EPA/600/R-03/057 

August 2003

A LANDSCAPE ATLAS OF ECOLOGICAL VULNERABILITY: 

ARKANSAS’ WHITE RIVER WATERSHED 

AND THE

MISSISSIPPI ALLUVIAL VALLEY ECOREGION

1

Ricardo D. Lopez, 1Daniel T. Heggem, 1Curtis M. Edmonds, 1K. Bruce Jones, 

2

Lee A. Bice, 2Matt Hamilton, 2Ed Evanston, 1Chad L. Cross, and 1Donald W. Ebert 

1

U.S. Environmental Protection Agency, Office of Research and Development, National 

Exposure Research Laboratory, Environmental Sciences Division, Las Vegas, Nevada 

2

Lockheed Martin Environmental Services, Las Vegas, Nevada 
NOTICE

The information in this document has been funded wholly by the United States Environmental 

Protection Agency (EPA) under a Regional Applied Research Effort agreement with EPA 

Region 6. It has been subjected to the Agency’s peer and administrative review and has been 

approved for publication as an EPA document. Mention of trade names or commercial products 

does not constitute endorsement or recommendation by EPA for use. 

COVER IMAGE DESCRIPTION

Selected study results showing mallard duck habitat suitability models in the (a) Lower White 

River Region, mallard duck habitat suitability models in the (b) Mississippi Alluvial Valley 

Ecoregion, and water quality vulnerability models in the (c) White River Watershed. (Right) 

The Lower White River Region mallard duck winter habitat suitability model with an overlay 

map of the mallard duck habitat Unified Vulnerability Index for the South Unit of the White 

River National Wildlife Refuge. (Lower left) Hillshaded digital elevation model of the White 

River Watershed, overlaid with a water quality vulnerability model based on percent agriculture. 
 

TABLE OF CONTENTS 

(CLICK ON LINK)

Cover 

Title Page 

Notice and Cover Image Description 

List of Tables 

List of Figures 

List of Appendices 

Executive Summary 

Acknowledgements and Dedication 

Chapter 1. Landscape Analysis Overview 

Project Overview and Summary of Regional Environmental Issues 

Objectives 

Approach and Methods 

 Habitat Vulnerability Assessments 

 Water Quality Vulnerability Assessments 

 The Landscape-Ecological Perspective 

 Data Reporting and Statistics 

Chapter 2. Habitat Assessments 

Study Areas 

How to Navigate and Interpret Habitat Assessment Maps 

Habitat Suitability 

 Mallard Duck Winter Habitat 

 Black Bear Wetland Habitat 

 Least Tern Breeding Habitat 

 Wetland Plant Habitat 

 Habitat Suitability Maps 

Habitat Vulnerability 
 

Chapter 3. Water Quality Vulnerability Assessments 

Study Area 

How to Navigate and Interpret Water Quality Vulnerability Maps 

Water Quality Vulnerability 

 Roads and Agriculture 

 Natural Vegetation 

Chapter 4. Assessment of Habitat Vulnerability in Wildlife Refuges 

National Wildlife Refuges in the Lower White River Region 

Results 

Discussion 

Recommendations and Conclusion 

Simulating Landscape Change in the White River National Wildlife Refuge 

Acronyms and Abbreviations Used 

Appendices 

Literature Cited 
LIST OF TABLES

(CLICK ON LINK)

Table 1a. GIS data sets used to derive mallard duck, black bear, least tern, and wetland plant 

habitat suitability and habitat vulnerability in the Lower White River Region. 

Table 1b. GIS data sets used to derive mallard duck, black bear, least tern, and wetland plant 

habitat suitability and habitat vulnerability in the Mississippi Alluvial Valley Ecoregion. 

Table 1c. GIS data sets used to derive water quality vulnerability models in the White River 

Watershed. 

Table 2a. Data classes used to produce habitat suitability GIS models for mallard ducks in the 

Lower White River Region and the Mississippi Alluvial Valley Ecoregion. 

Table 2b. Data classes used to produce habitat suitability GIS models for black bears in the 

Lower White River Region and the Mississippi Alluvial Valley Ecoregion. 

Table 2c. Data classes used to produce habitat suitability GIS models for least terns in the 

Lower White River Region and the Mississippi Alluvial Valley Ecoregion. 

Table 2d. Data classes used to produce habitat suitability GIS models for wetland plants in the 

Lower White River Region and the Mississippi Alluvial Valley Ecoregion. 

Table 3a. Data classes used to produce habitat vulnerability GIS models for mallard ducks, 

black bears, and wetland plants in the Lower White River Region and the Mississippi Alluvial 

Valley Ecoregion. 

Table 3b. Metrics used to produce water quality vulnerability GIS models in the Lower White 

River Region, measured among 8-digit Hydrologic Unit Codes (HUCs). 

Table 4. National Land Cover Dataset (NLCD) categories. 
Table 5. Spearman Rank Correlation of surface water quality parameters and percent agriculture 

land cover within 8-digit HUC subwatersheds in the White River Watershed. 

Table 6. Spearman Rank Correlation of surface water quality parameters and forest metrics 

within 8-digit HUC subwatersheds in the White River Watershed. 

Table 7. Area of potential mallard duck winter habitat in the Lower White River Region. 

Table 8. Area and perimeter of each of the 72 federal refuge zones in the Lower White River 

Region, in ascending order of refuge zone area. 

Table 9. Summary table of Spearman Rank Correlation between federal refuge zone area and 

habitat vulnerability parameters. 

Table 10. Rank of each of the 72 federal refuge zones in the Lower White River Region by (a) 

zone area, (b) zone perimeter, (c) mean area of habitat patches within a zone, and (d) mean 

percent contribution of habitat patches within a zone. 

Table 11. Rank of each of the 72 federal refuge zones in the Lower White River Region by 

habitat patch characteristics. 

Table 12. Rank of each of the 72 federal refuge zones in the Lower White River Region by 

habitat patch human-induced disturbance characteristics. 

Table 13. Assessment of the presence of wetlands less than or equal to 2 ha within each of 68 

federal refuge zones (i.e., excluding the four largest refuge zones) in the Lower White River 

Region. 
LIST OF FIGURES

(CLICK ON LINK)

Figure 1. Geographic overview of the Lower White River Region (LWRR) and White River 

Watershed (WRW) study areas. 

Figure 2a. Orientation map of Lower White River Region (LWRR), Mississippi Alluvial Valley 

Ecoregion (MAVE), and White River Watershed (WRW). Inset shows the states that intersect 

the MAVE study area. 

Figure 2b. Orientation map of Lower White River Region (LWRR), including the 13 counties 

that intersect the LWRR study area. 

Figure 2c. County map of all study areas including the 170 counties in Arkansas, Missouri, 

Louisiana, Tennessee, Illinois, and Kentucky that intersect the Lower White River Region 

(LWRR), Mississippi Alluvial Valley Ecoregion (MAVE), and the White River Watershed 

(WRW). 

Figure 3. Land cover (National Land Cover Dataset; NLCD) in the White River Watershed, 

showing subwatershed boundaries. 

Figure 4. Wetland conversion to crop agriculture, pasture, or urban land cover (pre-1600 to 

1992) in the Lower Mississippi River basin. 

Figure 5. “Loading logs on a lumber company railroad in early 1900” in an Arkansas 

bottomland hardwood swamp. 

Figure 6. Diesel generator pumping groundwater for the irrigation of row crops in Arkansas 

County, Arkansas. 

Figure 7. The Beouf-Tensas Project, Arkansas-Louisiana, an example of riparian vegetation 

destruction and wetland hydrologic alteration in the Mississippi Alluvial Valley Ecoregion. 
Figure 8. The McClellan-Kerr Navigation Channel on the Arkansas-Louisiana border, an 

example of riparian vegetation destruction and wetland hydrologic alteration in the Lower White 

River Region (LWRR). 

Figure 9a. Lower White River Region reference image with selected towns indicated with blue 

arrows. Wildlife refuge zones are indicated by white polygons. Single near-infrared band 

image. 

Figure 9b. Lower White River Region reference image with selected towns indicated with blue 

arrows. Wildlife refuge zones are indicated by white polygons. Three band false-color infra-red 

composite image. 

Figure 9c. Lower White River Region reference image with selected towns indicated with blue 

arrows. Wildlife refuge zones are indicated by white polygons. Three band false-color 

‘enhanced vegetation’ infra-red composite image. 

Figure 10. Land cover in the White River Watershed study area was measured within 

cumulative 30 m riparian “buffer zones”, from shorelines to a maximum of 300 m. 

Figure 11. How to interpret the habitat suitability, habitat vulnerability, and water quality 

vulnerability maps in this atlas. 

Figure 12. Hierarchical schematic of the mallard duck winter habitat model process for the 

Lower White River Region (LWRR). 

Figure 13. An “emergent wetland” in the Lower White River Region. 

Figure 14. A “scrub/shrub wetland”, across a pool with duckweed (Lemna sp.) and watermeal 

(Wolffia sp.) on the surface; Lower White River Region. 

Figure 15. A “forested wetland”, in the Lower White River Region. 
Figure 16. “Unconsolidated bottom wetland” types in the Lower White River Region. 

Figure 17. An “aquatic bed” with submersed and emergent vegetation present. 

Figure 18. A portion of the White River in the Lower White River Region study area. River 

areas provide areas for animals to feed, drink, and rest. 

Figure 19. Features and examples of classes and hydrologic modifiers in palustrine wetlands. 

Figure 20. Sandy shore of the White River in the Lower White River study area. 

Figures 21 a - m. Lower White River Region (LWRR) mallard duck, black bear, least tern, and 

wetland plant habitat suitability models. 

Figures 22 a - r. Lower White River Region (LWRR) mallard duck, black bear, and wetland 

plant habitat vulnerability models (> 2 ha) in terms of habitat patch size and shape. 

Figures 23 a – x. Lower White River Region (LWRR) mallard duck, black bear, and wetland 

plant habitat vulnerability models (> 2 ha) in terms of human-induced disturbance factors. 

Figures 24 a – m. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck, black bear, 

least tern, and wetland plant habitat suitability models. 

Figures 25 a - r. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck, black bear, and 

wetland plant habitat vulnerability models (> 2 ha) in terms of habitat patch size and shape. 

Figures 26 a - x. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck, black bear, and 

wetland plant habitat vulnerability models (> 2 ha) in terms of human-induced disturbance 

factors. 
Figure 27. Twenty-five 8-digit hydrologic unit code (HUC) subwatersheds in the White River 

Watershed. Yellow triangle indicates a National Water Quality Assessment (NAWQA) Program 

field sampling location, sampled during the 1990s (N = 35). 

Figure 28. Location of surface water in the White River Watershed. Surface water location data 

was used to calculate the percent of streams in proximity to roads. 

Figure 29. Road network in the White River Watershed. Data used to calculate the percent of 

streams within thirty meters of a road. 

Figure 30. White River Watershed percent of streams within 30 meters of a road. 

Figure 31. Proposed water quality vulnerability metrics, based on percent agriculture (i.e., 

NLCD codes 81, 82, 83, and 85 in Table 4) adjacent to shorelines, within cumulative thirtymeter riparian zones, and within entire HUCs. 

Figure 32. Proposed water quality vulnerability metrics, based on percent crop agriculture (i.e., 

NLCD codes 82, 83, and 85 in Table 4) adjacent to shorelines, within cumulative thirty-meter 

riparian zones, and within entire HUCs. 

Figure 33. Proposed water quality vulnerability metrics, based on percent pasture agriculture 

(i.e., NLCD codes 81 in Table 4) adjacent to shorelines, within cumulative thirty-meter riparian 

zones, and within entire HUCs. 

Figure 34. Hillshaded digital elevation model (DEM) of the White River Watershed, depicting 

areas from lower elevation (darker) to higher elevation (lighter). This DEM was used to 

calculate percent of total agriculture on slopes greater than three percent. 

Figure 35. White River Watershed percent total agriculture on slopes greater than three percent. 
Figure 36. Spatial analysis of twenty-five 8-digit hydrologic unit code (HUC) subwatersheds in 

the White River Watershed. There is a relatively greater risk of surface water quality impairment 

in 2 HUCs as a result of the presence of agriculture within the cumulative 120 meter riparian 

zones. 

Figure 37. Proposed water quality vulnerability metrics, based on (a) largest forest patch 

proportion of HUC, (b) mean area of forest patch, (c) largest forest patch area, (d) forest patch 

density, (e) forest patch number, and (f) percent of HUC that is forest (by area). 

Figure 38. Proposed water quality vulnerability metrics, based on percent forest (i.e., NLCD 

codes 41, 42, and 43 in Table 4) adjacent to shorelines, within cumulative thirty-meter riparian 

zones, and within entire HUCs. 

Figure 39. Proposed water quality vulnerability metrics, based on percent wetland (i.e., NLCD 

codes 91 and 92 in Table 4) adjacent to shorelines, within cumulative thirty-meter riparian zones, 

and within entire HUCs. 

Figure 40. Proposed water quality vulnerability metrics, based on percent natural land cover 

(i.e., NLCD codes 31, 41, 42, 43, 51, 71, 91, and 92 in Table 4) adjacent to shorelines, within 

cumulative thirty-meter riparian zones, and within entire HUCs. 

Figure 41. Spatial analysis of twenty-five 8-digit hydrologic unit code (HUC) subwatersheds in 

the White River Watershed. There is a relatively greater risk of surface water quality impairment 

in 6 HUCs as a result of forest loss in riparian zones of various widths. 

Figure 42. Seventy-two federal refuge zones in the Lower White River Region were compared 

using mallard duck winter habitat vulnerability models. 
Figure 43. The unified vulnerability index was applied to a hypothetical landscape where 

change in the riparian wetland hydrology has occurred. Red rectangle indicates the portion of 

the South Unit in the White River National Wildlife Refuge where landscape change is 

hypothesized. 

Figure 44. (A) Mallard duck winter habitat vulnerability under current hydrologic conditions in 

the South Unit of the White River National Wildlife Refuge; (B) Loss of mallard duck winter 

habitat under hypothetical decrease in flood stage and duration on the White River; (C) enlarged 

view of predicted net loss of mallard duck winter habitat under hypothetical decrease in flood 

stage and duration on the White River.
List of Appendices

(Click on Link)

Appendix A. U.S. Fish and Wildlife Service Publication: Habitat Suitability Index Models: 

Mallard (Winter Habitat, Lower Mississippi Valley) 

Appendix B. U.S. Fish and Wildlife Service Publication: Habitat Suitability Index Models: 

Black Bear, Upper Great Lakes Region 

Appendix C. U.S. Fish and Wildlife Service Publication: Habitat Suitability Index Models: 

Least Tern 

Appendix D. U.S. Fish and Wildlife Service Publication: Classification of Wetlands and 

Deepwater Habitats of the United States 
ACKNOWLEDGEMENTS

We thank Gerald Carney, Jack Hill, Richard Hines, Barbara Keeler, Joe Krystofik, Larry Mallard, Ryan Mollnow, 

Sharon Osowski, Betsy Smith, Kenneth Teague, and Donald Williams for their comments regarding this manuscript. 

Special thanks to Barbara Keeler and Norman E. Dyer of the U.S. Environmental Protection Agency’s Region 6

Office in Dallas, Texas for their support of this project. The authors thank the entire staff of the White River 

National Wildlife Refuge, Cache River National Wildlife Refuge, and the Bald Knob National Wildlife Refuge for 

their ongoing support of this research. 

DEDICATION

From Mark Twain’s, Life on the Mississippi - 1883 

Now when I had mastered the language of this water and had come to know every trifling 

feature that bordered the great river as familiarly as I knew the letters of the alphabet, I had 

made a valuable acquisition. But I had lost something, too. I had lost something which could 

never be restored to me while I lived. All the grace, the beauty, the poetry had gone out of the 

majestic river! I still keep in mind a certain wonderful sunset which I witnessed when 

steamboating was new to me. A broad expanse of the river was turned to blood; in the middle 

distance the red hue brightened into gold, through which a solitary log came floating, black 

and conspicuous; in one place a long, slanting mark lay sparkling upon the water; in another 

the surface was broken by boiling, tumbling rings, that were as many-tinted as an opal; where 

the ruddy flush was faintest, was a smooth spot that was covered with graceful circles and 

radiating lines, ever so delicately traced; the shore on our left was densely wooded, and the 

somber shadow that fell from this forest was broken in one place by a long, ruffled trail that 

shone like silver; and high above the forest wall a clean-stemmed dead tree waved a single 

leafy bough that glowed like a flame in the unobstructed splendor that was flowing from the 

sun. There were graceful curves, reflected images, woody heights, soft distances; and over the 

whole scene, far and near, the dissolving lights drifted steadily, enriching it, every passing 

moment, with new marvels of coloring. 

CHAPTER 1. LANDSCAPE ANALYSIS OVERVIEW

Project Overview and Summary of Regional Environmental Issues

The White River begins in mountainous northwestern Arkansas, flows through 

southwestern Missouri, reenters north central Arkansas, and flows down from the Ozark 

Mountains into Arkansas’ agricultural plain, where it meanders to its confluence with the 

Mississippi River (Figure 1). The catchment area of the White River Watershed (WRW; Figure 

2a) extends from the Fayetteville, Arkansas in the western Ozark Mountains to the Mississippi 

River, and drains from a wide range of landscapes containing farmland, upland forests, wetlands, 

lakes, streams, and urban areas (Figure 3). There are seven major dams that maintain large 

reservoirs along the White River, and a National Scenic River (Figure 1). Along the banks of the 

White River and its tributaries there are two National Wildlife Refuges, two National Forests, 

and a National Scenic River. The Cache River (a tributary to the White River; Figure 1) and its 

wetlands have been designated as Ramsar Wetlands of International Importance (Ramsar, 2002), 

along with 1,235 other wetlands around the world. The “Lower White River Region” (Figure 2a 

and Figure 2b) contains most of the White River channel that flows through Arkansas’ 

agricultural plain, which is a region currently dominated by row-crop agriculture (Figure 3) but 

also contains a large proportion of the Mississippi River Valley’s last-remaining bottomland 

hardwood wetland ‘swamps’ (Dahl, 1990; Figure 4). Thus, the Lower White River Region 

(LWRR) was used in this project, in addition to the entire Mississippi Alluvial Valley Ecoregion 

(MAVE), to assess the ecological vulnerability of habitat throughout the region (Figure 2a). 

