a b s t r a c tIn this article we present EnviroAtlas, a web-based, open access tool that seeks to meet a range of needs by bringing together environmental, economic and demographic data in an ecosystem services framework. Within EnviroAtlas, there are three primary types of geospatial data: research-derived ecosystem services indicator data in their native resolution, indicator data that have been summarized to standard reporting units, and reference data. Reporting units include watershed basins across the contiguous U.S. and Census block groups throughout featured urban areas. EnviroAtlas includes both current and future drivers of change, such as land use and climate, for addressing issues of adaptation, conservation, equity, and resiliency. In addition to geospatial data, EnviroAtlas includes geospatial and statistical tools, and resources that support research, education, and decision-making. With the development of EnviroAtlas, we facilitate the practice of ecosystem services science by providing a framework to track conditions across political boundaries and assess policies and regulations. EnviroAtlas is a robust research and educational resource, with consistent, systems-oriented information to support nationally, regionally, and locally focused decisions.Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Floodplains perform several important ecosystem services, including storing water during precipitation events and reducing peak flows, thus reducing flooding of downstream communities. Understanding the relationship between flood inundation and floodplains is critical for ecosystem and community health and well-being, as well as targeting floodplain and riparian restoration. Many communities in the United States, particularly those in rural areas, lack inundation maps due to the high cost of flood modeling. Only 60% of the conterminous United States has Flood Insurance Rate Maps (FIRMs) through the U.S. Federal Emergency Management Agency (FEMA). We developed a 30-meter resolution flood inundation map of the conterminous United States (CONUS) using random forest classification to fill the gaps in the FIRM. Input datasets included digital elevation model (DEM)-derived variables, flood-related soil characteristics, and land cover. The existing FIRM 100-year floodplains, called the Special Flood Hazard Area (SHFA), were used to train and test the random forests for fluvial and coastal flooding. Models were developed for each hydrologic unit code level four (HUC-4) watershed and each 30-meter pixel in the CONUS was classified as floodplain or non-floodplain. The most important variables were DEM-derivatives and flood-based soil characteristics. Models captured 79% of the SFHA in the CONUS. The overall F1 score, which balances precision and recall, was 0.78. Performance varied geographically, exceeding the CONUS scores in temperate and coastal watersheds but were less robust in the arid southwest. The models also consistently identified headwater floodplains not present in the SFHA, lowering performance measures but providing critical information missing in many low-order stream systems. The performance of the random forest models demonstrates the method's ability to successfully fill in the remaining unmapped floodplains in the CONUS, while using only publicly available data and open source software.

Landscapes are increasingly recognized for providing valuable cultural ecosystem services with numerous non-material benefits by serving as places of rest, relaxation, and inspiration that ultimately improve overall mental health and physical well-being. Maintaining and enhancing these valuable benefits through targeted management and conservation measures requires understanding the spatial and temporal determinants of perceived landscape values. Content contributed through mobile technologies and the web are emerging globally, providing a promising data source for localizing and assessing these landscape benefits. These georeferenced data offer rich in situ qualitative information through photos and comments that capture valued and special locations across large geographic areas. We present a novel method for mapping and modeling landscape values and perceptions that leverages viewshed analysis of georeferenced social media data. Using a high resolution LiDAR (Light Detection and Ranging) derived digital surface model, we are able to evaluate landscape characteristics associated with the visual-sensory qualities of outdoor recreationalists. Our results show the importance of historical monuments and attractions in addition to specific environmental features which are appreciated by the public. Evaluation of photo-image content highlights the opportunity of including temporally and spatially variable visual-sensory qualities in cultural ecosystem services (CES) evaluation like the sights, sounds and smells of wildlife and weather phenomena.

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Wetlands provide key functions in the landscape from improving water quality, to regulating flows, to providing wildlife habitat. Over half of the wetlands in the contiguous United States (CONUS) have been converted to agricultural and urban land uses. However, over the last several decades, research has shown the benefits of wetlands to hydrologic, chemical, biological processes, spurring the creation of government programs and private initiatives to restore wetlands. Initiatives tend to focus on individual wetland creation, yet the greatest benefits are achieved when strategic restoration planning occurs across a watershed or multiple watersheds. For watershed-level wetland restoration planning to occur, informative data layers on potential wetland areas are needed. We created an indicator of potential wetland areas (PWA), using nationally available datasets to identify characteristics that could support wetland ecosystems, including: poorly drained soils and low-relief landscape positions as indicated by a derived topographic data layer. We compared our PWA with the National Wetlands Inventory (NWI) from 11 states throughout the CONUS to evaluate their alignment. The state-level percentage of NWI-designated wetlands directly overlapping the PWA ranged from 39 to 95%. When we included NWI that was immediately adjacent to the overlapping NWI, our range of correspondence to NWI ranged from 60 to 99%. Wetland restoration is more likely on certain landscapes (e.g., agriculture) than others due to the lack of substantive infrastructure and the potential for the restoration of hydrology; therefore, we combined the National Land Cover Dataset (NLCD) with the PWA to identify potentially restorable wetlands on agricultural land (PRW-Ag). The PRW-Ag identified a total of over 46 million ha with the potential to support wetlands. The largest concentrations of PRW-Ag occurred in the glaciated corn belt of the upper Mississippi River from Ohio to the Dakotas and in the Mississippi Alluvial Valley. The PRW-Ag layer could assist land managers in identifying sites that may qualify for enrollment in conservation programs, where planners can coordinate restoration efforts, or where decision makers can target resources to optimize the services provided across a watershed or multiple watersheds.

Amphibians are often thought to have a metapopulation structure, which may render them vulnerable to habitat fragmentation. The red‐spotted toad (Bufo punctatus) in the southwestern United States and Mexico commonly inhabits wetlands that have become much smaller and fewer since the late Pleistocene. This study tests two predictions based on metapopulation theory, that the incidence of habitat patch occupancy is directly related to patch size and that it is inversely related to patch isolation, and a third, potentially competing hypothesis, that patch occupancy is influenced by local environmental conditions. In a 20 000 km2 area of the eastern Mojave Desert, 128 potential habitat patches (primarily springs) were identified and surveyed for local environmental characteristics and presence/absence of B. punctatus. Patch size metrics reflected extent of water and riparian vegetation of several types. Patch isolation metrics were based on nearest‐neighbor distances, calculated both as Euclidian distance and distance via connecting drainage channels. B. punctatus was found at 73% of the sites, including all of the 16 historic (pre‐1970) sites. Patches were generally quite small, with water extending a median distance of only 200 m and median area of 72 m2. Median nearest‐neighbor distances among patches were 1.8 km Euclidian distance (range: 0.4–22.0 km) and 6.8 km via drainage channels (range: 0.5–64.9 km). Based on stepwise multiple logistic regression, the incidence of patch occupancy increased significantly with patch size and was also significantly related to elevation, latitude, and four metrics that were associated with rocky terrain, periodic scouring water flows, and ephemeral water. In contrast, incidence of patch occupancy was not significantly related to patch isolation. These findings are consistent with a “patchy population” model, rather than the classical equilibrium metapopulation model, implying frequent dispersal among patches and virtually no local extinctions. We speculate that B. punctatus in the Mojave Desert today occurs primarily in a patchy population or populations within mountain ranges that are isolated from patchy populations in other ranges. The influence of local environmental characteristics on patch occupancy demonstrates the importance of including patch quality metrics in tests of predictions for patch occupancy based on metapopulation theory. Corresponding Editor: D. W. Pfenning.

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GIS-based measurements that combine native raster and na-