habitat-based drivers of arthropod abundance and richness
TRANSCRIPT
Habitat-based drivers of arthropod abundance and richness in an intensively
farmed agricultural landscape
by
Aleksandra Dolezal
A Thesis
presented to
The University of Guelph
In partial fulfilment of requirements for the degree of
Master of Science
in
Integrative Biology
Guelph, Ontario, Canada
© Aleksandra Dolezal, July 2019
ABSTRACT
HABITAT-BASED DRIVERS OF ARTHROPOD ABUNDANCE AND RICHNESS IN AN
INTENSIVELY FARMED AGRICULTURAL LANDSCAPE
Aleksandra Dolezal
University of Guelph, 2019
Advisor:
Dr. A. S. MacDougall
Habitat is critical for sustaining arthropod populations, but its influence in agricultural
landscapes where arthropods are declining is unclear. In a heavily farmed area of Ontario, I
examined habitat effects on arthropods at two scales: landscape (habitat isolation) and local
(farm cover type -crop, prairie, forest). Additional analyses tested differences in arthropod
communities among cover types, how prairie addition affects leaf defoliation, whether crop type
influences arthropods (soy, corn, orchard, mixed organic), and whether arthropod richness differs
between farm and non-farm sites. I found arthropod richness and abundance to be determined
locally, via the presence of restored high-quality prairie habitat. Prairie addition did not affect
leaf defoliation but influenced the spatial distribution of herbivores in crops. Crop type
influenced arthropod groups, especially herbivores, which preferred mixed organic cover. Lastly,
farm and non-farm sites revealed similar family-level richness, suggesting that conventional
farms can support arthropod biodiversity if habitat is not limiting.
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ACKNOWLEDGEMENTS
This thesis would not have been possible without the researchers who came together to
initiate an extraordinary project that was the basis for my work. It’s their guiding idea and the
efforts of all the participating scientists and non-scientists which make this project an on-going
success. Being a part of this collaborative effort has been a great privilege.
I want to express my gratitude to my supervisor Dr. Andrew MacDougall, who gave me
the opportunity to do my MSc under his guidance. I am especially thankful that he encouraged
me in skill-building activities such as workshops and conferences which added great experiences
to my time as a graduate student. I am also grateful to my committee members Kevin McCann
and Alex Smith for giving me input throughout my research journey. I would also like to
acknowledge Steve Marshall whose passion for entomological research has motivated me to start
my own journey as an entomologist. I look forward to following his work.
I wholeheartedly thank the MacDougall lab team for creating a positive and supportive
working atmosphere which cannot be taken for granted. A big thanks to Dr. Ellen Esch for all
her consulting days in the office for stats help. Thank you to all my field technicians who helped
during the summer of 2017. Thank you Brianna Maher for being so hardworking, Sam Kloke for
teaching me plant taxonomy in the field, Brock Roth for your dedication and work ethic, and
Bernal Arce for your expertise in all things arthropods. I learned from all of you as I hope you
took a piece of my work with you. Thank you Margeret Visser for putting in the time to edit my
work. I cannot forget Kevin, the love of my life, who held me together when I fell apart.
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I also want to thank the awesome participants in the ALUS program who gave us the
permission to do research on their farms. I hope my study contributes to the conservation of
these lands. Bryan Gilvesy was a huge contributor to the passion and work these participants
had, as well as Casey Whitelock and Alyssa Cousineau. Thank you to Sage Handler of the Nigel
Raine bee lab for identifying my bee species to genus level, you don’t know how many hours
this saved me, Sage.
I am thankful for the funding provided by the OMAFRA Highly Qualified Personnel
Scholarship that made this research possible. Tall Grass Ontario, and Food from Thought also
provided partial support for this work through travel grants. Lastly, I would like to thank my
family for their love and support for all my various endeavors over the years, and not freaking
out when bags of arthropods appeared in the freezer. Planting wildflowers, caring for insect pets,
running barefoot outside…. the childhood you gave me has made me who I am today.
“Nature holds a key to our aesthetic, intellectual, cognitive and even spiritual satisfaction”
-E.O. Wilson
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TABLE OF CONTENTS
Abstract…………………………………………………………………………………………....ii
Acknowledgements ........................................................................................................................ iii
Table of Contents ............................................................................................................................ v
List of Tables ................................................................................................................................. vi
List of Supplementary Tables ....................................................................................................... vii
List of Figures .............................................................................................................................. viii
List of Supplementary Figures ........................................................................................................ x
List of Appendices ........................................................................................................................ xii
1. Introduction ................................................................................................................................. 1
2. Methods....................................................................................................................................... 4
2.1 Site selection and description ................................................................................................ 4
2.2 Arthropod and habitat sampling ............................................................................................ 9
2.3 Main analysis ...................................................................................................................... 12
2.3.1 Landscape and local factors of habitat on 13 agricultural sites ................................... 12
2.3.2 Within-prairie habitat factors ....................................................................................... 14
2.4 Complementary analyses .................................................................................................... 16
2.5 Statistical analysis ............................................................................................................... 17
2.5.1 Landscape and local factors of habitat on 13 agricultural sites ................................... 18
2.5.2 Within-prairie habitat factors ....................................................................................... 19
2.5.3 CA1: Arthropod community composition among cover types .................................... 20
2.5.4 CA2: Assessment of arthropod-derived ecosystem service mediated by habitat ........ 20
2.5.5 CA3: Crop type differences for arthropod abundance and richness (family-level) ..... 21
2.5.6 CA4: Regional family pool comparison ...................................................................... 21
3. Results ....................................................................................................................................... 22
3.1 Landscape and local factors of habitat on 13 agricultural sites .......................................... 23
3.2 Within-prairie habitat factors .............................................................................................. 25
3.3 CA1: Arthropod community composition among cover types ........................................... 27
3.4 CA2: Assessment of arthropod-derived ecosystem service mediated by habitat ............... 27
3.5 CA3: Crop type differences for arthropod abundance and richness ................................... 28
3.6 CA4: Regional family pool comparison ............................................................................. 28
4. Discussion ................................................................................................................................. 29
5. Conclusion ................................................................................................................................ 39
6. References ................................................................................................................................. 40
List of Tables ................................................................................................................................ 66
List of Figures ............................................................................................................................... 72
List of Appendices ........................................................................................................................ 84
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LIST OF TABLES
Table 1: Results of linear models between percentage of landcover type of buffer radii (500,
1000, 1500, 2000 m) and total arthropod abundance and richness for 13 farms in Southern
Ontario, Canada. .............................................................................................................. 66
Table 2: Mantel test r-values (Pearson’s product-moment correlation) for the correlation
between similarity matrices for functional group abundance and richness (family-level) in
conventional farms and similarity matrices for spatial connectivity of farms. ......................... 67
Table 3: Summary of F- and p- values from analysis of arthropod abundance and family-level
richness across four focal functional groups in response to cover type. Significant effects (p ≤
0.05) in bold. ................................................................................................................... 68
Table 4: Results of Generalized Linear Mixed Models with negative binomial function of field
observations evaluating the effect of plant-related variables on arthropod abundance. ............. 69
Table 5: Results of Generalized Linear Mixed Models with negative binomial function of field
observations evaluating the effect of plant-related variables on arthropod family-level richness.
...................................................................................................................................... 70
Table 6: Results from ANOVA summary statistics analyzing factors prairie size and prairie age
for arthropod abundance and richness (family-level) response. ............................................. 71
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LIST OF SUPPLEMENTARY TABLES
Table S1: List of study sites and cover type variables. Farms located in Southern Ontario, field
research taken from May – July 2017. Annual mean temperature and annual mean precipitation
values are taken from WorldClim 1.4: current conditions data at 30 arc-seconds (~1 km). ....... 85
Table S2: Total plant species and associated functional groupings found within prairie cover.. 86
Table S3: Definitions of arthropod functional groups. ......................................................... 94
Table S4: Functional group categorization and literature search. .......................................... 95
Table S5: Proportion of agricultural and semi-natural landcover at four buffer radii (500 m, 1000
m, 1500 m, and 2000 m) of 13 farms in Southern Ontario, Canada. Landcover data was extracted
from Agri-Canada Annual Crop Inventory. ....................................................................... 111
Table S6: Lab methods for plant tissue quality analysis as provided by SGS Labs, Guelph,
Ontario. ........................................................................................................................ 112
Table S7: Summary of arthropod functional group abundance data. All data is presented as the
pooled number of individuals caught per site for both yellow pan traps and sweep nets samples.
.................................................................................................................................... 113
Table S8: Summary of arthropod functional group richness (family-level) data. All data is
presented as the pooled number of individuals caught per site for both yellow pan traps and
sweep nets samples. ....................................................................................................... 114
Table S9: Results of arthropod family identity in agricultural and non-farm (Naturalist) sites and
the shared or unique families between them. ..................................................................... 115
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LIST OF FIGURES
Figure 1: Total arthropod abundance and richness (family-level) sampled across (A) 22 total
farms and (B) 13 farms as they relate to 9 key functional groups. The focal functional groups are
given in colour (herbivores = purple, predators = yellow, parasitoids = blue, pollinators = green)
while all other functional groups are in grey. Bars are annotated to show the fraction of the total
arthropod community comprised by each functional group when the fraction was > 0.1. ......... 72
Figure 2: Parasitoid richness (family-level) as related to percent agricultural land use at a buffer
radius of 500 m. ............................................................................................................... 73
Figure 3: (A) Abundance (B) and richness (family-level) of arthropods across three different
cover types (crop = green, prairie = yellow, forest = brown). Error bars show ± 1 SE, and lower-
case letters indicate significance at p<0.050 as determined by post-hoc tests. ......................... 74
Figure 4: (A) Abundance (B) and richness (family-level) of arthropods belonging to the 4 focal
functional groups across three different cover types (crop = green, prairie = yellow, forest =
brown). Error bars show ± 1 SE, and lower-case letters indicate significance at p<0.050 as
determined by post-hoc tests. ............................................................................................ 75
Figure 5: Ordination of arthropod community composition using non-metric multidimensional
scaling (NMDS) showing variability between cover types on farm sites (n=13). Farm sites near
each other have a community composition that is more similar than farms that are separated by
greater distance. Ellipses represent 95% CI around the centroid. ........................................... 76
Figure 6: Average soybean leaf defoliation for farms which had prairie adjacent to the crop field
(n=7) and those without adjacent prairie (n=3). Error bars show ± 1 SE. ................................ 77
Figure 7: Relationship between abundance and richness of arthropod functional groups in farms
with prairie present (n=7) and farms with prairie absent (n=3). (A) Mean abundance of arthropod
functional groups in prairie absent farms. (B) Mean abundance of arthropod functional groups in
prairie present farms. (C) Mean richness (family-level) of arthropod functional groups in prairie
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absent farms. (D) Mean richness (family-level) of arthropod functional groups in prairie present
farms. ............................................................................................................................. 78
Figure 8: Relationship between abundance of arthropod functional groups in crop fields and
distance from edge from farms with (A) farms with prairie present adjacent to crop (n=7) and (B)
farms with no prairie present (n=3). ................................................................................... 79
Figure 9: Relationship between leaf defoliation and distance from edge on farms with prairie
present adjacent to crop (n=7) and farms with no prairie present (n=3). ................................. 80
Figure 10: (A) Mean abundance of arthropod functional groups for different crop types. (B)
Mean richness (family-level) of arthropod functional groups for different crop types. ............. 82
Figure 11: Data from non-farm iNaturalist observations in Southern Ontario compared to 13
agricultural field sites. Each bar in the graph represents the summed count of total arthropod
families in each dataset and segments in the graph represent the functional group composition of
that total. ......................................................................................................................... 82
Figure 12: Number of arthropod families unique or shared from data of non-farm iNaturalist
observations in Southern Ontario compared to 13 agricultural field sites. Each bar in the graph
represents the summed count of total arthropod families in each dataset and segments in the
graph represent the functional group composition of that total. ............................................. 83
x
LIST OF SUPPLEMENTARY FIGURES
Figure S1: Hierarchical framework of habitat-based drivers of arthropod abundance is shown.
Habitat occurs at multiple scales: landscape distance-based and local farm cover based. A
specialized plot-level analysis was conducted to explain my local-scale prairie cover which
consisted of plant-related factors of plant quantity, plant tissue quality, and plant composition. 84
Figure S2: Location of the 22 study sites within the Southern Ontario, Canada region. Farms
strictly used in my main analysis are coloured in green. Farms used in complementary analyses
include farms coloured in black. ........................................................................................ 88
Figure S3: Monthly temperature and precipitation data from historical records (in red) and
within my sampling year (in blue) during sampling months May-July 2017. .......................... 89
Figure S4: Quadrant sampling design for arthropod and plant measurements. All sampling
occurred from May-July 2017 on farms located in Southern Ontario, Canada. ........................ 90
Figure S5: Conceptual figure of sampling design used. For the majority of cases, a 5x 5 grid
would be overlaid onto the satellite image of each farm cover (crop, prairie, wood). In cases
where covers were unsymmetrical or where a 5 x 5 grid could not be easily overlaid, instead 25
grid squares that spanned the whole cover were overlaid. In all cases, five sampling locations
were chosen using a random number generator. .................................................................. 91
Figure S6: Sample-based rarefaction curves of arthropod richness were drawn three different
cover types (prairie, wood, crop). Solid lines represent the estimated number of species for each
cover and the shaded area between the lines indicates the estimated 95% confidence interval. . 92
Figure S7: (A) Mean abundance (± SE) of arthropod community per cover type and (B) Mean
richness (family-level) (± SE) of arthropod community per cover type. Bars with the same letter
are not significantly different (ANOVA, followed by comparisons between cover type using
Tukey’s HSD test, α = 0.05). ............................................................................................. 93
xi
Figure S8: Correlation matrix of landcover data carried out to assess the relationship among
predictor variables. Squares in blue indicate a strong negative correlation, squares in red indicate
a strong positive correlation. ........................................................................................... 118
Figure S9: Correlation matrix of plant resource data carried out to assess the relationship among
predictor variables. Squares in blue indicate a strong negative correlation, squares in red indicate
a strong positive correlation. ........................................................................................... 119
Figure S10: Locations of insect and arachnid iNaturalist observations logged from 05 May - 17
July 2017 spanning a 10,000 km² region of Southern Ontario, Canada. ................................ 120
Figure S11: Summary of arthropod (A) abundance and (B) richness (family-level) per farm from
a total of 22 farms in Southern Ontario, Canada. ............................................................... 121
xii
LIST OF APPENDICES
Appendix 1: Conceptual figures ................................................................................................... 84
Appendix 2: Site selection and description ................................................................................... 85
Appendix 3: Sampling methods .................................................................................................... 90
Appendix 4: Functional group categorization and definitions ...................................................... 94
Appendix 5: Supplementary methods and results ....................................................................... 111
1
1. Introduction
Arthropods are critical components of agricultural landscapes providing valuable
ecosystem services relating to nutrient recycling, pollination, biological control, and acting as a
food source for higher trophic levels (Losey & Vaughan, 2006; Zhang et al., 2007). Declines of
arthropod populations are reported globally (Dirzo et al., 2014; Hallmann et al., 2017; Sánchez-
Bayo & Wyckhuys, 2019). This includes the losses of beneficial groups such as predatory and
pollinating arthropods whose reductions may intensify insect pest attack on crops and pollination
deficits (Altieri, 1999; Deguines et al., 2014). These trends are closely related to the
intensification of farm practices in many parts of the world (Benton et al., 2003; Albrecht et al.,
2007; Geiger et al., 2010; Phalan et al., 2011) suggesting a widening dichotomy between a
critical need for arthropod services on farms and farm practices that may reduce their presence
and benefits.
Although arthropod declines in farm landscapes are well-described, the causal factors
regulating these changes are not fully clear. One central issue is the role of habitat loss relative to
the obvious impacts of insecticides (Robinson & Sutherland, 2002; De Clerck-Floate &
Cárcamo, 2011). Habitat loss, in many agricultural landscapes, derives from decades of change
towards fewer farms with larger fields supporting crop monocultures, fewer indigenous floral
and nesting resources, and reduced availability of habitat refuges against factors including
spraying and drought (Tillman et al., 2011). Habitat is a rigid limiting factor for many arthropod
populations, providing stable food resources, nesting sites, and overwintering refuge (Benton et
al., 2003; Duelli & Obrist, 2013; Schellhorn, Gagic & Bommarco, 2015). In many farm
landscapes lacking habitat there will be a loss of any arthropods unable to persist in crop
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monocultures. Many farm regions support some degree of habitat complexity such as grassland
borders, forest patches, and field edges with wildflowers. An increasing issue of interest is using
often small amounts of high-quality habitat in marginal edge areas (Paterson et al., 2019). How
do these refuge areas affect arthropod groups? The impacts appear to be highly variable by
habitat type (closed forest vs open edge or restored areas), by arthropod functional group
including whether such areas are a greater benefit to herbivores or predators, and the type of
crops planted (Tscharntke et al., 2012; Ma et al., 2019). This complexity in terms of responding
to limited habitat can apply to non-targeted species (i.e., not subject to pest control), the most
well studied and rapidly declining group (Ramsden et al., 2017). It can also apply to insect pests,
including possibly providing unintended beneficial alternative host plants for parts of their life
cycles and refuges from spraying. Additionally, there are intricate relationships between pests
and natural enemies mediated by habitat – sometimes in remnant patches the former does better,
sometimes the natural enemies control the pests (Tscharntke et al., 2007). In total, these
complexities indicate that we still lack a fundamental understanding of the habitat-based
dynamics that regulate arthropod groups – beneficials and pests - in agricultural landscapes.
Many of the uncertainties with habitat remnants and arthropods center on scale-
dependent mechanisms of habitat size, distance among required habitat, plant cover type, and
plant resources, which are rarely tested together and may act differently on some arthropod
groups over others (Harvey & MacDougall, 2014, 2015; Grainger & Gilbert, 2017). Habitat
impacts in agroecosystems can unfold at landscape and local scales (Soberon, 2007), each of
which I test. At the landscape scale, distance among habitat types may shape arthropod
communities strictly by influences on dispersal (closer sites have more similar arthropod
communities than sites farther away), but especially if preferred habitat such as semi-natural
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areas (prairie strips, forest, hedgerows, field edges) are relatively rare compared to crop
monocultures (Tscharntke et al., 2012). At the local scale, the presence of different habitat types-
specifically crop fields, forest, and permanent cover of herbaceous species found in field edges
or restored marginal lands- can be important determinants for sustaining populations of
arthropods (Hanski & Ovaskainen, 2000). Species richness of arthropods is expected to increase
in habitat with greater resources and patch size due to the increased likelihood of supporting
more life history strategies (Lawton, 1983; McCoy & Bell, 1991). In total, understanding how
habitat - be it landscape, local or both - affects arthropods in farm landscapes is an urgent focus,
given how herbivore and beneficial functional groups are both important for the future of food.
