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At least 19 recordsLinked to original sources
USGS2009

Tsunami exposure estimation with land-cover data: Oregon and the Cascadia subduction zone

A Cascadia subduction-zone earthquake has the potential to generate tsunami waves which would impact more than 1000 km of coastline on the west coast of the United States and Canada. Although the predictable extent of tsunami inundation is similar for low-lying land throughout the region, human use of tsunami-prone land varies, creating variations in community exposure and potential impacts. To better understand such variations, land-cover information derived from midresolution remotely-sensed imagery (e.g., 30-m-resolution Landsat Thematic Mapper imagery) was coupled with tsunami-hazard information to describe tsunami-prone land along the Oregon coast. Land-cover data suggest that 95% of the tsunami-prone land in Oregon is undeveloped and is primarily wetlands and unconsolidated shores. Based on Spearman rank correlation coefficients (r s ), correlative relationships are strong and statistically significant (p < 0.05) between city-level estimates of the amount of land-cover pixels classified as developed (impervious cover greater than 20%) and the amount of various societal assets, including residential and employee populations, homes, businesses, and tax-parcel values. Community exposure to tsunami hazards, described here by the amount and relative percentage of developed land in tsunami-prone areas, varies considerably among the 26 communities of the study area, and these variations relate to city size. Correlative relationships are strong and significant (p < 0.05) for community exposure rankings based on land-cover data and those based on aggregated socioeconomic data. In the absence of socioeconomic data or community-based knowledge, the integration of hazards information and land-cover information derived from midresolution remotely-sensed imagery to estimate community exposure may be a useful first step in understanding variations in community vulnerability to regional hazards.

Applied Geography↗
USGS2010

Influence of potential sea level rise on societal vulnerability to hurricane storm-surge hazards, Sarasota County, Florida

Although the potential for hurricanes under current climatic conditions continue to threaten coastal communities, there is concern that climate change, specifically potential increases in sea level, could influence the impacts of future hurricanes. To examine the potential effect of sea level rise on community vulnerability to future hurricanes, we assess variations in socioeconomic exposure in Sarasota County, FL, to contemporary hurricane storm-surge hazards and to storm-surge hazards enhanced by sea level rise scenarios. Analysis indicates that significant portions of the population, economic activity, and critical facilities are in contemporary and future hurricane storm-surge hazard zones. The addition of sea level rise to contemporary storm-surge hazard zones effectively causes population and asset (infrastructure, natural resources, etc) exposure to be equal to or greater than what is in the hazard zone of the next higher contemporary Saffir&ndash;Simpson hurricane category. There is variability among communities for this increased exposure, with greater increases in socioeconomic exposure due to the addition of sea level rise to storm-surge hazard zones as one progresses south along the shoreline. Analysis of the 2050 comprehensive land use plan suggests efforts to manage future growth in residential, economic and infrastructure development in Sarasota County may increase societal exposure to hurricane storm-surge hazards.

Florida↗
USGS2010

Stakeholder perspectives on land-use strategies for adapting to climate-change-enhanced coastal hazards: Sarasota, Florida

Sustainable land-use planning requires decision makers to balance community growth with resilience to natural hazards. This balance is especially difficult in many coastal communities where planners must grapple with significant growth projections, the persistent threat of extreme events (e.g., hurricanes), and climate-change-driven sea level rise that not only presents a chronic hazard but also alters the spatial extent of sudden-onset hazards such as hurricanes. We examine these stressors on coastal, long-term land-use planning by reporting the results of a one-day community workshop held in Sarasota County, Florida that included focus groups and participatory mapping exercises. Workshop participants reflected various political agendas and socioeconomic interests of five local knowledge domains: business, environment, emergency management and infrastructure, government, and planning. Through a series of alternating domain-specific focus groups and interactive plenary sessions, participants compared the county 2050 comprehensive land-use plan to maps of contemporary hurricane storm-surge hazard zones and projected storm-surge hazard zones enlarged by sea level rise scenarios. This interactive, collaborative approach provided each group of domain experts the opportunity to combine geographically-specific, scientific knowledge on natural hazards and climate change with local viewpoints and concerns. Despite different agendas, interests, and proposed adaptation strategies, there was common agreement among participants for the need to increase community resilience to contemporary hurricane storm-surge hazards and to explore adaptation strategies to combat the projected, enlarged storm-surge hazard zones.

