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USGS2023

Does post-fire recovery of native grasses across abiotic-stress and invasive-grass gradients match theoretical predictions, in sagebrush steppe?

Interactions among species can strongly affect how plant communities reassemble after disturbances, and variability among native and invasive species across environmental gradients must be known in order to manage plant-community recovery. The stress-gradient hypothesis (SGH) predicts species interactions will be more positive in abiotically stressful conditions and conversely, more negative in benign conditions, and the resistance-resilience concept (RRC) may predict where and when invasions will complicate ecosystem recovery. We evaluated how abiotic stress and biotic interactions determine native bunchgrass abundances across environmental gradients using additive models of cover data from over 500 plots re-measured annually for 5 years as they recovered naturally (untreated) after a megafire (>100,000 ha) in sagebrush steppe threated by the invasive-grass and fire cycle. The species included native bunchgrasses, bluebunch wheatgrass ( Pseudoroegneria spicata ) and Sandberg bluegrass ( Poa secunda ), and the exotic and invasive annual cheatgrass ( Bromus tectorum ). We asked whether associations between native bunchgrasses and cheatgrass were context dependent and if the SGH could help predict interspecific associations between species in a semiarid environment. The association of cover of each native bunchgrass to cheatgrass was not uniform, and instead varied from neutral to negative across environmental gradients in both space and time (i.e., weather), to which the species had nonlinear and sometimes threshold-like responses. Consistent with the SGH, bunchgrasses were generally more negatively related to cheatgrass (i.e., putative competition) in conditions which increased the cover of each bunchgrass – which were higher elevations and temperatures and lower solar heatload, and, for Sandberg bluegrass, drier conditions. There were few indications of positive interactions (i.e., putative facilitation) in stressful conditions, and instead associations were again negative, albeit weaker, in some of the conditions evaluated. Synthesis . These findings demonstrate that the negative association among native bunchgrasses and cheatgrass is context dependent and is determined by the abundances of both interacting species which is driven by environmental stress. This led to a hypothesis that together Sandberg bluegrass and bluebunch wheatgrass provide complementary resistance to cheatgrass at the landscape level, despite their different ecology and contrary to the management preference for bluebunch wheatgrass. Sandberg bluegrass might be critical for providing resistance against cheatgrass where invasion potential is greatest, i.e., at lower elevations, where bluebunch wheatgrass is scarce.

Global Ecology and Conservation↗

USGS2023

Macroscale analyses suggest invasive plant impacts depend more on the composition of invading plants than on environmental context

Aim Native biodiversity is threatened by the spread of non-native invasive species. Many studies demonstrate that invasions reduce local biodiversity but we lack an understanding of how impacts vary across environments at the macroscale. Using ~11,500 vegetation surveys from ecosystems across the United States, we quantified how the relationship between non-native plant cover and native plant diversity varied across different compositions of invading plants (measured by non-native plant richness and evenness) and environmental contexts (measured by productivity and human activity). Location Continental United States. Time Period Surveys from 1990s-present. Major Taxa Studied Terrestrial plant communities. Methods We fit mixed effects models to understand how native plant richness, diversity and evenness varied with non-native cover. We tested how this relationship varied when non-native cover interacted with non-native plant richness and evenness, and with productivity and human activity. Results Across the United States, communities with greater cover of non-native plants had lower native plant richness and diversity but higher evenness, suggesting rare native plants can be lost while dominant plants decline in abundance. The relationship between non-native cover and native community diversity varied with non-native plant richness and evenness but was not associated with productivity and human activity. Negative associations were strongest in areas with low non-native richness and evenness, characterizing plant communities that were invaded by a dominant non-native plant. Main Conclusions Non-native plant cover provides a first approximation of invasion impacts on native community diversity, but the magnitude of impact depended on non-native plant richness and evenness. Relationships between non-native cover and native diversity were consistent in strength across continental scale gradients of productivity and human activity. Therefore, at the macroscale, invasive plant impacts on native plant communities likely depend more on the characteristics of the invading plants, that is the presence of a dominant invader, than on the environmental context.

