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USGS2019

Adaptive variation, including local adaptation, requires decades to become evident in common gardens

Population‐level adaptation to spatial variation in factors such as climate and soils is critical for climate‐vulnerability assessments, restoration seeding, and other ecological applications in species management, and the underlying information is typically based on common‐garden studies that are short duration. Here, we show >20 yr were required for adaptive differences to emerge among 13 populations of a widespread shrub (sagebrush, Artemisia tridentata ssp wyomingensis ) collected from around the western United States and planted into common gardens. Additionally, >10 yr were required for greater survival of local populations, that is, local adaptation, to become evident. Variation in survival was best explained by the combination of populations’ home ecoregion combined with grouping of minimum temperature and aridity. Additional reductions in survival were explained by ungrouped (i.e., continuous) measures of garden‐to‐population‐origin separation in geographic distance (5% decrease in survival per 100 km increase in separation; R 2 = 0.22) and especially in minimum temperature in younger plants (−4% per + °C difference, R 2 = 0.56 vs. 0.29 in the 14th vs. 27th post‐planting years, respectively). Longer‐term common garden studies are needed. While we await them, uncertainty in adaptive variation resulting from short‐term observations could be quantitatively estimated and reported with seed‐transfer guidelines to reduce risks of introducing maladapted provenances in restoration.

Ecological Applications↗

USGS2010

Linking process to pattern: estimating spatiotemporal dynamics of a wildlife epidemic from cross‐sectional data

Underlying dynamic event processes unfolding in continuous time give rise to spatiotemporal patterns that are sometimes observable at only a few discrete times. Such event processes may be modulated simultaneously over several spatial (e.g., latitude and longitude) and temporal (e.g., age, calendar time, and cohort) dimensions. The ecological challenge is to understand the dynamic latent processes that were integrated over several dimensions (space and time) to produce the observed pattern: a so‐called inverse problem. An example of such a problem is characterizing epidemiological rate processes from spatially referenced age‐specific prevalence data for a wildlife disease such as chronic wasting disease (CWD). With age‐specific prevalence data, the exact infection times are not observed, which complicates the direct estimation of rates. However, the relationship between the observed data and the unobserved rate variables can be described with likelihood equations. Typically, for problems with multiple timescales, the likelihoods are integral equations without closed forms. The complexity of the likelihoods often makes traditional maximum‐likelihood approaches untenable. Here, using seven years of hunter‐harvest prevalence data from the CWD epidemic in white‐tailed deer (Odocoileus virginianus) in Wisconsin, USA, we develop and explore a Bayesian approach that allows for a detailed examination of factors modulating the infection rates over space, age, and time, and their interactions. Our approach relies on the Bayesian ability to borrow strength from neighbors in both space and time. Synthesizing a number of areas of event time analysis (current‐status data, age/period/cohort models, Bayesian spatial shared frailty models), our general framework has very broad ecological applicability beyond disease prevalence data to a number of important ecological event time analyses, including general survival studies with multiple time dimensions for which existing methodology is limited. We observed strong associations of infection rates with age, gender, and location. The infection rate appears to be increasing with time. We could not detect growth hotspots, or location by time interactions, which suggests that spatial variation in infection rates is determined primarily by when the disease arrives locally, rather than how fast it grows. We emphasize assumptions and the potential consequences of their violations.

Wisconsin↗

USGS2022

Evaluating temporal and spatial transferability of a tidal inundation model for foraging waterbirds