My research tests the influence of scale-dependent habitat factors on the abundance and
richness (family-level) of arthropod communities (see Appendix 1: Fig. S1) with a focus on four
agriculturally important functional groups (herbivores, predators, parasitoids, pollinators) on 22
farms across a 10,000 km2 agricultural landscape in Southern Ontario, Canada. My sampling was
structured around the examination of distance-based spatial constraints at the landscape-scale
and the influence of cover type at the local-scale within farms. My local-scale work on cover
type included restored high-diversity prairie on peripheral marginal lands (e.g., riparian strips,
field edges), as part of a farmer-led national program growing services on farms (ALUS Canada,
2019). To that end, I conducted additional analyses at the plot-level in prairies – the most
influential local factor that I detected (i.e., the presence of prairie for arthropods) – investigating
effects of plant species composition and related issues of tissue quantity and quality given that I
found variation in these factors largely absent in my sampling for crop fields and forest.
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I also conducted several complementary analyses relating to drivers of arthropod richness
(family-level) and abundance on farm landscapes. First, I tested if arthropod community
composition differed among cover types (crop, prairie, forest), specifically if the two semi-
natural habitats (prairie and forest) widely differed. Second, I tested the potential functional
implications of restored prairie habitat, given that they could support both pest and predator
populations with unclear implications for crop damage (Tscharntke et al., 2007). That is to say,
whether prairie habitat potentially harbors high herbivore populations that lead to higher leaf
defoliation, or the alternative where populations of beneficial arthropod predators curtail leaf
defoliation. Third, I tested the influence of crop type on arthropod abundance and richness
(family-level), and whether herbivore groups would show stronger responses due to host-
specificity or beneficial groups due to nectar and pollen resources by different crops. Finally,
given the widespread expectation that arthropods are declining with agricultural intensification
(Sánchez-Bayo & Wyckhuys, 2019) I pooled my farm data for family-level richness and
contrasted it with a regional database from non-farm areas in the surrounding region sampled
during the same time period. I was interested if total differences in richness (family-level) were
apparent and which families were absent from farms (e.g., ground nesting bees, butterflies).
2. Methods
2.1 Site selection and description
In this study, twenty-two farm study sites were selected from a 10,000 km² intensely
farmed agricultural region of Southern Ontario, Canada in Hillsburgh (43°47'30.7N
80°08'35.0W), Norfolk (42°52'17.4N 80°18'26.8W), and Elgin Counties (42°46'59.6N
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81°10'54.9W) (see Appendix 2: Fig. S2). Approximately 92% of this region supports crop
monocultures, especially soybean (41.7%), corn (28.7%), and wheat (12.5%) (Government of
Canada, 2017). The remainder of the landscape, not including settlement, is largely forest and
non-crop cover such as oldfield plant communities along roadsides and crop field margins. The
region has a temperate climate strongly influenced by the proximity to Lake Erie, with 7.75 ℃
mean annual temperature, 946 mm mean annual precipitation (Fick & Hijmans 2017; Appendix
2: Table S1), and frost-free days averaging between 160 to 170 from April to October
(OMAFRA Publication 811, 2017; Government of Ontario, 2019). Mean monthly temperature
and precipitation data during my sampling year were consistent with historical climate data
(years 1970-2000) (Appendix 2: Fig. S3). Soils have developed on glaciolacustrine deposits with
surface textures silty clay or silty clay loam, and some areas of silty or sandy loam (Presant,
1984). For each of the twenty-two farms, I conducted standardized arthropod sampling by cover
type depending on which covers were present on the farm (e.g., some farms lacked restored
prairie). The farms ranged in size from 2.37– 115.07 acres, with crop, prairie and forest cover of
various sizes (if present) per farm (Appendix 2: Table S1).
All farms were part of a national farmers program (ALUS, 2019) seeking to “grow”
ecosystem services in agricultural landscapes including providing good quality habitat to
beneficial insects. When present, prairie grassland covered 2% to 21% of total farm area. Four
farms were part of ALUS but had not yet planted prairie, providing me the opportunity to test
farms with and without this restored cover type. All sites with prairie were planted with a similar
mix of grasses and forbs, although species dominance, diversity, and composition of the
established plant community sometimes varied among farms (Appendix 1; Table S2). Overall,
forb species varied the most among farms while several C4 grasses tended to be present at all
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sites (e.g., big bluestem (Andropogon gerardii), Indian grass (Sorghastrum nutans), and
switchgrass (Panicum virgatum)). Given how prairie cover was different on each farm, I
conducted a specialized series of analyses using finer-scale plot-level data to investigate how
plant-based resources relating to plant quantity, tissue quality, and plant composition influenced
on-farm arthropod abundance and richness (family-level).
From the twenty-two farms, my primary focus (“Main Analysis” [MA]) involved thirteen
farms that represented the dominant landcover profile of the region – conventional crop
monocultures with soy, corn, or wheat. Additionally, these farms all had forests and small
marginal cropland areas (e.g., poorly drained soils, steep slopes) that had been converted to
permanent prairie grasslands. Using these thirteen MA farms, I tested my main hypotheses
regarding whether landscape versus local factors of habitat determined the abundance and
richness (family-level) of arthropods. Landscape-scale spatial factors were assessed as distance
among farms, and distance among similar nearby semi-natural habitat (described below). If
distance drives large spatial turnover in arthropod communities over my 10,000 km2 study area,
whether it be among farms or among semi-natural habitat, then I would predict spatial separation
among these factors should determine arthropod abundance and richness (family-level)
regardless of cover type variation. For example, my most isolated farms would likely have the
lowest numbers of species, especially if semi-natural habitat is lacking. Local-scale habitat
factors were assessed based on aspects of habitat type of crop, prairie, and woodland (described
below). If local habitat drives arthropod abundance and richness (family-level) over my
approximate 10,000 km2 study area, then I would predict ‘all insects to be everywhere’ (because
spatial factors are not limiting) with differences shaped instead by cover type on farm. In short,
this latter prediction turned out to be the case.
7
Given that local farm cover type largely ‘explained everything’, I conducted a series of
complementary analyses to flesh out some of the underlying details explaining this outcome.
First, I was interested in testing arthropod community composition overlap between three on-
farm cover types (crop, prairie, forest) (“Complementary Analysis 1” [CA1]). I was especially
interested in the forests which were present on all farms. Forests have received significant
attention on agricultural landscapes, including a recent study by Hallmann et al., (2017) testing
long-term changes in flying insect biomass in wooded areas of heavily farmed regions of
Germany. On one hand, forests may serve as important refuges for arthropods in heavily cropped
regions by providing sanctuary from spraying as well as food and nesting resources (e.g., floral
resources in early spring via “spring-ephemeral wildflowers (Dailey & Scott, 2006)). On the
other hand, there may be strong compositional differences between forest and open-field
arthropods based on microclimate: arthropods are ectothermic and thus highly sensitive to
temperature and humidity (Heinrich, 1996) such that large percentages of forest arthropods in
farm landscapes may not commonly venture forth (Duflot et al., 2015). To test for differences
between arthropod community composition by cover type I used the Bray-Curtis dissimilarity
index (Bray & Curtis, 1957) from my thirteen MA farms. These farms were used because they
contained all three cover types and with equal sampling effort.
Second, my results of MA and CA1 pointed to one clear finding – the presence of semi-
natural restored prairie habitat largely explained the variation I observed in arthropods across my
study region. Further, this habitat housed several groups of beneficial arthropods including
predatory insects known to attack crop pests. Thus, I tested whether the occurrence of prairie had
detectable differences in arthropod abundance, richness (family-level), and arthropod-derived
ecosystem services (“Complementary Analysis 2” [CA2]). In terms of services, I tested whether
8
the occurrence of prairie influenced crop leaf damage and arthropod distribution in edges or
interior of crop fields directly adjacent to the prairie. For this analysis, I compared data from
three soybean “complementary farms” that did not have the occurrence of prairie to seven “main
analysis” farms that also grew soybean but had prairie cover (more detailed sampling methods
provided below).
Third, I compared the influence of other forms of farm cover-type on the arthropod
richness (family-level), abundance, and functional group differences (“Complementary Analysis
3” [CA3]). To do this, five of my “complementary farms” grew crops other than soy, corn, wheat
(three farms: apple orchards, mixed organic, sorghum) or lacked crops altogether (two farms:
100% restored prairie). The lack of sampling power did not allow me to fully test the difference
in beneficial arthropods on these farms versus my core thirteen. However, I conducted non-
parametric assessments of arthropod richness (family-level), abundance, and functional group
differences between these complementary farms and my main analysis farms.
Finally, I compared broad difference in the arthropod families I sampled from my thirteen
MA farms, versus the total family pool for the region observed in non-farm areas
(“Complementary Analysis 4” [CA4]). I was especially interested whether farm richness (family-
level) was highly depauperate versus the regional pool and which major functional groups might
be missing. An increasing number of studies are suggesting large insect declines globally,
especially in farmed landscapes (Benton, Bryant & Cole, 2002). Given that I work in one of the
more intensively farmed regions of central North America, with crop cover >90% of my study
area, an obvious prediction would be that the richness (family-level) of arthropods on my farms
would be greatly reduced compared to more habitat-rich areas regionally. Relatedly, it would
9
also seem likely that the arthropods found on farms would be a non-random collection of
functional groups with life history traits that may favor persistence in areas dominated by crop
monocultures. For example, ground nesting bees (Hymenoptera: Andrenidae) are often assumed
to be rare or absent on farms because they lack nest sites and nest construction materials
(Williams & Kremen, 2007). To test these possibilities, I assembled a database representing the
“regional family pool” for Southern Ontario using data extracted from iNaturalist, a community
science program (https://www.inaturalist.org/ - details described below).
2.2 Arthropod and habitat sampling
Prior to field sampling, I randomly selected five sampling locations within each farm
cover type (crop, prairie, forest) to establish a 5 x 5 m quadrant that arthropods and plants would
be sampled (5 quadrants x [20 crop cover + 18 prairie cover + 22 wood cover] = 300 quadrants).
To do this, I overlaid a 5 x 5 grid onto a satellite image of each farm cover using the Agricultural
Information Atlas (AgMaps, 2017), then used a random number generator to select five sampling
locations in the grid. In situations where a 5 x 5 grid could not be easily overlaid on top of a
cover, such as strip prairies or unsymmetrical covers, I overlaid 25 grid squares that spanned the
whole cover and five sampling locations were chosen using a random number generator. The
position of all sampling locations was pre-recorded using the UTM coordinate system and later
used to find locations in the field using a Trimble GeoXT 2008 GPS (horizontal resolution of 2
meters set to zone 17 north and WGS 1984). See Appendix 3 for full details of sampling
methods.
10
Arthropod field sampling occurred between 15 May to 4 July 2017. This sampling
window was selected to maximize the number of farms in my study, while occurring prior to the
period of insecticide application regionally for corn, soy, and wheat. The recommended
application period date is 21 pre-harvest interval days for corn, 28 pre-harvest interval days for
soybean, and 30 pre-harvest interval days for wheat (OMAFRA Publication 812, 2018). Based
on these recommended dates, insecticide application would occur in late July or early August on
farm sites, thus after my sampling period. Given the extent of my sampling period and the
phenological shifts that may occur in arthropod communities over the summer growing season
(Welch & Harwood 2014), there was a risk of compositional turnover that might bias my
analyses. To test for this, differences in arthropod community composition between sampling
dates was assessed using the Bray-Curtis dissimilarity index (Bray & Curtis, 1957) on the
arthropod community. I conducted a permutational multivariate analysis of variance
(PERMANOVA) to test the null hypothesis of no differences in the arthropod community among
different sampling dates using the adonis function in vegan package of R (Oksanen et al., 2016).
Overall, I found no significant influence of sampling date on my arthropod community
dissimilarity (F= 0.78, R² = 0.014, p = 0.774), therefore indicating that my farms were
statistically comparable despite being sampled at different times.
Arthropods were sampled within each 5 x 5 m quadrant using two yellow pan traps
placed at different heights (H1 at soil level and H2 at canopy level) and filled with soapy water
to reduce surface tension (Leong & Thorp, 1999). Sampling occurred on sunny days between
10:00 a.m. and 4:30 p.m., at temperatures above 15 ⁰C, and with wind speeds less than 20 km/h.
Yellow pan trap samples were placed in whirl-pak bags, stored at 4 ⁰C until arthropods were
11
transferred to 90% ethanol for future identification. Yellow pan traps were used because they
attract a wide range of taxa which include flying and ground dwelling arthropods (Kirk, 1984;
Marshall et al., 1994).
Yellow pan trap samples were complemented with sweep-netting to capture the arthropod
community at a broader cover-level habitat scale. Sweep net samples consisted of a W-transect
standardized by beat frequency, net size, and occurred at the same time in each cover for five
minutes by three field technicians. This sampling method was chosen because it is the
recommended strategy for growers to scout their fields for arthropods (OMAFRA Publication
811, 2017). Each sweep-net sample was placed into individual top-closure polyethylene bags,
labeled according to its respective date, site, cover, and stored at -20° C for future identification.
In total, 60 sweep net samples were conducted because not all farms contained all cover types (1
sweep net x (20 crop cover + 18 prairie cover + 22 wood cover)).
In the laboratory, arthropods were identified to family-level at minimum, and lowest
taxonomic level when possible, using a combination of taxonomic keys, photographic guides and
natural history references. Bee specimens were identified to genus at minimum, and lower
taxonomic level when possible, by the Nigel Raine laboratory. Although identification to
relatively coarse levels of family or genus captures less diversity versus identification to the
species-level, the large number of arthropods collected (total 27,080) necessitated this approach.
Family-level taxonomic resolution has been found to be sufficient for detecting significant
patterns of community composition and habitat preference in temperate systems, including
agroecosystems (Cardoso et al., 2004; Timms et al., 2013). After identification I classified
arthropods into 9 functional groups (Appendix 4; Table S3) by life stage to account for
12
arthropods with ontogenetic shifts in feeding styles (e.g., Syrphidae larvae are predators, but
adults are pollinators). Functional group classification was based on the most common feeding
mode within the family and determined by an extensive literature search (Appendix 4; Table S4).
The primary goal was to examine key drivers of abundance and richness (family-level) of
four groups of arthropods considered agriculturally important: (1) herbivores because they are
the primary consumers, linked closely to the plant community and can be significant pests, (2)
predators, because they are important pest control agents, (3) parasitoids, because they are also
valuable pest control agents often used for biological control and (4) pollinators due to the
important pollination service they provide. Five other “secondary” functional groups were
categorized but grouped into a “total” arthropod functional group for subsequent analyses. These
functional groups were: kleptoparasite, aquatic, decomposer, anthophilous, and uncommon.
Individuals categorized into group “uncommon” were arthropods of unresolved feeding guilds or
when less than three individuals were collected total at a farm site, regardless of cover.
2.3 Main analysis
2.3.1 Landscape and local factors of habitat on 13 agricultural sites
My landscape-scale hypothesis was that distance among farms and, more specifically,
distance among nearby semi-natural habitat, would strongly determine arthropod richness
(family-level) and abundance on a farm. For this analysis, arthropod abundance and richness data
were compared among the farms (n=13).
More distant farms could have different arthropod communities due to spatial turnover.
Alternatively, there may be little influence of distance if “everything is everywhere” which might
13
be possible given that the dispersal dynamics of most arthropods are unknown except in a few
cases (e.g., pollinators). To test this, I calculated the between-farm distance (km) of each farm to
every other farm site using the ArcGIS measure tool. This resulted in 13 distance values per farm
which were averaged into one estimate and used as a measure of distance. A Mantel test was
used to examine if there were relationships between distance in geographic space and arthropod
family or abundance space (McCune & Mefford, 1999). The Mantel test is widely used in
ecological studies to examine whether populations are similar across sites (Koenig & Knops,
1998).
The distance of semi-natural areas could also be affecting abundance and richness
(family-level) with the assumption that this habitat (i) likely supports greater numbers of
arthropods due to higher availability of floral and nesting resources and the possibility that these
areas provide refuge against crop spraying, and (ii) that more nearby semi-natural habitats means
greater likelihood that these arthropods disperse into farms. To test this, I calculated the
landscape-level landcover surrounding the centroid of each of my 13 farms at four distances
(500, 1000, 1500, and 2000 m radii). The radii were chosen to capture likely dispersal distances
for less mobile (e.g., parasitoids) and highly mobile (e.g. flying predators) functional groups
(Schellhorn, Bianchi & Hsu, 2014). Landcover data were obtained from Agri-Canada annual
crop inventory (AAFC, 2017) and re-classified into aggregate percent agricultural landcover and
percent semi-natural landcover (see Appendix 5: Table S5). Specifically, agricultural landcover
contained the following specific land use types: corn, wheat, soybean, rye, ginseng, tobacco, fruit
and berry crops, orchard, vegetable, beans, oats, barley, fallow, vineyard, potato, peas. Semi-
natural land contained the following specific land use types: coniferous, broadleaved and mix
14
wood, grassland, shrub land, wetland. This analysis was conducted using ArcGIS version 10.3.1
(ESRI, 2014).
My local-scale hypothesis was that on-farm habitat type (crop, prairie, forest) would be
influencing arthropod abundance and richness (family-level). By studying the habitat use of
arthropods in agricultural landscapes, insights can be gained into how arthropod populations can
be regulated. To investigate local habitat use, I compared arthropod abundance and richness
(family-level) per cover type within farm (n = 39; 13 farms x 3 cover types).
Due to size differences among cover types, there was a risk that I over-sampled smaller
areas while under-sampling larger ones. I thus evaluated arthropod sampling effectiveness by
constructing sample-based rarefaction curves using the specaccum function in the vegan package
of R (Oksanen et al., 2016). Accumulation curves (Appendix 3; Fig. S6) tended towards
saturation and sampling was thus deemed sufficient for comparisons among cover types. Corn,
soy, and wheat were pooled to create the crop cover type as they represent the most common
rotated conventional cropping system in Southern Ontario. Further, arthropod abundance (F =
0.23, p = 0.798) and richness (family-level) (F = 0.55, p = 0.592) did not differ between crop
type (Appendix 3: Fig. S7).
2.3.2 Within-prairie habitat factors
As mentioned, my analyses showed that the presence of local prairie habitat largely
explained most of my variation in arthropod occurrences. As such, I examined finer-scale
measures within the prairie for plant quantity, quality, and composition to investigate effects on
arthropod abundance and richness (family-level).
15
To do this, plant variables were sampled in July in 1m2 plots inside or adjacent to the 5 x
5 m quadrant where arthropods were sampled (see Appendix 3: Fig. S4). For this analysis,
arthropod abundance and richness (family-level) data were the value per plot from farms with
prairie cover (n = 90; 18 farms x 5 plots per prairie). The following variables were measured:
photosynthetically active radiation (PAR intercept – the ratio of light above the canopy versus
ground level with 1.0 being full light and 0 being full light interception), litter (% cover), bare
ground (% cover), and plant species composition (% cover). I measured photosynthetically active
solar radiation (PAR) by placing an LI-190SA quantum sensor coupled to an LI-250 Light Meter
(LI- COR) on the ground surface in the center of each plot and at the canopy surface. I took the
intercept between the two measurements representing the proportion of available light reaching
the ground. I allowed the readings to stabilize for 10 seconds before recording a measurement.
