Research Interests

I am broadly interested in understanding how species respond to their environment in regards to physiology, behavior, and demography. Life-history theory and energy budget concepts underlie my dissertation research on how salamanders might persist under future environmental conditions.

Dissertation Research

For my Ph.D. I work as Network Coordinator for the Salamander Population and Adaptation Research Collaboration Network (SPARCnet). The network uses the red-backed salamander, Plethodon cinereus, as a model organism to understand how amphibians and other dispersal-limited species might adapt to climate change.

I aim to understand how fundamental aspects of energy allocation impact demographic and life-history traits under different environmental conditions. Because of SPARCnet’s collaborative nature, I am able to investigate energy allocation across the species’ range with the hopes of identifying populations that are particularly susceptible to future warming. This includes estimating plasticity and adaptation in metabolic thermal reaction norms, estimating seasonal and thermal reaction norms in growth rates, and estimating demographic sensitivity of red-backed salamanders.

My committee members play a large role in my research:

Rudolf Schilder Comparative and Ecological Physiology Lab

Laura Leites Quantitative Forest Ecology Lab

Julian Avery Lab

Other Current Projects

Impacts of Land-use on Mourning Dove Recruitment
Since the 1960s, mourning dove populations have been declining in the western and central management units, whereas in the eastern management unit, populations have been increasing. These trends have occurred simultaneously with large- scale shifts in agricultural practices and urbanization. David Miller and I are characterizing large-scale patterns in annual recruitment across the species’ range and assessing how land-use factors explain variation in local recruitment. We use 3 data sets to estimate patterns: data on age ratios from >135,000 wings collected from hunters from 2009-2014, annual agricultural crop data (National Cropland Data Layer), and land cover data (National Land Cover Database). We estimate patterns and drivers of recruitment using a hierarchical, conditional auto-regressive model fit within a Bayesian framework.

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