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USGS2014

Identifying monitoring gaps for amphibian populations in a North American biodiversity hotspot, the southeastern USA

I review the primary literature to ascertain the status of amphibian monitoring efforts in the southeastern USA, a “hotspot” for biodiversity in North America. This effort revealed taxonomic, geographic and ecological disparities in studies of amphibian populations in this region. Of the species of anurans and caudates known to occur in the Southeast, 73.8 and 33.3 %, respectively, have been monitored continuously for at least 4 years. Anurans are generally shorter-lived than are caudates and, thus, have been studied for the equivalent of at least one population turnover more than have caudates. The percentage of species (of those occurring in a given state) monitored continuously for at least 4 years was lowest for Alabama and Mississippi and highest for Florida for both taxa. The vast majority of studies (69.6 %) were conducted on species that inhabit natural freshwater wetlands, in contrast to other aquatic and terrestrial habitats. Species considered threatened by the International Union for Conservation of Nature comprised only 7.7 % of 65 species that have been studied consistently. The majority of comparative studies of contemporary versus historical occurrences were potentially biased by the use of “presence-only” historical data and resurveys of short duration. Other issues, such as inadequate temporal and spatial scale and neglect of different sources of error, were common. Awareness of these data gaps and sampling and statistical issues may help facilitate informed decisions in setting future monitoring priorities, particularly with respect to species, habitats and locations that have been largely overlooked in past and ongoing studies.

Alabama, Florida, Georgia, Louisiana, Mississippi,↗

USGS2004

Debris-flow hazards at San Salvador, San Vicente, and San Miguel volcanoes, El Salvador

Volcanic debris flows (lahars) in El Salvador pose a significant risk to tens of thousands of people as well as to property and important infrastructure. Major cities and nearly a third of the country's population are located near San Salvador, San Vicente, and San Miguel volcanoes. Debris flows traveling as little as 4 km from source at these volcanoes put hundreds to thousands of lives, property, and infrastructure at risk. We used a statistically based model that relates debris-flow volume to cross-sectional and planimetric inundation areas to evaluate spatial patterns of inundation from a suite of debris flows ranging in volume from 100,000 m 3 to as large as 100 million m 3 and examined prehistoric deposits and a limited number of historical events at these volcanoes to estimate probable frequencies of recurrence. Our analyses show that zones of greatest debris-flow hazard generally are focused within 10 km of the summits of the volcanoes. For typical debris-flow velocities (3–10 m/s), these hazard areas can be inundated within a few minutes to a few tens of minutes after the onset of a debris flow. Our analyses of debris-flow recurrence at these volcanoes suggest that debris flows with volumes of 100,000 m 3 to as large as 500,000 m 3 have probable return periods broadly in the range of ∼10 to 100 yr. Debris flows having volumes less than 100,000 m 3 probably recur more frequently, especially at San Miguel volcano. Despite the limited extents of the hazard zones portrayed in our analyses, even the smallest debris flows could be devastating. Urban and agricultural expansions have encroached onto the flanks of the volcanoes, and debris-flow–hazard zones extend well into areas that are settled densely or used for agriculture. Therefore, people living, working, or recreating along channels that drain the volcanoes must learn to recognize potentially hazardous conditions, be aware of the extents of debris-flow–hazard zones, and be prepared to evacuate to safer ground when hazardous conditions develop.

Special Paper of the Geological Society of America↗

USGS2007

Scaling field data to calibrate and validate moderate spatial resolution remote sensing models

Validation and calibration are essential components of nearly all remote sensing-based studies. In both cases, ground measurements are collected and then related to the remote sensing observations or model results. In many situations, and particularly in studies that use moderate resolution remote sensing, a mismatch exists between the sensor’s field of view and the scale at which in situ measurements are collected. The use of in situ measurements for model calibration and validation, therefore, requires a robust and defensible method to spatially aggregate ground measurements to the scale at which the remotely sensed data are acquired. This paper examines this challenge and specifically considers two different approaches for aggregating field measurements to match the spatial resolution of moderate spatial resolution remote sensing data: (a) landscape stratification; and (b) averaging of fine spatial resolution maps. The results show that an empirically estimated stratification based on a regression tree method provides a statistically defensible and operational basis for performing this type of procedure.

