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At least 145 records · Page 8Linked to original sources
USGS2023

Accounting for spatiotemporal sampling variation in joint species distribution models

Estimating relative abundance is critical for informing conservation and management efforts and for making inferences about the effects of environmental change on populations. Freshwater fisheries span large geographic regions, occupy diverse habitats and consist of varying species assemblages. Monitoring schemes used to sample these diverse populations often result in populations being sampled at different times and under different environmental conditions. Varying sampling conditions can bias estimates of abundance when compared across time, location and species, and properly accounting for these biases is critical for making inferences. We develop a joint species distribution model (JSDM) that accounts for varying sampling conditions due to the environment and time of sampling when estimating relative abundance. The novelty of our JSDM is that we explicitly model sampling effort as the product of known quantities based on time and gear type and an unknown functional relationship to capture seasonal variation in species life history. We use the model to study relative abundance of six freshwater fish species across the state of Minnesota, USA. Our model enables estimates of relative abundance to be compared both within and across species and lakes, and captures the inconsistent sampling present in the data. We discuss how gear type, water temperature and day of the year impact catchability for each species at the lake level and throughout a year. We compare our estimates of relative abundance to those obtained from a model that assumes constant catchability to highlight important differences within and across lakes and species. Synthesis and applications : Our method illustrates that assumptions relating indices of abundance to observed catch data can greatly impact model inferences derived from JSDMs. Specifically, not accounting for varying sampling conditions can bias inference of relative abundance, restricting our ability to detect responses to management interventions and environmental change. While our focus is on freshwater fisheries, this model architecture can be adopted to other systems where catchability may vary as a function of space, time and species.

Journal of Applied Ecology↗
USGS2024

Adaptive resource management: Achieving functional eradication of invasive snakes to benefit avian conservation

Natural resource management often co-occurs with considerable uncertainty. One approach to mitigating uncertainty is through adaptive resource management (ARM), a specialized form of structured decision-making that modifies management decisions or actions through monitoring and implementation. Here, we present a case study on the attempted eradication of an invasive brown treesnake ( Boiga irregularis ) in a 5-ha enclosure on Guam with uncertainty in approach. We applied an ARM process across three field phases of snake removal and evaluated whether (1) eradication was achievable and (2) eradication was necessary to achieve an avian response. Field phases included the application of aerial toxic baits, toxicant baiting large mouse and birds, trapping with live mouse and bird lures and hand capture. We found that each removal technique improved control by either removing many individuals or targeting a subset of individuals that resisted prior control approaches. Although the effort did not result in eradication, the evaluation of identified indicators allowed for timely adjustments to removal using the ARM process. The snake removal efforts yielded an avian response in the treatment area after integrating live birds as snake lures, suggesting functional eradication of snakes may be possible. We also, however, observed a release of invasive rodents following snake control, with birds being more sensitive to the presence of snakes than rodents. Synthesis and applications . We suggest that using adaptive resource management to evaluate each phase of action in relation to established goals allowed us to measure outcomes and was successful in eliminating uncertainty in the application of control tools for wildlife conservation. We were able to create a documented and successful approach towards removing snakes inside a snake-exclusion barrier by following the ARM process.

Journal of Applied Ecology↗
USGS2024

Geese migrating over the Pacific Ocean select altitudes coinciding with offshore wind turbine blades

