Open Final Dissertation Wanyizhu

The Pennsylvania State University The Graduate School Department of Entomology ASSESSING IMPACTS OF PESTICIDES AND OTHER STRESSORS ON HONEY BEE COLONY HEALTH: EXPERIMENTAL AND MODELING APPROACHES A Dissertation in Entomology and Operations Research by Wanyi Zhu © 2013 Wanyi Zhu Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy May 2013 The dissertation of Wanyi Zhu was reviewed and approved* by the following: James L. Frazier Professor of Entomology Dissertation Co-Advisor Co-Chair of Committee Michael C. Saunders Professor of Entomology Dissertation Co-Advisor Co-Chair of Committee Advisor of Operations Research Major Christopher A. Mullin Professor of Entomology Ottar Bjornstad Professor of Entomology, Biology, and Statistics Timothy Reluga Associate Professor of Mathematics Gary Felton Professor of Entomology Head of the Department of Entomology *Signatures are on file in the Graduate School ii ABSTRACT A healthy honey bee colony is a population of closely interacting individuals that form a highly complex society. However, the combinational energy-draining stresses of illness from environment, nutrition, and human migratory and cultural practices strike honey bee populations day after day, depriving them of long-term health. The possibility of a multi-factorial cause is one of the problems that make investigating colony declines especially complex. Pesticides are a major concern due to their widespread distribution within the hive. Beyond the effects of acute toxicity, pesticides are also likely to cause sublethal effects that result in behavior alteration or disorder of individual bees, together with the synergistic interactions among various pesticides in hive matrices, which eventually trigger serious harm to colony health. Therefore, a combination of mathematical modeling and experimental approaches were proposed to study the honey bee colony dynamics and quantify the colony-level effects of nutritional disturbance due to pesticides. First, a larval rearing method was adapted to assess experimentally the chronic oral toxicity to honey bee larvae of the four most common pesticides detected in pollen and wax - fluvalinate, coumaphos, chlorothalonil, and chlorpyrifos - tested alone and in all combinations. All individual or combined pesticides at hive-residue levels triggered a significant increase in larval mortality compared to untreated larvae by over two fold, with a strong increase after 3 days of exposure. Among these four pesticides, honey bee larvae were most sensitive to chlorothalonil compared to adults. Synergistic toxicity was observed in the binary mixture of chlorothalonil with fluvalinate at the concentrations of 34 mg/L and 3 mg/L, respectively; whereas, when diluted by 10 fold, the interaction switched to antagonism. Chlorothalonil at 34 mg/L was also found to synergize the miticide coumaphos at 8 mg/L. The three and four component mixtures of tested pesticides have mostly demonstrated additive effects in larval bees. One exception is that the addition of coumaphos significantly reduced the toxicity of the fluvalinate and chlorothalonil iii mixture, the only significant effect in all tested ternary mixtures. We also tested the common ‘inert’ ingredient N-methyl-2-pyrrolidone at seven realistic concentrations, and documented its unexpected high toxicity to larval bees compared to adults. Considering the extensive detection of chlorothalonil, its coexistence with other pesticides in diverse combinations especially in hive pollen and wax, and its substantial larval toxicity alone and in mixtures shown here, the potential impacts of fungicides on colony survival and development needed further investigation. Thus, we explored the potential hazard to honey bee larvae of frequently-found fungicides at environmentally relevant levels. Bravo®, its active ingredient (AI) chlorothalonil, and the formulations Nova® and Pristine® at environmentally realistic levels all triggered a significant increase in larval mortality through 6-d continuous dietary exposure. We also found a significant difference in larval toxicity of the fungicide formulation and its AI. Bravo® exhibited a monotonic and positive dose response for larval mortality, with hazard ratios increasing with concentrations; however, chlorothalonil showed a complex nonmonotonic dose response for larval mortality. A critical concentration of 3 mg/L was most toxic to honey bee larvae among those tested. Bravo® EC50 was significantly lower than the AI by a factor of 4.6 to 18.3. Enhanced toxicity of this formulation positively correlated with the length of exposure or the stage of larval development. The pairing of Bravo® and Nova® is the only mixture inducing significant synergism on mortality of larvae older than 3 days, with the mixture eliciting 2-times greater lethality than the expected concentration additive toxicity. This is the first study to report synergism for developing honey bee larvae between the non-systemic fungicide chlorothalonil and systemic EBI fungicide myclobutanil at environmentally relevant dietary levels. For further testing of pesticide impacts at the colony level, and linking of larval responses to effects on later honey bee life stages and colony health, we developed a worker-based, stage- structured model of honey bee population dynamics. This model was formulated with combined iv difference and differential equations, consisting of six discrete stages based on honey bee temporal polyethism: egg, larva, pupa, nurse, house bee and forager stage. It is unique in capturing the adaptive feedback mechanisms in the population and resource dynamics in a healthy bee colony, including the comb pattern formation, brood maintenance and collective foraging behavior. By validating with independent data sets for colony numbers at different latitudes and under different conditions, our model represents the most advanced population model for integrating the top-down differential model and bottom-up difference model that gives the most refined and realistic details of colony population and resource dynamics to date. Two variance-based sensitivity analyses of the model suggested that the disruption of the numerical basis of colony population dynamics has only delayed impacts on colony survival compared to minor changes in colony social structure, especially the nurse-to-forager transition, which can have immediate and drastic consequences. The model simulations also indicated that a balanced allocation of workers with respect to dynamic changes in colony task demands, particularly during the fall season, which is the sensitive stage for colonies to prepare for entering the winter season, is the key to sustain the colony survival. This colony‐level simulation model represents a useful tool that can be used to integrate exposure and effects data at individual bee levels of potential stressors within the social dynamics of a honey bee colony. Based on our modeling results of the most influential role of nurse-to-forager transition in maintaining colony homeostasis, the future risk assessments should include testing pesticide impacts on life-history transition in honey bees. Lastly, as a further step to extend our current modeling efforts, a decision support system (DSS) model (also called knowledge-based system model, expert system) was developed using the NetWeaverTM software, as a new and innovative method of transferring timely, up-to-date decision support to non-specialists and stakeholders for colony management and conservation. This expert system was built upon recent modeling work that determined, described and v quantified the major indicators of the colony dynamics including: 1) reproduction quality of the queen, 2) brood-caring quality of nurse bees, 3) foraging quality of foragers, 4) quality of the nectar and pollen resource in the environment, 5) quantity of the honey and pollen storage in the hive, and 6) disease levels of the hive (the levels of Varroa mite infection). Using associated reference conditions and thresholds for each indicator based on peer-reviewed literatures and model simulations, the DSS model can be developed to compare the current condition (user input) with reference conditions or threshold values, based on a range of arguments and logical relationships, which depict how the key factors characterize the colony dynamics and is defined by our existing model and domain experts. This system will offer effective directions for beekeepers and other stakeholders to determine the condition of honey bee colony, diagnose the potential stressors affecting colony health, and prioritize management plans based on relevance to colony health. vi TABLE OF CONTENTS List of Figures ......................................................................................................................... vii List of Tables ............................................................................................................................ xiii Acknowledgements .................................................................................................................. xv Chapter 1 Introduction ............................................................................................................. 1 Literature Review ............................................................................................................. 5 Background ............................................................................................................... 5 Honey bee colony as a superorganism

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