Prepared in cooperation with the Bureau of Land Management and 

the Great Plains Landscape Conservation Cooperative

Southern Great Plains Rapid Ecoregional Assessment

Volume I. Ecological Communities

Open-File Report 2017–1100

U.S. Department of the Interior

U.S. Geological Survey
Cover. Flint Hills, Nebraska. Photograph by Edwin Olson, 2003.
Southern Great Plains Rapid 

Ecoregional Assessment—

Volume 1. Ecological Communities

By Gordon C. Reese, Lucy Burris, Natasha B. Carr, Ian I.F. Leinwand, 

and Cynthia P. Melcher

Prepared in cooperation with the Bureau of Land Management and

the Great Plains Landscape Conservation Cooperative

Open-File Report 2017–1100

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

RYAN K. ZINKE, Secretary

U.S. Geological Survey

William H. Werkheiser, Acting Director

U.S. Geological Survey, Reston, Virginia: 2017

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Suggested citation:

Reese, G.C., Burris, Lucy, Carr, N.B., Leinwand, I.I.F., and Melcher, C.P., 2017, Southern Great Plains Rapid Ecoregional 

Assessment—Volume I. Ecological communities: U.S. Geological Survey Open-File Report 2017–1100, 126 p., 

https://doi.org/10.3133/ofr20171100.

ISSN 0196-1497 (print)

ISSN 2331-1258 (online)
iii

Acknowledgments

The impetus and oversight for this project was provided by the Southern Great Plains Rapid 

Ecoregional Assessment advisors including Steve Tryon, Melanie Barnes, David Wood, 

Kevin Miller, George Thomas, Janine Book, Laurence Levesque, Carolyn Russell of the Bureau of 

Land Management and Nicole Athearn of the Great Plains Landscape Conservation Cooperative. 

We greatly appreciate reviews of maps and the manuscript provided by Dan Baggao, 

Robert Means, Chuck Schmidt, and George Soehn of the Bureau of Land Management; 

Duane German, Russell Martin, and Amie Treuer-Kuehn, Texas Parks and Wildlife; Russ Horton, 

Oklahoma Department of Wildlife Conservation; Alex Daniels and Anne Bartuszevige, Playa 

Lakes Joint Venture; Lee Elliot, Missouri Resource Assessment Partnership; Rick Schneider, 

Nebraska Natural Heritage Program; Esteban Muldavin, New Mexico Natural Heritage 

Program; Bruce Hoagland, Oklahoma Natural Heritage Inventory; Joe Stevens, Joanna Lemly, 

Karin Decker, and Michael Menefee, Colorado Natural Heritage Program; Patrick J. Comer, 

NatureServe; Steven Rolfsmeier, Chadron State College; Kelly Kindscher, Kansas Biological 

Survey; and Timothy Assal, Greg Auble, Tammy Fancher, Steve Germaine, Daniel Manier, 

Laura Perry, Jonathan Friedman, Susan Skagen, Helen Sofaer, Colin Talbert, and David Walters, 

U.S. Geological Survey. Catherine Jarnevich, U.S. Geological Survey, assisted with the tamarisk 

and Russian olive models.

The following organizations developed or contributed to the datasets used in the Rapid 

Ecoregional Assessment: the Bureau of Land Management, Bureau of Meteorology (Australia), 

Colorado Division of Water Resources, Commonwealth Scientific and Industrial Research 

Organization, Community Earth System Model Contributors (National Science Foundation, 

Department of Energy, National Center for Atmospheric Research), Global Biodiversity 

Information Facility, IHS, Kansas Biological Survey, Kansas Geological Survey, National 

Agricultural Statistics Service, Nebraska Department of Natural Resources, New Mexico Office 

of the State Engineer, Oklahoma Water Resources Board, Playa Lakes Joint Venture, South 

Dakota Department of Environment and Natural Resources, Texas Commission on Environmental 

Quality, Texas Parks and Wildlife Department, Texas Tech University, Texas Water Development 

Board, U.S. Army Corps of Engineers, U.S. Census Bureau, U.S. Department of Transportation, 

U.S. Environmental Protection Agency, U.S. Forest Service, U.S. Geological Survey, World 

Climate Research Programme’s Working Group on Coupled Modelling, Wyoming Water 

Development Commission, and the Wyoming State Engineer’s Office.

v

Contents

Acknowledgments........................................................................................................................................iii

Executive Summary.......................................................................................................................................1

Chapter 1. Introduction and Overview...................................................................................................3

Introduction.....................................................................................................................................................3

Purpose of the Rapid Ecoregional Assessment ..............................................................................3

Management Implications ..................................................................................................................3

Components of the Rapid Ecoregional Assessment.......................................................................3

Overview of the Rapid Ecoregional Assessment Process.............................................................4

Southern Great Plains Rapid Ecoregional Assessment ..........................................................................4

Project Area...........................................................................................................................................4

Management Questions ......................................................................................................................7

Conservation Elements ........................................................................................................................7

Change Agents......................................................................................................................................7

Reports and Organization.............................................................................................................................7

Pre-Assessment Report.......................................................................................................................7

Rapid Ecoregional Assessment Reports...........................................................................................9

Accessing the Rapid Ecoregional Assessment Datasets..............................................................9

References Cited............................................................................................................................................9

Chapter 2. Methods Overview...............................................................................................................11

Assessment Framework .............................................................................................................................11

Core Management Questions...........................................................................................................11

Integrated Management Question...................................................................................................11

Scale of Analysis for Management Questions ..............................................................................11

Baseline Distributions for Evaluating Change Agents..................................................................11

Management Questions..............................................................................................................................12

Core Management Questions for Evaluating Current and Future Change Agents..................12

Core Management Questions for Evaluating Terrestrial Communities......................................14

Core Management Questions for Evaluating Aquatic Communities..........................................15

Integrated Management Question for Evaluating Terrestrial and Aquatic Communities.......15

Summary........................................................................................................................................................16

References Cited..........................................................................................................................................17

Chapter 3. Change Agents ......................................................................................................................19

Introduction...................................................................................................................................................19

Change Agents and Management Questions .........................................................................................19

Management Questions and Results .......................................................................................................20

Summary........................................................................................................................................................31

References Cited..........................................................................................................................................31

Chapter 4. Grasslands .............................................................................................................................33

Introduction...................................................................................................................................................33

Rapid Ecoregional Assessment Components Evaluated for Grasslands ...........................................33

Management Questions and Results .......................................................................................................35

Summary........................................................................................................................................................43

References Cited..........................................................................................................................................43

Chapter 5. Mixed-Grass Prairie ............................................................................................................45

Introduction...................................................................................................................................................45

Rapid Ecoregional Assessment Components Evaluated for Mixed-Grass Prairie ...........................45

Management Questions and Results .......................................................................................................47

Summary........................................................................................................................................................52

References Cited..........................................................................................................................................52
vi

Chapter 6. Shortgrass Prairie ................................................................................................................53

Introduction...................................................................................................................................................53

Rapid Ecoregional Assessment Components Evaluated for Shortgrass Prairie...............................53

Management Questions and Results .......................................................................................................55

Summary........................................................................................................................................................60

References Cited..........................................................................................................................................60

Chapter 7. Sand Prairie...........................................................................................................................61

Introduction...................................................................................................................................................61

Rapid Ecoregional Assessment Components Evaluated for Sand Prairie .........................................61

Management Questions and Results .......................................................................................................63

Summary........................................................................................................................................................69

References Cited..........................................................................................................................................69

Chapter 8. Riparian and Nonplaya Wetlands......................................................................................71

Introduction...................................................................................................................................................71

Rapid Ecoregional Assessment Components Evaluated for Riparian and Nonplaya Wetlands ........71

Management Questions and Results .......................................................................................................73

Summary........................................................................................................................................................80

References Cited..........................................................................................................................................80

Chapter 9. Playa Wetlands and Saline Lakes.....................................................................................83

Introduction...................................................................................................................................................83

Rapid Ecoregional Assessment Components Evaluated for Playa Wetlands and Saline Lakes........83

Management Questions and Results .......................................................................................................85

Summary........................................................................................................................................................93

References Cited..........................................................................................................................................93

Chapter 10. Prairie Streams and Rivers...............................................................................................95

Introduction...................................................................................................................................................95

Rapid Ecoregional Assessment Components Evaluated for Prairie Streams and Rivers ...............95

Management Questions and Results .......................................................................................................97

Summary......................................................................................................................................................106

References Cited........................................................................................................................................106

Chapter 11. Data Gaps, Limitations, and Uncertainty......................................................................107

Data Gaps, Limitations, and Uncertainty ...............................................................................................107

Overview.............................................................................................................................................107

Change Agents..................................................................................................................................107

Ecological Community Distributions.......................................................................................................108

Grassland Communities...................................................................................................................108

Riparian Areas and Nonplaya Wetlands.......................................................................................108

Playa Wetlands and Saline Lakes..................................................................................................109

Prairie Streams and Rivers .............................................................................................................109

Patch Size and Connectivity ....................................................................................................................109

Management Questions Not Addressed ...............................................................................................109

Altered Fire Regimes........................................................................................................................109

Shrub Dynamics................................................................................................................................109

Terrestrial Development ..................................................................................................................110

Aquatic Development.......................................................................................................................110

Invasive Species...............................................................................................................................110
vii

Conservation Reserve Program .....................................................................................................110

Climate Change .................................................................................................................................110

Poisoning/Herbicides/Pesticides...................................................................................................110

Grazing................................................................................................................................................110

Recreation..........................................................................................................................................110

References Cited........................................................................................................................................111

Appendix A. Methodological Details for Derived Datasets ..........................................................113

Introduction.................................................................................................................................................113

Change Agents...........................................................................................................................................113

Development......................................................................................................................................113

Invasive Species...............................................................................................................................116

Ecological Communities............................................................................................................................118

Terrestrial Community Baseline Distribution Mapping ..............................................................118

Aquatic Community Baseline Distribution Mapping...................................................................118

Landscape Structure.................................................................................................................................119

Patch Size...........................................................................................................................................119

Landscape-Level Area Ranking Breakpoints: Calculating Equal Subsets of the Data ..................120

Disclaimers .................................................................................................................................................124

References Cited........................................................................................................................................124

Data Sources .............................................................................................................................................124

Figures

1–1. Map showing Southern Great Plains Rapid Ecoregional Assessment boundary..............5

1–2. Map showing land ownership and jurisdictions for the Southern Great Plains ................6

1–3. Map showing distribution of ecological communities in the Southern Great Plains........8

2–1. Diagram showing overview of the process for deriving the terrestrial development 

index..............................................................................................................................................12

2–2. Diagram showing overview of the process for deriving the aquatic development 

index..............................................................................................................................................13

2–3. Diagram showing overall landscape-level ranks .......................................................................16

2–4. Diagram showing overview of the process used to address the core and integrated 

management questions .............................................................................................................17

3–1. Map showing terrestrial development index .........................................................................20

3–2. Graph showing total area in each terrestrial development index class ...........................21

3–3. Maps showing terrestrial development index for agricultural croplands and 

urban areas..................................................................................................................................22

3–4. Maps showing terrestrial development index for transportation and energy 

and minerals development........................................................................................................23

3–5. Maps showing aquatic development index ...........................................................................24

3–6. Graph showing total area in aquatic development index classes .....................................25

3–7. Maps showing aquatic development index for three classes of development ...............25

3–8. Map showing fire perimeters, 1984–2014 ...............................................................................26

3–9. Graph showing area burned annually, 1984–2014.................................................................27

3–10. Map showing potentially altered vegetation .........................................................................28

3–11. Maps showing average annual temperatures for 1981−2010, a hot and dry 

scenario for 2016−2045 and 2046−2075, and a warm and wet scenario for 

2016−2045 and 2046−2075 ..........................................................................................................29
viii

3–12. Maps showing average annual precipitation for 1981−2010, a hot and 

dry scenario for 2016−2045 and 2046−2075, and a warm and wet scenario 

for 2016−2045 and 2046−2075 ....................................................................................................30

4–1. Map showing estimated historical distribution of grasslands............................................35

4–2. Map showing baseline distribution of grasslands ................................................................36

4–3. Map showing terrestrial development index for baseline grasslands ..............................37

4–4. Graph showing area of baseline grasslands by terrestrial development index class ........... 38

4–5. Graph showing area of grasslands as a function of patch size..........................................38

4–6. Maps showing patch size of grasslands ................................................................................39

4–7. Maps showing occurrences of honey mesquite, eastern redcedar, and 

either species..............................................................................................................................40

4–8. Maps showing potential changes in grasslands distribution as projected by 

climate change scenarios.........................................................................................................41

4–9. Maps showing landscape-level summaries for grasslands................................................42

5–1. Map showing baseline and converted historical distributions of mixed-grass prairie.......... 47

5–2. Map showing terrestrial development index for baseline mixed-grass prairie...............48

