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At least 217 records · Page 12Linked to original sources
USGS2022

A biological condition gradient for Caribbean coral reefs: Part II. Numeric rules using sessile benthic organisms

The Biological Condition Gradient (BCG) is a conceptual model used to describe incremental changes in biological condition along a gradient of increasing anthropogenic stress. As coral reefs collapse globally, scientists and managers are focused on how to sustain the crucial structure and functions, and the benefits that healthy coral reef ecosystems provide for many economies and societies. We developed a numeric (quantitative) BGC model for the coral reefs of Puerto Rico and the US Virgin Islands to transparently facilitate ecologically meaningful management decisions regarding these fragile resources. Here, reef conditions range from natural, undisturbed conditions to severely altered or degraded conditions. Numeric decision rules were developed by an expert panel for scleractinian corals and other benthic assemblages using multiple attributes to apply in shallow-water tropical fore reefs with depths <30 m. The numeric model employed decision rules based on metrics (e.g., % live coral cover, coral species richness, pollution-sensitive coral species, unproductive and sediment substrates, % cover by Orbicella spp.) used to assess coral reef condition. Model confirmation showed the numeric BCG model predicted the panel’s median site ratings for 84% of the sites used to calibrate the model and 89% of independent validation sites. The numeric BCG model is suitable for adaptive management applications and supports bioassessment and criteria development. It is a robust assessment tool that could be used to establish ecosystem condition that would aid resource managers in evaluating and communicating current or changing conditions, protect water and habitat quality in areas of high biological integrity, or develop restoration goals with stakeholders and other public beneficiaries.

Ecological Indicators↗
USGS2022

Looking ahead, guided by the past: The role of U.S. national parks in amphibian research and conservation

Protected areas like national parks are essential elements of conservation because they limit human influence on the landscape, which protects biodiversity and ecosystem function. The role of national parks in conservation, however, often goes far beyond limiting human influence. The U.S. National Park Service and its system of land units contribute substantively to conservation by providing protected lands where researchers can document trends in species distributions and abundances, examine characteristics important for generating these trends, and identify and implement conservation strategies to preserve biodiversity. We reviewed the contribution of U.S. national parks to amphibian research and conservation and highlight important challenges and findings in several key areas. First, U.S. national parks were instrumental in providing strong support that amphibian declines were real and unlikely to be simply a consequence of habitat loss. Second, research in U.S. national parks provided evidence against certain hypothesized causes of decline, like UV-B radiation, and evidence for others, such as introduced species and disease. However, describing declines and identifying causes contributes to conservation only if it leads to management; importantly, U.S. national parks have implemented many conservation strategies and evaluated their effectiveness in recovering robust amphibian populations. Among these, removal of invasive species, especially fishes; conservation translocations; and habitat creation and enhancement stand out as examples of successful conservation strategies with broad applicability. Successful management for amphibians is additionally complicated by competing mandates and stakeholder interests; for example, past emphasis on increasing visitor enjoyment by introducing fish to formerly fishless lakes had devastating consequences for many amphibians. Other potential conflicts with amphibian conservation include increasing development, increased risk of introductions of disease and exotic species with increased visitation, and road mortality. Decision science and leveraging partnerships have proven to be key components of effective conservation under conflicting mandates in national parks. As resource managers grapple with large-scale drivers that are outside local control, public-private partnerships and adaptive strategies are increasing in importance. U.S. national parks have played an important role in many aspects of identifying and ameliorating the amphibian decline crisis and will continue to be essential for the conservation of amphibians in the future.

