THE EFFECT OF ANTHROPOGENIC HABITAT MODIFICATION ON INSECT- MEDIATED ECOSYSTEM SERVICES
Audrey Maran
A Dissertation
Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY
August 2020
Committee:
Shannon Pelini, Advisor
Nathan Hensley Graduate Faculty Representative
Kevin McCluney
Karen Root
Michael Weintraub © 2020
Audrey Maran
All Rights Reserved iii ABSTRACT
Shannon Pelini, Advisor
Human activity and land use negatively affect many arthropods that provide important
ecosystem services. Agricultural land and cities are both common types of human-modified
habitat that often have decreased arthropod richness and diversity, due in part to loss of habitat
quantity, habitat complexity (i.e. habitat types available), and structural complexity (i.e. habitat
density, shape, or height). This dissertation focuses on gaps in understanding of the relationship
between habitat complexity and arthropod-mediated ecosystem services in human-modified
environments. In agricultural fields, we examined the response of ground arthropods, microbial
activity, and nutrient pools to applications of a nutrient source (labile detritus: mayfly carcasses)
and/or habitat structure (recalcitrant detritus: corn stover) over a 7-week period. In urban
prairies, we examined the relationship between arthropod predators and complexity, and the
impact that urban features have on that relationship at multiple scales. Throughout the work
presented in this dissertation, we found that known patterns and relationships were disrupted or
dampened in human-modified habitats. Though labile detritus recruited ground arthropods to
agricultural fields, they did not stimulate nutrient cycling as they do in less-modified systems.
The labile detritus provided nutrients but had little impact on microbial activity. These findings suggest that low baseline bi otic activity in agricultural fields lessens our ability to promote biotic
nutrient cycling. In urban prairies, we found that on a small-scale, predator-prey ratio increased
with structural complexity, but only structural complexity at lower heights and when the
structural complexity was primarily due to forbs. Ons a larger cale, arthropod abundance and iv predation were best predicted by complexity within prairies, while arthropod community structure was best predicted by habitat complexity and urban features surrounding a prairie.
Taken together, these findings suggest that in urban prairies, the surrounding complexity may determine which arthropods can find and/or persist in a prairie, while complexity within the prairie determines how many of the arthropods can be supported and perhaps even the ecosystem services they provide, once in the prairie. Ultimately, this dissertation fills gaps in the literature regarding the impact of habitat and structural complexity on arthropod ecosystem services in human-modified environments. v
I dedicate this dissertation to my grandmother Jeanne Fair, whose interest in science could not be
smothered by the societal expectations that limited her career options. vi ACKNOWLEDGMENTS
The support system that I have been lucky enough to develop, comprised of family,
friends, peers, mentors, and furry four-legged companions, never fails to amaze me. Though
many of you have been there as long as I can remember, I specifically want to thank everyone
for their support while I pursued my doctoral degree. I have leaned on you throughout this process, but particularly in the last two years, first as I moved states away, and now as I have
worked to finish my dissertation while a pandemic has changed life as we all know it and our
society is rightfully rising up against years of systemic racism and oppression.
My parents, Norman and Wendy Maran have not just cheered for me from the sidelines,
but helped build experiment equipment and even come into the field. My friends, old and new,
have always been there to keep me sane and never complained when I had to cancel plans to get
work done or was especially grumpy from exhaustion. Melissa Fleig and Jordan Passmore even
helped me with tedious field equipment arts and crafts. I cannot begin to put into words how
much my partner, Paul Best has helped me throughout the process, from encouragement and
support to helping in the lab and field. He even got covered head-to-toe in mayflies for me,
something I’m sure not many people could say of their spouses.
At Bowling Green, so many wonderful people have contributed towards this dissertation,
by sharing their wisdom, ideas, commiserating, and more. The biology department at BG is a
truly special place, with faculty who have changed my life (thank you Jeff Miner, for
encouraging me to become a scientist) and let me laugh, think, and even cry in their offices.
Thank you to my committee, your guidance made this work possible; though you helped
in countless ways, in particular I thank Kevin McCluney for helping me with invertebrate
dynamics and statistics, Karen Root for helping me think on different scales and about vii community dynamics, Michael Weintruab for helping me think about soil, microbes, and soil
biogeochemistry, and Nathan Hensley for providing big picture guidance. Thank you to Mary-
Jon Ludy for stepping in for Nathan when I made a scheduling error.
Shannon Pelini, my wonderful, badass advisor is unparalleled. I mean, who else’s
description could elicit a “wow, she sounds great!” from anyone I tell about them? Shannon has
been there for my entire graduate school experience. I have grown with her and gone through a
number of life events with her, she helped me finally confront a long-standing battle with
anxiety, she taught me how to be both a scientist and a mentor, and she taught me so many of the
all-important “soft-skills” that helped me get where I am today. I could go on for ages about her, but instead I will say that if you haven’t been lucky enough to meet her, that you should, because she is a phenomenal person and you won’t leave an interaction with her without learning something about yourself or the world. Unsurprisingly, Shannon also recruits the most wonderful people to her lab (including her former lab manager and husband, Michael Pelini!). Rob Baroudi,
Cari Ritzenthaler, Caitlin Maloney, Eric Moore, Amanda Winters, Josie Lindsey-Robbins,
Preston Thompson, I could not have asked for better labmates and peers. Jessica Susser, honorary labmate, chapter 1 would literally not have happened without you. I also want to extend a special thank you to the undergraduate researchers who worked with me throughout my doctoral work, offering their help and growing into scientists alongside me: Jessica Swedick,
Kristina Flanigan, Rachel Goldman, Jamie Hawkins, Sydnee Fenn, Andrew Sotherland, Eric
Huber, and Catherine Freed.
I could fill a book with the names of everyone who has helped me—thank you all. viii
TABLE OF CONTENTS
Page
INTRODUCTION ...... 1
CHAPTER 1. DOES STIMULATING GROUND ARTHROPODS ENHANCE NUTRIENT
CYCLING IN CONVENTIONALLY MANAGED CORN FIELDS? ...... 8
Introduction ...... 8
Methods...... 12
Study sites ...... 12
Experimental design...... 12
Arthropod sampling ...... 14
Nutrient and microbial analyses ...... 14
Soil respiration ...... 15
Soil nutrients and microbial biomass C, N, and P ...... 15
Soil enzymes ...... 16
Data analysis ...... 17
Results ...... 19
Arthropods ...... 19
Soil microbial activity and nutrient dynamics ...... 19
Linking arthropods and microbial activity ...... 20
Discussion ...... 22
Conclusion ...... 26
Figures...... 27 ix
CHAPTER 2. DOES HABITAT STRUCTURAL COMPLEXITY PREDICT ARTHROPOD
PREDATOR-PREY RATIO IN URBAN PRAIRIES? ...... 35
Introduction ...... 35
Methods...... 38
Study sites ...... 38
Structural complexity ...... 39
Arthropod sampling ...... 40
Data analysis ...... 41
Results ...... 43
Plot characteristics ...... 43
Predator-prey ratio ...... 44
Arthropod abundance and richness ...... 44
Discussion ...... 45
Conclusion ...... 48
Figures...... 49
CHAPTER 3. HOW IS ARTHROPOD PREDATION IN URBAN PRAIRIES IMPACTED BY
HABITAT COMPLEXITY WITHIN AND SURROUNDING THE PRAIRIE? ...... 54
Introduction ...... 54
Methods...... 57
Study sites ...... 57
Habitat complexity ...... 59
Arthropod sampling ...... 60
Predation ...... 60 x
Data analysis ...... 61
Results ...... 64
Predation ...... 64
Arthropod abundance ...... 64
Arthropod richness and diversity ...... 64
Arthropod community composition ...... 66
Discussion ...... 66
Predation ...... 67
Arthropod abundance ...... 68
Arthropod richness and diversity ...... 69
Arthropod community composition ...... 70
Conclusion ...... 71
Figures...... 73
CONCLUSION ...... 82
REFERENCES ...... 86
APPENDIX A. AIC VALUES GENERATED DURING MODEL SELECTION ...... 105
APPENDIX B. CORRELATION COEFFICIENTS BETWEEN VARIABLES...... 108 xi
LIST OF FIGURES
Figure Page
1.1 Conceptual diagram of soil processes ...... 27
1.2 Response of detritivores and predators to each detritus treatment over the course
of the experiment ...... 28
3- 1.3 Response of microbial biomass carbon, microbial biomass PO4 , and microbial
biomass N to each detritus treatment at harvests 1, 2, and 3 ...... 29
1.4 Response of microbial respiration to each detritus treatment over the course of the
experiment...... 30
1.5 Response of microbial extracellular enzymes phosphatase, β-1,4-N-acetyl-
glucosaminidase, β-1,4-glucosidase, and leucine amino peptidase to each detritus
treatment over the course of the experiment ...... 31
+ - 1.6 Response of soluble- and adsorbed NH4 , NO3 , and cumulative exchangeable N
to each detritus treatment over the course of the experiment ...... 32
3- 1.7 Effect of detritus treatments on water extractable PO4 , adsorbed PO43-, and
3- cumulative exchangeable PO4 ...... 33
1.8 Relationship between arthropod abundance and microbial respiration at harvests
1, 2, and 3 ...... 34
2.1 The relationship between vertical complexity and vegetation biomass in each
prairie ...... 49
2.2 a. <50 cm horizontal complexity and biomass of vegetation below 50 cm b. The
relationship between >50 cm horizontal complexity and biomass of vegetation
above 50 cm ...... 50 xii
2.3 The relationship between predator-prey ratio and horizontal complexity and its
interaction with percent forbs in the plot ...... 50
2.4 The relationship between arthropod abundance and percent grass in the plot in each
prairie ...... 51
2.5 The relationship between springtail abundance and percent grass in the plot in each
prairie ...... 52
2.6 The relationship between aphid abundance and >50 cm horizontal complexity ...... 53
3.1 The buffer area surrounding each sampling area was created by measuring 30 m
from the center of each side of the sampling area ...... 73
3.2 The effect of prairie structural complexity and the number of bait predators on
predation ...... 74
3.3 The relationships between structural complexity and abundance and predator
abundance and the relationship between predator abundance and the size of the
sampling area ...... 75
3.4 The relationships between richness and size of the sampling area and %
impervious surface surrounding prairie ...... 76
3.5 The relationships between predator richness and the size of the sampling area, the
size complexity surrounding the prairie, and percent impervious surface ...... 77
3.6 The relationship between diversity and percent impervious surface surrounding
the prairie ...... 78
3.7 The relationship between predator diversity and complexity surrounding the
prairie ...... 79
3.8 The effect of complexity surrounding the prairie on community composition ...... 80 xiii
3.9 Top—The effect of complexity surrounding the prairie on predator community
composition. Bottom—The effect of prairie habitat complexity on predator
community composition...... 81
xiv
LIST OF TABLES
Table Page
1.1 Linear mixed effects model test statistics for the effect of experimental treatment
on each response variable...... 21
2.1 Locations, approximate size, and number of plots of urban prairies ...... 39
2.2 A description of complexity measurements used in this study ...... 43
2.3 Linear-mixed effects model statistics ...... 45
3.1 Location, size, and manager of each study site ...... 58
3.2 Statistics for the models that best predict each response variable ...... 65
1
INTRODUCTION
When people think of insects and other arthropods, it is not uncommon for them to react
with fear, immediately think of pests, or at least disregard the important role many insects play in
the ecosystem (Kellert, 1993; Prather et al., 2013; Snaddon, Turner, & Foster, 2008). Yet,
arthropods provide valuable ecosystem services, such as nutrient cycling, pest control,
pollination, and seed dispersal (Lavelle et al., 2006; Losey & Vaughan, 2006) and human activity
can negatively impact the same arthropods we depend on for these essential services.
Approximately 51% of Earth’s non-ice surface is impacted by human activity for purposes
ranging from infrastructure to resources or recreation (Hooke, Martín-Duque, & Pedraza, 2012).
This estimate is conservative, as widespread issues such as climate change and pollution are not factored into the percentage. Because of the variety and complexity of human impacts on arthropods, there is a need for careful study of arthropod community function in all human- impacted systems. Cropland and urban areas are two such systems that cover a large percentage of land: 17% of the United States is classified as cropland, while rapidly increasing urban areas already account for 4% of U.S. land (Bigelow & Borchers, 2017) and over half of the U.S. population lives in urban and suburban areas (Parker et al., 2018). Both cities and croplands have decreased insect richness and diversity (Birkhofer, Diekoetter, Meub, Stoetzel, & Wolters, 2015;
Faeth, Bang, & Saari, 2011; GarfieldJ House & Stinner, 1983; Lagucki, Burdine, & McCluney,
2017; Michael L McKinney, 2008), which is likely due to loss of habitat quantity, complexity,
and connectivity (Marzluff & Ewing, 2001; Michael L McKinney, 2008). Habitat complexity has the potential to be reduced in multiple ways, including structurally (e.g. variability in height or form, density) (Bultman & Uetz, 1982; Dale & Frank, 2014) and in the number of habitat types available (e.g. forest, shrub, ground vegetation) (Shrewsbury & Raupp, 2000). This dissertation 2
focuses on gaps in our understanding of the relationship between complexity and arthropod-
mediated ecosystem services in human-modified environments.
Croplands are often missing detrital matter (i.e. dead plant or animal tissue), which serves
as both habitat and food for ground- and soil arthropods that consume detritus (hereafter
detritivores). Therefore, a lack of detrital matter decreases not only food availability, but also
habitat complexity, which may be one reason that detritivore abundance and diversity is low in conventionally-managed croplands (Benton, Vickery, & Wilson, 2003; GarfieldJ House &
Stinner, 1983). However, this lack of detritivore abundance could be a factor in agricultural
nutrient cycling issues, which affect crop production and nutrient runoff, since detritivores play a
role in nitrogen (N) and phosphorus (P) cycling (Beare, Coleman, Crossley Jr, Hendrix, &
Odum, 1995; Lavelle, 1997; Seastedt, Mameli, & Gridley, 1981; Wolters, 2000). Yet, the human
impact on cropland detritivores and the role of detritivores in cropland nutrient cycling remain
understudied facets of agricultural research.
Research on detrital communities in croplands in general is sparse but suggests that
farming practices can alter the composition of the arthropod community (Birkhofer et al., 2015)
and lower abundance of ground-dwelling arthropods, especially when tilling is used (GarfieldJ
House & Stinner, 1983). No-till agriculture, cover crops, and crop rotation have been used to
mitigate the negative impacts of farming on soil function, but also benefit detritivores since
detrital crop residue is often left on the field (Brévault, Bikay, Maldès, & Naudin, 2007;
Derpsch, Friedrich, Kassam, & Li, 2010; Hendrix et al., 1986; GJ House & Alzugaray, 1989;
GarfieldJ House & Stinner, 1983; Witmer, Hough-Goldstein, & Pesek, 2003). Much of the
benefit to detritivores from the crop residue may be due to the increase in vertical habitat and
habitat complexity provided by the detritus (Blumberg & Crossley Jr, 1983). However, crop 3
residue is carbon-rich and may not provide food of high enough quality to support a rich detrital
community. The addition of a supplemental source of nutrient-rich detritus could have a greater
effect than crop residue alone (Song, Wu, Shao, Hui, & Wan, 2015; L. H. Yang, 2004, 2006;
Louie H. Yang, 2013). Combining a labile nutrient source with the habitat structure and carbon
provided by crop residue may attract detritivores and simultaneously reduce the need for
fertilizers containing high concentrations of soluble nutrients (e.g. manure, chemical fertilizer),
which readily enter runoff into aquatic systems (DeLaune et al., 2004).
Urban systems present habitat challenges to arthropods as well. While there are patches
of habitat that are high in quality, such as gardens or parks, cities are largely made up of
impervious surfaces and turf lawns that are poor-quality habitat for many arthropod species.
However, arthropod pests, such as beetle larvae that damage lawns and gardens, mosquitoes and
ticks that bite humans and transfer disease, weevils that eat stored goods, or flies that are
considered simply a nuisance are not uncommon in urban areas (Brunskill et al., 2011). By
improving habitat availability, connectivity, and complexity, we can support arthropod species
that control these pests. Restoring and conserving green spaces in cities is a popular means to
support pollinators, mitigate runoff, and provide recreation, but green spaces can also enhance
pest control via arthropod predation (Braman, Pendley, & Corley, 2002; A. K. Fiedler & D. A.
