Download Ecological monitoring in Cambridge Bay

Ecological monitoring in Cambridge Bay (Nunavut): testing
the effects of microhabitat and seasonal change on the
taxonomic and functional diversity of Arctic arthropod
communities
Elyssa R. Cameron
Department of Natural Resource Sciences
McGill University
Montreal, Quebec, Canada
April 2016
A thesis submitted to McGill University in partial fulfillment of the requirements
of the degree of Master of Science.
© Elyssa Cameron 2016
Table of Contents
Abstract
...4
Résumé
…5
List of Tables
…6
List of Figures
…8
Thesis Format
…11
Acknowledgements
…11
Contribution of Authors
…12
Chapter 1: Introduction and Literature Review
…13
1.1 Introduction
…13
1.1.1 Thesis rationale
…13
1.1.2 Research objectives
…15
1.2 Ecological monitoring
…16
1.3 The use of arthropods in long term ecological monitoring programs
…20
1.3.1 Arthropods as model organisms
…20
1.3.2 Where to sample: Habitat selection
…21
1.3.3 What level to sample: Measuring biodiversity
…22
1.3.4 When to sample: Time effects
…24
1.4 Ecological monitoring in the Arctic
…24
1.4.1 Why monitor the Arctic?
…24
1.4.2 Arctic monitoring in Canada
…25
1.5 References
…27
1.6 Connecting Statement
…33
Chapter 2: Seasonal change and microhabitat association of Arctic spider
assemblages (Arachnida: Araneae) on Victoria Island (Nunavut)
…34
2.1 Abstract
…34
2.2 Introduction
…34
2.3 Methods
…37
2.3.1 Experimental design and sampling
…37
2.3.2 Data analyses
…38
2.4 Results
…39
2
2.5 Discussion
…42
2.5.1 Microhabitats
…42
2.5.2 Seasonal change
…44
2.5.3 Conclusion
…45
2.6 Acknowledgements
…45
2.7 References
…46
2.8 Tables and Figures
…52
2.9 Connecting Statement
…61
Chapter 3: Arthropods as ecological indicators: a comparison of taxonomic and
functional diversity to sample entire communities in the Arctic
…62
3.1 Abstract
…62
3.2 Introduction
…63
3.3 Methods
…67
3.3.1 Experimental design
…67
3.3.2 Arthropod identification and classification
…68
3.3.3 Data analyses
…69
3.4 Results
…70
3.5 Discussion
…72
3.5.1 Taxonomic and functional diversity
…72
3.5.2 Food wed structure
…73
3.5.3 Functional roles
…75
3.5.4 Emergence times and peak abundance curves
…76
3.5.5 Recommendations and ecological monitoring in the Arctic
…77
3.6 Acknowledgements
…78
3.7 References
…78
3.8 Tables and Figures
…83
3.9 Connecting Statement
…88
Thesis summary and conclusion
…89
3
Abstract
Arctic ecosystems are experiencing rapid environmental change and as a result,
ecological processes are being altered and overall diversity is being threatened. The
establishment of long-term ecological monitoring programs allows governments to track
ecosystem changes in order to better adapt future conservation efforts. The overarching
goal of this thesis is to field test protocols for monitoring arthropod communities in
Cambridge Bay (Nunavut) using taxonomic and functional approaches and to provide
recommendations on future ecological monitoring in the Canadian Arctic.
Chapter 1 was a literature review and examined the key elements which
contributed to the success of monitoring programs. Chapter 2 used field collected data
on spider species, important Arctic predators, as a model for testing the effects of
microhabitat on biodiversity. Spider assemblages were strongly affected by microhabitat
type along a moisture-driven gradient. Species were found to be strongly associated to
a single habitat type and spider assemblages were dynamic over time. Chapter 3
examined the entire arthropod community and similar patterns to Chapter 2 were
observed. Habitat type and seasonality affected taxonomically (Family and Order levels)
and functionally described communities in similar ways. Habitat types contained
differently structured food webs and unique peaks of when different arthropods were
most commonly collected. There was also evidence to suggest that the same functional
roles may be performed by different taxa depending on the habitat type. Functional
roles showed variability in their abundance peaks when compared to total abundance
trends.
Monitoring programs in the Arctic should strive to include arthropods in their
protocols and this thesis provides baseline knowledge on how to do so efficiently and
cost-effectively. In this thesis, I provide details and recommendations on how, where,
when and what to sample along with some inferences as to how arthropods relate to
ecological processes and ecosystem structure in Canada’s north.
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Résumé
Les écosystèmes arctiques sont présentement menacés par les changements
climatiques. Par conséquence, les processus écologiques sont modifiés et la diversité
de l’écosystème entier est menacée. L’établissement de programmes de surveillance
écologique à long terme aiderait les gouvernements à mieux comprendre ces
changements pour faciliter leurs efforts de conservation. Le but global de cette thèse
est de fournir des recommandations sur la manière d’étudier la communauté
d’arthropodes de Cambridge Bay (Nunavut) avec l’espoir d’améliorer la surveillance
dans l’arctique.
Le chapitre 1 est un résumé de la littérature publiée. Ce chapitre a analysé les
éléments clés qui contribuent au succès des programmes de surveillance et a aussi
déterminé les informations essentielles à connaitre avant le commencement d’un projet.
Le chapitre 2 a examiné les données de ma recherche sur les araignées, des
prédateurs importants dans le Nord, pour déterminé les modèles de diversité au niveau
des espèces. Les assemblages d’araignées étaient influencés par le type d’habitat
selon un gradient d’humidité. Les espèces étaient localisées dans un habitat spécifique
et la communauté était dynamique au cours de la saison. Le chapitre 3 a examiné la
communauté entière d’arthropodes. Il démontre des tendances similaires que le
chapitre 2. Le type d’habitat et la saisonnalité ont influencés les communautés
indépendamment s’ils étaient décrit d’une perspective taxonomique (au niveau de la
famille et de l’ordre) ou fonctionnelle. Les différents habitats supportaient des réseaux
trophiques structurés différemment et présentaient des pics d’abondances uniques. Les
données suggéraient aussi que les mêmes rôles fonctionnels pouvaient être exécutés
par des arthropodes différents selon le type d'habitat. Les rôles fonctionnels ont même
démontré une variance dans leurs pics d’abondance par rapport à l’abondance totale.
Les programmes de surveillance devraient s’efforcer d’inclure les arthropodes
dans leurs protocoles. Cette thèse fournit des informations de base sur la façon de le
faire efficacement. Dans cette thèse, je fournis des recommandations et des détails sur
comment, où, quand et qu’est-ce qu’un programme de surveillance devrait
échantillonner. Je fais aussi des inférences sur la façon dont les arthropodes influence
les processus écologiques et la structure de l’écosystème arctique.
5
List of Tables
Table 2.1. Description of the four common ecosites used in this study. For the
classifications, the ecosite codes in bracket refer back to the Cambridge Bay
classifications.
Table 2.2 GPS coordinates for all sampled locations (samples and replicates) near
Cambridge Bay (Nunavut).
Table 2.3. List of spider species collected in each of the four ecosite types, collected in
Cambridge Bay, Nunavut in 2014. Ecosite descriptions are in Table 2.1. Abundance
values per ecosite represent the pooled totals from all sampling periods, replicates and
trap types. New territory records are denoted by an asterisk. Note the juveniles are not
included in these totals. Bolded values denote family level or habitat level totals.
Table 2.4. MANCOVA p-values for the effect of time (period) and microhabitat on the
total abundance, richness, and diversity of spiders in Cambridge Bay. Here, abundance
values are taken from the log total abundance. Significant values are denoted with an
asterisk. Additional Tukey HSD tests were conducted for “Microhabitat” and those pvalues can be found in Table 2.5.
Table 2.5. Tukey HSD test p-values for the factor “Microhabitat”. Abundance values are
taken from the log total abundance. Significant values are denoted by an asterisk.
Table 3.1. Description of the four common ecosites used in this study. For the
classifications, the ecosite codes in bracket refer back to the Cambridge Bay
classifications.
Table 3.2. Description of the functional group classifications and some examples of taxa
which would fall under them.
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Table 3.3. MANCOVA p-values for the effect of time (period) and microhabitat (ecosite)
on the distribution of our 6 functional roles. Here, abundance values represent the
average number of individuals per trap, rather than the raw abundance due to
differential sorting effort. Significant values are denoted by an asterisk. Additional Tukey
HSD tests were conducted for “Ecosite” and those p-values can be found in Table 3.4.
Table 3.4. Tukey’s HSD test p-values for the factor “Ecosite”. Abundance values
represent the average number of individuals per trap, rather than the raw abundance
due to differential sorting effort. Significant values are denoted by an asterisk.
Table 3.5. MANCOVA p-values for the effect of time (period) and microhabitat (ecosite)
on the total abundance and relative abundance of the taxonomic orders and dipteran
suborders (Nematocera and Brachycera) present in this study. Here, abundance values
represent the average number of individuals per trap, rather than the raw abundance
due to differential sorting effort. Significant values are denoted by an asterisk.
7
List of Figures
Fig. 2.1. NMDS ordination of the spider community across all replicates and time
periods using the log values of species relative abundance. Each point indicates the
location of a sampled microhabitat: where the triangles denote the two dry ecosites and
the squares denote the two wet habitats. Points which are located more closely together
are more similar than points located further away from one another. In A, the text
represents the individual species codes (two first letters of genus and two first letters of
species names (see Table 2.3)) and shows the location of each species within the
ordination space; where a strong association of a species to a particular habitat type
can be observed. The centroid variable of the plot is the microhabitat type (p-value <
0.001), making it the determining factor for point location in the ordination space. Of all
environmental variables tested, “Site” (p-value = 0.014) and “Period” (p-value = 0.103)
had the strongest vector effects. In B, the location of each habitat centroid along with
the 68% confidence areas is shown. There are significant differences between wet and
dry habitat pairings but no differences between the communities of Dry1 and Dry2 or
Wet1 and Wet2.
Fig. 2.2. Spider total relative abundance across the five sampling periods in each of the
four microhabitats. Abundance values include both adult and juvenile specimens and
represent the pooled total of all replicates and trap types. Sampling periods spanned the
entire 2014 summer season, and break down as follows: 1=3-8vii, 2=8-14vii, 3=19-26vii,
4=26-30vii, 5=5-11viii. See Table 2.1 for ecosite descriptions. Differences between the
microhabitats can be observed, but the overall pattern of spider peak abundance seems
to remain consistent. Associated p-values for the effect of time (period) and habitat
(ecosite) can be found in Table 2.4. Error bars represent one standard deviation.
Fig. 2.3. Rarefaction curves of species richness per microhabitat. See Table 2.1 for
ecosite descriptions. Only when sampling has reached asymptotic can species richness
be used as a measure of biodiversity – as is the case here. Rarefied species richness
values are: Dry1=7.854, Dry2=8.330, Wet1=9.992, Wet2=9.753.
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Fig. 2.4. Individual species turnover across the five sampling periods. Species codes
are given as the y-axis (associated species names can be found in Table 2.3). Plotted
values are the total abundance of a given species at each time period (microhabitats,
trap types and replicates are pooled). The figure only includes 20 of the 22 species
collected as the singleton and doubleton were excluded.
Fig. 2.5. Proportion of total individuals of each species found in dry and wet habitats.
Ecosites have been combined into the dry or wet categories to better show the pattern.
No significant differences have been observed between the two dry or the two wet
ecosites (Fig. 2.1 and Table 2.1). Species identity is portrayed on the x-axis by its code
(species names can be found in Table 2.3). The bolded values above the species code
denotes the total number of individuals collected for that species, across all time
periods, replicates and habitats.
Fig. 2.6. Relative dominance of the four spider families in each microhabitat and
through time. The habitats breakdown as follows: A – Dry1, B – Dry 2, C – Wet1, D –
Wet 2. Total abundance numbers come from pooled replicates and trap types.
Abundance values include both adult and juvenile specimens. Periods represent the
following dates: 1=3-8vii, 2=8-14vii, 3=19-26vii, 4=26-30vii, 5=5-11viii.
Fig. 3.1 NMDS ordination for the arthropod community of Cambridge Bay, Nunavut.
Ordination A describes the community from a taxonomic perspective, and ordination B
from a functional perspective. Each point represents a sampling location for a given
habitat, time and replicate. Triangles denote the dry habitat types and squares denote
the wet habitat types. See Table 3.1 for habitat descriptions. Both ordinations are
centered on the “microhabitat” environmental variable (p < 0.001 for both). The webs
show the location of each habitat centroid, and the lines connect each sampling point to
the center. The circles show the 68 % confidence intervals (1 standard deviation) from
the centroid. Only environmental variables which had a significant effect on the
community are shown on the ordinations.
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Fig. 3.2 Proportional representation of community composition in dry and wet habitat
types. Figure 3.2A characterizes the community taxonomically by order (and suborder
for flies). Figure 3.2B characterizes the community functionally into one of the functional
groups described in Table 3.2 (except Pollinators).
Fig. 3.3 Abundance by functional group and time period for dry and wet habitats.
Abundance values represent the average number of individuals per trap due to
differential sorting effort and are subsequently pooled by replicate. All sampling
occurred in Cambridge Bay, Nunavut over the 2014 summer season.
10
Format
This thesis is organized as a series of chapters written in manuscript style. Chapter 2
will be submitted to The Canadian Entomologist and Chapter 3 will be submitted to
Ecological Indicators. Chapter 1 is a literature review which introduces the main themes
of the thesis as well as the research questions and objectives.
Acknowledgements
I would first and foremost like to thank my supervisor, Chris Buddle, for his
support, insights, constructive criticism and guidance throughout every aspect of this
project. Thank you also to my immensely supportive lab mates, who have provided
moral support, statistical expertise, helpful edits, and fresh perspectives throughout this
entire process. My supervisor and lab mates have created a wonderful and open
working environment for new ideas to grow and develop, making this entire experience
extremely memorable. Thank you to my committee member, Jeffrey Cardille, for his
fresh perspectives and helpful prompts to be explicitly clear to non-entomologists. A
huge thank you to all the field and lab assistants who volunteered their time to collect,
sort and identify my tens of thousands of specimens. Special thanks to Anthony Dei
Tigli for his months of sorting through the “other” groups, Anne-Sophie Caron for the
identification of the wasps to subfamily, and Terry Wheeler for the identification of all the
Brachyceran flies. Finally, a big thank you to my friends and family for their love and
support.
I completed this work in collaboration with Polar Knowledge Canada. My
research was supported by the National Science and Engineering Research Council of
Canada (NSERC) (a post-graduate scholarship to me and a Discovery Grant and
Northern Research Supplement to Christopher Buddle).
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Contribution of authors
I wrote all of the original manuscripts and literature review in this thesis. I also collected
all the data and performed the data analyses. My supervisor, Christopher M Buddle
(McGill University), is co-author on both Chapters 2 and 3 and contributed to the
conceptual design and sampling of the data. He additionally provided inputs and edits
for both manuscripts. Terry A Wheeler (McGill University) is also a co-author on Chapter
3. He identified one of the 6 arthropod sorting categories (Brachyceran flies) and
contributed edits and intellectual inputs on the written draft. The other 5 arthropod
sorting categories, as well as all species level spider identifications, were performed by
me. All other contributions by colleagues and field assistants are outlined in the
acknowledgements section of each chapter.
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Chapter 1: Introduction and Literature Review
1.1 Introduction
1.1.1 Thesis rationale
Monitoring programs provide invaluable information on how to properly manage
and conserve ecosystems. They have the ability to detect changes early on and
hypothesize how these changes may spread through the ecosystem in order to develop
more targeted and efficient protocols (Lindenmayer and Likens 2009, 2010). This early
detection helps prevent ecosystem collapse (Noss 1990, Lindenmayer and Likens
2009). However, in order to develop these powerful models, we need detailed
information about all aspects of the ecosystem: a daunting, costly, and time consuming
task (Noss 1990, Lovett et al. 2007, Lindenmayer and Likens 2009, 2010). The Arctic,
despite being in the midst of rapid environmental change, is still understudied, and there
is much we do not understand about its basic ecological processes (Callaghan et al.
2004b, Parmesan 2006). Establishing a monitoring program in the Arctic, with the
present base of knowledge, would be unrealistic and unfocused.
The observation that the Arctic is changing is not a novel one as scientists have
noted irregular patterns for several decades (Danks 1992, Hoye and Sikes 2013).
However, the current rate of change, the true consequences and the ecosystem
impacts of these changes are only just being witnessed (Parmesan 2006, Hoye and
Sikes 2013). Northern environments are currently under major threat, and it is essential
that we develop monitoring programs now before the ecosystems are irreversibly
damaged (Danks 1992, Kim 1993, Parmesan 2006, Hoye 2013). In so doing, we will be
able to better conserve these ecosystems for future generations. One way to get around
our gaps in knowledge is to use model organisms to study our ecosystem – thereby
focusing our resources and efforts on a single group (Danks 1992, Kim 1993). From
these model organisms, we can make inferences about the entire ecosystem without
needing to study every component individually (Danks 1992, 1997). Still, a set of
baseline data must first be established in order to have a comparison to base future
studies on.
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Arthropods dominate terrestrial biodiversity and participate in most ecological
processes (Kim 1993). They therefore make great model organisms: especially in the
Arctic (Danks 1992, 1997). Arthropods provide researchers with basal knowledge of an
ecosystem and its food web: something few other organisms can provide (Danks 1992,
Kim 1993). Though the study of larger, more easily identified organisms (such as birds
and mammals) may be more appealing, it cannot provide the same in depth
connections and predictions as the study of arthropods (Danks 1992, Hoye and Sikes
2013). Environmentally linked changes can also be detected much earlier in arthropod
communities than in vertebrate ones (Danks 1992, Kim 1993, Hoye and Sikes 2013).
In the Arctic, arthropod community patterns are of particular importance due to
their low niche overlap (Ernst and Buddle 2015). The extreme environment promotes
low redundancy in arthropod communities (Danks 1992, Ernst and Buddle 2015),
making the ecosystem more vulnerable to collapse than other systems (Danks 1992,
Kim 1993). Changes noted in the arthropod community will thus more likely percolate
through the rest of the ecosystem as other organisms are less likely to take over the
place of a lost species. To be able to properly study the arthropod community of the
Arctic, researchers must first have a grasp on their spatial and temporal constrains
(Danks 1992, Kim 1993). Previous research suggests that arthropods can vary greatly
in what habitats they can be found in (Schaffers et al. 2008, Bowden and Buddle 2010,
Ernst and Buddle 2015) and when they are present in the system (Usher 1992, Ernst
and Buddle 2013). Understanding spatial and temporal patterns of Arctic arthropods is
thus the first step in establishing a monitoring program.
One additional hurdle that must be overcome is ensuring that arthropod data are
of high quality and useful for science (Kremen et al. 1993, Lindenmayer and Likens
2010). This goal is achieved by reducing the error in arthropod sorting and identification:
something which can be difficult with non-specialist researchers and incomplete
taxonomic keys (Kim 1993, Lovett et al. 2007, Lindenmayer and Likens 2010).
Arthropods can be taxonomically difficult, so the use of functional diversity has been
14
proposed as a way to include them in monitoring programs in a more meaningful way
(Danks 1997, Petchey and Gaston 2006, Cadotte et al. 2011). Classifying arthropods by
their ecological roles allows for the establishment of clearer links between diversity and
ecosystem functions (Petchey and Gaston 2002a, 2006). Before functional diversity can
be implemented into a monitoring program though, it must first be determined if it can
detect similar ecosystem patterns as taxonomic diversity. If so, then functional diversity
could act as a proxy for taxonomic diversity and allow arthropods to be more easily
integrated into Arctic monitoring programs. In short, arthropods can be of great use to
ecological monitoring programs if we first have a basic understanding of where, when
and at what level to sample them.
1.1.2 Research objectives
The overarching goal of this thesis is to provide recommendations on where to
sample, what to sample, and when to sample arthropods in the Canadian Arctic. This
will contribute invaluable knowledge for the establishment of an effective long-term
monitoring program, where arthropods play a central role as model organisms. Chapter
1 focuses on establishing the context of the research, highlighting the importance of
monitoring programs, the usefulness of arthropods, and the basic information required
to adequately study an ecosystem. Chapter 2 examines the effect of habitat and time on
spider assemblages, done at the species level, in order to quantify “where” and “when”
to sample. Chapter 3 examines the entire terrestrial arthropod community from both
taxonomic and functional diversity perspectives to address the “what” to sample. Again,
habitat and time were used to determine whether observed patterns behaved in the
same manner regardless of diversity type.
The research presented in this thesis investigates the arthropod diversity in
Cambridge Bay using a multidimensional (temporal and spatial) and multi-scale (within
and between microhabitats) approach. From this, a long-term ecological monitoring
program can be developed and aid in conservation and management of Arctic systems.
Specifically, I will be asking:
Chapter 1