Global Ecology and Biogeography↗

USGS2025

Leveraging artificial intelligence and machine learning to advance Chesapeake Bay research and management: A review of status, challenges, and opportunities

The Chesapeake Bay and its watershed (hereafter “Chesapeake Bay region”) have been the focus of extensive restoration efforts for several decades. These restoration efforts are guided by the Chesapeake Bay Watershed Agreement (Chesapeake Executive Council 2014) which outlines 10 goals and 31 measurable outcomes. The Chesapeake Bay is globally recognized as a model for coastal restoration due to long-term investments in monitoring, modeling, implementation and research by the Chesapeake Bay Program (CBP) partnership. These monitoring network spans tidal and non-tidal regions and provides data across multiple scales. Artificial intelligence (AI), particularly machine-learning (ML) and deep learning (DL), has emerged as a powerful tool for analyzing large, complex datasets. These techniques have gained widespread adoption across various disciplines, including ecology, hydrology, and environmental science. In the Bay context, AI/ML is increasingly being used to explore drivers of environmental change, analyze system dynamics, and predict conditions in areas with limited monitoring. The CBP partnership, particularly its Scientific and Technical Advisory Committee (STAC), has increasingly recognized the growing role of AI/ML in watershed and estuarine management. Recent Chesapeake Community Research Symposium sessions and initiatives such as the Chesapeake Global Collaboratory highlight increasing regional momentum to apply big data and AI/ML for environmental solutions. Together, these developments underscore the timely need to explore how AI/ML can help advance Chesapeake Bay restoration and management. This STAC workshop, titled “Leveraging Artificial Intelligence and Machine learning to Advance Chesapeake Bay Research and Management: A review of status, challenges, and opportunities,” was held from February 24-25, 2025, in Edgewater, Maryland to bring together over 50 federal, state, and academic scientists and partners to synthesize the current state of AI/ML applications and identify research gaps in Chesapeake Bay research and management. The workshop focused on three main objectives: 1. Summarize recent AI/ML applications and lessons learned in both tidal and nontidal areas of the Chesapeake Bay region. 2. Identify challenges and gaps in applying AI/ML approaches to Chesapeake Bay data. Such challenges and gaps may include data limitations, harmonization issues, ineffective communication of AI/ML insights, and a lack of coordination among research and management institutions. 3. Develop recommendations and identify opportunities for leveraging AI/ML to address issues across the Chesapeake Bay region. Key areas of focus may include generating new information to support watershed management, delivering AI/MLgenerated insights to managers in a clear and actionable way, and fostering greater collaboration among stakeholders within the CBP Partnership. Workshop participants engaged in science presentations and breakout sessions to develop recommendations for advancing the integration of AI/ML techniques into research and management across the Chesapeake Bay region. By synthesizing current applications, identifying challenges, and exploring new opportunities, the workshop has provided valuable insights and recommendations for better leveraging AI/ML approaches to support the success of Bay restoration efforts. Together, these recommendations provide a roadmap for enhancing data-driven, science-based decision making aligned with the goals and outcomes of the Chesapeake Bay Watershed Agreement.

Delaware, Maryland, Virginia↗

USGS2016

The automated reference toolset: A soil-geomorphic ecological potential matching algorithm

Ecological inventory and monitoring data need referential context for interpretation. Identification of appropriate reference areas of similar ecological potential for site comparison is demonstrated using a newly developed automated reference toolset (ART). Foundational to identification of reference areas was a soil map of particle size in the control section (PSCS), a theme in US Soil Taxonomy. A 30-m resolution PSCS map of the Colorado Plateau (366,000 km 2 ) was created by interpolating ∼5000 field soil observations using a random forest model and a suite of raster environmental spatial layers representing topography, climate, general ecological community, and satellite imagery ratios. The PSCS map had overall out of bag accuracy of 61.8% (Kappa of 0.54, p < 0.0001), and an independent validation accuracy of 93.2% at a set of 356 field plots along the southern edge of Canyonlands National Park, Utah. The ART process was also tested at these plots, and matched plots with the same ecological sites (ESs) 67% of the time where sites fell within 2-km buffers of each other. These results show that the PSCS and ART have strong application for ecological monitoring and sampling design, as well as assessing impacts of disturbance and land management action using an ecological potential framework. Results also demonstrate that PSCS could be a key mapping layer for the USDA-NRCS provisional ES development initiative.

