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TESTING THREE MODELS OF METACOMMUNITY STRUCTURE IN ARTHROPOD
COMMUNITIES IN SUBALPINE MEADOWS
Christopher Scott Carr
B.S., University of the Pacific, 2001
THESIS
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF SCIENCE
in
BIOLOGICAL SCIENCES
at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
FALL
2010
TESTING THREE MODELS OF METACOMMUNITY STRUCTURE IN ARTHROPOD
COMMUNITIES IN SUBALPINE MEADOWS
A Thesis
by
Christopher Scott Carr
Approved by:
__________________________________, Committee Chair
Jamie M. Kneitel, Ph.D.
__________________________________, Second Reader
William E. Avery, Ph.D.
__________________________________, Third Reader
Ronald M. Coleman, Ph.D.
__________________________________, Fourth Reader
Patrick Foley, Ph.D.
________________________
Date
ii
Student: Christopher Scott Carr
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
_________________________, Graduate Coordinator
Susanne Lindgren, Ph.D.
Department of Biological Sciences
iii
_________________
Date
Abstract
of
TESTING THREE MODELS OF METACOMMUNITY STRUCTURE IN ARTHROPOD
COMMUNITIES IN SUBALPINE MEADOWS
by
Christopher Scott Carr
Traditionally, patterns of species diversity were investigated exclusively at the local
community scale. However, it has since been recognized that processes at regional scales
also influence community dynamics. The integration of processes at local and regional
scales to better understand species diversity patterns at different scales is an emerging
focus in community ecology, and is examined with metacommunity theory. Several
paradigms utilized to examine metacommunity theory include the species-sorting, the
mass-effect, the patch dynamic, and the neutral models. Research was conducted in
several subalpine meadows in the El Dorado National Forest. The compositions of
several insect communities were compared, by utilizing species abundances and richness,
among microhabitats (i.e. local scale) and meadows (i.e. regional scale). Two types of
data were collected including insect community composition (i.e. abundance and
richness), and environmental variables (i.e. vegetation abundance and richness, and soil
moisture and pH). Insect community composition was categorized into dispersal groups
and functional groups, and vegetation community composition was categorized according
to growth forms (i.e. graminoid and forb/herb). The entire insect metacommunity was
iv
differentially structured according to meadows and microhabitats. Insect communities
responded to the soil moisture regimes and other environmental variables exhibited by
the various microhabitats. These results suggest that the entire insect metacommunity
may be structured according to the species-sorting model of metacommunity theory.
Analyzing distinct dispersal and functional groups within the insect metacommunity
suggested support for different metacommunity models. Herbivores, and both the good
disperser and poor disperser groups structured according to the species-sorting model.
The other functional groups (i.e. predators, parasitoids, pollinators, and omnivores) were
structured by a combination of the species-sorting, mass-effect, and neutral
metacommunity models.
___________________________, Committee Chair
Jamie M. Kneitel, Ph.D.
___________________________
Date
v
ACKNOWLEDGEMENTS
This study was conducted on land that is managed by the United States Forest Service. I
would like to thank Valerie Hendon of the Pacific Ranger District for granting me access
to the study area and providing a key to several locked gates. I would like to
acknowledge Dr. Arthur Shapiro who assisted in the identification of several species of
butterflies, and Dr. Patrick Foley who assisted in the identification of several bees. I
would especially like to thank my graduate professor, Dr. Jamie Kneitel, for consistently
providing valuable discussion and comments on drafts, and for providing invaluable
assistance with the statistical analyses. In addition, Dr. Patrick Foley, Dr. Ron Coleman,
and Dr. William Avery provided valuable discussion during the process of advancing to
candidacy, and provided comments on drafts.
vi
TABLE OF CONTENTS
Page
Acknowledgements ............................................................................................................ vi
List of Tables ................................................................................................................... viii
List of Figures .................................................................................................................... ix
Introduction ..................................................................................................................................... 1
Background ........................................................................................................................ 1
Metacommunities............................................................................................................... 2
Supporting Research .......................................................................................................... 4
Objectives .......................................................................................................................... 7
Predictions ......................................................................................................................... 7
Methods ........................................................................................................................................ 11
Study System ................................................................................................................... 11
Sampling Overview ......................................................................................................... 13
Insect Community Sampling............................................................................................ 18
Plant Community Sampling ............................................................................................. 25
Statistical Analysis ........................................................................................................... 26
Results ......................................................................................................................................... 28
Insect Abundance and Richness....................................................................................... 28
Dispersal Groups.............................................................................................................. 41
Functional Groups............................................................................................................. 41
Discussion ..................................................................................................................................... 44
Conclusion ....................................................................................................................... 52
Literature Cited ............................................................................................................................. 54
vii
LIST OF TABLES
Page
Table 1. Metacommunity Predictions for Significant Differences Among
Meadows and/or Microhabitats...........................................................................10
Table 2. Areas, Perimeters, and Elevations of sites 1 through 6. .....................................16
Table 3. Total Counts of Data Utilized, and not Utilized, in the Analyses
with a Breakdown into Insect Taxonomic Groups .............................................21
Table 4. Total Counts and Percentages of Insects Identified in each
Taxonomic Level ................................................................................................22
Table 5. Examples of Taxonomic Groups that are Included in each
Functional Group ................................................................................................24
Table 6. A Two-Way ANOSIM of the Effects of Meadow and
Microhabitat on Insect Community Composition During Two
Sampling Periods ................................................................................................29
Table 7. MANOVA of the Effects of Meadow and Microhabitat on Two
Measures of Insect Community Composition.....................................................31
Table 8. MANOVA of the Effects of Meadow and Microhabitat on
Environmental Variables. ...................................................................................35
Table 9. One-way ANOSIM of the Effects of Meadow and Microhabitat
on Insect Community Composition, Segregated by Functional
Group and Dispersal Ability Group, During Two Sampling
Periods.................................................................................................................43
viii
LIST OF FIGURES
Page
Figure 1. The Complete Study Area with Sites Identified by Circles ..............................15
Figure 2. Position of Transects in Study Sites ..................................................................17
Figure 3. Mean Insect Abundance Across Two Independent Variables ...........................32
Figure 4. Mean Insect Richness Across Two Independent Variables ..............................33
Figure 5. Mean Insect Abundance and Soil Moisture According to
Microhabitat Type .............................................................................................39
Figure 6. Mean Insect Richness and Soil Moisture According to
Microhabitat Type. ............................................................................................40
ix
1
INTRODUCTION
Background
Community diversity patterns may be attributed to processes at multiple spatial
scales and the interaction between these scales may affect the local and regional patterns
of coexistence (Wilson 1992; Debinski et al. 2000; Loreau 2000; Krawchuk and Taylor
2003). At the local scale, or within communities, species directly interact with biotic
(e.g., predation and competition) and abiotic (e.g., temperature and moisture) factors in
the environment (Kneitel and Chase 2004). At a regional scale, or across communities,
diversity can be predominately driven by patterns of colonization and extinction (Loreau
and Mouquet 1999; Mouquet and Loreau 2002; Chase 2005). These patterns may act
through dispersal limitation and are important for generating differences in species
diversity and composition among sites (Cornell and Lawton 1992; Shurin 2000). If the
dispersal abilities of species greatly exceed local extinction probabilities, then sites will
contain nearly all of the species in the region capable of invading. In this case, local
processes will dominate in shaping species diversity and composition within patches.
Alternatively, if dispersal abilities do not exceed local extinction probabilities, then
species will often be absent from suitable local sites. In this case, regional processes
(dispersal limitation) will dominate in shaping species diversity and composition within
patches (Ricklefs 1987; Cornell and Lawton 1992). The integration of local and regional
scales and the dynamics inherent may be studied with hypotheses put forward by
metacommunity theory (Levins and Culver 1971; Mouquet and Loreau 2003; Leibold et
al. 2004). This study will examine metacommunity theory by comparing insect
2
communities in various microhabitats (i.e. local scale) that were enclosed in subalpine
meadows (i.e. regional scale).