 The LWRR is unusual because (a) it provides suitable habitat for the largest winter 

concentration of mallard ducks (Anas platyrhynchos) in North America, (b) provides necessary 

habitat for recovering populations of black bears (Ursus americanus), (c) provides critical shore 

habitat for the endangered populations of least terns (Sterna antillarum), and (d) provides some 

of the last-remaining habitat for wetland plants in the region (Carreker, 1985; Allen, 1987; 

Rogers and Allen, 1987). The White River aquatic ecosystems also support an important 

riverine fishery, including sturgeon (Scaphirhynchus albus and S. platorynchus) and paddlefish 

(Polyodon spathula), and aquatic plant communities within the bottomland hardwood swamps, 

which represent some of the most biologically diverse and productive ecosystems of the world 

(Mitsch and Gosselink, 1993). The White River and the surrounding landscape also contain 
valuable resources for the people living and working in the region because they provide people 

with plentiful irrigation water, clean drinking water, flood control, transportation for agricultural 

commerce, recreation, commercial shelling and fishing products, and tourism. 

 Although the predominant land cover in the LWRR and MAVE is currently agriculture, 

forest land-cover predominates in the Ozark Mountain region of the WRW (Figure 3). Closer 

inspection of the land cover throughout the WRW reveals that the landscape is a mosaic of many 

different discernable land cover types, such as dry “upland” forests, wetlands, human-built and 

populated areas, pastures, and row-cropped land. This mixture of land cover occurs, for 

example, as a gradient (i.e., a gradual change in the relative proportions) of land cover types 

from the northwestern corner of the WRW to the southeastern corner of the WRW (Figure 3). 

Similar gradients to this are used in this assessment to predict habitat suitability (i.e., the 

applicability of land cover to organismal requirements) for mallard ducks, black bears, least 

terns, and wetland plants; to predict habitat vulnerability (i.e., the risk of loss or damage of 

suitable habitat) for mallard ducks, black bears, least terns, and wetland plants; and to predict the 

vulnerability of surface water to impairment from runoff. Vulnerability predictions were 

performed with the use of currently available land cover data for the Lower White River Region, 

the Mississippi Alluvial Valley Ecoregion (Omernik, 1987), and the White River Watershed 

(Figure 2; U.S. GS, 1994). 

 Historically, the Lower White River Region, the Mississippi Alluvial Valley Ecoregion, 

and the White River Watershed have all undergone substantial alterations in land cover (Dahl, 

1990; Figure 4), particularly the conversion of wetlands (Figure 5) to agricultural land (Figure 6). 

Consequently, there has been tremendous biological and hydrologic change throughout the 

landscape of the region (Dahl, 1990; Mitsch and Gosselink, 1993), particularly in riparian 

wetlands (Figure 7; Figure 8). Although the majority of wetland losses in the region occurred 

prior to the 1970s, the trend has continued in Arkansas, Mississippi, and Louisiana as a result of 

wetland conversion (Johnston, 1989; Dahl and Johnson, 1991; The Nature Conservancy, 1992; 

Kress et al., 1996; Heggem et al., 2000; NRI, 2000). In particular, seventy-percent of Arkansas’ 

wetlands have been destroyed since the late nineteenth century (Dahl, 1990), a loss of 

approximately 28,000 square kilometers of wetland, with greater than 4,000 square kilometers of 

wetland loss occurring in Arkansas during the first half of the twentieth century (Shaw and 

Fredine, 1956). The ongoing losses of wetlands in the region have been positively correlated 
with regional and local losses of biological diversity (Gosselink and Turner, 1978; Ewel, 1990; 

Kilgor and Baker, 1996; Smith, 1996; Wakeley and Roberts, 1996); an increase in frequency, 

severity, and duration of flood events (Hopkinson and Day, 1980a; Hopkinson and Day, 1980b; 

Brown, 1984); and the degradation of downstream water quality (Kitchens et al., 1975; Day et 

al., 1977; Hupp and Morris, 1990; Hupp and Bazemore, 1993; DeLaune et al., 1996; Dortch, 

1996; Kleiss, 1996; Long and Nestler, 1996; Walton et al., 1996a; Walton et al., 1996b; Wilber 

et al., 1996). This study is a first step towards determining how landscape scale (i.e., broad 

scale) land-cover changes may have influenced habitat loss or degradation, and surface water 

quality. This map-based “atlas” geographically depicts and quantifies some of the 

aforementioned relationships, and endeavors to provide information for future land use planning 

in the Lower Mississippi River region. This atlas focuses primarily on the loss and degradation 

of wetlands because future development in the vicinity of the White River has great potential for 

altering wetland and aquatic ecosystem habitat conditions, regional hydrology, and water quality. 

The results in this atlas are timely because there are several land development projects and 

human activities in the vicinity of the White River that are planned or ongoing, each of which 

has the potential to damage or destroy a substantial portion of the remaining wetlands in the area. 

In general, planned or ongoing development activities in the area include: construction of dikes 

or channel modifications to increase river flow rates (personal communications with U.S. Fish 

and Wildlife Service and U.S. Army Corps of Engineers, 2001); agricultural irrigation projects 

that involve increasing the removal of surface water from the White River to supplement 

agriculture irrigation shortages (Jehl, 2002); modification of reservoir release schedules 

(personal communication with U.S. Fish and Wildlife Service, 2000); and road construction 

projects (Arkansas State Highway Commission, 2002). 

 The White River has never before undergone a landscape-scale ecological assessment of 

this kind despite the fact that it contains important and rare habitat, is one of the major tributaries 

to the Mississippi River, and contributes a large amount of nutrients to the Mississippi River and 

the Gulf of Mexico (Presely et al., 1980; Meade, 1996; Rabalais et al., 2002). Thus, an 

ecological vulnerability assessment of the White River and surroundings is warranted to 

understand better the landscape-scale ecological relationships in the region and to contribute to 

the decision-making processes during future land development planning. To this end, this atlas 

was completed using readily available geographic information system (GIS) data, provides 
several forms of maps, and uses simple model calculations that consist of understandable 

components. Thus, the results in this atlas may be easily browsed (in the digital version of the 

report) and analyses may be reconstructed, recalculated, or applied to other data sets in the 

future. 

Objectives

 This atlas addresses two project objectives: (1) determining the risks of habitat damage 

or loss within the LWRR and the MAVE (i.e., habitat vulnerability) and (2) determining the risks 

of water quality impairment within the WRW (i.e., water quality vulnerability). The GIS-based 

habitat models are applied to the entire MAVE and to the LWRR using similar techniques, but 

with different data sets because the larger MAVE does not have the full coverage of GIS data 

that is available for the LWRR (see Approach and Methods). The results depicted in this atlas 

further the goals of U.S. EPA’s Landscape Sciences Program [e.g., see Jones et al., 2000a] by 

specifically contributing to the progressive strategy of (i) detecting cross-sectional landscape 

change by comparing thematic data sets in a GIS environment; (ii) quantifying landscape 

change; (iii) investigating landscape metrics (e.g., percent forest cover); and (iv) developing 

landscape indicators (e.g., forest cover as a indicator of surface water total phosphorus 

concentration). 

Approach and Methods 

Habitat Vulnerability Assessment 

Mallard ducks, black bears, least terns, and wetland plants were selected for the GISbased habitat vulnerability modeling in this atlas because: (a) sufficient habitat suitability 

literature was available for all taxa; (b) GIS data coverages were available and sufficient to 

represent published habitat requirements for all taxa; (c) the selected taxa require either wetland 

or shoreline conditions during at least a portion of the year; (d) the selected taxa have either 

undergone a population decline at some time in the study region, are recovering, or are presently 

listed as endangered; and (e) the selected taxa are of special interest to local, regional, or national 

natural resource professionals. 
 The habitat vulnerability assessment objectives of this project were met by addressing 

five landscape-ecological hypotheses within the relatively fine-scale LWRR and relatively 

broad-scale MAVE (Figure 2). For clarity, the hypotheses are stated below as questions: 

Question 1. What are the gradients of land cover in the LWRR (Table 1a) and the MAVE 

(Table 1b)? 

Question 2. How do the observed gradients of land cover in the LWRR and the MAVE affect 

the suitability of habitat for mallard ducks (Table 2a), black bears (Table 2b), 

least terns (Sterna antillarum; Table 2c), and regional wetland plants (Reed, 1988; 

Table 2d) based on basic organismal requirements? 

Question 3. How do the observed gradients of patch size, patch shape, and human-induced 

disturbance within the LWRR and the MAVE (Table 3a) affect the vulnerability 

of mallard duck, black bear, least tern, and wetland plant habitat? 

Question 4. How is mallard duck habitat suitability (from question 2) and mallard duck habitat 

vulnerability (from question 3) distributed among the seventy-two refuge areas 

that comprise the White River National Wildlife Refuge (NWR), Cache River 

NWR, and Bald Knob NWR (Figure 9a)? 

Question 5. What mallard habitat losses are likely to occur in the South Unit of the White 

River NWR, given current habitat vulnerability and a hypothetical decrease in 

flooding of river-adjacent riparian wetlands (Figure 9a)? 

Water Quality Vulnerability Assessment 

 The water quality vulnerability assessment objectives of this project were met by 

addressing two landscape-ecological hypotheses within the WRW (Figure 2). For clarity, the 

hypotheses are stated below as additional questions: 

Question 6. What are the landscape gradients among twenty-five 8-digit hydrologic unit code 

(U.S. GS, 1994) ‘HUC subwatersheds’ (Figure 2a) based on current land cover 

(Table 1c)? 

Question 7. How do the observed landscape gradients of land cover potentially affect surface 

water quality in the study areas, based on previously published correlations and 
validation with 1990s National Water Quality Assessment (NAWQA) Program 

surface water physio-chemical data (U.S. GS, 2001)? 

Water quality vulnerability assessments were performed by measuring land cover within a given 

8-digit hydrologic unit code (HUC) subwatershed and within cumulative 30 m riparian zones to a 

maximum of 300 m from shorelines within a HUC. The land-cover-derived metrics (Table 1c 

and Table 3b) within each 30 m riparian zone (Figure 10) and within each complete HUC were 

then compared among HUCs to compare the scale-dependency of such measurements (Figure 

11). 

The Landscape-Ecological Perspective 

 This study makes use of ecological indicators, landscape metrics, and landscape 

indicators. For the purposes of this atlas an ‘ecological indicator’ is a sample measurement of an 

ecological resource (Bromberg, 1990; Hunsaker and Carpenter, 1990; Hunsaker et al., 1990), 

typically from field sampling (e.g., a total phosphorus concentration at a single gauging station 

on the Cache River). When measured at a relatively broad ‘landscape scale’ (Forman, 1995), 

‘landscape metrics’ (e.g., percent forest cover) that are characteristic of the environment, as 

measured by a sufficient sample size of ecological indicators can provide quantitative 

information about ecological resources at broad scales, and are referred to as ‘landscape 

indicators’ (Jones et al., 1997). In this atlas we selected landscape metrics (Table 3a and Table 

3b) that are correlated with ecological indicators at several scales, i.e., at relatively broad scales 

(e.g., Riitters et al., 1995; Jones et al., 2000b; Jones et al., 2000c; Jones et al., 2001), at moderate 

scales (e.g., van der Valk and Davis, 1980; Roth et al., 1996; Nagasaka and Nakamura, 1999; 

Fauth et al., 2000; Lopez et al., 2002; Lopez and Fennessy, 2002), and at single-site or 

mesocosm scales (e.g., Peterjohn and Correll, 1984; Murkin and Kadlec, 1986; Ehrenfeld and 

Schneider, 1991; Willis and Mitsch 1995; McIntyre and Wiens, 1999a; Luoto, 2000). The 

combined use of these previously observed correlations, GIS mapping techniques, and statistical 

analysis techniques across the LWRR, MAVE, and WRW study areas facilitated the 

determination of correlations between land cover gradients and ecological vulnerability at each 

scale, further aided by relatively rapid computing rates and large computer memory storage 

space available on currently available personal computers (Scott et al., 1993; Jones et al., 1997). 
This atlas makes use of different landscape gradients. Each gradient is a range of a 

condition, which is observed across a selected landscape unit (e.g., across the LWRR) or among 

reporting units (e.g., among HUCs or among wildlife refuges). Thus, the two important selection 

criteria for study areas in a landscape gradient assessment are a sufficient range of conditions 

along each landscape gradient of interest within a study area, and a sufficient number of sites to 

compare among reporting units (Green, 1979; Karr and Chu, 1997). Accordingly, this atlas uses 

a complete coverage of GIS data (i.e., a “wall-to-wall” coverage of land cover data) to produce 

maps in the LWRR, MAVE, and WRW study areas. An initial visual analysis of available GIS 

and remote sensing data (Table 1a, Table 1b, and Table 1c), and meetings with local experts 

from the Arkansas Game and Fish Commission, the Arkansas Natural Heritage Commission, the 

Arkansas Soil and Water Conservation Commission, The Nature Conservancy, the U.S. Army 

Corps of Engineers, the U.S. Environmental Protection Agency; and the U.S. Fish and Wildlife 

Service also indicated sufficient land cover variability within each study area to conduct valid 

gradient analyses. 

 There is an inherent trade-off between conducting site-based studies, which are limited 

by a lack of contextual information about the surrounding landscape, and landscape-scale 

studies, which are limited by a lack of detailed information about small areas. Therefore, 

although the landscape-scale assessments in this atlas are founded on the ecological principals of 

site-specific studies, the enclosed models may be limited by a lack of detailed information about 

small habitat areas. Thus, these models are intended as a preliminary screening tool for large 

areas that would otherwise be impractical to assess in the field. For best results, these GIS-based 

models should be used in combination with detailed field investigations. 

Data Reporting and Statistics 

The habitat suitability maps (Figure 11) depict the different habitat requirements for 

mallard ducks (Table 2a), black bears (Table 2b), least terns (Table 2c), and wetland plants 

(Table 2d). Habitat suitability maps are color-coded to best distinguish between habitat types 

and patches. Habitat vulnerability maps depict the relative risk of habitat loss or damage for 

each taxon or guild (i.e., wetland plants) as a result of patch size, patch shape, human-induced 

disturbance(s), or a combination of these parameters. Habitat vulnerability maps are color-coded 
from greatest vulnerability (red tones) to least vulnerability (blue tones), based on standard 

deviations from the mean of each vulnerability parameter (Figure 11). 

In each water quality vulnerability map the data range in the legend shows the metric 

values within each of the 8-digit HUC subwatersheds, contained in each of five quintiles (Figure 

11). A quintile contains one-fifth of the distribution of each watershed metric. Quintiles are 

formed after ranking watersheds for each metric. The map of the WRW is color-coded to show 

relative conditions among watersheds, ranging from red (i.e., greatest vulnerability) to green 

(i.e., least vulnerability), potentially indicating a relative range in terms of surface water quality 

impairment from nutrient or sediment loading. 

Because one or both of the assumptions of parametric statistics tests (normality and 

equality of variance) are violated in all of the data, correlation analyses for all parameters were 

completed with Spearman Rank Correlation (Zar, 1984), α = 0.05. All statistical analyses were 

computed with Statview software (SAS Institute, v.5.0.1, Cary, North Carolina). All GIS 

calculations, gradient analyses, and mappings were performed using ArcView GIS software 

(Environmental Systems Research Institute, v.3.2, Redlands, California) and ATtILA GIS 

software (U.S. EPA, v.2.999, Las Vegas, Nevada). To allow for visual comparison of relative 

habitat vulnerability across the study area landscapes, vulnerability each metric was displayed as 

a mean value and standard deviations from the mean (Figure 11). 
CHAPTER 2. HABITAT ASSESSMENTS

Study Areas 

The Lower White River Region (LWRR) study site is an 8,970 square km region in 

eastern Arkansas (Figure 2b). The LWRR study site was selected such that it encompassed the 

White River National Wildlife Refuge (NWR), Cache River NWR, Bald Knob NWR (Figure 9), 

and a full range of land cover observed throughout the region. The final decision about the 

extent of the LWRR study area was determined by the geographic availability of digital National 

Wetland Inventory data (Table 1a). All LWRR habitat maps are overlaid on a grayscale Landsat 

Enhanced Thematic Mapper (ETM+) satellite image (single near infra-red band, 30 m spatial 

resolution) displaying a minimum of an additional 1500 m region outside the study boundary. 

The satellite image, county boundaries, reference cities, and federal wildlife refuge boundaries 

may be used to identify specific locations on all LWRR maps, referencing Figure 2b. 

The Mississippi Alluvial Valley Ecoregion (MAVE) study site is a 233,782 square km 

region in the Lower Mississippi River Valley, which primarily intersects Louisiana, Mississippi, 

Arkansas, and Missouri and small portions of Illinois and Kentucky (Figure 2). The MAVE 

delineates a regional area that contains similar land uses, soils, land surface forms, and potential

 natural vegetation types (Omernik, 1987). The MAVE contains the entire LWRR study area, 

and overlaps with a portion of the WRW (Figure 2a and Figure 2c). All MAVE habitat maps

are overlaid on county and state boundaries, which may be used to identify specific locations

 throughout the MAVE, referencing Figure 2c. 

