Ecosystems Analytics is a group of quantitative biologists and research statisticians with a diverse range of expertise and experience (summarized below). We collaborate with internal and external partners to answer challenging ecological questions that are a high priority of the U.S. Geological Survey Alaska Science Center, sister agencies within the Department of the Interior (DOI), and various state, national, and international institutions.
Our work is largely focused on DOI trust species residing in Arctic and subarctic ecosystems but is broadly based. We consult with partner agencies on monitoring plan design and the application of existing statistical methods, and conduct research to develop innovative analytical techniques and statistical models that generally advance the field of statistical ecology. Work products improve our understanding of ecosystem function and population dynamics, provide management authorities with critical information to support decision-making, and are often useful to forecast future population status.
Return to Ecosystems
About the Ecosystems Analytics Group
Group members may be contacted individually with the information located at bottom of this page or by clicking "View Staff Page" below. If you are unsure who to contact, Vijay Patil is the primary point of contact and can coordinate inquiries with other group members.
Vijay Patil
I conduct wildlife and ecosystems research with collaborators at the Alaska Science Center, Department of Interior agencies, and other state and local partners. Like Dr. Weiser, I provide statistical and programming support to partners at all stages of the research process, from study design to manuscript preparation.
Demographic rate estimation and population modeling - Much of my research involves vital rate estimation and modeling to identify demographic and environmental drivers of population growth. My recent work has included estimating survival costs of reproduction in marmots, testing tag effects and evaluating management strategies for shorebirds, and estimating waterfowl age-ratios. Current projects include a Bayesian hierarchical integrated population model (IPM) to evaluate the effects of phenology mismatch on Arctic-breeding goose populations.
GIS/remote sensing/spatial analysis - The recent proliferation of online datasets has created unprecedented opportunities for ecological and wildlife research at large spatial scales. Recently, I have used field and remote sensing data to model the distribution and abundance of polar bear dens, measure goose forage availability in Arctic wetlands, and design habitat protection scenarios to help mitigate potential impacts of oil and gas development on Alaskan wildlife.
Ecosystems research- I use field data and process-based models to investigate carbon and nutrient dynamics in terrestrial and aquatic systems, and to understand how community composition/biodiversity and physical ecosystem properties interact. For example, I am part of a collaborative effort to understand how climate change and extreme weather events affect lake ecosystems and phytoplankton communities.
Programming and software – I primarily use R for data analysis, modeling, visualization, and interfacing with other analytical tools such as JAGS and program MARK. More recently, I have begun exploring the world of R package development. I also have limited experience with Python, C++, and various flavors of SQL, and can provide assistance with Linux command line tools for data processing and automated data analysis.
Rebecca Taylor
I am a principal investigator who develops new statistical techniques and modifies state-of the-art analytical approaches for complex problems and intractable data. I focus on hard to study species, and I routinely work in both Bayesian and frequentist paradigms. I create explanatory and predictive models, and I also use methods such as causal inference techniques (directed acyclic graphs, propensity score matching), decision tree algorithms and boosting methods, and nonparametric methods. I program in compiled and interpreted languages, including R. I collaborate locally, nationally, and internationally, with other scientists and statisticians in federal agencies, state agencies, academia, and the private sector.
Joe Eisaguirre
My research generally involves developing and applying Bayesian hierarchical models and other quantitative methods to better understand the ecology and inform management of wildlife, in close collaboration with other Department of Interior agencies and state and local partners. This includes spatiotemporal models of population growth and spread, movement models, resource selection and space use models, and integrated data models. Currently, my primary interests relate to advancing spatiotemporal models to better understand the mechanisms governing the growth and spread of populations, as well as forecast changes in distribution and abundance, developing new movement modeling tools to directly incorporate individual animal movement data into population models, and expanding wildlife populations in Alaska.
Spatiotemporal models for wildlife populations - Mechanistic spatiotemporal population models explicitly account for how things like movement and habitat selection affect local and global population processes. They can also provide more precise inference than descriptive or phenomenological techniques, especially when forecasting ecological processes is of interest. My work continues to improve mechanistic spatiotemporal models for understanding wildlife population ecology and movement ecology. I’m particularly interested in accounting for effects of humans (e.g., harvest) in spatiotemporal population processes, combining data streams to better estimate process parameters, and forecasting future spread of populations in Alaska.
Movement ecology and population dynamics - The rapidly growing field of movement ecology, in part fueled by rapidly advancing animal telemetry technologies, has led to the development of numerous modeling tools for inferring things like behavioral changes and habitat selection. However, my interests lie in adapting existing tools and developing new ones to identify key life history events from movement data and scale inference to inform parameters in population models, such as reproductive success, survival, and cause-specific mortality.
Expanding wildlife populations in Alaska – Climate change and human activity are causing the distribution and abundance of wildlife populations to change in Alaska, resulting in ecosystem change and management challenges. For example, sea otter populations have recently experienced profound growth and spread following near extirpation from the maritime fur trade. However, their return is threatening commercial and subsistence fisheries. Additionally, barred owls are a newcomer to southeast Alaska, following their westward spread across North America, and could be having profound effects on other species (e.g., western screech owl) as has occurred in other regions. My interests in these areas relate to modeling and forecasting change to better inform monitoring activities and management plans. This also includes coupling spatiotemporal population models with bioeconomic models to inform “socially optimal” management strategies.