Photogrammetric Engineering and Remote Sensing↗

USGS2007

Estimating trend precision and power to detect trends across grouped count data

Ecologists commonly use grouped or clustered count data to estimate temporal trends in counts, abundance indices, or abundance. For example, the U.S. Breeding Bird Survey data represent multiple counts of birds from within each of multiple, spatially defined routes. Despite a reliance on grouped counts, analytical methods for prospectively estimating precision of trend estimates or statistical power to detect trends that explicitly acknowledge the characteristics of grouped count data are undescribed. These characteristics include the fact that the sampling variance is an increasing function of the mean, and that sampling and group-level variance estimates are generally estimated on different scales (the sampling and log scales, respectively). We address these issues for repeated sampling of a single population using an analytical approach that has the flavor of a generalized linear mixed model, specifically that of a negative binomial-distributed count variable with random group effects. The count mean, including grand intercept, trend, and random group effects, is modeled linearly on the log scale, while sampling variance of the mean is estimated on the log scale via the delta method. Results compared favorably with those derived using Monte Carlo simulations. For example, at trend = 5% per temporal unit, differences in standard errors and in power were modest relative to those estimated by simulation (???|11|% and ???|16|%, respectively), with relative differences among power estimates decreasing to ???|7|% when power estimated by simulations was ???0.50. Similar findings were obtained using data from nine surveys of fingernail clams in the Mississippi River. The proposed method is suggested (1) where simulations are not practical and relative precision or power is desired, or (2) when multiple precision or power calculations are required and where the accuracy of a fraction of those calculations will be confirmed using simulations. ?? 2007 by the Ecological Society of America.

Ecology↗

USGS1994

Surface-water-quality assessment of the Yakima River basin in Washington: major-and minor-element data for sediment, water, and aquatic biota, 1987-91

Major- and minor-element concentrations are presented for streambed and suspended sediment, filtered- and unfiltered-water, and aquatic-biota samples collected during 1987-91 from the Yakima River Basin in south-central Washington. The samples were collected as part of the U.S. Geological Survey's National Water-quality Assessment (NAWQA) program which is designed to provide results that are useful in understanding and managing the Nation's water resources. This report includes the sampling approach, field collection and processing techniques, and methods of chemical analysis, as well as a compilation of chemical data, statistical summaries, and quality- control data. These data may be used by scientists and resource managers to describe (1) spatial distribution of selected major and minor elements in sediment, water, and aquatic biota of the Yakima River Basin; (2) temporal variation for element concentrations in filtered water and in suspended sediment at selected sites; (3) suita- bility of surface water for preservation of aquatic life and protection of human health; and (4) major natural and anthropogenic sources of major and minor elements in the Yakima River Basin that affect observed water-quality conditions. Streambed-sediment samples were collected once from 27 sites in the basin during 1987-91. Suspended-sediment and filtered-water samples were collected monthly and during hydrologic events (including snowmelt and winter rainstorms) at seven sites, and filtered-water samples were collected at least once at an additional 37 sites during synoptic samplings. Unfiltered-water samples were collected at seven sites on a quarterly basis during 1987 only. Samples of aquatic plants were collected once in 1989, and aquatic insects, fish, and clams were collected from 34 sites three times during 1989-90.

Washington↗

USGS2009

Hydrodynamic simulations of physical aquatic habitat availability for Pallid Sturgeon in the Lower Missouri River, at Yankton, South Dakota, Kenslers Bend, Nebraska, Little Sioux, Iowa, and Miami, Missouri, 2006-07

