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2018 Insects in Variable Plant Patches Andrew Charles Merwin
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COLLEGE OF ARTS AND SCIENCES
INSECTS IN VARIABLE PLANT PATCHES
By
ANDREW CHARLES MERWIN
A Dissertation submitted to the Department of Biological Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy
2018
Andrew Merwin defended this dissertation on August 27, 2018. The members of the supervisory committee were:
Nora Underwood Professor Co-Directing Dissertation
Brian Inouye Professor Co-Directing Dissertation
Nick Cogan University Representative
Alice Winn Committee Member
Scott Burgess Committee Member
Joseph Travis Committee Member
The Graduate School has verified and approved the above-named committee members, and certifies that the dissertation has been approved in accordance with university requirements.
ii
Dedicated to Anita Merwin and Art Fabian, whose curiosity for the natural world was contagious.
iii ACKNOWLEDGMENTS
A staggering number of kind and brilliant people have helped me with my dissertation. I owe them a mountain of gratitude. Foremost, I would like to thank my advisers, Nora Underwood and Brian Inouye, for being incomparably generous with their time and for teaching me how to think more critically and communicate more clearly. They and the other members of my committee—Scott Burgess, Nick Cogan, Joe Travis, and Alice Winn—have all been exceptionally supportive of me, my research, and my passion for teaching. In particular, I thank Alice Winn for the opportunity to teach as instructor of record and for her insightful comments on both my teaching and research. I also thank current and former members of the Underwood and Inouye labs—Amanda Buchanan, Josh Grinath, Tania Kim, David McNutt, Jessie Mutz, Jane Ogilvie, Brian Spiesman, and Molly Wiebush—for an engaging and welcoming environment, long bike rides, and contra dancing. I was awfully lucky to be around a large group of creative and interesting peer colleagues at FSU. Thank you, Zach Boudreau, Jason Cassara, Katherine Easterling, Kate Hill, Eve Humphrey, Christina Kwapich, Liz Lange, Chris Malinowski, Abigail Pastore, Cheston Peterson, and Natalie Ramirez-Bullon for being great colleagues and friends. Special thanks to Jo Imhoff and Will Ryan for your energy and creativity, especially with regard to the homemade music fest. Has there ever in history been a biology department with as much musical talent? Thanks to the Oncce-ler, Superb Itch, Galactadactyl (formerly known as Survival Knife), Megalops, Marine Snow, and Meredith Cenzer for making beautiful sounds. In addition, I am deeply indebted to the many talented undergraduate and non-traditional student colleagues who have assisted with all stages of my research and who have served as an indispensable source of motivation. They include Alex Basili, Eric Bender, Shelby Biscoe, Elizabeth Elba, Zofia Haack, Chris Hahn, Jacob Hart, Andrew Ibarra, Ryan Kilbride, Zach Lankist, Melanie Larson, Jessica Latimer, Andrew Olsson, Abigail Plyant, Aaron Yilmaz, and other members of the el-fan club. Special thanks to Theresa Jepsen for greenhouse space, plant care, and tremendous tolerance; Roy Weidner for help constructing insect cages large and small; Sandy Heath for help constructing flight mills; Joe Funderburk for using his resources to provide field space for beetle experiments; and Christy and Maxx Manchester being great and giving me space in California to not think about my dissertation. Finally, I owe the completion (and formatting) of this dissertation to Meredith Steck, who enriches my life, deepens my understanding of kindness and sincerity, and who has the best Dumbledore voice of anyone ever. This work was funded in part by Godfrey and Trott Scholarships through the Departmnet of Biological Science, a Dissertation Research Gant from the Graduate School, and a Planning Grant from Office of Research. Caw.
iv TABLE OF CONTENTS
List of Tables ...... vi List of Figures ...... vii Abstract ...... viii
1. INTRODUCTION ...... 1
2. INCREASED CONSUMER DENSITY REDUCES THE STRENGTH OF NEIGHBORHOOD EFFECTS IN A MODEL SYSTEM ...... 5
3. HOMIER HOSTPLANT PATCHES: NATAL HOSTPLANT EXPERIENCE INFLUENCES THE RELATIONSHIP BETWEEN INSECT AND HOSTPLANT DENSITIES ...... 26
4. THE INFLUENCE OF HABITAT PATCH AREA AND PERIMETER-TO-AREA RATIO ON THE MOVEMENT AND DENSITIES OF INSECTS: PREDICTIONS AND OBSERVATIONS ...... 46
5. CONCLUSION ...... 66
References ...... 69
Biographical Sketch ...... 80
v LIST OF TABLES
2.1 List of neighborhood effects and their definitions as used in this manuscript...... 19
2.2 ANOVA table for GLMMs of damage per gram for black-eyed peas and mung beans...... 20
3.1 GLMM results for offspring on and damage to collards among patches ...... 42
3.2 GLMM results for offspring on and damage to plants within mixed patches...... 43
4.1 GLMM estimates of fixed effects on beetle densities within patches...... 61
4.2 Estimates of patch traits on recapture rates of C. sayi (n = 31)...... 61
vi LIST OF FIGURES
2.1 A conceptual model for damage to a focal resource type as a function of consumer density...... 21
2.2 Box (“landscape”) configuration ...... 22
2.3 Estimates of GLMM parameters for per gram damage to black-eyed peas (above diagonal) and mung beans (below diagonal) in treatments of low, medium, and high beetle densities...... 23
2.4 Per gram damage to focal resource (A) mung beans and (B) black-eyed peas as a function of neighbor and focal resource densities across three densities of bean beetles...... 24
2.5 Damage to 1 g of black-eyed peas, the less-preferred resource, with and without 4 g of neighboring mung beans, the preferred resource (median ± IQR) ...... 25
3.1 Example arrangement of patches within a field-cage. Position of patch types was randomized within each cage for the experiments and cages had a height of 0.75 m (not shown) ...... 44
3.2 Moth offspring per plant (caterpillars and pupae; Mean ± 1.96 SE) and estimated damage per plant (cm2 leaf tissue removed) on collards among patch types and between plant types within mixed patches...... 44
3.3 Results from small-cage experiments (mean ± 1.96 SE): (A) age at pupation (P < 0.001), (B) weight at pupation (P = 0.12)...... 45
4.1 An example of a randomly generated landscape showing all 15 area x perimeter-to-area ratio combinations for patches...... 62
4.2 Layout of soybean patches for large field experiment (35 x 95 m) ...... 62
4.3 Simulated patch density over time in patches that vary in their area and perimeter-to-area ratios ...... 63
4.4 Densities of C. sayi in relation to patch area (A) and patch height (B) and densities of T. carolina in relation to patch area (C) and patch height (D) from our large field experiment ...... 64
4.5 Untransformed recapture estimates of C. sayi (n = 31) from zero-inflated GLM...... 65
vii ABSTRACT
Animals move through landscapes where their resources are unevenly and often patchily distributed. When animals move and choose among their scattered resources in predictable ways, ecologists may be able to anticipate the spatial distribution of their populations and the relative strength of their trophic interactions (e.g. predation, or facilitation). Likewise, an understanding of animal movements can inspire the design and preservation of habitat for conservation or the promotion of ecosystem services. However, movement-based predictions of animal populations—and the human interventions these predictions inspire—are only as reliable as our understanding of the determinants of animal movement.
The research presented here addresses three basic aspects of animals’ environments and experiences that have the potential to influence our understanding of animal movement, population distributions, and ecological interactions: (1) animals’ conspecific density (2) prior experience with resources, and (3) the composition and geometry of habitat patches. These topics are well-studied, but rarely in the context of spatially heterogeneous landscapes, and many prior studies have confounded important explanatory variables.
For the first study, I performed a lab experiment using the bean beetle, Callosobruchus maculatus, and two of its host beans, Vigna unguiculata and V. radiata, to explore how consumer density influences resource choice and the relative distribution of damage among resources in a patch. My results demonstrated that the damage a focal resource type receives can depend on the frequency of neighboring resource types, and that this frequency dependence decreases with regional consumer density. These findings illustrate the importance of consumer density in mediating indirect effects among resources, and suggest that accounting for consumer density may improve our use of mixed-crop pest management strategies.
viii For the second study, I used a field experiment to test whether prior hostplant experience influences the distribution of offspring on and damage to hostplants among and within plant patches that varied in hostplant density and composition. Specifically, I reared diamondback moths, Plutella xylostella, on either collard or mustard plants (Brassica oleracea or B. juncea, respectively) and recorded the number of offspring on and damage to plants in three patch types within large field cages: two collards, four collards, and mixed patches of two collards and two mustards. I found that in cages with collard-reared moths, there were more offspring and damage per plant in four-collard patches than in two-collard patches, while mustard-reared moths did not respond to collard density. In contrast, we found no effect of natal hostplant experience on hostplant choice within mixed patches, and no influence of mustard plants on attacks on collards in mixed patches versus two-collard patches (i.e. there were no associational effects). These findings suggest that accounting for prior hostplant experience may improve our understanding of how some herbivores and their damage are distributed in patchy environments through time.
For my final dissertation study, I used a correlated random walk model to make predictions for animal density in patches that vary in area and perimeter-to-area ratio. I then tested predictions from this model by manipulating the area and perimeter-to-area ratios of plant patches and observing the densities of two predaceous beetles: a relative habitat specialist,
Calosoma sayi, and a relative habitat transient, Tetracha carolina carolina. Our model predicted that as habitat specialists spend more time in patches relative to non-habitat, patches with lower perimeter-to-area ratios should have higher animal density. However, for relatively transient species, defined as spending more time within non-habitat than in the habitat patches, our model predicted slightly higher densities in higher perimeter-to-area patches or no difference between patch types. Area per se, in contrast, did not influence mean density. Contrary to our model, we
ix found that in our field experiment patch area and perimeter-to-area ratio interacted to influence the movement and density of the relative habitat specialist, C. sayi. Their density increased significantly with patch size in high perimeter-to-area patches, but patch size had no influence on
C. sayi’s density within low edge-to-area patches. By contrast, densities of T. carolina were slightly higher in high perimeter-to-area ratio patches once the influence of plant height was accounted for, which was consistent with our model. These results underscore the importance of considering both patch area and perimeter-to-area ratio as well as species-specific movement behaviors for the management of habitat for ecosystem services.
Together these projects highlight exciting new areas of consideration for the study of plant-insect interactions in heterogeneous habitats, which could improve our ability to predict insect distributions and interactions in natural and managed populations.
x CHAPTER 1
INTRODUCTION
Animals move through landscapes where their resources are unevenly and often patchily distributed. For example, cabbage white butterflies search for hostplants in clumpy mixtures of host and non-hostplants (Jones 1977, Steck and Snell-Rood 2018). If animals, such as cabbage whites, move and choose among their scattered resources predictably, then ecologists may be able to anticipate the spatial distribution of their populations and the relative strength of their trophic interactions (e.g. predation, or facilitation) (Root and Kareiva 1984). Likewise, an understanding of animal movements can inspire the design and preservation of habitat for conservation or the promotion of ecosystem services (e.g., Haddad 1999, Schultz and Crone
2001). However, movement-based predictions of animal populations—and the human interventions they inspire—are only as reliable as our understanding of the determinants of animal movement. For this reason, an important challenge for ecologists is to identify the factors that are most informative for predicting the distributions of populations and ecological outcomes.
The research presented here addresses three aspects of animals’ environments and experiences that have the potential to influence animal movement, population distributions, and ecological interactions: (1) animals’ conspecific density (2) prior experience with resources, and
(3) the composition and geometry of habitat patches. These topics are, of course, well-studied, but rarely in the context of spatially heterogeneous landscapes or without confounding important explanatory variables.
My work focuses on insects and plants. Insects are endlessly fascinating creatures, worthy of research in their own right, and their sheer abundance and diversity render them indisputably important to ecosystems and human interests, including agriculture (Gullan and
1 Cranston 2010). Further, insects have a long history as study-subjects for work that links individual movements to population-level outcomes. And though ecologists and growers alike have long recognized that the arrangement and community of plants within patches can influence insect movement, densities, and subsequent feeding on plants (Root 1973, Andow 1991, Barbosa et al. 2009, Underwood et al. 2014), general mechanistic understandings of these processes have been lacking until relatively recently (Hambäck and Englund 2005, Hambäck et al. 2014,
Underwood et al. 2014). My dissertation contributes to our basic understanding of plant-insect interactions in heterogeneous landscapes and could help to inform sustainable pest management strategies in agriculture.
A common feature of the chapters in my dissertation is that they all consider insect movement within “patches” of plants. Animals experience hierarchical scales of patchiness— bounded by the physiological limits of their perception at the smallest scale and their lifetime home range at the largest scale (Kotliar and Wiens 1990). Thus, the term “patch” is ambiguous at best. Here, I consider “patches” to be areas of plants in which insects behave distinctly from the surrounding vegetation, at scales where the population dynamics of the patches are determined almost entirely by movement rates and not within patch vital rates.
For the first project, I took advantage of a model system, beans (Vigna radiata and V. unguiculata) and bean beetles, Callosobruchus maculatus, to explore how consumer density influences resource choice and the relative distribution of damage among resources. This work demonstrated that the damage a focal resource type receives depends on the frequency of neighboring resource types, and that this frequency dependence decreases with regional consumer density. These results suggest that regional insect densities need to be considered when
2 designing mixed-crop pest management strategies because so-called “neighborhood effects” may be as dynamic as the populations of consumers involved.
For my second project, I used a field experiment to test whether an herbivores’ prior hostplant experience influences the distribution of offspring on and damage to hostplants within and among patches that varied in hostplant density and composition. This work was done with diamondback moths (Plutella xylostella), collards (Brassica oleracea), and mustards (B. juncea).
My results demonstrate that natal hostplant experience can influence hostplant selection at the patch scale and can contribute to insect density-plant density relationships. This suggests that accounting for prior hostplant experience may improve our understanding of how some herbivores and their feeding damage are distributed in patchy environments through time.
Finally, in my last chapter, I investigated the influence of plant-patch geometry on the movement and density of two beneficial ground beetles (Calosoma sayi and Tetracha carolina carolina) which feed on herbivorous insects. In a large, fully factorial field experiment in which
I manipulated plant patch area and perimeter-to-area ratio, I demonstrated that these beetles can be influenced by an interaction between patch area and perimeter-to-area ratio and that this was the consequence of differential movement rates among patches. Using simulation models, I also determined that these patterns are not likely to be the consequence of simple rules that are commonly used to describe insect movement. This research highlights the importance of considering both patch area and area-to-perimeter ratio when designing habitats to optimize ecosystem services, and underscores the need for more research relating insect movement to patch use.
3 Together, these projects highlight exciting new areas of consideration for the study of plant-insect interactions in heterogeneous habitats. My research both complicates and improves our predictions of insect distributions and interactions in natural and managed populations.
4 CHAPTER 2
INCREASED CONSUMER DENSITY REDUCES THE STRENGTH OF NEIGHBORHOOD EFFECTS IN A MODEL SYSTEM
Note: The content and figures in this chapter were previously published as Merwin, A. C., Underwood, N. and Inouye, B. D. (2017), Increased consumer density reduces the strength of neighborhood effects in a model system. Ecology, 98: 2904-2913.
Introduction
An individual organism’s likelihood of interacting with consumers is often influenced by its neighbors. For example, a plant’s susceptibility to herbivory and disease or its probability of pollination may depend on the composition of the neighboring plant community if neighboring plants repel or attract herbivores, vectors, or pollinators (Power 1991, Barbosa et al. 2009,
Ogilvie and Thomson 2016, see Eigenbrode et al. 2016 for a mechanistic framework for insect herbivores in agriculture). Likewise, the predation risk experienced by prey may depend on other prey types in the same area that influence the numerical or functional responses of predators
(Holt 1977, Holt and Kotler 1987). Such consumer-mediated indirect effects among neighboring resources (hereafter referred to as neighborhood effects, Table 2.1) may contribute to long-term population-level processes such as competition and evolution (Lau and Strauss 2005, Agrawal et al. 2006, Rowland et al. 2007) and are exploited in applications such as mixed-crop pest management (Andow 1991) and the management of biodiversity in disease transmission hotspots
(Keesing et al. 2010). The strength and direction of neighborhood effects, however, have been notoriously difficult to predict (Andow 1991, Barbosa et al. 2009, Underwood et al. 2014).
One possible reason for the observed variation in neighborhood effects is that they may change as population dynamics or human interventions modify the abundances of both consumers and resources. Studies have begun to investigate how neighboring resource density and frequency
5 influence neighborhood effects (Kim and Underwood 2015, Hahn and Orrock 2016, Verschut et al. 2016). Other studies have examined the influence of consumer density on neighborhood effects (Smit et al. 2007, Graf et al. 2007, Fernandez-Conradi et al. 2017). However, these effects have yet to be examined experimentally in a context that separates the influence of consumer density as well as the densities and frequency of conspecific and heterospecific resource neighbors.