Litter, bare ground, and plant species composition were measured as the percent cover in each
plot. Plant species composition was used to calculate plant richness (species-level) and percent
cover of plant functional groups: C3 grasses, C4 grasses, and forbs (all in % cover). To the
southwest corner adjacent to the 5 x 5 m quadrant, in a 1 x 0.2 m plot, aboveground plant
biomass was cut using garden shears. Following this harvest, samples were dried in a 65°C oven
for 24 hours, weighed, then homogenized in a Wiley Mill using a 1 mm grinding plate and sent
for plant nutritional quality analyses by SGS-AgriFood labs in Guelph, Ontario (Appendix 5:
Table S6). Plant tissue quality variables included: carbon to nitrogen ratio (C:N), organic carbon,
phosphorous, actin-depolymerizing factors (ADF) proteins, and nitrogen. These variables were
chosen due to their influence on plant palatability to arthropod herbivores (see Appendix 4:
Table S6 for details). Similarly, light, bare ground cover, and litter can be important factors
16
affecting development, distribution and behavior of many insects (Michener, 2000; Schowalter,
2006).
2.4 Complementary analyses
Soybean farms were revisited in July to conduct leaf defoliation assessment, comparing
differences between farms that had prairie cover (n=7) to those without prairie cover (n=3). On
average, farms with prairie cover had the same sized crop fields as farms without prairie cover (t
= 2.23, p = 0.066). For this analysis, I was interested whether the occurrence of prairie was
associated with detectable differences in crop leaf damage ad arthropod abundance. I was also
interested whether distance from the edge of prairie altered arthropod distribution (i.e., more
herbivores closer to the prairie) and if this was true, if this affected leaf defoliation in the same
way (e.g., more towards the prairie). Several papers note that soybean yield is reduced when
defoliation occurs during bloom to pod formation (McWilliams et al., 1999; Pedersen, 2007;
Owen, 2013; Ohnesorg & Hunt, 2015) and therefore sampling in July was essential when
soybeans are in their reproductive stages (Pedersen et al., 2014). Measurement of crop leaf
damage occurred in 5 randomly placed 1 x 1 m plots in each crop field, varying distances from
edge to interior of field within dispersal capabilities of functional groups (randomly placed 1- 90
m from field edge). In each plot 20 leaves were randomly chosen from different plant heights
(top, middle, bottom) to assesses crop leaf damage by estimating leaf defoliation at 5 % intervals
(Ohnesorg & Hunt, 2015). Values of leaf defoliation from each plot were averaged resulting in a
mean leaf defoliation (%) value for each crop plot distance.
17
Finally, I assembled a database representing the “regional family pool” for Southern
Ontario using data extracted from iNaturalist, a community science program
(https://www.inaturalist.org/). A search query was created filtering family-level observations by
(i) place: Southern Ontario region, (10,000 km² region from non-farm locations such as
conservation areas, hiking trails, parks, forests; see Appendix 5, Fig. S10) (ii) taxon: arachnids
and insects, and (iii) date range: 2017-05-15 to 2017-07-04 (overlapping collection period for
farm sites). In total, 10,742 family-level observations were obtained from iNaturalist that fit this
search query and combined into a “regional family pool” list consisting of 194 families. Each
arthropod family was categorized into a functional group as discussed in the above arthropod
sampling methods. Observations from iNaturalist were compared to my 13 agricultural field
sites, with a total of 12,558 observations pooled into an “agricultural family pool” list consisting
of 183 families. The data from this analysis were only used as a comparison for my on-farm
arthropod sampling because community science data has the potential to over-emphasize
charismatic and easy-to-identify functional groups such as butterflies and under-emphasize
small-bodied arthropods such as parasitic wasps which are considered hyper-diverse in temperate
and tropical regions (Eagalle & Smith, 2017).
2.5 Statistical analysis
All analyses were conducted in Rstudio version 3.5.1 (Rstudio Team, 2015; R
Development Core Team, 2018). For all analyses, assumptions of normality and equal variance
were verified using residual plots, Shapiro-Wilk test of normality (Shapiro & Wilk, 1965), and
Bartlett’s test of equal variance (Snedecor & Cochran, 1989). Non-normal data were evaluated
18
using non-parametric tests. Significance of all factors was evaluated with Type II tests using the
Anova function in the car package (Fox & Weisberg, 2011).
2.5.1 Landscape and local factors of habitat on 13 agricultural sites
To test my hypothesis that distance among semi-natural habitat affects arthropod
abundance and richness (family-level), I performed linear models with the lm function with the
package “stats” (R Core Team, 2019). Fixed factors included land use (% agricultural and %
semi-natural) at each buffer radii. Given the high degree of multicollinearity between agricultural
and semi-natural landcover across the 4 buffer zones (Appendix 5: Fig. S8), each buffer zone
was analyzed separately.
To test my hypothesis that geographic distance among farms affects arthropod abundance
and richness (family-level), I performed Mantel tests using the mantel function in the package
“ape” (Paradis & Schliep, 2018) to assess spatial autocorrelation of measured geographic
location of farms and arthropod community data (n=13; summed response value per farm)
(Peres-Neto & Legendre, 2010). This test evaluates the similarity between two matrices: one
measuring ecological distance (e.g. differences in arthropod family richness or abundance) and
one as geographical distance (Legendre & Legendre, 2012). I used Monte Carlo permutations
with 999 randomizations to test for significance (Oksanen, 2014).
To test my hypothesis that cover type affects arthropod abundance and richness (family-
level), I performed a linear mixed model using the lmer function in the “lme4” package (Bates et
al., 2015). Cover type was used as a fixed effect and farm site was treated as a random effect to
account for site specific differences. Here, arthropod abundance and richness (family-level) was
19
aggregated by cover type within farm (n = 39; 13 farms x 3 covers). When there was a
significant effect of cover type, Tukey’s post-hoc tests were run using the emmeans call to
evaluate the response variable against the cover types while accounting for the random effect of
farm identity. I evaluated total arthropod responses as well as responses belonging to the four
focal functional groups in this manner.
2.5.2 Within-prairie habitat factors
To test my hypothesis that plant-based resources affect arthropod abundance and richness
(family-level), I used generalized linear mixed-effect models with the glmer.nb function, as my
data were over-dispersed, in the ‘MASS’ package in R (Venables & Ripley, 2002). Fixed effects
included the following plant-based resource variables relating to plant quantity, quality, and
composition: total plant biomass, PAR intercept, litter % cover, bare ground % cover, C:N,
phosphorous, ADF, organic carbon, nitrogen, C3 grasses, C4 grasses, forbs, and plant richness.
For this analysis arthropods collected from pan traps were used where plant data was also
collected (n = 90, 18 farms x 5 plots in prairie cover). Since plants indirectly provide prey
resources for predator groups, an additional predictor variable of herbivore abundance or
herbivore richness was used in predator or parasitoid models (whether testing abundance or
richness data, respectively).
In all models farm site was used as a random effect. I ran separate models for arthropod
abundance and richness (family-level) for the total arthropod community (all functional groups
pooled) and my four focal functional groups. Predictor variables used in my models were not
found to have high collinearity (r >0.06) and therefore no variables were removed (Appendix 5:
Fig. S9).
20
2.5.3 CA1: Arthropod community composition among cover types
To study differences in arthropod community composition (including taxa of all
functional groups) among cover type (crop, prairie, forest), I calculated the Bray-Curtis
dissimilarity index (Bray & Curtis, 1957) for each farm cover type. The Bray-Curtis dissimilarity
index is a version of the Sørensen index (Magurran, 2004) modified to include abundance. I
visualized the dissimilarity in reduced dimensionality using non-metric multi-dimensional
scaling (NMDS) using the R package “vegan” (Oksanen et al., 2013). A Monte Carlo
randomization test was performed on the final stress value to calculate goodness of. I tested the
significance of cover type with a permutation test using the adonis function in “vegan” (Oksanen
et al., 2018).
2.5.4 CA2: Assessment of arthropod-derived ecosystem service mediated by habitat
To test my hypothesis that the occurrence of prairie on a farm will affect response
variables leaf defoliation and arthropod abundance, I compared responses between farms with
prairie (n=7) to farms without prairie (n=3). For these analyses, means were calculated from crop
cover plots from each farm (prairie present n= 35 (5 plots x 7 farms), prairie absent n= 15 (5
plots x 3 farms). I used a Welch Two-Sample T-test that assumes unequal variances to account
for our unequal sampling design. I used the function t.test in the package “stats” with the
argument var.equal=FALSE to assume unequal variance (R Core Team, 2019). Analyses were
conducted separately for each response using fixed effect “presence” (with prairie absent or
prairie present as a categorical variable).
21
To test my hypothesis that the occurrence of prairie on a farm will affect arthropod
dispersal (of my four focal functional groups) and leaf defoliation from edges to interior of crop
fields, I analyzed the same crop plot data in the previous analysis with distance as a fixed effect.
First, I analyzed whether the abundance of my focal functional groups (herbivores, predators,
parasitoids, pollinators) differed with plot distance (1-90 m from a crop field edge) using
generalized linear mixed models with the glmer.nb function using farm as a random effect. This
statistical analysis was appropriate to deal with my unbalanced sample sites and my over
dispersed data. Second, I analyzed whether leaf defoliation differed with plot distance using the
same statistical analysis.
2.5.5 CA3: Crop type differences for arthropod abundance and richness (family-level)
I used a Kruskal-Wallis test to investigate if on-farm crop type affects arthropod
abundance and richness (family-level). A Kruskal-Wallis test is a non-parametric alternative to a
one-way ANOVA test and used in the situation where there are more than two groups being
compared. Here, my response variable was the summed arthropod abundance or richness
(family-level) in each crop type per farm. Separate analyses were run for the total arthropod
community, then for herbivore, predator, parasitoid, and pollinator functional groups. This
analysis was performed using the kruskal.test function in the package “stats” implemented in R
(R Core Team, 2019).
2.5.6 CA4: Regional family pool comparison
Since the number of families per site (non-farm iNaturalist vs farm field sites) was pooled
I could not use statistical analyses to compare the two sites. Rather, I broadly compared the two
22
sites using visual bar plots and compared differences between total number of families and
between functional groups in each site. Further, I described family differences between the two
sites in descriptive tables and whether families were found only in farms sites, only in non-farm
iNaturalist sites, or shared between sites.
3. Results
I tested how scale-dependent drivers of habitat affect arthropod abundance and richness
(family-level) in agricultural landscapes of Southern Ontario (see Appendix 1: Fig. S1). My
results show that arthropods responded to cover type at the farm level. The main habitat driver
was prairie grassland cover, which produced two times greater arthropod abundance than crop
fields or forests. Within prairie strips, plant composition was the primary factor at the plot-level.
Crop fields were generally depauperate while forests tended to have widely different arthropod
communities presumably due to habitat structure and microhabitat conditions that differed
significantly from open prairie and crop areas. In terms of service provision, the addition of
prairie adjacent to a crop field did not increase leaf defoliation, despite prairies having higher
herbivore abundances. Crop type was found to be an important factor influencing arthropod
groups, with herbivore groups preferring mixed organic crop type. Regional factors relating to
spatial distance were not key and, surprisingly, my comparison with iNaturalist data from
surrounding non-farm sites had similar arthropod richness (family-level) as agricultural sites
although with some differences in arthropod functional groups.
Over the 44-day sampling period a total of 27,080 arthropods were caught from 22 farms
with 223 families. Of these totals, 18,209 individuals belonged to one of the four focal functional
23
groups, consisting of 70 families of herbivores, 64 families of predators, 33 families of
parasitoids, and 17 families of pollinators. These key functional groups accounted for 82 % of
arthropod abundance and 82 % among the richness total numbers collected (Fig. 1A). Of my
remaining five functional groups, 25 families consisted of anthophilous groups, 9 families of
decomposer, 4 families of aquatic, 4 families of kleptoparasite, and 16 families of uncommon
groups. The most abundant families caught (> 1000 individuals) were: Chloropidae (Order:
Diptera), Cicadellidae (Order: Hemiptera), Anthomyiidae (Order: Diptera), and Chrysomelidae
(Order: Coleoptera), which were all categorized into the herbivore functional group. Summary of
arthropod abundance and richness data for all farm sites can be found in Appendix 5 (Table S7,
Table S8, Fig. S110).
My landscape and local analysis focused on 13 farms, of which, 14,175 arthropods were
collected from 183 families. Of these totals, 11,525 individuals belonged to one of the four focal
functional groups, consisting of 52 families of herbivores, 52 families of predators, 24 families
of parasitoids, and 10 families of pollinators. These key functional groups accounted for 81 % of
arthropod abundance and 80 % richness among the total numbers collected (Fig. 1B). Of my
remaining five functional groups, 23 families consisted of anthophilous groups, 9 families of
decomposer, 4 families of aquatic, 3 families of kleptoparasite, and 6 families of uncommon
groups.
3.1 Landscape and local factors of habitat on 13 agricultural sites
Percentage semi-natural landcover at the landscape-scale was not a major driver of
arthropod abundance or richness (family-level) at any buffer radius (Table 1). Similarly, percent
24
agricultural land use at the landscape-scale did not show any significant effects, with one
exception. At the 500 m buffer radius, parasitoid richness was highest with low agricultural
landcover, and declined with higher agricultural landcover (F = 7.12, p = 0.022, Fig. 2).
Although farm distance ranged from less than 1 km to greater than 60 km apart, farms
closer to each other were not more similar in total arthropod abundance or richness than farms
farther away (abundance: r = 0.086, p = 0.152; richness: r = 0.072, p = 0.146). This result was
true for the abundance and richness responses of each focal functional group (Table 2).
Basically, the same collections of arthropods tended to be represented at all farms.
Cover type had a significant influence on arthropod abundance and richness (abundance:
X2 = 19.56, p < 0.001; richness: X2 = 41.84, p < 0.001; Fig. 3). More specifically, prairie had
greater arthropod abundance than either the crop or forest covers (p = 0.002 and p = 0.003,
respectively) but there was no significant difference between overall arthropod abundance
between the crop and forest cover type (p = 0.949). For arthropod richness, crop cover type had
the lowest richness, while prairies and forests had greater richness than crops (p < 0.01 for both),
and forest and prairie cover were marginally significant (p = 0.054).
Abundance and richness of the four focal functional groups also varied significantly
between cover types (Table 3, Fig. 4). In all instances, there was greater abundance and richness
in the prairies as compared to the crop fields, while differences between forest-type covers
versus both prairies and crop fields depended on the functional group. For example, herbivore
abundance was not significantly different between crop or forest covers (Fig. 4A) but herbivore
richness was significantly different between all cover types (Fig. 4B).
25
3.2 Within-prairie habitat factors
My previous results indicate that within-farm prairie cover type is a large driver of
arthropod abundance and richness, but I further investigated how plant resources at a finer scale
(plot-level) influenced the arthropod community. By measuring plant resources of cover
quantity, quality, and plant composition, I found that plant composition was a more important
driver of arthropod abundance and richness, however, responses varied by functional group
(abundance: Table 4, richness: Table 5).
The abundance of the total arthropod community was positively associated to increasing
forb percent cover (Z = 2.22, p = 0.026) and greater solar radiation at ground level (PAR) (Z =
2.09, p = 0.036). Herbivore abundance was positively associated with percent cover of all plant
functional groups (C3 grass, forb, and C4 grass). In fact, only the herbivore functional group
responded positively to greater C4 grass percent cover (Z= 2.14, p = 0.033), which is the plant
functional group most pronounced in prairie grassland habitat. Herbivore abundance also
increased with increasing PAR (Z = 1.98, p = 0.048), which means that more herbivores were
found where light was able to penetrate the canopy of prairie cover. Predator abundance was
similarly affected by the same predictor variables as the herbivore functional group, with the
exception they were not influenced by C4 grass percent cover and were positively influenced by
increasing bare ground percent cover (Z = 2.36, p = 0.018). The strongest predictor variable for
predators was the abundance of herbivores (Z = 3.95, p < 0.001). Similarly, parasitoid
abundance, being natural enemies of herbivores, was also positively influenced with greater
herbivore abundance (Z = 2.99, p = 0.003). Additionally, parasitoid abundance was positively
influenced by litter percent cover (Z = 2.16, p = 0.031). Pollinator functional groups were
26
negatively associated with C3 grass percent cover (Z = -2.67, p = 0.008) and bare ground percent
cover (Z = -2.15, p = 0.032). All other variables, including plant tissue quality and quantity
(plant biomass), did not significantly influence arthropod functional group abundance.
Just as with arthropod abundance, richness of the total arthropod community was
positively associated with increasing forb percent cover (Z = 3.00, p = 0.003) and increasing
solar radiation at ground level (PAR) (Z = 2.80, p = 0.005). Total arthropod richness additionally
showed positive association to C3 grass percent cover (Z = 2.47, p = 0.014), and litter percent
cover (Z = 2.20, p = 0.028), variables which did not significantly affect total arthropod
abundance. Herbivore richness was only positively associated with one variable, which was an
increasing amount of PAR (Z = 2.25, p = 0.011), or the amount of light penetrating to ground
level. Predator richness was positively associated with all predictor variables in the abundance
model, apart from PAR. Parasitoid richness was positively associated with both the increasing
percent cover of C3 grasses (Z=2.10, p=0.036), bare ground (Z = 2.00, p = 0.045) and most
strongly by herbivore richness (Z = 3.22, p = 0.001). Lastly, pollinator richness showed no
significant positive or negative relationships to any predictor variable – similar numbers of
families were trapped in all parts of the prairie.
Even though prairie grasslands sampled ranged from age 1 to 10 years and size from
0.485 to 23.37 acres, I found no strong effect of age or size on arthropod abundance and richness
for any functional group (see Table 6). On the contrary, predator abundance and predator
richness showed a marginally significant response to prairie age (abundance: F=3.75, p=0.054;
richness: F=3.63, p=0.074). Moreover, herbivore abundance and richness showed a marginally
significant response to prairie size (abundance: F=3.34, p=0.086; richness: F=3.90, p=0.060).
27
3.3 CA1: Arthropod community composition among cover types
The NMDS analysis indicated that dissimilarity in arthropod community composition
was related to the cover type within a farm (k = 3, stress = 0.16, R² = 0.13, p = 0.001, Fig. 5). As
a rule of thumb, values of stress <0.2 are traditionally used to indicate a good representation of
data in multi-dimensional space (Kenkel & Orloci, 1993). Non-overlapping ellipses (represented
by 95 % CI) indicate that the community composition is different between crop, prairie and
forest cover. However, the closer distance between crop cover and prairie cover indicate that
they are more similar in terms of arthropod communities than forest or crop cover, presumably
due to plant structure or resources.
3.4 CA2: Assessment of arthropod-derived ecosystem service mediated by habitat
While average leaf defoliation was lower on the seven farms which had restored prairie
adjacent to crop fields as compared to the three farms without restored prairie (10.8 % ± 1.5 %
and 7.8 % ± 1.3 %, respectively), the difference was not significant (t = 1.53, p = 0.133, Fig. 6).
