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At least 217 records · Page 12Linked to original sources
USGS1999

Linking stressors and ecological responses

To characterize risk, it is necessary to quantify the linkages and interactions between chemical, physical and biological stressors and endpoints in the conceptual framework for ecological risk assessment (ERA). This can present challenges in a multiple stressor analysis, and it will not always be possible to develop a quantitative stressor-response profile. This review commences with a conceptual representation of the problem of developing a linkage analysis for multiple stressors and responses. The remainder of the review surveys a variety of mathematical and statistical methods (e.g., ranking methods, matrix models, multivariate dose-response for mixtures, indices, visualization, simulation modeling and decision-oriented methods) for accomplishing the linkage analysis for multiple stressors. Describing the relationships between multiple stressors and ecological effects are critical components of 'effects assessment' in the ecological risk assessment framework.

Book chapter↗
USGS2013

Using landscape epidemiological models to understand the distribution of chronic wasting disease in the Midwestern USA

Animal movement across the landscape plays a critical role in the ecology of infectious wildlife diseases. Dispersing animals can spread pathogens between infected areas and naïve populations. While tracking free-ranging animals over the geographic scales relevant to landscape-level disease management is challenging, landscape features that influence gene flow among wildlife populations may also influence the contact rates and disease spread between populations. We used spatial diffusion and barriers to white-tailed deer gene flow, identified through landscape genetics, to model the distribution of chronic wasting disease (CWD) in the infected region of southern Wisconsin and northern Illinois, USA. Our generalized linear model showed that risk of CWD infection declined exponentially with distance from current outbreaks, and inclusion of gene flow barriers dramatically improved fit and predictive power of the model. Our results indicate that CWD is spreading across the Midwestern landscape from these two endemic foci, but spread is strongly influenced by highways and rivers that also reduce deer gene flow. We used our model to plot a risk map, providing important information for CWD management by identifying likely routes of disease spread and providing a tool for prioritizing disease monitoring and containment efforts. The current analysis may serve as a framework for modeling future disease risk drawing on genetic information to investigate barriers to spread and extending management and monitoring beyond currently affected regions.

Landscape Ecology↗
USGS2016

Community exposure to potential climate-driven changes to coastal-inundation hazards for six communities in Essex County, Massachusetts

Introduction Understanding if and how community exposure to coastal hazards may change over time is crucial information for coastal managers tasked with developing climate adaptation plans. This report summarizes estimates of population and asset exposure to coastal-inundation hazards associated with sea-level-rise and storm scenarios in six coastal communities of the Great Marsh region of Essex County, Massachusetts. This U.S. Geological Survey (USGS) analysis was conducted in collaboration with National Wildlife Federation (NWF) representatives, who are working with local stakeholders to develop local climate adaptation plans for the Towns of Salisbury, Newbury, Rowley, Ipswich, and Essex and the City of Newburyport (hereafter referred to as communities). Community exposure was characterized by integrating various community indicators (land cover and land use, population, economic assets, critical facilities, and infrastructure) with coastal-hazard zones that estimate inundation extents and water depth for three time periods. Estimates of community exposure are based on the presence of people, businesses, and assets in hazard zones that are calculated from geospatial datasets using geographic-information-system (GIS) tools. Results are based on current distributions of people and assets in hazard zones and do not take into account projections of human population, asset, or land-use changes over time. Results are not loss estimates based on engineering analysis or field surveys for any particular facility and do not take into account aspects of individual and household preparedness before an extreme event, adaptive capacity of a community during an event, or long-term resilience of individuals and communities after an event. Potential losses would match reported inventories only if all residents, business owners, public managers, and elected officials were unaware of what to do if warned of an imminent threat, failed to take protective measures during an extreme event, or failed to implement any long-term strategies to mitigate potential impacts. This analysis is intended to serve as a foundation for additional risk-related studies, plans, and mitigation efforts that are tailored to local needs. After a summary of the geospatial methods used in the analysis, results are organized by community so that local officials can easily use them in their local adaptation planning efforts.

