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USGS2013

Including independent estimates and uncertainty to quantify total abundance of fish migrating in a large river system: walleye ( Sander vitreus ) in the Maumee River, Ohio

Walleye (Sander vitreus) in Lake Erie is a valuable and migratory species that spawns in tributaries. We used hydroacoustic sampling, gill net sampling, and Bayesian state-space modeling to estimate the spawning stock abundance, characterize size and sex structure, and explore environmental factors cuing migration of walleye in the Maumee River for 2011 and 2012. We estimated the spawning stock abundance to be between 431,000 and 1,446,000 individuals in 2011 and between 386,400 and 857,200 individuals in 2012 (95% Bayesian credible intervals). A back-calculation from a concurrent larval fish study produced an estimate of 78,000 to 237,000 spawners for 2011. The sex ratio was skewed towards males early in the spawning season but approached 1:1 later, and larger individuals entered the river earlier in the season than smaller individuals. Walleye migration was greater during low river discharge and intermediate temperatures. Our approach to estimating absolute abundance and uncertainty as well as characterization of the spawning stock could improve assessment and management of this species, and our methodology is applicable to other diadromous populations.

Ohio↗

USGS2017

Quantifying drivers of wild pig movement across multiple spatial and temporal scales

Background The movement behavior of an animal is determined by extrinsic and intrinsic factors that operate at multiple spatio-temporal scales, yet much of our knowledge of animal movement comes from studies that examine only one or two scales concurrently. Understanding the drivers of animal movement across multiple scales is crucial for understanding the fundamentals of movement ecology, predicting changes in distribution, describing disease dynamics, and identifying efficient methods of wildlife conservation and management. Methods We obtained over 400,000 GPS locations of wild pigs from 13 different studies spanning six states in southern U.S.A., and quantified movement rates and home range size within a single analytical framework. We used a generalized additive mixed model framework to quantify the effects of five broad predictor categories on movement: individual-level attributes, geographic factors, landscape attributes, meteorological conditions, and temporal variables. We examined effects of predictors across three temporal scales: daily, monthly, and using all data during the study period. We considered both local environmental factors such as daily weather data and distance to various resources on the landscape, as well as factors acting at a broader spatial scale such as ecoregion and season. Results We found meteorological variables (temperature and pressure), landscape features (distance to water sources), a broad-scale geographic factor (ecoregion), and individual-level characteristics (sex-age class), drove wild pig movement across all scales, but both the magnitude and shape of covariate relationships to movement differed across temporal scales. Conclusions The analytical framework we present can be used to assess movement patterns arising from multiple data sources for a range of species while accounting for spatio-temporal correlations. Our analyses show the magnitude by which reaction norms can change based on the temporal scale of response data, illustrating the importance of appropriately defining temporal scales of both the movement response and covariates depending on the intended implications of research (e.g., predicting effects of movement due to climate change versus planning local-scale management). We argue that consideration of multiple spatial scales within the same framework (rather than comparing across separate studies post-hoc) gives a more accurate quantification of cross-scale spatial effects by appropriately accounting for error correlation.

Movement Ecology↗

USGS2006

Annual dissolved nitrite plus nitrate and total phosphorous loads for the Susquehanna, St. Lawrence, Mississippi-Atchafalaya, and Columbia River basins, 1968-2004

Annual stream-water loads were calculated near the outlet of four of the larger river basins (Susquehanna, St. Lawrence, Mississippi-Atchafalaya, and Columbia) in the United States for dissolved nitrite plus nitrate (NO2 + NO3) and total phosphorus using LOADEST load estimation software. Loads were estimated for the period 1968-2004; although loads estimated for individual river basins and chemical constituent combinations typically were for shorter time periods due to limitations in data availability. Stream discharge and water-quality data for load estimates were obtained from the U.S. Geological Survey (USGS) with additional stream discharge data for the Mississippi-Atchafalaya River Basin from the U.S. Army Corps of Engineers. The loads were estimated to support national assessments of changes in stream nutrient loads that are periodically conducted by Federal agencies (for example, U.S. Environmental Protection Agency) and other water- and land-resource organizations. Data, methods, and results of load estimates are summarized herein; including World Wide Web links to electronic ASCII text files containing the raw data. The load estimates are compared to dissolved NO2 + NO3 loads for three of the large river basins from 1971 to 1998 that the USGS provided during 2001 to The H. John Heinz III Center for Science, Economics and the Environment (The Heinz Center) for a report The Heinz Center published during 2002. Differences in the load estimates are the result of using the most up-to-date monitoring data since the 2001 analysis, differences in how concentrations less than the reporting limit were handled by the load estimation models, and some errors and exclusions in the 2001 analysis datasets (which resulted in some inaccurate load estimates).