However, prairie presence influenced herbivore abundance (t = -2.23, p = 0.03) and pollinator
abundance (t = -2.39, p = 0.02) in the crop field, but had no effect on other functional groups or
arthropod richness (Fig. 7). Relatedly, prairie presence resulted in distance-decay effects on
herbivore distribution (χ2 = 4.52, p = 0.033) and pollinator distribution (X2 = 3.82, p = 0.050),
where abundance was greater near prairie borders and decreased further into the crop interior
(Fig. 8). Even though herbivore abundance was more concentrated near prairie borders, crop leaf
damage was not significantly different from crop edge or interior plots sampled (X2 = 0.549, p =
0.458, Fig. 9).
28
3.5 CA3: Crop type differences for arthropod abundance and richness
Crop type had a significant effect on total arthropod abundance (Kruskal-Wallis X2 =
22.54, p < 0.001) and total arthropod richness (Kruskal-Wallis X2 = 32.97, p < 0.001). All focal
functional groups (herbivore, predator, parasitoid, pollinator) had the greatest abundance and
richness in the mixed organic crop type (Fig. 10A, Fig. 10B). This response was due to the
disproportionately large number of herbivores caught which accounted for 50 % of the
abundance caught and 32% of the richness caught. In other crop types herbivore abundance
accounted for 16-38 % of the total arthropod abundance and 22-39 % of the richness. In terms of
percent total caught, the apple orchard had the greatest percentage of beneficial group
abundance, with 6.15 % parasitoid, 21.54 % pollinator and 36.92 % predator. Similarly, apple
orchards had the greatest percentage of beneficial group richness, with 7.32 % parasitoid, 24.39
% pollinator and 29.27 % predator. In fact, apple orchards had the least percentage of herbivore
abundance (22 %) and herbivore richness (16 %).
3.6 CA4: Regional family pool comparison
I found that the number of arthropod families on the farms I sampled (183) was similar to
the number of families present regionally on non-farm sites (193) (Fig. 11). Nevertheless, this
broad assessment between the family pool of arthropods in each site did show some interesting
patterns. My results show that agricultural sites had more families of herbivores, more families
of parasitoid wasps, less families of predators, and similar number of families of pollinators
when compared to non-farm (iNaturalist) sites (Fig. 11). A total of 89 families were shared
between non-farm (iNaturalist) sites and agricultural sites, while 94 families were found unique
29
to agricultural sites and 104 families were found unique to non-farm (iNaturalist) sites (Fig. 12;
see Appendix 5: Table S9 for details of families).
There were key differences in which functional groups were absent from non-farm
(iNaturalist) sites and agricultural sites. Non-farm (iNaturalist) areas over-emphasized groups
such as aquatic arthropods and under-emphasized small-bodied arthropods such as parasitic
wasps. A total of 27 aquatic arthropod families were found in non-farm sites with only 5 aquatic
families found in agricultural sites (Fig. 11). Of these 27 families found in non-farm sites, there
were 25 uniquely found in non-farm sites and one family shared between the two sites
(Hydropsychidae (order: Trichoptera)). Only 8 parasitoid families were found in non-farm sites
compared to 24 in farm sites, with 6 families shared between the two sites (Cynipidae,
Eulophidae, Eurytomidae, Ichneumonidae, Perilampidae, Platygastridae (all order:
Hympenoptera)). Not only did farm sites have a greater number of parasitoid families, but also a
greater number of herbivore families (Fig. 12). A total of 52 herbivore families were found in
farm sites, and of this total, 27 were unique to farm sites.
4. Discussion
My study presents a detailed analysis of arthropod abundance and richness in an
agricultural landscape of Southern Ontario. I tested two scales of habitat: landscape based on the
isolation of suitable habitat and local based on farm cover type (crop, prairie, forest). I also
examined what factors of prairie made this cover so important, testing overall plant species
composition, and more specific resource-based factors of tissue quantity (standing biomass) and
tissue quality (e.g., % N in foliage). Additionally, I tested how cover type affects community
30
composition, whether the addition of diverse prairie habitat affects crop damage (observable leaf
defoliation), how plant crop type affects arthropod communities, and whether regional
differences in family richness between farm and non-farm areas were apparent. These tests were
derived from intensively surveying 22 farms across a 10,000 km2 study area, sampling 27,080
arthropod individuals from 223 families. My key finding is that local habitat cover type, rather
than landscape-level distance-based factors, dictated arthropod abundance and richness in
agricultural landscapes. More specifically, it was the presence of high-quality resource-rich local
prairie that explained most of my findings, even though this habitat sometimes covered relatively
small percentages of my farms. I found that plant composition mattered most, especially the
percent cover of forbs and C3 grasses. In terms of potential service benefits against leaf
defoliation, I found no positive or negative effects. Prairie cover had a significant positive impact
for arthropod biodiversity including supporting larger numbers of herbivorous insects, many of
which were crop pests. However, these large herbivore populations did not increase leaf
defoliation in adjacent fields, possibly because beneficial predators were also highly abundant in
restored prairies. Indeed, the richness and abundance of predators and parasitoids were strongly
positively correlated with these same measures for herbivores. Lastly, I found that the family-
level richness of arthropods in agricultural sites versus non-farm sites were generally similar,
suggesting that even conventional farms can support arthropod biodiversity especially if high-
quality habitat is present.
My landscape scale analysis largely suggested that “everything is everywhere”, in terms
of family-level arthropod occurrences among farms. The only exception was parasitoid richness,
which decreased as percent agricultural landcover increased. This result was only found at the
lowest buffer radii of 500 m and is not surprising given that this arthropod group is heavily
31
dependent on woody nesting material and host density which can be limited at this small spatial
scale (Chaplin-Kramer et al., 2011; Inclan, Cerretti, & Marini, 2015). The lack of an effect of
landscape for other arthropod groups may be surprising, given that 92% of my study region
supported intensive farming. One possible explanation could be some form of “extinction debt”,
where remnant populations are ‘legacy populations’ from a time when these areas supported far
more suitable habitat. If true, then the large numbers of arthropod groups that I detected could be
doomed to disappear. This hypothesis is impossible to test with my data. However, given the vast
levels of habitat loss in this system combined with the short generation times of arthropods –
non-viable species would be likely to disappear relatively quickly. It might not have been
surprising that I would have observed my farms to be relatively depauperate, based on increasing
predictions of an “insect apocalypse”, especially on agricultural landscapes (Sánchez-Bayo &
Wyckhuys, 2019). Indeed, land clearing in my region started as far back as the late 19th century
(McCune et al. 2017), with habitat loss to crop fields even higher than today. However, it seems
difficult to classify my farms as “depauperate”, given the large and diverse collection of
arthropod families that I observed with 183 in agricultural sites and 193 families in the non-farm
surrounding region (iNaturalist data).
Despite finding a similar number of pooled arthropod families on agricultural and non-
farm sites, I found differences in functional group composition, family-specific feeding mode
(generalist vs. specialist), and tolerance to agricultural areas. I found farm sites to have a greater
number of herbivore and parasitoid families, several which were unique to agricultural sites and
considered having specialist feeding modes. For example, the family Agromyzidae is a soybean
stem borer (Arnemann et al., 2016), Thripidae contains species of soybean and corn pests
(Godfrey et al., 2019), Psyllidae tend to be host-specific (Ouvrard, 2013), and many families of
32
parasitoid wasps are regarded as specialists (Gordh, Legner & Caltagirone, 1999). On one hand,
this was not surprising given that farm sites are likely to be more easily colonized by crop-
associated specialist arthropods (Jarvis, Padoch & Cooper, 2007). On the other hand, it was
surprising to find a greater number of parasitoid families occurring in agricultural sites given my
previous finding that parasitoid richness decreased with increasing agricultural landcover at the
500 m scale. It is important to note that for my [CA4] analysis I pooled arthropod families
between agricultural sites and therefore effects of surrounding landcover or farm-specific
differences could not be considered. Regardless of this fact, my iNaturalist comparisons do show
that certain arthropod functional groups are absent from agricultural sites – those that may be
less “tolerant” and with life history strategies less equipped to establish and persist in these areas.
This includes families associated with aquatic habitat, which are often not found near open farm
areas (e.g., Baetidae, Caenidae, Ephemerellidae (all order: Ephemeroptera)), and groups such as
butterflies (e.g., Geometridae, Hesperiidae, Noctuidae (order: Lepidoptera)). Both butterflies and
aquatic insects in the EPT community (orders: Ephemeroptera, Plecoptera and Trichoptera) are
considered important environmental indicators for their intolerance to pollutants (Hamid &
Rawi, 2017) and rapid responses to loss of suitable habitat (Fleishman & Murphy, 2009).
As stated, the number of arthropod families on my farms was comparable to the non-farm
areas. An alternative explanation to extinction debt is that my farms actually contain sufficient
levels of habitat, that are adequately connected regionally such that many arthropod groups are
being maintained. Indeed, an increasing occurrence of semi-natural habitats in farmland is likely
to benefit biodiversity in all but the most heavily cleared landscapes (Tscharntke et al., 2004).
My agricultural sites were unique in that many possessed restored resource-rich tall-grass prairie
which may be supporting arthropod populations while serving as stepping stones that increase
33
dispersal among farms, within and possibly across generations. Again, this does not preclude the
possibility that the less tolerant arthropod communities have already been displaced regionally,
with the remaining subset being those that ‘can make a living’ within heavily farmed regions.
The population viability and dispersal dynamics of the families I detected remain to be
determined – my data are but a one-time snapshot of richness and abundance. However, my
findings suggest that many arthropod groups appear to be persisting through time and, given the
high degree of overlap among farms, may also be dispersing to some degree.
One potentially powerful factor thought to regulate arthropod communities in agricultural
landscapes is management including pesticides, whose effects were not directly tested by this
study (e.g., fully comparing conventional vs organic farms, comparing farm-level insect diversity
before and after spraying). However, I did make two relevant observations. First, all the
monoculture crop fields of corn, soy, and wheat were generally lacking large numbers of
arthropods. These results are consistent with other studies on farm management practices,
showing overall negative influences on arthropod abundance and richness (Weibull & Ostman,
2003; Ma et al. 2019). Second, although I only surveyed one organic farm, it supported the most
arthropods of any non-prairie cover type, mostly via large populations of herbivores. It is
impossible to know the exact cause of these patterns with my data, but this response could
potentially be due to chemical spraying. This would have to be from the year prior (chemical
residue) as no chemicals were applied before or during my sampling, or through dispersal from
adjoining crop fields (chemical drift). Alternatively, this large population response could reflect
the presence of high-quality habitat within the organic fields not present in other conventional
managed cultivated crops. It has been demonstrated that organic cropping systems generally
support greater levels of biodiversity, although this does not often translate into better provision
34
of ecosystem services because herbivores can be abundant (as I observed) and crop damage high
(Macfadyen et al., 2009). A clearer understanding of how farm management regulates
arthropods, especially regarding issues of habitat loss versus chemical spraying and their
impacts on crop damage remains an important research direction in agriculture (Larsen, Patton &
Martin, 2019).
Regardless of the causal factors driving low arthropod occurrences in crop fields, my
work clearly shows the significant positive responses of arthropods to the construction of
resource-rich prairie habitat on conventional farms, even if these areas occupy a relatively small
percentage of the total farm area. These prairies had the highest levels of both richness and
abundance, including many families of beneficial predators and parasitoids that prey on
herbivores and crop pests. This contrasts with cultivated crop habitats, which contained a low
abundance and richness of beneficial arthropods (predators, parasitoids, pollinators), a response
that has been shown in other studies (Pfiffner & Luka, 2003; Koh & Holland, 2014).
There were interesting differences as well, between restored prairie versus forests, with
the latter of interest given the recent work from European farm landscapes (Hallmann et al.,
2017). I found that prairies contained two times more abundance of arthropods than forest. My
complementary analysis [CA1] showed that, in addition to differences in abundance, these two
cover types had different arthropod community compositions, presumably related to vegetation
structure of forest vs open herbaceous grassland (Prevedello &Vieira, 2010). For example, the
permanent shade and high levels of nesting habitat in forest tends to support arthropod families
that are less common or even absent in the more open habitats of prairie and crop fields, where
desiccation risk in summer for arthropods can be high (Heinrich, 1996).
35
The addition of prairie as a strategy to combat arthropod biodiversity loss has not been
widely studied, other than for pollinators groups (e.g., Hopwood, 2008; Hormon-Threatt &
Hendric, 2014; Paterson, 2014; Tonietto, Ascher & Larkin, 2017; Paterson et al., 2019). Rather,
the common goal of prairie addition is to re-establish this formerly widespread plant community
while providing a range of ecosystem services usually centered on plant biodiversity soil carbon
storage (Wang, Vandenbygaart & Mcconkey, 2014). Clearly, however, my work shows that
these areas can support large arthropod communities. One additional question I sought to test
was why this occurs, focusing on four resource-based mechanisms that may attract arthropods to
prairie: the high standing biomass of restored prairie (tissue quantity), the potential for prairie
plants to provide high quality food resources especially for herbivores (tissue quality), factors
relating to habitat structure, and micro-environment such as PAR, bare ground, and litter; or
whether arthropods respond more to the presence of certain plant functional groups (plant
composition). My results clearly support the latter, while also suggesting an influence of habitat
structure. Generally, as the percentage of C3 grasses increased, so did the abundance of
functional groups herbivore, predator and pollinators. An increasing percentage of forb cover
increased the total arthropod community, as well herbivore and predator groups, but surprisingly
was not influential for pollinator or parasitoid groups. Previous research on the assemblage of
parasitoids and pollinators has shown that these groups are more sensitive to species identity
within plant functional groups such as the presence of different species of forbs (Thomson et al.,
2010; Perdikis et al., 2011). The plant species that occurred within my prairie sites were most
often generalists with a variety of nectar and pollen resources, not tailored to a specific
functional group such as pollinators or parasitoids. Further investigation is needed on nectar and
36
pollen quality within prairies and how certain functional groups such as native pollinators may
respond.
For habitat structure, there was a positive effect of PAR (light) on increasing abundance
of the total arthropod community, herbivores and predators. Additionally, my predictor variable
bare ground showed significant effects for beneficial functional groups. I found that an
increasing bare ground percent cover increased predator abundance, but decreased pollinator
abundance, and increased richness of predator and parasitoid groups. Higher PAR likely results
in changes to microclimate conditions, as more light can reach the soil surfaces. This contrast in
abundance responses between predators and pollinators could be attributed to the fact that
pollinators (e.g., ground nesters) may not prefer nesting sites exposed to full sunlight or predator
visibility. While predator groups could prefer an increasing amount of light at soil surface to
improve prey searching ability (Letourneau et al., 2009). Overall, these results suggest that PAR
and bare ground cover should be considered when monitoring prairie restoration efforts to target
the needs of beneficial functional groups.
Relatedly, I found strong indirect influences of plants on my arthropod groups.
Specifically, plant composition had strong effects on herbivores that, in turn, increased predatory
richness and abundance. These so-called “trophic dependencies” where prey affects predators via
plants has been described before (Harvey & MacDougall, 2014). Alternative prey via plants can
be important in attracting and retaining beneficial predatory groups which can sustain biological
control in crop fields. Previous work by Gravesen and Toft (1987) showed that grasslands
provided alternative prey to predators which increased populations to the point that aphid
populations were reduced in the adjacent field crops. Previous work has also shown that about
37
50% of herbivore species living in prairie grassland habitats were monophagous or oligophagous
(live only on a single host plant or a non-crop plant) and thus are not at risk of damaging
cultivated plants but can serve as alternative prey for predator and parasitoids (Lethmayer, 1995;
Hausammann, 1996).
Despite this, an important consideration to growers is the implications the addition of
semi-natural cover (e.g., prairie) has on crop fields. My work revealed two important effects
prairie cover has on adjacent crop fields: (1) the distribution of functional groups was altered due
to prairie grassland borders and (2) farms with prairie grasslands planted adjacent to crop fields
did not incur greater crop damage despite more on-farm herbivore abundance. Results showed
that farms with prairie present had the highest abundance of all focal functional groups
(herbivores, predators, parasitoids, pollinators) at the edges of crop fields closest to the prairie
border. Farms without prairie present had the same abundance of all arthropod groups, including
herbivores, at the edges and interior of crops. This arthropod movement between cultivated to
uncultivated habitat can be related to natural dispersal of arthropods, lack of suitable food, host
alteration, or major disturbance such as the use of pesticides (Gurr et al., 2017). Boetzl et al.
(2019) demonstrated that the proportion of multiple ground-dwelling predators declined from the
field edge and this was attributed to the fact that semi-natural vegetation strips, which provided
more suitable habitat and food resources, altered arthropod dispersal. Therefore, a strategic
placement of prairie cover adjacent to crop fields has the potential to reduce herbivore spill-over
into crop fields. This link between prairie cover and reduced herbivore distribution in the crop
field with no increase in crop leaf defoliation should encourage farmers to include more habitat
refuge areas.
38
Despite the striking results of my study, it is worth considering two limitations. First, my
one-year arthropod sampling misses known temporal variation. Populations of arthropods can
fluctuate strongly from year to year (Andrewartha & Birch, 1954; Stange et al., 2011) and
species turnover in habitat patches is high (Summerville et al., 2007). This highlights the
importance of long-term monitoring (Haaland et al., 2011) versus the single-season work of this
study. Indeed, my work has served as the basis for a long-term monitoring program set up by
BIO on my ALUS farms, using barcoding to sample arthropods every two weeks over multiple
years (Fryxell, Betini, personal communication). This should provide a clearer view on temporal
changes in arthropods, whether it be inter-annual climate differences, differing levels of prairie
availability (larger prairie cover could provide temporal stability) or the impacts of the timing of
pesticide applications vs the timing of insect phenology within and among years. Second, my
arthropod field sampling methods have limitations to what functional groups I could collect. Pan
traps and sweep netting occurred during the day, therefore omitting collection of important
nocturnal arthropods such as robber flies or moths (Wickramasinghe et al., 2004). Future
sampling should incorporate pitfall traps or malaise traps that will enable this nocturnal
arthropod capture. Establishing a sampling protocol that is cost-effective and adequate in
collecting whole arthropod communities in agricultural landscapes would be highly beneficial
for long-term monitoring.
Regardless of these limitations, my study represents one of the most comprehensive
arthropod surveys conducted in an agricultural landscape in Southern Ontario. From my results, I
provide the following recommendations: (i) land managers should acknowledge that “semi-
natural” areas are not all equal. For example, prairie grasslands produced different community
composition and produced two-times more arthropod abundance than forest covers, indicating it
39
may be a faster management strategy to foster arthropod biodiversity, (ii) prairie monitoring
efforts should consider the influence of plant composition, light penetration (related to plant
structure), and bare ground cover, which all showed strong effects on beneficial arthropod
groups (predators, parasitoids, pollinators), and (iii) prairie grasslands should be planted adjacent
to crop fields to reduce herbivore abundance in crop fields, but farmers should refrain from
spraying near crop edges to reduce detrimental effects to beneficial groups.