Soil Science Society of America Journal↗

USGS2020

Social Values for Ecosystem Services, version 4.0 (SolVES 4.0)—Documentation and user manual

The geographic information system tool, Social Values for Ecosystem Services (SolVES), was developed to incorporate quantified and spatially explicit measures of social values into ecosystem service assessments. SolVES 4.0 provides an open-source version of SolVES, which was designed to assess, map, and quantify the social values of ecosystem services. Social values—the perceived, nonmarket values the public ascribes to ecosystem services, particularly cultural services, such as aesthetics and recreation—can be evaluated for various stakeholder groups. These groups are distinguishable by factors such as their attitudes and preferences regarding public uses (for example, motorized recreation and logging). As with previous versions, SolVES 4.0 derives a quantitative 10-point, social-values metric—the value index—from a combination of spatial and nonspatial responses to public value and preference surveys. The tool also calculates metrics characterizing the underlying environment, such as average distance to water and dominant landcover. SolVES 4.0 has been developed with Python using a QGIS user interface and a PostgreSQL database for required data. SolVES is integrated with Maxent maximum entropy modeling software to generate more complete social-value maps and offer robust statistical models describing the relation between the value index and explanatory environmental variables. A model’s goodness of fit to a primary study area and its potential performance in transferring social values to similar areas using value-transfer methods can be evaluated. SolVES 4.0 provides an improved open-source, public-domain tool for decision makers and researchers to evaluate the social values of ecosystem services and to facilitate discussions among diverse stakeholders regarding the tradeoffs among ecosystem services in a variety of biophysical and social contexts including mountain, forest, coastal, riparian, agricultural, and urban environments around the globe.

Techniques and Methods↗

USGS2014

A bootstrap estimation scheme for chemical compositional data with nondetects

The bootstrap method is commonly used to estimate the distribution of estimators and their associated uncertainty when explicit analytic expressions are not available or are difficult to obtain. It has been widely applied in environmental and geochemical studies, where the data generated often represent parts of whole, typically chemical concentrations. This kind of constrained data is generically called compositional data, and they require specialised statistical methods to properly account for their particular covariance structure. On the other hand, it is not unusual in practice that those data contain labels denoting nondetects, that is, concentrations falling below detection limits. Nondetects impede the implementation of the bootstrap and represent an additional source of uncertainty that must be taken into account. In this work, a bootstrap scheme is devised that handles nondetects by adding an imputation step within the resampling process and conveniently propagates their associated uncertainly. In doing so, it considers the constrained relationships between chemical concentrations originated from their compositional nature. Bootstrap estimates using a range of imputation methods, including new stochastic proposals, are compared across scenarios of increasing difficulty. They are formulated to meet compositional principles following the log-ratio approach, and an adjustment is introduced in the multivariate case to deal with nonclosed samples. Results suggest that nondetect bootstrap based on model-based imputation is generally preferable. A robust approach based on isometric log-ratio transformations appears to be particularly suited in this context. Computer routines in the R statistical programming language are provided.

Journal of Chemometrics↗

USGS2026

Microclimate mediates the strength and direction of avian biotic interactions

Theory predicts that that the strength and direction of species interactions can shift from being competitive in benign environments toward being facilitative in stressful environments. However, the environmental context dependency of species interactions has rarely been tested in animal communities. We capitalized on a 15-year, landscape-scale dataset, collected annually in a relatively stable old-growth forest environment to test the long-held hypothesis that the strength and direction of species interactions might be mediated by climatic conditions. It is generally accepted that competitive and facilitative interactions drive the distributions of many species. Using multi-species dynamic occupancy models applied to long-term data, we tested whether annual settlement by bird species could affect either the persistence or settlement by other phylogenetically related species, and whether these interactions are mediated by microclimate. We found that species interactions were influenced by microclimate for some, but not all avian species pairs. Related species pairs more often showed settlement dynamics that were indicative of attraction rather than repulsion. In some cases, competitive interactions at warmer microclimates flipped to become facilitative in colder ones. However, the reverse was also true: facilitative interactions amplified for some species under warm conditions. Furthermore, species pairs that were closely related were more likely to exhibit competitive relationships along at least part of the microclimatic gradient. Our results highlight the importance of using long-term data to incorporate competitive and facilitative interactions into species distribution models and support the notion that the strength and direction of species interactions can be dependent on microclimatic environmental conditions.