Massachusetts↗
USGS2025

Pluvial and potential compound flooding in a coupled coastal modeling framework: New York City during post-tropical Cyclone Ida (2021)

Many coastal urban areas are prone to extreme pluvial flooding due to limitations in stormwater system capacity, with the additional potential for flooding compounded by storm surge, tides, and waves. Understanding and simulating these processes can improve prediction and flood risk management. Here, we adapt the Coupled Ocean–Atmosphere–Wave–Sediment Transport modeling framework (COAWST) to simulate pluvial flooding from post-tropical Cyclone Ida (2021) in the Jamaica Bay watershed of New York City (NYC). We modify the model to capture the volumetric effects of rainfall and parameterize soil infiltration and a stormwater conveyance system as the drainage rate. We generate a spatially continuous flood map of Ida with a root-mean-square error (RMSE) of 20 cm when compared to high-water marks, useful for understanding Ida's impacts and subsequent mitigation planning. Results show that over 23 km 2 and 4621 buildings were flooded deeper than 0.3 m during Ida. Sensitivity analyses are used to study the broader risk from events like Ida (pluvial flooding) as well as potential compound (pluvial–coastal) flooding. Spatial shifting of the storm track within a typical 12 h forecast uncertainty reveals a worst-case scenario that increases this flooded area to 62 km 2 (5907 buildings). Shifting Ida's rainfall to coincide with high tide increases this flooded area by 1 km 2 , a relatively small change due to the lack of significant storm surge. The application of COAWST to this storm event addresses a broader goal of developing the capability to model compound pluvial–coastal flooding by simultaneously representing coastal storm processes such as rain, tide, waves, erosion, and atmosphere–wave–ocean interactions. The sensitivity analysis results underscore the need for detailed flood risk assessments, showing that Ida, already NYC's worst rain event, could have been even more devastating with slight shifts in the storm track.

New York↗
USGS2026

Adapting wildfire risk assessment for a Tribal landscape: Mount Baldy and the Fort Apache Reservation in Arizona

Context The White Mountain Apache Tribe of the Fort Apache Reservation in Arizona manages their natural and cultural resources within a perpetually wildfire-risky environment. The Tribe’s values also extend beyond those of typical risk assessments to include the importance of cultural activities and sites, various recreational and revenue values, and culturally significant species. Objectives We worked with the Tribe to characterize a sample of these values and adapt them for use in a widely used risk assessment framework as part of an ongoing effort to improve wildfire mitigation and management with Tribes. Methods We used a three-factor experimental design to conduct a sensitivity analysis related to the treatment of values included in the risk assessment. It focused on (1) the inclusion of relative importance or not, (2) the inclusion of relative extent or not, and (3) absolute versus scaled net value change for each value. Results Our results demonstrate a pattern of risk related to the values expressed by the Tribe with a focus on the Mount Baldy landscape, but the contributions of individual values to an aggregate measure of risk vary. Conclusions Our assessments demonstrate an opportunity to incorporate Tribal values. Different depictions of risk can be used to accommodate diverse perspectives of decision-makers or managers, the values they are considering, and whether or not they are approaching risk in an emergency response or preparedness frame.

Arizona↗
USGS2015

Application of the FluEgg model to predict transport of Asian carp eggs in the Saint Joseph River (Great Lakes tributary)

The Fluvial Egg Drift Simulator (FluEgg) is a three-dimensional Lagrangian model that simulates the movement and development of Asian carp eggs until hatching based on the physical characteristics of the flow field and the physical and biological characteristics of the eggs. This tool provides information concerning egg development and spawning habitat suitability including: egg plume location, egg vertical and travel time distribution, and egg-hatching risk. A case study of the simulation of Asian carp eggs in the Lower Saint Joseph River, a tributary of Lake Michigan, is presented. The river hydrodynamic input for FluEgg was generated in two ways — using hydroacoustic data and using HEC-RAS model data. The HEC-RAS model hydrodynamic input data were used to simulate 52 scenarios covering a broad range of flows and water temperatures with the eggs at risk of hatching ranging from 0 to 93% depending on river conditions. FluEgg simulations depict the highest percentage of eggs at risk of hatching occurs at the lowest discharge and at peak water temperatures. Analysis of these scenarios illustrates how the interactive relation among river length, hydrodynamics, and water temperature influence egg transport and hatching risk. An improved version of FluEgg, which more realistically simulates dispersion and egg development, is presented. Also presented is a graphical user interface that facilitates the use of FluEgg and provides a set of post-processing analysis tools to support management decision-making regarding the prevention and control of Asian carp reproduction in rivers with or without Asian carp populations.

