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.
Habitat Selection Scenarios for Molting Waterfowl in the Goose Molting Area of the Teshekpuk Lake Special Area, for NPR-A Integrated Activity Plan/Environmental Impact Statement (2020)
Walrus used and available resource units for northeast Chukchi Sea, 2008-2012
Walrus Haulout and In-water Activity Levels Relative to Sea Ice Availability in the Chukchi Sea: 2008-2014
Fatty acid signature data of potential yellow-billed loon prey in the Arctic coastal plain of Alaska, 2009-2011
U.S. Geological Survey Polar Bear Mark-Recapture Records, Alaska Portion of the Southern Beaufort Sea, 2001-2010
Below are publications associated with this project.
Life-history attributes of Arctic-breeding birds drive uneven responses to environmental variability across different phases of the reproductive cycle
Long-term variation in polar bear body condition and maternal investment relative to a changing environment
Survival and abundance of polar bears in Alaska’s Beaufort Sea, 2001–2016
Evidence for interannual persistence of infectious influenza A viruses in Alaska wetlands
Fully accounting for nest age reduces bias when quantifying nest survival
Diet composition and body condition of polar bears (Ursus maritimus) in relation to sea ice habitat in the Canadian High Arctic
TrendPowerTool: A lookup tool for estimating the statistical power of a monitoring program to detect population trends
The extent and variability of storm‐induced temperature changes in lakes measured with long‐term and high‐frequency data
Seal body condition and atmospheric circulation patterns influence polar bear body condition, recruitment, and feeding ecology in the Chukchi Sea
USGS permafrost research determines the risks of permafrost thaw to biologic and hydrologic resources
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
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: 17Habitat Selection Scenarios for Molting Waterfowl in the Goose Molting Area of the Teshekpuk Lake Special Area, for NPR-A Integrated Activity Plan/Environmental Impact Statement (2020)
The dataset consists of a polygon shapefile. Each polygon represents a set of molt units (interconnected lakes used as habitat by molting waterfowl) within the Goose Molting Area of the Teshekpuk Lake Special Area in northern Alaska, in addition to a half-mile or 1-mile wide buffer, that were selected for restrictions on new surface occupancy or infrastructure development by the oil and gas industWalrus 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 quantifWalrus Haulout and In-water Activity Levels Relative to Sea Ice Availability in the Chukchi Sea: 2008-2014
An animal's energetic costs are dependent on the amount of time it allocates to various behavioral activities. For Arctic pinnipeds, the time allocated to active and resting behaviors could change with future reductions in sea ice cover and longer periods of open water. The Pacific walrus (Odobenus rosmarus divergens) is a large Arctic pinniped that rests on sea ice or land between foraging tripFatty acid signature data of potential yellow-billed loon prey in the Arctic coastal plain of Alaska, 2009-2011
This dataset contains fatty acid data expressed as mass percent of total fatty acids for several species potentially preyed upon by yellow-billed loons. These data were utilized in a quantitative fatty acid signature analysis to estimate the diet of yellow-billed loons nesting on the Arctic Coastal Plain of Alaska (Haynes et al. 2015).U.S. Geological Survey Polar Bear Mark-Recapture Records, Alaska Portion of the Southern Beaufort Sea, 2001-2010
These data were collected by the U.S. Geological Survey, Alaska Science Center, Polar Bear Research Program as part of long-term on the southern Beaufort Sea polar bear population. - Multimedia
- Publications
Below are publications associated with this project.
Filter Total Items: 102Life-history attributes of Arctic-breeding birds drive uneven responses to environmental variability across different phases of the reproductive cycle
Animals exhibit varied life-history traits that reflect adaptive responses to their environments. For Arctic-breeding birds, traits related to diet, egg nutrient allocation, clutch size, and chick growth are predicted to be under increasing selection pressure due to rapid climate change and increasing environmental variability across high-latitude regions. We compared four migratory birds (black bAuthorsDaniel R. Ruthrauff, Vijay P. Patil, Jerry W. Hupp, David H. WardLong-term variation in polar bear body condition and maternal investment relative to a changing environment
In the Arctic, warming air and ocean temperatures have resulted in substantial changes to sea ice, which is primary habitat for polar bears (Ursus maritimus). Reductions in extent, duration, and thickness have altered sea ice dynamics, which influences the ability of polar bears to reliably access marine mammal prey. Because nutritional condition is closely linked to population vital rates, a progAuthorsTodd C. Atwood, Karyn D. Rode, David C. Douglas, Kristin S. Simac, Anthony Pagano, Jeffrey F. BromaghinSurvival 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 inforAuthorsJeffrey F. Bromaghin, David C. Douglas, George M. Durner, Kristin S. Simac, Todd C. AtwoodEvidence for interannual persistence of infectious influenza A viruses in Alaska wetlands
