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At least 559 records · Page 31Linked to original sources
USGS2024

Declining ecological resilience and invasion resistance under climate change in the sagebrush region, United States

In water-limited dryland ecosystems of the Western United States, climate change is intensifying the impacts of heat, drought, and wildfire. Disturbances often lead to increased abundance of invasive species, in part, because dryland restoration and rehabilitation are inhibited by limited moisture and infrequent plant recruitment events. Information on ecological resilience to disturbance (recovery potential) and resistance to invasive species can aid in addressing these challenges by informing long-term restoration and conservation planning. Here, we quantified the impacts of projected future climate on ecological resilience and invasion resistance (R&R) in the sagebrush region using novel algorithms based on ecologically relevant and climate-sensitive predictors of climate and ecological drought. We used a process-based ecohydrological model to project these predictor variables and resulting R&R indicators for two future climate scenarios and 20 climate models. Results suggested widespread future R&R decreases (24%–34% of the 1.16 million km 2 study area) that are generally consistent among climate models. Variables related to rising temperatures were most strongly linked to decreases in R&R indicators. New continuous R&R indices quantified responses to climate change; particularly useful for areas without projected change in the R&R category but where R&R still may decrease, for example, some of the areas with a historically low R&R category. Additionally, we found that areas currently characterized as having high sagebrush ecological integrity had the largest areal percentage with expected declines in R&R in the future, suggesting continuing declines in sagebrush ecosystems. One limitation of these R&R projections was relatively novel future climatic conditions in particularly hot and dry areas that were underrepresented in the training data. Including more data from these areas in future updates could further improve the reliability of the projections. Overall, these projected future declines in R&R highlight a growing challenge for natural resource managers in the region, and the resulting spatially explicit datasets provide information that can improve long-term risk assessments, prioritizations, and climate adaptation efforts.

western United States↗
USGS2026

Restoration based on cost-benefit optimization: A grasslands pilot study

Ecological restoration is essential to meeting global biodiversity conservation goals. Given limited conservation budgets, deciding where to restore habitat is a key challenge for the coming decade. We developed a spatially explicit framework to optimize ecological restoration site selection by integrating land use history, species distributions, and economic costs. The framework includes the following steps: identify potential restoration area based on relevant environmental measures like land use; identify species of interest; calculate restoration benefits by modeling habitat and climate suitability and estimating reduced extinction risk associated with restoring a particular land parcel based on a modified species–area relationship; aggregate benefits across species; and compare to parcel-level land acquisition costs. We applied linear programming to maximize conservation benefit/restoration cost ratios to identify optimized restoration sites. We illustrate this approach using a case study for highly threatened grassland ecosystems in the Great Plains region of Kansas, USA. We selected five grassland animal species (greater prairie chickens [ Tympanuchus cupido ], lesser prairie chickens [ Tympanuchus pallidicinctus ], swift fox [ Vulpes velox ], pronghorn [ Antilocapra americana ], and regal fritillary [ Speyeria idalia ]) as indicators of restoration benefit across taxa. For the indicator species that we chose, shortgrass and mixed-grass prairies had the highest conservation benefit to cost ratio. Setting a minimum restoration threshold for each habitat type allowed us to identify high-priority tallgrass prairie sites. Despite increasing interest in ecological restoration, optimizing restoration site selection is challenging because one must consider habitat features that do not currently exist. The modeling approach described here is flexible and can be updated for different ecosystems, species, and conservation priorities. We outline potential alterations that can be made in future analyses, depending on desired restoration goals.

