DIVERSIFYING AGRICULTURAL LANDSCAPES FOR
   BIODIVERSITY AND ECOSYSTEM SERVICES
                              By
                Lindsey Renee Kemmerling
                     A DISSERTATION
                          Submitted to
                  Michigan State University
          in partial fulfillment of the requirements
                       for the degree of
       Integrative Biology ─ Doctor of Philosophy
 Ecology, Evolutionary Biology and Behavior – Dual Major
                             2022


                                             ABSTRACT
                     DIVERSIFYING AGRICULTURAL LANDSCAPES FOR
                        BIODIVERSITY AND ECOSYSTEM SERVICES
                                                   By
                                     Lindsey Renee Kemmerling
This dissertation focuses on strategies to restore biodiversity and ecosystem services in
agricultural landscapes through diversifying the plant community at the landscape level.
Biodiversity and ecosystem services are declining globally, and a leading cause of this decline is
large-scale row crop agriculture which results in habitat loss and pollution. Simultaneously, the
human population is growing, as are human demands for resources produced by agriculture.
Diversifying agricultural landscapes is one method to both mitigate the loss biodiversity while
providing essential human resources. I conducted three studies that test if diversifying cropping
systems can increase biodiversity and ecosystem services and maintain or even increase
agricultural yield. In Chapter 1, I tested the ability of multiple native, perennial bioenergy crops
(alternatives to annual bioenergy crops) to provide both crop yield and conserve pollinators. I
measured pollinator abundance and species richness, flower abundance and species richness, and
crop yield across four native perennial biofuel crop varieties: successional land (unmanaged),
restored prairie, a mix of native grasses, and seeded switchgrass. Successional land had the most
diverse community of pollinators but the lowest crop yield, native grasses had the highest yield
but the least diverse pollinator community, and switchgrass and restored prairie were
intermediate. If both pollinator conservation and crop yield are valued similarly, restored prairie
was the optimal biofuel crop. Chapter 2 tested the effects of crop management practices in row
crop agriculture, including the establishment of a conservation practice called “prairie strips” on
biodiversity and ecosystem services. Prairie strips are strips of farmland retired from production


and actively restored with native prairie plant species. I synthesized the tradeoffs and synergies
of a suite of biodiversity and ecosystem service measures across a land use intensity gradient, as
well as their spillover from prairie strips into cropland. The lowest land use intensity consistently
had the highest levels of biodiversity and ecosystem services other than crop yield. Treatments
with prairie strips had higher pollination services and a higher abundance of butterflies and
spiders than other row crop treatments. Crop yield in a treatment with low land use intensity and
prairie strips remained as highest land use intensity treatment, even when including the area
taken out of production for prairie strips. Biodiversity and ecosystem services decreased with
increasing distance from prairie strips and this effect was more pronounced in the second year of
the prairie strips than the first for several measures. These results show that, even in early
establishment, prairie strips can contribute to the conservation of biodiversity and ecosystem
services without a disproportionate loss of crop yield. Chapter 3 further investigated one of the
measures addressed in Chapter 2: butterfly biodiversity. I measured butterfly and plant species
richness and abundance across three years in the same land use intensity gradient. Butterfly
abundance and richness increased as land use intensity decreased. Prairie strips harbored unique
butterfly communities and had a higher abundance of butterflies than other row crop treatments,
including conservation land. Across the 1 ha plot of which 5% was prairie strip, butterfly
abundance was higher in row crops with prairie strips than in row crops without prairie strips,
likely as a result of prairie strips and other crop management practices in treatments with prairie
strips, such as reduced pesticides. Altogether, this work presents evidence that restoring habitat
within farms can support biodiversity and ecosystem services without disproportionately
impacting crop yields. Furthermore, when strategically placed, these conservation strategies can
prevent unnecessary greenhouse gas emissions, and potentially increase crop yield.


                                     ACKNOWLEDGEMENTS
Thank you to Nick Haddad for his endless support, enthusiasm, and kindness—this work would
not be possible without his mentorship. Also to the Haddad Lab, who has not only helped me so
much, but made it a joy to look for insects and conduct research, even via zoom during the
pandemic. Past Haddad Lab members—especially Sean Griffin, Elsita Kiekebusch, Erica Henry,
Sarah Cusser, Jackson Helms, and Christopher Warneke (honorary lab member)—thank you for
friendship and mentorship. Thank you to the Kellogg Biological Station community that I have
been lucky to be a part of—especially Kara Haas and Misty Klotz for mentorship in outreach,
and Sarah Reimer and Kay Gross for mentorship in coordinating academic programs. Thank you
to the MSU Ecology and Evolutionary Biology program for support and community. Also to the
North Carolina State University Applied Ecology Department that supported me in my first year.
Students and field assistance along the way—Ally Brown, Jessica Webb, Ian Ratcliff, Oren
Lerner, Cameron Brown, Annabelle McCarthy—I’m so grateful for your work, thank you. My
dear friends that fit into most of these categories—Corinn Rutkoski, Michaela Rose, Allie
Spring, Alice Puchalsky, and Stephanie Clark—thank you for everything. Thank you to my
committee members—Sarah Fitzpatrick, Phil Robertson, and Will Wetzel—for support and
energy and advice. Thank you to the people of the Kellogg Biological Station Long-Term
Ecological Research site and the Kellogg Biological Station Great Lakes Bioenergy Research
Center, all the people that develop and run these projects. My sincere gratitude to my family—
Mom, Dad, Reily, Ryan, and Cassy—for your care and support and love. And thank you dearly
to Aaron Kosobucki for all the adventure and learning and support along the way.
                                                  iv


                                                TABLE OF CONTENTS
LIST OF TABLES ........................................................................................................................ vii
LIST OF FIGURES ...................................................................................................................... ix
INTRODUCTION ........................................................................................................................ 1
LITERATURE CITED ................................................................................................................. 6
CHAPTER ONE ........................................................................................................................... 11
Optimizing pollinator conservation and crop yield among perennial bioenergy crops ................ 11
        Abstract ............................................................................................................................. 11
        Introduction ....................................................................................................................... 12
        Methods............................................................................................................................. 15
                Study sites.............................................................................................................. 15
                Agricultural management ..................................................................................... 16
                Flowers ................................................................................................................. 17
                Pollinators............................................................................................................. 18
                Crop yield.............................................................................................................. 20
                Optimizing crop yield and pollinator conservation .............................................. 20
        Results ............................................................................................................................... 21
                Flowers ................................................................................................................. 21
                Pollinators............................................................................................................. 22
                Crop yield.............................................................................................................. 22
                Optimizing crop yield and pollinator conservation .............................................. 23
        Discussion ......................................................................................................................... 23
        Acknowledgements ........................................................................................................... 29
APPENDIX ................................................................................................................................... 30
LITERATURE CITED ................................................................................................................. 41
CHAPTER TWO .......................................................................................................................... 51
Prairie strips and lower land use intensity increase biodiversity and ecosystem services ............ 51
        Abstract ............................................................................................................................. 51
        Introduction ....................................................................................................................... 52
        Materials and Methods ...................................................................................................... 55
                Study sites and sampling locations ....................................................................... 55
                Dung beetles.......................................................................................................... 58
                Ants ....................................................................................................................... 59
                Spiders................................................................................................................... 60
                Butterflies .............................................................................................................. 61
                Active carbon ........................................................................................................ 62
                Decomposition ...................................................................................................... 63
                Global warming impact (GWI) ............................................................................ 64
                Pollination............................................................................................................. 65
                                                                     v


                Soil organic carbon (SOC) ................................................................................... 66
                Crop yield.............................................................................................................. 67
                Statistical analyses ................................................................................................ 68
        Results ............................................................................................................................... 70
                Dung beetles.......................................................................................................... 70
                Ants ....................................................................................................................... 71
                Spiders................................................................................................................... 71
                Butterflies .............................................................................................................. 72
                Active carbon ........................................................................................................ 72
                Decomposition ...................................................................................................... 73
                GWI ....................................................................................................................... 73
                Pollination............................................................................................................. 74
                SOC ....................................................................................................................... 74
                Crop yield.............................................................................................................. 74
        Discussion ......................................................................................................................... 75
        Acknowledgements ........................................................................................................... 82
APPENDIX ................................................................................................................................... 84
LITERATURE CITED ............................................................................................................... 103
CHAPTER THREE .................................................................................................................... 115
Prairie strips and lower land use intensity increase butterfly biodiversity in row crop farms .... 115
        Abstract ........................................................................................................................... 115
        Introduction ..................................................................................................................... 116
        Methods........................................................................................................................... 118
                Study sites............................................................................................................ 118
                Plants .................................................................................................................. 120
                Butterflies ............................................................................................................ 121
        Results ............................................................................................................................. 123
                Plants .................................................................................................................. 123
                Butterflies ............................................................................................................ 124
        Discussion ....................................................................................................................... 126
        Acknowledgements ......................................................................................................... 130
APPENDIX ................................................................................................................................. 131
LITERATURE CITED ............................................................................................................... 146
                                                                    vi


                                                      LIST OF TABLES
Table 1.1. List of plant species with open flowers present in each treatment, and method used for
counting floral abundance and plant abundance ........................................................................... 31
Table 1.2. Seeded species and seeding rates for experimental treatments. Due to an intense rain
event within two weeks of planting in 2008, most of the seed was lost from treatment plots. After
evaluation of the plant composition in the experimental plots in the following summer, a decision
was made by scientists and practitioners to replant seed in the switchgrass, native grasses, and
restored prairie treatments in 2009. The rates of additional seeding were calculated by restoration
practitioners to make the total seeding rate equal to the attempted seeding rate of 2008.
Surveying, seeding rates, and planting densities were conducted by a local native seed and
restoration company Native Connections in Kalamazoo, MI, USA (occupied Anishinaabe land)
following local best-management practices (Sanford et al. 2016) ................................................ 34
Table 2.1. Native forb and grass species sowed to create prairie strips........................................ 85
Table 2.2. Dung beetle abundance per species found in each treatment each year. All distances
are combined for strip abundance counts...................................................................................... 86
Table 2.3. Effects of crop management treatments on measures of biodiversity and ecosystem
services treatments. Bolded p-values indicate statistically significant measures ......................... 87
Table 2.4. Effects of distance from prairie strips on biodiversity and ecosystem services,
including data from both reduced input and biologically based treatments. Bolded p-values
indicate statistically significant measures ..................................................................................... 88
Table 2.5. Ant abundance per species found in each treatment each year. All distances are
combined for strip abundance counts ........................................................................................... 89
Table 2.6. Spider abundance per family found in each treatment each year. All distances are
combined for strip abundance counts ........................................................................................... 91
Table 2.7. Butterfly abundance per species or morphogroup found in each treatment each year.
Unknown butterflies were butterflies that could not be identified in the field, for example if they
flew over too quickly; it is likely that these individuals are already included in this table (i.e., not
new species) .................................................................................................................................. 93
Table 3.1. Pollard index (sum of the average weekly abundances over the year) of butterflies
across crop management treatments. Unknown butterflies were butterflies that could not be
identified in the field, for example if they flew over too quickly. These individuals are most
likely species already included on this list .................................................................................. 132
                                                                    vii


Table 3.2 Presence/absence of plant species found in the prairie strips of each treatment in each
year. A black box indicates that the species was present. * denotes a species seeded into the
prairie strips. ** denotes that a species was seeded in that morphogroup and therefore could be a
seeded species, but we could not decipher if it was the seeded species ..................................... 136
                                                 viii


                                                     LIST OF FIGURES
Figure 1.1. (a) The layout of experimental plots in the Great Lakes Bioenergy Research Center
sites at Kellogg Biological Station. The intercrop distance between experimental plots is 15 m.
(b) The layout of a single plot in the Great Lakes Bioenergy Research site at Kellogg Biological
Station. Each plot is 30 × 40 m and contains a single biofuel crop. The survey area is where both
pollinators and flower surveys were conducted. Pollinator surveys occurred as timed walks along
the walking path, surveying 1 m on either side. Flower surveys were conducted in the same area
following pollinator surveys ......................................................................................................... 36
Figure 1.2. (a) Flower abundance, (b) flowering plant richness, (c) pollinator abundance, and (d)
pollinator group richness recorded in each of the four biofuel crop treatments. Each bar
represents an average across replicated plots and sampling rounds ±1 SE. Letters indicate
significant differences ................................................................................................................... 37
Figure 1.3. (a) Pollinator abundance as a function of flowering plant abundance, (b) pollinator
abundance as a function of flower richness, (c) pollinator group richness as a function of
flowering plant abundance, and (d) pollinator group richness as a function of flower richness.
Each plot depicts a linear model with a 95% confidence interval shaded in gray. Raw data points
are shown for each plot for all four sampling rounds. One data point in panels (a) and (c) that
describes pollinator abundance (z-score > 3) is not shown........................................................... 38
Figure 1.4. The yield of dry harvested biomass (Mg/ha) of each biofuel crop treatment. For
comparison, maize yielded on average 9.388 Mg/ha in this same replicated experiment in 2018.
Letters indicate significant differences among crop yields........................................................... 39
Figure 1.5. A Pareto frontier for mean crop yield (Mg/ha) and mean pollinator group richness
across biofuel crop treatments. The Pareto frontier is shown as a solid black line; this describes
the optimal values for crop yield and pollinator group richness over a range of preferences. The
means for each treatment are the larger, bolded points and the data points for each plot are the
smaller points ................................................................................................................................ 40
Figure 2.1. Characteristics of experimental crop management treatments. Standard rate refers to
Generally Accepted Agricultural and Management Practices. IPM (Integrated Pest Management)
in the reduced input treatment refers to a combination of extra tillage (maize years), narrow row
spacing (soy years), and reduced herbicide use, mostly related to the number of residual
herbicides. Fungicides and pesticides are applied as problems arise and severity is assessed. Also
regarding reduced input, N fertilizer is applied at 25% of the standard rate in maize years, and at
60% in wheat years ....................................................................................................................... 96
Figure 2.2. a. Experimental landscape with six blocks of five crop management treatments. b.
Plot layout. Prairie strips and Strip Sampling Stations are only present in the reduced input and
biologically based treatments ........................................................................................................ 97
                                                                    ix


Figure 2.3. Hedge’s g effect size (black and gray dots) with 95% confidence interval. The
baseline is the conventional management treatment. Negative values are effect sizes lower than
that of the baseline, and positive values are effect sizes higher than the baseline. Values that do
not cross zero are shaded gray. Note that lower levels of global warming impact (GWI) would
be a more positive ecosystem service. Conservation land was not harvested, therefore crop yield
is not applicable for that treatment................................................................................................ 98
Figure 2.4. Effects of crop management treatment on measures of biodiversity and ecosystem
services between years 2019 and 2020. For measures of biodiversity, bars depict means over an
entire year; for measures of ecosystem services, bars depict means for each sampling round.
Letters denote statistical differences among treatments. Error bars show standard errors. Ants and
active carbon were only measured in 2019 ................................................................................. 100
Figure 2.5. Effects of distance from prairie strips on measures of biodiversity and ecosystem
services in both reduced input and biologically based treatments. We considered linear effects of
distance from prairie strips. The interaction with year is presented, with 2019 being the first year
of prairie strip implementation and a wheat year, and 2020 being the second year and a maize
year. Distance 0 m is within the prairie strip. Black dashes on the y-axis represent the mean of
the plot level values for reduced input and biologically based for that measure in 2019; green
dashes represent 2020. Ant data from 2019 is modified from Helms et al. 2021 ....................... 101
Figure 3.1. a. A diagram of a plot and survey locations within the plot. The transect and quadrat
locations are consistent in every plot. Conventional, no till, and conservation land only have the
standard transect. b. Annabelle McCarthy surveys for butterflies along a prairie strip transect.
Photo taken by Jamie Smith ........................................................................................................ 142
Figure 3.2. NMDS (nonmetric multidimensional scaling) plot of prairie strip plant communities
across years and treatments. Each dot represents one plot per year. The ellipses represent a 95%
confidence interval around the centroid of each year grouping .................................................. 143
Figure 3.3. Butterfly abundance and richness indices across years for all surveys. Letters indicate
significant statistical differences ................................................................................................. 144
Figure 3.4. Butterfly abundance and richness across treatments each month ............................ 145
                                                             x


                                          INTRODUCTION
Biodiversity is a measure of the variety of life on earth, with inherent value in its existence and
vastness. Biodiversity is also valuable for its essential role to perform ecosystem functions that
sustain life on earth. Human activity is causing a decline in biodiversity globally with an
estimated one million species currently threatened with extinction, and the rate of loss is
accelerating. The loss of biodiversity can be mitigated if action is taken, but delayed response
will incur greater economic and ecological costs. Immediate transformative change is needed in
policy and action to conserve and restore biodiversity (IPBES 2019).
        The greatest threats to terrestrial biodiversity are land use change, overexploitation,
pollution, and climate change (in descending order; IPBES 2019). Agriculture is the principal
cause of land use change, occupying 37% of earth’s land (World Bank 2018). Agriculture is also
a global source of pollution due to fertilizer and pesticide use, and climate change due to
emissions from soil management and other sources (Novotny 1999, Paustian et al. 1995).
Simultaneously, the human population is growing, as are human demands for food, fuel, and
fiber (IPBES 2019). Furthermore, active and impending climate change hastens the need for
developing resilient farming systems (Lipper et al. 2018). The result is an urgent need to
transform agricultural landscapes into multifunctional systems that both meet human resource
needs and mitigate biodiversity loss (Kremen & Merenlander 2018, Schulte et al. 2021, Basso
2021).
        Agricultural diversification is one method of mitigating the loss of biodiversity while still
providing resources essential for human life (Kremen et al. 2012, Tscharntke et al. 2021). The
composition and configuration of landscapes are important factors determining their ability to
support biodiversity and ecosystem services (Tscharntke & Brandl 2004, Kremen et al. 2007,
                                                   1


Mitchell et al. 2014, Landis 2017). In agricultural landscapes, diversifying landscape
composition and configuration can positively affect biodiversity and ecosystem services by
increasing the amount of natural habitat, landscape connectivity, and structural complexity
(Tscharntke et al. 2005, M’Gonigle et al. 2015, Lamy et al. 2016). An open question, and one
that I address here, is: can landscape diversification increase biodiversity and maintain or even
increase agricultural yield and ecosystem services? We take an experimental approach to
understand the mechanisms by which landscape diversity affects biodiversity and the functioning
of agroecosystems.
        Methods of diversification in working landscapes include increased genetic diversity of
crops, the addition of crop rotations and cover crops, diversification of the number and
complexity of crops within the landscape, and habitat restoration within or surrounding crops
(Fischer et al. 2006, Isbell et al. 2017, Schulte et al. 2017, Renard & Tilman 2019). Restoring
land within agricultural landscapes benefits biodiversity by increasing available habitat and
increasing habitat connectivity within the broader landscape (Fischer et al. 2006, Kremen &
Merenlander 2018). Restoring habitat in agricultural landscapes can also support ecosystem
services which can have global effects (e.g., carbon sequestration, water purification, prevention
of nutrient runoff) and that can increase agricultural yield (e.g., pollination, decomposition, soil
microbial activity) (Werling et al. 2014, Schulte et al. 2017, Tamburini et al. 2020). This
multitude of goods, services, and functions increases resilience as climates shift and extreme
weather events become more common (Liebman & Schulte 2015). While diversification may
increase biodiversity, ecosystem services, and crop yield, the resulting tradeoffs and synergies
among these for specific agricultural diversification strategies are unknown.
                                                    2


