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USGS2025

The tortoise and the antilocaprid: Adapting GPS tracking and terrain data to model wildlife walking functions

Context The relationship between slope and terrestrial animal locomotion is key to landscape ecology but underexplored across species. This is partly due to a lack of scalable methodology that applies to a diversity of wildlife. Objectives This study investigates the slope-speed relationship for two species, Texas tortoise ( Gopherus berlandieri ) and pronghorn ( Antilocapra americana ), through the combined application of remote sensing, GPS tracking, behavior models, and parametric distribution. While using readily available Digital Elevation Models (DEM) for pronghorn, we explore the use of very high-resolution lidar Digital Terrain Models (DTM) from Unoccupied Aerial Systems (UAS) to characterize tortoise movements at micro-scales. Methods After classifying animal behavior with GPS tracking data and Hidden Markov Models (HMMs), we analyzed the relationship between the speed of the animals and the slope of the terrain using a 30-m DEM for pronghorn, and a fine-scale UAS DTM for Texas tortoise, and three nonlinear models: Laplace, Gauss, and Lorentz. Results High-resolution DTM, coupled with GPS tracking, accurately models the relationship of speed and slope at a micro-scale, while a DEM is suitable for a larger scale. Laplace models best predicted the speed of both the Texas tortoise and pronghorn. Models showed tortoises, which are not known for rapid and agile movement like the pronghorn, have a broader tolerance for varying slopes at a fine scale. Conclusions These findings enhance understanding of species-specific movement offering valuable insights for habitat management and conservation tailored to species’ behaviors and capabilities.

Colorado, Texas, Wyoming↗

USGS1999

Upgrade to MODFLOW-GUI; addition of MODPATH, ZONEBDGT, and additional MODFLOW packages to the U.S. Geological Survey MODFLOW-96 Graphical-User Interface

This report describes enhancements to a Graphical-User Interface (GUI) for MODFLOW-96, the U.S. Geological Survey (USGS) modular, three-dimensional, finitedifference ground-water flow model, and MOC3D, the USGS three-dimensional, method-ofcharacteristics solute-transport model. The GUI is a plug-in extension (PIE) for the commercial program Argus ONEe. The GUI has been modified to support MODPATH (a particle tracking post-processing package for MODFLOW), ZONEBDGT (a computer program for calculating subregional water budgets), and the Stream, Horizontal-Flow Barrier, and Flow and Head Boundary packages in MODFLOW. Context-sensitive help has been added to make the GUI easier to use and to understand. In large part, the help consists of quotations from the relevant sections of this report and its predecessors. The revised interface includes automatic creation of geospatial information layers required for the added programs and packages, and menus and dialog boxes for input of parameters for simulation control. The GUI creates formatted ASCII files that can be read by MODFLOW-96, MOC3D, MODPATH, and ZONEBDGT. All four programs can be executed within the Argus ONEe application (Argus Interware, Inc., 1997). Spatial results of MODFLOW-96, MOC3D, and MODPATH can be visualized within Argus ONEe. Results from ZONEBDGT can be visualized in an independent program that can also be used to view budget data from MODFLOW, MOC3D, and SUTRA. Another independent program extracts hydrographs of head or drawdown at individual cells from formatted MODFLOW head and drawdown files. A web-based tutorial on the use of MODFLOW with Argus ONE has also been updated. The internal structure of the GUI has been modified to make it possible for advanced users to easily customize the GUI. Two additional, independent PIEs were developed to allow users to edit the positions of nodes and to facilitate exporting the grid geometry to external programs.

Open-File Report↗

USGS2021

Forecasting drought probabilities for streams in the northeastern United States

Maximum likelihood logistic regression (MLLR) models for the northeastern United States forecast drought probability estimates for water flowing in rivers and streams using methods previously identified and developed. Streamflow data from winter months are used to estimate chances of hydrological drought during summer months. Daily streamflow data collected from 1,143 streamgages from April 1, 1877, through October 31, 2018, are used to provide hydrological drought streamflow probabilities for July, August, and September as functions of streamflows during October, November, December, January, and February. This allows estimates of outcomes from 5 to 11 months ahead of their occurrence. Models specific to the northeastern United States were investigated and updated. The MLLR models of drought stream-flow probabilities utilize the explanatory power of temporally linked water flows. Models with strong drought streamflow probability correct-classification rates were produced for streams throughout the northeastern United States. A test of northeastern United States drought streamflow probability predictions found that overall correct-classification rates for drought streamflow probabilities in the northeastern United States exceeded 97 percent when predicting July 2019 drought probability using February 2019 monthly mean streamflow data. Using hydrological drought probability estimates in a water-management context informs understandings of possible future streamflow drought conditions in the northeastern United States, provides warnings of potential future drought conditions, and aids water-management decision making and responses to changing circumstances.