How to Navigate and Interpret Habitat Assessment Maps

Figure 11 depicts the two types of habitat assessment maps that appear in this chapter 

along with a brief explanation of each map element. The habitat suitability maps depict the 

different parameters for each of the selected taxa, in accordance with available literature for 

mallard ducks (Table 2a), black bears (Table 2b), least terns (Table 2c), and wetland plants 

(Table 2d). Habitat suitability maps are color-coded to best distinguish between habitat types 

and patches, and are not intended to imply relative importance of habitat type(s) for any taxon. 

Habitat vulnerability maps are consistently color-coded from greatest vulnerability (red tones) to 
least vulnerability (blue tones), based on standard deviations from the mean of a particular 

vulnerability parameter, and are intended to imply the relative risk of loss or damage to each 

individual patch of habitat for the taxon or guild indicated on the map (Figure 11). 

Specific locations on all maps may be examined in detail with the aid of the zoom and 

pan buttons in the icon bar if you are using the digital version of this atlas. For both the LWRR 

and MAVE study areas a very small percentage (less than 4 percent by area) of the wetland 

habitat assessed intersects with the study area boundary. These ‘edge patches’ have no effect on 

the habitat suitability maps. The habitat patches that intersect the study area boundary exert a 

minimal effect on the distribution of relative habitat vulnerability classes, which are based on a 

mean vulnerability parameter (Figure 11). 

Habitat Suitability

 The ecological bases of the habitat suitability models in this atlas are summarized below. 

The GIS-based habitat suitability models in this atlas use specific habitat requirements for 

mallard ducks, black bears, least terns, and wetland plants, to the extent that digital data is 

available to model their ecology in prior field research. The following ecological overview of 

organisms is condensed to include the information relevant to the GIS models contained within 

this atlas. 

Mallard Duck Winter Habitat (summarized from Appendix A) 

 The Lower Mississippi Valley is the primary wintering habitat for mallard ducks in the 

Mississippi Flyway (Bellrose, 1976), resulting in a residence of an estimated 1.6 million ducks 

during the winter months (Bartonek et al., 1984). A wide variety of wetland hydrologic and 

vegetational conditions are required to meet different habitat requirements of various mallard 

sexes, ages, and behavioral segments of the mallard population. Generally, winter habitat 

conditions in the Lower Mississippi region influence all aspects of the socio-biology of mallards, 

which in turn affects the fecundity and survival of mallards. For example, mallard ducks in the 

Lower Mississippi region typically move 1.6 km to 8 km from roost sites to foraging areas. 

Flights of greater than 8 km are typically a response to changes in hydrology, temperature, 

depleted food resources, or other disturbances (Jorde et al., 1983). 
Mallard ducks are omnivores that feed on available foods that include aquatic 

invertebrates; wetland plant seeds, fruits, rootlets, and tubers; mast from trees; agricultural 

grains; mollusks; insects; small fish; fish eggs; and amphibians (Heitmeyer, 1985; Allen, 1987; 

Appendix A). Consequently, wetlands and other open water areas are extremely important for 

mallard ducks because such areas are where most of the naturally occurring duck food occurs. 

The timing of flooding, flood depth, and the duration of wetland flooding is a critical factor in 

determining the diversity and availability of organisms upon which mallard ducks feed 

(Fredrickson, 1979; Heitmeyer and Fredrickson, 1981; Nichols et al., 1983; Heitmeyer, 1985; 

Allen, 1987), and much larger numbers of mallards have been observed in the Lower Mississippi 

Valley when these wetter conditions exist (Nichols et al., 1983), particularly in the forested 

wetlands (Heitmeyer, 1985). Mallards typically feed from the water’s surface to a maximum of 

50 cm (Heitmeyer and Fredrickson, 1981; Krapu, 1981; Batema et al., 1985; Allen, 1987). Thus, 

the depth of flooding, the duration of flooding, and the type of vegetation, as it relates to food 

resources, is cited as the primary determiner of mallard duck habitat suitability (Allen, 1987). 

Accordingly, these factors were used to establish suitable mallard habitat and to distinguish 

between mallard habitat patches of differing suitability characteristics (Table 2a). Although 

some of the agricultural land in the LWRR is managed to provide winter mallard forage, 

agricultural grains are not a complete substitute for natural foods (Frederickson and Taylor, 

1982; Baldassarre et al., 1983; Jorde et al., 1984). Therefore, agricultural land was not 

considered to be suitable mallard habitat relative to non-agricultural wetland and was not used in 

the habitat vulnerability assessments in this atlas. A hierarchical schematic of the mallard duck 

habitat suitability model construction process is shown in Figure 12, depicting the links between 

the original digital data, the habitat suitability model, and the habitat vulnerability models. Black 

bear GIS models and wetland plant GIS models follow processes that are similar to the mallard 

duck GIS models, but use different GIS data layers. 

Because all of the naturally occurring and diverse habitat requirements of mallard ducks 

occur in wetlands, mallards require access to a variety of wetland types, including emergent 

wetlands (Figure 13), scrub/shrub wetlands (Figure 14), forested wetlands (Figure 15), 

unconsolidated bottom wetlands (Figure 16), aquatic bed wetlands (Figure 17), and the open 

water areas of lakes, impoundments, or rivers (Figure 18). The wetland types described in this 

atlas are based on the wetland classes and hydrologic modifiers used in the National Wetland 
Inventory (NWI; after Cowardin et al., 1979), as they apply to mallard duck habitat requirements 

(Figure 19). Specifically, mallard duck winter habitat suitability models in the LWRR and 

MAVE are based on optimal foraging-related habitat requirements of: (a) available wetland 

habitat; (b) the hydroperiod within a wetland (i.e., either temporarily-flooded/seasonally-flooded, 

permanently flooded, or dry); (c) the presence of woody vegetation; and (d) the presence of 

desirable mast producing oaks, specifically excluding overcup oak (Quercus lyrata) which 

produce acorns up to 2.5 cm long and are thus less suitable oak mast for forage (Allen, 1987; 

Table 2a). We chose mallard ducks as one of the modeled taxa for this atlas because they are 

abundant and ubiquitous, are dependent upon wetlands for most of the year, have well 

documented habitat requirements, require habitat that can be readily mapped using GIS data, and 

are a species that has recovered from previously lower numbers in the study area and throughout 

the Mississippi Flyway. 

Black Bear Wetland Habitat (summarized from Appendix B)

 Black bears are found throughout North America and, in the LWRR and MAVE, have a 

mean female home range from 9 km2

 to 12 km2. Male home ranges have been reported as large 

as 116 km2

 to 148 km2 (Pelton, 1982; Klepinger and Norton, 1983; Smith, 1985), while others 

have reported male home ranges from 13 km2 to 24 km2 (Rogers, 1992). The home ranges of 

male and female black bears are primarily dependent on the availability of resources (Jonkel and 

Cowan, 1971; Amstrup and Beecham, 1976; Garshelis and Pelton, 1980; LeCount, 1980; 

Reynolds and Beecham, 1980; McArthur, 1981; Elowe, 1984; Rogers, 1987), and are also 

influenced by population density, age of the individual, and seasonal conditions (Pelton, 1982). 

In 1997, one hundred eighty-seven black bears were legally killed in Arkansas using 

muzzleloader, modern gun, archery, and crossbow hunting techniques, solely in the mountainous 

regions. As a result of increasing numbers of black bears in the vicinity of the LWRR, hunting 

of black bears within selected areas to the west of the Mississippi River, using modern guns, 

became legal in 1999 excluding the White River National Wildlife Refuge (Arkansas Game and 

Fish Commission, 1998). 

 Black bears are omnivores that typically feed on easily digestible vegetative foods that 

are high in nutrients and low in cellulose (Rogers, 1976; Herrero, 1978; Herrero, 1979; Rogers, 

1987). Thus, the typical black bear diet consists of fruits, nuts, acorns, insects, and early-
sprouting green vegetation (Mealey, 1975; Herrero, 1979; Rogers, 1987). When naturally 

growing food items are scarce, black bears may alternatively feed on agricultural crops, such as 

orchard fruits or corn or at human-constructed food sources, such as centralized refuse disposal 

sites or at the residences of humans (Harger, 1967; Bray, 1974; Rogers, 1976; Rogers et al., 

1976; Hugie, 1979; Landers et al., 1979; Beeman and Pelton, 1980; Rogers, 1987). Scarcity of 

naturally growing foods for black bears has been positively correlated with occurrences of bear 

cannibalism (Tietje et al., 1986) and the occurrences of bear-human interactions. Black bears in 

the LWRR and MAVE may remain active throughout the winter to feed on corn and other foods 

if other naturally growing foods are scarce (Carpenter, 1973; Matula, 1974; Lindzey et al., 1976; 

Rogers, 1976; Hamilton, 1978; Hamilton and Marchington, 1980; Elowe, 1984). Black bear 

predation on vertebrates is relatively rare but such captures in the LWRR and MAVE may 

include newborn deer, nestling birds, fish, or other animals whose escape is hampered (Rowan, 

1928; Barmore and Stradley, 1971; Frame, 1974; Cardoza, 1976; Ozoga and Verme, 1984). 

 Wetlands and other open water areas are extremely important for black bears to survive 

because, aside from providing much of the food resources that they require, such areas are 

frequently the sole resource for drinking water and for providing water in which they can cool 

themselves. The home ranges of black bears are also closely tied to forested areas (Herrero, 

1979; Hugie, 1979; Pelton, 1982), and are limited by the fact that much of the remaining forested 

areas in the LWRR and MAVE are fragmented and therefore relatively inaccessible (Cowan, 

1972, Maehr and Brady, 1984; Twedt et al., 1999). Black bears in relatively fragmented 

landscapes, like the MAVE, are frequently observed in forest openings and clearings, which may 

provide a relatively higher degree of edge vegetation diversity than core forest areas (Herrero, 

1979; Hugie, 1979). Wetlands, particularly in riparian areas, are used by black bears for 

seasonal foraging; denning; cover for escape; and as travel corridors. Thus, as a result of the 

diverse resource and travel corridor requirements, black bears require access to a variety of 

wetland types and resources, including emergent wetlands (Figure 13), scrub/shrub wetlands 

(Figure 14), forested wetlands (Figure 15), unconsolidated bottom wetlands (Figure 16), aquatic 

bed wetlands (Figure 17), and the open water areas of lakes, impoundments, or rivers (Figure 

18). Specifically, the black bear habitat suitability models in the LWRR and MAVE are based 

on optimal habitat requirements of: (a) available wetland habitat; (b) the presence of woody 

vegetation; (c) the number of plant species within a patch, and (d) evidence of forest disturbance 
since the 1950s (Table 2b). We chose black bears as one of the modeled taxa for this atlas 

because they are a recovering species in the region, are dependent upon wetlands for most of the 

year, have well documented habitat requirements, and require habitat that can be readily mapped 

using GIS data. 

Least Tern Breeding Habitat (summarized from Appendix C) 

 Least terns breed along marine and freshwater coastal areas of North, Central, and South 

America and the Caribbean Islands (American Ornithologists’ Union, 1983). There are three 

subspecies of least terns in the United States, including the interior least tern (Sterna antillarum 

athalassos), which is modeled in this atlas. The interior least tern breeds along the major 

tributaries of the Mississippi River (Ducey, 1981; Cobb, 1992; U.S. ACOE, 1999) and within the 

Rio Grande River watersheds (Downing, 1980). The U.S. Fish and Wildlife Service lists the 

least tern (Sterna antillarum) as a federally endangered species (Endangered Species Act, 1973). 

The other two subspecies of least terns, not modeled in this atlas, are the eastern least tern (S. a. 

antillarum) that breeds along the Atlantic and Gulf of Mexico coasts (American Ornithologists’ 

Union, 1983), and the California least tern (S. a. browni) that breeds from San Francisco Bay to 

southern Baja California, Mexico (California Least Tern Recovery Team, 1980). 

 Least tern breeding habitat is generally characterized as open sand, soil, or dried mud in 

the proximity of water (Hardy, 1957; Craig, 1971; Massey, 1971; Massey and Atwood, 1982; 

Appendix C). Foraging areas of interior least terns include rivers, lakes, ponds, sloughs, and 

borrow pits (Ganier, 1930). Foraging areas of least terns are usually close to breeding areas, 

with typical ranges of breeding individuals reported from 100 m to 1.6 km, and as far away as 

6.4 km (Jernigan et al., 1978; Hays, 1980; Massey and Atwood, 1981; Atwood and Minsky, 

1983; Faanes, 1983; Carreker, 1985). Least terns tend to inhabit ephemeral sandy shorelines 

year after year (Burger, 1984) even if reproduction has declined in prior years at these locations 

(Massey and Atwood, 1979). Site intolerance among least terns is related to vegetation 

encroachment (Burger, 1984); beach erosion (Downing, 1973); human-related disturbances that 

result from replacement of natural land cover with built structures (Chambers, 1908; Massey and 

Atwood, 1980; Ducey, 1981; Grochfeld, 1983); replacement of natural land cover with 

agricultural land (Schulenburg and Ptacek, 1984); and river channel deepening (Downing, 1980). 

Least terns are predominantly fish-eating birds, hovering and diving from 3 m to 10 m above the 
surface of the water (Hardy, 1957; Tomkins, 1959; Moseley, 1976). Least terns may also skim 

the surface of the water for prey (Bent, 1921; Oberholser, 1974) and occasionally feed on insects 

over land (Bent, 1921; McDaniel and McDaniel, 1963; Moseley, 1976; Schulenberg et al., 1980). 

In general, adult least terns consume fish in the 2.5 cm to 9 cm length range (Massey, 1974; 

Moseley, 1976; Massey and Atwood, 1980; Schulenberg et al., 1980). Accordingly, least tern 

habitat suitability models in the LWRR and MAVE are based on the optimal habitat 

requirements of available bare shoreline of lakes, impoundments, and rivers (Figure 20) during 

the nesting season from June through August (Table 2c). The relative rarity of bare shoreline 

compared to other land cover types in the LWRR and MAVE eliminated the necessity for further 

division of habitat characteristics in this atlas, such as the proximity of breeding areas to foraging 

habitat. We chose least terns as one of the modeled taxa for this atlas because they are an 

endangered species, dependent upon wetlands and aquatic ecosystems for most of the year, have 

well documented habitat requirements, and require habitat that can be readily mapped using GIS 

data. 

Wetland Plant Habitat

 Wetland plants are adapted to surviving in soils that are saturated with water (and 

consequently have a low oxygen content). Plants that flourish in wetlands are thus generally 

referred to as hydrophytes, but may also include plants that can survive with only brief periods of 

soil saturation, and the low-oxygen soil conditions that accompany soil saturation (Reed, 1988; 

Appendix D). Wetland plant species in the LWRR and the MAVE are numerous and provide 

cover and forage for other organisms in emergent wetlands (Figure 13), scrub/shrub wetlands 

(Figure 14), forested wetlands (Figure 15), aquatic bed wetlands (Figure 17), and in the littoral 

zones of some lakes, impoundments, and rivers (Figure 18). Accordingly, wetland plant habitat 

suitability models in the LWRR and MAVE are based on the optimal wetland plant habitat 

requirements of: (a) the presence of wetland conditions (particularly, the presence of wetland 

soil types; Mitsch and Gosselink, 1993) and (b) the hydroperiod within each wetland type (Table 

2d). We chose the collective group (i.e., a guild) of regional wetland plant species for this study 

because they are strictly limited to the geographic extent of wetlands, are strictly dependent upon 

wetland hydrology, have well documented habitat requirements, have well understood 
physiological responses to hydrologic and other physical disturbances, and require habitat that 

can be readily mapped using GIS data. 

Habitat Suitability Maps 

 Wetland and upland habitat suitability is mapped for mallard ducks (LWRR, Figure 21a; 

MAVE, Figure 24a), black bears (LWRR, Figure 21b; MAVE, Figure 24b), and least terns 

(LWRR, Figure 21c and Figure 21d; MAVE, Figure 24c and Figure 24d). Maps that solely 

include wetland habitat are displayed for mallard ducks (LWRR, Figure 21e; MAVE, Figure 

24e), black bears (LWRR, Figure 21f; MAVE, Figure 24f), and wetland plants (LWRR, Figure 

21g; MAVE, Figure 24g). 

 Wetland habitat patches less than or equal to 2 ha were mapped separately for mallard 

ducks (LWRR, Figure 21h; MAVE, Figure 24h), black bears (LWRR, Figure 21i; MAVE, 

Figure 24i), and wetland plants (LWRR, Figure 21j; MAVE, Figure 24j) because these smaller 

patches of wetland habitat account for 20,093 cumulative ha of wetlands in the LWRR and 

196,360 cumulative ha of wetlands in the MAVE. These smaller wetlands are frequently 

overlooked, individually and collectively, in habitat assessments (Klett and Kirsch, 1976; 

Robinson, 1995; Naugle et al., 2002; Stevens et al., 2002). Thus, we included habitat suitability 

for patches less than or equal to 2 ha, but did not measure habitat patch vulnerability further in 

these smaller patches because of the accuracy limitations of the GIS data and the minimum 

mapping unit for each model (Table 1a and Table 1b). Suitable wetland habitat patches greater 

than 2 ha are mapped for mallard ducks (LWRR, Figure 21k; MAVE, Figure 24k), black bears 

(LWRR, Figure 21l; MAVE, Figure 24l), and wetland plants (LWRR, Figure 21m; MAVE, 

Figure 24m). The habitat suitability maps provide the bases for the habitat vulnerability models 

in the remainder of this atlas. 

Habitat Vulnerability

 Because the GIS models in this atlas are based on 30 m resolution data (e.g., AR-GAP in 

the LWRR; NLCD in the MAVE), caution should be exercised when using these results for fine 

scale interpretation (see Habitat Suitability section regarding habitat patches of 2 ha or less). The 

edge of each habitat patch was defined by a change in the wetland habitat suitability class for a 

particular taxa or guild (i.e., wetland plants). Thus, habitat vulnerability was determined for all 
suitable wetland habitat patches in the LWRR and MAVE for mallard duck, black bear, and 

wetland plants if a habitat patch was greater than 2 ha. All least tern habitat is extremely rare, 

and thus extremely vulnerable to loss or damage (LWRR, Figure 21c and Figure 21d; MAVE, 

Figure 24c and Figure 24d). Because of the relative rarity and minimal total area of least tern 

habitat, vulnerability measures were not calculated for their habitat patches. 