Michigan↗
USGS2019

Early-warning signals of individual tree mortality based on annual radial growth

Tree mortality is a key driver of forest dynamics and its occurrence is projected to increase in the future due to climate change. Despite recent advances in our understanding of the physiological mechanisms leading to death, we still lack robust indicators of mortality risk that could be applied at the individual tree scale. Here, we build on a previous contribution exploring the differences in growth level between trees that died and survived a given mortality event to assess whether changes in temporal autocorrelation, variance, and synchrony in time-series of annual radial growth data can be used as early warning signals of mortality risk. Taking advantage of a unique global ring-width database of 3065 dead trees and 4389 living trees growing together at 198 sites (belonging to 36 gymnosperm and angiosperm species), we analyzed temporal changes in autocorrelation, variance, and synchrony before tree death (diachronic analysis), and also compared these metrics between trees that died and trees that survived a given mortality event (synchronic analysis). Changes in autocorrelation were a poor indicator of mortality risk. However, we found a gradual increase in inter-annual growth variability and a decrease in growth synchrony in the last ∼20 years before mortality of gymnosperms, irrespective of the cause of mortality. These changes could be associated with drought-induced alterations in carbon economy and allocation patterns. In angiosperms, we did not find any consistent changes in any metric. Such lack of any signal might be explained by the relatively high capacity of angiosperms to recover after a stress-induced growth decline. Our analysis provides a robust method for estimating early-warning signals of tree mortality based on annual growth data. In addition to the frequently reported decrease in growth rates, an increase in inter-annual growth variability and a decrease in growth synchrony may be powerful predictors of gymnosperm mortality risk, but not necessarily so for angiosperms.

Frontiers in Plant Science↗
USGS2023

Advancing best practices for the analysis of the vulnerability of military installations in the Pacific Basin to coastal flooding under a changing climate – RC-2644

Coastal flooding takes many forms, ranging from major flooding associated with storms to minor flooding associated with exceptionally high tides and other oceanic and atmospheric phenomena on storm-free days. A major societal challenge is to understand and predict how flood magnitude and frequency will manifest at particular places and times, now and in the future. Of particular interest here is how coastal flooding will impact Department of Defense (DoD) installations. In response to this need, this work aims to advance the practical application of statistical and other analytical techniques that can be used to assess the exposure, and ultimately the vulnerability, of built and natural environments to the impacts of coastal flooding. A variety of methods are described and applied to assess exposure. This includes tide gauge station-based diagnosis and prognosis of patterns and trends of Still Water Level, techniques to characterize the expression of ‘lesser extremes’ (e.g., sub-annual to subdecadal event probabilities), and region-wide analysis that improves upon results obtained from conventional single-tide gauge analyses. A novel hybrid statistical and dynamical modeling approach is applied to the analysis of Total Water Levels, necessary for exposure assessment along shorelines exposed to wave action. The hybrid exposure assessment modeling approach is incorporated into a broader mission-based protocol for the assessment of resilience to coastal flooding at the installation level. Demonstrated via an exemplar assessment, which takes into account functional (lost day) as well as financial impacts (lost dollars), the protocol meets the demand for an actionable characterization of how DoD installations will be affected by coastal flooding and improves DoD’s ability to make informed decisions about how to adapt to its effects. The methods described, evaluated, and applied here, including innovative approaches and proof-of-concept products developed through this work, are incorporated into and considered within an analytical framework that serves as guidance as to their relative merits with respect to coastal flood exposure assessment in various circumstances and settings, and illustrates best practices. This will provide engineers, scientists and other practitioners with an enhanced capability to generate information that can be used to support area-wide assessment related to climate adaptation planning and disaster risk reduction as well as site-specific analysis related to design and maintenance of facilities and infrastructure. While the focus is on a select set of DoD sites in the Pacific Basin, the results have broad applicability nationally as well as globally.

