Geology Reports⌕ Search

SEARCH · Geology Reports

Results for “Ecological Modelling”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,387 records · Page 77Linked to original sources
USGS2018

Ecological neighborhoods as a framework for umbrella species selection

Umbrella species are typically chosen because they are expected to confer protection for other species assumed to have similar ecological requirements. Despite its popularity and substantial history, the value of the umbrella species concept has come into question because umbrella species chosen using heuristic methods, such as body or home range size, are not acting as adequate proxies for the metrics of interest: species richness or population abundance in a multi-species community for which protection is sought. How species associate with habitat across ecological scales has important implications for understanding population size and species richness, and therefore may be a better proxy for choosing an umbrella species. We determined the spatial scales of ecological neighborhoods important for predicting abundance of 8 potential umbrella species breeding in Nebraska using Bayesian latent indicator scale selection in N-mixture models accounting for imperfect detection. We compare the conservation value measured as collective avian abundance under different umbrella species selected following commonly used criteria and selected based on identifying spatial land cover characteristics within ecological neighborhoods that maximize collective abundance. Using traditional criteria to select an umbrella species resulted in sub-maximal expected collective abundance in 86% of cases compared to selecting an umbrella species based on land cover characteristics that maximized collective abundance directly. We conclude that directly assessing the expected quantitative outcomes, rather than ecological proxies, is likely the most efficient method to maximize the potential for conservation success under the umbrella species concept.

Biological Conservation↗
USGS2025

A new groundwater energy transport model for the MODFLOW hydrologic simulator

Heat transport in the subsurface is an important aspect of research related to the effects of a warming climate on ecological services (i.e., cold-water refugia); the development of geothermal resources for energy banking schemes (i.e., aquifer thermal energy storage [ATES]); and the effects of temperature on other aspects of groundwater quality, such as nutrient cycling. Historically, simulation of heat transport using the MODFLOW groundwater simulator and related codes was performed by scaling the input parameters of a solute-transport model to emulate heat transport. However, that approach required additional pre- and post-processing of input and output and could not account for the variation in effective thermal storage and transport properties during transient, unsaturated flow, for example. True heat-transport capabilities in the context of MODFLOW were first introduced in a variant called USG-Transport. More recently, a new groundwater energy-transport (GWE) model type has been added to MODFLOW 6, the core version of the MODFLOW hydrologic simulator. GWE supports the simulation of heat transport on structured or unstructured grids as well as within and between features of advanced packages that represent streams, lakes, multi-aquifer wells, and the unsaturated zone. GWE is integrated within MODFLOW 6 and is accessible through the FloPy Python package and the MODFLOW 6 application programming interface (API). An example simulation demonstrates conduction between grid cells through both the water and the solid aquifer material, including thermal bleeding from saturated overburden cells into a groundwater flow field.

Groundwater↗
USGS2019

The ecology of chronic wasting disease in wildlife

Prions are misfolded infectious proteins responsible for a group of fatal neurodegenerative diseases termed transmissible spongiform encephalopathy or prion diseases. Chronic Wasting Disease (CWD) is the prion disease with the highest spillover potential, affecting at least seven Cervidae (deer) species. The zoonotic potential of CWD is inconclusive and cannot be ruled out. A risk of infection for other domestic and wildlife species is also plausible. Here, we review the current status of the knowledge with respect to CWD ecology in wildlife. Our current understanding of the geographic distribution of CWD lacks spatial and temporal detail, does not consider the biogeography of infectious diseases, and is largely biased by sampling based on hunters' cooperation and funding available for each region. Limitations of the methods used for data collection suggest that the extent and prevalence of CWD in wildlife is underestimated. If the zoonotic potential of CWD is confirmed in the short term, as suggested by recent results obtained in experimental animal models, there will be limited accurate epidemiological data to inform public health. Research gaps in CWD prion ecology include the need to identify specific biological characteristics of potential CWD reservoir species that better explain susceptibility to spillover, landscape and climate configurations that are suitable for CWD transmission, and the magnitude of sampling bias in our current understanding of CWD distribution and risk. Addressing these research gaps will help anticipate novel areas and species where CWD spillover is expected, which will inform control strategies. From an ecological perspective, control strategies could include assessing restoration of natural predators of CWD reservoirs, ultrasensitive CWD detection in biotic and abiotic reservoirs, and deer density and landscape modification to reduce CWD spread and prevalence.

