Joseph A. Bishop

Predicting freshwater fish distributions using landscape-level variables

Abstract Management and conservation of aquatic systems requires the ability to identify species’ historical, current, and potential distributions. We explored how a geographic information system can be used in conjunction with a few broad landscape variables to provide watershed-scale information useful for identifying diverse aquatic areas and predicting potential fish habitat. We developed species habitat profiles for all fish species that are known to occur in Pennsylvania. Five landscape variables were used to characterize a species’ habitat profile to predict its statewide distribution: presence in a major drainage basin, presence in a physiographic region, median watershed slope, level of watershed disturbance, and watershed-stream size. Each of these variables was referenced to a small watershed boundary. Using these variables, we predicted a species potential habitat range. Distribution maps that we generated were then compared to known distributions with an average accuracy of 73%. While many collections have been made in Pennsylvania over the last 50 years, we determined that many areas still remain unexplored as potential sampling locations. Among those fishes whose predicted distribution was less than the actual sampled distribution, four receive special protection in Pennsylvania and one is federally endangered. Moreover, we determined that small watersheds (1:24,000 scale) in the Allegheny River drainage, in the Pittsburgh Low Plateau Section, of small size (3–4 order), with moderate slope (2–4%), and moderate watershed disturbance (25–75%) have the highest fish species richness. Our results should facilitate the conservation of fish species and our technique should be easily repeatable in other geographic areas.

A Stream-Wetland-Riparian (SWR) index for assessing condition of aquatic ecosystems in small watersheds along the Atlantic slope of the eastern U.S.

As part of a regional study by the Atlantic Slope Consortium (ASC) to develop ecological and socioeconomic indicators of aquatic ecosystem condition, we developed and tested a protocol for rapidly assessing condition of the stream, wetland, and riparian components of freshwater aquatic ecosystems. Aspects of hydrology, vegetation, in-stream and wetland characteristics, and on-site stressors were measured in the field. The resulting metrics were used to develop an index of overall condition, termed the Stream–Wetland–Riparian (SWR) Index. Values of this Index were compared to existing biotic indices and chemical measures, and to a Landscape Index created using satellite-based land cover data and a geographic information system (GIS). Comparisons were made at several levels of spatial aggregation and resolution, from site to small watershed. The SWR Index and associated Landscape Indices were shown to correlate highly with biological indicators of stream condition at the site level and for small contributing areas. The landscape patterns prevalent throughout the entire watershed do not necessarily match the patterns found adjacent to the stream network. We suggest a top-down approach that managers can use to sequentially apply these methods, to first prioritize watersheds based on a relative condition measure provided by the Landscape Index, and then assess condition and diagnose stressors of aquatic resources at the subwatershed and site level.

Use of landscape and land use parameters for classification and characterization of watersheds in the mid-Atlantic across five physiographic provinces

AbstractThe Atlantic Slope Consortium (ASC) is a project designed to develop and test a set of indicators in coastal systems that are ecologically appropriate, economically reasonable, and relevant to society. The suite of indicators will produce integrated assessments of the condition, health and sustainability of aquatic ecosystems based on ecological and socioeconomic information compiled at the scale of estuarine segments and small watersheds. The research mandate of the ASC project is the following:Using a universe of watersheds, covering a range of social choices, we ask two questions:• How “good” can the environment be, given those social choices?• What is the intellectual model of condition within those choices, i.e., what are the causes of condition and what are the steps for improvement?As a basis for compiling ecological indicators, a watershed classification system was required for the experimental design. The goal was to develop approximately five categories of watersheds for each physiographic province, utilizing landscape and land use parameters that would be predictive of aquatic resource condition. All 14-digit Hydrologic Unit Code (HUC) watersheds in the Mid-Atlantic region would then be classified according to the regime. Five parameters were utilized for the classification: three land cover categories, consisting of forested, agricultural, and urban, median slope or median elevation, and total variance of land covers in 1-km-radius circles positioned on all stream convergence points in a specified 14-digit␣HUC watershed. Cluster analysis utilizing these five parameters resulted in approximately five well-defined watershed classes per physiographic province. The distribution of all watersheds in the Mid-Atlantic region across these categories provides a unique report on the probable condition of watersheds in the region.

What’s a Stream Without Water? Disproportionality in Headwater Regions Impacting Water Quality

Headwater streams are critical components of the stream network, yet landowner perceptions, attitudes, and property management behaviors surrounding these intermittent and ephemeral streams are not well understood. Our research uses the concept of watershed disproportionality, where coupled social-biophysical conditions bear a disproportionate responsibility for harmful water quality outcomes, to analyze the potential influence of riparian landowner perceptions and attitudes on water quality in headwater regions. We combine social science survey data, aerial imagery, and an analysis of spatial point processes to assess the relationship between riparian landowner perceptions and attitudes in relation to stream flow regularity. Stream flow regularity directly and positively shapes landowners’ water quality concerns, and also positively influences landowners’ attitudes of stream importance—a key determinant of water quality concern as identified in a path analysis. Similarly, riparian landowners who do not notice or perceive a stream on their property are likely located in headwater regions. Our findings indicate that landowners of headwater streams, which are critical areas for watershed-scale water quality, are less likely to manage for water quality than landowners with perennial streams in an obvious, natural channel. We discuss the relationships between streamflow and how landowners develop understandings of their stream, and relate this to the broader water quality implications of headwater stream mismanagement.

