¼ 0:32, p ¼ 0.008), (b) May– Junet temperature (F1,16 ¼ 10.87,
estimate ¼ 0.11, R2Adj ¼ 0:37, p , 0.005) and (c) May – Augustt21 temperature (F1,16 ¼ 22.52, estimate ¼ 0.11, R2Adj ¼ 0:56, p ¼ 0.001) from
1996– 2013 at Zackenberg, Greenland.
correlated, there was no indication of multicollinearity
(electronic supplementary material, table S2).
Average wing length + s.e. in male (17.96 + 0.02 mm)
and female (18.87 + 0.02 mm) B. chariclea differed significantly
(F1,3626 ¼ 1145, p , 0.001, analysis of variance (ANOVA)). The
wing lengths of male (22.45 + 0.04 mm) and female (23.19 +
0.06 mm) C. hecla also differed (F1,845 ¼ 108.8, p , 0.001,
ANOVA). Wing lengths varied greatly in both species over
time (figure 2a,b), yet were strongly correlated between the
sexes in B. chariclea (r ¼ 0.88, p , 0.001) and C. hecla (r ¼ 0.80,
p , 0.001), and between species (all r . 0.6, p , 0.01).
Average annual body size decreased significantly in
response to previous year’s temperature (tempt21) for both
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(c)
3
rsbl.royalsocietypublishing.org
average wing length (mm)
(a) 24
23
22
Biol. Lett. 11: 20150574
21
(b)
(d)
average wing length (mm)
20
19
18
17
1996
2000
2004
year
2008
2012
0
3
2
average tempt–1 (°C)
1
4
5
Figure 2. Inter-annual variation in average male (open circles) and female (filled circles) wing length over the sampling period for (a) Colias hecla and (b) Boloria
chariclea and their responses (c,d, respectively) to average May– Augustt21 temperature. Error bars represent s.e. Data for 2010 are not available. (Online version in
colour.)
Table 1. Summary statistics showing the best-fit models as selected by AICc on wing length for each sex of Boloria chariclea and Colias hecla collected between
1996 and 2013 at Zackenberg, Greenland using annual averages. Significance of individual parameters is indicated by asterisks.
species
sex
intercept
snowt
snowt21
tempt
tempt21
p-value
R2Adj
F (d.f.)
Boloria chariclea
F
C
20.41**
19.38**
20.01
n.a.
n.a.
n.a.
n.a.
n.a.
20.31**
20.25**
0.01
0.007
0.42
0.38
6.46 (13)
10.16 (14)
Colias hecla
F
C
25.68**
26.25**
20.02
20.02
n.a.
n.a.
n.a.
n.a.
20.29*
20.32*
0.19
0.05
0.06
0.27
1.88 (13)
3.72 (13)
*p , 0.05; **p , 0.01. n.a., not applicable.
species (table 1 and figure 2c,d) and was consistently selected in
the top models for all tests (electronic supplementary material,
table S3). The models using individuals as replicates also
revealed significant effects of current year’s snowmelt on
wing length (electronic supplementary material, table S4).
DOY and plot significantly improved model fit for B. chariclea,
but not for C. hecla (electronic supplementary material,
table S5) and some sites differed significantly from one another
(electronic supplementary material, table S6). We re-analysed
the dataset excluding the plots that showed significant differences from others, but as temperature remained included in
the top models with very similar estimates (electronic supplementary material, table S5) data from all plots were
retained in the final models (table 1). We tested for interannual variation in size for B. chariclea, controlling for collection
date by re-analysing the data with individuals collected from
the same peak abundance day of each season; in this way, we
effectively controlled for seasonal variation. These models similarly included the negative effects of temperature on body size
in the top models (electronic supplementary material, table S7).
4. Discussion
We show that body size of males and females in two HighArctic butterfly species fluctuated in synchrony from year to
year, strongly supporting the influence of external factors on
inter-annual size variation in these species. We further show
that increasing summer temperatures lead to smaller adult
body size, thus corroborating earlier short-term experiments
suggesting that higher temperatures result in smaller adult
size [3,10]. Even though longer, warmer seasons may mean a
longer period of time to obtain resources, the seasonal quality
of resources, combined with the higher cost of obtaining them,
Downloaded from http://rsbl.royalsocietypublishing.org/ on August 28, 2016
bioclimatic models [23]. While these models include dispersal
capacity, in situ adaptation to climate change or plasticity
may enable some populations of a species to persist. Indeed,
the degree to which phenotypic plasticity and adaptation ultimately play a role in this system remains to be thoroughly
investigated. A recent review by Seebacher et al. [24] suggests
that terrestrial ectotherms in less stable environments are less
capable of physiological plasticity in response to climate
change, particularly at high latitudes. Hence, species such as
these found at high latitudes, adapted to cold climates, could
suffer from further warming.
repository (http://dx.doi.org/10.5061/dryad.43gt3).