What makes a good monitoring program?
15

What staring information is essential to ensure a program’s success?
Chapter 2

What is the biodiversity of spiders in Cambridge Bay?

Do spider assemblages differ between Arctic microhabitats?

Is there evidence of seasonal turnover within the community?
Chapter 3

What is the biological and functional diversity of arthropods in Cambridge Bay?

What is the community structure within microhabitats and do they differ between
one another?

Is there any seasonal variation in diversity or community structure?

Do spatial or temporal patterns vary when examining functional vs taxonomic
diversity?
With this information, I hope to:

Map patterns of arthropod diversity onto existing vegetation and soil data (which
characterize each microhabitat)
o In order to build a more complete and functional view of the ecosystem;
one with more predictive power and large scale implications

Investigate the variation of community structure across Arctic microhabitats
o In order to gain better insight as to what sampling scope is needed for
effective monitoring

Better understand the diversity and ecosystem functioning of the Arctic
o In order for Cambridge Bay to be used as a model for future/linked
monitoring programs

Create feasible recommendations and guidelines for a long-term ecological
monitoring program
1.2 Ecological monitoring
In order for an ecological monitoring program to be successful it is essential to
have clear goals and objectives, and a thorough grasp of the interactions, biodiversity
and processes which occur in the ecosystem of interest (Noss 1990, Kim 1993,
16
Lindenmayer and Likens 2009). Everyone involved, including the public, should
understand the necessity and importance of the program in order to have a strong,
lasting, and united partnership (Lindenmayer and Likens 2010). In this cooperative
manner, a monitoring program is less likely to fail and more likely to provide the desired
benefits.
Monitoring programs provide valuable information for scientific advances and
environmental policies and should be a fundamental component of any governmental
agency responsible for environmental policies (Lovett et al. 2007). When properly
developed, monitoring programs strike a balance between question-driven science and
long-term, large-scale observations (Lindenmayer and Likens 2010). Well-balanced
monitoring has the potential to generate high-quality data over long time scales which
can be continuously used to identify patterns and trends (Lovett et al. 2007,
Lindenmayer and Likens 2010). This data can then be integrated into environmental
management practices and policies (Lovett et al. 2007, Lindenmayer and Likens 2010).
Historical changes often provide the key to understanding present trends in ecology
and predicting future events (Hobbie et al. 2003). We must remember that
environmental and ecological problems rarely occur over a few years. They require
lifetimes to observe: being slow moving and difficult to detect without a baseline for
comparison (Hobbie et al. 2003). For all of these reasons, monitoring programs are
essential when considering ecosystem services, ecosystem health and managing
natural resources.
Though well supported in theory, some mishaps and challenges have given
some long-term monitoring programs a bad reputation (Lovett et al. 2007, Lindenmayer
and Likens 2009). This reputation has caused many to perceive them as unscientific
and pointless (Lovett et al. 2007) but these labels are often inaccurate. Monitoring
programs should not be dismissed as they provide the foundation for environmental
science based policies (Lovett et al. 2007). Instead, the framework from which
monitoring programs are conceived should be improved upon to reduce the risk of
failure. Historically, long-term programs fail due to one of three reasons: lack of funding,
unclear objectives or a disconnect between the significance of the work and the views of
the parties involved (Lovett et al. 2007, Lindenmayer and Likens 2009). Most of these
17
issues are linked with one another, and by designing a realistic, efficient protocol these
problems would no longer arise (Lindenmayer and Likens 2009).
By keeping in mind the principles of stable funding sources, clear objectives, and
a strong connection between objectives and stakeholder needs, it is possible to design
monitoring protocols that avoid common pitfalls. First, available funds and resources
govern the success of long-term monitoring programs (Danks 1997, Lindenmayer and
Likens 2009). Specifically, it is not uncommon for these long-term monitoring programs
to be funded by short-term contracts and political campaigns (Lindenmayer and Likens
2009). This is unsustainable, and the projects are often discontinued when the
resources dry out or move on. In addition, improper use of available resources can also
result in failure (Danks 1997, Lindenmayer and Likens 2009). Cost estimates should be
conducted vigorously to ensure the funding does not run out or result in low quality data
(Caughlan 2001). In this manner, researchers and policy makers have a clear path to
follow and do not waste time, energy, or resources. Having monitoring programs
become a priority for governments and funding agencies would allow more permanent
financial support (Lovett et al. 2007, Lindenmayer and Likens 2010).
Second, having a clear project direction not only ensures sustainable resources,
but also paves the way for good science (Lovett et al. 2007, Lindenmayer and Likens
2009). As such, goals should be implemented in the design early on in the planning
(Danks 1997, Lindenmayer and Likens 2009) and regular cost checkpoints should be
established (Caughlan 2001). These goals should then continuously adapt as new data
and questions present themselves (Lindenmayer and Likens 2009). Trying to assess
too much, or not studying appropriate organisms can not only waste money, but time as
well. Therefore, one should always establish the questions and framework for a
monitoring program before collecting data; and not the other way around (Danks 1997,
Lindenmayer and Likens 2009). In so doing, key issues are addressed before they
occur, data can be used effectively and meaningful science can be produced on both
short and long time frames (Lovett et al. 2007, Lindenmayer and Likens 2009). With a
clear design and fewer logistical problems, a monitoring program is more likely to
endure.
18
Last, ensuring that people understand the significance and importance of longterm studies will encourage their support in the future (Lovett et al. 2007, Lindenmayer
and Likens 2009). One must realise that monitoring is science, just on a larger scale
and with multiple hypotheses, all operating under a series of ecological and
environmental goals (Lovett et al. 2007). Ecosystems change slowly, so these long-term
ecological monitoring plans can provide science with insights which would otherwise be
unattainable in laboratories (Noss 1990, Lovett et al. 2007). The science that comes out
of these programs directly influences the policy makers, governments and
environmental bodies which determine their fate (Lovett et al. 2007). Failure to
effectively communicate intentions and results, bad science, unclear objectives, and
non-adaptive protocols can all cause a monitoring program to be shut down or deemed
“not of use” (Lovett et al. 2007, Lindenmayer and Likens 2009). Therefore, to be
successful, a monitoring program must have clearly defined goals and objectives, must
operate within its available resources, must adapt to changing goals or conditions and
must communicate the data effectively.
Additionally, the realm of monitoring must continue to evolve into an entity which
enlists the engagement of multiple parties. Government agencies and academics
traditionally have different roles: one monitors, the other researches (Lindenmayer and
Likens 2010). The communication between these two bodies should therefore be
explicit, and both parties should feedback into one another (Lindenmayer and Likens
2010). Programs can also greatly benefit from the integration of citizen based science
and community engagement into their protocols (Berkes et al. 2000, Ford and Martinez
2000). Indigenous knowledge can help fill knowledge gaps and interpret observations
more meaningfully. Traditional knowledge can be extremely useful in maintaining an
adaptive monitoring program and providing some baseline data if research is scarce.
Additionally, community initiatives create a network of people who are invested in the
health and conservation of the studied ecosystem (Berkes et al. 2000, Ford and
Martinez 2000).
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1.3 The use of arthropods in long term ecological monitoring programs
1.3.1 Arthropods as model organisms
When approaching an ecosystem to study or monitor, it is important to
understand that sampling everything is unrealistic, time consuming and costly (Noss
1990, Kim 1993, Danks 1997, Lindenmayer and Likens 2009). As such, the use of
model organisms can help focus research on the key groups which play the largest
roles in that particular ecosystem (Noss 1990, Danks 1992, Kim 1993). One can then
obtain the most accurate results without exceeding financial resources (Noss 1990, Kim
1993, Lindenmayer and Likens 2009). Models organisms act as indicators which help
assess the overall condition of the environment and serve as an early warning system
to help detect ecological problems (Dale and Beyeler 2001). Moreover, model
organisms must provide key information about the entire ecosystem, not simply a small
component of it (Dale and Beyeler 2001).
Danks (1992) and others (Kim 1993, Danks 1997, Hoye and Sikes 2013) suggest
arthropods for monitoring. In Arctic systems, not only do arthropods characterize the
vast majority of the total diversity, they also engage in key interactions which moderate
the proper health and function of the entire ecosystem (Danks 1992, Kim 1993,
Maleque et al. 2006, Hoye and Sikes 2013). Arthropods provide vital services such as
pollination, decomposition, predation, parasitism and herbivory control and are also
important food sources for local and migrating animals (Danks 1992, Kim 1993,
Maleque et al. 2006, Hoye and Sikes 2013). For instance, research supports a high
dependency of Arctic nesting birds on arthropod abundance in order to maximize their
reproductive success and chick fitness (Kremen et al. 1993, Milakovic and Jefferies
2003, Tulp and Schekkerman 2008, McKinnon et al. 2012, Bolduc et al. 2013).
Arthropods embody the ideal model organism due to their rapid response to
change, quick reproductive time and high abundance as well as their ease of access,
sampling, storing and (in some cases) identification (Danks 1992, Kim 1993, Hoye and
Sikes 2013). Arthropods can respond to small changes in ecosystem quality and finescale disturbances (Maleque et al. 2006); something which can help guide monitoring
programs before issues become widespread. The high taxonomic and functional
diversity found in Arctic arthropods is also an asset as it provides the opportunity to
20
monitor diversity from multiple viewpoints (Danks 1992, 1997, Kim 1993, Hoye and
Sikes 2013). In fact, to gain an accurate description of diversity, one must examine
three elements: composition, structure and function (Noss 1990, Franklin et al. 2011). It
is not only important to know what is there but also to understand how they are
distributed and what their roles are (composition, structure, and function; Noss 1990).
Still, having an appropriate model organism is not enough to ensure a viable monitoring
program; one must also study the ecosystem at multiple levels to gain an accurate view
of the whole picture and have insight about where, what and when to sample.
1.3.2 Where to sample: Habitat selection
Simply studying the diversity of a particular group does not allow a complete
overview of the system (Noss 1990). To be complete, one must also examine the
overall landscape, habitat patterns and community structures in conjunction with
biodiversity (Noss 1990, Kim 1993, Lindenmayer and Likens 2009, Hoye and Sikes
2013). In this manner, research should include both species and habitat information,
allowing for a more adaptable and predictive monitoring program to be implemented.
The Arctic as a whole, like many systems, is often viewed as homogenous at the
landscape level but it is quite heterogeneous at the microhabitat level. Studies across
multiple systems have already shown that arthropod communities differ between wet
and dry habitats (Usher 1992, Ernst and Buddle 2013, Ernst et al. 2015). A study by
Ernst and Buddle (2013) found that Arctic beetles exhibit high species turnover and
habitat segregation between wet and dry sites, leading to two distinct communities
being found 10s of meters apart from one another. In addition to soil moisture,
vegetation type (Schaffers et al. 2008) and dominance (Rich et al. 2013) are thought to
play key roles in determining arthropod diversity structure. A study done on Arctic
spiders found that shrub dominance altered snow melt time which in turn altered the
community structure (Legault and Weis 2013). Therefore, when considering a
microhabitat as a distinct unit in terms of its plant community and dominance, moisture
regime, percent openness and snow cover, one expects different communities of
arthropods to be present: and as such, different species. Microhabitat selection is an
important component of monitoring programs in all ecosystems. However, for complete
21
analysis of the Arctic ecosystem, there may be need to examine habitats in finer detail
than simply “wet” vs “dry”, as is the current microhabitat approach in many studies.
Knowing what level to sample in order to capture as many distinct communities as
possible is essential for a monitoring program.
1.3.3 What level to sample: Measuring biodiversity
Biodiversity research often favours the use of species-level data for
environmental and ecological research, but this may not be necessary for all monitoring
programs (Danks 1997, Timms et al. 2013). Though invaluable when answering
specific, fine scale questions, species level identifications can be difficult. Phylogenies
can be incomplete or unknown for some groups and identification keys non-existent or
poorly written; these reasons and more can create financial and logistical nightmares
(Kim 1993, Caughlan 2001, Lovett et al. 2007, Lindenmayer and Likens 2009, 2010).
Moreover, the research in monitoring programs is often performed by non-specialists
with little to no taxonomic background (Kim 1993, Lovett et al. 2007, Lindenmayer and
Likens 2010). As such, more merit is being placed on the use of functional diversity, in
lieu of taxonomic diversity, for larger-scaled projects (Petchey and Gaston 2002a, 2006,
Cadotte et al. 2011). Support for this, however, is not unanimous. Scientists still debate
whether it is more useful to know the identity of everything in a given system, or to have
a complete understanding of all the roles organisms play within that system (Petchey
and Gaston 2002a, 2002b, 2006, Cadotte et al. 2011).
Functional diversity places emphasis on understanding ecosystem components,
processes, and interactions (Pastor 1997, Tilman et al. 1997, Symstad et al. 2000,
Petchey and Gaston 2002a, 2006, Díaz et al. 2007, Spasojevic and Suding 2012,
Siemann et al. 2015). As these things are often targeted by monitoring programs,
functional diversity should be implemented into sampling protocols (Petchey and
Gaston 2002a, Cadotte et al. 2011). This does not necessarily imply that functional
diversity should replace taxonomic diversity; having a good taxonomic baseline is
imperative for tracking species invasions as well as geographic and phenologic shifts
(Callaghan et al. 2004a, Parmesan 2006, Hardy et al. 2014). Instead, functional
22
diversity should play a more central role in diversity sampling alongside taxonomic
diversity.
When studying functional diversity, two major approaches can be adopted
(Petchey and Gaston 2002a, 2002b, 2006, Cadotte et al. 2011). First, organisms can be
classified into categories and grouped based on similar characters (ex. trophic position).
Second, organisms can be plotted on a multi-dimensional matrix based on a suite of
individualized characters to illustrate a species unique niche within the ecosystem.
Grouping calls for major assumptions to be made but specimens can be left at higher
taxonomic divisions, whereas a trait-matrix often calls for species level identification but
gives much more accurate functional descriptions of a species’ role (Petchey and
Gaston 2002a, 2006). Statistical tests are also easier when dealing with the latter
method. However, at present, functional groups are easier to apply in monitoring
programs and they still allow researchers to examine changes within an ecosystem
(Petchey and Gaston 2002a, 2006, Cadotte et al. 2011).
Taxonomic-related issues for monitoring programs can also be resolved using
genetic resources such as DNA barcoding and next generation sequencing (NGS)
(Schwartz et al. 2007, De Barba et al. 2010). DNA barcoding provides useful
information when monitoring species invasions (Porco et al. 2013) and has improved
our ability to monitor understudied regions such as the Baltic Sea (Brodin et al. 2013).
With the rapid development of molecular tools however, DNA barcoding may become
obsolete to newer, more powerful methods such as NGS. Despite its high potential,
NGS has not been readily incorporated into most monitoring programs (Stenglein et al.
2010). At present, NGS has mostly been used to monitor single populations of
mammals (De Barba et al. 2010, Stenglein et al. 2010). However promising, both these
methods of genetic monitoring are relatively new, and as such, many issues still need to
be resolved (Schwartz et al. 2007). There may also be evidence to suggest that genetic
analysis does not provide any additional knowledge than what can be determined by
higher-level taxonomy (Pilgrim et al. 2011). Therefore, until these tools become more
developed, other methods to resolve taxonomic issues, such as functional diversity,
should be utilized.
23
1.3.4 When to sample: Time effects
Knowing when to sample in an ecosystem is imperative to accurately study the
arthropod community. Due to the Arctic’s compressed growing season, this knowledge
becomes an even greater priority. Arthropods have been shown to exhibit seasonal
turnover, where the community at the beginning of the season is not the same as the
community at the end of the season (Usher 1992, Ziesche and Roth 2008, Ernst and
Buddle 2013). Studying whether or not this occurs in an ecosystem will influence when
and how sampling should occur to best capture the arthropod diversity.
The emergence times and abundance peaks of arthropods are highly linked with
the services they provide (Kim 1993, Danks 1997, Hoye and Sikes 2013). Chick growth
and survival rates of some birds, for example, are highly dependent on arthropod
emergence dates and abundance (Tulp and Schekkerman 2008, Ims and Henden 2012,