For ecosystem models to be applicable outside their context of development, temporal and spatial transferability must be demonstrated. This presents a challenge for modeling intertidal ecosystems where spatiotemporal variation arises at multiple scales. Models specializing in tidal dynamics are generally inhibited from having wider ecological applications by coarse spatiotemporal resolution or high user competency. The Tidal Inundation Model of Shallow-water Availability (TiMSA) uniquely simulates tides to empirically derive a time-integrated measure of availability for a shallow-water depth range defined by the user. To evaluate temporal and spatiotemporal transferability, we employed TiMSA at the development site in the Florida Keys and at novel subsites in the Florida Bay (application site) under a different time period (application period). We used foraging little blue herons ( Egretta caerulea ) as the ecological unit with which to constrain the model's “water depth window,” that is, range of water depths to estimate shallow-water availability. At the development site, temporally consistent water depth windows contrasted with interannual variation in shallow-water availability, which revealed short-term changes in Little Blue Heron foraging habitat. At the application site, water depth accuracy varied by subsite and was correlated with spatial error in bathymetric elevation. Although TiMSA parameters were sensitive to environmental temporal variation and uncertainty in spatial data, a spatially explicit water depth window generated reliable estimates of shallow-water conditions over space and time at the development and application sites. By exploring the contributing factors to model error, we provide solutions to reduce uncertainty of TiMSA parameters at potential application sites and recommendations for addressing bathymetric inaccuracy in digital elevation models. Accurately quantifying spatiotemporal changes of shallow water has implications for monitoring habitat conditions for tidally influenced species and projecting future changes to coastal ecosystems in response to anthropogenic stressors and natural disturbances such as sea level rise.

Florida↗

USGS2021

Instrumental variable methods in structural equation models

Instrumental variable regression (RegIV) provides a means for detecting and correcting parameter bias in causal models. Widely used in economics, recently several papers have highlighted its potential utility for ecological applications. Little attention has thus far been paid to the fact that IV methods can also be implemented within structural equation models (SEMIV). In this paper I present the motivations, requirements and basic procedures for using SEMIV. I first consider causal inference and IVs from the perspective of a randomized experiment with partial control of the cause of interest. I consider common sources of bias, the role of randomization and limits to its capacity to exclude bias. Sources of bias include omitted confounders, reciprocal causation, reverse causation and measurement error, all of which can all be seen as a single problem—endogeneity. The approach to estimating IV models most commonly used in econometric practice, two-stage least squares regression (2SLS), is explained, followed by a brief exposition of the covariance modelling approach used in SEM. Using data from an ecological field experiment, I illustrate the use of the treatment variable as an IV and then illustrate procedures for evaluating candidate variables that might serve as additional IVs. IV methods are shown to be useful for both detecting endogeneity and removing its influences. I illustrate some of the ways that bias can be generated, as well as diagnostic capabilities and means for remedy embedded within SEM. Procedures for screening and evaluating additional IVs reveal valuable lessons regarding the theoretical requirements and empirical standards for IVs. SEMIV provides a useful way to detect and control for bias. I suggest that the use of IVs within the SEM framework can support the simultaneous pursuit of causal inference and explanatory modelling, a common pair of aspirations for ecologists. Moving forward, there is a need for a better understanding of the capabilities of SEMIV and requirements for successful application.

Methods in Ecology and Evolution↗

USGS2019

Selecting ecological models using multi-objective optimization

Choices in ecological research and natural resource management require balancing multiple, often competing objectives. Examples include maximizing species persistence in a wildlife conservation context, while minimizing cost, or balancing opposing stakeholder objectives when managing wildlife populations. Multiple-objective optimization (MOO) provides a unifying framework for solving multiple objective problems. Model selection is a critical component of ecological inference and prediction and requires balancing the competing objectives of model fit and model complexity. The tradeoff between model fit and model complexity provides a basis for describing the model-selection problem within the MOO framework. We discuss MOO and two strategies for solving the MOO problem; modeling preferences pre-optimization and post-optimization. Most conventional model selection methods can be formulated as solutions of MOO problems via specification of pre-optimization preferences. We reconcile model selection within the MOO framework. We also consider model selection using post-optimization specification of preferences. That is, by first identifying Pareto optimal solutions, and then selecting among them. We demonstrate concepts with an ecological application of model selection using avian species richness data in the continental United States.