Authors’ contributions. A.E. and T.T.H. conceived the idea and J.J.B.
measured specimens, generated data, performed analyses and
prepared the first draft. A.E., T.T.H., C.M.K., R.R.H. and K.O.
contributed to data interpretation, article revisions and final approval.
Competing interests. We have no competing interests.
Funding. T.T.H. and A.E. acknowledge 15 Juni Fonden, Denmark for
support.
Acknowledgements. Access to butterfly specimens and climate data were
kindly provided by the Greenland Ecosystem Monitoring Programme.
Specimens are curated by the Natural History Museum Aarhus.
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design and sampling procedures of the biological
monitoring programme within Zackenberg Basic.
Roskilde: Aarhus University.
Høye TT, Eskildsen A, Hansen RR, Bowden JJ,
Schmidt NM, Kissling WD. 2014 Phenology of higharctic butterflies and their floral resources: speciesspecific responses to climate change. Curr. Zool. 60,
243–251.
Eliasson CU, Ryrholm N, Gärdenfors U. 2005 The
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Fjärilar, Dagfjärilar [Swedish]: Hesperiidae—
Nymphalidae. Uppsala E: Artdatabanken, Sveriges
Lantbruksuniversitet.
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Lemoine NP, Burkepile DE, Parker JD. 2014 Variable
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Barrio IC, Bueno CG, Hik DD. In press. Warming the
tundra: reciprocal responses of invertebrate
herbivores and plants. Oikos. (doi:10.1111/oik.
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Settele J et al. 2008 Climatic risk atlas of European
butterflies. BioRisk 1, 1–710. (doi:10.3897/biorisk.1)
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MATE2457)
Biol. Lett. 11: 20150574
Data accessibility. Supporting data can be accessed via the Dryad data
4
rsbl.royalsocietypublishing.org
suggests that the larvae cannot compensate for energy losses.
While some species could be capable of increasing their feeding
rate to offset increased metabolic costs with warming, this
ability appears to be relatively uncommon and species likely
possess some locally adapted optimal feeding temperature
[21]. Indeed, Barrio et al. [22] recently showed that respiration
rates in the Arctic moth (Gynaephora groenlandica) were significantly higher and growth rates significantly lower at lower
elevation, adding support to the metabolism hypothesis.
While we believe our study presents the longest time series
available on body size variation in butterflies, the mechanistic
basis for the observed variation remains to be demonstrated.
We also cannot rule out that generation time is extended in
particularly short growing seasons.
We have demonstrated that two butterfly species in the
High-Arctic responded similarly and negatively to warming
temperatures over 18 years. Smaller body size in these Arctic
species could have significant consequences for their population dynamics by leading to decreased dispersal capacity or
lower fecundity and fitness. B. chariclea has already demonstrated a significant shift towards earlier and shorter flight
seasons at Zackenberg [18] and both B. chariclea and C. hecla
are considered under extremely high climate change risk by
the Climatic risk atlas of European Butterflies given future
Current Zoology
60 (2): 243–251, 2014
Phenology of high-arctic butterflies and their floral
resources: Species-specific responses to climate change
Toke T. HØYE1,2,3*, Anne ESKILDSEN2,4 , Rikke R. HANSEN3, Joseph J. BOWDEN3,
Niels M. SCHMIDT3,5, W. Daniel KISSLING6
1
Aarhus Institute of Advanced Studies, Høegh-Guldbergs Gade 6B, bldgs. 1630-1632, DK-8000 Aarhus C, Denmark
Department of Bioscience, Kalø, Aarhus University, Grenåvej 14, DK-8410 Rønde, Denmark
3
Arctic Research Centre, Aarhus University, C.F. Møllers Allé 8, bldg. 1110, DK-8000 Aarhus C, Denmark
4
Department of Bioscience, Aarhus, Aarhus University, Ny Munkegade, bldg. 1540, DK-8000 Aarhus C, Denmark
5
Department of Bioscience, Aarhus University, Frederiksborgvej 399, DK-4000 Roskilde, Denmark
6
Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, P.O. Box 94248, 1090 GE Amsterdam,
The Netherlands
2
Abstract Current global warming is particularly pronounced in the Arctic and arthropods are expected to respond rapidly to
these changes. Long-term studies of individual arthropod species from the Arctic are, however, virtually absent. We examined
butterfly specimens collected from yellow pitfall traps over 14 years (1996–2009) at Zackenberg in high-arctic, north-east