Final Report↗
USGS2021

Movement of synthetic organic compounds in the food web after the introduction of invasive quagga mussels (Dreissena bugensis) in Lake Mead, Nevada and Arizona, USA

Introductions of dreissenid mussels in North America have been a significant concern over the last few decades. This study assessed the distribution of synthetic organic compounds (SOCs) in the food web of Lake Mead, Nevada/Arizona, USA and how this distribution was influenced by the introduction of invasive quagga mussels. A clear spatial gradient of SOC concentrations in water was observed between lake basins downstream of populated areas and more rural areas. Within the food web, trophic magnification factors (TMF) indicated statistically significant biomagnification for nine, and biodilution for two, of 22 SOCs examined. The highest value recorded was for PCB 118 (TMF, 5.14), and biomagnification of methyl triclosan (TMF, 3.85) was also apparent. Biodilution was observed for Tonalide® (0.06) and Galaxolide® (0.38). Total SOC concentration in quagga mussels was higher than in three pelagic fishes. Also, 19 of 20 SOC examined in Largemouth Bass ( Micropterus salmoides ) had substantially lower concentrations in 2013, when quagga mussels had become well established, than in 2007/08, soon after quagga mussels were introduced. Estimates of SOC concentrations in the water column and quagga mussels suggest that a considerable portion (~10.5%) of the SOC mass in the lake has shifted from the pelagic to the benthic environments due to quagga mussel growth. These observations suggest that benthic species, such as the endangered Razorback Sucker, may be experiencing increased risk of SOC exposure. In addition, stable isotope analysis (carbon and nitrogen) indicated a decrease in the nutritional value of zooplankton to consumers (e.g., Razorback Sucker larvae) since quagga mussels became established. These changes could affect Razorback Sucker larval survival and recruitment. Results from this study strongly suggest that the introduction of quagga mussels has greatly altered the dynamics of SOCs and other processes in the food web of Lake Mead.

Arizona, Nevada↗
USGS2012

Guideline for assessing the performance of electric power systems in natural hazard and human threat events

Electric power utilities are familiar with and skilled in preparing for and responding to almost-routine natural hazard events such as strong wind and ice storms and seasonal floods, as well as intentional human acts such as vandalism. Recent extreme weather (hurricanes Katrina and Rita), extremely destructive international earthquakes (in Sumatra and Pakistan), and nation-wide concerns regarding future terrorist attacks have increased the pressure on utilities to take appropriate steps to avoid being overwhelmed by such infrequent and exceedingly severe events. Determining what constitutes the appropriate steps to take requires various levels of understanding of the specific hazards and the risks faced by the utility. The American Lifelines Alliance (www. americanlifelinesalliance.org) has prepared a Guideline that provides clear, concise, and nationally-applicable guidance on determining the scope and level of effort necessary to assess power system performance in the wide range of natural hazard or human threat events. Included in this Guideline are specific procedures to follow and information to consider in performing standardized assessments. With the results of such assessments, utility owners can effectively establish and carry out risk management programs that will lead to achieving appropriate levels of performance in future events. The Guideline incorporates an inquiry-driven process with a two-phase performance assessment that can be applied to power systems of any size. The screening phase enables systems or components that are clearly not at risk to be screened out early. The subsequent analysis phase uses results from the screening phase to prioritize and allocate resources for more detailed assessments of hazard, vulnerability, and system performance. This process helps assure that the scope of the assessment meets the specific performance objectives of the inquiry. A case history is presented to illustrate the type of experience with an inquiry-driven process that was considered in developing the Guideline to meet the diverse needs of utility personnel in engineering, operations, and management. Copyright ASCE 2007.