Open-File Report↗

USGS1995

Data for and adjusted regional regression models of volume and quality of urban storm-water runoff in Boise and Garden City, Idaho, 1993-94

The U.S. Environmental Protection Agency requires information on the volume and quality of urban storm-water runoff to apply for a permit to discharge this water into the Boise River under the National Pollutant Discharge Elimination System Program. Concentrations of selected chemical constituents in storm runoff were determined from samples collected at four storm-sewer outfalls in Boise from October 1993 through June 1994 and at one outfall in Garden City from September through October 1994. Samples were analyzed for specific conductance, pH, alkalinity, water temperature, oxygen demand, fecal indicator bacteria, major ions, dissolved and suspended solids, nutrients, trace elements, and numerous organic compounds. The measurement of storm-runoff volume and mean concentrations of constituents were used to estimate storm-runoff loads. Previously developed U.S. Geological Survey regional regression models of runoff and 11 chemical constituents were evaluated to assess their suitability for use in urban areas in Boise and Garden City. Data collected in the study area were used to develop adjusted regional models of storm-runoff volumes and mean concentrations and loads of chemical oxygen demand, dissolved and suspended solids, total nitrogen and total ammonia plus organic nitrogen as nitrogen, total and dissolved phosphorus, and total recoverable cadmium, copper, lead, and zinc. Explanatory variables used in these models were drainage area, impervious area, land-use information, and precipitation data. Mean annual runoff volume and loads at the five outfalls were estimated from 904 individual storms during 1976 through 1993. Two methods were used to compute individual storm loads. The first method used adjusted regional models of storm loads and the second used adjusted regional models for mean concentration and runoff volume. For large storms, the first method seemed to produce excessively high loads for some constituents and the second method provided more reliable results for all constituents except suspended solids. The first method provided more reliable results for large storms for suspended solids.

Idaho↗

USGS2016

Extending ordinal regression with a latent zero-augmented beta distribution

Ecological abundance data are often recorded on an ordinal scale in which the lowest category represents species absence. One common example is when plant species cover is visually assessedwithin bounded quadrats and then assigned to pre-defined cover class categories.We present an ordinal beta hurdle model that directly models ordinal category probabilitieswith a biologically realistic beta-distributed latent variable.Ahurdle-at-zero model allows ecologists to explore distribution (absence) and abundance processes in an integrated framework. This provides an alternative to cumulative link models when data are inconsistent with the assumption that the odds ofmoving into a higher category are the same for all categories (proportional odds). Graphical tools and a deviance information criterion were developed to assess whether a hurdle-at-zero model should be used for inferences rather than standard ordinal methods. Hurdle-at-zero and non-hurdle ordinal models fit to vegetation cover class data produced substantially different conclusions.The ordinal beta hurdle model yielded more precise parameter estimates than cumulative logit models, although out-of-sample predictions were similar. The ordinal beta hurdle model provides inferences directly on the latent biological variable of interest, percent cover, and supports exploration of more realistic ecological patterns and processes through the hurdle-at-zero or two-part specification.We provide JAGS code as an on-line supplement. Supplementary materials accompanying this paper appear on-line.

Journal of Agricultural, Biological, and Environme↗

USGS2009

Impacts of forest fragmentation on species richness: A hierarchical approach to community modelling

1. Species richness is often used as a tool for prioritizing conservation action. One method for predicting richness and other summaries of community structure is to develop species-specific models of occurrence probability based on habitat or landscape characteristics. However, this approach can be challenging for rare or elusive species for which survey data are often sparse. 2. Recent developments have allowed for improved inference about community structure based on species-specific models of occurrence probability, integrated within a hierarchical modelling framework. This framework offers advantages to inference about species richness over typical approaches by accounting for both species-level effects and the aggregated effects of landscape composition on a community as a whole, thus leading to increased precision in estimates of species richness by improving occupancy estimates for all species, including those that were observed infrequently. 3. We developed a hierarchical model to assess the community response of breeding birds in the Hudson River Valley, New York, to habitat fragmentation and analysed the model using a Bayesian approach. 4. The model was designed to estimate species-specific occurrence and the effects of fragment area and edge (as measured through the perimeter and the perimeter/area ratio, P/A), while accounting for imperfect detection of species. 5. We used the fitted model to make predictions of species richness within forest fragments of variable morphology. The model revealed that species richness of the observed bird community was maximized in small forest fragments with a high P/A. However, the number of forest interior species, a subset of the community with high conservation value, was maximized in large fragments with low P/A. 6. Synthesis and applications. Our results demonstrate the importance of understanding the responses of both individual, and groups of species, to environmental heterogeneity while illustrating the utility of hierarchical models for inference about species richness for conservation. This framework can be used to investigate the impacts of land-use change and fragmentation on species or assemblage richness, and to further understand trade-offs in species-specific occupancy probabilities associated with landscape variability.