        With mounting evidence that diversifying agricultural landscapes is essential for
conservation, widespread implementation has yet to occur for at least three reasons: funding and
policy, concerns of farmland displacement, and detailed assessments of place-based strategies.
Diversifying an agricultural landscape requires implementation costs that are not feasible for
farmers without aid, and political action is required to financially support farmers. Another
concern is that removing crop land from production will lead to the displacement of agricultural
land elsewhere. But advances in precision agriculture can direct efforts to parts of a farm that are
consistently underperforming and locations that have the most potential to conserve biodiversity
and provide ecosystem services (Chaplin-Kramer et al. 2019, Basso 2021). In addition, studies of
specific diversification strategies are needed to direct policy and to discern the interactions
among biodiversity, ecosystem services, and crop yield for specific practices. Taking action at
the necessary scale will require an understanding of both impacts on conservation and crop yield.
Understanding this relationship can inform land management decisions for designing resilient
working landscapes that both support biodiversity and yield goods and services (Robertson et al.
2017, Kremen & Merenlander 2018).
        I contribute to this body of knowledge with three studies that elucidate the tradeoffs and
synergies among biodiversity, ecosystem services, and yield across two diversified cropping
systems: perennial bioenergy cropping systems and row crop farms with prairie strips. One
specific strategy for diversifying bioenergy cropping systems is planting native, perennial
grassland as a bioenergy crop rather than annual grain crops such as maize. Perennial bioenergy
landscapes support biodiversity and ecosystem services, but currently produce lower yield than
annual grain crops (Robertson et al. 2017, Roozeboom et al. 2019). Most climate change
mitigation scenarios include biofuel agriculture, and understanding how different crops impact
                                                  3


biodiversity and crop yield is crucial for developing sustainable bioenergy policies (Gelfand et
al. 2020). Chapter 1 of this dissertation examines the performance of multiple native perennial
bioenergy cropping systems in their ability to optimize pollinator conservation and bioenergy
crop yield.
        Another method of landscape diversification is the implementation of prairie strips in
monoculture row crop farms. Prairie strips are strips of farmland retired from production and
actively restored with native prairie plant species. This conservation practice is designed to be
practicable for farmers and is a part of the USDA Conservation Reserve Program, which
subsidizes farmers for the cost of implementing prairie strips (Luther et al. 2022). Prairie strips
have the potential to promote biodiversity and ecosystem services on farms without sacrificing
crop yield (Kordbacheh et al. 2020, Schulte et al. 2017). However, this management practice has
not been tested against a range of other crop management practices for its impact on biodiversity,
ecosystem services, and crop yield, and has not been measured early in prairie strip
establishment. Chapter 2 seeks to elucidate the tradeoffs and synergies among biodiversity,
ecosystem services, and crop yield across a land use intensity gradient that ranges from cropping
systems without prairie strips to cropping systems with prairie strips to conservation land. We
also measured how prairie strips spillover into adjacent cropland. To understand the details by
which prairie strips affect diversity and ecosystem function as in Chapter 2, for Chapter 3 I
expand on one response—butterfly biodiversity—in detail.
        Approximately 26% of annual cropland in the Midwest USA is consistently
underperforming with the inputs of nutrients and greenhouse gas emissions into that area
providing relatively low yields. Continuing to farm this land wastes resources and results in
unnecessary nitrogen pollution and greenhouse gas emissions (Basso et al. 2019). These
                                                  4


underperforming areas of cropland could be targeted for restoration as prairie strips or for yield
as native perennial bioenergy crops without displacing cropland to another location. With my
dissertation, I use an experimental approach to assess the potential of these strategies to conserve
biodiversity and ecosystem services and produce crop yields.
                                                 5


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                                                10


                                        CHAPTER ONE
      Optimizing pollinator conservation and crop yield among perennial bioenergy crops1
Abstract
In order to both combat the decline of biodiversity and produce food, fuel, and fiber for a
growing human population, current agricultural landscapes must transition into diversified,
multifunctional systems. Perennial cellulosic biofuel crops have potential to meet both of these
challenges, acting as multifunctional systems that can enhance biodiversity. What is not well
understood, and what we test here, are the tradeoffs among different perennial crops in their
performance as biofuels and in biodiversity conservation. Working in an established bioenergy
experiment with four native, perennial, cellulosic biofuel crop varieties – ranging from
monoculture to diverse restoration plantings – we tested the effect of biofuel crop management
on flower communities, pollinator communities, and crop yield. The greatest abundance and
diversity of pollinators and flowers were in treatments that were successional (unmanaged),
followed by restored prairie (seeded mix of native grasses and forbs), switchgrass, and a mix of
native grasses. However, biofuel crop yield was approximately the inverse, with native grasses
having the highest yield, followed by switchgrass and prairie, then successional treatments.
Restored prairie was the optimal biofuel crop when both pollinator conservation and crop yield
are valued similarly. We add to mounting evidence that policy is needed to create sustainable
1
  Manuscript was published in Global Change Biology Bioenergy in March 2021 with coauthors
Sean R. Griffin and Nick M. Haddad
                                                 11


markets that value the multifunctionality of perennial biofuel systems in order to achieve greater
ecosystem services from agricultural landscapes.
Introduction
Human actions have caused a global biodiversity crisis with rapid and widespread rates of
biodiversity decline (Dirzo et al. 2014, Wepprich et al. 2019, van Klink et al. 2020, Wagner et al.
2021). Simultaneously, humans are faced with the challenge of sustainably and equitably
supporting a growing human population (Foley et al. 2011, Kremen & Merenlander 2018,
Bennett et al. 2021). Agricultural landscapes are the greatest source of biodiversity loss and have
the greatest potential for sustainable conservation (Intergovernmental Platform on Biodiversity
and Ecosystem Services 2019). Agriculture currently occupies over 38% of the world’s land
area, with a projected increase in crop demand of 100-110% from 2005 to 2050 (Tilman et al.
2011, World Bank 2016). A majority of agricultural landscapes rely on chemical and energy
intensive inputs to produce a single crop (Ramankutty et al. 2018). However, in order to both
conserve biodiversity and produce food, fiber, and fuel for humans, input-intensive agricultural
landscapes need to transition into multifunctional working landscapes (Jordan & Warner 2010,
Díaz et al. 2019).
        Diversified agricultural landscapes can mitigate the loss of biodiversity while sustainably
supporting a growing human population (Fischer et al. 2006, Albrecht al. 2020). An increase in
natural habitat in working landscapes increases resources available for a range of species and can
increase connectivity among habitat patches for some species that are otherwise relegated to
protected areas (Fischer et al. 2006, Nicholls & Altieri 2013, Isbell et al. 2017). In addition to the
benefits for biodiversity, diversified landscapes provide important ecosystem services that can
                                                 12


promote agricultural yield, including increased soil health, carbon sequestration, and pollination
(Morandin & Kremen 2013, Schulte et al. 2017, Kremen & Merenlander 2018). The effect of
diversifying working landscapes has the potential to create synergies or tradeoffs for biodiversity
and the yield of food, fiber, and fuel (Nelson et al. 2009, Meehan et al. 2013, Raudsepp-Earne et
al. 2010, Goldstein et al. 2012, Robertson et al. 2017), possible outcomes that we test here.
Weighing tradeoffs among environmental and economic factors is crucial for creating
sustainable agricultural landscapes that recognize biodiversity and social impacts (Wiens et al.
2011, Senapathi et al. 2015).
        One method of landscape diversification is to convert marginal lands—lands that are
under cultivation but consistently underperforming (Basso et al. 2019)—to natural or semi-
natural habitats that can also produce bioenergy (Robertson 2008, Cai et al. 2011, Núñez-
Regueiro et al. 2019). Biofuel agriculture is needed in most climate mitigation scenarios that
limit atmospheric warming to 1.5°C by 2100 due to its ability to reduce life cycle carbon
emissions (Rogelj et al. 2018, Gelfand et al. 2020). The impact of biofuel agriculture on
biodiversity depends on previous land use and biofuel crop choice, including crop diversity
(polyculture vs. monoculture) (Tilman et al. 2009, Wiens et al. 2011, Bennett et al. 2014,
Robertson et al. 2017). Currently, most biofuels are input-intensive monocultures of annual grain
crops, such as maize (Zea mays) (U.S. Environmental Protection Agency 2018). As an
alternative to annual grain crops, perennial cellulosic plants such as native grass species can be
planted as a biofuel crop. With current production infrastructure, perennial cellulosic biofuels
yield less ethanol per hectare than annual crops (Roozeboom et al. 2019), but potential exists to
decrease this gap (Brandes et al. 2018). In addition to biomass production, perennial cellulosic
biofuels increase ecosystem functions such as carbon sequestration and biodiversity (Hill et al.
                                                  13


2006, Werling et al. 2014, Landis et al. 2018), and ecosystem services, such as pollination, in the
broader landscape (Bennett & Isaacs 2014, Robertson et al. 2017). While switchgrass (Panicum
virgatum) is widely considered for its potential as a native perennial monoculture crop (Mitchell
et al. 2012), polycultures of native perennial species can be more productive (Tilman et al. 2006)
and can also increase benefits for biodiversity (Wiens et al. 2011, Robertson et al. 2017). Such
polycultures include those that we assess: mixes of native grasses (Tilman et al. 2006, Hill 2009),
unmanaged lands allowed to develop early successional plant communities, and restored prairies
planted with high-quality grasses and forbs (Fargione et al. 2009).
         An important ecosystem service among different biofuel crops is the potential habitat and
foraging and/or nesting resources provided to beneficial insects (Landis & Werling 2010).
Pollinators are of high conservation value due to recent declines (Potts et al. 2010), and due to
their high ecosystem service value in natural and agricultural systems through the pollination of
wild plants and crops (Klein et al. 2007). A major component structuring pollinator communities
is the quantity and quality of floral resources available and the associated pollen and nectar
resources (Potts et al. 2003, Ebeling et al. 2008, Williams et al. 2015, Kremen et al. 2018).
Within agricultural landscapes, more diverse plantings increase the resources and habitat
available for pollinators (Ebeling et al. 2008, Isbell et al. 2017, Mallinger et al. 2016, Kremen et
al. 2018). In addition, pollinators can spill over from diverse plantings into nearby habitats,
which can be especially important for nearby crops by providing pollination services that benefit
yields (Bennett & Isaacs 2014). Pollinator conservation has been of particular interest across the
globe in regard to biofuel crops and changing bioenergy landscapes (Gardiner et al. 2010,
Stanley & Stout 2013, Romero & Quezada-Euán 2013, Werling et al. 2014, Bennett et al. 2014,
Graham et al. 2017). However, the tradeoff between pollinator conservation and crop yield, and
                                                   14


therefore farmer benefit, is unknown (Garibaldi et al. 2014). Measuring these tradeoffs, as we do
here, increases the chance of identifying candidate biofuel crops that can both benefit farmers
and biodiversity (Power 2010, Waldron et al. 2012, Dale et al. 2014, Landis et al. 2018), a task
that is essential prior to policy adoption and implementation (Landis & Werling 2010, Robertson
et al. 2017).
         Working in an experiment with four native, perennial, cellulosic biofuel crop treatments,
ranging from monoculture to diverse restoration planting, we tested for optimal candidate crops
with respect to two variables: pollinator conservation and crop yield. We asked three questions:
1) How do candidate biofuel crops affect floral abundance and diversity available to pollinators?
2) How do candidate biofuel crops affect pollinator abundance and diversity? 3) What are the
tradeoffs among crop yield and pollinator conservation across candidate crops?
Methods
Study sites
We conducted this study at the Biofuel Cropping System Experiment at the U.S. Department of
Energy’s Great Lakes Bioenergy Research Center (GLBRC) experimental landscape (Figure
1.1). This site is located at the W.K. Kellogg Biological Station’s Long-Term Ecological
Research site in Hickory Corners, MI (occupied Anishinaabe land). Our study in 2018 occurred
within an experiment that was established in 2008 to test the environmental, biogeochemical, and
agricultural performance of potential biofuel crops. Prior to the establishment of this experiment
in 2008, alfalfa was farmed in the landscape (Sanford et al. 2016). The experiment was a
complete block design consisting of five replicate blocks (Figure 1.1 a). Within each block, we
studied four treatments that represented four bioenergy cropping systems: switchgrass, native
                                                 15


grasses, early succession, and restored prairie. These four bioenergy crops were perennial,
consisting of native grasses and/or forbs. Each plot was 30m by 40m with a permanent path in
the same location in each plot for sampling (Figure 1.1 b). See Gelfand et al. (2020) for detailed
soil and climate information at this experimental site.
Agricultural management
The switchgrass treatment was seeded in monoculture in 2008, although about 20% of biomass
in these switchgrass plots consisted of other species that naturally recruited into the system by
2014 (Werling et al. 2014) (see Table 1.1 for flowering species observed in each treatment in
2018). The native grasses treatment was a mix of one perennial C3 grass (Elymus canadensis)
and four C4 grasses (Panicum virgatum, Andropogon gerardii, Schizachyrium scoparium,
Sorghastrum nutans) seeded in polyculture. The restored prairie treatment was seeded in native
prairie species including the same C3 and C4 grasses as in the native grasses treatment plus one
C3 grass (Koeleria cristata), three leguminous forbs (Desmodium canadense, Lespedeza
capitata, Baptisia leucantha), and nine non-leguminous forbs (Rudbeckia hirta, Anemone
canadensis, Asclepias tuberosa, Silphium perfoliatum, Monarda fistulosa, Ratibida pinnata,
Solidago rigida, Solidago speciosa, and Symphyotrichum novae-angliae). The early successional
treatment was not seeded, and inhabiting species are those that have colonized the plots via
surrounding habitats, from within the seedbank, and potentially from nearby experimental plots.
The species sown in each treatment in 2008 and 2009 are described in Table 1.2.
        The land for all four crop treatments was prepared in the same way in 2008. Switchgrass
and native grasses treatments were herbicided in 2009 and 2010 to suppress weed competition.
Fertilizer was applied annually in the switchgrass, native grasses, and successional treatments at
                                                  16


56 kg of Nitrogen per hectare applied as 28% N fertilizer. Fertilizer was not applied to the
restored prairie treatment as per standard agricultural practice for these candidate biofuel crops
(Sanford et al. 2016). Plots were harvested annually in the fall, also as per standard practice in
these cropping systems (Stahlheber et al. 2016).
Flowers
Four sampling rounds of flowers and pollinators occurred monthly between June and October of
2018. We conducted flower surveys within five days of each pollinator survey in every plot to
quantify resources available to pollinators. We measured flower richness and abundance along a
2x60m transect in each plot (Fig. 1b). We only recorded actively flowering forbs; we did not
record grasses. Table 1.1 lists plant species with open flowers that we detected in each treatment.
For each species along the transect, we counted the total number of individuals with open
flowers. Then, for ten individuals of each species (or all individuals if fewer than ten were
observed within the transect), we counted the number of open flowers on each plant. We used
those open flower counts to calculate an average number of open flowers per forb species per
transect. We multiplied those values by the total number of flowering individuals of that species
on the transect, thus providing an estimate of total floral abundance for each species on each
transect. In some cases, we counted floral units rather than individual flowers. For instance, we
considered a head of a Rudbeckia hirta the equivalent to an individual flower (Table 1.1).
        To determine the effects of bioenergy crop on flower abundance, we used linear mixed
effects models with a negative binomial distribution. Main effects of the model were biofuel crop
and sampling round, and the random effect was the experimental block. We rarefied flower
richness by abundance using the ‘iNext’ package in R to control for the effects of flower
                                                  17


abundance on flower species richness (Chao et al. 2014, Hsieh et al. 2016). To determine the
effects of bioenergy crops on rarefied flower richness, we used a linear mixed effects model with
a normal distribution. The response variable was the rarefied number of plant species with open
flowers, the main effect was biofuel crop, and the random effect was the experimental block.
Sampling round was not included in this model because we did not have enough observations to
calculate rarefied richness for each plot per sampling round; a single measure was calculated
across all sampling periods.
Pollinators
To test effects of bioenergy crops on pollinators, we conducted pollinator surveys in each plot.
Pollinator diversity and abundance were measured along the same 2x60m transects as the flower
surveys (Figure 1.1 b). Each plot was surveyed twice per day per sampling round, with one
survey occurring between 10am-12pm, and the second survey occurring between 1-4pm. To
limit bias, only two scientists conducted all surveys, each of which consisted of walking a single
transect over a ten-minute period and recording all insects actively visiting a flower and the
associated species of flower they were visiting. We defined pollinator groups a priori based on
our knowledge of pollinator diversity in the system (Gardiner et al. 2010). Pollinators were
visually identified into 13 groups: Andrena spp., Apis mellifera, Bombus spp. (includes
Xylocopa, only three total were detected), butterflies, large dark bees (brown or black bees,
>16mm), large green bees (>10 mm including Agapostemon spp.), medium dark bees (brown or
black bees, 10-16mm), moths, small dark bees (brown or black bees, <10mm), small green bees
(<10mm), syrphid flies (non-Toxomerus spp. syrphids), Toxomerus spp., and wasps. These
groups were chosen as they are possible to identify on the wing. We chose to visually identify
                                                 18


pollinators rather than destructively sample pollinators because we noticed strong treatment
effects across groups that can be visually identified. Visual identification could allow more
accurate measurements in the field (without having to stop and execute the collections) and
prevents disrupting other pollinators which could skew abundance. We were able to assess key
differences without destructive sampling of pollinators. The alternative of collecting pollinators
requires long time lags in data processing and high levels of funding for species level
identification. We use the term “pollinator group richness” to equal the number of pollinator
groups we recorded.
        To determine the effect of bioenergy crop on pollinator abundance and pollinator group
richness, we used linear mixed effects models. We constructed the pollinator abundance model
using a negative binomial distribution with pollinator abundance as the response variable, biofuel
crop and sampling round as the main effects, and experimental block as the random effect. There
was one outlier (z-score=8.1, threshold of z-score>3) in the pollinator abundance dataset that was
removed from the linear regression analyses. We constructed the pollinator group richness model
similarly, but used a normal distribution with pollinator group richness (number of pollinator
groups) as the response variable.
        The relationship between the pollinator community and the floral community was
determined in separate models. Four simple linear regressions were created to investigate the
relationships between: 1) pollinator abundance and flowering plant abundance, 2) pollinator
group richness and flowering plant abundance, 3) pollinator abundance and flowering plant
richness, and 4) pollinator group richness and flowering plant richness. All four models were
separated because floral abundance and floral richness were correlated despite rarefaction, and
treatment caused differences in floral abundance.
                                                 19


Crop yield
Plots were harvested after the first frost of the season, on October 25, 2018, using a JD 7350 self-
propelled forage harvester equipped with a JD 676 cutting head. Yield was defined as dry
harvested biomass (Mg/ha). Yield was measured by chopping plant material into a Gnuse forage
wagon fitted with load cells.
        To determine the difference in yield among crops, we used a linear mixed effects model
with a normal distribution. Crop yield in dry matter yield (Mg/ha) was the response variable,
treatment was the main effect, and experimental block was the random effect. We used R
package ‘lme4’ (Bates et al. 2015) to construct models with normal distributions and
‘glmmADMB’ (Fournier et al. 2012, Skaug et al. 2013) to construct models with negative
binomial distributions. A Tukey test was used for post-hoc analyses for all models using R
package ‘multcomp’ (Hothorn et al. 2008).
Optimizing crop yield and pollinator conservation
Pareto optimality—a concept from economics and engineering—was used to determine the
tradeoffs between biofuel yield and ecosystem service generation (pollinator conservation).
Pareto optimality establishes a set of tradeoffs in which one criterion must decrease in order for
another to increase. It is a tool for determining efficient use of resources (Polasky et al. 2005,
Kennedy et al. 2008, Tendler et al. 2015). A Pareto frontier is the boundary in graphical space
that connects all optimal points. Because the optimum is dependent on how the two variables are
valued or preferred against each other, there is a range of optimal points. If the variable on the y-
axis is preferred, the corresponding optimal point would be located where the slope of the Pareto
frontier is shallow; if the variable on the x-axis is preferred, the corresponding optimal point
                                                   20


would be located where the slope is steep. If the variables on the x and y axes are valued
similarly, the optimal point will fall where the slope is intermediate. Points will not fall on the
Pareto frontier if they are not optimal for any tradeoff between the two variables.
        We constructed a Pareto frontier for crop yield (an economic return) and pollinator
conservation (an ecosystem service), using the ‘rPref’ package in R. Pollinator group richness
was used as a measure of pollinator conservation (Senapathi et al. 2015). Mean pollinator group
richness and mean yield across all plots were used to calculate the Pareto frontier. We used the
‘rPref’ package to calculate optimal datapoints with respect to a preference for both high crop
yield and high pollinator conservation (Roocks 2016). Points do not fall on the Pareto frontier if
they are not optimal between crop yield and pollinator conservation. All graphical plots were
constructed using R package ‘ggplot2’ (Wickham 2016) and the ‘Rmisc’ package (Hope 2013)
was used to calculate standard error.
Results
Flowers
Flower abundance did not differ among successional (mean: 17734.7 ± SE: 5355.7), restored
prairie (4730.1 ± 1732.4), and switchgrass (5717.2 ± 2255.6) treatments. These treatments were
all significantly higher in floral abundance than the native grasses treatment (1128.7 ± 893.8)
(χ2=51.9, p<0.001, Figure 1.2 a). Results did not change when the number of individual plants
replaced flower abundance as the response variable. Flower richness was highest in the
successional treatment (mean: 17.6 ± 1.3) followed by prairie (14.1 ± 0.4), then switchgrass
(11.1 ± 0.7), then the native grasses treatment (3.5 ± 0.5) (χ2=235.1, p<0.001, Figure 1.2 b).
                                                  21