Consumer density could modify two types of neighborhood effects: focal resource density effects and associational effects. Focal resource density effects occur when individuals of a particular resource type of interest (i.e. the “focal resource”, which can be either a preferred or less preferred resource depending on the context) become more or less susceptible to consumers as their own density changes (Stiling 1987, Lehtonen and Jaatinen 2016, Table 2.1). In the plant- insect literature, these focal resource density effects are referred to as resource concentration and resource dilution effects, respectively (Root 1973, Otway et al. 2005, Table 2.1), and are thought to be the net result of increased movement into and residency times within denser plant patches, countered by the “dilution” of those herbivores across more plants (Andersson et al. 2013). One mechanism by which consumer density might modify the strength of dilution effects is conspecific aversion. If consumers avoid each other they may be more likely to leave crowded patches in favor of patches with fewer conspecifics or lower consumer-resource ratios (Fretwell and Lucas 1970, Kennedy and Gray 1993). If consumers distribute themselves so as to minimize the consumer-resource ratio across patches, this would reduce the magnitude of focal resource density effects. In contrast to the effects of conspecific aversion, Turchin (1989) suggested that aggregative consumer behavior should amplify resource concentration effects.
6 Likewise, consumer density could modify the strength of associational effects, which occur when neighboring resources of a different type than a focal resource (e.g. heterospecifics or different genotypes) increase or decrease a focal resource individual’s susceptibility to consumers. These effects are referred to more specifically as associational susceptibility or associational resistance, respectively (Tahvanainen and Root 1972, Letourneau 1995, reviewed in Barbosa et al. 2009 and Underwood et al. 2014, Table 2.1). A variety of mechanisms may contribute to associational effects, including differential consumer preference or palatability among neighboring resource types (e.g. Hay 1986, Hahn and Orrock 2016). Depending on how consumers respond to conspecific cues, increasing consumer density could increase or decrease the strength of associational resistance (Fig 2.1). If consumers do not respond to conspecific cues and simply feed on a focal resource a certain percentage of the time, as consumer density increases the absolute reduction of damage a preferred neighbor confers to the focal resource should increase linearly with consumer density (Fig. 2.1 compare dotted and dashed lines).
Conversely, increased consumer density could reduce the strength of associational resistance if the depletion of palatable resources forces consumers to accept less palatable resources (Graff et al. 2007, Smit et al. 2007), or if consumers avoid conspecific cues. For instance, at higher consumer densities, consumers might avoid preferred plants that are coupled with direct or indirect conspecific cues, such as oviposition pheromones (Nufio and Papaj 2001) or herbivore- induced volatile organic compounds (e.g. De Moraes 2001), and might instead feed or oviposit on the focal resource where fewer conspecific cues are present, thus reducing the strength of associational resistance (Fig. 2.1 compare dotted and solid lines).
Predicting the strength of neighborhood effects is not only complicated by the influence of consumer density, but also by neighbor density, neighbor frequency and total density. Three
7 recent investigations (Kim and Underwood 2015, Hahn and Orrock 2016, and Verschut et al.
2016) used response surface designs (Inouye 2001) to distinguish between density-dependent and frequency-dependent associational effects and found evidence for frequency-dependent but not density-dependent associational effects. However, research in other systems and across a wider range of resource densities is needed to determine this generality.
In this study we performed a lab experiment to investigate the role that consumer density plays in mediating the strength of neighborhood effects using the bean beetle, Callosobruchus maculatus, and two seed hosts: preferred mung beans (Vigna radiata) and less preferred black- eyed peas (V. unguiculata). Because of the tractability of this model system, we were able to use a high-resolution response surface design with a range of consumer and resource densities at relatively high replication. For each treatment, we measured the effects of beetle density and the composition of beans in a local neighborhood on damage to the beans. We asked: (1) Does beetle density reduce the strength of neighborhood effects?; and (2) at each beetle density, how does focal resource damage depend on focal resource density, neighboring resource density, and neighbor frequency? Knowing that bean beetles produce compounds that deter oviposition by female conspecifics (Credland and Wright 1990),we predicted that the less preferred resource would receive less damage as the preferred resource increased in density and/or frequency and that these effects would diminish with beetle density.
Methods
Study System
Callosobruchus maculatus is a cosmopolitan pest of dried legumes, and a model organism for lab-based ecological and evolutionary studies (e.g. Wasserman and Futuyma 1981,
8 Bellows 1982, Miller and Inouye 2011). Adult females make oviposition decisions at fine scales
(e.g. among adjacent beans or within beans)(Cope and Fox 2003) and oviposit eggs singly on the surface of beans. After hatching, larvae burrow into the bean, where they feed and grow until pupation. When they emerge from their puparia, adults chew a clearly visible exit hole through the seed coat. Adults do not require food, and females spend their 1-2 week lifespan mating and ovipositing (Miller and Inouye 2011). The population used in these experiments has been lab- reared on various beans for over 25 years, but mostly on mung beans (Vigna radiata). Although black-eyed peas are larger (V. unguiculata, mean 0.18g) compared to mung beans (mean 0.06g), adult females of this population prefer to oviposit on mung beans. Within the different hosts, beetles tend to develop at similar rates (Roesli 1991, Inouye unpublished data) and emerge as adults that are similar in size (Timms 1998), although females emerging from mung beans tend to have a higher lifetime fecundity than those from black-eyed peas (Timms 1998, Inouye unpublished data). This preference for mung beans over black-eyed peas may provide associational resistance to black-eyed peas. Both sexes can produce compounds that deter subsequent beetles from ovipositing on beans, which operate at small spatial scales (e.g. among adjacent beans, Credland and Wright 1990). Beetles do not always reject previously used hosts outright, but may reuse hosts that have lower than average amounts of eggs (Cope and Fox
2003).
Whether or not the presence of feeding and development of a single beetle in a bean kills it depends on the bean host and the biotype of C. maculatus (Fox et al. 2010). We found that a single pupal chamber reduces seedling development (germination to the production of cotyledons) by 6% and 81% in black-eyed peas and mung beans, respectively (unpublished data).
9 Experiment
We studied neighborhood effects on attack by C. maculatus on mung beans and black- eyed peas in simulated landscapes in which local neighborhoods varied in resource (i.e. bean) composition. Each “landscape” consisted of a wooden box (50 x 50 x 7.5cm) with a clear acrylic top containing 24 local “neighborhoods”: open petri dish lids (50mm diameter) each with a different, randomly assigned combination of beans. Petri dish lids were placed in two concentric circles in each box (Fig. 2.2.). The inner circle (10.25 cm radius) consisted of 8 lids, each 8 cm apart from one another. The outer circle (20.5 cm radius) consisted of 16 lids, also 8 cm apart from one another. The lips of the lids were short enough (10.5mm) that beetles could readily move among the discrete neighborhoods of beans, and lids were wide enough that the highest density mixture of beans fit in a single layer. We made neighborhoods of beans with factorial combinations of zero, one, two, three, or four grams of mung beans and black-eyed peas (all possible combinations, except zero mung beans and zero black-eyed peas). Each box thus provided a complete response surface of bean densities. We chose to manipulate resource abundance by gram, rather than by individual bean, as C. maculatus has been shown to oviposit so as to maximize the amount of mass available to each developing larva (Cope and Fox 2003).
To investigate how consumer density mediates neighborhood effects, we introduced low
(40 beetles), medium (80 beetles), or high (160 beetles) densities of beetles into each box in an even sex ratio. As a pest of stored legumes, C. maculatus can reach devastating densities (Jackai and Daoust 1986). These densities are well within their range, however, our chosen densities are not meant to reflect a particular field condition. There were 12, 13, and 20 replicate response surfaces (boxes) for low, medium, and high beetle densities, respectively. We had more replicates at higher beetle densities where we expected neighborhood effects to be weaker. To
10 account for this unbalanced design we used type-two sums of squares in our interpretations of the data.
Beetles were allowed to mate and oviposit for 48hrs in each experimental landscape before being removed, which allowed for all but three of 288 neighborhoods at low beetle densities to experience damage, yet at high densities beans were still far from reaching their maximum number of pupal chambers (six for black-eyed peas, four for mung beans, personal observations). When the next generation of adults emerged, ~40 days later at room temperature
(25°C), the beans were frozen to halt development of eggs oviposited by newly emerged adults.
Damage was assessed by counting the number of pupal chambers—both the exit holes of emerged adults and the pupal chambers of adults that were frozen before emergence—in each bean, and was recorded and analyzed as damage per gram of bean in a neighborhood.
Experiments were carried out in seven temporal blocks from November 2014 to March 2015.
Each treatment was replicated within blocks.
Analyses
To address our first question of how consumer density influences the strength of neighborhood effects, we fit generalized linear mixed-effects models (GLMMs) separately to per gram damage of mung beans and black-eyed peas. Beetle density (low, medium, high), focal resource density, neighbor resource density and frequency (neighbor (g) / total (g)), and all beetle density-resource interactions were included as independent variables. Beetle density was treated as a factor and not as a continuous variable because its effects were non-linear. To account for non-independence of damage among petri dishes within a “landscape”, box number was included as a random effect. Temporal block was included in models as a fixed effect to account for
11 temporal variation. In earlier models, there were no significant interactions between temporal block and the other fixed effects, so these interactions were excluded from analyses.
To more easily interpret how focal resource damage depends on focal resource density, neighboring resource density, and neighbor frequency at the different beetle densities, we fit generalized linear mixed models to the data from each beetle density independently. Again, replicate (i.e. box) was treated as a random effect, while temporal block was treated as a fixed factor.
All models used a gamma distribution with a log link function (glmmADMB, R version
3.2.2, R Core Team 2015,Skaug et al. 2015, Fournier et al. 2012). Gamma distributions are appropriate for continuous, non-negative data such as ours, and provided good model fits. We determined the statistical significance of effects with type II Wald-Chi square tests using the
Anova function in the package car (Fox and Weisburg 2011). In preliminary analyses the location of the petri dish lids within the box did not influence results and thus was not included in the analyses. Because R2-analogues for gamma GLMMs are not yet developed, we instead report R2 values from linear models of observed damage per gram as a function of their predicted values from the GLMMs. These values should be interpreted with caution, as they do not account for variance explained by random effects.
To visualize results, response surfaces were parameterized from the GLMMs for individual beetle-density treatments and an interaction plot was constructed showing the data for damage to black-eyed peas with no neighbors or with four grams of mung beans. This latter figure was included to facilitate direct comparison of our results with predictions in our conceptual model (Fig. 2.1). Estimates from all fixed factors were used, with the exception of temporal block. We used median intercept values.
12 In addition to looking for effects within a neighborhood (petri dish), we looked for an influence of surrounding neighborhoods on the total damage and per capita damage of within a focal neighborhood, but found no significant influence (See Appendix S1).
Results
We found clear effects of beetle density on the strength of neighborhood effects (Fig. 2.3,
2.4 & 2.5). Beetle density reduced the strength of resource dilution for black-eyed peas
2 (GLMM; beetle density*black-eyed pea density χ 2 = 13.57, p = 0.001, Table 2.2) and mung
2 beans (GLMM; beetle density*mung bean density χ 2 = 59.27, p < 0.001, Table 2.2), and also reduced the strength of the frequency-dependent associational resistance of mung beans on
2 black-eyed peas (GLMM; χ 2 = 11.10, p = 0.004, Table 2.2, Fig. 2.5). In fact, at high beetle densities, no significant resource density or neighborhood effects were observed (Figure 2.3).
Our full models seem to have accounted for much of the variation seen in damage among black-
2 2 eyed peas (LMobs~pred: R = 0.77) and mung beans (LMobs~pred: R = 0.71).
The influence of focal resource density, neighbor density, and/or neighbor frequency on per-capita damage to the focal resource depended on the focal resource identity (black-eyed pea or mung bean) and the beetle density (Fig. 2.3). At low beetle density, we observed focal resource dilution effects for both black-eyed peas and mung beans (GLMMs; effect of focal density p = 0.019 and p < 0.001, Fig. 2.3): as pea or bean neighbor density increased, damage per pea or bean decreased. We also observed associational resistance (reduced damage) conferred to black-eyed peas in the presence of mung beans. This associational resistance depended on the
2 frequency of mung beans at low beetle densities (GLMM; χ 1 = 3.76, p = 0.053), but was better
2 explained by the density of mung beans at medium beetle densities (GLMM; χ 1 = 5.03, p =
13 0.025). Although the frequency-dependent associational effect was only marginally significant at low beetle density (p = 0.053), when performing AICc-based model selection, the best fit model dropped neighbor density and neighbor frequency is highly significant (p < 0. 0005,
ΔAICc = 2.0 vs full model, the second best fit), suggesting that resource frequency strongly affected oviposition patterns at low density.
Discussion
Using model organisms in simplified landscapes, we found that a resource organism’s susceptibility to attack could be influenced by conspecific density and, for the less preferred resource (in this study, black-eyed peas), a neighbor’s density and frequency. Importantly, the strength of these effects depended on the density of the consumer. At high beetle density, the composition of beans in a neighborhood did not influence damage to individual beans, while at low and medium beetle densities we found that damage to the preferred bean was reduced as the density of that bean increased (i.e. resource dilution) and that damage to the less preferred bean was reduced by the presence of neighbors (i.e. associational resistance).
Empirical studies in natural systems have demonstrated that consumers display habitat selection and resource-use that depend on the density of conspecific consumers (e.g. Brown
1969, Gill et al. 2001, Svanbäck and Bolnick 2007), though few have considered the influence this might have on indirect effects among resource organisms. A couple studies on grazing mammals have found that when grazing pressure increases associational resistance decreases, as grazers are forced to accept lower quality hosts (Graff et al. 2007, Smit et al. 2007). Conversely, a prior study of insect herbivory on oak genotypic diversity (Fernandez-Conradi et al. 2017) showed that genotypic diversity reduces damage to host plants more at ambient herbivore levels
14 than when herbivores were suppressed with insecticides. These studies, however, did not separate the effects of density and frequency of neighboring plants. Here, we disentangle all three factors of resource composition (focal density and neighbor density and frequency) to provide a more complete description of how consumer density may influence neighborhood effects.
Like the studies of mammalian herbivores, our results may be explained by density- dependent host selection. In our case, this was likely mediated by oviposition deterring compounds (Credland and Wright 1990). Ovipositing females prefer mung beans to black-eyed peas at low beetle density, which could lead to the observed associational resistance for black- eyed peas in the presence of mung beans. At high beetle densities, where many beans already have eggs, aversion to oviposition compounds could out-weigh the preference for mung beans, resulting in the observed pattern of black-eyed peas being damaged equally among neighborhoods regardless of the density or frequency of neighboring mung beans.
Model systems, such as ours, are powerful tools for exploring ecological phenomena that are otherwise intractable to study. However, directly relating studies of model systems to nature is not straightforward. Our system allowed us to manipulate consumer and resource densities at scales and with replication that would have been infeasible in a field study. While our system does not directly parallel any natural consumer-resource interactions and made use of highly simplified landscapes, the host preference and conspecific avoidance behavior that likely led to patterns of host use in this study are commonly seen in natural systems (e.g. Cronin and Hay
1996, De Moraes et al 2001, Nufio and Papaj 2001, Prokopy and Roitberg 2001) and should be considered in future studies of neighborhood effects as they may account for diminished associational resistance in mixed-crop agricultural systems during outbreak years (e.g. Latheef
15 and Ortiz 1984, Buntin 1998, Ludwig and Kok 1998) and inconsistencies in the strength of associational effects observed among years in the same system (Andow 1991).
Although we found that increasing consumer density reduces the strength of associational resistance in our system (Fig. 2.4 & 2.5), as we had predicted would occur with conspecific aversion (Fig. 2.1 compare dotted and solid lines), in other systems we might expect the opposite pattern—that the strength of associational resistance would increase with consumer density. In particular, this could occur when consumer densities are relatively low and when consumers are aggregative or show no aversion to conspecifics (Fig. 2.1. compare dotted and dashed lines). For example, Fernandez-Conradi et al. (2017) found an increase in the strength of neighborhood effects with increased gypsy moth density; gypsy moths may have stronger preferences at higher density (Skaller 1985). In addition, a previous study found strong neighborhood effects at outbreak densities of an insect herbivore (Stastny and Agrawal 2014), suggesting that consumer density will not always swamp neighborhood effects in more natural settings.
