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At least 649 records · Page 36Linked to original sources
USGS2018

Nutrient and metal loads estimated by using discrete, automated, and continuous water-quality monitoring techniques for the Blackstone River at the Massachusetts-Rhode Island State line, water years 2013–14

Flow-proportional composite water samples were collected in water years 2013 and 2014 by the U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, from the Blackstone River at Millville, Massachusetts (U.S. Geological Survey station 01111230), about 0.5 mile from the border with Rhode Island. Samples were collected in order to better understand the dynamics of selected nutrient and metal constituents, assist with planning, guide activities to meet water-quality goals, and provide real-time water-quality information to the public. An automated system collected the samples at 14-day intervals to determine total and dissolved nitrogen and phosphorus concentrations, to provide accurate monthly nutrient concentration data, and to calculate monthly load estimates. Concentrations of dissolved trace metals and total aluminum were determined from 4-day composite water samples that were collected twice monthly by the automated system. Results from 4-day composites provide stakeholders with information to evaluate trace metals on the basis of chronic 4-day exposure criteria for aquatic life, and the potential to use the biotic ligand model to evaluate copper concentrations. Nutrient, trace metal, suspended sediment, dissolved organic carbon, and chlorophyll a concentrations were determined from discrete samples collected at the Millville station and from across the stream transect at the upstream railroad bridge, and these concentrations served as a means to evaluate the representativeness of the Millville point location. Analytical results from samples collected with the automated flow-proportional sampling system provided the means to calculate monthly and annual loading data. Total nitrogen and total phosphorus loads in water year (WY) 2013 were about 447,000 and 36,000 kilograms (kg), respectively. In WY 2014, annual loads of total nitrogen and total phosphorus were about 342,000 and 21,000 kg, respectively. Total nitrogen and total phosphorus loads from WYs 2013 and 2014 were about 56 and 65 percent lower than those reported for WYs 2008 and 2009. The higher loads in 2008 and 2009 may be explained by the higher than average flows in WY 2009 and by facility upgrades made by wastewater treatment facilities in the basin. Median loads were determined from composite samples collected with the automated system between October 2012 and October 2014. Median dissolved cadmium and chromium 4-day loads were 0.55 and 0.84 kg, respectively. Dissolved copper and total lead median 4-day loads were 8.02 and 1.42 kg, respectively. The dissolved nickel median 4-day load was 5.45 kg, and the dissolved zinc median 4-day load was 36 kg. Median total aluminum 4-day loads were about 197 kg. Spearman’s rank correlation analyses were used with discrete sample concentrations and continuous records of temperature, specific conductance, turbidity, and chlorophyll a to identify correlations between variables that could be used to develop regression equations for estimating real-time concentrations of constituents. Correlation coefficients were generated for flow, precipitation, antecedent precipitation, physical parameters, and chemical constituents. A 95-percent confidence limit for each value of Spearman’s rho was calculated, and multiple linear regression analysis using ordinary least squares regression techniques was used to develop regression equations for concentrations of total phosphorus, total nitrogen, suspended sediment concentration, total copper, and total aluminum. Although the correlations are based on the limited amount of data collected as part of this study, the potential to monitor water-quality changes in real time may be of value to resource managers and decision makers.