5–3. Graph showing area of baseline mixed-grass prairie by terrestrial development 

index class ...................................................................................................................................49

5–4. Graph showing area of mixed-grass prairie as a function of patch size...............................49

5–5. Maps showing patch size of mixed-grass prairie .................................................................50

5–6. Maps showing landscape-level summaries for mixed-grass prairie ................................51

6–1. Map showing baseline and converted historical distributions of shortgrass prairie.........55

6–2. Map showing terrestrial development index for baseline shortgrass prairie..................56

6–3. Graph showing area of baseline shortgrass prairie by terrestrial development 

index class ...................................................................................................................................57

6–4. Graph showing area of shortgrass prairie as a function of patch size..................................57

6–5. Maps showing patch size of shortgrass prairie ....................................................................58

6–6. Maps showing landscape-level summaries for shortgrass prairie ...................................59

7–1. Map showing baseline and converted historical distributions of sand prairie................63

7–2. Map showing baseline distributions of sand sagebrush and shinnery oak 

within the sand prairie community ..........................................................................................64

7–3. Map showing terrestrial development index for baseline sand prairie ............................65

7–4. Graph showing area of baseline sand prairie by terrestrial development index class.......... 66

7–5. Graph showing area of sand prairie as a function of patch size........................................66

7–6. Maps showing patch size of sand prairie...............................................................................67

7–7. Maps showing landscape-level summaries for sand prairie..............................................68

8–1. Map showing baseline distribution of riparian and nonplaya wetlands ...........................73

8–2. Map showing percentage of each sixth-level watershed classified as riparian 

and nonplaya wetlands..............................................................................................................74

8–3. Map showing aquatic development index for baseline riparian and nonplaya 

wetlands.......................................................................................................................................75

8–4. Graph showing area of baseline riparian and nonplaya wetlands by aquatic 

development index class...........................................................................................................76

8–5. Graph showing area of riparian and nonplaya wetlands as a function of patch size............ 76

8–6. Map showing probability of suitable habitat and reported occurrences for

Russian olive................................................................................................................................77
ix

8–7. Map showing probability of suitable habitat and reported occurrences for tamarisk .......... 78

8–8. Maps showing landscape-level summaries for riparian and nonplaya wetlands...........79

9–1. Map showing baseline distribution of playa wetlands..............................................................85

9–2. Map showing baseline distribution of saline lakes...............................................................86

9–3. Map showing area of baseline playa wetlands as a percentage of sixth-level 

watershed ....................................................................................................................................87

9–4. Map showing local aquatic development index for baseline playa wetlands.................88

9–5. Map showing local aquatic development index for baseline saline lakes.......................89

9–6. Graphs showing area of playa wetlands and saline lakes by local aquatic 

development index class...........................................................................................................90

9–7. Graphs showing area of playa wetlands and saline lakes as a function of patch size.......... 91

9–8. Maps showing landscape-level summaries for playa wetlands and saline lakes ..........92

10–1. Map showing baseline distribution of prairie streams and rivers by hydroperiod..........97

10–2. Map showing total length of perennial streams....................................................................98

10–3. Map showing total length of intermittent streams................................................................99

10–4. Map showing aquatic development index for baseline perennial streams....................100

10–5. Map showing aquatic development index for baseline intermittent streams................101

10–6. Graphs showing length of prairie streams and rivers by aquatic development 

index class .................................................................................................................................102

10–7. Graph showing total length of perennial streams as a function of stream-segment 

size class...........................................................................................................................................103

10–8. Map showing potential barriers in perennial streams .......................................................104

10–9. Maps showing landscape-level summaries for prairie streams and rivers ...................105

A–1. Maps showing terrestrial development index score and the contributing footprint 

from development for two landscapes.......................................................................................115

A–2. Diagram of the local and upstream contributing areas for a local catchment..............116

A–3. Maps showing components of the aquatic development index summarized 

by catchment.............................................................................................................................117

A–4. Graphs showing total area in aquatic development index classes .................................118

Tables

1–1. Ecological communities evaluated as conservation elements.............................................7

3–1. Change agents and associated indicators used to address management questions........19

3–2. Management questions addressed for change agents .......................................................19

3–3. Area of ecological communities burned, 1984–2014..................................................................27

4–1. Key ecological attributes and associated indicators used to address core 

management questions for grasslands...................................................................................34