Ecological Indicators↗
USGS2022

Importance of local weather and environmental gradients on demography of a broadly distributed temperate frog

Amphibian populations are sensitive to environmental temperatures and moisture, which vary with local weather conditions and may reach new norms and extremes as contemporary climate change progresses. Using long-term (11–16 years) mark-recapture data from 10 populations of the Columbia spotted frog ( Rana luteiventris ) from across its U.S. range, we addressed hypotheses about how demographic relationships to weather depend upon a population’s position along climate gradients. We estimated the effect of seasonal weather on annual survival probability and recruitment rates both within populations and across the species’ range from subalpine forests to semi-arid deserts. We calculated population-specific weather variables that captured seasonal temperature and precipitation between summer sampling events, both for periods when frogs were active (spring to fall) and inactive (winter). Across all populations, we marked 15,885 adult frogs, with 33% of frogs recaptured at least once. Population demography varied with seasonal weather across the species’ range. Annual adult survival probability and recruitment rates of each population were influenced by a unique set of seasonal temperature and precipitation variables, particularly in winter and spring. Hence, adult survival varied with local conditions but, when analyzed across all populations, was predictable along a species-environment response curve associated with the timing of snowmelt and spring moisture. In contrast, recruitment rates for each population peaked at different values along an environmental gradient associated with the amount of snow during winter, and fall temperature and moisture levels, suggesting that recruitment may be responding to local conditions independently within each population. These findings highlight that sampling across the environmental (i.e., elevational and meteorological) gradients within a species range is necessary to predict species-level responses to regional climate change. This study also provides evidence of the importance of winter conditions on the demography of temperate amphibians, conditions that are already responding to climate change. Finally, this study further emphasizes that local context and spatiotemporal scale of inquiry remain paramount to understanding and potentially managing for climate effects on populations of amphibian species with broad geographic ranges.

Idaho, Montana, Nevada, Oregon↗
USGS2022

Power analysis for detecting the effects of best management practices on reducing nitrogen and phosphorus fluxes to the Chesapeake Bay watershed, USA

In 2010 the U.S. Environmental Protection Agency established the Total Maximum Daily Load (TMDL) which is a “pollution diet” that aims to reduce the amount of nitrogen and phosphorus entering the Chesapeake Bay, the largest estuary in the United States, by 25 and 24% percent, respectively. To achieve this goal the TMDL requires the implementation of Best Management Practices (BMPs), which are accepted land management practices for reducing pollutant runoff to nearby bodies of water. While the TMDL requires that the necessary management actions be in place by 2025 to eventually reach targeted nutrient loads, the ability to detect an effect of BMPs while assuming that one has occurred (i.e. statistical power) is still not well understood. The goal of this study was to investigate the power and required timelines to detect nutrient reductions in streams and rivers as the result of BMP implementation at the Chesapeake Watershed scale. Power estimates were produced using SPAtially Referenced Regression On Watershed attributes (SPARROW) models, which offer a flexible statistical framework and were recently extended to allow for modeling multiple time steps. Nitrogen and phosphorus focused models were calibrated to estimate the power to detect reductions in flux from numerous constituent sources. To confidently detect a decrease in constituent flux reaching the Chesapeake Bay’s tidal waters from a specific constituent source, reductions ranging from 30–60% were required for the nitrogen model. In contrast, reductions of up to 80% were not detectable under the phosphorus model. The timelines necessary to detect reductions in nitrogen flux ranged from 11 to several hundred years under different rates-of-change and management scenarios. The approach proposed here can help better understand the ability to detect the effects of BMPs on a regional scale and help guide future management actions and monitoring programs.

Delaware, Maryland, New York, Pennsylvania, Virgin↗
USGS2022

A biological condition gradient for coral reefs in the US Caribbean Territories: Part I. Coral narrative rules

As coral reef condition and sustainability continue to decline worldwide, losses of critical habitat and their ecosystem services have generated an urgency to understand and communicate reef response to management actions, environmental contamination, and natural disasters. Increasingly, coral reef protection and restoration programs emphasize the need for robust assessment tools for protecting high-quality waters and establishing conservation goals. Of equal importance is the need to communicate assessment results to stakeholders, beneficiaries, and the public so that environmental consequences of decisions are understood. The Biological Condition (BCG) model provides a structure to evaluate the condition of a coral reef in increments of change along a gradient of human disturbance. Communication of incremental change, regardless of direction, is important for decision makers and the public to better understand what is gained or lost depending on what actions are taken. We developed a narrative (qualitative) Biological Condition Gradient (BCG) from the consensus of a diverse expert panel to provide a framework for coral reefs in US Caribbean Territories. The model uses narrative descriptions of biological attributes for benthic organisms to evaluate reefs relative to undisturbed or minimally disturbed conditions. Using expert elicitation, narrative decision rules were proposed and deliberated to discriminate among six levels of change along a gradient of increasing anthropogenic stress. Narrative rules for each of the BCG levels are presented to facilitate the evaluation of benthic communities in coral reefs and provide specific narrative features to detect changes in coral reef condition and biological integrity. The BCG model can be used in the absence of numeric, or quantitative metrics, to evaluate actions that may encroach on coral reef ecosystems, manage endangered species habitat, and develop and implement management plans for marine protected areas, watersheds, and coastal zones. The narrative BCG model is a defensible model and communication tool that translates scientific results so the nontechnical person can understand and support both regulatory and non-regulatory water quality and natural resource programs.

Puerto Rico, U.S. Virgin Islands↗
USGS2022

How do accuracy and model agreement vary with versioning, scale, and landscape heterogeneity for satellite-derived vegetation maps in sagebrush steppe?

Maps of the distribution and abundance of dominant plants derived from satellite data are essential for ecological research and management, particularly in the vast semiarid shrub-steppe. Appropriate application of these maps requires an understanding of model accuracy and precision, and how it might vary across space, time, and different vegetation types. For a 113 k Ha burn area, we compared modeled maps of different vegetation cover types created from satellite data to ‘benchmark” models based on intensive field sampling (~1500-2000 plots resampled annually for 5 years) for three new satellite-derived models: USDA Rangeland Analysis Platform (RAP), the USGS Rangeland Condition Monitoring Assessment and Projection (RCMAP), and USGS fractional estimate of exotic annual grass cover (USGS-fractional-EAG). We assessed out-of-sample point accuracy and asked if and how accuracy changed each year due to vegetation shifts, new images, and model improvements (i.e. model versions). We also assessed how map agreement between satellite-based and field-based models changed with scale of application, topography, and time since fire. Accuracy and map agreement varied considerably among the vegetation types and across time and space (r 2 ranging from 0 to 0.53), and some of the variability was predictable. All models tended to over or underestimate cover when field-measured cover was relatively low or high, respectively, i.e. a “false moderating effect”. Accuracy was greater and improved with newer versions of RAP (+0.05 to 0.29 r 2 ) compared to RCMAP and USGS fractional model estimates, and in some cases was greater than field-based models. Variability in map agreement tended to decrease with larger areas sampled (particularly in areas >12 km), and this scale dependency was more evident in RAP and USGS-fractional-EAG models. Creating a “fair” basis for comparison of spatial models of low-statured semiarid vegetation derived from satellite compared to field data is not trivial because scaling the field data to the scale of large satellite pixels (or downscaling satellite-based models to field scale) requires modeling and associated model uncertainty. Accuracy can vary considerably and understanding the variation can help guide application of the models to the appropriate time, place, and variables.

Idaho, Oregon↗
USGS2022

Projected stream fish community risk to climate impacts in the Northeastern and Midwestern United States

Climate change is expected to alter stream fish habitat potentially leading to changes in the composition and distribution of fish communities. In the Northeastern and Midwestern United States we identified the distribution and characteristics of those fish communities most and least at risk of experiencing changes in climate which deviate from the climate they are associated with. We classified stream fish communities based on a suite of climate and environmental variables with multivariate regression trees under both recent and future conditions based on eight climate models. Our findings showed that some areas, such as the majority of the Illinois, Wisconsin, and Iowa), have high levels of risk of change in stream class, while much of Kentucky, West Virginia, Virginia, Pennsylvania, Eastern Ohio, Southern Michigan, and the Atlantic Coast are at relatively low risk. Stream class shifts ranged from over 75% of segments lost (associated with cooler temperatures) to gains of over 40% (associated with warmer temperatures). Common warmwater species such as green sunfish ( Lepomis cyanellus ), bluegill ( Lepomis macrochirus ) and largemouth bass ( Micropterus salmoides ) are expected to have the largest net gains in associated stream classes, while species associated with cooler streams such as Southern redbelly dace ( Chrosomus erythrogaster ), slimy sculpin ( Cottus cognatus ), and Eastern blacknose dace ( Rhinichthys atratulus ) were expected to experience the largest proportional losses. By pairing our climate risk predictions with other stressors such anthropogenic land use, habitat fragmentation, and water quality impairment, we identified opportunities for preservation (low risk due to all threats), restoration (low risk due to climate, high risk due to other stressors), and adaptation (high climate risk with low risk from other stressors). Understanding which communities are at risk due to climate change will aid in developing adaptation strategies to help sustain them in the future.

Connecticut, Delaware, Illinois, Indiana, Iowa, Ke↗
USGS2022

Hydrologic and environmental thresholds in stream fish assemblage structure across flow regimes

The characteristic pattern of variation in flow magnitude, frequency, duration, timing, and rate of change defines the flow regime of rivers and streams and is a key driver of ecosystem processes in fluvial ecosystems. Understanding how freshwater biotic assemblages change across gradients of hydrology and anthropogenic-source disturbance in different streamflow regimes is crucial to managing for sustainable environmental flows and watershed conservation. We compiled long-term (1916–2016) occurrence records for fishes collected in the Ouachita-Ozark Interior Highlands and West Gulf Coastal Plain streams, together with hydrologic metrics calculated from daily streamflow data measured at USGS stream gauging stations (n = 111), to examine important drivers and thresholds for fish assemblage turnover in groundwater (GW), runoff (RO), and intermittent (INT) flow regimes. We also examined the importance of spatial gradients (latitude, longitude, elevation, drainage area) and anthropogenic-source stressors (Hydrologic Disturbance Index; HDI) for fish assemblage turnover using a gradient forest modeling approach. Watershed fragmentation was of high importance for fish assemblage turnover in RO and INT streams, while changes in dam storage were more important for fishes in GW streams. Hydrologic metrics describing seasonal and stochastic properties of daily streamflow (Mag6) were most important for fish assemblage turnover in INT streams. Timing of high flow events had significantly higher importance compared to flow magnitude, duration, and frequency metrics, especially for fish assemblages in GW and INT streams. The frequency and timing of low flow events had high importance for fish assemblage turnover across all stream flow classes, while the magnitude of low flows and the magnitude and rate of change of average flows was most important for INT stream fish assemblages. In addition to benefiting multi-species conservation and management actions through identification of local and regional flow-ecology relationships generalized across different flow regimes, the results of this study provide a better understanding of complex nonlinear threshold effects, which is critical to anticipating changes in aquatic ecosystems and communities.