Oregon↗

USGS2014

Beach science in the Great Lakes

Monitoring beach waters for human health has led to an increase and evolution of science in the Great Lakes, which includes microbiology, limnology, hydrology, meteorology, epidemiology, and metagenomics, among others. In recent years, concerns over the accuracy of water quality standards at protecting human health have led to a significant interest in understanding the risk associated with water contact in both freshwater and marine environments. Historically, surface waters have been monitored for fecal indicator bacteria (fecal coliforms, Escherichia coli , enterococci), but shortcomings of the analytical test (lengthy assay) have resulted in a re-focusing of scientific efforts to improve public health protection. Research has led to the discovery of widespread populations of fecal indicator bacteria present in natural habitats such as soils, beach sand, and stranded algae. Microbial source tracking has been used to identify the source of these bacteria and subsequently assess their impact on human health. As a result of many findings, attempts have been made to improve monitoring efficiency and efficacy with the use of empirical predictive models and molecular rapid tests. All along, beach managers have actively incorporated new findings into their monitoring programs. With the abundance of research conducted and information gained over the last 25 years, “Beach Science” has emerged, and the Great Lakes have been a focal point for much of the ground-breaking work. Here, we review the accumulated research on microbiological water quality of Great Lakes beaches and provide a historic context to the collaborative efforts that have advanced this emerging science.

Great Lakes↗

USGS2022

Bayesian applications in environmental and ecological studies with R and Stan

Modern ecological and environmental sciences are dominated by observational data. As a result, traditional statistical training often leaves scientists ill-prepared for the data analysis tasks they encounter in their work. Bayesian methods provide a more robust and flexible tool for data analysis, as they enable information from different sources to be brought into the modelling process. Bayesian Applications in Evnironmental and Ecological Studies with R and Stan provides a Bayesian framework for model formulation, parameter estimation, and model evaluation in the context of analyzing environmental and ecological data. Features: An accessible overview of Bayesian methods in environmental and ecological studies Emphasizes the hypothetical deductive process, particularly model formulation Necessary background material on Bayesian inference and Monte Carlo simulation Detailed case studies, covering water quality monitoring and assessment, ecosystem response to urbanization, fisheries ecology, and more Advanced chapter on Bayesian applications, including Bayesian networks and a change point model Complete code for all examples, along with the data used in the book, are available via GitHub The book is primarily aimed at graduate students and researchers in the environmental and ecological sciences, as well as environmental management professionals. This is a group of people representing diverse subject matter fields, who could benefit from the potential power and flexibility of Bayesian methods.

Book↗

USGS2013

Developing an outcome-based biodiversity metric in support of the field to market project: Final report