Conference Paper↗
USGS2001

Flood frequency estimates and documented and potential extreme peak discharges in Oklahoma

Knowledge of the magnitude and frequency of floods is required for the safe and economical design of highway bridges, culverts, dams, levees, and other structures on or near streams; and for flood plain management programs. Flood frequency estimates for gaged streamflow sites were updated, documented extreme peak discharges for gaged and miscellaneous measurement sites were tabulated, and potential extreme peak discharges for Oklahoma streamflow sites were estimated. Potential extreme peak discharges, derived from the relation between documented extreme peak discharges and contributing drainage areas, can provide valuable information concerning the maximum peak discharge that could be expected at a stream site. Potential extreme peak discharge is useful in conjunction with flood frequency analysis to give the best evaluation of flood risk at a site. Peak discharge and flood frequency for selected recurrence intervals from 2 to 500 years were estimated for 352 gaged streamflow sites. Data through 1999 water year were used from streamflow-gaging stations with at least 8 years of record within Oklahoma or about 25 kilometers into the bordering states of Arkansas, Kansas, Missouri, New Mexico, and Texas. These sites were in unregulated basins, and basins affected by regulation, urbanization, and irrigation. Documented extreme peak discharges and associated data were compiled for 514 sites in and near Oklahoma, 352 with streamflow-gaging stations and 162 at miscellaneous measurements sites or streamflow-gaging stations with short record, with a total of 671 measurements.The sites are fairly well distributed statewide, however many streams, large and small, have never been monitored. Potential extreme peak-discharge curves were developed for streamflow sites in hydrologic regions of the state based on documented extreme peak discharges and the contributing drainage areas. Two hydrologic regions, east and west, were defined using 98 degrees 15 minutes longitude as the dividing line.

Oklahoma↗
USGS2020

Selecting three components of ground motions from Conditional Spectra for multiple stripe analyses

For complex structures where the seismic response depends appreciably on the vertical (V) component of ground motion (GM) (e.g., base-isolated buildings, long-span bridges, dams, nuclear power plants), incremental dynamic analysis (IDA) is commonly utilized to estimate seismic risk, where the V components of GM are selected and scaled based on the corresponding horizontal (H) components. The resulting seismic risk (e.g., fragility estimates, annual rates of failure) will likely be significantly biased when the scale factors in IDA are very large. As an alternative to IDA, multiple stripe analyses (MSA) with GMs for each stripe selected from the Conditional Spectrum (CS) can be used to estimate the seismic risk; however, the V components are still commonly selected and scaled based on the corresponding H components. Consequently, these V components may still be inconsistent relative to the corresponding target hazard, again yielding biased estimates of seismic risk. To improve the accuracy of seismic risk estimates, we extend the CS to include the V component of GM and present an approach to select multicomponent GMs that are hazard consistent with respect to all three components of GM. Using the target and the GM selection approach developed in this study, we then evaluate typical current practice for selecting and scaling V components of GM. We observe that the latter approach can yield hazard-inconsistent multicomponent GMs, but hazard consistency can be improved by including the V component in the selection process, constraining the scale factors, or widening the period range for selecting GMs.

Conference Paper↗
USGS2018

Hierarchical multi-population viability analysis

Population viability analysis (PVA) uses concepts from theoretical ecology to provide a powerful tool for quantitative estimates of population dynamics and extinction risks. However, conventional statistical PVA requires long-term data from every population of interest, whereas many species of concern exist in multiple isolated populations that are only monitored occasionally. We present a hierarchical multi-population viability analysis model that increases inference power from sparse data by sharing information among populations to assess extinction risks while accounting for incomplete detection and sampling biases with explicit observation and sampling sub-models. We present a case study in which we customized this model for historical population monitoring data (1985–2015) from federally threatened Lahontan cutthroat trout populations in the Great Basin, USA. Data were counts of fish captured during backpack electrofishing surveys from locations associated with 155 isolated populations. Some surveys (25%) included multi-pass removal sampling, which provided valuable information about capture efficiency. GIS and remote sensing were used to estimate August stream temperatures, peak flows, and riparian vegetation condition in each population each year. Field data were used to derive an annual index of nonnative trout densities. Results indicated that population growth rates were higher in colder streams and that nonnative trout reduced carrying capacities of native trout. Extinction risks increased with more environmental stochasticity and were also related to population extent, water temperatures, and nonnative densities. We developed a graphical user interface to interact with the fitted model results and to simulate future habitat scenarios and management actions to assess their influence on extinction risks in each population. Hierarchical multi-population viability analysis bridges the gap between site-level field observations and population-level processes, making effective use of existing datasets to support management decisions with robust estimates of population dynamics, extinction risks, and uncertainties.

