The USGS Alaska Science Center Ecosystems Analytics program is a group of quantitative biologists and research statisticians who provide analytical support to USGS scientists to answer challenging ecological topics and management questions for USGS partners.
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
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 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.
Below are other science projects associated with this project.
Walrus Research
Polar Bear Population Dynamics
Chugach Imaq Research Collaborative
Annual Data and Model-based Estimates of Pacific Black Brant Age Ratios
Q&A: Improving Aerial Surveys of Geese in Alaska with Aerial Imagery
Below are data or web applications associated with this project.
Movement Data for Migrating Geese Over the Northeast Pacific Ocean, 2018-2021
Walrus Haulout and In-water Activity Levels Relative to Vessel Interactions in the Chukchi Sea, 2012-2015
Polar Bear Continuous Time-Correlated Random Walk (CTCRW) Location Data Derived from Satellite Location Data, Chukchi and Beaufort Seas, July-November 1985-2017
Aerial Photo Imagery from Fall Waterfowl Surveys, Izembek Lagoon, Alaska, 2017-2019
Counts of Birds in Aerial Photos from Fall Waterfowl Surveys, Izembek Lagoon, Alaska, 2017-2019
Arthropod Abundance Data from the Colville River Delta, Alaska
Environmental Data from the Colville River Delta, Alaska
Avian Demographic Data from the Colville River Delta, Alaska
Polar Bear Continuous Time-Correlated Random Walk (CTCRW) Location Data Derived from Satellite Location Data, Southern Beaufort Sea, 1986-2016
Temporal Viral Viability Data from Avian Influenza A Viruses Maintained in Alaska Wetlands Under Experimental and Environmental Conditions
Data and Model-based Estimates from Black Brant (Branta bernicla nigricans) Fall Age Ratio Surveys at Izembek Lagoon, Alaska
Multistate capture and search data from the southern Beaufort Sea polar bear population in Alaska, 2001-2016
The USGS Alaska Science Center Ecosystems Analytics program is a group of quantitative biologists and research statisticians who provide analytical support to USGS scientists to answer challenging ecological topics and management questions for USGS partners.
Below are publications associated with this project.
Assessing the population consequences of disturbance and climate change for the Pacific walrus
Understanding sea otter population change in southeast Alaska
Geese migrating over the Pacific Ocean select altitudes coinciding with offshore wind turbine blades
Rayleigh step-selection functions and connections to continuous-time mechanistic movement models
Informing management of recovering predators and their prey with ecological diffusion models
A hierarchical modelling framework for estimating individual- and population-level reproductive success from movement data
Optimizing surveys of fall-staging geese using aerial imagery and automated counting
Estimating reproductive and juvenile survival rates when offspring ages are uncertain: A novel multievent mark-resight model with beluga whale case study
Brown bear–sea otter interactions along the Katmai coast: Terrestrial and nearshore communities linked by predation
Barrier islands influence the assimilation of terrestrial energy in nearshore fishes
Diet energy density estimated from isotopes in predator hair associated with survival, habitat, and population dynamics
Estimating Pacific walrus abundance and survival with multievent mark-recapture models
Arctic marine ecosystems are undergoing rapid physical and biological change associated with climate warming and loss of sea ice. Sea ice loss will impact many species through altered spatial and temporal availability of resources. In the Bering and Chukchi Seas, the Pacific walrus Odobenus rosmarus divergens is one species that could be impacted by rapid environmental change, and thus, population
Proportion of time that Pacific Flyway geese are at risk of wind-turbine strikes during transoceanic migrations
This tool shows the proportion of goose locations expected to be in or below a user-specified rotor-swept zone based on expected goose flight altitudes.
Below are software products associated with this project.
North Pacific Pelagic Seabird Database Visualization Tool (NPPSD_viz)
Eelgrass Biomass Model
Scripts to Analyze Altitude Selection in Migrating Pacific Flyway Geese
Rayleigh Step-Selection Functions
algaeClassify
Reproductive Success from Movement Data
QFASA Robustness to Assumption Violations: Computer Code
R scripts for analysis of fall photographic waterfowl surveys, Izembek NWR, Alaska, 2017-2019
Code for analysis of polar bear maternal den abundance and distribution in four regions of northern Alaska and Canada within the Southern Beaufort Sea subpopulation boundary (1982-2015)
Nest Survival Bias Analysis
This R script will run one replicate of one scenario used by Weiser (in review) to quantify biases in estimates of nest survival when nests are not found at the beginning of the nesting interval (age 0). The script simulates nest monitoring histories based on input parameters, applies models with or without an age effect to estimate daily survival rates, and calculates nest survival (to the end of
Arctic Shorebird Population Model
qfasar: Quantitative Fatty Acid Signature Analysis in R
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
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 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.
