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At least 91 records · Page 5Linked to original sources
USGS2010

Visualizing NetCDF Files by Using the EverVIEW Data Viewer

Over the past few years, modelers in South Florida have started using Network Common Data Form (NetCDF) as the standard data container format for storing hydrologic and ecologic modeling inputs and outputs. With its origins in the meteorological discipline, NetCDF was created by the Unidata Program Center at the University Corporation for Atmospheric Research, in conjunction with the National Aeronautics and Space Administration and other organizations. NetCDF is a portable, scalable, self-describing, binary file format optimized for storing array-based scientific data. Despite attributes which make NetCDF desirable to the modeling community, many natural resource managers have few desktop software packages which can consume NetCDF and unlock the valuable data contained within. The U.S. Geological Survey and the Joint Ecosystem Modeling group, an ecological modeling community of practice, are working to address this need with the EverVIEW Data Viewer. Available for several operating systems, this desktop software currently supports graphical displays of NetCDF data as spatial overlays on a three-dimensional globe and views of grid-cell values in tabular form. An included Open Geospatial Consortium compliant, Web-mapping service client and charting interface allows the user to view Web-available spatial data as additional map overlays and provides simple charting visualizations of NetCDF grid values.

Fact Sheet↗
USGS2014

Thresholds for conservation and management: structured decision making as a conceptual framework

A conceptual framework is provided for considering the threshold concept in natural resource management and conservation. We define three kinds of thresholds relevant to management and conservation. Ecological thresholds are values of system state variables at which small changes bring about substantial or specified changes in system dynamics. They are frequently incorporated into ecological models used to project system responses to management actions. Utility thresholds are components of management objectives and are values of state or performance variables at which small changes yield substantial changes in the value of the management outcome. Decision thresholds are values of system state variables at which small changes prompt changes in management actions in order to reach specified management objectives. Decision thresholds are derived from the other components of the decision process.We advocate a structured decision making (SDM) approach within which the following components are identified: objectives (possibly including utility thresholds), potential actions, models (possibly including ecological thresholds), monitoring program, and a solution algorithm (which produces decision thresholds). Adaptive resource management (ARM) is described as a special case of SDM developed for recurrent decision problems that are characterized by uncertainty. We believe that SDM, in general, and ARM, in particular, provide good approaches to conservation and management. Use of SDM and ARM also clarifies the distinct roles of ecological thresholds, utility thresholds, and decision thresholds in informed decision processes.

Book↗
USGS2014

Estimation of potential evapotranspiration from extraterrestrial radiation, air temperature and humidity to assess future climate change effects on the vegetation of the Northern Great Plains, USA

The potential evapotranspiration (PET) that would occur with unlimited plant access to water is a central driver of simulated plant growth in many ecological models. PET is influenced by solar and longwave radiation, temperature, wind speed, and humidity, but it is often modeled as a function of temperature alone. This approach can cause biases in projections of future climate impacts in part because it confounds the effects of warming due to increased greenhouse gases with that which would be caused by increased radiation from the sun. We developed an algorithm for linking PET to extraterrestrial solar radiation (incoming top-of atmosphere solar radiation), as well as temperature and atmospheric water vapor pressure, and incorporated this algorithm into the dynamic global vegetation model MC1. We tested the new algorithm for the Northern Great Plains, USA, whose remaining grasslands are threatened by continuing woody encroachment. Both the new and the standard temperature-dependent MC1 algorithm adequately simulated current PET, as compared to the more rigorous PenPan model of Rotstayn et al. (2006) . However, compared to the standard algorithm, the new algorithm projected a much more gradual increase in PET over the 21st century for three contrasting future climates. This difference led to lower simulated drought effects and hence greater woody encroachment with the new algorithm, illustrating the importance of more rigorous calculations of PET in ecological models dealing with climate change.