Metacommunities
Wilson (1992) defined a metacommunity as a set of local communities linked by
the dispersal of multiple interacting species. Several perspectives within this framework
predict metacommunity dynamics based on the relative role of local (Welsh Jr. and
Hodgson 2010) versus regional (Shurin et al. 2000) factors, and have been summarized
by Leibold et al. (2004). These perspectives include: patch dynamics, species-sorting,
mass-effect, and neutral theory. The terms perspective and model both refer to a specific
metacommunity theory and may be used interchangeably throughout the rest of this
report.
The patch dynamic perspective assumes that all local communities (patches) are
homogeneous and equally capable of supporting populations. An assumption inherent to
the patch dynamic perspective is that patches are either vacant or occupied by
populations at equilibrium. Dispersal is of great importance and limits species diversity
in each patch. Spatial dynamics are dominated by local extinction and colonization rates
(Leibold et al. 2004). Regional coexistence is generated by a trade-off whereby
differences in competitive ability and dispersal facilitate colonization of an empty patch
by the poorer competitor (Kneitel and Chase 2004; Cadotte et al. 2006).
The species-sorting perspective is based on community composition change over
environmental gradients (Smith 1977; Danielson 1991). Patches are heterogeneous in a
region varying in some abiotic factor, with species interactions and community structure
3
dependent on this variance. The dynamics of this system are generated by the
existence of trade-offs among species that allow them to specialize on different patch
types. Dispersal is important and enables covariance between the community and
environment (Leibold et al. 2004).
The mass-effect perspective is based, largely, on source-sink dynamics (Pulliam
1988). Mass-effects are processes that occur at the regional scale and may modify both
species abundances and interactions, and consequently alter community structure and
dynamics (Mouquet and Loreau 2002). In this perspective, immigration and emigration
determine the dynamics of local populations. Species may be rescued from extinction by
emigrating from patches where they are good competitors to patches where they are poor
competitors (Brown and Kodric-Brown 1977; Schoener and Spiller 1987).
The neutral perspective may be considered the null hypothesis in relation to the
previous views described (Bell 2001). The neutral model assumes that species are
functionally equivalent and that species richness within a habitat is set by habitat
characteristics that determine the probability of species loss and gain in local sites. This
model assumes no difference in species’ niche requirements (i.e. species-sorting
perspective), in their ability to disperse (i.e. patch-dynamic perspective), or in their ability
to avoid local extinctions (i.e. mass-effect perspective) ;( Bell 2001). Critically, the
neutral model predicts that dispersal limitation will have a greater impact on community
assembly than species-sorting mechanisms and adaptations to local conditions (Hubbell
1979).
4
Supporting Research
Metacommunity ecology is an emerging discipline (Liebold et al. 2004; Chase
2005) based on previous empirical studies (Ricklefs 1987; Robinson and Dickerson 1987;
Cornell and Lawton 1992; Belyea and Lancaster 1999; Shurin 2000; Chave 2004; Fukami
2004; Ricklefs 2004) which recognize the interaction of population and community
dynamics at multiple spatial scales. Empirical studies utilizing metacommunity theory to
explain the structuring of organisms at local and regional scales are numerous with
support for each perspective in many different ecological settings (Holyoak et al. 2005).
These metacommunity perspectives are not mutually exclusive and may exist
along a continuum with potentially multiple perspectives shaping the dynamics of the
metacommunity, and at different intensities (Leibold et al. 2004; Cottenie 2005).
Difficulty exists in separating metacommunity dynamics. Hence, relatively few
experiments have utilized field data to examine several different metacommunity
perspectives in the same analysis (Cottenie 2005; Etienne and Olff 2005). Several studies
have found support for both neutral and niche-based processes structuring communities
(Thompson and Townsend 2006; Chu et al. 2007; Ellwood et al 2009). Urban (2004)
found species-sorting and mass-effect perspectives explain the structure of a freshwater
pond metacommunity. Furthermore, different groups of species in a metacommunity
may respond differently to metacommunity dynamics (Leibold et al. 2004). Species
associated with different feeding guilds, or exhibiting different dispersal abilities may be
structured according to different metacommunity perspectives (Townsend et al. 2003;
Urban 2004; Thompson and Townsend 2006).
5
Cottenie (2005) performed a meta-analysis on various published data sets with
information on community structure, environmental and spatial variables and found
evidence of community structuring processes inherent in three of the metacommunity
perspectives; the neutral, the species-sorting, and the mass-effect perspectives. The
majority of the communities examined were structured by species-sorting dynamics
illustrating the presence of an environmental component in the community structuring
processes. A lesser number of the data sets consisted of metacommunities structured by
both environmental and spatial variables, suggesting the presence of species-sorting and
mass-effect dynamics. Neutral processes were found to structure the fewest number of
data sets. This brief summary of supporting research should not be considered a
comprehensive list of literature available on metacommunities.
According to Leibold et al. (2004) the application of metacommunity models (see
previous section) to actual communities is not straightforward. Two requirements for an
experimental system, in which metacommunity dynamics may be accurately assessed, are
local communities should have discrete boundaries, and different species should respond
to processes at different spatial scales. Experimental tests can be categorized into: 1)
assemblages of discrete, permanent habitat patches, 2) temporary patches distinct from a
background habitat matrix, but varying in position and frequency with time, and 3)
permanent habitats with indistinct boundaries.
The experimental tests in the present study were performed in subalpine
meadows. Subalpine meadows can be considered permanent habitat patches that exhibit
discrete boundaries form the surrounding forest habitat. Thus, Leibold’s first
6
requirement for an experimental system in which metacommunity dynamics may be
accurately assessed is satisfied. Additionally, each patch may be linked by the dispersal
of multiple interacting species. As for Leibold’s second requirement, it is assumed that
different insect species will respond to processes at different spatial scales. This system
was therefore appropriate for a metacommunity study because both local (within a patch)
and regional (across patches) scales were represented.
Previous studies focusing on insects in subalpine meadows have only included a
specific taxa or functional group. Sharp et al. (1974) found there was no significant
correlation between the distribution and diversity of an assemblage of adult subalpine
butterflies and their larval food plants, and Gutierrez and Menendez (1995) found the
regional distribution of butterflies was correlated with the altitudinal range and
abundance of larval food plants. Bowers (1985) found that bumble bee diversity was a
function of the composition of flowers within subalpine meadows, and Bowers (1986)
found that competition limits local bumble bee densities. The distribution of several
pollinator insect taxa has been found to be correlated with elevational gradients (Warren
et al. 1988). Sampling numerous taxa encompassing several functional groups, this study
may provide broader insights into the patterns of insect community assembly. This study
is set apart from previous studies that have utilized the responses of insects to changes in
various environmental variables in order identify population and/or community
structuring dynamics.
7
Objectives
The overall objective of this study was to examine the patterns of insect species
diversity and community structure in different microhabitats of subalpine meadows of the
Sierra Nevada, CA. The terms microhabitat and patch refer to a distinct area of the
meadow that displayed a particular moisture regime and plant physiognomy and will be
used interchangeably throughout the rest of this document. The specific objectives
consist of comparing insect community composition using species abundance and
richness among microhabitats (i.e. local scale) and among meadows (i.e. regional scale);
determining the effect vegetation structure and measures of the abiotic environment have
on insect communities; assessing which metacommunity models are reflected in the
patterns of diversity of the insect communities; and determining if metacommunity
processes structure insect communities differentially according to dispersal ability and
functional groups. In order to accomplish these objectives a comparative study was
performed to ascertain the effects of environment (meadows and microhabitats), space
(different meadows) and time (two time periods) on insect communities.
Predictions
The species-sorting model emphasizes niche differentiation among species,
whereas the neutral model assumes ecological equivalence of species. The mechanisms
for species coexistence in these models are in direct opposition (Gravel et al. 2006;
Mouillot 2007). The species-sorting model predicts decreased community similarity with
lower similarity in ecological conditions (Thompson and Townsend 2006). For example,
insect community composition may be determined by environmental variables (i.e. soil
8
moisture and pH) and the composition of vegetation (i.e. abundance and richness of
graminoids and forbs), resulting in a relationship between habitat type and species
composition. In other words, the corn lily, wet, and dry microhabitats will exhibit
different insect communities.
