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
(Credit: Rebecca Taylor, USGS. 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
(Credit: Mike Lockhart, USGS. 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.
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.
Annual Data and Model-based Estimates of Pacific Black Brant Age Ratios
Below are data or web applications associated with this project.
Aerial Photo Imagery from Fall Waterfowl Surveys, Izembek Lagoon, Alaska, 2017-2019
Data and Model-based Estimates from Black Brant (Branta bernicla nigricans) Fall Age Ratio Surveys at Izembek Lagoon, Alaska
Walrus used and available resource units for northeast Chukchi Sea, 2008-2012
Below are publications associated with this project.
Modeling the spatial and temporal dynamics of land-based polar bear denning in Alaska
Prioritizing habitats based on abundance and distribution of molting waterfowl in the Teshekpuk Lake Special Area of the National Petroleum Reserve, Alaska
Survival and abundance of polar bears in Alaska’s Beaufort Sea, 2001–2016
Fully accounting for nest age reduces bias when quantifying nest survival
TrendPowerTool: A lookup tool for estimating the statistical power of a monitoring program to detect population trends
Analyses on subpopulation abundance and annual number of maternal dens for the U.S. Fish and Wildlife Service on polar bears (Ursus maritimus) in the southern Beaufort Sea, Alaska
Drivers and consequences of apex predator diet composition in the Canadian Beaufort Sea
Sample-size considerations for a study of shorebird nest survival in the 1002 Area, Arctic National Wildlife Refuge, Alaska
Dietary fat concentrations influence fatty acid assimilation patterns in Atlantic pollock (Pollachius virens)
Visualizing populations of North American sea ducks: Maps to guide research and management planning
Balancing sampling intensity against spatial coverage for a community science monitoring programme
Spatio-temporal population change of Arctic-breeding waterbirds on the Arctic Coastal Plain of Alaska
Below are software products associated with this project.
QFASA Robustness to Assumption Violations: Computer Code
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
- 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
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
Example of a photo from the USFWS fall aerial survey for Black Brant at Izembek Lagoon. Inset shows a closer view of four Black Brant (bottom) with a Cackling Goose (top) foraging on eelgrass. We can use artificial intelligence to automate counting birds in each photo to develop population estimates. 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.
Animated plot of migration tracks for adult Snow Geese in the Western Arctic Population, from Fall 2018 to Winter 2019. Each colored dot represents an individual goose. The red polygon on the north slope of Alaska represents the 1002 area of the Arctic National Wildlife Refuge, where these geese briefly stage each fall. Locations are derived from GPS/GSM collars that were deployed on adult Snow Geese from Wrangel Island (n=9) and the Colville River Delta (n=13) in August 2018. These GPS data are being used to better understand the factors that control the timing of spring and fall migrations, to compare the migration pathways of geese from different nesting areas, and to learn about fine-scale patterns of habitat use on their wintering grounds. 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
Walrus reflections in the water. Taken from a USGS research cruise in the Chukchi Sea.
(Credit: Rebecca Taylor, USGS. 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
Large polar bear
(Credit: Mike Lockhart, USGS. 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.
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.
Sources/Usage: Public Domain. Visit Media to see details.A sea otter mom nursing her pup. Photo taken in Prince William Sound, Alaska. A newborn sea otter needs to stay with its mother for six months to learn how to survive on its own. 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.
Annual Data and Model-based Estimates of Pacific Black Brant Age Ratios
Pacific brant are an Arctic-breeding sea goose that stage and feed on seagrasses during the non-breeding season in coastal areas of Alaska. Brant are an important subsistence and sport harvest species and the focus of several population surveys by state and federal agencies. Each fall the entire population stages during migration in Izembek Lagoon, Alaska, presenting a unique opportunity to survey... - Data
Below are data or web applications associated with this project.
Aerial Photo Imagery from Fall Waterfowl Surveys, Izembek Lagoon, Alaska, 2017-2019
The imagery and annotations presented here were generated while testing an aerial photographic survey design to improve repeatability, transparency, and estimation of variance for annual population estimates of geese staging at Izembek Lagoon, Alaska. This dataset includes 1) 131,031 .JPG images captured from a small fixed-wing occupied aircraft, usually at an altitude of about 457 m, over IzembekData and Model-based Estimates from Black Brant (Branta bernicla nigricans) Fall Age Ratio Surveys at Izembek Lagoon, Alaska
These data are in two tables relating to fall age ratios (number of juvenile birds : total birds aged) of Black brant (Branta bernicla nigricans) staging in Izembek Lagoon, Alaska since 1963. The first file is observation data for the birds' age classes during surveys and associated survey characteristics. The second file contains model-based estimates of age ratios by year along with SE, and 95%Walrus used and available resource units for northeast Chukchi Sea, 2008-2012
Sea ice loss represents a stressor to the Pacific walrus, which feeds on benthic macroinvertebrates in the Bering and Chukchi seas. However, no studies have examined the effects of sea ice on foraging walrus space use patterns. Thus, we examined walrus foraging resource selection as a function of proximity to resting substrates and prey biomass with a matched use-availability design. We quantif - Publications
Below are publications associated with this project.