Detection and delineation of critical areas using echelons and spatial scan statistics with synoptic cellular data

Geographical surveillance for hotspot detection and delineation has become an important area of investigation both in geospatial ecosystem health and in geospatial public health. In order to find critical areas based on synoptic cellular data, geospatial ecosystem health investigations apply recently discovered echelon tools. In order to find elevated rate areas based on synoptic cellular data, geospatial public health investigations apply recently discovered spatial scan statistic tools. The purpose of this paper is to conceptualize a joint role for these together in the spirit of a cross-disciplinary cross-fertilization to accomplish more effective and efficient geographical surveillance for hotspot detection and delineation, and early warning system.

Contextual clustering for configuring collaborative conservation of watersheds in the Mid-Atlantic Highlands

The environmental purpose is to characterize watersheds in a region regarding vulnerability and resiliency relative to present and potential degradation of water quality due to human impact based on available spatial information and multidisciplinary expertise. Available information is of six general types as (1) physical and topographic conformation, (2) soil factors, (3) climatic factors, (4) hydrologic characteristics, (5) land-cover/land-use, and (6) prior records of sampling at selected locations for water quality and biological indicators. The strategy is first to develop cluster-based classes of watersheds that are expected to have similar responses to anthropogenic stressors, without using indicators of landscape condition that are directly influenced by local human activity. Watersheds in these classes can then be analyzed for degree of human influence as indicated by land-cover/land-use demographics. More sparse data on water quality and biological indicators at stream sampling locations provide a basis for determining the degradation response to human-induced stressors in each class along with potential for remediation. Focus in this paper is on the first task of cluster-based classification.Statistical adaptation comes in combining empirical objectivity of clustering with interdisciplinary environmental expertise, such that the trajectory of investigation arises from team expertise while the formulation is shaped statistically. Expertise enters initially in recognizing subsets of available descriptors that characterize different aspects of the watershed context needing to be explored separately rather than being completely confounded. Reduction of redundancy among available descriptors and removal of outliers are preliminary concerns. Clustering then proceeds through a series of phases using the sets of variables individually and in selected combinations. Contingency of composite clustering relative to separately clustered sets is examined via special cross tabulations in order to elucidate interactions between sets of variables. The spatial nature of the investigation contributes the major contextual capability for exercising team expertise through visualization using geographic information systems (GIS) that enhances and integrates insights from clustering, particularly with regard to spatial distribution of cluster membership.

Hydrogeomorphic (HGM) Classification, Inventory, and Reference Wetlands

Classifying wetlands is useful for describing and managing their natural variability. The hydrogeomorphic (HGM) approach, which covers classification, reference, and functional assessment aspects, has proven to be helpful in classifying wetlands as to their position in the landscape, their source of water, and the flow of that water. In this chapter, we review the origins and characteristics of freshwater wetlands for ecoregions of the Mid-Atlantic region (MAR), which are dominated by riverine types. Inventories of wetlands in the MAR are dated, so we discuss what is known with regard to status and trends, and potential solutions. We discuss the value of establishing a reference set to assist with classification, assessment, and mitigation of wetlands, and describe the set of reference wetlands compiled for Pennsylvania by Riparia. Preliminary results from a regional condition assessment of wetlands in the MAR are provided.

Habitat Characteristics and Revised Gap Landscape Analysis for the Northern Flying Squirrel (Glaucomys sabrinus), a State Endangered Species in Pennsylvania

Abstract We quantified the habitat characteristics associated with capture sites (2003–2006) of the state endangered northern flying squirrel (Glaucomys sabrinus) in Pennsylvania to develop management recommendations that help ensure its continued persistence in the Appalachians. In addition, we used this refined understanding of northern flying squirrel habitat to update the Pennsylvania Gap analysis model for this species. We examined habitat at both the landscape and local level and determined that northern flying squirrels preferred sites that contained mature (>95 y old) mixed coniferous forests and were adjacent to a permanent water source. In addition, sites where northern flying squirrels were captured contained significantly more overstory trees (all species), saplings and rock cover. After using these data to refine the Pennsylvania Gap model for northern flying squirrels, we reduced the primary or core habitat predicted for this species in the state by 90%. Our findings further support the reliance of this species on specialized habitat in the Appalachians and help reinforce the need to conserve and manage mature mixed-coniferous forest stands, which are threatened by exotic pests and human development throughout the region.