The neutral model predicts dissimilarity as distance between sites increases
(Thompson and Townsend 2006). Insect communities within the meadows would be
expected to be similar regardless of microhabitat type. Because dispersal limitation
underlies processes driving the neutral model, poor dispersers within the insect
communities will be strongly negatively associated with spatial separation of sites, while
good dispersers will overcome dispersal limitation and be strongly positively associated
with local ecological conditions. Therefore, species with low dispersal will exhibit the
neutral model of metacommunity theory, and species with high dispersal will exhibit the
species-sorting and mass-effect models.
The mass-effect model is difficult to identify from the neutral model because
dispersal among habitats are central to this perspective. Therefore, any interpretation of
both the neutral and mass-effect models in structuring the insect metacommunity should
be made with caution. In the mass-effect model, very high dispersal rates cause regional
diversity to decline because of increasing homogenization of the community (Mouqet and
Loreau 2003). In the context of this study, the homogenization of the community at the
regional level will be indicated by the good dispersers exhibiting similar community
composition between meadows.
9
Insect communities segregated into functional groups (i.e. herbivores,
predators, parasitoids, pollinators, and omnivores) will exhibit disparate metacommunity
models. The communities consisting of herbivores, omnivores, and pollinators consume
vegetation, and their associated parts, and do not need to go out in search of prey. The
insect species within these communities, accordingly, may behave like poor dispersers,
and structure according to the neutral model of metacommunity theory. The
communities consisting of predators and parasitoids go out in search of prey, and will
reflect the species-sorting and mass-effect models.
Measuring competitive abilities of species are beyond the scope of this study and
will not be performed which will inhibit evaluation of the patch dynamic model.
Furthermore, neglecting dispersal characteristics potentially limits the statistical power of
any results considering the theoretical importance of dispersal processes in distinguishing
the metacommunity models.
As discussed above, support for the various models of metacommunity theory will
depend on how insect species interact with their environment. According to the
predictions presented above, and for ease of interpretation, Table 1 summarizes the
inferences that can be made from the results.
10
Model supported
Significant
Not significant
species-sorting
species-sorting
neutral
mass-effect
microhabitat and meadow
microhabitat
meadow
―――――
―――――
meadow
microhabitat
microhabitat and meadow
Table 1. Metacommunity Predictions for Significant Differences Among Meadows
and/or Microhabitats.
11
METHODS
Study System
The research was conducted in the El Dorado National Forest located in the
central Sierra Nevada mountains in California. The habitat type of the study location is a
subalpine forest zone ranging from 1,676 m to 3,505 m and reaching its maximum extent
between 2,743 m 3,048 m. The subalpine forest zone consists of open forests with
needle-leaved evergreen trees of low to medium stature, with heights typically exceeding
30 m. Shrubby vegetation and herbaceous ground cover are generally sparse or lacking.
Litter accumulation is typically scanty, but fallen woody material persists for long
periods in the cold climate (Rundel et al. 1977).
Several conifer species dominate within the subalpine zone, either singly or in
mixtures of two or more species. These include Engelmann spruce (Picea engelmannii),
red fir (Abies magnifica), subalpine fir (Abies lasiocarpa), mountain hemlock (Tsuga
mertensiana), western white pine (Pinus monticola), lodgepole pine (Pinus contorta),
whitebark pine (Pinus albicaulis), and limber pine (Pinus flexilis). A shrub understory
includes species such as wax currant (Ribes cereum), purple mountainheather
(Phyllodoce breweri), oceanspray (Holodiscus discolor), and big sagebrush (Artemisia
tridentate). California brome (Bromus carinatus), several species of lupines, and a
variety of flowering annuals are common in the sparse ground cover. The moist areas
within this region are composed of meadows and quaking aspen (Populus tremuloides)
12
stands, with the meadows consisting of several species of Vaccinium (e.g. Western
huckleberry (Vaccinium membranaceum), and Sierra bilberry (Vaccinium cespitosum),
while the woodlands consist of Jeffrey pine (Pinus jeffreyi) and western juniper
(Juniperus occidentalis);(Rundel et al. 1977; Parsons 1980).
The study area is 946.4 hectares, ranges in elevation from approximately 1,996 m
to 2,164 m, and is owned by the United States Forest Service (USFS) Pacific Ranger
District. The study area contains approximately 20 variably sized meadows within the
surrounding forest matrix that have been set aside for restoration purposes. These
meadows are managed by the USFS and not subjected to significant human disturbance.
The termination of anthropogenic burning, and other historic land-use practices has
facilitated the encroachment of conifers, particularly lodgepole pines (Pinus contorta)
and red fir (Abies magnifica), and caused the subalpine meadow system to be threatened
and subject to slow decline (USFS 2005). As a result, the meadows have been identified
as restoration habitat and are part of the Pacific Ranger District Van Vleck subalpine
meadow restoration project.
The meadows range in size, shape, and elevation, and are distributed among the
forest at varying distances from each other within the study area. Each meadow exhibits
three distinct microhabitats (wet, dry, and corn lily dominated) which are distinguished
by divergent moisture regimes, and noticeable changes in the physiognomy and structure
of the vegetation. The edges among the microhabitats are fairly well defined with a
relatively small transition from one microhabitat to another. A small stream meanders
13
through the wet microhabitat within each meadow which gives rise to the wetter
moisture soil regime indicative of that microhabitat.
The vegetation in the wet portions of meadows consists mostly of graminoids,
including Poaceae and Cyperaceae (dominant species include Carex sp., Juncus sp.,
Deschampsia sp.), whereas the vegetation in the dry portions consists of mostly forbs,
including species in the taxonomic groups Asteraceae, Apiaceae, Liliaceae,
Saxifragaceae, and Valerinaceae (dominant species include Senecio triangularis,
Phalacroseris bolanderi, Hilenium bigelovii, Aster alpigenus, Mimulus primuloides,
Castilleja miniata). The corn lily habitats contains a mixture of graminoids and forbs,
with forbs, and especially the species Corn lily (Veratrum californicum) dominating the
community.
Sampling Overview
Sampling was conducted in the summer of 2007 from late June to early August.
Two sampling events were conducted within this period: June 29 – July 1, and August 3
– 5. Several meadows were chosen for sampling based upon the characteristics of easy
accessibility, the availability of three distinct microhabitats, and the availability of
sufficient area for the placement of two transects and two quadrats within each
microhabitat.
Data were collected from six meadows (Figure 1), which included the three
microhabitats within each meadow: wet, dry, and corn lily dominated microhabitats. The
elevation of the six meadows consistently increased from South to North such that
meadow 1 exhibited the lowest elevation and meadow 6 exhibited the highest elevation.
14
The sizes of the meadows (area and perimeter) were randomly distributed, and did not
consistently increase from South to North (Table 2). Two 5 m transects (for insect
community sampling) and two 1 m² quadrats (for plant community sampling) were
sampled within each microhabitat of each meadow (Figure 2). Incorporating variance by
measuring at more than one location within each microhabitat ensured that any
significant differences found between microhabitats was a result of a treatment effect
(Hurlbert 1984). In total, 36 transects and 36 quadrats were sampled from six meadows.
Two types of data were collected including insect community composition and ecological
factors. The variables that defined insect community composition included individual
insect abundance and species richness. The variables that defined ecological factors
included vegetation composition (i.e. plant species abundances measured as percent
cover, and richness), as well as soil moisture and pH.
15
Site 6
Site 5
Site 4
Site 2
Site 3
Site 1
Figure 1. The Complete Study Area with Sites Identified by Circles.
16
Site
1
2
3
4
5
6
Meadow
Lower Van Vleck 1
Lower Van Vleck 2
Upper Van Vleck
Highland
Coyote 1
Coyote 2
Area (m²) Perimeter (m) Elevation (m)
37,661
1,241
1,996
243,728
2,920
2,002
40,551
1,120
2,011
17,933
551
2,048
14,344
730
2,095
28,179
1,311
2,107
Table 2. Areas, Perimeters, and Elevations of sites 1 through 6.
17
Site 1
Site 2
Site 4
Site 5
Site 3
Site 6
Figure 2. Position of Transects in Study Sites. The Black, Dark Gray, and Light Gray
Bars Represent the Transects in the Different Microhabitats; Dry, Wet, and Corn Lily,
Respectively. The Placement, and Scale is not Eact but Should Give a General Idea of
Transect Locations.