Florida↗
USGS2011

A GIS application for assessing, mapping, and quantifying the social values of ecosystem services

As human pressures on ecosystems continue to increase, research involving the effective incorporation of social values information into the context of comprehensive ecosystem services assessments is becoming more important. Including quantified, spatially explicit social value metrics in such assessments will improve the analysis of relative tradeoffs among ecosystem services. This paper describes a GIS application, So cia l V alues for E cosystem S ervices (SolVES), developed to assess, map, and quantify the perceived social values of ecosystem services by deriving a non-monetary Value Index from responses to a public attitude and preference survey. SolVES calculates and maps the Value Index for social values held by various survey subgroups, as distinguished by their attitudes regarding ecosystem use. Index values can be compared within and among survey subgroups to explore the effect of social contexts on the valuation of ecosystem services. Index values can also be correlated and regressed against landscape metrics SolVES calculates from various environmental data layers. Coefficients derived through these analyses were applied to their corresponding data layers to generate a predicted social value map. This map compared favorably with other SolVES output and led to the addition of a predictive mapping function to SolVES for value transfer to areas where survey data are unavailable. A more robust application is being developed as a public domain tool for decision makers and researchers to map social values of ecosystem services and to facilitate discussions among diverse stakeholders involving relative tradeoffs among different ecosystem services in a variety of physical and social contexts.

Applied Geography↗
USGS2011

Landscape unit based digital elevation model development for the freshwater wetlands within the Arthur C. Marshall Loxahatchee National Wildlife Refuge, Southeastern Florida

The hydrologic regime is a critical limiting factor in the delicate ecosystem of the greater Everglades freshwater wetlands in south Florida that has been severely altered by management activities in the past several decades. "Getting the water right" is regarded as the key to successful restoration of this unique wetland ecosystem. An essential component to represent and model its hydrologic regime, specifically water depth, is an accurate ground Digital Elevation Model (DEM). The Everglades Depth Estimation Network (EDEN) supplies important hydrologic data, and its products (including a ground DEM) have been well received by scientists and resource managers involved in Everglades restoration. This study improves the EDEN DEMs of the Loxahatchee National Wildlife Refuge, also known as Water Conservation Area 1 (WCA1), by adopting a landscape unit (LU) based interpolation approach. The study first filtered the input elevation data based on newly available vegetation data, and then created a separate geostatistical model (universal kriging) for each LU. The resultant DEMs have encouraging cross-validation and validation results, especially since the validation is based on an independent elevation dataset (derived by subtracting water depth measurements from EDEN water surface elevations). The DEM product of this study will directly benefit hydrologic and ecological studies as well as restoration efforts. The study will also be valuable for a broad range of wetland studies.

Florida↗
USGS2011

Characterizing fragmentation of the collective forests in southern China from multitemporal Landsat imagery: A case study from Kecheng district of Zhejiang province

Tropical and subtropical forests provide important ecosystem goods and services including carbon sequestration and biodiversity conservation. These forests are facing increasing socioeconomic pressures and are rapidly being degraded and fragmented. This analysis focuses on the rate of change and patterns of fragmentation in a collective forest area in Zhejiang province, China, during the time period 1988–2005. The research consisted of two parts. The first was the development of general land cover maps and the identification of land cover changes by interpreting Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) time series imagery. The second part involved the computation and analysis of forest fragmentation metrics. For this portion of the study, fragmentation statistics were analyzed, and images were developed to depict forest fragmentation patterns and trends. Results revealed that there was a net loss of 7.8% in forest coverage, dropping from 66.8% in 1988 to 59.0% in 2005, primarily caused by agricultural expansion and poor forest management practices. An acceleration of forest fragmentation was also witnessed during the time intervals, which was evidenced by a decreasing trend in interior forest (57.2% in 1988, 55.0% in 1996 and 54.8% in 2005 respectively) coupled with the scales of the selected geospatial metrics. Continued forest loss and fragmentation are closely correlated with the existing political, educational, institutional and economic processes of contemporary China. To unlock the developmental potentials of the collective forests and to effectively mitigate the rate of forest loss and fragmentation, reforms of forest tenure and ecological immigration practices are recognized as a prospective alternative. The produced fragmentation maps further illustrates the importance of assessing landscape change history, especially the spatiotemporal patterns of forest fragments, when developing landscape level plans for biodiversity conservation, land use management and ecologically sustainable forestry.