Ecosystems Analytics is a group of quantitative biologists and research statisticians with a diverse range of expertise and experience (summarized below). We collaborate with internal and external partners to answer challenging ecological questions that are a high priority of the U.S. Geological Survey Alaska Science Center, sister agencies within the Department of the Interior (DOI), and various state, national, and international institutions.
Our work is largely focused on DOI trust species residing in Arctic and subarctic ecosystems but is broadly based. We consult with partner agencies on monitoring plan design and the application of existing statistical methods, and conduct research to develop innovative analytical techniques and statistical models that generally advance the field of statistical ecology. Work products improve our understanding of ecosystem function and population dynamics, provide management authorities with critical information to support decision-making, and are often useful to forecast future population status.
Return to Ecosystems
About the Ecosystems Analytics Group
Group members may be contacted individually with the information located at bottom of this page or by clicking "View Staff Page" below. If you are unsure who to contact, Vijay Patil is the primary point of contact and can coordinate inquiries with other group members.
Vijay Patil
I conduct wildlife and ecosystems research with collaborators at the Alaska Science Center, Department of Interior agencies, and other state and local partners. Like Dr. Weiser, I provide statistical and programming support to partners at all stages of the research process, from study design to manuscript preparation.
Demographic rate estimation and population modeling - Much of my research involves vital rate estimation and modeling to identify demographic and environmental drivers of population growth. My recent work has included estimating survival costs of reproduction in marmots, testing tag effects and evaluating management strategies for shorebirds, and estimating waterfowl age-ratios. Current projects include a Bayesian hierarchical integrated population model (IPM) to evaluate the effects of phenology mismatch on Arctic-breeding goose populations.
GIS/remote sensing/spatial analysis - The recent proliferation of online datasets has created unprecedented opportunities for ecological and wildlife research at large spatial scales. Recently, I have used field and remote sensing data to model the distribution and abundance of polar bear dens, measure goose forage availability in Arctic wetlands, and design habitat protection scenarios to help mitigate potential impacts of oil and gas development on Alaskan wildlife.
Ecosystems research- I use field data and process-based models to investigate carbon and nutrient dynamics in terrestrial and aquatic systems, and to understand how community composition/biodiversity and physical ecosystem properties interact. For example, I am part of a collaborative effort to understand how climate change and extreme weather events affect lake ecosystems and phytoplankton communities.
Programming and software – I primarily use R for data analysis, modeling, visualization, and interfacing with other analytical tools such as JAGS and program MARK. More recently, I have begun exploring the world of R package development. I also have limited experience with Python, C++, and various flavors of SQL, and can provide assistance with Linux command line tools for data processing and automated data analysis.
Rebecca Taylor
I am a principal investigator who develops new statistical techniques and modifies state-of the-art analytical approaches for complex problems and intractable data. I focus on hard to study species, and I routinely work in both Bayesian and frequentist paradigms. I create explanatory and predictive models, and I also use methods such as causal inference techniques (directed acyclic graphs, propensity score matching), decision tree algorithms and boosting methods, and nonparametric methods. I program in compiled and interpreted languages, including R. I collaborate locally, nationally, and internationally, with other scientists and statisticians in federal agencies, state agencies, academia, and the private sector.
Joe Eisaguirre
My research generally involves developing and applying Bayesian hierarchical models and other quantitative methods to better understand the ecology and inform management of wildlife, in close collaboration with other Department of Interior agencies and state and local partners. This includes spatiotemporal models of population growth and spread, movement models, resource selection and space use models, and integrated data models. Currently, my primary interests relate to advancing spatiotemporal models to better understand the mechanisms governing the growth and spread of populations, as well as forecast changes in distribution and abundance, developing new movement modeling tools to directly incorporate individual animal movement data into population models, and expanding wildlife populations in Alaska.
Spatiotemporal models for wildlife populations - Mechanistic spatiotemporal population models explicitly account for how things like movement and habitat selection affect local and global population processes. They can also provide more precise inference than descriptive or phenomenological techniques, especially when forecasting ecological processes is of interest. My work continues to improve mechanistic spatiotemporal models for understanding wildlife population ecology and movement ecology. I’m particularly interested in accounting for effects of humans (e.g., harvest) in spatiotemporal population processes, combining data streams to better estimate process parameters, and forecasting future spread of populations in Alaska.
Movement ecology and population dynamics - The rapidly growing field of movement ecology, in part fueled by rapidly advancing animal telemetry technologies, has led to the development of numerous modeling tools for inferring things like behavioral changes and habitat selection. However, my interests lie in adapting existing tools and developing new ones to identify key life history events from movement data and scale inference to inform parameters in population models, such as reproductive success, survival, and cause-specific mortality.
Expanding wildlife populations in Alaska – Climate change and human activity are causing the distribution and abundance of wildlife populations to change in Alaska, resulting in ecosystem change and management challenges. For example, sea otter populations have recently experienced profound growth and spread following near extirpation from the maritime fur trade. However, their return is threatening commercial and subsistence fisheries. Additionally, barred owls are a newcomer to southeast Alaska, following their westward spread across North America, and could be having profound effects on other species (e.g., western screech owl) as has occurred in other regions. My interests in these areas relate to modeling and forecasting change to better inform monitoring activities and management plans. This also includes coupling spatiotemporal population models with bioeconomic models to inform “socially optimal” management strategies.