Renewable energy facilities are a key part of mitigating climate change, but can pose threats to wild birds and bats, most often through collisions with infrastructure. Understanding collision risk and the factors affecting it can help minimize impacts on wild populations. For wind turbines, flight altitude is a major factor influencing collision risk, and altitude-selection analyses can evaluate when and why animals fly at certain altitudes under certain conditions. We used GPS tags to track Pacific Flyway geese (Pacific greater white-fronted goose, tule greater white-fronted goose and lesser snow goose) on transoceanic migrations between Alaska and the Pacific Coast of the contiguous United States, an area where offshore windfarm development is beginning. We evaluated how geographic and environmental covariates affected (1) whether birds were at rest on the water versus in flight (binomial model) and (2) altitude selection when in flight (similar to a step-selection framework). We then used a Monte Carlo simulation to predict the probability of flying at each altitude under various conditions, considering both the fly/rest decision and altitude selection. In both spring and fall, geese showed strong selection for altitudes within the expected rotor-swept zone (20–200 m asl), with 56% of locations expected to be within the rotor-swept zone under mean daylight conditions and 28% at night. This indicates a high possibility that migrating geese may be at risk of collision when passing through windfarms. Although there was some variation across subspecies, geese were most likely to be within the rotor-swept zone with little wind or light tailwinds, low clouds, little to no precipitation and moderate to cool air temperatures. Geese were unlikely to be in the rotor-swept zone at night, when most individuals were at rest on the water. Synthesis and applications . These results could be used to inform windfarm management, including decisions to shut down turbines when collision risk is high. The altitude-selection framework we demonstrate could facilitate further study of other bird species to develop a holistic view of how windfarms in this area could affect the migratory bird community as a whole.

Journal of Applied Ecology↗
USGS2024

Integrating presence-only and detection/non-detection data to estimate distributions and expected abundance of difficult-to-monitor species on a landscape-scale

Estimating species distribution and abundance is foundational to effective management and conservation. Using an integrated species distribution model that combines presence-only data from various sources with detection/non-detection data from structured surveys, we estimated the distribution and expected abundance of three difficult-to-monitor mammals of management concern across New York State, namely, coyotes ( Canis latrans ), bobcats ( Lynx rufus ) and black bears ( Ursus americanus ). Three distinct landscape-scale camera trap surveys provided detection/non-detection data over 9 years between 2013 and 2021, and we augmented those data with incidental records of our focal species from public repositories. We used an inhomogeneous Poisson point process to construct an integrated model that fit both data types simultaneously. We demonstrate a simple application of spatial point density of all species records in the accessed public databases to inform the thinning process to account for unknown spatial sampling in the presence-only data, often referred to as the ‘magic covariate’. Using this approach, we examine habitat associations and provide spatially explicit estimates in expected abundance across the entirety of New York State for all three focal species. As expected, coyotes were the most widely distributed and abundant species, with a strong positive association with agricultural land uses. Bobcats exhibited low expected abundance throughout the state and showed positive associations with deciduous forest and forest edge, and a negative association with road density. Finally, we observed considerable spatial variation in abundance of black bears with expected abundance increasing in association with various forest cover and composition covariates and decreasing with crop cover. We present insights into habitat associations and spatial variation in abundance, and provide management implications for each of the species of interest. Synthesis and applications . Our integrated modelling method allows for managers to use citizen sightings combined with detection/non-detection surveys to estimate robust indices of abundance for both high- and low-density, and wide-spread versus patchily distributed species. Through comparison with previous studies, we highlight how broad-scale programmes, such as the statewide efforts to estimate species distributions undertaken here, can benefit substantively from integrated models that leverage additional data (here, incidental records) from a larger region of space, and thus capture more landscape heterogeneity than is plausible within formalized surveys alone.

New York↗
USGS2024

Propensity score matching mitigates risk of faulty inferences in observational studies of effectiveness of restoration trials

