University of Nevada, Reno Aquatic Community Interaction Diversity
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University of Nevada, Reno Aquatic Community Interaction Diversity and Mosquito Larvae A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Ecology Evolution and Conservation Biology by Will Patrick Lumpkin Lee A. Dyer Dissertation Advisor May, 2020 i Abstract Mosquitoes comprise a diverse group of small flies (Diptera) in the family Culicidae which includes an estimated 3,600 described species. Colloquially we know mosquitoes as biting insects that pose a threat to humans and domestic animals as important vectors of disease. Although a minority of the described species of mosquitoes are not known as competent disease vectors, many competent vector species are highly common surrounding human habitations. Despite being important flying insects, mosquitoes undergo an entirely aquatic life cycle as developing larvae and pupae. During these developmental stages immature mosquitoes are most vulnerable to predation and competition for resources. Their habitats are highly variable in many factors including size, invertebrate diversity, and spatial heterogeneity. My dissertation research focuses on the larval stages of mosquitoes. The main questions of my research include: 1. What are the important interactions of co-inhabiting invertebrates including predators and competitors, with mosquito larvae? 2. Does environmental heterogeneity in the form of plant complexity influence the structure of invertebrate diversity in aquatic communities? 3. Does interaction diversity affect the abundance of mosquito larvae? My research includes four complimentary approaches to answering these questions. First, I conducted a meta-analysis on the use of natural enemies to control mosquito populations. Second, I developed simulation models to test the effects of plant, herbivore, and enemy diversity, abundance and diet breadths on sampling interaction diversity in artificial communities. Third, I conducted two identical mesocosm experiments with experimental manipulations of plant diversity and structural complexity in order to test the effects of those on aquatic invertebrate diversity and ii mosquito abundance. Finally, I measured plant diversity and environmental heterogeneity in unmanipulated aquatic field environments to test the effects of plant diversity and environmental heterogeneity on invertebrate diversity and mosquito abundance. The results from my research show several important relationships between environmental heterogeneity, invertebrate diversity, interaction diversity, and mosquito abundance: 1. Natural enemy groups including predators, competitors, parasites, and pathogens can have important negative effects on mosquitoes. 2. Increased predator and competitor diversities reduce larval mosquito abundance through direct and indirect effects. 3. Plant diversity and environmental heterogeneity have positive effects on community invertebrate diversity. 4. Greater interaction diversity in aquatic systems reduces larval mosquito abundance. These results show the importance of protecting and encouraging biodiversity as components of effective larval mosquito control programs. Careful management of aquatic macroyphyte diversity and environmental heterogeneity will help reduce larval mosquito abundance. iii Acknowledgments I have many people to thank for helping me in my doctoral research and guiding me along the way. I would like to thank my committee members Lee Dyer, Angela Smilanich, Mike Teglas, Steven Juliano, and Beth Leger. Throughout the years they have each helped me focus my curiosities into the meaningful questions and projects that are presented in this dissertation. Their experience and support helped me focus my ideas into practical scientific processes and research designs. Most importantly, this manuscript would not have been possible without their open mindedness and belief in my research questions. I would also like to thank the many friends and colleagues that I have had over the years in the Ecology Evolution and Conservation Biology program. I have been constantly impressed by the diversity of people and diversity of scientific interests encompassing the program. It is truly an inspirational place to conduct research and collaborate with others. iv Table of Contents Chapter 1. Introduction…………………………………………………………………………….1 2. A Meta-analysis on the Effectiveness of Biological Mosquito Control………......…...7 Abstract……………………………………………..…………………………......7 Introduction………………………………………..………………………………8 Methods……………………………………………...…………………………...11 Results……………………………………………...………………………….....14 Discussion…………………………………………...…………………………...16 3. Simulated tri-trophic networks reveal complex relationships between species diversity and interaction diversity………………………………….