Ecological Modelling↗

USGS2001

Coastal sage scrub case study

In ecological applications of large-scale spatial data to management decisions concerning land planning and conservation, errors and biases may creep into the analysis and decision making at several steps (see Chaps. 1, 2, and 3), including: • Uncertainty in positions of spatial locations of relevant ecological and physiographic features of the landscape. • Uncertainty of the type and attributes of land cover at a particular location. • Uncertainty in how different land covers at a position in space and the geometric arrangement of land covers nearby might influence an animal species occurrence or distribution, or the magnitude of some ecological process. • Uncertainty about the relative importance of each spatial location to the overall success or persistence of a population or ecological process. • Uncertainty about how to weight each species or ecological process in determining the overall biodiversity and functioning of ecosystems, local and national resource priorities, and consistency with legislative mandates. We would like to be able to quantify the errors at each step, identify biases, and pass these along to the next analysis step so that our degree of uncertainty regarding potential outcomes is evident at each level (e.g., Stoms et al. 1992).

Book chapter↗

USGS2023

Toward absolute abundance for conservation applications: Estimating the number of contributors via microhaplotype genotyping of mixed-DNA sample

Molecular methods including metabarcoding and quantitative polymerase chain reaction have shown promise for estimating species abundance by quantifying the concentration of genetic material in field samples. However, the relationship between specimen abundance and detectable concentrations of genetic material is often variable in practice. DNA mixture analysis represents an alternative approach to quantify specimen abundance based on the presence of unique alleles in a sample. The DNA mixture approach provides novel opportunities to inform ecology and conservation by estimating the absolute abundance of target taxa through molecular methods; yet, the challenges associated with genotyping many highly variable markers in mixed-DNA samples have prevented its widespread use. To advance molecular approaches for abundance estimation, we explored the utility of microhaplotypes for DNA mixture analysis by applying a 125-marker panel to 1179 Chinook salmon ( Oncorhynchus tshawytscha ) smolts from the Sacramento-San Joaquin Delta, California, USA. We assessed the accuracy of DNA mixture analysis through a combination of mock mixtures containing DNA from up to 20 smolts and a trophic ecological application enumerating smolts in predator diets. Mock DNA mixtures of up to 10 smolts could reliably be resolved using microhaplotypes, and increasing the panel size would likely facilitate the identification of more individuals. However, while analysis of predator gastrointestinal tract contents indicated DNA mixture analysis could discern the presence of multiple prey items, poor and variable DNA quality prevented accurate genotyping and abundance estimation. Our results indicate that DNA mixture analysis can perform well with high-quality DNA, but methodological improvements in genotyping degraded DNA are necessary before this approach can be used on marginal-quality samples.

California↗

USGS2009

Molecular detection of native and invasive marine invertebrate larvae present in ballast and open water environmental samples collected in Puget Sound

Non-native marine species have been and continue to be introduced into Puget Sound via several vectors including ship's ballast water. Some non-native species become invasive and negatively impact native species or near shore habitats. We present a new methodology for the development and testing of taxon specific PCR primers designed to assess environmental samples of ocean water for the presence of native and non-native bivalves, crustaceans and algae. The intergenic spacer regions (IGS; ITS1, ITS2 and 5.8S) of the ribosomal DNA were sequenced for adult samples of each taxon studied. We used these data along with those available in Genbank to design taxon and group specific primers and tested their stringency against artificial populations of plasmid constructs containing the entire IGS region for each of the 25 taxa in our study, respectively. Taxon and group specific primer sets were then used to detect the presence or absence of native and non-native planktonic life-history stages (propagules) from environmental samples of ballast water and plankton tow net samples collected in Puget Sound. This methodology provides an inexpensive and efficient way to test the discriminatory ability of taxon specific oligonucleotides (PCR primers) before creating molecular probes or beacons for use in molecular ecological applications such as probe hybridizations or microarray analyses. This work addresses the current need to develop molecular tools capable of diagnosing the presence of planktonic life-history stages from non-native marine species (potential invaders) in ballast water and other environmental samples. ?? 2008 Elsevier B.V.