Pollinators
Across four sampling rounds, we visually identified 1640 pollinator individuals. Pollinator
abundance was highest in successional (mean: 59.1 ± SE: 23.2) and restored prairie (15.3 ± 3.3)
treatments, followed by switchgrass (6.9 ± 1.8) (although restored prairie was not statistically
different than successional or switchgrass treatments), and lowest in the native grasses treatment
(0.9 ± 0.3) (χ2=153.4, p<0.001, Figure 1.2 c). A similar pattern resulted for pollinator group
richness, with the highest richness in the successional treatment (7.4 ± 0.5), followed by prairie
(5.2 ± 0.5), then switchgrass (3.7 ± 0.5), then the native grasses treatments (1.6 ± 0.2), although
prairie was not statistically different than switchgrass or successional treatments (χ2=76.5,
p<0.001, Figure 1.2 d). Native bees, honeybees, and non-bee pollinators all responded similarly
to the biofuel crop treatments when analyzed independently.
        Flowering plant abundance and richness were always significantly, positively related to
pollinator abundance and richness (pollinator abundance and flower abundance R2=0.29,
p<0.001, Figure 1.3 a; pollinator group richness and flowering plant abundance R2=0.47,
p<0.001, Figure 1.3 b; pollinator abundance and flowering plant richness R2=0.25, p<0.001,
Figure 1.3 c; pollinator group richness and flower richness R2=0.49, p<0.001 Figure 1.3 d).
Crop yield
Crop yield (Mg/ha) was highest in native grasses (mean: 7.1 ± 0.5) and prairie (mean: 5.8 ± 0.7)
treatments, followed by switchgrass (mean: 5.5 ± 0.3), although prairie was not statistically
different from native grasses or switchgrass treatments). The successional treatment (mean: 2.3 ±
0.2) had the lowest crop yield (χ2=87.4, p<0.001, Figure 1.4).
                                                  22


Optimizing crop yield and pollinator conservation
The Pareto frontier analysis created an optimal boundary for valuing high crop yield and high
pollinator conservation (Figure 1.5). The frontier, which connects all optimal points, included the
native grasses, restored prairie, and successional treatments. The only treatment not included on
the boundary was switchgrass. The frontier shows that restored prairie treatment was the optimal
crop when pollinator conservation and biofuel crop yield are valued equally. The native grasses
treatment was the optimal crop when crop yield was valued higher than pollinator conservation.
The successional treatment was the optimal crop when pollinator conservation was valued higher
than crop yield.
Discussion
Our results reveal that choice of perennial biofuel crop produced a measurable tradeoff between
crop yield and pollinator conservation, mediated by the flower community. Higher floral
abundance and diversity drove higher pollinator abundance and diversity in all treatments, but
the treatments with the most flowers did not have the highest yield. We found that restored
prairie best balanced objectives to achieve high crop yield and high pollinator richness. These
results indicate that working landscapes with biofuels may need to balance conservation with
production goals.
         The agricultural management of each of the four perennial native biofuel crops
established a distinct floral community, as expected. The abundance and diversity of flowers in
each treatment explained the abundance and diversity of pollinators. All combinations of flower
and pollinator abundance and diversity were positively correlated, with the strongest correlation
occurring between pollinator group richness and flower richness. Therefore, agricultural
                                                 23


management determined the pollinator community in each biofuel crop. Incorporating a greater
abundance and, more importantly, a greater richness of native flower species in agricultural
landscapes can attract a more abundant and more diverse community of pollinators.
        The richness and abundance of pollinators was highest in the successional treatment,
followed by restored prairie, switchgrass, and lowest in the native grasses treatment. Unlike our
study that showed a clear ordering of pollinator richness and abundance, past studies have had
inconsistent results—some finding higher bee species richness and abundance in restored prairie
than switchgrass (Ridgway 2016, Graham et al. 2017), while another found no difference
(Gardiner et al. 2010). When measuring across a broad range of pollinators, we found a greater
abundance and richness of pollinators in prairie than in switchgrass treatments. This result adds
evidence to other studies that have found an increase in insect species richness from maize to
switchgrass to prairie (Harrison & Berenbaum 2013, Kempski 2013). Biofuel crop choice
impacts biodiversity, and the more species-rich the crop, the higher diversity of pollinators and
associated services the crop can conserve within and around biofuel landscapes.
        The yield of each crop was inversely related to floral diversity and abundance (Fig. 5).
The high yields in native grasses were likely due to the number of tall, tightly spaced grasses
with highly productive species. The switchgrass treatment was similar, but a thick cover of tall
grasses interspersed with more forbs likely caused the lower yield than that of the native grasses.
The restored prairie treatment shifted that balance by maintaining tall grasses throughout, but
with more forbs than the switchgrass treatment. The low yields in the successional treatment
were likely due to the inconsistent, patchy plant cover across the plot. The 2018 yield results
presented here are similar to that of the post-establishment yields that were measured in these
plots in 2009-2014 (native grasses: mean 5.0 ± SE 1.2 Mg/ha per year; restored prairie: 3.3±0.7;
                                                  24


successional: 2.5 ± 0.5; switchgrass: 7.3 ± 0.9) (Gelfand et al. 2020). Whereas in 2018 native
grasses and restored prairie had the highest yields, switchgrass outperformed prairie and native
grasses when averaged over 2009-2014. In early years, switchgrass is a monoculture, and is
slowly filled in with a greater diversity of plants. This change in plant diversity over time
corresponds with a change in yield, and results in changes in pollinator diversity. Perennial
biofuel crop yield is largely determined by species composition, agricultural management, and
year since establishment (Jarchow et al. 2012, Sanford et al. 2016), as seen in our study. These
variables should be considered with biodiversity and other environmental and economic
variables in selecting a biofuel crop.
         Our yield results fall within the expected range for candidate native, perennial biofuel
crops (Roozeboom et al. 2019, Gelfand et al. 2020). The yield of these candidate bioenergy crops
is lower than that of maize, but this lower productivity does not imply that they are unsuitable for
bioenergy. It does imply that it will take more landcover to get the same ethanol produced as
these candidate biofuel crops. As we find that these candidate crops benefit biodiversity and
ecosystem services, an increase in landcover would then further benefit biodiversity and
ecosystem services. For example, the successional treatment had the lowest yield in our study.
But, restored prairie and successional treatments had the highest greenhouse gas benefits (the
lowest greenhouse gas intensities) compared to these other candidate crops and maize (Gelfand
et al. 2020). Increasing the area planted of a crop that benefits biodiversity and ecosystem
services, especially on consistently low-yielding land (Basso et al. 2019), creates a more
multifunctional and sustainable outcome.
         We found that restored prairie treatments provided the optimal response in the tradeoff
between production and conservation goals. Farmers who strongly value crop yield over
                                                  25


pollinator conservation would farm consistent with a crop that falls along a shallow slope on the
Pareto front in Fig. 4b. In our study, this corresponds to the native grasses treatment where yield
was high, but it performed the lowest for pollinator conservation. Oppositely, farmers who
strongly value pollinator conservation over crop yield are depicted by a steep slope on the Pareto
front. This end corresponds to the successional treatment which had the highest diversity of
pollinators, but the lowest yield. Pollinator conservation and crop yield are attributed similar
values where the slope of the Pareto frontier is intermediate between shallow and steep. Restored
prairie is the optimum over this range of preferences where pollinator conservation and yield are
both valued similarly. Switchgrass treatments performed poorly as they were not the highest in
production nor conservation value, and they did not perform optimally when both variables are
valued similarly.
         Restoring prairie as a biofuel crop in agricultural landscapes could meet both global
challenges of combatting biodiversity decline and supporting the growing human population.
Tallgrass prairie was a dominant ecosystem in central North America pre-European colonization,
but is currently endangered due to row crop expansion (Samson & Knopf 1994). Restoring
prairie for harvest is not equivalent to replacing natural grassland ecosystems; however,
grasslands are historically fire maintained ecosystems and harvest can imitate the biomass
removal of fire (Stahlheber et al. 2016). In addition, best practices for wildlife for biomass
production and harvest of grasslands have been established to prevent creating habitat sinks
(McGuire & Rupp 2013). Therefore, planting prairie could restore habitat for other native
organisms in the region. Planting could occur as the conversion of suitable marginal land, the
implementation of buffer strips, the conversion of annual biofuel crops, or introducing prairie
                                                  26


strips to agricultural landscapes. Such transformative efforts would meet the level of change
needed to create multifunctional landscapes.
         In the broader context of bioenergy crops, all of the perennial crops we studied were
more diverse than the most common bioenergy crop in the upper Midwestern US: maize. Bee
abundance and pollination services are higher in switchgrass and restored prairie than maize
(Gardiner et al. 2010, Werling et al. 2014), and as maize has close to zero floral resources within
the landscape, it is likely that all four of our tested biofuel crops outcompete maize in terms of
pollinator conservation. This agrees with a modeling scenario in which annual bioenergy crops
reduced wild bee abundance and diversity and perennial bioenergy crops increased bee
abundance and diversity (Bennett et al. 2014). In considering other measures of biodiversity and
ecosystem services, restored prairie harbors more diverse ant communities, more diverse
predatory arthropod communities, and have higher predation of pest eggs by arthropod predators
than switchgrass or maize biofuel crops (Helms et al. 2020, Werling et al. 2014).
         Choice of biofuel crop should also consider tradeoffs among other variables, among them
climate and water quality. Perennial crops provide climate benefits through carbon storage. They
also provide a greater richness of methanotrophs and the consumption of methane occurring in
restored prairie and switchgrass compared to maize (Werling et al. 2014). Perennial biofuel crops
also provide aesthetic and recreation benefits such as birdwatching during the growing season
with a higher richness of breeding birds and a higher abundance of grassland birds in prairie and
switchgrass than maize (Werling et al. 2014). Current biofuel markets and policies support
annual biofuels such as maize and do not account for the environmental impact of those crops
(Landis et al. 2018). These markets and policy should shift to value the multifunctionality of
perennial biofuels (Jordan & Warner 2010, Mishra et al. 2019).
                                                     27


        The management and costs of these different crops also plays an important role in their
value as biofuel crops. Successional, switchgrass, and native grass treatments were fertilized
every year, while the restored prairie was not fertilized. The initial cost of the seed mix for
restored prairie was higher than the seed mixes for the other three treatments due to the number
of species sowed, however the restored prairie biofuel crop treatment required the least amount
of management and costs over time. Restored prairie is the optimal biofuel crop for optimizing
pollinator conservation and crop yield with the additional environmental and economic benefits.
Our results have implications for pollinator conservation and biofuel futures. Pollinators respond
to increased floral diversity across spatial scales from within fields to across landscapes (Kremen
& Miles 2012, Kennedy et al. 2013, Isbell et al. 2017). In our study, honeybees and non-
managed native pollinators both responded similarly to the biofuel crop treatments. Therefore,
planting a greater abundance and diversity of perennial, native biofuel crops can positively
impact a range of wild and managed pollinator populations and communities within and across
landscapes. Compared to low diversity of native grasses, diversifying biofuel landscapes by
planting crops such as restored prairie can both support pollinator conservation and maintain
yield.
        Biofuel agriculture has the potential to diversify agricultural landscapes, increase habitat
for beneficial insects and other biodiversity, provide climate benefits, and provide other
ecosystem services. Incorporating a greater abundance and, more importantly, a greater richness
of native flower species in agricultural landscapes can attract a more abundant and more diverse
community of pollinators. Directed policies could encourage the use of multifunctional, resilient
crops that benefit both farmers and biodiversity across diverse agroecosystems.
                                                  28


Acknowledgements
This project was completed at Michigan State University’s Kellogg Biological Station which
occupies the ancestral, traditional, and contemporary Lands of the Anishinaabeg–Three Fires
Confederacy of Ojibwe, Odawa, and Potawatomi peoples. Financial support for this research was
provided by the Great Lakes Bioenergy Research Center, U.S. Department of Energy, Office of
Science, Office of Biological and Environmental Research (Award DE-SC0018409), by the
National Science Foundation Long-term Ecological Research Program (DEB 1832042) at the
Kellogg Biological Station, by Michigan State University AgBioResearch, and by Jean and Dr.
William Maxey’s Kellogg Biological Station graduate fellowship. Thank you Dr. Brook Wilke
and Joe Simmons for harvesting the biofuels, conducting agricultural operations, and managing
the field sites, and to Ally Brown, Ian Ratcliff, and Jessica Webb for field support identifying
flowers. We appreciate Stacey VanderWulp for her help organizing fieldwork. Thank you to Dr.
Phil Robertson for advice in developing questions and reviewing this manuscript, and to Dr. Tom
Getty for suggesting a Pareto frontier analysis. Thank you to the Great Lakes Bioenergy
Research Center and the Long-Term Ecological Research researchers at KBS for collecting
biomass data, and to the GLBRC data managers for organizing the biofuel yield data. And thank
you to three anonymous reviewers for their constructive feedback on this manuscript.
                                                  29


APPENDIX
   30


Table 1.1. List of plant species with open flowers present in each treatment, and method used for
counting floral abundance and plant abundance.
                                    Treatments present
 Flower species                     Native grasses   Restored prairie   Successional   Switchgrass   Flower equivalent Plant equivalent
 Achillea millefolium               x                x                  x                            flower             flower cluster
 Ambrosia artemisiifolia                                                x                            raceme             plant
 Anthemis cotula                                                        x                            head               plant
 Asclepias syriaca                                                      x                            flower             umbel
 Aster sp. 1                        x                x                  x              x             head               plant
 Aster sp. 2                                                            x              x             head               plant
 Centaurea maculosa                                                     x                            head               plant
 Daucus carota                      x                x                  x              x             flower             umbel
 Desmodium sp.                                       x                                               flower             plant
 Dianthus armeria                                    x                  x                            flower             plant
 Erigeron annuus                                     x                  x              x             flower             plant
 Euthamia graminifolia              x                x                  x              x             flower             raceme
 Geum canadense                                                         x                            flower             plant
 Hieracium kalmii                                                       x              x             flower             plant
 Hieracium sp.                                                          x                            head               plant
 Hypericum perforatum                                                   x              x             flower             plant
                                                                         31


Table 1.1 (cont’d)
Lactuca serriola                 x  x flower plant
Medicago lupulina              x      head   plant
Monarda fistulosa            x x x  x flower head
Oenothera biennis                x  x flower plant
Oxalis sp.                       x    flower plant
Phytolacca americana             x    flower plant
Plantago lanceolata              x    head   plant
Potentilla sp.                   x    flower plant
Ratibida pinnata             x x x  x head   plant
Rudbeckia hirta                x x  x head   plant
Silene latifolia             x   x  x flower plant
Silphium perfoliatum           x x  x head   plant
Solanum carolinense              x    flower plant
Solidago canadensis          x x x  x raceme plant
Solidago rigida                x x    raceme plant
Solidago speciosa              x      raceme plant
Solidago sp.                   x x  x raceme plant
Symphyotrichum novae-angliae x x x  x head   plant
Torilis japonica                 x  x flower umbel
Trifolium pratense             x x    head   plant
Verbascum thapsus                x    flower plant
Verbena urticifolia              x    flower plant
                                 32


Table 1.1 (cont’d)
Veronica persica     x    flower plant
Vicia americana    x      flower plant
unknown 1          x      flower stem
unknown 2          x      flower raceme
unknown 3          x      flower head
unknown 4               x head   flower
                     33


Table 1.2. Seeded species and seeding rates for experimental treatments. Due to an intense rain
event within two weeks of planting in 2008, most of the seed was lost from treatment plots. After
evaluation of the plant composition in the experimental plots in the following summer, a decision
was made by scientists and practitioners to replant seed in the switchgrass, native grasses, and
restored prairie treatments in 2009. The rates of additional seeding were calculated by restoration
practitioners to make the total seeding rate equal to the attempted seeding rate of 2008.
Surveying, seeding rates, and planting densities were conducted by a local native seed and
restoration company Native Connections in Kalamazoo, MI, USA (occupied Anishinaabe land)
following local best-management practices (Sanford et al. 2016).
 Treatment             Species                                      Grass/forb Oz per acre
 Native grasses        Schizachyrium scoparium (Michx.) Nash        grass               45.4
                       Sorghastrum nutans (L.) Nash                 grass               34.1
                       Andropogon gerardii                          grass               34.1
                       Elymus canadensis                            grass               22.7
                       Panicum virgatum var. Southlow               grass               22.7
 Restored prairie      Elymus canadensis                            grass                 17
                       Sorghastrum nutans (L.) Nash                 grass                 17
                       Schizachyrium scoparium (Michx.) Nash        grass                 17
                       Andropogon gerardii                          grass                 17
                       Desmodium canadense                          forb                    5
                       Lespedeza capitata                           forb                    5
                                                 34


Table 1.2 (cont’d)
                   Baptisia lactea var. lactea (Raf.) Thieret forb        5
                   Rudbeckia hirta L.                         forb        5
                   Anemone canadensis L.                      forb        5
                   Asclepias tuberosa L.                      forb        5
                   Monarda fistulosa L.                       forb        5
                   Silphium perfoliatum L.                    forb        5
                   Ratibida pinnata (Vent.) Barnh.            forb        5
                   Solidago rigida L.                         forb        5
                   Solidago speciosa L.                       forb        5
                   Symphyotrichum novae-angliae L.            forb        5
                   Koeleria cristata                          grass    11.4
                   Panicum virgatum var. Southlow             grass    11.4
Switchgrass        Panicum virgatum var. Cave-in-Rock         grass   201.6
Successional       -                                          -     -
                                              35


Figure 1.1. (a) The layout of experimental plots in the Great Lakes Bioenergy Research Center
sites at Kellogg Biological Station. The intercrop distance between experimental plots is 15 m.
(b) The layout of a single plot in the Great Lakes Bioenergy Research site at Kellogg Biological
Station. Each plot is 30 × 40 m and contains a single biofuel crop. The survey area is where both
pollinators and flower surveys were conducted. Pollinator surveys occurred as timed walks along
the walking path, surveying 1 m on either side. Flower surveys were conducted in the same area
following pollinator surveys.
                                                 36


Figure 1.2. (a) Flower abundance, (b) flowering plant richness, (c) pollinator abundance, and (d)
pollinator group richness recorded in each of the four biofuel crop treatments. Each bar
represents an average across replicated plots and sampling rounds ±1 SE. Letters indicate
significant differences.
                                                37


Figure 1.3. (a) Pollinator abundance as a function of flowering plant abundance, (b) pollinator
abundance as a function of flower richness, (c) pollinator group richness as a function of
flowering plant abundance, and (d) pollinator group richness as a function of flower richness.
Each plot depicts a linear model with a 95% confidence interval shaded in gray. Raw data points
are shown for each plot for all four sampling rounds. One data point in panels (a) and (c) that
describes pollinator abundance (z-score > 3) is not shown.
                                                38


Figure 1.4. The yield of dry harvested biomass (Mg/ha) of each biofuel crop treatment. For
comparison, maize yielded on average 9.388 Mg/ha in this same replicated experiment in 2018.
Letters indicate significant differences among crop yields.
                                                39


Figure 1.5. A Pareto frontier for mean crop yield (Mg/ha) and mean pollinator group richness
across biofuel crop treatments. The Pareto frontier is shown as a solid black line; this describes
the optimal values for crop yield and pollinator group richness over a range of preferences. The
means for each treatment are the larger, bolded points and the data points for each plot are the
smaller points.
                                                 40