5. Conclusion
Overall, my findings revealed that distance-based constraints at the landscape scale were
not detected, but rather on-farm cover type prairie grassland was the driving factor for arthropod
abundance and richness. Local habitat addition on conventional farms is much more important
than previously thought in conserving biodiversity and requires a management perspective and
policy regulations to match.
With 40% of the world’s total land area dedicated to agriculture (Matson et al., 1997;
Ramankutty & Foley, 1999) agroecosystems have the potential to conserve a large proportion of
arthropod biodiversity if high-quality habitat is present. Understanding how habitat-based factors
positively influence agriculturally important arthropod functional groups will be essential to
conserve arthropod-derived ecosystem services (Tilman et al., 2002; Foley et al., 2005; Scherr &
McNeely, 2008). Future research should focus on quantifying arthropod-derived ecosystem
services provided by habitat (i.e, pollination, nutrient recycling, decomposition) which will
provide better incentive for farmers and leverage policy to include more refuge areas on farms.
40
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66
LIST OF TABLES
Table 1: Results of linear models between percentage of landcover type of buffer radii (500,
1000, 1500, 2000 m) and total arthropod abundance and richness for 13 farms in Southern
Ontario, Canada.
Total Herbivore Predator Parasitoid Pollinator
Response Buffer
distance
(m)
F-
model
p -
value
F-
model
p -
value
F-
model
p -
value
F-
model
p -
value
F-
model
p -
value
Abundance
% semi-natural
landcover
500 1.27 0.321 3.83 0.121 0.87 0.401 0.00 0.948 0.98 0.381
1000 1.94 0.235 2.09 0.221 0.07 0.796 0.69 0.450 0.17 0.696
1500 1.18 0.337 1.19 0.335 0.20 0.675 1.27 0.321 0.05 0.822
2000 0.89 0.398 0.79 0.422 0.36 0.579 1.94 0.235 0.18 0.688
% agricultural
landcover
500 0.87 0.403 3.11 0.152 1.09 0.353 0.00 0.989 1.10 0.352
1000 2.18 0.213 2.63 0.179 0.15 0.710 0.99 0.374 0.14 0.719
1500 0.00 0.988 0.00 0.947 0.53 0.505 3.15 0.150 0.10 0.761
2000 1.05 0.361 0.86 0.403 0.24 0.645 1.72 0.259 0.17 0.696
Richness
% semi-natural
landcover
500 0.03 0.869 0.12 0.746 0.33 0.592 0.11 0.757 0.12 0.739
1000 0.42 0.548 0.01 0.921 0.29 0.618 0.19 0.678 0.00 0.950
1500 0.15 0.711 0.14 0.726 0.26 0.636 0.17 0.698 0.01 0.911
2000 1.19 0.336 0.73 0.440 0.04 0.843 0.00 0.970 0.55 0.498
% agricultural
landcover
500 0.00 0.988 0.06 0.814 0.72 0.442 9.21 0.01 0.04 0.836
1000 0.21 0.670 0.09 0.778 0.34 0.588 0.13 0.729 0.01 0.905
1500 0.31 0.605 0.94 0.385 0.03 0.863 0.01 0.901 0.50 0.517
2000 1.14 0.345 0.49 0.521 0.09 0.769 0.00 0.959 0.49 0.519
Observations 13 13 13 13 13
67
Table 2: Mantel test r-values (Pearson’s product-moment correlation) for the correlation
between similarity matrices for functional group abundance and richness (family-level) in
conventional farms and similarity matrices for spatial connectivity of farms.
Total Herbivore Predator Parasitoid Pollinator
Response r p r p r p r p r p
Abundance 0.067 0.242 -0.025 0.455 -0.144 0.853 -
0.104
0.609 -0.029 0.451
Richness
(family-level)
0.095 0.283 -0.000 0.275 -0.073 0.500 0.045 0.222 0.149 0.182
Observations 13 13 13 13 13
68
Table 3: Summary of F- and p- values from analysis of arthropod abundance and family-level
richness across four focal functional groups in response to cover type. Significant effects (p ≤
0.05) in bold.
Functional
Group
Abundance Richness (family-level)
F - value p - value F - value p - value
Total 19.56 <0.001 41.84 <0.001
Herbivore 26.11 <0.001 17.70 <0.001
Predator 26.98 <0.001 59.43 <0.001
Parasitoid 6.92 0.031 15.30 <0.001
Pollinator 50.87 <0.001 44.86 <0.001
69
Table 4: Results of Generalized Linear Mixed Models with negative binomial function of field
observations evaluating the effect of plant-related variables on arthropod abundance.
Total
Abundance
Herbivore
Abundance
Predator
Abundance
Parasitoid
Abundance
Pollinator
Abundance
Predictors Z-
statistic
p-
value
Z-
statistic
p-
value
Z-
statistic
p-
value
Z-
statistic
p-
value
Z-
statistic
p-
value
(Intercept) 2.34 0.019 -0.81 0.418 -2.11 0.035 -1.73 0.084 2.68 0.007
C3 % cover 1.80 0.072 3.34 0.001 3.06 0.002 1.59 0.113 -2.67 0.008
C4 % cover 1.01 0.314 2.14 0.033 0.30 0.766 -1.00 0.316 -1.66 0.097
Forb % cover 2.22 0.026 2.91 0.004 3.35 0.001 0.55 0.584 -0.85 0.396
Richness 0.18 0.857 -0.86 0.388 1.82 0.068 0.96 0.337 0.27 0.784
C:N 0.21 0.836 -0.42 0.674 1.39 0.166 1.40 0.161 0.57 0.569
Phosphorous -0.39 0.695 1.08 0.280 -1.96 0.051 0.22 0.824 -1.20 0.229
ADF -0.76 0.448 1.26 0.208 1.79 0.074 -1.22 0.222 -1.48 0.140
Organic
carbon
0.84 0.403 1.14 0.254 -0.91 0.362 -1.06 0.289 0.44 0.657
Nitrogen -0.66 0.507 -0.49 0.622 -0.32 0.751 0.15 0.885 -0.39 0.695
Biomass -0.63 0.530 0.32 0.748 -0.31 0.758 1.48 0.140 -1.59 0.113
Litter 1.41 0.159 1.13 0.259 1.87 0.062 2.16 0.031 0.50 0.615
Bare ground 0.93 0.354 1.91 0.056 2.36 0.018 1.46 0.146 -2.15 0.032
PAR 2.09 0.036 1.98 0.048 2.06 0.039 -0.10 0.918 0.72 0.470
Herbivore - - - - 3.95 <0.001 2.99 0.003 - -
Random Effects
σ2 0.32 0.42 0.49 1.01 0.63
τ00 0.22 Lot 0.63 Lot 0.57 Lot 0.64 Lot 0.17 Lot
ICC 0.41 Lot 0.60 Lot 0.54 Lot 0.39 Lot 0.21 Lot
Observations 90 90 90 90 90
Marginal R2 /
Conditional R2
0.165 / 0.503 0.165 / 0.667 0.517 / 0.776 0.394 / 0.629 0.267 / 0.423
70
Table 5: Results of Generalized Linear Mixed Models with negative binomial function of field
observations evaluating the effect of plant-related variables on arthropod family-level richness.
Total
Richness
Herbivore
Richness
Predator
Richness
Parasitoid
Richness
Pollinator
Richness
Predictors Z-
statistic
p-
value
Z-
statistic
p-
value
Z-
statistic
p-
value
Z-
statistic
p-
value
Z-
statistic
p-
value
(Intercept) 3.49 <0.001 0.44 0.660 -2.27 0.023 -2.03 0.043 2.05 0.040
C3 % cover 2.47 0.014 1.94 0.053 2.69 0.007 2.10 0.036 -1.16 0.248
C4 % cover 0.87 0.383 0.48 0.634 0.37 0.712 -0.28 0.782 -1.10 0.269
Forb % cover 3.00 0.003 1.29 0.196 2.75 0.006 0.48 0.632 0.70 0.487
Richness -0.49 0.622 -0.51 0.608 0.94 0.346 0.76 0.447 -1.60 0.109
C:N 0.92 0.359 0.89 0.373 0.15 0.884 -0.87 0.386 0.57 0.568
Phosphorous -0.64 0.522 -0.47 0.637 -1.48 0.138 -0.18 0.857 -0.35 0.725
ADF 0.08 0.935 0.57 0.568 1.21 0.228 0.60 0.548 0.00 0.996
Organic carbon -1.90 0.058 -1.57 0.116 -0.42 0.677 -1.04 0.300 -0.89 0.374
Nitrogen -0.11 0.910 0.58 0.560 -0.17 0.868 -0.19 0.852 -0.57 0.570
Biomass -0.35 0.723 -0.84 0.402 -0.04 0.967 1.54 0.124 -1.30 0.193
Litter 2.20 0.028 1.39 0.164 1.39 0.164 0.65 0.517 0.85 0.396
Bare ground 1.16 0.246 -0.25 0.801 2.17 0.030 2.00 0.045 -1.07 0.284
PAR 2.80 0.005 2.53 0.011 1.83 0.067 0.17 0.863 0.89 0.376
Herbivore - - - - 2.02 0.044 3.22 0.001 - -
Random Effects
σ2 0.12 0.31 0.53 1.06 0.50
τ00 0.09 Lot 0.03 Lot 0.19 Lot 0.01 Lot 0.01 Lot
ICC 0.44 Lot 0.09 Lot 0.26 Lot 0.01 Lot 0.02 Lot
Observations 90 90 90 90 90
Marginal R2 /
Conditional R2
0.332 / 0.628 0.290 / 0.357 0.546 / 0.664 0.334 / 0.459 0.215 / 0.233
71
Table 6: Results from ANOVA summary statistics analyzing factors prairie size and prairie age
for arthropod abundance and richness (family-level) response.
Prairie size Prairie age
Response F-value p - value F-value p - value
Abundance
Total 3.44 0.082 0.855 0.367
Herbivore 3.34 0.086 0.155 0.698
Predator 0.19 0.662 3.75 0.054
Parasitoid 0.08 0.780 1.88 0.189
Pollinator 1.05 0.319 0.62 0.439
Richness
(family-level)
Total 1.14 0.299 1.80 0.197
Herbivore 3.90 0.06 0.92 0.349
Predator 0.02 0.902 3.63 0.074
Parasitoid 0.36 0.554 0.003 0.957
Pollinator 0.04 0.842 3.06 0.098
72
LIST OF FIGURES
Figure 1: Total arthropod abundance and richness (family-level) sampled across (A) 22 total
farms and (B) 13 farms as they relate to 9 key functional groups. The focal functional groups are
given in colour (herbivores = purple, predators = yellow, parasitoids = blue, pollinators = green)
while all other functional groups are in grey. Bars are annotated to show the fraction of the total
arthropod community comprised by each functional group when the fraction was > 0.1.
0.17
0.08
0.04
0.13
0.56
0.04
0.040.02
0.08
0.09
0.13
0.25
0.33
0.00
0.25
0.50
0.75
1.00
Abundance Richness
Fra
ctio
n o
f C
om
mu
nity
FunctionalGroup
herbivore
predator
parasitoid
pollinator
anthophilous
kleptoparasite
aquatic
decomposer
uncommon
A) 22 total farm profile B) 13 “main” farms
0.17
0.08
0.04
0.13
0.56
0.04
0.040.02
0.08
0.09
0.13
0.25
0.33
0.00
0.25
0.50
0.75
1.00
Abundance Richness
Fra
ctio
n o
f C
om
mu
nity
FunctionalGroup
herbivore
predator
parasitoid
pollinator
anthophilous
kleptoparasite
aquatic
decomposer
uncommon
73
Figure 2: Parasitoid richness (family-level) as related to percent agricultural land use at a buffer
radius of 500 m.
5
10
15
20
20 40 60 80
Agricultural land coverwithin 500 m buffer (%)
Pa
rasitoid
ric
hn
ess
74
Figure 3: (A) Abundance (B) and richness (family-level) of arthropods across three different
cover types (crop = green, prairie = yellow, forest = brown). Error bars show ± 1 SE, and lower-
case letters indicate significance at p<0.050 as determined by post-hoc tests.
Ric
hn
ess
Abu
nda
nce
Crop Prairie Forest Crop Prairie Forest
0
20
40
60
0
200
400
600
Cover type
a
b
a
a
b
b
A. B.
(fam
ily-l
evel
)
75
Figure 4: (A) Abundance (B) and richness (family-level) of arthropods belonging to the 4 focal
functional groups across three different cover types (crop = green, prairie = yellow, forest =
brown). Error bars show ± 1 SE, and lower-case letters indicate significance at p<0.050 as
determined by post-hoc tests.
1
2
Ric
hne
ss
Abun
dan
ce
herb
ivor
e
para
sitoid
pollina
tor
pred
ator
herb
ivor
e
para
sitoid
pollina
tor
pred
ator
0
5
10
15
20
0
100
200
300
400
Functional Group
Covertype
Crop
Prairie
Forest
A. B.
a
b
a
a b ab a
ba a
b bc
a
b
ab
a
b
a a
b
c
a
b ab
(fam
ily-l
evel
)
76
Figure 5: Ordination of arthropod community composition using non-metric multidimensional
scaling (NMDS) showing variability between cover types on farm sites (n=13). Farm sites near
each other have a community composition that is more similar than farms that are separated by
greater distance. Ellipses represent 95% CI around the centroid.
77
Figure 6: Average soybean leaf defoliation for farms which had prairie adjacent to the crop field
(n=7) and those without adjacent prairie (n=3). Error bars show ± 1 SE.
0.0
2.5
5.0
7.5
10.0
12.5
Prairie absent Prairie present
Le
af
de
folia
tio
n (
%)
78
A B
C
A D
A
Figure 7: Relationship between abundance and richness of arthropod functional groups in
farms with prairie present (n=7) and farms with prairie absent (n=3). (A) Mean abundance of
arthropod functional groups in prairie absent farms. (B) Mean abundance of arthropod
functional groups in prairie present farms. (C) Mean richness (family-level) of arthropod
functional groups in prairie absent farms. (D) Mean richness (family-level) of arthropod
functional groups in prairie present
79
Figure 8: Relationship between abundance of arthropod functional groups in crop fields and
distance from edge from farms with (A) farms with prairie present adjacent to crop (n=7) and (B)
farms with no prairie present (n=3).
A B
80
Figure 9: Relationship between leaf defoliation and distance from edge on farms with prairie
present adjacent to crop (n=7) and farms with no prairie present (n=3).
A B
81
Figure 10: (A) Mean abundance of arthropod functional groups for different crop types. (B)
Mean richness (family-level) of arthropod functional groups for different crop types.
A
B
N=5 N=10
N=13
N=2
N=13
N=1
N=2
N=13
N=1
N=2
N=13
N=1
N=2
N=13
N=5
N=10
N=13
N=2
N=13
N=1
N=2
N=13
N=1
N=2
N=13
N=1
N=2
N=13
(organic)
(fam
ily-leve
l)
(organic)
82
Figure 11: Data from non-farm iNaturalist observations in Southern Ontario compared to 13
agricultural field sites. Each bar in the graph represents the summed count of total arthropod
families in each dataset and segments in the graph represent the functional group composition of
that total.
83
Figure 12: Number of arthropod families unique or shared from data of non-farm iNaturalist
observations in Southern Ontario compared to 13 agricultural field sites. Each bar in the graph
represents the summed count of total arthropod families in each dataset and segments in the
graph represent the functional group composition of that total.
Nu
mb
er
of fa
mili
es (
sh
are
d o
r u
niq
ue)
84
Appendix 1: Conceptual figures
Figure S1: Hierarchical framework of habitat-based drivers of arthropod abundance is shown.
Habitat occurs at multiple scales: landscape distance-based and local farm cover based. A
specialized plot-level analysis was conducted to explain my local-scale prairie cover which
consisted of plant-related factors of plant quantity, plant tissue quality, and plant composition.
85
Appendix 2: Site selection and description
Table S1: List of study sites and cover type variables. Farms located in Southern Ontario, field
research taken from May – July 2017. Annual mean temperature and annual mean precipitation
values are taken from WorldClim 1.4: current conditions data at 30 arc-seconds (~1 km).
Farm Location Prairie
size
(acre)
Forest
size
(acre)
Cover
type
Crop
size
(acre)
Julian
date
Sampling
date
temp.
(℃)
Annual
mean
temp.
(℃)
Annual
mean
prec.
(mm)
1 Waterford - 7.55 Corn 31.94 158 15 7.75 950
2 Waterford 12.3 0.58 Soybean 20.51 149 20 7.71 950
3 Langton 1.00 6.89 Soybean 23.81 135 15 8.02 961
4 Straffordville
e 0.51 0.069 Soybean 1.8 143 15 8.09 930
5 Straffordville 1.08 0.995 Wheat 25.45 144 16 8.09 930
6 Norfolk 5.47 0.87 Corn 29.29 138 24 7.67 951
7 Simcoe 14.21 1.72 Wheat 12.34 150 18 7.71 951
8 Simcoe 9.9 2.67 Grasslan
ds
- 137 20 7.83 949
9 Delhi 2.50 1.97 Soybean 15.68 153 15 7.72 951
10 Norwich 3.91 0.61 Soybean 37.61 163 26 7.78 949
11 Delhi 7.9 1.683 Corn 13.92 152 15 7.54 946
12 Port Dover 4.87 2.6 Soybean 10.12 146 16 7.71 947
13 Dunwich - 2.65 Soybean 30.08 170 20 7.76 960
14 Dunwich - 2.19 Soybean 40.25 170 20 7.76 950
15 Aylmer 23.37 1.95 Corn 14.39 185 19 7.67 951
16 Aylmer 13.21 3.25 Corn 34.41 179 17 7.97 959
17 Eden - 1.68 Soybean 30.88 180 22 8.12 951
18 Tillsonburg 15.9 100 Sorghu
m
15.04 172 17 7.97 954
19 Simcoe 20.49 6.22 Grasslan
ds
- 159 16 7.66 947
20 Rodney 3.20 1.91 Soybean 30.96 171 18 7.8 943
21 Tillsonburg 4.27 0.85 Apple
orchard
11.37 165 18 5.85 922
22 Hillsburgh 3.84 0.96 Organic
mixed
5.7 167 20 8.45 915
86
Table S2: Total plant species and associated functional groupings found within prairie cover.