4–2. Anthropogenic change agents and associated indicators used to address core

management questions for grasslands ........................................................................................34

4–3. Landscape-level variables used to address the integrated management question

for grasslands ....................................................................................................................................34

4–4. Management questions addressed for grasslands ..............................................................34

5–1. Key ecological attributes and associated indicators used to address core 

management questions for mixed-grass prairie ...................................................................46
x

5–2. Anthropogenic change agents and associated indicators used to address core 

management questions for mixed-grass prairie ...................................................................46

5–3. Landscape-level variables used to address the integrated management question 

for mixed-grass prairie.....................................................................................................................46

5–4. Management questions addressed for mixed-grass prairie...............................................46

6–1. Key ecological attributes and associated indicators used to address core 

management questions for shortgrass prairie ......................................................................54

6–2. Anthropogenic change agents and associated indicators used to address core 

management questions for shortgrass prairie............................................................................54

6–3. Landscape-level variables used to address the integrated management question 

for shortgrass prairie........................................................................................................................54

6–4. Management questions addressed for shortgrass prairie..................................................54

7–1. Key ecological attributes and associated indicators used to address core 

management questions for sand prairie.................................................................................62

7–2. Anthropogenic change agents and associated indicators used to address core 

management questions for sand prairie.................................................................................62

7–3. Landscape-level variables used to address the integrated management question 

for sand prairie...................................................................................................................................62

7–4. Management questions addressed for sand prairie ............................................................62

8–1. Key ecological attributes and associated indicators used to address core 

management questions for riparian and nonplaya wetlands..............................................72

8–2. Anthropogenic change agents and associated indicators used to address core 

management questions for riparian and nonplaya wetlands..............................................72

8–3. Landscape-level variables used to address the integrated management question 

for riparian and nonplaya wetlands ..............................................................................................72

8–4. Management questions addressed for riparian and nonplaya wetlands .........................72

9–1. Key ecological attributes and associated indicators used to address core 

management questions for playa wetlands and saline lakes .............................................84

9–2. Anthropogenic change agents and associated indicators used to address 

core management questions for playa wetlands and saline lakes....................................84

9–3. Landscape-level variables used to address the integrated management 

question for playa wetlands and saline lakes........................................................................84

9–4. Management questions addressed for playa wetlands and saline lakes.........................84

10–1. Key ecological attributes and associated indicators used to address core 

management questions for prairie streams and rivers ........................................................96

10–2. Anthropogenic change agents and associated indicators used to address core 

management questions for prairie streams and rivers ........................................................96

10–3. Landscape-level variables used to address the integrated management question 

for prairie streams and rivers ...................................................................................................96

10–4. Management questions addressed for prairie streams and rivers....................................96

A–1. Data sources used to quantify change agents....................................................................114

A–2. Relations between component variables and metrics for the overall aquatic 

development index and key ecological attributes for prairie streams and rivers .........115

A–3. Variables used in the Russian olive and tamarisk distribution models............................119

A–4. Data sources used to map the distribution of baseline ecological communities..........119

A–5. Cover types classified from LANDFIRE Existing Vegetation Types.......................................120
xi

Conversion Factors

SI to Inch/Pound

Multiply By To obtain

Length

meter (m) 3.281 foot (ft) 

meter (m) 1.094 yard (yd) 

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2

) 10.76 square foot (ft2) 

hectare (ha) 2.471 acre

square kilometer (km2

) 0.3861 square mile (mi2)

Volume

cubic meters (m3

) 0.0008107 acre-feet

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F = (1.8 × °C) + 32

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows:

°C = (°F – 32)/1.8

Scientific Notation

Symbol Meaning

< Less than

≤ Less than or equal to

= Equals

> Greater than

≥ Greater than or equal to

Abbreviations

ADI aquatic development index

BLM Bureau of Land Management

BpS Biophysical Settings

DOI Department of the Interior

EVT Existing Vegetation Type

GAP Gap Analysis Program (U.S. Geological Survey)

GPLCC Great Plains Landscape Conservation Cooperative

NHD National Hydrography Dataset (U.S. Geological Survey)

NRCS Natural Resources Conservation Service

REA Rapid Ecoregional Assessment

SAHM Software for Assisted Habitat Modeling

SGP Southern Great Plains

TDI terrestrial development index
xii

Species Names

Plants

big bluestem (Andropogon gerardii)

blowout penstemon (Penstemon haydenii)

blue grama (Bouteloua gracilis)

buffalograss (Bouteloua dactyloides)

cottonwood (Populus spp.)

little bluestem (Schizachyrium scoparium)

honey mesquite (Prosopis glandulosa)

eastern redcedar (Juniperus virginiana)

one-seeded juniper (Juniperus monosperma)

Russian olive (Elaeagnus angustifolia)

sand sagebrush (Artemisia filifolia)

sand shinnery oak (Quercus havardii)

sideoats grama (Bouteloua curtipendula)

tamarisk (Tamarix spp.)

western wheatgrass (Pascopyrum smithii)

willow (Salix spp.)