Filter Total Items: 83Modeling the spatial and temporal dynamics of land-based polar bear denning in Alaska
Although polar bears (Ursus maritimus) of the Southern Beaufort Sea (SBS) subpopulation have commonly created maternal dens on sea ice in the past, maternal dens on land have become increasingly prevalent as sea ice declines. This trend creates conditions for increased human–bear interactions associated with local communities and industrial activity. Maternal denning is a vulnerable period in thePrioritizing habitats based on abundance and distribution of molting waterfowl in the Teshekpuk Lake Special Area of the National Petroleum Reserve, Alaska
The National Petroleum Reserve in Alaska (NPR-A) encompasses more than 9.5 million hectares of federally managed land on the Arctic Coastal Plain of northern Alaska, where it supports a diversity of wildlife, including millions of migratory birds. Within the NPR-A, Teshekpuk Lake and the surrounding area provide important habitat for migratory birds and this area has been designated by the BureauSurvival and abundance of polar bears in Alaska’s Beaufort Sea, 2001–2016
The Arctic Ocean is undergoing rapid transformation toward a seasonally ice-free ecosystem. As ice-adapted apex predators, polar bears (Ursus maritimus) are challenged to cope with ongoing habitat degradation and changes in their prey base driven by food-web response to climate warming. Knowledge of polar bear response to environmental change is necessary to understand ecosystem dynamics and inforFully accounting for nest age reduces bias when quantifying nest survival
Accurately measuring nest survival is challenging because nests must be discovered to be monitored, but nests are typically not found on the first day of the nesting interval. Studies of nest survival therefore often monitor a sample that overrepresents older nests. To account for this sampling bias, a daily survival rate (DSR) is estimated and then used to calculate nest survival to the end of thTrendPowerTool: A lookup tool for estimating the statistical power of a monitoring program to detect population trends
A simulation-based power analysis can be used to estimate the sample sizes needed for a successful monitoring program, but requires technical expertise and sometimes extensive computing resources. We developed a web-based lookup app, called TrendPowerTool (https://www.usgs.gov/apps/TrendPowerTool/), to provide guidance for ecological monitoring programs when resources are not available for a simulAnalyses on subpopulation abundance and annual number of maternal dens for the U.S. Fish and Wildlife Service on polar bears (Ursus maritimus) in the southern Beaufort Sea, Alaska
The long-term persistence of polar bears (Ursus maritimus) is threatened by sea-ice loss due to climate change, which is concurrently providing an opportunity in the Arctic for increased anthropogenic activities including natural resource extraction. Mitigating the risk of those activities, which can adversely affect the population dynamics of the southern Beaufort Sea (SBS) subpopulation, is an eDrivers and consequences of apex predator diet composition in the Canadian Beaufort Sea
Polar bears (Ursus maritimus) rely on annual sea ice as their primary habitat for hunting marine mammal prey. Given their long lifespan, wide geographic distribution, and position at the top of the Arctic marine food web, the diet composition of polar bears can provide insights into temporal and spatial ecosystem dynamics related to climate-mediated sea ice loss. Polar bears with the greatest ecolSample-size considerations for a study of shorebird nest survival in the 1002 Area, Arctic National Wildlife Refuge, Alaska
Authorization of lease sales for oil development in the 1002 Area of the Arctic National Wildlife Refuge has highlighted gaps in information about biological communities in the area. The U.S. Fish and Wildlife Service, which is planning a study to evaluate spatial variation in the nest survival of tundra-breeding shorebirds to identify hotspots with high nest survival, sought advice from the U.S.Dietary fat concentrations influence fatty acid assimilation patterns in Atlantic pollock (Pollachius virens)
A key aspect in the use of fatty acids (FA) to estimate predator diets using Quantitative FA Signature Analysis (QFASA) is the ability to account for FA assimilation through the use of calibration coefficients (CC). Here, we tested the assumption that CC are independent of dietary fat concentrations by feeding Atlantic pollock (Pollachius virens) three formulated diets with very similar FA proportVisualizing populations of North American sea ducks: Maps to guide research and management planning
North American sea ducks generally breed in mid- to northern-latitude regions and nearly all rely upon marine habitats for much of their annual cycle. Most sea duck species remained poorly studied until the 1990s when declines were noted in several species and populations. Subsequent research, much of which was funded by the Sea Duck Joint Venture, began in the late 1990s with an emphasis on definBalancing sampling intensity against spatial coverage for a community science monitoring programme
Community science is an increasingly integral part of biodiversity research and monitoring, often achieving broad spatial and temporal coverage but lower sampling intensity than studies conducted by professional scientists. When designing a community‐science monitoring programme, careful assessment of sampling designs that could be both feasible and successful at meeting programme goals is essentiSpatio-temporal population change of Arctic-breeding waterbirds on the Arctic Coastal Plain of Alaska
Rapid physical changes that are occurring in the Arctic are primary drivers of landscape change and thus may drive population dynamics of Arctic-breeding birds. Despite the importance of this region to breeding and molting waterbirds, lack of a comprehensive analysis of historic data has hindered quantifying avian population change. We estimated distribution, abundance, and spatially explicit popu - Software
Below are software products associated with this project.
QFASA Robustness to Assumption Violations: Computer Code
Quantitative fatty acid signature analysis (QFASA; Iverson et al. 2004) has become a common method of estimating diet composition, especially for marine mammals, but the performance of the method has received limited investigation. Bromaghin et al. (In press) used computer simulation to compare the bias of several QFASA estimators and developed recommendations regarding estimator selection. SimulaCode 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)
We have archived the derived data files and R/JAGS code for our analysis as a U.S. Geological Survey data release (link ). The code is divided into three R scripts: 1) pbdens_landdens_JWM.r contains R code for fitting hierarchical Bayesian models of polar bear maternal den abundance and distribution for the Southern Beaufort Sea (SBS) subpopulation, 1982-2015. This script requires the installationNest 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
R script to run one example of the stochastic matrix models run by Weiser et al. to simulate shorebird population trends and elasticities.qfasar: Quantitative Fatty Acid Signature Analysis in R
Knowledge of predator diets provides numerous insights into their ecology. Diet estimation therefore remains an active area of research in quantitative ecology. Quantitative Fatty Acid Signature Analysis (QFASA) is a method of estimating the diet composition of predators. The fundamental unit of information in QFASA is a fatty acid signature (signature), which is a vector of proportions describing - News