…………...………………….24 Abstract……………………………………………..……………………………25 Introduction………………………………………...…………………..………...26 Methods……………………………………………...……………………..…….29 Results……………………………………………………...…………………….36 Discussion…………………….……………………...…………………...……...45 4. Macrophyte Diversity and Complexity Reduce Larval Mosquito Abundance……….57 Abstract……………………………………………..……………………………57 Introduction…………………………………………..…………………………..58 Methods……………………………………………...………………….………..63 Results………………………………………………...…………………...……..67 Discussion…………………………………………...………….………………..73 v 5. Aquatic Complexity and Interaction Diversity Reduce Mosquito Abundance…..…..78 Abstract…………………………………………….…….………..………...…...78 Introduction………………………………………….…….……..…………...….79 Methods…………………………………………….…………..……………..….83 Results……………………………………………….…………..……………….85 Discussion…………………………………………….……………..……..…….89 6. Conclusions……………………………………………….….……………..………...97 7. Literature Cited…………………………………..………..…..…………………….101 vi List of Tables Chapter 2. 1. Important taxa used for classical and neoclassical biological control of mosquitoes…………………………………………………….10 2. Below are the number of effect sizes collected by antagonist category, experimental location (field versus laboratory), container type (artificial, mesocosm, and natural), and by effect measurement target (adult, larval behavior, larval only)..….…………..…..…………..15 3. The mean posterior probability coefficients, standard deviations and 95 % credibility intervals for the effect of total mosquito measurements (A. adult effects, behavioral effects, and larval effects) and larval only effects (B). ………………………………………..…….………….19 Chapter 4. 1. Results from individual Bayesian Regression models based on hypothetical relationships (Figure 1). Estimates with mosquito larvae as response variables were modeled assuming negative binomial distributions.………………………………………….………..72 Chapter 5. 1. The results for the Bayesian linear regression models are shown below including the mean posterior beta coefficients, 95 % credibility intervals, and posterior standard deviations. Below each estimate are the effects of site name as a covariate and the intercepts. Site shows stronger coefficient values for regressions on larval mosquito abundance……………………………………….……..………88 vii List of Figures Chapter 2. 1. Bayesian posterior probability estimates and 95 % credibility intervals for enemy effects of trophic groups on mosquitoes are shown here for all effects (A, adults, larval behavior, larval development) and for only larval development……………………..…...20 2. Bayesian posterior probability estimates and 95 % credibility intervals for enemy effects between experimental container types on mosquitoes are shown here for all effects (A, adults, larval behavior, larval development) and for only larval development (B).…...21 3. Bayesian posterior probability estimates and 95 % credibility intervals for enemy effects between field versus laboratory- conducted experiments on mosquitoes are shown here for all effects (A, adults, larval behavior, larval development) and for only larval development (B)……………………………………………….... 22 4. Bayesian posterior probability estimates and 95 % credibility Intervals for natural enemy effects between family or taxonomic grouping, on mosquitoes are shown here for all effects (A, adults, larval behavior, larval development) and for only larval development (B)………………………………………………………………………..22 Chapter 3. 1. A randomly selected tri-tropic network produced from one of the 1000 simulations. Each black bar is a node representing a unique species, while the grey bars are edges connecting the black bars and represent observed interactions between those two species. Green sections within some of the black bars represent individuals within that particular species that were present in the community, but not involved in trophic interactions (e.g., plants without herbivores). The width of each edge and node within the network denotes the abundance of sampled interactions or species. Only species that were sampled are shown in this network. Numbers above each node denote the species identification number from that particular simulation………………………………………….……..38 viii 2. Posterior probabilities of: A) mean Chao1 estimates of richness for species and interactions, and B) the mean slope of rarefaction curves for species and interactions. Interactions are displayed in grey, while species are shown in white. The error bars represent the 95% High Density Intervals (HDI). Mean slopes were acquired by calculating the slope of each rarefaction curve when half of the species or interactions were sampled. Chao1 estimates of richness were acquired using the `estimateR' function in the vegan package in R………………………..………………………...……………………39 3. Summary plots of semi-partial correlations between the