Our ability to predict the strength of neighborhood effects for any particular set of organisms in a specific location may depend on how well we can predict consumer density and on knowledge of how consumers respond to conspecifics. Though consumer density may fluctuate wildly among years, especially for insect herbivores (Cappuccino and Price 1995,
Barbosa et al. 2012), spatial patterns in consumer density may be easier to predict. For example, gradients or ecotones of insect density may exist across a resource organism’s range. Although these gradients are likely to be confounded with environmental factors, careful research may be able to tease apart the influences of consumers (e.g. Graff et al. 2007, Miller et al. 2009) and determine whether neighborhood effects vary predictably along these gradients. The influence of consumer density on the strength of neighborhood effects is likely to depend on how consumers
16 respond to conspecifics, however, this behavior is variable both across taxa and within individuals. We know that consumers respond to cues directly from conspecifics (Nufio and
Papaj 2001) and to indirect cues, such as when herbivores (terrestrial or marine) avoid plants or algae that have previously been damaged (Cronin and Hay 1996, De Moraes et al. 2001), but whether consumers are attracted to or avoid conspecifics depends both on how natural selection has shaped behavior and on the physiological state and information available to an individual
(Prokopy and Roitberg 2001). Theory tells us that individuals should join conspecifics when the cost of finding new resources is greater than the cost of joining a group (Clark and Mangel
1986), but the costs and benefits of conspecific aggregation are dynamic and may change with context. When responses to conspecifics are known, however, we may be able to make general predictions about the influence consumer density will have on neighborhood effects.
The dispersion and abundances of individual resource types across a landscape will determine their frequency of co-occurrence and composition of patches and thus the presence and strength of associational effects. Often, this results in foraging consumers that must choose among resources that exist in hierarchical scales of patchiness (Kotliar and Wiens 1990). For example, organisms may choose among patches or among resources within a patch (Hambäck et al. 2014, Verschut et al. 2016), such that neighborhood effects could occur at either of these scales Our study was designed to investigate choice among beans within a patch because C. maculatus are known to choose primarily at the among-bean scale (Cope and Fox 2003). Though in our system neighborhood effects occurred only within a patch (we found no among-dish neighborhood effects; Appendix S1), in other systems associational effects among neighboring resources may operate at larger spatial scales. For example, highly attractive neighbors may lead to the immigration of more consumers into a patch, which then “spill over” onto less attractive
17 focal plants in the vicinity, creating associational susceptibility (e.g. White and Whitham 2000 ,
Ogilvie and Thomson 2016, Kim 2017). In addition, larger-scale habitat context, such as land use history, may determine aspects of herbivore abundance and behavior that could also influence associational effects (Kim et al. 2015, Hahn and Orrock 2016).
Here, we demonstrate that resource densities and frequency interact with consumer density to influence the strength of neighborhood effects, suggesting that neighborhood effects are likely to be as dynamic as the interacting populations themselves. Our results indicate that when consumers avoid conspecifics and their densities are low, the presence of preferred, neighboring resources may be especially beneficial for a less preferred focal resource. It is interesting to consider the potential for associational effects to influence population dynamics of herbivores and that this might “feedback” to again influence the strength of associational effects among plants (Holt 1977). However, establishing the existence of such feedback loops will likely be complicated by the differing scales at which associational effects operate and consumer populations are regulated. Future studies of neighborhood effects should attempt to characterize the range of conditions under which consumer density influences neighborhood effects either through analyses of large data sets in which consumer densities vary through time and space, or by further use of consumer density manipulations. In addition, theory for neighborhood effects
(e.g. Hambäck et al. 2014) may need to incorporate consumer responses to conspecifics to improve our understanding of the variability of neighborhood effects we see in nature.
18 Tables
Table 2.1 List of neighborhood effects and their definitions as used in this manuscript. Definitions may vary among authors. Term Definition Example Ref. the indirect influence of neighboring resource organisms on the consumption of a focal resource organism. Neighborhood effects may result from Kim & Underwood Neighborhood effect neighboring resource organisms of the same or 2015 different type (e.g. conspecifics or heterospecifics) Neighborhood effects caused by changes to a focal resource type: a neighborhood effect that occurs when changes Focal resource to the density of a focal resource type (e.g. This study density effect conspecifics) influence per capita consumption on that type a focal resource density effect in which per capita Resource Root 1973; Anderson consumption of a focal resource type increases as concentration effect et al. 2013 the density of that type increases a focal resource density effect in which per capita Resource dilution consumption of a focal resource type decreases as Otway et al. 2005 effect the density of that type increases
Neighborhood effects caused by changes to neighboring resource type(s):
a neighborhood effect that occurs when per capita consumption of the focal resource type, at a given density of that type, is a function of the Associational effect Underwood et al. 2014 neighborhood composition (e.g. density or frequency) of other resource types (e.g. heterospecifics)
an associational effect that occurs when per capita Tahvanainen & Root Associational consumption of the focal resource type decreases 1972; Barbosa et al. resistance with increasing density or frequency of a 2009; Underwood et al. neighboring resource type (e.g. heterospecifics) 2014
19
Table 2.1 – Continued
Term Definition Example Ref. an associational effect that occurs when per capita Letourneau 1995; Associational consumption of the focal resource type increases Barbosa et al. 2009; susceptibility with increasing density or frequency of a neighboring Underwood et al. 2014 resource type (e.g. heterospecifics)
Table 2.2 ANOVA table for GLMMs of damage per gram for black-eyed peas and mung beans. Results are from Wald chi-square tests.
Black-eyed Peas Mung Beans Damage g-1 Damage g-1
Factor DF χ2 P DF χ2 P Beetle Density (Beetle) 2 10.950 0.004 2 8.571 0.014 Focal Resource Density (Focal) 1 13.262 < 0.001 1 69.669 < 0.001 Neighbor Density 1 0.196 0.658 1 0.024 0.878 Neighbor Frequency 1 9.001 0.003 1 1.857 0.173 Temporal Block 6 50.774 < 0.001 6 79.283 < 0.001 Beetle * Focal 2 13.570 0.001 2 59.267 < 0.001 Beetle * Neighbor Density 2 1.598 0.450 2 0.686 0.710 Beetle * Neighbor Frequency 2 11.097 0.004 2 2.960 0.228
20 Figures
w/ neighbors & w/ avoidance Damage no neighbors to focal resource AE w/ neighbors & w/o avoidance
Consumer density
Figure 2.1 A conceptual model for damage to a focal resource type as a function of consumer density. The strength of the associational effects (AE) is measured as the difference between the damage to a resource type with and without neighbors (Underwood et al. 2014). When consumer preference remains constant, with consumers feeding on the focal plants a fixed proportion of the time, this leads to stronger absolute AEs as consumer density increases. Conversely, conspecific avoidance leads to diminished associational effects as consumer density increases.
21 8 cm
50 cm 10.25 cm 20.5 cm
50 cm
Figure 2.2 Box (“landscape”) configuration. The 24 combinations 0, 1, 2, 3, or 4 g of mung beans and black-eyed peas were randomly arranged within each petri dish (“neighborhood”). One petri dish is shown containing a mung bean and two black-eyed peas (not drawn to scale) as an example.
22 Beetle Density
Low Med High Focal -0.20* 0.02 -0.03 FRDE Density -0.19*** -0.07*** 0.01 Neighbor 0.06 -0.10* 0.01 NE Density AE 0.004 0.03 -0.02 Neighbor -1.44! 0.24 -0.004
Frequency -0.26 -0.24 0.17 = black eyed pea damage g-1 = mung bean damage g-1
Figure 2.3 Estimates of GLMM parameters for per gram damage to black-eyed peas (above diagonal) and mung beans (below diagonal) in treatments of low, medium, and high beetle densities. Models explained relatively high variance of the observed damage to black-eyed peas 2 (LMobs~pred: R = 0.77 for pooled data ,0.33, 0.54, & 0.20 for low, med, and high beetle densities, 2 respectively) and mung beans (LMobs~pred: R = 0.71 for pooled data, 0.46, 0.41, & 0.18 for low, med, and high beetle densities, respectively). NE = neighborhood effects, FRDE = focal resource density effect, AE = associational effects. *** p < 0.001; ** p < 0.01; * p < 0.025; • p = 0.053
23 Beetle Density Low Medium High
25 25 25 e
g 20 20 20 ) a g
m 15 15 15 (
A a B
D 10 10 10 M B 5 5 5 M 0 0 0 0 1 0 1 0 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4
15 15 15 e g ) a
g 10 10 10 m ( a P
B D E
P 5 5 5 B E B 0 0 0 0 1 0 1 0 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4 e g a
) 2 2 2 m g ( a D
C P 0 0 0 E P B E
B -2 -2 -2 Δ
0 0 1 0 1 1 1 1 1 Neighbor (g) Neighbor (g) 2 Neighbor (g) 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4 Focal (g) Focal (g) Focal (g)
Figure 2.4 Per gram damage to focal resource (A) mung beans and (B) black-eyed peas as a function of neighbor and focal resource densities across three densities of bean beetles. (C) depicts the isolated associational effects of mung beans on per gram damage to black-eyed peas, that is, the focal resource density effect is subtracted from the surface. Surfaces are parameterized from GLMMs. See Table 2 for estimates and significance values.
24 resource resource Damage to less-preferred to Damage
Beetle density
Figure 2.5 Damage to 1 g of black-eyed peas, the less-preferred resource, with and without 4 g of neighboring mung beans, the preferred resource (median ± IQR). Points are offset along the x- axis for clarity.
25 CHAPTER 3
HOMIER HOSTPLANT PATCHES: NATAL HOSTPLANT EXERIENCE INFLUENCES THE RELATIONSHIP BETWEEN INSECT AND HOSTPLANT DENSITIES
Introduction
The type of habitat where an animal is born can influence the type of habitat it selects later in life (Immelmann 1975, Stamps and Blozis 2006, Davis 2008). For this reason, ecologists and evolutionary biologists have suggested that natal habitat experiences can bias dispersal among habitat types, influence colonization dynamics in meta-populations, and contribute to patterns of local adaptation (Papaj and Prokopy 1988, Davis and Stamps 2004, Benard and
McCauley 2008). Although the consequences of natal habitat experience have been studied across taxa, much research over the last century has focused on insect herbivores, whose hostplants often serve as both their food source and their habitat (Barron 2001, Petit et al. 2017).
Evidence from lab and small-cage choice tests demonstrates that for some insect species, the type of hostplant on which they feed as a juvenile can influence the type of hostplant on which they lay their eggs as adults (Davis 2008, Blackiston et al. 2008, Lhomme et al. 2017). Because hostplants are unevenly distributed across landscapes, however, insects often must choose not only among adjacent hostplants, but must search among patches of plants as well (Bukovinszky et al. 2005). Further, patch finding, a critical step in the multi-scale process of host selection, can depend on different cues than those used for selecting individual hosts within patches (Verschut et al. 2017a).To our knowledge, however, studies have only examined how natal-hostplant experiences influence insect herbivore choices among adjacent hostplants, and have not yet explored the effect of natal experience on the distribution of offspring among patches of
26 hostplants. Our understanding of the consequences of natal host-plant experience for plant- herbivore interactions in the heterogeneous landscapes they encounter in the field is thus still quite limited. Understanding whether natal-hostplant experience does modify hostplant selection in the field could inform our predictions of the spatial-dynamics of insect populations and our ability to manage them through applications such as mixed-crop pest management and the reintroduction of endangered species.
Natal-habitat experience can influence subsequent habitat selection through at least two non-mutually exclusive processes. First, the quality of natal habitat can influence the physiological condition of dispersing animals. Depending on how animals allocate their resources, higher quality natal habitats may increase or decrease selectivity. Animals with more resources available to devote to habitat search are likely to be more selective and to reject low quality habitat during sequential hostplant encounters—a process referred to as the Silver Spoon
Effect (Stamps and Blozis 2006, Davis 2007). By contrast, when animals devote more resources to egg production, theory and empirical evidence suggests that they are often less selective among habitats or hosts (Minkenberg et al. 1992). A second way that natal-habitat experience can influence habitat selection is through associative or non-associative learning. When dispersing animals positively respond to cues experienced on their natal hostplant, this is referred to as natal-habitat preference induction, which is known to occur broadly across animal taxa
(Davis 2008). For holometabolous insect herbivores, such learning can happen as a larva or as an adult emerging from a puparium (Blackiston et al. 2008, Facknath and Wright 2007). Although evidence for natal-habitat preference induction is not universal for insect herbivores (Barron
2001, Petit et al. 2017) it is expected to be most common when temporal changes in habitat
27 quality are maximized between generations and minimized within generations, so that learned environmental cues are more adaptive than innate cues (Snell-Rood 2013)
While we know that natal-hostplant experience can influence insect preference between adjacent plant individuals, it is also possible that this experience can modify how herbivores respond to patches of plants. Here, we consider an herbivore to perceive a “patch” when it senses the collective characteristics of multiple individual plants (Hambäck et al. 2014). A vast amount of literature is dedicated to understanding how the density of plants within patches and species or genotype composition influence herbivore densities and subsequent damage to hostplants
(Kareiva 1983, Andow 1991, Barbosa et al. 2009, Underwood et al. 2014). Because these relationships are thought to be shaped by patch finding behavior (Andow 1990, Hambäck and
Englund 2005, Hambäck et al. 2014), characterizing the ecological significance of natal-habitat experience requires research that explores its consequences for patch selection.
Natal-hostplant experience has the potential to influence the relationship between insect density and a focal plant type’s density. Herbivore density may increase (resource concentration effect, Root 1973) or decrease (resource dilution effect, Otway et al. 2005) with plant density and, at scales where population dynamics are determined by net migration rates, the direction of this relationship is likely to depend on the type of cues—contact, visual, or olfactory—that insects use when searching for plants (Bukovinszky et al. 2005, Hambäck and Englund 2005,
Andersson et al. 2013); insects using olfactory cues are more likely to exhibit a positive insect density-plant density relationship than contact or visual searchers. However, insects often respond to multiple cue types from plants (e.g. visual and olfactory, Kulahci et al. 2008) and it is possible that a heightened sensitivity to olfactory cues associated with a natal-plant type
(Thoming et al. 2013, Lhomme et al. 2017) could bias their search towards olfactory cues of that
28 plant type and could thereby result in a more positive insect density-natal plant type density relationship.
Neighboring plant types within patches can increase or decrease a focal plant type’s susceptibility to herbivores—phenomena referred to as associational susceptibility and resistance, respectively (Tahvanainen and Root 1972, Letourneau 1995). Predicting the strength of these associational effects, however, may depend on an understanding of how hostplant selection changes with natal-hostplant experiences. Associational effects occur when neighboring plant types influence herbivores’ patch-finding behavior, within patch movements, and/or patch-leaving behavior (Hambäck et al. 2014, Eigenbrode et al. 2016). If natal-hostplant experiences modify the relative sensitivity of herbivores to neighboring plants’ cues, this will change the focal plant type’s susceptibility to attack. Although associational effects influence population-level processes, such as the evolution of hostplant defenses (Sato and Kudoh 2017) and plant-plant competition (Lau and Strauss 2005), we do not yet know how herbivores’ natal experiences modify the strength of association effects.
Here we report the results of a study that examines how natal host experiences influence insect interactions both among and within patches. For this experiment, we used the diamondback moth, Plutella xylostella, which, in prior studies, has been shown to change its hostplant preferences in response to natal hostplant cues (Liu et al. 2005, Liu and Liu 2006, Ryan and Bidart-Bouzat 2014) experienced as newly emerged adults. We reared P. xylostella on either collards (Brassica oleracea) or mustards (Brassica juncea) and recorded subsequent offspring on and damage to hostplants in three different patch types: two collards, four collards, and mixed patches of two collards and two mustards. We predicted that within mixed patches, a higher proportion of offspring and damage would be observed on the moth’s natal hostplant type
29 compared to moths reared on the other hostplant. We also predicted that we would observe a more positive response to increasing collard density among patches with collard-reared moths than with mustard-reared moths. Further, we predicted that the presence of neighboring mustard plants would influence the offspring numbers on and damage to collards differently between the moth-rearing treatments (i.e. different associational effects), likely through changes in patch finding, within patch movement, or patch leaving behavior. However, we lacked sufficient knowledge of P. xylostella behavior to predict the direction of this effect. Finally, we measured hostplant quality in greenhouse experiments, using time to pupation and weight at pupation as proxies, to provide insights into the potential mechanisms contributing to observed experience- dependent responses of moths in the larger field cage experiment.
Methods
System
Plutella xylostella is a cosmopolitan pest of cruciferous crops, including collards
(Brassica oleracea) and Indian mustards (Brassica juncea var integrefolia). These moths are thought to rely mainly on olfactory cues to locate hosts (Couty et al. 2006), but also respond to visual cues when searching (Finch and Collier 2000). We selected collards and mustards as hostplants because they are commonly grown in the Southeastern Unites States and because some studies suggest that mustards mays be used as a trap crop (Bender et al. 1999); they confer associational resistance to collards.