Landis, 2007; Steven D. Frank, Shrewsbury, & Esiekpe, 2008; Isaacs, Tuell, Fiedler, Gardiner, &
Landis, 2009). By investigating controls on natural predator communities and predation rates in these spaces, we can better understand how to control such pests without chemical intervention.
As with detritivores, predators are impacted by habitat complexity. Habitat complexity is
generally found to benefit predators and predation by supporting more prey, creating a more
favorable microclimate, making detection (e.g. through vibrations carrying) and/or capture of 4 prey more efficient, making escape from predation and cannibalism easier, and providing supplemental resources (e.g. alternate prey, pollen, dead items) (Langellotto & Denno, 2004).
Several studies aimed at understanding factors that impact predation have focused on habitat complexity, which they have defined in various ways (e.g. density of vegetation or litter, biomass of vegetation or litter, types of habitat available) depending on the focal predator species
(Bultman & Uetz, 1982; Corcuera et al., 2016; S. D. Frank & Shrewsbury, 2004; Langellotto &
Denno, 2004; McNett & Rypstra, 2000; Podgaiski & Rodrigues, 2017; Schmidt, Roschewitz,
Thies, & Tscharntke, 2005; Shrewsbury & Raupp, 2000). Despite the varied approaches, habitat complexity has consistently been shown to increase predator abundance (Bultman & Uetz, 1982;
Corcuera et al., 2016; S. D. Frank & Shrewsbury, 2004; Langellotto & Denno, 2004; Podgaiski
& Rodrigues, 2017; Schmidt et al., 2005). The effect of complexity on predation rate has been measured less frequently and the relationship between the two has been variable, from no effect to positive or nonlinear relationships. (S. D. Frank & Shrewsbury, 2004; Frey et al., 2018;
Shrewsbury & Raupp, 2006; Yadav, Duckworth, & Grewal, 2012).
Features of the urban habitat that are different than those found in natural systems may be contributing further to the variable relationship between habitat complexity and predation. One such feature is impervious surfaces in cities, which cover approximately 50% of the area in urban cores (Michael L. McKinney, 2002). Impervious surfaces absorb heat (Bolund & Hunhammar,
1999), which increases temperature, and in temperate cities, lengthens the growing season, and leads to fewer frost days relative to surrounding rural areas. This effect on urban temperature alone may fundamentally alter arthropods’ interactions and their impact on the environment because arthropods are ectotherms and they are effected by ambient temperature (Berger,
Walters, & Gotthard, 2008; Bokhorst, Huiskes, Convey, van Bodegom, & Aerts, 2008; Briones, 5
Ostle, McNamara, & Poskitt, 2009; Maran & Pelini, 2015; Pelini et al., 2014; Zhang, Li, Lu,
Zhang, & Liang, 2013). For example, Dale and Frank (2014) found that the temperature within
urban tree canopies increased with the percent impervious surface within 100 m, which
ultimately led to a decoupling of the positive relationship between complexity and parasitoid
wasps’ ability to control pests.
An urban landscape may impact the relationship between arthropod predators and habitat
complexity for reasons beyond the warming from impervious surfaces. Green spaces in urban
systems are also unique because they are generally small and nested in a fragmented landscape
that is inhospitable to many arthropods (Burkman & Gardiner, 2014; Faeth et al., 2011). The
diversity, type, and amount of landcover surrounding green spaces impacts arthropod predators,
particularly those that are capable of long-distance movement (Brose et al., 2012; Egerer et al.,
2017; Lagucki et al., 2017; Sattler, Duelli, Obrist, Arlettaz, & Moretti, 2010; Tews et al., 2004).
These factors also impact the connectivity between metapopulations, which can impact
community structure (Opdam, 1991). Though habitat complexity is known to promote arthropod
predators at multiple scales (Chaplin‐Kramer, O’Rourke, Blitzer, & Kremen, 2011; Langellotto
& Denno, 2004; Letourneau et al., 2011), few studies have looked at the relative impact of landscape and local habitat complexity on predation. Furthermore, studies that have compared local and landscape scale are often in agricultural settings and are focused on biocontrol of one pest (Östman, Ekbom, & Bengtsson, 2001). These studies have varied results; there is evidence that landscape scale and local scale are both important (Chaplin-Kramer & Kremen, 2012;
Östman et al., 2001) and that landscape scale is only important for certain local habitat types (i.e. organically- vs conventionally-managed crops) (Winqvist et al., 2011). Understanding the impact 6
of local and landscape habitat complexity on predation will aid in determining location and
content of future establishment of green spaces in urban systems.
In both cropland and urban systems, loss of habitat and reduced habitat structure has led
to a decrease in arthropod-mediated ecosystem services. This dissertation focuses on ecosystem services that are particularly important in the northwest Ohio region. In cropland systems, nutrient-rich runoff has ultimately led to harmful algal blooms in Lake Erie, which makes understanding the role of detritivores in nutrient cycling imperative. Pest species that spread disease and damage property flourish with the warm climate and fragmented structure of urban systems. Investigating how to use our green spaces to promote predation on these pest species will increase the benefit of restoring natural spaces in cities. Additionally, this work may provide valuable information to city planners for effective pest control as new pest species move into northwest Ohio in response to climate change.
In chapter one, we discuss a field experiment that we conducted to determine the relative or combined importance of detrital habitat and food sources on arthropod-mediated insect cycling in conventionally-managed corn fields. We examined the effect of recalcitrant (corn stover) and labile (mayfly carcasses) detrital additions on detrital communities, microbial activity, and nitrogen and phosphorus nutrient cycling.
In chapter two, we took an observational approach to explore the relationship between habitat complexity and predator-prey ratios, and how that relationship is impacted by proximity to impervious surface. We conducted the study at a small scale, in plots within six urban prairies in Toledo, OH. In each plot, we sampled the arthropod community and measured the structural and habitat complexity of the vegetation. 7
We conducted a larger scale observational study for work reported in chapter three. We used individual prairies as the experimental unit and looked at the effects of habitat complexity surrounding the prairies and within the prairie on predation and arthropod communities. 8
CHAPTER 1. DOES STIMULATING GROUND ARTHROPODS ENHANCE
NUTRIENT CYCLING IN CONVENTIONALLY MANAGED CORN FIELDS?
This chapter was published in the journal Agriculture, Ecosystems, and Environment. Its inclusion in this dissertation is permitted by Elsevier’s Personal Use allowance. Citation: Maran
AM, Weintraub MN, Pelini SL (2020) Does stimulating ground arthropods enhance nutrient cycling in conventionally managed corn fields? Agriculture, Ecosystems & Environment
297:106934. Available online here.
Introduction
High nitrogen and phosphorous (N and P) nutrient runoff associated with conventional agriculture can cause harmful algal blooms (Michalak et al., 2013) and decrease water quality
(Comly, 1945; Knobeloch, Salna, Hogan, Postle, & Anderson, 2000). Despite efforts to combat this issue through reductions in fertilizer use in the past few decades, the concentration of nutrients, particularly P, in runoff has not been similarly reduced (Powers et al., 2016). This is partially due to the accumulation and steady release of soil P from a long history of fertilizer application (i.e. legacy P) (Kleinman et al., 2011), which could be drawn down by plant uptake to reduce runoff potential, particularly if the availability of this mineral-bound P is increased.
One potential management solution that could enhance legacy P availability is stimulating soil food webs, particularly the arthropod detritivores that consume dead plant or animal tissue matter
(hereafter detritivores). The most significant role played by detritivores in nutrient cycling is through their effect on microbial communities, which occurs due to action that changes soil structure, grazing, and excretion of labile nutrients into soil (T. W. Crowther, Jones, & Boddy,
2011; Del Toro, Ribbons, & Ellison, 2015; Lavelle et al., 2006; Ritzenthaler et al., 2018; D. A.
Wardle, 2006). Detritivores often graze on microbes, whether directly as in the case of fungi, or 9
indirectly by consuming microbial substrate (i.e. detritus). Such grazing changes microbial
abundance (reviewed in David A. Wardle, 2002) and community structure (T. W. Crowther et al., 2011; Ronn, McCaig, Griffiths, & Prosser, 2002; D. A. Wardle, 2006). Detritivores also alter the soil environment, creating more substrate for microbes through actions like burrowing, shredding detritus, and excreting labile carbon and nutrient sources (Del Toro et al., 2015;
Lavelle, 1997; Nielson, Ayres, Wall, & Bardgett, 2011; D. A. Wardle, 2006). By changing the abundance of microbes, the diversity and richness of microbes, and the available substrate, these interactions with the microbial community can affect rates of microbial nutrient cycling, including activities of the extracellular enzymes that decompose organic matter (Bünemann,
2015; Oehl, Frossard, Fliessbach, Dubois, & Oberson, 2004), and immobilization of nutrients in microbial biomass (Achat et al., 2010; Oberson & Joner, 2005).
Through their impact on the microbial community and movement of soil particles and nutrients, detritivores are integral to nutrient cycling in soils (Beare et al., 1995; Lavelle, 1997;
Seastedt et al., 1981; Wolters, 2000). Though there is a large body of research exploring ways to reduce high-nutrient agricultural runoff, detritivores and their role in nutrient cycling is not often considered. Therefore, the question arises as to whether stimulating detritivores by alleviating substrate and nutrient limitation may increase the potential for agricultural soil biota to cycle nutrients.
Research on detrital communities in croplands is sparse, but suggests that farming practices can alter the composition of the invertebrate community (Birkhofer et al., 2015).
Practices such as tilling reduce abundance of ground-dwelling invertebrates (Badejo, Tian, &
Brussaard, 1995; Benton et al., 2003; GarfieldJ House & Stinner, 1983), while cover crops, crop rotation, and low pesticide use can positively impact beneficial ground arthropod communities 10
(GJ House & Alzugaray, 1989; Witmer et al., 2003), and organic amendments have variable impacts depending on their type (Gunadi, Edwards, & Arancon, 2002). A companion study
(Susser, Pelini, & Weintraub, 2020 (accepted)), as well as preliminary data from this project (S.
Pelini, unpublished data) revealed few detritivores in NW Ohio corn fields, which is unsurprising because conventionally managed farm fields have little to no detritus on which the detritivores can feed or inhabit. Providing a labile (i.e. easily accessible to soil biota) detrital source during the crop growing season could therefore attract detritivores, which may promote nutrient cycling, while also reducing the need for fertilizers containing high concentrations of soluble nutrients
(e.g. manure, chemical fertilizer), which readily runoff (DeLaune et al., 2004).
One such labile detrital resource is arthropod carcasses, which provide high quality
(nutrient-rich) detritus (Dreyer & Gratton, 2014). In our study system and in other systems near freshwater lakes, the carcasses of mayflies are abundant. Mayflies are insects that spend nearly all their lives in aquatic systems and emerge as adults to mate and abruptly die (Needham, 1935).
In populated areas the carcasses of mated adults become a nuisance, causing slippery roadways and unpleasant smells (Henry, 2010). The potential to use the carcasses as a soil amendment has not gone unrecognized; for several years, Port Clinton, OH, USA had a program that composted mayflies and provided the compost to residents (Blade, 2002). However, composting large quantities requires extra expense and time that may be unnecessary for the soil to receive the benefits of the mayfly carcasses.
Furthermore, insect carcasses have been shown to affect ecosystem processes in natural systems. For example, the addition of cicada carcasses stimulated aboveground plant biomass
(Louie H. Yang, 2013), microbial biomass (L. H. Yang, 2004) and promoted the activity of
ground arthropods (L. H. Yang, 2006). A similar study added locust carcasses to experimental 11 plots at varying densities and found a positive relationship between the number of carcasses
+ added and soil respiration and net ecosystem CO2 exchange, and ammonium (NH4 ) concentrations (Song et al., 2015). This finding suggests that locust carcasses relieved the soil microbial community from N limitation, which increased overall carbon cycling. These studies each occurred in a natural system (forested, temperate steppe, or in a greenhouse using low nutrient soil), and it is unclear if their findings are generalizable to agroecosystems, as heavily managed agroecosystems likely have fewer detritivore species and less consistent inputs of leaf litter than natural or less managed systems (Duelli, Obrist, & Schmatz, 1999). However, use of a labile detrital source over time could attract ground arthropods that lead to a more abundant and diverse community (Halaj & Wise, 2002; Hoekman, Dreyer, Jackson, Townsend, & Gratton,
2011; L. H. Yang, 2006), which could ultimately stimulate microbial activity and promote nutrient cycling. Supplementing labile detritus with recalcitrant detritus has the potential of amplifying the response of detritivores. Though recalcitrant detritus is less effective than labile detritus in attracting detritivores (Badejo et al., 1995), it does serve as refugia (Moore et al.,
2004). Corn stover (i.e. dead leaves and stalks of corn) is one such recalcitrant detrital source that may be left on fields deliberately as part of some management practices.
Our study assessed the impact of arthropod carcass and corn stover addition on short- term (7-week) nutrient availability and flow through the soil food web in conventionally managed corn fields. We asked how arthropod and microbial activity respond to labile and recalcitrant detritus, and how their response and/or the detrital additions alone can impact N and
P pools in the soil. We quantified the activity and composition of the ground arthropod community (detritivores, herbivores, and their predators), soil microbial biomass and activity
(microbial respiration and extracellular enzymes), and both water soluble (can enter runoff 12
through leaching and correlates with runoff) and adsorbed (available to organisms through cation-exchange) N and P pools in croplands (Figure 1.1). We expected arthropods to recruit to plots with detritus, and most readily to plots with mayflies (i.e. nutritious food) added. Because arthropods both release labile nutrients and stimulate microbes, which further break down
detritus, we predict that arthropod activity would be positively correlated with microbial activity
and with N and P availability in both the water-extractable and adsorbed pools.
Methods
Study sites
We conducted our experiment between June 28th and August 10th 2016 in four
conventionally managed corn (Zea mays) fields in Luckey, Ohio, USA (N 41° 27', W 83° 30'),
owned by t he same farmer and are all located within a 3 km rl adius.co Al rn fields are on a two-
year rotation with soy (Glycine max) and were planted during the week of May 23rd 2016.
During the week of June 6th 2016, 349 liters per hectare of 28% nitrogen fertilizer (a 2:1:1 mixture of urea, ammonium, and nitrate; 28-0-0) was applied to the fields (~125 kg N ha -1).
They applied no P and no insecticides. The syoil lo is a cla H am in the oytville series, classified
as a Mesic Mollic Epiaqualf (USDA, 2014) and contains an average of 2.0% organic matter
(analyzed by Spectrum Analytical Inc., Washington Court House, OH, USA). The soil has an
average pH of 6.7, though pH did vary throughout the season, starting with an average of 7.7 and reached an average of 6.7 midway through the experiment. This variation is likely due to the
fertilizer application nearer to the beginning of the experiment.
Experimental design
The experiment consisted of three treatments: a mayfly carcass addition (i.e. labile
detritus), a corn stover (dead leaves and stalks) addition (i.e. recalcitrant detritus), and a 50:50 13 combination of the mayfly carcass and corn stover addition. We collected live mayflies
(Hexagenia spp.) as they swarmed near a light in Point Place, OH (41°42'58.1"N 83°28'46.5"W) and stored them at -20°C. The treatments were applied to the soil surface of 0.25 m2 plots between rows of corn and replicated 3 times in each of the 4 fields, with an additional 3 unmanipulated control plots per field. The plots were separated by 10 m (Yang 2006) and randomly assigned a treatment. In mayfly treatment plots, we added 120 g frozen weight
(equivalent to 36 g dry weight) of mayflies, in the corn stover treatment plots we added 40 g corn stover (equivalent to 36 g dry weight), and in the combination corn stover/mayfly plots (hereafter mayfly+corn) we added 18 g dry weight equivalent of both mayflies and corn stover.