Determining effectiveness of restoration treatments is an important requirement of adaptive management, but it can be non-trivial where only portions of large and heterogeneous landscapes of concern can be treated and sampled. Bias and non-randomness in the spatial deployment of treatment and thus sampling is nearly unavoidable in the data available for large-scale management trials, and the biophysical landscape characteristics underlying the bias are key but rare considerations in analyses of treatment effects. Treatment effects from large-scale management trials are typically estimated with multivariable regression (MVR) models. However, this method is unsuited to reliable estimations of treatment effects when treated and untreated areas differ in their underlying biophysical variability. An alternative to conventional regression is to use propensity score (PS) matching, which can limit the differences in confounding variables among treatment groups and assure the data collected or selected for analysis are more consistent with a randomized and unconfounded experiment. Thus, PS is expected to identify treatment effects more accurately. We used data from a large-scale monitoring effort of a megafire to evaluate the efficacy of PS matching in making inferences on treatment effects when treatments are applied non-randomly over a large heterogeneous area. We compared the resulting inference to both traditional MVR methods and to “naïve” methods that do not consider treatment allocation bias. Treatment effects varied between the different statistical methods for controlling selection bias and confounding biophysical factors. The PS-matched model revealed a weaker treatment effect of drill seeding and a greater effect of herbicide spraying on the cover of perennial bunchgrasses when compared to MVR or naïve modelled estimates. The inferences from the PS-matched model are considered more reliable because the treated and untreated plots are more similar in their underlying biophysical characteristics. Synthesis and applications . Failure to consider the non-random and selective deployment of restoration treatments by managers leads to faulty inference on their effectiveness. However, tools such as propensity-score matching can be used to remove the bias from analyses of the outcomes of management trials or to devise sampling plans that efficiently protect against the bias.

Journal of Applied Ecology↗
USGS2024

Leveraging relationships between species abundances to improve predictions and inform conservation

Many management and conservation contexts can benefit from understanding relationships between species abundances, which can be used to improve predictions of species occurrence and abundance. We present conditional prediction as a tool to capture information about species abundances via residual covariance between species. From a fitted joint species distribution model, this framework produces a species coefficient matrix that contains relationships between species abundances. The species coefficients allow co-observed species to be treated as a second set of predictors supplementing covariates in the model to improve prediction. We use simulations to demonstrate the potential benefits and limitations of conditional prediction across data types and species covariance before applying conditional prediction to two management contexts with real data. Simulations demonstrate that conditional prediction provides the largest benefits to continuous data and when there is residual covariance between many species. In our first application, we show that conditioning on other species improves in-sample and out-of-sample predictions of fish and invertebrate species, including Atlantic cod. In our second application, we show that the species coefficient matrix can be used to identify bird species at risk of nest parasitism by Brown-headed Cowbirds. Synthesis and applications . We present guidelines for using conditional prediction, which can help understand relationships between species abundances, improve predictions and inform conservation in a variety of contexts.

Journal of Applied Ecology↗
USGS2025

Site-level connectivity identified from multiple sources of movement data to inform conservation of a migratory bird

Migratory birds depend on a suite of sites across their annual cycles, making them vulnerable to a wide variety of anthropogenic pressures. Current area-based conservation measures have been found inadequate to safeguard migratory birds, in part due to a lack of consideration for the connectivity between sites mediated by the movements of individuals. To address this issue, we develop a network analysis integrating different types of individual movement data for a migratory shorebird, the Black-tailed Godwit ( Limosa limosa ), across the East Atlantic Flyway. Leveraging metal-ring recoveries, colour-ring re-sightings and satellite tracking from over 10,000 individual godwits, we quantify variation in connectivity between sites across the migratory range, using two weighted metrics to address sampling biases. Colour-ring re-sightings provided the largest number of sites (70%) and links (60% and 43% per season) overall, followed by tracking data (50% of sites, 49% and 63% of links per season) and ring recoveries (25% of sites, <1% of links per season), with clear regional variation in datatype contributions. Sampling completeness of the network structure varied with longitude, with information particularly lacking in central and eastern countries of both Europe and Africa. We identified 49 sites playing a disproportionate role in the site network, each with direct connections to 48 (interquartile range 32–84) other sites, on average. Just 23 (47%) top sites are formally recognized for their international importance for Black-tailed Godwits, and 33 (67%) were robust to sampling incompleteness. Across all 1058 sites, 20% lacked protected area coverage, and per site, 44% (44% ± SD) of bird relocations fell within protected areas. Integrating multiple sources of data improved geographical coverage and completeness of the site network, allowing us to quantify the importance of sites in terms of connectivity across the flyway. Our results highlight shortcomings of existing area-based conservation measures and add value to ongoing efforts to identify important sites for migratory birds. Policy implications . The increasing availability of individual movement data provides valuable opportunities to reveal the inter-dependence of sites used by migratory species, which can help identify priority areas and facilitate flyway-scale management.