Applied Geography↗
USGS2012

Climate change and human health: Spatial modeling of water availability, malnutrition, and livelihoods in Mali, Africa

This study develops a novel approach for projecting climate trends in the Sahel in relation to shifting livelihood zones and health outcomes. Focusing on Mali, we explore baseline relationships between temperature, precipitation, livelihood, and malnutrition in 407 Demographic and Health Survey (DHS) clusters with a total of 14,238 children, resulting in a thorough spatial analysis of coupled climate-health dynamics. Results suggest links between livelihoods and each measure of malnutrition, as well as a link between climate and stunting. A ‘front-line’ of vulnerability, related to the transition between agricultural and pastoral livelihoods, is identified as an area where mitigation efforts might be usefully targeted. Additionally, climate is projected to 2025 for the Sahel, and demographic trends are introduced to explore how the intersection of climate and demographics may shift the vulnerability ‘front-line’, potentially exposing an additional 6 million people in Mali, up to a million of them children, to heightened risk of malnutrition from climate and livelihood changes. Results indicate that, holding constant morbidity levels, approximately one quarter of a million children will suffer stunting, nearly two hundred thousand will be malnourished, and over one hundred thousand will become anemic in this expanding arid zone by 2025. Climate and health research conducted at finer spatial scales and within shorter projected time lines can identify vulnerability hot spots that are of the highest priority for adaptation interventions; such an analysis can also identify areas with similar characteristics that may be at heightened risk. Such meso-scale coupled human-environment research may facilitate appropriate policy interventions strategically located beyond today’s vulnerability front-line.

Applied Geography↗
USGS2012

A land-use and land-cover modeling strategy to support a national assessment of carbon stocks and fluxes

Changes in land use, land cover, disturbance regimes, and land management have considerable influence on carbon and greenhouse gas (GHG) fluxes within ecosystems. Through targeted land-use and land-management activities, ecosystems can be managed to enhance carbon sequestration and mitigate fluxes of other GHGs. National-scale, comprehensive analyses of carbon sequestration potential by ecosystem are needed, with a consistent, nationally applicable land-use and land-cover (LULC) modeling framework a key component of such analyses. The U.S. Geological Survey has initiated a project to analyze current and projected future GHG fluxes by ecosystem and quantify potential mitigation strategies. We have developed a unique LULC modeling framework to support this work. Downscaled scenarios consistent with IPCC Special Report on Emissions Scenarios (SRES) were constructed for U.S. ecoregions, and the FORE-SCE model was used to spatially map the scenarios. Results for a prototype demonstrate our ability to model LULC change and inform a biogeochemical modeling framework for analysis of subsequent GHG fluxes. The methodology was then successfully used to model LULC change for four IPCC SRES scenarios for an ecoregion in the Great Plains. The scenario-based LULC projections are now being used to analyze potential GHG impacts of LULC change across the U.S.

Applied Geography↗
USGS2012

Mapping socio-environmentally vulnerable populations access and exposure to ecosystem services at the U.S.-Mexico borderlands

Socio-environmental vulnerable populations are often unrepresented in land-use planning yet have great potential for loss when exposed to changes in ecosystem services. Administrative boundaries, cultural differences, and language barriers increase the disassociation between land-use management and marginalized populations living in the U.S.&ndash;Mexico borderlands. This paper describes the development of a Modified Socio-Environmental Vulnerability Index (M-SEVI), using determinants from binational census and neighborhood data that describe levels of education, access to resources, migratory status, housing, and number of dependents, to provide a simplified snapshot of the region's populace that can be used in binational planning efforts. We apply this index at the SCW, located on the border between Arizona, USA and Sonora, Mexico. For comparison, the Soil and Water Assessment Tool is concurrently applied to assess the provision of erosion- and flood control services over a 9-year period. We describe how this coupling of data can form the base for an ecosystem services assessment across political boundaries that can be used by land-use planners. Results reveal potential disparities in environmental risks and burdens throughout the binational watershed in residential districts surrounding and between urban centers. The M-SEVI can be used as an important first step in addressing environmental justice for binational decision-making.