We digested a subsample of mayflies in alkaline potassium persulfate for 1 hour at 121°C
(Metzner, 2018) to determine their initial N and P content. The corn stover was SQC10715 variety corn (Shininger’s Quality Crop Seed, Fulton, OH), ground into approximately 2.5 cm pieces. This size is on the smaller end of corn residue found on fields, but we chose this size due to its similarity to mayfly size and the small size of our plots. We also digested corn stover grown in a greenhouse for general comparison (Lindsey-Robbins, 2019), as we did not have litter left over from the experiment. The digested samples were analyzed using a SEAL Analytical
Discrete analyzer (SEAL Analytical Inc., Mequon, WI). We found that the mayflies contained
0.5% P and 6.5% N and the tested corn stover had about 0.15 % P and 1.1% N. From these percentages, we determined that mayfly plots received a subsidy with approximately 7.2 kg ha-1
P and 93.6 kg ha-1 N, the corn plots a subsidy with approximately 2.16 kg ha-1 P and 15.4 kg ha-1 N, and the mayfly+corn plots a subsidy with approximately 4.8 kg ha-1 P and 63.6 kg ha-1
N. 14
To determine how additions of mayflies and corn stover affected soil nutrient availability over the course of the experiment, we measured cumulative phosphate and inorganic N availability using ion exchange resins (Binkley and Matson 1983, Saggar et al. 1990). We inserted 2 cm wide x 5 cm tall strips of anion and cation exchange resin membranes (GE Power and Water, Trevose, PA, USA via Maltz sales: anion model #AR204SZRA-MKIII, cation model
#CR67-MKIII), which were regenerated using 0.5 M HCl and 0.5 M NaHCO3, into the soil after treatment. Each plot was then covered with 0.25 m2 cages made of 1.27 cm galvanized steel hardware cloth to deter large scavengers (e.g., raccoons, birds) (Everbilt, model #308221EB,
Atlanta, GA, USA).
Arthropod sampling
To measure arthropod activity-density (hereafter arthropod abundance), we installed two pitfall traps-4 oz, 5.5 cm wide polypropylene cups (Dynarex, model #4256, Orangeburg, NY) - on opposite ends of each 0.25 m2 plot. Each trap was filled to approximately 3/4 capacity with a
50% ethanol:water solution, and a drop of dish soap to break surface tension for captured arthropods, and left for 48 hours. Upon collection, the two traps from each plot were combined and all arthropods from the plot were transferred to a 90% ethanol solution for storage. We identified arthropods to the finest resolution needed to categorize them by trophic level (e.g. scavenger/detritivore, predator, herbivore), which was most frequently to taxonomic family.
Nutrient and microbial analyses
We measured soil biological activity and nutrient availability at three time points during the experiment; during the weeks of 3-July-2016, 17-July-2016, and 7-August-2016 (hereafter harvests 1, 2, and 3). We had capacity to process only 24 samples each day, therefore during each harvest we randomly selected two fields to be sampled on the Monday of the week and two 15
fields to be sampled on the Wednesday of the week. To determine how our amendments affected
soil biological activity and nutrient availability, we collected and combined two 5 cm diameter
by 5 cm deep, soil cores from each plot to measure extractable N and P pools, as well as soil
microbial biomass, extracellular enzyme activities and respiration (i.e. measures of microbial
activity). We homogenized each soil sample by hand for 5 minutes, removing roots, arthropods,
and rocks, and maintained them in an incubator at 20 °C until analysis, all of which occurred
within 17 hours of collection.
Soil respiration
For each plot, we added 20 g of homogenized soil to a half-pint mason jar and added
enough distilled water for the soil to reach 55% water-holding capacity, which promotes
microbial activity and ensured a consistent level of moisture for respiration measurements
throughout the experiment. The jars were pre-incubated at 20 °C for approximately 24 hours to
allow the soils to equilibrate to the adjusted moisture content, then vented to remove any build-
up of CO2. We sealed the jars with a lid containing a gas septum and allowed CO2 to accumulate
for 4 hours at 20 °C, then jar headspace samples were collected with a syringe. CO2
concentrations were immediately analyzed using a using a LI-820 Infrared Gas Analyzer (LI-
COR Biosciences, Lincoln NE, USA).
Soil nutrients and microbial biomass C, N, and P
We extracted 5 g of soil in 25 mL of nanopure water and 5 g in 0.5 M potassium sulfate
(K2SO4) to assess N and P availability, following Darrouzet-Nardi and Weintraub (2014). The water extraction provides water-soluble nutrients that are readily available to soil organisms.
K2SO4 extracts water-soluble nutrients plus those adsorbed to ion exchange sites on soil
particles, which can be available to soil organisms through cation exchange (reviewed in 16
Darrouzet-Nardi & Weintraub, 2014). By subtracting water-soluble nutrients from K2SO4 extracted nutrients, we estimated adsorbed nutrient pool sizes.
All extracts were analyzed for total dissolved N (TN) and dissolved organic carbon
(DOC) using a Shimadzu TOC-VCPN total organic carbon analyzer with a total N module
(Shimadzu Scientific Instruments Inc., Columbia, MD, USA). Microplate colorimetric assays
3- - were used to measure phosphate (PO4 ), nitrate (NO3 ) (Doane & Horwáth, 2003) and
+ ammonium (NH4 ) (D'Angelo, Crutchfield, & Vandiviere, 2001; Rhine, Sims, Mulvaney, &
Pratt, 1998), using a BioTek Synergy HT microplate reader (BioTek Instruments Inc., Winooski
VT, USA). To determine microbial biomass C, N, and P, we used a modified (Scott-Denton et al.
2006) chloroform fumigation-extraction (Brookes et al. 1985) followed by K2SO4 extraction and
3- DOC, TN, and PO4 analyses as described above. Concentrations in extracts from non- fumigated samples were subtracted from fumigated samples to estimate microbial C, N, and P.
The anion and cation exchange resin strips placed in the plots were removed during the final sampling date (7-August-2016). While in the soil, these strips accumulate ions in soil pore water, which provides a metric of cumulative exchangeable nutrient availability, rather than the snapshot view from instantaneous concentrations in soil extractions. Upon harvest, the resin strips were rinsed with nanopore water to remove soil particles, then shaken with 35 mL of 2 M
- + 3- KCl for 1 hour, vacuum filtered, and stored until analysis for NO3 , NH4 and PO4 as described above.
Soil enzymes
To measure relative microbial activities and assess their need for and potential acquisition of C, N, and P, we analyzed 4 extracellular enzymes produced by microbes: β-1,4-glucosidase
(BG), which releases glucose from the last step of cellulose breakdown; β-1,4-N-acetyl- 17 glucosaminidase (NAG), which releases N-acetyl glucosamine from the last step in step in chitin or peptidoglycan decay; leucine amino peptidase (LAP), which releases amino acids, especially
3- leucine, from peptide hydrolysis; and phosphatase (PHOS), which releases PO4 from the hydrolysis of phosphate monoesters.
We quantified enzyme activities using a fluorometric microplate method (modified from
Saiya-Cork, Sinsabaugh, & Zak, 2002) but with a modified universal buffer (MUB) adjusted to a pH of 7.7. The pH was chosen based on initial soil pH measurements. As the season went on, the average pH of the soil decreased to 6.7. We maintained the initial MUB pH for consistency throughout the experiment. We mixed 1 g of soil with 125 mL of MUB using a tissue homogenizer (Biospec Tissue Tearer, BioSpec Products, Bartlesville, OK, USA), then separately combined each soil slurry in microplates with four substrates: 4-methylumbelliferyl-β-D- glucopyrandoside (BG); 4-methylumbelliferyl-N-acetyl-β-D-glucosaminide (NAG); L-leucine-7- amino-4-methyl coumarin (hydrochloride) (LAP); and 4-methylumbelliferyl-phosphate (PHOS).
After the substrate was added, the slurries were incubated for 4 hours at 20 °C, and then read by a microplate reader at 365 nm (excitation) and 460 nm (emission) (BioTek Instruments Inc.,
Winooski, VT, USA). For analysis, we considered absolute enzyme activity and activity normalized to microbial biomass C (i.e. enzyme activity/microbial biomass C) to determine whether any changes in activity were due to altered microbial biomass or changes in microbial function.
Data analysis
All analyses were carried out in R version 3.1.3 (R Core Team, 2013). We used linear mixed effects models (lme) in the R package nlme (Pinheiro, Bates, DebRoy , Sarkar, & R Core
Team, 2015) to determine the effect of detritus treatment on arthropod diversity (Shannon- 18
Weaver, vegan package (Oksanen et al., 2016)) abundance (total and that of each functional
group) during each harvest. Abundance was square-root transformed to meet test assumptions of
normality. We accounted for effects of the corn fields by including field as a random variable in
the models. To determine the best fixed effects structure, we used backwards model selection
with Kenward-Rogers tests in the pbkrtest package (Halekoh & Hojsgaard, 2014). Post-hoc
multiple comparisons Tukey tests (glht) in the multcomp package (Hothorn, Bretz, & Westfall,
2008) were used to determine treatment effects when predictors were found to be significant. For
linear relationships, we used the r.squaredGLMM function in the MuMIn package (Barton,
2014) to obtain R2 values when predictors were found to be significant. Two R2 values are given:
2 2 2 2 a “marginal R ” (R m) for main effects and a “conditional R ” (R c) for main effects with the
field effect included. The same process was used to determine treatment effect on soil nutrients,
microbial biomass, soil respiration, and enzymes, and the relationship between detritivore,
predator, and total arthropod abundance and each of these measures. Herbivore abundance was
- so low (<15% of all arthropods), that we did not look for associations. Water-extractable NO3
required extensive dilution to be within the detection range of the assay; however, with each
- dilution the readings become less reliable, so we did not include water-extractable NO3 in
- - statistical analysis. Because NO3 adsorption is low in this soil, K2SO4 extracted NO3 was
- considered to represent available NO3 . In all cases, the response variables were normalized
through log-transformation. Rather than considering the harvest date as a factor in one model, we
analyzed the data from each of the three soil harvests separately. This decision was made
because even if a plot was missing a measurement for one date (i.e. due to sampling or analytical
error), it would be included in the analyses for other harvests, and because we expected
considerable differences between the harvests as the mayflies decomposed rapidly. 19
Results
Arthropods
Total arthropod abundance in the mayfly and mayfly+corn plots was approximately 70%
higher than the control in harvest 1, and approximately 80% higher than both the control and the
corn plots in harvests 2 and 3 (Table 1.1; Figure 1.2a). This effect was largely driven by detritivores, which were twice as abundant (relative to controls) in mayfly and mayfly+corn plots throughout the experiment. Arthropod abundance decreased in all treatments by approximately
50% between harvests 1 and 3. Predatory arthropods also responded to the treatments, but differently from the detritivores. Predator abundance was approximately 80% higher in mayfly+corn plots than in control plots at harvest 1, and approximately 120% higher in mayfly+corn plots than in control and corn plots in harvest 2. In harvest 3, predator abundance was approximately 80% higher in mayfly and mayfly+corn plots than the control plots (Table
1.1; Figure 1.2b). Herbivorous arthropods did not respond to the treatments. Arthropod diversity
and richness did not respond to experimental treatments at either the morphospecies or
taxonomic family level.
Soil microbial activity and nutrient dynamics
Microbial biomass C and N were not significantly different among treatments at any
harvest period, however microbial biomass C was approximately 25% lower and microbial
biomass N was approximately 55% lower overall at harvest 3 than at harvest 1 (F1,138=6.5,
p=0.002; F1,138=3.48, p=0.03 ) (Figure 1.3a; Figure 1.3c). Microbial biomass P was approximately 80% higher in mayfly plots than in corn and control plots during harvest 3 only
(Table 1.1.1; Figure 1.3b). Microbial respiration was approximately 50% higher in the mayfly and mayfly+corn treatments than the control and corn treatments at harvest 2 and 100% higher at 20
harvest 3 (Table 1.1; Figure 1.4). Microbial extracellular enzymes BG, NAG, LAP, and PHOS
did not differ between treatments during harvest 1, harvest 2, or harvest 3 (Table 1.1). Average
enzyme activity was BG: 43.04 ± 20.54 nmol·h-1·g ; LAP: 97.75 ± 55.70 nmol·h-1·g; NAG:
11.74 ± 9.55 nmol·h-1·g; PHOS: 46.03 ± 22.00 nmol·h-1·g (see Figure 1.5 for mean by harvest).
+ Water-extractable ammonium (NH4 ) was approximately 150% higher in mayfly plots than in corn and control plots at harvest 1 only (Table 1.1; Figure 1.6a). There was no significant
+ change in water-extractable NH4 between harvests, but generally declined over time. Adsorbed
+ - NH4 and NO3 did not differ between treatments at any harvest and did not significantly differ
throughout the experiment, but were typically highest in the mayfly plots (Table 1.1; Figure
1.6b; Figure 6c). Cumulative exchangeable N did not differ between treatments (Table ; Figure
1.6d).
3- Water-extractable phosphate (PO4 ) was approximately 80% higher in the mayfly plots
3- than in corn and control plots at harvest 3 only (Table 1.1; Figure 1.7a). Water-extractable PO4
did not differ between harvests but tended to be higher in the mayfly-only treatment. Adsorbed
3- PO4 did not differ between treatments or over time; though the response to treatment was
marginally significant (p>0.07), a Tukey HSD test confirmed no difference between treatments
(Table 1.1; Figure 1.7b). Cumulative exchangeable P was approximately 400% higher in the
mayfly plots than in control and corn plots, and mayfly+corn plots had approximately 100%
higher cumulative exchangeable P than in control plots (Table 1.1; Figure 1.7c).
Linking arthropods and microbial activity
Total arthropod abundance was positively correlated with respiration at harvests 1
(F1,42=7.96, p=0.007), 2 (F1,43=10.23, p=0.003) and 3 (F1,43=17.03, p=0.0004) (Figure 1.8). 21
Nutrient pools and enzymes were not correlated with arthropod abundance (figures included in
Supplementary Information).
Table 1.1 Linear mixed effects model test statistics for the effect of experimental treatment on
each response variable.
Response Harvest df F statistic p-value variable Harvest 1 3, 40 7.02 <0.0007 Arthropod Harvest 2 3, 41 9.23 <0.0001 abundance Harvest 3 3, 41 10.32 <0.0001 Predatory Harvest 1 3, 40 3.08 <0.04 arthropod Harvest 2 3, 41 3.90 <0.02 abundance Harvest 3 3, 41 3.78 <0.01 Harvest 1 3, 41 1.50 0.23 Microbial Harvest 2 3, 41 1.77 0.17 biomass C Harvest 3 3, 41 1.71 0.18 Harvest 1 3, 41 0.33 0.80 Microbial Harvest 2 3, 41 0.07 0.98 biomass N Harvest 3 3, 41 0.79 0.51 Harvest 1 3, 41 0.89 0.45 Microbial Harvest 2 3, 41 0.49 0.69 biomass P Harvest 3 3, 41 2.85 <.05 Harvest 1 3, 41 1.20 0.32 Microbial Harvest 2 3, 41 8.17 <0.0003 respiration Harvest 3 3, 41 13.95 <0.0001 β-1,4- Harvest 1 3, 41 0.7300 >0.54 glucosidase Harvest 2 3, 41 1.01 >0.39 (BG) Harvest 3 3, 41 0.724 >0.54 β-1,4-N-acetyl- Harvest 1 3, 41 1.859 >0.15 glucosaminidase Harvest 2 3, 41 1.175 >0.33 (NAG) Harvest 3 3, 41 1.4007 >0.25 Harvest 1 3, 41 0.8743 >0.46 Leucine amino Harvest 2 3, 41 0.2887 >0.83 peptidase (LAP) Harvest 3 3, 41 0.4614 >0.71 Harvest 1 3, 35 0.4811 >0.69 Phosphatase Harvest 2 3, 41 0.9119 >0.44 (PHOS) Harvest 3 3, 41 2.716 >0.06 22
Water- Harvest 1 3, 41 3.26 <0.03 extractable Harvest 2 3, 41 0.39 0.75 ammonium Harvest 3 3, 41 0.53 0.67 Harvest 1 3, 41 1.39 0.26 Adsorbed Harvest 2 3, 41 0.98 0.41 ammonium Harvest 3 3, 41 0.58 0.63 Harvest 1 3, 41 0.14 0.94 Adsorbed Harvest 2 3, 41 0.11 0.95 nitrate Harvest 3 3, 41 1.80 0.16 Cumulative Harvest 3 3, 41 1.58 >0.20 exchangeable N Water Harvest 1 3, 41 1.07 0.37 extractable Harvest 2 3, 41 1.53 0.22 phosphate Harvest 3 2, 41 3.73 0.02 Harvest 1 3, 41 0.46 0.71 Adsorbed Harvest 2 3, 41 2.43 0.08 phosphate Harvest 3 3, 41 0.67 0.58 Cumulative Harvest 3 3, 41 6.51 <0.002 exchangeable P
Discussion
Understanding the controls on nutrient pools in croplands is essential if we are to mitigate nutrient-rich runoff. This study examined how arthropods and microbes responded to the addition of labile and recalcitrant detritus and how water-extractable and adsorbed N and P pools varied with the detrital additions over a 7-week period. We also considered correlations between arthropod abundance, microbial biomass and activity, and nutrient pools to examine the potential for arthropods and microbes to impact nutrient cycling in agricultural fields. We predicted that arthropods would recruit to plots supplemented with detritus, especially mayfly carcasses, resulting in increased nutrient cycling and availability. Because we expected the arthropods to stimulate microbes, and also for the microbes to directly respond to the detritus, we predicted that the N and P pools would be increased by detritus, especially in plots with added mayflies. 23
Overall, arthropod abundance was low in the fields; in fact, in a recent experiment, we
found approximately 5x the number of arthropods in nearby urban prairies (Pelini unpublished
data, 2018). This pattern that has also been reported in studies that compared agricultural fields
to nearby prairies (A. F. Fox et al., 2016; Werling, Meehan, Robertson, Gratton, & Landis,
2011). Though during this growing season there were no insecticides applied, the farmer does
use insecticides as needed, which may affect arthropod abundance. Despite low arthropod
abundance, detritivores were found in increased numbers in both treatment types with mayfly
addition (mayfly and mayfly+corn) through all three harvests, even though by harvest 2, mayfly
carcasses were no longer visible on the plots. Contrary to other carcass addition studies (e.g. L.