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                                           CHAPTER TWO
      Prairie strips and lower land use intensity increase biodiversity and ecosystem services 2
Abstract
Agricultural landscapes can be managed to protect biodiversity and maintain ecosystem services.
One approach to achieve this is to restore native perennial vegetation within croplands. Where
rowcrops have displaced prairie, as in the US Midwest, restoration of native perennial vegetation
can align with crops in so called “prairie strips”. We tested the effect of prairie strips in addition
to other management practices on a variety of taxa and on a suite of ecosystem services. To do
so, we worked within a 33-year-old experiment that included treatments that varied methods of
agricultural management across a gradient of land use intensity. In the two lowest intensity crop
management treatments, we introduced prairie strips that occupied 5% of crop area. We
addressed three questions: 1) What are the effects of newly established prairie strips on the
spillover of biodiversity and ecosystem services into cropland? 2) How does time since prairie
strip establishment affect biodiversity and ecosystem services? 3) What are the tradeoffs and
synergies among biodiversity conservation, non-provisioning ecosystem services, and
provisioning ecosystem services (crop yield) across a land use intensity gradient (which includes
prairie strips)? Within prairie strip treatments, where sampling effort occurred within and at
increasing distance from strips, dung beetle abundance, spider abundance and richness, active
2
  Manuscript is in revision as of March 2022 at the journal Frontiers in Ecology and with
coauthors Corinn E. Rutkoski, Sarah E. Evans, Jackson A. Helms IV, Esbeiry Cordova-Ortiz,
Jamie D. Smith, Jorge A. Vázquez Custodio, Carmella Vizza, and Nick M. Haddad.
                                                   51


carbon, decomposition, and pollination decreased with distance from prairie strips, and this
effect increased between the first and second year. Across the entire land use intensity gradient,
treatments with prairie strips and reduced chemical inputs had higher butterfly abundance, spider
abundance, and pollination services. In addition, soil organic carbon, butterfly richness, and
spider richness increased with a decrease in land use intensity. Crop yield in one treatment with
prairie strips was equal to that of the highest intensity management, even while including the
area taken out of production. We found no effects of strips on ant biodiversity and greenhouse
gas emissions (N2O and CH4). Our results show that, even in early establishment, prairie strips
and lower land use intensity can contribute to the conservation of biodiversity and ecosystem
services without a disproportionate loss of crop yield.
Introduction
Two global challenges of our time are supporting a growing human population and preventing
the loss of biodiversity and ecosystem services (Kremen & Merenlander 2018, IPBES 2019).
The capacity to address these challenges depends largely on management of agricultural lands
that dominate the landscape globally (Campbell et al. 2017, Raven & Wagner 2021). In the US
Midwest, for example, 38% of the landscape is planted in principal row crops (USDA 2019).
These agroecosystems were designed to maximize the production of food, fuel, and fiber, and
they contribute to greenhouse gas emissions, pollution, and the loss of natural ecosystems and
biodiversity (Tilman & Clark 2015). To prevent further ecological harm and to sustain food, fuel,
and fiber production for future generations, agricultural landscapes must be managed for
multifunctionality and biodiversity (Asbjornson et al. 2013, IPBES 2019, Mitchell et al. 2021).
Yet, there are few assessments of the tradeoffs and synergies of biodiversity and ecosystem
                                                   52


service responses across crop management and conservation practices required to address the
two grand challenges (Wittwer et al. 2021).
        Diversifying agricultural landscapes can promote biodiversity and non-provisioning
ecosystem services without compromising crop yield (Tamburini et al. 2020, Tscharntke et al.
2021). Landscape diversification, an approach to land management rooted in indigenous
knowledge, has been experimentally studied by the scientific community for applications in
input-intensive cropping systems (IPBES 2016, Nkuba et al. 2020). One method of diversifying
agricultural landscapes, as is done in the US Midwest, is to establish prairie strips on row crop
farms. This conservation practice consists of retiring areas of farmland and actively restoring
them by seeding native perennial vegetation. Supported by the United States Department of
Agriculture (USDA) Conservation Reserve Program (CRP), prairie strips are one of many “edge
of field” practices–including riparian buffers, hedgerows, and wildflower strips–aimed at
incorporating native and diverse habitat into agricultural landscapes (The Nature Conservancy
2021).
        Once established, prairie strips in contoured farm landscapes can reduce soil erosion,
improve water quality, and support biodiversity. This can provide benefits to the farm and farmer
at disproportionately higher levels than the amount of farmland removed from production
(Schulte et al. 2017). Prairie strips also have the potential to provide resources and habitat for
beneficial insects and increase their spillover into the farm, where they can provide ecosystem
services such as biocontrol and pollination (Blitzer et al. 2012, Morandin & Kremen 2013,
Kordbacheh et al. 2020). While prairie strips are known to benefit biodiversity and ecosystem
services once they have been established for multiple years, these impacts have not been studied
during their initial years of establishment. Measuring prairie strips’ conservation potential during
                                                   53


their early establishment period can address this gap and increase the precision of farmers’
expectations of prairie strips.
        The conservation potential of prairie strips relative to other agricultural conservation
practices such as no tillage is also unknown. Biodiversity and ecosystem services in
agroecosystems are driven not only by the presence of natural habitat on or near farms, but also
by the agricultural management practices used in row crop areas. Crop rotations and cover crops
generally increase biodiversity and enhance nutrient availability (Rusch et al. 2013, de Pedro et
al. 2020, Bowers et al. 2021), whereas the use of tillage, pesticides, and fertilizer generally
decrease biodiversity and increase greenhouse gas emissions (Syswerda & Robertson 2014,
Bowles et al. 2016, Raven & Wagner 2021). But when implemented jointly, landscape
diversification and crop management practices can interact to produce unique impacts on on-
farm biodiversity and ecosystem services (Schmidt et al. 2005, Tscharntke et al. 2005, Landis
2017). There is some evidence that conventionally managed farm fields with prairie strips
increase ecosystem services compared to conventionally managed farm fields without strips, and
research has focused on the reduction of nutrient runoff and erosion in contoured agricultural
landscapes (Schulte et al. 2017). By directly comparing responses in fields with prairie strips
with responses in fields managed with other practices, we can better identify combinations of
prairie strips and crop management that may optimize crop yield, biodiversity, and ecosystem
services.
        We address how targeted conservation can promote multifunctionality including
biodiversity, agricultural production, and other ecosystem services. Working in a 33-year-old
experiment, we tested the effects of prairie strips and a gradient of crop management strategies
across a suite of invertebrate biodiversity metrics and ecosystem services during the first two
                                                  54


years of prairie strip establishment. First, we asked: what are the effects of newly established
prairie strips on the spillover of biodiversity and ecosystem services into cropland? To test this,
we measured how services changed with distance from a prairie strip. Second, we asked: how
does time since prairie strip establishment affect biodiversity and ecosystem services in
agricultural plots? Third, we asked: what are the tradeoffs and synergies among biodiversity
conservation, non-provisioning ecosystem services, and crop yield across a gradient of land use
intensity? To answer our third question, we examined all services across a land use gradient
(including treatments with prairie strips) in relation to yield changes. Our study includes
biodiversity measurements of ants, butterflies, dung beetles, and spiders, all of which can
provide ecosystem services to farms. Our study also includes ecosystem service measurements of
microbial activity, decomposition, greenhouse gas emissions, pollination, soil carbon, and crop
yield.
Materials and Methods
Study sites and sampling locations
We conducted our study at the Kellogg Biological Station Long Term Ecological Research (KBS
LTER) site in Hickory Corners, MI USA (occupied Anishinaabe land) that was established in
1987. The KBS LTER is located in a temperate climate with a mean temperature of 10.1°C and
mean annual precipitation of 100.5 cm (1981-2011 means) with increasing trends in temperature
over the past few decades (Robertson & Hamilton 2015, Liang & Robertson 2021). Surface soils
are 17% clay/43% sand Alfisol loams developed on glacial till and outwash (Robertson &
Hamilton 2015).
                                                  55


         This study occurred in 2019–2020, the first two years after prairie strip planting. We
worked in five treatments of KBS-LTER’s Main Cropping System Experiment: conventionally
managed row crops, no till row crops, reduced input row crops, biologically based (organic) row
crops, and conservation land (details of crop management treatments are compared in Figure 2.1
and in Robertson and Hamilton (2015)). The experiment consisted of six replicated plots of each
treatment (six experimental blocks; Figure 2.2 a), with each plot having an area of one hectare
(87 m x 105 m; Figure 2.2 b). Conventional and no till treatments received levels of chemical
inputs that follow Generally Accepted Agricultural and Management Practices (GAAMP) in
Michigan, USA and are typical for the US Midwest (details on the dates and quantities of
fertilizer application, pesticide application, weed management, and soil preparation can be found
at https://aglog.kbs.msu.edu). The conventional treatment was tilled with a chisel plow, and the
no till treatment was managed as the conventional treatment but was left unplowed. The reduced
input treatment received lower levels of inputs (nitrogen at planting and pesticides) than
conventional and no till and had a legume cover crop in the winter. The biologically based
treatment did not receive any chemical inputs, compost, or manure, and it had a legume cover
crop and was rotary hoed five times after planting in 2020 to control weeds. Conservation land
(referred to as early successional in site maps and earlier publications) was unmanaged other
than yearly burning in the spring to suppress woody vegetation. This treatment was a grassland
with the dominant bloom period in the fall when goldenrods and asters flower. All treatments
except conservation land were on a three-year maize (Zea mays L.)—winter wheat (Triticum
aestivum L.)—soybean (Glycine max L.) rotation. In the years of our study, wheat was planted in
2019 and maize in 2020.
                                                  56


        Prairie strips were introduced in the reduced input and biologically based treatments in
April 2019. In five percent of each plot, configured as a strip parallel to row crops down the
middle of each plot, we sowed a native prairie plants species mix. The mix consisted of 4 grass
species and 18 forb species (Table 2.1) purchased from Native Connections, Kalamazoo,
Michigan, USA. The mix was chosen to have species bloom throughout the growing season
(Isaacs et al. 2009). The mixes sown in each plot contained the same weight and proportion of
each species. The first year’s plant community was dominated by agricultural weeds, but with
some seeded species flowering. The second year’s plant community contained more seeded
species, and we expect more seeded species to establish as the prairie strips mature. Prairie strips
were mowed three times during the 2019 season to reduce weeds and support establishment of
native seeds.
        We compared a variety of biodiversity and ecosystem service measures by sampling three
sets of sampling locations within the plots (Figure 2.2 b). First, to compare among plot-level
treatments, each plot had five sampling locations distributed throughout, all located outside of
prairie strips. These sampling locations are referred to as Standard Sampling Stations. Second,
also to compare among plot-level treatments, each plot had six sampling stations at the northeast
corner for destructive sampling that could not occur at the Standard Sampling Stations. These
sampling locations are referred to as Subplot Sampling Stations. Third, reduced input and
biologically based treatments had an additional three transects perpendicular to the prairie strips
with sampling locations at distances of 0 m, 1 m, 5 m, and 20 m from the prairie strip (the station
at 0 m was located within the prairie strip). These sampling stations were used to measure
biodiversity and ecosystem services at different distances from the prairie strips and are referred
to as Strip Sampling Stations.
                                                 57


         Because prairie strips were implemented in all replicates of the reduced input and
biologically based treatments, we do not have a fully factorial experiment. Therefore, we cannot
isolate the effect of prairie strips from effects of crop management and year on measures of
biodiversity and ecosystem services across all crop management treatments. However, we can (i)
make conclusions about how treatments with prairie strips, in the context of their associated
management strategies (including historical data on these treatments), compare to other crop
management strategies without prairie strips, (ii) examine trends of prairie strips with time since
establishment, where increases from year to year would suggest prairie strips play a role for
processes that stabilized with the background management treatments (though are confounded by
crop rotation), and (iii) we can also attribute significant effects of distance from prairie strips on
response variables to the presence of the strip, though benefits of prairie strips are not isolated to
spillover effects.
Dung beetles
Soil dwelling macroarthropods contribute to decomposition in agricultural landscapes by
fragmenting litter, altering soil structure, and feeding on other soil dwelling fauna. Dung beetles
break apart manure, mobilize nutrients in the soil (Coleman et al. 2018a), and suppress human
and livestock pathogens (Nichols et al. 2008, Jones et al. 2016, Sands & Wall 2017). Dung beetle
communities are vulnerable to the effects of crop management and are negatively affected by
agricultural intensification (Barbero et al. 1999, Hutton & Giller 2003).
         Across management treatments: To compare dung beetle diversity (as well as ants and
spiders which are described next) across crop management treatments, we installed pitfall traps
at Subplot Sampling Stations within each plot. Pitfall traps consisted of plastic containers (5.1
                                                    58


cm diameter, 120 mL) buried so the container’s rim was flush with the soil surface. We partially
filled containers with 95% ethanol mixed with a few drops of detergent to break surface tension.
To protect the traps from rain and flooding, we mounted clear Plexiglass rain covers (15 x 15
cm) 10 cm above the ground over each trap. We baited traps with approximately 10 g of cow
manure per trap that was collected from the Kellogg Pasture Dairy Farm (located approximately
0.4 km from experimental sites) and homogenized by stirring in a bucket. Cows were treated
with an ingested larvicide for fly control (unpublished data shows this had no effect on
abundance and richness of dung beetles collected or manure decomposition). We deployed pitfall
traps at all Subplot Sampling Stations three times during the growing season in 2019, once each
in June, July, and August. For each sampling event, traps were collected after 48 h in the field
and the samples collected from the traps were stored in ethanol at −20 °C. Dung beetles
(Coleoptera: Scarabaeidae of the subfamily Scarabaeinae) were identified to species using a
regional guide (Nemes & Price 2015).
         Distances from prairie strips: To measure dung beetle richness at distances from prairie
strips, baited pitfall traps were deployed at Strip Sampling Stations for each of three sampling
rounds each year occurring in June, July, and August of 2019 and 2020.
Ants
Ants comprise half of global insect biomass and perform many ecosystem services (Hölldobler
& Wilson 1990, Folgarait 1998, Wills & Landis 2018). Ants are the major predators of
agricultural pests at our study site and elsewhere in the US Midwest (Grieshop et al. 2012, Wills
et al. 2019, Helms et al. 2020). They also disperse plant material, seeds, and nitrifying bacteria
and pool nutrients in the soil (Benckiser 2010, Hölldobler & Wilson 2009, Mueller et al. 2005).
                                                   59


Ants are sensitive to harvesting and management practices that can reduce ant activity (Peck et
al. 1998, Agosti et al. 2000, Wodika et al. 2014, Helms et al. 2021, Hussain et al. 2021).
         Across management treatments: We collected ants across crop management treatments
using baited pitfall traps at Subplot Sampling Stations as described for dung beetles. Captured
ants were identified using regional guides (Coovert 2005, Ellison et al. 2012) and vouchers were
stored in the senior author’s reference collection.
         To test effects of treatments on species richness, we first combined species occurrences
from all repeated pitfall traps (maximum of 3 traps per each of 6 sampling stations, 16 to 18 total
pitfall traps per plot). The occurrence of workers of a given species at least once at any of the 6
sampling stations was conservatively treated as indicating the presence of a single colony of that
species (abundance = 1) within a plot during the study year, regardless of how many or how
frequently workers were captured (Ellison et al. 2007; Gotelli et al. 2011). In this way, we
derived one species list for each of the 30 plots (6 plots per each of 5 treatments).
         Distances from prairie strips: To measure ant richness at different distances from prairie
strips, ants were collected with non-baited pitfall traps at the Strip Sampling Stations (reduced
input and biologically based treatments). Non-baited traps were sampled on a rolling weekly
basis (three weeks on, one week off during which baited traps were deployed) from May-
September with a total of five sampling rounds per station in 2019 and four in 2020. 2019 prairie
strip ant data are modified from those used in Helms et al. (2021).
Spiders
Spiders are generalist predators that can contribute to pest control in agricultural landscapes.
Spider communities generally respond positively to agricultural conservation practices, such as
                                                   60


cover crops and reduced tillage (Sunderland & Samu 2000, de Pedro et al. 2020). Increased
natural habitat in an agricultural landscape can increase spider abundance and richness, but there
is little evidence of spillover of spiders from natural habitat into cropland (Sunderland & Samu
2000, Schmidt et al. 2005, but see Hussain et al. 2021).
          Across management treatments: We collected spiders across crop management
treatments as described for ants. Spiders were identified to family with a key to spiders of North
America (Ubick et al. 2017). Distances from prairie strips: Pitfall traps were used to collect
spiders at distances from the prairie strips as described for ants.
Butterflies
Butterflies are diverse pollinators, herbivores, and indicators of insect response to habitat change,
and they hold cultural value (Ghazanfar et al. 2016). Butterflies are declining in abundance at a
rate of 2% per year in the US Midwest with agriculture as a main reason for this decline due to
habitat loss, pesticides, and fertilizers, which are sources of direct mortality and destroy host
plants and food resources (Wepprich et al. 2019, van Klink et al. 2020). Reducing the use of
pesticides and fertilizer and restoring habitat on farms helps mitigate the loss of butterfly
biodiversity in agricultural landscapes (Reeder et al. 2005, Rundlöf et al. 2008).
          Across management treatments: Unlike samples for all the other species and services, we
sampled butterfly species richness and abundance using transect counts, modified from Pollard
(1977). We conducted surveys along a 12-minute one-way walking transect through each plot.
Observers recorded butterflies within 5 m on both sides and above the transect in front of the
observer. Transects were surveyed between 10:00am and 4:00pm weekly from June 2019–
                                                   61


September 2019 and May 2020–September 2020. Butterflies were identified to species using a
regional guide (Nielsen 1999) and supplementary sources as needed.
Active carbon
Biologically available soil carbon, also termed “active carbon”, reflects a fraction of total soil
carbon that is readily mineralized by soil microorganisms and serves as an early indicator of
longer-term soil carbon accrual (Coleman et al. 2018b, Culman et al. 2012). Conversion of
agricultural fields to perennial vegetation has been shown to increase soil active carbon
compared to conventionally managed agricultural soils by increasing the production of fine root
biomass (Sprunger et al. 2017, Sprunger & Robertson 2018). We expect prairie strips to increase
levels of active carbon and for active carbon to spillover from prairie strips into cropland at short
distances from prairie strips if roots from perennials extend into cropland, if nitrogen from
farming doesn’t reach to exactly the edge of the prairie strip, or if litter from prairie strips spills
over into cropland.
        Across management treatments: Active carbon was determined via a 24-hour assay based
on Franzluebbers et al. (2000) that measures CO2 respired from soils rewetted to a common
water holding capacity. We collected soil cores at the Standard Sampling Stations in June, July,
and August of 2019. Samples were analyzed individually for active carbon, then data were
pooled across June, July and September to form a single dataset for each year in each treatment.
We collected field soil with a soil push probe at 0–10 cm depth then sieved to 2 mm. Soil water
holding capacity (WHC) and gravimetric soil moisture were determined from fresh sieved soil.
We added 5 g of air-dried soil and sterile ultrapure water to a 125 mL Wheaton serum bottle to
achieve 70% WHC. Bottles were sealed and incubated at room temperature for 24 hours. We
                                                 62


collected gas samples from bottle headspace at two time points following the incubation period
(0 and 24 hr). CO2 samples were collected in overpressurized 6 mL glass vials (Exetainers,
Labco Ltd) flushed with N2. We analyzed samples with a gas chromatograph (Agilent 7890A)
coupled to an autosampler (Gerstel MPS2XL) as described in Shcherbak and Robertson (2019).
        We calculated short-term mineralizable C as the difference between 0 and 24-hr CO2
measurements. We report active carbon in micrograms (μg) of CO2 per day per g of dry soil.
Distances from prairie strips: Soil cores were collected at Strip Sampling Stations and processed
as described above in both 2019 and 2020.
Decomposition
Decomposition is essential for suppressing pathogens, cycling nutrients, and creating soil organic
matter (Barrios 2007, Coleman et al. 2018c). Diversified landscapes can increase decomposition
by increasing the abundance and richness of beneficial soil fauna (Landis et al. 2000, Karp et al.
2016, Jones et al. 2019). To quantify decomposition services, we measured mass loss of manure
over time.
        Across management treatments: We placed one patty of fresh cow manure (20 g) at each
of the Subplot Sampling Stations (Jones et al. 2019). Manure (fresh) was weighed in the lab and
separated into individual packets prior to deployment. We left manure under a rain cover (same
rain cover as described for ants) for seven days immediately following pitfall trap collection of
dung beetles for all sampling rounds. We then collected manure in an envelope, placed the
envelope in a drying oven until moisture evaporated, and then weighed it. The dry weight after
deployment was divided by the dry weight of the manure (20 g of fresh manure was equivalent
to 6 g dried manure). We defined decomposition as the proportion of manure removed. Distances
                                                 63


from prairie strips: We placed sentinel cow manure patties (20g) at the Strip Sampling Stations
and processed samples as described above.
Global warming impact (GWI)
Agriculture produces 10–14% of global anthropogenic greenhouse gas emissions (Barker et al.
2007, Smith et al. 2007). Prairie strips, no till management, and cover crops are among
management practices that have the potential to sequester carbon in cropping systems (Robertson
et. al 2000, Gelfand & Robertson 2015). We use 100-year global warming impact (GWI) as a
measure to convert greenhouse gas emissions (N2O and CH4) to units of CO2 equivalent
emissions.
         Across management treatments: We sampled greenhouse gas fluxes per Kahmark et al.
(2020) approximately biweekly May-September and monthly October-April in both 2019 and
2020 using a stainless-steel gas chamber (14.3 cm radius, 22.8 cm height) with a plastic lid. After
placing the lid on the chamber, a needle was inserted into the chamber lid septum to relieve any
induced pressure changes. We inserted another individual needle into the septum of a 5.9-mL
exetainer sample vial to act as a vent. Then we mixed the chamber headspace three times with a
10-mL sampling syringe. After mixing, we withdrew 10-mL and injected the air into the sample
vial with the vent needle in place. After flushing the vial three times, we removed the vent
needle, drew a 10-mL sample from the chamber, and injected it into the flushed sample vial (so
that it was overpressurized). We collected a sample of ambient air at the same time in each
sampling round and also a duplicate chamber sample using the same gas sampling procedure
described above. We also recorded soil temperature and moisture next to the gas chamber during
the sampling period. We collected four gas samples at 15-minute intervals over each sampling
                                                   64


period. Post gas sampling and flux calculations were conducted following the protocol of
Holland et al. (1999).
Pollination
Pollinators are necessary for the function of natural and managed ecosystems. Pollinators have
experienced a steep decline in abundance and richness, and prairie strips could restore pollinators
and their services to agricultural landscapes (IPBES 2016, Wepprich et al. 2019, van Klink et al.
2020, Kordbacheh et al. 2020).
         Across management treatments: Pollination was measured with sentinel plants placed at
the Standard Sampling Stations in the conventional, no till, and conservation land treatments, as
well as at the Strip Sampling Stations in the reduced input and biologically based treatments. We
used Black-eyed Susans (Rudbeckia hirta) as our sentinel plants, as it is native to southwest
Michigan and was also included in the prairie strip seed mix. We propagated plants from seed
(purchased from the same location as the prairie strip seed mix) in a greenhouse. Seedlings were
transplanted into 16.5 cm pots with a low dose of 12-12-12 N-P-K controlled release organic
fertilizer. To avoid pollination prior to receiving experimental treatment, we marked and covered
two flower heads on each plant with pollinator exclusion bags just before they started producing
pollen. The following day, we deployed plants with exclusion bags on both flowers into the field.
Upon placement in the field, we removed one bag to be exposed to pollinators (called open
flowers). One bag remained over the flower through the duration in the field (called closed
flowers). Closed flowers acted as a measure of potential self-pollination. We deployed plants in
experimental treatments for 14 days during each of the three sampling rounds starting on June
18, 2019, August 7, 2019, and July 8, 2020. We bagged all experimental flower heads prior to
                                                  65