Common name Family Genus Species Functional group
redtop Poaceae Agrostis Agrostis gigantea C3 grass
smooth brome Poaceae Bromus Bromus inermis C3 grass
drooping brome Poaceae Bromus Bromus tectorum C3 grass
orchard grass Poaceae Dactylis Dactylis glomerata C3 grass
couch grass Poaceae Elymus Elymus repens C3 grass
Timothy-grass Poaceae Phleum Phleum pratense C3 grass
annual bluegrass Poaceae Poa Poa annua C3 grass
Kentucky
bluegrass Poaceae Poa Poa pratensis C3 grass
Indian grass Poaceae Sorghastrum Sorghastrum nutans C4 grass
big bluestem Poaceae Andropogon Andropogon gerardii C4 grass
hairy crabgrass Poaceae Digitaria Digitaria sanguinalis C4 grass
ryegrass Poaceae Lolium Lolium spp. C4 grass
switchgrass Poaceae Panicum Panicum virgatum C4 grass
common yarrow Asteraceae Achillea Achillea millefolium Forb
redroot pigweed Amaranthaceae Amaranthaceae Amaranthus retroflexus Forb
common ragweed Asteraceae Ambrosia Ambrosia artemisiifolia Forb
swamp milkweed Apocynaceae Asclepias Asclepias incarnata Forb
hoary alyssum Brassicaceae Berteroa Berteroa incana Forb
mouse-ear
chickweed Caryophyllaceae Caryophyllaceae Cerastium vulgatum Forb
creeping thistle Asteraceae Cirsium Cirsium arvense Forb
pigweed Amaranthaceae Chenopodium Chenopodium album Forb
bird's-foot trefoil Apiaceae Daucus Daucus carota Forb
showy tick trefoil Fabaceae Desmodium Desmodium canadense Forb
common horsetail Equisetum Equisetum Equisetum arvense Forb
fleabane Asteraceae Erigeron Erigeron annuus Forb
Canadian
horseweed Asteraceae Erigeron Erigeron canadensis Forb
Philadelphia
fleabane Asteraceae Erigeron Erigeron philadelphicus Forb
wild strawberry Rosaceae Fragaria Fragaria vesca Forb
rough oxeye Asteraceae Heliopsis Heliopsis helianthoides Forb
meadow
hawkweed Asteraceae Hieracium Hieracium pratense Forb
87
honeysuckle Caprifoliaceae Lonicera Lonicera spp. Forb
bird's-foot trefoil Fabaceae Lotus Lotus corniculatus Forb
black medick Fabaceae Medicago Medicago lupulina Forb
sweet clover Fabaceae Melilotus Melilotus alba Forb
common mallow Malvaceae Malva Malva neglecta Forb
wild bergamot Lamiaceae Monarda Monarda fistulosa Forb
narrow leaf
plantain Plantaginaceae Plantago Plantago lanceolata Forb
broadleaf plantain Plantaginaceae Plantago Plantago major Forb
Virginia knotweed Polygonaceae Persicaria Persicaria virginiana Forb
common
cinquefoil Rosaceae Potentilla Potentilla simplex Forb
common bracken Dennstaedtiaceae Pteridium Pteridium spp. Forb
wild radish Brassicaceae Raphanus Raphanus raphanistrum Forb
black-eyed Susan Asteraceae Rudbeckia Rudbeckia hirta Forb
trailing pearlwort Caryophyllaceae Sagina Sagina decumbens Forb
green foxtail Poaceae Setaria Setaria viridis Forb
bladder campion Caryophyllaceae Silene Silene vulgaris Forb
Canadian
goldenrod Asteraceae Solidago Solidago canadensis Forb
chickweed Caryophyllaceae Stellaria Stellaria media Forb
white heath aster Asteraceae Symphyotrichum
Symphyotrichum
ericoides Forb
New England aster Asteraceae Symphyotrichum
Symphyotrichum novae-
angliae Forb
purple-stemmed
aster Asteraceae Symphyotrichum
Symphyotrichum
puniceum Forb
common dandelion Asteraceae Taraxacum Taraxacum officinale Forb
red clover Fabaceae Trifolium Trifolium pratense Forb
great mullein Crophulariaceae Verbascum Verbascum thapsus Forb
cow vetch Fabaceae Vicia Vicia cracca Forb
riverbank grape Vitaceae Vitis Vitis riparia Forb
field pansy Violaceae Viola Viola arvensis Forb
88
Figure S2: Location of the 22 study sites within the Southern Ontario, Canada region. Farms
strictly used in my main analysis are coloured in green. Farms used in complementary analyses
include farms coloured in black.
Hillsburgh
Elgin County
Norfolk County
Prairie
89
Figure S3: Monthly temperature and precipitation data from historical records (in red) and
within my sampling year (in blue) during sampling months May-July 2017.
90
Appendix 3: Sampling methods
Figure S4: Quadrant sampling design for arthropod and plant measurements. All sampling
occurred from May-July 2017 on farms located in Southern Ontario, Canada.
91
Figure S5: Conceptual figure of sampling design used. For the majority of cases, a 5x 5 grid
would be overlaid onto the satellite image of each farm cover (crop, prairie, wood). In cases
where covers were unsymmetrical or where a 5 x 5 grid could not be easily overlaid, instead 25
grid squares that spanned the whole cover were overlaid. In all cases, five sampling locations
were chosen using a random number generator.
92
Figure S6: Sample-based rarefaction curves of arthropod richness were drawn three different
cover types (prairie, wood, crop). Solid lines represent the estimated number of species for each
cover and the shaded area between the lines indicates the estimated 95% confidence interval.
Ric
hness (
fam
ily-level)
93
Figure S7: (A) Mean abundance (± SE) of arthropod community per cover type and (B) Mean
richness (family-level) (± SE) of arthropod community per cover type. Bars with the same letter
are not significantly different (ANOVA, followed by comparisons between cover type using
Tukey’s HSD test, α = 0.05).
A
B
A
bu
nd
an
ce
Ric
hne
ss (
fam
ily-le
vel)
94
Appendix 4: Functional group categorization and definitions
Table S3: Definitions of arthropod functional groups.
Functional groups Definition
Primary groups
Herbivore plant feeder, economically important pests lumped here because all pests
are herbivores but not all herbivores are pests
Predator beneficial arthropod; eats other arthropods
Parasitoid beneficial arthropod; larvae are parasites that kill their hosts
Pollinator beneficial arthropod; bee and non-bee that are effective pollinators
Secondary groups
Anthophilous
flower visiting, or flower eating but are not as effective in spreading
pollen to gametes of flowers
Aquatic has an aquatic life cycle stage to become an adult; usually non-feeding
adult stage.
Decomposer
decomposes organic material; often found in soil habitats or within
ground
Kleptoparasite arthropod that robs other species of food, not a beneficial arthropod
Uncommon less than three total abundance found on farm; or unresolved feeding guild
95
Table S4: Functional group categorization and literature search.
Order/Family Functional
group
Feeding description References
Tabanidae anthophilous Often visits flowers. Both males and females
collect nectar from flowers females later
switch to blood meals after mating.
Larson & Inouye,
2001; Pratt & Pratt,
1972
Lonchopteridae anthophilous Adults feed on nectar and are associated with
flower visitation.
Larson & Inouye,
2001; Proctor, Yeo &
Lack, 1996; Klymko &
Marshall, 2008
Mycetophilidae anthophilous The small gnat-like adults occasionally visit
flowers with exposed nectaries, but they are
not important pollinators.
Marhsall, 2006; Larson
& Inouye, 2001
Phalacridae anthophilous Larvae feed on fluid material within flower
heads of Asteraceae and pollen feeders as
adults.
Gimmel, 2013;
Marhsall, 2006
Ceratopogonidae anthophilous Feed on flower nectar or other sugar source.
Females and males commonly visit flowers
with exposed nectar, All males feed only on
sugars.
Larson & Inouye,
2001; Bystrak, 1978;
Kearns, 2001
Chironomidae anthophilous Adults are short-lived but are ranging from
opportunistic nectar and pollen consumers.
Larson & Inouye,
2001; Kevan & Baker,
1983; Kearns, 2001
Mordellidae anthophilous Common on flowers and foliage. Adults feed
on pollen and nectar from a variety of plants.
Ford & Jackson, 1996;
Dillon, 1961
Phoridae anthophilous Of the Cyclorrapha, recent reviews indicate
that they are flower visiting.
Larson & Inouye,
2001; Disney, 1983
Tipulidae anthophilous Long-beaked members can be found
abundantly on flowers of some Lamiaceae in
Ontario.
Larson & Inouye,
2001; Marhsall, 2006
Bibionidae anthophilous Observed early in the year on flowers
because mating swarms often forage on
nearby flowering plants for sustenance. Their
effectiveness needs to be quantified.
Larson & Inouye,
2001; Fitzgerald, 2005;
D'arcy-Burt &
Blackshaw, 1991;
Kearns, 2001
Scatopsidae anthophilous Observed on flowers with exposed nectar or
pollen.
Proctor, Yeo & Lack,
1996; Speight, 1978
Conopidae anthophilous Often regarded as flower visitors, adults take
nectar.
Proctor, Yeo & Lack,
1996; Proctor & Yeo,
1973
Culicidae anthophilous The abundance of these common flower
visitors may compensate for their low
individual effectiveness; mosquitoes often
visit flowers at night.
Downes, 1958;
Stockholm & Price,
1962; Hocking, 1968;
Kevan, 1970; Kearns,
2001
96
Simuliidae anthophilous Observed on flowers with exposed nectar,
such as umbellifers (Apiacaea).
Davies & Peterson
1956; Burgin & Hunter
1997
Calliphoridae anthophilous Common visitors to flowers of Asclepias spp.
(Asclepiadaceae) Well known as anthophiles
and nectarophages.
Punchihewa, 1984;
Ambrose et al., 1985;
Beaman, Decker &
Beaman 1988
Formicidae anthophilous . Ants are commonly anthophilous (flower
loving), but rarely pollinate the plants that
they visit.
Gullan & Cranston,
2014
Muscidae anthophilous Muscidae are often considered the most
significant anthophiles after syrphids.
Pont, 1993; Speight,
1978; Proctor, Yeo &
Lack, 1996; Larson &
Inouye, 2001
Sarcophaginae anthophilous Flesh flies (Sarcophagidae) and dung flies
(Scathophagidae) are occasionally reported as
anthophilous. Some sarcophagids and
scathophagids may be pollinators, but their
true roles have not been well elucidated.
Larson & Inouye, 2001
Sepsidae anthophilous Feed on nectar at umbels of Apiaceae but are
not likely pollinators.
Larson & Inouye,
2001; Marshall, 2006
Sphaeroceridae anthophilous Found in trap flowers and the spathes of
Araceae; sparse records of anthophily.
Yafuso 1993; Proctor,
Yeo & Lack, 1996
Sarcophagidae anthophilous Flesh flies (Sarcophagidae) and dung flies
(Scathophagidae) are occasionally reported as
anthophilous but may be more prevalent than
presently thought. Some sarcophagids and
scathophagids may be pollinators, but their
true roles have not been well elucidated.
Larson & Inouye,
2001; Kevan, 1970;
Proctor, Yeo & Lack,
1996
Stratiomyidae anthophilous Adults have been recorded from the flowers
of 22 plant families: they were observed in
low numbers but often on the flowers.
Knuth, 1908; Ambrose
et al., 1985
Cerambycidae anthophilous Many adults (especially the brightly coloured
ones) feed on flowers. Many of the flower-
feeding cerambycids pollinate their food
plants as they feed on pollen and nectar.
Wang, 2017;
Hawkeswood &
Turner, 2007
Phlaeothripidae anthophilous Includes many common species, commonly
found on daisy flowers.
Marshall, 2006
Gasteruptiidae anthophilous Larvae feed on stored pollen or prey of host's
nest (usually bees or wasps), adults feed on
nectar of primarily carrot family (Apiaceae).
Marshall, 2006
Plecoptera aquatic Nymphs feed on algae, diatoms, mosses, and
immature aquatic invertebrates, including
mayflies and midges; most spring and
summer adults do not feed, and are nocturnal;
White & Borror, 1970
97
winter stoneflies are day-flying, and feed on
blue-green algae and foliage.
Ephemeroptera aquatic Adults have no functional mouthparts and do
not feed.
Arnett, 2000
Trichoptera aquatic Some adults take liquid food, such as nectar,
others do not feed.
Holzenthal et al., 2015;
Marshall, 2006
Ephydridae aquatic Larvae of most spp. filter microorganisms
(bacteria, unicellular algae, yeasts) from the
surrounding semiliquid medium. Associated
with semi aquatic or aquatic habitats. Only 3
instances of this family were found.
Arnett, 2000
Hydropsychidae aquatic non-feeding adult stage Holzenthal et al., 2015
Nemouridae aquatic see Pecoptera. non -feeding adult stage. White & Borror ,1970
Brachyptera aquatic see Plecoptera. non -feeding adult stage. White & Borror ,1970
Entomobryidae decomposer Springtails are "decomposers" that thrive
mostly on decaying organic matter, especially
vegetable matter.
Marshall, 2006;
Hopkin, 1997
Sminthuridae decomposer Springtails are "decomposers" that thrive
mostly on decaying organic matter, especially
vegetable matter.
Marshall, 2006;
Hopkin, 1997
Dryomyzidae decomposer North American Dryomizids are stickly
microbioal grazers in decomposing material
such as rotting fungus and dead invertebrates.
Mathis & Rung, 2011;
Marshall, 2006
Tetratomidae decomposer mostly fruiting bodies of hymenomycete
fungi, esp. Polyporaceae and
Tricholomataceae; adults usually on the
surface, larvae bore inside.
Arnett et al., 2002
Fanniidae decomposer Larvae live as scavengers in various kinds of
decaying organic matter.
Marshall, 2006
Lauxaniidae decomposer Associated with mining dead or decaying
leaves. Their habitat includes forests and
forests.
Marshall, 2006
Leiodidae decomposer Their habitat includes litter, various decayed
matter, vertebrate and ant nests/burrows.
Most common encountered beetle in this
family is attracted to dead bodies.
Marshall, 2006
Lonchaeidae decomposer The larvae of most Lonchaeidae are invaders
in diseased or injured plants. Known to aid in
decomposition.
Savely, 1939
Trichoceridae decomposer Larvae occur in moist habitats where they
feed on decaying vegetable matter. They are
of no economic importance.
Pratt, 2003
Berytidae herbivore Most are phytophagous, many may be host-
specific, often associated with plants with
glandular trichomes in Geraniaceae,
Arnett 2000
98
Onagraceae, Scrophulariaceae, and
Solanaceae.
Heteronemiidae herbivore All Phasmids feed on foliage, and possess
chewing mouthparts designed for breaking
down plant material.
Chad 2006
Membracidae herbivore Most species are host-specific and feed on
trees and shrubs, some on herbaceous plants.
Only a few species are considered pests; most
of the damage is caused by egg-laying.
Arnett, 2000
Nitidulidae herbivore They feed mainly on decaying vegetable
matter, over-ripe fruit, and sap.
Downie & Arnett,
1996
Buprestidae herbivore The larvae burrow through roots and logs,
from within the bark to within the cambium
layers, or are leaf and stem miners of
herbaceous and woody plants, including
grasses.
Jackson & Jewiss-
Gaines, 2012; Donald
& Bright, 1987
Issidae herbivore Herbivore, sapsucking, but not seen as pest
on crops
Arnett, 2000
Languriini herbivore Observed as herbivorous (unlike other
erotylid lineages).
Ernett et al., 2002
Lygaeidae herbivore Mostly seeds, although some species are
considered crop pests.
Arnett, 2000
Pergidae herbivore Food plant diversity exceeds that found in
any other sawfly family. No observations of
Pergidae consuming crop plants.
Schmidt & Smith,
2016
Psyllidae herbivore Associated almost exclusively with
dicotyledons, with a few species developing
on monocots and on conifers. Known pest of
tomato and potatoes and occasionally serious
pest on citrus.
Hill, 2008; Ouvrard,
2013
Rhopalidae herbivore Seeds of herbaceous plants, but some are
arboreal.
MacGavin, 1999;
Johnson & Triplehorn,
2004
Rhyparochromidae herbivore Eat seeds, formerly treated as subfamily of
Lygaeidae.
Arnett, 2000; Henry,
2009
Xyelidae herbivore Both adults and larvae feed mainly on
staminate pine flowers, buds or developing
cones, although some are known to feed on
deciduous trees.
Marshall, 2006
Alydidae herbivore Herbaceous plants and seeds, primarily that
of legumes (Fabaceae).
Marshall, 2006
Agridae herbivore Feeds on wide range of trees and shrubs,
including poison ivy by some species.
Marshall, 2006
Siricidae herbivore Larvae feed on fungi inside the decaying tree
trunk, adults feed only on nectar and water
Marshall, 2006
99
Oedemeridae herbivore Adults feed on pollen of variety of plants,
such as Asteraceae, Poaceae, and Rosaceae.
Arnett, 2000
Largidae herbivore Feeds on plant seeds and sap. Marshall, 2006
Anobiidae herbivore Larvae feed inside the trunk of decaying oak,
beech, or pine trees, adults do not eat.
Marshall, 2006
Dictyopharidae herbivore Most adults feed on plant sap, nymphs of
some species are known to feed on fungi.
Marshall, 2006
Caterpillar
(unknown family)
herbivore Feeds on variety of trees, shrubs and
herbaceous plants, usually young foliage.
Marshall, 2006
Cymidae herbivore Feeds on the seeds of sedges (Cyperaceae),
rushes (Juncaceae), and occasionally grasses
(Poaceae).
Marshall, 2006
Oedemeridae herbivore Pollen feeders often found on flowers in early
spring.
Marshall, 2006
Passalidae herbivore Both adult and larvae feed on
microorganism-rich rotting wood to digest
cellulose.
Marshall, 2006
Cephidae herbivore Adults and larvae of most species feed on
tree or shrub foliage, adults of some species
feed on pollen and nectar.
Marshall, 2006
Cimbicidae herbivore Adults and larvae of most species feed on
tree or shrub foliage, adults of some species
feed on pollen and nectar.
Marshall, 2006
Thyreocoridae herbivore Feed on flowers and developing seeds. They
are exclusively herbivorous.
Marshall, 2006;
Matesco & Grazia,
2015
Taeniopterygidae herbivore Adults and nymphs are plant feeders. DeWalt 2013
Tingidae herbivore Feed mainly on leaves of trees and shrubs,
causing yellow spotting and sometimes
browning and death of the leaves. Known as
a sap-sucker with toxic saliva.
Johnson & Triplehorn,
2004; Hill, 2008
Acrididae herbivore Typically eat foliage of forbs, grasses. Some
species take a variety of plants, while others
are restricted to a few species of closely
related plants. Cited as major maize pest.
Hill, 2008
Agromyzidae herbivore Larvae are leaf miners, some are stem or seed
borers, cited as minor pest of soybean.
Hill, 2008
Anthomyiidae herbivore It is an important pest of germinating
soybeans and corn. It also attacks a wide
range of horticultural crops including beans,
peas, cucumber, melon, onion, pepper,
potato, and other vegetables.
Hill, 2008; Philip,
2015; Funderburk et
al., 1983
Aphididae herbivore Aphids suck juices from plants and may be
quite damaging. Cited as major pest of wheat
in North America, cited as major maize pest.
Hill, 2008; Philip,
2015
100
Cecidomyiidae herbivore Most are gall makers. Others feed on plants.
Cited as major pest of wheat (including
barley and oats) in Europe and North
America.
Hill, 2008
Cercopidae herbivore Grass-feeders (xylem). Cited as minor
agricultural pests of forage crops.
Hill, 2008
Chloropidae herbivore In most species, larvae feed on grass stems of
the families Poaceae, Cyperaceae, and
Typhaceae. Some are serious pests of cereals.
Hill, 2008
Cicadellidae herbivore Nymphs and adults feed on sap of above-
ground stems or leaves of plants; some
species are more host-specific than others,
cited as major pest of maize.