Kansas↗
USGS2023

Hidden Markov movement models reveal diverse seasonal movement patterns in two North American ungulates

Animal movement is the mechanism connecting landscapes to fitness, and understanding variation in seasonal animal movements has benefited from the analysis and categorization of animal displacement. However, seasonal movement patterns can defy classification when movements are highly variable. Hidden Markov movement models (HMMs) are a class of latent-state models well-suited to modeling movement data. Here, we used HMMs to assess seasonal patterns of variation in the movement of pronghorn ( Antilocapra americana ), a species known for variable seasonal movements that challenge analytical approaches, while using a population of mule deer ( Odocoileus hemionus ), for whom seasonal movements are well-documented, as a comparison. We used population-level HMMs in a Bayesian framework to estimate a seasonal trend in the daily probability of transitioning between a short-distance local movement state and a long-distance movement state. The estimated seasonal patterns of movements in mule deer closely aligned with prior work based on indices of animal displacement: a short period of long-distance movements in the fall season and again in the spring, consistent with migrations to and from seasonal ranges. We found seasonal movement patterns for pronghorn were more variable, as a period of long-distance movements in the fall was followed by a winter period in which pronghorn were much more likely to further initiate and remain in a long-distance movement pattern compared with the movement patterns of mule deer. Overall, pronghorn were simply more likely to be in a long-distance movement pattern throughout the year. Hidden Markov movement models provide inference on seasonal movements similar to other methods, while providing a robust framework to understand movement patterns on shorter timescales and for more challenging movement patterns. Hidden Markov movement models can allow a rigorous assessment of the drivers of changes in movement patterns such as extreme weather events and land development, important for management and conservation.

Wyoming↗
USGS2024

Updated range map of an endangered salamander and congeneric competitor reveals different niche preferences

Estimating distributions for cryptic and highly range-restricted species induces unique challenges for species distribution modeling. In particular, bioclimatic covariates that are typically used to model species ranges at regional and continental scales may not show strong variation at scales of 100s and 10s of meters. This limits both the likelihood and usefulness of correlated occurrence to data typically used in distribution models. Here, we present analyses of species distributions, at 100 × 100 m resolution, for a highly range restricted salamander species (Shenandoah salamander, Plethodon shenandoah ) and a closely related congener (red-backed salamander, Plethodon cinereus ). We combined data across multiple survey types, account for seasonal variation in availability of our target species, and control for repeated surveys at locations– all typical challenges in range-scale monitoring datasets. We fit distribution models using generalized additive models that account for spatial covariates as well as unexplained spatial variation and spatial uncertainty. Our model accommodates different survey protocols using offsets and incorporates temporal variation in detection and availability resulting from survey-specific variation in temperature and precipitation. Our spatial random effect was crucial in identifying small-scale differences in the occurrence of each species and provides cell-specific estimates of uncertainty in the density of salamanders across the range. Counts of both species were seen to increase in the 3 days following a precipitation event. However, P. cinereus were observed even in extremely wet conditions, while surface activity of P. shenandoah was associated with a more narrow range. Our results demonstrate how a flexible analytical approach improves estimates of both distribution and uncertainty, and identify key abiotic relationships, even at small spatial scales and when scales of empirical data are mismatched. While our approach is especially valuable for species with small ranges, controlling for spatial autocorrelation, estimating spatial uncertainty, and incorporating survey-specific information in estimates can improve the reliability of distribution models in general.

Ecology and Evolution↗
USGS2017

The evolution of different maternal investment strategies in two closely related desert vertebrates

We compared egg size phenotypes and tested several predictions from the optimal egg size (OES) and bet-hedging theories in two North American desert-dwelling sister tortoise taxa, Gopherus agassizii and G. morafkai , that inhabit different climate spaces: relatively unpredictable and more predictable climate spaces, respectively. Observed patterns in both species differed from the predictions of OES in several ways. Mean egg size increased with maternal body size in both species. Mean egg size was inversely related to clutch order in G. agassizii , a strategy more consistent with the within-generation hypothesis arising out of bet-hedging theory or a constraint in egg investment due to resource availability, and contrary to theories of density dependence, which posit that increasing hatchling competition from later season clutches should drive selection for larger eggs. We provide empirical evidence that one species, G. agassizii , employs a bet-hedging strategy that is a combination of two different bet-hedging hypotheses. Additionally, we found some evidence for G. morafkai employing a conservative bet-hedging strategy. (e.g., lack of intra- and interclutch variation in egg size relative to body size). Our novel adaptive hypothesis suggests the possibility that natural selection favors smaller offspring in late-season clutches because they experience a more benign environment or less energetically challenging environmental conditions (i.e., winter) than early clutch progeny, that emerge under harsher and more energetically challenging environmental conditions (i.e., summer). We also discuss alternative hypotheses of sexually antagonistic selection, which arise from the trade-offs of son versus daughter production that might have different optima depending on clutch order and variation in temperature-dependent sex determination (TSD) among clutches. Resolution of these hypotheses will require long-term data on fitness of sons versus daughters as a function of incubation environment, data as yet unavailable for any species with TSD.