Massachusetts, Rhode Island↗
USGS2019

Rigorously valuing the role of U.S. coral reefs in coastal hazard risk reduction

The degradation of coastal habitats, particularly coral reefs, raises risks by increasing the exposure of coastal communities to flooding hazards. The protective services of these natural defenses are not assessed in the same rigorous economic terms as artificial defenses, such as seawalls, and therefore often are not considered in decision making. Here we combine engineering, ecologic, geospatial, social, and economic tools to provide a rigorous valuation of the coastal protection benefits of all U.S. coral reefs in the States of Hawaiʻi and Florida, the territories of Guam, American Samoa, Puerto Rico, and Virgin Islands, and the Commonwealth of the Northern Mariana Islands. We follow risk-based valuation approaches to map flood zones at 10-square-meter resolution along all 3,100+ kilometers of U.S. reef-lined shorelines for different storm probabilities to account for the effect of coral reefs in reducing coastal flooding. We quantify the coastal flood risk reduction benefits provided by coral reefs across storm return intervals using the latest information from the U.S. Census Bureau, Federal Emergency Management Agency, and Bureau of Economic Analysis to identify their annual expected benefits, a measure of the annual protection provided by coral reefs. Based on these results, the annual protection provided by U.S. coral reefs is estimated in: avoided flooding to more than 18,180 people; avoided direct flood damages of more than \$825 million to more than 5,694 buildings; avoided flooding to more than 33 critical infrastructure facilities, including essential facilities, utility systems, and transportation systems; and avoided indirect damages of more than \$699 million in economic activity of individuals and more than \$272 million in avoided business interruption annually. Thus, the annual value of flood risk reduction provided by U.S. coral reefs is more than 18,000 lives and \$1.805 billion in 2010 U.S. dollars. These data provide stakeholders and decision makers with spatially explicit, rigorous valuation of how, where, and when U.S. coral reefs provide critical coastal storm flood reduction benefits. The overall goal is to ultimately reduce the risk to, and increase the resiliency of, U.S. coastal communities.

American Samoa, Commonwealth of the Northern Maria↗
USGS2024

Fish invasion of prairie pothole wetlands reduces amphipod abundance, a key vertebrate forage

Fishes have spread into previously fishless wetlands, likely affecting other species. In the Prairie Pothole Region of North America, the invasion of fish into wetlands is facilitated by interactions of altered land use, climate, and hydrology. We aimed to understand the effects of fishes on amphipods, which are macroinvertebrates that vertebrates rely on as forage. We hypothesized the presence and abundance of fish, particularly benthivores, would have detrimental effects on amphipod abundance. Our study design targeted a large gradient of amphipod abundances among wetlands, including very high abundances of two amphipod species: Gammarus lacustris and Hyalella azteca . We found that fishless basins had twice as many amphipods as those with fish, on average. Gammarus lacustris were not detected in the presence of Black Bullhead Ameiurus melas . The abundance of both amphipod species had negative associations with the most common fishes, Fathead Minnow Pimephales promelas and Brook Stickleback Culaea inconstans . A multivariate community analysis showed the benthivore-fish functional feeding guild was negatively associated with the amphipod community, as hypothesized. However, our study design captured several wetlands with anomalies of high abundances of both fish and amphipods, obscuring their relationships. Our results aid resource managers by confirming several fish guilds and species are associated with lower abundances of amphipods. These findings can inform resource managers who make decisions about managing for fish and wildlife; for example, they may choose to manage existing fish populations or protect existing wetlands with high amphipod densities from new fish invasions.

Wetlands↗
USGS2019

Socioecological determinants of drought impacts and coping strategies for ranching operations in the Great Plains

In Great Plains rangelands, drought is a recurring disturbance. Ranchers in this region expect to encounter drought but may not be adequately prepared for it. Efforts to encourage drought preparedness would benefit from a better understanding of the conditions under which managers make decisions to minimize the impacts of drought. We tested the direct and moderating roles of the drought hazard and the social-ecological context on drought impacts and response. This study was conducted with ranchers in western and central South Dakota and Nebraska following the drought that began in 2012. We surveyed ranchers regarding the effects of the drought and their responses and used multimodel analysis to explore the relationships among measures of drought preparedness, drought response, and drought impacts. Drought severity was the primary predictor of all impacts, but specific types of impacts were varied depending on the operation’s enterprise mix, resources, and management. The socioecological characteristics of the ranch system predicted drought response actions taken, by either providing the necessary resources and capacity to take action or creating sensitivity in the system that required action to be taken. We conclude with recommendations for learning from current drought experiences in order to better adapt to future drought events.