Arizona↗
USGS2012

Mapping outdoor recreationists' perceived social values for ecosystem services at Hinchinbrook Island National Park, Australia

Coastal ecosystems are increasingly faced with human impacts. To better understand these changing conditions, biophysical and economic values of nature have been used to prioritize spatial planning efforts and ecosystem-based management of human activities. Less is known, however, about how to characterize and represent non-material values in decision-making. We collected on-site and mailback survey data ( n = 209), and analyzed these data using the Social Values for Ecosystem Services (SolVES) GIS application to incorporate measures of social value and natural resource conditions on Hinchinbrook Island National Park, Australia. Our objectives in this paper are to: 1) determine the spatial distribution and point density of social values for ecosystem services; 2) examine the relationship between social values and natural resource conditions; and 3) compare social value allocations between two subgroups of outdoor recreationists. Results suggest that high priority areas exist on Hinchinbrook's land and seascapes according to the multiple values assigned to places by outdoor recreationists engaged in consumptive (e.g., fishing) and non-consumptive (e.g., hiking) activities. We examine statistically significant spatial clustering across two subgroups of the survey population for three value types that reflect Recreation, Biological Diversity, and Aesthetic qualities. The relationship between the relative importance of social values for ecosystem services and spatially-defined ecological data is explored to guide management decision-making in the context of an island national park setting.

Hinchinbrook Island National Park↗
USGS2013

Conflation and aggregation of spatial data improve predictive models for species with limited habitats: a case of the threatened yellow-billed cuckoo in Arizona, USA

Riparian vegetation provides important wildlife habitat in the Southwestern United States, but limited distributions and spatial complexity often leads to inaccurate representation in maps used to guide conservation. We test the use of data conflation and aggregation on multiple vegetation/land-cover maps to improve the accuracy of habitat models for the threatened western yellow-billed cuckoo (Coccyzus americanus occidentalis). We used species observations (n = 479) from a state-wide survey to develop habitat models from 1) three vegetation/land-cover maps produced at different geographic scales ranging from state to national, and 2) new aggregate maps defined by the spatial agreement of cover types, which were defined as high (agreement = all data sets), moderate (agreement ≥ 2), and low (no agreement required). Model accuracies, predicted habitat locations, and total area of predicted habitat varied considerably, illustrating the effects of input data quality on habitat predictions and resulting potential impacts on conservation planning. Habitat models based on aggregated and conflated data were more accurate and had higher model sensitivity than original vegetation/land-cover, but this accuracy came at the cost of reduced geographic extent of predicted habitat. Using the highest performing models, we assessed cuckoo habitat preference and distribution in Arizona and found that major watersheds containing high-probably habitat are fragmented by a wide swath of low-probability habitat. Focus on riparian restoration in these areas could provide more breeding habitat for the threatened cuckoo, offset potential future habitat losses in adjacent watershed, and increase regional connectivity for other threatened vertebrates that also use riparian corridors.

Arizona↗
USGS2014

Comparing population exposure to multiple Washington earthquake scenarios for prioritizing loss estimation studies

Scenario-based, loss-estimation studies are useful for gauging potential societal impacts from earthquakes but can be challenging to undertake in areas with multiple scenarios and jurisdictions. We present a geospatial approach using various population data for comparing earthquake scenarios and jurisdictions to help emergency managers prioritize where to focus limited resources on data development and loss-estimation studies. Using 20 earthquake scenarios developed for the State of Washington (USA), we demonstrate how a population-exposure analysis across multiple jurisdictions based on Modified Mercalli Intensity (MMI) classes helps emergency managers understand and communicate where potential loss of life may be concentrated and where impacts may be more related to quality of life. Results indicate that certain well-known scenarios may directly impact the greatest number of people, whereas other, potentially lesser-known, scenarios impact fewer people but consequences could be more severe. The use of economic data to profile each jurisdiction’s workforce in earthquake hazard zones also provides additional insight on at-risk populations. This approach can serve as a first step in understanding societal impacts of earthquakes and helping practitioners to efficiently use their limited risk-reduction resources.

Washington↗
USGS2014

Characterizing recent and projecting future potential patterns of mountain pine beetle outbreaks in the Southern Rocky Mountains