Journal of Applied Ecology↗
USGS2025

The relative influence of geographic and environmental factors on rare plant translocation outcomes

Conservation translocations are an established method for reducing the extinction risk of plant species through intentional movement within or outside the indigenous range. Unsuitable environmental conditions at translocation recipient sites and a lack of understanding of species–environment relationships are often identified as critical barriers to translocation success. However, previous syntheses have drawn these inferences from analyses of qualitative feedback rather than quantitative environmental data. In this study, we use a data set of 235 translocations conducted in the US to understand the influences of geographic and environmental factors on three metrics of translocation success: population persistence, next-generation recruitment and next-generation maturity. We use random forest models to quantify the relative importance of geographic and environmental factors that characterize dissimilarity between source and recipient locations, the position of recipient sites relative to species' ranges and niche metrics derived from these ranges. We also compare the importance of these variables with more conventional predictors (e.g. founder population size). Our results indicate that geographic and environmental variables can be as insightful as conventional variables for predicting plant translocation outcomes. The climate suitability of recipient sites, estimated using species distribution models, was the strongest relative predictor of whether a population persisted, with populations situated in more suitable climates displaying greater persistence. Next-generation recruitment and maturity were best predicted by niche metrics; species in more biotically limiting environments, including tropical regions and soils with high relative nutrient retention, as well as species with the broadest precipitation niches, were the least likely to attain these next-generation benchmarks. Synthesis and applications . Our study is one of the first to quantify the important role of spatial and climatic factors in rare plant translocation outcomes. We provide a novel geographic and environmental perspective on outcomes in plant translocations and demonstrate opportunities to improve translocation success not only by adhering to established best practice guidelines but also by integrating spatial modelling approaches into planning and management processes.

Journal of Applied Ecology↗
USGS2025

Post-fire recovery of sagebrush-steppe communities is better explained by elevation than climate-derived indicators of resistance and resilience

More landscapes require restoration than can feasibly be treated, and so decision-support tools to prioritize areas for treatment are needed. Moreover, restoration is complicated by the threat of biological invasion in disturbed areas, and so indicators of ecosystem resistance to invasion and resilience to disturbance (hereafter R&R) are important candidate criteria for prioritizing sites for restoration. We asked how climate-based R&R indicators that differed in being either categorical or continuous compared in their ability to explain plant-community recovery after six wildfires that collectively encompassed >750,000 ha and 7803 plot-year observations in sagebrush steppe of the western USA. Unique associations of species that most frequently co-occurred were identified using structural topic modelling. Mixed effect random forests were used to identify the relative importance of various R&R indicators in explaining post-fire plant associations compared with weather, landscape characteristics and treatment history. Simple metrics (elevation, latitude, longitude and year of monitoring) were more informative predictors of post-fire recovery than climate-based R&R indicators. However, small differences in the abundances of perennial grass and especially annual grass associations were predicted by the spring modified Thornthwaite Moisture Index (difference between precipitation and potential evapotranspiration). Synthesis and applications : The convenience of categorical resistance and resilience indicators has led to their widespread adoption for large-scale planning of restoration. Our results reveal that none of the resistance and resilience indicators assessed effectively explained post-fire restoration better than elevation, although a simple continuous resistance and resilience indicator describing water balance performed better than categorical indicators for explaining small but critical differences in cheatgrass association abundances.