Our objective was to create a metric that would calculate the relative impact of common commercial agricultural practices on terrestrial vertebrate richness. We sought to define impacts in fields (including field borders) of the southeastern region’s commercial production of corn, wheat, soy, and cotton. The metric is intended to serve as an educational tool, allowing producers to see how operational decisions made at the field level impact overall vertebrate species richness and to explore decision impacts to targeted species groups (e.g. game, pest, or beneficial species). Agricultural landscapes are often mistakenly thought to be unsuitable habitat for most species. However, as demonstrated by results reported here, even large-scale, conventional agricultural producers are potentially important partners in biodiversity conservation. Many vertebrate species do inhabit agricultural landscapes, benefitting from the provision of water, food, or shelter within cultivated fields and their immediate borders (e.g., Holland et al. 2012). In the Southeastern US, of the 613 terrestrial vertebrate species modeled by the Southeast Gap Analysis Program (SEGAP) (http://www.basic.ncsu.edu/segap/index.html), 263 utilize row crop and associated agricultural land cover classes as potential habitat (Box 1). While some species may be sensitive to certain operational practices (e.g., tillage, pest management, or field border management practices), others are generally tolerant, and some may benefit either directly or indirectly. For example, field margins and ditches often serve as semi-natural habitats providing foraging resources and shelter for vertebrates and are shown to positively influence species richness and abundance (Billeter et al. 2007; Herzon & Helenius 2008; Marshall & Moonen 2002; Shore et al. 2005; Weibull et al. 2003; Wuczyńskia et al. 2011). Biodiversity responses are, therefore, complex, as an individual species’ responses to agricultural production practices depends on that animal’s resource specialization, mobility, and life history strategies (Jeanneret et al. 2003a, b; Jennings & Pocock 2009). The knowledge necessary to define the biodiversity contribution of agricultural lands is specialized, dispersed, and nuanced, and thus not readily accessible. Given access to clearly defined biodiversity tradeoffs between alternative agricultural practices, landowners, land managers and farm operators could collectively enhance the conservation and economic value of agricultural landscapes. Therefore, Field to Market: The Keystone Alliance for Sustainable Agriculture and The Nature Conservancy jointly funded a pilot project to develop a biodiversity metric to integrate into Field to Market’s existing sustainability calculator, The Fieldprint Calculator (http://www. fieldtomarket.org/). Field to Market: The Keystone Alliance for Sustainable Agriculture is an alliance among producers, agribusinesses, food companies, and conservation organizations seeking to create sustainable outcomes for agriculture. The Fieldprint Calculator supports the Keystone Alliance’s vision to achieve safe, accessible, and nutritious food, fiber and fuel in thriving ecosystems to meet the needs of 9 billion people in 2050. In support of this same vision, our project provides proof-of-concept for an outcome-based biodiversity metric for Field to Market to quantify biodiversity impacts of commercial row crop production on terrestrial vertebrate richness. Little research exists examining the impacts of alternative commercial agricultural practices on overall terrestrial biodiversity (McLaughlin & Mineau 1995). Instead, most studies compare organic versus conventional practices (e.g. Freemark & Kirk 2001; Wickramasinghe et al. 2004), and most studies focus on flora, avian, or invertebrate communities (Jeanneret et al. 2003a; Maes et al. 2008; Pollard & Relton 1970). Therefore, we used an expert-knowledge-based approach to develop a metric that predicts expected impacts to shelter and forage resources, individual species, and overall biodiversity (species richness). This approach is modeled after an ecosystems services concept (WRI 2005), except that we examine services (i.e., resources) provided to vertebrate wildlife rather than service provided to the human population. SEGAP predicts species that are potentially present in an area given landscape-scale habitat availability, configuration, and context (e.g., patch size, proximity to resources, connectivity, potential for disturbance). Based on the prediction of species that may be potentially present, the impacts of management decisions within fields and around their borders can be analyzed based on the impact of those practices to the availability of species’ resources. The final metric provides an index of a producer’s relative impact, but perhaps even more importantly, the underlying database allows producers to explore details such as which species are most impacted or how alternative decisions would impact their score.

Technical Bulletin↗

USGS2003

Quaternary vegetation and climate change in the western United States: Developments, perspectives, and prospects

This chapter explores the strengths and shortcomings of the major sources of data on Quaternary vegetation and climate change and discusses the use of models as a means to explore past and potential future environmental changes. The flora and major vegetation types of the western United States are present for several million years. Ongoing changes in atmospheric chemistry , climate, and human activities may lead to major vegetation changes over the coming decades to centuries. The combination of observations from the paleoenvironmental record, modern ecological studies, and modeling now permit assessments of the magnitude of potential future changes in the context of natural variability. They also provide opportunities for hypothesis testing and identification of the processes driving past changes in vegetation and climate. Understanding the dynamics of paleoenvironmental change can contribute to current conservation and natural resource management efforts and will help conservation and natural resource managers anticipate the potential rate, magnitude, and complexity of future vegetation change.

western United States↗

USGS2017

Local environmental context conditions the impact of Russian olive in a heterogeneous riparian ecosystem