Biological Reviews↗
USGS2009

Inference about species richness and community structure using species-specific occupancy models in the National Swiss Breeding Bird Survey MUB

Species richness is the most widely used biodiversity measure. Virtually always, it cannot be observed but needs to be estimated because some species may be present but remain undetected. This fact is commonly ignored in ecology and management, although it will bias estimates of species richness and related parameters such as occupancy, turnover or extinction rates. We describe a species community modeling strategy based on species-specific models of occurrence, from which estimates of important summaries of community structure, e.g., species richness, occupancy, or measures of similarity among species or sites, are derived by aggregating indicators of occurrence for all species observed in the sample, and for the estimated complement of unobserved species. We use data augmentation for an efficient Bayesian approach to estimation and prediction under this model based on MCMC in WinBUGS. For illustration, we use the Swiss breeding bird survey (MHB) that conducts 2?3 territory-mapping surveys in a systematic sample of 267 1 km2 units on quadrat-specific routes averaging 5.1 km to obtain species-specific estimates of occupancy, and estimates of species richness of all diurnal species free of distorting effects of imperfect detectability. We introduce into our model species-specific covariates relevant to occupancy (elevation, forest cover, route length) and sampling (season, effort). From 1995 to 2004, 185 diurnal breeding bird species were known in Switzerland, and an additional 13 bred 1?3 times since 1900. 134 species were observed during MHB surveys in 254 quadrats surveyed in 2001, and our estimate of 169.9 (95% CI 151?195) therefore appeared sensible. The observed number of species ranged from 4 to 58 (mean 32.8), but with an estimated 0.7?11.2 (mean 2.6) further, unobserved species, the estimated proportion of detected species was 0.48?0.98 (mean 0.91). As is well known, species richness declined at higher elevation and fell above the timberline, and most species showed some preferred elevation. Route length had clear effects on occupancy, suggesting it is a proxy for the size of the effectively sampled area. Detection probability of most species showed clear seasonal patterns and increased with greater survey effort; these are important results for the planning of focused surveys. The main benefit of our model, and its implementation in WinBUGS for which we provide code, is its conceptual simplicity. Species richness is naturally expressed as the sum of occurrences of individual species. Information about species is combined across sites, which yields greater efficiency or may even enable estimation for sites with very few observed species in the first place. At the same time, species detections are clearly segregated into a true state process (occupancy) and an observation process (detection, given occupancy), and covariates can be readily introduced, which provides for efficient introduction of such additional information as well as sharp testing of such relationships.

Book chapter↗
USGS2011

Integrating estimates of ecosystem services from conservation programs and practices into models for decision makers

Most government agencies involved in land management are seeking consistent approaches to evaluate the effects of specific management actions on ecological processes and concurrent changes on ecosystem services. This is especially true within the context of anthropogenic influences, such as land use and climate change. The Conservation Effects Assessment Project—Wetlands National Component (CEAP–Wetlands) was developed by the U.S. Department of Agriculture (USDA) to evaluate effects of conservation practices on ecosystem services including carbon sequestration for climate stability, groundwater recharge, runoff and flood attenuation, water storage, nutrient and contaminant retention, and wildlife habitat. A primary purpose of CEAP–Wetlands is to provide science‐based information in an adaptive monitoring framework for use by the USDA to facilitate policy and management decisions, and to document effects of conservation programs and practices to the federal Office of Management and Budget. Herein, we propose a modeling framework to allow estimation of conservation practice and program effects on various ecosystem services at different temporal and spatial scales. This modeling approach provides the broad view needed by decision‐makers to avoid unintended negative environmental outcomes, and to communicate to society the positive effects of conservation actions on a broad suite of ecosystem services.