Arkansas, Missouri, Oklahoma↗
USGS2023

Where forest may not return in the western United States

Droughts that are hotter, more frequent, and last longer; pest outbreaks that are more extensive and more common; and fires that are more frequent, more extensive, and perhaps more severe have raised concern that forests in the western United States may not return once disturbed by one or more of these agents. Numerous field-based studies have been undertaken to better understand forest response to these changing disturbance regimes. Meta-analyses of these studies provide broad guidelines on the biotic and abiotic factors that hinder forest recovery, but study-to-study differences in methods and objectives do not support estimation of the total extent of potentially impaired forest succession. In this research, we provide an estimate of the area of potentially impaired forest succession. The estimate was derived from modeling of an 18-year land cover and Normalized Difference Vegetation Index (NDVI) time series supported by an extensive ancillary dataset. We estimate an upper bound of approximately 3470 km 2 of disturbed forest that may not return or reattain prior composition and structure. Based on the data used, fire appears to be the main disturbance agent of impaired forest succession, although climatic factors cannot be discounted. The numerous field studies routinely cite distal seed sources as a factor that hinders forest recovery, and we estimate that 20 % of the upper bound estimate has no forest cover within a 4.4-ha neighborhood. Our upper bound estimate is about 0.5 % of the 2001 mapped extent of western United States forests. The estimate is cognizant of measurement and modeling uncertainties (i.e., upper bound) and uncertainties related to successional rates and trajectories (i.e., potential).

Ecological Indicators↗
USGS2023

Long-term monitoring in transition: Resolving spatial mismatch and integrating multistate occupancy data

The success of long-term wildlife monitoring programs can be influenced by many factors and study designs often represent compromises between spatial scales and costs. Adaptive monitoring programs can iteratively manage this tension by adopting new cost-efficient technologies, which can provide projects the opportunity to reallocate costs to address new hypotheses, adapt to changing ecological conditions, or adjust sampling scale or resolution. If there is interest in longer time series of monitoring data, methodological transitions may necessitate integrated models to link newer data with historical data. However, data integration can be difficult if spatial or temporal scales are mismatched. Here, we develop an integrated multistate site-occupancy model and resolve sample unit spatial mismatch to link datasets from two northern spotted owl ( Strix occidentalis caurina ) monitoring schemes that broadly overlapped during a methodological transition. The first dataset was obtained from a decades-long spotted owl monitoring program using call-playback and mark-resight surveys on historical territories of varying size and shape. This monitoring program has recently transitioned to passive acoustic monitoring of randomly selected 5-km 2 hexagons over larger spatial extents. Both monitoring datasets overlapped with areas in which barred owl ( Strix varia ), an invasive competitor that has played an important role in northern spotted owl declines, were being removed experimentally. Reconciling spatial mismatch substantially increased the representation of the call-playback dataset and integrating the two datasets increased precision of spotted owl use and paired occupancy estimates relative to single dataset estimates. Estimates of spotted owl pair occupancy across the study area were lower than previous territory-based estimates based on call-playback surveys. Our integrated model further showed that a concurrent barred owl removal experiment increased landscape use and site occupancy by pairs of spotted owls. Our empirical application of an integrated modelling approach demonstrates a useful analytical framework for long-term monitoring efforts undergoing methodological transitions (e.g. mark-recapture to non-invasive population monitoring). This framework allows monitoring programs to maintain continuity of monitoring objectives across methodological transitions, rigorously incorporate previous findings, and adaptively respond to changing ecological conditions.

Oregon↗
USGS2023

Satellite-derived plant cover maps vary in performance depending on version and product

Understanding the accuracy and appropriate application scale of satellite-derived maps of vegetation cover is essential for effective management of the vast, remote rangelands of the world. However, the underlying models are updated frequently and may combine with rapidly changing vegetation conditions to cause variations in accuracy and precision over time. We sought to assess how model performance changed between different versions of satellite-derived cover products (Rangeland Analysis Platform, RAP, and Rangeland Condition Monitoring and Assessment Protocol, RCMAP) and how the performance of LandCart compared to RAP and RCMAP. Additionally, we asked how variability in agreement between LandCart and field-based models varied with scale. We utilized an intensive dataset of grid-point intercept functional group cover data collected between 2016 and 2020 across the ∼113 kHA 2015 Soda Wildfire to 1) evaluate r 2 agreement between versions of each satellite-derived product and plot-level field data and 2) assess relative standard error of agreement in cover between LandCart and continuous field-based Empirical Bayesian Kriging (EBK) regression models. Agreement between satellite- compared to field-plot values of cover (r 2 ) increased for RCMAP Version 5.0 compared to Version 2.0, but there were negligible changes between versions of RAP. Despite this, r 2 values of RCMAP and LandCart were nearly always less than RAP. Variability in agreement between EBK regression model cover and LandCart-derived cover decreased with the scale of consideration. Variability in agreement between satellite-derived cover products and field-based metrics is lowest at larger scale (mega-fire or regional) and varies from year to year and across versions, which could complicate detection of temporal changes in plant cover.