removal from experimental stations. For analysis of pollination across treatments, we used all
plants from Standard Sampling Stations in conventional, no till, and conservation land, and we
randomly selected five plants from the Strip Sampling Stations in the reduced input and
biologically based treatments to compare consistently among all treatments. For analysis of
pollination at distances from prairie strips, we used all plants from the Strip Sampling Stations.
        Following experimental deployment, we returned plants to a greenhouse where they
senesced and set seed. Seeds were then harvested and stored in a refrigerator from September to
January each year. We randomly selected thirty seeds from each flower head for a germination
trial. We placed these seeds in petri dishes in a greenhouse, watered them regularly over a 14-day
period, and counted the number of individuals that germinated. We calculated seedset as the ratio
of not-germinated:germinated seeds for each seed head (two measures per plant). We measured
pollination services as the difference between seedset of the open flower and seedset of the
closed flower for each plant (one measure per plant). Distances from prairie strips: The sentinel
plants from the Strip Sampling locations were used to measure pollination services at distances
from the prairie strips.
Soil organic carbon (SOC)
SOC is a measure of total carbon in soil organic matter. Agricultural management practices
influence SOC accrual and loss. SOC accrual can be stimulated by the addition of high-quality
organic inputs like cover crops (Syswerda et al. 2011), as well as the establishment of perennial
vegetation (Kravchenko et al. 2019). On the other hand, practices that involve physical soil
disturbance, such as tillage, generally reduce SOC by disrupting soil aggregates and releasing
organic matter for decomposition (Paul et al. 2015). Whereas active carbon responds quickly to
                                                  66


land management changes, SOC generally responds on the order of years to decades, as it is a
measure of total carbon across both labile and recalcitrant soil organic matter pools (Culman et
al. 2012).
         Across management treatments: We collected one soil core with a soil push probe at each
Standard Sampling Station in April 2019 and May 2020 at a depth of 0–25 cm. Soil cores within
each plot (5 stations per plot) were combined into a pooled sample. We air dried soil samples and
then pulverized them to a powder using a Shatterbox grinding mill. We then weighed soil
samples (15-20 mg) and packed them into tins. Samples were analyzed for total carbon in
triplicate (three soil tins for each sample) on a Costech Elemental Combustion System 4010.
Because these soils did not contain carbonates, we express these data as percent SOC.
Crop yield
Across management treatments: Crops were harvested from the entire crop area of each plot
across all agronomic treatments. Prairie strips were not harvested, but we area-scaled yields in
the reduced input and biologically based treatments by reducing yields 5% to account for area in
strips. Wheat was harvested from conventional and no till plots on July 24, 2019 and from
reduced input and biologically based plots on July 25, 2019. Maize was harvested from all
treatments and plots on October 29, 2020. Crops were harvested with a harvest combine, and
yield for the entire crop area of each plot was measured with a weigh wagon. We report yield as
kg/ha at crop harvest at standard moisture content (13% for wheat, 15.5% for maize). We did not
compare yield with the conservation land treatment, although perennial grasslands have potential
to be harvested for bioenergy (Robertson et al. 2017).
                                                   67


        In addition to measuring crop yields for 2019 and 2020, we separately compared
historical crop yields in the same plots prior to the sowing of prairie strips. We used yield
measurements from 2013–2018—two cycles of the crop rotation prior to prairie strips.
Statistical analyses
Across management treatments: We aggregated the individuals of our measures of invertebrate
biodiversity (ants, butterflies, dung beetles, and spiders) surveyed over each year within each
plot of each treatment (six plots for each of five treatments) by summing. For all measures of
biodiversity and ecosystem services, we calculated effect sizes of no till, reduced input,
biologically based, and conservation land treatments relative to the conventional treatment. We
measured the Hedge’s g effect size and 95% confidence intervals using the “compute.es”
package in R (Del Re 2013). The conventional treatment served as the baseline, which does not
include a confidence interval.
        To determine the differences in arthropod richness (ants, butterflies, dung beetles, and
spiders) across treatments, we used generalized linear mixed effects models with normal
distributions. All model assumptions were met. Richness was used as the response variable; main
effects were treatment and year (except for ants which were sampled in only one year), and the
random effect was experimental block. We calculated the estimated species richness of
butterflies within each replicate of each treatment per year using the R package “iNext” with
Chao1 abundance-based rarefaction (Chao et al. 2014, Hsieh et al. 2016). We used measures of
raw richness for ants, dung beetles, and spiders; we recognize that abundance affects richness for
these measures, but because our abundances were low, rarefaction was not possible.
                                                   68


        To determine the differences in arthropod abundances across treatments, we used
generalized linear mixed effects models constructed similarly but with negative binomial
distributions (except for ants for which we used a normal distribution to meet model
assumptions). R package “lme4” was used to construct the models (Bates et al. 2015). An
ANOVA followed by a Tukey test was used for post-hoc analyses for all models using R
packages “car” (Fox & Weisberg 2019) and “multcomp” respectively (Hothorn et al. 2008). The
same method was used for all measured ecosystem services including crop yield (except GWI),
but with the measure of the service as the response variable and sampling round included as a
fixed effect when multiple sampling rounds occurred within a year (decomposition and
pollination). We also modeled crop yield independently for each year using the same method to
measure relative yields among treatments for each crop. For GWI, we constructed a generalized
linear mixed effects model with log transformed CO2 as the response variable, treatment, year,
sampling round, and temperature as fixed effects, and experimental block as the random effect.
        Distances from prairie strips: We aggregated the individuals of our measures of
invertebrate biodiversity (ants, dung beetles, and spiders) collected over each year within each
distance of each plot of each treatment (four distances for each of six plots for each of two
treatments) by summing. To determine the effect of distance from prairie strip and year on
measures of arthropod richness (ants, dung beetles, and spiders), we constructed a generalized
linear mixed effects model with a normal distribution. Richness was the response variable, crop
management treatment, distance from prairie strip, and year were fixed effects with an
interaction between distance and year, and experimental block was a random effect. We followed
this with an ANOVA. The same method was used for measures of abundance, but with a
negative binominal distribution (except ants for which we used a normal distribution). We
                                                 69


constructed similar models with normal distributions for measures of ecosystem services but
included sampling round as a fixed effect when relevant (decomposition and pollination).
Distance was treated as a continuous variable. To test if trends were occurring across distances
from the prairie strips, or if they were driven solely by high values within the prairie strips, we
also ran analyses for all measures with datapoints at 0 m removed.
Results
Dung beetles
Across management treatments: We collected a total of 553 dung beetles in Standard Sampling
Plots (Table 2.2). The effect sizes of dung beetle richness across all treatments did not differ
from baseline (Figure 2.3). Species richness did not differ among treatments, however dung
beetle abundance was 128–992% higher in conservation land than in all other treatments (Figure
2.4, Table 2.3). Distances from prairie strips: We collected a total of 284 dung beetles at Strip
Sampling Stations (Table 2.2). There was no relationship between distance and dung beetle
richness, but dung beetle richness was higher in 2020 than in 2019 (Figure 2.5, Table 2.4). There
was an interaction between year and distance from prairie strip for dung beetle abundance, with
2019 having no relationship with distance from strip and 2020 abundance decreasing with
distance from strip (Figure 2.5, Table 2.4). When 0 m samples were removed, there was no effect
of distance or year on dung beetle abundance (distance: χ2=0.6, df=1, p=0.4; year: χ2=0.5, df=1,
p=0.8) or richness (distance: χ2=0.4, df=1, p=0.53; year: χ2=1.7, df=1, p=0.2), meaning the linear
trend of abundance was driven by high dung beetle abundance in the prairie strips.
                                                  70


Ants
Across management treatments: We collected a total of 1821 worker ants from the Subplot
Sampling Stations (Table 2.5). The Hedge’s g effect sizes of ant richness in conservation land
were higher than the baseline (Figure 2.3). Species richness was higher in conservation land
treatments than in all row crop treatments (Figure 2.4, Table 2.3). Ant abundance was the same
as ant richness because abundance of any particular species could only be 0 or 1 at a single
sampling station, and sampling stations were summed per plot per year (see methods). Distances
from prairie strips: We collected a total of 3218 ants from the Strip Sampling Stations (Table
2.5). Ant species richness did not vary by year nor with distance from the prairie strip (Figure
2.5, Table 2.4). Ant abundance did not differ by distance from prairie strip, but differed by year,
decreasing from 2019 to 2020 (Figure 2.5, Table 2.4).
Spiders
Across management treatments: We collected a total of 1522 spiders from Subplot Sampling
Stations (Table 2.6). The effect size of spider richness for treatments with prairie strips and the
conservation land treatment were higher than baseline; no till did not differ from the baseline
(Figure 2.3). Spider richness increased from conventional to no till and biologically based, to
reduced input, to conservation land (Figure 2.4, Table 2.3). Spider abundance was highest in the
treatments with prairie strips followed by conservation land and no till, and lowest in
conventional (Figure 2.4, Table 2.3). Distances from prairie strips: We collected a total of 3626
spiders from Strips Sampling Stations (Table 2.6). There was an interaction between distance
from prairie strip and year to explain spider richness and abundance, with 2019 having no change
                                                  71


in richness and abundance with increasing distance and 2020 having a decrease in richness with
increasing distance (Figure 2.5, Table 2.4).
Butterflies
Across management treatments: We visually identified 5329 butterflies during transect counts
(Table 2.7). The effect sizes of butterfly richness were higher than the baseline of conventional
in the biologically based and conservation land treatments; no till and reduced input did not
differ from baseline (Figure 2.3). Butterfly richness was the highest in the conservation land and
biologically based treatments followed by reduced input and no till treatments, and lowest in
conventional (Figure 2.4, Table 2.3). Giant Swallowtails, Checkered Skippers, and Red Spotted
Purples were species identified in treatments with prairie strips that were never observed in
conventional or no till treatments. Butterfly abundance was 134–349% higher in treatments with
prairie strips than crop treatments without prairie strips (conventional and no till; Figure 2.4).
Active carbon
Across management treatments: The effect sizes of active carbon in the reduced input and the
conservation land treatments were higher than the baseline; no till and biologically based did not
differ from the baseline (Figure 2.3). Active carbon was highest in the conservation land
treatment, lowest in the no till treatment, and intermediate in the reduced input, biologically
based, and conventional (Figure 2.4, Table 2.3). Distances from prairie strips: Distance from
prairie strip and year had significant effects on active carbon, with 2019 having higher active
carbon than 2020, and 2019 having a decrease in active carbon with distance from prairie strip
(Figure 2.5, Table 2.4). In the crop area alone, (0 m datapoints removed from analyses), there
                                                  72


was a significant interaction between distance and year with a decrease in active carbon with
increasing distance from prairie strips in 2019 and no change with distance from prairie strip in
2020 (χ2=4.9, df=1, p<0.05).
Decomposition
Across management treatments: The effect size of decomposition in conservation land was
higher than the baseline; no other treatment differed from the baseline (Figure 2.3). Conservation
land and no till treatments had the highest rates of decomposition, followed by conventional and
reduced input, and then by biologically based (Figure 2.4, Table 2.3). Distances from prairie
strips: There was an interaction between year and distance from prairie strip, with 2019 having a
lower rate of decomposition and no change with distance from prairie strip and 2020 having a
higher rate of decomposition with a decreasing trend with increasing distance from prairie strip
(Figure 2.5, Table 2.4).
GWI
Across management treatments: The effect size of GWI in conservation land was lower than the
baseline; no other treatments differed from the baseline (Figure 2.3). GWI in the conservation
land treatment was at least 25x lower than all other treatments (Figure 2.4, Table 2.3). Methane
tended to be consumed in the soils rather than emitted to the atmosphere across all land use
types. Fluxes in conservation land across 2019 and 2020 were lowest at −2.68 ± 2.46 (mean ±
SD) g CH4-C ha−1 day−1, while those in the biologically based treatment were highest at −0.79 ±
1.78 g CH4-C ha−1 day−1. Similarly, N2O fluxes were lowest in conservation land and highest in
                                                 73


the biologically based treatment at 0.34 ± 0.51 and 7.27 ± 17.13 g N2O-N ha−1 day−1,
respectively.
Pollination
Across management treatments: The effect size of pollination was higher in reduced input and
biologically based treatments than the baseline (Figure 2.3). Pollination rates were 72–222%
higher in the treatments with prairie strips than all other treatments (Figure 2.4, Table 2.3).
Distances from prairie strips: Pollination services decreased with distance from prairie strip, and
pollination services increased from 2019 to 2020 (Figure 2.5, Table 2.4).
SOC
Across management treatments: SOC was higher than baseline in biologically based and
conservation land treatments (Figure 2.3). SOC was lowest in conventional, intermediate in the
no till, reduced input, and biologically based treatments, and highest in conservation land
treatment (Figure 2.4, Table 2.3).
Crop yield
         Across management treatments: The effect size was lower than baseline in the
biologically based treatment but did not differ from the baseline for no till and reduced input
treatments (Figure 2.3). When including both wheat and maize in analyses, crop yield was
highest in the no till treatment, intermediate in the conventional and reduced input treatments,
and lowest in the biologically based treatment (Figure 2.4, Table 2.3). Crop yield was scaled to
include the land area of prairie strips in analyses (kg/ha measurements include area of the prairie
                                                   74


strips). These relative crop yields across treatments were consistent with the previous two crop
rotations where, across all the whole crop rotation, crop yields were highest in no till,
intermediate in conventional and reduced input (although conventional and no till were not
significantly different), and lowest in the biologically based treatment.
         When considering the wheat and maize years independently, the pattern among
treatments changed from the previous two crop rotations. When measuring just the wheat year
(2019), crop yield was lowest in the biologically based, followed by the reduced input treatment,
then no till, then conventional (χ2=1921.2, df=3, p=<0.01). Historically (2013–2017), however,
wheat yield was lowest in biologically based but did not differ among other crop treatments.
When measuring just the maize year (2020), crop yield was lowest in the biologically based and
conventional, intermediate in the reduced input, and highest in no till (χ2=70.3, df=3, p<0.01).
This also differs from the previous two crop rotations, where maize yield was lowest in
biologically based, intermediate in reduced input and conventional, and highest in no till.
Therefore, maize yields in the conventional treatment in 2020 were low compared to the
previous two crop rotations. The third crop in the rotation, soy, historically was lowest in
conventional and biologically based and highest in no till and reduced input.
Discussion
We show that prairie strips, even early in their establishment, combined with lower land use
intensity can promote biodiversity and ecosystem services without compromising crop yield.
Within reduced input and biologically based treatments, where sampling effort occurred within
prairie strips and at increasing distance from strips, biodiversity and ecosystem services spilled
over into agronomic areas for five out of six measures. Among all treatments, using data from
                                                  75


the entire plot area (not sampling within prairie strips for treatments with strips), pollination
services and the abundance of butterflies and spiders were higher in plots with prairie strips. In
addition, soil organic carbon, butterfly richness, and spider richness increased with a decrease in
land use intensity. Crop yield in the reduced input treatment was equal to that of conventional
management, even while including the area taken out of production. These effects were evident
early in strip establishment, during which prairie strip plant communities changed from mostly
weeds to a diversity of planted species. We expect the effects of strips to grow over time as
native plants establish and become more abundant, and as lagged effects of historic agronomic
disturbances abate.
        The benefits of prairie strips decreased with distance into cropland for spider abundance
and richness, dung beetle abundance, active carbon, decomposition, and pollination (Figure 2.5).
Each of these responses decreased with distance from the prairie strip, but did so by different
mechanisms. For spiders, the spillover into cropland was delayed one year after the prairie strips
were sown. Prairie strips provided new habitat and sources of prey for spiders (Hussain et al.
2021), such that spiders could move into cropland to capture additional prey. The impact on
dung beetles and decomposition are likely due to the prairie strips harboring dung beetles in the
year after restoration, which in turn caused a higher rate of manure removal near the prairie strips
and a decrease in dung beetle abundance and manure removal with increasing distance from the
strips (Manning & Cutler 2018). For pollination, prairie strips increased floral resources which
attracted pollinators and then exported them into the surrounding habitat (Garibaldi et al. 2011).
We were surprised that prairie strips increased pollination as our plot sizes are a fraction of
pollinator foraging range (Ricketts et al. 2008), and we suspect that pollinators were attracted to
the high concentration of resources that contrasted strongly with crops. For active carbon, the
                                                 76


effect of distance from prairie strip may have been driven by high prairie litter inputs. Mowing
prairie strips in 2019 may have caused a spillover of prairie litter inputs into cropland that did not
occur in 2020, leading to higher levels of active carbon in crop soils immediately adjacent to
prairie strips. While the increase of active carbon in nearby sites was subtle, it still highlights the
biogeochemical benefits of adjacent prairies for agricultural lands, which are generally
underappreciated (Pérez-Suárez et al. 2014).
        Our study shows that spillover effects from prairie strips extend across measures of
biodiversity and ecosystem services. Pollinators and pollination services have been studied at
distances from restorations and habitat edges, often with higher numbers of pollinators and rates
of pollination near non-crop habitat (Ricketts 2004, Carvalheiro et al. 2010, Kordbacheh et al.
2020); by also demonstrating this phenomenon in dung beetles, spiders, active carbon, and
decomposition, we show that these spatial effects apply to a broader array of organisms and
ecosystem services. These results, with detailed attention to mechanism in our controlled
experiment, strengthen evidence that suggests that strategic placement and amount of natural
habitat in agricultural landscapes can add both conservation value and ecosystem services to an
agricultural landscape (Basso et al. 2019, Mitchell et al. 2021). Our study focused on ecosystem
services, and we did not measure potential ecosystem disservices from prairie strips that could
impact yield, such as herbivory, however such disservices could be addressed in future studies.
        Prairie strips require several years after planting to resemble a restored prairie
community, over which time diversity and ecosystem services have shown to accrue (Kurtz
2013, Griffin et al. 2017). Supporting this, we found that year since prairie strip establishment
affected all responses that we measured at distances from prairie strips except ant richness
(Figure 2.5). Lack of response of ant species richness is consistent with other grassland
                                                   77


restoration projects where it takes several years for ant communities to turnover (Dauber &
Wolters 2005, Menke et al. 2015, Scharnhorst et al. 2021, Hussain et al. 2021). With the
exception of active carbon, variables displayed a general progression of the first year having
small to no effect of distance from prairie strip, to the second year showing a stronger negative
effect of distance. It is important to note that year differences in our experiment are confounded
by crop type (wheat or maize). These effects will become easier to separate from effects of
prairie strips as measurements are repeated over the next four years (after two full crop rotations)
and then in seven years (one full rotation after strip maturation). We expect that as more plant
species establish, prairie strips will increase biodiversity even further.
        Prairie strips are also likely to continue to increase the provision of soil services; for
example, we found higher levels of active carbon and SOC in the prairie strips in this study, with
potential for these benefits to extend into the cropland at short distances. Over time, prairie strips
could thus be a significant carbon sink, which could provide benefits to agricultural landscapes
and may come with economic reward with future carbon pricing. Quantifying the aggregated
potential of this sequestration should be a priority, and continued measurement of these variables
in our study after the early establishment phase of prairie strips will provide insight to their long-
term potential for conservation and impact on crop yield, and more comprehensive opportunities
for synergies.
        Across all crop management treatments, there was a variable effect of land use intensity
on measures of biodiversity (Figure 2.4). Conservation land consistently had the highest level of
richness, and often had the highest level of ecosystem services. We found that lower land use
intensity treatments with prairie strips increased butterfly abundance and pollination services
compared to other crop management schemes. This may be the consequence of pollinators and
                                                   78


butterflies being mobile agents with large ranges. They can therefore find and utilize the
resources of prairie strips early in strip establishment (Cant et al. 2005, Pasquet et al. 2008). In
addition to the prairie strips, reduced input and biologically based treatments have reduced
pesticides which likely contributes to increased butterfly richness and pollination services; but
the decreasing pollination services with distance from the prairie strips suggests that pollinators
are attracted to the strips. Butterflies were most diverse in the conservation land treatment due to
the increased floral and habitat diversity throughout the plot (Menéndez et al. 2007). We attribute
the low level of pollination services in the conservation land treatment, especially in 2019, to the
forager dilution effect, in which pollination services are diluted in an area of mass flowering
(Holzschuh et al. 2011). While butterflies and pollination services do not improve the yield of
wheat or maize crops, they may improve yield in soy crops (Cunningham-Minnick et al. 2019) or
other crops in the landscapes that benefit from pollinators, and the potential of these services
remain among the most important to surveyed farmers (Arbuckle 2019, Hevia et al. 2021).
         Our hypotheses of increasing biodiversity with a decrease in land use intensity were not
supported uniformly. For ground dwelling arthropods, conventional management had
surprisingly high species richness. This finding is not without precedent; despite previous
findings that organic farms support more biodiversity than conventional farms (Bengtsson et al.
2005, Tuck et al. 2014), ants and dung beetles show mixed results (Hutton & Giller 2003, Jones
et al. 2019, Piccini et al. 2019, Helms et al. 2021). In addition, while diversifying farms generally
increases spider diversity (Schmidt et al. 2005), prairie strips in our study have not increased
spider diversity at the plot level, but have increased spider abundance.
         Soil-related ecosystem services (active carbon, decomposition, GWI, and SOC) across
the land use intensity gradient were highest in conservation lands, with variable differences
                                                    79


among cropping treatments. Decomposition was highest in untilled treatments (no till and
conservation land), possibly due to the higher microbial activity and soil moisture maintained by
soil aggregates with greater physical protection (Paul et al. 2015), or due to mesofaunal
differences that we did not measure, such as earthworms (Smith et al. 2008). As strips develop,
we expect decomposition to increase with dung beetle abundance and diversity in prairie strip
treatments (Hosler et al. 2021). Conservation land had increased active carbon likely due to the
fine root production of diverse perennial vegetation, which we also expect to increase in and near
prairie strips as they mature (Sprunger et al. 2017, Sprunger & Robertson 2018). The increased
SOC along the land use intensity gradient was likely due to carbon from perennial plants
(Syswerda et al. 2011, Mosier et al. 2021). GWI was almost entirely driven by N2O in our study.
The reduced GWI in the conservation land treatment was likely due to reduced fertilizer inputs.
Although there was no difference in overall GWI across row crop management treatments, as
Gelfand et al. (2016) has also found for N2O emissions, there was higher GWI in the wheat year,
compared to maize. This was likely due to several days of tillage early in the growing season for
biologically based maize, management that is different from other treatments that receive
alternative methods of weed management. We note that GWI only represents soil emissions and
is not a full life cycle analysis.
        The potential for prairie strips to enhance biodiversity and ecosystem services at large
scales will be most powerful if they do not sacrifice agricultural yield. Historically in our
experiment, the no till treatment has the highest yield, followed by the conventional and reduced
input treatments with intermediate yields, and the biologically based treatment with the lowest
yield. We show that converting 5% of crop area to prairie strips (and using yield measures that
include the area taken out of production) does not change differences in yield across treatments
                                                  80


beyond differences already induced by existing management. Reduced input management with
prairie strips maintained a high yield, equivalent to conventional, while having high levels of
pollination, spider abundance, and butterfly richness; biologically based crop management
similarly maximized these services, but at the cost of a large cut in yields. This reduction was
likely due to limitation of inorganic nitrogen (Robertson et al. 2015), unrelated to prairie strips.
While no single method of crop management performed highest or lowest across all measures
(e.g., no till treatments had the highest yield but did not have the highest levels of biodiversity or
other ecosystem services), there was a synergy among crop yield, biodiversity, and non-
provisioning ecosystem services in the reduced input treatment. Prairie strips are an effective
conservation practice that can be combined with other techniques—reduced fertilizer and
pesticides—to conserve biodiversity and ecosystem services without compromising crop yield.
        When considering both 2019 and 2020 yields together, yield in reduced input treatments
may have remained equivalent to yield in conventional treatments for at least three reasons. First,
converting 5% of cropland to prairie was not enough area to result in significant changes among
treatments. Second, ecosystem services generated by strips could increase yield in the remainder
of the plot. Several of the responses we measured were higher at the plot scale outside strips. For
example, relative to conventional treatments, active carbon was higher in reduced input
treatments, and SOC trended toward higher (Figure 2.3). Third, climate or other environmental
conditions during the time of our study could have had stronger negative impact in conventional
treatments. As the strips mature, and with results through multiple rotations, the mechanism of
yield response will become clearer.
        Prairie strips were implemented on land that had previously been in crop production in
this experiment, but prairie strips could also be implemented on the landscape in a way that does
                                                   81


not reduce farm profitability. For instance, prairie strips can be strategically placed on marginal
land—land that has consistent low yields relative to nutrient and greenhouse gas inputs. Marginal
land occupies 26% of annual cropland land in the US Midwest, resulting in excessive pollution
and wasted monetary and nutrient resources (Basso et al. 2019). Converting marginal cropland to
prairie strips would reduce inputs without sacrificing crop yield. In addition, prairie strips could
be harvested as perennial biofuel for added profitability, and we suggest future studies address
how this would impact biodiversity and ecosystem services. Future studies might also examine
how the optimal benefits of prairie strips could be achieved with strategic location, particularly
cropland that is consistently underperforming and therefore not as profitable, on slopes to
prevent soil erosion, or in locations that could increase habitat connectivity in the broader
landscape (Basso 2021).
        We expect that as prairie strips mature their effects on biodiversity and ecosystem
services will grow. We converted just 5% of cropland to prairie, however optimal benefit may be
achieved by even larger strips, such as the 10% conversion of cropland to prairie as
recommended by Schulte et al. (2017). Our study supports that prairie strips are an effective
strategy for conserving biodiversity, and can in some cases be created without impacting crop
yield in the US Midwest. More broadly, diversifying agricultural landscapes can help mitigate
the loss of biodiversity and ecosystem services while supporting the growing human population.
Acknowledgments
Thank you Stacey VanderWulp and Mark Hammond for project coordination, Sven Bohm and
Hsun-yi Hsieh for data management, and Kevin Kahmark and Joe Simmons for fieldwork
management. Thank you Doug Landis for efforts to create prairie strip treatments. Thank you
                                                  82


Alexandra Brown, Michaela Rose, Oren Lerner, and other field technicians for assistance with
data collection and processing. We acknowledge that W.K. Kellogg Biological Station’s LTER
Site, where this work was conducted, occupies the ancestral land of the Peoria Tribe and the
ancestral, traditional, and contemporary Lands of the Anishinaabeg – Three Fires Confederacy of
Ojibwe, Odawa, and Potawatomi peoples. We honor with gratitude the land itself and the people
who have stewarded it through the generations. Support for this research was provided by the
NSF Long-term Ecological Research Program (DEB 1832042) at the Kellogg Biological Station
and by Michigan State University AgBioResearch. Support for this research was also provided
by the Great Lakes Bioenergy Research Center, U.S. Department of Energy, Office of Science,
Office of Biological and Environmental Research (Award DE-SC0018409). This research was
also partially funded by the Kellogg Biological Station, the Michigan State University Graduate
School, and the Michigan State University Ecology, Evolution, and Behavior graduate program.
                                                83


APPENDIX
   84


Table 2.1. Native forb and grass species sowed to create prairie strips.
                                                                           Seeds   % by    % by
                     Species                    Common Name                per m2 weight   seed
           Andropogon gerardii            Big Bluestem                         9.9 6.3%     3.2%
           Bouteloua curtipendula         Side-oats Grama                   20.8   21.9%   6.8%
 Grasses   Elymus canadensis              Canada Wild Rye                    5.2   6.3%    1.7%
           Schizachyrium scoparium        Little Bluestem                   37.0   15.6%   12.2%
           Achillea millefolium           Yarrow                            22.1   0.8%    7.2%
           Asclepias syriaca              Common Milkweed                    3.6   5.6%    1.2%
           Asclepias tuberosa             Butterfly Milkweed                 3.2   4.7%    1.1%
           Symphyotrichum novae-angliae   New England Aster                 12.3   1.2%    4.0%
           Coreopsis lanceolata           Lance-leaf Coreopsis              19.8   6.3%    6.5%
           Coreopsis tripteris            Tall Coreopsis                     2.8   1.3%    0.9%
           Desmodium canadense            Showy Tick Trefoil                 2.7   3.1%    0.9%
           Echinacea purpurea             Purple Coneflower                  6.6   6.3%    2.1%
           Lespedeza capitata             Round-headed Bush Clover           3.2   2.6%    1.1%
 Forbs     Monarda fistulosa              Wild Bergamot                     13.0   1.2%    4.3%
           Ratibida pinnata               Yellow Coneflower                  7.4   1.6%    2.4%
           Rudbeckia hirta                Black-eyed Susan                  56.8   3.9%    18.6%
           Silphium perfoliatum           Cupplant                           0.4   2.0%    0.2%
           Solidago juncea                Early Goldenrod                   28.6   0.6%    9.4%
           Solidago nemoralis             Old-field Goldenrod               29.6   0.6%    9.7%
           Tradescantia ohiensis          Common Spiderwort                  1.9   1.6%    0.7%
           Verbena stricta                Hoary Vervain                     10.3   2.3%    3.4%
           Zizia aurea                    Golden Alexander                   7.8   4.5%    2.5%
                                                   85


Table 2.2. Dung beetle abundance per species found in each treatment each year. All distances
are combined for strip abundance counts.
                                                                                                            Strip - Reduced input   Strip - Biologically based
                                 Conventional             Reduced input                 Conservation land
                                                No till                    Bio. based
             2019
 Copris fricator                       0           0             0              0                1                     0                          0
 Onthophagus hecate               12               5             3          24 118                            27                      87
 Onthophagus orpheus                   1           0             0              0                0                     0                          0
 Onthophagus pennsylvanicus            4           6             9          13                   4                     4                          8
 Onthophagus taurus                    4           5             3              2                0                     1                          0
             2020
 Copris fricator                       1           0             1              0                0                     0                          0
 Copris minutus                        0           3             4              1                7                     7                          3
 Onthophagus hecate               21            39         18               15            95                  45                      31
 Onthophagus nuchicornis               1           0             0              0                0                     0                          0
 Onthophagus orpheus                   1           0             1              1                1                     4                          1
 Onthophagus pennsylvanicus       23            12         13               19            61                  32                      33
 Onthophagus taurus                    0           0             0              1                0                     0                          1
                                                                          86


Table 2.3. Effects of crop management treatments on measures of biodiversity and ecosystem
services treatments. Bolded p-values indicate statistically significant measures.
                                                            treatment             year
         measure                      unit               χ2     df     p     χ2    df    p
 ant abundance             number of individuals        42.6     4 <0.01     NA   NA    NA
 ant richness              number of species            42.6     4 <0.01     NA   NA    NA
 butterfly abundance       number of individuals       223.9     4 <0.01     67.3   1 <0.01
 butterfly richness        species richness (chao1)     19.3     4 <0.01      0.8   1    0.4
 dung beetle abundance number of individuals            95.9     4 <0.01     15.2   1 <0.01
 dung beetle richness      number of species              6.4    4      0.2  12.2    1 <0.01
 spider abundance          number of individuals        26.9     4 <0.01     17.1   1 <0.01
 spider family richness    number of families           48.0     4 <0.01     13.1   1 <0.01
 active carbon             μg CO2/day/g                144.4     4 <0.01     NA   NA    NA
 decomposition             proportion removed           40.8     4 <0.01     86.6   1 <0.01
 GWI                       kg CO2/ha/day               117.5     4 <0.01      3.7   1   0.05
 pollination               proportion germinated        70.2     4 <0.01     32.0   1 <0.01
 SOC                       soil C weight%              102.9     4 <0.01      2.7   1    0.1
 crop yield                kg/ha                        68.3     3 <0.01 130.7      1 <0.01
                                                87


Table 2.4. Effects of distance from prairie strips on biodiversity and ecosystem services,
including data from both reduced input and biologically based treatments. Bolded p-values
indicate statistically significant measures.
                                   distance               year           distance * year
         measure               χ2     df     p      χ2     df      p     χ2    df     p
 ant abundance                 2.8     1     0.1   0.004    1      0.9   1.7    1     0.2
 ant richness                  2.8     1     0.1   0.004    1      0.9   1.7    1     0.2
 dung beetle abundance         0.3     1     0.6      5.9   1 <0.01      4.6    1 <0.05
 dung beetle richness          0.2     1     0.7      8.8   1 <0.01 0.08        1     0.8
 spider abundance             0.06     1     0.8    48.7    1 <0.01 13.5        1 <0.01
 spider family richness       0.03     1    0.86    0.02    1      0.9   6.2    1 <0.01
 active carbon                 8.2     1 <0.01     155.1    1 <0.01      3.4    1    0.06
 decomposition services        1.3     1     0.2    33.3    1 <0.01      6.1    1 <0.01
 pollination services          6.0     1 <0.01      40.9    1 <0.01      0.4    1     0.5
                                                  88


Table 2.5. Ant abundance per species found in each treatment each year. All distances are
combined for strip abundance counts.
                                                                                                      Strip - Reduced Input   Strip - Biologically Based
                               Conventional                                       Conservation land
                                              No Till   Red. input   Bio. based
            2019
 Aphaenogaster carolinensis         0             0         0           0             0                    1                        0
 Aphaenogaster rudis                1             0         0           0             6                    1                        1
 Brachymyrmex depilis               0             0         0           0             0                    2                        0
 Crematogaster cerasi               0             0         0           0             0                    0                        2
 Crematogaster lineolata            0             0         0           1             0                    0                        0
 Formica glacialis                  2             0         0           2             5                    1                        3
 Formica incerta                    0             0         0           0             1                    4                        2
 Formica pallidefulva               0             0         0           0             2                    0                        0
 Formica subsericea                 0             0         0           0             0                    0                        1
 Formica vinculans                  0             0         0           0             0                    0                        1
 Lasius americanus                  1             0         0           0             3                    0                        0
 Lasius latipes                     0             0         0           0             1                    0                        0
 Lasius neoniger                    6             6         6           6             6               68                      59
 Myrmica americana                  1             0         1           3             4                    7                  19
                                                                     89


Table 2.5 (cont’d)
Myrmica detritinodis  1 0 1  0 3  0  1
Myrmica pinetorum     0 0 0  0 1  0  0
Prenolepis imparis    1 3 5  4 2 56 52
Solenopsis molesta    0 1 0  0 0  1  0
Tapinoma sessile      0 0 0  0 0  0  1
Tetramorium immigrans 3 1 2  2 1 44 49
            2020
Brachymyrmex depilis  - - -  - -  1  1
Formica glacialis     - - -  - -  1  1
Formica incerta       - - -  - -  3  1
Formica subsericea    - - -  - -  1  0
Lasius neoniger       - - -  - - 50 43
Myrmica americana     - - -  - -  2  6
Ponera pennsylvanica  - - -  - -  0  1
Prenolepis imparis    - - -  - - 53 52
Tapinoma sessile      - - -  - -  0  1
Tetramorium immigrans - - -  - - 47 46
                            90


Table 2.6. Spider abundance per family found in each treatment each year. All distances are
combined for strip abundance counts.
                                                                                            Strip - Reduced input   Strip - Biologically based
                  Conventional             Reduced input                Conservation land
                                 No till                   Bio. based
      2019
 Agelenidae                 0        0                0            0             0                            0                           1
 Araneidae                  0        0                0            0             0                            8                           3
 Clubionidae                0        0                0            0             3                            0                           0
 Corinnidae                 0        0                0            0             0                            1                           0
 Dictynidae                 0        0                0            0             4                            0                           0
 Gnaphosidae                0        0                0            0             0                            2                           3
 Hahniidae                  0        0                1            0             0                            0                           1
 Linyphiidae           16        16             96             45         38 446 492
 Lycosidae             10        74             53 159                    29 409 868
 Salticidae                 0        1                0            0             2                            0                           1
 Tetragnathidae             0        1          13             16                3                  60 145
 Theridiidae                0        1                2            0      11                                  0                           0
 Thomisidae                 0        1                0            0      19                                  6                           3
      2020
 Agelenidae                 0        0                1            0             0                            0                           0
                                                                                                 91


Table 2.6 (cont’d)
Araneidae           0  0   0  0  0   6   4
Clubionidae         0  0   0  1  1   0   0
Corinnidae          0  0   0  0  1   1   2
Dictynidae          0  0   0  0  2   0   0
Gnaphosidae         2  4   5  3  7   2   2
Hahniidae           0  1   1  0  0   0   0
Linyphiidae        28 50 104 96 37 361 275
Lycosidae        133  37 109 83 76 232 243
Pisauridae          0  0   0  0  0   0   1
Salticidae          0  0   0  0  0   1   1
Tetragnathidae      0  0   1  1  2   3   2
Theridiidae         1  9   1  2  3   0   0
Thomisidae         13 33  19  5 36  21  19
Zodariidae          0  0   0  0  0   1   0
                                    92


Table 2.7. Butterfly abundance per species or morphogroup found in each treatment each year.
Unknown butterflies were butterflies that could not be identified in the field, for example if they
flew over too quickly; it is likely that these individuals are already included in this table (i.e., not
new species).
                                                               Conventional             Reduced input   Biologically based   Conservation land
                                                                              No Till
             2019
 Baltimore Checkerspot         Euphydryas phaeton                    0           0             0                 0                    1
 Black Swallowtail              Papilio polyxenes                    1           4             9                 6                    3
 Blue sp                                                             0           0             0                 0                    1
 Common Buckeye                 Junonia coenia                       1           1             3                 3                    3
 Common Sootywing               Pholisora catullus                   0           0             0                 0                    2
 Crescent sp                                                         1           2             1                 3            34
 Eastern Tailed Blue            Cupido comyntas                      1           1             1                 7            14
 Fritillary sp                                                       0           0             0                 2                    0
 Giant Swallowtail              Papilio cresphontes                  0           0             0                 1                    0
 Gray Hairstreak                Strymon melinus                      2           1             1                 4                    6
 Meadow Fritillary              Boloria bellona                      0           1             0                 0                    0
 Monarch                        Danaus plexippus                40            71         70 113 170
 Mourning Cloak                 Nymphalis antiopa                    0           0             0                 0                    1
 Peck's Skipper                 Polites peckius                      1           1             0                 1                    0
 Red Admiral                    Vanessa atalanta                18            20         27              15                   11
                                                   93


Table 2.7 (cont’d)
Silver Spotted Skipper Epargyreus clarus    25  23  65  51 174
Skipper sp                                   1   0   0   4  11
Spicebush Swallowtail  Papilio troilus       2   1   2   8   0
Sulphur sp                                 149 210 479 806 580
Tiger Swallowtail      Papilio glaucus       3   2   3   4   1
Vanessa sp                                  15   6  17  38  31
Variegated Fritillary  Euptoieta claudia     0   1   0   0   0
Viceroy                Limenitis archippus   0   1   0   0   1
unknown butterflies                          0   1   0   0   1
           2020
Baltimore Checkerspot  Euphydryas phaeton    1   0   0   1   2
Black Swallowtail      Papilio polyxenes     2   2   1   3  10
Blue sp                                      1   0   0   2   2
Checkered Skipper      Prygus commuis        0   0   0   1   0
Common Buckeye         Junonia coenia        1   1   2   3  11
Crescent sp                                  2   2   2   5  15
Eastern Tailed Blue    Cupido comyntas       1   0   2   5  13
Monarch                Danaus plexippus     11  12  12  26  47
Mourning Cloak         Nymphalis antiopa     0   0   0   0   1
Peck's Skipper         Polites peckius       0   2   1   3   4
Red Admiral            Vanessa atalanta      0   0   1   0   2
                                        94


Table 2.7 (cont’d)
Red Spotted Purple     Limenitis arthemis   0  0   1   0   2
Silver Spotted Skipper Epargyreus clarus   15 11 104 134 216
Skipper sp                                  3  1   9   3  20
Spicebush Swallowtail  Papilio troilus      0  0   3   1   3
Sulphur sp                                 57 47 191 340 523
Swallowtail sp                              0  1   1   0   0
Tiger Swallowtail      Papilio glaucus      0  4   2   8   4
Vanessa sp                                  0  1   0   1   2
Variegated Fritillary  Euptoieta claudia    0  0   0   0   1
Viceroy                Limenitis archippus  0  0   0   0   3
unknown butterflies                         3  1   0   0   2
                                        95


Figure 2.1. Characteristics of experimental crop management treatments. Standard rate refers to
Generally Accepted Agricultural and Management Practices. IPM (Integrated Pest Management)
in the reduced input treatment refers to a combination of extra tillage (maize years), narrow row
spacing (soy years), and reduced herbicide use, mostly related to the number of residual
herbicides. Fungicides and pesticides are applied as problems arise and severity is assessed. Also
regarding reduced input, N fertilizer is applied at 25% of the standard rate in maize years, and at
60% in wheat years.
                                                            Crop management treatments
                     Conventional               No till             Reduced input           Biologically based    Conservation land
 Tillage                  yes                     no                        yes                     yes                  no
 Cover crops               no                     no                        yes                     yes                  no
 Pest management     standard rate          standard rate                  IPM         tillage/mechanical control        no
                                                                  `
 Crop varieties   genetically modified   genetically modified     genetically modified  not genetically modified         NA
 N fertilizer        standard rate          standard rate       25%/60% standard rate                no                  no
 Prairie strips            no                     no                    yes – 5%                 yes – 5%                no
 Burning                   no                     no                prairie strip area       prairie strip area      entire area
                 Land use intensity gradient
                                                                 96