McKinnon et al. 2012). Disconnect between arthropod emergence and chick hatching
dates can have disastrous effects on bird breeding success (Tulp and Schekkerman
2008, McKinnon et al. 2012, Bolduc et al. 2013). Similarly, plant flowering dates must
also line up with arthropods to ensure pollination occurs at the right stages of
development (Walker et al. 2006, Hoye and Sikes 2013, Hardy et al. 2014). With climate
change threatening to further exaggerate these timing gaps in the Arctic, monitoring
programs must strive to include a time profile to their arthropod community data to be
able to track and predict future changes.
1.4 Ecological Monitoring in the Arctic
1.4.1 Why monitor the Arctic?
In the last decade, the Arctic has experienced rapid, dramatic changes in
response to global drivers such as climate change (Callaghan et al. 2004a, 2004b,
Hoye and Sikes 2013). These changes render species and habitats vulnerable (Wookey
2007) and real concern arises for organisms who may be unable to adapt on pace with
their changing environment (Callaghan et al. 2004a). The low redundancy of Arctic
species is also cause for concern (Ernst and Buddle, 2015). It is expected that with
increasing temperatures and atmospheric carbon, there will be pronounced changes in
the distribution, phenology and diversity of species (Strathdee and Bale 1998). In fact,
24
scientist have already reported range shifts and contractions of animal species and
shifts in plant phenology (Parmesan 2006, Wookey 2007). Such changes are predicted
to continue and even accelerate in the coming years (Parmesan 2006).
As such, there is a growing need for a general understanding of fundamental
ecosystem processes and functioning in the North (Noss 1990, Callaghan et al. 2004a,
Hoye and Sikes 2013). The ecosystem is at risk because its flora and fauna are highly
specialized: restricting where they can thrive and how fast they can adapt (Strathdee
and Bale 1998). Moreover, there is a high probability that different organisms will evolve
and migrate at different rates (Parmesan 2006, Hardy et al. 2014). Consequently, local
predator-prey and insect-host relationships may be disrupted (Callaghan et al. 2004a,
Parmesan 2006). New competition may also arise with migrating southern species
(Parmesan 2006, Wookey 2007). The change is thus multifaceted, affecting organisms
directly and indirectly (Wookey 2007). Natural chains and webs could then be deformed,
and molded into a completely new system (Wookey 2007). Without proper
understanding of the current system, we will have no way of monitoring change,
conserving natural diversity, or studying novel assemblages.
This urgency propels research into the realm of ecological monitoring. In fact,
multiple studies have stressed the necessity of developing accurate, efficient and
focused long-term monitoring programs now, before additional changes further alter
these biomes (Noss 1990, Danks 1992, Callaghan et al. 2004a, Lindenmayer and
Likens 2009, Hoye and Sikes 2013). These programs can then provide guidelines for
conservation and management strategies (Noss 1990, Lindenmayer and Likens 2009)
as well as providing predictive power to prevent future change and the collapse of entire
ecosystems (Noss 1990, Callaghan et al. 2004a, Hoye and Sikes 2013).
1.4.2 Arctic monitoring in Canada
As of yet, no arthropod focused long-term ecological monitoring programs exist in
the Canada. The closest is the Zackenberg Ecological Research Operations (ZERO)
located in Greenland. Since 1996, this station has been collecting varied biological data
(plants, arthropods, birds and mammals) as well as climatic and geological data, and
has been reporting on annual changes and trends (Jensen et al. 2013). In so doing, the
25
ZERO program aims to “produce a coherent set of numerical and phenological data
facilitating the understanding of the intricate dynamics of a terrestrial High Arctic
ecosystem” (Schmidt et al. 2012). The first of its kind, ZERO operates in a simplified
manner, with protocols and regular revisions for each organism type occurring
independently of one another (Schmidt et al. 2012). ZERO is not ideal in all aspects in
that it does not examine the system from various scales nor does it consider any
aspects of the ecosystem besides biological diversity. Though a great starting point for
long-term programs, ZERO lacks a comprehensive framework from which ecosystem
wide information can be drawn.
Another program, based out of Alaska, is the Arctic Long term Ecological
Research Site. It is one of 24 projects operated by the National Science Foundation’s
Long Term Ecological Research Network (Hobbie et al. 2003). The Alaska program has
not been operating for as long as the program in Zackenberg, but it too has the goal of
understanding and predicting environmental change on Arctic landscapes (Hobbie et al.
2003). Its research focuses on broader, landscape level patterns and interactions. Here,
there is lack of focus on the hierarchical complexity of Arctic food webs and ecological
impacts. Additionally, there is no specific or direct establishment of an arthropod
sampling protocol; meaning arthropods are never studied on their own. However, like
the ZERO project, the use of long-term sites to gather both temporal and spatial data is
something that must become the norm in the future of Arctic monitoring-research.
Despite these two large projects, there is still need for comprehensive and
extensive research to be conducted in the Arctic if global problems, such as climate
change, are to be addressed. POLAR knowledge, a branch of Environment Canada, is
in the process of establishing a long-term ecological monitoring network in the Arctic.
Cambridge Bay, Nunavut (69.117° N, 105.053° W) embodies an ideal starting location
as an extensive analysis into its geology and flora has already been conducted. From
past POLAR research, a series of 11 microhabitats have been established and can
serve as building blocks for additional layers of information to be collected about the
ecosystem. My research will serve to establish the baseline data for Cambridge Bay’s
arthropod monitoring. This baseline data will allow for a basic understanding of the
overall Arctic ecosystem to be determined and will serve as a benchmark for future
26
work. I will investigate species and community level diversity patterns, using a crosshabitat approach. Through my research I hope to encourage a new focus on Arctic
conservation and management. By building a concrete image of the diversity and
community structure of arthropods in partnership with existing soil, vegetation and
environmental data, I hope to contribute recommendations for a more effective and
applicable monitoring program; one which incorporates all aspects of Arctic
ecosystems.
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climate change? Hindcasting the abundance of tundra arthropods using weather
and seasonal variation. Arctic, 61: 48–60. doi: 10.14430/arctic6.
Usher, M.B. 1992. Management and diversity of arthropods in Calluna heathland.
Biodivers. Conserv., 1: 63–79. doi: 10.1007/bf00731035.
Walker, M.D., Wahren, C.H., Hollister, R.D., Henry, G.H.R., Ahlquist, L.E., Alatalo, J.M.,
Bret-Harte, M.S., Calef, M.P., Callaghan, T. V, Carroll, A.B., Epstein, H.E.,
Jonsdottir, I.S., Klein, J.A., Magnusson, B., Molau, U., Oberbauer, S.F., Rewa,
32
S.P., Robinson, C.H., Shaver, G.R., Suding, K.N., Thompson, C.C., Tolvanen, A.,
Totland, O., Turner, P.L., Tweedie, C.E., Webber, P.J., and Wookey, P.A. 2006.
Plant community responses to experimental warming across the tundra biome.
Proc. Natl. Acad. Sci. U. S. A., 103: 1342–1346. doi: 10.1073/pnas.0503198103.
Wookey, P.A. 2007. Climate change and biodiversity in the Arctic; Nordic perspectives.
Polar Res., 26: 96–103. doi: doi:10.1111/j.1751-8369.2007.00035.x.
Ziesche, T.M., and Roth, M. 2008. Influence of environmental parameters on smallscale distribution of soil-dwelling spiders in forests: What makes the difference, tree
species or microhabitat? For. Ecol. Manage., 255: 738–752. doi:
10.1016/j.foreco.2007.09.060.
1.6 Connecting Statement
This chapter provided the context and background information for the research
presented in the subsequent two chapters. It outlined the objectives of the thesis and
introduced the specific questions which will be in Chapters 2 and 3. Chapter 2 is about
the effects of microhabitat and seasonal change on spider assemblages, studied at the
species level.
33
Chapter 2: Seasonal change and microhabitat association of Arctic spider
assemblages (Arachnida: Araneae) on Victoria Island (Nunavut)
1
Elyssa R. Cameron and Christopher M. Buddle
1
1
Department of Natural Resource Sciences, McGill University, 21111 Lakeshore Rd, Ste-Anne-de-
Bellevue, QC, H9X 3V9
2.1 Abstract
Arctic ecosystems are characterized by a mosaic of distinct microhabitats which play a
key role in structuring biodiversity. Understanding variation in species in relation to
these microhabitats, and how communities are structured seasonally, is imperative to
properly conserve, monitor and manage northern biodiversity. Spiders (Arachnida:
Araneae) are dominant arthropod predators in the Arctic, yet the seasonal change in
their communities in relation to microhabitat variation is relatively unknown. This
research quantified how spider assemblages are structured seasonally and by
microhabitat, near the hamlet of Cambridge Bay, Nunavut. In the summer of 2014
spiders were collected in 256 pan and pitfall traps placed in common microhabitat types
(2 wet and 2 dry) over 5 sampling periods from 3 July – 11 August, the active season in
the high Arctic. In total, 10 353 spiders from 22 species and 4 families were collected.
NMDS ordinations revealed that spider assemblages from wet habitats were distinct
from those occurring in drier habitats, but that differences within each of those habitats
were not evident. Abundance and diversity was highest in wet habitats and differed
significantly from dry habitats; both these variables decreased seasonally. Spider
assemblages in the north are structured strongly along moisture gradients, and such
data informs planning for future ecological monitoring in the Arctic.
2.2 Introduction
In Arctic systems, microhabitat differences are on a much finer scale than in other
ecosystems. Terrestrial landscapes are not delineated by abrupt transition zones such
as forests to fields, or canopies to forest floors. Instead, all microhabitats experience
harsh open conditions, and vegetation structure is limited and ranges from mosses and
lichens to grasses and small shrubs, seldom more than knee-height. Still, the few
studies which have been done in the Arctic suggest that even these slight differences
34
between Arctic habitats can produce distinct arthropod communities; at least between
more broadly-defined wet and dry habitat types (Koponen 1992, Marusik and Koponen
2002, Wyant et al. 2011, Rich et al. 2013).
Biodiversity in the north is dominated by arthropods (Danks 1992), and among
terrestrial species, spiders are the dominant apex predators. Spiders have been
observed to not only alter herbivore density, which sustains a diverse and beneficial
plant community (Schmitz 2003, Estes et al. 2011), but to also alter herbivore behavior
by displacing them and therefore minimizing herbivory stress on local plants
(Beckerman et al. 1997). Loss or change in top predator communities can have ripple
effects down through the food chain leading to a loss of plant life and lowered diversity
(Schmitz 2003, Estes et al. 2011). This reduction in plant biomass could lead to a drop
in suitable nesting grounds for migrating birds and a loss of food resources for
mammals (Ims and Henden 2012). Conserving Arctic systems begins from the
producers up; adding more support for the need to understand basic spider distribution
across the tundra.
Historically, research with Arctic spiders has focused on distributional data and overall
biodiversity checklists or inventories (Marusik and Koponen 2000). With climate change
occurring at accelerated rates in the Arctic (Parmesan 2006, Høye et al. 2007), present
research must shift focus to better understand the factors that govern northern
biodiversity, including how spider communities are structured across space and time.
Recent research predicts that the habitat mosaics found in the Arctic will respond at
different rates and in different ways to climate change (Bowden et al. 2015). As such,
the Arctic we see today will not have the same microhabitat distribution, proportions,
and characteristics as the Arctic of the coming decades. Shrub encroachment, snow
melt timing, permafrost melt, and disturbance regimes (Myers-Smith et al. 2011, Naito
and Cairns 2011, Legault and Weis 2013) are already being observed in the north and
will continue to progress. To be able to accurately predict how spider assemblages may
respond to these habitat changes requires knowledge of the determinants of community
structure.
35
Current research shows that habitat complexity and type govern spider community
composition (Greenstone 1984, Uetz 1991, Halaj et al. 2000, Weeks and Holtzer 2000,
Schaffers et al. 2008, Bowden and Buddle 2010a). Though abiotic (Willis and Whittaker
2002, DeVito et al. 2004, Bowden et al. 2015) and landscape (Willis and Whittaker
2002, Finch et al. 2008, Schaffers et al. 2008, Bowden and Buddle 2010a) factors can
also shape spider assemblages, they are often considered to be of lesser importance
than habitat complexity (Bowden and Buddle 2010a), notably changes in plant
community structure, complexity and diversity (Greenstone 1984, Uetz 1991, Rypstra et
al. 1999, Weeks and Holtzer 2000, Beals 2006).
Spider communities differ along biome-level (Willis and Whittaker 2002, Bowden and
Buddle 2010b) and altitudinal gradients (Greenstone 1984, Willis and Whittaker 2002,
Bowden and Buddle 2010b, Cardoso et al. 2011), between different forest types in the
same region (Pearce et al. 2004, Ziesche and Roth 2008), and between different forest
(Pinzon et al. 2012) and agricultural (Rypstra et al. 1999) management strategies.
These variations can even be perceived at various microhabitat levels, whereby canopy
vs ground assemblages in a single stand (Larrivee and Buddle 2009) or the relative
decay of deadwood (Varady-Szabo and Buddle 2006) can support different spider
communities. However, specific knowledge about the factors structuring spiders in the
high Arctic remains limited.
In addition to habitat complexity, seasonal turnover can influence Arctic arthropod
assemblages. One study on Arctic beetles noted that assemblages found at the
beginning of the season were distinct from those found at the end (Ernst and Buddle
2013). Due to the accelerated growing season in Arctic systems, this may also be true
of spider communities, though this is not well studied. In other ecosystems, spiders can
respond to plant succession and seasonal change (Usher 1992, Ziesche and Roth
2008) but inconsistencies remain (Mallis and Hurd 2005, Ziesche and Roth 2008).
Therefore, to understand the assemblage patterns of northern spiders, the influence of
seasonal change requires attention.
36
The objective of this study is to quantify the relationship between Arctic spider
assemblages and microhabitats, and to assess how spider assemblages change over
the short Arctic growing season. Doing so will not only allow us to enhance our
knowledge of Arctic spider communities as a whole, but also identify any betweenhabitat differences which may occur at the micro-habitat level.
2.3 Methods
2.3.1 Experimental design and sampling
We sampled spiders in Cambridge Bay, Nunavut (69.117° N, 105.053° W) in 2014.
Cambridge Bay is a hamlet on Victoria Island and experiences a polar climate with
summer averages for temperature and precipitation of 7.9°C and 24.9mm, respectively.
We determined the sampling sites based in part on existing environmental data.
Specifically, the Canadian High Arctic Research Station (CHARS), run by Polar
Knowledge Canada (POLAR), has produced an Arctic microhabitat classification system
(each called an ecosite (ES), analogous to microhabitats) which relates the biotic plant
components with local abiotic components to describe the ecosystem at a fine scale
(McLennan et al. 2013). We sampled the four most abundant ecosites of the Cambridge
Bay region (ES01, ES03, ES07, ES08 – descriptions in Table 2.1), herein referred to as
Dry1, Dry2, Wet1 and Wet2 (Table 2.1).
We selected four locations (i.e., replicates) all within 12 km of the hamlet of Cambridge
Bay. Each replicate needed to contain the four above mention habitats in large,
representative patches and in proximity to one another (Table 2.2).
In each microhabitat, we established a grid of 9 yellow pan traps and 6 pitfall traps.
Each trap was approximately 10 meters from each other, and the trap type order was
randomly determined. The use of both trap types ensured we were sampling the
complete spider assemblage, and helped reduce trap bias (Ernst et al. 2015). This
design was repeated in each of the four microhabitats and at all four locations, leading
to a total of 16 grids and 240 traps.
37
We installed both pan and pitfall traps with approximately 3 cm (depth) of glycol mixture
(50:50 water and propylene glycol with a drop of dish soap) in each. The traps were
open between 3 July (vii) 2014 and 11 August (viii) 2014 inclusively, and were
separated into 7 sampling periods (1 = 3-8vii, 2 = 8-14vii, 3 = 14-19vii, 4 = 19-26vii, 5 =
26-30vii, 6 = 26vii-5viii, 7 = 5-11viii). Data from periods 1, 2, 4, 5, and 7 are included in
the analyses. Sampling occurred over the entire summer season to increase the
chances of capturing the full spider diversity: and not miss species which may be less
active at certain periods. Upon servicing a trap, we rinsed all samples with water, then
placed them in a whirl pak and immersed them with 70% ethanol. All samples were
taken back to the laboratory for processing.
Spider identification keys and guides were used to determine the species identity of
adult specimens, including the Insects and Arachnids of Canada series (Dondale et al.
2003) and the Guide d’indentification des Araignées (Araneae) du Quebec (Paquin and
Duperre 2003). Juveniles were only identified to family. Vouchers were made for both
male and female adult specimens of each species, and are deposited at The Lyman
Entomological Museum of McGill University (Ste-Anne-de-Bellevue, Quebec).
2.3.2 Data analyses
To test the overall effects of microhabitats and seasonal change on spider
assemblages, we considered measures of relative abundance and diversity. Spider
community matrices were log transformed and plotted in ordination space using the
metaMDS function in the vegan package (Oksanen et al. 2015) of R 3.1.1 (R Core
Team 2014). This gives a visual representation of community similarity where each
point in the ordination space represents a spider assemblage at a given time, replicate
and microhabitat. Environmental variables (maximum temperature, minimum
temperature, mean temperature, total precipitation, maximum wind gust, trap type, site,