The recent widespread mountain pine beetle (MPB) outbreak in the Southern Rocky Mountains presents an opportunity to investigate the relative influence of anthropogenic, biologic, and physical drivers that have shaped the spatiotemporal patterns of the outbreak. The aim of this study was to quantify the landscape-level drivers that explained the dynamic patterns of MPB mortality, and simulate areas with future potential MPB mortality under projected climate-change scenarios in Grand County, Colorado, USA. The outbreak patterns of MPB were characterized by analysis of a decade-long Landsat time-series stack, aided by automatic attribution of change detected by the Landsat-based Detection of Trends in Disturbance and Recovery algorithm (LandTrendr). The annual area of new MPB mortality was then related to a suite of anthropogenic, biologic, and physical predictor variables under a general linear model (GLM) framework. Data from years 2001–2005 were used to train the model and data from years 2006–2011 were retained for validation. After stepwise removal of non-significant predictors, the remaining predictors in the GLM indicated that neighborhood mortality, winter mean temperature anomaly, and residential housing density were positively associated with MPB mortality, whereas summer precipitation was negatively related. The final model had an average area under the curve (AUC) of a receiver operating characteristic plot value of 0.72 in predicting the annual area of new mortality for the independent validation years, and the mean deviation from the base maps in the MPB mortality areal estimates was around 5%. The extent of MPB mortality will likely expand under two climate-change scenarios (RCP 4.5 and 8.5) in Grand County, which implies that the impacts of MPB outbreaks on vegetation composition and structure, and ecosystem functioning are likely to increase in the future.

Colorado↗
USGS2015

Sensitivity of tsunami evacuation modeling to direction and land cover assumptions

Although anisotropic least-cost-distance (LCD) modeling is becoming a common tool for estimating pedestrian-evacuation travel times out of tsunami hazard zones, there has been insufficient attention paid to understanding model sensitivity behind the estimates. To support tsunami risk-reduction planning, we explore two aspects of LCD modeling as it applies to pedestrian evacuations and use the coastal community of Seward, Alaska, as our case study. First, we explore the sensitivity of modeling to the direction of movement by comparing standard safety-to-hazard evacuation times to hazard-to-safety evacuation times for a sample of 3985 points in Seward's tsunami-hazard zone. Safety-to-hazard evacuation times slightly overestimated hazard-to-safety evacuation times but the strong relationship to the hazard-to-safety evacuation times, slightly conservative bias, and shorter processing times of the safety-to-hazard approach make it the preferred approach. Second, we explore how variations in land cover speed conservation values (SCVs) influence model performance using a Monte Carlo approach with one thousand sets of land cover SCVs. The LCD model was relatively robust to changes in land cover SCVs with the magnitude of local model sensitivity greatest in areas with higher evacuation times or with wetland or shore land cover types, where model results may slightly underestimate travel times. This study demonstrates that emergency managers should be concerned not only with populations in locations with evacuation times greater than wave arrival times, but also with populations with evacuation times lower than but close to expected wave arrival times, particularly if they are required to cross wetlands or beaches.

Applied Geography↗
USGS2018

The sensitivity of ecosystem service models to choices of input data and spatial resolution

Although ecosystem service (ES) modeling has progressed rapidly in the last 10–15 years, comparative studies on data and model selection effects have become more common only recently. Such studies have drawn mixed conclusions about whether different data and model choices yield divergent results. In this study, we compared the results of different models to address these questions at national, provincial, and subwatershed scales in Rwanda. We compared results for carbon, water, and sediment as modeled using InVEST and WaSSI using (1) land cover data at 30 and 300 m resolution and (2) three different input land cover datasets. WaSSI and simpler InVEST models (carbon storage and annual water yield) were relatively insensitive to the choice of spatial resolution, but more complex InVEST models (seasonal water yield and sediment regulation) produced large differences when applied at differing resolution. Six out of nine ES metrics (InVEST annual and seasonal water yield and WaSSI) gave similar predictions for at least two different input land cover datasets. Despite differences in mean values when using different data sources and resolution, we found significant and highly correlated results when using Spearman's rank correlation, indicating consistent spatial patterns of high and low values. Our results confirm and extend conclusions of past studies, showing that in certain cases (e.g., simpler models and national-scale analyses), results can be robust to data and modeling choices. For more complex models, those with different output metrics, and subnational to site-based analyses in heterogeneous environments, data and model choices may strongly influence study findings.