Arizona, California↗
USGS2018

Size‐assortative choice and mate availability influences hybridization between red wolves (Canis rufus) and coyotes (Canis latrans)

Anthropogenic hybridization of historically isolated taxa has become a primary conservation challenge for many imperiled species. Indeed, hybridization between red wolves ( Canis rufus ) and coyotes ( Canis latrans ) poses a significant challenge to red wolf recovery. We considered seven hypotheses to assess factors influencing hybridization between red wolves and coyotes via pair‐bonding between the two species. Because long‐term monogamy and defense of all‐purpose territories are core characteristics of both species, mate choice has long‐term consequences. Therefore, red wolves may choose similar‐sized mates to acquire partners that behave similarly to themselves in the use of space and diet. We observed multiple factors influencing breeding pair formation by red wolves and found that most wolves paired with similar‐sized conspecifics and wolves that formed congeneric pairs with nonwolves (coyotes and hybrids) were mostly female wolves, the smaller of the two sexes. Additionally, we observed that lower red wolf abundance relative to nonwolves and the absence of helpers increased the probability that wolves consorted with nonwolves. However, successful pairings between red wolves and nonwolves were associated with wolves that maintained small home ranges. Behaviors associated with territoriality are energetically demanding and behaviors (e.g., aggressive interactions, foraging, and space use) involved in maintaining territories are influenced by body size. Consequently, we propose the hypothesis that size disparities between consorting red wolves and coyotes influence positive assortative mating and may represent a reproductive barrier between the two species. We offer that it may be possible to maintain wild populations of red wolves in the presence of coyotes if management strategies increase red wolf abundance on the landscape by mitigating key threats, such as human‐caused mortality and hybridization with coyotes. Increasing red wolf abundance would likely restore selection pressures that increase mean body and home‐range sizes of red wolves and decrease hybridization rates via reduced occurrence of congeneric pairs.

Ecology and Evolution↗
USGS2019

Differential plague susceptibility in species and populations of prairie dogs

Laboratory trials conducted over the past decade at U.S. Geological Survey National Wildlife Health Center indicate that wild populations of prairie dogs ( Cynomys spp.) display different degrees of susceptibility to experimental challenge with fully virulent Yersinia pestis , the causative agent of plague. We evaluated patterns in prairie dog susceptibility to plague to determine whether the historical occurrence of plague at location of capture was related to survival times of prairie dogs challenged with Y. pestis . We found that black-tailed prairie dogs ( Cynomys ludovicianus ) from South Dakota (captured prior to the detection of plague in the state), Gunnison's prairie dogs ( Cynomys gunnisoni ) from Colorado, and Utah prairie dogs ( Cynomys parvidens ) from Utah were most susceptible to plague. Though the susceptibility of black-tailed prairie dogs in South Dakota compared with western locations supports our hypothesis regarding historical exposure, both Colorado and Utah prairie dogs have a long history of exposure to plague. It is possible that for these populations, genetic isolation/bottle necks have made them more susceptible to plague outbreaks.