California, Idaho, Nevada, Oregon↗
USGS2025

Accounting for non-random samples with distance sampling to estimate population density

A critical assumption of standard distance sampling is that sampling lines are located such that animals are uniformly distributed as a function of distance from the line. Failure to meet this assumption can introduce bias in the estimator. Many studies have used landscape features, such as roads or rivers, as lines, which can violate assumptions of distance sampling in two ways. First, animals may be attracted or repelled by the landscape feature due to human activity (e.g. along roads) or habitat characteristics associated with the feature (e.g. rivers). Second, sampling along landscape features may not be representative of the larger area of interest. We used auxiliary data to generalize the distance sampling estimator and relax assumptions of a uniform distribution of animals relative to distance from the line (i.e. density gradient) and to allow the distribution of animals to differ by habitat type. The generalized estimator provides unbiased estimates of density within the area sampled but may not be representative of the study area. To address the problem of landscape features providing unrepresentative sampling, we used a resource selection model to estimate the proportion of the population that occurred within the surveyed area to obtain an estimate of abundance for the desired area of inference. We demonstrate our modified distance sampling estimator using white-tailed deer ( Odocoileus virginianus ) in a 972-km 2 study area. We conducted infrared surveys of deer from roads to collect distance-to-transect data. We used locations of radio-collared deer to model the distribution of deer relative to the transects and to develop a resource selection model of deer based on distance to roads, habitat type, elevation and slope to account for roads being a non-representative sample of the study area. Synthesis and applications . When using landscape features as survey lines, the density gradient and deer distribution can introduce either positive or negative bias, which makes it impossible to assess the bias introduced without auxiliary data. The estimator we developed can improve precision because we obtained a better fit to distance observations and accounts for non-random placement of transects with minimal loss of precision.

Journal of Applied Ecology↗
USGS2025

Linking environmental variability to long-term demographic change of an endangered species using integrated population models

Understanding how species populations change with environmental conditions is important for implementing effective habitat management and conservation strategies. Challenges to evaluating population-level responses to environmental conditions arise when data are sparse or not spatiotemporally aligned, especially for at-risk species with small, declining numbers. We synthesized 30 years (1992–2021) of three partially aligned data sets to build a Bayesian integrated population model (IPM) and evaluate demographic and environmental drivers of growth rates for six separately managed ‘subpopulations’ (A–F) of the federally endangered Cape Sable seaside sparrow endemic to the Florida Everglades. We found that juvenile survival peaked at inundation periods (hydroperiods) around 100–220 days and dropped sharply outside those values, while adult survival increased with longer periods of water depth <20 cm, but not with longer periods of water depth >20 cm. Fecundity increased when water depths were more stable, more area was dry, intervals between fires were longer and less area was burned. Changes in population growth rates tended to occur in years that juvenile and adult survival were associated with hydroperiod, especially in the two largest subpopulations B and E. Population growth rates were also associated with hydrologic conditions during the breeding season and fire dynamics through changes in fecundity, most notably in the smaller subpopulations A, C/F and D. Synthesis and applications . Our IPM represents the first long-term population analysis of the Cape Sable seaside sparrow connecting demographic processes to environmental factors. Our results suggest that sustaining periods of shallow water year-round may enhance Cape Sable seaside sparrow survival and population growth. Also, limiting water depth variability and maintaining dry conditions during the breeding season and inhibiting fires in consecutive years may increase fecundity and population growth. Identifying the mechanistic links between environmental and population dynamics could inform how species are expected to respond to management decisions and anticipated ecosystem changes.