Birds

burrowing owl (Athene cunicularia hypugaea)

ferruginous hawk (Buteo regalis)

interior least tern (Sternula antillarum athalassos)

lesser prairie chicken (Tympanuchus pallidicinctus)

long-billed curlew (Numenius americanus)

mountain plover (Charadrius montanus)

snowy plover (Charadrius nivosus)

Mammals

black-tailed prairie dog (Cynomys ludovicianus)

mule deer (Odocoileus hemionus)

swift fox (Vulpes velox)
xiii

Fish

Arkansas River shiner (Notropis girardi)

Reptiles

dunes sagebrush lizard (Sceloporus arenicolus)

western massasauga (Sistrurus catenatus tergeminus)

Contributing Authors

Lucy Burris 

U.S. Geological Survey, Fort Collins Science Center, Fort Collins, Colo.

Natasha B. Carr 

U.S. Geological Survey, Fort Collins Science Center, Fort Collins, Colo.

Ian I.F. Leinwand 

Cherokee National Technologies, on contract to U.S. Geological Survey, 

Fort Collins Science Center, Fort Collins, Colo.

Cynthia P. Melcher 

U.S. Geological Survey, Fort Collins Science Center, Fort Collins, Colo.

Gordon C. Reese 

U.S. Geological Survey, Fort Collins Science Center, Fort Collins, Colo.

Executive Summary

Rapid Ecoregional Assessments

The overall goal of the Bureau of Land Management 

(BLM) Rapid Ecoregional Assessments (REAs) is to compile 

and synthesize regional datasets to facilitate evaluation of the 

cumulative effects of change agents on priority species and 

communities. More specifically, the REAs identify and map 

the distribution of priority communities and wildlife habitats 

at broad spatial extents and provide assessments of ecological conditions. The REAs also identify where and to what 

degree ecological resources are currently at risk from change 

agents—natural processes or human activities that drive ecosystem change—such as development, fire, invasive species, 

and climate change. The REAs can help managers identify and 

prioritize potential areas for conservation or restoration, assess 

cumulative effects as required by the National Environmental 

Policy Act, and inform landscape-level planning and management decisions for multiple uses of public lands. Overall, the 

REAs provide a vehicle for creating stronger, more effective, 

and more efficient collaboration and cooperation among all 

parties interested in regional land and resource management and thereby support the BLM landscape approach to 

resource management.

Rapid Ecoregional Assessment 

Components

There are several components to the REAs. Management questions, developed by the BLM and other stakeholders, identify the regionally significant information needed 

to address land-management responsibilities. Conservation 

elements represent ecological communities and species that 

are of regional management concern. The emphasis on ecological communities is based on the premise that intact and 

functioning ecological systems are more resistant and resilient 

to change agents, including both natural and human stressors. 

Because it is not feasible to manage or monitor all species 

individually, the protection of intact ecological communities 

may serve as a safety net for species not addressed specifically 

by the REA. Species or species assemblages of management 

concern not adequately addressed at the community level may 

be specifically addressed as conservation elements. The REA 

identifies and assesses the primary factors, or change agents, 

that currently affect or are likely to affect the condition of 

communities and species in the future.

The Southern Great Plains Rapid 

Ecoregional Assessment

The BLM partnered with the Great Plains Landscape 

Conservation Cooperative (GPLCC) to ensure that the results 

of the Southern Great Plains REA provide information useful 

in addressing management issues identified by a diverse set of 

stakeholders representing both the REA and the GPLCC. The 

Southern Great Plains (SGP) REA project area includes the 

full extent of the GPLCC area and four level-III ecoregions: 

High Plains, Central Great Plains, Southwestern Tablelands, 

and Nebraska Sand Hills. The project area for this REA is the 

largest of all completed REAs; it encompasses 961,105 square 

kilometers (371,085 square miles) and includes portions of 

Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South 

Dakota, Texas, and Wyoming.