Greenland. Specimens were previously sorted to the family level. We identified them to the species level and examined long-term
species-specific phenological responses to recent summer warming. Two species were rare in the samples (Polaris fritillary Boloria polaris and Arctic blue Plebejus glandon) and statistical analyses of phenological responses were therefore restricted to the
two most abundant species (Arctic fritillary, B. chariclea and Northern clouded yellow Colias hecla). Our analyses demonstrated
a trend towards earlier flight seasons in B. chariclea, but not in C. hecla. The timing of onset, peak and end of the flight season in
B. chariclea were closely related to snowmelt, July temperature and their interaction, whereas onset, peak and end of the flight
season in C. hecla were only related to timing of snowmelt. The duration of the butterfly flight season was significantly positively
related to the temporal overlap with floral resources in both butterfly species. We further demonstrate that yellow pitfall traps are
a useful alternative to transect walks for butterfly recording in tundra habitats. More phenological studies of Arctic arthropods
should be carried out at the species level and ideally be analysed in context with interacting species to assess how ongoing climate change will affect Arctic biodiversity in the near future [Current Zoology 60 (2): 243–251, 2014].
Keywords
Arctic, Arthropod, Flight period, Greenland, Pitfall trap, Zackenberg
Phenology is a key indicator of species responses to
climate change (Parmesan, 2006) and among terrestrial
animals, ectotherms are particularly sensitive, probably
because their developmental rates are closely related to
variation in the abiotic environment (Høye et al., 2007;
Thackeray et al., 2010). Butterflies in particular are
frequently used as model organisms in phenological
studies, because they respond rapidly to environmental
change (Diamond et al., 2011; Illan et al., 2012; Roy
and Sparks, 2000; Westwood and Blair, 2010). Additionally, their appeal to the general public has stimulated monitoring programs and has generated high-quality
data, especially in temperate and boreal regions
(Karlsson, In press; Roy and Sparks, 2000). Recent
global warming, however, is particularly pronounced in
the Arctic and empirical evidence suggests that Arctic
Received Dec. 17, 2013; accepted Mar. 20, 2014.
 Corresponding author. E-mail: [email protected].
© 2014 Current Zoology
species across broad taxonomic scales are responding
rapidly to these changes (Høye et al., 2007; Post et al.,
2009). Unfortunately, long-term records of individual
arthropod species from the Arctic are virtually absent
and the phenological sensitivity to climate change in
most arctic arthropod species remains to be estimated
(Bale et al., 2002; Hodkinson et al., 1996; Hodkinson
and Bird, 1998). The biological monitoring program at
Zackenberg, north-east Greenland, represents an exception, where arthropods have been collected weekly during the growing season since 1996 and subsequently
sorted to the family level (Schmidt et al., 2012).
Very little information exists about the drivers of
phenological variation at the species level for Arctic
arthropods, but family-level studies suggest that timing
of snowmelt and temperature are important (Danks and
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Current Zoology
Oliver, 1972; Høye and Forchhammer, 2008a, b;
Strathdee and Bale, 1998). Snow cover acts as a constraint on arthropod growth and development in arctic
and alpine environments (Forrest and Thomson, 2011;
Iler et al., 2013a). Effectively, the timing of snowmelt
opens and closes the activity season for many arthropods in the Arctic. For many species, this time period
dictates the amount of resources they can accrue and
utilize in the current season e.g. for reproduction, dispersal or to carry them over the winter into the following season. Temperature during the active season may
modulate the effect of snowmelt on arthropod phenology by affecting their developmental rate (Hodkinson et
al., 1996). In addition, many Arctic arthropod species,
like spiders (Bowden and Buddle, 2012; Høye et al.,
2009) and insects (Butler, 1982; Morewood and Ring,
1998), have multi-annual life cycles and their phenological responses to climate change may further depend
on overwintering strategy or species-specific developmental cues.