18
Insect Community Sampling
Insect community sampling was conducted using 5 m transects with sweep nets
because previous work has established sweeping covers the largest area and can
accurately represent plot level characteristics of the plant and insect community within
the vegetation swept (Siemann 1998; Haddad et al. 2001). However, it is recognized that
sweeping does not accurately capture the entire community of insects, but only the active
subcommunity of the vegetation in the height zone swept (Janzen and Schoener 1968).
Exhaustive representation of the entire insect community would entail sampling of
microhabitats in addition to the vegetation such as soil, forest floor, and canopy (where
present). In addition, certain species of Diptera, Hymenoptera, and Lepidoptera that are
strong flyers may escape the sweep net by flying before the sweeper arrives as a result of
movement in the vegetation (Janzen and Schoener 1968). This bias should be consistent
across sampling transects.
To maintain consistency among transects, sweeps were limited to approximately
50 sweeps per 5 M transect. Sweeps were taken at heights between 0.25 m and 1.0 m
above the ground within the vegetation of each sampling area. This corresponded to the
height of the vegetation within each microhabitat, which ranged from several inches off
the ground, to approximately 1 m.
Insect community sampling occurred for six days within the two sampling
periods. A total of 12 transects were sampled each day, consisting of two transects from
19
each microhabitat, three microhabitats within each meadow, with two meadows
sampled. All six meadows were sampled on days of similar weather with temperatures
ranging from 80-90 degrees Fahrenheit with clear skies and minimal wind speed.
Sampling was conducted at mid-day (initiated at approximately 12:00 p.m.) during peak
hours of insect activity (Warren et al. 1988; Forehand et al. 2006; Martinko et al. 2006;
Rand and Louda 2006). Each transect was sampled in approximately 10 minutes, with all
transects for a particular day being sampling in 120 minutes. Incorporating additional
time to transfer samples to storage and travel to each site, the duration for completing
insect sampling for a particular day was 180 minutes.
Richness and abundances for each transect were collated. All individuals were
separated based on taxonomic group and stored in a solution of 66% alcohol and 33%
water, with the exception of butterflies, which were spread and pinned. Invertebrate
identification was carried out at 10-30X magnification, with insects being identified to
the taxonomic categories family, superfamily, or suborder depending on difficulty. The
Peterson Field Guides (Borror and White 1970; White 1983) were consulted to assist
identification. The quantity of individual insects (23,688; Tables 3 and 4) captured from
both sampling periods necessitated relatively quick identification. The insects readily
identified to family included members of the orders Homoptera, Hemiptera, Orthoptera,
Coleoptera, Neuroptera, Lepidoptera (i.e. only butterflies were included in the analysis)
and the superfamily Apoidea. The insects readily identified to superfamily and suborder
included members of the orders Hymenoptera and Diptera, respectively. The 6 meadows
contained a total of 77 taxonomic groups (Table 3) of which 56% were identified to
20
family (Table 4). Moths, caterpillars, and unknown individuals were not included in
the analysis and represented 0.32% of the entire collection of insects sampled.
21
Data Utilized in Analyses
Orthoptera
Acrididae
Tettigoniidae
Tetrigidae
Hemiptera
Pentatomidae
Nabidae
Miridae
Lygaeidae
Piesmatidae
Corimelaenidae
Cercopidae
Berytidae
Aphididae
Anthocoridae
Alydidae
Reduviidae
Scutelleridae
Rhopalidae
Neuroptera
Chrysopidae
Raphidiidae
Homoptera
Membracidae
Delphacidae
Cicadellidae
Psyllidae
Lepidoptera
Pieridae
Nymphalidae
Lycaenidae
Hesperiidae
Coleoptera
Pedilidae
Mordellidae
Melyridae
Elateridae
Curculionidae
Coccinellidae
Chrysomelidae
Cerambycidae
Cantharidae
Bruchidae
Anthribidae
Anthicidae
Staphylinidae
Rhipiphoridae
Pyrrhocoridae
Diptera
313 Nematocera
7 Tipulidae
1 Cyclorrhapha
Otitidae
26 Pipunculidae
4 Tephritidae
1,861 Syrphidae
103 Sarcophagidae
2 Sepsidae
4 Brachycera
1 Stratiomyidae
2 Tabanidae
1,951 Asilidae
22 Bombyliidae
13
Hymenoptera
135
Apocrita
56 Icneumonoidea
1 Proctotrupoidea
Cynipoidea
36 Scolioidea
3 Sphecoidea
Sphecidae
7 Bethyloidea
273 Chrysididae
6,302 Chalcidoidea
361 Perilampidae
Chalcididae
1 Apidae
6 Halictidae
4 Megachilidae
4 Colletidae
Anthophoridae
103 Andrenidae
27 Vespidae
4 Formicidae
8
Symphata
27 Diprionidae
48 Tenthredinidae
140 Total
2
Data Not Utilized
4 Caterpillar
6 Moth
4 Unknown
4 Total
6
88
1
253
9
9,196
6
21
38
2
1
827
24
1
2
3
1
625
13
1
6
7
1
8
1
264
2
25
1
33
2
6
1
4
2
94
1
236
23,688
97
117
15
229
Table 3. Total Counts of Data Utilized, and not Utilized, in the Analyses with a
Breakdown into Insect Taxonomic Groups.
22
Taxonomic Level
Family
Superfamily
Suborder
Total
Counts
13,291
924
9,473
23,688
Percentages
56%
4%
40%
100%
Table 4. Total Counts and Percentages of Insects Identified in each Taxonomic Level.
23
Two broadly defined dispersal ability guilds identified within these taxa
included good dispersers (i.e. “winged”) and poor dispersers (i.e. “not winged”).
“Winged” insects were defined as those species that possessed a complete pair of wings
and a strong ability to fly, including, but not limited to Dipterans, Hymenopterans, and
Coleopterans. Insects categorized into the dispersal ability group “not winged” did not
contain wings, such as Aphidids, Formicids, and some Homopterans (most have wings);
or were considered jumping insects (e.g. some Homopterans). The good dispersers
consisted of 68 taxonomic groups, and the poor dispersers consisted of 9 taxonomic
groups.
Five broadly defined functional guilds identified within the communities
included: herbivores, parasitoids, predators, omnivores, and pollinators. The functional
guilds consisted of a suite of different insects (Table 5). The individual insects were not
identified to species; therefore, the exact functional guild which corresponded to a given
individual was unknown in some cases. If a particular taxon contained species of
differing functional guilds, the functional guild representing the majority of insect species
in that taxon was chosen to represent the functional group for that taxon. For example,
the majority of species of shield-backed bugs (Pentatomidae) are herbivorous; however,
some species are predacious. In the case of this study, shield-backed bugs were
considered herbivores. A taxonomic group consisting of species that were scavengers,
predators, and herbivores (e.g. Formicidae) was categorized into the functional group of
omnivore.
24
Herbivore
Cicadellidae
Miridae
Pentatomidae
Scutelleridae
Acanthosomatidae
Curculionidae
Chrysomelidae
Acrididae
Tetrigidae
Parasitoid
Icneumonoidea
Chalcidoidea
Proctotrupoidea
Cynipoidea
Scolioidea
Bethyloidea
Chalcididae
Chrysididae
Perilampidae
Predator
Coccinellidae
Reduviidae
Chrysopidae
Anthocoridae
Asilidae
Cantharidae
Nabidae
Raphidiidae
Staphylinidae
Omnivore
Formicidae
Cyclorrapha
Brachycera
Nematocera
Pollinator
Lycaenidae
Hesperiidae
Apidae
Halictidae
Megachilidae
Colletidae
Bombyliidae
Nymphalidae
Syrphidae
Table 5. Examples of Taxonomic Groups that are Included in each Functional Group.
25
Two functional groups that were represented within the taxonomic groups
collected, but not included in the statistical analyses, were bloodsuckers and scavengers.
These functional groups were not represented in a large enough quantity of insects to
utilize statistical analyses. A functional group was included in the analyses if four or
more taxonomic groups were represented. Herbivores consisted of 32 taxonomic groups;
predators consisted of 14 taxonomic groups; pollinators consisted of 12 taxonomic
groups; parasitoids consisted of 11 taxonomic groups; and omnivores consisted of 4
taxonomic groups.