Florida↗
USGS2025

Fine-scale spatial risk models to predict avian collisions with power lines

1. Avian fatalities caused by collisions with overhead power lines are an important conservation issue worldwide. Although mitigation strategies can help reduce mortalities, given their considerable cost and the vast scale of power line infrastructure, cost-effective action requires that these efforts be prioritised to areas with the highest potential risk to birds. To date, this risk assessment has usually been guided by potentially biased information on the location of recorded fatalities. 2. Here we use five years of GPS tracking data from endangered Tasmanian wedge-tailed eagles to develop an alternative approach to risk assessment: fine-scale spatial risk models based on behavioural analyses. We built and cross-validated a model that generates spatially explicit predictions of the probability that eagles would cross power lines at hazardous altitudes throughout the entire Tasmanian electricity distribution network. 3. In our model, probability of power line crossings was most strongly associated with the proportion of forest edges, wet forest, open habitat, freshwater sources, and rural residential developments in the area surrounding the power lines. Cross-validation indicated that the model effectively predicted where Tasmanian wedge-tailed eagles cross power lines at low altitude. 4. Model validation suggested our approach was a powerful predictor of the locations of power line collisions involving eagles. The locations of almost all (94%) confirmed eagle fatalities were in the half of the total Tasmanian power line area assigned the higher risk by the model, and 50% of incidents occurred in the 30% of the power line area estimated to be highest risk. 5. Synthesis and applications . Our study illustrates a framework for using bird movement data to provide insights into avian behaviour and the risk they encounter around power line infrastructure. Electricity delivery industries can use these models to identify the electrical infrastructure that poses the highest risk to avian survival and prioritise mitigation efforts, thereby optimizing the benefit of investments to reduce detrimental effects on biodiversity. Our model can direct pre-emptive mitigation across Tasmania’s 20,310 km of distribution infrastructure to meet management targets aiming to reduce the negative effects of power lines on the Tasmanian wedge-tailed eagle.

Tasmania↗
USGS2025

Spatially explicit power analyses to inform occupancy‐based multi‐species wildlife monitoring programmes

1. Current and accurate information on wildlife populations is integral to successful biodiversity management and conservation globally. Nevertheless, many monitoring programs fail in their attempts to accurately monitor populations of interest due to interlinked issues including insufficient sample sizes, inappropriate duration, lack of reproducibility, and lack of clearly stated objectives. These common pitfalls could be avoided through the elicitation of explicit monitoring objectives and the a priori use of simulations to inform minimum sampling design requirements to meet said objectives. 2. Here, we provide a blueprint for using spatially explicit power analyses to inform the design and implementation of multi-species monitoring programs on landscape-scales. As a demonstration, we used spatially explicit simulations to devise a suitable sampling regime to meet clearly specified monitoring objectives in New York State: to use annual occupancy-based monitoring to be able to detect 25% and 50% changes in abundance of populations over five- and ten- year periods for all species of management interest in New York State, USA. We focused our simulation efforts on three challenging focal species (black bear, Ursus americanus, bobcat, Lynx rufus, and American marten, Martes americana) that differ notably in their morphology, life histories, space use, detection probability, habitat suitability, and population sizes/trajectories, and thus provide extremes in the challenges presented when it comes to sampling appropriately to detect changes in abundance. 3. Our simulations demonstrate variable context dependent trade-offs in sampling designs (i.e. number of sites [J] and number of sampling occasions [K]), and identify necessary minimum detection probabilities that must be attained to achieve statistical power to detect changes of varying magnitudes in populations of varying sizes in the three focal species. The simulations also highlight that monitoring population increases is likely beyond the reach of occupancy-based monitoring programs for wide-ranging or locally abundant species. 4. Synthesis and applications : We combine the results from the single-species simulations to produce a multi-species sampling design that meets the specified objectives for all three species. While the case study is centered on developing a multi-species sampling regime for New York State, it provides a reproducible step-by-step framework using established methods for wildlife managers and other practitioners to inform their own context- and objective- specific multi-species occupancy-based monitoring programs.