Journal of Experimental Marine Biology and Ecology↗

USGS2023

Challenges and solutions for automated avian recognition in aerial imagery

Remote aerial sensing provides a non-invasive, large geographical-scale technology for avian monitoring, but the manual processing of images limits its development and applications. Artificial Intelligence (AI) methods can be used to mitigate this manual image processing requirement. The implementation of AI methods, however, has several challenges: (1) imbalanced (i.e., long-tailed) data distribution, (2) annotation uncertainty in categorization, and (3) dataset discrepancies across different study sites. Here we use aerial imagery data of waterbirds around Cape Cod and Lake Michigan in the United States to examine how these challenges limit avian recognition performance. We review existing solutions and demonstrate as use cases how methods like Label Distribution Aware Marginal Loss with Deferred Re-Weighting, hierarchical classification, and FixMatch address the three challenges. We also present a new approach to tackle the annotation uncertainty challenge using a Soft-fine Pseudo-Label methodology. Finally, we aim with this paper to increase awareness in the ecological remote sensing community of these challenges and bridge the gap between ecological applications and state-of-the-art computer science, thereby opening new doors to future research.

Remote Sensing in Ecology and Conservation↗

USGS2022

What common-garden experiments tell us about climate responses in plants

Common garden experiments are indoor or outdoor plantings of species or populations collected from multiple distinct geographic locations, grown together under shared conditions. These experiments examine a range of questions for theory and application using a variety of methods for analysis. The eight papers of this special feature comprise a cross section of contemporary approaches, summarized and synthesized here by what they tell us about the relationships between climate-related trait spectra and fitness optima. Four of the eight papers are based on field experiments in prairie, desert, Mediterranean and boreal biomes. Representative of many common garden experiments, these experiments reveal consistent evidence of traits varying with population climate provenance, but evidence of a tradeoff between growth and tolerance traits or of consistent fitness optimization at home is scant, in contrast to trait theory. Two synthesis papers highlight dominant patterns of trait divergence, including for an exotic invasive species. One theoretical paper warned that unknown kinship relationships between populations can result in the misidentification of adaptive trait divergence. A third synthesis paper formulated novel and ambitious goals for common-garden studies through including measurement of response variables at multiple levels of biological organization. The featured papers discuss multiple avenues for improving common garden studies. Genomic analysis, together with the quantification of kinship relationships, will continue to reveal the influence of environmental drivers on gene selection. Measuring a more complete set of fitness traits, especially for traits related to regeneration, will permit the development of projection models to explicitly link trait spectra, climate patterns and fitness consequences. More standardized data reporting will additionally improve abilities to synthesize findings across experiments. Testing population performance in competition with other species will produce more robust fitness comparisons between genotypes, especially for slower-growing genotypes in higher-resource environments. Adding gardens in and beyond climatic edge locations will furthermore strengthen the understanding of population failure and species exclusion. Finally, there is unrealized potential in adding ecosystem-level observations to common-garden studies that will enhance integrative analysis across scales of biological organization and scientific domains. Synthesis . With novel, creative designs, data integration and synthesis, common garden experiments will continue to advance the understanding of trait ensembles interacting with climate across scales of biological organization, provide pivotal data for global change models and guide ecological applications such as restoration of habitats for rare and climate sensitive species.