Like their predicted shifts in distribution under climate change (Eskildsen et al., 2013; Leroux et al.,
2013), phenological responses to climatic change in
arthropods could be species-specific and have concealed
consequences for trophic interactions. In arctic insects,
the emergence of adults normally coincides with the
peak in food resources, the availability of mates, or
suitable egg-laying habitats (Danks, 2004; Høye and
Forchhammer, 2008b; MacLean, 1980). Arctic pollinator communities are dominated by Diptera species that
are generalized in their flower visitation patterns
(Elberling and Olesen, 1999; Lundgren and Olesen,
2005). Among arctic flower visitors, however, butterflies visit only a subset of the available flowering plants
(Olesen et al., 2008). Hence, butterflies form a relevant
taxonomic group with which to examine species-specific phenological responses to climate change. Even if
different butterfly species respond to changes in the
same phenological cue, they can do so at different rates
(Hegland et al., 2009). Hence, more subtle changes in
the phenological response of a pollinator community
may only be fully resolved with species-level information (Iler et al., 2013b).
Pollinator populations are often limited by the availability of floral resources (Potts et al., 2010). Such resource limitation can emerge if the flight season of a
pollinator shifts relative to the flowering season of its
plant resource causing a decrease in the temporal overlap of pollinators and flowers (Høye et al., 2013; MillerRushing et al., 2010). Moreover, in butterflies, the
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No. 2
availability of nectar resources can affect adult longevity and population dynamics (Boggs and Inouye, 2012;
Cahenzli and Erhardt, 2012). Hence, if adult butterflies
emerge in asynchrony with the flowering season or
during a period of low flower availability, their flight
season may become shorter with potential detrimental
effects on population dynamics (Nilsson et al., 2008).
We have recently demonstrated that the flowering season at our study site is shortening and this could affect
resource availability and flight season duration in butterfly species (Høye et al., 2013).
Here, we examine species-specific phenological responses to recent warming in high-arctic butterflies and
their temporal synchrony with floral resources. We use
specimens collected as part of the Zackenberg Basic
monitoring programme (Meltofte and Rasch, 2008) that
were previously identified to the family level. We identify these specimens to the species level and establish
species-specific estimates of onset, peak and end of the
flight season. For two species with sufficient data, we
ask two specific questions: 1) Are onset, peak, and
end of the flight seasons affected by recent rapid
warming and changes in timing of snowmelt at Zackenberg? 2) To what extent is the duration of the flight
season related to the timing of the flight season and the
temporal overlap with the flowering season of relevant
plant species.
1
Material and Methods
1.1 Study area and data
Data were collected at Zackenberg, north-east
Greenland (74°28'N, 20°34'W) as part of the Zackenberg Basic monitoring programme. Although expanding,
the growing season is currently limited to between early
June and early September during which the average air
temperature is around 4.5°C. Throughout the study period, temperature and snow depth were recorded hourly
by an automated weather station (Hansen et al., 2008).
Timing of snowmelt was estimated as the first date
when less than 10 cm of snow was measured (Hinkler et
al., 2008). The vegetation of the study area can be
roughly divided into five major plant communities: fen,
grassland, Salix snow-bed, Cassiope heath, and Dryas
heath (Elberling et al., 2008).
Arthropods were monitored during 14 consecutive
years (1996–2009) in one window trap plot and six pitfall trap plots in the vicinity (<2 km) of the weather
station. Each pitfall trap plot (10 m × 20 m) consisted of
eight pitfall traps during 1996–2006 and four pitfall
traps thereafter. The pitfall traps were yellow plastic
245
HØYE TT et al.: Phenology of high-arctic butterflies
cups 10 cm in diameter. The colour was chosen to attract flying insects while also catching surface-active
arthropods. The window trap plot consisted of two traps
each with two chambers. The two traps were placed
perpendicular to each other. Arthropods caught in the
traps were collected weekly during June, July and August. The window trap plot and the pitfall traps in arid
heath (one plot), mesic heath (two plots) and in the fen
(one plot) were in operation during the entire study period 1996–2009, while one snow-bed plot and one additional arid heath plot were only operated during the periods 1996–1998 and 1999– 2009, respectively (see
Schmidt et al., 2012 for details).
The entire butterfly community at Zackenberg consists of four species: Arctic fritillary, Boloria chariclea
(Schneider), Polaris fritillary, B. polaris (Boisduval),
Arctic blue, Plebejus glandon (de Prunner), and Northern clouded yellow, Colias hecla (Lefèbvre). Only one
additional species, Small copper, Lycaena phlaeas
(Linnaeus) has been observed in Greenland. All specimens caught in all years were previously identified to
the family level (Nympalidae, Lycaenidae and Pieridae)
as part of Zackenberg Basic monitoring program. We
identified the specimens to the species level and calculated species-specific estimates of onset, peak and end
of the flight season.