Influenza A viruses (IAVs) deposited by wild birds into the environment may lead to sporadic mortality events and economically costly outbreaks among domestic birds. There is a paucity of information, however, regarding the persistence of infectious IAVs within the environment following deposition. In this investigation, we assessed the persistence of 12 IAVs that were present in the cloaca and/orAuthorsAndrew M. Ramey, Andrew B. Reeves, Benjamin Joel Lagassé, Vijay P. Patil, Laura E. Hubbard, Dana W. Kolpin, R. Blaine McCleskey, Deborah A. Repert, David E. Stallknecht, Rebecca L. PoulsonFully 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 thAuthorsEmily L. WeiserDiet composition and body condition of polar bears (Ursus maritimus) in relation to sea ice habitat in the Canadian High Arctic
Polar bears (Ursus maritimus) rely on sea ice for hunting marine mammal prey. Declining sea ice conditions associated with climate warming have negatively affected polar bears, especially in the southern portion of their range. At higher latitudes, the transition from multi-year ice to thinner annual ice has been hypothesized to increase biological productivity and potentially improve polar bear fAuthorsKatie R. N. Florko, Gregory W. Thiemann, Jeffrey F. Bromaghin, Evan S. RichardsonTrendPowerTool: 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 simulAuthorsEmily L. Weiser, James E. Diffendorfer, Laura Lopez-Hoffman, Darius J. Semmens, Wayne E. ThogmartinThe extent and variability of storm‐induced temperature changes in lakes measured with long‐term and high‐frequency data
The intensity and frequency of storms are projected to increase in many regions of the world because of climate change. Storms can alter environmental conditions in many ecosystems. In lakes and reservoirs, storms can reduce epilimnetic temperatures from wind‐induced mixing with colder hypolimnetic waters, direct precipitation to the lake's surface, and watershed runoff. We analyzed 18 long‐term aAuthorsJonathan P. Doubek, Orlane Anneville, Gael Dur, Aleksandra M. Lewandowska, Vijay P. Patil, James A. Rusak, Nico Salmaso, Christian T. Seltmann, Dietmar Straile, Pablo Urrutia‐Cordero, Patrick Venail, Rita Adrian, Maria B. Alfonso, Curtis L. DeGasperi, Elvira de Eyto, Heidrun Feuchtmayr, Evelyn Gaiser, Scott F Girdner, Jennifer L. Graham, Hans-Peter Grossart, Josef Hejzlar, Stéphan Jacquet, Georgiy Kirillin, María E. Llames, Shin-Ichiro S. Matsuzaki, Emily Nodine, Maria Cintia Piccolo, Donald C. Pierson, Alon Rimmer, Lars G. Rudstam, Steven Sadro, Hilary M. Swain, Stephen J. Thackeray, Wim Thiery, Piet Verburg, Tamar Zohary, Jason D. StockwellSeal body condition and atmospheric circulation patterns influence polar bear body condition, recruitment, and feeding ecology in the Chukchi Sea
Polar bears (Ursus maritimus) are experiencing loss of sea ice habitats used to access their marine mammal prey. Simultaneously, ocean warming is changing ecosystems that support marine mammal populations. The interactive effects of sea ice and prey are not well understood yet may explain spatial‐temporal variation in the response of polar bears to sea ice loss. Here, we examined the potential comAuthorsKaryn D. Rode, Eric V. Regehr, Jeffrey F. Bromaghin, Ryan H. Wilson, Michelle St. Martin, Justin A. Crawford, Lori T. QuakenbushUSGS permafrost research determines the risks of permafrost thaw to biologic and hydrologic resources
The U.S. Geological Survey (USGS), in collaboration with university, Federal, Tribal, and independent partners, conducts fundamental research on the distribution, vulnerability, and importance of permafrost in arctic and boreal ecosystems. Scientists, land managers, and policy makers use USGS data to help make decisions for development, wildlife habitat, and other needs. Native villages and citiesAuthorsMark P. Waldrop, Lesleigh Anderson, Mark Dornblaser, Li H. Erikson, Ann E. Gibbs, Nicole M. Herman-Mercer, Stephanie R. James, Miriam C. Jones, Joshua C. Koch, Mary-Cathrine Leewis, Kristen L. Manies, Burke J. Minsley, Neal J. Pastick, Vijay Patil, Frank Urban, Michelle A. Walvoord, Kimberly P. Wickland, Christian ZimmermanByNatural Hazards Mission Area, Water Resources Mission Area, Climate Research and Development Program, Coastal and Marine Hazards and Resources Program, Land Change Science Program, Volcano Hazards Program, Earth Resources Observation and Science (EROS) Center , Geology, Geophysics, and Geochemistry Science Center, Geology, Minerals, Energy, and Geophysics Science Center, Geosciences and Environmental Change Science Center, Pacific Coastal and Marine Science Center, Volcano Science CenterAnalyses 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 eAuthorsTodd C. Atwood, Jeffrey F. Bromaghin, Vijay P. Patil, George M. Durner, David C. Douglas, Kristin S. SimacDrivers 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 ecolAuthorsKatie R. N. Florko, Gregory W. Thiemann, Jeffrey F. Bromaghin - Web Tools
- Software
Below are software products associated with this project.
- News