Performing Department
Entomology
Non Technical Summary
Global warming is impacting biological processes at both organismal and population levels, with profound effects on species interactions and ecological function. Bees play a critical role as ecosystem service providers, facilitating the reproduction of wild plants and crops, but their populations are in decline. Multiple interacting biotic and abiotic stressors, such as pathogens and pesticides, have been linked to bee declines but it remains unknown how global warming modulates effects of these stressors to impact wild and managed pollinator health. Here, we propose a novel and integrative approach that includes rigorous laboratory and field experiments in physiology, toxicology, and disease ecology, to achieve a mechanistic understanding of how temperature variation, pesticides, and pathogens interact to mediate fitness and survival in crop pollinators at individual and population levels. We will use a comparative approach that includes three critical and representative bee pollinators of agroecosystems in North America: squash bees (wild solitary), bumble bees (wild social), and honey bees (managed social). Empirical data will be used to build mechanistic species distribution models that incorporate data on pesticide exposure, disease pressure, and microclimatic conditions. Our results will address the current gap of knowledge about how multiple stressors impact pollinator health in agroecosystems that are critical to our food supply. Our combined lab, field, and modeling approach will facilitate the identification of key stressors in different habitats to facilitate recommendations for mitigation measures to enhance pollinator health. The research component of this project addresses the AFRI Priority Area of (i) Food safety, nutrition, and health, and the Program Area Priority of (ii) Investigating factors that influence the abundance, diversity, and health of pollinators.
Animal Health Component
0%
Research Effort Categories
Basic
80%
Applied
20%
Developmental
(N/A)
Goals / Objectives
Our goal is to mechanistically quantify how biotic and abiotic stressors shape individual-level and population-level responses in order to better manage and mitigate regional and national pollinator declines in the face of global change.Objective 1. Quantify the interacting effects of pathogen pressure and pesticide exposure on individual-level heat tolerance, energetics, and fitness for social and solitary bee crop pollinators.Objective 2. Determine how microclimate variation, pathogen pressure, and pesticide exposure impact population-level thermal tolerance and fitness across heterogeneous agricultural landscapes.Objective 3. Map stressors and population vulnerability across heterogeneous agricultural landscapes under varying climatic conditions.
Project Methods
Objective 1. Quantify the interacting effects of pathogen pressure and pesticide exposure on individual-level heat tolerance, energetics, and fitness for social and solitary bee crop pollinators.Full factorial experiment combiningpathogen and pesticide treatments. Response variables will include thermal tolerance, energetics at rest (indicative of stress level and/or maintenance costs incurred), and lipid reserves.Objective 2. Determine how microclimate variation, pathogen pressure, and pesticide exposure impact population-level thermal tolerance and fitness across heterogeneous agricultural landscapes. We will characterize thermal tolerance and fitness traits in bees from the 3 focal species collected from 15 cucurbit landscapes in Pennsylvania, North Carolina, and Texas (total = 45 sites). E. pruinosa (~30 females, ~30 males), B. impatiens (~30 female workers) and A. mellifera (~30 foragers) will be collected from each site (n=15 location/region and 450 individuals/species/region). Individuals will be randomly split into the CTmin and CTmax experiments at the beginning of the field season in each region (late-May Texas, early-July North Carolina, mid-July Pennsylvania).Objective 3. Map stressors and population vulnerability across heterogeneous agricultural landscapes under varying climatic conditions.We will integrate our results from objectives 1 and 2 to inform the parameterization of physiological models that will be incorporated into mechanistic species distribution models of E. pruinosa, B. impatiens, and A. mellifera populations. For this objective, we will take advantage of existing spatial modeling tools (e.g., Maxent and Niche MapperR) to develop pollinator suitability models incorporating climate variables at broad and fine scales, as well as species-specific thermal tolerance data under different scenarios (e.g., presence or absence of biotic and abiotic stressors).