Ecological Applications↗
USGS2010

Faunal assemblages and multi-scale habitat patterns in headwater tributaries of the South Fork Trinity River - an unregulated river embedded within a multiple-use landscape

Headwaters can represent 80% of stream kilometers in a watershed, and they also have unique physical and biological properties that have only recently been recognized for their importance in sustaining healthy functioning stream networks and their ecological services. We sampled 60 headwater tributaries in the South Fork Trinity River, a 2,430 km2, mostly forested, multiple-use watershed in northwestern California. Our objectives were: (1) to differentiate unique headwater types using 69 abiotic and vegetation variables measured at three spatial scales, and then to reduce these to informative subsets; (2) determine if distinct biota occupied the different tributary types; (3) determine the environmental attributes associated with the presence and abundance of these biotic assemblages; and (4) using niche modeling, determine key attribute thresholds to illustrate how these biota could be employed as metrics of system integrity and ecological services. Several taxa were sufficiently abundant and widespread to use as bio-indicators: the presence and abundance of steelhead trout (Oncorhynchus mykiss), herpetofauna (reptile and amphibian) species richness, and signal crayfish (Pacifastacus leniusculus) represented different trophic positions, value as commercial resources (steelhead), sensitivity to environmental stress (amphibians), and indicators of biodiversity (herpetofauna species richness). Herpetofauna species richness did not differ, but abundances of steelhead trout, signal crayfish, and amphibian richness all differed significantly among tributary types. Niche models indicated that distribution and abundance patterns in both riparian and aquatic environments were associated with physical and structural attributes at multiple spatial scales, both within and around reaches. The bio-indicators responded to unique sets of attributes, reflecting the high environmental heterogeneity in headwater tributaries across this large watershed. These niche attributes represented a wide range of headwater environments, indicating responses to a number of natural and anthropogenic conditions, and demonstrated the value of using a suite of bio-indicators to elucidate watershed conditions, and to examine numerous disturbances that may influence ecological integrity.

Animal Biodiversity and Conservation↗
USGS2017

Pinyon and juniper encroachment into sagebrush ecosystems impacts distribution and survival of greater sage-grouse

In sagebrush ( Artemisia spp.) ecosystems, encroachment of pinyon ( Pinus spp.) and juniper ( Juniperus spp.; hereafter, “pinyon-juniper”) trees has increased dramatically since European settlement. Understanding the impacts of this encroachment on behavioral decisions, distributions, and population dynamics of greater sage-grouse (Centrocercus urophasianus) and other sagebrush obligate species could help benefit sagebrush ecosystem management actions. We employed a novel two-stage Bayesian model that linked avoidance across different levels of pinyon-juniper cover to sage-grouse survival. Our analysis relied on extensive telemetry data collected across 6 yr and seven subpopulations within the Bi-State Distinct Population Segment (DPS), on the border of Nevada and California. The first model stage indicated avoidance behavior for all canopy cover classes on average, but individual grouse exhibited a high degree of heterogeneity in avoidance behavior of the lowest cover class (e.g., scattered isolated trees). The second stage modeled survival as a function of estimated avoidance parameters and indicated increased survival rates for individuals that exhibited avoidance of the lowest cover class. A post hoc frailty analysis revealed the greatest increase in hazard (i.e., mortality risk) occurred in areas with scattered isolated trees consisting of relatively high primary plant productivity. Collectively, these results provide clear evidence that local sage-grouse distributions and demographic rates are influenced by pinyon-juniper, especially in habitats with higher primary productivity but relatively low and seemingly benign tree cover. Such areas may function as ecological traps that convey attractive resources but adversely affect population vital rates. To increase sage-grouse survival, our model predictions support reducing actual pinyon-juniper cover as low as 1.5%, which is lower than the published target of 4.0%. These results may represent effects of pinyon-juniper cover in areas with similar ecological conditions to those of the Bi-State DPS, where populations occur at relatively high elevations and pinyon-juniper is abundant and widespread.