 Habitat vulnerability measures (Table 3a) within habitat patches for each taxa are based 

on patch size, patch shape, human-induced disturbances, and combinations of these metrics. All 

of the habitat vulnerability metrics within a habitat patch may affect the likelihood that a 

particular habitat patch will rebound after patch disturbance(s). That is, habitat vulnerability 

metrics are based on predicted habitat degradation as a result of patch destruction (i.e., total 

loss), patch fragmentation (i.e., partial loss), or patch degradation (i.e., stress) [after Odum, 

1985). 

 Patch size metrics in this atlas (Table 3a) are based on previously observed ecosystem 

trends regarding the effects of patch size on habitat quality for specific taxa, in many different 

regions (e.g., MacArthur and Wilson, 1967; Simberloff and Wilson, 1970; Diamond, 1974; 

Forman et al., 1976; Pickett and Thompson, 1978; Soule et al., 1979; Hermy and Stieperaere, 

1981; van der Valk, 1981; Simberloff and Abele, 1982; McDonnell and Stiles, 1983; Harris, 

1984; McDonnell, 1984; Moller and Rordam, 1985; Brown and Dinsmore, 1986; Dzwonko, and 

Loster, 1988; Gutzwiller and Anderson, 1992; Opdam et al., 1993; Hamazaki, 1996; Kellman, 

1996; Bastin and Thomas, 1999; McIntyre and Wiens, 1999a; McIntyre and Wiens, 1999b; 

Twedt and Loesch, 1999; Jones et al., 2000b; Lopez et al., 2002; Lopez and Fennessy, 2002). 

Accordingly, the ‘patch size’ habitat vulnerability models map the habitat patch area, habitat 

patch perimeter length, and habitat patch interior-to-edge ratio in the LWRR (Figure 22) and the 

MAVE (Figure 25). Smaller habitat patches (as measured by area, perimeter, or interior-to-edge 

ratio) are relatively less likely to rebound from disturbances (i.e., are more likely to be 

fragmented or destroyed after changes in hydrology, destruction of vegetation, or the 

establishment of opportunistic flora and fauna) than larger habitat patches. 

 The ecological vulnerability metrics in this atlas regarding patch shape (Table 3a) are 

based on trends previously observed for specific taxa, in many different regions (e.g., MacArthur 

and Wilson, 1967; Gilpin, 1981; McDonnell and Stiles, 1983; Harris, 1984; McDonnell, 1984; 

Gutzwiller and Anderson, 1992; Hamazaki, 1996; Kellman, 1996; Bastin and Thomas, 1999; 
Jones et al., 2000b; Lopez et al., 2002; Lopez and Fennessy, 2002), and include patch ‘sinuosity’ 

(after Bosch, 1978; Davis, 1986) in the LWRR (Figure 22) and the MAVE (Figure 25). Patches 

with a smaller sinuosity index are less winding or convoluted in shape, thus are relatively less 

likely to rebound from disturbances than patches with a greater sinuosity index. That is, smaller 

index values indicate that a habitat patch has a greater likelihood of fragmentation or loss as a 

result of environmental change, such as changes in hydrology, destruction of vegetation, or the 

establishment of opportunistic flora and fauna. Another metric of patch shape is patch 

‘circularity’ (after Stoddart, 1965; Unwin, 1981) in the LWRR (Figure 22) and the MAVE 

(Figure 25). Habitat patches with a smaller circularity index are more circular in shape, thus are 

relatively less likely to rebound from disturbances than patches with a larger circularity index. 

That is, smaller index values indicate that a habitat patch has a greater likelihood of 

fragmentation or loss as a result of environmental change, such as changes in hydrology, 

destruction of vegetation, or the establishment of opportunistic flora and fauna. The unified 

patch index (Table 3a) multiplicatively combines the patch interior-to-edge metric, the patch 

sinuosity index, and the patch circularity index into a single metric that depicts patch area, patch 

perimeter, and patch shape simultaneously as a measure of habitat patch vulnerability in the 

LWRR (Figure 22) and the MAVE (Figure 25). 

 Human-induced disturbance factors within habitat patches in the LWRR and MAVE 

(Table 3a) are based on previously observed positive correlations between ecosystem 

degradation and amount of land cover conversion during road construction, road maintenance, 

and other human-activities (e.g., Connell and Slatyer, 1977; van der Valk, 1981; Ehrenfeld, 

1983; Johnston, 1989; Scott et al., 1993; Johnston, 1994; Poiani and Dixon, 1995; Strittholt and 

Boerner, 1995; Jenning, 1995; Wilcox, 1995; Ogutu, 1996; Stiling, 1996; Heggem et al., 2000; 

Lopez et al., 2002; Lopez and Fennessy, 2002). Thus, the directly measurable human-induced 

disturbance metrics within habitat patches are road length and road density in the LWRR (Figure 

23) and the MAVE (Figure 26). Patches with greater total road length or road density (or their 

combined index value; Table 3a) are relatively less likely to rebound from disturbances than 

patches with a lesser road presence. That is, greater road length, road density, or road index 

values indicate that a patch has a greater likelihood of fragmentation or loss as a result of 

environmental change, such as changes in hydrology, destruction of vegetation, or the 

establishment of opportunistic flora and fauna. The increased presence of roads within a patch 
may also bring about a decrease in patch size or shape metrics. The road index additively 

combines road length and road density metrics to depict the combined potential habitat 

degradation effects of road construction and maintenance on habitat patches in the LWRR 

(Figure 23) and the MAVE (Figure 26). 

 The indirect metrics of human-induced disturbance within habitat patches are human 

population density in 1990, estimated human population density in 2011 (i.e., future human 

population density), and estimated human population density change from 1990 to 2011 for the 

LWRR (Figure 23) and the MAVE (Figure 26). The human population density change metric 

was normalized to a positive number by adding 50 to the calculation for the LWRR, and by 

adding 8000 to the calculation for the MAVE models (Table 3a). Habitat patches that exist 

within in census block groups (Table 1a and Table 1b) with a greater population density now, or 

in the future are relatively less likely to rebound from disturbances than patches that exist in 

areas of lesser population density, now or in the future. That is, greater human population 

density values indicate that a habitat patch has a greater likelihood of fragmentation or loss as a 

result of environmental change, such as changes in hydrology, destruction of vegetation, or the 

establishment of opportunistic flora and fauna. The increased presence of humans near habitat 

patches may also bring about a decrease in patch size metrics, patch shape metrics, an increase in 

road length, or an increase in road density. The unified human index additively combines road 

length, road density, and human population density change (from 1990 to 2011) to depict the 

combined effects of direct and indirect human-induced disturbance in the LWRR (Figure 23) and 

the MAVE (Figure 26). The unified vulnerability index (Table 3a) additively combines the 

unified patch index, direct human-induced disturbance metrics, and indirect human-induced 

disturbance metrics, so that the combined effects of patch size, patch shape, road metrics, and 

human population density metrics can be depicted simultaneously in the LWRR (Figure 23) and 

the MAVE (Figure 26). Applications of the habitat vulnerability metrics to wildlife refuges and 

for landscape simulations are described in Chapter 4.
CHAPTER 3. WATER QUALITY VULNERABILITY ASSESSMENTS

Study Area 

The White River Watershed (WRW) study site is a 101,533 square km region in 

Arkansas and southern Missouri (Figure 2a and Figure 2c). The study site was selected such that 

it encompassed the twenty-five 8-digit HUC subwatersheds (Figure 27) comprising the entire 

catchment area of the White River, and contained a full range of the land cover types in the 

region (Figure 3). Thirty-eight National Water Quality Assessment (NAWQA) surface water 

sampling locations are located within the WRW (Figure 27). Field-based water quality data at 

the thirty-eight NAWQA locations were used to validate some of the metrics described in this 

chapter, and were sampled from 1992 through 2000 in the Spring and Summer months (U.S. GS, 

2001). All land cover metric maps in this atlas are overlaid on 8-digit HUC subwatershed 

reference boundaries (Figure 27) so that they may be referenced in relationship to the locations 

of counties in the WRW (Figure 2c). Additional information about a specific watershed may be 

accessed at the following URL: http://www.epa.gov/surf/. 

How to Navigate and Interpret Water Quality Vulnerability Maps

Figure 11 illustrates the types of watershed-based land cover maps that appear in this 

chapter. Using the land cover types provided in the National Land Cover Dataset (NLCD; Table 

4), several land cover metrics were developed to assess the vulnerability of surface water to 

pollution from various land cover types and configurations (Table 3b), including the proximity of 

land cover to surface water. Measurements of land cover in proximity to surface water were 

calculated by intersecting a ‘buffer zone’ around each surface water body (Figure 10) with each 

land cover metric (Table 3b). These measurements were then used to create a watershed map 

that depicts the percent of a selected land cover metric at the bank, and within cumulative 30 m 

buffer zone intervals (Figure 11) from all water bodies. For each map, the data range in the 

legend shows the metric values within each of the 8-digit HUC subwatersheds, contained in each 

of five quintiles. A quintile contains one-fifth of the distribution of each watershed metric. 

Quintiles are formed after ranking watersheds for each metric. The map of the WRW is colorcoded to show relative conditions among watersheds, ranging from red (i.e., greatest 
vulnerability) to green (i.e., least vulnerability), potentially indicating a relative range in terms of 

surface water quality impairment from nutrient or sediment loading. 

Water Quality Vulnerability 

 Land-cover gradients within the White River Watershed were used to develop landscape 

indicators of surface water quality at a broad scale. The theoretical relationships between 

watershed land cover and the surface water chemistry within the WRW are based on previously 

published field results. For example, there is a strong negative correlation between the presence 

of ‘natural vegetation’ (i.e., trees, grassland, and wetland vegetation) and the amount of 

nutrients (e.g., nitrogen and phosphorus) and sediment in downstream surface water (Gregory et 

al., 1991; Valett et al., 2002). Peterjohn and Correll (1984) demonstrated that the negative 

correlation between the presence of natural vegetation in riparian zones and nutrient loading 

from agricultural land to streams is primarily a result of nutrient uptake in the root zone, and 

assimilation of these nutrients into vegetative plant parts. Other research strongly suggests that 

the high degree of clay and organic components in wetland soils, anoxic soil conditions, and low 

through-flow in wetlands (in combination with natural vegetation) are responsible for the strong 

negative correlations observed between the presence of ‘natural areas’ and the concentrations of 

nutrients, sediment, or trace metals in connected surface water bodies (e.g., Hey et al., 1989; 

Johnston, 1989; Gregory et al., 1991; Poiani et al., 1996; Fennessy and Cronk, 1997; Crumpton 

and Baker; 1998l Giese et al., 2000). Historically, the loss of forests and other natural vegetation 

in the MAVE is a result of the expansion of agricultural land development (see Chapter 1) and 

there is a strong positive correlation between the presence of agricultural land cover and the 

amount of nutrients and sediment in downstream surface water bodies (e.g., Johnston et al., 

1965; Triplett et al., 1969; Romkens et al., 1973; Hergert et al., 1981; Robinson and Sharpley, 

1995). Therefore, we selected road, agriculture, and natural vegetation metrics (NLCD codes 31, 

41, 42, 43, 51, 71, 91, 92 in Table 4) as potential landscape indicators of surface water quality, 

and mapped these metrics among HUC subwatersheds in the WRW (Figure 27). 

Roads and Agriculture 

 There are 132,042 kilometers of streams and 130,165 kilometers of roads in the WRW. 

Information about the location of surface water in the WRW, including the location of streams, 
rivers, lakes, impoundments, and ditches (Figure 28) was used in combination with information 

about the location of roads (Figure 29) to determine the number of streams within thirty meters 

of roads within the WRW (Figure 30). The placement of roads in close proximity to streams is a 

surface water quality impairment risk because runoff from paved surfaces flows relatively 

quickly to low areas (e.g., streams or lakes) and carries with it any substances that spill onto 

roads. These substances can include hydrocarbons, metals, or other chemicals that are toxic to 

plants or animals (Warren, 1981; Carter, 1982; Merian, 1990; Ehrenfeld and Schneider, 1991). 

Information about the location of current land cover in the WRW from the NLCD (Figure 

3; Table 4) was used to determine the percent of all agriculture (Figure 31; NLCD codes 81, 82, 

83, and 85 in Table 4), percent crop agriculture (Figure 32; NLCD codes 82, 83, and 85 in Table 

4), and percent agricultural pasture (Figure 33; NLCD code 81 in Table 4) within each HUC 

subwatershed (Figure 27). Greater amounts of agricultural activities within a HUC subwatershed 

increase the risks of surface water quality impairment because of the greater potential for soil 

loss from tilled land, runoff of agricultural fertilizers and pesticides, or the runoff of livestock 

byproducts. Modifying tilling practices may mitigate soil loss and runoff from crop-agricultural 

land, because less soil is disturbed during plowing. 

Information about the slope of the terrain within the WRW (Figure 34) was used in 

combination with the land cover information from the NLCD to determine the percent of area 

within each HUC subwatershed that contains agriculture on slopes greater than three percent 

(Figure 35). Agricultural activities on steep slopes are a surface water quality impairment risk 

because of the increased likelihood of soil erosion and loss to downhill areas and streams. Soil 

loss and runoff on steep slopes may be mitigated by soil conservation practices such as terracing 

pastures and fields parallel to the elevational contours of the land. 

We selected ‘percent total agriculture’ for validity testing, using the 1990s NAWQA field 

data, because it is an easily measurable variable that is relatively unchanging throughout the 

WRW, thus repeatable with other available data sets. Significant positive correlations between 

percent total agriculture and surface water concentrations of dissolved organic carbon (DOC), 

amino and organic nitrogen (OrgN), total phosphorus (P), and suspended sediment (Sed) [Table 

5] suggest that ‘percent total agriculture’ is an appropriate landscape indicator of actual increases 

in nutrient and sediment concentrations in surface water of the WRW. The same significant 

positive correlation trend exists for ‘percent total agriculture’ within the riparian zone, up 
through 300 m from shorelines (Figure 31). The significant positive correlations between 

surface water chemistry measurements and streamside percent total agriculture (P < 0.0001) 

suggests that a GIS data set containing solely the agricultural land cover data along streams is 

sufficient to initially detect risk of surface water impairment from nutrient and sediment loading 

as a result of agriculture in the WRW. These initial water quality vulnerability results, in 

combination with a map analysis of percent total agriculture, among HUCs (Figure 36), suggest 

that HUC 8020204 and HUC 8020201 are extremely vulnerable to water quality impairment as a 

result of agricultural activities in proximity to surface water, particularly within 120 m of 

shorelines (Figure 27). 

Natural Vegetation 

Information about the location of current land cover in the WRW from the NLCD was 

used to create six landscape forest metrics (Figure 37), measuring ‘largest ‘forest patch 

proportion of HUC’, ‘mean forest patch area’, ‘largest forest patch proportion of HUC’, ‘forest 

patch density’, ‘forest patch number’, and ‘percent forest’ (areal measurement) within a HUC. 

The ‘percent forest’ metric (NLCD codes 41, 42, and 43 in Table 4) was compared among HUCs 

and within cumulative 30 m riparian zones (Figure 38). Replacement of forests with other land 

cover types is a surface water quality impairment risk because the root systems of trees and 

forest understory vegetation tend to absorb substances that are plant nutrients (e.g., nitrogen) and 

accumulate these substances in vegetation. Riparian forests also decrease the rate of overland 

flow, and intercept a substantial amount of nutrients (e.g., phosphorus) and sediment, 

incorporating these substances into wetland soils for long periods of time. Thus, ‘percent 

wetland’ (including forested and emergent wetland vegetation; NLCD codes 91 and 92 in Table 

4) was compared among HUCs and within cumulative 30 m riparian zones (Figure 39). All 

natural land cover types in the NLCD (i.e., NLCD codes 31, 41, 42, 43, 51, 71, 91, and 92 in 

Table 4) were combined to measure the percent of natural land cover in the landscape, and were 

compared among HUCs and within cumulative 30 m riparian zones (Figure 40). 

Although many of the theoretical ‘natural vegetation loss’ risks of surface water quality 

impairment are important, we selected ‘mean forest patch area’, ‘largest forest patch proportion 

of HUC’, and ‘percent forest’ for validity testing, using the 1990s NAWQA field data, because 

these metrics are easily measurable using existing GIS data sets. Significant positive correlations 
between the selected landscape forest metrics and the surface water concentrations of DOC, 

OrgN, P, and Sed (Table 6) suggest that ‘mean forest patch area’, ‘largest forest patch proportion 

of HUC’, and ‘percent forest’ are appropriate landscape indicators of actual increases in nutrient 

and sediment concentrations in surface water of the WRW. The same significant positive 

correlation trend exists for ‘percent forest’ within riparian zone distances, up through 300 m 

from shorelines (Figure 38). The significant positive correlations between surface water 

chemistry measurements, ‘mean forest patch area’, ‘largest forest patch proportion of HUC’, and 

either HUC or streamside percent forest (P < 0.0001) suggests that a GIS data set containing 

solely forest cover data is sufficient to detect risk of surface water impairment from nutrient or 

sediment loading in the WRW. These initial water quality vulnerability results, in combination 

with a map analysis of percent forest among HUCs, suggest that six HUCs are extremely 

vulnerable to water quality impairment as a result of forest loss in proximity to streams, with 

HUC 11010014 as the subwatershed with the greatest loss of forest on land that is directly 

adjacent to streams (Figure 41). 
CHAPTER 4. ASSESSMENT OF HABITAT 

VULNERABILITY IN WILDLIFE REFUGES

National Wildlife Refuges in the Lower White River Region

Mallard duck winter habitat vulnerability models were used to assess the 895 square 

kilometers of federal refuge lands within the 8,921 square kilometer Lower White River Region 

(LWRR). LWRR mallard duck winter habitat (Table 7) is contained within thirteen Arkansas 

counties (Figure 2b), of which a portion intersects the seventy-two separate parcels of land that 

comprise the White River National Wildlife Refuge (NWR), Cache River NWR, and Bald Knob 

NWR (Figure 9 and Figure 42). The U.S. Fish and Wildlife Service (DeWitt, Arkansas, 2001) 

supplied the boundary of each National Wildlife Refuge Zone (RZ, hereafter) as ArcView shape 

files (Environmental Systems Research Institute, v.3.2, Redlands, California). 