The data set included a total of 3,868 specimens retrieved from the pitfall and window trap plots. As part
of this study, we revisited a subset of 1,660 Boloria
specimens, distributed evenly across all years. Because
our subset represents more than half (51%) of all Boloria specimens in the collection, differences in phenology between B. chariclea and B. polaris in the subset
would also likely reflect the total sample of specimens.
A total of 35 specimens (out of 1,660) were identified as
B. polaris, equalling just 2.1% of the Boloria specimens.
For the purpose of making robust phenological esti-
mates, we assumed that all Boloria specimens not subject to species identification were B. chariclea since B.
polaris made up such a small subset of the Boloria sp.
specimens in the subset subject to species identification.
The Lycaenid species P. glandon was even rarer (n = 23
specimens) than B. polaris in the samples across all
years (Table 1). The low sample sizes prevented us from
estimating inter-annual variation in the phenology of B.
polaris and P. glandon. Adults of all four species of butterflies were only observed during July, August and early
September (Table 1). Traps were occasionally flooded,
destroyed by arctic foxes (Vulpes lagopus, Linneaus), or
trampled by muskoxen (Ovibos moschatus, Zimmermann), so the capture numbers in each plot were converted to individuals caught per trap per day for each
trapping period, i.e. scaled by the number of active traps
and the number of days each trap was active (see Høye
and Forchhammer 2008b for further information).
The Zackenberg Basic monitoring program also included weekly observations of the number of buds,
open and senescent flowers from the same locality and
the same time periods within and across years on six
flowering plant species (Cassiope tetragona, Dryas
octopetala, Papaver radicatum, Salix arctica, Saxifraga
oppositifolia and Silene acaulis). These plant species
are very common at the study site. The butterfly species
B. chariclea has been observed on flowers of all six
species and C. hecla has only been observed on D. octopetala, S. arctica and S. acaulis (Olesen et al., 2008).
For B. chariclea, we used data on all six plant species
and for C. hecla we used only data on the three plant
species upon whose flowers the species has been observed. Each plant species was monitored in 3–6 plots
(see Schmidt et al., 2012 for details). Both butterfly
species have also been observed on flowers of other
plant species, for which we have no data on flowering
phenology. We used the data on flower phenology to
Table 1 Summary of phenological observations for four species of butterflies at Zackenberg, north-east Greenland (Boloria chariclea, B. polaris, Colias hecla and Plebejus glandon)
Species
First
Onset
Peak
End
B. chariclea
168
200±2.0
212±2.3
B. polaris
176
190
217
C. hecla
176
194±2.4
P. glandon
196
196
st
Last
n
224±1.7
245
3,235*
232
238
35*
205±2.6
218±2.3
238
575
204
211
224
23
All dates are given as days after 1 January. The first observation is the first date at which a butterfly of a particular species was found in a trap
across all years (1996–2009) and the last observation is the last date across all years. Onset, peak and end indicate the mean day of year ± SE at
which 10%, 50% and 90% of the annual sum of individuals were caught, respectively. Asterisks indicate that only 51% of the total collection of
Boloria specimens was checked for B. polaris and the remaining specimens were assumed to be B. chariclea. The sample size, n gives the total
number of specimens of a particular species identified from the traps.
246
Current Zoology
characterize the temporal variability in floral resources
available for each of the two butterfly species. An independent study of the pollination network at our site
documented visits by B. chariclea to 15 plant species
and to 5 plant species by C. hecla (Olesen et al., 2008).
1.2 Data analysis
Following Høye and Forchhammer (2008b), we defined onset, peak and end of the flight season of adult
butterflies by dates at which 10%, 50% and 90% of the
seasonal capture of a species was reached, respectively.
The dates for onset, peak and end of the flight season
were estimated by linear interpolation between weekly
trapping periods. For instance, peak flight season was
estimated by interpolation between the latest weekly
trapping period in which less than 50% of the individuals were caught and the earliest weekly trapping period
in which more than 50% of the individuals were caught.