Rangeland Ecology and Management↗
USGS2012

Experimental and environmental factors affect spurious detection of ecological thresholds

Threshold detection methods are increasingly popular for assessing nonlinear responses to environmental change, but their statistical performance remains poorly understood. We simulated linear change in stream benthic macroinvertebrate communities and evaluated the performance of commonly used threshold detection methods based on model fitting (piecewise quantile regression [PQR]), data partitioning (nonparametric change point analysis [NCPA]), and a hybrid approach (significant zero crossings [SiZer]). We demonstrated that false detection of ecological thresholds (type I errors) and inferences on threshold locations are influenced by sample size, rate of linear change, and frequency of observations across the environmental gradient (i.e., sample-environment distribution, SED). However, the relative importance of these factors varied among statistical methods and between inference types. False detection rates were influenced primarily by user-selected parameters for PQR (τ) and SiZer (bandwidth) and secondarily by sample size (for PQR) and SED (for SiZer). In contrast, the location of reported thresholds was influenced primarily by SED. Bootstrapped confidence intervals for NCPA threshold locations revealed strong correspondence to SED. We conclude that the choice of statistical methods for threshold detection should be matched to experimental and environmental constraints to minimize false detection rates and avoid spurious inferences regarding threshold location.

Ecology↗
USGS2008

Integrating modelling and remote sensing to identify ecosystem performance anomalies in the boreal forest, Yukon River Basin, Alaska

High-latitude ecosystems are exposed to more pronounced warming effects than other parts of the globe. We develop a technique to monitor ecological changes in a way that distinguishes climate influences from disturbances. In this study, we account for climatic influences on Alaskan boreal forest performance with a data-driven model. We defined ecosystem performance anomalies (EPA) using the residuals of the model and made annual maps of EPA. Most areas (88%) did not have anomalous ecosystem performance for at least 6 of 8 years between 1996 and 2004. Areas with underperforming EPA (10%) often indicate areas associated with recent fires and areas of possible insect infestation or drying soil related to permafrost degradation. Overperforming areas (2%) occurred in older fire recovery areas where increased deciduous vegetation components are expected. The EPA measure was validated with composite burn index data and Landsat vegetation indices near and within burned areas.

International Journal of Digital Earth↗
USGS2014

Scaling up watershed model parameters: flow and load simulations of the Edisto River Basin, South Carolina, 2007-09

As part of an ongoing effort by the U.S. Geological Survey to expand the understanding of relations among hydrologic, geochemical, and ecological processes that affect fish-tissue mercury concentrations within the Edisto River Basin, analyses and simulations of the hydrology of the Edisto River Basin were made using the topography-based hydrological model (TOPMODEL). A primary focus of the investigation was to assess the potential for scaling up a previous application of TOPMODEL for the McTier Creek watershed, which is a small headwater catchment to the Edisto River Basin. Scaling up was done in a step-wise manner, beginning with applying the calibration parameters, meteorological data, and topographic-wetness-index data from the McTier Creek TOPMODEL to the Edisto River TOPMODEL. Additional changes were made for subsequent simulations, culminating in the best simulation, which included meteorological and topographic wetness index data from the Edisto River Basin and updated calibration parameters for some of the TOPMODEL calibration parameters. The scaling-up process resulted in nine simulations being made. Simulation 7 best matched the streamflows at station 02175000, Edisto River near Givhans, SC, which was the downstream limit for the TOPMODEL setup, and was obtained by adjusting the scaling factor, including streamflow routing, and using NEXRAD precipitation data for the Edisto River Basin. The Nash-Sutcliffe coefficient of model-fit efficiency and Pearson’s correlation coefficient for simulation 7 were 0.78 and 0.89, respectively. Comparison of goodness-of-fit statistics between measured and simulated daily mean streamflow for the McTier Creek and Edisto River models showed that with calibration, the Edisto River TOPMODEL produced slightly better results than the McTier Creek model, despite the substantial difference in the drainage-area size at the outlet locations for the two models (30.7 and 2,725 square miles, respectively). Along with the TOPMODEL hydrologic simulations, a visualization tool (the Edisto River Data Viewer) was developed to help assess trends and influencing variable in the stream ecosystem. Incorporated into the visualization tool were the water-quality load models TOPLOAD, TOPLOAD–H, and LOADEST. Because the focus of this investigation was on scaling up the models from McTier Creek, water-quality concentrations that were previously collected in the McTier Creek Basin were used in the water-quality load models.