The objective of this study was to assess the sensitivity of habitat availability in the Lower Missouri River to discharge variation, with emphasis on habitats that might support spawning of the endangered pallid sturgeon. We constructed computational hydrodynamic models for four reaches that were selected because of evidence that sturgeon have spawned in them. The reaches are located at Miami, Missouri (river mile 259.6–263.5), Little Sioux, Iowa (river mile 669.6–673.5), Kenslers Bend, Nebraska (river mile 743.9–748.1), and Yankton, South Dakota reach (river mile 804.8–808.4). The models were calibrated for a range of measured flow conditions, and run for a range of discharges that might be affected by flow modifications from Gavins Point Dam. Model performance was assessed by comparing modeled and measured water velocities. A selection of derived habitat units was assessed for sensitivity to hydraulic input parameters (drag coefficient and lateral eddy viscosity). Overall, model results were minimally sensitive to varying eddy viscosity; varying lateral eddy viscosity by 20 percent resulted in maximum change in habitat units of 5.4 percent. Shallow-water habitat units were most sensitive to variation in drag coefficient with 42 percent change in unit area resulting from 20 percent change in the parameter value; however, no habitat unit value changed more than 10 percent for a 10 percent variation in drag coefficient. Sensitivity analysis provides guidance for selecting habitat metrics that maximize information content while minimizing model uncertainties. To assess model sensitivities arising from topographic variation from sediment transport on an annual time scale, we constructed separate models from two complete independent surveys in 2006 and 2007. The net topographic change was minimal at each site; the ratio of net topographic change to water volume in the reaches at 95 percent exceedance flow was less than 5 percent, indicating that on a reach-average basis, annual topographic change contributed little to habitat area variation. Net erosion occurred at Yankton (the upstream reach) and because erosion was distributed uniformly, there was little affect on many habitat metrics. Topographic change was spatially nonuniform at Little Sioux and Kenslers Bend reaches. Shallow water habitat units and some reach-scale patch statistics (edge density, patch density, and Simpson’s Diversity Index) were affected by these changes. Erosion dominated at the downstream reach but habitat metrics did not vary substantially from 2006 to 2007. Among habitat metrics that were explored, zones of convergent flow were identified as areas that most closely correspond to spawning habitats of other sturgeon species, as identified in the scientific literature, and that are consistent with sparse data on pallid sturgeon spawning locations in the Lower Missouri River. Areas of convergent zone habitat varied little with discharges that would be associated with spring pulsed flows, and relations with discharge changed negligibly between 2006 and 2007. Other habitat measures show how physical habitat varies with discharge and among the four reaches. Wake habitats defined by velocity gradients seem to correspond with migration pathways of adult pallid sturgeon. Habitats with low Froude-number correspond to low energy areas that may accumulate passively transporting particles, organic matter, and larval fish. Among the modeled reaches, Yankton had substantially longer water residence time for equivalent flow exceedances than the other three modeled reaches. Longer residence times result from greater flow resistance in the relatively wide, shallow channel and may be associated with longer residence times of passively transported particulate materials.

Scientific Investigations Report↗

USGS2019

Framework for using downscaled climate model projections in ecological experiments to quantify plant and soil responses

Soil and plant responses to climate change can be quantified in controlled settings. However, the complexity of climate projections often leads researchers to evaluate ecosystem response based on general trends, rather than specific climate model outputs. Climate projections capture spatial and temporal climate extremes and variability that are lost when using mean climate trends. In addition, application of climate projections in experimental settings remains limited. Our objective was to develop a framework to incorporate statistically downscaled climate model projections into the design of temperature and precipitation treatments for ecological experiments. To demonstrate the utility of experimental treatments derived from climate projections, we used wetlands in the Great Plains as a model ecosystem for evaluating plant and soil responses. Spatial and temporal projections were selected to capture variability and intensity of projected future conditions for exemplary purposes. To illustrate climate projection application for ecological experiments, we developed temperature and precipitation treatments based on moderate-emissions scenario climate outputs (i.e., RCP4.5–650 ppm CO 2 equivalent). Our temperature treatments captured weekly trends that represented cool, average, and warm temperature predictions, and our daily precipitation treatments mimicked various seasonal precipitation trends and extreme events projected for the late 21st century. Treatments were applied to two short-term controlled experiments evaluating (1) plant germination (temperature treatment applied in growth chamber) and (2) soil nitrogen cycling (precipitation treatment applied in greenhouse) responses to projected future conditions in the Great Plains. Our approach provides flexibility for selecting appropriate and precise climate model outputs to design experimental treatments. Using these techniques, ecologists can better incorporate variation in climate model projections for experimentally evaluating ecosystem responses to future climate conditions, reduce uncertainty in predictive ecological models, and apply predicted outcomes when making management and policy decisions.