Hill, 2008
Cixiidae herbivore Nymphs subterranean, feeding on roots (and
possibly fungi). Many are presumed to be
polyphagous as adults, most often on woody
plants.
Hill, 2008
Coreidae herbivore All are plant feeders. Hill, 2008
Curculionidae herbivore Most larvae and adults occur and feed on all
parts of plants, and many species are
important pests because they chew holes in
fruits, nuts, and other parts of cultivated
plants, cited as major crop pest of wheat in
North America.
Hill, 2008; Philip,
2015
Delphacidae herbivore Mostly grass feeders, cited as minor pest of
wheat in North America.
Hill, 2008
Elateridae herbivore Adults usually eat plants. Larvae eat newly
planted seeds, roots, cited as minor pest of
wheat.
Hill, 2008
Gryllidae herbivore Cited as minor pest of maize. Hill, 2008
Meloidae herbivore Adults feed on leaves and flowers of several
families of plants, particularly Asteraceae,
Fabaceae, and Solanaceae.
Hill, 2008
Pentatomidae herbivore The majority are herbivorous, but members
of one subfamily (Asopinae) are predaceous
on other arthropods. Both adults and nymphs
of plant-feeding species may damage plants,
mostly by piercing the plant tissues and thus
opening a path for pathogens to enter the
plant, cited as wheat crop pest.
Hill, 2008
Scarabaeidae herbivore Adults take a variety of foods, some of these
are agricultural pests., cited as major crop
pest of wheat in North America.
Hill, 2008
Scathophagidae herbivore Larvae of Delininae are leaf miners in
Liliaceae, Orchidaceae, and Commelinaceae.
Hill, 2008
101
Larvae of Scathophaginae have been reported
as phytophagous.
Scutelleridae herbivore Though most jewel bugs do little harm to
crop plants, a few members of Scutelleridae
are considered major agricultural pests.
Hill, 2008
Tenthredinidae herbivore Most of the larvae feed on foliage, but a few
are leaf miners or stem borers, observed
study of crop plants and leaf feeding damage
found laminae sclerotized.
Hill, 2008
Tettigoniidae herbivore Most common form of leaf damage by
defoliating arthropods is by chewing insects
such as grasshoppers. Grasshoppers cited as
major maize pest.
Hill, 2008
Ullidiidae herbivore All three species studied here should be
considered primary pests that can render
unprotected sweet corn ears unmarketable.
Goyal et al., 2012
Drosophilidae herbivore A serious fruit fly pest of soft fruit and
berries.
AgriService BC, 2017
Miridae herbivore In Canada, the plant bug genus Lygus
(Heteroptera: Miridae) contains 27 native
species, of which 14 are recorded as
agricultural field pests.
Kelton, 1982; Maw et
al., 2000
Tephritidae herbivore Leaf mining fruit fly which attacks many
crops in Canada, both larval and adult stages
damages crops.
Philip, 2015; Hill,
2008
Tenebrionidae herbivore Cited a minor crop pest of wheat in North
America and Europe.
Hill, 2008
Opomyzidae herbivore Food includes grasses and cereal crops. Larva
bore shoots.
Arnett, 2000; Hill,
2008
Galerucinae herbivore Known as leaf skeletonizes. This family has
some serious agricultural pests, causing
damage directly by plant feeding or indirectly
by transmitting viruses.
Wilcox, 1965; Hill,
2008
Bruchidae herbivore Cited as minor pest of soybean. Hill, 2008
Psilidae herbivore Some species are serious crop pests. Hill, 2008
Acanthosomatidae herbivore All specimens were Brown Marmorated
Stink Bug, considered serious crop pest. All
adults are phytophagous.
Hoebeke & Carter,
2003
Lasiocampidae herbivore Family of tent caterpillar. Larvae feed on
foliage of many fruit crops in the Rosaceae
family, such as apple, pear, cherry, peach,
and plum.
Alford, 2016
Oedipodinae herbivore Most families of grasshoppers seen as general
pests. Noted as leaf chewers.
Hill, 2008
102
Lygaeidae herbivore Cited as minor pest of wheat in North
America.
Hill, 2008
Thripidae herbivore Plant feeding common thrips in this family
are frequent problems in greenhouses and are
considered pests, also because they carry and
spread disease.
Marshall, 2006
Chrysomelidae herbivore Chrysomelids are phytophagous and are
among the most diverse and conspicuous
arthropod families on plants. The adults feed
on living plant material, usually consuming
leaves or sometimes various flower parts
including pollen. Cited as minor crop pest of
wheat in North America.
Hill, 2008; Philip,
2015
Bostrichidae herbivore Most species attack wood, either living, or in
some cases, dead, including seasoned lumber.
Marshall, 2006
Miltogramminae klepto-
parasite
Kleptoparasites of Digger Wasps; the prey
may include arthropods from a variety of
families and orders.
Pape et al., 2012
Chrysididae kepto-
parasite
Some species are parasitoids and others
kleptoparasites. Either way the host larva
dies. All specimens found were from
subfamily Chrysidinae which are generally
kleptoparasites.
Xerces Society, 2014;
Goulet & Huber, 2001;
Wilson & Carril, 2015
Megachilidae klepto-
parasite
All species feed on nectar and pollen, but a
few are kleptoparasites, parasitic species do
not possess scopae and all specimens found
in this study had one present. The genera
found in this study were majority Osmia,
Megachile, and Hoplitis.
Goulson & Ebrary,
2010; O'Toole et al.,
1999; Mader et al.,
2011; Wilson & Carril,
2015
Tanaostigmatidae parasitoid They are almost exclusively phytophagous
arthropods, forming galls in plant stems,
leaves, or seeds.
Universal
Chalcidoidea
Database, 2017, 2017
Tiphiinae parasitoid Paralyze grubs of scarab beetles, lay an egg
on it and leave the grub for the young to eat.
Xerces Society, 2014;
Bethylidae parasitoid Parasites of coleopteran and lepidopteran
larvae.
Evans, 1978
Braconidae parasitoid Many life histories adapted to parasitizing
hosts as diverse as aphids, bark beetles, and
foliage-feeding caterpillars. Many species are
egg-larval parasitoids, laying eggs within
host eggs and then not developing until the
host is in the larval stage.
Xerces Society, 2014;
Goulet & Huber, 1993
Pipunculidae parasitoid Adults feed on honeydew secretions; larvae
mostly parasitize leafhoppers and
planthoppers.
Xerces Society, 2014;
Goulet & Huber, 1993
103
Tiphiidae parasitoid Parasitoids of subterranean beetle larvae
(especially of Scarabaeoidea, Tenebrionidae,
and Cicindelinae) and various bees/wasps
occurring in soil or rotten wood.
Xerces Society, 2014;
Goulet & Huber, 1993
Ichneumonidae parasitoid A great variety of hosts (mostly immature
stages) is used, though most species attack
only a few host types; some infest spiders and
other non-arthropod arthropods.
Xerces Society, 2014;
Goulet & Huber, 1993
Orussidae parasitoid Larvae are parasites of xylobiontic arthropod
larvae, primarily of Buprestidae, but also of
Cerambycidae, Xiphydriidae and Siricidae.
Goulet & Huber, 1993
Ripiphoridae parasitoid Lays eggs on families of Hymenoptera,
wood-boring beetle larvae.
Arnett et al., 2002
Eurytomidae parasitoid The Eurytomidae contains species which
exhibit a wide range of biologies, but the
majority seem to be endophytic, either as
phytophages or as parasitoids of
phytophagous arthropods.
Goulet & Huber, 1993;
Universal
Chalcidoidea
Database, 2017, 2017
Eupelmidae parasitoid Parasites on a wide range of species of
different orders (Lepidoptera, Hemiptera,
Hymenoptera, Coleoptera, Neuroptera,
Orthoptera).
Goulet & Huber, 1993;
Universal
Chalcidoidea
Database, 2017, 2017
Diapriidae parasitoid Typically, parasites of fungus gnats and other
dipterans.
Goulet & Huber, 1993
Eucoilidae parasitoid Parasitoids of fly pupa. Goulet & Huber, 1993
Figitidae parasitoid Mostly parasitoids of fly pupae. Goulet & Huber, 1993
Scelionidae parasitoid They generally attack the eggs of many
different types of arthropods, spiders,
butterflies. Many are important in biological
control.
Goulet & Huber, 1993
Chalcididae parasitoid Hosts: mostly Lepidoptera and Diptera,
though a few attack Hymenoptera,
Coleoptera or Neuroptera.
Goulet & Huber, 1993;
Universal
Chalcidoidea
Database, 2017, 2017
Aphelinidae parasitoid Majority are primary parasitoids on
Hemiptera, though other hosts are attacked,
and details of the life history can be variable
(e.g., some attack eggs, some attack pupae).
They are found throughout the world in
virtually all habitats and are extremely
important as biological control agents.
Goulet & Huber, 1993;
Universal
Chalcidoidea
Database, 2017, 2017
Encyrtidae parasitoid Parasites of an extremely wide range of
species. Some attack eggs, larvae, adults or
they are hyperparasitoids.
Goulet & Huber, 1993;
Grissell & Schauff,
1997; Universal
Chalcidoidea
Database, 2017
104
Eulophidae parasitoid The majority are primary parasitoids on a
huge range of arthropods at all stages of
development.
Goulet & Huber, 1993;
Universal
Chalcidoidea
Database, 2017
Mymaridae parasitoid Fairyflies are tiny wasps that are egg
parasitoids belonging to the Chalcidoidea,
and have a global distribution.
Goulet & Huber, 1993;
Yoshimoto, 1990;
Universal
Chalcidoidea
Database, 2017
Tetracampidae parasitoid They are parasitoids of phytophagous
arthropods, primarily flies.
Goulet & Huber, 1993;
Universal
Chalcidoidea
Database, 2017
Perilampidae parasitoid Most are hyperparasitoids. Some are primary
parasitoids of wood-boring beetle larvae.
Goulet & Huber, 1993;
Universal
Chalcidoidea
Database, 2017
Torymidae parasitoid Many are parasitoids on gall-forming
arthropods.
Goulet & Huber, 1993;
Universal
Chalcidoidea
Database, 2017
Platygastridae parasitoid They are generally idiobionts, attacking the
eggs of either beetles or Hemiptera.
Goulet & Huber, 1993
Scoliidae parasitoid These wasps’ larvae are parasitoids of beetle
larva, particularly scarab beetles, for
example, Japanese beetles.
Xerces Society, 2014
Proctotrupidae parasitoid Endoparasitoids of beetle larva (Coleoptera)
or less commonly fly larva (Mycetophilidae:
Diptera).
Goulet & Huber, 1993;
Masner, 1993
Megaspilidae parasitoid It is a poorly known group, though most are
believed to be parasitoids
Goulet & Huber, 1993;
Masner, 1993
Ormyridae parasitoid Parasitoids of gall-forming Hymenoptera or
possibly primary gall-formers. Worldwide
distribution
Goulet & Huber, 1993;
Gibson, 1993
Bombyliidae pollinator Adults take nectar/pollen. Larson & Inoyue 2001
Apidae pollinator Many are valuable pollinators in natural
habitats and for agricultural crops. The
genera found in this study are Apis (Apis
Melifera), Bombus, Ceratina which were
classified as pollinators and Nomada which
was classified as a kleptoparasite.
Goulson & Ebrary,
2010; O'Toole et al.,
1999; Mader et al.,
2011; Wilson & Carril,
2015
Colletidae pollinator These relatively hairless bees lack an external
structure to carry pollen and resemble wasps.
The common genus found in this study was
Hylaeus.
Goulson & Ebrary,
2010; O’Toole et al.,
1999; Mader et al.,
2011; Wilson & Carril,
2015
105
Papilionidae pollinator Adults of all species visit flowers for nectar. Marshall, 2006
Syrphidae pollinator Flower flies play a particularly important role
in pollination.
Rader et al. 2016;
Marshall, 2006; Xerces
Society, 2014
Andrenidae pollinator The Andrenidae (commonly known as
mining bees) are a large, nearly cosmopolitan
family of solitary, ground-nesting bees. This
family had genus Andrena.
Goulson & Ebrary,
2010; O’Toole et al.,
1999; Mader et al.,
2011; Wilson & Carril,
2015
Halictidae pollinator All species are pollen feeders and may be
important pollinators. The genus Sphecodes
is considered a kleptoparasite in this study.
Other genera found was Lasioglossum,
Halictus, Augochlora, Augochloropsis and
Agapostomon which were all considered
pollinators.
Goulson & Ebrary,
2010; O’Toole et al.,
1999; Mader et al.,
2011; Wilson & Carril,
2015
Melittidae pollinator Melittids are typically small to moderate-
sized bees, which are well known for their
specialist and oligolectic foraging habits.
Goulson & Ebrary,
2010; O’Toole et al.,
1999; Mader et al.,
2011; Wilson & Carril,
2015; Michener, 2000
Drepanidae pollinator Nectar feeding as adult and notes as effective
pollinators.
Marshall, 2006
Pieridae pollinator Most larva feed on leaves of Brassicaceae,
while some feed on Fabaceae.
Marshall, 2006
Nymphalidae pollinator Adults show wide range of feeding behavior;
some feed on nectar, sap fluid, or rotting
fruits.
Marshall, 2006
Erebidae pollinator Nectar feeding on common prairie plant
species including the Asteraceae family.
Marshall, 2006
Hesperidae pollinator Nectar feeding on common prairie plant
species including the Asteraceae family.
Marshall, 2006
Lycaenidae pollinator Adults of most species are avid flower
visitors and derive most of their nutrition in
this way.
Marshall, 2006
Empididae predator Adults have long piercing mouthparts that
they use for predaceous feeding. They feed
on other small flies, beetles, and moths.
Downes, 1969;
Marshall, 2006; Arnett,
2000
Phytoseiidae predator Feed on spider mites and small arthropods
such as thrips. They are important for
biocontrol
Xerces Society, 2014
Anthicidae predator Adult beetles are omnivorous, being known
to consume small arthropods, pollen, fungi,
and whatever else they can find. Some
species are of interest as biological control
Marshall, 2006
106
agents, as they can eat the eggs or larvae of
pests.
Anthocoridae predator All known to eat small arthropods. This
family is used for biocontrol purposes.
Xerces Society, 2014;
Horton, 2008
Asilidae predator Diurnal ambush predators, feed on variety of
arthropods depending on the size. Larger
species feed on beetles, wasps or dragonflies,
smaller species feed on springtails or other
small arthropods. Some feed preferentially on
honey bees.
Marshall, 2006
Carabidae predator Most adults rapidly pursue their prey (other
arthropods) at night. A few eat pollen,
berries, and seeds. Most larvae are predators.
Xerces Society, 2014
Chrysopidae predator Some adults are predators, others take liquids
such as honeydew and some feed on pollen.
Larvae are predatory on other arthropods,
especially aphids.
Xerces Society, 2014
Cleridae predator Larvae feeds on larvae of wood-burrowing
arthropods, adults feeds on adults of those
wood-burrowing arthropods.
Marshall, 2006
Coenagrionidae predator Both nymphs and adults are predators;
nymphs feed on small aquatic arthropods,
tadpoles and fish, while adults feed on flies,
aphids, mosquitoes.
Marshall, 2006
Crabronidae predator Feeds on variety of arthropods such as
aphids, bees, butterflies, moths, cicadas,
cockroaches, crickets, flies, and
grasshoppers. Adults hunt by stinging and
paralyzing, larvae feed on the prey captured
by the adults.
Marshall, 2006
Lampyridae predator They feed on snails, earthworms, and larvae
of other arthropods. Some may consume
nectar or pollen.
Xerces Society, 2014
Mesostigmata predator Many of these mites are not parasitic but
free-living and predatory. Some species feed
on Nematoda and Collembola.
Marshall, 2006
Odonata predator Dragonflies are generalists. According to
most studies, the main diet of adult Odonates
consists of small arthropods, especially
Diptera (flies).
Marshall, 2006
Opiliones predator Many species are omnivorous, eating
primarily small arthropods and all kinds of
plant material and fungi.
Marshall, 2006
Pompilidae predator Larvae feed on spiders. Adults usually found
on flowers or on the ground searching for
prey.
Marshall, 2006
107
Reduviidae predator Most prey on arthropods. Their saliva is
commonly effective at killing prey
substantially larger than itself.
Marshall, 2006
Rhagionidae predator Both adults and larvae are predaceous on a
variety of small arthropods.
Marshall, 2006
Scenopinidae predator Prey are small arthropods; adults take nectar
from flowers, and feed on honeydew.
Marshall, 2006
Sciomyzidae predator Prey on freshwater or terrestrial snails which
are pests to many crops.
Marshall, 2006
Sphecidae predator Larvae feed on paralyzed arthropods (the host
varies according to wasp species) provided
by adult; common hosts include spiders,
grasshoppers, and caterpillars.
Marshall, 2006
Staphylinidae predator Most adults and larvae are predatory on other
invertebrates. Some larvae feed on decaying
vegetation.
Marshall, 2006
Tachinidae predator Feed on arthropods including millipedes,
spiders, scorpions. Adults may take nectar.
Marshall, 2006
Therevidae predator Feed on beetles, butterflies, moths and flies
that live either on ground or in leaf litter.
Marshall, 2006;
Rodrigues & Calhau,
2016
Vespidae predator Adults feed masticated small arthropods and
caterpillars to larvae by trophallaxis. Adults
primarily feed on larval saliva which is rich
in amino acids.
Hunt et al., 1982;
Xerces Society, 2014
Xylophagidae predator Larvae are scavengers or predators, feeding
on arthropod larvae.
Marshall, 2006
Cantharidae predator Adults eat nectar, pollen, other arthropods;
larvae are fluid-feeding predators, feed on
arthropod eggs and larvae.
Xerces Society, 2014
Dolichopodidae predator Mouthparts are for piercing (with a short
proboscis). Adults and larvae prey on small
arthropods.
Marshall, 2006
Hybotidae predator Known as predators from other studies. They
have piercing mouthparts to capture the prey.
Marshall, 2006
Nabidae predator Both adults and nymphs are predaceous.
They feed on mites, aphids, caterpillars, as
well as other arthropods eggs and nymphs.
Adults both ambush and actively pursue the
prey.
Marshall, 2006
Melyridae predator Adults evidently feed on flower-visiting
arthropods and pollen, larvae are primarily
predators of other arthropods.
Xerces Society, 2014
Mantidae predator Almost exclusively predatory and
carnivorous. They feed on variety of
Xerces Society, 2014
108
arthropods such as grasshoppers, moths, bees,
wasps, katydids, and flies.
Cicindelidae predator Both larvae and adults are predaceous and
feed on other small arthropods and spiders.
Jaskula, 2013;
Marshall, 2006
Athericidae predator Larvae are aquatic and can be predaceous on
other invertebrates. Adults noted as
predaceous.
Marshall, 2006
Histeridae predator Adults and larvae feed on other arthropods
(such as maggots), and other small
invertebrates.
Marshall, 2006
Platystomatidae predator Some species are predators of other
arthropods, others feed on decaying
vegetations and animals.