Rangeland Ecology and Management↗
USGS2009

Predictive Models of the Hydrological Regime of Unregulated Streams in Arizona

Three statistical models were developed by the U.S. Geological Survey in cooperation with the Arizona Department of Environmental Quality to improve the predictability of flow occurrence in unregulated streams throughout Arizona. The models can be used to predict the probabilities of the hydrological regime being one of four categories developed by this investigation: perennial, which has streamflow year-round; nearly perennial, which has streamflow 90 to 99.9 percent of the year; weakly perennial, which has streamflow 80 to 90 percent of the year; or nonperennial, which has streamflow less than 80 percent of the year. The models were developed to assist the Arizona Department of Environmental Quality in selecting sites for participation in the U.S. Environmental Protection Agency's Environmental Monitoring and Assessment Program. One model was developed for each of the three hydrologic provinces in Arizona - the Plateau Uplands, the Central Highlands, and the Basin and Range Lowlands. The models for predicting the hydrological regime were calibrated using statistical methods and explanatory variables of discharge, drainage-area, altitude, and location data for selected U.S. Geological Survey streamflow-gaging stations and a climate index derived from annual precipitation data. Models were calibrated on the basis of streamflow data from 46 stations for the Plateau Uplands province, 82 stations for the Central Highlands province, and 90 stations for the Basin and Range Lowlands province. The models were developed using classification trees that facilitated the analysis of mixed numeric and factor variables. In all three models, a threshold stream discharge was the initial variable to be considered within the classification tree and was the single most important explanatory variable. If a stream discharge value at a station was below the threshold, then the station record was determined as being nonperennial. If, however, the stream discharge was above the threshold, subsequent decisions were made according to the classification tree and explanatory variables to determine the hydrological regime of the reach as being perennial, nearly perennial, weakly perennial, or nonperennial. Using model calibration data, misclassification rates for each model were 17 percent for the Plateau Uplands, 15 percent for the Central Highlands, and 14 percent for the Basin and Range Lowlands models. The actual misclassification rate may be higher; however, the model has not been field verified for a full error assessment. The calibrated models were used to classify stream reaches for which the Arizona Department of Environmental Quality had collected miscellaneous discharge measurements. A total of 5,080 measurements at 696 sites were routed through the appropriate classification tree to predict the hydrological regime of the reaches in which the measurements were made. The predictions resulted in classification of all stream reaches as perennial or nonperennial; no reaches were predicted as nearly perennial or weakly perennial. The percentages of sites predicted as being perennial and nonperennial, respectively, were 77 and 23 for the Plateau Uplands, 87 and 13 for the Central Highlands, and 76 and 24 for the Basin and Range Lowlands.

Open-File Report↗
USGS2010

Developing an ecosystem services online decision support tool to assess the impacts of climate change and urban growth in the Santa Cruz watershed: Where we live, work, and play

Using respective strengths of the biological, physical, and social sciences, we are developing an online decision support tool, the Santa Cruz Watershed Ecosystem Portfolio Model (SCWEPM), to help promote the use of information relevant to water allocation and land management in a binational watershed along the U.S.-Mexico border. The SCWEPM will include an ES valuation system within a suite of linked regional driver-response models and will use a multicriteria scenario-evaluation framework that builds on GIS analysis and spatially-explicit models that characterize important ecological, economic, and societal endpoints and consequences that are sensitive to climate patterns, regional water budgets, and regional LULC change in the SCW.

Arizona↗
USGS2024

Predicted potential for aquatic exposure effects of per- and polyfluorinated alkyl substances (PFAS) in Pennsylvania’s statewide network of streams

Per- and polyfluoroalkyl substances (PFAS) are contaminants that can lead to adverse health effects in aquatic organisms, including reproductive toxicity and developmental abnormalities. To assess the ecological health risk of PFAS in Pennsylvania stream surface water, we conducted a comprehensive analysis that included both measured and predicted estimates. The potential combined exposure effects of 14 individual PFAS to aquatic biota were estimated using the sum of exposure-activity ratios ( Σ EARs) in 280 streams. Additionally, machine learning techniques were utilized to predict potential PFAS exposure effects in unmonitored stream reaches, considering factors such as land use, climate, and geology. Leveraging a tailored convolutional neural network (CNN), a validation accuracy of 78% was achieved, directly outperforming traditional methods that were also used, such as logistic regression and gradient boosting (accuracies of ~65%). Feature importance analysis highlighted key variables that contributed to the CNN’s predictive power. The most influential features highlighted the complex interplay of anthropogenic and environmental factors contributing to PFAS contamination in surface waters. Industrial and urban land cover, rainfall intensity, underlying geology, agricultural factors, and their interactions emerged as key determinants. These findings may help to inform biotic sampling strategies, water quality monitoring efforts, and policy decisions aimed to mitigate the ecological impacts of PFAS in surface waters.