Journal of Ecology↗

USGS2012

A generalized model for estimating the energy density of invertebrates

Invertebrate energy density (ED) values are traditionally measured using bomb calorimetry. However, many researchers rely on a few published literature sources to obtain ED values because of time and sampling constraints on measuring ED with bomb calorimetry. Literature values often do not account for spatial or temporal variability associated with invertebrate ED. Thus, these values can be unreliable for use in models and other ecological applications. We evaluated the generality of the relationship between invertebrate ED and proportion of dry-to-wet mass (pDM). We then developed and tested a regression model to predict ED from pDM based on a taxonomically, spatially, and temporally diverse sample of invertebrates representing 28 orders in aquatic (freshwater, estuarine, and marine) and terrestrial (temperate and arid) habitats from 4 continents and 2 oceans. Samples included invertebrates collected in all seasons over the last 19 y. Evaluation of these data revealed a significant relationship between ED and pDM ( r 2 = 0.96, p < 0.0001), where ED (as J/g wet mass) was estimated from pDM as ED = 22,960pDM − 174.2. Model evaluation showed that nearly all (98.8%) of the variability between observed and predicted values for invertebrate ED could be attributed to residual error in the model. Regression of observed on predicted values revealed that the 97.5% joint confidence region included the intercept of 0 (−103.0 ± 707.9) and slope of 1 (1.01 ± 0.12). Use of this model requires that only dry and wet mass measurements be obtained, resulting in significant time, sample size, and cost savings compared to traditional bomb calorimetry approaches. This model should prove useful for a wide range of ecological studies because it is unaffected by taxonomic, seasonal, or spatial variability.

Freshwater Science↗

USGS2008

Using the FORE-SCE model to project land-cover change in the southeastern United States

A wide variety of ecological applications require spatially explicit current and projected land-use and land-cover data. The southeastern United States has experienced massive land-use change since European settlement and continues to experience extremely high rates of forest cutting, significant urban development, and changes in agricultural land use. Forest-cover patterns and structure are projected to change dramatically in the southeastern United States in the next 50 years due to population growth and demand for wood products [Wear, D.N., Greis, J.G. (Eds.), 2002. Southern Forest Resource Assessment. General Technical Report SRS-53. U.S. Department of Agriculture, Forest Service, Southern Research Station, Asheville, NC, 635 pp]. Along with our climate partners, we are examining the potential effects of southeastern U.S. land-cover change on regional climate. The U.S. Geological Survey (USGS) Land Cover Trends project is analyzing contemporary (1973-2000) land-cover change in the conterminous United States, providing ecoregion-by-ecoregion estimates of the rates of change, descriptive transition matrices, and changes in landscape metrics. The FORecasting SCEnarios of future land-cover (FORE-SCE) model used Land Cover Trends data and theoretical, statistical, and deterministic modeling techniques to project future land-cover change through 2050 for the southeastern United States. Prescriptions for future proportions of land cover for this application were provided by ecoregion-based extrapolations of historical change. Logistic regression was used to develop relationships between suspected drivers of land-cover change and land cover, resulting in the development of probability-of-occurrence surfaces for each unique land-cover type. Forest stand age was initially established with Forest Inventory and Analysis (FIA) data and tracked through model iterations. The spatial allocation procedure placed patches of new land cover on the landscape until the scenario prescriptions were met, using measured Land Cover Trends data to guide patch characteristics and the probability surfaces to guide placement. The approach provides an efficient method for extrapolating historical land-cover trends and is amenable to the incorporation of more detailed and focused studies for the establishment of scenario prescriptions.

Ecological Modelling↗

USGS2025

Heterogeneity of locked‐pasture snow conditions modulate habitat and movement choices of a facultative migrant

Habitat selection and movement are key mechanisms by which animals can respond to and potentially cope with highly variable environmental conditions. Optimal responses likely vary, however, depending on the severity and scope of conditions. We tested this hypothesis using a facultative migrant species, the Great Gray Owl ( Strix nebulosa ), which exhibits high inter- and intra-individual variation in the timing, direction, and distance of winter movements. Specifically, we evaluated whether episodic, spatiotemporally variable “locked-pasture” snow conditions, which restrict access to subnivean food, prompted shifts in habitat selection or long-distance movements by owls. We quantified the movement of 42 owls using global positioning system (GPS) data within the Greater Yellowstone Ecosystem, USA, during 2017–2022. We used a novel ecological application of SnowModel, a snow evolution modeling system, to estimate fine-scale, physical snow properties likely to influence access to prey. Variables included snow depth, snow crusts produced by wind, and ice crusts produced by melt-freeze and rain-on-snow events. Owls avoided heterogeneously distributed wind crusts via local shifts in habitat selection. More homogenous ice crusts elicited long-distance movements away from affected home ranges. Finally, owls employed both proximate shifts in habitat selection and long-distance movements to avoid deeper snow. Ultimately, owls exhibited behavioral flexibility in response to limiting snow conditions that can vary in terms of severity, spatial extent, and duration. Such behavioral responses determine species distribution, with implications for population and community dynamics in spatiotemporally variable systems. Understanding the effects of, and responses to, environmental controls is increasingly important given the scope of on-going global change.

