Ecosystems Analytics Active
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
View video about our ecosystems analytics group.
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
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.
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, 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
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.
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 and habitat selection 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, and developing new movement modeling tools to directly incorporate individual animal movement data into population models.
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 and movement ecology. I’m particularly interested in accounting for effects of humans (e.g., harvest) in spatiotemporal population processes, as well as combining data streams to better estimate process parameters.
Mechanistic movement models for informing demographic parameters - 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 and cause-specific mortality.
Bayesian computation for combining data streams in hierarchical models - Most traditional methods for estimating parameters in Bayesian models applied in ecology rely on computationally intensive algorithms (e.g., MCMC). However, recent advances in computing techniques, such as recursive Bayesian inference, offer potential avenues for improving computation. These improvements are becoming increasingly important as we continue along the path of developing and applying more complex integrated data models (e.g., integrated population models) to refine inferences about ecological processes. My interests with this lie at the interface of mathematical statistics and computing to find ways to efficiently combine different data streams and scale inference in complex hierarchical statistical models.
Below are other science projects associated with this project.
Below are data or web applications associated with this project.
Below are publications associated with this project.
Estimating survival rates with time series of standing age‐structure data
Results and evaluation of a survey to estimate Pacific walrus population size, 2006
Using a genetic mixture model to study phenotypic traits: Differential fecundity among Yukon river Chinook Salmon
A likelihood framework for joint estimation of salmon abundance and migratory timing using telemetric mark-recapture
Divergent movements of walrus and sea ice in the northern Bering Sea
Modeling haul-out behavior of walruses in Bering Sea ice
An improved procedure for detection and enumeration of walrus signatures in airborne thermal imagery
Estimation of walrus populations on sea ice with infrared imagery and aerial photography
Application of airborne thermal imagery to surveys of Pacific walrus
Nesting habitat of the Tule Greater White-fronted Goose Anser albifrons elgasi
Evaluation of aerial survey methods for Dall's sheep
Observer variability in pinniped counts: Ground-based enumeration of walruses at haul-out sites
Below are software products associated with this project.
- Overview
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
View video about our ecosystems analytics group.
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
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.
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, 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
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.
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 and habitat selection 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, and developing new movement modeling tools to directly incorporate individual animal movement data into population models.
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 and movement ecology. I’m particularly interested in accounting for effects of humans (e.g., harvest) in spatiotemporal population processes, as well as combining data streams to better estimate process parameters.
Mechanistic movement models for informing demographic parameters - 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 and cause-specific mortality.
Bayesian computation for combining data streams in hierarchical models - Most traditional methods for estimating parameters in Bayesian models applied in ecology rely on computationally intensive algorithms (e.g., MCMC). However, recent advances in computing techniques, such as recursive Bayesian inference, offer potential avenues for improving computation. These improvements are becoming increasingly important as we continue along the path of developing and applying more complex integrated data models (e.g., integrated population models) to refine inferences about ecological processes. My interests with this lie at the interface of mathematical statistics and computing to find ways to efficiently combine different data streams and scale inference in complex hierarchical statistical models.
- Science
Below are other science projects associated with this project.
- Data
Below are data or web applications associated with this project.
Filter Total Items: 17No Result Found - Multimedia
- Publications
Below are publications associated with this project.
Filter Total Items: 102Estimating survival rates with time series of standing age‐structure data
It has long been recognized that age‐structure data contain useful information for assessing the status and dynamics of wildlife populations. For example, age‐specific survival rates can be estimated with just a single sample from the age distribution of a stable, stationary population. For a population that is not stable, age‐specific survival rates can be estimated using techniques such as inverAuthorsMark S. Udevitz, Peter J. GoganResults and evaluation of a survey to estimate Pacific walrus population size, 2006