Pennsylvania↗
USGS2018

Landsat time series analysis of fractional plant cover changes on abandoned energy development sites

Oil and natural gas development in the western United States has increased substantially in recent decades as technological advances like horizontal drilling and hydraulic fracturing have made extraction more commercially viable. Oil and gas pads are often developed for production, and then capped, reclaimed, and left to recover when no longer productive. Understanding the rates, controls, and degree of recovery of these reclaimed well sites to a state similar to pre-development conditions is critical for energy development and land management decision processes. Here we use a multi-decadal time series of satellite imagery (Landsat 5, 1984–2011) to assess vegetation regrowth on 365 abandoned well pads located across the Colorado Plateau in Utah, Colorado, and New Mexico. We developed high-frequency time series of the Soil-Adjusted Total Vegetation Index (SATVI) for each well pad using the Google Earth Engine cloud computing platform. BFAST time-series models were used to fit temporal trends, identifying when vegetation was cleared from the site and the magnitudes and rates of vegetation change after abandonment. The time series metrics are used to calculate the relative fractional vegetation cover (RFVC) of each pad, a measure of post-abandonment vegetation cover relative to pre-drilling condition. Mean and median RFVC were 36% (s.d. 33%) and 26%, respectively, five years after abandonment, with one third of well pads having RFVC greater than 50%. Statistical analyses suggest that much of the high vegetation cover is associated with weedy invasive annual species such as cheatgrass (Bromus tectorum) and Russian thistle (Salsola spp.). Climate conditions and the year of abandonment also play a role, with increased cover in later years associated with a wetter period. Non-linear change at many pads suggests longer recovery times than would be estimated by linear extrapolation. New techniques implemented here address a complex response of cover change to soils, management, and climate over time, and can be extended to the operational monitoring of energy development across large areas.

International Journal of Applied Earth Observation↗
USGS2014

Causal networks clarify productivity-richness interrelations, bivariate plots do not

Perhaps no other pair of variables in ecology has generated as much discussion as species richness and ecosystem productivity, as illustrated by the reactions by Pierce (2013) and others to Adler et al.'s (2011) report that empirical patterns are weak and inconsistent. Adler et al. (2011) argued we need to move beyond a focus on simplistic bivariate relationships and test mechanistic, multivariate causal hypotheses. We feel the continuing debate over productivity–richness relationships (PRRs) provides a focused context for illustrating the fundamental difficulties of using bivariate relationships to gain scientific understanding. Pierce (2013) disputes Adler et al.'s (2011) conclusion that bivariate productivity–richness relationships (PRRs) are ‘weak and variable’. He argues, instead, that relationships in the Adler et al. data are actually strong and, further, that failure to adhere to the humped-back model (HBM; sensu Grime 1979) threatens scientists' ability to advise conservationists. Here, we show that Pierce's reanalyses are invalid, that statistically significant boundary relations in the Adler et al. data are difficult to detect when proper methods are used and that his advice neither advances scientific understanding nor provides the quantitative forecasts actually needed by decision makers. We begin by examining Grimes' HBM through the lens of causal networks. We first translate the ideas contained in the HBM into a causal diagram, which shows explicitly how multiple processes are hypothesized to control biomass production and richness and their interrelationship. We then evaluate the causal diagram using structural equation modelling and example data from a published study of meadows in Finland. Formal analysis rejects the literal translation of the HBM and reveals additional processes at work. This exercise shows how the practice of abstracting systems as causal networks (i) clarifies possible hypotheses, (ii) permits explicit testing and (iii) provides more powerful and useful predictions. Building on the Finnish meadow example, we contrast the utility of bivariate plots compared with structural equation models for investigating underlying processes. Simulations illustrate the fallibility of bivariate analysis as a means of supporting one theory over another, while models based on causal networks can quantify the sensitivity of diversity patterns to both management and natural constraints. A key piece of Pierce's critique of Adler et al.'s conclusions relies on upper boundary regression which he claims to reveal strong relationships between production and richness in Adler et al.'s original data. We demonstrate that this technique shows strong associations in purely random data and is invalid for Adler et al.'s data because it depends on a uniform data distribution. We instead perform quantile regression on both the site-level summaries of the data and the plot-level data (using mixed-model quantile regression). Using a variety of nonlinear curve-fitting approaches, we were unable to detect a significant humped-shape boundary in the Adler et al. data. We reiterate that the bivariate productivity–richness relationships in Adler et al.'s data are weak and variable. We urge ecologists to consider productivity–richness relationships through the lens of causal networks to advance our understanding beyond bivariate analysis. Further, we emphasize that models based on a causal network conceptualization can also provide more meaningful guidance for conservation management than can a bivariate perspective. Measuring only two variables does not permit the evaluation of complex ideas nor resolve debates about underlying mechanisms.