H. Yang, 2006), the effect size did not diminish throughout the experiment—arthropods were
consistently ~70% greater in mayfly and mayfly+corn plots, and scavenger abundance was
always about twice as high. However, there was an overall decrease in arthropod abundance over
the course of the experiment in all plots. In general, the increased abundance of detritivores in
mayfly addition plots was expected because detritivores are attracted to invertebrate carcasses
(Halaj & Wise, 2002; Hoekman et al., 2011; L. H. Yang, 2006). However, the lack of detritivore
response to corn stover addition was surprising, as even litter of lower quality, such as corn
stover, is known to attract detritivores (Badejo et al., 1995). One potential reason for the lack of
detritivore response to corn stover in this study may have to do with the size we used (~2.5 cm);
larger pieces or more variation in the size of pieces, which would better reflect stover often
found on fields, likely impacts microclimate differently.
Arthropod predators of detritivores responded primarily to the combined addition of mayfly carcasses and corn (~100% increase over control plot abundance). Several studies show that predators, particularly spiders, are found in higher abundance in habitats that are more 24 structurally complex (Bultman & Uetz, 1982; Langellotto & Denno, 2004; Mathews, Bottrell, &
Brown, 2004), and an experiment that altered ground habitat in an apple orchard found that predation was higher when mulch with detritus was added than just mulch alone (Mathews et al.,
2004). The corn stover addition may have provided additional habitat complexity that attracted predators, particularly when it occurred in combination with mayfly carcasses. Though not statistically significant, trends of higher predator abundance in corn only and mayfly only plots during harvests 1 and 3 suggest that corn stover alone or mayfly carcasses alone may have attracted predators. However, when added in combination, increases to predator abundance were amplified, possibly because the carcasses not only attracted potential detritivore prey, but also could have served as a nutrient source for predators supplementing their diet by scavenging.
Unlike arthropods, microbes had a limited response to the detritus additions. Microbial respiration increased by ~50% in response to the mayfly and mayfly+corn treatment. Microbial biomass and enzyme activities did not respond to detritus additions. Though not significant, both microbial C and N appear to be decreasing between harvests 2 and 3. There was also a statistically non-significant increase in microbial biomass C and N in mayfly plots from harvest 1 to harvest 2, and then a drop in microbial biomass C and N in all by harvest 3. This trend is consistent with patterns of soil respiration, and suggests initial microbial use and subsequent depletion of mayfly C. Though the patterns were consistent, microbial biomass was not significantly correlated with soil respiration, which may be due to variations in microbial C use efficiency (Stefano Manzoni, Taylor, Richter, Porporato, & Ågren, 2012) or dormancy (Salazar-
Villegas, Blagodatskaya, & Dukes, 2016) .
Furthermore, respiration was positively correlated with arthropod abundance, regardless of detritus treatment. This relationship was weak in harvest 1 but became stronger throughout the 25
experiment. Though we cannot be certain of a causal relationship, the association may reflect
how detritivores can influence microbial activity by physically altering the soil environment,
providing labile nutrients, and selective feeding (T. W. Crowther et al., 2011; Thomas W.
Crowther et al., 2015; Ritzenthaler et al., 2018).
N and P nutrient pool responses to mayfly carcass additions were staggered in time— with N increasing initially and then decreasing, and P slowly increasing over time. We observed
+ an initial increase in water-extractable NH4 from mayfly addition; otherwise, there was no
change to N pools. This pattern suggests that soluble N from the mayfly carcasses was rapidly
used and depleted. Since the chitin in mayfly exoskeletons are a labile source of N (Boyer &
Kator, 1985), there was likely still N released from the carcasses over time, but this input could have been small compared to the N provided by fertilizer additions. However, our results suggest that mayfly additions increased cumulative P availability, which was approximately 400% higher in the mayfly treatment plots. We cannot be certain what proportion of this P was derived from the mayfly carcasses and what proportion was released from the soil due to the increased biological activity caused by carcass addition. We also cannot be certain why cumulative P availability was not also increased in the mayfly + corn treatment, but one possibility would be that the corn stover in the mayfly + corn stover addition may have provided C that promoted P immobilization, thereby reducing measured P derived from mayfly carcasses. Overall, we found that labile detrital addition had little impact on N pools in this agricultural soil over the 7-week period, but can provide a source of P. When in combination with a more recalcitrant form of detritus, such as corn stover, this P may be taken up by microbes and immobilized rather than becoming incorporated in runoff. 26
Conclusion
This study investigated how adding labile and recalcitrant detritus impacted arthropod and microbial activity, and how their response or the detrital addition itself would impact N and
P pools in an agricultural soil over a 7-week period. We found that the addition of labile, but not recalcitrant, litter provides nutrients and recruits detritivores and their arthropod predators.
Furthermore, recalcitrant litter may attract predatory ground arthropods by providing physical structure and habitat, and also provides a C source that could promote P immobilization and therefore retention on-field. However, the arthropod response did not correlate with changes in nutrient pools, possibly due to the low numbers of arthropods present on the fields. Additionally, the duration of this study was only seven weeks, which may not be long enough to stimulate biogeochemical pathways and see the impact of arthropods on those pathways. However, past studies in natural systems have found stronger responses to similar subsidies within a similar time frame (e.g. Song et al., 2015; L. H. Yang, 2006), suggesting that the lack of biological activity in conventionally managed croplands may have dampened any response. Thus, the ability of labile detritus additions, with or without recalcitrant detritus, to enhance soil nutrient availability and reduce the need for fertilizer additions on a conventional agricultural field is still unclear beyond the input of water-soluble P. Future studies aimed at exploring similar subsidies would benefit from using a longer time-period, repetition, and/or studying organic agricultural fields with different management methods (e.g. no till, biological or integrated pest management, crops other than corn), which may have higher baseline biological activity (Gurr et al., 2016;
Lichtenberg et al., 2017). 27
Figures
Figure 1.1 Conceptual diagram of soil processes. Boxes represent pools and arrows represent processes. Bolded terms indicate components that we quantified in this study. Figure Credit:
Josephine Lindsey-Robbins (Lindsey-Robbins 2019), modified from (Susser et al., 2020
(accepted)) 28
Figure 1.2 Response of detritivores (left) and predators (right) to each detritus treatment over the course of the experiment. Bars indicate standard error. Asterisks indicate p<0.05. 29
a
Figure 1.3 Response of (a). microbial biomass carbon (µg-C·g soil-1), (b) microbial biomass
3- PO4 (µg-P·g soil-1), and (c) microbial biomass N (µg-N·g soil-1) to each detritus treatment at
harvests 1, 2, and 3. Asterisks indicate p<0.05 between treatments, letters indicate significant difference in the average biomass over time. 30
Figure 1.4 Response of microbial respiration (µg-C·g soil-1·day-1) to each detritus treatment over the course of the experiment. Bars indicate standard error. Asterisks indicate p<0.05. 31
Figure 1.5 Response of microbial extracellular enzymes (a) phosphatase (PHOS), (b) β-1,4-N- acetyl-glucosaminidase (NAG), (c) β-1,4-glucosidase (BG), and (d) leucine amino peptidase
(LAP) (nmol·h-1·g) to each detritus treatment over the course of the experiment. Bars indicate standard error. 32
+ - Figure 1.6 Response of (a) soluble- and (b) adsorbed NH4 , (c) NO3 (µg N·g soil-1), and (d) cumulative exchangeable N (µg N·g soil-1) to each detritus treatment over the course of the experiment. Bars indicate standard error. Asterisks indicate p<0.05 33
3- Figure 1.7 Effect of detritus treatments on (a) water extractable PO4 (µg P·g soil-1) (b)
3- 3- adsorbed PO4 , and (c) cumulative exchangeable PO4 .Asterisks indicate p<0.05, letters indicate
significant difference over time. 34
Figure 1.8 Relationship between arthropod abundance and microbial respiration (µg-C·g soil-
1·day-1) at harvests 1, 2, and 3. Asterisks indicate p<0.05. 35
CHAPTER 2. DOES HABITAT STRUCTURAL COMPLEXITY PREDICT
ARTHROPOD PREDATOR-PREY RATIO IN URBAN PRAIRIES?
Introduction
The conversion of land into urban areas can negatively impact arthropods, which is
concerning since arthropods play a role in nearly all ecosystem services (Prather et al., 2013),
including pest control, pollination, seed dispersal, and nutrient cycling (Lavelle et al., 2006;
Losey & Vaughan, 2006). Four percent of U.S. land is covered by urban areas and the percentage
is rapidly increasing (Bigelow & Borchers, 2017) and over half of the U.S. population lives in
urban and suburban areas (Parker et al., 2018). However, within these urban areas, people make
efforts that can support beneficial arthropods. Restoring and conserving green spaces (here and
throughout the paper, refers to spaces like parks, prairies, and gardens, but not turf lawns) in
cities is a popular means to support pollinators, mitigate runoff, and provide recreation. Green
spaces have higher abundance and especially species richness when compared to turfgrass
(Braman et al., 2002; Burkman & Gardiner, 2015; A. K. Fiedler & D. A. Landis, 2007; Steven D.
Frank et al., 2008; Isaacs et al., 2009). This is important because cities often have decreased
insect abundance, richness and diversity, especially nearer to city centers (Faeth et al., 2011;
Lagucki et al., 2017; Michael L McKinney, 2008), which is likely due to loss of habitat quantity,
heterogeneity, and structure. Arthropod communities have also been shown to shift in urban
areas (Egerer et al., 2017; Lagucki et al., 2017; Uno, Cotton, & Philpott, 2010). Despite the
negative impact of urbanization on some arthropod communities, others, such as several pest
species (e.g. mosquitoes, ticks, weevils, nuisance flies) and ants adapt well to cities (Brunskill et al., 2011; Michael L McKinney, 2006; Uno et al., 2010) and occur in higher abundances
(McIntyre, 2000). Though green spaces may be installed for the purposes listed above, they also 36 can support arthropod predators that may prey on these pests (Ferrante, Lo Cacciato, & Lövei,
2014). By investigating controls on natural predator communities and predation rates in these spaces, we can better understand how to maximize the ability of the spaces to promote biological control of pests.
Habitat structure may be key to understanding predation in urban green spaces. Several studies have found that habitat structure, particularly habitat complexity, impacts predation
(Bultman & Uetz, 1982; Corcuera et al., 2016; S. D. Frank & Shrewsbury, 2004; Frey et al.,
2018; Langellotto & Denno, 2004; McNett & Rypstra, 2000; Podgaiski & Rodrigues, 2017;
Schmidt et al., 2005; Shrewsbury & Raupp, 2000). The way that each study defines habitat complexity varies based on the needs of the focal predator species. For example, density of vertical vegetative growth is important for web-building spiders, while litter shape and depth are more relevant for hunting spiders and beetles. Furthermore, some studies include variation in biomass of substrate (i.e. litter, vegetation) as a metric of habitat complexity (e.g. Dale & Frank,
2014; S. D. Frank & Shrewsbury, 2004), while others control for substrate biomass and manipulate only structural complexity, such as density of vertical stems or litter shape (e.g.
Bultman & Uetz, 1982; Podgaiski & Rodrigues, 2017). Despite the varied approaches, habitat complexity has consistently been shown to increase predator abundance (Bultman & Uetz, 1982;
Corcuera et al., 2016; S. D. Frank & Shrewsbury, 2004; Langellotto & Denno, 2004; Podgaiski
& Rodrigues, 2017; Schmidt et al., 2005). Habitat complexity can be beneficial to predators through multiple mechanisms: increased abundance of prey, more favorable microclimate, more efficient detection (e.g. through vibrations carrying) and/or capture of prey, escape from predation and cannibalism, and supplemental resources (e.g. alternate prey, pollen, dead items)
(Langellotto & Denno, 2004). 37
Features of the urban habitat that are different than those found in natural systems may be
contributing to the variable relationship between habitat complexity and predation. One such
feature is impervious surfaces in cities, which cover approximately 50% of the area in urban
cores (Michael L. McKinney, 2002). Impervious surfaces absorb heat (Bolund & Hunhammar,
1999), which increases temperature, and in temperate cities, lengthens the growing season and leads to fewer frost days in cities relative to surrounding rural areas. This effect on urban temperature alone may fundamentally alter arthropods’ interactions and their impact on the environment. Since arthropods are ectotherms, their activity (Chown & Gaston, 2010), fecundity
(Dale & Frank, 2014), development (Berger et al., 2008), and community structure (Bokhorst et al., 2008; Briones et al., 2009; Maran & Pelini, 2015; Pelini et al., 2014; Zhang et al., 2013) are all affected by warming, positively or negatively depending on their thermal tolerance (Diamond et al., 2012). Dale and Frank (2014) found that the temperature within urban tree canopies increased with the percent impervious surface within 100 m. Since scale insect fecundity is positively associated with temperature, scale insect reproduction was heightened in trees surrounded by a high percentage of impervious surfaces. The hastening of scale insect reproduction rate outpaced any impact of vegetative complexity on predator and parasitoid control of the pests.
In this study we ask how the structural complexity of vegetation impacts predator-prey ratios in restored urban prairies in Toledo, OH and whether proximity to impervious surfaces alters the relationship between complexity and predator-prey ratio. We expected that predator- prey ratio would be positively correlated with structural complexity, but that the relationship would diminish with closer proximity to impervious surface due to the potential for temperature to increase nearer to impervious surface. 38
Methods
Study sites
We conducted this experiment from July-August 2018 in 6 urban prairies planted and managed by the Toledo Zoo in Toledo, OH (Table 2.1). The prairies were chosen because they were seeded with native prairie plants and are similar in age and management. The prairies were
3-5 years old and are mowed rather than burned. Mowing does not take place on a consistent schedule, but frequency is similar between all prairies. The prairies are restored prairies, which often have lower plant species richness (Kindscher & Tieszen, 1998; Sluis, 2002) than remnant prairies. However, because these prairies are all young, their arthropod communities are likely to have high richness and diversity more similar to what would be observed in remnant prairies, since arthropod species richness and diversity tend to be initially high in restored prairies and slowly decrease over time (Dahms, Lenoir, Lindborg, Wolters, & Dauber, 2010; Orlofske,
Ohnesorg, & Debinski, 2011).