Montana, Nebraska, North Dakota, South Dakota, Wyo↗
USGS2021

Space matters: Host spatial structure and the dynamics of plague transmission

The development of models to elucidate the transmission pathways and dynamics of wildlife diseases remains challenging. Sylvatic plague, caused by the bacterium Yersinia pestis ( Yp ), is an infectious zoonotic disease that primarily affects wild rodents, including prairie dogs ( Cynomys spp.) in North America. Proposed transmission pathways for Yp include flea bites, direct contacts between hosts, and environmental reservoirs (e.g. soil, carcasses). We developed a spatially explicit, agent-based model of Yp transmission to explore the effects of alternative transmission pathways, different disease initiation mechanisms (host or fleas), parameter uncertainty, and spatial structure of hosts. A particularly novel aspect of our model was the integration of ecological models with traditional disease models. Specifically, we used estimates from spatial capture-recapture models to generate data-driven spatial distributions, densities, and contact rates to capture the spatial structure of prairie dogs. We simulated ~9 million scenarios across a wide range of parameter values and conducted sensitivity analyses to determine the most influential parameters on the number of flea-days (sum of the mean number of fleas on hosts each day of the simulation), number of newly infected hosts per day, the time to depopulation (<20 prairie dogs remaining), and the proportion of the prairie dog population remaining at the end of the simulation (after 150 days). When including spatial structure, we found the probability of transmission via environmental sources of Yp (i.e. carcasses) had the greatest influence on model results when Yp infection was initiated in prairie dog hosts, rather than in fleas. Conversely, the mechanism of transmission by fleas to prairie dogs had the greatest influence on model results when Yp infection was initiated in fleas (i.e. via introduction by carnivores, a migrant prairie dog, or other mammalian host). Uncertainty in parameter estimates, particularly those related to the transmission pathways of Yp, continue to hamper efforts to realistically model plague dynamics in wild rodents. Our results elucidate the complexity of the flea-plague-prairie dog system and reiterate the importance of research on Yp transmission mechanisms to provide a full understanding of this disease. Our results also emphasize the importance of realistic estimates of spatial structure for exploring transmission dynamics of wildlife diseases and provide a framework for generating a data-driven description of spatial structure.

Ecological Modelling↗
USGS2007

Using simulated historical time series to prioritize fuel treatments on landscapes across the United States: The LANDFIRE prototype project

Canopy and surface fuels in many fire-prone forests of the United States have increased over the last 70 years as a result of modern fire exclusion policies, grazing, and other land management activities. The Healthy Forest Restoration Act and National Fire Plan establish a national commitment to reduce fire hazard and restore fire-adapted ecosystems across the USA. The primary index used to prioritize treatment areas across the nation is Fire Regime Condition Class (FRCC) computed as departures of current conditions from the historical fire and landscape conditions. This paper describes a process that uses an extensive set of ecological models to map FRCC from a departure statistic computed from simulated time series of historical landscape composition. This mapping process uses a data-driven, biophysical approach where georeferenced field data, biogeochemical simulation models, and spatial data libraries are integrated using spatial statistical modeling to map environmental gradients that are then used to predict vegetation and fuels characteristics over space. These characteristics are then fed into a landscape fire and succession simulation model to simulate a time series of historical landscape compositions that are then compared to the composition of current landscapes to compute departure, and the FRCC values. Intermediate products from this process are then used to create ancillary vegetation, fuels, and fire regime layers that are useful in the eventual planning and implementation of fuel and restoration treatments at local scales. The complex integration of varied ecological models at different scales is described and problems encountered during the implementation of this process in the LANDFIRE prototype project are addressed.

Ecological Modelling↗
USGS2013

Modelling dendritic ecological networks in space: anintegrated network perspective

Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of ecological networks, or in 2-D space, may be inadequate for studying the influence of structure and connectivity on ecological processes within DENs. We propose a conceptual taxonomy of network analysis methods that account for DEN characteristics to varying degrees and provide a synthesis of the different approaches within the context of stream ecology. Within this context, we summarise the key innovations of a new family of spatial statistical models that describe spatial relationships in DENs. Finally, we discuss how different network analyses may be combined to address more complex and novel research questions. While our main focus is streams, the taxonomy of network analyses is also relevant anywhere spatial patterns in both network and 2-D space can be used to explore the influence of multi-scale processes on biota and their habitat (e.g. plant morphology and pest infestation, or preferential migration along stream or road corridors).