Connecticut, Delaware, Massachusetts, Maine, New H↗

USGS2001

Carbon balance of the terrestrial biosphere in the twentieth century: analyses of CO 2 , climate and land use effects with four process-based ecosystem models

The concurrent effects of increasing atmospheric CO 2 concentration, climate variability, and cropland establishment and abandonment on terrestrial carbon storage between 1920 and 1992 were assessed using a standard simulation protocol with four process-based terrestrial biosphere models. Over the long-term(1920–1992), the simulations yielded a time history of terrestrial uptake that is consistent (within the uncertainty) with a long-term analysis based on ice core and atmospheric CO 2 data. Up to 1958, three of four analyses indicated a net release of carbon from terrestrial ecosystems to the atmosphere caused by cropland establishment. After 1958, all analyses indicate a net uptake of carbon by terrestrial ecosystems, primarily because of the physiological effects of rapidly rising atmospheric CO 2 . During the 1980s the simulations indicate that terrestrial ecosystems stored between 0.3 and 1.5 Pg C yr−1, which is within the uncertainty of analysis based on CO 2 and O 2 budgets. Three of the four models indicated (in accordance with O 2 evidence) that the tropics were approximately neutral while a net sink existed in ecosystems north of the tropics. Although all of the models agree that the long-term effect of climate on carbon storage has been small relative to the effects of increasing atmospheric CO 2 and land use, the models disagree as to whether climate variability and change in the twentieth century has promoted carbon storage or release. Simulated interannual variability from 1958 generally reproduced the El Niño/Southern Oscillation (ENSO)-scale variability in the atmospheric CO 2 increase, but there were substantial differences in the magnitude of interannual variability simulated by the models. The analysis of the ability of the models to simulate the changing amplitude of the seasonal cycle of atmospheric CO 2 suggested that the observed trend may be a consequence of CO 2 effects, climate variability, land use changes, or a combination of these effects. The next steps for improving the process-based simulation of historical terrestrial carbon include (1) the transfer of insight gained from stand-level process studies to improve the sensitivity of simulated carbon storage responses to changes in CO 2 and climate, (2) improvements in the data sets used to drive the models so that they incorporate the timing, extent, and types of major disturbances, (3) the enhancement of the models so that they consider major crop types and management schemes, (4) development of data sets that identify the spatial extent of major crop types and management schemes through time, and (5) the consideration of the effects of anthropogenic nitrogen deposition. The evaluation of the performance of the models in the context of a more complete consideration of the factors influencing historical terrestrial carbon dynamics is important for reducing uncertainties in representing the role of terrestrial ecosystems in future projections of the Earth system.

Global Biogeochemical Cycles↗

USGS2015

Social Values for Ecosystem Services, version 3.0 (SolVES 3.0): documentation and user manual

The geographic information system (GIS) tool, S ocial V alues for E cosystem S ervices (SolVES), was developed to incorporate quantified and spatially explicit measures of social values into ecosystem service assessments. SolVES 3.0 continues to extend the functionality 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 their attitudes and preferences regarding public uses, such as motorized recreation and logging. As with previous versions, SolVES 3.0 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 3.0 is integrated with Maxent maximum entropy modeling software to generate more complete social-value maps and offer robust statistical models describing the relationship 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 methodology can be evaluated. SolVES 3.0 provides an improved 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 physical and social contexts ranging from forest and rangeland to coastal and marine.

Open-File Report↗

USGS2021

Are Cisco and Lake Whitefish competitors? An analysis of historical fisheries in Michigan waters of the Upper Laurentian Great Lakes