Colorado, South Dakota, Utah↗
USGS2019

Spatially explicit models of seasonal habitat for greater sage‐grouse at broad spatial scales: Informing areas for management in Nevada and northeastern California

Defining boundaries of species' habitat across broad spatial scales is often necessary for management decisions, and yet challenging for species that demonstrate differential variation in seasonal habitat use. Spatially explicit indices that incorporate temporal shifts in selection can help overcome such challenges, especially for species of high conservation concern. Greater sage‐grouse Centrocercus urophasianus (hereafter, sage‐grouse), a sagebrush obligate species inhabiting the American West, represents an important case study because sage‐grouse exhibit seasonal habitat patterns, populations are declining in most portions of their range and are central to contemporary national land use policies. Here, we modeled spatiotemporal selection patterns for telemetered sage‐grouse across multiple study sites (1,084 sage‐grouse; 30,690 locations) in the Great Basin. We developed broad‐scale spatially explicit habitat indices that elucidated space use patterns (spring, summer/fall, and winter) and accounted for regional climatic variation using previously published hydrographic boundaries. We then evaluated differences in selection/avoidance of each habitat characteristic between seasons and hydrographic regions. Most notably, sage‐grouse consistently selected areas dominated by sagebrush with few or no conifers but varied in type of sagebrush selected by season and region. Spatiotemporal variation was most apparent based on availability of water resources and herbaceous cover, where sage‐grouse strongly selected upland natural springs in xeric regions but selected larger wet meadows in mesic regions. Additionally, during the breeding period in spring, herbaceous cover was selected strongly in the mesic regions. Lastly, we expanded upon an existing joint–index framework by combining seasonal habitat indices with a probabilistic index of sage‐grouse abundance and space use to produce habitat maps useful for sage‐grouse management. These products can serve as conservation planning tools that help predict expected benefits of restoration activities, while highlighting areas most critical to sustaining sage‐grouse populations. Our joint–index framework can be applied to other species that exhibit seasonal shifts in habitat requirements to help better guide conservation actions.

California, Nevada↗
USGS2016

Building the team for team science

The ability to effectively exchange information and develop trusting, collaborative relationships across disciplinary boundaries is essential for 21st century scientists charged with solving complex and large-scale societal and environmental challenges, yet these communication skills are rarely taught. Here, we describe an adaptable training program designed to increase the capacity of scientists to engage in information exchange and relationship development in team science settings. A pilot of the program, developed by a leader in ecological network science, the Global Lake Ecological Observatory Network (GLEON), indicates that the training program resulted in improvement in early career scientists’ confidence in team-based network science collaborations within and outside of the program. Fellows in the program navigated human-network challenges, expanded communication skills, and improved their ability to build professional relationships, all in the context of producing collaborative scientific outcomes. Here, we describe the rationale for key communication training elements and provide evidence that such training is effective in building essential team science skills.

Ecosphere↗
USGS2018

Twenty-nine years of population dynamics in a small-bodied montane amphibian

Identifying population declines before they reach crisis proportions is imperative given the current global decline in vertebrate fauna and associated challenges and expense of recovery. Understanding life histories and how the environment influences demography are critical aspects of this challenge, as is determining the biological relevance of covariates that are best supported by the data. We used 29 yr of data on chorus frogs at two sites to estimate demographic parameters, examine life history, assess weather‐related covariates, and determine the magnitude of process variation in target parameters. Average estimates of survival probabilities were 0.51 (Standard Error [SE] = 0.04) and 0.43 (SE = 0.04), and average estimates of recruitment probabilities were 0.64 (SE = 0.07) and 0.44 (SE = 0.04). Process variation accounted for ≥76% of the total temporal variation in both parameters at one pond and in survival probability alone at the other, suggesting that the covariates in our top models were explaining predominantly process rather than sampling variation. Estimates of population growth rates indicated a declining population at one pond (i.e., negative population growth rates in 15 of 18 yr), and comparisons with historical estimates suggested declines in survival probability at the other. The amount of deviance explained was low, providing little support for the influence of covariates on target parameters, despite model selection support. Synthesis and applications: This analysis illustrates the value of disentangling components of variance when assessing demographic drivers and highlights the need for adequate demographic information in assigning conservation labels.