Plant Community Sampling
Vegetation sampling was conducted using 1 m² quadrats. Each quadrat took
approximately 20 minutes to collect samples from. Each quadrat was sampled on the
same day the corresponding transect was sampled (see previous section on insect
community sampling). The presence of an experimenter collecting plant samples caused
insects to leave the area being sampled, therefore, an hour was allowed to elapse between
the completion of plant community sampling and the onset of insect community
sampling.
Plant community richness and abundances were collated for each quadrat.
Percent cover was utilized as an estimate of abundance for each species to the nearest
5%. Species with negligible cover were assigned a value of 1%. Plant species richness
was categorized according to growth forms (i.e. graminoid and forb/herb). A Kelway soil
tester was used to determine the moisture content and pH of the soil.
26
Statistical Analysis
In order to examine the composition of the insect communities at local
(microhabitat) and regional (meadow) scales several statistical procedures were
implemented including one-way ANOSIM, two-way ANOSIM, and MANOVA. The
one-way and two-way ANOSIM tests were run using the program PAST (Hammer et al.
2001). For these tests insect communities were analyzed by utilizing species
presence/absence data. Insect presence/absence data (and abundances) were summarized
into matrices of pairwise Bray-Curtis similarities between sites (Thompson and
Townsend 2006). The MANOVA tests were run using the IBM program SPSS Statistics
version 18. For this test, total counts of insect abundances and richness were correlated
with meadows, microhabitats, and environmental variables. In total 34 statistical tests
were run.
In order to test the first hypothesis that insect community composition is
determined by environmental variables, and structured by species-sorting processes, all
statistical procedures were utilized. The entire insect presence/absence data set
(consisting of all species in all microhabitats and meadows) was run with two-way
ANOSIM tests to examine differences in the composition of communities. The structure
of the two-way ANOSIM analysis permitted meadow and microhabitat to be run in the
same test; however, sampling periods were required to be run separately. Bonferroni post
hoc analyses were run with one-way ANOSIM tests to determine which specific
communities differed among meadows and microhabitats. The structure of the one-way
ANOSIM analysis required meadows and microhabitats in each sampling period (i.e. 1
27
and 2) to be run separately. Two different MANOVA tests were run. The
independent variables for both tests were meadows and microhabitats. The dependent
variables were total insect abundance and diversity, for test1; and total vegetation
abundance, gramioid abundance, forb abundance, total vegetation richness, graminoid
richness, forb richness, soil moisture, and soil pH for test 2. Wilk’s lamba statistic was
utilized for the analysis because it is the most widely used test statistic for a MANOVA
analysis.
In order to test the second hypothesis, that insect communities segregated into
good dispersers (i.e. winged) and poor dispersers (i.e. not winged) should exhibit
disparate metacommunity models, one-way ANOSIM tests were run. Separate BrayCurtis similarity matrices for each dispersal group were extracted from the data set.
In order to test the third hypothesis, that insect communities segregated into
functional groups should exhibit disparate metacommunity models, one-way ANOSIM
tests were run. Separate Bray-Curtis similarity matrices for each functional group
(herbivores, parasitoids, predators, omnivores, and pollinators) were extracted from the
data set.
28
RESULTS
Insect Abundance and Richness
The corn lily, wet, and dry microhabitats all supported significantly different
insect communities. The two-way ANOSIM tests, utilizing the entire data set of insect
species presence/absence counts, indicated that species composition differed significantly
among meadows (P < 0.001) and microhabitats (P < 0.001). These results were
consistent across sampling periods (Table 6). Bonferroni post-hoc tests indicated that all
the communities were significantly different between the microhabitats. Insect
communities between the various meadows were not all different. For example, the
Bonferroni post-hoc test indicated that for sampling period 1, meadows 2 and 4 were not
significantly different (Table 6).
29
Time
Sampling Period 1
Sampling Period 2
Treatment
Meadow
Microhabitat
Meadow
Microhabitat
Two-Way ANOSIM
R
0.55
0.75
0.62
0.66
P
<0.001
<0.001
<0.001
<0.001
Bonferroni post-hoc
1 and 3, 6; 2 and 3; 3
and 5, 6
all
1 and 2, 3, 5, 6
all
Table 6. A Two-Way ANOSIM of the Effects of Meadow and Microhabitat on Insect
Community Composition During Two Sampling Periods.
30
A MANOVA test, utilizing total counts of insect abundance and richness
within each community, indicated that both dependent variables differed significantly
among meadows, microhabitats, and sampling periods (Table 7). Average insect
abundances differed significantly among meadows (P = 0.003), microhabitats (P <
0.001), and sampling periods (P < 0.001). In addition, average insect abundances varied
significantly by the interaction between sampling periods and microhabitats (P = 0.012);
and meadows and microhabitiats (P = 0.002). On average, insect abundances were
highest in the corn lily microhabitat type (Figure 3). Average insect richness differed
significantly among meadows (P = 0.041), microhabitats (P < 0.001), and sampling
periods (P < 0.001). On average, insect richness across communities exhibited the same
pattern as insect abundances: the corn lily microhabitat contained the highest richness of
insects (Figure 4).
31
Source
Sampling Period
Meadow
Microhabitat
Sampling
Period*Meadow
Sampling
Period*Microhabitat
Meadow*Microhabitat
Sampling
Period*Meadow*Micr
ohabitat
Source
Sampling Period
Meadow
Microhabitat
Sampling
Period*Meadow
Sampling
Period*Microhabitat
Meadow*Microhabitat
Sampling
Period*Meadow*Micr
ohabitat
Source
Sampling Period
Meadow
Microhabitat
Sampling
Period*Meadow
Sampling
Period*Microhabitat
Meadow*Microhabitat
Sampling
Period*Meadow*Micr
ohabitat
MANOVA
F
Wilks'λ
19.114
0.478
3.374
0.455
21.223
0.204
df
2, 35
10, 70
4, 70
P
<0.001
0.001
<0.001
0.856
0.568
10, 70
0.835
0.718
3.148
4, 70
0.019
0.404
2.009
20, 70
0.017
0.558
1.184
20, 70
0.294
Average Insect Abundance
F
ss
df
24.847
944854.222
1
4.378
832399.833
5
37.816
2.88E+06
2
P
<0.001
0.003
<0.001
5
49715.944
0.261
0.931
2
383826.194
5.047
0.012
10
1.38E+06
3.628
0.002
10
613584.139
1.614
0.142
Average Insect Richness
ss
df
268.347
1
141.292
5
374.25
2
F
24.739
2.605
17.251
P
<0.001
0.041
<0.001
5
42.403
0.782
0.569
2
15.194
0.7
0.503
10
121.583
1.121
0.374
10
88.306
0.814
0.617
.
Table 7. MANOVA of the Effects of Meadow and Microhabitat on Two Measures of
Insect Community Composition.
32
Figure 3. Mean Insect Abundance Across Two Independent Variables. Each Bar
Represents an Average of Four Data Points (Two Transects X Two Sampling Periods).
Error Bars = 1 SD.
33
Figure 4. Mean Insect Richness Across Two Independent Variables. Each Bar
Represents an Average of Four Data Points (Two Transects X \Two Sampling Periods).
Error Bars = 1 SD.
34
A second MANOVA test, utilizing environmental variables (i.e total
vegetation abundance, gramioid abundance, forb abundance, total vegetation richness,
graminoid richness, forb richness, soil moisture, and soil pH) within each community,
indicated that all dependent variables differed significantly across meadows,
microhabitats, and sampling periods (Table 8). Average vegetation abundance differed
significantly among microhabitats (P < 0.001), and sampling periods (P = 0.001). In
addition, average vegetation abundance varied significantly by the interaction between
sampling periods and microhabitats (P = 0.001); and meadows and microhabitats (P =
0.009). Average vegetation richness differed significantly among meadows (P < 0.001),
microhabitats (P < 0.001), and sampling periods (P = 0.022). In addition, average
vegetation richness varied significantly by the interaction between and meadows and
microhabitats (P = 0.038).