Ecology Letters↗
USGS2019

Bighorn sheep habitat and model extrapolation across remote landscapes

Determining a species’ habitat use is an essential first step in any wildlife conservation action. We described habitat use, animal movements and probable lambing areas in a remote, restricted-access region of the Mojave Desert. Differences in habitat use between sexes was apparent, supporting the often-reported concept of risk-aversion by females. Animals exhibited low variability in distances travelled, although males travelled further and with more variability than females. All females demonstrated what we interpret as lambing behavior during the same 2 ½ month periods over the two years, strongly supporting our inference of lambing sites. Water appeared critical to animal long-range movements, with no animal moving beyond 8.5 km from known sources. Modeling habitat use across the landscape of concern is another necessary step for conservation of species, allowing managers to plan for and predict the outcomes of management actions. In ecology, models are often created within relatively small areas, then extrapolated across larger regions of concern. The ability to extrapolate ecological models may be especially useful across remote areas, where consistent access by wildlife managers may be highly restricted. These restrictions on access require that most data be collected remotely, necessitating the need for extrapolating models developed in other areas. We used data from GPS-collared desert bighorn sheep to describe and model habitat use across the Pintwater Range, located on the Nevada Test and Training Range of southern Nevada, a highly restricted military training ground. We tested the efficacy of habitat model extrapolation by comparing the performance of two models derived from adjacent but independent desert bighorn sheep populations. The predictive power of seasonal habitat models derived from adjacent mountain ranges was lower than those derived from the local population. However, the performance of the extrapolated model suggests it could still be a feasible alternative for estimating general habitat use.

Nevada↗
USGS2020

Linking land and sea through an ecological-economic model of coral reef recreation

Coastal zones are popular recreational areas that substantially contribute to social welfare. Managers can use information about specific environmental features that people value, and how these might change under different management scenarios, to spatially target actions to areas of high current or potential value. We explored how snorkelers’ experience would be affected by separate and combined land and marine management actions in West Maui, Hawaiʻi, using a Bayesian Belief Network (BBN) and a spatially explicit ecosystem services model. The BBN simulates the attractiveness of a site for recreation by combining snorkeler preferences for coastal features with expert opinions on ecological dynamics, snorkeler behavior, and management actions. A choice experiment with snorkelers elucidated their preferences for sites with better ecological and water-quality conditions. Linking the economic elicitation to the spatially explicit BBN to evaluate land-sea management scenarios provides specific guidance on where and how to act in West Maui to maximize ecosystem service returns. Improving coastal water quality through sediment runoff and cesspool effluent reductions (land management), and enhancing coral reef ecosystem conditions (marine management) positively affected overall snorkeling attractiveness across the study area, but with differential results at specific sites. The highest improvements were attained through joint land-sea management, driven by strong efforts to increase fish abundance and reduced sediment; however, the effects of management at individual beaches varied.

Hawaii↗
USGS2001

. Ecological conceptual models: a framework and case study on ecosystem management for South Florida sustainability

The Everglades and South Florida ecosystems are the focus of national and international attention because of their current degraded and threatened state. Ecological risk assessment, sustainability and ecosystem and adaptive management principles and processes are being used nationally as a decision and policy framework for a variety of types of ecological assessments. The intent of this study is to demonstrate the application of these paradigms and principles at a regional scale. The effects-directed assessment approach used in this study consists of a retrospective, eco-epidemiological phase to determine the causes for the current conditions and a prospective predictive risk-based assessment using scenario analysis to evaluate future options. Embedded in these assessment phases is a process that begins with the identification of goals and societal preferences which are used to develop an integrated suite of risk-based and policy relevant conceptual models. Conceptual models are used to illustrate the linkages among management (societal) actions, environmental stressors, and societal/ecological effects, and provide the basis for developing and testing causal hypotheses. These models, developed for a variety of landscape units and their drivers, stressors, and endpoints, are used to formulate hypotheses to explain the current conditions. They are also used as the basis for structuring management scenarios and analyses to project the temporal and spatial magnitude of risk reduction and system recovery. Within the context of recovery, the conceptual models are used in the initial development of performance criteria for those stressors that are determined to be most important in shaping the landscape, and to guide the use of numerical models used to develop quantitative performance criteria in the scenario analysis. The results will be discussed within an ecosystem and adaptive management framework that provides the foundation for decision making.