microhabitat and period) were then plotted on the ordination as vectors, using the envfit
function in vegan (Oksanen et al. 2015), to determine their relative influence on
community composition. Microhabitat centroids (ordispider function in vegan) and 68%
38
confidence intervals (ordiellipse function in vegan) were included on the ordination to
obtain statistically testable values and delimit the ecosite boundaries.
Using MANCOVAs, we determined the influence of habitat and time on spider total
relative abundance. Tukey’s HSD tests determined the differences between
microhabitat pairings. Time was considered as a continuous variable because we were
interested in whether or not communities changed over time, and were less interested in
determining whether individual time periods differed from each other. All trap types and
replicates were pooled for this analysis and the data was log transformed in order for
the residuals to be normally distributed.
We examined species diversity by first constructing rarefaction curves to determine if
adequate sampling had been conducted (Buddle et al. 2005). These were created using
the rarefy function (Oksanen et al. 2015) in R 3.1.1 (R Core Team 2014). Our
rarefaction curves of our sampled communities did approach an asymptote (Fig. 2.3), so
species richness was used as a metric of diversity along with other measures of species
diversity: Shannon, Simpson, Pielou’s evenness, and Fisher alpha. To infer about
statistical significance, we again preformed MANCOVAs and Tukey HSD tests. As with
the abundance data, all trap types and replicates were pooled.
2.4 Results
Project-wide, 10 353 spiders were collected representing 4 families and 22 species
(Table 2.3). Lycosidae (wolf spiders) were the most commonly collected spiders (7 523
individuals, 73% of the total sample) and represented only two species. The Linyphiidae
(micro sheet web spiders) were the most diverse family – with its 18 species; Linyphiids
were also the second most commonly collected spiders (2 020 individuals, 20% of the
total sample). A complete list of species, and their associated species code and
abundance values, can be found in Table 2.3.
39
This study identified four new territory records for Nunavut: a long-jawed orb weaver
Pachygnatha clerckii Sundevall, and three species from the Linyphiidae: Agyneta
allosubtillis Loksa, Masikia indistincta Kulczynski, and Bathyphantes simillimus Koch.
Spider assemblages were oriented along a moisture-driven microhabitat gradient,
where dry ecosites differed from wet ecosites but there was no discernable difference
within them (Fig. 2.1). Microhabitat identity was the most important variable in
determining where communities fall within the ordination space (Fig. 2.1; p-value <
0.001). Site location also seemed to influence spider communities but to a lesser
degree (Fig. 2.1; p-value = 0.034). All other tested environmental variables
(temperature, precipitation, wind, etc.) had no effect on overall spider assemblages.
Spider abundance and diversity varied significantly by microhabitat. Total abundance
trends over time remained consistent across all four ecosites, but relative values
differed between them (Fig. 2.2). Wet habitats contained significantly more individuals
than dry habitats (Fig. 2.2, Table 2.4, 2.5) though differences between similar
microhabitats were non-significant (Table 2.5).
Rarefaction curves illustrated an adequate sampling of microhabitats (Fig. 2.3) and
therefore, species richness could be used as a measure of diversity, along with a suite
of common biodiversity indices. However, results did differ depending on the metric
used. Simpson’s diversity index, Pielou’s evenness and Shannon diversity index all
revealed a significant effect of microhabitat on diversity, though Fisher’s alpha diversity
index did not (Table 2.4). Of those diversity measures which had a significant effect,
both wet ecosites consistently differed with Dry1 (Table 2.5). This pattern was not
always true with Dry2. In most cases, the wet ecosites did not differ significantly in their
diversity when compared to Dry2 (Table 2.5). Again, we concluded that wet ecosites
supported different communities than dry ecosites, but that finer habitat divisions were
not apparent.
40
At a species-level, many specialists (defined here as a species for which 70% or more
of the captured individuals were found in a single habitat type (dry or wet)) and very few
generalists (defined as a species with a more even distribution between both habitat
types) were present (Fig. 2.5). Species tended to be more specialized for either a wet or
a dry microhabitat type, and were rarely found with a similar relative abundance in both.
This observation did not hold true at the family level (Fig. 2.6). No apparent ecosite
preference existed for Lycosidae or Linyphiidae, the two dominant families.
In the ordination, sampling period was insignificant and did not seem to affect the
community composition (Fig. 2.1; p-value = 0.123). Other environmental variables, such
as mean temperature (p-value = 0.554), total precipitation (p-value = 0.422) and
maximum wind gust (p-value = 0.672), also had no effect. However, when the
community was considered at the species level, the effect of time told a more complex
story (Fig. 2.6). Here, we observed that some species were active early in the season,
and then had varying relative abundance. Others showed a peak activity level in the
middle of the season, and some experienced their highest relative abundance at the
end of the season (Fig. 2.6). This suggested that the community was dynamic over
time, and that the relative proportions of different species were not static even if the
species presence/absence (as was measured by the ordination) did not change.
Seasonality had a significant effect on overall community abundance and diversity
(Table 2.4). The relative abundance of species changed over the course of the season.
Community abundance peaked around the second sampling period (July 8 th – 14th) and
subsequently declined over the remainder of the season, with a slight increase in the
final sampling period (Fig. 2.2). Spider diversity also proved to be influenced by
sampling period, according to the significant values of Simpson’s diversity index,
Pielou’s evenness and Shannon diversity index (Table 2.2). As with microhabitats,
Fisher’s alpha diversity index showed no significant effect.
41
2.5 Discussion
The objective of this research was to characterize Arctic spider assemblages in relation
to microhabitats and seasonal change, near Cambridge Bay, Nunavut. Our main results
show that Arctic microhabitats are non-uniform, and spiders are structured along a
moisture gradient; assemblages from dry habitat types differ significantly from those
collected from wet habitat types. Spiders do not seem to select for differences within
broader “dry” or “wet” ecosites and the community can be adequately described based
on these broader habitat classifications. We also documented a shift in spider
assemblages in relation to seasonal change: a pattern that was most evident at the
species-level. The spider assemblages were dynamic, with different species showing
higher catch rates at specific times of the short growing season.
2.5.1 Microhabitats
Spider communities respond to small-scale habitat differences within the tundra as
distinct assemblages were observed in dry and wet habitats, but there was a high
degree of overlap within each of the two community types (Fig. 2.1). This distinction
between wet and dry communities has also been found for arctic beetles (Ernst and
Buddle 2013) and in similar studies of Arctic spiders (Koponen 1992, Usher 1992,
Wyant et al. 2011). Finer differences within the same ecosite were not apparent. Legault
and Weis (2013) found that snow melt timing (the main distinction between the two wet
habitats in this study) of different wet habitat types had no effect on spider assemblage
structure or emergence timing. In contrast, one study on Arctic spiders in Alaska
demonstrated that communities of two different wet habitat types were significantly
distinct (Rich et al. 2013). So at finer scales there is still conflicting evidence of effects
on spiders, although on Victoria Island, spider assemblages are mostly structured by
broad habitat categories.
Microhabitats support a high degree of species specificity (Fig. 2.5). Only two species
could be classified as generalists, Alopecosa hirtipes Kulczynski (Araneae: Lycosidae)
and Walckenaeria karpinskii Pickard-Cambridge (Araneae: Linyphiidae), as they are
found just over 50% of the time in dry ecosites and the rest of the time in wet habitats.
42
The remaining species can be defined as specialists, with more than 70% of the
individuals having been caught in a single microhabitat type (Fig. 2.5). The idea that
spiders can be specialists has been reported indirectly in other studies (Mallis and Hurd,
2005; Rypstra et al., 1999). This habitat specialization could be explained by a species’
guild or hunting strategy (Uetz, 1991), abiotic restrictions (Bowden and Buddle, 2010a;
DeVito et al., 2004), or habitat complexity requirements (Bowden and Buddle, 2010b;
Schaffers et al., 2008). Further research is needed in order to determine the influence of
abiotic components in shaping spider communities in the high Arctic: and if they are
driven directly by the abiotic conditions in that habitat (wet or dry) or indirectly by the
subsequent plant community and prey type under those abiotic conditions.
In Cambridge Bay, dry habitats are characterized by relatively flat, low lying vegetation
and differ dramatically from the taller, more structurally complex vegetation at the wet
habitats. The variance in the plant species dominance, vegetation complexity and
overall habitat architecture between these habitats most likely explains the majority of
the observed differences in spider assemblages and species preferences (Uetz 1991,
Rypstra et al. 1999, Halaj et al. 2000, Weeks and Holtzer 2000, Willis and Whittaker
2002, Larrivee and Buddle 2009). Given the importance of habitat type and complexity,
it can be expected that as Arctic habitats continue to undergo changes which affect the
timing of snowmelt (Legault and Weis, 2013), the vegetation dominance and the habitat
complexity, the abundance and diversity of the spider community will also likely change.
One study found that a loss of lichen/moss dominated habitats to graminoid dominated
habitats triggered an overall loss in species diversity (Walker et al., 2006). Halaj et al.
(2000) also observed a decrease in overall abundance and diversity when habitats were
made less complex and more uniform. Even habitat change from overgrazing by geese
caused shifts in local beetle and spider communities (Milakovic and Jefferies, 2003).
Yet, possibly the greatest threat to microhabitat mosaics in the Arctic is shrub
expansions, as it causes greater uniformity of microhabitats (Eldridge et al., 2011;
Myers-Smith et al., 2011; Naito and Cairns, 2011). In Cambridge Bay, this may lead to
neighbouring Dry2 and Wet1 habitats being overtaken by the Wet2 willow type habitat.
43
This could have important implications to local diversity, as the species Pachygnatha
clerckii Sundevall (Araneae: Tetragnathidae) is found almost exclusively in Wet1 and
Emblyna borealis Pickard-Cambridge (Araneae: Dictynidae) is most commonly found in
Dry2 (Fig. 2.6). Increase in shrub cover also leads to changes in local abiotic
environmental factors, nutrient cycling, disturbance regimes and decomposition, all of
which could affect spider communities and their prey in unpredictable ways (Eldridge et
al., 2011; Myers-Smith et al., 2011; Naito and Cairns, 2011; Walker et al., 2006).
2.5.2 Seasonal change
Arctic spider assemblages exhibited seasonal change patterns, though this change
cannot be defined specifically as species turnover. Ernst and Buddle (2013) found that
beetle communities exhibited strong species turnover and that communities were very
different at the start and end of the season. With our work, most species were present
throughout the entire sampling season, leading to an insignificant effect of sampling
period in the ordination (Fig. 2.1). However, sampling period did have a significant effect
on overall spider abundance and diversity (Table 2.1), and differences in abundance
peaks of individual species were observed (Fig. 2.4).
With climate change altering the timing of snow melt and accelerating the warming
process (Parmesan 2006, Høye et al. 2007), these abundance curves may shift with
time – potentially leading to new community dynamics based upon how quickly
individual species will react or adapt to these temperature changes. Since we know that
ecologically similar species are often restricted by different temperature profiles (DeVito
et al. 2004), climate change may give certain species a competitive edge over others.
Also although all communities change over time, they may not always change at a
similar rate within each habitat type – restricted by different conditions associated with
those habitats. Weeks and Holtzer (2000), for example, observed a distinct seasonal
effect in steppe grass systems, where communities from different habitats changed in
different ways throughout the season. The reason we did not concretely observe this in
our study could be due to differences in growing season length and ecoclimatic zones.
44
In the Arctic, presence/absence of a species seems less important than a species’
relative dominance in the community as a function of time. In this way, spider
communities are not static throughout the season but do not exhibit a true turnover (Fig.
2.4). The pattern may be a strategy to decrease competition with species of similar
guilds without sacrificing emergence time in the short growing season (Uetz and Uetz
1977, Uetz 1991, Uetz et al. 1999, Halaj et al. 2000, Weeks and Holtzer 2000, DeVito et
al. 2004). Or it may potentially be a function of timing with favorite prey sources.
2.5.3 Conclusion
In the Arctic, both microhabitat type and seasonal change play a role in structuring
spider communities. Of the four tested ecosites, distinct communities emerge when
comparing wet microhabitats to dry microhabitats but the differences between more
similar microhabitats were insignificant. This is most likely explained by the dramatic
differences in the habitat complexity of the ecosites: something spiders are known to
respond strongly to in other ecosystems, even at finer scales. Still, it would be ideal to
test all 11 microhabitat types in Cambridge Bay for community differences before
making concrete statements about the uniformity of all wet or dry microhabitat types.
Though the effect of time isn’t as clear cut as the effect of microhabitats, communities
are not static; their abundance, diversity and species dominance all change throughout
the season. This research has therefore shown that fine scale microhabitat level
sampling is necessary to capture the full complement of Arctic spiders. This knowledge
will aid in the development of future ecological monitoring programs as well as more
accurate and meaningful sampling designs.
2.6 Acknowledgments
We would like to thank the many volunteers (Anthony Dei Tigli, Amanda Fishman, Julie
Hamel, Jessica Turgeon, Anne-Sophie Caron) who helped with the sorting and
identification of samples. Special thanks to the members of the Buddle Lab (Chris
Cloutier, Shaun Turney, Crystal Ernst) for their ideas and support and to our two field
assistants: Sarah Loboda and Jessica Turgeon. This project was funded by NSERC
(Post-graduate scholarship to EC and a Discovery Grant to CMB), and a NSERC
45
Northern Research Supplement (to CMB). The project was done in collaboration with
Polar Knowledge Canada.
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2.8 Tables and Figures
Table 2.1. Description of the four common ecosites used in this study. For the classifications,
the ecosite codes in bracket refer back to the Cambridge Bay classifications.
Classification
Dry1 (ES01)
Dry2 (ES03)
Wet1 (ES07)
Wet2 (ES08)
Characteristics
Dominant Vegetation
Moisture
Flat and rocky, often
located on the tops of
hills. Highly exposed to the
elements
Located on slope sides,
often between Dry1 and
Wet1.
Moss and dryas vegetation
Mesic – driest
of all habitats
Least snow
accumulation and
earliest snow melt
Snow Depth/Melt
Hummic vegetation
Mesic
transitional
Flat habitat with
waterlogged soil, located
in close proximity to water
bodies. Often sheltered by
valleys – less exposed
Flat and slightly rocky,
with clay soil and highest
vegetation complexity.
Located along edges of
water bodies.
Sedges
Wet – prone to
short-term
seasonal
flooding
Low accumulation
and early melt but
higher and later
than Dry1
Mid accumulation
and mid to late
snow melt
Dwarf willows
Wet
Highest
accumulation and
latest snowmelt
Table 2.2 GPS coordinates for all sampled locations (samples and replicates) near Cambridge
Bay (Nunavut).
Location
Replicate 1 – Dry1
Replicate 1 – Dry2
Replicate 1 – Wet1
Replicate 1 – Wet 2
GPS Coordinates
N69.1398 W104.9517
N69.1396 W104.9510
N69.1394 W104.9494
N69.1385 W104.9504
Locations
Replicate 3 – Dry1
Replicate 3 – Dry2
Replicate 3 – Wet1
Replicate 3 – Wet 2
GPS coordinates
N69.1564 W104.8990
N69.1562 W104.9004
N69.1561 W104.8980
N69.1597 W104.9017
Replicate 2 – Dry1
Replicate 2 – Dry2
Replicate 2 – Wet1
Replicate 2 – Wet 2
N69.1574 W104.9115
N69.1579 W104.9120
N69.1581 W104.9126
N69.1586 W104.9093
Replicate 4 – Dry1
Replicate 4 – Dry2
Replicate 4 – Wet1
Replicate 4 – Wet 2
N69.1664 W104.8576
N69.1661 W104.8550
N69.1654 W104.8560
N69.1657 W104.8563
52
Table 2.3. List of spider species collected in each of the four ecosite types, collected in
Cambridge Bay, Nunavut in 2014. Ecosite descriptions are in Table 2.1. Abundance values per
ecosite represent the pooled totals from all sampling periods, replicates and trap types. New
territory records are denoted by an asterisk. Note the juveniles are not included in these totals.
Bolded values denote family level or habitat level totals.
Family/Species
Microhabitat
#per
ecosite
Dry1
#per
ecosite
Dry2
#per
ecosite
Wet1
#per
ecosite
Wet2
PAAL
ALHI
6
330
15
633
2088
258
1166
417
EMBO
27
85
1
2
PACL
3
37
289
23
ERAR
ERPS
HIPR
HIVE
DIBA
HYAQ
MAIN
SIPA
SEBE
WAKA
TALA
AGMA
AGAL
BAGR
ORES
BASI
HAHO
HATH
3
0
1
1
5
1
0
0
0
4
0
4
0
0
2
2
0
0
54
0
0
6
11
4
0
2
0
4
0
14
0
0
0
3
0
1
196
117
67
8
0
33
44
4
71
1
6
5
2
98
1
0
4
0
291
66
6
13
0
56
34
3
107
4
8
1
0
32
0
0
6
0
Total
collected
4913
3275
1638
115
115
352
352
1406
544
183
74
28
16
94
78
9
178
13
14
24
2
130
3
5
10
1
389
869
3293
2235
6786
Species
Code
Lycosidae
Pardosa algens (Kulczynski 1908)
Alopecosa hirtipes (Kulczynski 1907)
Dictynidae
Emblyna borealis (O. Pickard-Cambridge 1877)
Tetragnathidae
Pachygnatha clercki (Sundevall 1823)*
Linyphiidae
Erigone arctica (White 1852)
Erigone psychrophila (Thorell 1871)
Hilaria proletaria (L. Koch 1879)
Hilaria vexatrix (O. Pickard-Cambridge 1877)
Diplocephalus barbiger (Roewer 1955)
Hybauchenidium aquilonare (L. Koch 1879)
Masikia indistincta (Kulczynski 1908)*
Silometopoides pampia (Chambelin 1949)