Functional Ecology↗
USGS2013

Internet-based Modeling, Mapping, and Analysis for the Greater Everglades (IMMAGE; Version 1.0): web-based tools to assess the impact of sea level rise in south Florida

South Florida's Greater Everglades area is particularly vulnerable to sea level rise, due to its rich endowment of animal and plant species and its heavily populated urban areas along the coast. Rising sea levels are expected to have substantial impacts on inland flooding, the depth and extent of surge from coastal storms, the degradation of water supplies by saltwater intrusion, and the integrity of plant and animal habitats. Planners and managers responsible for mitigating these impacts require advanced tools to help them more effectively identify areas at risk. The U.S. Geological Survey's (USGS) Internet-based Modeling, Mapping, and Analysis for the Greater Everglades (IMMAGE) Web site has been developed to address these needs by providing more convenient access to projections from models that forecast the effects of sea level rise on surface water and groundwater, the extent of surge and resulting economic losses from coastal storms, and the distribution of habitats. IMMAGE not only provides an advanced geographic information system (GIS) interface to support decision making, but also includes topic-based modules that explain and illustrate key concepts for nontechnical users. The purpose of this report is to familiarize both technical and nontechnical users with the IMMAGE Web site and its various applications.

Florida↗
USGS2021

A rapid assessment function to estimate common raven population densities: Implications for targeted management

Common raven ( Corvus corax ; raven) populations have increased over the past 5 decades within the western United States. Raven population increases have been largely attributed to growing resource subsidies from expansion of human enterprise. Concomitantly, managers are becoming increasingly concerned about elevated adverse effects on multiple sensitive prey species, damage to livestock and agriculture, and human safety. Managers could benefit from a rapid but reliable method to estimate raven densities across spatiotemporal scales to monitor raven populations more efficiently and inform targeted and adaptive management frameworks. However, obtaining estimates of raven density is data- and resource-intensive, which renders monitoring within an adaptive framework unrealistic. To address this need, we developed a rapid survey protocol for resource managers to estimate site-level density based on the average number of ravens per survey. Specifically, we first estimated raven densities at numerous field sites with robust distance sampling procedures and then used regression to investigate the relationship between those density estimates and the number of ravens per survey, which revealed a strong correlation ( R 2 = 0.86). For management application, we provide access to R function software through a web-based interface to estimate density using number of ravens per survey, which we refer to as a Rapid Assessment Function (RAF). Then, using a simulation analysis of data from sites with abundant surveys and the RAF, we estimated raven density based on different numbers of surveys to help inform how many surveys are needed to achieve reliable estimates within this rapid assessment. While more robust procedures of distance sampling are the preferred methods for estimating raven densities from count surveys, the RAF tool presented herein provides a reliable approximation for informing management decisions when managers are faced with resource and small sample size constraints.