New York↗
USGS2025

Aggregating three sources of long-term trends of swallows and martins to identify priority conservation areas in the Great Lakes region

1. Long-term monitoring of bird populations across scales is important in evaluating conservation targets and creating effective conservation strategies. For nearly six decades, the Breeding Bird Survey (BBS) has served as the primary broad-scaled source of relative abundance trends of swallows and martins in North America. Recently, however, it has become possible to obtain breeding population trends using semi-structured eBird community science data. Moreover, weather surveil-lance radar data of swallow and martin roosting populations yield a third complementary source of trend information. 2. Using results from these three approaches, we propose a novel method of spatially combining estimates of percent change per year into a probability of directional agreement and/or disagreement that describes (1) the direction of the trend within a given region, (2) the amount of evidence associated with the estimate and (3) how much uncertainty surrounds it. We focus our efforts on an area of high Hirundinidae concentration in the North American Great Lakes region and predict trends from 2012 to 2022. 3. We found a high probability of agreement between all three sources about ob-served declines in swallow and martin trends in the region surrounding Lake Ontario and to the west of Lake Michigan. Focusing future research on these regions could improve our understanding of these declines and help build more targeted conservation initiatives. 4. Synthesis and applications. Our data integration methodology allows managers to identify regions that accumulate evidence of concerning trends across multiple wildlife monitoring schemes. These regions can thus be prioritized in conservation and management efforts. This approach can be generalized to other sources of long-term monitoring data of different species, at different stages of their annual cycle, in any geographic location.

Great Lakes region↗
USGS2026

Spatially concentrating logging could mitigate climate-magnified fragmentation risks to a globally endangered bird

1. Rising timber demand is transforming forest structure globally, profoundly affecting biodiversity and climate resilience. Logging-driven fragmentation is potentially a major driver of biodiversity loss in production landscapes, yet its interactions with escalating climate stressors remain poorly understood. 2. We combine two decades of Landsat-derived habitat metrics with 29,000 surveys of the marbled murrelet ( Brachyramphus marmoratus )—an iconic Pacific Northwest old-forest specialist seabird affecting management of >10 million hectares. Controlling for habitat amount and detection probability, increasing landscape-scale forest edge amount sharply reduces murrelet occupancy, with impacts worsening under unfavourable climate-driven ocean conditions. 3. Comparing alternative landscape-scale timber harvest strategies, spatially concentrated logging consistently supports higher murrelet populations than fragmented approaches producing equivalent wood volumes, with benefits amplified under adverse ocean conditions. However, historical harvesting policies in the Pacific Northwest have instead driven severe habitat fragmentation, which we show is eroding the value of core set-aside forests on federal and conservation lands and ultimately rendering murrelets more vulnerable to climate change. 4. Synthesis and applications : We map key opportunities to boost populations by reducing edginess around remaining nesting habitat and investigate these opportunities' spatial distribution across land ownership and timber productivity gradients. Concentrating logging could be critical for mitigating fragmentation and climate threats for murrelets and potentially other forest-dependent species amid rising timber demand.

California, Oregon, Washington↗
USGS2026

Biocrust and seed placement influence emergence rates of perennial grass Elymus elymoides across five North American deserts

1. Dryland vascular plant emergence is often limited by water availability especially with projected climate and precipitation changes. Biological soil crusts (biocrusts) can promote water retention and nutrient availability that benefit germination, and emergence yet can also act as a surface barrier preventing critical seed soil contact and hindering emergence. Alongside these factors, dryland fire frequency has increased with the inclusion of invasive species and vegetation structural changes. With enhanced continuous fine fuel distribution there is a high potential to disrupt biocrust-plant interactions and influence associated management actions. 2. This study explores the dynamic relationship between biocrusts and fire-related heating effects on seedling emergence across five North American deserts: the Chihuahuan Desert, Colorado Plateau, Great Basin, Mojave Desert and Sonoran Desert. We conducted a greenhouse-based seedling emergence experiment using Elymus elymoides (bottlebrush squirreltail), a common perennial grass, with biocrust and bare soil mesocosms in which half were heated to mimic the effects of wildfire temperature. 3. The variables that had the greatest influence on germination rate and germination timing were the presence of biocrust and seed placement (on top of vs within the biocrust/soil matrix). Emergence rate was greatest atop bare soil followed by seeds inserted into biocrust. Emergence timing was faster with biocrust present, but only when seeds were inserted into it. Desert origin of biocrust and soil collection also influenced germination where the probability of any one seed emerging was highest in the Chihuahuan and Mojave desert sites relative to the Sonoran desert site which showed the lowest probability. Heating had mixed effects whereby it increased the likelihood of emergence but did not affect the overall rate or timing. 4. Synthesis and applications . This study underscores the importance of healthy and impaired biocrusts in dryland systems and suggests ways in which the combination of biocrust and seed placement can influence plant establishment, in addition to providing insight into seeding strategies for managers and restoration practitioners working in dryland sites.