Results 

The 89,529 ha of federal refuge land in the LWRR ranges in size from 7.6 ha (RZ 12) to 

64,552 ha (RZ 72), with a mean RZ area of 2,558 ha (S.D. = 10,838 ha; Table 8). Refuge 

perimeter length ranges from 1,182 m (RZ 12) to 335 km (RZ 72), with a mean RZ perimeter 

length of 25 km (S.D. = 55 km). RZ area is strongly negatively correlated with mean percent 

contribution of habitat patches within the RZ (ρ = - 0.723, P = <0.0001) and weakly positively 

correlated with mean area of habitat patches within the RZ (ρ = 0.127, P = <0.0001). To 

examine the influence of the four largest RZs (contributing 83% of the area to all RZs in the 

LWRR) we excluded them (i.e., RZs 72, 67, 62, and 8) from the analyses (Table 9) but 

correlations between habitat area and refuge area did not substantially change (Table 9). The 

rank order of RZs with regard to RZ area, RZ perimeter, area of habitat patches within RZ, and 

mean percent contribution of habitat patches within a RZ suggests that larger RZs tend to capture 

larger wetland habitat patches, but this relationship is nonlinear, and the relationship is most 

clearly demonstrated among either relatively larger RZs or relatively smaller RZs (Table 10). 

Eight specific RZs demonstrate how a larger refuge does not necessarily result in larger mallard 

habitat within the refuge. RZ 29 is relatively large with very small intersecting habitat patches; 

and RZs 48, 44, 40, 32, 18, 13, and 11 are relatively small with large intersecting habitat patches. 
Table 9 summarizes the general trend that RZ area is weakly negatively correlated with 

(a) original area of habitat patches inside and outside of the RZ (ρ = - 0.141, P = <0.0001); (b) 

habitat patch perimeter within a RZ (ρ = - 0.095, P = <0.0001); (c) habitat patch interior-to-edge 

ratio within a RZ (ρ = - 0.188, P = <0.0001); (d) population density in the year 1990 (ρ = - 

0.214, P = <0.0001); (e) estimated population density in the year 2011 (ρ = - 0.233, P = 

<0.0001); and (f) estimated population density change from 1990 to 2011 in a RZ (ρ = - 0.039, P 

= <0.031). To hold aside the influence of the four largest RZs we excluded them from the metric 

comparisons, resulting in trends similar to the full group of seventy-two RZs (Table 9). 

Rankings of mallard duck wetland habitat patch metrics and indexes (Table 11) indicate that (a) 

RZs 72, 67, 62, and 8 are relatively more vulnerable to disturbance as a result of a smaller habitat 

patch interior-to-edge ratio; (b) RZs 72, 67, and 8 are relatively more vulnerable to disturbance 

as a result of a smaller habitat patch area; (c) RZs 72 and 8 are relatively more vulnerable to 

disturbance as a result of a smaller habitat patch perimeter, sinuosity index, and circularity index; 

and (d) RZ 8 is relatively more vulnerable to disturbance as a result of a smaller habitat patch 

unified patch index (Table 11). 

Table 12 describes the seventy-two RZs, ranked by ‘human-induced disturbance’ metrics 

and indexes, with the median indicated for each value range. The ranks of RZs indicate that (a) 

RZs 72, 67, 62, and 8 are relatively more vulnerable to disturbance as a result of a greater habitat 

patch road density, road length, road index, and unified human index; (b) RZs 67, 62, and 8 are 

relatively more vulnerable to disturbance as a result of a greater habitat patch population density 

in 1990 and expected population density in 2011; (c) RZ 8 is relatively more vulnerable to 

disturbance as a result of a greater habitat patch population density change expected in the year 

2011; and (d) RZs 72, 67, 62, and 8 are relatively less vulnerable to disturbance as indicated by 

the relatively larger unified vulnerability index (Table 12). 

 For wetland habitat patches less than or equal to 2 ha there is a greater percent 

contribution of the original habitat patch area in smaller RZs (by area, ρ = -0.897, P < 0.0001; by 

perimeter, ρ = -0.868, P < 0.0001) than in larger RZs. The ranks of RZs with regard to wetland 

habitat patches less than or equal to 2 ha within them indicate that all RZs have patches that are 

less than or equal to 2 ha, with the exception of RZs 31, 28, 22 and 2 (Table 13; Figure 42; 

Figure 9). For wetland habitat patches less than or equal to 2 ha the mean original habitat patch 

area is weakly positively correlated with the area of RZ (ρ = 0.285, P = 0.0197) and the 
perimeter of the RZ (ρ = 0.310, P = 0.0112). For wetland habitat patches less than or equal to 2 

ha, the strongest positive correlation exists between total habitat patch count and the area of RZ 

(ρ = 0.909, P < 0.0001) and the perimeter of the RZ (ρ = 0.905, P < 0.0001). Because wetlands 

that are 2 ha or smaller are too small to reliably monitor with currently available satellite remotesensing-derived land cover products, routine broad-scale monitoring of such small wetland areas 

may be difficult. 

Discussion 

RZ gradient analyses in the LWRR suggest that larger habitat patches are relatively more 

likely to rebound (i.e., are less likely to be fragmented or destroyed) than smaller habitat patches 

after disturbances, such as changes in hydrology, destruction of vegetation, or the establishment 

of opportunistic flora and fauna. However, large size alone may not be sufficient to ensure that a 

patch is capable of existing and flourishing in a disturbed setting; other contributing disturbance 

factors such as patch perimeter length, interior-to-edge ratio, sinuosity, and circularity may also 

be relevant (see Chapter 2). Additionally, human-induced disturbances, such as runoff, 

agriculture, and land conversion may function as drivers of habitat degradation, fragmentation, 

and loss. Thus, those areas with increased human-induced disturbance (as evidenced by present 

and future population density, population density change, and road development) in the LWRR 

may bring about future net wetland degradation or loss in the LWRR. 

The positive correlation between wetland habitat area or the percent contribution of 

habitat area, with area of RZ indicates that larger refuges contain more wetland habitat than 

smaller refuges. However, the percent contribution of wetland habitat to a refuge is inversely 

correlated with the area of the refuge. The weakness of the correlation between RZ area and 

mean area of habitat patches within a RZ, and outside a RZ, (Table 9) indicates that the presence 

of a relatively large refuge is not necessarily a predictor of large habitat area within the refuge. 

The weak negative correlation between RZ area, and the original area of the habitat patches 

inside and outside the RZ; the habitat patch perimeter within a RZ; and the habitat patch interiorto-edge ratio within a RZ (Table 11) also indicates that relatively large available and suitable 

mallard duck winter habitat patches in the LWRR are not encompassed within current federal 

refuge boundaries. 
The weak negative correlations between RZ area and human population density metrics 

in the LWRR indicate that larger RZs tend to exist in areas of lower human population densities, 

and smaller RZs tend to exist in areas of higher human population densities. Results of 

estimated future human population density change in the RZs (from 1990 to 2011) are 

inconclusive, but could be expected to follow the same trend as the population density metrics 

because large RZs in rural areas would likely lessen the effects of increased population density 

change. 

Recommendations and Conclusion 

The ranks of the four largest RZs (i.e., RZ 72, 67, 62, and 8) indicate that RZs are 

vulnerable to degradation as a result of smaller habitat patch size, less complex habitat patch 

shape, and human-induced disturbances within a habitat patch. All four of the largest RZs have a 

substantial proportion of very small wetland habitat patches within them (i.e., habitat ≤ 2 ha). 

Because of the high likelihood that these very small wetland habitat patches may be lost in the 

future (by definition), and because of the intrinsic difficulty in monitoring their loss, we 

recommend that relatively small patches of wetland habitat in the LWRR be a high priority for 

future remote-sensing and field-based monitoring and conservation efforts. 

Based on the number of human-induced disturbance and patch metric vulnerabilities 

among the four largest RZs, wetland habitat in RZ 8 is likely to experience the highest levels of 

disturbance and patch fragmentation and/or loss in the future because its vulnerability to all 

factors except the unified vulnerability index is greater than the median. The next most 

vulnerable RZ among the four largest RZs are: RZ 72 because it has greater than the median 

vulnerability to all factors except the unified patch index, population density factors, and the 

unified vulnerability index, and RZ 67 because it has greater than the median vulnerability to all 

factors except the patch perimeter metric, sinuosity index, circularity index, unified patch index, 

and population density factors. The least vulnerable among the four largest RZs is RZ 62 

because it has greater than the median vulnerability to all factors except the patch area metric, 

patch perimeter metric, sinuosity index, circularity index, unified patch index, unified 

vulnerability index, and population density factors. Considering the relative total area of the four 

largest RZs (Table 8), the number of small habitat patches (i.e., ≤ 2 ha) in RZs 72, 67, and 62 is 

approximately proportional to each refuge’s area, leaving RZ 8 with approximately half the 
expected number of small habitat patches. RZ 8 (Figure 42; Figure 9) is a unit of the Bald Knob 

National Wildlife Refuge, a relatively recent wildlife refuge land acquisition, and is currently 

predominated by agricultural land (Figure 9). Thus, RZ 8 is the largest federally owned parcel in 

the LWRR to have been recently (and almost completely) impacted by wetland fragmentation, 

loss, and human-induced disturbances (Figure 9). Therefore, we recommend that the Bald Knob 

National Wildlife Refuge be the highest priority for wetland habitat restoration and protection 

among the four largest RZs in the LWRR. 

Because indexes are (by definition) derived from other more directly measured metrics 

they may be less sensitive to the relative differences among patches than the metrics themselves 

(Yoder, 1991; Karr and Chu, 1997). We found that the indexes that included ‘patch 

characteristics’ (i.e., the unified patch index and the unified vulnerability index) tended to rank 

the four largest RZs as less vulnerable than their component metrics. That is, the ‘patch 

characteristic’ vulnerability indexes used in this atlas tend to indicate that habitat is less 

vulnerable than the component metrics of that index. The purely ‘human-induced disturbance’ 

indexes (i.e., the road index and the unified human index) tended to rank the four largest RZs 

consistently with their component metrics, with regard to the median parameter value. Thus, the 

results of the unified patch index and unified vulnerability index for RZ 72 (indicating that RZ 

72 is relatively less vulnerable than other RZs) may be misleading because of this ‘dilution 

effect’ of combining the sub-component metrics (Table 3a). Accordingly, if index results are 

held aside, wetland vulnerability to landscape-ecological degradation factors in RZ 72 and 8 is 

similar. Consequently, we recommend that RZ 72, the largest RZ, be given the second highest 

priority for wetland restoration and protection among the four largest RZs in the LWRR. 

Results of the habitat vulnerability assessment of RZ 67 suggest that it is predominantly 

vulnerable to the influence of road construction and the presence of relatively smaller wetland 

habitat patches. Human-induced disturbance factors related to the presence of roads in RZ 67 

may be partially mitigated by the robustness of patch characteristics within this RZ, with the 

exception of patch area. Therefore, we recommend that RZ 67 be given the third highest priority 

for continued wetland restoration and protection among the four largest RZs in the LWRR. 

Results of habitat vulnerability assessments for RZ 62 suggest that it is primarily 

vulnerable to the influence of roads. The human-induced disturbance factors related to the 

presence of roads in RZ 62 may be partially mitigated by the robustness of patch characteristics 
within this RZ, because all of the habitat size and shape metrics for RZ 62 are relatively high. 

Thus, we recommend that RZ 62 be given the lowest priority for continued wetland restoration 

and protection among the four largest RZs in the LWRR. 

Simulating Landscape Change in the White River National Wildlife Refuge

Because the unified vulnerability index (UVI) integrates habitat patch size, habitat patch 

shape, and human-induced disturbance metrics into a single index value it is the most 

conservative (i.e., describes the ‘least potential impact’ scenario) of the measures used to model 

habitat vulnerability. That is, a high UVI indicates component metrics that may range from low 

vulnerability to extreme vulnerability, but it encompasses all of the subcomponent metrics (see 

Table 11 and Table 12; see discussion of using indexes versus component metrics in previous 

section). Thus, the UVI was selected to model potential future land cover change in riparian 

wetland mallard duck winter habitat, in a portion of RZ 72 (LWRR), given a hypothetical 

decrease in the extent and duration of riparian wetland flooding. The hypothetical decrease in 

the extent and duration of flooding was assumed to involve a change from “permanently 

flooded” or “semi-permanently flooded” wetland conditions to “intermittently flooded” or 

“rarely flooded” wetland conditions (after Cowardin et al., 1979; see Table 2a for mallard duck 

habitat hydrologic parameters). One hundred-thirteen kilometers of river channel and the 

surrounding landscape in the vicinity of the South Unit of the White River National Wildlife 

Refuge (Figure 43) was used to simulate these potential future hydrologic changes. 

Results of the simulation from an arbitrary upstream point (UTM 15 = 663318E, 

3814972N) to an arbitrary downstream terminus (UTM 15 = 674296E, 3769144N) indicate 

current wetland habitat vulnerability along the adjacent 226 km of riparian habitat (Figure 44A), 

per the UVI. The UVI in patches of mallard duck winter habitat grossly determines the potential 

affects of the hypothesized future hydrologic changes (Figure 44B), within wetlands adjacent to 

a 30 m region on the riverbank. Mallard duck winter habitat under the simulated future 

hydrologic conditions indicates a decrease in the periodicity and duration of flooding along the 

226 km of river-adjacent riparian wetlands, which could result in a conversion of twenty-one 

percent (2,822 ha) of the 13,514 ha of riparian wetland along the 113 km of White River system 

tested (Figure 44B and Figure 44C). Accordingly, seventy-nine percent of the tested wetlands 
are relatively less vulnerable to conversion from such decreases in periodicity and duration of 

flooding in the future. 

The results of this simulation are substantially simplified because they describe solely the 

conversion of wetlands from relatively ‘wetter conditions’ to relatively ‘drier conditions’. 

Actual wetland change in riparian areas is more complex, involving many biophysical 

constraints (Vannote et al., 1980; Gregory et al., 1991; Middleton, 1999). Additionally, because 

the UVI is a conservative model, hydrologic changes in this region of the LWRR may result in 

substantially greater biological affects, such that facultative-wetland or obligate-wetland plants 

(Reed, 1988) might be less able to flourish, resulting in facultative or facultative-upland (Reed, 

1988) plant influx and establishment. Thus, future changes in the hydrology of the White River 

could result in the loss of plant species that are important resources for wetland organisms (e.g., 

Potamogeton spp. or Polygonum spp. for waterfowl forage). Improved hydrologic models for 

the White River could be used to improve upon the assumptions made in this simple example. 

Such improvements would help to determine the important linkages between hydrology and the 

vulnerability of biological resources for wetland organisms in the Lower White River Region. 
ACRONYMS AND ABBREVIATIONS USED

AR-GAP = Arkansas GAP Program

DEM = Digital Elevation Model 

EPA = Environmental Protection Agency 

ETM+ = Enhanced Thematic Mapper 

GAP = GAP Program

GIS = Geographic Information System

HUC = Hydrologic Unit Code 

LWRR = Lower White River Region study area 

MAVE = Mississippi Alluvial Valley Ecoregion study area 

MAVA-LULC = Mississippi Alluvial Valley of Arkansas Landuse/Landcover Project 

NAWQA = National Water Quality Assessment Program

NED = National Elevation Dataset 

NHD = National Hydrography Dataset 

NLCD = National Land Cover Dataset 

NWR = National Wildlife Refuge 

ORD = Office of Research and Development 

PCI = Habitat patch circularity index 

PIER = Habitat patch interior to edge ratio 

Popdenchg = Habitat patch human population density change from 1990 to 2011 

Popdens 1990 = Habitat patch human population density in 1990 

Popdens 2011 = Habitat patch human population density in 2011 

PRI = Habitat patch road index 

PSI = Habitat patch sinuosity index 

RARE = Regional Applied Research Effort 

Rddens = Habitat patch road density 

Rdlen = Habitat patch road length 

RZ = Refuge Zone 

URL = Universal Reference Locator 

USACOE = United States Army Corps of Engineers 

U.S. EPA = United States Environmental Protection Agency 

U.S. GS = United States Geological Survey 

UTM 15 = Universal Transverse Mercator, Zone 15

UVI = Habitat patch unified vulnerability index 

WRW = White River Watershed study area 
APPENDICES

(CLICK ON LINK) 

Appendix A. U.S. Fish and Wildlife Service Publication: Habitat Suitability Index Models: 

Mallard (Winter Habitat, Lower Mississippi Valley) 

Appendix B. U.S. Fish and Wildlife Service Publication: Habitat Suitability Index Models: 

Black Bear, Upper Great Lakes Region 

Appendix C. U.S. Fish and Wildlife Service Publication: Habitat Suitability Index Models: 

Least Tern 

Appendix D. U.S. Fish and Wildlife Service Publication: Classification of Wetlands and 

Deepwater Habitats of the United States [Available online at URL: 

http://wetlands.fws.gov/Pubs_Reports/Class_Manual/class_titlepg.htm] 
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Tables 
Table 1a. GIS data sets used to derive mallard duck, black bear, least tern, and wetland plant habitat suitability and habitat vulnerability in the Lower White River Region.