Below are other science projects associated with this project.
Walrus Research
Polar Bear Population Dynamics
Chugach Imaq Research Collaborative
Annual Data and Model-based Estimates of Pacific Black Brant Age Ratios
Q&A: Improving Aerial Surveys of Geese in Alaska with Aerial Imagery
Below are data or web applications associated with this project.
Movement Data for Migrating Geese Over the Northeast Pacific Ocean, 2018-2021
Walrus Haulout and In-water Activity Levels Relative to Vessel Interactions in the Chukchi Sea, 2012-2015
Polar Bear Continuous Time-Correlated Random Walk (CTCRW) Location Data Derived from Satellite Location Data, Chukchi and Beaufort Seas, July-November 1985-2017
Aerial Photo Imagery from Fall Waterfowl Surveys, Izembek Lagoon, Alaska, 2017-2019
Counts of Birds in Aerial Photos from Fall Waterfowl Surveys, Izembek Lagoon, Alaska, 2017-2019
Arthropod Abundance Data from the Colville River Delta, Alaska
Environmental Data from the Colville River Delta, Alaska
Avian Demographic Data from the Colville River Delta, Alaska
Polar Bear Continuous Time-Correlated Random Walk (CTCRW) Location Data Derived from Satellite Location Data, Southern Beaufort Sea, 1986-2016
Temporal Viral Viability Data from Avian Influenza A Viruses Maintained in Alaska Wetlands Under Experimental and Environmental Conditions
Data and Model-based Estimates from Black Brant (Branta bernicla nigricans) Fall Age Ratio Surveys at Izembek Lagoon, Alaska
Multistate capture and search data from the southern Beaufort Sea polar bear population in Alaska, 2001-2016
The USGS Alaska Science Center Ecosystems Analytics program is a group of quantitative biologists and research statisticians who provide analytical support to USGS scientists to answer challenging ecological topics and management questions for USGS partners.
The USGS Alaska Science Center Ecosystems Analytics program is a group of quantitative biologists and research statisticians who provide analytical support to USGS scientists to answer challenging ecological topics and management questions for USGS partners.
Below are publications associated with this project.
Assessing the population consequences of disturbance and climate change for the Pacific walrus
Understanding sea otter population change in southeast Alaska
Geese migrating over the Pacific Ocean select altitudes coinciding with offshore wind turbine blades
Rayleigh step-selection functions and connections to continuous-time mechanistic movement models
Informing management of recovering predators and their prey with ecological diffusion models
A hierarchical modelling framework for estimating individual- and population-level reproductive success from movement data
Optimizing surveys of fall-staging geese using aerial imagery and automated counting
Estimating reproductive and juvenile survival rates when offspring ages are uncertain: A novel multievent mark-resight model with beluga whale case study
Brown bear–sea otter interactions along the Katmai coast: Terrestrial and nearshore communities linked by predation
Barrier islands influence the assimilation of terrestrial energy in nearshore fishes
Diet energy density estimated from isotopes in predator hair associated with survival, habitat, and population dynamics
Estimating Pacific walrus abundance and survival with multievent mark-recapture models
Arctic marine ecosystems are undergoing rapid physical and biological change associated with climate warming and loss of sea ice. Sea ice loss will impact many species through altered spatial and temporal availability of resources. In the Bering and Chukchi Seas, the Pacific walrus Odobenus rosmarus divergens is one species that could be impacted by rapid environmental change, and thus, population
Proportion of time that Pacific Flyway geese are at risk of wind-turbine strikes during transoceanic migrations
This tool shows the proportion of goose locations expected to be in or below a user-specified rotor-swept zone based on expected goose flight altitudes.
Below are software products associated with this project.
North Pacific Pelagic Seabird Database Visualization Tool (NPPSD_viz)
Eelgrass Biomass Model
Scripts to Analyze Altitude Selection in Migrating Pacific Flyway Geese
Rayleigh Step-Selection Functions
algaeClassify
Reproductive Success from Movement Data
QFASA Robustness to Assumption Violations: Computer Code
R scripts for analysis of fall photographic waterfowl surveys, Izembek NWR, Alaska, 2017-2019
Code for analysis of polar bear maternal den abundance and distribution in four regions of northern Alaska and Canada within the Southern Beaufort Sea subpopulation boundary (1982-2015)
Nest Survival Bias Analysis
This R script will run one replicate of one scenario used by Weiser (in review) to quantify biases in estimates of nest survival when nests are not found at the beginning of the nesting interval (age 0). The script simulates nest monitoring histories based on input parameters, applies models with or without an age effect to estimate daily survival rates, and calculates nest survival (to the end of