Historically, Cisco Coregonus artedi and Lake Whitefish Coregonus clupeaformis were abundant throughout the Laurentian Great Lakes, but overharvest, habitat degradation, and interactions with exotic species caused most populations to collapse by the mid-1900s. Strict commercial fishery regulations and improved environmental and ecological conditions allowed Cisco to partially recover only in Lake Superior, whereas Lake Whitefish recovered in all the upper Great Lakes (Superior, Michigan, and Huron). The differential responses of Cisco and Lake Whitefish to improved environmental and ecological conditions in lakes Michigan and Huron have led to questions about potential negative interactions between these species. To provide context for fishery managers, we tested for positive and negative correlations between historical (1929–1970) Cisco and Lake Whitefish commercial gill net catch per effort (CPE; kg/km of net) at a variety of spatial scales in Michigan waters of the upper Great Lakes. The three best-fit spatial models—LAKEWIDE, REGIONAL 10, and SIMPLE—all had similar levels of support (scaled second-order Akaike Information Criterion < 3.0), and we used these models to determine whether there was a significant correlation between Cisco and Lake Whitefish CPE (positive and negative). There was either no correlation between Cisco and Lake Whitefish CPE or a positive correlation for most (12 of 13) pairwise (Cisco–Lake Whitefish) comparisons. We identified no strong positive or negative correlations in the lakewide (LAKEWIDE) or reduced (SIMPLE) models. In the regional model (REGIONAL 10), we identified strong and positive correlations between Cisco and Lake Whitefish CPE in two regions (ρ = 0.59–0.71) and a weak negative correlation in one region (ρ = −0.45). Collectively, our findings suggest that Cisco and Lake Whitefish CPE were largely independent of each other; thus, these species likely did not interact to the detriment of one another in Michigan waters of the upper Great Lakes during 1929–1970.

Michigan↗

USGS2025

Future forest conditions under alternative management and hydrological scenarios in the Upper Mississippi River floodplain

Context Floodplain forests are being transformed by multiple pressures, prompting widespread management and restoration efforts. It is uncertain how disturbances, including hydrologic change, and management actions will interact to influence the ecology of these threatened forests. Objectives This study examined the effects of alternative management and hydrologic regimes on forest succession at an Upper Mississippi River floodplain site with a restoration project in planning. Methods We used the spatially explicit forest landscape model, LANDIS-II, to simulate forest succession for 100 years under four hydrogeomorphic management scenarios, three forest management scenarios, and two scenarios of future hydrologic conditions. We evaluated changes in forest biomass and composition over time and assessed the relative importance of management actions and hydrologic change on succession. Results Forest aboveground biomass decreased in all management-hydrology scenarios, especially in the wetter hydrological scenario. Intensified hydrogeomorphic and forest management scenarios reduced the magnitude and extent of biomass declines; however, they were unable to prevent overall declines in biomass or cause large shifts in tree species composition. Silver maple ( Acer saccharinum ) was projected to decrease in biomass, while increases in biomass were projected for several late-successional species including swamp white oak ( Quercus bicolor ). Among the factors influencing variation in biomass, forest management had the largest influence in the first 50 years of our simulations, but hydrological regime became the most important factor by the end of the century. Conclusions Our simulations indicate that management actions could play an important role in the conservation of floodplain forests, but their effectiveness will likely be limited if recent upward trends in flooding conditions in this system continue in the future. Thus, our results highlight both the potential benefits and limitations of management actions in the face of hydrologic change.

Iowa, Minnesota, Wisconsin↗

USGS2024

Integrating monitoring and modeling information to develop an indicator of watershed progress toward nutrient reduction goals

Eutrophication has been a major environmental issue in many coastal and inland ecosystems, which is primarily attributed to excessive anthropogenic inputs of nutrients. Restoration efforts have therefore focused on the reduction of watershed nutrient loads, including in the Chesapeake Bay (USA). To facilitate watershed management, watershed models are often developed and used to assess the expected impact of scenarios of past and future management policies and practices and the impact of watershed conditions. However, the level of load reductions estimated using monitoring data often does not match with model predictions, which may cast doubt on the effectiveness of the restoration efforts, the reliability of the model, and the prospect of achieving pre-established reduction goals. To better reconcile such inconsistencies between expectation (i.e., modeling estimates) and reality (i.e., monitoring information), a watershed-wide indicator was developed for the Chesapeake Bay watershed to explicitly quantify the progress toward nutrient reduction goals in the context of the Chesapeake Bay Total Maximum Daily Load (TMDL). Results of the indicator show that since 1995 long-term progress has been made toward the TMDL planning targets for both nitrogen and phosphorus. Specifically, management practices that are implemented and realized (in monitoring data) have been increasing over time, whereas management practices that need to be implemented in the future to meet the goals have been decreasing. In addition, the progress of nutrient reduction toward meeting the goals has varied with source sectors and watershed locations: i.e., point source management has been fully or nearly fully implemented, whereas nonpoint source management has been implemented by 50%-70%. In summary, this indicator, which is largely based on monitoring data, can provide at least four benefits: (1) evaluating the validity of the modeled estimates of nutrient reductions by comparing them to monitoring information; (2) placing the monitored riverine trends into a management context; (3) comparing progress between different nutrient source sectors and watershed locations; and (4) facilitating communication of the progress to the Chesapeake Bay Program Partnership and the public. Although we focus on the indicator development and interpretation for the Chesapeake Bay watershed, the framework can be transferred to watersheds within and beyond this watershed, where similar modeling and monitoring information exists, to gauge expectations on the trajectory and pace of the progress toward meeting restoration goals.