Nebraska, Texas↗

USGS1997

Detecting changes in the spatial distribution of nitrate contamination in ground water

Many studies of ground water pollution in general and nitrate contamination in particular have often relied on a one-time investigation, tracking of individual wells, or aggregate summaries. Studies of changes in spatial distribution of contaminants over time are lacking. This paper presents a method to compare spatial distributions for possible changes over time. The large-scale spatial distribution at a given time can be considered as a surface over the area (a trend surface). The changes in spatial distribution from period to period can be revealed by the differences in the shape and/or height of surfaces. If such a surface is described by a polynomial function, changes in surfaces can be detected by testing statistically for differences in their corresponding polynomial functions. This method was applied to nitrate concentration in a population of wells in an agricultural drainage basin in Iowa, sampled in three different years. For the period of 1981-1992, the large-scale spatial distribution of nitrate concentration did not show significant change in the shape of spatial surfaces; while the magnitude of nitrate concentration in the basin, or height of the computed surfaces showed significant fluctuations. The change in magnitude of nitrate concentration is closely related to climatic variations, especially in precipitation. The lack of change in the shape of spatial surfaces means that either the influence of land use/nitrogen management was overshadowed by climatic influence, or the changes in land use/management occurred in a random fashion.

Journal of the American Water Resources Associatio↗

USGS2009

Evaluating the power to detect temporal trends in fishery independent surveys: A case study based on Gillnets Set in the Ohio waters of Lake Erie for walleye

Fishery-independent (FI) surveys provide critical information used for the sustainable management and conservation of fish populations. Because fisheries management often requires the effects of management actions to be evaluated and detected within a relatively short time frame, it is important that research be directed toward FI survey evaluation, especially with respect to the ability to detect temporal trends. Using annual FI gill-net survey data for Lake Erie walleyes Sander vitreus collected from 1978 to 2006 as a case study, our goals were to (1) highlight the usefulness of hierarchical models for estimating spatial and temporal sources of variation in catch per effort (CPE); (2) demonstrate how the resulting variance estimates can be used to examine the statistical power to detect temporal trends in CPE in relation to sample size, duration of sampling, and decisions regarding what data are most appropriate for analysis; and (3) discuss recommendations for evaluating FI surveys and analyzing the resulting data to support fisheries management. This case study illustrated that the statistical power to detect temporal trends was low over relatively short sampling periods (e.g., 5–10 years) unless the annual decline in CPE reached 10–20%. For example, if 50 sites were sampled each year, a 10% annual decline in CPE would not be detected with more than 0.80 power until 15 years of sampling, and a 5% annual decline would not be detected with more than 0.8 power for approximately 22 years. Because the evaluation of FI surveys is essential for ensuring that trends in fish populations can be detected over management-relevant time periods, we suggest using a meta-analysis–type approach across systems to quantify sources of spatial and temporal variation. This approach can be used to evaluate and identify sampling designs that increase the ability of managers to make inferences about trends in fish stocks.

Ohio↗

USGS2002

Statistical mapping of count survey data

We apply a Poisson mixed model to the problem of mapping (or predicting) bird relative abundance from counts collected from the North American Breeding Bird Survey (BBS). The model expresses the logarithm of the Poisson mean as a sum of a fixed term (which may depend on habitat variables) and a random effect which accounts for remaining unexplained variation. The random effect is assumed to be spatially correlated, thus providing a more general model than the traditional Poisson regression approach. Consequently, the model is capable of improved prediction when data are autocorrelated. Moreover, formulation of the mapping problem in terms of a statistical model facilitates a wide variety of inference problems which are cumbersome or even impossible using standard methods of mapping. For example, assessment of prediction uncertainty, including the formal comparison of predictions at different locations, or through time, using the model-based prediction variance is straightforward under the Poisson model (not so with many nominally model-free methods). Also, ecologists may generally be interested in quantifying the response of a species to particular habitat covariates or other landscape attributes. Proper accounting for the uncertainty in these estimated effects is crucially dependent on specification of a meaningful statistical model. Finally, the model may be used to aid in sampling design, by modifying the existing sampling plan in a manner which minimizes some variance-based criterion. Model fitting under this model is carried out using a simulation technique known as Markov Chain Monte Carlo. Application of the model is illustrated using Mourning Dove (Zenaida macroura) counts from Pennsylvania BBS routes. We produce both a model-based map depicting relative abundance, and the corresponding map of prediction uncertainty. We briefly address the issue of spatial sampling design under this model. Finally, we close with some discussion of mapping in relation to habitat structure. Although our models were fit in the absence of habitat information, the resulting predictions show a strong inverse relation with a map of forest cover in the state, as expected. Consequently, the results suggest that the correlated random effect in the model is broadly representing ecological variation, and that BBS data may be generally useful for studying bird-habitat relationships, even in the presence of observer errors and other widely recognized deficiencies of the BBS.

