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Dive into the research topics where Wayne L. Myers is active.

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Featured researches published by Wayne L. Myers.


Environmental and Ecological Statistics | 1997

Echelon approach to areas of concern in synoptic regional monitoring

Wayne L. Myers; G. P. Patil; Kyle Joly

Echelons provide an objective approach to prospecting for areas of potential concern in synoptic regional monitoring of a surface variable. Echelons can be regarded informally as stacked hill forms. The strategy is to identify regions of the surface which are elevated relative to surroundings (Relative ELEVATIONS or RELEVATIONS). These are areas which would continue to expand as islands with receding (virtual) floodwaters. Levels where islands would merge are critical elevations which delimit echelons in the vertical dimension. Families of echelons consist of surface sectors constituting separate islands for deeper waters that merge as water level declines. Pits which would hold water are disregarded in such a progression, but a complementary analysis of pits is obtained using the surface as a virtual mould to cast a counter-surface (bathymetric analysis). An echelon tree is a family tree of echelons with peaks as terminals and the lowest level as root. An echelon tree thus provides a dendrogram representation of surface topology which enables graph theoretic analysis and comparison of surface structures. Echelon top view maps show echelon cover sectors on the base plane. An echelon table summarizes characteristics of echelons as instances or cases of hill form surface structure. Determination of echelons requires only ordinal strength for the surface variable, and is thus appropriate for environmental indices as well as measurements. Since echelons are inherent in a surface rather than perceptual, they provide a basis for computer-intelligent understanding of surfaces. Echelons are given for broad-scale mammalian species richness in Pennsylvania.


Fisheries Research | 2003

Predicting freshwater fish distributions using landscape-level variables

David G. Argent; Joseph A. Bishop; Jay R. Stauffer; Robert F. Carline; Wayne L. Myers

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.


Environmental and Ecological Statistics | 2005

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

Denice H. Wardrop; Joseph A. Bishop; M. Easterling; Kristen C. Hychka; Wayne L. Myers; G. P. Patil; C. Taillie

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.


Environmental and Ecological Statistics | 2006

Spatially constrained clustering and upper level set scan hotspot detection in surveillance geoinformatics

G. P. Patil; Reza Modarres; Wayne L. Myers; Pushkar Patankar

We discuss upper level set (ULS) scan as a type of spatially constrained clustering in relation to two ways of imposing the spatial constraint, retrospectively versus progressively. We show that ULS scan produces the same results both ways; whereas two popular clustering techniques, single-linkage and K-means, can yield different results when spatial constraints are imposed retrospectively versus progressively. The ULS scan approach examines spatially connected components of a tessellation as a threshold is moved from the highest level (value) in the data to the lowest level. When the variable of interest on the tessellation is a rate of incidence, then a significance test is available based on binomial or Poisson null models and Monte Carlo techniques. This is a common context for detecting hotspots of diseases in epidemiological work. We also discuss an approach for extending the univariate methodology to accommodate multivariate contexts.


Landscape Ecology | 2001

Characterizing watershed-delineated landscapes in Pennsylvania using conditional entropy profiles

Glen D. Johnson; Wayne L. Myers; G. P. Patil; C. Taillie

When the objective is to characterize landscapes with respect to relative degree and type of forest (or other critical habitat) fragmentation, it is difficult to decide which variables to measure and what type of discriminatory analysis to apply. It is also desirable to incorporate multiple measurement scales. In response, a new method has been developed that responds to changes in both the marginal and spatial distributions of land cover in a raster map. Multiscale features of the map are captured in a sequence of successively coarsened resolutions based on the random filter for degrading raster map resolutions. Basically, the entropy of spatial pattern associated with a particular pixel resolution is calculated, conditional on the pattern of the next coarser ‘parent’ resolution. When the entropy is plotted as a function of changing resolution, we obtain a simple two-dimensional graph called a ‘conditional entropy profile’, thus providing a graphical visualization of multi-scale fragmentation patterns.Using eight-category raster maps derived from 30-meter resolution LANDSAT Thematic Mapper images, the conditional entropy profile was obtained for each of 102 watersheds covering the state of Pennsylvania (USA). A suite of more conventional single-resolution landscape measurements was also obtained for each watershed using the FRAGSTATS program. After dividing the watersheds into three major physiographic provinces, cluster analysis was performed within each province using various combinations of the FRAGSTATS variables, land cover proportions and variables describing the conditional entropy profiles. Measurements of both spatial pattern and marginal land cover proportions were necessary to clearly discriminate the watersheds into distinct clusters for most of the state; however, the Piedmont province essentially only required the land cover proportions. In addition to land cover proportions, only the variables describing a conditional entropy profile appeared to be necessary for the Ridge and Valley province, whereas only the FRAGSTATS variables appeared to be necessary for the Appalachian Plateaus province. Meanwhile, the graphical representation of conditional entropy profiles provided a visualization of multi-scale fragmentation that was quite sensitive to changing pattern.