Ecosphere↗
USGS2021

A greener future for the Galapagos: Forecasting ecosystem productivity by finding climate analogs in time

Forecasting ecosystem response to climate change is critical for guiding policymaking but challenging due to: complicated relationships between microclimates and regional climates; species’ responses that are driven by extremes rather than averages; the multifaceted nature of species’ interactions; and the lack of historical analogs to future climates. Given these challenges, even model systems such as the Galapagos Islands, a world-famous biodiversity hotspot and World Heritage Site, lack clear forecasts for future environmental change. Here, we developed a novel nonparametric method for simulating the ecosystem futures based on observed vegetation productivity (NDVI) during analogous weather observed historically. Using satellite images taken from the past to piece together a simulated future, we projected that productivity of terrestrial vegetation of the Galapagos will increase over the next century by approximately one standard deviation archipelago-wide, with largest increases during the wet season (January to June) and in the arid zones. Such greening would impact a variety of ecological and evolutionary processes, species of conservation concern, and agricultural practices. Our straightforward approach can be applied to many other regions, particularly those with rapid ecosystem responses to stochastic inter-annual climatic fluctuations that provide appropriate climate analogs for forecasting.

Galápagos Islands↗
USGS2022

Democratizing macroecology: Integrating unoccupied aerial systems with the National Ecological Observatory Network

Macroecology research seeks to understand ecological phenomena with causes and consequences that accumulate, interact, and emerge across scales spanning several orders of magnitude. Broad-extent, fine-grain information (i.e., high spatial resolution data over large areas) is needed to adequately capture these cross-scale phenomena, but these data have historically been costly to acquire and process. Unoccupied aerial systems (UAS or drones carrying a sensor payload) and the National Ecological Observatory Network (NEON) make the broad-extent, fine-grain observational domain more accessible to researchers by lowering costs and reducing the need for highly specialized equipment. Integration of these tools can further democratize macroecological research, as their strengths and weaknesses are complementary. However, using these tools for macroecology can be challenging because mental models are lacking, thus requiring large up-front investments in time, energy, and creativity to become proficient. This challenge inspired a working group of UAS-using academic ecologists, NEON professionals, imaging scientists, remote sensing specialists, and aeronautical engineers at the 2019 NEON Science Summit in Boulder, Colorado, to synthesize current knowledge on how to use UAS with NEON in a mental model for an intended audience of ecologists new to these tools. Specifically, we provide (1) a collection of core principles for collecting high-quality UAS data for NEON integration and (2) a case study illustrating a sample workflow for processing UAS data into meaningful ecological information and integrating it with NEON data collected on the ground—with the Terrestrial Observation System—and remotely—from the Airborne Observation Platform. With this mental model, we advance the democratization of macroecology by making a key observational domain—the broad-extent, fine-grain domain—more accessible via NEON/UAS integration.

Ecosphere↗
USGS2022

Environmental drivers of demography and potential factors limiting the recovery of an endangered marine top predator

Understanding what drives changes in wildlife demography is fundamental to the conservation and management of depleted or declining populations, though making inference about the intrinsic and extrinsic factors that influence survival and reproduction remains challenging. Here we use mark–resight data from 2000 to 2018 to examine the effects of environmental variability on age-specific survival and natality for the endangered western distinct population segment (wDPS) of Steller sea lions ( Eumetopias jubatus ) in Alaska, USA. Though this population has been studied extensively over the last four decades, the causes of divergent abundance trends that have been observed across the wDPS range remain unknown. We developed a Bayesian multievent mark–resight model that accounts for female reproductive state uncertainty. Annual survival probabilities for male pups (0.44; 0.36–0.53), female yearlings (0.63; 0.49–0.73), and male yearlings (0.62; 0.51–0.71) born in the western portion of the wDPS range, estimated here for the first time, were lower than those in the eastern portion of the wDPS range, estimated as: male pups (0.69; 0.65–0.74), female yearlings (0.76; 0.71–0.81), and male yearlings (0.71; 0.65–0.78). There was a higher proportion of young female breeders in the western portion of the range, but overall natality was lower (0.69; 0.47–0.96) than in the eastern portion of the range (0.80; 0.74–0.84). Additionally, pup mass had a positive effect on pup survival in the eastern portion of the range and a negative effect in the western portion of the range, potentially due to earlier weaning of heavier pups. Local- and basin-scale oceanographic features such as the Aleutian Low, the Arctic Oscillation Index, the North Pacific Gyre Oscillation, chlorophyll concentration, upwelling, and wind in certain seasons were correlated with vital rates. However, drawing strong inferences from these correlations is challenging given that relationships between ocean conditions and an adaptive top predator in a dynamic ecosystem are exceedingly complex. This study provides the first demographic rate estimates for the western portion of the range where abundance estimates continue to decline. These results will advance efforts to identify factors driving regionally divergent abundance trends, with implications for population-level responses to future climate variability.