Book chapter↗

USGS2017

What mediates tree mortality during drought in the southern Sierra Nevada?

Severe drought has the potential to cause selective mortality within a forest, thereby inducing shifts in forest species composition. The southern Sierra Nevada foothills and mountains of California have experienced extensive forest dieback due to drought stress and insect outbreak. We used high-fidelity imaging spectroscopy (HiFIS) and light detection and ranging (LiDAR) from the Carnegie Airborne Observatory (CAO) to estimate the effect of forest dieback on species composition in response to drought stress in Sequoia National Park. Our aims were: (1) to quantify site-specific conditions that mediate tree mortality along an elevation gradient in the southern Sierra Nevada Mountains; (2) to assess where mortality events have a greater probability of occurring; and (3) to estimate which tree species have a greater likelihood of mortality along the elevation gradient. A series of statistical models were generated to classify species composition and identify tree mortality, and the influences of different environmental factors were spatially quantified and analyzed to assess where mortality events have a greater likelihood of occurring. A higher probability of mortality was observed in the lower portion of the elevation gradient, on southwest and west-facing slopes, in areas with shallow soils, on shallower slopes, and at greater distances from water. All of these factors are related to site water balance throughout the landscape. Our results also suggest that mortality is species-specific along the elevation gradient, mainly affecting Pinus ponderosa and Pinus lambertiana at lower elevations. Selective mortality within the forest may drive long-term shifts in community composition along the elevation gradient.

Sierra Nevada↗

USGS2019

Nonlinear reaction–diffusion process models improve inference for population dynamics

Partial differential equations (PDEs) are a useful tool for modeling spatiotemporal dynamics of ecological processes. However, as an ecological process evolves, we need statistical models that can adapt to changing dynamics as new data are collected. We developed a model that combines an ecological diffusion equation and logistic growth to characterize colonization processes of a population that establishes long-term equilibrium over a heterogeneous environment. We also developed a homogenization strategy to statistically upscale the PDE for faster computation and adopted a hierarchical framework to accommodate multiple data sources collected at different spatial scales. We highlighted the advantages of using a logistic reaction component instead of a Malthusian component when population growth demonstrates asymptotic behavior. As a case study, we demonstrated that our model improves spatiotemporal abundance forecasts of sea otters in Glacier Bay, Alaska. Furthermore, we predicted spatially varying local equilibrium abundances as a result of environmentally driven diffusion and density-regulated growth. Integrating equilibrium abundances over the study area in our application enabled us to infer the overall carrying capacity of sea otters in Glacier Bay, Alaska.

Alaska↗

USGS2021

Nutrient limitation of phytoplankton in Chesapeake Bay: Development of an empirical approach for water-quality management

Understanding the temporal and spatial roles of nutrient limitation on phytoplankton growth is necessary for developing successful management strategies. Chesapeake Bay has well-documented seasonal and spatial variations in nutrient limitation, but it remains unknown whether these patterns of nutrient limitation have changed in response to nutrient management efforts. We analyzed historical data from nutrient bioassay experiments (1992–2002) and data from long-term, fixed-site water-quality monitoring program (1990–2017) to develop empirical approaches for predicting nutrient limitation in the surface waters of the mainstem Bay. Results from classification and regression trees (CART) matched the seasonal and spatial patterns of bioassay-based nutrient limitation in the 1992–2002 period much better than two simpler, non-statistical approaches. An ensemble approach of three selected CART models satisfactorily reproduced the bioassay-based results (classification rate = 99%). This empirical approach can be used to characterize nutrient limitation from long-term water-quality monitoring data on much broader geographic and temporal scales than would be feasible using bioassays, providing a new tool for informing water-quality management. Results from our application of the approach to 21 tidal monitoring stations for the period of 2007–2017 showed modest changes in nutrient limitation patterns, with expanded areas of nitrogen-limitation and contracted areas of nutrient saturation (i.e., not limited by nitrogen or phosphorus). These changes imply that long-term reductions in nitrogen load have led to expanded areas with nutrient-limited phytoplankton growth in the Bay, reflecting long-term water-quality improvements in the context of nutrient enrichment. However, nutrient limitation patterns remain unchanged in the majority of the mainstem, suggesting that nutrient loads should be further reduced to achieve a less nutrient-saturated ecosystem.