We acquired eggs of P. xylostella from a commercial insectary (Benzon Research,
Carlisle, PA, USA) where they were reared on artificial diet; prior studies of natal hostplant experience have used moths from this same colony (Ryan and Bidart-Bouzat 2014). We reared
30 developing caterpillars in trays of six-week-old mustards or collards (Tang et al. 1989). As third or fourth instars, caterpillars were separated by sex (Liu and Tabashnik 1997) into groups of ten and placed in 24 oz (700 ml) plastic containers with leaves of their respective hostplants. These containers were then placed in a growth chamber (Percival Scientific Inc., Perry, IA, USA) at
27°C on a L14:D10 photoperiod. Fresh leaves were provided as needed until adult moths emerged. Moths used in these experiments were thus exposed to natal hostplants as feeding larvae and as recently emerged adults.
Mustard and collard plants for this study were grown from seed (Red Giant” and
“Georgia Southern”, respectively; Botanical Interests, Broomfield, CO, USA) in the Florida
State University Mission Road Greenhouse Facility with 6 g of fertilizer (NPK 10-10-10) and water provided as needed.
Field-Cage Experiment
To determine if natal hostplant experiences influence moth responses to plant types within and among patches, we used a split-plot design in which we manipulated the density and composition of plants in patches in large field cages (5 x 1 x 0.75 m) and introduced adult diamondback moths that were reared on either collard or mustard hostplants into these cages.
This experiment was conducted at the Florida State University Mission Road Greenhouse
Facility in recently mown old-field habitat and was repeated in two temporal blocks, starting on
22 March, and 10 May 2017. Each cage consisted of a wooden frame draped by black fiberglass screen that was tucked under the cage and fastened to the ground with steel sod staples. The cages included groundcover of short vegetation and any herbivores therein, although we
31 manually removed any flowers (potential nectar sources) before introducing moths to cages. For each temporal block 14 field cages were used (seven for each moth-rearing treatment).
Within each cage we randomly positioned three patches of potted plants: a two-collard patch, a four-collard patch, and a mixed patch of two collards and two mustards (Fig. 3.1). These patches were placed 1.5 m apart from each other. Within the four-collard and mixed patches, plants were placed in a square, with each plant 25 cm apart from its nearest neighbor (measuring from the center of pots). Plants in the two-collard patch were also placed 25 cm apart from each other, perpendicular to the length of the cage. All plants used in this experiment were approximately eight weeks old and foliage was not touching among neighboring plants.
At the start of each temporal block, twenty adult moths with an even sex ratio—reared on mustard or on collard—were introduced into randomly assigned cages. Diamondback adults had emerged from their puparia between 2 and 48 h prior to being placed in a cage.
Two weeks after adult moths were placed in a cage, we removed the fiberglass screen from that cage, and surveyed each leaf from each plant for diamondback moth life stages (eggs, larvae, and pupae), other arthropods, and herbivore damage. Diamondback moth eggs were rarely observed, likely because of their small size (0.44 mm diam.) and were not included in any analyses. To observe the effects of natal-habitat experience under more natural conditions, we kept the screens off of the cages after the first survey. This exposed offspring to greater predation and the plants to more non-diamondback moth herbivores. A second survey was conducted two weeks after the first survey (four weeks after moths were introduced to the cage), during which each leaf from each plant was again examined for diamondback moth life stages, herbivores, and herbivore damage.
32 We estimated the area damaged on each leaf by visually estimating the proportion of leaf area removed and multiplying this by the leaf area predicted by a linear model based on leaf length, derived from a sample of mustard (n = 54) and collard leaves (n = 66) from plants not used in the experiment (ImageJ, Schneider et al. 2012). Assuming an intercept of zero, we used leaf length and the square of leaf length as predictor variables. These models fit remarkably well to the data (r2 = 0.98 and 0.96 for collards and mustard models, respectively).
Patterns of damage clearly reflect primarily diamondback moth larval feeding, though our estimates included feeding from all chewing herbivores (e.g. Chrysomelid beetles, grasshoppers, and other Lepidoptera). Diamondback feeding damage could not be reliably distinguished from other chewing damage, however in statistical models of damage, the number of diamondback offspring (larvae and pupae) was a highly significant predictor of plant damage (GLMM, p <
0.001), while the number of other observed herbivores was not (GLMM, p = 0.12). Therefore, our estimates of damage appear to be reliable estimates of diamondback larval feeding.
In our analyses, we used offspring numbers from the first surveys, since these offer the best proxy of adult oviposition preference and because few offspring remained on plants during the second surveys, likely because they had already emerged as adults or had been eaten.
However, estimates of plant damage were taken from our second survey to capture damage from the last and most damaging caterpillar instar (Harcourt 1957).
Analysis of Field-Cage Experiment
Within mixed patches (two collards and two mustards), we looked for an influence of natal hostplant experience on diamondback responses (offspring number and their feeding damage) using generalized linear mixed-effect models (GLMMs) built with the ‘lme4’ package
33 in (Bates et al. 2015, R Core Team 2017). To account for non-independence of data resulting from the spatial and temporal structure of the experiment, these models included the random effect of cage, nested within temporal block. Because plant damage has a lower bound of zero and therefore violates assumptions of normality, plant damage was modeled with a gamma distribution and a log link function. Similarly, because offspring numbers are counts and are bounded by zero, models of offspring used a negative binomial distribution with a log link function. Models included temporal block, natal hostplant experience (collard- or mustard- reared), plant type (collard or mustard), and their interaction as fixed effects. In addition, the model of plant damage included number of non-diamondback moth herbivores as a covariate.
We also tested the influence of natal habitat experience on moth responses among patches with GLMMs. To look for the influence of natal-hostplant experience on the insect density-collard density relationship (e.g. resource concentration), we compared responses between two-collard patches and four-collard patches with models that included either offspring numbers or damage as a response variable. Temporal block, patch type (two-collard or four- collard patches), natal hostplant experience, and their interactions were included as fixed effects.
In addition, we used the number of non-diamondback moth herbivores as a covariate for models of plant damage. Likewise, to examine how natal habitat experience influences the associational effect of mustard on collards (i.e. associational resistance or susceptibility), we used similar models to those used to compare two- and four-collard patches, but compared diamondback moth responses between two-collard patches and mixed patches. Like the within patch analysis, to account for non-independence of data due to the spatial and temporal structure of our experiment, among-patch models included the random effect of patch nested within cage, nested within temporal block. We also explored models with neighboring biomass as a continuous
34 variable, rather than a discrete variable (patch type), and observed qualitatively similar results
(results not shown).
For all models, we determined the significance of factors by performing Type II Wald chi-squared tests with the Anova function in the ‘car’ package (Fox and Weisberg 2011).
Interactions with temporal block that were not significant were removed from the models.
Hostplant Quality Experiment and Analyses
To test for potential differences in host quality between collards and mustards, which could help distinguish between physiological and learning-based responses to natal hosts, we transferred 40 second instar larvae from rearing trays of each host type onto their respective hostplants in cubic-foot mesh cages (30 x 30 x 30 cm, Raising Butterflies Inc., Salt Lake City,
UT, USA) with ten larvae on each hostplant (eight cages total). We measured time to pupation and weight of pupae as proxies for hostplant quality and used mixed effects models with cage as a random effect to compare these metrics between hostplant types. Two larvae from mustards and four larvae from collards were not recovered as pupae, likely indicating larval mortality and were excluded from analyses. We used models with Gaussian error distributions. An identity link function was used for our analysis of pupal weight and a log link function was used for time to pupation.
Results
Field-Cage Experiment
Offspring on and damage to collard plants were highly variable. Numbers of offspring ranged from 0 to 24 per plant (mean ± SE: 1.86 ± 0.19) and estimated damage to collards ranged from 0 to 245 cm2 (44.05 ± 3.05 cm2) per plant. For both response variables, temporal block was
35 significant, with the second temporal block having fewer diamondback moth offspring but more damage overall (Table 1), possibly reflecting differences in temperature between blocks.
We found no evidence that diamondback moths’ natal hostplant experience influenced their selection between mustards and collards within mixed patches. In cages with mustard- reared moths there were slightly more offspring on mustards than collards, but this was not significantly different from cages with collard-reared moths (GLMM moth type x plant type, P =
0.189, Table 2). Damage was greater on mustards than collards for moths from both rearing environments, but only in the first temporal block (block x plant: P < 0.001, Table 2).
Among patches, we found that natal hostplant experience influenced the insect density-collard density relationship, but that there were no associational effects of mustards on collards regardless of the natal habitat. In cages with collard-reared moths there were significantly more diamondback moth offspring on and greater damage to collards plants in four-collard patches than in two-collard patches (GLMM moth type x patch type(2 col. & 4 col.), ), P = 0.015 & P = 0.027 for offspring and damage, respectively, Figs 3.2 & 3.3), indicating a resource concentration effect when moths are reared on collards. In contrast, we found no evidence that neighboring mustard plants influenced the number of offspring on or damage to collard plants (GLMM moth type x patch type(2 col. & mixed) , P = 0.12 , P = 0.30, respectively) nor that natal-hostplant experience modified this effect (GLMM moth type x patch type(2 col. & mixed), P = 0.152 & P =
0.614 for offspring and damage, respectively, Figs 3.2 & 3.3).
36 Hostplant Quality Experiment
Larvae reared on mustards were significantly younger when they reached pupation than those reared on collards (11.8 v 14.0 days, P < 0.001, Fig 3.4A) and tended to weigh more (8.6 v.
8.0 mg, P = 0.12 Fig 3.4B).
Discussion
When an insect chooses among adjacent hostplants, many lab studies demonstrate that the type of hostplant on which it fed as a juvenile can influence the type of hostplant on which it lays its eggs as an adult. To our knowledge, however, prior research has not explored the ecological consequences of hostplant experience under field conditions—where insects must search over different hierarchical spatial scales and where their preferences may change throughout their lifespan. In this study, we have taken a step towards understanding the influence of host experience on plant-insect interactions under field conditions, finding that natal hostplant experience can contribute to hostplant patch selection, but not necessarily choices between hostplant types. We found that, in cages with collard-reared diamondback moths, offspring on and damage to collards increased with collard patch density, though we saw no insect density- collard density pattern in cages with mustard-reared moths. By contrast, we did not observe differences in the selection of hostplants within mixed patches nor an associational effect of mustards on collards, regardless of the diamondback moths’ natal hostplant.
The most parsimonious explanation for the positive offspring density - collard density relationship observed for collard-reared moths is that they were sensitized to cues associated with their natal hostplants (collards). Prior research with P. xylostella confirms that cues experienced as emerging adults may influence the hostplants on which females lay their eggs
(Liu et al. 2005, Liu and Liu 2006, Ryan and Bidart-Bouzat 2014a). That a clear influence of
37 natal hostplant experience was observed among patches and not within mixed patches is surprising, but could potentially be explained by the use of different cues during the distinct stages of hostplant selection. Although diamondback moths, like other moths, are thought to search for hostplant patches primarily through olfaction (Bukovinszky et al. 2005, Couty et al.
2006, Hambäck et al. 2007), final hostplant selection may be influenced by visual, tactile, and gustatory cues (Sarfraz et al. 2006). Research with other lepidoptera (Blackiston et al. 2008,
Lhomme et al. 2017) demonstrates that experience with olfactory cues, in particular, can influence hostplant selection, but studies have not yet demonstrated how experience with visual, tactile, and gustatory cues affect later hostplant selection (Anderson and Anton 2014). It is possible, therefore, that sensitization to olfactory cues associated with collards could lead to differences in patch selection, but not plant selection within patches.
By contrast, neither egg limitation nor a silver spoon effect alone seem likely explain the observed offspring density-collard density relationship for collard-reared moths. That collard- reared moths developed more slowly and weighed less than mustard-reared moths in our hostplant quality experiment suggests that collards are a lower quality host than mustards. If collard-reared moths were more egg limited, they would be predicted to select the higher quality host—mustards—within mixed patches (Minkenberg et al. 1992) and perhaps be more likely to select the mixed patch than other patches. A silver spoon effect, by contrast, would predict that collard-reared moths, being on a lower quality host, would be less choosy among hostplants
(Stamps and Blozis 2006) and, by extension, among hostplant patches. Neither of these sets of predictions are consistent with the patterns observed. However, an individual’s physiological state can influence not only their selectivity among plants, as suggested by egg-limitation and silverspoon hypotheses, but also their sensitivity to cues (Root et al. 2011, Verschut et al.
38 2017b). Therefore, the increased sensitivity of collard-reared moths to patches with more collard plants may be the result of both physiological difference due to hostplant quality as well as their prior experience with hostplant cues.
Although the experimental design for our large field-cage experiment limited our ability to observe behavioral mechanisms of the effects of natal hostplant experience, it afforded us an opportunity to examine its influence on moth responses over ecologically relevant timescales and under semi-natural conditions. Our cages, which were too short for human observers, did not allow us to observe adult moth searching, oviposition, or leaving behaviors, so we cannot identify the particular selection behaviors that were modified by prior hostplant experience.
Further, we cannot rule out that larvae may have moved among plants and contributed to patterns of offspring distribution. However, given that collard and mustard plants sustained relatively little damage (3.5% ± 0.2%) and that adjacent patches were a meter and a half away, it seems unlikely that larvae would be motivated to leave hostplants or that their successful movement between patches would have greatly contributed to the observed patterns of larval distribution and damage. Although adjacent plants within patches were not touching, it is possible that larval movement among adjacent plants may have masked an influence of natal hostplant experience on hostplant selection among plants within mixed patches. A particular strength of our study, however, is the timespan over which it was conducted. Natal host preference can diminish over time (Vafaie et al. 2013). By caging adult moths for the entirety of their reproductive lives, our experiment confirms that whether or not the sensitization to their natal hostplant diminished throughout the lifespan of the adult moths, a signal of that experience (offspring density-collard density relationship) was nevertheless observable, both for the distribution of offspring and the
39 distribution of damage to plants. In addition, this pattern was observed despite the susceptibility of offspring to predation and parasitism.
Our results suggest that accounting for prior hostplant experience may improve our understanding of how some herbivores and their damage are distributed in patchy habitats through time. For instance, the first generation of an insect herbivore to spread into a region with a previously unexperienced hostplant species may exhibit a different insect density-hostplant density relationship than the next generation, which will have experienced the new hostplant species as larvae and as newly emerged adults. Likewise, insect herbivores in habitats with numerous hostplant species may have different hostplant density relationships with dominant hostplants than with less common hostplants as a consequence of the herbivore population’s prevailing natal hostplant experience. Further, if the observed patterns hold for patches at larger spatial scales, natal hostplant experience could bias dispersal between ‘patches’ (sensu Levins
1969), increasing colonization rates to patches of the same hostplant type (Hanski and Singer
2001).
Our results may also have implications for conservation and pest management. For instance, estimates of critical patch size, the minimum patch size for a sustainable population
(Schultz and Crone 2005), could be influenced by an insects’ natal hostplant experience. And the planting arrangement of a crop that most reduces pest immigration could depend on whether pests are immigrating from adjacent agricultural fields of the same hostplant or from wild or agricultural hostplants of different types.
The work presented here adds several new dimension to our understanding of natal habitat experience. To our knowledge, this is the first demonstration that natal hostplant experience can influence consumer-resource interactions in patchy environments and that the
40 ecological consequences of natal hostplant experience may persist despite repeated host selection events. Future work should focus on identifying the physiological and behavioral mechanisms by which natal hostplant experience produces differential patterns of patch use and should explore the consequences of natal hostplant experience at larger spatial scales. For instance, physiological studies that identify how natal hostplant experience influences an insect’s detection of single cue types and the integration of multiple cue types (e.g. olfactory v visual) as well as behavioral studies that identify the stages of patch use (patch finding, moving among plants within patches, and leaving patches) that are most influenced, could help us predict the occurrence and direction of experience-dependent patterns of patch use in other systems. Further, because migration to new patches at larger spatial scales may represent a fundamentally different behavior from movement at smaller spatial scales (Dingle and Drake 2007), large-scale studies using mark release recapture or molecular methods that identify the natal hostplant of insects
(Jackson et al. 2012) are necessary to test the potential influence of natal hostplant experience on metapopulation dynamics and local adaptation.
41 Tables
Table 3.1 GLMM results for offspring on and damage to collards among patches. All models included the random effect of patch ID, nested within cage ID, nested within temporal block to account for non-independence of plants within patches. P values less than 0.05 are in bold.
four- v. two-collard mixed v. two-collard patches patches
response predictor estimate P estimate P num. of offspring temporal block -0.707 0.034 -1.35 <0.001 per collard plant natal host 0.516 0.298 0.557 0.788 patch type 1.3979 0.012 0.892 0.12 natal host x patch type -1.326 0.015 -0.838 0.152 estimated damage temporal block 0.467 0.028 0.327 0.203 per collard plant non-DBM herbivores 0.256 0.235 0.170 0.517 (cm2) natal host 0.590 0.933 0.593 0.041 patch type 0.822 0.085 0.359 0.305 natal host x patch type -0.921 0.027 -0.234 0.614
42 Table 3.2 GLMM results for offspring on and damage to plants within mixed patches. All models included the random effect of cage ID, nested within temporal block to account for non- independence of plants within cages. P-values less than 0.05 are in bold.
collard v mustard plants
response predictor estimate P num. of offspring temporal block -2.063 <0.001 per collard plant natal host -0.405 0.666 plant type -0.039 0.274 natal host x patch type 0.496 0.189 estimated damage temporal block 0.322 0.076 per collard plant non-DBM herbivores 0.066 0.386 (cm2) natal host 0.366 0.552 plant type 1.145 0.238 block x plant type -1.261 <0.001 natal host x patch type -0.528 0.128
43 Figures
Figure 3.1 Example arrangement of patches within a field-cage. Position of patch types was randomized within each cage for the experiments and cages had a height of 0.75 m (not shown).