Alaska↗
USGS2020

Tall building performance-based seismic design using SCEC broadband platform site-specific ground motion simulations

The scarcity of strong ground motion records presents a challenge for making reliable performance assessments of tall buildings whose seismic design is controlled by large-magnitude and close-distance earthquakes. This challenge can be addressed using broadband ground-motion simulation methods to generate records with site-specific characteristics of large-magnitude events. In this paper, simulated site-specific earthquake seismograms, developed through a related project that was organized through the Southern California Earthquake Center (SCEC) Ground Motion Simulation Validation (GMSV) Technical Activity Group, are used for nonlinear response history analyses of two archetype tall buildings for sites in San Francisco, Los Angeles, and San Bernardino. The SCEC GMSV team created the seismograms using the Broadband Platform (BBP) simulations for five site-specific earthquake scenarios. The two buildings are evaluated using nonlinear dynamic analyses under comparable record suites selected from the simulated BBP catalog and recorded motions from the NGA-West database. The collapse risks and structural response demands (maximum story drift ratio, peak floor acceleration, and maximum story shear) under the BBP and NGA suites are compared. In general, this study finds that use of the BBP simulations resolves concerns about estimation biases in structural response analysis which are caused by ground motion scaling, unrealistic spectral shapes, and overconservative spectral variations. While there are remaining concerns that strong coherence in some kinematic fault rupture models may lead to an overestimation of velocity pulse effects in the BBP simulations, the simulations are shown to generally yield realistic pulse-like features of near-fault ground motion records.

California↗
USGS2013

Interactions between chemical and climate stressors: A role for mechanistic toxicology in assessing climate change risks

Incorporation of global climate change (GCC) effects into assessments of chemical risk and injury requires integrated examinations of chemical and nonchemical stressors. Environmental variables altered by GCC (temperature, precipitation, salinity, pH) can influence the toxicokinetics of chemical absorption, distribution, metabolism, and excretion as well as toxicodynamic interactions between chemicals and target molecules. In addition, GCC challenges processes critical for coping with the external environment (water balance, thermoregulation, nutrition, and the immune, endocrine, and neurological systems), leaving organisms sensitive to even slight perturbations by chemicals when pushed to the limits of their physiological tolerance range. In simplest terms, GCC can make organisms more sensitive to chemical stressors, while alternatively, exposure to chemicals can make organisms more sensitive to GCC stressors. One challenge is to identify potential interactions between nonchemical and chemical stressors affecting key physiological processes in an organism. We employed adverse outcome pathways, constructs depicting linkages between mechanism-based molecular initiating events and impacts on individuals or populations, to assess how chemical- and climate-specific variables interact to lead to adverse outcomes. Case examples are presented for prospective scenarios, hypothesizing potential chemical–GCC interactions, and retrospective scenarios, proposing mechanisms for demonstrated chemical–climate interactions in natural populations. Understanding GCC interactions along adverse outcome pathways facilitates extrapolation between species or other levels of organization, development of hypotheses and focal areas for further research, and improved inputs for risk and resource injury assessments.