South Carolina↗
USGS2001

Population dynamics and the ecological stability of obligate pollination mutualisms

Mutualistic interactions almost always produce both costs and benefits for each of the interacting species. It is the difference between gross benefits and costs that determines the net benefit and the per-capita effect on each of the interacting populations. For example, the net benefit of obligate pollinators, such as yucca and senita moths, to plants is determined by the difference between the number of ovules fertilized from moth pollination and the number of ovules eaten by the pollinator's larvae. It is clear that if pollinator populations are large, then, because many eggs are laid, costs to plants are large, whereas, if pollinator populations are small, gross benefits are low due to lack of pollination. Even though the size and dynamics of the pollinator population are likely to be crucial, their importance has been neglected in the investigation of mechanisms, such as selective fruit abortion, that can limit costs and increase net benefits. Here, we suggest that both the population size and dynamics of pollinators are important in determining the net benefits to plants, and that fruit abortion can significantly affect these. We develop a model of mutualism between populations of plants and their pollinating seed-predators to explore the ecological consequences of fruit abortion on pollinator population dynamics and the net effect on plants. We demonstrate that the benefit to a plant population is unimodal as a function of pollinator abundance, relative to the abundance of flowers. Both selective abortion of fruit with eggs and random abortion of fruit, without reference to whether they have eggs or not, can limit pollinator population size. This can increase the net benefits to the plant population by limiting the number of eggs laid, if the pollination rate remains high. However, fruit abortion can possibly destabilize the pollinator population, with negative consequences for the plant population.

Oecologia↗
USGS1999

Robustness of closed capture-recapture methods to violations of the closure assumption

Closed-population capture–recapture methods have been used extensively in animal ecology, both by themselves and within the context of Pollock’s robust design and multistate models, to estimate various parameters of population and community dynamics. The defining assumption of geographic and demographic closure (i.e., no births, deaths, immigration, or emigration) for the duration of sampling is restrictive and is likely to be violated in many field situations. I evaluated several types of violations of the closure assumption and found that completely random movement in and out of a study area does not introduce bias to estimators from closed-population methods, although it decreases precision. In addition, if capture probabilities vary only with time, the closed-population Lincoln-Petersen estimator is unbiased for the size of the superpopulation when there are only births/immigration or only deaths/emigration. However, for other cases of nonrandom movement, closed-population estimators were biased when movement was Markovian (dependent on the presence/absence of the animal in the previous time period), when an animal was allowed one entry to and one exit from the study area, or when there was trap response or heterogeneity among animals in capture probability. In addition, the probability that an animal is present and available for capture (e.g., breeding propensity) can be estimated using Pollock’s robust design only when movement occurs at a broader temporal scale than that of sampling.