Data Used Derived Land Cover or Land Use Reference or Source Relevant InteMississippi Alluvial Valley of Arkansas Landuse/Landcover (MAVA-LULC)

Agriculture Crop Cover Gorham, 1999 http://www.c

National Hydrographic Dataset (NHD) v.1 Surface Water Location USGS and USEPA , 1999 http://nhd.usgNational Wetland Inventory (NWI) Wetland Cover and Hydrology Type U.S. FWS, Various http://www.nArkansas GAP Program (AR-GAP) Vegetation Community Cover, Vegetation Taxa Cover, Surface Water Location, 

Agriculture Cover, Urban Cover

Smith et al., 1998 http://web.ca

Historical Forest Cover 1950s Forest Cover Llewellyn et al., 1996U.S. Census Bureau Statistics Current and Future Estimates of Human Population Applied Geographic Solutions, 2001 http://www.a

ussf1

Wessex Streets v.7.0 Road Length and Density Geographic Data Technology, 1999
Table 1b. GIS data sets used to derive mallard ducks, black bears, least terns, and wetland plant habitat suitability and habitat vulnerability in the Mississippi Alluvial V

Data Used Derived Land Cover or Land Use Reference or Source Relevant Internet UNational Land Cover Dataset (NLCD) Open Water, Residential/Commercial/Industrial, Barren, Agriculture, 

Forest, Shrubland, Grassland/Herbaceous, Woody Wetland, and Emergent Herbaceous Wetland Cover

Vogelmann et al., 2001 http://landcover.us

National Hydrographic Dataset (NHD) v.1 Surface Water Location U.S. GS and U.S. EPA , 1999 http://nhd.usgs.govU.S. Census Bureau Statistics Current and Future Estimates of Human Population Applied Geographic Solutions, 2001 http://www.appliedWessex Streets v.7.0 Road Length and Density Geographic Data Technology, 1999 http://www.geogra
Table 1c. GIS data sets used to derive water quality vulnerability models in the White River Watershed.

Data Used Derived Land Cover or Land Use Reference or Source Relevant INational Land Cover Dataset (NLCD) Open Water, Residential/Commercial/Industrial, Barren, Agriculture, Forest, 

Shrubland, Grassland/Herbaceous, Woody Wetland, and Emergent Herbaceous Wetland Cover

Vogelmann et al., 2001 http://land

National Hydrographic Dataset (NHD) v.1 Surface Water Location U.S. GS and U.S. EPA , 1999 http://nhd.Wessex Streets v.7.0 Road Length and Density Geographic Data Technology, 1999 http://wwwNational Elevation Dataset (NED) Topographic slope Gesch et al., 2002 http://gisd
Table 2a. Data classes used to produce habitat suitability GIS models for mallard ducks in the Lower

White River Region and the Mississippi Alluvial Valley Ecoregion. Hydroperiodicity per Cowardin

et al. (1979).

Habitat Suitability Class in GIS Models

Lower White River 

Region Models

Mississippi Alluvial Valley 

Ecoregion Models

Wetland with fluctuating hyrdoperiod; trees/shrubs present; oak included; 

overcup oak excluded √

Wetland with fluctuating hyrdoperiod; trees/shrubs present; oak included; 

overcup oak included √

Wetland with fluctuating hyrdoperiod; trees/shrubs present; oak excluded √

Wetland with fluctuating hydroperiod; solely herbaceous plants present √

Wetland with fluctuating hydroperiod; no plants present √

Wetland with infrequent flooding or a lake, impoundment, river, or stream; 

trees/shrubs present; oak included; overcup oak excluded √

Wetland with infrequent flooding or a lake, impoundment, river, or stream; 

trees/shrubs present; oak included; overcup oak included √

Wetland with infrequent flooding or a lake, impoundment, river, or stream; 

trees/shrubs present; oak excluded √

Wetland with infrequent flooding or a lake, impoundment, river, or stream; 

solely herbaceous plants present √

Wetland with infrequent flooding or a lake, impoundment, river, or stream; no 

plants present √

Upland; Agriculture √ √

Upland; Non-agriculture √ √

Wetland; not open water; solely herbaceous plants present √

Wetland; not open water; woody plants present √

Open water √
Table 2b. Data classes used to produce habitat suitability GIS models for black bears in the Lower White

River Region and the Mississippi Alluvial Valley Ecoregion.

Habitat Suitability Classes used in GIS Models

White River 

Region 

Valley Ecoregion 

Models

Wetland; woody plants present; 4 tree species present; forest present in the 1950s √

Wetland; woody plants present; 3 tree species present; forest present in the 1950s √

Wetland; woody plants present; 2 tree species present; forest present in the 1950s √

Wetland; woody plants present; 1 tree species present; forest present in the 1950s √

Wetland; woody plants present; 4 tree species present; forest present solely after 1950s √

Wetland; woody plants present; 3 tree speciespresent; forest present solely after 1950s √

Wetland; woody plants present; 2 tree speciespresent; forest present solely after 1950s √

Wetland; woody plants present; 1 tree species present; forest present solely after 1950s √

Non-woody wetland; 4 herbaceous plant species present; forest absent in 1950s √

Non-woody wetland; 3 herbaceous plant species present; forest absent in 1950s √

Non-woody wetland; 2 herbaceous plant species present; forest absent in 1950s √

Non-woody wetland; 1 herbaceous plant species present; forest absent in 1950s √

Non-woody wetland; no herbaceous plant species present; forest absent in 1950s √

Non-woody wetland; 4 herbaceous plant species present; forest present in 1950s √

Non-woody wetland; 3 herbaceous plant species present; forest present in 1950s √

Non-woody wetland; 2 herbaceous plant species present; forest present in 1950s √

Non-woody wetland; 1 herbaceous plant species present; forest present in 1950s √

Non-woody wetland; no herbaceous plant species present; forest present in 1950s √

Open water √ √

Wetland; trees present, > 250 meters from trees √

Wetland; non-agriculture herbaceous plants present; < 250 meters from trees √

Wetland; non-agriculture herbaceous plants present; > 250 meters from trees √

Upland; trees present; < 250 meters from trees √

Upland; non-agriculture herbaceous plants present; < 250 meters from trees √

Upland; non-agriculture herbaceous plants present; > 250 meters from trees √

Upland, agriculture, urban, or non-vegetated; < 250 meters from trees √

Upland, agriculture, urban, or non-vegetated; > 250 meters from trees √
Table 2c. Data classes used to produce habitat suitability GIS models for least terns in the Lower White River Region

and the Mississippi Alluvial Valley Ecoregion.

Habitat Suitability Classes used in GIS Models

Lower White River Regio

Models

Sandy shore present during June-August time period; lotic or lentic ecosystem within 30 meters

√

Sandy shore present during June-August time period
Table 2d. Data classes used to produce habitat suitability GIS models for wetland plants in the Lower White River Region 

and the Mississippi Alluvial Valley Ecoregion.

Habitat Suitability Classes used in GIS Models

Lower White River Region 

Models

Wetland; lacustrine; littoral; rooted vascular plants present; semipermanently flooded √

Wetland; lacustrine; littoral; rooted vascular plants present; permanently flooded √

Wetland; lacustrine; littoral; unconsolidated bottom; permanently flooded √

Wetland; palustrine; aquatic bed; rooted vascular plants present; permanently flooded √

Wetland; palustrine; persistent emergent plants present; temporarily flooded √

Wetland; palustrine; persistent emergent plants present; seasonally flooded √

Wetland; palustrine; persistent emergent plants present; semipermanently flooded √

Wetland; palustrine; broad-leaved deciduous forest present; seasonally flooded √

Wetland; palustrine; broad-leaved deciduous forest present; temporarily flooded √

Wetland; palustrine; broad-leaved deciduous forest present; semipermanently flooded √

Wetland; palustrine; deciduous forest present; temporarily flooded √

Wetland; palustrine; deciduous forest present; seasonally flooded √

Wetland; palustrine; needle-leaved deciduous forest present; seasonally flooded √

Wetland; palustrine; needle-leaved deciduous forest present; semipermanently flooded √

Wetland; palustrine; broad-leaved deciduous shrubs present; seasonally flooded √

Wetland; palustrine; broad-leaved deciduous shrubs present; temporarily flooded √

Wetland; palustrine; broad-leaved deciduous shrubs present; semipermanently flooded √

Wetland; palustrine; unconsolidated bottom √

Wetland; palustrine; unconsolidated shore; seasonally flooded √

Wetland; riverine; lower perennial; unconsolidated bottom; permanently flooded √

Wetland; riverine; lower perennial; unconsolidated bottom; temporarily flooded √

Wetland; riverine; lower perennial; unconsolidated bottom; seasonally flooded √

Upland √

Wetland; woody plants present

Wetland; soley herbaceous plants present

Open water

Habitat Vulnerability Parameter Interpretation and calculation

Wetland habitat patch total road length for patches greater than 2 ha

Patches with greater total road length are relatively less likely to rebound from disturbances than patches with a lesser tolength. That is, greater length values indicate that a patch has a greater likelihood of fragmentation or loss as a result of environmental change, such as changes in hydrology, destruction of vegetation, or the establishment of opportunistic floThe increased presence of roads may also bring about the aforementioned disturbances.

Wetland habitat patch total road density for patches greater than 2 ha

Patches with greater total road density are relatively less likely to rebound from disturbances than patches with a lesser t

density. That is, greater density values indicate that a patch has a greater likelihood of fragmentation or loss as a result 

environmental change, such as changes in hydrology, destruction of vegetation, or the establishment of opportunistic floThe increased presence of roads may also bring about the aforementioned disturbances.

Wetland habitat patch road index for patches greater than 2 ha

Patches with a greater road index are relatively less likely to rebound from disturbances than patches with a lesser Road is, greater index values indicate that a patch has a greater likelihood of fragmentation or loss as a result of environmentaas changes in hydrology, destruction of vegetation, or the establishment of opportunistic flora and fauna. The increased 

roads may also bring about the aforementioned disturbances. Calculation: Rddens + Rdlen, where Rddens = the numbe

patch area and Rdlen is the total length of road per patch area.

Wetland habitat patch human population density in 1990 for patches greater than 2 ha

Patches that reside in census block groups with a greater population density are relatively less likely to rebound from dithan patches that reside in areas of lesser population density. That is, greater density values indicate that a patch has a glikelihood of fragmentation or loss as a result of environmental change, such as changes in hydrology, destruction of ve

the establishment of opportunistic flora and fauna. The increased presence of residents near patches may also bring aboaforementioned disturbances.

Wetland habitat patch human population density in 2011 for patches greater than 2 ha

Patches that reside in census block groups with a greater population density in the future are relatively less likely to rebo

disturbances than patches that reside in areas of lesser population density in the future. That is, greater density values in

patch has a greater likelihood of fragmentation or loss as a result of environmental change, such as changes in hydrologof vegetation, or the establishment of opportunistic flora and fauna. The increased presence of residents near patches mabout the aforementioned disturbances.

Wetland habitat patch human population density change from 1990 to 2011 for patches greater than 2 ha

Patches that reside in census tracts with a greater increase in population density are relatively less likely to rebound from than patches that reside in areas of lesser population density change. That is, greater (predicted) increases in human pop

density indicate that a patch has a greater likelihood of fragmentation or loss as a result of environmental change, such ahydrology, destruction of vegetation, or the establishment of opportunistic flora and fauna. The increased density of hupopulation near patches may also bring about the aforementioned disturbances. Calculation: popden2-popden1, where p2011 population density and popden1 = population density in 1990.

Wetland habitat patch unified human index for patches greater than 2 ha

Patches with a greater unified human index are relatively less likely to rebound from disturbances than patches with a smhuman index. That is, greater index values indicate that a patch has a greater likelihood of fragmentation or loss as a reenvironmental change, such as changes in hydrology, destruction of vegetation, or the establishment of opportunistic floThe increased presence of roads and growth of residential areas near patches may also bring about the aforementioned dCalculation: [(popdenchg90_11) + C] + [√ (rdden+len)], where popdenchg90_11 = the population density change betwe2011, C = a normalization quantity to ensure that the net change value is positive, and rdden+len = the Road Index.

Wetland habitat patch unified vulnerability index for patches greater than 2 ha

Patches with a smaller unified vulnerability index are less likely to rebound from disturbances than patches with a largevulnerability index. That is, smaller index values indicate that a patch has a greater likelihood of fragmentation or loss a

environmental change, such as changes in hydrology, destruction of vegetation, or the establishment of opportunistic flo

Calculation: [1 / (√UPI)] + (√ UHI), where UPI = the patch Unified Patch Index and UHI = the patch Unified Human In

Table 
Table 3b. Metrics used to produce water quality vulnerability GIS models in the

Lower White River Region, measured among 8-digit Hydrologic Unit Codes (HUCs).

"Natural land cover" is a combination of National Land Cover Dataset codes 31, 41, 

42, 43, 51, 71, 91, and 92 in Table 4).

Water Quality Vulnerability Metric (compared among HUCs)

Largest forest patch proportion of HUC

Mean forest patch area

Largest forest patch area

Forest patch number

Forest patch density

Percent streams within 30 meters of roads

Percent total agriculture on slopes greater than 3%

Percent total agriculture within entire HUC

Percent crop agriculture within entire HUC

Percent pasture within entire HUC

Percent forest within entire HUC

Percent wetland within entire HUC

Percent natural land cover within entire HUC

Percent total agriculture within a 300 meter riparian zones at 30 meter increments

Percent crop agriculture within a 300 meter riparian zones at 30 meter increments

Percent pasture within a 300 meter riparian zones at 30 meter increments

Percent forest within a 300 meter riparian zones at 30 meter increments

Percent wetland within a 300 meter riparian zones at 30 meter increments

Percent natural land cover within a 300 meter riparian zones at 30 meter increments
Table 4. National Land Cover Dataset (NLCD) categories. NLCD

codes were used to develop land cover metrics in this atlas. See 

Table 1c for more information about this data set. 

Land Cover Category NLCD Land Cover Code

Open Water 11

Perennial Ice/Snow 12

Low Intensity Residential 21

High Intensity Residential 22

Commercial/Industrial/Transportation 23

Bare Rock/Sand/Clay 31

Quarries/Strip Mines/Gravel Pits 32

Transitional 33

Deciduous Forest 41

Evergreen Forest 42

Mixed Forest 43

Deciduous Shrubland 51

Evergreen Shrubland 52

Mixed Shrubland 53

Orchards/Vineyards/Other 61

Grasslands/Herbaceous 71

Pasture/Hay 81

Row Crops 82

Small Grains 83

Fallow 84

Urban/Recreational Grasses 85

Woody Wetlands 91

Emergent Herbaceous Wetlands 92
Table 5. Spearman Rank Correlation of surface water parameters and 

percent agriculture land cover within 8-digit HUC subwatersheds in the 

White River Watershed. Correlation (Rho) values shown, P < 0.0001 

Water Quality Parameter N

Dissolved organic carbon 344 0.697 0.651

Amino and organic nitrogen 188 0.510 0.532

Total phosphorus 367 0.707 0.682

Suspended sediment 424 0.692 0.643

Watershed Percent 

Total Agriculture 

Streamside Percent 

Total Agriculture 
Table 6. Spearman Rank Correlation of surface water parameters and forest land cover metrics within 8-digit HUC 

subwatersheds in the White River Watershed. Correlation (Rho) values shown, P < 0.0001 

Water Quality Parameter N

Dissolved organic carbon 344 -0.548 -0.716 -0.722 -0.693 Amino and organic nitrogen 188 -0.202 -0.507 -0.480 -0.570 Total phosphorus 367 -0.495 -0.706 -0.624 -0.709 Suspended sediment 424 -0.496 -0.713 -0.687 -0.692 

Forest Metric

Largest forest patch 

proportion of HUC

Mean forest 

patch area

Largest forest patch 

area

Watershed Percent 

Forest 
Table 7. Area of potential mallard duck winter habitat in the Lower White River Region.

Mallard Duck Winter Habitat Suitability Class 

Wetland with fluctuating hyrdoperiod; trees/shrubs present; oak included; overcup oak excluded Wetland with fluctuating hyrdoperiod; trees/shrubs present; oak included; overcup oak included Wetland with fluctuating hyrdoperiod; trees/shrubs present; oak excluded 

Wetland with fluctuating hydroperiod; solely herbaceous plants present Wetland with fluctuating hydroperiod; no plants present Wetland with infrequent flooding or a lake, impoundment, river, or stream; trees/shrubs present; oak included; overcup oak excluded Wetland with infrequent flooding or a lake, impoundment, river, or stream; trees/shrubs present; oak included; overcup oak included Wetland with infrequent flooding or a lake, impoundment, river, or stream; trees/shrubs present; oak excluded Wetland with infrequent flooding or a lake, impoundment, river, or stream; solely herbaceous plants present Wetland with infrequent flooding or a lake, impoundment, river, or stream; no plants present Upland; Agriculture Upland; Non-agriculture Total 
Table 8. Area and perimeter of each of the 72 federal

refuge zones in the Lower White River Region, in 

ascending order of refuge zone area. Note: four 

largest (area) federal refuge zones (8, 62, 67, and 72) 

are listed in red type. 

