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Group Member Research

Group members may be contacted individually with the information located at the right side or bottom of this page. If you are unsure who to contact, Emily Weiser will coordinate with other group members. If you need more information, clicking on each group member’s name will redirect you to their individual USGS page.

Emily Weiser

Sources/Usage: Public Domain.

I develop and use quantitative tools to inform on-the-ground conservation and management of birds and other wildlife, often in close collaboration with partners such as the U.S. Fish and Wildlife Service. My areas of expertise include demographic analyses, population modeling, monitoring design, power analysis, data simulation, Bayesian modeling, and use of R for analysis and visualization.

Demography and population modeling – My work has involved estimating survival in a mark-recapture framework, quantifying daily survival rates, and building simulation-based or matrix-based population models to evaluate population trends. Recent examples include estimating annual adult survival, nest survival, and influence of vital rates on population trends in Arctic-breeding shorebirds.

Monitoring design and power analysis – I have a keen interest in designing monitoring programs or studies to effectively and safely address the question at hand. Previously, I’ve worked to inform the design of a continental-scale monitoring program for monarch butterflies, evaluated how markers or tracking tags affect shorebirds, and identified statistically robust options for monitoring nest survival of shorebirds. Current work includes evaluating the design of a photographic aerial survey for brant.

Programming and software – I have experience with high-performance computing on the USGS supercomputers and Bayesian analysis in JAGS. I use R extensively for data manipulation, simple or complex modeling, data simulation, spatial analysis and mapping, producing publication-quality graphics, and interfacing with JAGS.

Sources/Usage: Public Domain.

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

Sources/Usage: Public Domain.

I am a principal investigator who develops new statistical techniques and modifies state-of the-art analytical approaches for complex problems and intractable data, which are frequently sparse, biased and/or imprecise, possibly with large knowledge gaps. I focus on critical management decisions involving hard to study species, often in a changing environment. I routinely work in both Bayesian and frequentist paradigms.

Estimating demographic rates and abundance with emerging methods and multiple data types - Recent work under this theme includes evaluation of survival rate estimators based on standing age structure data that relax the (often used but generally unrealistic) stable age structure assumption, and integrated population modeling to estimate demographic rates using multiple data types, sparsely scattered over a multi-decade timespan. For example, the integrated population models have provided the only rigorous, robust estimates of Pacific walrus demographic rates and population trend to date. Current projects include work with age at death distributions, state-misclassification (as opposed to state-uncertainty) in multistate mark recapture models, developing explicit maximum likelihood estimators that combine capture-mark-recapture data with other data types, and close kin mark recapture estimation.

Mechanistic models and causal inference methods - This theme is geared toward to understanding and predicting effects of environmental change and anthropogenic disturbance on wildlife populations. Recent work has forecasted effects of sea ice loss on Pacific walruses and evaluated effects of increased vessel traffic (which occurs secondary to sea ice loss), also on walruses. The mechanistic models link environmental change and anthropogenic influences to 1) animal movement and behavior, 2) bioenergetics and body condition and 3) demography and population dynamics. Causal inference methods focus on obtaining unbiased estimates of a single link in the chain in the presence of multiple confounding factors: they use a combination of treatment and outcome modeling, including techniques such as propensity score-based matching that are rarely used in wildlife studies. 

Jeff Bromaghin

Sources/Usage: Public Domain.

My research involves the development and application of statistical methods and models to improve our understanding of the ecology and population dynamics of species residing in Arctic and sub-Arctic ecosystems, with an emphasis on polar bears and other DOI trust species. The remote and harsh habitats, rapid rate of environmental change, and paucity of data on ecological drivers present tremendous challenges that require innovative solutions to overcome. Research products provide critical information to the public and management authorities from local to international levels, and many have broad applicability that advance the discipline of statistical ecology.

Modeling Population Dynamics - Research in this area generally involves the development and application of models to estimate key demographic rates, such as reproduction and survival, that underly change in population abundance and composition through time. Past work has included mark-recapture methodology, the integration of multiple data sources to estimate the timing and abundance of migrating mixtures of salmon populations, and the effects of animal capture and handling. Current research involves mark-recapture models that integrate multiple data sources and spatial multistate mark-recapture models for polar bear populations.

Methods in Statistical Ecology - I develop and test the performance of new models and analytical techniques in quantitative ecology. Past work has involved nest survival models, statistical methods in genetics, and size-selectivity in fishery harvests. Most recent research in this area has concerned the use of biotracers (e.g., fatty acids, stable isotopes) to estimate consumer diet composition and animal origins and movements. This research is timely because the diversity and complexity of biotracer methods in ecology is expanding rapidly.