Within each prairie, we identified 1 m2 plots that were varying distances from the nearest impervious surface. To select the plots, we created a digital grid consisting of 1 m2 squares and randomly selected plots. If two plots were within 8 m of one another, we randomly selected a different location on the grid to avoid spatial correlation. The number of plots in each prairie scaled with the area of the prairie (i.e. each prairie only had the number of plots that could be used without any being closer than 8 m to another plot). There were a total of 45 plots across all prairies. 39
Table 2.1 Locations, approximate size, and number of plots of urban prairies. Prairie ID Location Approximate Size (m2) Number of plots 1 41.616433, -83.582033 405 6 2 41.620918, -83.582024 404 4 3 41.712978, -83.557235 1214 12 4 41.621700, -83.583300 400 4 5 41.621350, -83.583483 809 7 6 41.621181, -83.584735 1497 12
Structural complexity
Structural complexity of the vegetation in each plot was quantified horizontally and vertically at the beginning of July and end of July with methods adapted from McNett and
Rypstra (2000), which essentially measures vegetation density. We measured vertical complexity by randomly selecting a point in the plot and placing a meter stick perpendicular to the ground at that location. The random points were selected by creating a 1m2 coordinate grid and using a random number generator (random.org) to select coordinates. We then counted how many pieces of vegetation were touching the meter stick and recorded the lowest and highest point of contact with vegetation. Since we were concerned with overall structural complexity, any piece of vegetation was counted as an independent piece, even if the leaves or stems were on the same plant (e.g. if a plant had leaves touch the meter stick at 5 cm and 15 cm, they each counted as a point of contact). We repeated this for a total of five locations in each plot. Horizontal complexity was measured twice from each four sides of the plot, once at a random height between 1 and 50 cm (<50 cm horizontal complexity) and once between 51 and 100 cm (>50 cm horizontal complexity). These height categories were chosen since plots were most frequently around 100 cm in height. We placed a meter stick horizontally at the selected height and counted 40 how many pieces of vegetation touched the meter stick. At the end of the season, we measured the biomass of vegetation from each plot by removing the vegetation and harvesting it, separating it into vegetation below 50 cm and above 50 cm, and then drying it at 90°C for 7 days and weighing it.
During these vegetation surveys, we measured other plot characteristics in addition to complexity. We recorded surface temperature in 4 random locations in the plot, soil moisture, soil temperature, number of open floral blooms, and the number of different species of floral blooms. We also estimated the percent ground cover of grasses and forbs in the plot, and presence of shrubs or tree canopy. We monitored ambient air temperature throughout the experiment by housing Thermochrom iButton temperature sensor and loggers (Model DS1921G-
F5#, Dallas, TX) under small styrofoam cups and attaching them to a plant in the plot.
Arthropod sampling
Each plot was sampled for arthropods biweekly using an insect vacuum, which we created by modifying (Zou et al., 2016) a Homelite leaf blower (model # UT26HBV, Anderson,
SC, USA). Before vacuuming, we would visually survey the plot for 1 minute to record any large flying insects that would not be captured in the vacuum (e.g. bees, large beetles). We did not record insects that flew through the plot without interacting with the vegetation. After the visual survey, we vacuumed the vegetation in the plot for 1.5 minutes. One and a half minutes was chosen after preliminary trials because it allowed us to consistently vacuum all parts of the vegetation in the plot. We then identified most arthropods on site and preserved those we could not identify in 90% ethanol for later identification in the lab. We identified insects to the finest resolution needed to categorize them by trophic level (e.g. predator, parasitoid, herbivore), which was most frequently to taxonomic family. Spiders were identified to family when possible and 41 considered to be predators. Other arthropod orders (e.g. isopods, centipedes) were not identified further, and due to their rarity in our samples, were not classified by trophic level.
Data analysis
All analyses were carried out in R version 3.1.3 (R Core Team, 2013). We used linear mixed effects models (lmer) in the R package lme4 (Bates, Maechler, Bolker, & Walker, 2014) to examine relationships between predator-prey ratios and structural complexity and impervious surface. In addition, the effect of these predictors on arthropod abundance (normalized through square-root transformation) and richness were tested. Spiderlings were removed from abundance numbers since they are often carried on the adult or are found in masses. Springtails were removed and considered separately since their high numbers tend to mask the response of other arthropod groups. For analysis of vertical complexity, we averaged all the vertical complexity measurements taken in the plot across both sampling dates. Initially the dates were considered separately, but when the plots were found not to have changed significantly between sampling days despite any ongoing growth, we combined all measurements taken in the plot. The minimum and maximum point of contact measured in each plot were also considered as potential predictors. Similarly, <50 cm horizontal complexity measurements were averaged for each plot, as were >50 cm complexity measurements. See Table 2.2 for a summary of complexity measures.
We accounted for effects of the prairies by including the prairie site as a random variable in the linear mixed effects models. We used Kenward-Roger approximation tests (KRmodcomp function, pbkrtest package (Halekoh & Hojsgaard, 2014)) to determine whether fixed effects were significant predictors. We used the r.squaredGLMM function in the MuMIn package
(Barton, 2014) to obtain R2 values when predictors were found to be significant. Two R2 values 42
2 2 2 2 are given: a “marginal R ” (R m) for main effects and a “conditional R ” (R c) for main effects with the site effect included. The same process was used to assess the potential for other variables to act as covariate predictors of predator-prey ratios and arthropod abundance or richness, such as plant richness, number of blooms in the plot, distance to the nearest edge of the prairie, percent forbs, percent grasses, surface temperature, soil temperature, and soil moisture.
We also looked for a correlation between complexity measures and plant biomass. We completed a post-hoc analyses to assess potential predictors of certain important pest groups that were found to be common during the study (aphids, thrips, and whiteflies) and one very abundant group (springtails). Since at larger scales insect communities have been altered by increased temperature and decreased moisture (Bokhorst et al., 2008; Briones et al., 2009; Maran & Pelini,
2015; Pelini et al., 2014; Zhang et al., 2013), which can be an effect of impervious surface (Dale
& Frank, 2014), we also looked for significant correlations between distance from impervious surface and soil moisture, surface temperature, and soil temperature. 43
Table 2.2. A description of complexity measurements used in this study
Factor Description Vertical measures that describe the number of pieces of vegetation touching a meter stick Vertical The average number of pieces of vegetation that touched the meter stick across Complexity all 10 random points (5 measured during each sampling period) where we measured vertical complexity in the plot. Vertical measures that describe the height of pieces touching a meter stick Vertical Minimum The lowest piece of vegetation touching the meter stick during the 10 vertical random points where we measured vertical complexity in the plot. Vertical Maximum The highest piece of vegetation touching the meter stick during the 10 vertical random points where we measured vertical complexity in the plot. Vertical Average The average height of vegetation across all 10 vertical complexity Height measurements in the plot Horizontal measures of complexity >50 cm Horizontal The average number of pieces of vegetation that touched the meter stick during Complexity the 8 horizontal complexity measurements from 50-100 mm (4 measured during each sampling period). <50 cm Horizontal The average number of pieces of vegetation that touched the meter stick during Complexity the 8 horizontal complexity measurements from 0-50 mm.
Results
Plot characteristics
Measured plots differed in their distance from impervious surface (15.06 ± 7.07 m (mean
± SD, here and throughout this section)). Soil moisture (13.06 ± 5.23%), soil temperature (21.75
± 1.44 °C), and surface temperature (21.92 ± 2.51 °C) were not correlated with distance from impervious surface at the scales measured. Vertical complexity was, on average, 7 ± 2 pieces of vegetation touching the vertically-placed meter stick. Average height of plots varied, with an average height of 64 ± 23.5 cm. Horizontal complexity varied more substantially than vertical complexity between plots: <50 cm horizontal complexity had on average 20 ± 12 pieces of vegetation making contact with the meter stick, while the >50 cm horizontal complexity had on average 6 ± 4 pieces of vegetation making contact. Vertical complexity was positively correlated 44
2 2 with the plot’s total vegetation biomass (F1,29=10.32, p=0.003, R m=0.18, R c=0.55; Figure 2.1).
For horizontal complexity, <50 cm horizontal complexity was not correlated with vegetation biomass (F1,27=0.0011, p=0.97; Figure 2.2a). However, >50 cm horizontal complexity was
2 correlated with vegetation in the top 50 cm of the plot (F1,25=34.51, p<.0001, R m=0.55,
2 R c=0.55; Figure 2.2b).
Predator-prey ratio
Predator-prey ratio was associated with <50 cm horizontal complexity, but not >50 cm
horizontal complexity or vertical complexity. Predator-prey ratio was best explained by an interaction between horizontal complexity and percent forbs in the plot (Table 2.3, Figure 2.3).
Predator-prey ratio increased with horizontal complexity in general, but with greater effect as percent forbs in the plot increased. No other potential covariates, including distance from impervious surface, were correlated with predator-prey ratio.
Arthropod abundance and richness
Arthropod abundance was significantly negatively associated with percent grass in the plot (Table 2.3, Figure 2.4). Conversely, abundance of springtails, one of the most frequently collected arthropods, was positively associated with percent grass in the plots (Table 2.3, Figure
2.5). No complexity measures or other plot characteristics were associated with overall arthropod or springtail abundance. However, during analyses on subsets of that data for each abundant pest species, other predictors were important. Aphid abundance was negatively correlated with >50 cm horizontal complexity (Table 2.3, Figure 2.6) and no other plot characteristics. Whiteflies and thrips were not significantly associated with any predictors. Neither family nor morphospecies richness were predicted by any plot characteristics. 45
Table 2.3. Linear-mixed effects model statistics. Effect df F p-value Predator-Prey ratio <50 cm horizontal complexity x percent 1, 36 5.08 0.05* forbs <50 cm horizontal complexity 1, 36 11.49 <.0003* percent forbs 1, 36 1.14 0.35 Abundance percent grass 1, 38 14.48 <.0008 Abundance (springtails) percent grass 1,38 7.01 0.02 Abundance (aphids) >50 cm horizontal complexity 1,38 9.01 0.01
Discussion
As the trend towards adding green spaces to urban areas continues, managers of those
spaces will benefit from more information about how which species planted and planting
location will affect the services provided by arthropods in the space. This study provides data
about what types of vegetation will promote arthropod predator abundance and how planting
them close to impervious surfaces may change the outcome.
As we predicted, structural complexity was positively correlated with predator-prey ratio;
however, the relationship was only significant when complexity was driven by forbs and only in
the lower portion of the plot. Contrary to our predictions and the findings of other studies
measuring predator-prey ratio in an urban environment (Dale & Frank, 2014), proximity of
impervious surface did not impact the relationship between any sources of complexity and
predator-prey ratio. However, this is unsurprising, since impervious surface is thought to
negatively impact predator-prey ratio because of its warming and drying effect on nearby
habitats (Bolund & Hunhammar, 1999), and within our prairies we did not find a significant relationship between the temperature or moisture in a plot and its proximity to impervious 46
surface. We may not have seen a difference from impervious surface due to the small scale—
plots within the same prairie may not be different enough in their distance from impervious
surface to see an effect.
Predator-prey ratio was best predicted by the interaction between <50 cm horizontal
complexity and percent forbs in the plot. Though predator-prey ratio increased with <50 cm
horizontal complexity in general, the effect was amplified when there were more forbs in the plot
(Figure 2.3). This finding suggests that the source of complexity (e.g., forbs vs grass) in the plot
may be just as important as the structural complexity of vegetation in the plot. Forbs may be a
better source of complexity for arthropod predator-prey ratios because their broader leaves provide more surface area on which arthropods can move and have a more branched structure compared to blades of grass. Few studies have focused on the impact of complexity in shape of habitat on predators, but some have reported that shape does have an effect (Bultman & Uetz,
1982; Podgaiski & Rodrigues, 2017). Though we did not measure surface area, we did measure
other features of the plots which represent other differences between the forbs and grasses, such
as number of blooms and bloom species richness, which did not have explanatory power.
However, other studies have found bloom characteristics to promote predators (A. K. Fiedler &
D. Landis, 2007). Additionally, studies have found that vegetation type can affect the arthropod
community; prairies dominated by grasses, particularly short grasses have lower arthropod
abundance, richness, and biomass (Jerrentrup, Wrage‐Mönnig, Röver, & Isselstein, 2014; Norton
et al., 2019) and that vegetation height can drive arthropod communities (Norton et al., 2019).
There was no significant interaction between >50 cm horizontal complexity and forbs, likely
because most of the vegetation at that height is made up of forbs. 47
Vertical complexity did not vary significantly with predator-prey ratio, which may be
because the horizontal structural complexity data was only correlated with predator-prey ratio in
the lower portions of plots. If the importance of structural complexity for predator-prey ratio is
vertically stratified, then we would not expect a vertical measurement to predict predator-prey
ratio well. The arthropod community in our prairies could also contribute to the vertically
stratified result: different types or locations of complexity have been shown to have varied
impacts on the abundance and richness of arthropods (Rusch, Valantin-Morison, Sarthou, &
Roger-Estrade, 2011), as well as predator-prey ratio (Dale & Frank, 2014; Langellotto & Denno,
2004; Podgaiski & Rodrigues, 2017), likely due to the focal species and their habitat needs.
Overall, since >50 cm horizontal complexity and vertical complexity did not vary with predator- prey ratio, but <50 cm horizontal complexity did, our findings suggest that structural complexity across the lower portion of vegetation has a greater impact on arthropod predator-prey ratio than structural complexity of the upper portion in urban prairies in northwest Ohio.
Arthropod abundance, not including springtails, was negatively associated with the percent grass in the plot; however, the prairie in which the plot was located also had high explanatory power (Figure 2.4). The negative relationship between grass and arthropod abundance appears to be most apparent in plots that occur in prairies with high abundances of arthropods in general and that were large enough to have more sampling points within them.
Conversely, springtail abundance was positively correlated with percent grass and also predicted by the prairie in which the plot occurred (Figure 2.5). Taken together, these results suggest that prairie grasses do not support as many arthropods as forbs, excluding springtails, which are abundant areas with a lot of grass. These findings are similar to those of other studies, which report a higher abundance of arthropods in forb-dominated green spaces, with the exception of 48 grass-dwelling species (e.g. springtails) (Morris, 2000; Norton et al., 2019). Of the pest arthropod species that were common in plots, whitefly and thrips abundances were not correlated with any of our measures, but aphids were negatively correlated with horizontal complexity >50 cm (Figure 2.6). Along with the general increase in aphid abundance in plots with low horizontal complexity >50 cm, we observed that aphid colonies often occurred on isolated stems in a plot, and stems were more likely to be isolated in plots with low horizontal complexity >50 cm.
However, we this observation was not a standardized effort and may be due to aphids being easier to see on isolated stems.
Conclusion
Our findings align with the large body of literature showing that predator abundance is positively impacted by structural complexity, while also providing evidence that the location and type of structural complexity is important. Unlike other studies in urban systems, we did not find evidence of the relationship between predators and complexity being altered by nearby impervious surface. Because our study focused on small-scale complexity within only six urban prairies, we may not have detected impacts of impervious surface that would be found on a larger scale. We found that arthropod abundance is higher in areas of prairies that have more forbs than grasses. Overall, these findings suggest that urban prairies are more likely to maintain high predator-prey ratios if the prairies are planted with a higher proportion of forbs than grasses and if they have high structural complexity (e.g. more densely planted). This is particularly true in lower heights of vegetation (i.e. <50 cm horizontal complexity). The benefit of complexity derived from forbs builds onto a variety of other benefits from forbs, including pollinator resources and support for native grasses (Isaacs et al., 2009; New, 2015), which ultimately demonstrates the importance of inclusion of forbs in restored urban prairies. 49
Figures
Figure 2.1 The relationship between vertical complexity and vegetation biomass in each prairie.
2 2 R m=0.18, R c=0.55, p=0.003. 50
Figure 2.2 a. <50 cm horizontal complexity and biomass of vegetation below 50 cm
(F1,27=0.0011, p=0.97). b. The relationship between >50 cm horizontal complexity and biomass
2 2 of vegetation above 50 cm (F1,25=34.51, p<.0001, R m=0.55, R c=0.55).