For each weekly trapping period, we pooled the number
of individuals from all plots to base the phenological
estimates on the largest number of individuals and because previous analyses of Boloria specimens demonstrated limited phenological variation among plots
(Høye and Forchhammer, 2008b). Although low sample
sizes prohibited us from estimating yearly phenology
metrics for B. polaris and P. glandon, we calculated an
average across years of onset, peak, and end of the
flight season for future comparisons. All dates were
expressed as day of year with 1st January equalling day
1. Flight season duration was estimated as the number
of days between onset and end of the flight season. We
quantified onset and end of the community-wide flowering season at a landscape scale based on the relevant
plant species for each of the two butterfly species following the approach of Høye et al. (2013).
At the study site, snowmelt typically takes place
during June, while the flight season of butterflies is
typically in July and early August. We were interested in
separating the effect of snowmelt from the effect of
temperature after snowmelt on the timing of the butterfly flight season. Hence, we used mean daily July temperature as a predictor of timing of the butterfly flight
season in addition to timing of snowmelt. We applied
generalized linear models with a Gaussian error distribution (McCullagh and Nelder, 1998) with onset, peak,
end, and duration of the flight season of B. chariclea
and C. hecla as response variables. As predictors for
models with onset, peak and end of the flight season, we
used the mean daily temperature during July, the timing
of snowmelt, and their interaction. Non-significant
terms were removed successively using F-tests based on
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No. 2
type III sums of squares and starting with interaction
terms and evaluating main effects only if interaction
terms were non-significant. The correlation between
timing of snowmelt and July temperature as well as
between peak flight time of B. chariclea and C. hecla
was estimated with Pearson correlation analysis. We
calculated the temporal extent of overlap between the
flowering season of relevant plant species (see above)
and the butterfly flight season in days for B. chariclea
and C. hecla, separately. We tested for trends in the duration of overlap during the study period and whether
duration of the flight season was affected by onset of
the flight season, the duration of the overlap with floral
resources or their interaction. Finally, for analyses of
temporal trends in peak flight time and overlap with
flower seasons, year was used as a continuous predictor
variable in simple linear regression models.
2 Results
The number of specimens was highest for B. chariclea (n = 3,235) and C. hecla (n = 575) and very low
for B. polaris (n = 35) and P. glandon (n = 23). The
earliest and the latest butterfly records were observations of B. chariclea, while the earliest observation of P.
glandon was later than all other species and the latest
observation of this species was also earlier than all other
species. The average peak flight time was late July for C.
hecla and P. glandon, and about ten days later in B.
chariclea and B. polaris (Table 1). For B. chariclea and
C. hecla, the timing of peak flight season varied markedly from year to year (Fig. 1), with a significant trend
towards earlier peak flight season in B. chariclea (slope =
-1.22, F1,12 = 6.14, P = 0.029), but not in C. hecla (slope =
-0.68, F1,12 = 1.12, P = 0.31). In B. chariclea, onset,
peak, and end of the flight period showed a statistically
significant relationship with timing of snowmelt, mean
daily July temperature, and their interaction (Table 2).
In C. hecla, the onset, peak, and end of the flight season
was, however, only significantly related to timing of
snowmelt (Table 2). Timing of snowmelt and July temperatures were not significantly correlated (Pearson correlation: r = -0.38, n = 13, P = 0.18). The flight season of
both butterfly species was generally later than the flowering season of the plant species that are visited by the
butterflies and for which we had flower phenology data
(Fig. 2). The duration of the flight season showed a statistically significant and positive relationship to the duration of overlap with floral resources in both B. chariclea (Fig. 3a) and C. hecla (Fig. 3b), but not with onset
of the flight period or their interaction in any of the two
247
HØYE TT et al.: Phenology of high-arctic butterflies
species (Table 3). The duration of overlap became smaller
during the study period, but the relationship was not signi-
ficant for B. chariclea (slope = -0.080, F1,12 = 0.13, P =
0.72) or C. hecla (slope = -0.77, F1,12 = 3.56, P = 0.084).
Fig. 1 Peak flight times of two butterfly species A) Boloria chariclea and B) Colias hecla during 1996–2009 at Zackenberg,
north-east Greenland. Summary statistics (R2 and P-values) based on simple linear regression analysis are given in each panel
Regression lines indicate that slopes are significantly different from zero.