Ecology↗
USGS2012

A spatial cluster analysis of tractor overturns in Kentucky from 1960 to 2002

Background: Agricultural tractor overturns without rollover protective structures are the leading cause of farm fatalities in the United States. To our knowledge, no studies have incorporated the spatial scan statistic in identifying high-risk areas for tractor overturns. The aim of this study was to determine whether tractor overturns cluster in certain parts of Kentucky and identify factors associated with tractor overturns. Methods: A spatial statistical analysis using Kulldorff's spatial scan statistic was performed to identify county clusters at greatest risk for tractor overturns. A regression analysis was then performed to identify factors associated with tractor overturns. Results: The spatial analysis revealed a cluster of higher than expected tractor overturns in four counties in northern Kentucky (RR = 2.55) and 10 counties in eastern Kentucky (RR = 1.97). Higher rates of tractor overturns were associated with steeper average percent slope of pasture land by county (p = 0.0002) and a greater percent of total tractors with less than 40 horsepower by county (p<0.0001). Conclusions: This study reveals that geographic hotspots of tractor overturns exist in Kentucky and identifies factors associated with overturns. This study provides policymakers a guide to targeted county-level interventions (e.g., roll-over protective structures promotion interventions) with the intention of reducing tractor overturns in the highest risk counties in Kentucky.

Kentucky↗
USGS2023

Flood-frequency analysis in the Midwest: Addressing potential nonstationarity of annual peak-flow records

Flood-frequency analysis is essential in numerous water-resource management applications, including critical structure design and flood-plain mapping. A basic assumption within Bulletin 17C [1], the standardized guidelines for conducting flood-frequency analysis, is that basins without major hydrologic alterations, such as regulation or urbanization, exhibit stationary statistical properties of the distribution of annual peak streamflow. That is, the mean, variance, and skew are constant over time and the peak-flow record is a representative sample of the population of future floods [1]. In recent decades, better understanding of long-term climatic persistence and concerns about climate and land-use change have caused the assumption of stationarity in peak-flow records to be reexamined [2, 3, 4, 5]. Under nonstationary conditions, the long-term distributional properties (mean, variance, and/or skew) of peak-flow series change one or more times, either gradually or abruptly. Nonstationarities may be attributed to one source, but are often a result of a mixture of drivers, making detection and attribution of nonstationarities challenging [6, 7, 8]. Failure to incorporate observed trends and abrupt changes into flood-frequency analysis may result in a poor representation of the true flood risk. Bulletin 17C currently offers no guidance on how to account for nonstationarities when estimating floods and acknowledges the benefit additional flood frequency studies that incorporate changing climate or basin characteristics into the analysis would provide[1].

Midwest↗
USGS2007

Seismic hazard and risk assessment in the intraplate environment: The New Madrid seismic zone of the central United States

Although the causes of large intraplate earthquakes are still not fully understood, they pose certain hazard and risk to societies. Estimating hazard and risk in these regions is difficult because of lack of earthquake records. The New Madrid seismic zone is one such region where large and rare intraplate earthquakes (M = 7.0 or greater) pose significant hazard and risk. Many different definitions of hazard and risk have been used, and the resulting estimates differ dramatically. In this paper, seismic hazard is defined as the natural phenomenon generated by earthquakes, such as ground motion, and is quantified by two parameters: a level of hazard and its occurrence frequency or mean recurrence interval; seismic risk is defined as the probability of occurrence of a specific level of seismic hazard over a certain time and is quantified by three parameters: probability, a level of hazard, and exposure time. Probabilistic seismic hazard analysis (PSHA), a commonly used method for estimating seismic hazard and risk, derives a relationship between a ground motion parameter and its return period (hazard curve). The return period is not an independent temporal parameter but a mathematical extrapolation of the recurrence interval of earthquakes and the uncertainty of ground motion. Therefore, it is difficult to understand and use PSHA. A new method is proposed and applied here for estimating seismic hazard in the New Madrid seismic zone. This method provides hazard estimates that are consistent with the state of our knowledge and can be easily applied to other intraplate regions. ?? 2007 The Geological Society of America.

