My overarching research interests are fairly broad, drawing from a somewhat eclectic educational background that has been in constant development. Below, I've tried to summarize some overarching research themes and foci to date.
Sampling Design, Efficiency, and Inference
Inference and prediction are both constrained by sampling design and analytical objectives. Prior to initiating any study, it is critical to think about study objectives (what to estimate? at what spatiotemporal extent or resolution?) and how to collect (what methodology and design?) and analyze (what class of model?l) data to meet objectives. The early stages of data collection are an ideal time to refine and optimize sampling, and I place a lot of emphasis on iteratively using data collection to improve data collection.
Accounting For Observational Error and Uncertainty
Imperfect detection is recognized as a truism within most observational studies focusing on wildlife populations. Although many studies account for false negative errors, false positive error--which is both common and can cause extreme bias--is often ignored. I develop and apply methodologies to broadly account for known or potential species misclassification across a range of model types.
Inferring, Predicting, and Forecasting Species Distributions
Like many other ecologists, I spend a lot of time working on trying to figure out where organisms are located during specific times and why. I use many different flavors of distribution model to predict, forecast, and make inference about spatial or temporal population variability to address both theoretical and applied objectives.
Species interactions are a key interest for community and behavioral ecologists that are also starting to see greater importance for conservation or management decision-making. I am particularly interested in exploring variation (and general rules for variation) in species interactions and behaviors across different environmental contexts.