Overview
We study the spatiotemporal ecology of insects, their communities, and the ecological systems that shape human, animal, and environmental health. Our lab sits at the intersection of global change entomology and One Health, working to understand how insect distributions, phenology, and community dynamics are impacted under changing environmental conditions.
Why It Matters
Insects are both indicators and drivers of environmental change, and they are at the center of many One Health challenges from vector borne disease, food security, and conservation. By building a clearer, more predictive understanding of how insect communities respond to a changing world, our goal is to advance science and contribute usable tools that support decisions protecting human, animal, and environmental health.
Our Approach
Many of the questions we care about cannot be answered by individual field studies. A core part of our work is building reproducible workflows that integrate and harmonize multisource and disparate data across spatiotemporal scales. By integrating ecological theory, geospatial technologies, quantitative modeling, and data science approaches, we aim to understand and predict insect responses to a changing world to advance wellbeing on our planet.
We have strong domain expertise in the ecology of zoonotic arbovirus systems, but also work more broadly across other taxa and systems. We are innovative in integrating data sources and information across sectors (conservation, agriculture, and veterinary and public health) to scale our understanding to improve knowledge and management decisions to improve planetary health.
Our work has been funded through multiple federal and state programs, including NSF, the USDA, Department of Defense, CDC, and Florida Department of Agriculture and Consumer Services, as well and University of Florida Seed Funds
Research Themes
Our work sits within the field of distributional ecology asking questions about where, when, why… and ultimately, what will happen in the future across insect taxa and systems. Because ecological processes operate at multiple spatiotemporal scales, our work spans local, regional, and macroscales, as well as short term and longer time scales.
Distributions
How are insect species' ranges shifting in response to climate, land use, and other drivers? We model past, current, and future distributions of insects and their ecological systems.
Phenology
Timing matters. We study how insect life cycles, activity, and seasonal dynamics are impacted under variable environmental conditions, and what these dynamics mean for trophic interactions and ecosystem processes.
Prediction & Forecasting
We develop predictive and forecasting models to anticipate insect population dynamics, distributions, and transmission hazard under current and future environmental conditions. Our goal is 1) understand the predictability of ecological systems and 2) support proactive, rather than reactive, decision-making.
Decision Support
We work closely with partners across multiple sectors to develop tools that can be used to support data driven management decisions.
Open Data and Tools
Across the broader scientific community, we are active in developing new methods, open data, and tools to facilitate additional research following FAIR and CARE principles.
Phenological overlap increases host–vector interactions and transmission potential, while mismatch reduces both.
Yasmin Tavares used Gaussian Markov Random Fields to capture spatiotemporal dynamics of 2018 Florida Department of Health West Nile virus sentinel chicken seroconversion. Red indicates greater spatiotemporal structure; blue lower structure.
Alex Baecher developed spatiotemporal predictive toward ecological forecasting of West Nile Virus sentinel chickens across Florida. New work will automate forecasting for decision support. https://doi.org/10.1016/j.scitotenv.2025.180308
Amely Bauer is developing R tools to parse overlapping insect generations and predict insect phenological onset and offset. NSF funded work will develop these tools further.
We are collaborative and want to connect!
https://scholar.google.com/citations?hl=en&user=MVSUB9sAAAAJ&view_op=list_works&sortby=pubdate
Campbell Lab GitHub: