Phenological change and the implications of temporal mismatch
The timing of seasonal events (phenology) plays an integral role in ecosystem functionality. Organisms must often time key life-history events to coincide with favorable environmental conditions and/or peaks in resource availability. As a result of recent climate change, phenological patterns are changing, leading to temporal mismatches in ecological interactions. The principal goals of my work in this area are to: 1) quantify phenological change across time, space, and trophic levels, 2) asses the role of abiotic conditions in driving these patterns, and 3) evaluate the demographic consequences of such changes. These efforts integrate large-scale datasets derived from community science projects, remote sensing, captive studies, and field research. Current work is focused on North American songbirds, as a part of the NSF Macrosystems Program-funded Phenomismatch Project, with past efforts focused on Southern Ocean seabirds.
Remote sensing of trophic interactions and population dynamics
The impacts of global change are both widespread and varied. Assessing how ecological dynamics are responding to these changes, however, remains a difficult task. Data collection efforts in the field are often limited in spatial and temporal scope. Satellite-based remote sensing represents a key opportunity in this regard. Recent advances in satellite sensor technology (e.g., improved spatial, temporal, and spectral resolution) and image classification (e.g., deep learning) have opened up a host of new opportunities for understanding the dynamics of animal populations. One component of this work is focused on characterizing dietary shifts in Antarctic penguin populations, to understand how food web dynamics are changing across time and space. This NASA-funded work relies on the spectral profiles of penguin colonies in satellite imagery to assess dietary change on decadal, continental scales, and seeks to link these changes to demographic processes. Additional, ongoing work seeks to leverage high spatial, high temporal resolution satellite imagery to assess how the abundance, spatial distribution, and movement of large and/or gregarious animals are responding to global change using deep learning and hierarchical Bayesian models.
Environmental forcing and demographic processes
Understanding the factors that drive population dynamics is key if we are to assess the impacts of environmental change on ecological systems. However, demographic processes are complex, often having non-linear associations with relevant environmental conditions. My work in this area aims to answer questions related to niche partitioning and community-level synchrony in demographic processes, the roles of environmental variability and extreme events in population dynamics, and what these might tell us about the resilience of these systems to future environmental change. This research uses a variety of data streams, from bird banding data to remote time-lapse camera images, to address these questions.