Figure 3.2 Moth offspring per plant (caterpillars and pupae; Mean ± 1.96 SE) and estimated damage per plant (cm2 leaf tissue removed) on collards among patch types and between plant types within mixed patches. Values for offspring are the deviance from temporal block mean, while values for damage are the deviance from a linear model including block mean and non- diamondback moth herbivores. Offspring on and damage to collards within mixed patches are the same between the figure panels and is presented twice for clarity. * p < 0.05
44
Figure 3.3 Results from small-cage experiments (mean ± 1.96 SE): (A) age at pupation (P < 0.001), (B) weight at pupation (P = 0.12).
45 CHAPTER 4
THE INFLUENCE OF HABITAT PATCH AREA AND PERIMETER-TO- AREA RATIO ON THE MOVEMENT AND DENSITIES OF INSECTS: PREDICTIONS AND OBSERVATIONS
Introduction
Understanding how animal movement and density relate to the spatial distribution of their habitat is a central goal of ecology, and is critical for informing the preservation of habitat to optimize conservation and ecosystem services (Fretwell and Lucas 1970, Debinski and Holt
2000, Kristan et al. 2003, Schultz and Crone 2005, Nathan et al. 2008). When habitats are patchy, the geometric features of patches, such as area and perimeter-to-area ratio, can influence the net migration of animals and their relative densities among patches (Haddad 1999, Tanner
2003, Davis 2004). In larger patches with greater area, for instance, we expect more immigrants and a greater abundance of individuals than in smaller patches (Gilpin and Diamond 1976,
Lomolino 1990, Bowman et al. 2002). By contrast, the relationship between patch area and density—animals per unit area—is less clear (Bowers and Matter 1997, Bender et al. 1998,
Hambäck and Englund 2005). Because density, not abundance, often determines interaction rates among potential mates, competitors, and predators, understanding the geometric features of patches that influence the density of animals is critical to predicting ecological outcomes in patchy landscapes.
Much research has demonstrated that the densities of animals may be influenced by the geometric features of patches, both because they can affect animals’ likelihoods of entering and exiting patches and because one feature—patch area—determines the ‘dilution’ of animals within a given patch (Root 1973, Bowman et al. 2002, Otway et al. 2005, Hambäck and Englund
2005). An early and ambiguously defined verbal model of animal density-patch relationships,
46 ‘the resource concentration hypothesis’, has usually been interpreted to predict that animal densities should increase as patch area increases (Root 1973). However, theoretical and empirical research suggests that the geometric features of patches that influence animal densities are likely to depend on the search behavior of the animal (Bukovinszky et al. 2005, Hambäck and Englund
2005). For example, models of short-range searchers—those that search with short movements, can only detect patches from short distances, or do not have directed movements (i.e. locate patches through kinesis)—are likely to enter patches in proportion to the perimeter (Bowman et al. 2002, Hambäck and Englund 2005). Because patch perimeter is proportionate to the square root of patch area for a given patch shape, the densities of animals with these search behaviors should decrease as patch area increases and their abundances are “diluted” within a greater patch area (Bowman et al. 2002, Hambäck and Englund 2005, Edwards et al. 2018).
While the perimeter-to-area ratio is thus thought to influence the density of short-range searchers, area per se—area independent of the perimeter—may also influence densities of patch inhabitants. As patch area increases, the occurrence of conspecific cues could increase independently of perimeter-to-area ratio and thereby contribute to animal density-patch size relationships. Theoretical and empirical research suggests that when animals use conspecific cues to select habitat, emigration rates may be reduced and early immigration events may feedback to increase later immigration (Turchin 1989, Fletcher 2006, 2009, Hjermann 2018).
Because larger patches are more likely to acquire early immigrants (though not necessarily higher densities of early immigrants), this process could give rise to positive animal density- patch area relationships that are not driven by perimeter-to-area ratio.
Importantly, different animal species are sensitive to different habitat cues and, therefore, the degree to which geometric features of a particular habitat type influence movements and
47 densities of animals is likely to be species specific (Haddad 1999, Pasinelli et al. 2013). For example, relative habitat specialists—animals with a preference for a particular habitat type— may exhibit distinct patterns of density with patch area or area-to-perimeter ratio that are different from those of relative habitat transients—animals that spend more time in the surrounding matrix than a given habitat type.
Experimental tests are necessary to resolve the influence of patch area and perimeter-to- area ratio. Although observational studies have documented the effects of habitat size and perimeter-to-area ratio (e.g. Helzer and Jelinski 1999, Davis 2004, Pasinelli et al. 2013), these studies cannot disentangle the influence of habitat perimeter-to-area ratio from edge effects that may reduce the quality of marginal habitat (Ries et al. 2004). To date, experimental tests of the geometric features of patches that influence animal movement and density have been limited by experimental designs that either confound the influence of area and perimeter-to-area ratio or do not investigate potential interactions between them (Fletcher et al. 2007). For example, the vast majority of experimental studies of patch geometry have manipulated patch area while keeping patch shape constant (e.g. Turchin 1989). These studies confound the influence of area and perimeter-to-area ratio and thus the effect of area per se remains unclear. Other studies have manipulated patch perimeter, while holding area constant and, thus, ignore potential interactions between area and perimeter-to-area ratios (Hamazaki 1996, Grez and Prado 2000, Muriel and
Grez 2002, Collinge and Palmer 2002).
Despite the tendency for patch area and perimeter-to-area ratios to be highly correlated
(Rex and Malanson 1990), natural and human influenced habitats can range from small, low perimeter-to-area ratios (e.g. clumps of sage brush and small garden beds) to large, nearly linear patches with high perimeter-to-area ratios (e.g. riparian habitat and crop rows). Given this
48 variability, it is critical to perform manipulative experiments that tease apart the independent and interactive influences of these two patch attributes to better inform our understanding of how animal densities relate to the spatial distribution of their habitats.
Here, we describe results from a simulation model and a field experiment in which we explored independently the influence of area and perimeter-to-area ratio on the movement and densities of relative patch specialists and transients. We modeled animal movement as a correlated random walk and defined habitat specialists as animals that stay longer in the habitat patch relative to the matrix and transients as animals that stay longer in the matrix relative to the habitat. For the field experiment, we manipulated patches of soybean plants (Glycine max) and recorded the densities and movements of two predacious ground beetles (Carabidae) that differ in their habitat use: Calosoma sayi, which prefers dense vegetation (Young 2008), and Tetracha carolina var. carolina, which forages in more open habitat (Pearson et al. 2006). We had two goals: (1) to establish general predictions for how patch area and perimeter-to-area ratio should influence the density of randomly searching patch specialists and transients and (2) to test those predictions in a large field experiment.
Methods
The Model
To make general predictions about the influence of patch area and perimeter-to-area ratio on animal density, we simulated the undirected movement (i.e., kinesis) of animals using a correlated random walk model in R (R Core Team 2017). Correlated random walk models describe animal movement as a series of steps in which the direction of each step is correlated with the direction of the previous step. In these models, each step is characterized by its length
49 and turn angle, both of which may be influenced by an animal’s location within a landscape (e.g. within a patch or within the matrix). Such models have a rich history in theoretical and conservation ecology for their ability to predict population-level patterns from individual movement and have been described in greater detail in other publications (Turchin 1998).
Rectangular habitat patches of specified areas and area-to-perimeter ratios were randomly positioned within a lattice landscapes (1224 x 1225 cells) with wrapped boundaries. To fit our landscapes into smaller lattices, we oriented the long axis of patches in the same direction.
However, in prior iterations of the model, patch orientation and “isolation” did not influence the qualitative results of our models (results not shown).
For each step in our model, individuals were assigned a direction, Q, and a step length, lp.
The direction of each step was determined by an individual’s current direction with the addition of a turn angle that was drawn from a wrapped normal distribution with a mean of zero and a standard deviation, sp. The standard deviation of the turn angle took one of two values according to whether an individual was in the matrix or in a patch. Likewise, the length of each individual’s step was assigned one of two values according to whether the individual was in the matrix or a patch. Although we would seldom expect animals to exercise such rigid self-discipline in their step lengths (with the exception, perhaps, of military troops and marching bands), assuming fixed step lengths does not influence the qualitative outcomes of correlated random walk models when compared to models that, for example, draw step lengths from exponential distributions
(Turchin 1991). The position of an individual in its next time step was, thus, determined by the following formulae:
� = � + � (0, � ) � = � + � ∗ cos(� ) � = � + � ∗ sin(� )
50 where x and y indicated the individual’s horizontal and vertical position, respectively, on a
Cartesian plane.
Simulations
Our simulations (annotated code will be included at publication) began by generating 20 unique landscapes, each containing one of every specified patch type. We explored densities of individuals in patches with areas that were 400, 600, 800, 1000, or 1200 step units squared and had perimeter to area ratios that were 2:1, 3:1, or 4:1. Because we explored all combinations of these patch areas and perimeter-to-area ratios, each landscape had 15 distinct patches.
Simulations explored how the differential movements of individuals (e.g. step lengths and standard deviations of turn angles) in patches and matrix habitat contributed to the density of animals within patches. We consider relative patch specialists to be animals with shorter step lengths and smaller standard deviations of turn angles within patches. In contrast, we consider relative patch transients to have shorter step lengths and smaller standard deviations of turn angles within the matrix. We explored step lengths that were 1, 15, or 30 arbitrary step units and two extreme cases for standard deviations of turn angles: 2π and , which correspond to extremely low and high correlation among consecutive turn angels, respectively.
For each simulation, 1000 individuals were randomly assigned initial starting coordinates and angles of orientation from uniform distributions. Simulations were run until patch densities appeared to reach a equilibrium: 60,000 time steps. Each parameterization of step lengths and standard deviations of turn angles was run separately in the same 20 landscapes. Simulations were examined for qualitative trends in mean patch density at equilibrium (the last 5000 time steps).
51 Field Experiment System
Calosoma sayi and Tetracha carolina carolina are predaceous beetles of the ground beetle family, Carabidae. They frequently colonize agricultural fields in the southeastern United
States, and are considered important naturally occurring biological control agents (Price and
Shepard 1978, House and All 1981). Both species are nocturnal and crepuscular as adults and take refuge in small burrows or under debris during the day. Calosoma sayi, a.k.a. the black caterpillar hunter, is thought to prey mostly on lepidopterous larvae and pupae (Price and
Shepard 1978) although they feed opportunistically in lab experiments and have been observed hunting both on the ground and in crop canopies (Young 2008). Prior research on congeners demonstrates that they move more slowly and spend more time in dense vegetation than in mown areas (Wallin 1991). In contrast, T. carolina is mostly found in moist areas with sparse vegetation, where it feeds opportunistically on arthropod prey on the soil surface (Pearson et al.
2006, Young 2012).
To study the influence of habitat area and perimeter-to-area ratio on these beetles, we planted soybean patches that were 4, 6, or 12 m2 and had perimeter-to-area ratios that were 2:1 or 4:1 m:m2. Our experiment was thus a 3x2 fully-factorial design with six replicates of patch types that were 4 and 6 m2 and five replicates for patches that were 12 m2 due to spatial limitation. For convenience, patches with low perimeter-to-area ratios will be referred to as
“thick patches”, while those with high perimeter-to-area ratios will be referred to as “thin patches”. These patches were established during the late spring and summer of 2016 on an abandoned agricultural field (approx. 35 x 95m) at the North Florida Research and Education
Center in Quincy, Florida (30.54,-84.59°). Prior to the experiment, the field was tilled and fertilized. We selected a soybean variety with determinate growth and a bushy canopy structure
52 (variety 96M60, DuPont Pioneer, Johnston, IA, USA) to provide dense vegetation. On 19 May
2016, we planted our seeds in swaths consisting of 8 rows each, with 25 cm spacing among rows and approximately 13 seeds per meter of row. Swaths were oriented east to west on the northern half of the field and north to south on the southern half of the field to account for the potential influence of patch orientation. Adjacent swaths of plants were three meters apart from one another, with the exception that the northern and southern halves of the field were separated by nine meters to accommodate the turning radius of the tractor and planting implement. Further, we planted a continuous swath of soybeans in a perimeter around the field as a buffer?, three meters apart from all adjacent patches, which had a three-meter break between the north and south side of the field to allow for the entry and exit of a riding mower. Two weeks after planting, seedlings were sprayed with glyphosate (Roundup, Monsanto, St. Louis, MO, USA) to suppress competition with weeds.
To establish shapes of patches that could reasonably be grown given the constraints of the planting equipment and the size of the field, we used an algorithm in R that explored the area and perimeter-to-area ratio of rectangular patches from which 625 cm2 of area were progressively removed from the patch corners. This process resulted in thick patches with few plants removed from the corners and thin patches with many plants removed from the corners, giving the appearance of short “wings” (Fig. 4.2). Four weeks after planting, we “carved” these patch shapes out of the swaths using a “weed eater” to clip seedlings to ground height.
To estimate relative beetle densities, we used two pitfall traps in each patch, placed 180 cm apart from each other and 25 cm from the edge of the patch. For our purposes pitfall traps were a reliable method, given that we were sampling the same beetle species in a relatively homogeneous habitat (within soybean patches) with repeated sampling (Baars 1979). Pitfall traps
53 consisted of two stacked 475 ml. plastic cups with a 9.5 cm diameter opening buried to surface level. The bottom cup ensured that the surrounding soil was not disturbed as the top cup was checked for captures. Pitfall traps were shielded from the rain by Styrofoam® plates positioned approximately 10 cm above the trap with three wooden skewers. Traps were unbaited and were checked every 24 hrs. On days when traps were not checked, they were covered with tight- fitting lids to prevent unintended captures. We began checking traps on 28 June 2016—when plants were well-established and patch canopies were filled in—and continued until 28 August, when soybean fruit were mature and plants were showing clear signs of senescence.
To understand how C. sayi movements contributed to patterns of relative densities, we marked all captured beetles individually (Sharpie oil-based paint pens; Newell Rubbermaid, Oak Brook,
Illinois, USA) and recorded their collection location. Although we attempted to mark T. carolina, their extremely smooth elytra thwarted our attempts at non-abrasive marking.
Analyses
To analyze the relative densities of beetles among patch types, we used generalized linear mixed models with negative binomial distributions (function ‘glmm.nb’ in package ‘lme4’,
(Bates et al. 2015). These models included Julian date, patch height, patch area, perimeter-to- area ratio, and the interaction between patch area and perimeter-to-area ratio as fixed effects. In addition, the patch identity was treated as a random effect to account for non-independence of repeated measures of the same patch and the effect of Julian date was allowed to vary randomly with patch identity as well (i.e., a random slope model). To determine significance of fixed effects, we used type II Wald’s chi-square tests (function ‘Anova’, package ‘car’, (Fox and
Weisberg 2011).
54 We also examined the influence of patch characteristics on the movement of C. sayi. To do this, we considered all possible “paths”—pairwise combinations of capture and recapture patches. We tallied the number of times each path was “occupied” and fit models treating path occupancy as a function of path length as well as the area and perimeter-to-area ratio of the patch in which the beetle was recaptured. Because we observed only 30 paths out of 782 possible
(recaptured beetles were initially captured in 23 patches x 34 possible recapture paths = 782 possible paths), we modeled recaptures using a zero-inflated Poisson distribution in a generalized linear model with the ‘glmmTBM’ package (Brooks et al. 2017). We determined statistical significance with a Wald Z-test.
Results
Simulations
Results from our models suggest that as animals become more specialized for habitat patches—that is, as individuals’ step lengths increase within the matrix relative to within patches—low perimeter-to-area patches tend to have greater mean density at equilibrium than thin patches (Fig. 4.3). More transient individuals—individuals with longer step lengths within the habitat, have no difference or less pronounced differences among patch types, showing slightly higher densities within high perimeter-to-area patches for some step length combinations.
Density within patches was relatively unaffected by patch area per se, though the variance in equilibrium density (i.e., variance over the last 5000 time steps) decreased as patch area increased (results not shown).