Science of the Total Environment↗
USGS2026

Incorporating location uncertainty improves inference with stop-level North American Breeding Bird Survey data

Ecological models should account for uncertainty to be most effective and useful. Yet, uncertainty from model covariates—unlike that from other sources, such as sampling error or process variability—is seldom explicitly incorporated. This can cause underestimates of uncertainty to cascade through model parameter estimates, predictions, and downstream uses. Burner et al. proposed a method for quantifying uncertainty in covariates and incorporating it into models using informative Bayesian priors. This method was applied to stop-level Breeding Bird Survey (BBS) analyses, where land cover uncertainty at each stop arises from substantial stop location uncertainty. A limited validation of model-estimated land cover, using stops with known locations, indicated the method’s potential effectiveness, but it was not rigorously evaluated. We conduct a robust simulation-based test, generating stop locations, extracting land cover, and simulating bird communities across 210 BBS routes in the upper Midwest. We compare 3 models: a “known” model with true land cover, a “naive” model assuming consistent 800-m stop spacing, and a “full” model using informative priors to estimate land cover. Species parameter estimates and predicted prevalence patterns across gradients in land cover from the full model approached those of the known model and were substantially closer to the true values used in simulations relative to those from the naive model. Naive model parameters were more biased relative to the other models, and credible intervals of predicted species prevalence rarely included the true simulated values. The full model also produced land cover covariate estimates closer to true simulation values relative to the mean informative priors. Our results show that, for the BBS, informative priors enable more accurate stop-level analyses despite location uncertainty. In contrast, naive models that ignore this uncertainty yield poor inferences. More broadly, we demonstrate empirically the utility of informative priors to account for covariate uncertainty in ecological models.

Michigan, Minnesota, Wisconson↗
USGS2008

Land use and the structure of western US stream invertebrate assemblages: Predictive models and ecological traits

Inferences drawn from regional bioassessments could be strengthened by integrating data from different monitoring programs. We combined data from the US Geological Survey National Water-Quality Assessment (NAWQA) program and the US Environmental Protection Agency Wadeable Streams Assessment (WSA) to expand the scope of an existing River InVertebrate Prediction and Classification System (RIVPACS)-type predictive model and to assess the biological condition of streams across the western US in a variety of landuse classes. We used model-derived estimates of taxon-specific probabilities of capture and observed taxon occurrences to identify taxa that were absent from sites where they were predicted to occur (decreasers) and taxa that were present at sites where they were not predicted to occur (increasers). Integration of 87 NAWQA reference sites increased the scope of the existing WSA predictive model to include larger streams and later season sampling. Biological condition at 336 NAWQA test sites was significantly (p < 0.001) associated with basin land use and tended to be lower in basins with intensive landuse modification (e.g., mixed, urban, and agricultural basins) than in basins with relatively undisturbed land use (e.g., forested basins). Of the 437 taxa observed among reference and test sites, 180 (41%) were increasers or decreasers. In general, decreasers had a different set of ecological traits (functional traits or tolerance values) than did increasers. We could predict whether a taxon was a decreaser or an increaser based on just a few traits, e.g., desiccation resistance, timing of larval development, habit, and thermal preference, but we were unable to predict the type of basin land use from trait states present in invertebrate assemblages. Refined characterization of traits might be required before bioassessment data can be used routinely to aid in the diagnoses of the causes of biological impairment. ?? 2008 by The North American Benthological Society.

Arizona, California, Colorado, Idaho, Montana, New↗
USGS2011

Regression models of ecological streamflow characteristics in the Cumberland and Tennessee River Valleys

Predictive equations were developed using stepbackward regression for 19 ecologically relevant streamflow characteristics grouped in five major classes (magnitude, ratio, frequency, variability, and date) for use in the Tennessee and Cumberland River watersheds. Basin characteristics explain 50 percent or more of the variation for 10 of the 19 equations. Independent variables identified through stepbackward regression were statistically significant in 81 of 304 coefficients tested across 19 models (⬚ < 0.0001) and represent four major groups: climate, physical landscape features, regional indicators, and land use. The most influential variables for determining hydrologic response were in the land-use and climate groups: daily temperature range, percent agricultural land use, and monthly mean precipitation. These three variables were major explanatory factors in 17, 15, and 13 models, respectively. The equations and independent datasets were used to explore the broad relation between basin properties and streamflow and its implications for the study of ecological flow requirements. Key results include a high degree of hydrologic variability among least disturbed Blue Ridge streams, similar hydrologic behavior for watersheds with widely varying degrees of forest cover, and distinct hydrologic profiles for streams in different geographic regions.