New York↗

USGS2011

Quantifying differences in the impact of variable chemistry on equilibrium uranium(VI) adsorption properties of aquifer sediments

Uranium adsorption-desorption on sediment samples collected from the Hanford 300-Area, Richland, WA varied extensively over a range of field-relevant chemical conditions, complicating assessment of possible differences in equilibrium adsorption properties. Adsorption equilibrium was achieved in 500-1000 h although dissolved uranium concentrations increased over thousands of hours owing to changes in aqueous chemical composition driven by sediment-water reactions. A nonelectrostatic surface complexation reaction, >SOH + UO 2 2+ + 2CO 3 2- = >SOUO 2 (CO 3 HCO 3 ) 2- , provided the best fit to experimental data for each sediment sample resulting in a range of conditional equilibrium constants (log K c ) from 21.49 to 21.76. Potential differences in uranium adsorption properties could be assessed in plots based on the generalized mass-action expressions yielding linear trends displaced vertically by differences in log K c values. Using this approach, log K c values for seven sediment samples were not significantly different. However, a significant difference in adsorption properties between one sediment sample and the fines (<0.063 mm) of another could be demonstrated despite the fines requiring a different reaction stoichiometry. Estimates of log K c uncertainty were improved by capturing all data points within experimental errors. The mass-action expression plots demonstrate that applying models outside the range of conditions used in model calibration greatly increases potential errors.

Washington↗

USGS2011

Baseline ecological risk assessment of the Calcasieu Estuary, Louisiana: Part 2. An evaluation of the predictive ability of effects-based sediment-quality guidelines

Three sets of effects-based sediment-quality guidelines (SQGs) were evaluated to support the selection of sediment-quality benchmarks for assessing risks to benthic invertebrates in the Calcasieu Estuary, Louisiana. These SQGs included probable effect concentrations (PECs), effects range median values (ERMs), and logistic regression model (LRMs)-based T 50 values. The results of this investigation indicate that all three sets of SQGs tend to underestimate sediment toxicity in the Calcasieu Estuary (i.e., relative to the national data sets), as evaluated using the results of 10-day toxicity tests with the amphipod, Hyalella azteca , or Ampelisca abdita , and 28-day whole-sediment toxicity tests with the H. azteca . These results emphasize the importance of deriving site-specific toxicity thresholds for assessing risks to benthic invertebrates.

Louisiana↗

USGS2019

Sampling designs for landscape-level eDNA monitoring programs using three-level occurrence models

Resource managers conduct landscape-level monitoring using environmental DNA (eDNA). These managers must contend with imperfect detection in samples and sub-samples (i.e., molecular analyses). This imperfect detection impacts their ability to both detect species and estimate occurrence. Although occurrence (synonymously occupancy) models can estimate these probabilities, most models and guidance for their application do not consider three levels. We studied this with three aims. First, we examined the number of samples required to detect a species at a site given imperfect detection. Second, we examined the ability of a three-level occurrence model to recover parameter estimates. Third, we examined the number of samples required to reliably recover parameter estimates. We found detecting eDNA in 1 sample at a site required 12 samples under most condition, but detection eDNA in situations that might be expected when looking for species at very low abundance required >50 samples. We found our occupancy model generally recovered known parameters unless detection and sample occurrence probabilities were <0.3. In these situations, >50 samples per site and 8 molecular replicates were required. Conversely, estimating and comparing occurrence and detection probabilities for species with moderate to high abundance may require 4 molecular replicates and 20-30 samples per site. More broadly, our findings illustrate the importance of study design, sample sizes, and molecular replicates for eDNA-based research, monitoring, and management.