Applied Geography↗
USGS2019

Estimating forest canopy cover dynamics in Valles Caldera National Preserve, New Mexico, using LiDAR and Landsat data

Increasing tree canopy cover has led to increasing wildfire activity in conifer dominated areas of the southwestern United States. Estimating historical changes in the spatial distribution of tree canopy cover can provide further insights into the dynamics of forest and fuel conditions in these landscapes and help prioritize areas for restoration to mitigate wildfire risks and restore biological functioning. In this study, we explored the relationship between LiDAR derived canopy cover data and Landsat reflectance values, and derived a model to estimate percent canopy cover (PCC) on historical Landsat data from 1987 to 2015 for the Valles Caldera National Preserve (VCNP), located in the southwest Jemez Mountains of New Mexico. We developed a regression model between LiDAR generated canopy cover collected in June 2010 and Landsat Thematic Mapper (TM) reflectance values (bands 1–7 except band 6) and vegetation indices collected for the same date. About 5% (17,000) of the total LiDAR points (329,102) were used as training points and a separate, non-overlapping set of 17,000 points as test points to validate the regression model. A simple linear model with the red band (band 3; R 2 = 0.70) was selected as the best model to predict PCC in the rest of the images for 1987–2015. In general, we found a strong consistency between the spatial dynamics of modelled tree canopy cover based on historical Landsat data, wildfire events and forest management practicesthat occurred during the same period. Results showed that about 11% of the study area experienced an increase in PCC for the period of 1987–2015 while 41% of the study area experienced a reduction in PCC during the same time period, mostly in the areas which were affected by stand replacing wildfires in 2011 and 2013. The results indicate an overall increase in medium and high canopy cover classes in specific regions of the study area, which could lead to hazardous wildfires such as those in 2011 and 2013. In the context of ongoing ecological restoration of these montane forests, predicted PCC of contemporary forests could help local managers to identify the areas in the need of immediate restoration efforts by focusing management practices on the areas with closed canopy.

New Mexico↗
USGS2024

Identifying priority science information needs for managing public lands

Public lands worldwide provide diverse resources, uses, and values, ranging from wilderness to extractive uses. Decision-making on public lands is complex as a result and is required by law to be informed by science. However, public land managers may not always have the science they need. We developed a methodology for identifying priority science needs for public land management agencies. We relied on two core data sources: environmental effects analyses conducted for agency decisions and legal challenges to those decisions. We considered needs in four categories: data, science, methods, and mitigation measures. We classified topics as primary science needs when (1) the topic was analyzed frequently in agency environmental analyses, (2) our metric of quality/defensibility was low or mitigation measures were frequently included for the topic, and (3) the agency was challenged on its use of science for the topic. We applied our methodology to the Bureau of Land Management—the largest public land manager in the United States—in Colorado, a state with abundant and diverse public lands. Primary identified needs were data on vegetation; science about effects of oil and gas development and livestock grazing on multiple resources, including terrestrial wildlife; methods for analyzing environmental effects for many topics; and mitigation measures for protecting vegetation, soils, water quality, and archaeological and historic resources. Science needs often reflect needs for facilitating and supporting the use of existing science in agency decision-making. Our method can be applied across agencies, geographies, and timeframes to help strengthen science use in public lands decision-making.

Environmental Management↗
USGS2014

Geographic variability in elevation and topographic constraints on the distribution of native and nonnative trout in the Great Basin

Understanding local and geographic factors influencing species distributions is a prerequisite for conservation planning. Our objective in this study was to model local and geographic variability in elevations occupied by native and nonnative trout in the northwestern Great Basin, USA. To this end, we analyzed a large existing data set of trout presence (5,156 observations) to evaluate two fundamental factors influencing occupied elevations: climate-related gradients in geography and local constraints imposed by topography. We applied quantile regression to model upstream and downstream distribution elevation limits for each trout species commonly found in the region (two native and two nonnative species). With these models in hand, we simulated an upstream shift in elevation limits of trout distributions to evaluate potential consequences of habitat loss. Downstream elevation limits were inversely associated with latitude, reflecting regional gradients in temperature. Upstream limits were positively related to maximum stream elevation as expected. Downstream elevation limits were constrained topographically by valley bottom elevations in northern streams but not in southern streams, where limits began well above valley bottoms. Elevation limits were similar among species. Upstream shifts in elevation limits for trout would lead to more habitat loss in the north than in the south, a result attributable to differences in topography. Because downstream distributions of trout in the north extend into valley bottoms with reduced topographic relief, trout in more northerly latitudes are more likely to experience habitat loss associated with an upstream shift in lower elevation limits. By applying quantile regression to relatively simple information (species presence, elevation, geography, topography), we were able to identify elevation limits for trout in the Great Basin and explore the effects of potential shifts in these limits that could occur in response to changing climate conditions that alter streams directly (e.g., through changes in temperature and precipitation) or indirectly (e.g., through changing water use).

California;Idaho;Nevada;Oregon↗