Federal Refuge Zone ID Area (Ha) Perimeter (m)

12 7.6 1182

64 7.7 1237

2 8.0 1208

22 8.4 1211

53 8.4 1204

9 15.0 1548

59 15.1 2427

26 15.9 1596

25 16.0 1600

41 16.2 2025

31 16.3 1617

28 16.9 1646

24 17.7 1684

47 19.6 2112

51 22.3 1895

55 22.8 1899

57 28.1 2702

52 31.3 2353

14 33.7 2485

13 34.0 2475

19 35.3 2514

33 39.9 3799

17 40.0 2808

35 40.1 3823

54 40.3 2970

3 56.3 3132

16 65.0 4024

50 65.6 3773

37 66.5 3263

34 67.7 5196

21 78.5 4815

1 79.4 7156

11 80.8 3847

38 81.3 4032

42 89.4 5606

45 92.3 5224

40 95.1 4971

44 96.4 4822

63 99.5 5824

66 106.4 4380

32 114.2 4459

27 124.4 8113

65 128.7 4811

60 131.4 6252

68 161.1 7609

36 167.3 9596

39 174.3 10229

71 205.2 8099

10 212.4 7158

18 213.5 8143

30 230.7 10935

5 250.9 14060

46 251.4 9844

61 287.3 10260

69 295.1 11304

6 372.8 13889

23 401.8 10756

49 407.6 9278

48 441.6 9046

70 510.3 13622

7 738.1 25561

20 740.6 18248

4 757.3 25088

58 767.9 19509

43 1094.4 30733

15 1261.4 30217

29 1629.0 31502

56 1756.1 32308

67 2622.5 42974

62 2899.8 44889

8 5325.8 52555

72 64552.1 335315

Sum 89529.1 891387

Mean 2558.0 25468

1 S.D. 10838.3 55365
Table 9. Summary table of Spearman Rank Correlation between federal refuge zone area and: (a) area of habitat patches, 

(b) percent contribution of habitat patches, (c) the original area of the habitat patches (inside and outside of the zone), (d) habitat 

patch perimeter, (e) habitat patch interior:edge ratio, (f) habitat patch sinuosity index, (g) habitat patch circularity index, (h) unified 

patch index, (i) habitat patch road density, (j) habitat patch road length, (k) habitat patch road index, (l) population density in 

the year 1990, (m) estimated population density in the year 2011, (n) estimated population density change from 1990 to 2001,

 (o) unified human index, and (p) unified vulnerability index within a zone. Correlation (Rho) values and significance are shown 

for all refuge zones (N = 72), and for all refuge zones except the four largest (N = 68, i.e., excluding refuge zones 8, 62, and 67,

 and 72); ns = not significant, * = 0.05, ** = 0.01, *** = 0.001,   = 0.0001. All calculations are based upon habitat patches > 2 ha.

Habitat Patch Metric or Index Name Area of All Federal Refuge 

Zones

Area of All Federal Refuge Zones Except 

Four Largest Refuge Zones

Area of habitat patches within a refuge zone 0.127 ** ** 0.116 **

Percent contribution of habitat patches within a refuge zone -0.723   -0.503 ** **

Original (i.e., inside and outside) area of the habitat patches for a refuge zone -0.141   -0.149 ** **

Habitat patch perimeter within a refuge zone -0.095   -0.125 ** *

Habitat patch interior:edge ratio within a refuge zone -0.188 **** -0.148  

Habitat patch sinuosity index within a refuge zone ns ns

Habitat patch circularity index within a refuge zone ns ns

Unified patch index within a refuge zone ns ns

Habitat patch road density within a refuge zone 0.032 ** ** -0.026  

Habitat patch road length within a refuge zone ns ns

Habitat patch road index within a refuge zone ns ns

Population density in the year 1990 within a refuge zone -0.214 ** ** -0.113 **

Estimated population density in the year 2011 within a refuge zone -0.233   -0.114 ** 

Estimated population density change from 1990 to 2001 within a refuge zone -0.039 * ns

Unified human index within a refuge zone ns ns

Unified vulnerability index within a refuge zone ns ns
Table 10. Rank of each of the 72 federal refuge zones in the Lower White River Region by (a) zone area, (b) zone perimeter, 

(c) mean area of habitat patches within a zone, and (d) mean percent contribution of habitat patches within a zone. Federal 

refuge zone ranks are in ascending order for each parameter. All calculations are based upon habitat patches > 2 ha.

Note: four largest (area) federal refuge zones (8, 62, 67, and 72) are listed in red type.

Federal Refuge Zone 

Area (ha)

Federal Refuge Zone 

Perimeter (km)

Mean Original Habitat Patch Area Within 

Federal Refuge Zone 

Mean Percent Contribution of Each Habitat Patch to 

Federal Refuge Zone

Relative Metric Value 

Guide

12 12 12 72 Low

64 53 24 29

2 2 25 8

22 22 2 67

53 64 53 15

9 9 44 62

59 26 29 4

26 25 64 56

25 31 40 48

41 28 22 7

31 24 50 43

28 51 17 58

24 55 9 44

47 41 59 6

51 47 19 18

55 52 42 40

57 59 33 20

52 13 26 24

14 14 34 61

13 19 14 30

19 57 18 12

33 17 16 23

17 54 28 69

35 3 47 5

54 37 48 71

3 50 60 42

16 33 51 50

50 35 3 60

37 11 54 46

34 16 55 39

21 38 30 10

1 66 31 34

11 32 71 25

38 65 8 17

42 21 6 70

45 44 61 27

40 40 39 16

44 34 4 65

63 45 35 36

66 42 45 33

32 63 5 45

27 60 7 19

65 1 21 68

60 10 27 3

68 68 15 66

36 71 41 21

39 27 13 49

71 18 69 14

10 48 10 1

18 49 1 54

30 36 46 63

5 46 65 37

46 39 37 2

61 61 66 9

69 23 58 53

6 30 23 35

23 69 36 59

49 70 63 26

48 6 68 51

70 5 43 47

7 20 67 38

20 58 57 32

4 4 20 64

58 7 56 22

43 15 38 55

15 43 52 13

29 29 11 11

56 56 72 28

67 67 62 31

62 62 32 41

8 8 70 57

72 72 49 52 High

Rank of Federal Wildlife Refuge Zone Metric (Zones 1-72)
Table 11. Rank of each of the 72 federal refuge zones in the Lower White River Region by mallard duck winter habitat patch 

characteristics: (a) habitat patch area, habitat patch perimeter, [c] habitat patch interior:edge ratio within a zone (PIER), 

[d] habitat patch sinuosity index within a zone (PSI), [e] habitat patch circularity index within a zone (PCI), and [f] unified

patch index within a zone (UPI). The gradients of parameter values and vulnerability among the 72 federal refuge zones are 

indicated from ‘High’ to ‘Low’. A double line indicates median. All calculations are based upon habitat patches > 2 hectares. 

Note: four largest (area) federal refuge zones (8, 62, 67, and 72) are listed in red type.

Patch Area (ha) Patch Perimeter (km) PIER PSI PCI UPI

Relative Metric or Index 

Value Guide

Relative Vulnerability to 

Disturbance Guide

24 24 50 54 54 24 Low High

50 40 24 40 52 40

40 59 44 52 40 54

59 60 59 24 24 60

53 53 62 60 53 59

60 50 47 53 60 53

18 54 72 2 2 37

51 37 8 59 59 50

54 51 71 18 9 51

8 18 20 9 18 9

47 9 46 5 3 18

44 8 53 3 42 2

9 65 6 42 5 65

6 6 67 1 51 3

37 30 48 4 30 8

30 47 40 30 33 47

65 2 15 10 4 52

4 4 26 6 10 6

17 5 60 51 6 5

15 3 4 33 1 30

19 15 30 43 65 42

26 44 69 15 37 34

34 48 7 14 15 33

3 7 17 65 8 48

7 19 58 8 57 4

71 34 45 7 7 7

5 17 19 37 43 15

2 33 43 36 14 19

16 42 65 19 34 17

1 1 18 23 19 44

48 10 34 72 31 57

10 72 1 57 48 10

42 43 39 17 17 26

67 58 36 34 23 1

72 26 35 58 47 16

14 71 10 48 72 43

23 52 37 56 56 14

43 14 9 62 36 58

57 67 51 39 44 31

58 23 5 31 50 23

36 57 56 44 58 38

33 16 21 67 39 71

62 62 23 47 38 55

39 36 61 20 62 39

68 39 68 50 67 36

55 55 70 38 16 67

38 56 16 29 20 21

46 38 66 69 55 72

21 31 14 55 26 56

45 21 3 16 29 62

69 69 64 71 21 22

22 68 22 26 71 69

61 46 33 46 69 29

31 22 11 21 11 11

52 29 25 68 22 68

56 20 54 11 68 13

11 11 63 61 46 46

70 61 42 22 12 12

66 45 38 45 13 20

29 70 2 49 27 70

35 13 28 27 61 28

13 12 55 12 25 45

63 27 27 70 70 27

28 28 57 13 49 61

32 66 49 25 45 25

12 25 29 66 28 41

64 63 13 63 41 66

27 35 32 28 66 63

25 32 41 32 63 32

20 41 31 41 32 35

41 64 12 64 35 49

49 49 52 35 64 64 High Low

Rank of Federal Wildlife Refuge Zone Metric or Index (Zones 1-72)
Table 12. Rank of each of the 72 federal refuge zones in the Lower White River Region by mallard duck winter habitat patch 

human-induced disturbance characteristics: [a] habitat patch road density within a zone (Rddens), [b] habitat patch road length 

within a zone (Rdlen), [c] habitat patch road index within a zone (PRI), [d] population density in the year 1990 within a zone 

(Popdens1990), [e] estimated population density in the year 2011 within a zone (Popdens2011), [f] estimated population 

density change from 1990 to 2001 in a zone (Pdenchg 1990-2011), [g] unified human index within a zone (UHI), and [h] unified 

vulnerability index within a zone (UVI). The gradients of parameter values and vulnerability among the 72 federal refuge zones

are indicated from ‘High’ to ‘Low’. A double line indicates median. All calculations are based upon habitat patches > 2 ha. 

Note: four largest (area) federal refuge zones (8, 62, 67, and 72) are listed in red type. 

Rddens Rdlen PRI Popdens1990 Popdens2011

Pdenchg 

1990-2011 UHI UVI

Relative Metric or Index 

Value Guide

Relative Vulnerability to 

Disturbance Guide

37 49 49 66 66 1 49 12 High High

65 20 20 12 1 2 12 41

56 12 12 63 2 66 13 57

3 56 56 8 88 1 28

19 13 13 64 12 20 64 52

31 31 31 61 64 57 37 50

69 37 37 13 61 64 31 49

13 64 64 41 57 35 2 20

72 58 58 43 41 45 41 56

62 3 3 42 13 55 20 31

49 70 70 57 63 32 3 63

12 41 41 40 43 56 66 66

10 11 11 31 42 61 56 1

40 63 63 38 20 46 11 58

8 1 1 49 45 41 8 64

71 72 72 45 38 11 70 70

34 69 69 44 62 10 19 37

64 10 10 62 49 71 58 11

58 19 19 2 47 14 25 8

30 67 67 47 31 65 69 72

39 62 62 70 40 17 21 69

15 21 21 52 70 48 10 3

48 66 66 1 52 7 71 10

67 34 34 3 71 18 45 40

1 71 71 71 44 28 57 67

11 8 8 27 55 68 63 62

41 27 27 58 58 16 72 21

63 14 14 67 3 52 27 19

18 45 45 46 67 47 14 27

70 25 25 20 46 9 62 71

46 61 61 5 35 51 39 14

23 39 65 39 68 50 67 45

17 65 39 30 32 53 65 61

43 30 30 68 60 59 34 25

21 23 23 60 28 60 61 65

29 40 40 28 51 54 23 34

20 15 15 55 39 24 29 2

14 46 46 51 56 58 15 39

4 17 17 36 65 69 46 30

27 29 29 32 16 15 17 43

66 43 43 56 5 70 18 46

45 7 48 65 69 21 48 15

6 48 7 34 27 62 35 23

61 18 18 4 33 33 30 17

7 6 6 26 72 25 55 29

25 4 4 16 30 67 7 4

32 32 35 69 34 39 32 13

35 35 32 35 6 22 6 48

28 28 28 33 36 19 28 7

22 22 68 72 11 72 68 26

68 68 22 6 17 6 16 18

38 38 36 29 29 13 52 44

55 55 55 37 25 29 47 6

36 36 38 23 4 23 9 5

16 16 16 25 37 49 51 36

57 57 26 11 10 37 50 55

52 52 57 17 7 42 53 38

26 26 44 22 23 5 59 35

42 42 33 10 15 12 60 42

33 33 42 7 22 38 54 22

44 44 5 15 48 34 24 32

5 5 52 19 14 3 33 33

2 2 47 21 21 36 40 68

47 47 2 48 26 4 22 16

9 9 9 14 19 43 43 47

51 51 51 18 18 30 42 9

54 54 50 9 9 44 5 51

50 50 53 54 54 27 4 53

53 53 59 50 50 26 38 59

60 60 60 53 53 31 36 60

59 59 54 59 59 40 44 54

24 24 24 24 24 63 26 24 Low Low

Rank of Federal Wildlife Refuge Zone Metric or Index (Zones 1-72)
Table 13. Assessment of the presence of wetlands less than or equal to 2 ha within each of 

68 federal refuge zones in the Lower White River Region. Four federal refuge zones (2, 22, 28, 

and 31) did not contain habitat patches less than 2 ha. Federal refuge zones are ranked by 

ascending areal contribution of wetlands less than 2 ha within the zone, and include (a) areal 

percent contribution, (b) mean patch area (+/- S.D.), and (c) total number of habitat patches 

less than 2 ha in federal refuge zone. All calculations are based upon habitat patches less than 

2 ha. Note: four largest (area) federal refuge zones (8, 62, 67, and 72) are listed in red type.

Federal Refuge Zone 

ID

Mean Percent Contribution 

to Federal Zone Area (ha)

Mean Habitat Patch 

Area (ha) Std. Dev. (ha) Total Habitat Patch Count

72 0.000004 2814 3550 11391

8 0.000058 3110 3944 510

62 0.000081 2356 3157 460

67 0.000098 2563 3291 631

56 0.000120 2107 2741 552

29 0.000144 2343 2915 199

20 0.000168 1244 1816 184

15 0.000235 2961 3773 281

43 0.000258 2825 3306 182

23 0.000349 1400 1600 21

7 0.000380 2801 3502 237

58 0.000411 3157 4212 151

48 0.000423 1869 2661 76

4 0.000435 3293 4089 156

54 0.001000 508 795 18

37 0.001000 817 306 4

45 0.001000 931 1299 17

11 0.001000 1080 380 10

32 0.001000 1261 2154 33

60 0.001000 1415 2109 72

63 0.001000 1450 1755 36

68 0.001000 1462 1915 63

71 0.001000 1509 2152 154

46 0.001000 1747 1554 99

61 0.001000 1845 2513 75

18 0.001000 1880 2460 52

36 0.001000 2082 2777 33

69 0.001000 2139 2544 127

10 0.001000 2400 2578 64

30 0.001000 2600 2926 111

70 0.001000 2941 3859 36

6 0.001000 2950 3511 153

5 0.001000 3279 3627 86

49 0.001000 3460 4664 29

40 0.002000 1664 2289 27

66 0.002000 2163 2965 18

39 0.002000 2769 4365 37

27 0.002000 2931 4334 20

55 0.003000 640 247 5

19 0.003000 900 - 1

35 0.003000 1259 1848 28

16 0.003000 2006 1815 7

42 0.003000 2319 1765 4

44 0.003000 2696 3853 39

65 0.003000 3707 3967 48

14 0.004000 1319 - 1

50 0.004000 2482 3597 11

38 0.004000 2879 5049 23

34 0.004000 2893 3636 7

24 0.005000 805 862 3

59 0.005000 808 986 13

13 0.005000 1572 1958 8

17 0.006000 2399 2640 8

21 0.006000 4487 5975 7

1 0.006000 4519 5104 12

57 0.007000 1842 2133 12

33 0.007000 2645 3572 26

64 0.008000 649 823 4

47 0.009000 1684 1108 2

51 0.011000 2537 2922 3

12 0.012000 900 0 2

52 0.015000 4656 5776 4

3 0.015000 8593 - 1

41 0.016000 2590 2986 3

26 0.018000 2885 2594 3

25 0.019000 3022 2329 5

53 0.021000 1801 - 1

9 0.023000 3459 2695 5

Sum 0.282164 157082 174630 16701

Mean 0.004149 2310 2729 246

1 S.D. 0.005525 1228 1323 1378
Figures 

Study Site 

Orientation Maps 

Figure 2a. Orientation map of Lower White River Region (LWRR), Mississippi Alluvial 

Valley Ecoregion (MAVE), and White River Watershed (WRW). Inset shows the states 

that intersect the MAVE study area. 

Figure 2b. Orientation map of Lower White River Region (LWRR), including the 13 

counties that intersect the LWRR study area. 

Figure 2c. County map of all study areas including 170 counties in Arkansas, Missouri, 

Louisiana, Tennessee, Illinois, and Kentucky that intersect the Lower White River 

Region (LWRR), Mississippi Alluvial Valley Ecoregion (MAVE), and the White River 

Watershed (WRW). 









Lower White 

River Region 

Landsat Images 

(November, 1999) 

Figure 9a. Lower White River Region reference image with selected towns indicated 

with blue arrows; Landsat ETM+ single near-infrared band; 30 meter spatial resolution; 

November, 1999. Red rectangle indicates habitat vulnerability focus area in the South 

Unit of the White River National Wildlife Refuge. 

Figure 9b. Lower White River Region reference image with selected towns indicated 

with blue arrows; Landsat ETM+ 3 band false-color infra-red composite; 30 meter spatial 

resolution; November, 1999. Red tones indicate photosynthetic vegetation. Red 

rectangle indicates habitat vulnerability focus area in the South Unit of the White River 

National Wildlife Refuge. 

Figure 9c. Lower White River Region reference image with selected towns indicated 

with blue arrows; Landsat ETM+ 3 band false-color ‘enhanced vegetation’ infra-red 

composite; 30 meter spatial resolution; November, 1999. Green tones indicate 

photosynthetic vegetation. Red rectangle indicates habitat vulnerability focus area in the 

South Unit of the White River National Wildlife Refuge. 














Lower White River 

Region Habitat 

Suitability Models 
Figure 21a. Lower White River Region (LWRR) mallard duck winter habitat suitability 

(All Land), All Sizes. 