Local abiotic and biotic conditions can alter the strength of exotic species impacts. To better understand the effects of exotic species on invaded ecosystems and to prioritize management efforts, it is important that exotic species impacts are put in local environmental context. We studied how differences in plant community composition, photosynthetically active radiation (PAR), and available soil N associated with Russian olive presence are conditioned by local environmental variation within a western U.S. riparian ecosystem. In four sites along the South Fork of the Republican River in Colorado, we established 200 pairs of plots (underneath and apart from Russian olive) to measure the effects of invasion across the ecosystem. We used a series of a priori mixed models to identify environmental variables that altered the effects of Russian olive. For all response variables, models that included the interaction of environmental characteristics, such as presence/absence of an existing cottonwood canopy, with the presence/absence of Russian olive canopy were stronger candidate models than those that just included Russian olive canopy presence as a factor. Compared with reference plots outside of Russian olive canopy, plots underneath Russian olive had higher relative exotic cover (exotic/total cover), lower perennial C4 grass cover, and higher perennial forb cover. These effects were reduced, however, in the presence of a cottonwood canopy. As expected, Russian olive was associated with reduced PAR and increased N, but these effects were reduced under cottonwood canopy. Our results demonstrate that local abiotic and biotic environmental factors condition the effects of Russian olive within a heterogeneous riparian ecosystem and suggest that management efforts should be focused in open areas where Russian olive impacts are strongest.

Colorado↗

USGS2024

Prioritizing river basins for nutrient studies

Increases in fluxes of nitrogen (N) and phosphorus (P) in the environment have led to negative impacts affecting drinking water, eutrophication, harmful algal blooms, climate change, and biodiversity loss. Because of the importance, scale, and complexity of these issues, it may be useful to consider methods for prioritizing nutrient research in representative drainage basins within a regional or national context. Two systematic, quantitative approaches were developed to (1) identify basins that geospatial data suggest are most impacted by nutrients and (2) identify basins that have the most variability in factors affecting nutrient sources and transport in order to prioritize basins for studies that seek to understand the key drivers of nutrient impacts. The “impact” approach relied on geospatial variables representing surface-water and groundwater nutrient concentrations, sources of N and P, and potential impacts on receptors (i.e., ecosystems and human health). The “variability” approach relied on geospatial variables representing surface-water nutrient concentrations, factors affecting sources and transport of nutrients, model accuracy, and potential receptor impacts. One hundred and sixty-three drainage basins throughout the contiguous United States were ranked nationally and within 18 hydrologic regions. Nationally, the top-ranked basins from the impact approach were concentrated in the Midwest, while those from the variability approach were dispersed across the nation. Regionally, the top-ranked basin selected by the two approaches differed in 15 of the 18 regions, with top-ranked basins selected by the variability approach having lower minimum concentrations and larger ranges in concentrations than top-ranked basins selected by the impact approach. The highest ranked basins identified using the variability approach may have advantages for exploring how landscape factors affect surface-water quality and how surface-water quality may affect ecosystems. In contrast, the impact approach prioritized basins in terms of human development and nutrient concentrations in both surface water and groundwater, thereby targeting areas where actions to reduce nutrient concentrations could have the largest effect on improving water availability and reducing ecosystem impacts.

Environmental Monitoring and Assessment↗

USGS2012

Wildlife contact analysis: Emerging methods, questions, and challenges

Recent technological advances, such as proximity loggers, allow researchers to collect complete interaction histories, day and night, among sampled individuals over several months to years. Social network analyses are an obvious approach to analyzing interaction data because of their flexibility for fitting many different social structures as well as the ability to assess both direct contacts and indirect associations via intermediaries. For many network properties, however, it is not clear whether estimates based upon a sample of the network are reflective of the entire network. In wildlife applications, networks may be poorly sampled and boundary effects will be common. We present an alternative approach that utilizes a hierarchical modeling framework to assess the individual, dyadic, and environmental factors contributing to variation in the interaction rates and allows us to estimate the underlying process variation in each. In a disease control context, this approach will allow managers to focus efforts on those types of individuals and environments that contribute the most toward super-spreading events. We account for the sampling distribution of proximity loggers and the non-independence of contacts among groups by only using contact data within a group during days when the group membership of proximity loggers was known. This allows us to separate the two mechanisms responsible for a pair not contacting one another: they were not in the same group or they were in the same group but did not come within the specified contact distance. We illustrate our approach with an example dataset of female elk from northwestern Wyoming and conclude with a number of important future research directions.

