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I use quantitative approaches to empirical research and computer modelling to address questions revolving around animal movement ecology. Animal movement data is used for a range of purposes, including:
Here are a few research areas in which I'm currently interested:
- Informing conservation and management of special status species;
- As an indicator of environmental “health” (e.g. management of public lands);
- Improving understanding of human-wildlife conflicts/interactions (e.g. vehicle collisions, livestock depredation, spread of zoonotic diseases);
- Informing management of commercial species (e.g. hunting, commercial fisheries);
- Various basic research questions.
Here are a few research areas in which I'm currently interested:
Why move?
A revolution in animal tracking technology is currently underway. We are now able to track a greater diversity of species than ever before with increasingly sophisticated tracking tools and bio-loggers. As the data come pouring in, movement ecologists are increasingly in need of a unifying theory. Why do organisms move? Or not move? What can we say about the relative fitness of various movement strategies? Why do animals solve the same problem with such stunningly different approaches (e.g., hibernation and migration can both be seen as "solutions" to winter).
Allison Pierce and I are currently developing a simulation modeling tool aimed at uncovering the links between life history/energetics and movement behaviors. This tool uses a genetic algorithm to search for optimal movement solutions to given environmental situations. We use dynamic energy budgeting theory to use energetics/reproductive fitness as the "fitness function" in the genetic algorithm, thereby solving for both life history and movement behavior simultaneously. This approach allows us to generate quantitative theoretical models that attempt to explain the diversity of movement strategies observed in the context of fundamental eco-evolutionary theory.
A revolution in animal tracking technology is currently underway. We are now able to track a greater diversity of species than ever before with increasingly sophisticated tracking tools and bio-loggers. As the data come pouring in, movement ecologists are increasingly in need of a unifying theory. Why do organisms move? Or not move? What can we say about the relative fitness of various movement strategies? Why do animals solve the same problem with such stunningly different approaches (e.g., hibernation and migration can both be seen as "solutions" to winter).
Allison Pierce and I are currently developing a simulation modeling tool aimed at uncovering the links between life history/energetics and movement behaviors. This tool uses a genetic algorithm to search for optimal movement solutions to given environmental situations. We use dynamic energy budgeting theory to use energetics/reproductive fitness as the "fitness function" in the genetic algorithm, thereby solving for both life history and movement behavior simultaneously. This approach allows us to generate quantitative theoretical models that attempt to explain the diversity of movement strategies observed in the context of fundamental eco-evolutionary theory.
Simulation models and the method of multiple working hypotheses
In 1890 Thomas Chamberlin published an article in Science emploring researchers to consider multiple working hypotheses in any inferential endeavor. Chamberlin contended that doing so strengthened inference and guarded against biased outcomes predicated on a researcher's favored ideas. This article is the single most requested reprint the journal Science ever published. Despite its popularity and the critical message it conveys, most researchers in ecology and evolutionary biology still fail to consider multiple hypotheses. I have developed a framework that seeks to further enable researchers to overcome barriers to considering multiple hypotheses in their work. Specifically, I have shown that simulation modeling during the design phase of research can be a powerful tool to avoid collecting data that cannot be used to provide conclusive support for a given hypothesis (model identifiability). My R package checkyourself provides simple code for basic examples of this process in movement ecology and species distributions.
In 1890 Thomas Chamberlin published an article in Science emploring researchers to consider multiple working hypotheses in any inferential endeavor. Chamberlin contended that doing so strengthened inference and guarded against biased outcomes predicated on a researcher's favored ideas. This article is the single most requested reprint the journal Science ever published. Despite its popularity and the critical message it conveys, most researchers in ecology and evolutionary biology still fail to consider multiple hypotheses. I have developed a framework that seeks to further enable researchers to overcome barriers to considering multiple hypotheses in their work. Specifically, I have shown that simulation modeling during the design phase of research can be a powerful tool to avoid collecting data that cannot be used to provide conclusive support for a given hypothesis (model identifiability). My R package checkyourself provides simple code for basic examples of this process in movement ecology and species distributions.
Resource Selection Behavior in a Spatially and Temporally Heterogeneous Environment
I use a small migratory owl (Flammulated Owl; Psiloscops flammeolus) as a model system for studying animal movement behavior. The old-growth forests the species inhabits in North America are highly fire dependent and historically would experience frequent but low severity fires that would “clean out” the underbrush but leave the forest canopy intact. Climate change and recent management practices are moving these fire regimes towards much higher severity fire behavior which results in destructive stand replacing fires which drastically modify the species’ habitat. I am working with the U.S. Forest Service in Colorado’s San Juan Mountains to use archival GPS technology to study habitat use before and after a prescribed fire, to better understand how this species (and other species that inhabit these environments) might respond to changing fire regimes. This study will leverage approaches suggested by the simulation project.
I use a small migratory owl (Flammulated Owl; Psiloscops flammeolus) as a model system for studying animal movement behavior. The old-growth forests the species inhabits in North America are highly fire dependent and historically would experience frequent but low severity fires that would “clean out” the underbrush but leave the forest canopy intact. Climate change and recent management practices are moving these fire regimes towards much higher severity fire behavior which results in destructive stand replacing fires which drastically modify the species’ habitat. I am working with the U.S. Forest Service in Colorado’s San Juan Mountains to use archival GPS technology to study habitat use before and after a prescribed fire, to better understand how this species (and other species that inhabit these environments) might respond to changing fire regimes. This study will leverage approaches suggested by the simulation project.
Migratory Connectivity of a Long-distance Migrant Owl
I am working with the Smithsonian Institute’s Migratory Connectivity Project to better understand the migratory behaviors of Flammulated Owls across the species' range. Generally, our understanding of the biology of migratory populations is limited to the breeding season only, leaving large gaps in our understanding of what is required to maintain healthy populations of many species. (e.g. resource requirements may vary seasonally, species may face survival “bottlenecks” on migration or during winter). By tracking animals with micro-GPS technology across their entire ranges, year-round we can increase our understanding of their full life cycle behaviors, resource requirements, and population trends allowing us to make better management decisions in the context of all the uses of these data described above. This project is still in the early design and pilot data stage.
I am working with the Smithsonian Institute’s Migratory Connectivity Project to better understand the migratory behaviors of Flammulated Owls across the species' range. Generally, our understanding of the biology of migratory populations is limited to the breeding season only, leaving large gaps in our understanding of what is required to maintain healthy populations of many species. (e.g. resource requirements may vary seasonally, species may face survival “bottlenecks” on migration or during winter). By tracking animals with micro-GPS technology across their entire ranges, year-round we can increase our understanding of their full life cycle behaviors, resource requirements, and population trends allowing us to make better management decisions in the context of all the uses of these data described above. This project is still in the early design and pilot data stage.
