Seasonality, phenology, and synchrony in ecological systems

The timing of seasonal events (phenology) plays an integral role in ecosystem function. Organisms must often time key life-history events to coincide with favorable environmental conditions and/or peaks in resource availability. There is growing concern that as phenology changes in response to rapid climatic change, ecological interactions (i.e., predation, competition, mutualisms) are becoming increasingly decoupled in time. However, much remains unknown regarding how and why phenological responses vary across time, space, and trophic levels, and the implications of phenological change for population and coexistence dynamics. My work in this area focuses on exploring the degree to which birds are keeping pace with climate change and the consequences that this might have for the demographics of both North American songbirds and Antarctic seabirds. I am also interested in how individual-level phenological processes manifest themselves as population-level responses, and the role that plasticity, life history traits, and breeding strategies play in these dynamics.

Harnessing the data revolution to understand population responses to global change

Given the complexities of ecological systems, understanding and predicting how population processes are responding to changing environmental conditions is a challenging task. Concurrent with technological advances, new and forthcoming data resources, particularly those from terrestrial- and space-based remote sensors, are fundamentally changing the landscape of opportunity in this area. My work leverages these data resources using advances in statistical modeling and artificial intelligence to quantify how animal populations are responding to global change and to support the long-term monitoring of ecological systems. My NASA-funded work in this area sought to characterize dietary change and its links to demographic trends in penguin populations over decadal, continental scales using the spectral characteristics of guano at Antarctic penguin colonies. Additional, ongoing work uses newly available quantitative tools in conjunction with satellite imagery to understand how Pacific walrus are responding to a changing Arctic. This research leverages AI-based deep learning approaches to identify walrus in satellite imagery, making it possible to collect data at regular time intervals at a range-wide scale. Data streams such as these then provide a means to characterize how the spatiotemporal distribution, abundance, and phenology of animals are responding to rapidly changing conditions, with applications for conservation management and environmental decision making.

Characterizing the spatiotemporal dimensions of intraspecific biodiversity

Quantifying patterns of biodiversity provides a means to understand the eco-evolutionary dynamics that shape ecological communities. While differences among species are often the focus of biodiversity studies, substantial variation also exists within species. Using insights from population ecology, community ecology, and evolutionary ecology, my work explores the role that biotic and abiotic interactions play in driving intraspecific variation over space and time. In this work, I use a set of bioinformatic tools, hierarchical Bayesian models, and advances in spatial statistics to characterize the drivers of intraspecific variation in hundreds of bird species across the Americas. This work explores the role of functional tradeoffs in shaping intraspecific variation, with implications for understanding both existing and new large-scale ecomorphological 'rules'. This work seeks to link morphological differences across space and time to additional axes of variation, including genetic, through collaborative efforts. Future work will extend this framework to other taxa, with implications for understand differences within species, an often ignored but important element of biodiversity.

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