Integrated Environmental Assessment and Management↗

USGS2021

Land management strategies influence soil organic carbon stocks of prairie potholes of North America

Soil organic carbon (SOC) stocks of Prairie Pothole Region (PPR) wetlands in the central plains of Canada and the United States are highly variable due to natural variation in biota, soils, climate, hydrology, and topography. Land-use history (cropland, grassland) and land-management practices (drainage, restoration) also affect SOC stocks. We conducted a region-wide assessment of wetland SOC stocks using data from the Canadian and US portions of the PPR under various management types. Natural wetlands with no disturbance history in the wetland basin or surrounding catchment had considerably greater average SOC stocks in the upper (0–15 cm) soil profile than wetlands surrounded by cropland. Hydrologically restored wetlands did not show significantly greater SOC stocks than drained wetlands, but wetlands surrounded by restored grasslands did have greater SOC stocks in the upper soil profile than those surrounded by croplands. Similarities among cropped and restored wetlands likely were due to insufficient time since restoration, as well as high variability attributable to several environmental factors within the region. We conclude that avoided loss of natural wetlands from drainage and avoided loss of native grasslands from cropping have the most benefit for preserving wetland SOC stocks. Robust PPR SOC models that incorporate multiple abiotic, biotic, and land-use factors are required to determine where and when restoration is most effective for SOC sequestration.

Book chapter↗

USGS2022

Precision of headwater stream permanence estimates from a monthly water balance model in the Pacific Northwest, USA

Stream permanence classifications (i.e., perennial, intermittent, ephemeral) are a primary consideration to determine stream regulatory status in the United States (U.S.) and are an important indicator of environmental conditions and biodiversity. However, at present, no models or products adequately describe surface water presence for regulatory determinations. We modified the Thornthwaite monthly water balance model (MWBM) with a flow threshold parameter to estimate flow permanence and evaluated the model’s accuracy and precision for more than 1.3 million headwater stream reaches in the U.S. Pacific Northwest (PNW). Stream reaches were assigned to one of eight calibration groups by unsupervised classification based on sensitivity to MWBM parameters. Suitable MWBM parameter sets were identified by comparing modeled stream permanence estimates to surface water presence observations (SWPO). Parameter sets with accuracies > 65% were considered suitable. The MWBM estimated stream permanence with high precision at 40% of reaches, with poor precision at 20% of reaches, and no suitable parameter sets were identified for 40% of reaches. Results highlight the need for increased SWPO collection to improve calibration and assessment of stream permanence models. Additionally, implementation of the MWBM to estimate surface water presence indicates potential for process-based models to predict stream permanence with future development.

California, Idaho, Montana, Nevada, Oregon, Utah, ↗

USGS2011

Chapter 7: Occurrence and abundance of ants, reptiles, and mammals

Sagebrush ( Artemisia spp.)- associated wildlife are threatened by habitat loss and fragmentation and by impacts associated with anthropogenic disturbances, including energy development. Understanding how species of concern as well as other wildlife including insects, reptiles, and mammals respond to type and spatial scale of disturbance is critical to managing future land uses and identifying sites that are important for conservation. We developed statistical models to describe species occurrence or abundance, based on area searches in 7.29-ha survey blocks, across the Wyoming Basins Ecoregional Assessment (WBEA) area for six shrub steppe-associated species: harvester ant ( Pogonomyrmex spp.), thatch ant ( Formica spp.), short-horned lizard ( Phrynosoma hernandesi ), white-tailed jackrabbit ( Lepus townsendii ), cottontail ( Sylvilagus spp.) and least chipmunk ( Tamius minimus ). We modeled patterns in occupancy or abundance relative to multi-scale measures of vegetation type and pattern, abiotic site characteristics, and anthropogenic disturbance factors. Sagebrush habitat was a strong predictor of occurrence for shorthorned lizards and white-tailed jackrabbits, but weak for the other four species. Vegetation and abiotic characteristics were strong determinants of species occurrence, although the scale of response was not consistent among species. All species, with the exception of the short-horned lizard, responded to anthropogenic disturbance, although responses again varied as a function of scale and direction (negative and positive influences). Our results improve our understanding of how environmental and anthropogenic factors affect species distributions across the WBEA area and facilitate a multi-species approach to management of this sagebrush ecosystem.