Ecology↗
USGS2004

Selenium loading through the Blackfoot River watershed--linking sources to ecosystem

The upper Blackfoot River watershed in southeast Idaho receives drainage from 11 of 16 phosphate mines that have extracted ore from the Phosphoria Formation, three of which are presently active. Toxic effects from selenium (Se), including death of livestock and deformity in aquatic birds, were documented locally in areas where phosphatic shales are exposed ( Piper et al., 2000 ; Presser et al., Chapter 11). Current drainage conditions are leading to Se bioaccumulation at concentrations that pose a risk to fish in the Blackfoot River and its tributaries (Hamilton et al., Chapter 18). A gaging station on the Blackfoot River was re-activated in April 2001 to assess hydrologic conditions and concentration, load, and speciation for Se discharges on a watershed scale. The gaging-station data are considered to represent regional drainage conditions in the upper Blackfoot River water- shed because of its location near the outlet of the watershed and directly upstream of the Blackfoot Reservoir. Watershed discharges for 2001 and 2002 were below minimum hydrologic conditions for the gage as documented by the historical record. Drought emergencies were declared in the area in both 2001 and 2002. Unmonitored diversions for irrigation that routinely take place during the snowmelt season also affected conditions downstream. Annual cycles in Se concentration, load, and selenate (Se 6+ ) reached maxima in the spring during the period of maximum flow at the gaging station. Thirty-seven to 44% of annual flow occurred dur- ing the three-month high-flow season (April through June) in 2001 and 56% of annual flow occurred during that time period in 2002. Extrapolation from historical hydrographs for average and wet years and a limited data set of regional Se concentrations for 2001 and 2002 indicated potential for a 3.6- to 7.4-fold increase in Se loading because of increased seasonal flows in the Blackfoot River watershed. Supplementation data indicate that: (a) the difference between total Se and dissolved Se, as a measure of the contribution of particulate Se, was < 10% except at the peak of con- centration when total Se was 18% more than dissolved Se; (b) selenite (Se 4+ ) represented less than 10% of the dissolved species during all months of 2001; and (c) dissolved Se was approximately a 50:50 mixture of selenate and organic selenide (operationally defined Se 2- ) during summer 2001 (June through August). Ecological risk based on regional Se drainage occurred during both the high- and low-flow seasons. Seventy to 83% of the Se load occurred during the high-flow season. During early May of both years, dissolved-Se concentrations exceeded the criterion for the protection of aquatic life and the ecological threshold of 5 gL 1 Se at which sub- stantive risk occurs. During the majority of the three-month high-flow season, dissolved- Se concentrations exceeded the 2 gL 1 Se concern level for aquatic biota. The Se concentration in suspended material during high flow in 2002 was within the range of marginal risk to aquatic life (2-4 gg 1 Se, dry weight). Selenate was the major species during peak flows, with both selenate and organic selenide being major species during relatively low-flow periods in summer. A change in speciation to reduced Se may indicate elevated biotic productivity during summer months and could result in enhanced Se uptake in food webs. In addition to the magnitude of regional Se release in the Blackfoot River watershed, Se concentrations in individual source drains and waste-rock seeps, and those predicted by experimental column leaching of proposed mining overburden materials, also indicate that drainage options that currently meet existing demands for phosphate mining cause eco- logical risk thresholds to be exceeded. At times, the drinking-water Se standard (50 g L 1 Se) and the criterion for hazardous Se waste (1000 L -1 Se) ( US Department of the Interior, 1998 ; US Environmental Protection Agency, 1987 ) are also exceeded. For water-years 2001 and 2002, seasonal increased input of water in the mining area resulted in increased Se transport, suggesting a mechanism of contamination that involves a significant Se reservoir. Hence, recognition and monitoring of Se loading to the envi- ronment on a mass balance basis (i.e. inputs, fluxes and storage within environmental media, and outputs) are essential to evaluating how to control Se concentrations within environmentally protective ranges ( Presser and Piper, 1998 ). In areas where release of Se to aquatic systems is anticipated as a product of future expansion of phosphate mining, continuous monitoring of flow and development of seasonal Se loading patterns would help to model watersheds in terms of sources, flow periods, and environmental-Se con- centrations that most influence bioavailability. These data, in turn, could be linked to Se- bioaccumulation models specific to food webs and vulnerable species of the impacted areas to accurately project ecological effects. Gaging at this site on the Blackfoot River is planned to continue in order to establish a long-term (>10 year) record of hydrologic conditions.