Arizona, California, Colorado, Nevada, New Mexico↗
USGS2026

Space between houses influences movement and habitat selection of ungulates: Width as a novel metric of development

Wildlife often lose access to habitat due to housing development. The magnitude of indirect habitat loss can be conditional on the configuration of individual houses, but commonly used metrics (i.e. density or distance) can overlook the configuration of development. We introduce a novel framework to index the configuration of development based on the width of space between houses and associated structures. We use resource selection functions to assess the degree that GPS-collared elk ( Cervus canadensis ), mule deer ( Odocoileus hemionus ), pronghorn and moose ( Alces alces ) on winter range and on migration routes use space between houses within northwest Wyoming, USA, near the towns of Cody and Jackson. Further, to help inform regulations aiming to promote wildlife movement across a gradient of land uses, we differentiated between individuals residing in primarily rural and exurban areas. Rural populations of elk, mule deer, pronghorn and moose avoided spaces narrower than 2 km and never used spaces narrower than 50 m between houses, whereas exurban populations of elk, mule deer and moose selected for spaces narrower than 2 km but avoided spaces narrower than 50 m. We identified cutoffs in rural and exurban areas where space may become too narrow for most animals to use. Through this metric, managers and policy makers can inform the necessary width to maintain wildlife movement through corridors. Our width metric can be applied to other systems and our workflow is publicly available ( https://wildlifemovetools.org/width-calculator ) so users can estimate the width of space between structures in their focal areas. Synthesis and applications . In rural areas, maintaining spaces between houses >500 m will likely facilitate ungulate movement. To fully conserve functional habitat, such as unimpeded habitat use along migration corridors, maintaining spaces approximately >2.5 km between houses will likely be necessary.

Wyoming↗
USGS2026

Testing the efficacy of industrial mitigation measures for caribou in the Arctic

1. Mitigation measures are commonly employed to reduce the negative effects of industrial development on wildlife but are not often evaluated for their efficacy. For example, oil fields in the Arctic typically incorporate design features intended to increase permeability for migratory, barren-ground caribou ( Rangifer tarandus) , even though there is limited empirical evidence of the effectiveness of some of these features. 2. Given expected increases in energy development in the North American Arctic, we examined whether two mitigation measures commonly used for migratory caribou, elevating pipelines and separating roads and pipelines, were effective at increasing the probability caribou would cross infrastructure. 3. We conducted our investigation on adult female caribou in the Central Arctic Herd of Alaska during summer, analyzing movement data from telemetry collars (2015-2020) in conjunction with spatial data on oil field infrastructure. To evaluate whether caribou would cross infrastructure as a function of the mitigation measures, we employed a generalized additive modeling framework capable of detecting non-linear and threshold responses. 4. We found that caribou were more likely to cross a pipeline when the nearest pipeline was elevated (≥1.9-m), a result that supports current mitigation recommendations. We also found that caribou were more likely to cross both a road and pipeline when they were directly adjacent to one another, as opposed to being spatially separated, a result that contradicts recommended mitigation strategies. 5. Synthesis and applications . As new energy projects are designed and implemented in environments around the globe, it is important to ensure that mitigation efforts for wildlife are scientifically validated for their efficacy. In the North American Arctic, such efforts will be critical for minimizing the impacts of expanding industrial development on migratory caribou and on the human communities that rely on them for subsistence.

Alaska↗