Behavioral Ecology and Sociobiology↗

USGS2013

Comparing approaches to spatially explicit ecosystem service modeling: a case study from the San Pedro River, Arizona

Although the number of ecosystem service modeling tools has grown in recent years, quantitative comparative studies of these tools have been lacking. In this study, we applied two leading open-source, spatially explicit ecosystem services modeling tools – Artificial Intelligence for Ecosystem Services (ARIES) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) – to the San Pedro River watershed in southeast Arizona, USA, and northern Sonora, Mexico. We modeled locally important services that both modeling systems could address – carbon, water, and scenic viewsheds. We then applied managerially relevant scenarios for urban growth and mesquite management to quantify ecosystem service changes. InVEST and ARIES use different modeling approaches and ecosystem services metrics; for carbon, metrics were more similar and results were more easily comparable than for viewsheds or water. However, findings demonstrate similar gains and losses of ecosystem services and conclusions when comparing effects across our scenarios. Results were more closely aligned for landscape-scale urban-growth scenarios and more divergent for a site-scale mesquite-management scenario. Follow-up studies, including testing in different geographic contexts, can improve our understanding of the strengths and weaknesses of these and other ecosystem services modeling tools as they move closer to readiness for supporting day-to-day resource management.

Arizona;Sonora↗

USGS2017

Harvesting wildlife affected by climate change: a modelling and management approach for polar bears

The conservation of many wildlife species requires understanding the demographic effects of climate change, including interactions between climate change and harvest, which can provide cultural, nutritional or economic value to humans. We present a demographic model that is based on the polar bear Ursus maritimus life cycle and includes density-dependent relationships linking vital rates to environmental carrying capacity ( K ). Using this model, we develop a state-dependent management framework to calculate a harvest level that (i) maintains a population above its maximum net productivity level (MNPL; the population size that produces the greatest net increment in abundance) relative to a changing K , and (ii) has a limited negative effect on population persistence. Our density-dependent relationships suggest that MNPL for polar bears occurs at approximately 0·69 (95% CI = 0·63–0·74) of K . Population growth rate at MNPL was approximately 0·82 (95% CI = 0·79–0·84) of the maximum intrinsic growth rate, suggesting relatively strong compensation for human-caused mortality. Our findings indicate that it is possible to minimize the demographic risks of harvest under climate change, including the risk that harvest will accelerate population declines driven by loss of the polar bear's sea-ice habitat. This requires that (i) the harvest rate – which could be 0 in some situations – accounts for a population's intrinsic growth rate, (ii) the harvest rate accounts for the quality of population data (e.g. lower harvest when uncertainty is large), and (iii) the harvest level is obtained by multiplying the harvest rate by an updated estimate of population size. Environmental variability, the sex and age of removed animals and risk tolerance can also affect the harvest rate. Synthesis and applications . We present a coupled modelling and management approach for wildlife that accounts for climate change and can be used to balance trade-offs among multiple conservation goals. In our example application to polar bears experiencing sea-ice loss, the goals are to maintain population viability while providing continued opportunities for subsistence harvest. Our approach may be relevant to other species for which near-term management is focused on human factors that directly influence population dynamics within the broader context of climate-induced habitat degradation.