Idaho↗
USGS2026

Sediment vulnerability ranking for river basins across the contiguous U.S. using integrated biophysical and social-ecological data

Nearly half of streams and rivers in the United States (U.S.) have fair to poor conditions for aquatic ecosystem health as declared by the U.S. Environmental Protection Agency, and sediment was identified as a primary factor impairing these waterbodies. We rank sediment vulnerability using physical, ecological, and socioeconomic datasets in 163 4-digit Hydrologic Unit Codes within 18 regions across the contiguous U.S. based on the National Hydrography Dataset. We refer to sediment vulnerability as the potential for excess sediment to adversely impact social-ecological aspects of streams and rivers. Metrics were organized into six variable groups: (1) geomorphic setting, (2) modeled sediment and phosphorus, (3) hydrologic conditions, (4) anthropogenic factors and land use, (5) socioeconomic considerations, and (6) measures of ecosystem sensitivity. Correlations and principal component analysis were used to reduce and select metrics that explained the highest variability across basins. Results from the regional analysis identify basins with higher sediment vulnerability in each region, whereas national results identify hotspots of highly ranked basins in the Midwest and the central U.S. with a concentration of moderately to highly ranked basins in the south-central and southwestern U.S. Indicators of socioeconomic and ecological vulnerability integrated with physical drivers of sediment production help identify important gaps in sediment monitoring and research.

contiguous United States↗
USGS2021

Integrating observations and models to determine the effect of seasonally frozen ground on hydrologic partitioning in alpine hillslopes in the Colorado Rocky Mountains, USA

This study integrated spatially distributed field observations and soil thermal models to constrain the impact of frozen ground on snowmelt partitioning and streamflow generation in an alpine catchment within the Niwot Ridge Long-Term Ecological Research site, Colorado, USA. The study area was comprised of two contrasting hillslopes with notable differences in topography, snow depth and plant community composition. Time-lapse electrical resistivity surveys and soil thermal models enabled extension of discrete soil moisture and temperature measurements to incorporate landscape variability at scales and depths not possible with point measurements alone. Specifically, heterogenous snowpack thickness (~0–4 m) and soil volumetric water content between hillslopes (~0.1–0.45) strongly influenced the depths of seasonal frost, and the antecedent soil moisture available to form pore ice prior to freezing. Variable frost depths and antecedent soil moisture conditions were expected to create a patchwork of differing snowmelt infiltration rates and flowpaths. However, spikes in soil temperature and volumetric water content, as well as decreases in subsurface electrical resistivity revealed snowmelt infiltration across both hillslopes that coincided with initial decreases in snow water equivalent and early increases in streamflow. Soil temperature, soil moisture and electrical resistivity data from both wet and dry hillslopes showed that initial increases in streamflow occurred prior to deep soil water flux. Temporal lags between snowmelt infiltration and deeper percolation suggested that the lateral movement of water through the unsaturated zone was an important driver of early streamflow generation. These findings provide the type of process-based information needed to bridge gaps in scale and populate physically based cryohydrologic models to investigate subsurface hydrology and biogeochemical transport in soils that freeze seasonally.

Colorado↗
USGS2016

FishVis, A regional decision support tool for identifying vulnerabilities of riverine habitat and fishes to climate change in the Great Lakes Region