35
Source
Sampling Period
Meadow
Microhabitat
Sampling
Period*Meadow
Sampling
Period*Microhabitat
Meadow*Microhabitat
Sampling
Period*Meadow*Micr
ohabitat
Source
Sampling Period
Meadow
Microhabitat
Sampling
Period*Meadow
Sampling
Period*Microhabitat
Meadow*Microhabitat
Sampling
Period*Meadow*Micr
ohabitat
Source
Sampling Period
Meadow
Microhabitat
Sampling
Period*Meadow
Sampling
Period*Microhabitat
Meadow*Microhabitat
Sampling
Period*Meadow*Micr
ohabitat
MANOVA
Wilks'λ
F
0.17
25.179
0.68
4.03
0.008
54.149
df
6, 31
30, 126
12, 62
P
<0.001
<0.001
<0.001
0.308
1.44
30, 126
0.086
0.316
4.02
12, 62
<0.001
0.011
3.848
60, 176.474 <0.001
0.17
1.125
60, 176.474 0.277
Vegetation Abundance
df
ss
1
2211.125
5
618.403
2
8478.694
F
11.883
0.665
22.784
P
0.001
0.653
<0.001
5
1199.958
1.29
0.29
2
3145.75
8.453
0.001
10
5367.472
2.885
0.009
10
2132.417
1.146
0.357
F
0.003
11.56
188.707
P
0.954
<0.001
<0.001
Graminoid Abundance
df
ss
1
0.347
5
5844.792
2
38166.083
5
503.903
0.997
0.434
2
107.028
0.529
0.594
10
15086.75
14.919
<0.001
10
963.472
0.953
0.499
Forb Abundance
df
ss
1
2266.889
5
10126.61
2
41201.44
Source
Sampling Period
Meadow
Microhabitat
Sampling
Period*Meadow
Sampling
Period*Microhabitat
1096.611
0.754
0.589
2
2772.111
4.764
0.015
10
22497.06
7.732
<0.001
10
1654.389
0.569
0.828
Vegetation Richness
df
ss
F
1
10.125
5.74
5
64.625
7.328
2
66.083
18.732
P
0.022
<0.001
<0.001
5
13.292
1.507
0.212
2
2.583
0.732
0.488
10
39.417
2.235
0.038
10
30.25
1.715
0.115
Graminoid Richness
df
ss
F
1
5.556
18.182
5
3.667
2.4
2
4.333
7.091
P
<0.001
0.056
0.003
Meadow*Microhabitat
Sampling
Period*Meadow*Micr
ohabitat
Source
Sampling Period
Meadow
Microhabitat
Sampling
Period*Meadow
Sampling
Period*Microhabitat
P
0.008
<0.001
<0.001
5
Meadow*Microhabitat
Sampling
Period*Meadow*Micr
ohabitat
Source
Sampling Period
Meadow
Microhabitat
Sampling
Period*Meadow
Sampling
Period*Microhabitat
F
7.791
6.961
70.806
Meadow*Microhabitat
Sampling
Period*Meadow*Micr
ohabitat
5
2.278
1.491
0.217
2
3.444
5.636
0.007
10
10.5
3.436
0.003
10
4.722
1.545
0.164
Table 8. MANOVA of the Effects of Meadow and Microhabitat on Environmental
Variables. This Table Continues on the Following Page.
36
Forb Richness
df
ss
1
0.681
5
62.792
F
0.405
7.473
P
0.529
<0.001
Microhabitat
Sampling
Period*Meadow
Sampling
Period*Microhabitat
2
88.083
26.207
<0.001
5
8.903
1.06
0.399
2
0.861
0.256
0.775
Meadow*Microhabitat
Sampling
Period*Meadow*Micro
habitat
10
57.75
3.436
0.003
10
20.306
1.208
0.319
F
42.96
4.098
106.156
P
<0.001
0.005
<0.001
Source
Sampling Period
Meadow
Source
Sampling Period
Meadow
Microhabitat
Sampling
Period*Meadow
Sampling
Period*Microhabitat
Meadow*Microhabitat
Sampling
Period*Meadow*Micro
habitat
Soil Moisture
df
ss
1
875.014
5
417.292
2
4324.396
5
259.736
2.55
0.045
2
10
355.007
667.812
8.715
3.279
0.001
0.004
10
347.368
1.705
0.117
Soil pH
Source
Sampling Period
Meadow
Microhabitat
Sampling
Period*Meadow
Sampling
Period*Microhabitat
Meadow*Microhabitat
Sampling
Period*Meadow*Micro
habitat
df
1
5
2
ss
0.329
0.478
2.855
F
13.583
3.939
58.857
P
0.001
0.006
<0.001
5
0.353
2.908
0.026
2
10
0.315
0.596
6.497
2.457
0.004
0.024
10
0.324
1.335
0.25
Table 8 continued. MANOVA of the Effects of Meadow and Microhabitat on
Environmental Variables.
37
The two different growth forms (i.e. graminoid and forb) exhibited by the
vegetation significantly differed among meadows and microhabitats. Average graminoid
abundance differed significantly among meadows (P < 0.001) and microhabitats (P <
0.001). In addition, average graminoid abundance varied significantly by the interaction
between meadows and microhabitats (P < 0.001). Average graminoid richness differed
significantly among microhabitats (P = 0.003) and sampling periods (P < 0.001). In
addition, average graminoid richness varied significantly by the interaction between
sampling periods and microhabitats (P = 0.007); and meadows and microhabitats (P =
0.003). Average forb abundance differed significantly among meadows (P < 0.001),
microhabitats (P < 0.001), and sampling periods (P = 0.008). In addition, average forb
abundance varied significantly by the interaction between sampling periods and
microhabitats (P = 0.015); and meadows and microhabitats (P < 0.001). Average forb
richness differed significantly among meadows (P < 0.001), and microhabitats (P <
0.001). In addition, average forb richness varied significantly by the interaction between
meadows and microhabitats (P = 0.003).
The two abiotic measurements (i.e soil moisture and pH) differed significantly
across meadows and microhabitats. Average soil moisture differed significantly among
meadows (P = 0.005), microhabitats (P < 0.001), and sampling periods (P < 0.001). In
addition, average soil moisture varied significantly by the interaction between sampling
periods and meadows (P = 0.045); sampling periods and microhabitats (P = 0.001); and
meadows and microhabitats (P = 0.004). On average, insect abundances and richness
were highest at intermediate levels of soil moisture exhibited by the corn lily
38
microhabitats (Figures 5, and 6). Average soil pH differed significantly among
meadows (P = 0.006), microhabitats (P < 0.001), and sampling periods (P = 0.001). In
addition, average soil pH varied significantly by the interaction between sampling periods
and meadow (P = 0.026); sampling periods and microhabitats (P = 0.004); and meadows
and microhabitats (P = 0.024).
39
Insect Abundance Estimates
corn lily
dry
wet
1200
1000
800
600
400
200
0
0
5
10
15
20
25
30
35
Soil Moisture (%)
Figure 5. Mean Insect Abundance and Soil Moisture According to Microhabitat Type.
Each Data Point Represents an Average of Four Data Points (Two Transects X Two
Sampling Periods).
40
corn lily
dry
wet
Insect Richness Estimates
25
20
15
10
5
0
0
5
10
15
20
25
30
35
Soil Moisture (%)
Figure 6. Mean Insect Richness and Soil Moisture According to Microhabitat Type.
Each Data Point Represents an Average of Four Data Points (Two Transects X Two
Sampling Periods).
41
Dispersal Groups
The one-way ANOSIM tests, utilizing a subset of the entire data set consisting of
insects differentiated into poor and good disperser groups, indicated that species
compositions differed significantly among meadows and microhabitats (Table 9). Poordisperser species composition was significantly different among meadows (P = 0.028 and
P = 0.046 for sampling periods 1 and 2, respectively) and microhabitats (P = 0.002 and P
= 0.003 for sampling periods 1 and 2, respectively). Good-disperser species composition
was significantly different among meadows (P <0.001 for sampling period 2) and
microhabitats (P < 0.001 and P < 0.001 for sampling periods 1 and 2, respectively).
Meadows did not have a significant effect on species composition of the communities in
sampling period 1.