55 Results from our model also revealed transient dynamics within landscapes, whereby patches with higher perimeter-to-area ratios temporarily exhibited greater density than patches with lower perimeter-to-area ratios (Fig. 4.3).
Field Experiment
We caught a total of 186 Calosoma sayi (median = 5, IQR = 3 to 7) and 118 T. carolina
(median = 2, IQR = 1 to 4) over 61 days and found that both patch size and perimeter-to-area ratios have the potential to influence beetle densities, but that their effects depend on beetle identity.
For Calosoma sayi, patch area and perimeter-to-area ratio interacted to determine densities (p = 0.042, Fig. 4.4A, Table 4.1). While patch size had little influence on the C. sayi density within low perimeter-to-area patches (GLMM with data subsetted to thick patches, p =
0.91) beetle density increased significantly with patch size in high perimeter-to-area patches
(GLMM with data subsetted to thin patches, p = 0.002)—with the largest patches having more than twice the density of the smallest patches on average. Plant height was not a significant predictor of C. sayi density (P= 0.501, Table 4.1, Fig. 4.4B).
By contrast, the density of Tetracha carolina was not influenced by patch area (P = 0.93,
Fig. 4.4C), but was best explained by a negative relationship with the height of plants (P < 0.001) and a positive relationship with perimeter-to-area ratio (P = 0.026, figure 4.4D).
Examination of recaptures (n = 31), which accounted for the distance among patches, was consistent with observations of density; beetles were least likely to be recaptured in small patches with high edge-to-area ratios and most likely to be recaptured in large patches with high edge-to-area ratios (Fig. 4.5, Table 4.2).
56 Discussion
To our knowledge, no prior theoretical nor experimental studies have sought to explicitly disentangle the influence of area and perimeter-to-area ratio on the movement and density of animals within habitat patches, despite calls from other authors for such research (Fletcher et al.
2007). By using a correlated random walk model, we developed qualitative predictions relating animal density to patch area and perimeter-to-area ratio for short-range and undirected searchers.
We then tested these predictions for ground beetles in patches of soybean plants and found that patch area and perimeter-to-area ratio influenced beetle movements and densities in a species- specific manner, but not as our models predicted.
Our simple simulations predict a pattern of animal density that is partially consistent with the verbal predictions of the resource concentration hypothesis (Root 1973). Our simulations predict that, when patch step lengths are shorter than matrix step lengths, patches with lower perimeter-to-area ratio should have higher animal densities on average than patches with higher perimeter-to-area ratio. Similarly, the resource concentration hypothesis predicts that larger habitat patches should have greater densities of animals. Because larger patches of a given shape
(e.g., circles) have lower perimeter-to-area ratios, our model is consistent with the resource concentration hypothesis for a given patch shape. Importantly, our model predictions specify that perimeter-to-area ratio, not area per se, determines the mean density of animals within a patch.
For this reason, our simulations depart from the resource concentration hypothesis. Patches with greater area but lower perimeter-to-area ratio would have lower densities of animals than smaller patches with higher perimeter-to-area ratios; the density of animals is not a function of area per se.
57 In contrast to our simulations, other models predict that randomly searching animals should have lower densities in patches with lower perimeter-to-area ratios either because they consider isolated patches with infinite potential immigrants (Hambäck and Englund 2005), or because they do not consider emigration (Bowman et al. 2002, Edwards et al. 2018). In our model, a finite number of animals move among patches. Although patches with lower perimeter- to-area ratios have lower immigration rates, they also retain their immigrants longer (Stamps et al. 1987). As individuals accumulate in patches with lower perimeter-to-area ratios, there are fewer immigrants available for patches with higher perimeter-to-area ratios. If we simulate landscapes with isolated patches, our model predictions align with Hambäck and Englund (2005; results not shown). Our models are also consistent with models that do not consider emigration if we only examine the early, transient dynamics of our model (Fig. 4.4). Because our simulation includes the movement of animals among patches, it provides a more realistic approximation for the spatial distribution of animals at smaller spatial scales where movement among patches is frequent.
Our field experiment demonstrates that patch area and perimeter-to-area ratio can affect species’ densities independently and interactively, but also illustrates the limitations of our theoretical predictions. Contrary to our model predications that lower perimeter-to-area ratio, and not area per se, should increase the densities of relative patch specialists, we found that patch area and perimeter-to-area ratio interacted to influence the density of Calosoma sayi within soybean patches; while beetle density increased with patch area in thin patches, there was no effect of patch area in thick patches. This observation is difficult to interpret, but must result from behaviors or ecological interactions not considered in our model. The inclusion of boundary behavior—turn angles that bias animal movements towards habitat patches when crossing the
58 patch-matrix boundary—has improved model predictions of animal distributions in other empirical studies (Kindvall 1999, Haddad 1999, Schultz and Crone 2001), but was not measured in our study nor considered in our simulation model. The possibility remains, therefore, that some parameterization of boundary behaviors—perhaps boundary behavior that occurs some distance from the patch itself (Crone et al. 2001) could account for the observed pattern of habitat use by C. sayi. Likewise, theoretical and empirical work suggest that conspecific attraction may contribute to positive animal density-patch area relationships (Turchin 1989,
Fletcher 2006, 2009). Perhaps olfactory cues associated with conspecifics were more detectable from thinner patches (e.g. volatile pheromones). Further, it is also possible that the distribution of prey items and predators influenced the patch-use patterns of C. sayi among patches. Although we did not study the predators of C. sayi, the preferred prey item of Calosoma, caterpillars, were collected in pitfall traps during our survey and during a sweep net survey (data not shown) and, consistent with prior work at the same site, their densities did not differ among patches
(Funderburk et al. 1990). Further research, therefore, is needed to understand the behavioral and ecological mechanisms underlying patterns of patch use by C. sayi.
In contrast to C sayi, the pattern of patch use by T. carolina, the relative patch transient, is consistent with both our knowledge of their habitat preference and our theoretical models.
Densities of Tetracha carolina diminished significantly with plant height and, after accounting for plant height, densities were slightly higher in thiner patches (Figure 4.4D). Thinner patches and those with shorter plants are likely to have thinner canopies, thus, T. carolina patch use likely reflects an aversion to dense canopies..
Our field study provides experimental evidence that both area and perimeter-to-area ratio can independently influence animal densities and suggests that both patch characteristics need to
59 be considered when designing habitat patches to optimize ecosystem services and animal conservation. For example, our study suggests that small-scale growers of soybeans attempting to optimize patch shapes for predatory beetles may want to plant patches with greater area and higher perimeter-to-area ratios as this will maximize the density of C. sayi and T. carolina beetles. While studies of habitat fragmentation have demonstrated the importance of considering edge-to-area ratios for conservation because this increased edge habitat may reduce the amount of available core habitat (Ries et al. 2004), our study demonstrates that densities of animals can be influenced by the geometry of patches even for relatively homogenous patch quality. Thus, patch geometry should be considered not only for the potential influence of habitat quality, but because of its influence on animal movement.
This work, through the use of corelated random walks, provides a different approach to predicting the relative densities of animals among habitat patches, but also illustrates the limitations of these predictions. Though our simple movement models predict that perimeter-to- area ratio should determine the relative densities of animals, our field study revealed that both patch area and perimeter-to-area ratio can interact to determine animal densities. This work underscores the need for future research to better understand the movement-related behaviors and ecological interactions that determine patterns of patch use by animals, but also highlights the importance of considering patch area, area-to-perimeter ratio, and species identity when designing or conserving habitat to either augment or reduce animal densities.
60 Tables
Table 4.1 GLMM estimates of fixed effects on beetle densities within patches. P values less than 0.05 are in bold. PAR = perimeter-to-area ratio
C. sayi T. carolina factor β SE DF χ2 p β SE DF χ2 p Julian date 0.090 0.011 1 68.875 <0.001 -0.029 0.010 1 7.992 0.005 patch height 0.008 0.012 1 0.455 0.501 -0.058 0.014 1 21.059 <0.001 area 0.116 0.042 1 3.576 0.058 0.001 0.041 1 0.007 0.933 PAR 1.014 0.493 1 0.256 0.613 -0.637 0.291 1 4.981 0.026 area x PAR -0.122 0.060 1 4.151 0.042 -0.125 0.082 1 1.919 0.166
Table 4.2 Estimates of patch traits on recapture rates of C. sayi (n = 31). Results are from a GLM with a zero-inflated Poisson distribution. P-values less than 0.05 are in bold.
C. sayi factor β SE DF z-value p distance -0.042 0.013 1 -3.264 0.001 patch area 0.156 0.070 1 2.235 0.025 perimeter-to-area ratio (PAR 0.942 0.930 1 1.014 0.311 area x PAR -0.142 0.105 1 -1.347 0.178
61 Figures
Figure 4.1 An example of a randomly generated landscape showing all 15 area x perimeter-to- area ratio combinations for patches.
Figure 4.2 Layout of soybean patches for large field experiment (35 x 95 m).
62
Figure 4.3 Simulated patch density over time in patches that vary in their area and perimeter-to- area ratios. Each line represents the mean of value of a particular patch size and perimeter-to- area ratio combination from 20 distinct landscapes. For ease of interpretation, only the smallest and largest patches are displayed (400 step units2 and 1200 step units2, respectively). Along the diagonal (grey boxes) individuals have the same step lengths in the matrix as the habitat patch. Above the diagonal are transients, those with shorter step lengths in the matrix, while below the diagonal are the relative patch specialists that have shorter step lengths in the patch. In these graphs, spatch = 2π and smatrix = , except for the transients above that diagonal, for whom spatch = and smatrix = 2π.
63
Figure 4.4 Densities of C. sayi in relation to patch area (A) and patch height (B) and densities of T. carolina in relation to patch area (C) and patch height (D) from our large field experiment. Each point represents the total number of beetles found in each patch during the study. Lines are the best-fit estimates from GLMs with negative binomial distributions and are displayed on panels where the variable on the horizontal axis is significant.
64
Figure 4.5 Untransformed recapture estimates of C. sayi (n = 31) from zero-inflated GLM. Patch area and area-to-perimeter ratio refer to the patch of recapture.
65 CHAPTER 5
CONCLUSION
Animals’ resources are often unevenly and patchily distributed across landscapes. For insects moving among plant patches, features of patches—such as the community composition and patch geometry—can influence the movement and densities of insects and the interactions in which they participate. Here, I considered aspects of insect populations—density, natal habitat experience, and species identity—that interact with these patch features to determine the distribution of insects within patches and/or the strength of the indirect effects they mediate among neighboring plants. To address these topics, I used three different systems and found considerable variation among them, but also revealed complexities in insect patch use that have the potential to advance ecological theory and applications, such as pest management.
Insect population densities responded differently to larger patches in my three chapters.
Offspring of bean beetles, Callosobruchus maculatus, had lower densities in larger patches of beans, while offspring of Plutella xylostella and adults of the predaceous beetle, Calosoma sayi, had higher densities in larger plant patches under certain conditions. Reconciling the distinct patterns of insects’ patch use among these systems requires further research into the movement- behaviors of these insects. For example, boundary behavior, diurnal changes in resource- dependent movement, and movements that are relatively independent of resources may explain these differences in insect density-plant density relationships. Correlated random walk models
(e.g. chapter 3) that include boundary behavior—insect movement back towards patches (e.g.
Schultz and Crone 2001)—have the potential to create resource dilution effects (i.e., negative insect density-plant density relationships), such as those seen in the bean beetles (results not shown). Likewise, diurnal changes in resource-dependent movement have the potential to “reset”
66 insect density-plant density relationships on a daily basis, which could allow for the “transient” patterns of resource dilution effects to persist (e.g. figure 4.3 bottom-center panel). Further, insects may exhibit patch-leaving behavior that is relatively independent of the quantity and geometry of resource patches, which might also contribute to negative insect density-plant density relationships (e.g. Root and Karieva 1984). Certainly, fertile ground remains for future empirical and theoretical work to resolve the distinct patch-use patterns of insects and other animals.
My work is likely to inform ecological applications and theory of plant-insect interactions. For instance, my lab work on bean beetles suggests that the effectiveness of using neighboring hostplants to reduce pest insects in agriculture may depend on the density of insect herbivores. Because insect densities often vary widely among years, growers may only want to use mixed-crop pest management strategies certain years. If a grower experiences a strong freeze one year that is likely to reduce regional pest densities, this could inform their use of mixed-crop planting regimes. Likewise, growers seeking to increase the densities of beneficial insects, such as ground beetles, may want to plant their crops in larger, high perimeter-to-area ratio patches, as these were found to enhance the densities of ground beetles in our field experiments.
Much theory linking insect movement to plant patch traits, for example, does not consider the role of conspecific aggregation or aversion (e.g. Hämback and Englund 2005,
Hämback et al. 2014, Edwards et al. 2018, but see Turchin 1989), however, my experimental work on bean beetles suggests that conspecific aversion may be an important factor mediating consumer-resource interactions. Explicitly considering conspecific interactions in theoretical models might, therefore, improve our predictions. Further, theory has not considered how natal
67 habitat experience influences ecological interactions for insect herbivores in patchy landscapes across generations, though the empirical work presented here suggests that this may be important.
In conclusion, the work I have presented here advances our understanding of insect-plant patch relationships by highlighting the independent and interactive roles of patch features that are often confounded in experimental studies (e.g. focal resource density and neighboring resource frequency; patch size and perimeter-to-area ratio) and by accounting for consumer traits that can mediate insect density responses to patch features. This work has the potential to influence how we design and preserve habitat to either reduce the insect interactions we don’t want or increase the ones that we do want.
68 REFERENCES
Agrawal, A. A., J. A. Lau, and P. A. Hambäck. 2006. Community heterogeneity and the evolution of interactions between plants and insect herbivores. The Quarterly Review of Biology 81:349–376.
Anderson, P. and Anton, S. 2014. Experience-based modulation of behavioural responses to plant volatiles and other sensory cues in insect herbivores. - Plant Cell Environ 37: 1826– 1835.
Andersson, P., C. Löfstedt, and P. A. Hambäck. 2013. Insect density–plant density relationships: a modified view of insect responses to resource concentrations. Oecologia 173:1333– 1344.
Andow, D. 1990. Population-dynamics of an insect herbivore in simple and diverse habitats. - Ecology 71: 1006–1017.
Andow, D. 1991. Vegetational Diversity and arthropod population response. Annual Review of Entomology 36:561–586.
Baars, M. A. 1979. Catches in pitfall traps in relation to mean densities of carabid beetles. Oecologia 41:25–46.
Barbosa, P., D. Letourneau, and A. Agrawal. 2012. Insect Outbreaks Revisited. Wiley- Blackwell, Hoboken, New Jersey, USA.
Barbosa, P., J. Hines, I. Kaplan, H. Martinson, A. Szczepaniec, and Z. Szendrei. 2009. Associational resistance and associational susceptibility: Having right or wrong neighbors. Annual Review of Ecology, Evolution, and Systematics 40:1–20.
Barron, A. 2001. The life and death of Hopkins' host-selection principle. - Journal of Insect Behavior 14: 725–737.
Bates, D., M. Mächler, B. Bolker, and S. Walker. 2015. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software 67.
Benard, M. F. and McCauley, S. J. 2008. Integrating across life‐history stages: consequences of natal habitat effects on dispersal. - Am Nat 171: 553–567.
Bender, D. A. et al. 1999. Intercropping cabbage and indian mustard for potential control of lepidopterous and other insects. - HortScience 34: 275–279.
Bender, D. J., T. A. Contreras, and L. Fahrig. 1998. Habitat Loss and Population Decline: A Meta-Analysis of the Patch Size Effect. Ecology 79:517.
Blackiston, D. J. et al. 2008. Retention of memory through metamorphosis: Can a moth remember what it learned as a caterpillar? - PLoS ONE. 69 Bowers, M. A., and S. F. Matter. 1997. Landscape ecology of mammals: relationships between density and patch size. Journal of Mammalogy 74:999–1013.
Bowman, J., N. Cappuccino, and L. Fahrig. 2002. Patch size and population density: the effect of immigration behavior. Conservation Ecology 6.
Brooks, M., K. Kristensen, K. van Benthem, A. Magnusson, C. W. Berg, A. Nielsen, H. J. Skaugh, M. Mächler, and B. Bolker. 2017. glmmTMB balances speed and flexibility among packages for zero-inflated generalized mixed modeling. The R Journal 9:378–400.
Brown, J. L. 1969. Territorial behavior and population regulation in birds: a review and re- evaluation. The Wilson Bulletin 81:293-329.
Bukovinszky, T., R. P. J. Potting, Y. Clough, J. C. van Lenteren, and L. E. M. Vet. 2005. The role of pre- and post- alighting detection mechanisms in the responses to patch size by specialist herbivores. Oikos 109:435–446.