Idaho, Oregon↗
USGS2023

Incremental evolution of modeling a prognosis for polar bears in a rapidly changing Arctic

Updating predictions of the response of high-profile, at-risk species to climate change and anthropogenic stressors is vital for informing effective conservation action. Here, we review two prior generations of Bayesian network probability models predicting changes in global polar bear ( Ursus maritimus ) population status, and provide a contemporary update based on recent research findings and sea-ice projections by newer climate models. We compare predictions of polar bear population response from all 3 models among four circumpolar Arctic ecoregions, using sea ice projections based on three IPCC greenhouse gas emissions scenarios (SSP2.6, 4.5, 8.5). Consistent with the previous two model generations, polar bears will continue to experience increasing probability of declining or greatly declining populations throughout the 21st century, varying by emission scenario. Populations within the Polar Basin Divergent Ice Ecoregion have the highest predicted probability of declines, but predictions were slightly less dire relative to the previous model generation. Most of the influence, denoted by model sensitivity analysis, is from expected degradation and loss of sea ice and reduced access to marine prey. The lack of terrestrial prey adequate to substitute for loss of access to marine prey, as well as human-caused bear morality associated with hunting and defense of life and property encountered when polar bears are increasingly forced ashore also contributed to predicted declines. Although some tidewater glacial fjords and other localized onshore resources may provide local refugia, their benefit is transient. Our findings continue to inform priorities for inventory, monitoring, and research needs, and suggest that similar updates to models of other at-risk species can capitalize on the comparison framework we present here.

Ecological Indicators↗
USGS2024

A new method for bioassessment of ecosystems with complex communities and environmental gradients

Bioassessment of complex and heterogeneous ecosystems is a challenge when there are multiple, strong, natural environmental gradients; unknown, or spatially varying, mixtures of stressors; and large numbers of taxa with unknown responses to both the environmental gradients and the stressors. Current methods of bioassessment are not designed for use under this set of constraints. To address this gap, we have developed an assessment method appropriate for well-sampled, heterogeneous systems with many taxa. In the bioassessment described below, we model taxa occurrence as a function of natural environmental gradients, then use residual covariance patterns between all pairs of taxa to estimate the impact of human disturbance across sites as a latent construct. The derivation of the method from an underlying causal model allows the metric value at each site and the associated taxa responses to be partitioned into contributions from a set of putative stressors. We apply this method as a case study to the subtidal benthic invertebrate community of Puget Sound, WA (USA) and demonstrate a partial decomposition of the metric values to a set of stressors including sediment organic carbon, nitrogen, metals, and organic pollutants. While this method provides new opportunities to estimate, communicate, and understand the ecological condition of complex, heterogeneous ecosystems, due to the requirement for broad, detailed data to inform its estimates, it will likely be most appropriate for monitoring programs.

Washington↗
USGS2024

Passive acoustic monitoring and convolutional neural networks facilitate high-resolution and broadscale monitoring of a threatened species