California, Idaho, Nevada, Oregon↗
USGS2014

Uncertainty analysis of a groundwater flow model in east-central Florida

A groundwater flow model for east-central Florida has been developed to help water-resource managers assess the impact of increased groundwater withdrawals from the Floridan aquifer system on heads and spring flows originating from the Upper Floridan aquifer. The model provides a probabilistic description of predictions of interest to water-resource managers, given the uncertainty associated with system heterogeneity, the large number of input parameters, and a nonunique groundwater flow solution. The uncertainty associated with these predictions can then be considered in decisions with which the model has been designed to assist. The “Null Space Monte Carlo” method is a stochastic probabilistic approach used to generate a suite of several hundred parameter field realizations, each maintaining the model in a calibrated state, and each considered to be hydrogeologically plausible. The results presented herein indicate that the model’s capacity to predict changes in heads or spring flows that originate from increased groundwater withdrawals is considerably greater than its capacity to predict the absolute magnitudes of heads or spring flows. Furthermore, the capacity of the model to make predictions that are similar in location and in type to those in the calibration dataset exceeds its capacity to make predictions of different types at different locations. The quantification of these outcomes allows defensible use of the modeling process in support of future water-resources decisions. The model allows the decision-making process to recognize the uncertainties, and the spatial/temporal variability of uncertainties that are associated with predictions of future system behavior in a complex hydrogeological context.

Florida↗
USGS2020

Exploring the potential value of satellite remote sensing to monitor chlorophyll-a for U.S. lakes and reservoirs

Assessment of chlorophyll-a, an algal pigment, typically measured by field and laboratory in situ analyses, is used to estimate algal abundance and trophic status in lakes and reservoirs. In situ-based monitoring programs can be expensive, may not be spatially, and temporally comprehensive and results may not be available in the timeframe needed to make some management decisions, but can be more accurate, precise, and specific than remotely sensed measures. Satellite remotely sensed chlorophyll-a offers the potential for more geographically and temporally dense data collection to support estimates when used to augment or substitute for in situ measures. In this study, we compare available chlorophyll-a data from in situ and satellite imagery measures at the national scale and perform a cost analysis of these different monitoring approaches. The annual potential avoided costs associated with increasing the availability of remotely sensed chlorophyll-a values were estimated to range between $5.7 and $316 million depending upon the satellite program used and the timeframe considered. We also compared sociodemographic characteristics of the regions (both public and private lands) covered by both remote sensing and in situ data to check for any systematic differences across areas that have monitoring data. This analysis underscores the importance of continued support for both field-based in situ monitoring and satellite sensor programs that provide complementary information to water quality managers, given increased challenges associated with eutrophication, nuisance, and harmful algal bloom events.

Environmental Monitoring and Assessment↗
USGS2006

Pigs on the plains: Institutional analysis of a Colorado water quality initiative

We used the Legal-Institutional Analysis Model (LIAM) and Advocacy Coalition Framework (ACF) to analyze the campaign over passage of the Colorado Hogs Rule, an initiative passed by the voters in 1998 to require regulation of swine production facilities in Colorado. Used in tandem, LIAM and ACF provided an opportunity to develop a robust understanding of the obstacles and opportunities that face water quality managers in a state-centered multi-organizational decision process. We found that combining the LIAM with the ACF enhanced the understanding that could be achieved by using either model in isolation. The predictive capacity of the LIAM would have been reduced without information from the ACF, and the ACF by itself would have missed the importance of a single-case study.

International Journal of Public Administration↗
USGS2025

Using probability difference to compare streamflow information of alternatives for efficient operation of monitoring networks

Efficient operation of streamflow monitoring networks requires investments in technology and labor that provide the greatest benefits from available resources. Economic analyses comparing the costs and benefits from different types of alternatives for monitoring have not been practical to implement. Streamflow information provides a generic measure of benefits that can be incorporated into operational decisions as an objective for monitoring networks. A methodology for comparing how accuracy, monitoring period, and monitoring instead of modeling affects streamflow information is developed from information-theoretic approaches for network design but contributes three novel features: (1) a probability-difference model for conditional probability of monotonically paired variables, (2) explicit discounting of unverified information that may exceed the accuracy of streamflow records, and (3) run analysis to account for non-stationarity in streamflow probabilities. Application of the methodology to the U.S. Geological Survey streamflow monitoring network indicates the value of monitoring period to reduce the uncertainty of streamflow probabilities and, thus, increase streamflow information. The methodology has important limitations, particularly for sites with non-perennial streamflow, but demonstrates that probability difference could be used to evaluate operational alternatives to increase the efficiency of monitoring networks.