Maryland, Virginia↗

USGS2016

Uncertainty in biological monitoring: a framework for data collection and analysis to account for multiple sources of sampling bias

Biological monitoring programmes are increasingly relying upon large volumes of citizen-science data to improve the scope and spatial coverage of information, challenging the scientific community to develop design and model-based approaches to improve inference. Recent statistical models in ecology have been developed to accommodate false-negative errors, although current work points to false-positive errors as equally important sources of bias. This is of particular concern for the success of any monitoring programme given that rates as small as 3% could lead to the overestimation of the occurrence of rare events by as much as 50%, and even small false-positive rates can severely bias estimates of occurrence dynamics. We present an integrated, computationally efficient Bayesian hierarchical model to correct for false-positive and false-negative errors in detection/non-detection data. Our model combines independent, auxiliary data sources with field observations to improve the estimation of false-positive rates, when a subset of field observations cannot be validated a posteriori or assumed as perfect. We evaluated the performance of the model across a range of occurrence rates, false-positive and false-negative errors, and quantity of auxiliary data. The model performed well under all simulated scenarios, and we were able to identify critical auxiliary data characteristics which resulted in improved inference. We applied our false-positive model to a large-scale, citizen-science monitoring programme for anurans in the north-eastern United States, using auxiliary data from an experiment designed to estimate false-positive error rates. Not correcting for false-positive rates resulted in biased estimates of occupancy in 4 of the 10 anuran species we analysed, leading to an overestimation of the average number of occupied survey routes by as much as 70%. The framework we present for data collection and analysis is able to efficiently provide reliable inference for occurrence patterns using data from a citizen-science monitoring programme. However, our approach is applicable to data generated by any type of research and monitoring programme, independent of skill level or scale, when effort is placed on obtaining auxiliary information on false-positive rates.

Methods in Ecology and Evolution↗

USGS2007

Problems and methods involved in relating land use to ground-water quality

Efforts to relate shallow ground-water quality to the land use near a well lead to several statistical difficulties. These include potential uncertainty in land-use categorical data due to misclassification, data closure, distributional skewing, and spatial autocorrelation. Methods of addressing these problems are, respectively, the establishment of limits on minimum buffer radius, the estimation of contrasts, rank-based tests of association, and sub-sampling to prevent buffer overlap. Relations between the presence of purgeable organic compounds in ground water and land use are used to illustrate these problems and methods.

Journal of the American Water Resources Associatio↗

USGS2010

An evaluation of sex-age-kill (SAK) model performance

The sex-age-kill (SAK) model is widely used to estimate abundance of harvested large mammals, including white-tailed deer ( Odocoileus virginianus ). Despite a long history of use, few formal evaluations of SAK performance exist. We investigated how violations of the stable age distribution and stationary population assumption, changes to male or female harvest, stochastic effects (i.e., random fluctuations in recruitment and survival), and sampling efforts influenced SAK estimation. When the simulated population had a stable age distribution and λ > 1, the SAK model underestimated abundance. Conversely, when λ < 1, the SAK overestimated abundance. When changes to male harvest were introduced, SAK estimates were opposite the true population trend. In contrast, SAK estimates were robust to changes in female harvest rates. Stochastic effects caused SAK estimates to fluctuate about their equilibrium abundance, but the effect dampened as the size of the surveyed population increased. When we considered both stochastic effects and sampling error at a deer management unit scale the resultant abundance estimates were within ±121.9% of the true population level 95% of the time. These combined results demonstrate extreme sensitivity to model violations and scale of analysis. Without changes to model formulation, the SAK model will be biased when λ ≠ 1. Furthermore, any factor that alters the male harvest rate, such as changes to regulations or changes in hunter attitudes, will bias population estimates. Sex-age-kill estimates may be precise at large spatial scales, such as the state level, but less so at the individual management unit level. Alternative models, such as statistical age-at-harvest models, which require similar data types, might allow for more robust, broad-scale demographic assessments.