Semljicola beringianus (Eskov 1989)
Walckenaeria karpinskii (O. Pickard-Cambridge 1873)
Tarsiphantes latithorax (Strand 1905)
Agyneta maritima (Emerton 1919)
Agyneta allosubtilis (Loksa 1965)*
Baryphyma groenlandium (Holm 1967)
Oreonata eskimopoint (Saaristo & Marusik 2004)
Bathyphantes simillimus (L.Koch 1879)*
Halorates holmgrenii (Thorell 1871)
Halorates thulensis (Jackson 1934)
TOTAL
53
Table 2.4. MANCOVA p-values for the effect of time (period) and microhabitat on the total
abundance, richness, and diversity of spiders in Cambridge Bay. Here, abundance values are
taken from the log total abundance. Significant values are denoted with an asterisk. Additional
Tukey HSD tests were conducted for “Microhabitat” and those p-values can be found in Table
2.5.
Response
Abundance
Species
Richness
Simpson
Pielou’s
Evenness
Fisher Alpha
Shannon
Factor
Period
Microhabitat
Period:Microhabitat
Period
Microhabitat
Period:Microhabitat
Period
Microhabitat
Period:Microhabitat
Period
Microhabitat
Period:Microhabitat
Period
Microhabitat
Period:Microhabitat
Period
Microhabitat
Period:Microhabitat
Df
1
3
3
1
3
3
1
3
3
1
3
3
1
3
3
1
3
3
Sum Sq
30.9253
26.9514
0.1536
104.0
471.4
16.8
0.1099
0.4173
0.1914
0.05342
0.02612
0.02908
1.386
1.724
0.911
0.6016
2.3899
0.0950
Mean Sq
30.9253
8.9838
0.0512
104.01
157.13
5.61
0.10992
0.13910
0.01595
0.05342
0.00871
0.00055
1.386
0.5748
0.3036
0.6016
0.7966
0.0317
F Value
99.4402
28.8874
0.1646
41.655
62.932
2.245
6.891
8.721
0.288
22.044
3.592
0.226
2.543
1.054
0.557
8.326
11.026
0.438
P Value
3.325e-15*
2.267e-12*
0.9198
1.1e-08*
2e-16*
0.0903
0.02217*
0.00242*
0.83342
0.000519*
0.046372*
0.876452
0.137
0.404
0.653
0.013691*
0.000917*
0.729861
Table 2.5. Tukey HSD test p-values for the factor “Microhabitat”. Abundance values are taken
from the log total abundance. Significant values are denoted by an asterisk.
Abundance
Species
Richness
Simpson
Pielou’s
Evenness
Fisher Alpha
Shannon
ES03-ES01
0.3103954
0.0381997*
ES07-ES01
0.0000000*
0.0000000*
ES08-ES01
0.0000000*
0.0000000*
ES07-ES03
0.0000001*
0.0000000*
ES08-ES03
0.0000302*
0.0000000*
ES08-ES07
0.4780271*
0.2151235
0.2175107
0.2446323
0.0102793*
0.1479482
0.0024773*
0.0356243*
0.3178110
0.9870846
0.0882300
0.6534586
0.8389519
0.8322009
0.9101405
0.2998644
0.4934607
0.0031135*
0.4458575
0.0016212*
0.8565161
0.0762420
0.8150465
0.0389084*
0.9997438
0.9787119
54
Fig 2.1. NMDS ordination of the spider community across all replicates and time periods using
the log values of species relative abundance. Each point indicates the location of a sampled
microhabitat: where the triangles denote the two dry ecosites and the squares denote the two
wet habitats. Points which are located more closely together are more similar than points
located further away from one another. In A, the text represents the individual species codes
(two first letters of genus and two first letters of species names (see Table 2.3)) and shows the
location of each species within the ordination space; where a strong association of a species to
a particular habitat type can be observed. The centroid variable of the plot is the microhabitat
type (p-value < 0.001), making it the determining factor for point location in the ordination space.
Of all environmental variables tested, “Site” (p-value = 0.014) and “Period” (p-value = 0.103)
had the strongest vector effects. In B, the location of each habitat centroid along with the 68%
55
confidence areas is shown. There are significant differences between wet and dry habitat
pairings but no differences between the communities of Dry1 and Dry2 or Wet1 and Wet2.
Fig 2.2. Spider total relative abundance across the five sampling periods in each of the four
microhabitats. Abundance values include both adult and juvenile specimens and represent the
pooled total of all replicates and trap types. Sampling periods spanned the entire 2014 summer
season, and break down as follows: 1=3-8vii, 2=8-14vii, 3=19-26vii, 4=26-30vii, 5=5-11viii. See
Table 2.1 for ecosite descriptions. Differences between the microhabitats can be observed, but
the overall pattern of spider peak abundance seems to remain consistent. Associated p-values
for the effect of time (period) and habitat (ecosite) can be found in Table 2.4. Error bars
represent one standard deviation.
56
Fig 2.3. Rarefaction curves of species richness per microhabitat. See Table 2.1 for ecosite
descriptions. Only when sampling has reached asymptotic can species richness be used as a
measure of biodiversity – as is the case here. Rarefied species richness values are:
Dry1=7.854, Dry2=8.330, Wet1=9.992, Wet2=9.753.
57
Fig 2.4. Individual species turnover across the five sampling periods. Species codes are given
as the y-axis (associated species names can be found in Table 2.3). Plotted values are the total
abundance of a given species at each time period (microhabitats, trap types and replicates are
pooled). The figure only includes 20 of the 22 species collected as the singleton and doubleton
were excluded.
58
Fig 2.5. Proportion of total individuals of each species found in dry and wet habitats. Ecosites
have been combined into the dry or wet categories to better show the pattern. No significant
differences have been observed between the two dry or the two wet ecosites (Fig. 2.1 and
Table 2.1). Species identity is portrayed on the x-axis by its code (species names can be found
in Table 2.3). The bolded values above the species code denotes the total number of individuals
collected for that species, across all time periods, replicates and habitats.
59
Fig 2.6. Relative dominance of the four spider families in each microhabitat and through time.
The habitats breakdown as follows: A – Dry1, B – Dry 2, C – Wet1, D – Wet 2. Total abundance
numbers come from pooled replicates and trap types. Abundance values include both adult and
juvenile specimens. Periods represent the following dates: 1=3-8vii, 2=8-14vii, 3=19-26vii, 4=2630vii, 5=5-11viii.
60
2.9 Connecting statement
The overarching theme of this thesis is to provide baseline information on the arthropod
community of Cambridge Bay, Nunavut in order to offer recommendations for the longterm monitoring program being developed. Chapter 2 took a more traditional, taxonomic
approach to defining arthropod communities and used spiders as a model group. It
supplied critical information about where and when to sample spiders in order to gain
the most complete and accurate view of the community. It illustrated that microhabitats
cannot be treated as uniform (dry assemblages differ significantly from wet
assemblages) and that there is evidence of seasonal change within the community.
These results support a recommendation which favors sampling multiple habitat types
throughout the entire sampling season. However, sampling in monitoring programs will
rarely use species-level taxonomic resolution when using arthropods. Instead, Family
and Order level taxonomy and/or functional diversity dependent on ecological roles will
be favored. Both of these classifications will be analysed in Chapter 3. Hence, now that
these patterns are known to exist at a species level in this system, we have a baseline
with which to compare the results of Chapter 3. Whether the results from the two
chapters differ or not will provide important information about how observed patterns
could change with sampling method (taxonomic or functional focuses), and how this
may alter our ability to provide accurate and meaningful monitoring recommendations.
61
Chapter 3: Arthropods as ecological indicators: a comparison of taxonomic and
functional diversity to sample entire communities in the Arctic
1
Elyssa R. Cameron , Terry A. Wheeler
1, 2
, and Christopher M. Buddle
1
1
Department of Natural Resource Sciences, McGill University, 21111 Lakeshore Rd, Ste-Anne-de-
Bellevue, QC, H9X 3V9
2
Lyman Entomological Museum, McGill University, 21111 Lakeshore Rd, Ste-Anne-de-Bellevue, QC, H9X
3V9
3.1 Abstract
Arthropods make ideal model organisms for monitoring programs although their high
taxonomic diversity can pose challenges. It is often difficult to identify arthropods to
species, especially for non-specialists. However, the use of functional diversity in
monitoring may allow scientists to avoid dealing with fine-scale taxonomic divisions.
This solution would make it more feasible to accurately integrate arthropods into
ecological monitoring programs. Functional diversity can also allow monitoring
programs to link arthropod community data with ecological processes. However, it is
unclear if common community level patterns can be detected using functional diversity
as readily as with taxonomic diversity. Using an Arctic tundra system for study, the
objectives of this research were to (1) examine the effects of microhabitat type and
seasonality on functionally and taxonomically described terrestrial arthropod
communities, (2) determine if these patterns were similar between the two diversity
measures and (3) describe the food webs of wet and dry habitat types. Functional and
taxonomic communities were both significantly affected by a moisture-driven habitat
gradient and seasonality. Food webs also differed: wet habitats had a
predator/decomposer-dominated abundance whereas dry habitats had a herbivoredominated abundance. It was also observed that the same functional roles may be
62
performed by different taxa depending on the habitat. Total abundance peaks varied by
habitat type (in both abundance and timing), and within a given habitat, abundance
peaks of each functional role sometimes differed from the total community. Our findings
suggest that functional diversity can be used as a proxy for arthropod monitoring in the
Arctic and that habitat types support different communities, food webs, and abundance
peaks. With climate change threatening Arctic systems, ecological monitoring programs
must be able to better predict ecosystem responses. We provide recommendations on
how arthropods can be better integrated into monitoring programs in order to track
environmental change in the Arctic.
3.2 Introduction
Arthropods make ideal model organisms for ecological monitoring programs. They are
hyper-diverse, highly fecund, reproduce rapidly, and are easy to collect, sample and
store (Danks, 1992; Kim, 1993). Moreover, they respond to fine-scale changes in the
environment, play central roles in ecological processes and can be used to help detect
environmental problems: important criteria for ecological indicators (Dale and Beyeler,
2001; Danks, 1992; Maleque et al., 2006). Recent research from long-term monitoring
in Greenland illustrates how well arthropods can indicate effects of climate change
(Hoye and Sikes, 2013, Jensen et al., 2013; Schmidt et al., 2012).
At present, the Arctic is undergoing accelerated change and requires immediate
attention before the damage becomes irreversible (Parmesan, 2006). Monitoring
programs can provide important recommendations on how to best preserve the health
and diversity of the ecosystem: and arthropods will be at the forefront of these insights
63
(Danks, 1992; Kim, 1993). In the Arctic, arthropods perform essential ecological
functions, such as pollination, decomposition, and herbivore population control, and
these in turn help maintain the proper functioning and health of the ecosystem (Danks,
1992; Kim, 1993). Local plant success is strongly linked with pollinator emergence dates
(Walker et al., 2006; Wookey, 2007) and plant diversity depends on arthropod herbivory
control by predators (Beckerman et al., 1997; Estes et al., 2011). Some birds rely on
arthropods as a food resource for chicks (Bolduc et al., 2013; McKinnon et al., 2012;
Tulp and Schekkerman, 2008) and benefit from a healthy plant community for nesting
sites (Ims and Henden, 2012). Ecological monitoring programs should strive to include
arthropods in their protocols and objectives as they provide insights for the entire
ecosystem (Dale and Beyeler, 2001; Danks, 1992; Kim, 1993; Lindenmayer and Likens,
2010).
One major challenge when using arthropods in monitoring programs is also a great
asset: they are hyper-diverse. This, coupled with outdated taxonomic keys and
incomplete knowledge of phylogenetic relationships can cause major problems (Danks,
1997). Scientists working on these monitoring programs are often non-specialists with
little taxonomic training, further magnifying the issue (Kim, 1993; Lindenmayer and
Likens, 2010, 2009; Lovett et al., 2007). Parataxonomy can be used as an alternative to
named species taxonomy and is generally accepted in the field of conservation biology
(Krell, 2004). However, morphospecies diversity measures tend to overestimate the
total diversity of a system and are prone to high sorting error: making it more difficult to
conduct analyses (Krell, 2004). When looking at an ecosystem from a purely taxonomic
64
perspective of arthropod communities, community links with ecological processes and
ecosystem functions may be overlooked; yet these aspects are important for monitoring
programs.
Using functional diversity as an ecological indicator can help to address a more
ecosystem-wide focus (Cadotte et al., 2011; Petchey and Gaston, 2002, 2006).
Functional diversity is defined as a biodiversity measure which explains the array of
roles that organisms play within communities and ecosystems (Cadotte et al., 2011;
Petchey and Gaston, 2006). It can provide insight on how organisms influence the biotic
and abiotic components of the system along with the processes which govern it (Diaz
and Cabido, 2001; Spasojevic and Suding, 2012; Tilman et al., 2013). Arthropods are
functionally diverse and contribute to many ecological processes (Danks, 1992; Kim,
1993). Studying arthropods as indicators from a functional perspective could therefore
allow scientists to better understand food webs, to identify ecosystem vulnerabilities,
and to link arthropod diversity with other ecosystem components (Dale and Beyeler,
2001; Danks, 1992; Kim, 1993; Maleque et al., 2006). Research suggests that plant
functional diversity is highly correlated with ecological processes (Spasojevic and
Suding, 2012; Symstad et al., 2000; Tilman et al., 2013, 1997) and species richness of
other groups (Siemann et al., 2015). Arthropods, like plants, form the base of most
ecosystems, and can be expected to influence the ecosystem in similar ways. One
study discovered that arthropods select for different plant functional traits (Pastor,
1997): indicating that functional diversity can link different trophic levels.
65
Functional diversity can help answer the broad-viewed, large-scaled questions often
asked by monitoring programs (Petchey and Gaston, 2002, 2006). Still, before they can
be readily implemented into ecological monitoring, it must first be established if
functional diversity is comparable to taxonomic diversity in its ability to detect patterns in
arthropod communities (Petchey and Gaston, 2002, 2006). Taxonomic studies have
already shown that insects and spiders respond strongly to changes in habitat type
(Ernst et al., 2015; Rich et al., 2013; Schaffers et al., 2008) and seasonality (Danks,
1997; Ernst and Buddle, 2013; Hoye and Sikes, 2013). There is a slowly growing body
of literature which examines the functional diversity of arthropods in relation to
monitoring (Andersen et al., 2002; Anderson, 1995; Taillefer and Wheeler, 2012).
Though to our knowledge, work to quantify the difference between functional and
taxonomic diversity is still required, especially in vulnerable Arctic ecosystems.
Monitoring programs need fundamental data about where and when to sample model
organisms to avoid wasting limited time and resources (Caughlan, 2001; Danks, 1997;
Lindenmayer and Likens, 2009).
The overarching goal of this study is to characterize the terrestrial arthropod community
of Cambridge Bay (Nunavut) to establish a benchmark for future monitoring programs.
Our objectives are the following: (1) to determine the effects of habitat type and time
(i.e. seasonal change) on communities (2) quantify whether functional and taxonomic
diversity produce the same patterns, (3) to compare food webs, emergence times and
functional roles between habitat types, and (4) to provide recommendations for Arctic
monitoring programs.
66
3.3 Methods
3.3.1 Experimental design
We sampled the arthropod community of Cambridge Bay, Nunavut (69.117° N,
105.053° W), a hamlet of Victoria Island. Characterized by a polar climate, this area
supports a mosaic of heterogeneous Arctic microhabitats – from exposed rock and
moss outcrops to riparian areas dominated by sedges and willows. We selected the four
most common microhabitats (2 dry and 2 wet) from which to sample. These
microhabitats are synonymous with ecosites (ES): ecosystem classification units which
relate the local plant components with abiotic conditions. In Cambridge Bay, the
Canadian High Arctic Research Station (CHARS), run by Polar Knowledge Canada
(POLAR), has already described the ecosites of the region (see McLennan et al. 2013)
Based on their system, we selected ES01, ES03, ES07 and ES08 (descriptions in Table
1) for our study. For simplicity, this paper will refer to these ecosites as Dry1, Dry2,
Wet1 and Wet2. Sites were sampled in 2014.
We chose four locations (replicates) to sample, all within 11.5 km of the hamlet of
Cambridge Bay. Each replicate contained each of the four above mentioned
microhabitats. In each microhabitat, we created a grid of 9 yellow pan traps and 6 pitfall
traps (though only the pan trap data will be included in the analyses). Trap order was
randomly determined and distance between traps was approximately 10 m. We then
applied this design to all microhabitats, in all four locations, for a total of 16 grids and
144 pan traps and 96 pitfall traps.
67
In each trap, we placed approximately 3 cm of glycol mixture (50:50 water and
propylene glycol with a drop of dish soap). Sampling occurred between the 3 rd of July
(vii) and the 11th of August (viii) 2014 inclusively and were separated into 7 sampling
periods (1 = 3-8vii, 2 = 8-14vii, 3 = 14-19vii, 4 = 19-26vii, 5 = 26-30vii, 6 = 26vii-5viii, 7 =
5-11viii). For our analyses, only the data from sampling periods 1, 2, 4, 5 and 7 are
included. Upon servicing a trap, we rinsed all samples with water, then placed them in a
whirl-pak bag with 70% ethanol. All samples were taken back to the laboratory for
processing.
3.3.2 Arthropod identification and classification
Samples were initially sorted into one of six taxonomic groups: Araneae, Coleoptera,
Hymenoptera, Nematocera (Diptera), Brachycera (Diptera) and Other. For spiders,
beetles and higher flies we included data from all 9 pan traps into the analyses. For the
more cryptic groups of bees and wasps, lower flies and “other” (which included
arthropods such as true bugs, springtails, mites, etc.), we only analyzed data from 5 (all
odd numbered) pan traps. For this reason, the “abundance” values represented in this
paper are the average number of individuals per trap in order to make the groups
comparable. Samples were then identified to the taxonomic level necessary to
determine a broad functional role, representing a group’s major contribution to Arctic