Environmental Toxicology and Chemistry↗
USGS2020

Metal bioavailability models: Current status, lessons learned, considerations for regulatory use, and the path forward

Since the early 2000s, biotic ligand models and related constructs have been a dominant paradigm for risk assessment of aqueous metals in the environment. We critically review 1) the evidence for the mechanistic approach underlying metal bioavailability models; 2) considerations for the use and refinement of bioavailability-based toxicity models; 3) considerations for the incorporation of metal bioavailability models into environmental quality standards; and 4) some consensus recommendations for developing or applying metal bioavailability models. We note that models developed to date have been particularly challenged to accurately incorporate pH effects because they are unique with multiple possible mechanisms. As such, we doubt it is ever appropriate to lump algae/plant and animal bioavailability models; however, it is often reasonable to lump bioavailability models for animals, although aquatic insects may be an exception. Other recommendations include that data generated for model development should consider equilibrium conditions in exposure designs, including food items in combined waterborne–dietary matched chronic exposures. Some potentially important toxicity-modifying factors are currently not represented in bioavailability models and have received insufficient attention in toxicity testing. Temperature is probably of foremost importance; phosphate is likely important in plant and algae models. Acclimation may result in predictions that err on the side of protection. Striking a balance between comprehensive, mechanistically sound models and simplified approaches is a challenge. If empirical bioavailability tools such as multiple-linear regression models and look-up tables are employed in criteria, they should always be informed qualitatively and quantitatively by mechanistic models. If bioavailability models are to be used in environmental regulation, ongoing support and availability for use of the models in the public domain are essential.

Environmental Toxicology and Chemistry↗
USGS2019

Brodifacoum toxicity in American Kestrels (Falco sparverius) with evidence of increased hazard upon subsequent anticoagulant rodenticide exposure

A seminal question in ecotoxicology is the extent to which contaminant exposure evokes prolonged effects on physiological function and fitness. A series of studies were undertaken with American kestrels ingesting environmentally realistic concentrations of the second-generation anticoagulant rodenticide (SGAR) brodifacoum (BROD). Kestrels fed BROD at 0.3, 1.0 or 3.0 µg/g diet wet wt for 7 d exhibited dose-dependent hemorrhage, histopathological lesions and coagulopathy (prolonged prothrombin and Russell’s viper venom times). Following termination of a 7 d exposure to 0.5 µg BROD/g diet, prolonged blood clotting time returned to baseline values within a week, but BROD residues in liver and kidney (terminal half-life estimates >50 d) persisted during the 28 d recovery period. In order to examine the hazard of sequential AR exposure, kestrels were exposed to either the firstgeneration AR chlorophacinone (CPN; 1.5 µg/g diet) or the SGAR BROD (0.5 µg/g diet) for 7 d, and following a recovery period, were challenged with a low dose of CPN (0.75 µg/g diet) for 7 d. In BROD-exposed kestrels, the challenge exposure clearly prolonged prothrombin time compared to naïve controls and kestrels previously exposed to CPN. These data provide evidence that the SGAR BROD may have prolonged effects that increase toxicity of subsequent AR exposure. As free-ranging predatory and scavenging wildlife are often repeatedly exposed to ARs, such protracted toxicological effects need to be considered in hazard and risk assessments.

Environmental Toxicology and Chemistry↗
USGS2022

Linking evolutionary potential to extinction risk: Applications and future directions

Extinction-risk assessments play a major role in prioritizing conservation action at national and international levels. However, quantifying extinction risk is challenging, especially when including the full suite of adaptive responses to environmental change. In particular, evolutionary potential (EP), the capacity to evolve genetically based changes that increase fitness under changing conditions, has proven difficult to evaluate, limiting its inclusion in risk assessments. Theory, experiments, simulations, and field studies all highlight the importance of EP in characterizing and mitigating extinction risk. Disregarding EP can therefore result in ineffective allocation of resources and inadequate recovery planning. Fortunately, proxies for EP can be estimated from environmental, phenotypic, and genetic data. Some proxies can be incorporated into quantitative extinction-risk assessments, whereas others better inform basic conservation actions that maximize resilience to future change. Integration of EP into conservation decision-making is challenging but essential and remains an important area for innovation in applied conservation science.

Frontiers in Ecology and the Environment↗