Journal of Wildlife Management↗

USGS2009

Multiscale sagebrush rangeland habitat modeling in southwest Wyoming

Sagebrush-steppe ecosystems in North America have experienced dramatic elimination and degradation since European settlement. As a result, sagebrush-steppe dependent species have experienced drastic range contractions and population declines. Coordinated ecosystem-wide research, integrated with monitoring and management activities, would improve the ability to maintain existing sagebrush habitats. However, current data only identify resource availability locally, with rigorous spatial tools and models that accurately model and map sagebrush habitats over large areas still unavailable. Here we report on an effort to produce a rigorous large-area sagebrush-habitat classification and inventory with statistically validated products and estimates of precision in the State of Wyoming. This research employs a combination of significant new tools, including (1) modeling sagebrush rangeland as a series of independent continuous field components that can be combined and customized by any user at multiple spatial scales; (2) collecting ground-measured plot data on 2.4-meter imagery in the same season the satellite imagery is acquired; (3) effective modeling of ground-measured data on 2.4-meter imagery to maximize subsequent extrapolation; (4) acquiring multiple seasons (spring, summer, and fall) of an additional two spatial scales of imagery (30 meter and 56 meter) for optimal large-area modeling; (5) using regression tree classification technology that optimizes data mining of multiple image dates, ratios, and bands with ancillary data to extrapolate ground training data to coarser resolution sensors; and (6) employing rigorous accuracy assessment of model predictions to enable users to understand the inherent uncertainties. First-phase results modeled eight rangeland components (four primary targets and four secondary targets) as continuous field predictions. The primary targets included percent bare ground, percent herbaceousness, percent shrub, and percent litter. The four secondary targets included percent sagebrush (Artemisia spp.), percent big sagebrush (Artemisia tridentata), percent Wyoming sagebrush (Artemisia tridentata wyomingensis), and sagebrush height (centimeters). Results were validated by an independent accuracy assessment with root mean square error (RMSE) values ranging from 6.38 percent for bare ground to 2.99 percent for sagebrush at the QuickBird scale and RMSE values ranging from 12.07 percent for bare ground to 6.34 percent for sagebrush at the full Landsat scale. Subsequent project phases are now in progress, with plans to deliver products that improve accuracies of existing components, model new components, complete models over larger areas, track changes over time (from 1988 to 2007), and ultimately model wildlife population trends against these changes. We believe these results offer significant improvement in sagebrush rangeland quantification at multiple scales and offer users products that have been rigorously validated.

Wyoming↗

USGS2026

End-user needs for remote sensing wetlands of the Prairie Pothole Region of North America

The Prairie Pothole Region (PPR) of North America comprises globally important grassland and wetland ecosystems critical for numerous populations of migratory birds. Due to the importance of this region for migratory birds, and particularly waterfowl, and the threats of habitat loss due to intensifying agriculture, there is a mature and diverse system of conservation organizations, agencies, and partnerships that spends hundreds of millions of dollars annually on habitat conservation to support migratory bird populations. Remote sensing can be a powerful tool for observing and evaluating global change at large scales as well as expanding inferences from field studies to the broader landscape with statistical models. However, development and utilization of these tools has lagged behind their demand for several reasons, including concerns over spatial and temporal resolution and accuracy of products; perception of a misalignment with decision-maker needs; technological barriers such as skill sets of conservation professionals, computing resources, data access, and usability. In this report, we summarize the needs of conservation professionals and scientists who use or want to use remote sensing data products to inform science about wetland change and conservation of wetlands in the PPR. We assembled this information through several methods leading up to, during, and following a January 2026 PPR Wetland Remote Sensing Workshop. The workshop included United States and Canadian scientists, conservation professionals, and policy experts. Our goal was to bring together end-users and remote sensing product developers jointly to explore reducing the lag between product development and utilization of products to inform science and conservation. Specifically, we aimed to identify gaps in wetland remote sensing that limit effective monitoring, management, and conservation in the PPR, and to develop a framework that outlines pathways to address these gaps by fostering collaboration, improving communication networks, encouraging discussion, and building on existing and ongoing efforts. This report summarizes our participants’ descriptions of end-user needs and the outcomes of the workshop.

Prairie Pothole region↗