Marshall, 2006
Micropezidae predator Adults feed on small arthropods, while some
species are attracted to feces or decaying
fruit.
Marshall, 2006
Geocoridae predator Opportunistic predators that show omnivory;
feeds on variety of plants (grasses, and
soybeans) and arthropods (primarily mites,
thrips, and aphids).
Crocker & Whitcomb,
1980; Marshall, 2006
Chamaemyiidae predator Larvae feed on aphids and scale arthropods,
and some species have been used for pest
controls.
Marshall, 2006
Phymatidae predator They hunt variety of small arthropods and
other arthropods, but primarily hunt for
winged adults of Diptera, Homoptera, and
Hymenoptera.
Yong, 2005; Marshall,
2006
Libellulidae predator Both nymphs and adults are predators.
Nymphs feed on small aquatic arthropods,
adults feed on variety of winged arthropods
such as flies, moths, mayflies, and sometimes
bees.
Marshall, 2006
Forficulidae predator They feed on wide range of prey including
caterpillars, foliage feeds, small arthropods.
Marshall, 2006
Aeshnidae predator Nymphs are aquatic predators, feeding on
aquatic arthropods and small fish. Adults
feed on flying arthropods such as flies,
mosquitoes, grasshoppers, and moths.
Marshall, 2006
Agelenidae predator Opportunistic hunters. Captures mainly
arthropods such as grasshoppers, although
they have been noted of preying upon other
funnel weavers.
Foelix, 2011; Bradley
& Buchanan, 2013
Araneidae predator Any small arthropods that they are capable of
capturing. Larger Araneidae members in
genus Argiope is known to capture small
frogs and humming birds.
Dondale et al., 2003;
Foelix, 2011; Bradley
& Buchanan, 2013
109
Dictynidae predator Web builder; primarily captures flies,
occasionally grasshoppers, moths, and other
small spiders.
Foelix, 2011; Bradley
& Buchanan, 2013
Hahniidae predator Web builder; they build webs on the small
depressions in the soil. They eat small
arthropods or other soil arthropods.
Foelix, 2011; Bradley
& Buchanan, 2013
Linyphiidae predator Webs are built in trees, grasses, fences, or
directly on the soil. They are known to eat
small arthropods such as flies, grasshoppers,
small wasps, as well as soil arthropods
(mainly springtails) and other spiders.
Foelix, 2011; Bradley
& Buchanan, 2013
Tetragnathidae predator Builds a web usually near the bodies of water
(rivers, ponds, lakes, swamps) and captures
flies, moths, and other arthropods.
Dondale et al., 2003;
Foelix, 2011; Bradley
& Buchanan, 2013
Theridiidae predator They will eat any arthropods such as flies,
moths, and grasshoppers that are small
enough to be caught in the web.
Foelix, 2011; Bradley
& Buchanan, 2013
Theridisomatidae predator Web builder; they build a conical orb web
and captures any small arthropods that fall
onto or attempts to fly through the web.
Dondale et al., 2003;
Foelix, 2011; Bradley
& Buchanan, 2013
Amaurobiidae predator Ambush predator; feeds on small arthropods. Foelix, 2011; Bradley
& Buchanan, 2013
Anyphaenidae predator Ambush predator; mostly eats small
arthropods, although some are known to eat
flower nectar. They play a role in pest control
such as aphids in tree crops.
Dondale & Rener,
1982; Foelix, 2011;
Bradley & Buchanan,
2013
Clubionidae predator Ambush predator; they hunt for small
arthropods and soil arthropods in the night.
Dondale & Rener,
1982; Foelix, 2011;
Bradley & Buchanan,
2013
Thomisidae predator Ambush predator; they wait motionless on
the leave or flowers until the prey
approaches. They eat small arthropods such
as flies or moths.
Dondale & Rener
1978; Foelix, 2011;
Bradley & Buchanan,
2013
Lycosidae predator Active predator that hunts without the web.
Mainly consumes any small arthropods, as
well as sometimes other small spiders.
Dondale & Rener
1990; Foelix, 2011;
Bradley & Buchanan,
2013
Philodromidae predator Active predator; they hunt similar prey as
Thomisidae (small arthropods such as flies or
moths), and they can also pursue escaping
prey.
Dondale & Rener
1978; Foelix, 2011;
Bradley & Buchanan,
2013
Salticidae predator Active predators as they are excellent at
climbing and jumping. They eat mainly small
arthropods such as flies, beetles and crickets.
Foelix, 2011; Bradley
& Buchanan, 2013
110
Phalangiidae predator Active predator. Common in disturbed,
anthropogenic habitats (e.g., agricultural
fields, urban areas).
Marshall, 2006; Foelix,
2011; Bradley &
Buchanan, 2013
Panorpidae uncommon Adult mecopterans are mostly scavengers,
feeding on decaying vegetation and the soft
bodies of dying invertebrates.
Cheung et al., 2006;
Dunford et al., 2008
Ixodidae uncommon Feed on blood of medium- to large-sized
mammals by attaching themselves.
Marshall, 2006
Clusiidae uncommon Most species feed on nectar, sap, rotting
vegetation, and feces of birds and mammals,
while others are known to feed on other
arthropods such as beetle larvae.
Marshall, 2006
Pscoptera uncommon This order is common on bark and on foliage
but are not an economically important group.
Little is known about any damage to crops in
this group.
Marshall, 2006
Hippoboscidae uncommon Feed on blood of birds or mammals, often
associated with road-killed birds.
Marshall, 2006
Erotylidae uncommon Larvae and adults feed on the fruiting bodies
of fungi growing in decaying wood.
Arnett et al., 2002
Piophilidae uncommon Both adult and larvae feed on decaying
materials ranging from fungi to mammal.
Marshall, 2006
Heleomyzidae uncommon Usually in shaded areas, typically near rotting
matter (compost, dung, carrion etc.) or fungi.
Marshall, 2006
Acartophthalmidae uncommon The biology of Acartophthalmidae is almost
unknown. The adults have mainly been found
in forests.
Marshall, 2006
Chyromyidae uncommon A small but widely distributed group of
rather uncommon flies.
Marshall, 2006
111
Appendix 5: Supplementary methods and results
Table S5: Proportion of agricultural and semi-natural landcover at four buffer radii (500 m, 1000
m, 1500 m, and 2000 m) of 13 farms in Southern Ontario, Canada. Landcover data was extracted
from Agri-Canada Annual Crop Inventory.
Farm ag500 ag1000 ag1500 ag2000 nat500 nat1000 nat1500 nat2000
1 57.00 37.41 36.99 26.20 36.12 58.69 60.27 71.53
2 42.75 45.86 45.86 49.98 54.05 52.49 52.49 46.45
3 59.26 44.44 44.44 48.42 35.06 50.74 50.74 47.34
4 45.39 65.48 65.48 74.95 49.38 30.95 30.95 21.28
5 38.92 49.32 49.32 49.13 42.86 26.36 26.36 35.75
6 10.19 14.10 34.22 31.25 83.33 63.44 59.14 65.40
7 24.69 39.43 39.43 41.46 72.10 58.40 58.40 55.93
8 84.84 74.63 74.63 73.64 8.31 21.49 21.49 22.35
9 69.23 39.79 39.79 50.76 26.55 55.52 55.52 43.10
10 27.59 37.95 37.95 54.57 59.11 52.87 52.87 34.64
11 54.63 58.01 58.01 54.50 41.46 38.35 38.35 42.11
12 66.09 77.44 77.44 77.14 16.71 14.32 14.32 17.61
13 64.52 60.04 60.04 64.84 32.51 36.28 36.28 32.17
*Note: ag= agricultural landcover, nat= semi-natural cover
112
Table S6: Lab methods for plant tissue quality analysis as provided by SGS Labs, Guelph,
Ontario.
Test Analysis Description Purpose
Nitrogen % Dumas
method
Sample is weighed than heated
at temperatures ranging
between 800–1,000 °C. Next is
a water removal procedure
followed by gas separation. The
last step is to measure gaseous
nitrogen concentration. A
thermal conductivity detector is
used in which differences in
thermal conductivity of the
gases are detected (Shea &
Watts, 1939).
Nitrogen (N) plays a key role in the
plant life cycle. It is the main plant
mineral nutrient needed for
chlorophyll production and other
plant cell components (proteins,
nucleic acids, amino acids). A strong
positive relationship between foliar N
concentration and arthropod
survivorship, development, growth,
and reproductive (Lerdau & Throop,
2004) rates has been demonstrated for
many arthropod species.
Phosphorus
%
Inductively
coupled
plasma (ICP)
spectrometry
Metals and other elements in
plant tissues by ICP – Sample is
dry ashed, followed by acid
digestion with HCl. Minerals
are then analyzed by
inductively couple plasma after
appropriate dilution.
Phosphorus participates in a number
of processes determining the growth,
development and the productivity of
the plant: formation of cell nucleus
and cell multiplication, synthesis of
lipids and specific proteins,
transmission of hereditary properties,
breathing and photosynthesis, energy
transmission from richer to poorer
energetic compounds (Taiz et al.,
2015)
Organic
carbon %
Loss on
ignition
The percentage organic matter
is the ratio of the weight loss
during ignition to the weight of
plant tissue dried at 10SoC X
100. The ignition method was
adapted from Ball (1964)
Including the effects of herbivory in
ecosystem studies, particularly in
systems where rates of herbivory are
high and linked to plant C:N.
Deposition induced shifts in plant
C:N may therefore strongly affect
plant–herbivore interactions (Lerdau
& Throop, 2004)
ADF % ANKOM
Technology
Method 12.
Filter Bag
Technique
AOAC 973.18 – Fiber (Acid
Detergent) and ADF in Animal
Feed. It is the residue remaining
after boiling a forage
sample acid detergent solution.
ADF. contains cellulose, lignin,
and silica, not hemicellulose. It
often is used to calculate
digestibility. ADF is the
percentage of plant material
which is insoluble (Moore &
Jung, 2001)
There are large differences in
lignification between grasses and
legumes and differences in the impact
of ADF per se on their forage quality.
This can influence herbivore and
arthropod foraging. Carbon-based
secondary compounds such as
phenolics and ADF are important
controls of palatability (Schädler et
al., 2014)
113
Table S7: Summary of arthropod functional group abundance data. All data is presented as the
pooled number of individuals caught per site for both yellow pan traps and sweep nets samples.
Farm Anthophilous Decomposer Kleptoparasite Aquatic Uncommon Herbivore Parasitoid Pollinator Predator
1 223 11 8 1 0 495 57 43 175
2 111 3 2 0 0 237 23 138 69
3 85 3 1 3 0 355 53 141 52
4 61 2 1 0 1 113 29 21 44
5 57 3 1 0 1 90 12 27 42
6 215 11 3 1 3 924 17 76 77
7 468 51 2 1 7 1707 141 158 353
8 172 3 1 0 1 1083 40 121 226
9 193 24 4 0 1 1261 72 85 177
10 146 48 2 0 0 793 46 121 214
11 51 14 1 0 9 799 28 74 150
12 135 13 0 0 2 1332 100 190 316
13 255 7 4 0 1 1628 96 122 218
14 176 224 1 0 0 433 43 73 184
15 77 6 3 0 0 195 21 36 56
16 166 10 0 2 0 196 26 72 169
17 443 14 2 0 1 206 142 25 81
18 100 11 0 2 4 363 36 9 111
19 311 6 4 8 5 433 41 64 75
20 62 4 0 0 0 114 8 15 23
21 243 20 11 1 3 2421 200 149 290
22 177 10 1 5 0 836 54 104 275
114
Table S8: Summary of arthropod functional group richness (family-level) data. All data is
presented as the pooled number of individuals caught per site for both yellow pan traps and
sweep nets samples.
Farm Anthophilous Decomposer Kleptoparasite Aquatic Uncommon Herbivore Parasitoid Pollinator Predator
1 17 4 3 1 0 29 10 8 22
2 12 2 2 0 0 18 9 10 15
3 9 1 1 1 0 25 9 13 20
4 10 2 1 0 1 21 13 9 15
5 11 2 1 0 1 20 9 9 13
6 17 4 3 1 3 24 6 11 23
7 17 6 1 1 2 34 19 9 36
8 12 2 1 0 1 43 16 9 29
9 14 3 2 0 1 31 14 13 25
10 12 4 1 0 0 38 15 9 37
11 13 3 1 0 1 29 9 9 24
12 15 2 0 0 2 30 11 11 35
13 18 6 2 0 1 35 11 10 39
14 12 2 1 0 0 25 15 3 26
15 9 2 3 0 0 17 8 6 17
16 14 4 0 1 0 22 5 8 19
17 15 2 2 0 1 22 16 7 22
18 12 5 0 1 3 25 10 4 19
19 13 4 3 2 1 27 11 16 20
20 8 2 0 0 0 14 3 5 11
21 22 4 2 1 3 48 23 15 43
22 17 3 1 2 0 30 12 14 29
115
Table S9: Results of arthropod family identity in agricultural and non-farm (Naturalist) sites and
the shared or unique families between them.
Agricultural (unique)
(n=94)
iNaturalist (unique)
(n=104) Shared (n=89)
Anthophilous Anthophilous Anthophilous Bibionidae Gasteruptiidae Calliphoridae
Conopidae Limoniidae Ceratopogonidae
Lonchopteridae Platypezidae Chironomidae
Mycetophilidae Psychodidae Culicidae
Phalacridae Pterophoridae Formicidae
Phlaeothripidae Stratiomyidae Mordellidae
Phoridae Aquatic Muscidae
Sarcophaginae Baetidae Sarcophagidae
Scatopsidae Baetiscidae Sepsidae
Sphaeroceridae Bdellidae Simuliidae
Aquatic Belostomatidae Tabanidae
Ephydridae Blastobasidae Tipulidae
Nemouridae Bolboceratidae Aquatic
Perlidae Caenidae Hydropsychidae
Taeniopterygidae Corixidae Decomposer Decomposer Corydalidae Elateridae
Clusiidae Dytiscidae Fanniidae
Dryomyzidae Elmidae Lauxaniidae
Entomobryidae Ephemerellidae Herbivore Leiodidae Ephemeridae Acrididae
Lonchaeidae Gerridae Anthomyiidae
Sminthuridae Gyrinidae Aphididae
Herbivore Haliplidae Berytidae
Agridae Heptageniidae Buprestidae
Agromyzidae Hydrophilidae Cecidomyiidae
Alydidae Leptoceridae Cerambycidae
Anobiidae Naucoridae Cercopidae
Chloropidae Nepidae Chrysomelidae
Cimbicidae Notonectidae Cicadellidae
Delphacidae Phryganeidae Cixiidae Drosophilidae Psephenidae Coreidae
Galerucie Veliidae Curculionidae
Issidae Decomposer Gryllidae
Languriidae Aradidae Lygaeidae
Largidae Silphidae Meloidae
Nitidulidae Herbivore Membracidae
116
Oedemeridae Acanthosomatidae Miridae Pergidae Aphrophoridae Pentatomidae
Phaleothripidae Cicadidae Rhopalidae
Phasmatodea Coccidae Scarabaeidae
Psilidae Crambidae Tenthredinidae
Psyllidae Cydnidae Tettigoniidae
Rhyparochromidae Derbidae Thyreocoridae
Scathophagidae Diprionidae Tingidae
Scutelleridae Elachistidae Kleptoparasite
Siricidae Flatidae Apidae
Tephritidae Gelechiidae Chrysididae
Thripidae Gracillariidae Parasitoid Ullidiidae Lasiocampidae Cynipidae
Xyelidae Lycidae Eulophidae
Kleptoparasite Margarodidae Eurytomidae
Miltogramminae Pseudococcidae Ichneumonidae
Parasitoid Pyralidae Perilampidae
Bethylidae Rhaphidophoridae Platygastridae
Braconidae Tetranychidae Pollinator Ceraphronidae Tetrigidae Bombyliidae
Chalcididae Tortricidae Halictidae
Diapriidae Parasitoid Lycaenidae
Encyrtidae Carnidae Nymphalidae
Eucoilidae Pelecinidae Papilionidae
Eupelmidae Uropodidae Syrphidae
Figitidae Pollinator Predator Megaspilidae Erebidae Araneidae
Mymaridae Geometridae Asilidae Orussidae Hesperiidae Cantharidae
Pipunculidae Noctuidae Carabidae
Proctotrupidae Notodontidae Chrysopidae
Pteromalidae Sphingidae Clubionidae
Scelionidae Predator Coccinellidae
Tiphiidae Aeshnidae Coenagrionidae
Torymidae Ascidae Crabronidae
Pollinator Coniopterygidae Dictynidae
Andrenidae Damaeidae Dolichopodidae
Colletidae Eremaeidae Empididae
Megachilidae Euphthiracaridae Lampyridae
Pieridae Eupodidae Linyphiidae
Predator Gnaphosidae Lycosidae
Agelenidae Gomphidae Phalangiidae
117
Amaurobiidae Hemerobiidae Philodromidae
Anthicidae Histeridae Pholcidae
Anthocoridae Hypochthoniidae Pompilidae
Anyphaenidae Lestidae Reduviidae
Bittacidae Libellulidae Rhagionidae
Cleridae Macromiidae Salticidae
Coegrionidae Mimetidae Sciomyzidae
Forficulidae Myrmeleontidae Sphecidae
Hybotidae Oribotritiidae Staphylinidae
Liocranidae Pachylaelapidae Tachinidae
Melyridae Panorpidae Tetragnathidae
Mesostigmata Parasitidae Theridiidae
Micropezidae Parholaspididae Thomisidae
Nabidae Phytoseiidae Vespidae
Phymatidae Pisauridae Uncommon Platystomatidae Punctoribatidae Chyromyidae
Scenopinidae Scheloribatidae Heleomyzidae
Tetragthidae Scutacaridae Ixodidae
Therevidae Siteroptidae Tenebrionidae
Theridisomatidae Sperchontidae
Xylophagidae Trematuridae
Uncommon Trombidiidae
Bostrichidae Tydeidae
Erotylidae Uropodellidae
Veigaiidae
Uncommon
Astegistidae
Coleophoridae
Eniochthoniidae
Eriophyidae
Psocidae
Psychidae
118
Figure S8: Correlation matrix of landcover data carried out to assess the relationship among
predictor variables. Squares in blue indicate a strong negative correlation, squares in red indicate
a strong positive correlation.
119
Figure S9: Correlation matrix of plant resource data carried out to assess the relationship among
predictor variables. Squares in blue indicate a strong negative correlation, squares in red indicate
a strong positive correlation.
120
Figure S10: Locations of insect and arachnid iNaturalist observations logged from 05 May - 17
July 2017 spanning a 10,000 km² region of Southern Ontario, Canada.
10,000 km²
Southern Ontario, Canada
121
Figure S11: Summary of arthropod (A) abundance and (B) richness (family-level) per farm from
a total of 22 farms in Southern Ontario, Canada.
A
B
Tota
l art
hro
po
d a
bu
nd
ance
To
tal a
rth
rop
od
ric
hn
ess
Farm site
(fam
ily-le
vel)