Tennessee and Cumberland Rivers↗
USGS2019

Selecting a landscape model for natural resource management applications

Purpose of Review: Climate change and associated ecological impacts have challenged many conventional, observation-based approaches for predicting ecosystem and landscape responses to natural resource management. Complex spatial ecological models provide powerful, flexible tools which managers and others can use to make inferences about management impacts on future, no-analog landscape conditions. However, land managers who wish to use ecosystem and landscape models for natural resource applications are faced with the difficult task of deciding among many models that differ in important ways. Here, we summarize a process to aid managers in the selection of an appropriate model for natural resource management. Recent Findings: To guide management planning, scientifically credible information on how landscapes will respond to management actions under changing climate is required. Landscape models are increasingly used in a management context to evaluate of impacts of changing climate and interacting stressors on ecosystems and to test effects of alternative management options on desired conditions. However, the wide range of available models makes selection of appropriate and viable models a complex process. Summary: We present a series of preliminary steps to define critical scales of time, space, and ecological organization to guide an experimental design for a modeling project and then list a set of criteria for selecting a landscape or ecological model. Material presented includes the preliminary steps (crafting modeling objective, designing modeling project), organizational concerns (resources available, expertise on hand, timelines), and modeling details (complexity, design, documentation) of model selection.

Current Landscape Ecology Reports↗
USGS2016

Resprouting and seeding hypotheses: A test of the gap-dependent model using resprouting and obligate seeding subspecies of Arctostaphylos

Ecological factors favoring either postfire resprouting or postfire obligate seeding in plants have received considerable attention recently. Three ecological models have been proposed to explain patterns of these two life history types. In this study, we test these three models using data from California chaparral. We take an innovative approach to testing these models by not testing community or landscape patterns, but instead, investigating vegetation structure characteristic of four pairs of resprouting and (non-resprouting) obligate seeding subspecies of Arctostaphylos (Ericaceae), a dominant and diverse shrub genus in California chaparral. Data were analyzed for percentage bare ground, elevation, annual precipitation, number of fires, and time between fires and were compared independently for each subspecies pair. Results were consistently supportive of the gap-dependent model suggesting that obligate seeders are favored when post-disturbance gaps are large. Results were inconclusive or contrary to expectations for both of the other two models.

Plant Ecology↗
USGS2020

Testing prediction accuracy in short-term ecological studies

Applied ecology is based on an assumption that a management action will result in a predicted outcome. Testing the prediction accuracy of ecological models is the most powerful way of evaluating the knowledge implicit in this cause-effect relationship, however, the prevalence of predictive modeling and prediction testing are spreading slowly in ecology. The challenge of prediction testing is particularly acute for small-scale studies, because withholding data for prediction testing (e.g., via k -fold cross validation) can reduce model precision. However, by necessity small-scale studies are common. We use one such study that explored small mammal abundance along an elevational gradient to test prediction accuracy of models with varying degrees of information content. For each of three small mammal species, we conducted 5000 iterations of the following process: (1) randomly selected 75 % of the data to develop generalized linear models of species abundance that used detailed site measurements as covariates, (2) used an information theoretic approach to compare the top model with detailed covariates to habitat type-only and null models constructed with the same data, (3) tested those models’ ability to predict the 25 % of the randomly withheld data, and (4) evaluated prediction accuracy with a quadratic loss function. Detailed models fit the model-evaluation data best but had greater expected prediction error when predicting out-of-sample data relative to the habitat type models. Relationships between species and detailed site variables may be evident only within the framework of explicitly hierarchical analyses. We show that even with a small but relatively typical dataset ( n = 28 sampling locations across 125 km over two years), researchers can effectively compare models with different information content and measure models’ predictive power, thus evaluating their own ecological understanding and defining the limits of their inferences. Identifying the appropriate scope of inference through prediction testing is ecologically valuable and is attainable even with small datasets.