Figure 21b. Lower White River Region (LWRR) black bear habitat suitability (All 

Land), All Sizes. 

Figure 21c. Lower White River Region (LWRR) least tern habitat suitability reference 

aid (All Land), All Sizes. 

Figure 21d. Lower White River Region (LWRR) least tern habitat image overlay (All 

Land), All Sizes. 

Figure 21e. Lower White River Region (LWRR) mallard duck winter habitat suitability 

(Wetlands Only), All Sizes. 

Figure 21f. Lower White River Region (LWRR) black bear habitat suitability 

(Wetlands Only), All Sizes. 

Figure 21g. Lower White River Region (LWRR) wetland plant habitat suitability 

(Wetlands Only), All Sizes. Classes and hydrologic modifiers per Cowardin et al. 

(1979). 

Figure 21h. Lower White River Region (LWRR) mallard duck winter habitat suitability 

(Wetlands Only), ≤ 2 ha. 

Figure 21i. Lower White River Region (LWRR) black bear habitat suitability (Wetlands 

Only), ≤ 2 ha. 

Figure 21j. Lower White River Region (LWRR) wetland plant habitat suitability 

(Wetlands Only), ≤ 2 ha. Classes and hydrologic modifiers per Cowardin et al. (1979). 

Figure 21k. Lower White River Region (LWRR) mallard duck winter habitat suitability 

(Wetlands Only), > 2 ha. 

Figure 21l. Lower White River Region (LWRR) black bear habitat suitability (Wetlands 

Only), > 2 ha. 

Figure 21m. Lower White River Region (LWRR) wetland plant habitat suitability 

(Wetlands Only), > 2 ha. Classes and hydrologic modifiers per Cowardin et al. (1979). 
Lower White River 

Region Habitat 

Suitability Models 

(All Land, All Sizes) 




Lower White 

River Region 

Wetland Habitat 

Suitability Models 

(All Sizes) 



Lower White 

River Region 

Wetland Habitat 

Suitability Models 

(≤ 2 ha) 



Lower White 

River Region 

Wetland Habitat 

Suitability Models 

(> 2 ha) 



Lower White 

River Region 

Wetland Habitat 

Vulnerability Models 

(Patch Size and 

Patch Shape) 
Figure 22a. Lower White River Region (LWRR) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch total area. 

Figure 22b. Lower White River Region (LWRR) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch total area. 

Figure 22c. Lower White River Region (LWRR) wetland plant habitat vulnerability (> 

2 ha) in terms of habitat patch total area. 

Figure 22d. Lower White River Region (LWRR) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch total perimeter length. 

Figure 22e. Lower White River Region (LWRR) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch total perimeter length. 

Figure 22f. Lower White River Region (LWRR) wetland plant habitat vulnerability (> 2 

ha) in terms of habitat patch total perimeter length. 

Figure 22g. Lower White River Region (LWRR) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch interior-to-edge ratio. 

Figure 22h. Lower White River Region (LWRR) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch interior-to-edge ratio. 

Figure 22i. Lower White River Region (LWRR) wetland plant habitat vulnerability (> 2 

ha) in terms of habitat patch interior-to-edge ratio. 

Figure 22j. Lower White River Region (LWRR) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch circularity index. 

Figure 22k. Lower White River Region (LWRR) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch circularity index. 

Figure 22l. Lower White River Region (LWRR) wetland plant habitat vulnerability (> 2 

ha) in terms of habitat patch circularity index. 

Figure 22m. Lower White River Region (LWRR) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch sinuosity index. 

Figure 22n. Lower White River Region (LWRR) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch sinuosity index. 

Figure 22o. Lower White River Region (LWRR) wetland plant habitat vulnerability (> 

2 ha) in terms of habitat patch sinuosity index. 

Figure 22p. Lower White River Region (LWRR) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch unified patch index. 

Figure 22q. Lower White River Region (LWRR) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch unified patch index. 

Figure 22r. Lower White River Region (LWRR) wetland plant habitat vulnerability (> 2 

ha) in terms of habitat patch unified patch index. 
Lower White 

River Region 

Wetland Habitat 

Vulnerability Models 

(Total Area) 



Lower White 

River Region 

Wetland Habitat 

Vulnerability Models 

(Total Perimeter) 



Lower White 

River Region 

Wetland Habitat 

Vulnerability Models 

(Interior-to-Edge 

Ratio) 



Lower White 

River Region 

Wetland Habitat 

Vulnerability Models 

(Circularity Index) 



Lower White 

River Region 

Wetland Habitat 

Vulnerability Models 

(Sinuosity Index) 



Lower White 

River Region 

Wetland Habitat 

Vulnerability Models 

(Unified Patch Index) 



Lower White 

River Region 

Wetland Habitat 

Vulnerability Models 

(Patch HumanInduced Disturbance) 
Figure 23a. Lower White River Region (LWRR) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch total road length. 

Figure 23b. Lower White River Region (LWRR) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch total road length. 

Figure 23c. Lower White River Region (LWRR) wetland plant habitat vulnerability (> 

2 ha) in terms of habitat patch total road length. 

Figure 23d. Lower White River Region (LWRR) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch total road density. 

Figure 23e. Lower White River Region (LWRR) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch total road density. 

Figure 23f. Lower White River Region (LWRR) wetland plant habitat vulnerability (> 2 

ha) in terms of habitat patch total road density. 

Figure 23g. Lower White River Region (LWRR) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch road index. 

Figure 23h. Lower White River Region (LWRR) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch road index. 

Figure 23i. Lower White River Region (LWRR) wetland plant habitat vulnerability (> 2 

ha) in terms of habitat patch road index. 

Figure 23j. Lower White River Region (LWRR) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch human population density in 1990. 

Figure 23k. Lower White River Region (LWRR) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch human population density in 1990. 

Figure 23l. Lower White River Region (LWRR) wetland plant habitat vulnerability (> 2 

ha) in terms of habitat patch human population density in 1990. 

Figure 23m. Lower White River Region (LWRR) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch estimated human population density in 

2011. 

Figure 23n. Lower White River Region (LWRR) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch estimated human population density in 

2011. 

Figure 23o. Lower White River Region (LWRR) wetland plant habitat vulnerability (> 

2 ha) in terms of habitat patch estimated human population density in 2011. 

Figure 23p. Lower White River Region (LWRR) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch human population density change from 

1990 to 2011. 

Figure 23q. Lower White River Region (LWRR) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch human population density change from 

1990 to 2011. 

Figure 23r. Lower White River Region (LWRR) wetland plant habitat vulnerability (> 2 

ha) in terms of habitat patch human population density change from 1990 to 2011. 
Figure 23s. Lower White River Region (LWRR) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch unified human index. 

Figure 23t. Lower White River Region (LWRR) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch unified human index. 

Figure 23u. Lower White River Region (LWRR) wetland plant habitat vulnerability (> 

2 ha) in terms of habitat patch unified human index. 

Figure 23v. Lower White River Region (LWRR) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch unified vulnerability index. 

Figure 23w. Lower White River Region (LWRR) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch unified vulnerability index. 

Figure 23x. Lower White River Region (LWRR) wetland plant habitat vulnerability (> 

2 ha) in terms of habitat patch unified vulnerability index. 
Lower White 

River Region 

Wetland Habitat 

Vulnerability Models 

(Total Road Length) 



Lower White 

River Region 

Wetland Habitat 

Vulnerability Models 

(Total Road Density) 



Lower White 

River Region 

Wetland Habitat 

Vulnerability Models 

(Road Index) 



Lower White 

River Region 

Wetland Habitat 

Vulnerability Models 

(Human Population 

Density in 1990) 



Lower White 

River Region 

Wetland Habitat 

Vulnerability Models 

(Estimated Human 

Population Density 

in 2011) 



Lower White 

River Region 

Wetland Habitat 

Vulnerability Models 

(Estimated Human 

Population Density 

Change from 

1990 to 2011) 



Lower White 

River Region 

Wetland Habitat 

Vulnerability Models 

(Unified Human 

Index) 



Lower White 

River Region 

Wetland Habitat 

Vulnerability Models 

(Unified Vulnerability 

Index) 



Mississippi Alluvial 

Valley Ecoregion 

Habitat Suitability 

Models 
Figure 24a. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck winter habitat 

suitability (All Land), All Sizes. 

Figure 24b. Mississippi Alluvial Valley Ecoregion (MAVE) black bear habitat 

suitability (All Land), All Sizes. 

Figure 24c. Mississippi Alluvial Valley Ecoregion (MAVE) least tern habitat suitability 

reference aid (All Land), All Sizes. 

Figure 24d. Mississippi Alluvial Valley Ecoregion (MAVE) least tern habitat image 

overlay (All Land), All Sizes. 

Figure 24e. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck winter habitat 

suitability (Wetlands and Open Water Only), All Sizes. 

Figure 24f. Mississippi Alluvial Valley Ecoregion (MAVE) black bear habitat 

suitability (Wetlands and Open Water Only), All Sizes. 

Figure 24g. Mississippi Alluvial Valley Ecoregion (MAVE) wetland plant habitat 

suitability (Wetlands and Open Water Only), All Sizes. 

Figure 24h. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck winter habitat 

suitability (Wetlands and Open Water Only), ≤ 2 ha. 

Figure 24i. Mississippi Alluvial Valley Ecoregion (MAVE) black bear habitat 

suitability (Wetlands and Open Water Only), ≤ 2 ha. 

Figure 24j. Mississippi Alluvial Valley Ecoregion (MAVE) wetland plant habitat 

suitability (Wetlands and Open Water Only), ≤ 2 ha. 

 

Figure 24k. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck winter habitat 

suitability (Wetlands and Open Water Only), > 2 ha. 

Figure 24l. Mississippi Alluvial Valley Ecoregion (MAVE) black bear habitat 

suitability (Wetlands and Open Water Only), > 2 ha. 

Figure 24m. Mississippi Alluvial Valley Ecoregion (MAVE) wetland plant habitat 

suitability (Wetlands and Open Water Only), > 2 ha. 
Mississippi Alluvial 

Valley Ecoregion 

Habitat Suitability 

Models 

(All Land, All Sizes) 




Mississippi Alluvial 

Valley Ecoregion 

Wetland Habitat 

Suitability Models 

(All Sizes) 



Mississippi Alluvial 

Valley Ecoregion 

Wetland Habitat 

Suitability Models 

(≤ 2 ha) 



Mississippi Alluvial 

Valley Ecoregion 

Wetland Habitat 

Suitability Models 

(> 2 ha) 



Mississippi Alluvial 

Valley Ecoregion 

Wetland Habitat 

Vulnerability Models 

(Patch Size and 

Patch Shape) 
Figure 25a. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch total area. 

Figure 25b. Mississippi Alluvial Valley Ecoregion (MAVE) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch total area. 

Figure 25c. Mississippi Alluvial Valley Ecoregion (MAVE) wetland plant habitat 

vulnerability (> 2 ha) in terms of habitat patch total area. 

Figure 25d. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch total perimeter length. 

Figure 25e. Mississippi Alluvial Valley Ecoregion (MAVE) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch total perimeter length. 

Figure 25f. Mississippi Alluvial Valley Ecoregion (MAVE) wetland plant habitat 

vulnerability (> 2 ha) in terms of habitat patch total perimeter length. 

Figure 25g. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch interior-to-edge ratio. 

Figure 25h. Mississippi Alluvial Valley Ecoregion (MAVE) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch interior-to-edge ratio. 

Figure 25i. Mississippi Alluvial Valley Ecoregion (MAVE) wetland plant habitat 

vulnerability (> 2 ha) in terms of habitat patch interior-to-edge ratio. 

Figure 25j. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch circularity index. 

Figure 25k. Mississippi Alluvial Valley Ecoregion (MAVE) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch circularity index. 

Figure 25l. Mississippi Alluvial Valley Ecoregion (MAVE) wetland plant habitat 

vulnerability (> 2 ha) in terms of habitat patch circularity index. 

Figure 25m. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck winter 

habitat vulnerability (> 2 ha) in terms of habitat patch sinuosity index. 

Figure 25n. Mississippi Alluvial Valley Ecoregion (MAVE) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch sinuosity index. 

Figure 25o. Mississippi Alluvial Valley Ecoregion (MAVE) wetland plant habitat 

vulnerability (> 2 ha) in terms of habitat patch sinuosity index. 

Figure 25p. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch unified patch index. 

Figure 25q. Mississippi Alluvial Valley Ecoregion (MAVE) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch unified patch index. 

Figure 25r. Mississippi Alluvial Valley Ecoregion (MAVE) wetland plant habitat 

vulnerability (> 2 ha) in terms of habitat patch unified patch index. 
Mississippi Alluvial 

Valley Ecoregion 

Wetland Habitat 

Vulnerability Models 

(Total Area) 



Mississippi Alluvial 

Valley Ecoregion 

Wetland Habitat 

Vulnerability Models 

(Total Perimeter) 



Mississippi Alluvial 

Valley Ecoregion 

Wetland Habitat 

Vulnerability Models 

(Interior-to-Edge 

Ratio) 



Mississippi Alluvial 

Valley Ecoregion 

Wetland Habitat 

Vulnerability Models 

(Circularity Index) 



Mississippi Alluvial 

Valley Ecoregion 

Wetland Habitat 

Vulnerability Models 

(Sinuosity Index) 



Mississippi Alluvial 

Valley Ecoregion 

Wetland Habitat 

Vulnerability Models 

(Unified Patch Index) 



Mississippi Alluvial 

Valley Ecoregion 

Wetland Habitat 

Vulnerability Models 

(Patch HumanInduced Disturbance) 
Figure 26a. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch total road length. 

Figure 26b. Mississippi Alluvial Valley Ecoregion (MAVE) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch total road length. 

Figure 26c. Mississippi Alluvial Valley Ecoregion (MAVE) wetland plant habitat 

vulnerability (> 2 ha) in terms of habitat patch total road length. 

Figure 26d. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch total road density. 

Figure 26e. Mississippi Alluvial Valley Ecoregion (MAVE) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch total road density. 

Figure 26f. Mississippi Alluvial Valley Ecoregion (MAVE) wetland plant habitat 

vulnerability (> 2 ha) in terms of habitat patch total road density. 

Figure 26g. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch road index. 

Figure 26h. Mississippi Alluvial Valley Ecoregion (MAVE) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch road index. 

Figure 26i. Mississippi Alluvial Valley Ecoregion (MAVE) wetland plant habitat 

vulnerability (> 2 ha) in terms of habitat patch road index. 

Figure 26j. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch human population density in 1990. 

Figure 26k. Mississippi Alluvial Valley Ecoregion (MAVE) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch human population density in 1990. 

Figure 26l. Mississippi Alluvial Valley Ecoregion (MAVE) wetland plant habitat 

vulnerability (> 2 ha) in terms of habitat patch human population density in 1990. 

Figure 26m. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck winter 

habitat vulnerability (> 2 ha) in terms of habitat patch estimated human population 

density in 2011. 

Figure 26n. Mississippi Alluvial Valley Ecoregion (MAVE) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch estimated human population density in 

2011. 

Figure 26o. Mississippi Alluvial Valley Ecoregion (MAVE) wetland plant habitat 

vulnerability (> 2 ha) in terms of habitat patch estimated human population density in 

2011. 

Figure 26p. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch human population density change from 

1990 to 2011. 

Figure 26q. Mississippi Alluvial Valley Ecoregion (MAVE) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch human population density change from 

1990 to 2011. 
Figure 26r. Mississippi Alluvial Valley Ecoregion (MAVE) wetland plant habitat 

vulnerability (> 2 ha) in terms of habitat patch human population density change from 

1990 to 2011. 

Figure 26s. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch unified human index. 

Figure 26t. Mississippi Alluvial Valley Ecoregion (MAVE) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch unified human index. 

Figure 26u. Mississippi Alluvial Valley Ecoregion (MAVE) wetland plant habitat 

vulnerability (> 2 ha) in terms of habitat patch unified human index. 

Figure 26v. Mississippi Alluvial Valley Ecoregion (MAVE) mallard duck winter habitat 

vulnerability (> 2 ha) in terms of habitat patch unified vulnerability index. 

Figure 26w. Mississippi Alluvial Valley Ecoregion (MAVE) black bear wetland habitat 

vulnerability (> 2 ha) in terms of habitat patch unified vulnerability index. 

Figure 26x. Mississippi Alluvial Valley Ecoregion (MAVE) wetland plant habitat 

vulnerability (> 2 ha) in terms of habitat patch unified vulnerability index. 
Mississippi Alluvial 

Valley Ecoregion 

Wetland Habitat 

Vulnerability Models 

(Total Road Length) 



Mississippi Alluvial 

Valley Ecoregion 

Wetland Habitat 

Vulnerability Models 

(Total Road Density) 



Mississippi Alluvial 

Valley Ecoregion 

Wetland Habitat 

Vulnerability Models 

(Road Index) 



Mississippi Alluvial 

Valley Ecoregion 

Wetland Habitat 

Vulnerability Models 

(Human Population 

Density in 1990) 



Mississippi Alluvial 

Valley Ecoregion 

Wetland Habitat 

Vulnerability Models 

(Estimated Human 

Population Density 

in 2011) 



Mississippi Alluvial 

Valley Ecoregion 

Wetland Habitat 

Vulnerability Models 

(Estimated Human 

Population Density 

Change from 

1990 to 2011) 



Mississippi Alluvial 

Valley Ecoregion 

Wetland Habitat 

Vulnerability Models 

(Unified Human 

Index) 



Mississippi Alluvial 

Valley Ecoregion 

Wetland Habitat 

Vulnerability Models 

(Unified Vulnerability 

Index) 



White River 

Watershed Water 

Quality Vulnerability 

Models and Figures 















Lower White River 

Region Federal 

Wildlife Refuge 

Analyses