Figure 2.3 The relationship between predator-prey ratio and horizontal complexity and its
2 2 interaction with percent forbs in the plot. R m=0.32, R c=0.32, p=0.05. 51
bundance Arthropod a Arthropod
Figure 2.4 The relationship between arthropod abundance and percent grass in the plot in each
prairie. Shown by prairie since the conditional R2, which takes prairie into consideration, has
2 2 high explanatory power. R m=0.28, R c=0.43, p<0.00004. 52
Figure 2.5 The relationship between springtail abundance and percent grass in the plot in each
prairie. Shown by prairie since the conditional R2, which takes prairie into consideration, has
2 2 high explanatory power. R m=0.13, R c=0.47, p=0.01. 53
Figure 2.6 The relationship between aphid abundance and >50 cm horizontal complexity. - R 2 2 m=0.17, R c=0.24, p=0.006. 54
CHAPTER 3. HOW IS ARTHROPOD PREDATION IN URBAN PRAIRIES IMPACTED BY HABITAT COMPLEXITY WITHIN AND SURROUNDING THE PRAIRIE? Introduction
This work builds upon Chapter 2, in which we found that structural complexity positively correlated with predator-prey ratio when complexity was provided by forbs. However, we did
not see some expected relationships, such as a negative impact of impervious surfaces on the
arthropod community and other findings were heavily dependent on the prairie in which the plot
was found. We also did not directly measure predation in the previous study (Chapter 2). Here,
we consider how the scale of the study may have affected those findings and measure predation directly.
Both smalll-scale and arge-scale habitat and structural complexity can impact predation
(Tscharntke et al., 2016), and has been shown to do so in many systems (Denno, Finke, &
Langellotto, 2005), including urban ones. A study of predation in residential yards found that
interactions between complexity of woody vegetation at different spatial scales best predicted
arthropod and bird predation on a pest caterpillar species (Frey et al., 2018). The fragmented
landscape and reduced habitat complexity in urban areas (Michael L McKinney, 2006) leads not
only to reduced insect abundance, richness and diversity, but also to decreased arthropod
predation and predator abundance, particularly nearer to urban centers (Faeth et al., 2011;
Lagucki et al., 2017; Michael L McKinney, 2008; Turrini, Sanders, & Knop, 2016). These
effects are further impacted by impervious surface, which causes a large part of urban
fragmentation.
Green spaces, such as urban prairies, are often installed to support pollinators, store
carbon, or mitigate runoff, but they also may benefit arthropod predators at multiple scales by 55
both providing habitat for them and more resource patches in the fragmented urban landscape
(Ferrante et al., 2014). By investigating controls on natural predator communities and predation rates in these spaces, we can better understand how to maximize the ability of the spaces to promote biological control of pests.
Habitat structure may be key to understanding predation in urban green spaces. Several studies have found that habitat structure, particularly habitat complexity, impacts predation
(Bultman & Uetz, 1982; Corcuera et al., 2016; S. D. Frank & Shrewsbury, 2004; Frey et al.,
2018; Langellotto & Denno, 2004; McNett & Rypstra, 2000; Podgaiski & Rodrigues, 2017;
Schmidt et al., 2005; Shrewsbury & Raupp, 2000). The way that each study defines habitat
complexity varies based on the needs of the focal predator species. For example, density of
vertical vegetative growth is important for web-building spiders, while litter shape and depth are
more relevant for hunting spiders and beetles. Furthermore, some studies include variation in
biomass of substrate (i.e. litter, vegetation) as a metric of habitat complexity (e.g. Dale & Frank,
2014; S. D. Frank & Shrewsbury, 2004), while others control for substrate biomass and
manipulate only structural complexity, such as density of vertical stems or litter shape (e.g.
Bultman & Uetz, 1982; Podgaiski & Rodrigues, 2017). Despite the varied approaches, habitat
complexity has consistently been shown to increase predator abundance (Bultman & Uetz, 1982;
Corcuera et al., 2016; S. D. Frank & Shrewsbury, 2004; Langellotto & Denno, 2004; Podgaiski
& Rodrigues, 2017; Schmidt et al., 2005). The effect of habitat complexity on predation rate has
been measured less frequently and the relationship between the two has been variable (no effect:
S. D. Frank & Shrewsbury, 2004; non-linear relationship: Frey et al., 2018; Shrewsbury &
Raupp, 2006; positive relationship: Yadav et al., 2012). Habitat complexity can be beneficial to
predators through multiple mechanisms: increased abundance of prey, more favorable 56
microclimate, more efficient detection (e.g. through vibrations) and/or capture of prey, escape
from predation and cannibalism, and supplemental resources (e.g. alternate prey, pollen, dead
items) (Langellotto & Denno, 2004).
Features of the urban habitat that are different than those found in natural systems may be
contributing to the variable relationship between habitat complexity and predation. In fact, the
urban setting, particularly impervious surface, has been demonstrated to play a stronger role than
habitat complexity in determining predator-prey interactions (Dale & Frank, 2014). Impervious surfaces cover approximately 50% of the area in urban cores (Michael L. McKinney, 2002) and they contribute to reduced complexity surrounding urban green spaces because they do not provide habitat. Impervious surfaces also absorb heat (Bolund & Hunhammar, 1999), which increases temperature, and in temperate cities, lengthens the growing season and leads to fewer
frost days in cities relative to surrounding rural areas. This effect on urban temperature alone
may fundamentally alter arthropods’ interactions and their impact on the environment because
arthropods are ectotherms and they are effected by ambient temperature (Berger et al., 2008;
Bokhorst et al., 2008; Briones et al., 2009; Maran & Pelini, 2015; Pelini et al., 2014; Zhang et
al., 2013). Though we did not see a relationship between temperature and proximity to
impervious surface on a small scale in Chapter 2, Dale and Frank (2014) found that the
temperature within urban tree canopies increased with the percent impervious surface within a
100 m radius. This increase in temperature impacted a relationship between parasitic wasps and
scale insects. Because scale insect fecundity is positively associated with temperature, scale
insect reproduction was heightened in trees surrounded by a high percentage of impervious
surfaces. The hastening of scale insect reproduction rate outpaced any impact of vegetative
complexity on predator and parasitoid control of the pests. 57
In this study we ask how habitat and structural complexity impact predation rate and the richness, diversity, and composition of arthropod communities in restored prairies across an urban gradient (i.e. in urban, suburban, and rural areas in and surrounding Toledo, OH). Because complexity at varying spatial scales can impact arthropod communities and predation, we consider the effect of both prairie complexity and complexity in the 30 m surrounding each prairie. This work also builds upon Chapter 2, which looked at the effect of structural complexity at an even smaller scale (plots within prairies). We predict that structural and habitat complexity within prairies would correlate positively with predation rate. We expect that the arthropod community will become more abundant and diverse with greater distance from the city center and with higher complexity within the prairie and its surroundings.
Methods
Study sites
We conducted this experiment from June-August 2018 in 16 restored prairies located at varying distances (3 km to 23 km) from the urban center of Toledo, OH. The prairies were all planted with prairie species native to Northwest Ohio. Seven of the prairies were managed by the
Toledo Zoo, two were managed by the Metroparks of the Toledo Area, one was managed by The
Olander Park System, three were managed by the University of Toledo, one was managed by
Owens Corning, and two were managed by private landowners (Table 3.1). The prairies are restored prairies, which often have lower plant species richness (Kindscher & Tieszen, 1998;
Sluis, 2002) than remnant prairies. However, because these prairies are all young, their arthropod communities are likely to have high richness and diversity more similar to what would be observed in remnant prairies, since arthropod species richness and diversity tend to be initially 58
high in restored prairies and slowly decrease over time (Dahms et al., 2010; Orlofske et al.,
2011).
Prairies varied in size from 260 m2 to 50,000 m2 (Table 3.1). In the prairies larger than
1000 m2, we randomly selected a point in the prairie and used that point to serve as the northwest
corner of a 1000 m2 sampling area to improve tractability. In each sampling area we identified
ten, 15 m long transects that were separated from the next transect by 5 m to ensure no overlap of
sweep-netted area. We completed all sampling along these transects. In the prairies smaller than
1000 m2, we identified the number of transects necessary to sample the same percentage of the sampling area that was sampled in the 1000 m2 sampling areas (15%).
Table 3.1. Location, size, and manager of each study site.
Distance from city Location Size (m2) Manager center (km)
41.618139, -83.578452 2768 5.44 Toledo Zoo 41.616433, -83.582033 361 5.84 Toledo Zoo 41.630325, -83.514541 210 3.22 Toledo Zoo 41.644055, -83.824899 1725 23.81 Private landowner 41.712977, -83.557235 1038 6.65 Toledo Zoo 41.589614, -83.783814 9383 21.85 Metroparks of the Toledo Area 41.651106, -83.780539 7529 20.3 Metroparks of the Toledo Area 41.644202, -83.535247 39817 16.85 Owens Corning 41.528605, -83.646810 1164 1.27 Toledo Zoo 41.653661, -83.742471 260 11.63 University of Toledo 41.653838, -83.722996 249 17.18 University of Toledo 41.640144, -83.674247 1000 15.58 Toledo Zoo 41.668751, -83.697003 2033 15.31 Private landowner 41.682517, -83.716312 37107 13.51 University of Toledo 41.699778, -83.751877 50000 18.64 Olander Park System 41.621181, -83.584735 1189 5.55 Toledo Zoo 59
Habitat complexity
We measured elements of habitat complexity within and surrounding the prairies. Within
the prairie, we measured structural complexity (based on plant physical attributes, similar to
vegetation density, hereafter prairie structural complexity) in the same manner as in Chapter 2 at
two randomly chosen locations along each of the transects, using the point intercept quadrat
method: one location between 1-7 m and one between 8-15 m. We started walking at the
beginning of the transect and stopped at each location, turned to our left (90 degrees counterclockwise), laid a 1 m2 quadrat, and measured prairie structural complexity. The start
point for each transect was designated at the beginning of the study period and was consistently
used throughout the study. Structural complexity was calculated as the sum of vertical and
horizontal complexities. Though horizontal complexity <50 cm was the important measurement
in chapter two, we considered all aspects of structural complexity because this chapter looks at
different responses (predation rather than predator-prey ratio) and is at a different scale
(experimental unit is the prairie, rather than plots in the prairie). In addition to structural complexity, we recorded prairie habitat complexity in each quadrat by counting the number of different types of habitat in the quadrat (bare ground, ground cover, grass, forbs, shrubs, canopy
(Shrewsbury & Raupp, 2000)). We also recorded surface temperature in a shaded part of the plot, soil moisture, and the number of open floral blooms. In each prairie, one of these locations was randomly chosen to house a Thermochrom iButton temperature sensor and logger (Model
DS1921G-F5#, Dallas, TX), which was shielded by a small Styrofoam cup for the duration of the experiment, and attached to a plant in the plot at approximately 25 cm from the ground.
In a 30 m buffer surrounding the prairie sampling area, we recorded the number of different land cover types (impervious surface, bare ground, lawn, prairie, crop, wooded, water) 60 and percentage of each (Figure 3.1). We summed the number of different types of cover
(excluding impervious surface) to calculate the surrounding habitat complexity, hereafter surrounding complexity. For example, if a buffer contained wooded area, lawn, and cornfield it would have a surrounding complexity of three. We directly observed the habitat or cover type in approximately 2% of the buffer by walking transects and recording the land cover type in 1 m2 quadrats every 6 m along the transect. We also recorded the total number of trees within the buffer.
Arthropod sampling
During June-August we sampled each prairie twice for arthropods by sweep netting with a 38 cm diameter insect net. We walked along each transect at approximately 7 km/hr and swept the upper 25% of vegetation in an arc approximately 2.5 m wide as we walked (Doxon, Davis, &
Fuhlendorf, 2011). We counted 30 sweeps for each transect. At the end of each transect, we twisted the top of the net to prevent insect escape and then emptied the net into a plastic 7.57 liter bag with a zip closure. We examined the insects in the field and released any bees or lepidopterans in the field, which we identified to the finest resolution possible (finest was family level with the exception of distinct species such as monarch butterflies), due to permit requirements at some locations. The rest of the arthropods were left in the bag, which was placed in a cooler on ice and transferred to a -18 °C freezer until identification. We identified insects in the same manner as Chapter 2.
Predation
To measure predation in the prairies, we used painted lady butterfly (Vanessa cardui) caterpillars as baits. We ordered eggs from Carolina Biological (Item # 144078, Burlington, NC), raised them with Carolina Biological culture media (Item # 144068) and conducted baiting trials 61
approximately 10 days after they hatched. To create the baits, using a low-temperature hot glue
gun (Adtech Mini Lo Low Temp Hot Glue Gun, Hampton, NH), we put hot glue (AdTech
MultiTemp 4" Hot Glue Sticks, Hampton, NH) on a piece of cardstock, then waited until the glue
was slightly cooled, but still not hardened, and placed the back half of the live caterpillar in the
glue. We pinned the paper with the glued caterpillars to a plant at approximately 25 cm from the
ground in the same plots that were sampled for complexity. Because we placed the caterpillars at
25 cm, ground arthropod predation was not accounted for in this study.
We placed baits in the same locations where we measured structural complexity, so there
were two baits per transect (20 baits in 1000 m2 sampling areas). Caterpillar baits were left out
for 24 hours. Because we conducted several lab trials to confirm that caterpillars could not
escape, we considered any of the caterpillars that were missing or partially missing to be preyed
upon (S. D. Frank & Shrewsbury, 2004). For analysis, predation was calculated as the percentage
of baits preyed upon. Any that remained alive were immediately put on ice for euthanization. We
measured predation once in this manner in the first week of August. We attempted another
measurement earlier in the experiment by pinning the caterpillars to plants (S. D. Frank &
Shrewsbury, 2004); however, we observed that almost all of the caterpillars were eaten by ants in
this trial and could not use the data.
Data analysis
Prior to analysis, we combined data from both sampling dates. Arthropods were summed for all transects and both sampling dates for each prairie so that we had one abundance measurement for each arthropod morphospecies for each prairie. To account for varying sampling area size, abundance was divided by number of transects. Complexity and environmental covariate measurements within the prairie were averaged so that for each prairie 62
we had one measurement for structural complexity, one for prairie habitat complexity, one for
surface temperature and one for moisture. For habitat complexity surrounding the prairie, we
considered only the percentage of each type of land cover and not the number of different types
of land cover. We made this decision because there was little variation in the number of types of
land cover and the data were not informative.
We calculated abundance as the total arthropod individuals captured divided by the
number of transects in the prairie. Richness was the total number of arthropod families found in
each prairie. We calculated arthropod Shannon-Weaver diversity using the vegan package
(Oksanen et al., 2015). For analyses of abundance, we had to remove one site due to abundance
an order of magnitude higher than other prairies. The high abundance was mostly due to high
numbers of thrips and aphids, and their predators (e.g. insidious flower bug). A different site was
removed from predation analyses due to missing data.
All analyses were carried out in R version 3.1.3 (R Core Team, 2013). We used linear
models to determine the effect of complexity measures (i.e., habitat and structural) and distance
from the city center on predation on caterpillars, arthropod abundance, predatory arthropod
abundance, arthropod family richness, predatory arthropod richness, and arthropod diversity and
predatory arthropod diversity. We also considered covariates, such as moisture, surface
temperature, percent of forbs, grass, and bare ground in the plot. For predation only, we
considered the abundance of bait predatory arthropods (i.e. actively hunting morphospecies that
may prey on caterpillars placed above ground level; hereafter “bait predators”) as a potential covariate.
For the analysis of predation, we used an ANOVA to test our hypothesis. Test statistics for each model were determined using the Anova function in the car package (J. Fox & 63
Weisberg, 2019). Because the remaining portion of this study was largely exploratory, we
determined the best predictor(s) for our other responses (arthropod and predator abundance,
richness, and diversity) using Akaike Information Criterion using the AIC function in R with a
selection threshold of ΔAIC greater than 2 (AIC values for each predictor for each response in
Appendix A). Due to the large number of possible predictors, we used forward selection to create
models using predictors with the lowest AIC and any other predictors with a ΔAIC from that
predictor less than 2. When the response was predicted best by sampling area size only, we
completed another round of analysis on a subset of data that only included our maximum size
sampling area (1000 m2).
We ensured that model predictors were not colinear by using the correlation function
(cor) in R, and if predictors were found with a correlation coefficient of at least r = ± 0.70, we
eliminated one of them from the analysis (full correlation table in Appendix B). We tested the
residuals of models for spatial autocorrelation with Moran’s I in the ape package (Paradis &
Schliep, 2018). We also ensured normality and equal variance in model residuals.