Idaho, Wyoming↗

USGS2002

A statistical evaluation of non-ergodic variogram estimators

Geostatistics is a set of statistical techniques that is increasingly used to characterize spatial dependence in spatially referenced ecological data. A common feature of geostatistics is predicting values at unsampled locations from nearby samples using the kriging algorithm. Modeling spatial dependence in sampled data is necessary before kriging and is usually accomplished with the variogram and its traditional estimator. Other types of estimators, known as non-ergodic estimators, have been used in ecological applications. Non-ergodic estimators were originally suggested as a method of choice when sampled data are preferentially located and exhibit a skewed frequency distribution. Preferentially located samples can occur, for example, when areas with high values are sampled more intensely than other areas. In earlier studies the visual appearance of variograms from traditional and non-ergodic estimators were compared. Here we evaluate the estimators' relative performance in prediction. We also show algebraically that a non-ergodic version of the variogram is equivalent to the traditional variogram estimator. Simulations, designed to investigate the effects of data skewness and preferential sampling on variogram estimation and kriging, showed the traditional variogram estimator outperforms the non-ergodic estimators under these conditions. We also analyzed data on carabid beetle abundance, which exhibited large-scale spatial variability (trend) and a skewed frequency distribution. Detrending data followed by robust estimation of the residual variogram is demonstrated to be a successful alternative to the non-ergodic approach.

Environmental and Ecological Statistics↗

USGS2025

Direct effects of pesticides and other grassland management practices on the North American Monarch Butterfly (Danaus plexippus plexippus): A systematic review

This review synthesizes recent scientific literature on the effects of grassland management practices and pesticide applications on monarch butterflies ( Danaus plexippus ), focusing on studies published since the 2020 U.S. Fish and Wildlife Service Species Status Assessment (SSA). While the SSA highlighted habitat conservation efforts to enhance monarch populations, there has been limited empirical research on the direct effects of these management strategies on monarch survival and fitness. Following PRISMA guidelines, we conducted a systematic review to evaluate research published during and after the SSA (2019-2024) on the direct effects of common grassland management practices (mowing, grazing, burning, haying) and pesticides (herbicides, insecticides, fungicides) across monarch life stages. This search identified 29 studies that met our criteria. Of these, only two studies addressed the impacts of grassland management on monarchs, indicating a substantial and important information gap. The other 27 studies reviewed indicate significant risks of pesticide exposure to monarchs, particularly at immature life stages from lethal and sublethal effects. The effects of chronic insecticide exposure, particularly neonicotinoids, are especially critical to consider, as research shows insecticides can result in impairment of growth, development, and reproductive behaviors. Additionally, fungicides pose overlooked risks to monarchs due to their toxicity to non-target organisms. Notably, most studies evaluating pesticide effects were conducted in a laboratory setting and thus it is unclear how these results relate to fieldrealistic exposure, as well as exposure over multiple life history stages and potential synergistic effects of pesticides. Additional relevant information gaps remain, especially on studies of southern migratory paths critical for monarch population persistence. Data gaps at relevant scales for grassland management combined with growing evidence of pesticide persistence and subsequent threats to insects, highlight how more ecologically applicable metrics for monitoring environmental chemicals could help improve understanding of the effects of pesticides on monarchs and characterize contamination of milkweed and nectar resources.