PLOS Water↗
USGS2025

Environmental drivers of Greater Sage-grouse population trends over 25 years in Idaho, USA

Greater Sage-grouse ( Centrocercus urophasianus ) populations have been in decline for decades across much of the US Intermountain West. However, findings from 25 years of lek counts in Idaho indicate that some populations are stable or even increasing. After accounting for potential biases in past lek count data, we sought to explain the variability in population trends among all 70 lek clusters (i.e., populations) we identified in the state. For each population, we identified lek count troughs, or low-point years, that occurred between the mid-1990s and 2021 and used a regression slope of those abundance low points to quantify each population's trend over the 25-year time span. We related the 70 populations' slopes to climate, fire, topographic, vegetation, and landcover variables. Our analyses revealed that populations with negative trends tend to occur toward the ends of climate gradients (i.e., extremes of occupied habitats) and in locations with more wildfire, agriculture, and riparian landcover. Populations with positive trends generally occur in landscapes toward the middle of the climate gradient, with high amounts of low sagebrush ( Artemisia arbuscula ) landcover and intermediate amounts of riparian and agricultural landcover. Post hoc analysis indicated that the latter two drivers were strongly associated with high raven occupancy rates, which may contribute to the negative sage-grouse population trends we observed in areas with high riparian or agricultural landcover. When modeled separately for different regions however, various region-specific drivers were identified, including tree cover, annual herbaceous cover, and human development. This information can help guide sage-grouse habitat management decisions and set expectations for population recovery, given the diversity of habitats occupied by the species and the cyclic nature of sage-grouse populations.

Idaho↗
USGS2022

Framework for assessing and mitigating the impacts of offshore wind energy development on marine birds

Offshore wind energy development (OWED) is rapidly expanding globally and has the potential to contribute significantly to renewable energy portfolios. However, development of infrastructure in the marine environment presents risks to wildlife. Marine birds in particular have life history traits that amplify population impacts from displacement and collision with offshore wind infrastructure. Here, we present a broadly applicable framework to assess and mitigate the impacts of OWED on marine birds. We outline existing techniques to quantify impact via monitoring and modeling (e.g., collision risk models, population viability analysis), and present a robust mitigation framework to avoid, minimize, or compensate for OWED impacts. Our framework addresses impacts within the context of multiple stressors across multiple wind energy developments. We also present technological and methodological approaches that can improve impact estimation and mitigation. We highlight compensatory mitigation as a tool that can be incorporated into regulatory frameworks to mitigate impacts that cannot be avoided or minimized via siting decisions or alterations to OWED infrastructure or operation. Our framework is intended as a globally-relevant approach for assessing and mitigating OWED impacts on marine birds that may be adapted to existing regulatory frameworks in regions with existing or planned OWED.

Biological Conservation↗
USGS2019

Using out-of-sample yield forecast experiments to evaluate which earth observation products best indicate end of season maize yields

In East Africa, accurate grain yield predictions can help save lives and protect livelihoods. Regional grain yield forecasts can inform decisions regarding the availability and prices of key staples, food aid, and large humanitarian responses. Here, we use earth observation (EO) products to develop and evaluate subnational grain yield forecasts for 56 regions located in two severely food insecure countries: Kenya and Somalia. We identify, for a given region and time of year, which, if any, product is the best indicator for end-of-season maize yields. Our analysis seeks to inform a real-world situation in which analysts have access to multiple regularly updated EO data products, but predictive skill corresponding to each may vary across these regions and throughout the season. We find that the most accurate predictions can be made for high-producing areas, but that the relationship between production and forecast accuracy diminishes in areas with yields averaging greater than one metric ton per hectare. However, while forecast accuracy is highest in high production areas, in many of these regions, the forecast accuracy of models using EO products is not better than a set of baseline models that do not use EO products. Overall, we find that rainfall is the best indicator in low-producing regions and that other EO products work best in areas where yields are relatively consistent, but production is still limited by environmental factors.

Environmental Research Letters↗