Climate change is expected to alter the distributions and community composition of stream fishes in the Great Lakes region in the 21st century, in part as a result of altered hydrological systems (stream temperature, streamflow, and habitat). Resource managers need information and tools to understand where fish species and stream habitats are expected to change under future conditions. Fish sample collections and environmental variables from multiple sources across the United States Great Lakes Basin were integrated and used to develop empirical models to predict fish species occurrence under present-day climate conditions. Random Forests models were used to predict the probability of occurrence of 13 lotic fish species within each stream reach in the study area. Downscaled climate data from general circulation models were integrated with the fish species occurrence models to project fish species occurrence under future climate conditions. The 13 fish species represented three ecological guilds associated with water temperature (cold, cool, and warm), and the species were distributed in streams across the Great Lakes region. Vulnerability (loss of species) and opportunity (gain of species) scores were calculated for all stream reaches by evaluating changes in fish species occurrence from present-day to future climate conditions. The 13 fish species included 4 cold-water species, 5 cool-water species, and 4 warm-water species. Presently, the 4 cold-water species occupy from 15 percent (55,000 kilometers [km]) to 35 percent (130,000 km) of the total stream length (369,215 km) across the study area; the 5 cool-water species, from 9 percent (33,000 km) to 58 percent (215,000 km); and the 4 warm-water species, from 9 percent (33,000 km) to 38 percent (141,000 km). Fish models linked to projections from 13 downscaled climate models projected that in the mid to late 21st century (2046–65 and 2081–2100, respectively) habitats suitable for all 4 cold-water species and 4 of 5 cool-water species under present-day conditions will decline as much as 86 percent and as little as 33 percent, and habitats suitable for all 4 warm-water species will increase as much as 33 percent and as little as 7 percent. This report documents the approach and data used to predict and project fish species occurrence under present-day and future climate conditions for 13 lotic fish species in the United States Great Lakes Basin. A Web-based decision support mapping application termed “FishVis” was developed to provide a means to integrate, visualize, query, and download the results of these projected climate-driven responses and help inform conservation planning efforts within the region.

Great Lakes Region↗
USGS2020

Ecological prediction at macroscales using big data: Does sampling design matter?

Although ecosystems respond to global change at regional to continental scales (i.e., macroscales), model predictions of ecosystem responses often rely on data from targeted monitoring of a small proportion of sampled ecosystems within a particular geographic area. In this study, we examined how the sampling strategy used to collect data for such models influences predictive performance. We subsampled a large and spatially-extensive dataset to investigate how macroscale sampling strategy affects prediction of ecosystem characteristics in 6,784 lakes across a 1.8 million km2 area. We estimated model predictive performance for different subsets of the dataset to mimic three common sampling strategies for collecting observations of ecosystem characteristics: random sampling design, stratified random sampling design, and targeted sampling. We found that sampling strategy influenced model predictive performance such that (1) stratified random sampling designs did not improve predictive performance compared to simple random sampling designs and (2) although one of the scenarios that mimicked targeted (non-random) sampling had the poorest performing predictive models, the other targeted sampling scenarios resulted in models with similar predictive performance to that of the random sampling scenarios. Our results suggest that although potential biases in datasets from some forms of targeted sampling may limit predictive performance, compiling existing spatially-extensive datasets can result in models with good predictive performance that may inform a wide range of science questions and policy goals related to global change.

Ecological Applications↗
USGS2025

The influence of scale in modeling social vulnerability and disaster assistance

Understanding how social vulnerability relates to disaster impacts is critical for addressing social equity, yet the role of spatial scale in this relationship is often overlooked. Most studies use aggregated data, risking ecological fallacy—misinterpreting individual outcomes from group-level data. This study examines how spatial scale influences the relationship between social vulnerability and federal disaster assistance after Hurricane Harvey. Using spatial econometric models at both household and census tract levels, we assessed the strength of key vulnerability indicators in explaining disaster assistance. Results show that disability, housing tenure, household size, and income predict assistance at the household level, but their influence shifts across scales. Income and disability remain strong predictors at the tract level, whereas housing factors weaken or reverse. These findings suggest that using aggregated data to model household-level relationships between social vulnerability and disaster assistance can distort understanding of vulnerability processes, potentially leading to misinformed disaster policies and inequitable outcomes. Our findings have implications for disaster management, but the primary contribution of this study is methodological, in providing a critical evaluation of how spatial scale and data aggregation shape the statistical interpretation of social vulnerability and aid distribution.

Texas↗