Special Paper of the Geological Society of America↗
USGS2024

Using integrated step selection to determine effects of predation risk on bison habitat selection and movement

Animal movement is a fundamental mechanism that shapes communities and ecosystems. Ungulates alter the ecosystems they inhabit and understanding their movements and distribution is critical for linking habitat with population dynamics. Predation risk has been shown to strongly influence ungulate movement patterns, such that ungulates may select habitat where predation risk is lower (refugia), adjust movement rates, temporal patterns, or selection of cover variables in areas with greater predation risk. We evaluated potential predation avoidance behavior in a population of plains bison inhabiting the north rim of Grand Canyon National Park (GRCA) and adjacent Kaibab National Forest (KNF). The KNF has year-round hunting managed by Arizona Game and Fish Department, whereas hunting is not allowed in GRCA. Human-maintained water sources on the KNF are particularly important resources for bison wherein they may be exposed to higher predation risk to access these resources. We used 2-h GPS locations for three years from 31 bison ( n = 9 males; n = 22 females), and integrative step selection analysis to test four hypotheses about the potential for bison to reduce their risk from human predation by avoiding areas of high predation risk; moving faster in areas with high predation risk; entering high-risk areas at night when risk is reduced; and entering high-risk areas in habitats that provide cover (coniferous forest). The highest performing model indicated bison movement was 1.3 times faster per 2-h step interval than in areas with no hunting across all vegetation classes (coniferous forest, shrub, quaking aspen, grass-forb meadow) and across all topography classes (valley, slope, ridge). Bison moved more slowly in grass-forb meadows than all other vegetation types, and in valleys relative to slopes and ridges. Several radio-collared individuals had no GPS locations in KNF for the duration of the study. Bison avoided predation risk using two strategies: moving faster while in the KNF, and fully avoiding high-risk areas by remaining within GRCA. Management that manipulates or reduces timing of hunting seasons may reduce perceived predation risk and encourage bison to distribute into the KNF and across a broader range of available habitat.

Arizona↗
USGS2026

Nonstationary demographic state-space models using unreplicated counts for species undergoing environmental stressors

A fundamental task in ecological statistics is to estimate abundance and growth rate distributions from wildlife monitoring data to inform conservation management. Modeling time series of wildlife populations presents a number of challenges from both statistical and ecological perspectives, including discreteness; lack of replication; nonstationarity; and observation, demographic, and other phenomenological processes. Nonstationary dynamics are often exhibited by populations undergoing environmental stressors. Models must account for these characteristics to produce reliable estimates of abundance and trends, yet estimation can be challenging with unreplicated data. We propose nonstationary demographic state-space models using unreplicated counts for populations undergoing environmental stressors. A reduced growth rate model matches the complexity of the unreplicated count data, and a fecundity bound on growth rate distributions allows the separation of processes affecting growth rates like environmental stressors from those affecting abundance external to growth rates like migration. NDSSMs allow for the embedding of nonstationary model components, and we explore the use of changepoints, volatility clustering, and migration processes. We apply the proposed nonstationary models in case studies of herons affected by predator/competitor reestablishment and three bat species affected by a fungal pathogen causing white-nose syndrome. Nonstationary models outperform stationary models and generalized linear mixed effects models according to model scoring and visual inspection of predictions, and provide estimates more consistent with published values. Incorporating migration improves model fit universally, even with approximate one-way immigration, most likely because populations are extirpated, recolonized, and increase multiple-fold over the upper bound set by species fecundity. In addition, estimates of the timing and severity of the environmental stressor differed for models with migration. Including nonstationary and demographic components in a fecundity-bounded growth rate model improves inference and benefits interpretability of hyperparameters. In turn, this adjusts uncertainties in predictions of abundance and growth rates over time, providing the ingredients needed for informed conservation analysis and for directing future monitoring of at-risk species.

Journal of Agricultural, Biological and Environmen↗