Chesapeake Bay watershed↗

USGS2024

Using global remote camera data of a solitary species complex to evaluate the drivers of group formation

The social system of animals involves a complex interplay between physiology, natural history, and the environment. Long relied upon discrete categorizations of “social” and “solitary” inhibit our capacity to understand species and their interactions with the world around them. Here, we use a globally distributed camera trapping dataset to test the drivers of aggregating into groups in a species complex (martens and relatives, family Mustelidae , Order Carnivora ) assumed to be obligately solitary. We use a simple quantification, the probability of being detected in a group, that was applied across our globally derived camera trap dataset. Using a series of binomial generalized mixed-effects models applied to a dataset of 16,483 independent detections across 17 countries on four continents we test explicit hypotheses about potential drivers of group formation. We observe a wide range of probabilities of being detected in groups within the solitary model system, with the probability of aggregating in groups varying by more than an order of magnitude. We demonstrate that a species’ context-dependent proclivity toward aggregating in groups is underpinned by a range of resource-related factors, primarily the distribution of resources, with increasing patchiness of resources facilitating group formation, as well as interactions between environmental conditions (resource constancy/winter severity) and physiology (energy storage capabilities). The wide variation in propensities to aggregate with conspecifics observed here highlights how continued failure to recognize complexities in the social behaviors of apparently solitary species limits our understanding not only of the individual species but also the causes and consequences of group formation.

PNAS↗

USGS2015

The influence of prefire tree growth and crown condition on postfire mortality of sugar pine following prescribed fire in Sequoia National Park

Tree mortality is a vital component of forest management in the context of prescribed fires; however, few studies have examined the effect of prefire tree health on postfire mortality. This is especially relevant for sugar pine ( Pinus lambertiana Douglas), a species experiencing population declines due to a suite of anthropogenic factors. Using data from an old-growth mixed-conifer forest in Sequoia National Park, we evaluated the effects of fire, tree size, prefire radial growth, and crown condition on postfire mortality. Models based only on tree size and measures of fire damage were compared with models that included tree size, fire damage, and prefire tree health (e.g., measures of prefire tree radial growth or crown condition). Immediately following the fire, the inclusion of different metrics of prefire tree health produced variable improvements over the models that included only tree size and measures of fire damage, as models that included measures of crown condition performed better than fire-only models, but models that included measures of prefire radial growth did not perform better. However, 5 years following the fire, sugar pine mortality was best predicted by models that included measures of both fire damage and prefire tree health, specifically, diameter at breast height (DBH, 1.37 m), crown scorch, 30-year mean growth, and the number of sharp declines in growth over a 30-year period. This suggests that factors that influence prefire tree health (e.g., drought, competition, pathogens, etc.) may partially determine postfire mortality, especially when accounting for delayed mortality following fire.

California↗

USGS2019

Employing an ecosystem services framework to deliver decision ready science

Public land managers have limited information to allow for the integration and balancing of multiple objectives in land management decisions including the social (cultural and health), economic (monetary and nonmonetary), and environmental aspects. In this article, we document an approach to consider the many facets of decision making by incorporating them into a decision context using an ecosystem services framework. This analysis is based on a multi-partner project led by the US Geological Survey and the US Fish and Wildlife Service to provide land management decision support for the Great Dismal Swamp National Wildlife Refuge. It is an integrated ecologic-economic analysis of baseline (current) and potential future quantities, qualities, and values of selected ecosystem services from the Refuge. Alternative management scenarios are modeled to consider the impact of specific management actions or natural disturbances on priority ecosystem services. We examine the benefits and challenges of using this framework. Key lessons learned from this effort include the mismatch in timing between physical and social science; the challenge of integrating methods from multiple disciplines; the importance of frequent communication to overcome siloed research; and the utility of an integrating framework for ecosystem services and supporting tools such as the dynamic ecosystem model.

Advances in Ecological Research↗

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↗