In spring 2006, we conducted a collaborative U.S.-Russia survey to estimate abundance of the Pacific walrus (Odobenus rosmarus divergens). The Bering Sea was partitioned into survey blocks, and a systematic random sample of transects within a subset of the blocks was surveyed with airborne thermal scanners using standard strip-transect methodology. Counts of walruses in photographed groups wereAuthorsSuzann G. Speckman, Vladimir I. Chernook, Douglas M. Burn, Mark S. Udevitz, Anatoly A. Kochnev, Alexander Vasilev, Chadwick V. Jay, Alexander Lisovsky, Anthony S. Fischbach, R. Bradley BenterUsing a genetic mixture model to study phenotypic traits: Differential fecundity among Yukon river Chinook Salmon
Fecundity is a vital population characteristic that is directly linked to the productivity of fish populations. Historic data from Yukon River (Alaska) Chinook salmon Oncorhynchus tshawytscha suggest that length‐adjusted fecundity differs among populations within the drainage and either is temporally variable or has declined. Yukon River Chinook salmon have been harvested in large‐mesh gill‐net fiAuthorsJeffrey F. Bromaghin, D.F. Evenson, T.H. McLain, Blair G. FlanneryA likelihood framework for joint estimation of salmon abundance and migratory timing using telemetric mark-recapture
Many fisheries for Pacific salmon Oncorhynchus spp. are actively managed to meet escapement goal objectives. In fisheries where the demand for surplus production is high, an extensive assessment program is needed to achieve the opposing objectives of allowing adequate escapement and fully exploiting the available surplus. Knowledge of abundance is a critical element of such assessment programs. AbAuthorsJeffrey F. Bromaghin, Kenneth S. Gates, Douglas E. PalmerDivergent movements of walrus and sea ice in the northern Bering Sea
The Pacific walrus Odobenus rosmarus divergens is a large Arctic pinniped of the Chukchi and Bering Seas. Reductions of sea ice projected to occur in the Arctic by mid-century raise concerns for conservation of the Pacific walrus. To understand the significance of sea ice loss to the viability of walruses, it would be useful to better understand the spatial associations between the movements of seAuthorsChadwick V. Jay, Mark S. Udevitz, Ron Kwok, Anthony S. Fischbach, David C. DouglasModeling haul-out behavior of walruses in Bering Sea ice
Understanding haul-out behavior of ice-associated pinnipeds is essential for designing and interpreting popula-tion surveys and for assessing effects of potential changes in their ice environments. We used satellite-linked transmitters to obtain sequential information about location and haul-out state for Pacific walruses, Odobenus rosmarus divergens (Il-liger, 1815), in the Bering Sea during ApriAuthorsMark S. Udevitz, Chadwick V. Jay, Anthony S. Fischbach, J. L. Garlich-MillerAn improved procedure for detection and enumeration of walrus signatures in airborne thermal imagery
In recent years, application of remote sensing to marine mammal surveys has been a promising area of investigation for wildlife managers and researchers. In April 2006, the United States and Russia conducted an aerial survey of Pacific walrus (Odobenus rosmarus divergens) using thermal infrared sensors to detect groups of animals resting on pack ice in the Bering Sea. The goal of this survey was tAuthorsDouglas M. Burn, Mark S. Udevitz, Suzann G. Speckman, R. Bradley BenterEstimation of walrus populations on sea ice with infrared imagery and aerial photography
Population sizes of ice-associated pinnipeds have often been estimated with visual or photographic aerial surveys, but these methods require relatively slow speeds and low altitudes, limiting the area they can cover. Recent developments in infrared imagery and its integration with digital photography could allow substantially larger areas to be surveyed and more accurate enumeration of individualsAuthorsMark S. Udevitz, D. M. Burn, M.A. WebberApplication of airborne thermal imagery to surveys of Pacific walrus
We conducted tests of airborne thermal imagery of Pacific walrus to determine if this technology can be used to detect walrus groups on sea ice and estimate the number of walruses present in each group. In April 2002 we collected thermal imagery of 37 walrus groups in the Bering Sea at spatial resolutions ranging from 1-4 m. We also collected high-resolution digital aerial photographs of the sameAuthorsD. M. Burn, M.A. Webber, Mark S. UdevitzNesting habitat of the Tule Greater White-fronted Goose Anser albifrons elgasi
This paper presents the first information on the availability and use of nesting habitat by the rare Tule Greater White-fronted Goose Anser albifrons elgasi. The breeding range was sampled by marking geese with radio transmitters on wintering and moulting areas, and tracking them to nest sites in Alaska. Nesting habitat was described at the scales of ecoregion, wetland ecosystem (National WetlandsAuthorsR.V. Densmore, Craig R. Ely, K.S. Bollinger, S. Kratzer, Mark S. Udevitz, D.J. Fehringer, T.C. RotheEvaluation of aerial survey methods for Dall's sheep
Most Dall's sheep (Ovis dalli dalli) population-monitoring efforts use intensive aerial surveys with no attempt to estimate variance or adjust for potential sightability bias. We used radiocollared sheep to assess factors that could affect sightability of Dall's sheep in standard fixed-wing and helicopter surveys and to evaluate feasibility of methods that might account for sightability bias. WorkAuthorsMark S. Udevitz, Brad S. Shults, Layne G. Adams, Christopher KlecknerObserver variability in pinniped counts: Ground-based enumeration of walruses at haul-out sites
Pinnipeds are often monitored by counting individuals at haul-out sites, but the often large numbers of densely packed individuals at these sites are difficult to enumerate accurately. Errors in enumeration can induce bias and reduce precision in estimates of population size and trend. We used data from paired observers monitoring walrus haul-outs in Bristol Bay, Alaska, to quantify observer variaAuthorsMark S. Udevitz, C.V. Jay, M.B. Cody - Web Tools
- Software
Below are software products associated with this project.
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