Anne C. Neale

Predicting nutrient and sediment loadings to streams from landscape metrics: A multiple watershed study from the United States Mid-Atlantic Region

There has been an increasing interest in evaluating the relative condition or health of water resources at regional and national scales. Of particular interest is an ability to identify those areas where surface and ground waters have the greatest potential for high levels of nutrient and sediment loadings. High levels of nutrient and sediment loadings can have adverse effects on both humans and aquatic ecosystems. We analyzed the ability of landscape metrics generated from readily available, spatial data to predict nutrient and sediment yield to streams in the Mid-Atlantic Region in the United States. We used landscape metric coverages generated from a previous assessment of the entire Mid-Atlantic Region, and a set of stream sample data from the U.S. Geological Survey. Landscape metrics consistently explained high amounts of variation in nitrogen yields to streams (65 to 86% of the total variation). They also explained 73 and 79% of the variability in dissolved phosphorus and suspended sediment. Although there were differences in the nitrogen, phosphorus, and sediment models, the amount of agriculture, riparian forests, and atmospheric nitrate deposition (nitrogen only) consistently explained a high proportion of the variation in these models. Differences in the models also suggest potential differences in landscape-stream relationships between ecoregions or biophysical settings. The results of the study suggest that readily available, spatial data can be used to assess potential nutrient and sediment loadings to streams, but that it will be important to develop and test landscape models in different biophysical settings.

Bird Species Assemblages as Indicators of Biological Integrity in Great Basin Rangeland

The study evaluates the potential for bird species assemblages to serve as indicators of biological integrity of rangelands in the Great Basin in much the same way that fish and invertebrate assemblages have been used as indicators in aquatic environments. Our approach was to identify metrics of the bird community using relatively simple sampling methods that reflect the degree of rangeland degradation and are consistent over a variety of vegetation types and geographic areas. We conducted the study in three range types (i.e., potential natural plant community types) in each of two widely separated areas of the Great Basin: south-eastern Idaho (sagebrush steppe range types) and west-central Utah (salt-desert shrub range types). Sites were selected in each range type to represent three levels of grazing impact, and in Idaho included sites modified for crested wheatgrass production. Birds were sampled by point counts on 9 100-m radius plots at 250-m spacing on each of 20 sites in each area during the breeding season. In sagebrush-steppe, 964 individuals in 8 species of passerine birds were used in analyses. Five metrics were significantly related to impact class, both when analyzed within range type and when analyzed with all range types combined. Species richness, relative abundance of shrub obligate species, and relative abundance of Brewers sparrow were generally lower for the higher impact classes, whereas the reverse was true for dominance by a single species and for relative abundance of horned larks. In contrast, total number of individuals did not differ significantly as a function of impact class. In salt-desert shrub, a total of 843 birds in 4 species were included in analyses, 98% of which were horned larks. None of the metrics identified above was significantly related to impact class. Two metrics for breeding birds in sagebrush steppe (species richness and dominance) showed little overlap between values for the extremes of impact class, and thus they have potential as indicators of biological integrity. However, the sensitivity of these metrics appears to be greatest at the high impact end of the spectrum, which suggests they may have limited utility in distinguishing between sites having light and moderate impact.

Assessing Landscape Condition Relative to Water Resources in the Western United States: A Strategic Approach

The Environmental Monitoring and Assessment Program (EMAP) is proposing an ambitious agenda to assess the status of streams and estuaries in a 12-State area of the western United States by the end of 2003. Additionally, EMAP is proposing to access landscape conditions as they relate to stream and estuary conditions across the west. The goal of this landscape project is to develop a landscape model that can be used to identify the relative risks of streams and estuaries to potential declines due to watershed-scale, landscape conditions across the west. To do so, requires an understanding of quantitative relationships between landscape composition and pattern metrics and parameters of stream and estuary conditions. This paper describes a strategic approach for evaluating the degree to which landscape composition and pattern influence stream and estuary condition, and the development and implementation of a spatially-distributed, landscape analysis approach.

A Comparison of Vector and Raster GIS Methods for Calculating Landscape Metrics Used in Environmental Assessments

GIS-based measurements that combine native raster and native vector data are commonly used in environmental assessments. Most of these measurements can be calculated using either raster or vector data formats and processing methods. Raster processes are more commonly used because they can be significantly faster computationally than vector, but error is introduced in converting vector data to raster. This conversion error has been widely studied and quantified, but the impact on environmental assessment results has not been investigated. We examined four GIS-based measurements commonly used in environmental assessments for approximately 1000 watersheds in the state of Maryland and Washington, D.C. Each metric was calculated using vector and raster methods, and estimated values were compared using a paired t-test, Spearman rank correlation, and cluster analyses. Paired t-tests were used to determine the statistical significance of quantitative differences between methods, and Spearman rank correlation and cluster analyses were used to evaluate the impact of the differences on environmental assessments. Paired t-test results indicated significant quantitative differences between methods for three of the four metrics. However, Spearman ranks and cluster analyses indicated that the quantitative differences would not affect environmental assessment results. Spearman rank correlations between vector and raster values were greater than 0.98 for all comparisons. Cluster analyses resulted in identical assignment for 88 percent to over 98 percent of watersheds analyzed among vector and various raster methods.