Buntin, G. D. 1998. Cabbage seedpod weevil (Ceutorhynchus assimilis, Paykull) management by trap cropping and its effect on parasitism by Trichomalus perfectus (Walker) in oilseed rape. Crop Protection 17:294-305
Cappuccino, N., and P. W. Price, editors. 1995. Population Dynamics: New Approaches and Synthesis. Academic, San Diego, CA.
Champagne, E., J.-P. Tremblay, and S. D. Côté. 2016. Spatial extent of neighboring plants influences the strength of associational effects on mammal herbivory. Ecosphere 7:e01371.
Clark, C. W., and M. Mangel. 1986. The evolutionary advantages of group foraging. Theoretical Population Biology 30:45–75.
Collinge, S. K., and T. M. Palmer. 2002. The influences of patch shape and boundary contrast on insect response to fragmentation in California grasslands. Landscape ecology 17:647– 656.
Cope, J. M., and C. W. Fox. 2003. Oviposition decisions in the seed beetle, Callosobruchus maculatus (Coleoptera : Bruchidae): effects of seed size on superparasitism. Journal of Stored Products Research 39:355–365.
Couty, A. et al. 2006. The roles of olfaction and vision in host-plant finding by the diamondback moth, Plutella xylostella. - Physiological Entomology 31: 134–145.
Credland, P. F., and A. W. Wright. 1990. Oviposition deterrents of Callosobruchus maculatus (Coleoptera: Bruchidae). Physiological Entomology 15:285–298.
Crone, E. E., D. Doak, and J. Pokki. 2001. Ecological influences on the dynamics of a field vole metapopulation. Ecology 82:831–843. 70 Cronin, G., and M. E. Hay. 1996. Induction of seaweed chemical defenses by amphipod grazing. Ecology. 78:2287-2301
Dávalos, A., and B. Blossey. 2011. Matrix habitat and plant damage influence colonization of purple loosestrife patches by specialist leaf-beetles. Environmental Entomology 40:1074– 1080.
Davis, J. 2007. Preference or desperation? Distinguishing between the natal habitat's effects on habitat choice. - Animal Behaviour 74: 111–119.
Davis, J. M. 2008. Patterns of variation in the influence of natal experience on habitat choice. - The Quarterly Review of Biology 83: 363–380.
Davis, J. M. and Stamps, J. A. 2004. The effect of natal experience on habitat preferences. - Trends in Ecology and Evolution 19: 411–416.
Davis, S. K. 2004. Area sensitivity in grassland passerines: effects of patch size, patch shape, and vegetation structure on bird abundance and occurrence in southern Saskatchewan. The Auk 121:1130–1145. De Moraes, C. M., M. C. Mescher, and J. H. Tumlinson. 2001. Caterpillar-induced nocturnal plant volatiles repel conspecific females. Nature 410:577-578
Debinski, D. M., and R. D. Holt. 2000. A Survey and Overview of Habitat Fragmentation Experiments. Conservation Biology 14:342–355.
Dingle, H. and Drake, V. A. 2007. What Is Migration? - BioScience 57: 113.
Edwards, C. B., J. A. Rosenheim, and M. Segoli. 2018. Aggregating fields of annual crops to form larger-scale monocultures can suppress dispersal-limited herbivores:1–11.
Eigenbrode, S. D., A. N. E. Birch, S. Lindzey, R. Meadow, and W. E. Snyder. 2016. Review: A mechanistic framework to improve understanding and applications of push-pull systems in pest management. Journal of Applied Ecology 53:202–212.
Facknath, S. and Wright, D. 2007. Is host selection in leafminer adults influenced by pre‐ imaginal or early adult experience? - Journal of Applied Entomology 131: 505–512.
Fernandez-Conradi, P., H. Jactel, A. Hampe, M. J. Leiva, and B. Castagneyrol. 2017. The effect of tree genetic diversity on insect herbivory varies with insect abundance. Ecosphere 8:e01637.
Finch, S. and Collier, R. H. 2000. Host-plant selection by insects – a theory based on “appropriate/inappropriate landings” by pest insects of cruciferous plants. - Entomologia Experimentalis et Applicata: 91–102.
Fletcher, R. J., Jr. 2006. Emergent Properties of Conspecific Attraction in Fragmented Landscapes. The American Naturalist 168:207–219.
71 Fletcher, R. J., Jr. 2009. Does attraction to conspecifics explain the patch-size effect? An experimental test. Oikos 118:1139–1147.
Fletcher, R. J., Jr, L. Ries, J. Battin, and A. D. Chalfoun. 2007. The role of habitat area and edge in fragmented landscapes: definitively distinct or inevitably intertwined? Canadian Journal of Zoology 85:1017–1030.
Fox, C. W., M. L. Bush, and F. J. Messina. 2010. Biotypes of the seed beetle Callosobruchus maculatus have differing effects on the germination and growth of their legume hosts. Agricultural and Forest Entomology 12:353–362.
Fox, J. and S. Weisberg. 2011. An R Companion to Applied Regression, Second Edition. Thousand Oaks CA: Sage. URL http://socserv.socsci.mcmaster.ca/jfox/Books/Companion
Fretwell, S. D., and H. L. Lucas. 1970. On territorial behavior and other factors influencing habitat distribution in birds. Acta Biotheoretica 19:37–44.
Funderburk, J. E., A. R. Soffes, and R. D. Barnett. 1990. Plot size and shape in relation to soybean resistance for velvetbean caterpillar (Lepidoptera: Noctuidae). Journal of Economic Entomology 83:2107–2110.
Gill, J. A., K. Norris, P. M. Potts, T. G. Gunnarsson, P. W. Atkinson, and W. J. Sutherland. 2001. The buffer effect and large-scale population regulation in migratory birds. Nature 412:436–438.
Gilpin, M. E., and J. M. Diamond. 1976. Calculation of immigration and extinction curves from the species-area-distance ration. Proceedings of the National Academy of Sciences 73:4130–4134.
Grez, A. A., and E. Prado. 2000. Effect of plant patch shape and surrounding vegetation on the dynamics of predatory coccinellids and their prey Brevicoryne brassicae (Hemiptera : Aphididae). Environmental entomology 29:1244–1250.
Gullan, P. J., and P. Cranston. 2010. The insects: an outline of entomology. Fourth. Wiley- Blackwell, Malaysia.
Haddad, N. M. 1999. Corridor use predicted from behaviors at habitat boundaries. American Naturalist 153:215–227.
Hahn, P. G., and J. L. Orrock. 2016. Neighbor palatability generates associational effects by altering herbivore foraging behavior. Ecology 97:2103-2111
Hamazaki, T. 1996. Effects of patch shape on the number of organisms. Landscape ecology 11:299–306.
72 Hambäck, P. A. and Englund, G. 2005. Patch area, population density and the scaling of migration rates: the resource concentration hypothesis revisited. - Ecology Letters 8: 1057–1065.
Hambäck, P. A., B. D. Inouye, P. Andersson, and N. Underwood. 2014. Effects of plant neighborhoods on plant-herbivore interactions: resource dilution and associational effects. Ecology 95:1370-1383.
Hambäck, P. A. et al. 2007. Habitat specialization, body size, and family identity explain lepidopteran density-area relationships in a cross-continental comparison. - Proceedings of the National Academy of Sciences of the United States of America 104: 8368–8373.
Hanski, I. and Singer, M. C. 2001. Extinction-colonization dynamics and host-plant choice in butterfly metapopulations. - American Naturalist 158: 341–353.
Harcourt, D. G. 1957. Biology of the diamondback moth Plutella maculipennis (Curt.) (Lepidoptera; Plutellidae), in eastern Ontario. II. Life history, behavior, and host relationships. - The Canadian Entomologist 89: 554–564.
Hay, M. E. 1986. Associational plant defenses and the maintenance of species diversity - turning competitors into accomplices. American Naturalist 128:617–641.
Helzer, C. J., and D. E. Jelinski. 1999. The relative importance of patch area and perimeter-area ratio to grassland breeding birds. Ecological Applications 9:1448–1458.
Hjermann, D. 2018. Emigration rates and mechanisms Why does emigration increase with decreasing patch size? An experimental test of the boundary encounter rate hypothesis.:1–19.
Holt, R. D. 1977. Predation, apparent competition, and the structure of prey communities. Theoretical Population Biology.12:197-229
Holt, R. D., and B. P. Kotler. 1987. Short-term apparent competition. American Naturalist. 130:412-430
House, G. J., and J. N. All. 1981. Carabid beetles in soybean agroecosystems. Environmental entomology 10:194–196.
Immelmann, K. 1975. Ecological significance of imprinting and early learning. - Annual Review of Ecology and Systematics 6: 15–37.
Inouye, B. D. 2001. Response surface experimental designs for investigating interspecific competition. Ecology 82:2696–2706.
Jackai, L. E. N., and R. A. Daoust. 1986. Insect pests of cowpeas. Annual Review of Entomology 31:95–119.
73 Jackson, R. E. et al. 2012. Analysis of carbon and nitrogen isotopes for natal host determination of tarnished plant bug (Hemiptera: Miridae) adults. - Southwestern Entomologist 37: 123–132.
Jones, R. E. 1977. Movement Patterns and Egg Distribution in Cabbage Butterflies. Journal of Animal Ecology 46:195–212.
Karban, R., I. T. Baldwin, K. J. Baxter, G. Laue, and G. W. Felton. 2000. Communication between plants: induced resistance in wild tobacco plants following clipping of neighboring sagebrush. Oecologia 125:66-71
Kareiva, P. M. 1983. Influence of vegetation texture on herbivore populations: resource concentration and herbivore movement. - In: Denno, R. F. and McClure, M. S. (eds), Variable plants and herbivores in natural and managed systems. Academic Press, ppp. 259–290.
Keesing, F., L. K. Belden, P. Daszak, A. Dobson, C. D. Harvell, R. D. Holt, P. Hudson, A. Jolles, K. E. Jones, C. E. Mitchell, S. S. Myers, T. Bogich, and R. S. Ostfeld. 2010. Impacts of biodiversity on the emergence and transmission of infectious diseases. Nature 468:647–652.
Kennedy, M., and R. D. Gray. 1993. Can ecological theory predict the distribution of foraging animals? A critical analysis of experiments on the ideal free distribution. Oikos 68:158- 166
Kim, T. N. 2017. How plant neighborhood composition influences herbivory: Testing four mechanisms of associational resistance and susceptibility. PLoS ONE. 12 e01796499
Kim, T. N., and N. Underwood. 2015. Plant neighborhood effects on herbivory: damage is both density and frequency dependent. Ecology 96:1431-1437
Kim, T. N., B. J. Spiesman, A. L. Buchanan, A. S. Hakes, S. L. Halpern, B. D. Inouye, A. L. Kilanowski, N. Kortessis, D. W. McNutt, A. C. Merwin, and N. Underwood. 2015. Selective manipulation of a non-dominant plant and its herbivores affects an old-field plant community. Plant Ecology 216:1029–1045.
Kindvall, O. 1999. Dispersal in a metapopulation of the bush cricket, Metrioptera bicolor (Orthoptera : Tettigoniidae). Journal of Animal Ecology 68:172–185.
Kotliar, N. B., and J. A. Wiens. 1990. Multiple scales of patchiness and patch structure: a hierarchical framework for the study of heterogeneity. Oikos 59:253-260.
Kristan, W., A. J. Lynam, J. T. Rotenberry, and M. V. Price. 2003. Alternative causes of edge- abundance relationships in birds and small mammals of California Coastal Sage Scrub. Ecography 26:29–44.
Kulahci, I. G. et al. 2008. Multimodal signals enhance decision making in foraging bumble-bees. 74 - Proceedings of the Royal Society B: Biological Sciences 275: 797–802.
Latheef, M. A., and J. H. Ortiz. 1984. Influence of companion herbs on Phyllotreta cruciferae (Coleoptera: Chrysomelidae) on collard plants. Journal of Economic Entomology 77:80– 82.
Lau, J. A., and S. Y. Strauss. 2005. Insect herbivores drive important indirect effects of exotic plants on native communities. Ecology 86:2990–2997.
Lehtonen, J., and K. Jaatinen. 2016. Safety in numbers: the dilution effect and other drivers of group life in the face of danger. Behavioral Ecology and Sociobiology 70:449–458.
Letourneau, D. K. 1995. Associational susceptibility: effects of cropping pattern and fertilizer on Malawian bean fly levels. Ecological Applications 5:823-829
Levins, R. 1969. Some demographic and genetic consequences of environmental heterogeneity for biological control. - Bulletin of the Entomological Society of America 15:237–240
Lhomme, P. et al. 2017. A context-dependent induction of natal habitat preference in a generalist herbivorous insect. - Behavioral Ecology 29: 360–367.
Liu, S.-S. and Liu, T.-X. 2006. Preimaginal conditioning does not affect oviposition preference in the diamondback moth. - Ecological Entomology 31: 307–315.
Liu, S.-S. et al. 2005. Experience-induced preference for oviposition repellents derived from a non-host plant by a specialist herbivore. - Ecology Letters 8: 722–729.
Liu, Y.-B. and Tabashnik, B. E. 1997. Visual determination of sex of diamondback moth larvae. - The Canadian Entomologist 129: 585–586.
Lomolino, M. V. 1990. The target area hypothesis - the influence of island area on immigration rates of non-volant mammals. Oikos 57:297–300.
Ludwig, S. W., and L. T. Kok. 1998. Evaluation of trap crops to manage harlequin bugs, Murgantia histrionica (Hahn)(Hemiptera: Pentatomidae) on broccoli. Crop Protection 17:123-128
Miller, T. E., and B. D. Inouye. 2011. Confronting two-sex demographic models with data. Ecology 92:2141–2151.
Miller, T. E., S. M. Louda, K. A. Rose, and J. O. Eckberg. 2009. Impacts of insect herbivory on cactus population dynamics: experimental demography across an environmental gradient. Ecological Monographs 79:155–172.
Minkenberg, O. P. J. M. et al. 1992. Egg load as a major source of variability in insect foraging and oviposition behavior. - Oikos 65: 134–142.
75 Muriel, S. B., and A. A. Grez. 2002. Effect of plant patch shape on the distribution and abundance of three lepidopteran species associated with Brassica oleracea. Agricultural and Forest Entomology.
Nathan, R., W. Getz, E. Revilla, M. Holyoak, R. Kadmon, D. Saltz, and P. Smouse. 2008. A movement ecology paradigm for unifying organismal movement research. Proceedings of the National Academy of Sciences 105:19052.
Nufio, C. R., and D. R. Papaj. 2001. Host marking behavior in phytophagous insects and parasitoids. Entomologia Experimentalis et Applicata.99:273-293
Ogilvie, J. E., and J. D. Thomson. 2016. Site fidelity by bees drives pollination facilitation in sequentially blooming plant species. Ecology 97:1442-1451
Otway, S. J., A. Hector, and J. H. Lawton. 2005. Resource dilution effects on specialist insect herbivores in a grassland biodiversity experiment. Journal of Animal Ecology 74:234– 240.
Papaj, D. R. and Prokopy, R. J. 1988. The effect of prior adult experience on components of habitat preference in the apple maggot fly (Rhagoletis pomonella). - Oecologia 76: 538– 543.
Pasinelli, G., K. Meichtry-Stier, S. Birrer, B. Baur, and M. Duss. 2013. Habitat quality and geometry affect patch occupancy of two orthopteran species. PLoS ONE 8:e65850.
Pearson, D., C. B. Knisley, and C. J. Kazilek. 2006. A field guide to the tiger beetles of the United States and Canada. Oxford University Press.
Petit, C. et al. 2017. Do the mechanisms modulating host preference in holometabolous phytophagous insects depend on their host plant specialization? A quantitative literature analysis. - Journal of Pest Science 90: 797–805.
Power, A. G. 1991. Virus spread and vector dynamics in genetically diverse plant populations. Ecology:232–241.
Price, J. F., and M. Shepard. 1978. Calosoma sayi and Labidura riparia predation on noctuid prey in soybeans and locomotor activity. Environmental entomology.
Prokopy, R. J., and B. D. Roitberg. 2001. Joining and avoidance behavior in nonsocial insects. Annual Review of Entomology 46:631–665.
R Core Team 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Rex, K. D., and G. P. Malanson. 1990. The fractal shape of riparian forest patches. Landscape ecology 4:249–258.
76 Ries, L., R. J. Fletcher Jr, J. Battin, and T. D. Sisk. 2004. Ecological responses to habitat edges: mechanisms, models, and variability explained. Annual Review of Ecology, Evolution, and Systematics 35:491–522.
Roesli, R., P. Dobie, and B. M. Gerard. 1991. Strain differences in two species of Callosobruchus (Coleoptera: Bruchidae) developing on seeds of cowpea {Vigna unguiculata (L.)} and green gram {V. radiata (L.)}. Biotropia 4:19-30.
Root, C. M. et al. 2011. Presynaptic facilitation by neuropeptide signaling mediates odor-driven food search. - Cell 145: 133–144.