The Southern Great Plains REA is summarized in a 

series of three reports. The pre-assessment report summarizes 

the process used by the REA stakeholders to select management questions, conservation elements, and change agents. It 

also provides background information for each conservation 

element selected including a description of the key ecological attributes and change agents, which are summarized in a 

conceptual model and tables. Volume I of the Southern Great 

Plains REA report (this volume) provides background information, methods, summaries, and data gaps for all the ecological communities evaluated for the Southern Great Plains REA. 

Volume II will address the species and species assemblages 

evaluated for the Southern Great Plains REA.

Seven major ecological communities were evaluated as 

conservation elements for the Southern Great Plains REA. Of 

those seven, three are grassland communities: mixed-grass 

prairie, shortgrass prairie, and sand prairie. We also addressed 

management questions for the three grassland communities collectively and included other grassland types present 

in the project area, but not addressed individually: tallgrass, 

northwest mixed-grass, and cool-season bunchgrass prairies; 

foothill and saline grasslands; and semidesert grassland and 

shrubland. The three aquatic communities evaluated are riparian and nonplaya wetlands, playa wetlands and saline lakes, 

and prairie streams and rivers. A total of 13 species and species assemblages were selected for the Southern Great Plains 

REA (addressed in volume II): freshwater mussel assemblage, Arkansas River shiner (Notropis girardi), ferruginous 

hawk (Buteo regalis), lesser prairie chicken (Tympanuchus 

pallidicinctus), snowy plover (Charadrius nivosus), 
2 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

mountain plover (Charadrius montanus), long-billed curlew (Numenius americanus), interior least tern (Sternula 

antillarum athalassos), burrowing owl (Athene cunicularia

hypugaea), black-tailed prairie dog (Cynomys ludovicianus), 

bat assemblage, swift fox (Vulpes velox), and mule deer 

(Odocoileus hemionus).

Assessment Framework

Management questions form the basis of the REA framework. Core management questions relate to the key ecological 

attributes and change agents associated with each conservation element. Integrated management questions synthesize the 

results of the primary core management questions into overall 

landscape-level ranks for each conservation element. The 

change agents evaluated vary among conservation elements 

depending on the core management questions and the availability of data.

Four change agents were evaluated for the Southern Great

Plains REA: development (agricultural croplands, urban areas,

roads, railroads, and energy and minerals), fire, invasive species,

and climate change. We evaluated development for all conservation elements by using either the terrestrial development index 

or the aquatic development index, which were used to quantify 

the cumulative landscape-level effects of development. Fire was 

addressed by compiling recent (1984–2014) fire occurrences

for the entire SGP. Because information on fire occurrence is

limited, it was difficult to evaluate how fire suppression has

altered communities and habitats in the SGP; this issue was a

priority management question, but it remains a data gap. Similarly, available information and models on invasive species are

limited for most, so we used available habitat suitability models

and focused on the potential expansion of woody species in

grasslands (honey mesquite [Prosopis glandulosa] and eastern

redcedar [Juniperus virginiana]) and riparian areas (Russian

olive [Elaeagnus angustifolia] and tamarisk [Tamarix spp.]). To 

evaluate the potential effects of climate change, we summarized 

projected changes in precipitation and temperature for several 

climate change scenarios for the entire SGP. We also used 

available vegetation models to evaluate potential changes to 

grassland communities. We did not evaluate climate change for 

aquatic communities because of the lack of available models.

The methods and results are organized with respect to the 

management questions for each change agent and conservation element. The information needed to address management 

questions is organized by chapters, one chapter for all change 

agents and one chapter for each of the conservation elements. 

All source and derived datasets used to produce the maps and 

graphs for REAs are available online at the BLM Landscape 

Approach Data Portal (https://landscape.blm.gov/geoportal/

catalog/main/home.page).