Table 2 Results of generalized linear models of onset, peak and end of the flight season for two butterfly species (Boloria
chariclea and Colias hecla) at Zackenberg, north-east Greenland, across 1996–2009 as a function of mean daily temperature
during July and timing of snowmelt, as well as their interaction as continuous variables
Species
Event
B. chariclea
Onset
-144.2±111.8
2.05±0.66
35.4±14.4
-0.21±0.09
Peak
-266.1±136.6
2.90±0.80
54.5±17.6
-0.33±0.10
End
-54.4±103.9
1.71±0.61
30.9±13.4
-0.19±0.08
Onset
109.7±27.5
0.50±0.16
-
-
C. hecla
Intercept
Snowmelt
Temp
Snowmelt:Temp
R2
P
10
0.73
0.0032
10
0.73
0.0037
10
0.69
0.0069
12
0.44
0.0096
df
Peak
104.1±26.5
0.60±0.16
-
-
12
0.55
0.0025
End
131.3±24.6
0.51±0.15
-
-
12
0.51
0.0043
Only models with significant parameters (at α = 0.05) are shown. Parameters ± SE are presented along with residual degrees of freedom, coefficient
of determination (R2) and P-value.
Fig. 2 The onset and end of the flight season of A) Boloria chariclea and community-wide flowering seasons for six flowering plant species (Cassiope tetragona, Dryas octopetala, Papaver radicatum, Salix arctica, Saxifraga oppositifolia and Silene
acaulis) which the butterfly species has been observed visiting and B) Colias hecla and community-wide flowering seasons
for three flowering plant species (Dryas octopetala, Salix arctica and Silene acaulis) on which the butterfly species has been
observed visiting
The lower line in each set is onset and the upper line is end of butterfly flight seasons (hatched lines) and flowering season (full lines).
248
Current Zoology
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No. 2
Fig. 3 Duration (end minus onset) in days of the flight season for A) Boloria chariclea and B) Colias hecla in relation to the
overlap with the flowering season of six relevant plant species for B. chariclea and three relevant plant species for C. hecla
(see text for details)
The overlap is negative in some years, because onset and end of seasons are defined by the date of 10% and 90% of butterflies and flowers observed
during the season, respectively. Regression lines based on simple linear regression analysis along with R2 and p-values are given in each panel.
Table 3 Results of generalized linear models of flight season duration for two butterfly species (Boloria chariclea and
Colias hecla) at Zackenberg, north-east Greenland, from 1996–2009 as a function of the overlap with the flowering season of
their respective plant species (upon which they have been observed), the onset of flight season, and their interaction as continuous variables
Species
Intercept
Overlap
Onset
Overlap:Onset
df
R2
P
B. chariclea
21.41±1.46
0.83±0.33
-
-
12
0.35
0.027
C. hecla
17.32±2.52
0.49±0.17
-
-
12
0.39
0.016
Only models with significant parameters (at α = 0.05) are shown. Parameters ± SE are presented along with residual degrees of freedom, coefficient
of determination (R2) and P-value.
3
Discussion
The literature on species-specific phenological responses to ambient climatic variability of Arctic arthropods is scant and to our knowledge this is the first
study to span more than a decade of observations
(Høye and Sikes, 2013; Leung and Reid, 2013). Given
the species richness of arthropods in marine, freshwater and terrestrial environments in the Arctic, this illustrates an important knowledge gap that limits our
ability to predict the consequences of rapid climate
change for Arctic biodiversity (Meltofte 2013; Callaghan et al., 2004b; Post et al., 2009). We have previously demonstrated large family-level variation in estimates of timing of emergence over a broad range of
terrestrial arthropods at Zackenberg (Høye et al., 2007;
Høye and Forchhammer, 2008b). However, with familylevel taxonomic resolution, it is not possible to separate the effects of changing species composition between years from the inter-annual variation in phenology of individual species (Callaghan et al., 2004a).
By identifying the individual species of a butterfly
assemblage from the monitoring program at Zackenberg, we have made progress towards estimating species-specific phenological responses to recent climatic
variation in the Arctic. The two abundant butterfly
species indeed differ in their trend towards earlier
flight time, their phenological responses to climatic
variability, and in inter-annual variation in degree of
temporal overlap with floral resources. This suggests
that phenological responses measured at coarse taxonomic resolutions can mask important variability at the
species level.