Population monitoring is an essential component of biodiversity conservation and management, but low detection probabilities for rare and/or cryptic species makes estimating abundance and occupancy challenging. Passive acoustic monitoring combined with machine learning algorithms represents a potential path forward to effectively and efficiently monitor the occurrence of rare vocalizing species across entire forest landscapes. Our objectives were to develop and implement a convolutional neural network (PNW-Cnet) to identify vocalizations of a rare and threatened forest nesting bird species – the marbled murrelet ( Brachyramphus marmoratus ) – in the Pacific Northwest, U.S.A., 2018–2021. We used PNW-Cnet predictions from broadscale passive acoustic monitoring data to examine spatiotemporal patterns in the distribution of murrelets. PNW-Cnet showed sufficiently high prediction accuracy (overall precision > 0.9) to enable broadscale population monitoring. Spatiotemporal analysis showed that annual peak murrelet call abundance occurs in ordinal weeks 28–32 (late July–Mid August) but this varied by study area. The greatest number of detections typically occurred in the Olympic Peninsula and Oregon Coast Range where late-successional forest dominates and nearer to ocean habitats. We demonstrate that passive acoustic monitoring can be used to understand intensity of use across broad scales for a rare and cryptic species in addition to the typical detection/non-detection data that are often collected. Passive acoustic monitoring combined with PNW-Cnet offers considerable promise for species distribution modeling and long-term population monitoring for rare species.

Oregon, Washington↗
USGS2024

Estimating traffic volume and road age in Wyoming to inform resource management planning: An application with wildlife-vehicle collisions

Road networks and their associated vehicular traffic disturb many terrestrial systems, but inventories of roads used to assess these effects often focus on the ‘where’ (e.g., local road type and density) and neglect the ‘when’ (e.g., temporal disturbance) or ‘how much’ (e.g., traffic volume disturbance). We developed annual estimates of the ‘when’ (road age) and ‘how much’ (vehicular traffic volume) across 148,172 km of highways, arterials, collectors, local, and gravel/graded roads within the state of Wyoming for the years 1986 to 2020 to provide a comprehensive dataset for future ecological investigations. We leveraged a suite of ancillary data on surface disturbances (e.g., oil & gas drilling operations, wind turbines, and open pit mines) with known establishment dates and combined them using graph theory and centrality metrics to estimate the age of each road. We then predicted traffic volume obtained from the Wyoming Department of Transportation for each year across Wyoming using a machine learning method, XGBoost, and a separate set of spatial covariates hypothesized to explain traffic patterns across large regions. We found that 132,476 km of these roads likely existed before 1986, but that 16,693 km (10.7 %) of roads have been built since 1986. Overall, our estimates of road age were 89 % accurate when assessed on a subset of 1,330 roads with high-resolution aerial imagery. Mean absolute error for predicting traffic volume ranged from 35.2 to 77.9 annual average daily traffic (aadt) for trucks and 269.2 to 516.7 aadt for all-vehicles across the 35 years. We found that mean traffic volume across the state increased by 23 % for both truck-only traffic and all vehicular traffic from 1986 to 2020. However, changes in traffic volume have varied substantially across the state (e.g., 100 % increases in volume in some areas, while other areas experienced declines of up to 1,786 %). We also illustrate a novel application of these data by predicting rates of reported wildlife-vehicle collisions (WVCs) along a subset of roads. We found evidence of a non-linear relationship that supported a threshold hypothesis for WVCs, wherein increases in traffic volume equate to increases in WVCs up to a threshold, above which increases in traffic volume result in declines in WVCs. The data provided here will enable better-informed studies of road ecology to address how roads may affect wildlife populations and key ecosystems across Wyoming.

Wyoming↗
USGS2024

Connectivity patterns between floodplain lakes and neighboring streams in the historical floodplain of the Lower Mississippi River