Functional Groups
The one-way ANOSIM tests, utilizing a subset of the entire data set consisting of
insects differentiated into functional groups (herbivores, parasitoids, predators,
omnivores, and pollinators), indicated species composition among meadows and
microhabitats differed depending on these groups (Table 9). Herbivore species
composition was significantly different among meadows (P = 0.039 and P = 0.018 for
sampling periods 1 and 2, respectively) and microhabitats (P < 0.001 and P = 0.002 for
sampling periods 1 and 2, respectively). Parasitoid, predator, and pollinator species
compositions were significantly different among meadows and microhabitats only part of
the time. Parasitoid species composition was significantly different among microhabitats
in sampling period 1 (P = 0.001); predator species composition was significantly different
42
among microhabitats in sampling period 2 (P = 0.007); and pollinator species
composition was significantly different among meadows in sampling period 2 (P =
0.012). Omnivore species composition was significantly different among microhabitats
in sampling period 1 (P < 0.001), and among meadows in sampling period 2 (P < 0.001).
43
One-Way ANOSIM
Treatment
R
Good Dispersers
Meadow
0.029
Microhabitat
0.436
0.253
<0.001
Sampling Period 2
Meadow
Microhabitat
<0.001
<0.001
Sampling Period 1
Meadow
Microhabitat
Meadow
Microhabitat
<0.001
0.070
Time
Sampling Period 1
Sampling Period 1
Meadow
Microhabitat
0.273
0.188
Poor Dispersers
0.1218
0.1956
0.1079
0.1819
Parasitoid
0.02389
0.1546
0.06029
0.04339
Predator
-0.03109
0.06508
-0.04998
0.1388
Herbivore
0.09868
0.2549
0.1432
0.1982
Pollinator
0.0719
0.04474
0.1185
0.01212
Omnivore
0.07798
0.3721
Sampling Period 2
Meadow
Microhabitat
0.2242
0.06411
Sampling Period 2
Sampling Period 1
Sampling Period 2
Sampling Period 1
Sampling Period 2
Sampling Period 1
Sampling Period 2
Sampling Period 1
Sampling Period 2
Meadow
Microhabitat
Meadow
Microhabitat
Meadow
Microhabitat
Meadow
Microhabitat
Meadow
Microhabitat
Meadow
Microhabitat
Meadow
Microhabitat
Meadow
Microhabitat
P
Bonferroni post-hoc
N.A.
all
1 and 2, 3, 4, 5, 6; 2
and 3; 3 and 5, 6
all
0.028
0.002
0.046
0.003
1 and 3, 6; 2 and 3
C and D, W
1 and 2, 3, 5, 6
C and W; D and W
0.268
0.001
0.125
0.129
N.A.
C and W; D and W
N.A.
N.A.
0.720
0.070
0.830
0.007
N.A.
N.A.
N.A.
C and D, W
0.039
<0.001
0.018
0.002
1 and 3, 6; 2 and 3
all
1 and 2, 3, 6
C and W; D and W
0.063
0.095
0.012
0.269
N.A.
N.A.
5 and 1, 2, 3, 4, 6
N.A.
0.087
<0.001
N.A.
all
1 and 2, 5, 6; 3 and 6;
4 and 6
N.A.
Table 9. One-way ANOSIM of the Effects of Meadow and Microhabitat on Insect
Community Composition, Segregated by Functional Group and Dispersal Ability Group,
During Two Sampling Periods. Within the Last Column that Displays Bonferroni PostHoc Pairwise Comparisons: W = Wet Microhabitat, C = Corn Lily Microhabitat, D =
Dry Microhabitat, and N.A. = Not Applicable
44
DISCUSSION
The insect metacommunity was structured at both local (microhabitat) and
regional (meadow) community scales. Insect community composition was
predominantly associated with local environmental conditions and differed among
microhabitats. These results suggest that mid-elevation Sierra Nevada insect
communities structure according to the species-sorting model of metacommunity theory.
Dispersal and functional groups revealed different patterns in response to meadows and
microhabitats. Herbivores, and both dispersal groups appeared to structure according to
the species-sorting model. The other functional groups (i.e. predators, parasitoids,
pollinators, and omnivores) were likely structured by a combination of the speciessorting, mass-effect, and neutral metacommunity models.
The results were consistent with the prediction that environmental variables play
an important role in structuring insect communities. The entire metacommunity was
associated with local environmental conditions (total vegetation abundance, graminoid
abundance, forb abundance, total vegetation richness, graminoid richness, forb richness,
soil moisture, and soil pH) present within each microhabitat. These results support the
species-sorting model of metacommunity theory. This finding is consistent with
numerous other systems whereby niche-based processes determined the structure of
communities (Shurin 2000; Cottenie et al. 2003; Shurin et al. 2003; Cottenie 2005;
Jenkins 2006; Thompson and Townsend 2006; Chu et al. 2007). Purves and Turnbull
(2010) argue that the majority of communities in the field are niche-structured (species-
45
sorting model). Approaching this view from analyzing the feasibility of the neutral
model, Purves and Turnbull (2010) conclude that neutrality is inherently highly
implausible because real communities actually contain species that are observably
different in almost every respect. These observable differences translate to discrepancies
in baseline fitness between species, which give rise to species-sorting dynamics.
The species compositions (species abundances and richness) of the insect
communities differed significantly according to the environmental variables within each
microhabitat. The composition of the insect community varied significantly with
vegetation abundance and richness. These results are consistent with other studies that
have found the composition of the insect community is affected by the composition of the
plant community (Knops et al. 1999; Haddad et al. 2001; Crist et al. 2006). The
composition of the insect community also varied significantly with plant growth forms.
Insect communities changed along with the composition of plant growth forms because
plants exhibiting the same growth form have some similarities in tissue quality, and are
more similar taxonomically (Haddad et al. 2001). Ultimately, soil moisture appeared to
be the driving factor responsible for conditions within each microhabitat. Allen Diaz
(1991) examined the relationship between meadow plant communities and the soil water
table in Sierra Nevada meadows near Truckee, and found the highly variable hydrology
of the meadows served as a good predictor to plant abundance and distribution. Kneitel
and Alford (unpublished data) sampled subalpine meadows in the same geographic
location as the present study, and found hydrology was an important variable for
explaining patterns in the meadow plant community.
46
Habitats with an intermediate moisture regime exhibited, on average, the
highest abundance and richness of insects. This finding is consistent with Janzen and
Schoener (1968) who found that absolute numbers of insects were highest in moist
bottom-lands, which exhibited an intermediate moisture regime when compared to other
sites. Whittaker (1952) examined several mesic to xeric forest habitats in the Smoky
Mountains and found that greatest diversity of insects in forest types of intermediate
moisture. This common response suggests soil moisture plays an important role in
structuring insect communities.
The results are not consistent with the prediction that poor dispersers will
structure according to the neutral model of metacommunity theory, and good dispersers
will structure according to the species-sorting and mass-effect models of metacommunity
theory. Results indicated that community similarity was positively associated with local
environmental variables. This was indicated by communities in both groups structuring
differentially among microhabitats which lends support to the species-sorting model of
metacommunity theory. Urban (2004) found similar results in a freshwater pond
metacommunity consisting of invertebrates and amphibians. Urban (2004) segregated
the metacommunity into two dispersal categories consisting of active and passive
dispersers, and found that both groups structured according to local conditions in the
environment.
The results were inconsistent with Townsend et al. (2003) who found that the
geographic location and separation of sites accounted for variation in the invertebrate
assemblages. Townsend et al. (2003) examined separately a group of good dispersers
47
(i.e. strong flyers) and found that species with a strong flying ability can more readily
overcome dispersal limitation. A more recent study by Thompson and Townsend (2006)
showed that neutral processes influenced species with poor dispersal to a greater degree
than those with moderate and high dispersal. Thompson and Townsend (2006) sampled
aquatic insect communities in ten streams located in grassland catchments of New
Zealand and found that species with a low dispersal ability were unable to overcome
dispersal limitation which resulted in a strong negative correlation with spatial separation
of sites. In other words, communities close to each other were more similar than distant
communities.