Journal of Applied Ecology↗

USGS2015

A collision risk model to predict avian fatalities at wind facilities: an example using golden eagles, Aquila chrysaetos

Wind power is a major candidate in the search for clean, renewable energy. Beyond the technical and economic challenges of wind energy development are environmental issues that may restrict its growth. Avian fatalities due to collisions with rotating turbine blades are a leading concern and there is considerable uncertainty surrounding avian collision risk at wind facilities. This uncertainty is not reflected in many models currently used to predict the avian fatalities that would result from proposed wind developments. We introduce a method to predict fatalities at wind facilities, based on pre-construction monitoring. Our method can directly incorporate uncertainty into the estimates of avian fatalities and can be updated if information on the true number of fatalities becomes available from post-construction carcass monitoring. Our model considers only three parameters: hazardous footprint, bird exposure to turbines and collision probability. By using a Bayesian analytical framework we account for uncertainties in these values, which are then reflected in our predictions and can be reduced through subsequent data collection. The simplicity of our approach makes it accessible to ecologists concerned with the impact of wind development, as well as to managers, policy makers and industry interested in its implementation in real-world decision contexts. We demonstrate the utility of our method by predicting golden eagle ( Aquila chrysaetos ) fatalities at a wind installation in the United States. Using pre-construction data, we predicted 7.48 eagle fatalities year -1 (95% CI: (1.1, 19.81)). The U.S. Fish and Wildlife Service uses the 80th quantile (11.0 eagle fatalities year -1 ) in their permitting process to ensure there is only a 20% chance a wind facility exceeds the authorized fatalities. Once data were available from two-years of post-construction monitoring, we updated the fatality estimate to 4.8 eagle fatalities year-1 (95% CI: (1.76, 9.4); 80 th quantile, 6.3). In this case, the increased precision in the fatality prediction lowered the level of authorized take, and thus lowered the required amount of compensatory mitigation.

PLoS ONE↗

USGS2008

Hydrochemical Regions of the Glacial Aquifer System, Northern United States, and Their Environmental and Water-Quality Characteristics

The glacial aquifer system in the United States is a large (953,000 square miles) regional aquifer system of heterogeneous composition. As described in this report, the glacial aquifer system includes all unconsolidated geologic material above bedrock that lies on or north of the line of maximum glacial advance within the United States. Examining ground-water quality on a regional scale indicates that variations in the concentrations of major and minor ions and some trace elements most likely are the result of natural variations in the geologic and physical environment. Study of the glacial aquifer system was designed around a regional framework based on the assumption that two primary characteristics of the aquifer system can affect water quality: intrinsic susceptibility (hydraulic properties) and vulnerability (geochemical properties). The hydrochemical regions described in this report were developed to identify and explain regional spatial variations in ground-water quality in the glacial aquifer system within the hypothetical framework context. Data analyzed for this study were collected from 1991 to 2003 at 1,716 wells open to the glacial aquifer system. Cluster analysis was used to group wells with similar ground-water concentrations of calcium, chloride, fluoride, magnesium, potassium, sodium, sulfate, and bicarbonate into five unique groups. Maximum Likelihood Classification was used to make the extrapolation from clustered groups of wells, defined by points, to areas of similar water quality (hydrochemical regions) defined in a geospatial model. Spatial data that represented average annual precipitation, average annual temperature, land use, land-surface slope, vertical soil permeability, average soil clay content, texture of surficial deposits, type of surficial deposit, and potential for ground-water recharge were used in the Maximum Likelihood Classification to classify the areas so the characteristics of the hydrochemical regions would resemble the characteristics of the clusters. The result of the Maximum Likelihood Classification is a map showing five hydrochemical regions of the glacial aquifer system. Statistical analysis of ion concentrations (calcium, chloride, fluoride, magnesium, sodium, potassium, sulfate, and bicarbonate) in samples collected from wells completed in the glacial aquifer system illustrates that variations in water quality can be explained, in part, by related environmental characteristics that control the movement of ground water through the aquifer system. A comparison of median concentrations of chemical constituents in ground water among the five hydrochemical regions indicates that ground water in the Midwestern Agricultural Region, the Urban-Influenced Region, and the Western Agriculture and Grassland Region has the highest concentrations of major and minor ions, whereas ground water in the Northern and Great Lakes Forested Region and the Mountain and Coastal Forested Region has the lowest concentrations of these ions. Median concentrations of barium, arsenic, lithium, boron, strontium, and nitrite plus nitrate as nitrogen also are significantly different among the hydrochemical regions.

Scientific Investigations Report↗