ecosystems: carnivore, herbivore, decomposer, parasitoid, blood feeder/nuisance or
input (see Table 3.2 for descriptions). For some groups, we assigned the role that best
characterized the taxon based on the commonly known species in the area. For higher
flies, the assigned role was based on the larval stage, in accordance with common
practice in Diptera, since individuals spend most of their life as larvae. The adult stage
was used for all other groups. Flies, bumble bees, butterflies and moths could also be
considered important flower visitors and were additionally classified as “pollinators”. Due
to the repetition of data (i.e. when a taxon could be classified as a pollinator and another
group) we omitted pollinators from the main analyses and treated them separately.
68
3.3.3 Data analyses
To test the effects of habitat and time on arthropod communities, we completed NMDS
(non-metric multidimensional scaling) ordinations using the metamds function of the
vegan package (Oksanen et al., 2015) in R 3.1.1 (R Core Team 2014). All data were
first log transformed, then one plot was constructed with the taxonomic information and
another with the functional role information. Ordinations allow for a visual representation
of community similarity, where points located closer together are more similar than
points located further away from each other. We then imposed additional environmental
variables (habitat type, time period, precipitation, minimum, maximum and average
temperature, and maximum wind gust speed) on the plot as vectors using the envfit
function in vegan (Oksanen et al., 2015). These vectors were tested for a significant
effect in shaping the observed communities. To generate statistical values for each
habitat, centroids were determined using the ordispider function (in vegan) and 68%
confidence intervals were added using the ordiellipse function (in vegan).
Trends in total relative abundance, abundance by Order and taxonomic diversity (using
multiple indices: Shannon, Simpson, Pielou’s Evenness) were tested using
MANCOVA’s for an effect of habitat type and sampling period. For significant habitat
effects, Tukey’s HSD tests were performed to identify the pairings with a significant
difference. Each functional category was also subjected to the same analysis.
We compared the differences in functional and taxonomic community composition
between wet and dry microhabitats. We calculated the relative proportions of the total
abundance, for each habitat (pooled replicates and time periods), for the functional roles
and taxonomic orders. Special attention was placed on understanding differences in
group dominance and relating functional role observations to their taxonomic
counterparts.
69
3.4 Results
Project-wide, 87,880 arthropods were collected from 10 orders and 74 taxa. In
decreasing order of abundance, these orders were Diptera (59,795), Collembola
(11,716), Araneae (8,052), Hymenoptera (3,603), Coleoptera (2,339), Hemiptera
(1,174), Acari (929), Lepidoptera (156), Trichoptera (117), Plecoptera (2). Within the
Diptera, the most abundant families were the Chironomidae (21,237), Muscidae
(13,464), Dolichopodidae (6,507), and Sciaridae (5,118).
NMDS ordinations revealed a significant effect of microhabitats in shaping Arctic
arthropod communities (Fig. 3.1). Both taxonomic and functional assemblages were
centered on microhabitats (p < 0.001 in both cases) and significantly affected by
sampling period (p < 0.001 for both). We observed a moisture-driven microhabitat
gradient, where arthropod communities from dry ecosites differed from those in wet
ecosites but there was no difference within them (Fig. 3.1). This difference appeared
more pronounced when examining taxonomic groups compared to functional groups,
but both were significant. Taxonomic groups were additionally mediated by maximum,
minimum, and mean temperature, and total precipitation (p = 0.016, p < 0.001, p <
0.001, and p = 0.004 respectively). Functional groups were affected by maximum wind
gust speed (p = 0.022) as well as the four above mentioned environmental variables (all
with p < 0.001).
Ecosite and sampling period had significant effects on the number of individuals within
each functional role. Carnivores, herbivores, decomposers and parasitoids were all
affected by microhabitat type (Table 3.3), but only when a wet habitat was compared to
a dry habitat (Table 3.4). No differences were noted between the two wet or the two dry
ecosites. There was also an overall effect of microhabitat (p < 0.001) and period (p <
0.001) on the functional roles. Additionally, sampling period was found to significantly
affect carnivores, herbivores, and parasitoids (Table 3.3). Pollinators were tested
separately, and both microhabitat (p = 0.488) and period (p = 0.100) had no effect.
70
Microhabitats not only supported different community compositions and abundances,
they were also characterized by a unique food web when viewed as the relative
abundance by functional group. Dry habitats contained proportionally more individuals
from the herbivore, nuisance, and input groups and proportionally less carnivores,
decomposers and parasitoids than wet habitats (Fig. 3.2.). Additionally, evidence
supported the assumption that not all taxonomic groups play the same roles in each
habitat type (Fig. 3.2). For example, we noticed an increase in the proportion of
decomposers in wet sites (Fig. 3.2B), but this increase was less pronounced than the
difference in Collembola and Acari (most abundant decomposer groups) between the
two habitats (Fig 3.2A). Also, though the proportion of spiders and beetles (most
abundant carnivore groups) remained relatively constant between the two habitat types
(Fig. 3.2A), there were proportionally more carnivores in wet habitats than dry (Fig.
3.2B).
Figure 3.3 further illustrated community differences between wet and dry habitats.
Overall, wet habitats had a higher relative abundance and reached peak abundance
earlier in the season than dry habitats (Fig. 3.3). Individual functional groups also
appeared to peak earlier in wet habitats, though not always at the same time as the
overall community peak (Fig. 3.3). In the wet habitat, carnivore and decomposer
abundance peaked earliest in early July (8-14vii) whereas herbivore and input
abundance peaked in mid-July (19-26vii) and parasitoid abundance in late July (2630vii) (Fig. 3.3). In the dry habitats, though total community abundance peaked one
week later, certain groups (carnivores and herbivores) peaked at the same time as in
the wet habitats. Comparatively, decomposers peaked later in dry habitats (19-26vii)
compared to wet habitats; as did the input group (26-30vii) (Fig. 3.3).
Total relative abundance differed significantly by microhabitat (p = 0.001) however it did
not vary significantly by sampling period (p = 0.724). When examined individually, the
abundance of some taxonomic groups also varied by microhabitat and/or period (Table
3.5). Diversity, regardless of the index used (Shannon, Simpson, Pielou’s Evenness),
was not significantly affected by microhabitat or period.
71
3.5 Discussion
The objectives of this research were to determine if Arctic arthropod communities
described by functional and taxonomic diversity responded to habitat type and
seasonality in the same way, and to compare the structure of arthropod assemblages
between the different habitat types. We found that wet and dry habitats support different
taxonomic and functional communities and communities also respond to seasonal
changes. In general, both measures of diversity produced the same community
patterns, a finding with significant implications for ecological monitoring. For taxonomic
groups, the abundance of many orders was affected by period and microhabitat but
diversity was not. For functional roles, carnivores, herbivores, decomposers and
parasitoids responded to microhabitat type, and other than decomposers, also
significantly changed with time. Additionally, the timing of peak abundance, the
seasonal trends in abundance, and the relative proportions of functional roles differed
between wet and dry habitats. This suggested that habitats based along a moisture
gradient contained distinct arthropod food webs.
3.5.1 Taxonomic and functional diversity
Monitoring programs must focus their scope and sampling due to limited resources
(Danks, 1997; Kim, 1993; Lindenmayer and Likens, 2009; Noss, 1990). Based on our
findings, Arctic arthropod communities may be adequately sampled by using one dry
and one wet habitat type (Fig. 3.1), and further microhabitat divisions may be
unnecessary. Other studies have observed similar patterns of habitat-mediated
arthropod communities (Bowden et al., 2015; Ernst et al., 2015; Halaj et al., 2000; Rich
et al., 2013; Schaffers et al., 2008). Although many of these studies examine
communities at a species-level, our results at higher taxonomic resolutions had the
same conclusions. This suggests that when conducting arthropod-related monitoring
programs in the Arctic, species level classifications may not always be necessary
(Danks, 1997; Timms et al., 2013). Keeping taxonomic classifications at the Family and
Order levels allows non-specialists to analyze entire communities with relative ease,
72
reduces sorting error, and improves data quality (Danks, 1997; Lindenmayer and
Likens, 2010; Timms et al., 2013).
Functionally defined communities exhibited the same pattern as the taxonomically
defined ones, whereby communities (Fig. 3.1), along with 4 of the 7 functional roles
(Table 3.3), differed significantly between wet and dry habitats: though there was no
difference within similar habitat types. This means that functional diversity may be a
valid proxy for taxonomic diversity in our Arctic study system. The use of functional
groupings instead of multi-trait matrices is not always supported in the literature
(Cadotte et al., 2011; Petchey and Gaston, 2006). However, our findings suggest that
the use of simplified measures of quantifying functional diversity can give meaningful
results. This observation, along with the congruence to taxonomic patterns, supports the
use of functional categories (roles) to describe arthropod communities in the Arctic.
Functional roles are easily determined from higher taxonomic resolutions (Petchey and
Gaston, 2002, 2006) and can be directly correlated with ecological processes and
ecosystem services (Diaz and Cabido, 2001; Lacroix and Abbadie, 1998; Tilman et al.,
2013): these are two clear benefits for monitoring programs.
Seasonality influenced both functional and taxonomic communities in a significant way
(Fig. 3.1; Table 3.3 and 3.4). In Cambridge Bay, peak abundance occurred in mid-July
for the overall community, though the exact date differed between habitat types (Fig.
3.3). If time and money were limited, programs could restrict sampling to the peak
abundance times of arthropods (Caughlan, 2001; Danks, 1997; Kim, 1993;
Lindenmayer and Likens, 2009).
3.5.2 Food web structure
Arthropods are known to play key roles in Arctic ecosystems (Danks 1992, Kim 1993,
Hoye and Sikes 2013). In our study, functional roles are directly related to food web
position. It is therefore argued that the food webs of wet and dry habitats were
structured differently. Dry habitats contain proportionally more herbivores, nuisances,
and inputs and proportionally fewer carnivores, decomposers and parasitoids than wet
73
habitats (Fig. 3.2). This depicts a food web where the majority of the abundance is
located in the “primary consumer” trophic level (the “center” of the food web). In
contrast, wet habitats contain proportionally more carnivores, decomposers and
parasitoids and proportionally less herbivores, nuisance species, and inputs than dry
habitats (Fig. 3.2). This illustrates a food web where the majority of the abundance is
located at the “extremes” (top and bottom) of the food web. The mechanisms behind
the creation of a “center-heavy” vs. “top-heavy” food web are unknown but most likely
influenced by the difference in plant communities.
Plant composition and plant functional diversity help explain much of an environment’s
abiotic conditions (Tilman et al. 1997), and can be differentially selected by arthropods
(Pastor 1997, Symstad et al. 2000, Siemann et al. 2015). Plant functional diversity is
also highly correlated with ecological processes such as decomposition and nutrient
cycling (Spasojevic and Suding, 2012; Symstad et al., 2000; Tilman et al., 1997). One
habitat may therefore be more favorable to one group than another. Additionally, habitat
complexity is linked with diversity (Schaffers et al. 2008); and with a difference in
taxonomic diversity, there is most likely a difference in arthropod functional traits
(Petchey and Gaston 2006, Cadotte et al. 2011). Ecosystem processes are
subsequently governed by the suite of functional traits present in that habitat. It can
therefore be implied that different habitats, with their distinct plant communities and
varied complexity, select for different arthropod species and mediate different
ecosystem processes.
Functional diversity thus allows monitoring programs to detect changes in the arthropod
community and then infer how these changes may percolate through the food web
(Cadotte et al., 2011; Danks, 1992; Kim, 1993; Petchey and Gaston, 2002, 2006).
Potential consequences and predicted vulnerabilities can then be targeted and properly
managed in the future (Cadotte et al., 2011; Petchey and Gaston, 2002, 2006). Pairing
functional diversity with the use of arthropods as ecological indicators then allows for
quick response to environmental disturbances and more efficient recovery efforts
74
(Cadotte et al., 2011; Dale and Beyeler, 2001; Danks, 1992; Kim, 1993; Petchey and
Gaston, 2002).
3.5.3 Functional roles
The overall arthropod abundance differs significantly between habitats (Table 3.5), and
the functional groups which are dominant in a habitat remain that way regardless of time
(Fig. 3.3). Since the habitat food webs, abundance, and community composition all
differ, we can also extrapolate that ecosystem functions (roles) may be performed by
different taxa in each habitat (and most likely by different genera and species within a
taxon). For example, mites (order: Acari), springtails (order: Collembola), and some
higher flies (suborder: Brachycera) can all be classified as “Decomposers”. There is a
much higher abundance of decomposers in wet habitats (Fig. 3.3), but proportionally,
this difference is less pronounced (Fig. 3.2B). However, the proportional difference in
the abundance of mites and springtails between the two habitats is much greater (Fig.
3.2A). This suggests the decomposer fly species must be much more abundant in dry
sites than in wet sites for the difference in overall decomposers (Fig. 3.2B) to remain so
slight. Therefore, it may be possible that different taxa are necessary for the same
ecological process depending on the habitat type. A similar pattern can be observed for
carnivores: spiders (order: Araneae), beetles (order: Coleoptera) and some brachyceran
flies. There are proportionally more carnivores in wet habitats than dry, but the same
proportion of spiders and beetles: implying a larger occurrence of carnivorous fly
species in wet habitats to account for the difference (Fig. 3.2).
This observation that different taxa can perform the same functions depending on the
habitat has important implications for ecological monitoring. It suggests that many
groups are responsible for ecological processes, and that sampling should not focus on
a single taxon. Biological diversity ensures functional diversity which in turn promotes
ecosystem stability (Danks, 1992; Kim, 1993).
75
3.5.4 Emergence times and peak abundance curves
Understanding the emergence times of arthropods has important implications for
pollination and bird reproduction success (Bolduc et al., 2013; McKinnon et al., 2012;
Walker et al., 2006). When examining the functional community, abundance peaks were
not consistent between ecological roles or habitat types (Fig. 3.3). Environmental
variables such as snow melt timing can alter emergence times of arthropods (Tulp and
Schekkerman 2008, Legault and Weis 2013). Though in this study, wet habitats (which
have later snow melt dates), have earlier peak abundances than dry habitats. Other
factors, such as overwinter stage, development times, temperature requirements, etc.,
(Danks, 1992), must therefore also be at play. More research would need to be
conducted in our system to determine their significance. Wet habitats also maintain a
higher abundance throughout the season. This is unsurprising as wet habitats are
known to have more abundant arthropod communities than dry habitats (Ernst and
Buddle 2013, Ernst et al. 2015).
Interestingly, the abundance peaks of individual roles are not always consistent with the
overall abundance peaks (Fig. 3.3). Monitoring can therefore target specific functional
roles based on their peak abundance, and not based on the seasonal abundance of all
arthropods. Researchers can then focus their sampling effort in the habitat and time
period which holds the highest abundance of their target group. This will promote a
more efficient use of available resources and waste less time as the researcher will not
need to sort through massive samples of non-target groups (Noss 1990, Danks 1992,
Kim 1993, Lindenmayer and Likens 2009, Hoye and Sikes 2013). Monitoring programs
will also be able to target ecological process related questions more easily. A study on
soil decomposition should be carried out early in the season (in wet habitats) to ensure
the highest volume of decomposers. By knowing the temporal distribution of each
functional group, more meaningful and targeted research can be conducted.
76
3.5.5 Recommendations and ecological monitoring in the Arctic
Our study demonstrates a multi-leveled distinction between communities of wet and dry
habitats and the importance of functional diversity in describing the arthropod
assemblages. With climate change, the arrangement and dominance of these
microhabitats is likely to change (Parmesan, 2006). Perhaps most importantly, shrub
expansion in the Arctic threatens to increase the uniformity of habitats (Eldridge et al.,
2011; Myers-Smith et al., 2011; Naito and Cairns, 2011). In Cambridge Bay, this could
mean an expansion of wet habitats at the expense of dry habitats. Severe and
unpredictable biological change may then occur since these habitat types are so
unique. The impacts of the increasing uniformity on diversity (both taxonomic and
functional) as well as ecosystem function is largely unknown (Lacroix and Abbadie,
1998; Tilman et al., 1997). One study found many functionally important taxa decreased
with environmental homogeneity (Diekotter et al., 2010). An ecosystem would then also
be at much greater risk of losing functional trait diversity, which can destabilize the
entire system (Lacroix and Abbadie, 1998; Tscharntke et al., 2008).
Based on our results, we offer the following recommendations for ecological monitoring
in the Arctic:

Monitoring programs with objectives focused on assessing ecosystem health and
processes, detecting ecosystem changes, and monitoring diversity of arthropods,
plants and vertebrates will benefit from the use of arthropod functional diversity
as a proxy for taxonomic diversity. When monitoring arthropods, functional
diversity can be used in lieu of taxonomic diversity in most cases (unless
species-level resolution is absolutely required to answer the scientific question or
monitoring objective). Giving a larger role to functional diversity will allow
arthropods to be more easily integrated into ecological monitoring programs. This
will result in saving valuable time and resources without sacrificing data quality

To gain a complete view of the diversity and abundance of arthropods,
monitoring programs in the Arctic should minimally sample in at least one wet
and one dry habitat and over the entire season, and replicate this design in at
least two locations. This recommendation is important since we observed
77
changes in communities across space and time. However, the more
microhabitats which can be integrated into the protocols, the better.

Arthropod based food webs are structurally different between the two habitat
types. The same functional role may also be performed by different taxa
depending on habitat. Monitoring programs should therefore analyze habitats
separately to better detect ecosystem changes and predict future ecosystem
consequences and vulnerabilities.

Functional groups can be more efficiently sampled by targeting them based on
sampling date and habitat type. This will allow monitoring programs to answer
more specific research questions without wasting limited resources.
3.6 Acknowledgements
We would like to thank the many volunteers (Anthony Dei Tigli, Amanda Fishman, Julie
Hamel, Jessica Turgeon, Anne-Sophie Caron) who helped with the sorting and
identification of samples. Special thanks to the members of the Buddle Lab (Chris
Cloutier, Shaun Turney, Crystal Ernst) for their ideas and support and to our two field
assistants: Sarah Loboda and Jessica Turgeon. This project was funded by NSERC
(Post-graduate scholarship to EC and a Discovery Grant and Northern Research
Supplement to CMB). The project was done in collaboration with Polar Knowledge
Canada.
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3.8 Tables and Figures
Table 3.1. Description of the four common ecosites used in this study. For the classifications, the ecosite
codes in bracket refer back to the Cambridge Bay classifications.
Classification
Dry1 (ES01)
Characteristics
Flat and rocky, often
Dominant Vegetation
Moisture
Moss and dryas vegetation
Mesic – driest
Least snow
of all habitats
accumulation and
located on the tops of
hills. Highly exposed to the
Snow Depth/Melt
earliest snow melt
elements
Dry2 (ES03)
Located on slope sides,
Hummic vegetation
often between Dry1 and
Mesic
Low accumulation
transitional
and early melt but
Wet1.
higher and later
than Dry1
Wet1 (ES07)
Flat habitat with
Sedges
Wet – prone to
Mid accumulation
waterlogged soil, located
short-term
and mid to late
in close proximity to water
seasonal
snow melt
bodies. Often sheltered by
flooding
valleys – less exposed
Wet2 (ES08)
Flat and slightly rocky,
Dwarf willows
Wet
Highest
with clay soil and highest
accumulation and
vegetation complexity.
latest snowmelt
Located along edges of
water bodies.
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Table 3.2. Description of the functional group classifications and some examples of taxa which would fall
under them
Functional Role
Description
Example of Taxa
Carnivore
A taxon which mostly or exclusively feeds on
Spiders, Carabidae, Staphylinidae
other arthropods
Herbivore
A taxon which mostly or exclusively feeds on
True bugs, Caterpillars, Sawflies
plant matter (leaves, stem, seeds, fruits, etc)
Decomposer
A taxon which mostly or exclusively feeds on
Springtails, Mites
dead or decaying matter
Parasitoid
A taxon which parasitizes other arthropods
Wasps
Blood
A taxon which takes blood meals from hosts
Mosquitoes, Black flies
feeder/nuisance
and can act as a nuisance to people or the
resources (caribou, birds, etc) they rely on
Input
A taxon which has a mostly aquatic life cycle
Midges, Crane Flies
but emerges as an adult in massive numbers
and dies off a few days later. It therefore
serves as an allochtonous input of food
Pollinator
A taxon which regularly visits flowers and may
Bumble bees, Muscidae,
actively or indirectly spread pollen
Syrphidae
Table 3.3. MANCOVA p-values for the effect of time (period) and microhabitat (ecosite) on the
distribution of our 6 functional roles. Here, abundance values represent the average number of
individuals per trap, rather than the raw abundance due to differential sorting effort. Significant values
are denoted by an asterisk. Additional Tukey HSD tests were conducted for “Ecosite” and those p-values
can be found in Table 3.4.
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Carnivore
Herbivore
Decomposer
Parasitoid
Blood
Input
Period
0.002641*
0.0091305*
0.707163
0.0007575* 0.8668
0.1031
Ecosite
1.488e-07*
0.0004355*
0.000256*
5.874e-10*
0.1799
0.1542
Interaction
0.383820
0.0039831
0.818361
0.0130699
0.4026
0.8852
Table 3.4. Tukey’s HSD test p-values for the factor “Ecosite”. Abundance values represent the average
number of individuals per trap, rather than the raw abundance due to differential sorting effort.
Significant values are denoted by an asterisk.
Carnivore
Herbivore
Decomposer
Parasitoid
03-01
0.9666946
0.9750886
0.9969325
0.997457
07-01
0.0000182*
0.0134030*
0.0258641*
0.0000001*
08-01
0.0000574*
0.0031354*
0.0050935*
0.0000205*
07-03
0.0001043*
0.0414315*
0.0147182*
0.0000003*
08-03
0.0003106*
0.0111933*
0.0026777*
0.0000425*
08-07
0.9903100
0.9629605
0.9424056
0.5827459
Table 3.5. MANCOVA p-values for the effect of time (period) and microhabitat (ecosite) on the total
abundance and relative abundance of the taxonomic orders and dipteran suborders (Nematocera and
Brachycera) present in this study. Here, abundance values represent the average number of individuals
per trap, rather than the raw abundance due to differential sorting effort. Significant values are denoted
by an asterisk.
Taxa
Period
Microhabitat
Interaction
Total Abundance
0.724
5.15e-06*
0.928
Coleoptera
1.414e-10*
0.1332
0.9556
Araneae
4.752e-11*
9.735e-09*
0.0003899*
Nematocera
0.06712
0.06621
0.66989
Brachycera
0.1757
0.0528
0.9968
Hymenoptera
0.001554*
7.88e-10*
0.022240*
Hemiptera
0.0065897*
0.0001758*
0.6243233
85
Collembola
0.2463576
0.0009683*
0.7763536
Acari
0.3460416
0.0004851*
0.8296607
Lepidoptera
0.0006286*
0.0365716*
0.7818006
Trichoptera
0.0007777*
0.0619490
0.0141150*
Plecoptera
0.4877
0.4103
0.6927
Fig. 3.1 NMDS ordination for the arthropod community of Cambridge Bay, Nunavut. Ordination A
describes the community from a taxonomic perspective, and ordination B from a functional perspective.
Each point represents a sampling location for a given habitat, time and replicate. Triangles denote the
dry habitat types and squares denote the wet habitat types. See Table 3.1 for habitat descriptions. Both
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ordinations are centered on the “microhabitat” environmental variable (p < 0.001 for both). The webs
show the location of each habitat centroid, and the lines connect each sampling point to the center. The
circles show the 68 % confidence intervals (1 standard deviation) from the centroid. Only environmental
variables which had a significant effect on the community are shown on the ordinations.
Fig. 3.2 Proportional representation of community composition in dry and wet habitat types. Figure 3.2A
characterizes the community taxonomically by order (and suborder for flies). Figure 3.2B characterizes
the community functionally into one of the functional groups described in Table 3.2 (except Pollinators).
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Fig. 3.3 Abundance by functional group and time period for dry and wet habitats. Abundance values
represent the average number of individuals per trap due to differential sorting effort and are
subsequently pooled by replicate. All sampling occurred in Cambridge Bay, Nunavut over the 2014
summer season.
3.9 Connecting statement
Chapter 3 provided insight on the use of functional diversity in Arctic monitoring
programs. It showed how functional diversity can improve the feasibility of including
arthropods in a monitoring program without sacrificing data integrity. It also described
the effects of space (microhabitat) and time (seasonality) on the functional community.
The results obtained in this chapter provide the same conclusions as those from
Chapter 2 (which used a more traditional, species-level approach). This agreement
suggests that monitoring programs can utilize functional diversity to describe arthropod
communities and still be able to track changes in ecosystem patterns and ecological
processes – perhaps even more easily than with taxonomic diversity. Chapter 3 also
provided recommendations on how to more efficiently monitor Arctic arthropods without
wasting valuable resources. The final section of this thesis is a general summary and
conclusion.
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Thesis summary and conclusions
This thesis established important benchmark data on the arthropod community of
Cambridge Bay, Nunavut. The monitoring program being developed in this region by
Polar Knowledge Canada will greatly benefit from this baseline. With it, long-term
datasets will have a base of comparison to better detect and track patterns in the rapidly
changing Arctic environment. Additionally, recommendations on how to improve the
arthropod monitoring protocols were generated and will surely contribute to the more
efficient use of time and resources without sacrificing data integrity.
Chapter 1 provided an introduction to the operation of monitoring programs. It
outlined the usefulness of arthropods as ecological indicators and explained the three
key pieces of information which must be known about the community before a
monitoring program is established: where arthropods should be sampled, when
arthropods should be sampled, and what level of diversity arthropods should be studied
at. Chapter 2 explored the first two key questions using spiders as a model taxon. Novel
assemblages were observed for wet and dry microhabitat types, but finer habitat
divisions were non-significant. Species composition was also found to be dynamic over
time: whereby the proportion of individuals present for a given species changed over the
course of the season. Spider assemblages thus did not exhibit seasonal turnover, as
most species were present (with varying abundance and dominance) throughout the
summer but the effect of time was none the less important. A high degree of habitat
specificity at the species level was also uncovered.
Chapter 3 examined the entire arthropod community from two diversity
perspectives: taxonomic and functional. Communities were found to be significantly
affected by microhabitat type and seasonality regardless of the diversity measure.
Evidence also suggested the presence of differently structured food webs in wet and dry
habitat types. Habitat type even influenced the abundance peaks of functional roles. It is
additionally possible that the same functional role may be performed by different taxa
89
depending on the microhabitat. From the results of chapter 3, the following
recommendations were generated for the monitoring of Arctic arthropod communities:

Monitoring programs with objectives focused on assessing ecosystem health and
processes, detecting ecosystem changes, and monitoring diversity of arthropods,
plants and vertebrates will benefit from the use of arthropod functional diversity
as a proxy for taxonomic diversity. When monitoring arthropods, functional
diversity can be used in lieu of taxonomic diversity in most cases (unless
species-level resolution is absolutely required to answer the scientific question or
monitoring objective). Giving a larger role to functional diversity will allow
arthropods to be more easily integrated into ecological monitoring programs. This
will result in saving valuable time and resources without sacrificing data quality

To gain a complete view of the diversity and abundance of arthropods,
monitoring programs in the Arctic should minimally sample in at least one wet
and one dry habitat and over the entire season, and replicate this design in at
least two locations. This recommendation is important since we observed
changes in communities across space and time. However, the more
microhabitats which can be integrated into the protocols, the better.

Arthropod based food webs are structurally different between the two habitat
types. The same functional role may also be performed by different taxa
depending on habitat. Monitoring programs should therefore analyze habitats
separately to better detect ecosystem changes and predict future ecosystem
consequences and vulnerabilities.

Functional groups can be more efficiently sampled by targeting them based on
sampling date and habitat type. This will allow monitoring programs to answer
more specific research questions without wasting limited resources.
The future success of Arctic conservation depends on our ability to understand
and asses these ecosystems, along with their associated threats, vulnerabilities and
consequences. Establishing a viable, easily maintained and smoothly operating
ecological monitoring program will contribute greatly to this goal. We hope that more
90
programs will invest in preliminary assessment of their study system before its
establishment: subsequently limiting its changes of failure.
91