Our results indicate that timing of the flight season in
high-arctic butterfly species is closely related to the
timing of snowmelt. While the timing of the flight season in C. hecla is only related to timing of snowmelt,
there is an additional effect of July temperature in B.
chariclea, and this temperature effect interacts with
timing of snowmelt. This suggests that the advancement
of the flight season in B. chariclea with warmer temperatures is most pronounced with late snowmelt. The
HØYE TT et al.: Phenology of high-arctic butterflies
lack of relationship to summer temperatures in timing of
the flight season for C. hecla may be the reason why C.
hecla is not showing a trend towards earlier flight time
during the study period. The summer temperature has
increased dramatically during the study period at our
site (Høye et al., 2013). With future warming, the flight
seasons of B. chariclea and C. hecla may shift relative
to each other. Since peak flight date is generally ten
days later in B. chariclea than in C. hecla, their flight
times are likely to become more synchronized. A knowledge gap is whether future timing of snowmelt in the
region will advance or be delayed, which is currently
uncertain (Stendel et al., 2008).
The duration of butterfly flight seasons could be affected by the timing and duration of flowering seasons if
the lifespan of each individual butterfly is constrained
by access to floral resources. Adult longevity has been
linked to nectar resources in some butterfly species
(Cahenzli and Erhardt, 2012; Murphy et al., 1983). Our
results demonstrate that the duration of the flight season
varied by a factor of two over the study period in both
butterfly species and was related to the duration of
overlap with relevant floral resources. Other studies
have found extending flight seasons, but these studies
have used observations collected at much larger spatial
extents (Roy and Sparks, 2000; Westwood and Blair,
2010). For instance, a recent study demonstrated extended flight seasons in northern butterfly communities
in response to recent warming across Sweden (Karlsson
In press). Flight seasons may also be extended when the
flight seasons start earlier. This could happen by more
pronounced protandry (i.e. males emerging earlier than
females) in years of early snowmelt and warmer temperatures.
Our estimates of the onset and end of the flowering
season of plant species known to be visited by the studied butterfly species is generally earlier than the flight
season of the butterflies. Both species of butterflies are
known to visit flowers of other plants as well and some
of these are late flowering species (e.g. Arnica angustifolia and Bistorta vivipara). Hence, the apparent mistiming of the butterfly flight season with the flowering
season presented here could be due to the subset of
plant species for which we have data on flowering phenology. The butterfly flight season may also be timed
primarily with particular phenological stages of the
plant species used for oviposition, although this would
not explain why flight season duration is related to the
overlap with our subset of plant species. We consider it
more likely that our measure of onset and end of the
249
flowering season is rather conservative. Indeed, in a
pollination network study from our site, the same plant
species were observed to flower later than the end of
flowering in our permanent plots (J.-M. Olesen unpublished data).
Our results demonstrate that for high-arctic study
sites it is possible to use pitfall trapping to census butterfly species. We used yellow pitfall traps as a method
to trap both ground-active and flying insects in the same
trap (Böcher and Meltofte, 1997). The large sample
sizes for at least two species of butterflies using this
technique suggests that this approach is useful for remote tundra localities as it does not require trained butterfly observers who are otherwise needed for conventional transect counts (Pollard and Yates, 1993). The
separation of B. chariclea and B. polaris is difficult in
the field and we provide new information on their relative abundance. We found less than 3% of the Boloria
specimens to be B. polaris, suggesting that this species
is much rarer at Zackenberg than B. chariclea. This
could be linked to the distribution of their larval food
plants or because of habitat segregation not picked up
by our sampling design. We consider it unlikely that our
subsampling procedure, where we identified 51% of the
Boloria specimens, could have accidentally missed
samples with a large proportion of B. polaris specimens.
The low capture numbers for B. polaris and P. glandon
could potentially be the result of these butterfly species
not being attracted to the yellow colour of our pitfall
traps, but independent data from a study of the pollination network at our study site support the idea that B.
polaris and P. glandon are rare species in the area (J.-M.
Olesen unpublished data).
The strong phenological response to climate variation that we document for high-arctic butterflies and the
clear differences between the two most abundant species suggest that species-specific studies of populations
of arctic arthropods should be a research priority. Future
phenological studies of Arctic arthropods should ideally
be analysed in context with interacting species to assess
how ongoing climate change will affect Arctic biodiversity. Long-term records will be particularly helpful in
attempts to identify the climatic constraints on the future persistence of high-arctic butterfly species (Post
and Høye, 2013).
Acknowledgements We thank Zackenberg Basic for access
to the ecosystem data. T.T.H. and A.E. acknowledge 15. Juni
Fonden for funding and W.D.K. acknowledges a University of
Amsterdam (UvA) starting grant. We appreciate comments on
250
Current Zoology
a previous version of the manuscript by Oliver Schweiger and
two anonymous reviewers.
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