We also looked at the effect of each predictor on arthropod community composition and
predatory arthropod community composition with nonparametric permutational anova (adonis, in
the vegan package). We focused analysis on community composition at the taxonomic family-
level.
Most data were visualized using the package ggplot2 (Wickham, 2016), with the
exception of interaction plots (interact_plot, interactions package (Long, 2019)) and community
composition ordination plots (metaMDS and envfit, vegan) function to show associations with environmental factors. 64
Results
Predation
The rate of predation was not significantly correlated with prairie habitat complexity,
prairie structural complexity, surrounding complexity, bait predator abundance, or distance from
the city center. Interactions between these measures also did not significantly predict predation.
However, the interaction between prairie structural complexity and bait predator abundance was
2 marginally significant F1,11=3.75, p=0.08, R =0.21; Figure 3.2). Predation was not significantly
correlated with any other measured potential covariates (e.g. surface temperature, moisture).
Arthropod abundance
Arthropod abundance was best predicted by prairie structural complexity (Table 3.2,
Figure 3.3) (Δ AIC= 2.73 from the next best fit model). In a posthoc analysis to determine whether certain arthropod families drove the relationship between arthropod abundance and structural complexity, we found that subtracting the abundance of thysanopterans (thrips) from arthropod abundance reduced the strength of the correlation between structural complexity and
abundance (R2=0.42 and R2=0.12, respectively), though the general trend was still present.
Predator abundance was best predicted by prairie structural complexity and the size of the
sampling area (Table 3.2, 3.3) (Δ AIC= 2.13 from the next best fit model).
Arthropod richness and diversity
Overall arthropod richness was best predicted by the size of the sampling area (Table 3.2,
Figure 3.4). When analyzing a subset of data with only prairies with maximum size sampling
areas (1000m2), we found that richness was best predicted by percent impervious surface
surrounding the prairie (Table 3.2, Figure 3.4) (Δ AIC= 2.00 from the next best fit model). 65
Richness of predator families was best predicted by the size of the sampling area (Table
3.2). When analyzing a subset of data with only prairies with maximum size sampling areas
(1000m2), we found that predator richness was best predicted by percent impervious surface surrounding the prairie and complexity surrounding the prairie (Table 3.2; Figure 3.5) (Δ AIC=
3.67 from the next best fit model).
Overall arthropod diversity was best predicted by percent impervious surface surrounding the prairie (Table 3.2, Figure 3.6) (Δ AIC= 2.00 from the next best fit model). Diversity of predators was best predicted by complexity surrounding the prairie (Table 3.2, Figure 3.7) (Δ
AIC= 2.11 from the next best fit model.
Table 3.2 Statistics for the models that best predict each response variable.
Factors AIC R2 Abundance prairie structural complexity 124.84 0.42 Predator abundance prairie structural complexity + size of sampling area 62.87 0.47 Richness size of sampling area 113.92 0.75 Richness (1000 m sampling area only) percent impervious surface surrounding prairie 76.80 0.33 Predator richness size of sampling area 82.72 0.60 Predator richness (1000 m sampling area only) percent impervious surface surrounding prairie + complexity 48.52 0.53 surrounding prairie Diversity percent impervious surface surrounding prairie 1.48 0.54 Predator diversity complexity surrounding prairie 1.77 0.37 66
Arthropod community composition
Community composition of all arthropods was predicted by surrounding complexity
2 (F1,14=1.76, p=0.05, R =0.12) (Figure 3.8), but not by distance from city center, prairie habitat
complexity, prairie structural complexity, or any other potential covariates. Community
composition of predatory arthropods was predicted by both surrounding complexity (F1,14=2.38,
2 2 p=0.02, R =0.11) and prairie habitat complexity (F1,14=2.58, p=0.01, R =0.15) (Figure 3.9), but
not by distance from city center, prairie structural complexity, or any other potential covariates.
Discussion
This study built upon our Chapter 2 findings, which suggested that on a small-scale complexity positively impacts predator-prey ratio only in vegetation below 50 cm and when forbs are the source of complexity. Here, we examined the effect of structural complexity of vegetation (i.e. vegetation density) and the number of different types of habitat (habitat complexity) within and surrounding prairies on the rate of arthropod predation across an urban gradient. We also looked at how these factors correlate with arthropod community abundance, richness, diversity, and community composition. We predicted that structural and habitat complexity within prairies would correlate positively with predation rate and that the arthropod community would become more abundant and diverse with greater distance from the city center and higher complexity within the prairie and its surroundings. Our findings were not as straightforward as our predictions; we found that arthropod abundance and predation rate were predicted best by complexity within the prairie, while arthropod community richness, diversity, and composition were more dependent on features surrounding the prairies. 67
Predation
We found that predation on the caterpillar baits was not significantly predicted by surrounding complexity or other environmental factors; however, there was a marginally significant relationship between predation and the interaction between prairie structural complexity and the number of bait predators. Though the relationship is not strong, there was a general trend that showed predation rate increasing with structural complexity when there were higher than average bait predators present and decreasing when there were lower than average bait predators present. Our data do not point to a mechanism for this relationship, but hypotheses that researchers have proposed to explain why structural complexity benefits predators can provide possible mechanisms worth further investigation. One hypothesis posits that complexity leads to increased prey abundance and/or supplemental resources (Langellotto & Denno, 2004).
If that were the case, it suggests that the observed interaction could be due to higher structural complexity leading to higher prey abundance, and when fewer predators are present, less need for the predators to be searching out and eating prey. However, we saw decreased arthropod abundance with increasing structural complexity, which makes this explanation less likely. Other researchers hypothesize that structural complexity helps predators avoid cannibalism or predation (Langellotto & Denno, 2004, 2006). If this were the case, perhaps predators are more likely to encounter the baited prey in a structurally complex environment due to partitioning habitat with other predators or more actively needing to escape predation. This need would become more important with more predators present.
Generally, the weak relationship between structural complexity and predation aligns with the literature on complexity and predation, which documents a relationship in some studies, but not others. In our study, we may have seen this weak potential relationship in part if edge effects 68 are driving dynamics in our prairies. Edge effects have been shown to become dominant drivers in small, irregularly shaped prairies like ours (Evans, Turley, Levey, & Tewksbury, 2012; Ries,
Murphy, Wimp, & Fletcher, 2017). The effect can be variable, but is known to depend on the habitat on the other side of the edge; therefore sharing an edge with a habitat containing few resources, such as turf lawns or impervious surfaces, is more likely to lead to negative edge effects (Wimp, Ries, Lewis, & Murphy, 2019) and may have altered habitat structure (Murphy,
Battocletti, Tinghitella, Wimp, & Ries, 2016). Further, though generalist predators have been thought to perform well along edges (Fagan, Cantrell, & Cosner, 1999), the response of arthropod predators is variable (Murphy et al., 2016) and seems to be more dependent on their habitat needs (e.g. hunting spiders respond positively, while web-builders do not) (Wimp et al.,
2019).
Arthropod abundance
Counter to our expectation that structural complexity would provide more habitat for arthropods and therefore increase arthropod abundance, we found the opposite pattern. Though we did not find that one morphospecies completely drove the pattern, we did observe that thrips had a similar relationship as overall arthropod abundance with structural complexity. In fact, when we removed thrips abundance from the total arthropod abundance, the trend was weaker and no longer supported. This suggests that some of the high abundance with low structural complexity may be due to pests, such as thrips occurring in high numbers in less complex vegetation. The relationship between abundance and structural complexity may also be due to a sampling artifact in part; sweep nets’ sampling efficiency can vary with vegetation and environmental factors (Larson, 1996) and we experienced more difficulty sweep netting in 69
prairies with higher structural complexity than in prairies with low structural complexity, which
could lead to reduced numbers of arthropods being captured in dense vegetation.
When looking at predator abundance alone, the best fit model suggested a slight negative
correlation with prairie structural complexity. There was also an increase in predator abundance
as the size of the sampling area increased, even though abundance was corrected for the number
of transects sampled. Surprisingly, arthropod abundance was not affected by distance from the
city center alone; a recent study by another group in the same geographical area found increases
in arthropod abundance further from the city center (Lagucki et al., 2017). The contrast between
our findings and those from Lagucki et al. (2017) may be caused by differences in focal species,
since the Lagucki study focused on flying arthropods and ours focused on species found in
vegetation.
Arthropod richness and diversity
While predation rate and, to a lesser degree, arthropod abundance were predicted by factors within the prairie, arthropod richness and diversity were more often determined by features surrounding the prairies. Richness of all arthropods and predatory arthropods were both best predicted by, and positively associated with, the size of the sampling area, likely due to insufficient sampling; we did not reach the asymptote of species accumulation curves for any of the sites. We then looked at a subset with only prairies where we sampled the maximum size
(1000 m2) and found arthropod richness was negatively associated with the percent of
impervious surface surrounding the prairie. Richness of predatory arthropods followed similar
trends, but in the 1000 m2 sampling subset, complexity surrounding the prairie and percent
impervious surface surrounding the prairie were both associated with richness. These findings
suggest that although complexity within the prairie impacts arthropod abundance, impervious 70
surface surrounding the prairie still ultimately reduces the variety of arthropod species present.
This pattern has been documented more often than not in studies of arthropod richness along
urbanization gradients and could be due to loss of surrounding habitat area (Michael L
McKinney, 2008). Further, for predatory arthropods, the complexity surrounding the prairie is
more likely to impact how many predatory species are found in a prairie than the complexity
within it. Diversity of arthropods was not correlated with size of the sampling area as richness
was, but the area surrounding the prairies were still important. Overall arthropod diversity was
negatively correlated with the amount of impervious surface surrounding the prairie, while
predatory arthropods were positively associated with habitat complexity surrounding the prairie.
The contrast between our abundance results and our richness and diversity results is
interesting, particularly because it suggests that attempts to understand arthropod abundance, richness, and diversity may need to consider different scales for different questions. Along similar lines, we did not see strong evidence of the documented positive relationship between predators and complexity in abundance data, but in the richness and diversity data, we see that complexity surrounding the prairie does positively affect predatory arthropods. This could suggest that predatory arthropods may be hunting in the prairies, but more reliant on habitat availability surrounding the prairies than complexity within the prairie, and potentially more so than other arthropod groups.
Arthropod community composition
The composition of arthropod communities was impacted by habitat complexity
surrounding the prairie, though the relationship was not strongly predictive. We did not detect
any obvious similarities between families that drove the separation (e.g. similar distance
dispersal, similar food source). When visualizing the data in an ordination plot, communities in 71
prairies surrounded by low habitat complexity separate from the prairies surrounded by mid-low,
mid-high, and high surrounding complexity. This separation may mean that particularly low
surrounding habitat complexity can shift the composition of a community, while moderate and
high levels of surrounding habitat complexity can support a similar community. Similarly,
another potential explanation is that with low surrounding complexity, there are not enough
patches of habitat relative to the surrounding urban matrix to support well-connected metapopulations of some arthropods, which would shift the community in the prairie. This pattern also aligns with landscape ecology literature, which often reports critical connectivity thresholds, above which patches are well-connected and below which they are isolated (Turner,
2005; With, Gardner, & Turner, 1997).
When looking only at predatory arthropods, we see a similar separation with low habitat complexity surrounding the prairie. However, predatory arthropods community composition was also significantly impacted by habitat complexity within the prairie. The separation is difficult to parse when looking at the ordination plot but may be due to a cluster of a few prairies with particularly high prairie habitat complexity. These communities were clustered, but not well- separated from the other communities. This suggests that prairie structural complexity may shift the make-up of the predatory arthropod community, but not with the same degree of difference as surrounding habitat complexity.
Conclusion
Our study found that both the level of complexity of a prairie’s habitat and the complexity surrounding it affects arthropod communities and predation rate within them, but in varying ways. When it comes to predation rate, structural complexity of the vegetation in the prairie was more important than the prairie’s surroundings. This makes intuitive sense, since 72 structural complexity in the location where a predation event would take place should impact how and whether that event occurs. Our arthropod abundance trends are not as strong, but also suggest that complexity within the prairies is more important than complexity in the surrounding areas. When taken in consideration with our Chapter 2 findings, this weak relationship may be due to stronger effects of structural complexity at a small scale that are more difficult to detect when observed at a larger scale. However, the prairie’s surroundings become more important predictor of arthropod communities when looking at arthropod richness, diversity, and community composition. Ultimately, in our system, the area surrounding the prairie may determine which arthropods can find and/or persist in a prairie, while complexity within the prairie determines how many of the arthropods can be supported and perhaps even the ecosystem services they provide, such as pest control, once in the prairie. In cities this could be particularly important because arthropod predation on pest predators impacts human actions and well-being.
However, we may be able to boost arthropod ecosystem services, such as predation, by better understanding the way that choices about where to develop new green spaces and what plants are seeded in them impact arthropod communities. 73
Figures
30m
30m 30m
30m
Sampling area Buffer
Figure 3.1 The buffer area (gray striped) surrounding each sampling area (green solid) was created by measuring 30 m ftrom he center of each side of the sampling area. Example of a square (left) and rectangular (right) sampling area. 74
Figure 3.2 The effect of prairie structural complexity and the number of bait predators on predation. The wrelationship as marginally significant (p=0.08). R2=0.21. 75
Figure 3.3 The relationships between structural complexity and abundance and predator abundance and the relationship between predator abundance and the size of the sampling area.
Top left—The relationship between abundance and structural complexity (R2 =0.42). Top
right—The relationship between predator abundance and structural complexity (R2 = 0.48).
Bottom left—The relationship between predator abundance and the size of the sampling area
(R2=0.26). 76
Figure 3.4 The relationships between richness and size of the sampling area and % impervious surface surrounding prairie. Left—The relationship between richness and the size of the
sampling area (R2=0.63). Right—The relationship between richness and percent impervious
surface surrounding the prairie (R2=0.33) in sampling areas that were 1000 m2; the black points
are the prairies with sampling areas over 1000 m2. 77
Figure 3.5 The relationships between predator richness and the size of the sampling area, the size complexity surrounding the prairie, and percent impervious surface. Top left—The relationship between predator richness and the size of the sampling area (R2=0.60). Top right—
The relationship between predator richness and complexity surrounding the prairie in sampling areas that were 1000 m2 (R2=0.25). Bottom right—The relationship between predator richness and percent of impervious surface surrounding the prairie in sampling areas that were 1000 m2
(R2=0.46); the black points are the prairies with sampling areas over 1000 m2. 78
Figure 3.6 The relationship between diversity and percent impervious surface surrounding the prairie (R2=0.54). 79
Figure 3.7 The relationship between predator diversity and complexity surrounding the prairie
(R 2=0.37). 80
Figure 3.8 The effect of complexity surrounding the prairie on community composition. Colors are along a gradient from low to high, with blue representing low surrounding complexity and
red representing high surrounding complexity. Family identity is not pictured for readability. 81
Figure 3.9 Top—The effect of complexity surrounding the prairie on predator community
composition. Bottom—The effect of prairie habitat complexity on predator community
composition. Colors are along a gradient from low to high, with blue representing low values and red representing high values. 82
CONCLUSION
This dissertation identified and investigated gaps in understanding about how complexity
(physical structure provided by the habitat, including vegetation and detritus, and the variety of habitat types present) impacts arthropod communities and their ecosystem services in human- impacted environments. In agricultural fields, we looked at how ground arthropods and their role in nutrient cycling were affected by subsidies of recalcitrant detritus (i.e. primarily habitat structure) and labile detritus additions (i.e. primarily a food source). Both the relationship between arthropods and nutrient cycling and the impact of habitat complexity on that relationship are understudied facets of agricultural arthropod research. In urban areas, we built upon research suggesting that the urban environment may alter the expected positive impact of complexity on predators and predation. Our studies looked at the relative and combined impacts of structural complexity and complexity in habitat types present at multiple scales on predators and predation.