A Landscape Ecology Assessment of the Tensas River Basin

A group of landscape ecological indicators were applied to biophysical data masked to the Tensas River Basin. The indicators were used to identify and prioritize sources of nutrients in a Mississippi/Atchafalaya River System sub-basin. Remotely sensed data were used for change detection assessment. With these methods, we were able to look at land use practices over the past twenty years in the Tensas River Basin of Louisiana. A simple land use classification was applied to multispectral scanner (MSS) data from 1972 and 1991. The landscape analysis methods described in this paper will show how to use these methods to assess the impact of human land use practices that are being implemented to improve environmental quality. Landscape assessment methods can be used as a simple, timely, cost effective approach for monitoring, targeting, and modeling ecosystem health in watersheds. Although this study was conducted in the southeast, the methods described in this paper may be applicable to western landscapes.

Distribution of soil organic carbon in the conterminous United States

The U.S. Soil Survey Geographic (SSURGO) database provides detailed soil mapping for most of the conterminous United States (CONUS). These data have been used to formulate estimates of soil carbon stocks, and have been useful for environmental models, including plant productivity models, hydrologic models, and ecological models for studies of greenhouse gas exchange. The data were compiled by the U.S. Department of Agriculture Natural Resources Conservation Service (NRCS) from 1:24,000-scale or 1:12,000-scale maps. It was found that the total soil organic carbon stock in CONUS to 1 m depth is 57 Pg C and for the total profile is 73 Pg C, as estimated from SSURGO with data gaps filled from the 1:250,000-scale Digital General Soil Map. We explore the non-linear distribution of soil carbon on the landscape and with depth in the soil, and the implications for sampling strategies that result from the observed soil carbon variability.

Association between Natural Resources for Outdoor Activities and Physical Inactivity: Results from the Contiguous United States

Protected areas including national/state parks and recreational waters are excellent natural resources that promote physical activity and interaction with Nature, which can relieve stress and reduce disease risk. Despite their importance, however, their contribution to human health has not been properly quantified. This paper seeks to evaluate quantitatively how national/state parks and recreational waters are associated with human health and well-being, taking into account of the spatial dependence of environmental variables for the contiguous U.S., at the county level. First, we describe available natural resources for outdoor activities (ANROA), using national databases that include features from the Protected Areas Database, NAVSTREETS, and ATTAINSGEO 305(b) Waters. We then use spatial regression techniques to explore the association of ANROA and socioeconomic status factors on physical inactivity rates. Finally, we use variance analysis to analyze ANROA’s influence on income-related health inequality. We found a significantly negative association between ANROA and the rate of physical inactivity: ANROA and the spatial effect explained 69%, nationwide, of the variation in physical inactivity. Physical inactivity rate showed a strong spatial dependence—influenced not only by its own in-county ANROA, but also by that of its neighbors ANROA. Furthermore, community groups at the same income level and with the highest ANROA, always had the lowest physical inactivity rate. This finding may help to guide future land use planning and community development that will benefit human health and well-being.

An inventory of continental U.S. terrestrial candidate ecological restoration areas based on landscape context

Abstract Landscape context is an important factor in restoration ecology, but the use of landscape context for site prioritization has not been as fully developed. We used morphological image processing to identify candidate ecological restoration areas based on their proximity to existing natural vegetation. We identified 1,102,720 candidate ecological restoration areas across the continental United States. Candidate ecological restoration areas were concentrated in the Great Plains and eastern United States. We populated the database of candidate ecological restoration areas with 17 attributes related to site content and context, including factors such as soil fertility and roads (site content), and number and area of potentially conjoined vegetated regions (site context) to facilitate its use for site prioritization. We demonstrate the utility of the database in the state of North Carolina, U.S.A. for a restoration objective related to restoration of water quality (mandated by the U.S. Clean Water Act), wetlands, and forest. The database will be made publicly available on the U.S. Environmental Protection Agencys EnviroAtlas website (http://enviroatlas.epa.gov) for stakeholders interested in ecological restoration.

Development of a spatially complete floodplain map of the conterminous United States using random forest

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 methods ability to successfully fill in the remaining unmapped floodplains in the CONUS, while using only publicly available data and open source software.

New Interoperable Web Tools to Facilitate Decision-Making to Support Community Sustainability

The US Environmental Protection Agency’s Office of Research and Development Sustainable and Healthy Communities Research Program (SHCRP) is building on past decision-support efforts and expanding the scope and accessibility of available tools to empower decision-makers ranging from individuals up to national policymakers to move towards sustainability goals. Initially, the SHCRP is integrating a National Atlas of Ecosystem Services (developed in ESRI’s ArcServer application for geographical information analysis) with the Regional Vulnerability Assessment (ReVA) Program’s Environmental Decision Toolkit (EDT) that focuses on analysis of multiple stresses (e.g., air pollution, land-use change) with multiple resources (e.g., habitat, drinking water supplies) (developed in the SPlus statistical application) for prioritizing protection, mitigation, and restoration actions. Future plans for the interoperable suite of tools include the incorporation of the use of social media to identify issues and solutions, as well as potential connections among different communities.