The insect functional groups exhibited patterns consistent with different
metacommunity models. Results did not support the specific prediction that communities
segregated into the functional groups of herbivores, omnivores and pollinators will
structure according to the neutral model. Results indicated that herbivores consistently
(sampling periods 1 and 2) structured according to the species-sorting model, omnivores
structured according to the neutral and species-sorting models, and pollinators structured
according to the neutral and mass-effect models. Conversely, results supported the
specific prediction that communities segregated into the functional groups of parasitoids
and predators will structure according to the species-sorting and mass-effect models.
The composition of the community, consisting exclusively of herbivores, differed
significantly among meadows and microhabitats. These results indicated that community
similarity was positively associated with local environmental conditions. A synthesis of
herbivores and their community dynamics also indicated that herbivores respond to
48
changes in environmental conditions (Huntly 1991). Similarly, Thompson and
Townsend (2006) found that grazer (i.e. species that consume algae and are functionally
equivalent to herbivores) communities responded to changes in local environmental
conditions (i.e. species-sorting model). These common results suggest that herbivore
communities structure according to the species-sorting model of metacommunity theory.
Communities consisting exclusively of parasitoids and predators appeared to
structure according to different metacommunity models. Parasitoid communities
structured differentially among microhabitats in sampling period 1, and predator
communities structured differentially among microhabitats in sampling period 2. These
results indicate that, for parasitoids and predators, community similarity was positively
associated with local environmental variables in each respective sampling period (see
above). These results suggest the dynamics structuring these communities originate from
the species-sorting model of metacommunity theory. Similarly, Thompson and
Townsend (2006) found that within an aquatic macroinvertebrate metacommunity, the
composition of predators strongly correlated with environmental conditions. This pattern
may be consistent across very different habitats (aquatic vs. terrestrial) because predators
actively search for prey. The feeding behavior that predators exhibit necessitates a high
dispersal ability such that predators overcome dispersal limitation and structure according
to niche-based processes (species-sorting).
Parasitoids and predators did not appear to structure according to the speciessorting model in all sampling periods. Communities not affected by either meadows or
microhabitats included parasitoids in sampling period 2, and predators in sampling period
49
1. The absence of an effect from the microhabitats rules out the presence of speciessorting dynamics structuring the above communities, and an absence of an effect from the
meadows rules out the presence of neutral dynamics structuring the above communities.
These results suggest the dynamics structuring these communities originate from the
mass-effect model of metacommunity theory. This also illustrates how different
processes may drive diversity patterns over time.
The species-sorting and mass-effect models have two assumptions in common:
species differences (both trophic and others) are significant drivers of community
structuring processes, and species have the ability to overcome dispersal limitation.
Distance between sites does not inhibit immigration of species that overcome dispersal
limitation. These species are capable of dispersing to all sites within a region.
Consequently, sites may contain nearly all of the species in the region capable of
invading and local processes may dominate in shaping species diversity and composition
within patches (Ricklefs 1987; Cornell and Lawton 1992).
The mass-effect model differs from the species-sorting model by assuming that
dispersal rates are very high and exceed the rate at which environmental conditions
exclude taxa from certain habitats, such that immigrants influence the structure of the
communities in recipient habitats (Mouquet and Loreau 2003; Leibold et al. 2004; Urban
2004). The communities consisting of parasitoids and predators that did not vary
significantly with meadows or microhabitats could have been exhibiting very high
dispersal, which caused regional (i.e. meadow) diversity to decline because of increasing
50
homogenization of the community (Mouquet and Loreau 2003). These regional
dynamics support the mass-effect model of metacommunity theory.
The communities of predators and parasitoids structuring according to different
metacommunity models between sampling periods could be a consequence of the amount
of time that elapsed between sampling periods. Insects emerge and are active at different
times during the summer months (Janzen 1973). The species represented in each set of
communities (of parasitoids or predators) could have differed across sampling periods
and exhibited different dispersal abilities. For example, the communities consisting of
parasitoids in sampling period 1 may have exhibited high enough dispersal for speciessorting processes to dominate community structuring mechanisms. In sampling period 2
the parasitoid communities may have consisted of species exhibiting very high dispersal;
higher than the species in sampling period 1. Dispersal could have been high enough to
exceed the rate at which environmental conditions exclude taxa such that immigrants
influence the structuring of species in recipient habitats. These results are consistent with
Urban (2004) who found evidence of a freshwater pond metacommunity structuring
according to both the species-sorting and mass-effect models.
The communities consisting of omnivores and pollinators appeared to structure
according to different metacommunity models between sampling periods. These results
indicate how different processes may drive diversity patterns over time. However, unlike
the predator and parasitoid communities which overcame dispersal limitation (i.e.
structured according to species-sorting and mass-effect models) consistently (for both
sampling periods), the communities consisting of omnivores and pollinators overcame
51
dispersal limitation part of the time. Moreover, the prediction that omnivores and
pollinators would be inhibited by dispersal limitation and structure according to the
neutral model was only supported part of the time. Other studies have found support for
neutral processes structuring communities. McPeek and Brown (2000) found little
differences among some competing damselfly species, leaving the neutral model of
metacommunity theory as a potential explanation for assemblages of coexisting
Enallagma damselfly species exhibiting very similar ecological characteristics.
Similarly, French (1999) found evidence of neutral processes structuring dipteran
communities in an Australian eucalypt forest. The similarities of dipteran communities
appeared to decrease as distance between sites increased.
Omnivores overcame dispersal limitation during sampling period 1, and may have
structured according to the species-sorting model; pollinators overcame dispersal
limitation during sampling period 2, and may have structured according to the masseffect model. The inconsistency of the prediction (that omnivores and pollinators will
structure according to the neutral model) with the study results may be an artifact
resulting from the method used to categorize insects into functional groups. Several
individuals that were parsed into the omnivore and pollinator communities included
species that were defined as good dispersers in the previous analyses (i.e. good vs. poor
disperser community comparisons), and good dispersers were predicted to overcome
dispersal limitation and structure according to the species-sorting and mass-effect
models. These individuals included Dipterans (for the omnivore communities),
Lepidopterans, and Apoideans (for the pollinator communities). Good dispersers
52
included individuals that exhibit a strong ability to fly and contain at least one full set
of wings. However, this ability to fly is at odds with the behavior indicative of the
functional group, such that pollinators (as an example) do not go out in search of prey
and may behave like poor dispersers. Apidae and many Anthophoridae are much better
dispersers than Halictidae and Andrenidae. In addition, Megachilidae are probably also
good dispersers, but strongly tied to nest sites in trees off the meadows.
The taxonomic group Diptera contained many species that belong to various
functional groups including predators, scavengers, pollinators, etc. For ease of
identification and in the essence of time (due to the quantity of dipterans collected), a
majority of the Dipterans in this study were only identified to suborder (exceptions
included Tabanidae, Syrphidae, Sepsidae, Pipinculidae, Otitidae, Sarcophagidae,
Bombyliidae and Asilidae). Thus, many individuals could not be associated with a
specific functional group. Identifying Dipterans to a lower taxonomic level (beyond
suborder) would permit the categorization of these insects into functional groups other
than omnivore. This process of categorization may have significantly underestimated the
dispersal ability of many dipterans.
Conclusion
The results of this study suggest that various models of metacommunity theory
are not mutually exclusive and may exist along a continuum with local and regional
dynamics shaping the structure of the metacommunity (Leibold et al. 2004). The entire
insect metacommunity was strongly positively associated with local environmental
conditions, lending support to the species-sorting model of metacommunity theory.
53
Looking at a finer scale view into the functional groups and dispersal abilities of
species within the metacommunity revealed the neutral and mass-effect models of
metacommunity theory. Future experiments that utilize species composition patterns in
the field to differentiate between models of metacommunity theory should extract subsets
of data and categorize them according to species traits (e.g. functional group, and
dispersal ability). Grouping categories of animals appropriately may expose any
underlying community structuring mechanisms that exist. When restoring meadow
habitat, such as the subalpine meadows that were examined in this study, the restoration
goals should strive to protect and enhance the communities which use the habitat.
Meadows have been shown to be very important habitat for insects and the results of this
study suggest that heterogeneity is an important factor that influences the structure of
insect communities. This was evidenced by communities structuring according to
different meadows and microhabitats. Consequently, any restoration goals for meadows
should include activities that when implemented will maintain and even enhance local
and regional heterogeneity.
54
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