Throughout the work presented in this dissertation, we found that known patterns and relationships were disrupted or dampened in ecosystems heavily modified by humans. The impact of detritivores on nutrient cycling is well-documented in other systems (T. W. Crowther et al., 2011; Del Toro et al., 2015; Lavelle et al., 2006; Ritzenthaler et al., 2018; D. A. Wardle,
2006), yet in the corn fields of our experiment, we saw only little evidence of their impact. Even a simple expectation that organisms would come to a food source was altered; while detritivores did respond to the addition of labile detritus, and predators responded to the combination of labile detritus and habitat structure, the response was not as strong as those observed in less- modified systems (Song et al., 2015; L. H. Yang, 2004, 2006; Louie H. Yang, 2013). Similarly, we saw evidence that adding a carbon source may have modestly increased uptake of nutrients, 83
but not to the degree that would have been expected (Stefano Manzoni et al., 2012; S. Manzoni,
Trofymow, Jackson, & Porporato, 2010).
In both Chapters 2 and 3 of this dissertation, we saw evidence of the positive effect that
complexity is expected to have on predators and predation, yet the relationship was not
straightforward. The relationship was dependent on scale, source of complexity (i.e. forbs,
grasses), and type of complexity (i.e. structural, habitat). This dissertation considered three scales
in urban environments: plots within a prairie (Chapter 2), prairie (Chapter 3), and 30 m
surrounding a prairie (Chapter 3). We saw that certain responses, such as abundance and
predator-prey ratio were better predicted at the smallest scale of plots within prairies, and even dependent on a smaller scale (e.g. predator-prey ratio increased with complexity only when complexity was driven by forbs). Yet, other responses, such as predation were predicted by prairie-level measurements, and arthropod richness, diversity, and community composition were predicted by larger-scale complexity. The impact that urban features had on these responses was also dependent on scale; proximity to impervious surface had no measurable effect on plots within prairies, but the percent of impervious surface surrounding prairies drove what arthropods were present in the prairie and negatively affected arthropod richness and diversity.
The comparison of two scales in Chapter 3 provides further illumination on the impact of scale and the way that complexity correlates with predation and arthropod communities. For predation within the prairie we saw potential evidence of structural complexity within the prairie being important, but no evidence of habitat within or surrounding the prairie having an effect.
When looking at the arthropod community, abundance was negatively related to structural complexity within the prairie; a relationship that is counter to what one may expect (Langellotto
& Denno, 2004). While in other urban studies this type of disruption in expectations could be 84
explained by the heat generated by impervious surfaces, we saw that the more likely explanation
was that pest species drove the relationship and thrived in more structurally simple habitat.
However, the stronger relationship between abundance and complexity at the prairie-plot level in
Chapter 2 may suggest that abundance is better predicted at smaller scales. Ultimately, in our
system, surrounding complexity may determine which arthropods can find and/or persist in a
prairie, while complexity within the prairie determines how many of the arthropods can be
supported and perhaps even the ecosystem services they provide, such as pest control, once in
the prairie.
All three of these studies point to a need for us to consider that expectations based on
ecology in natural systems may not translate to human-modified systems. In an environment like
conventional croplands, the considerable disruption by human activity may lead to small, low
diversity arthropod communities that don’t respond to the sudden addition of habitat or resources
in an expected way. Though, it is possible that with more time and intervention, responses could
become closer to what literature from natural systems would predict. However, even an urban prairie, which is meant to mimic a natural system, but is located within a heavily modified landscape, shows weakening or disruption of relationships seen in less-modified environments.
Aside from the consistent theme of human-modified systems altering expectations from research conducted in less modified environments, our findings could be useful to managers of croplands or urban green spaces. Conventionally managed croplands may be too heavily disturbed for land managers to apply methods that are meant to boost detritivore communities without long-term intervention. Similarly, the addition of corn stover alone as a carbon resource meant to increase nutrient uptake may not be enough without other changes in management or consistent use over a long period of time. Managers of urban areas should seek locations with a 85
variety of habitat types and little impervious surface immediately surrounding the planned green
space to maximize the potential for a rich, diverse arthropod community. However, for
abundance, predator-prey ratio, and predation, concentrating on the density of vegetation, the
percentage of forbs versus grasses, and types of habitat offered in the space may be more
important.
Overall, the findings of this dissertation suggest that we should expect the unexpected, or
at least expect to see something slightly different that we anticipated in environments that are
heavily modified by human activity. And when it comes to making decisions about land
management in such systems, we should consider alternative solutions to achieve the desired
outcome. Our studies also add to a body of literature documenting variable findings on the
relationship between predators and complexity. We find that a relationship between the two is
sometimes weak and potentially impacted by other known phenomena, such as edge effects
(Chapter 3), and when it is observed, dependent on other factors (Chapter 2). This adds further evidence that the ecological hypothesis that complexity promotes predators and/or predation may be true only under some conditions. 86
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APPENDIX A. AIC VALUES GENERATED DURING MODEL SELECTION
Table A.1. AIC values generated during model selection in Chapter 3.
Factors AIC Abundance prairie structural complexity 124.01 prairie habitat complexity 130.41 distance from city center 133.14 temperature difference at night 126.74 surrounding complexity 133.17 % forbs in prairie 130.24 % grass in prairie 132.50 average number blooms in prairie 133.05 moisture in prairie 133.30 number of trees surrounding prairie 133.23 % wooded surrounding prairie 133.34 % lawn surrounding prairie 132.72 % impervious surrounding prairie 131.68 % bare surrounding prairie 132.38 % crops surrounding prairie 132.08 prairie sampling area size 133.24 Predator Abundance prairie structural complexity 66.35 prairie habitat complexity 69.86 distance from city center 70.55 temperature difference at night 65.74 surrounding complexity 69.22 % forbs in prairie 67.98 % grass in prairie 68.92 average number blooms in prairie 70.58 moisture in prairie 69.71 number of trees surrounding prairie 70.19 % wooded surrounding prairie 70.53 % lawn surrounding prairie 70.57 % impervious surrounding prairie 70.53 % bare surrounding prairie 65.67 % crops surrounding prairie 70.43 prairie sampling area size 66.1 Richness prairie structural complexity 140.02 106
prairie habitat complexity 136.55 distance from city center 135.52 temperature difference at night 130.30 surrounding complexity 134.47 % forbs in prairie 139.71 % grass in prairie 140.03 average number blooms in prairie 139.84 moisture in prairie 139.74 number of trees surrounding prairie 140.10 % wooded surrounding prairie 135.57 % lawn surrounding prairie 135.56 % impervious surrounding prairie 131.20 % bare surrounding prairie 135.08 % crops surrounding prairie 139.72 prairie sampling area size 124.28 Predator Richness prairie structural complexity 93.33 prairie habitat complexity 88.86 distance from city center 90.57 temperature difference at night 88.37 surrounding complexity 86.78 % forbs in prairie 93.26 % grass in prairie 93.26 average number blooms in prairie 91.12 moisture in prairie 92.08 number of trees surrounding prairie 91.58 % wooded surrounding prairie 80.20 % lawn surrounding prairie 86.50 % impervious surrounding prairie 87.84 % bare surrounding prairie 88.21 % crops surrounding prairie 92.65 prairie sampling area size 82.18 Diversity prairie structural complexity 13.32 prairie habitat complexity 13.31 distance from city center 3.97 temperature difference at night 6.58 surrounding complexity 11.60 % forbs in prairie 11.15 % grass in prairie 11.84 average number blooms in prairie 10.00 moisture in prairie 13.26 number of trees surrounding prairie 12.75 107
% wooded surrounding prairie 11.12 % lawn surrounding prairie 11.19 % impervious surrounding prairie 1.49 % bare surrounding prairie 11.67 % crops surrounding prairie 10.37 prairie sampling area size 9.66 Predator Diversity prairie structural complexity 5.14 prairie habitat complexity 5.18 distance from city center 3.47 temperature difference at night 3.47 surrounding complexity 1.77 % forbs in prairie 4.95 % grass in prairie 4.97 average number blooms in prairie 5.11 moisture in prairie 4.75 number of trees surrounding prairie 4.94 % wooded surrounding prairie 4.29 % lawn surrounding prairie 3.45 % impervious surrounding prairie 2.46 % bare surrounding prairie 3.37 % crops surrounding prairie 4.15 prairie sampling area size 1.34 108
APPENDIX B. CORRELATION COEFFICIENTS BETWEEN VARIABLES
Table B.1. Correlation coefficients between all variables in Chapter 3. The left-most column repeats after each bold line. A red highlighted cell with red text indicates correlation coefficient above 0.70.
temperature distance from difference at number of the city center night blooms distance from the city center 1.00 -0.55 -0.38 temperature difference at night -0.55 1.00 0.00 number of blooms -0.38 0.00 1.00 habitat complexity within prairie -0.22 -0.07 -0.05 % bare ground in prairie 0.06 -0.02 -0.21 % grass in prairie -0.20 0.32 -0.42 % forbs in prairie 0.21 -0.38 0.57 moisture in prairie -0.42 0.15 -0.01 % bare ground surrounding prairie -0.48 0.23 0.86 % crops surrounding prairie 0.58 -0.34 0.16 % landscaped surrounding prairie -0.41 0.24 0.45 % lawn surrounding prairie -0.50 -0.06 0.60 % prairie surrounding prairie 0.31 0.15 -0.47 % water surrounding prairie 0.44 -0.35 -0.05 % wooded surrounding prairie 0.09 0.08 -0.37 number of trees surrounding prairie -0.35 0.18 0.22 complexity surrounding prairie 0.26 -0.14 -0.11 size of sampling area 0.23 -0.01 -0.40 structural complexity in prairie 0.09 0.15 0.61 % impervious surface surrounding prairie -0.43 0.06 0.03 habitat complexity % bare ground % grass in within prairie in prairie prairie distance from the city center -0.22 0.06 -0.20 temperature difference at night -0.07 -0.02 0.32 number of blooms -0.05 -0.21 -0.42 habitat complexity within prairie 1.00 -0.15 0.45 % bare ground in prairie -0.15 1.00 -0.32 % grass in prairie 0.45 -0.32 1.00 % forbs in prairie -0.36 -0.04 -0.91 109 moisture in prairie 0.07 -0.01 0.10 % bare ground surrounding prairie -0.13 -0.35 -0.45 % crops surrounding prairie -0.28 0.13 -0.33 % landscaped surrounding prairie 0.42 -0.29 -0.07 % lawn surrounding prairie -0.04 0.16 -0.26 % prairie surrounding prairie -0.09 -0.42 0.33 % water surrounding prairie -0.06 0.29 -0.09 % wooded surrounding prairie 0.46 0.26 -0.06 number of trees surrounding prairie 0.80 -0.25 0.16 complexity surrounding prairie 0.17 0.09 -0.20 size of sampling area 0.30 0.10 0.36 structural complexity in prairie -0.28 -0.29 -0.49 % impervious surface surrounding prairie 0.01 0.14 0.24 % bare ground % forbs in moisture in surrounding prairie prairie prairie distance from the city center 0.21 -0.42 -0.48 temperature difference at night -0.38 0.15 0.23 number of blooms 0.57 -0.01 0.86 habitat complexity within prairie -0.36 0.07 -0.13 % bare ground in prairie -0.04 -0.01 -0.35 % grass in prairie -0.91 0.10 -0.45 % forbs in prairie 1.00 -0.05 0.57 moisture in prairie -0.05 1.00 0.09 % bare ground surrounding prairie 0.57 0.09 1.00 % crops surrounding prairie 0.38 -0.28 -0.07 % landscaped surrounding prairie 0.15 0.03 0.53 % lawn surrounding prairie 0.20 0.35 0.52 % prairie surrounding prairie -0.15 -0.07 -0.32 % water surrounding prairie -0.01 0.10 -0.16 % wooded surrounding prairie -0.01 0.18 -0.27 number of trees surrounding prairie -0.08 0.08 0.29 complexity surrounding prairie 0.22 0.14 -0.11 size of sampling area -0.29 0.38 -0.54 structural complexity in prairie 0.66 0.07 0.63 % impervious surface surrounding prairie -0.41 -0.18 0.00 % landscaped % crops garden % lawn surrounding surrounding surrounding prairie prairie prairie distance from the city center 0.58 -0.41 -0.50 110 temperature difference at night -0.34 0.24 -0.06 number of blooms 0.16 0.45 0.60 habitat complexity within prairie -0.28 0.42 -0.04 % bare ground in prairie 0.13 -0.29 0.16 % grass in prairie -0.33 -0.07 -0.26 % forbs in prairie 0.38 0.15 0.20 moisture in prairie -0.28 0.03 0.35 % bare ground surrounding prairie -0.07 0.53 0.52 % crops surrounding prairie 1.00 -0.20 -0.29 % landscaped surrounding prairie -0.20 1.00 0.18 % lawn surrounding prairie -0.29 0.18 1.00 % prairie surrounding prairie -0.01 -0.11 -0.74 % water surrounding prairie 0.45 -0.24 0.15 % wooded surrounding prairie -0.17 -0.02 -0.30 number of trees surrounding prairie -0.34 0.71 0.00 complexity surrounding prairie 0.44 0.43 -0.30 size of sampling area 0.23 -0.05 -0.48 structural complexity in prairie 0.41 0.42 0.10 % impervious surface surrounding prairie -0.43 0.00 0.38 % prairie % water % wooded surrounding surrounding surrounding prairie prairie prairie distance from the city center 0.31 0.44 0.09 temperature difference at night 0.15 -0.35 0.08 number of blooms -0.47 -0.05 -0.37 habitat complexity within prairie -0.09 -0.06 0.46 % bare ground in prairie -0.42 0.29 0.26 % grass in prairie 0.33 -0.09 -0.06 % forbs in prairie -0.15 -0.01 -0.01 moisture in prairie -0.07 0.10 0.18 % bare ground surrounding prairie -0.32 -0.16 -0.27 % crops surrounding prairie -0.01 0.45 -0.17 % landscaped surrounding prairie -0.11 -0.24 -0.02 % lawn surrounding prairie -0.74 0.15 -0.30 % prairie surrounding prairie 1.00 -0.22 0.03 % water surrounding prairie -0.22 1.00 -0.20 % wooded surrounding prairie 0.03 -0.20 1.00 number of trees surrounding prairie -0.16 -0.18 0.44 complexity surrounding prairie 0.18 0.13 0.23 size of sampling area 0.43 0.27 0.31 111 structural complexity in prairie -0.04 0.03 -0.07 % impervious surface surrounding prairie -0.53 -0.16 -0.35 number of trees complexity surrounding surrounding size of sampling prairie prairie area distance from the city center -0.35 0.26 0.23 temperature difference at night 0.18 -0.14 -0.01 number of blooms 0.22 -0.11 -0.40 habitat complexity within prairie 0.80 0.17 0.30 % bare ground in prairie -0.25 0.09 0.10 % grass in prairie 0.16 -0.20 0.36 % forbs in prairie -0.08 0.22 -0.29 moisture in prairie 0.08 0.14 0.38 % bare ground surrounding prairie 0.29 -0.11 -0.54 % crops surrounding prairie -0.34 0.44 0.23 % landscaped surrounding prairie 0.71 0.43 -0.05 % lawn surrounding prairie 0.00 -0.30 -0.48 % prairie surrounding prairie -0.16 0.18 0.43 % water surrounding prairie -0.18 0.13 0.27 % wooded surrounding prairie 0.44 0.23 0.31 number of trees surrounding prairie 1.00 0.16 0.13 complexity surrounding prairie 0.16 1.00 0.46 size of sampling area 0.13 0.46 1.00 structural complexity in prairie 0.12 0.30 -0.03 % impervious surface surrounding prairie 0.03 -0.55 -0.49 % impervious structural surface complexity in surrounding prairie prairie distance from the city center 0.09 -0.43 temperature difference at night 0.15 0.06 number of blooms 0.61 0.03 habitat complexity within prairie -0.28 0.01 % bare ground in prairie -0.29 0.14 % grass in prairie -0.49 0.24 % forbs in prairie 0.66 -0.41 moisture in prairie 0.07 -0.18 % bare ground surrounding prairie 0.63 0.00 % crops surrounding prairie 0.41 -0.43 % landscaped surrounding prairie 0.42 0.00 112
% lawn surrounding prairie 0.10 0.38 % prairie surrounding prairie -0.04 -0.53 % water surrounding prairie 0.03 -0.16 % wooded surrounding prairie -0.07 -0.35 number of trees surrounding prairie 0.12 0.03 complexity surrounding prairie 0.30 -0.55 size of sampling area -0.03 -0.49 structural complexity in prairie 1.00 -0.49 % impervious surface surrounding prairie -0.49 1.00