Hydrologic connectivity, the network of water pathways linking aquatic habitats, is vital for the exchange of organisms and abiotic materials between rivers and adjacent waterbodies. This study quantified hydrologic connectivity for 1,283 lakes in the Lower Mississippi River floodplain using satellite imagery, streamgauge data, and geospatial information. We aimed to assess connection frequency patterns between lakes and streams. Eight metrics describing temporal aspects of hydrologic connectivity were estimated, identifying trends by lake features and by stream size. Each lake exhibited a distinct pattern of connection, with specific months of connectivity followed by disconnection, likely influenced by lake characteristics and seasonal precipitation. Larger lakes showed increased connectivity, likely due to their surface area and volume, while smaller lakes were more prone to isolation, especially during dry periods. Lakes connected to large streams exhibited more prolonged and recurring connections, with less seasonal variation. In contrast, lakes near agricultural areas experienced reduced connectivity. However, local factors such as levees and artificial channels often disrupted these general trends. This hydrologic connectivity analysis can provide insight to support floodplain management, facilitate development of frameworks that restore connectivity, promote preservation of ecological integrity, and support management of invasive species spread in agricultural floodplains.

Lower Mississippi River floodplain↗
USGS2025

An accuracy assessment of satellite-derived rangeland fractional cover

Satellite-derived maps of vegetation cover provide detailed information about vegetation spatiotemporal patterns and are increasingly used to better understand and manage rangelands. Despite their utility, questions remain regarding the regional and site level accuracy for these maps, especially compared to field-collected data. We conducted an accuracy assessment of the Rangeland Analysis Platform (RAP), using over 17,000 field plots sampled through nationwide rangeland vegetation monitoring programs in the continental U.S. We observed higher overall nationwide map error compared to previous validations of RAP, and absolute error (Mean Absolute Error [MAE] and Root Mean Square Error [RMSE]) was highest for perennial herbaceous and bare ground and lowest for trees (MAE range = 2.98 –10.22 %). There were also differences in map agreement with field data across ecoregions. Generally, map agreement was highest in the Great Basin and lowest in the Great Plains and Desert Southwest. Additionally, we assessed the suitability of using RAP in riparian and wetland areas, which are absent in the current version’s training. Errors for bare ground in riparian areas were lower than errors of upland accuracy assessments (upland MAE = 10.22 %, riparian MAE = 7.22 %), but for all other functional groups, riparian error was higher (ΔMAE range: 0.21 – 20.49 %). We examine how our results could inform regional applications of fractional cover data while considering error and uncertainty and identify areas for potential model improvement. Our findings inform the use of RAP regionally and provide a technique for evaluating other vegetation mapping products for use in rangeland management.

Ecological Indicators↗
USGS2025

Effects of northern bobwhite habitat management on avian species of conservation concern

The umbrella species concept is often used as a tool to guide management decisions and focus efforts towards one focal species whose habitat needs overlap that of other species. We assessed this concept in the context of an agriculturally dominant landscape using one of the most well-studied avian species in North America as a target for conservation efforts: Northern Bobwhite ( Colinus virginianus ). This species is often viewed as an umbrella species for grassland and shrubland bird conservation throughout its native range due to its complex, year-round habitat requirements. We assessed the influence of Northern Bobwhite habitat management on six songbird species of conservation concern in Iowa by evaluating similarities and differences in habitat associations between each species. Our objectives were to (1) assess which vegetation characteristics were most strongly associated with Northern Bobwhite occupancy and (2) evaluate whether those characteristics were also associated with abundance of the focal songbird species. We used occupancy and N-mixture models to assess relationships between vegetation characteristics and Northern Bobwhite occupancy and songbird abundance, respectively. We found that the vegetation characteristics most strongly associated with Northern Bobwhite occupancy probability were the amounts of closed canopy forest, early successional woody vegetation, non-vegetated areas, and percent cover of bare ground. We found that for some of these covariates, including the amounts of forest and non-vegetated area, the effect on focal songbird species abundance aligned with Northern Bobwhite occupancy. For others, including the amount of early successional woody vegetation, the effects differed. This assessment of overlap and variability in habitat associations suggests that Northern Bobwhite-targeted management can provide benefits to other grassland and shrubland birds, but may also come with some trade-offs. This work adds to existing literature, further highlighting the nuances of the umbrella species concept in that land management benefits from the assessment of trade-offs and inclusion of local community dynamics.

Iowa↗