Research Interests

 

Pattern, Scale, and Complexity

 

logistic_map

The ecological world shows a huge variety of patterns, with spatial dimensions running from the microscopic to the continental, and temporal durations from seconds to millenia. Yet ultimately, all of them emerge from the interactions of individual organisms with each other and with their abiotic environment.

The realization that patterns and dynamics at one scale or level of organization can emerge from seemingly unrelated behaviors and dynamics at a lower level was a huge paradigm shift in ecology. A classic example is Robert May’s logistic map, pictured above: a complex, chaotic pattern with surprising fractal properties emerges from the most comically oversimplified model for population growth imaginable. However, much ecological theory still assumes populations tend towards equilibrium and are distributed evenly in space. “Scaling up” the conclusions of small-scale experiments to accurate large-scale predictions remains extremely difficult.

These are the biggest-picture questions I am interested in. What is predictable, and what is not? How do ecological cause and effect propagate from one scale to another? And what can patterns observed at the macro scale tell us about the micro-scale processes that generated them?

 

Animal Movement

 

simulated_tracks

Animals move to search for food or mates, to escape predators, and to seek out good habitat. Even sessile animals often have a planktonic larval phase. Yet movement is ignored in most traditional ecological models, since its inclusion makes them much more complex, and the mean field approximation is often assumed to apply.

Movement ecology is currently experiencing a research explosion, largely driven by electronic tagging data. Animal movements are interesting because they can teach us about the aspects of the environment that matter to animals. Learning about animal movement is useful because it may help us predict how animal populations will respond to changes in their environment, and the distribution of their predators and prey.

 

Fisheries Oceanography

 

plankton_bloom1

The ocean is a fundamentally different environment for life than terrestrial landscapes.  The ocean “landscape” is a turbulent fluid: it moves, deforms, overturns, and rearranges itself at all spatial and temporal scales.  This uncertain environment has a profound influence on the distribution and abundance of the organisms living in it, and in turn they have evolved to exist in this uncertain environment.  Despite over a century of research on how physical processes influence fish and other marine life, there is still a great deal we don’t know.

 

Fisheries Acoustics

 

Echogram

The ocean is a terrible medium for visually-oriented animals like ourselves, because light dies out quickly in water, especially when it is green with phytoplankton.  Active acoustic instruments–i.e., sonar–let us “see like dolphins” by emitting pulses of sound and listening for echoes from fish and zooplankton in the water column.  Echosounders can be deployed on boats, autonomous underwater vehicles, moorings, or ocean observatories, and can map the distribution of animals in the water quickly and at high spatio-temporal resolution.

 

Quantitative Ecology

 

Statistical Analysis

Natural ecosystems are variable and stochastic, but not entirely unpredictable.  Using the tools of statistics, we can account for this variability and incorporate it into our models in robust and honest ways.

Radar Ornithology

 

Radar ornithology

Above the sea surface, electromagnetic waves travel farther and faster than sound waves, making them the natural choice for observing flying animals.  For observing seabirds—most of which nest in dense colonies—radar has the great advantage of being able to track a significant proportion of a colony simultaneously, even in fog or darkness.  This enables us to see population-level movement dynamics, including social interactions, which would otherwise be invisible.