Figure 2.2. a. Experimental landscape with six blocks of five crop management treatments. b.
Plot layout. Prairie strips and Strip Sampling Stations are only present in the reduced input and
biologically based treatments.
                                                 97


Figure 2.3. Hedge’s g effect size (black and gray dots) with 95% confidence interval. The
                                                98


Figure 2.3 (cont’d)
baseline is the conventional management treatment. Negative values are effect sizes lower than
that of the baseline, and positive values are effect sizes higher than the baseline. Values that
cross zero are shaded gray. Note that lower levels of global warming impact (GWI) would be a
more positive ecosystem service. Conservation land was not harvested, therefore crop yield is
not applicable for that treatment.
                                                  99


Figure 2.4. Effects of crop management treatment on measures of biodiversity and ecosystem
services between years 2019 and 2020. For measures of biodiversity, bars depict means over an
entire year; for measures of ecosystem services, bars depict means for each sampling round.
Letters denote statistical differences among treatments. Error bars show standard errors. Ants and
active carbon were only measured in 2019.
                                                100


Figure 2.5. Effects of distance from prairie strips on measures of biodiversity and ecosystem
                                                 101


Figure 2.5 (cont’d)
services in both reduced input and biologically based treatments. We considered linear effects of
distance from prairie strips. The interaction with year is presented, with 2019 being the first year
of prairie strip implementation and a wheat year, and 2020 being the second year and a maize
year. Distance 0 m is within the prairie strip. Black dashes on the y-axis represent the mean of
the plot level values for reduced input and biologically based for that measure in 2019; green
dashes represent 2020. Ant data from 2019 is modified from Helms et al. 2021.
                                                 102


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                                        CHAPTER THREE
    Prairie strips and lower land use intensity increase butterfly biodiversity in row crop farms
Abstract
One third of butterfly species are currently declining and butterfly abundance across all species is
declining at a rate of 2% per year. Agriculture contributes to butterfly decline through habitat
loss, pollution, and climate change. Prairie strips, strips of farmland actively restored to native
perennial vegetation, are a conservation practice with potential to mitigate biodiversity loss, but
their impact on butterfly biodiversity is not known. Similar to other prairie restorations, the plant
community of prairie strips is expected to fluctuate over the first few years after sowing, and
therefore higher trophic levels, such as butterflies are also expected to fluctuate. We measured
the plant and butterfly community of newly established prairie strips during the first three years
of establishment in a row crop agricultural experiment that included a three-year crop rotation of
wheat, maize, and soy. We also measured butterfly abundance and richness over across a broader
gradient of land use intensity in the same experimental setting including conventional row crops,
no till row crops, reduced input row crops with prairie strips, biologically based row crops with
prairie strips, and conservation land. Butterfly abundance was higher within prairie strips than in
all other treatments. Across the land use intensity gradient and at the plot level (not surveying
within prairie strips), treatments with prairie strips had a higher abundance of butterflies than
other row crop treatments, and the biologically based treatment which does not use pesticides
had a higher abundance than the reduced input treatment. Also across entire plots, butterfly
richness increased as land use intensity decreased. Treatments with prairie strips and reduced
land use intensity had distinct butterfly communities, as they harbored several butterfly species
that were not found in other row crop treatments. We show that prairie strips, in combination
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with other crop management practices, can increase overall butterfly biodiversity by supporting
unique species and increasing abundance compared to crop management treatments without
prairie strips.
Introduction
Biodiversity is declining worldwide, with an estimated one million species threatened with
extinction (IPBES 2019). Butterflies alone are decreasing in abundance by 2% per year, with
approximately 30% of butterfly species declining (Dirzo et al. 2014, Wepprich et al. 2019, van
Klink et al. 2020). In addition to their inherent value as species, butterflies are of particular
conservation interest because of their high cultural value and their roles as herbivores and
pollinators. Butterflies also have potential to serve as indicators of ecosystem health because they
are sensitive to environmental change, they respond to more environmental qualities than
vegetation such as chemicals, their ecology is well known, they can be found across the globe,
and they can be monitored by scientists and non-scientists alike (Dennis et al. 2003, Thomas
2005, Fleishman & Murphy 2009).
         The principal reasons for butterfly decline are habitat loss, chemical pollution, and
climate change (Wepprich et al. 2019, Wagner 2021). Agriculture is a leading cause of both
habitat loss and chemical pollution from pesticide and fertilizer use, which directly and indirectly
result in the destruction of host plants, habitat, and nectar resources for butterflies (Wepprich et
al. 2019, van Klink et al. 2020). Mitigating this loss of biodiversity is possible by diversifying
monoculture agricultural landscapes into sources of food, shelter, and habitat connectivity
(Kremen & Merenlander 2018), which is the focus of this study.
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         One conservation strategy for diversifying agricultural landscapes in the US Midwest is
with prairie strips. Prairie strips are strips of farmland that are retired from production and sown
with native, perennial grassland species (Schulte et al. 2017). Prairie strips are multifunctional
with their abilities to reduce erosion and nutrient runoff in contoured agricultural landscapes, and
support biodiversity on farms. Moreover, prairie strips can promote biodiversity without
compromising yield (Kemmerling et al. in review), and farmers can be compensated for
implementing prairie strips through the USDA Conservation Reserve Program (Luther et al.
2022). Prairie strips are similar to other management practices such as filter strips and hedgerows
that harbor butterflies (Dover & Sparks 2000, Haaland et al. 2011, Dover 2019). While prairie
strips are known to increase the prevalence of pollinators and specific species of butterflies like
Monarch butterflies (Schulte et al. 2017, Kordbacheh et al. 2020, Murray 2021), the effects of
prairie strips on butterfly biodiversity are not known, and is the subject of this study.
         One goal of restoring prairie strips, and the approach we take in this study, is to optimize
biodiversity, ecosystem services, and yield in agricultural landscapes. This differs from that of
typical prairie restorations with the intent to achieve an ecosystem similar to that of reference
conditions (Hallett et al. 2013). In management for prairie strips, we can draw on prairie
restoration ecology theory to predict how the plant community and higher trophic levels will
develop over time. Prairie restorations take several years of management before they resemble a
native grassland (Kurtz 2013). During early years of establishment, the plant community and
therefore higher trophic levels will fluctuate in biomass, species composition, and diversity
(Camill et al. 2004, Griffin et al. 2017). Measuring the impact of prairie strips on biodiversity in
their first years of establishment addresses how their structure impacts higher trophic levels and
is useful for informing farmers implementing this conservation practice.
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        Prairie strips are not created in isolation; they are embedded within croplands that are
used at different levels of intensity. Treatments in our experiment include other conservation
strategies on farms such as cover crops, reduced fertilizer, and integrated pest management
including reduced pesticides to mitigate loss of biodiversity. In this study, we measured how
prairie strips and crop management impact butterfly biodiversity, addressing three questions.
First, how do prairie strips and other agricultural management practices impact butterfly
biodiversity? Second, because butterflies are dependent on the plant community for food at larval
and adult life stages, how does year since establishment impact prairie strip plant community?
And third, how does crop management surrounding the prairie strip impact the plant and
butterfly community? We measured butterfly abundance and species richness across a gradient
of agricultural management intensity including prairie strips over three years of a wheat, maize,
and soy crop rotation.
Methods
Study sites
We conducted this study at the Kellogg Biological Station Long Term Ecological Research
(KBS LTER) site, established in 1987, in Hickory Corners, MI USA (occupied Anishinaabe
land). The climate of the KBS LTER is temperate with a 100.5 cm average annual precipitation
(1981-2011 average) and a 10.1°C average annual temperature (Liang & Robertson 2021). The
experiment consisted of five treatments spanning a gradient of land use intensity within the KBS
LTER’s Main Cropping System Experiment: conventionally managed row crops, no till row
crops, reduced input row crops, biologically based row crops, and conservation land. Row crop
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treatments (all treatments except conservation land) are planted on a three-year rotation of maize
(Zea mays L.), soy (Glycine max L.), and wheat (Triticum aestivum L.).
         The experiment consists of 6 replicated 1 ha plots of each treatment within the same
experimental landscape (plot layout in Figure 3.1 a). Conventional management includes tillage,
no cover crops, pest management and fertilizer at rates recommended by Generally Accepted
Agricultural Management Practices (GAAMP), and genetically modified crop varieties. No till
management is the same as conventional except without mechanical weed management. Reduced
input management includes tillage, cover crops, integrated pest management, 33% the rate of
fertilizer compared to conventional and no till, and genetically modified crop varieties.
Biologically based management includes tillage, cover crops, certified organic weed
management through mechanical control (cultivation and tillage), no fertilizer or manure, and
crop varieties that are not genetically modified. Conservation land – unmanaged, with the
exception of annual spring burning (this is equivalent to early successional in earlier publications
and site maps within the same experiment). The conservation land treatment is mainly grassland
with peak bloom in the fall of asters and goldenrod. Additional details on management of
treatments are detailed in Robertson and Hamilton (2015).
         In April 2019, prairie strips were added to reduced input and biologically based
treatments. Prairie strips were configured as a 4.5m strip (5% of the plot area) running the entire
length through the center of the plot oriented with the rows of crops (Fig. 3.1 a). Prairie strips
were implemented by sowing a mix of native prairie plant species consisting of 18 forb species
and 4 grass species (Table 2.1). The seed mix is a “pollinator mix”, having at least two species
per bloom period (spring, summer, and fall; Isaacs et al 2009) plus Common Milkweed and
Butterfly Milkweed that combined make up 2.2% of the mix. Annual ryegrass (Lolium sp.) and
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spring oats (Avena sativa, also an annual) were added to the seed mix to increase the seeding rate
for the seeding machinery. Every prairie strip was sown with the same seed mix—the same
weight and proportion of each species sourced from Native Connections in Kalamazoo, MI, USA
(Table 2.1). Strips were sprayed with herbicide prior to seeding. In 2019, prairie strips were
mowed three times strategically over the growing season to reduce weeds and support
establishment of native seeds. In 2021, prairie strips were burned in the spring. Our study
occurred in 2019 (initial year of prairie strip planting and a wheat year), 2020 (maize year), and
2021 (soy year).
Plants
We surveyed plants using quadrats counts within the prairie strips (Figure 3.1 a). All plants
rooted within five 1x1m quadrats in each plot were identified to species. We measured percent
cover for each plant species, and for bare ground, litter, and rocks; percent cover for each quadrat
added up to 100% or greater, with a majority greater than 100% as plants overlapped each other.
After the five quadrats within a plot were surveyed, we surveyed the entire strip with a single
pass walkthrough to record the presence of additional species that were not captured in the
quadrats. Plants were surveyed in later summer (July-September) every year.
        To visualize the prairie strip plant community changes across years and across
treatments, we created an NMDS (nonmetric multidimensional scaling) plot with three
dimensions and using Bray-Curtis dissimilarity using data from the plant quadrat surveys. We
analyzed the prairie strip plant communities across years using a PERMANOVA (permutational
multivariate analysis of variance) with replicate as a blocking factor and also using Bray-Curtis
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dissimilarity. The NMDS plot and PERMANOVA analysis were created with the “vegan”
package in R (Oksanen et al. 2020).
Butterflies
We surveyed butterflies using the Pollard Walk method, which involved weekly transect counts
along one-way walking transects (Pollard 1977). Butterflies were identified to species visually,
either with use of binoculars or, rarely, after capture with a net. Surveys occurred along two
different transects: prairie strip transects and standard transects (Figure 3.1 a). Prairie strip
transects (105m) were located directly adjacent to the prairie strips in reduced input and
biologically based treatments for the whole length of the prairie strips (Figure 3.1 a). Observers
visually identified butterflies within and above the prairie strip (4.5m wide) in front of the
observer. These surveys were 8 minutes long at a slow and steady pace. Standard transects are
permanent walking transects replicated in all plots. Standard transects (152m) were located on a
walking transect throughout entire plots originally established for other purposes in conventional,
no till, reduced input, biologically based, and conservation land treatments (Figure 3.1 a).
Observers visually identified butterflies within 5m on both sides and above the transect in front
of the observer (Figure 3.1 b). These transects were surveyed at the same walking rate as the
Prairie Strip transects and were 12 minutes long. We conducted all surveys between 10:00am
and 4:00pm weekly from June 2019-September 2019, June 2020-September 2020, and May-
September 2021. Surveys were conducted in conditions without rain and above 15°C.
         We identified individuals to species whenever possible (23 species; Table 3.1). Some
groups of butterflies are difficult to identify on the wing to species, in which case the species
were grouped (7 morphogroups). Spring Azures, Summer Azures, and Eastern-Tailed Blues were
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categorized as Blue sp. Fritillary sp. are fritillaries that could not be identified on the wing.
Skipper sp. can be any skipper species found in this range except Checkered Skippers, Silver
Spotted Skippers, Peck’s Skippers, and Common Sootywings. Cabbage Whites were identified
when possible, and undetermined Cabbage Whites were called Sulpur sp. Sulphur sp. were
mainly Orange Sulphurs, Clouded Sulphurs, and Cabbage Whites that are nearly impossible to
identify on the wing (we almost never observed Cloudless Sulphurs or Pink-Edged Sulphurs).
Swallowtail sp. are swallowtails that could not be identified on the wing, as they tend to fly over
quickly (including Black Swallowtails, Eastern Tiger Swallowtails, and Spicebush Swallowtails).
Lady sp. includes American Ladies and Painted Ladies. Some individuals could not be identified
to any group, for example they flew over quickly before being identified. It is likely that these
individuals were of a species that had already been identified. These individuals are referred to as
“unknown butterflies” in Table 3.1. These individuals were included for analyses of abundance,
but were excluded from analyses of richness.
        Butterfly abundance values were summed over each plot for each year. To standardize
prairie strip transect surveys and standard transect surveys for comparison, we created an index
of butterflies per minute by dividing abundance values per plot per year by eight (minutes) for
prairie strip transect surveys and 12 (minutes) for standard transect surveys. We constructed a
generalized linear mixed effects model to measure the differences in butterfly abundance among
treatments and year. We used a negative binomial distribution with butterfly abundance as the
response variable, crop management treatment and year as fixed effects, surveys per plot per year
as an offset, and replicate as the random effect.
        To measure butterfly richness across treatments, we constructed a similar model as for
butterfly abundance, but we used rarefied species richness as the response variable. We rarefied
                                                   122


butterfly richness by abundance, treating the species surveyed across a plot each year as a sample
using the “iNEXT” package in R (Chao et al. 2014, Hsieh et al. 2016). Models were constructed
using the R package “lme4” (Bates et al. 2015). Post-hoc analyses included an ANOVA (R
package “car”; Fox & Weisbery 2019) followed by a Tukey test (R package “multcomp”;
Hothorn et al. 2008). To visualize butterfly abundance and richness over the year, we plotted
butterfly abundance index and butterfly richness using raw values of richness as abundance was
too low to rarefy by plot per month.
Results
Plants
All plant species identified in the prairie strips across all years are listed in Table 3.2. All seeded
species were present in both treatments by the second year (2020) with the exception of
Tradescantia ohiensis and potentially Solidago juncea and Solidago nemoralis as we could not
differentiate among Solidago species in surveys (Table 3.2). We found distinct communities
among plants in prairie strips across years (R2=0.55, p=<0.01) and across treatments (R2=0.073,
p=<0.01), which are visualized in Figure 3.2. There was also a significant interaction among year
and treatment (R2=0.071, p=<0.01), with reduced input and biologically based treatments
becoming more similar to each other over time.
        Annual ragweed (Ambrosia artemisiifolia) is historically the dominant weed in the
biologically based treatment, common throughout entire plots, as that treatment is not treated
with herbicides; annual ragweed is uncommon in the reduced input treatment as it is treated with
herbicide. Annual ragweed was the dominant plant in the biologically based treatment in 2019,
with more than double the coverage of the next most common species—Anthemis cotula.
                                                   123


Ryegrass (Lolium sp.) and common oat (Avena sativa) that were planted with the seed mix in
both treatments for weed suppression and thus dominated the reduced input in the first year. By
the second year, the three dominant species in the reduced input and biologically based
treatments were the same: Black-eyed Susan (Rudbeckia hirta), red clover (Trifolium pratense),
and Lanceleaf Coreopsis (Coreopsis lanceolata). Annual ragweed was the fourth most abundant
in biologically based and goldenrod (Solidago sp.) was the fourth most abundant in reduced
input. By 2021, ten out of twelve most abundant species in reduced input and biologically based
were the same with five of them being sown species. The proportion of forbs to grasses in
percent cover was 1.6 in 2019, 6.5 in 2020, and 4.1 in 2021.
Butterflies
We observed 6,835 butterflies during prairie strip transect surveys (12 plots) across 24 different
species/morphogroups, and we observed 7,145 butterflies during standard transect surveys (30
plots including the 12 that contain prairie strips) across 28 different species/morphogroups.
Sulphurs, Silver Spotted Skippers, and Monarchs were the most common species identified
across all treatments every year (Table 3.1). Several species were identified in treatments with
prairie strips that were not found in conventional or no till treatments, including Coppers
(American and Bronze), Checkered Skippers, Checkered Whites, Common Sootywings, and
Giant Swallowtails. Mourning Cloaks were only observed in the conservation land treatment.
During standard transect surveys across the entire plots, treatments with prairie strips increased
the average abundance of Monarchs by 25-222% compared to conventional and no till
treatments.
                                                  124


        Butterfly richness in the prairie strips did not differ between treatments or years and the
three dominant butterfly species in all prairie strips were the same over the three years: Sulphur
sp., Silver Spotted Skippers, and Monarchs. However, there was a turnover in the next most
abundant species where (in descending order of abundance) Lady sp., Red Admirals, Eastern
Tailed Blues, and Skipper sp. were the most common in 2019, Skipper sp., Eastern Tailed Blues,
Pearl Crescents, and Tiger Swallowtails were the most common in 2020, and Eastern Tailed
Blues, Pearl Crescents, Black Swallowtails, and Tiger Swallowtails were the most common in
2021. Butterfly abundance in the prairie strips also differed among years, with the highest
butterfly abundance in 2020.
        Butterfly abundance increased as land use intensity of crop management treatments
decreased (Figure 3.3); conventional and no till treatments had the lowest abundance (a), reduced
input (b), biologically based (bc), and conservation land (c) had an intermediate level of
abundance, and strip surveys had the highest abundance (d) (χ2=325.7, df=6, p<0.01). Butterfly
abundance in 2019 and 2020 was higher than butterfly abundance than 2021 (χ2=37.1, df=2,
p<0.01). Butterfly richness also increased as land use intensity decreased, with conventional
having the lowest richness (a), followed by no till (ab), reduced input (ac), biologically based
(bc), prairie strip in the biologically based treatment (bc), conservation land (c), and prairie strips
in the reduced input prairie strip (c) (χ2=31.5, df=6, p<0.01; Figure 3.3). Butterfly richness did
not vary across years (χ2=4.8, df=2, p=0.09).
        Across the growing season, butterfly abundance and richness fluctuated for each crop-
year (Figure 3.4). In 2019, wheat was harvested in July, after which crop fields were mostly litter
(conventional and no till) or had a red clover cover crop planted in August (reduced input and
biologically based). Both richness and abundance of butterflies increased after harvest for all
                                                   125


treatments, but treatments with prairie strips, which also have cover crops, increased further in
abundance later in the season alongside the conservation land treatment. In 2020, butterfly
richness and abundance in the prairie strips peaked in August across all years. This time aligns
with the bloom period for the most abundant plants in the prairie strips in 2020, including Lance-
leaf Coreopsis and Black-eyed Susan. From August to September 2020, there was a steep decline
across all treatments, except conservation land, which is likely a result of obscured visibility as
maize grew overhead. The pattern of butterfly richness and abundance across all treatments was
relatively consistent across the growing season in 2021.
Discussion
A critical question for butterfly conservation is: how close does a restored patch within a field
come to the conservation potential of a whole field of grassland? We asked this question for
restored prairie strips in row crop agricultural fields and their effects on the biodiversity of
butterflies. Across the entire 1 ha plots, of which prairie strips constituted 5% of the area,
butterfly abundance in the biologically based treatment was 72% of that of conservation land
(although not statistically different), and butterfly abundance in the reduced input treatment was
52% of that of conservation land. This recovers much of the biodiversity that is lost in
conventional agriculture treatments (abundance 20% of the that in conservation land) and no till
treatments (abundance 17% of that in conservation land). Butterfly richness did not differ among
conventional, no till, and reduced input at the plot scale (Figure 3.3). However, reduced input
and biologically based treatments, which both contain prairie strips, attracted several butterfly
species that were not found in any other treatment. Although prairie strips did not fully recover
biodiversity to levels found in conservation land, reduced input and biologically based treatments
                                                  126