Wyoming↗

USGS1999

Mapping the glacial geology of the Central Great Lakes region in three dimensions: A model for state-federal cooperation

Planners need to evaluate complex and competing public-policy options for managing water, land, and biological resources; they must ensure economic growth, meet the needs of an increasing population, assess hazards, and manage the environment in a sustainable manner. The State Geological Surveys of Illinois, Indiana, Michigan, and Ohio and the U.S. Geological Survey (USGS) receive many requests from local, State, and Federal planners and officials for geologic information. Thick and complex layers of glacial and related sediments blanket the bedrock of these States: a three-dimensional understanding of these deposits is critical to making informed resource-management and other planning decisions. At two recent forums (in Indianapolis, March 1997, and Columbus, Ohio, February 1999), more than 200 attendees from more than 60 local, State, and Federal agencies provided a clear message. They need three-dimensional geologic information to use in making decisions on the following issues: • Quality, quantity, distribution, and accessibility of surface and ground water • Aggregate sources and land-use conflicts • Energy and mineral resource management • Environmental management and mitigation of land and water contamination • Acceleration of the permitting processes of regulatory agencies • Industrial, commercial, residential, and infrastructure siting and construction • Agricultural land loss, erosion, sedimentation, and agrichemical use • Waste-disposal planning and mitigation • Habitat alteration and biodiversity • Coastal erosion, landslides, radon, floods, and earthquakes

Illinois, Indiana, Michigan, Ohio↗

USGS2015

Long-term effects of wildfire on greater sage-grouse - integrating population and ecosystem concepts for management in the Great Basin

Greater sage-grouse ( Centrocercus urophasianus ; hereinafter, sage-grouse) are a sagebrush obligate species that has declined concomitantly with the loss and fragmentation of sagebrush ecosystems across most of its geographical range. The species currently is listed as a candidate for federal protection under the Endangered Species Act (ESA). Increasing wildfire frequency and changing climate frequently are identified as two environmental drivers that contribute to the decline of sage-grouse populations, yet few studies have rigorously quantified their effects on sage-grouse populations across broad spatial scales and long time periods. To help inform a threat assessment within the Great Basin for listing sage-grouse in 2015 under the ESA, we conducted an extensive analysis of wildfire and climatic effects on sage-grouse population growth derived from 30 years of lek-count data collected across the hydrographic Great Basin of Western North America. Annual (1984–2013) patterns of wildfire were derived from an extensive dataset of remotely sensed 30-meter imagery and precipitation derived from locally downscaled spatially explicit data. In the sagebrush ecosystem, underlying soil conditions also contribute strongly to variation in resilience to disturbance and resistance to plant community changes (R&R). Thus, we developed predictions from models of post-wildfire recovery and chronic effects of wildfire based on three spatially explicit R&R classes derived from soil moisture and temperature regimes. We found evidence of an interaction between the effects of wildfire (chronically affected burned area within 5 kilometers of a lek) and climatic conditions (spring through fall precipitation) after accounting for a consistent density-dependent effect. Specifically, burned areas near leks nullifies population growth that normally follows years with relatively high precipitation. In models, this effect results in long-term population declines for sage-grouse despite cyclic periods of high precipitation. Based on 30-year projections of burn and recovery rates, our population model predicted steady and substantial long-term declines in population size across the Great Basin. Further, example management scenarios that may help offset adverse wildfire effects are provided by models of varying levels of fire suppression and post-wildfire restoration that focus on areas especially important to sage-grouse populations. These models illustrate how sage-grouse population persistence likely will be compromised as sagebrush ecosystems and sage-grouse habitat are degraded by wildfire, especially in a warmer and drier climate, and by invasion of annual grasses that can increase wildfire frequency and size in the Great Basin.

California, Idaho, Nevada, Oregon, Utah↗

USGS2013

A comparative assessment of tools for ecosystem services quantification and valuation

To enter widespread use, ecosystem service assessments need to be quantifiable, replicable, credible, flexible, and affordable. With recent growth in the field of ecosystem services, a variety of decision-support tools has emerged to support more systematic ecosystem services assessment. Despite the growing complexity of the tool landscape, thorough reviews of tools for identifying, assessing, modeling and in some cases monetarily valuing ecosystem services have generally been lacking. In this study, we describe 17 ecosystem services tools and rate their performance against eight evaluative criteria that gauge their readiness for widespread application in public- and private-sector decision making. We describe each of the tools′ intended uses, services modeled, analytical approaches, data requirements, and outputs, as well time requirements to run seven tools in a first comparative concurrent application of multiple tools to a common location – the San Pedro River watershed in southeast Arizona, USA, and northern Sonora, Mexico. Based on this work, we offer conclusions about these tools′ current ‘readiness’ for widespread application within both public- and private-sector decision making processes. Finally, we describe potential pathways forward to reduce the resource requirements for running ecosystem services models, which are essential to facilitate their more widespread use in environmental decision making.