Cooperator Science Series↗

USGS2006

Longitudinal hydraulic analysis of river‐aquifer exchanges

A longitudinal analysis of transient flow between a river and an underlying aquifer is developed to calculate flow rates between the river and the aquifer and the location of groundwater seepage into the river as it changes over time. Two flow domains are defined in the analysis: an upstream domain of fluvial recharge, where water flows vertically from the river into the unsaturated portion of the aquifer and horizontally in saturated parts of the aquifer, and a downstream domain of groundwater seepage to the river, where groundwater flows parallel to the underlying impermeable base. The river does not necessarily penetrate completely through the aquifer. A one‐dimensional, unsteady flow equation is derived from mass conservation, Darcy's law, and the geometry of the river‐aquifer system to calculate the water table position and the groundwater seepage rate into the river. Models based on numerical and analytical solutions of the flow equation were applied to a reach of the Methow River in north central Washington. The calibrated models simulated groundwater seepage with a root‐mean‐square error less than 5% of the mean groundwater seepage rates for three low‐flow evaluation periods. The analytical model provides a theoretical basis for a nonlinear exponential base flow recession generated by a draining aquifer, but not an explicit functional form for the recession. Unlike cross‐sectional approaches, the longitudinal approach allows the analysis of the length and location of groundwater seepage to a river, which have important ecological implications in many rivers. In the numerical simulations, the length of the groundwater seepage varied seasonally by about 4 km and the upstream boundary of groundwater seepage was within 689 m of its location at a stream gage on 9 September 2001 and within 91 m of its location on 6 October 2002. To demonstrate its utility in ecological applications, the numerical model was used to calculate differences in length of groundwater seepage to the Methow River under an early runoff scenario and the timing of those differences with respect to life stages of chinook salmon.

Water Resources Research↗

USGS2007

The FORE-SCE model: a practical approach for projecting land cover change using scenario-based modeling

A wide variety of ecological applications require spatially explicit, historic, current, and projected land use and land cover data. The U.S. Land Cover Trends project is analyzing contemporary (1973–2000) land-cover change in the conterminous United States. The newly developed FORE-SCE model used Land Cover Trends data and theoretical, statistical, and deterministic modeling techniques to project future land cover change through 2020 for multiple plausible scenarios. Projected proportions of future land use were initially developed, and then sited on the lands with the highest potential for supporting that land use and land cover using a statistically based stochastic allocation procedure. Three scenarios of 2020 land cover were mapped for the western Great Plains in the US. The model provided realistic, high-resolution, scenario-based land-cover products suitable for multiple applications, including studies of climate and weather variability, carbon dynamics, and regional hydrology.

Journal of Land Use Science↗

USGS2021

Using expert knowledge to support Endangered Species Act decision‐making for data‐deficient species

Many questions relevant to conservation decision making are characterized by extreme uncertainty due to lack of empirical data and complexity of the underlying ecological processes, leading to a rapid increase in the use of structured protocols to elicit expert knowledge. Published ecological applications often employ a modified Delphi method, where experts provide judgments anonymously and mathematical aggregation techniques are used to combine judgments. The Sheffield Elicitation Framework (SHELF) differs in its behavioral approach to synthesizing individual judgments into a fully specified probability distribution for an unknown quantity. This study demonstrates the remote use of the SHELF protocol for an extinction risk assessment of three subterranean aquatic species petitioned for listing under the US Endangered Species Act. Experts were provided an empirical threat assessment for each known locality using video conferencing and asked for judgments on the probability of population persistence over four generations using online submission forms and R‐shiny apps available through the SHELF package. Despite large uncertainty for all populations, results reveal key differences between species’ risk of extirpation based on spatial variation in dominant threats, local land use and management practices, and microhabitat use. The resulting probability distributions provide decision makers with a full picture of uncertainty that is consistent with the probabilistic nature of risk assessments, and discussions during the behavioral aggregation stage clearly document dominant threats (e.g., development, timber harvest, animal agriculture, and cave visitation) and their interactions with local cave geology and species’ habitat preferences. Our virtual implementation of the SHELF protocol demonstrates the flexibility of this approach for conservation applications operating on budgets and timelines that can limit in‐person meetings of geographically dispersed experts.

Conservation Biology↗