Root, R. 1973. Organization of a plant-arthropod association in simple and diverse habitats: the fauna of collards (Brassica oleracea). Ecological Monographs 43:95–124.
Root, R. B., and P. M. Kareiva. 1984. The search for resources by cabbage butterflies (Pieris rapae): ecological consequences and adaptive significance of Markovian movements in a patchy environment. Ecology:147–165.
Rowland, H. M., E. Ihalainen, L. Lindström, J. Mappes, and M. P. Speed. 2007. Co-mimics have a mutualistic relationship despite unequal defences. Nature 448:64–67.
Ryan, S. F. and Bidart-Bouzat, M. G. 2014. Natal insect experience with Arabidopsis thalianaplant genotypes influences plasticity in oviposition behavior. - Entomologia Experimentalis et Applicata 152: 216–227.
Sarfraz, M. et al. 2006. Diamondback moth–host plant interactions: Implications for pest management. - Crop Protection 25: 625–639.
Sato, Y. and Kudoh, H. 2017. Herbivore-mediated interaction promotes the maintenance of trichome dimorphism through negative frequency-dependent selection. - American Naturalist 190: E67–E77.
Schneider, C. A. et al. 2012. NIH Image to ImageJ: 25 years of image analysis. - Nature Methods 9: 671–675.
Schultz, C. B., and E. E. Crone. 2001. Edge-mediated dispersal behavior in a prairie butterfly. Ecology 82:1879–1892.
Schultz, C. B. and Crone, E. E. 2005. Patch size and connectivity thresholds for butterfly habitat restoration. - Conservation Biology 19: 887–896.
Skaller, P. M. 1985. Patterns in the distribution of gypsy-moth (Lymantria dispar) (Lepidoptera, Lymantriidae) egg masses over an 11-year population-cycle. Environmental Entomology 14:106–117.
Skaug H., D. Fournier, B. Bolker, A. Magnusson, and A. Nielsen 2016. Generalized linear mixed models using 'AD Model Builder'. R package version 0.8.3.3.
77 Snell-Rood, E. C. 2013. An overview of the evolutionary causes and consequences of behavioural plasticity. - Animal Behaviour 85: 1004–1011.
Stamps, J. A., M. Buechner, and V. V. Krishnan. 1987. The effects of edge permeability and habitat geometry on emigration from patches of habitat. American Naturalist 129:533– 552.
Stamps, J. A. and Blozis, S. A. 2006. Effects of natal experience on habitat selection when individuals make choices in groups: a multilevel analysis. - Animal Behaviour 71: 663– 672.
Stastny, M., and A. A. Agrawal. 2014. Love thy neighbor? Reciprocal impacts between plant community structure and insect herbivory in co‐occurring Asteraceae. Ecology 95:2904- 2914
Steck, M. K., and E. C. Snell-Rood. 2018. Specialization and accuracy of host-searching butterflies in complex and simple environments. Behavioral Ecology 29:486–495.
Stiling, P. D. 1987. The frequency of density dependence in insect host‐parasitoid systems. Ecology 68:844-856
Svanbäck, R., and D. I. Bolnick. 2007. Intraspecific competition drives increased resource use diversity within a natural population. Proceedings of the Royal Society B: Biological Sciences 274:839–844.
Tahvanainen, J. O., and R. B. Root. 1972. The influence of vegetational diversity on the population ecology of a specialized herbivore, Phyllotreta cruciferae (Coleoptera: Chrysomelidae). Oecologia 10:321–346.
Tang, J.D., Hawley, J., Mitchell, B.M., and Shelton, A.M. 1989 ‘Rearing Diamondback Moth on Artificial Diet and Foliage’ (30 min).
Tanner, J. E. 2003. Patch shape and orientation influences on seagrass epifauna are mediated by dispersal abilities. Oikos 100:517–524.
Turchin, P. 1989. Population Consequences of Aggregative Movement. Journal of Animal Ecology 58:75–100.
Turchin, P. 1991. Translating Foraging Movements in Heterogeneous Environments Into the Spatial-Distribution of Foragers. Ecology 72:1253–1266.
Turchin, P. 1998. Quantitative analysis of movement: measuring and modeling population redistribution in animals and plants. Sunderland: Sinauer Associates.
Thoming, G. et al. 2013. Comparison of plant preference hierarchies of male and female moths and the impact of larval rearing hosts. - Ecology 94: 1744–1752.
78 Timms, R. 1998. Size‐independent effects of larval host on adult fitness in Callosobruchus maculatus. Ecological Entomology 23:480-483
Turchin, P. 1989. Population consequences of aggregative movement. Journal of Animal Ecology 58:75–100.
Underwood, N., B. D. Inouye, and P. A. Hambäck. 2014. A conceptual framework for associational effects: When do neighbors matter and how would we know? The Quarterly Review of Biology 89:1–19.
Vafaie, E. K. et al. 2013. Does rearing an aphid parasitoid on one host affect its ability to parasitize another species? - Agricultural and Forest Entomology 15: 366–374.
Verschut, T. A. et al. 2017a. Sensory mutations in Drosophila melanogaster influence associational effects between resources during oviposition. - Scientific Reports: 1–10.
Verschut, T. A. et al. 2017b. Mating affects resource selection and modulates associational effects between neighbouring resources. - Oikos 126: 1708–1716.
Verschut, T. A., P. G. Becher, P. Anderson, and P. A. Hambäck. 2016. Disentangling associational effects: both resource density and resource frequency affect search behaviour in complex environments. Functional Ecology 30:1826–1833.
Wallin, H. 1991. Movement patterns and foraging tactics of a caterpillar hunter inhabiting alfalfa fields. Functional Ecology 5:740–749.
Wasserman, S. S., and D. J. Futuyma. 1981. Evolution of host plant utilization in laboratory populations of the southern cowpea weevil, Callosobruchus maculatus Fabricius (Coleoptera: Bruchidae). Evolution 35:605-617.
White, J. A., and T. G. Whitham. 2000. Associational susceptibility of cottonwood to a box elder herbivore. Ecology 81:1795–1803.
Young, O. P. 2008. Body weight and survival of Calosoma sayi (Coleoptera: Carabidae) during laboratory feeding regimes. Annals of the Entomological Society of America 101:104– 112.
Young, O. P. 2012. Laboratory evaluation of Tetracha carolina (Coleoptera: Carabidae: Cicindelinae) as a predator of ground-surface arthropods in an old-field habitat. Entomological news 122:192–197.
79 BIOGRAPHICAL SKETCH
Andrew C. Merwin
PROFESSIONAL POSITIONS: Postdoctoral Fellow 2018 - Present University of Nebraska, Lincoln Instructor of Record Summers 2017 & 2018 Florida State University
EDUCATION: Ph.D. Biological Science 2018 Florida State University M.S. Entomology 2012 University of California, Davis B.S. Biology 2008 California State University, Long Beach B.A. Spanish 2006 University of California, Los Angeles
PUBLICATIONS: * indicates student co-author Merwin, A.C., A. Yilmaz*. 2018 Flight capacity and diel flight activity of the kudzu bug, Megacopta cribraria. Journal of Applied Entomology 2018:00;1-6 Merwin, A.C., N. Underwood, B.D. Inouye. 2017 Consumer density reduces the strength of neighborhood effects in a model system. Ecology 98(11): 2904-2913 Kim, T. N., B. J. Spiesman, A. L. Buchanan, A. Hakes, S. L. Halpern, B. D. Inouye, A. L. Kilanowski, N. Kortessis, D. W. McNutt, A. C. Merwin, and N. Underwood. 2015. Selective removal of insect herbivores influences an old-field plant community. Plant Ecology 216: 1029-1045.
80 Merwin, A. C., & M.P. Parrella. 2014. Preference induction and the benefits of floral resources for a facultative florivore. Ecological Entomology 39, 405–411. Merwin, A.C., M.A. Aghaee, J. Carlson, M. Shelomi. 2014 The land-grant mission of entomology departments is more economically relevant than ever, American Entomologist; 59(4):217.
MANUSCRIPTS IN REVIEW AND IN PREP: * indicates student co-author
Basili, A.*, A.C. Merwin. In review Parasitism declines with distance from the site of introduction for the kudzu bug, Megacopta cribraria, and depends on host density at different spatial scales. (Target journal – Biological Invasions) Merwin, A.C., B. Inouye, N. Underwood, In prep Homier hostplant patches: natal hostplant experience influences the relationship between insect and hostplant densities. (Target journal – Oikos) Merwin, A.C., J. Hart*, N. Underwood, B. Inouye In prep The influence of habitat patch area and perimeter-to-area ratio on the movement and densities of insects: predictions and observation. (Target journal – American Naturalist) Merwin, A.C In prep Flight capacity and body size decline with increasing distance from core for an invasive herbivorous insect, Megacopta cribraria (Target journal – Biology Letters)
GRANTS AND AWARDS Population Biology Program of Excellence Postdoctoral Fellowship - $90,000 UNL, 2018 - 20 Dissertation Research Grant – $934 FSU, Fall 2017 Outstanding Teaching Assistant Award Nomination – FSU, Spring 2017 Planning Grant – $14,000 FSU, Fall 2016 Trott Grant – $1000, FSU Department of Biological Science, Fall 2015 Godfrey Endowment – $1000, FSU Department of Biological Science, Fall 2012 UCD and Humanities Research Award – $1000 UCD, Winter 2010
81 TEACHING Instructor of record: Summer 2018 BSC 1005 Biology for Non-majors - Genetics Summer 2017 PCB 4674 Evolution
Graduate Teaching Assistantships Fall 2017 BSC 3402L Introduction to Experimental Design Summer 2016 BSC 4933L Animal Diversity Lab Spring 2016 PCB 3043 Introduction to Ecology Fall 2015 PCB 4933 Introduction to Mathematical Modeling in Biology Spring 2015 BSC 3402L Meta-analysis in Biomedical Research Fall 2013 Bot 3143C Field Botany Spring 2013 Bot 3015L Introduction to Plant Biology Lab Fall 2012 BSC 2085L Human Anatomy and Physiology Lab Winter 2012 ENT 10 Natural History of Insects Fall 2010 BIS 2B Introduction to Evolution and Ecology & Fall 2011
Research and Teaching Mentorship Andrew Ibarra – Directed Individual Study Spring 2018 Direct and indirect effects of light environment on Callophrys iris--an imperiled butterfly. Melanie Larson – FSU Teach Intern 2017-2018 Everglades Jenga, outreach activity for tables Zofia Haack – FSU Teach Intern 2016-2017 “Head, thorax, abdomen”, outreach instruction Aaron Yilmaz – Post graduate research volunteer – Summer 2016 Diel flight activity of the kudzu bug, Megacopta cribraria Jacob Hart – NSF REU – Summer 2016 Habitat preference of ground beetles in an agricultural field.
82 Alex Basili – Undergraduate Research Opportunities Program Fall 2015 – Spring 2016 Kudzu bug wingloading along an invasion gradient Kudzu bug egg parasitism along an invasion gradient Ryan Kilbride – Directed Individual Study Spring 2015 Insect Collection and Agricultural Insects Brochure
INVITED PRESENTATIONS Merwin, A.C. Insects in variable landscapes: spatial ecology with beans and beetles. Ecology and Evolution Seminar, Tallahassee, FL. April 21, 2017 Merwin, A.C., Underwood N., Inouye B. Resource density, resource frequency, and herbivore density: Assembling associational effects from behavioral choices. Entomology Society of America, Minneapolis, MN. November 19, 2015 Merwin, A.C. Science Friction: A quick look at America’s “anti-science” undercurrents, their origins, and ways to address them. Entomology Society of America Pacific Branch, Portland, OR March 27, 2012
CONFERENCE PRESENTATIONS AND POSTERS * indicates student co-author
Merwin A.C., Hart J.*, Inouye B., Underwood N. Habitat patch size and shape interact to determine movement and densities of an insect predator. Ecological Society of America, Portland, OR August 11, 2017 Basili A.*, Merwin A.C. Parasitism rate declines with increasing distance from core and decreasing clutch size. Undergraduate Research Opportunities Program, March 2017 (Poster) Kelley C., Mende M., Merwin A.C., Scott M. Involving Others in Sustainable Consumption: The Positive Impact of Exemplary Behavior. Marketing and Public Policy Conference. San Louis Obispo, CA June 2016 (Poster)
Merwin A.C., Consumer density and resource frequency determine the strength of an associational effect. SEEC, Tallahassee, FL March 2016
83 Merwin A.C. A simulation model for how spatial variance in movement probabilities influences population spread. SEEC, Athens, GA March 2015 Merwin A.C., Underwood N., Inouye B. Measuring the importance of neighbors: using a model system to understand how neighboring plants and herbivore density influence herbivore damage. Entomological Society of America, Portland, OR November 2014 Merwin A.C., Underwood N., Inouye B. Measuring the importance of neighbors: consumer and focal-resource densities mediate associational effects in a model system. Ecological Society of America, Sacramento, CA August 2014 Merwin A.C., Cassara J., Hoover D., Ralicki H., Saltzberg C., Inouye B, Miller T. The effects of size structure for a top predator cascade down trophic levels in a pitcher plant community. ESA, Minneapolis, MN August 2013 (Poster) Merwin, A.C. and Parrella, M.P. Within-plant preference induction and the benefits of floral resources for the facultative florivore, Liriomyza trifolii (Burgess). Gordon Research Conference on Plant-Insect Interactions, Ventura, CA, February 2013 (Poster) Merwin, A.C. Petal-feeding behavior of Liriomyza trifolii in Gerbera cut-flower production. Entomological Society of America. Reno, NV November 15, 2011
GUEST LECTURES Introduction to Entomology, FSU, BSC 3402L Fall 2017 & Spring 2018 Introduction to R, FSU, BSC 3402L Fall 2017 & Spring 2018 Agricultural Ecology, FSU, PCB 3043 Spring 2016 Plant-Animal Interactions, FSU, PCB 3043 Spring 2016 w/ McNutt, D.W, Kwapich, C. Entomophagy and the Wonderful World of Insects, FSU, GEO 4093 Fall 2012 November 8, 2012 Insects in Animation. UC Davis, ENT 009 Winter 2011 w/ Maxwell D. Introduction to Biological Control. UC Davis, ENT 009 Fall 2010 Entomologists and the Methods of their Madness. UC Davis, ENT 009, Spring 2010
K- 12 education Afterschool Science Instructor – Shanks Middle School, Quincy, FL. Biweekly, Fall 2015
84 Instructor, Bio Boot Camp, Bohart Museum of Entomology, UC Davis, Summer 2011 Afterschool Science Instructor – Star Inc. Culver City, CA 2008 – 2009 Outreach Instructor – Cabrillo Marine Aquarium, San Pedro, CA 2006-2009 Outdoor Educator – Clear Creek Outdoor School, LAUSD, Los Angeles, CA 2008 – 2009. Bilingual Teaching Assistant (Spanish) – Venice High School, Venice, CA 2005 – 2007
SERVICE
PROFESSIONAL SERVICE Judging committee chair – Southeastern Ecology and Evolution Conference, Tallahassee Fl. March 11-13, 2016 Buell/Braun Award Judge, Ecological Society of America annual meeting, Minneapolis, MN, August 2013 Moderator, Pacific Branch Entomological Society of America, PhD Paper Competition. Portland, OR March 26, 2012.
DEPARTMENTAL SERVICE Co-chair Committee for Diversity and Inclusion Statement. FSU Fall 2017 – Spring 2018 Secretary of Ecology and Evolution Research and Discussion Group. FSU Fall 2015 – Spring 2016 Treasurer of Ecology and Evolution Research and Discussion Group. FSU Fall 2014 – Spring 2015 Tour of Lab and Discussion of Research for FSU WIMSE (Women in Math Science and Engineering) April 2, 2013. Chair for Entomology Picnic Day Events. UC Davis Spring 2011 Vice President of Entomology Graduate Student Association . UC Davis Fall 2011 & Spring 2012
PUBLIC OUTREACH Larson, M. and Merwin, A.C. “Everglades Jenga”, Farmers Market, Tallahassee, FL. April 22, 2018
85 Haack, Z., Merwin, A.C. “Head, Thorax, Abdomen” Unitarian Universalist Church preschool, Tallahassee, FL. February 22, 2017 Vice-chair – Plastic Bag Reduction Committee, Transition Tallahassee. Tallahassee, FL 2015 – 2016 Merwin, A.C. “Our six-legged friends”, Front Porch Children’s Library, Tallahassee, FL. September 12, 2015 McNutt, D.W, Jones, M., Merwin, A.C. Kwapich, C. “Entomophagy and the Wonderful World of Insects”, Science Salon Night, Waterworks. Tallahassee, FL. October 25, 2012 Volunteer Entomologist, Solano Youth Ag Day Solano Co, CA March 2011
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