shifted strongly toward that level, harboring unique species and a greater abundance of
butterflies than row crop treatments without prairie strips.
        Within the prairie strips, the richness of butterflies was the same as the conservation land
treatments and the entire plots of the reduced input and biologically based treatments, but there
was a consistently higher abundance of butterflies in the prairie strips than in the conservation
land treatment and all other treatments. This was the result of the intentional sowing of species
that flower throughout the growing season in the prairie strips, whereas the conservation land
treatment had a peak in bloom in later summer/early fall (Isaacs et al. 2009). Prairie strips can be
managed to support particular species or ecosystem services through seed mix selection and
other management practices.
        The plant community of the prairie strips between the reduced input and biologically
based treatments became more similar to each other over time. During the year that strips were
seeded, the plant community in prairie strips was largely a result of the agricultural weeds, and
these differed among treatments. Over the next two years, sown species emerged in both
treatments and the plant communities grew more similar in species composition and plant cover.
We expect the prairie strip plant communities to continue to shift over time toward a greater
abundance of native species (Carter & Blair 2012, Bach & Kleiman 2021).
        Butterfly abundance in prairie strips differed among years, with the highest butterfly
abundance in 2020 when the proportion of forbs to grasses was three times higher than in 2019
and 59% higher than in 2021. Butterfly species composition in the prairie strips shifted over the
three years as well, and became more similar over time. These changes in butterfly species
composition and abundance were the result of local factors (prairie strip floral abundance, crop
type within plots, litter cover) and landscape factors among years (crop type in the surrounding
                                                  127


landscape) (Davis et al. 2007, Wepprich et al. 2019). As prairie strips are expected to increase in
native plant species over time, in turn, butterfly communities in prairie strips are expected to
concomitantly become more diverse over time (Davis et al. 2007, Griffin et al. 2017).
        Butterfly abundance and richness was likely the result of both prairie strips and other
crop management practices across all treatments. Treatments without herbicides and pesticides
(biologically based, conservation land, and prairie strips) had the greatest butterfly richness and
abundance, likely caused by the increase in floral resources throughout each plot (Rundlöf et al.
2008). The higher levels of butterfly abundance within the prairie strips suggests that the
increase in butterfly abundance was not solely due to the other management practices, but also a
result of the prairie strips themselves. However, the higher abundance of butterflies in the
biologically based treatment than in the reduced input treatment suggests that prairie strips
combined with other conservation management strategies can increase butterfly abundance on
row crop farms.
        Monarchs are of high conservation interest internationally due to their unique life cycle
that includes a migration from Mexico to the United States and Canada. Monarchs are a leading
example of the cultural value of nature to humans; in Mexico, Monarchs hold significance to
indigenous cultures and serve as sources of ecotourism at their overwintering sites, and in the
United States, they are the subject of school activities and inspiration for household pollinator
gardens. Cultural value is a principal motivator for conservation (Díaz 2018, Doak et al. 2015)
and can lead to conservation at the community level (Preston et al. 2021, Caro 2010). The ability
of prairie strips to increase the abundance of Monarchs on farms may be of high value to policy
or to farmers considering implementing prairie strips. Treatments with prairie strips increased the
average abundance of Monarch butterflies by up to two-fold compared to other row crop
                                                  128


treatments. This aligns with another study which found a higher abundance of Monarchs and
other pollinators in farms with prairie strips than without strips (Murray 2021). Moreover,
although there is concern that restoring habitat within croplands sprayed with insecticides can
make prairie strips ecological traps for species, a recent study showed that pesticide levels in
milkweed within prairie strips are not at harmful levels to Monarchs (Hall et al. 2022).
         While prairie strips on individual farms can boost butterfly populations, landscape-scale
restoration is crucial for the long-term persistence of butterfly biodiversity. Networks of
connected restored grassland enhance butterfly biodiversity more than isolated fragments
(Shephard & Debinski 2005). Prairie strips are a conservation practice that can be scaled up and
connected across the US Midwest, with potential to increase species richness even on farms
embedded in complex landscapes (Kordbacheh et al. 2020). Farmland that is consistently
underperforming is of particular interest for restoration to minimize the waste of agricultural
inputs (e.g., fertilizer, pesticides, etc.) and the displacement of farmland (Basso 2021). Large,
connected strips and patches of prairie on marginal land across agricultural landscapes would
benefit butterflies and other species and ecosystem functions with minimal effect on yield
(Schulte et al. 2017, Kemmerling et al. in review).
         Because of agriculture’s pervasive negative effect on biodiversity, it is imperative that
strategies are implemented to mitigate the further loss of butterfly biodiversity. Prairie strips are
one potential management strategy for supporting butterfly biodiversity in addition to providing
benefits for other species and ecosystem services. We suggest future studies examine the ability
of prairie strips to support breeding populations of butterflies. And, important for the adoption of
this strategy, future studies should assess the role that the presence of butterflies, or of specific
species such as Monarchs, serves as motivation for implementing prairie strips on farms. Lastly,
                                                    129


future studies should address how much habitat across agricultural landscapes is needed to
conserve biodiversity at the level of a grassland while still meeting human resource needs.
Acknowledgements
Thank you dearly to the Haddad Lab for surveying butterflies every week and making surveys so
enjoyable. Thank you to members of the KBS LTER Joe Simmons, Brook Wilke, and Ryan
Anthony for maintaining field sites and keeping detailed field logs, Stacey VanderWulp for
project coordination, and Hsun-yi Hsieh and Sven Bohm for data management. Financial support
for this research was provided by the Great Lakes Bioenergy Research Center, U.S. Department
of Energy, Office of Science, Office of Biological and Environmental Research (Award DE-
SC0018409), by the National Science Foundation Long-term Ecological Research Program
(DEB 1832042) at the Kellogg Biological Station, by Michigan State University AgBioResearch.
                                                130


APPENDIX
   131


Table 3.1. Pollard index (sum of the average weekly abundances over the year) of butterflies
across crop management treatments. Unknown butterflies were butterflies that could not be
identified in the field, for example if they flew over too quickly. These individuals are most
likely species already included on this list.
                                                         Conventional                                       Successional   Strip - Red. Input   Strip - Bio. Based
                                                                        No Till   Red. input   Bio. based
           2019
Baltimore Checkerspot           Euphydryas phaeton             0           0           0            0             1                  0                    1
Black Swallowtail               Papilio polyxenes              1           4           9            6             3                  5                    6
Blue sp                                                        0           0           0            0             1                  0                    0
Checkered White                 Pontia protodice               0           0           0            0             0                  0                    2
Common Buckeye                  Junonia coenia                 1           1           3            3             3                  1                    0
Common Sootywing                Pholisora catullus             0           0           0            0             2                  2                    3
Eastern Tailed Blue             Cupido comyntas                1           1           1            7        14                      7                    5
Fritillary sp                                                  0           0           0            2             0                  0                    0
Giant Swallowtail               Papilio cresphontes            0           0           0            1             0                  0                    1
Gray Hairstreak                 Strymon melinus                2           1           1            4             6                  0                    0
Meadow Fritillary               Boloria bellona                0           1           0            0             0                  0                    0
Monarch                         Danaus plexippus          40            71         70          113          170              56                   81
Mourning Cloak                  Nymphalis antiopa              0           0           0            0             1                  0                    0
Pearl Crescent                  Phyciodes tharos               1           2           1            3        34                      5                    5
                                                 132


Table 3.1 (cont’d)
Peck's Skipper            Polites peckius       1   1   0   1   0   4   5
Red Admiral               Vanessa atalanta     18  20  27  15  11  18  11
Red Spotted Purple        Limenitis arthemis    0   0   0   0   0   0   2
Silver Spotted Skipper    Epargyreus clarus    25  23  65  51 174 137  83
Skipper sp                                      1   0   0   4  11   9   3
Spicebush Swallowtail     Papilio troilus       2   1   2   8   0   3   1
Sulphur sp                                    149 210 479 806 580 478 564
Tiger Swallowtail         Papilio glaucus       3   2   3   4   1   5   5
Lady sp                                        15   6  17  38  31  67  45
Variegated Fritillary     Euptoieta claudia     0   1   0   0   0   0   0
unknown butterflies                             0   1   0   0   1   2   2
            2020
Baltimore Checkerspot     Euphydryas phaeton    1   0   0   1   2   0   1
Black Swallowtail         Papilio polyxenes     2   2   1   3  10  12   4
Blue sp                                         1   0   0   2   2   5  16
Checkered Skipper         Prygus commuis        0   0   0   1   0   0  15
Common Buckeye            Junonia coenia        1   1   2   3  11   5   6
Common Sootywing          Pholisora catullus    0   0   0   0   0   3   3
Eastern Tailed Blue       Cupido comyntas       1   0   2   5  13  14  27
Great Spangled Fritillary Speyeria cybele       0   0   0   0   0   1   0
Monarch                   Danaus plexippus     11  12  12  26  47  60  56
Mourning Cloak            Nymphalis antiopa     0   0   0   0   1   0   0
                                          133


Table 3.1 (cont’d)
Pearl Crescent         Phyciodes tharos     2  2   2   5  15  17  13
Peck's Skipper         Polites peckius      0  2   1   3   4  13  12
Red Admiral            Vanessa atalanta     0  0   1   0   2   5   0
Red Spotted Purple     Limenitis arthemis   0  0   1   0   2   1   0
Silver Spotted Skipper Epargyreus clarus   15 11 104 134 216 653 420
Skipper sp                                  3  1   9   3  20  24  22
Spicebush Swallowtail  Papilio troilus      0  0   3   1   3   2   0
Sulphur sp                                 57 47 191 340 523 104 989
                                                               7
Swallowtail sp                              0  1   1   0   0   1   0
Tiger Swallowtail      Papilio glaucus      0  4   2   8   4  18  12
Lady sp                                     0  1   0   1   2   8   7
Variegated Fritillary  Euptoieta claudia    0  0   0   0   1   0   0
Viceroy                Limenitis archippus  0  0   0   0   3   0   1
unknown butterflies                         3  1   0   0   2   5   1
            2021
American Copper        Lycaena phlaeas      0  0   0   0   0   0   1
Baltimore Checkerspot  Euphydryas phaeton   0  0   0   0   2   1   4
Black Swallowtail      Papilio polyxenes    1  2   4   7   5   9  10
Blue sp                                     0  0   0   0   4   4   1
Bronze Copper          Lycaena hyllus       0  0   0   1   0   0   0
Checkered Skipper      Prygus commuis       0  0   4   0   0   0   0
Common Sootywing       Pholisora catullus   0  0   3   0   0   2   1
                                       134


Table 3.1 (cont’d)
Eastern Tailed Blue       Cupido comyntas      1  3   7   5  27  21  22
Fritillary sp                                  0  0   0   0   1   0   0
Gray Hairstreak           Strymon melinus      1  0   0   0   1   0   0
Great Spangled Fritillary Speyeria cybele      0  0   1   0   2   0   0
Monarch                   Danaus plexippus    13 16  42  67 123 112 104
Mourning Cloak            Nymphalis antiopa    0  0   0   0   2   0   0
Pearl Crescent            Phyciodes tharos     1  0   2   4  16  17  22
Peck's Skipper            Polites peckius      0  0   0   0   1   1   0
Red Admiral               Vanessa atalanta     0  0   0   0   0   1   0
Red Spotted Purple        Limenitis arthemis   0  1   0   0   1   1   0
Silver Spotted Skipper    Epargyreus clarus   31 23 123  59 214 275 143
Skipper sp                                     5  3   2   3  10   5   7
Spicebush Swallowtail     Papilio troilus      0  0   1   3   2   3   6
Sulphur sp                                    51 55 208 203 387 459 440
Swallowtail sp                                 2  1   0   8   1   1   2
Tiger Swallowtail         Papilio glaucus      2  4   1   2   4   8   8
Lady sp                                        0  0   1   1   1   1   0
Viceroy                   Limenitis archippus  0  2   1   3   4  10   6
unknown butterflies                            3  4   5   5   7   1   3
                                          135


Table 3.2. Presence/absence of plant species found in the prairie strips of each treatment in each
year. A black box indicates that the species was present. * denotes a species seeded into the
prairie strips. ** denotes that a species was seeded in that morphogroup and therefore could be a
seeded species, but we could not decipher if it was the seeded species.
                                                               2019                         2020                         2021
                                                         Reduced input   Bio. based   Reduced input   Bio. based   Reduced input   Bio. based
 Family              Species
 Amaranthaceae       Amaranthus sp.                            0              0             1              0             1              0
                     Chenopodium album                         1              1             1              1             1              1
 Apiaceae            Daucus carota                             0              1             1              1             1              1
                     Zizia aurea*                              1              1             1              1             1              1
 Apocynaceae         Apocynum cannabinum                       1              1             1              1             1              1
                     Asclepias syriaca*                        1              1             1              1             1              1
                     Asclepias tuberosa*                       1              1             1              1             1              1
 Asteraceae          Achillea millefolium*                     1              1             1              1             1              1
                     Ambrosia artemisiifolia                   1              1             1              1             1              1
                     Anthemis cotula                           1              1             1              1             1              1
                     Artemisia absinthium                      0              0             1              0             0              0
                     Aster dumosum                             0              0             0              0             1              0
                     Aster novae-angliae*                      1              1             1              1             1              1
                     Aster sp.                                 0              0             1              1             1              1
                     Bidens frondosa                           1              0             0              0             0              0
                                               136


Table 3.2 (cont’d)
                   Brickellia eupatorioides      0 0 0 1 0 0
                   Centaurea stoebe              1 1 0 1 1 1
                   Cirsium altissimum            0 0 0 0 1 0
                   Cirsium arvense               1 1 1 1 1 1
                   Cirsium sp.                   1 0 1 0 1 1
                   Cirsium vulgare               0 0 1 1 1 1
                   Conyza canadensis             1 1 1 1 1 1
                   Coreopsis lanceolata*         1 1 1 1 1 1
                   Coreopsis tripteris*          0 0 1 1 1 1
                   Crepis capillaris             0 1 0 0 1 0
                   Echinacea purpurea*           1 1 1 1 1 1
                   Erigeron sp.                  1 1 1 1 1 1
                   Euthamia graminifolia         1 1 1 1 1 1
                   Hieracium sp.                 1 0 1 1 0 1
                   Hypochaeris radicata          0 1 0 0 0 0
                   Pseudognaphalium obtusifolium 0 0 1 0 0 0
                   Ratibida pinnata*             1 1 1 1 1 1
                   Rudbeckia hirta*              1 1 1 1 1 1
                   Senecio jacobaea              0 0 0 0 1 0
                   Silphium perfoliatum*         1 1 1 1 1 1
                   Solidago sp.**                1 1 1 1 1 1
                   Sonchus asper                 0 1 1 0 0 1
                                            137


Table 3.2 (cont’d)
                   Symphyotrichum pilosum       0 0 1 1 1 1
                   Symphyotrichum sagittifolium 1 1 1 1 1 1
                   Taraxacum officinale         1 1 1 1 1 1
Boraginaceae       Hackelia virginiana          1 0 0 0 0 0
Brassicaceae       Arabidopsis sp.              1 1 0 0 0 0
                   Barbarea vulgaris            1 1 1 1 1 1
                   Capsella bursa-pastoris      1 1 0 0 1 1
                   Lepidium sp.                 1 1 0 0 0 0
Caryophyllaceae    Dianthus armeria             0 0 1 0 0 0
                   Silene latifolia             0 0 0 1 0 0
                   Stellaria sp.                1 1 0 1 0 0
Fabaceae           Baptisia alba                0 0 1 0 0 0
                   Desmodium canadense*         1 1 1 1 1 1
                   Desmodium sp.                0 0 0 0 1 1
                   Glycine max                  1 1 0 0 1 0
                   Lespedeza capitata*          1 1 1 1 1 1
                   Medicago lupulina            0 0 0 0 1 0
                   Securigera varia             0 0 0 1 1 0
                   Trifolium pratense           1 1 1 1 1 1
                   Trifolium repens             1 1 1 1 1 1
                   Vicia sp.                    0 0 0 0 1 1
Hypericaceae       Hypericum sp.                0 1 1 1 1 1
                                            138


Table 3.2 (cont’d)
Juncaceae          Juncus sp.                  0 0 0 0 1 1
Lamiaceae          Monarda citriodora          0 0 0 1 0 0
                   Monarda fistulosa*          1 0 1 1 1 1
                   Monarda punctata            1 0 0 0 0 0
Malvaceae          Abutilon theophrasti        1 1 1 1 1 1
Moraceae           Morus alba                  1 0 0 0 0 0
Onagraceae         Circaea lutetiana           1 0 0 0 0 0
Oxalidaceae        Oxalis stricta              1 1 1 1 1 1
Phytolaccaceae     Phytolacca americana        0 1 0 0 0 0
Plantaginaceae     Penstemon digitalis         0 0 0 0 0 1
                   Plantago lanceolata         1 1 1 1 1 1
                   Plantago major              1 1 1 1 1 1
                   Veronica sp.                1 1 0 0 0 0
Poaceae            Andropogon gerardii*        1 1 1 1 1 1
                   Avena sativa                1 1 0 0 0 0
                   Bouteloua curtipendula*     0 1 1 1 1 1
                   Bromus sp.                  0 0 0 0 0 1
                   Dactylis glomerata          1 0 1 1 1 1
                   Digitaria sp.               0 1 1 1 1 1
                   Echinochloa sp.             0 0 0 0 1 0
                   Elymus canadensis*          1 1 1 1 1 1
                   Lolium sp.                  1 1 1 1 1 1
                                           139


Table 3.2 (cont’d)
                   Phleum pratense            0 1 0 1 1 1
                   Poa sp.                    1 1 1 0 1 0
                   Schizachyrium scoparium*   1 0 1 1 1 1
                   Setaria sp.                1 1 1 1 0 1
                   Sorghastrum nutans         0 0 0 0 1 1
                   Triticum aestivum          1 1 1 1 0 1
Polygonaceae       Fallopia sp.               0 0 0 0 0 1
                   Persicaria sp.             1 1 1 1 0 0
                   Polygonum aviculare        1 1 1 0 0 0
                   Polygonum convolvulus      1 1 0 0 0 0
                   Rumex crispus              1 1 1 1 1 1
                   Rumex obtusifolius         1 1 1 1 1 1
Rosaceae           Malus sp.                  1 0 0 0 1 0
                   Potentilla norvegica       1 1 0 1 1 1
                   Prunus serotina            1 0 1 0 0 0
                   Prunus sp.                 1 0 0 0 0 0
                   Pyrus sp.                  0 0 1 0 0 0
                   Rosa sp.                   0 0 0 0 1 0
                   Rubus sp.                  0 0 0 0 1 0
Sapindaceae        Acer rubrum                1 0 0 1 0 0
Scrophulariaceae Verbascum blattaria          0 0 0 0 1 0
                   Verbascum thapsus          0 0 0 0 0 1
                                          140


Table 3.2 (cont’d)
Solanaceae         Solanum ptychanthum         1 0 0 0 0 0
Ulmaceae           Ulmus sp.                   0 1 0 0 0 0
unknown            Aster-like seedlings        1 1 0 0 0 0
                   unknown forb 1              0 0 0 0 0 1
                   unknown forb 2              0 0 0 1 0 0
                   unknown forb 3              0 0 0 1 0 0
                   unknown forb 4              0 0 1 0 0 0
                   unknown forb 5              0 0 0 0 0 1
                   unknown forb 6              0 0 0 0 1 0
                   unknown grasses             0 1 1 1 1 1
                   unknown seedlings           0 1 0 1 1 1
Verbenaceae        Verbena hastata             1 1 0 1 0 0
                   Verbena stricta*            1 0 1 1 1 1
                   Verbena urticifolia         1 1 0 0 0 0
Vitaceae           Parthenocissus quinquefolia 1 1 0 0 0 0
                   Vitis riparia               1 0 1 0 0 0
                                           141


Figure 3.1. a. A diagram of a plot and survey locations within the plot. The transect and quadrat
locations are consistent in every plot. Conventional, no till, and conservation land only have the
standard transect. b. Annabelle McCarthy surveys for butterflies along a prairie strip transect.
Photo taken by Jamie Smith.
a.                                    b.
        N
             Prairie strip transect
             Standard transect
             Prairie strip
             Plant survey quadrat
105m
       87m
                                                142


Figure 3.2. NMDS (nonmetric multidimensional scaling) plot of prairie strip plant communities
across years and treatments. Each dot represents one plot per year. The ellipses represent a 95%
confidence interval around the centroid of each year grouping.
                                               143


Figure 3.3. Butterfly abundance and richness indices across years for all surveys. Letters indicate
significant statistical differences.
                                                         d
                                                                   d
                                       bc       c
                                 b
              a         a
                                   ac           c        c
                                      bc                          bc
                      ab
             a
                                              144


Figure 3.4. Butterfly abundance and richness across treatments each month.
                                              145


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       146


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