Ecosystem Services↗

USGS2022

Modeling risk dynamics of contaminants of emerging concern in a temperate-region wastewater effluent-dominated stream

Wastewater effluent-dominated streams are becoming increasingly common worldwide, including in temperate regions, with potential impacts on ecological systems and drinking water sources. We recently quantified the occurrence/spatiotemporal dynamics of pharmaceutical mixtures in a representative temperate-region wastewater effluent-dominated stream (Muddy Creek, Iowa) under baseflow conditions and characterized relevant fate processes. Herein, we quantified the ecological risk quotients (RQs) of 19 effluent-derived contaminants of emerging concern (CECs; including: 14 pharmaceuticals, 2 industrial chemicals, and 3 neonicotinoid insecticides) and 1 run-off-derived compound (atrazine) in the stream under baseflow conditions, and estimated the probabilistic risks of effluent-derived CECs under all-flow conditions ( i.e. , including runoff events) using stochastic risk modeling. We determined that 11 out of 20 CECs pose medium-to-high risks to local ecological systems ( i.e. , algae, invertebrates, fish) based on literature-derived acute effects under measured baseflow conditions. Stochastic risk modeling indicated decreased, but still problematic, risk of effluent-derived CECs ( i.e. , RQ ≥ 0.1) under all-flow conditions when runoff events were included. Dilution of effluent-derived chemicals from storm flows thus only minimally decreased risk to aquatic biota in the effluent-dominated stream. We also modeled in-stream transport. Thirteen out of 14 pharmaceuticals persisted along the stream reach (median attenuation rate constant k < 0.1 h −1 ) and entered the Iowa River at elevated concentrations. Predicted and measured concentrations in the drinking water treatment plant were below the human health benchmarks. This study demonstrates the application of probabilistic risk assessments for effluent-derived CECs in a representative effluent-dominated stream under variable flow conditions (when measurements are less practical) and provides an enhanced prediction tool transferable to other effluent-dominated systems.

Iowa↗

USGS2020

A novel quantitative framework for riverscape genetics

Riverscape genetics, which applies concepts in landscape genetics to riverine ecosystems, lack appropriate quantitative methods that address the spatial autocorrelation structure of linear stream networks and account for bidirectional geneflow. To address these challenges, we present a general framework for the design and analysis of riverscape genetic studies. Our framework starts with the estimation of pairwise genetic distance at sample sites and the development of a spatially structured ecological network (SSEN) on which riverscape covariates are measured. We then introduce the novel bidirectional geneflow in riverscapes (BGR) model that uses principles of isolation-by-resistance to quantify the effects of environmental covariates on genetic connectivity, with spatial covariance defined using simultaneous autoregressive models on the SSEN and the generalized Wishart distribution to model pairwise distance matrices arising through a random walk model of geneflow. We highlight the utility of this framework in an analysis of riverscape genetics for brook trout ( Salvelinus fontinalis ) in north central Pennsylvania, USA. Using the fixation index ( F ST ) as the measure of genetic distance, we estimated the effects of 12 riverscape covariates on geneflow by evaluating the relative support of eight competing BGR models. We then compared the performance of the top-ranked BGR model to results obtained from comparable analyses using multiple regression on distance matrices (MRM) and the program STRUCTURE. We found that the BGR model had more power to detect covariate effects, particularly for variables that were only partial barriers to geneflow and/or uncommon in the riverscape, making it more informative for assessing patterns of population connectivity and identifying threats to species conservation. This case study highlights the utility of our modeling framework over other quantitative methods in riverscape genetics, particularly the ability to rigorously test hypotheses about factors that influence geneflow and probabilistically estimate the effect of riverscape covariates, including stream flow direction. This framework is flexible across taxa and riverine networks, is easily executable, and provides intuitive results that can be used to investigate the likely outcomes of current and future management scenarios.

Ecological Applications↗

USGS2015

Evaluation of habitat suitability index models by global sensitivity and uncertainty analyses: a case study for submerged aquatic vegetation

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low-quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision-making framework will result in better-informed, more robust decisions.

Florida↗