Maine, New Hampshire↗
USGS2017

Multi-model comparison highlights consistency in predicted effect of warming on a semi-arid shrub

A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance, and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi-model approach to predict climate change impacts on big sagebrush ( Artemisia tridentata ), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata , we developed four predictive models, two based on empirically derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi-model approach in forecasting climate change impacts and uncertainties and should help land managers to maximize the value of conservation investments.

Global Change Biology↗
USGS2017

Model-based estimators of density and connectivity to inform conservation of spatially structured populations

Conservation and management of spatially structured populations is challenging because solutions must consider where individuals are located, but also differential individual space use as a result of landscape heterogeneity. A recent extension of spatial capture–recapture (SCR) models, the ecological distance model, uses spatial encounter histories of individuals (e.g., a record of where individuals are detected across space, often sequenced over multiple sampling occasions), to estimate the relationship between space use and characteristics of a landscape, allowing simultaneous estimation of both local densities of individuals across space and connectivity at the scale of individual movement. We developed two model-based estimators derived from the SCR ecological distance model to quantify connectivity over a continuous surface: (1) potential connectivity—a metric of the connectivity of areas based on resistance to individual movement; and (2) density-weighted connectivity (DWC)—potential connectivity weighted by estimated density. Estimates of potential connectivity and DWC can provide spatial representations of areas that are most important for the conservation of threatened species, or management of abundant populations (i.e., areas with high density and landscape connectivity), and thus generate predictions that have great potential to inform conservation and management actions. We used a simulation study with a stationary trap design across a range of landscape resistance scenarios to evaluate how well our model estimates resistance, potential connectivity, and DWC. Correlation between true and estimated potential connectivity was high, and there was positive correlation and high spatial accuracy between estimated DWC and true DWC. We applied our approach to data collected from a population of black bears in New York, and found that forested areas represented low levels of resistance for black bears. We demonstrate that formal inference about measures of landscape connectivity can be achieved from standard methods of studying animal populations which yield individual encounter history data such as camera trapping. Resulting biological parameters including resistance, potential connectivity, and DWC estimate the spatial distribution and connectivity of the population within a statistical framework, and we outline applications to many possible conservation and management problems.

Ecosphere↗
USGS2019

Digital mapping of ecological land units using a nationally scalable modeling framework

Ecological site descriptions (ESDs) and associated state-and-transition models (STMs) provide a nationally consistent classification and information system for defining ecological land units for management applications in the United States. Current spatial representations of ESDs, however, occur via soil mapping and are therefore confined to the spatial resolution used to map soils within a survey area. Land management decisions occur across a range of spatial scales and therefore require ecological information that spans similar scales. Digital mapping provides an approach for optimizing the spatial scale of modeling products to best serve decision makers and have the greatest impact in addressing land management concerns. Here, we present a spatial modeling framework for mapping ecological sites using machine learning algorithms, soil survey field observations, soil survey geographic databases, ecological site data, and a suite of remote sensing-based spatial covariates (e.g., hyper-temporal remote sensing, terrain attributes, climate data, land-cover, lithology). Based on the theoretical association between ecological sites and landscape biophysical properties, we hypothesized that the spatial distribution of ecological sites could be predicted using readily available geospatial data. This modeling approach was tested at two study areas within the western United States, representing 6.1 million ha on the Colorado Plateau and 7.5 million ha within the Chihuahuan Desert. Results show our approach was effective in mapping grouped ecological site classes (ESGs), with 10-fold cross-validation accuracies of 70% in the Colorado Plateau based on 1405 point observations across eight expertly-defined ESG classes and 79% in the Chihuahuan Desert based on 2589 point observations across nine expertly-defined ESG classes. Model accuracies were also evaluated using external-validation datasets; resulting in 56 and 44% correct classification for the Colorado Plateau and Chihuahuan Desert, respectively. National coverage of the training and covariate data used in this study provides opportunities for a consistent national-scale mapping effort of ecological sites.

Chihuahuan Desert, Colorado Plateau↗