Applied and Environmental Soil Science | 2013

The Use of LiDAR Terrain Data in Characterizing Surface Roughness and Microtopography

Kristen M. Brubaker; Wayne L. Myers; Patrick J. Drohan; Douglas A. Miller; Elizabeth W. Boyer

The availability of light detection and ranging data (LiDAR) has resulted in a new era of landscape analysis. For example, improvements in LiDAR data resolution may make it possible to accurately model microtopography over a large geographic area; however, data resolution and processing costs versus resulting accuracy may be too costly. We examined two LiDAR datasets of differing resolutions, a low point density (0.714 points/m2 spacing) 1 m DEM available statewide in Pennsylvania and a high point density (10.28 points/m2 spacing) 1 m DEM research-grade DEM, and compared the calculated roughness between both resulting DEMs using standard deviation of slope, standard deviation of curvature, a pit fill index, and the difference between a smoothed splined surface and the original DEM. These results were then compared to field-surveyed plots and transects of microterrain. Using both datasets, patterns of roughness were identified, which were associated with different landforms derived from hydrogeomorphic features such as stream channels, gullies, and depressions. Lowland areas tended to have the highest roughness values for all methods, with other areas showing distinctive patterns of roughness values across metrics. However, our results suggest that the high-resolution research-grade LiDAR did not improve roughness modeling in comparison to the coarser statewide LiDAR. We conclude that resolution and initial point density may not be as important as the algorithm and methodology used to generate a LiDAR-derived DEM for roughness modeling purposes.


Ecological Modelling | 1999

Multiresolution fragmentation profiles for assessing hierarchically structured landscape patterns

Glen D. Johnson; Wayne L. Myers; G. P. Patil; C. Taillie

For landscapes that are cast as categorical raster maps, we present an entropy based method for obtaining a multiresolution characterization of spatial pattern. The result is a conditional entropy profile which reflects the rate of information loss as map resolution is degraded by increasing the pixel size through a resampling filter. We choose a random filter because of desirable properties that simplify calculations. Neutral landscapes that are simulated by stochastic generating models provide a way to evaluate the behavior of conditional entropy profiles under known hierarchically scaled generating mechanisms. When the random filter is used, we provide a method to directly compute the conditional entropy profile for specified generating models. Such profiles can provide benchmarks for comparing results obtained from raster maps of actual landscapes that are classified from satellite images. These profiles appear to capture much of the information about a landscape pattern that is otherwise obtained by a suite of landscape measurements which characterize different aspects of spatial pattern.


Archive | 2006

Exploring Patterns of Habitat Diversity Across Landscapes Using Partial Ordering

Wayne L. Myers; G. P. Patil; Yun Cai

Potential habitat suitability was assessed for species groupings of vertebrate fauna in the State of Pennsylvania, USA as part of a nationally coordinated GAP Analysis Program to find gaps in provision for conservation of important habitats. Diversity values were compiled spatially at a resolution of one square kilometre from species models developed at 30-meter resolution. Diversity patterns differ in varying degrees among species groups for mammals, birds, amphibians, snakes/lizards, turtles, and fishes. Comparing the patterns for partial ordering on watershed extents using statistical indices of ranking can facilitate determination of inter-group commonality and contrast. This helps to designate watersheds as having importance from multi-group and particular group perspectives. Partial ordering on the basis of rank-range runs is particularly informative when combined with levels of counter-indication corresponding to levels in a Hasse diagram. This serves to segregate sets having combinatorial clarity of condition relative to conservation from settings where disparate conditions may offer opportunities for targeted restoration. Disparity of conditions on multiple bio-indicators may arise from habitat heterogeneity as well as differential degradation. Broadening the spectrum of indicators will usually increase the apparent complexity of the conservation context.


Biological Conservation | 2001

Patterns of mammalian species richness and habitat associations in Pennsylvania

Kyle Joly; Wayne L. Myers

Abstract Landscape variables were employed as indices of habitat heterogeneity, fragmentation, and human influence on the environment to characterize constituent units of a 635 km2 grid covering the state of Pennsylvania. Species richness was determined by overlaying the distributions of all 60 terrestrial mammalian species found within the state. All landscape variables investigated were correlated with species richness. Areas with high topographic variation and low road density had the highest species richness. Species sensitive to habitat fragmentation were also associated with large forest patches and low road density. These landscape variables may be useful in identifying areas that are important for the conservation of these species. Associations between species distributions and landscape variables were substantiated by published habitat associations. Species with extremely limited distributions were not associated with landscape variables and represent special cases for conservation planners. Rare species, as defined by their limited geographical distribution, were not associated with areas of high species richness (hotspots). The utility of species richness hotspots for conservation planning is disputable. Hotspots of species richness were associated with large forest patches and low road density.


Landscape Ecology | 1999

Stochastic generating models for simulating hierarchically structured multi-cover landscapes

Glen D. Johnson; Wayne L. Myers; G. P. Patil

For simulating hierarchically structured raster maps of landscapes that consist of multiple land cover types, we extend the concept of neutral landscape models to provide a general Markovian model. A stochastic transition matrix provides the probability rules that govern landscape fragmentation processes by assigning finer resolution land cover categories, given coarser resolution categories. This matrix can either be changed or remain the same at different resolutions. The probability rules may be defined for simulating properties of an actual landscape or they may be specified in a truly neutral manner to evaluate the effects of particular transition probability rules.For illustration, model parameters are defined heuristically to simulate properites of actual watershed-delineated landscapes in Pennsylvania. Three landscapes were chosen; one is mostly forested, one is in a transitional state between mostly forested and a mixture of agriculture, urban and suburban land, while the third is fully developed with only remnant forest patches that are small and disconnected. For each landscape type, a small sample of raster maps are simulated in a Monte Carlo fashion to illustrate how an empirical distribution of landscape measurements can be obtained.

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G. P. Patil

Pennsylvania State University

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Joseph A. Bishop

Pennsylvania State University

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C. Taillie

Pennsylvania State University

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Glen D. Johnson

Pennsylvania State University

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Robert P. Brooks

Pennsylvania State University

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Barry M. Evans

Pennsylvania State University

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Denice H. Wardrop

Pennsylvania State University

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Kristen C. Hychka

Pennsylvania State University

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Kyle Joly

Pennsylvania State University

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Raj Acharya

Pennsylvania State University

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