Management Implications

REAs summarize information at broad spatial extents and 

can be used with information at local levels to inform management decisions. For example, REAs can be used as a screening 

tool to identify potential areas for conservation, restoration, or 

development projects. Local-level information, including additional surveys and research, can be used to assess conditions 

not quantified by REAs because of a lack of regional data 

(such as population sizes of species and occurrence of invasive 

species). Additionally, REAs can provide assessments of spatially explicit cumulative effects of change agents, especially 

development. REAs also can augment information from local 

projects to provide a broader spatial context for evaluating 

potential effects of proposed actions and alternatives that cannot be determined with local-level information alone. REAs, 

therefore, contribute to multiscale information necessary for 

implementing the BLM’s landscape approach.
Chapter 1. Introduction and Overview

Introduction

Purpose of the Rapid Ecoregional Assessment

The overall goal of the Bureau of Land Management 

(BLM) Rapid Ecoregional Assessments (REAs) is to compile 

and synthesize regional datasets to facilitate evaluation of the 

cumulative effects of change agents on priority species and 

communities. More specifically, the REAs identify important 

ecosystems and wildlife habitats at broad spatial scales and 

provide assessments of ecological conditions. The REAs also 

identify where and to what degree ecological resources are 

currently at risk from change agents such as development, fire, 

invasive species, and climate change. They can help managers identify and prioritize potential areas for conservation 

or restoration, assess cumulative effects as required by the 

National Environmental Policy Act, and inform landscapelevel planning and management decisions for multiple uses of 

public lands. The REAs support the BLM landscape approach 

to resource management by facilitating collaboration and 

cooperation among all parties interested in regional land and 

resource management (Carter and others, 2017).

Specific outcomes of the REAs include (1) assessments 

of current (baseline) conditions for long-term monitoring of 

conditions and trends at broad spatial extents; (2) assessments of 

landscape-level intactness of ecological communities, habitats 

for priority species, and the ecoregion overall; and (3) a predictive capacity for evaluating future risks. They also can identify 

data gaps and key ecological attributes of communities and 

habitats, which can inform the development of monitoring 

strategies for assessing status and trends. The BLM State and 

field offices and other stakeholders may use this information to 

facilitate land-use planning and prioritize actions for conservation, restoration, and development, including ascribing bestmanagement practices and usage authorizations. By addressing 

priority management issues identified by multiple Federal and 

State agencies working collaboratively, the REAs also foster 

interagency collaboration and help to ensure that REA results 

and products are relevant to many stakeholders.

Management Implications

Ecoregional assessments, such as the REAs, involve 

spatial analyses conducted at broad spatial scales to quantify 

landscape-level features of habitats (such as habitat fragmentation and connectivity), identify the most intact areas of ecological communities and wildlife habitats, and quantify both natural 

and anthropogenic disturbances (Hanser and others, 2011). For 

example, the REAs can be used as a screening tool to identify 

potential areas for conservation, restoration, and development 

projects. The REAs can lead to improved understanding of 

systems across jurisdictional boundaries and thereby can provide the context needed for assessing broad-scale cumulative 

effects (Wood and others, 2017). Broad-scale assessments are 

intended to be used in conjunction with local-level assessments 

for planning and management activities. Local-level assessments, such as the BLM Assessment, Inventory, and Monitoring 

program, provide more detailed information on individuals, 

populations, and habitats than can be determined from 

ecoregional assessments (Wood and others, 2017). The REAs 

augment local-level information and provide a broader spatial 

context for evaluating potential effects of proposed actions and 

alternatives that cannot be determined with local-level information alone. The REAs, therefore, contribute to multiscale 

information necessary for a landscape approach.

The BLM’s REA program is closely aligned with the 

U.S. Department of Interior (DOI) strategy for DOI lands, “A 

Strategy for Improving the Mitigation Policies and Practices of 

the Department of the Interior” (Clement and others, 2014). A 

primary objective of the DOI strategy is to shift from projectlevel management to broad-scale, science-based management 

that helps to avoid, minimize, and compensate for adverse 

effects on natural resources. Specifically, the REAs address 

the following key components outlined by the DOI strategy: 

(1) development of assessment methods that promote consistency in management decisions, (2) identification of ecological 

characteristics that promote ecosystem resilience in rapidly 

changing environmental conditions, and (3) fostering collaboration among land-management agencies.

Components of the Rapid Ecoregional 

Assessment

Management Questions

For each REA, BLM land managers and other REA 

stakeholders identify regionally important management 

questions that serve as the foundation for the REA process 

and products. The management questions not only frame the 

conservation planning and land-management priorities for a 

given ecoregion, but they also help to ensure that the most 

relevant datasets are compiled, analyzed, and summarized for 

the REA. Additionally, the management questions address 

information needs for developing best-management practices and establishing priorities for conservation, restoration, 

and development.
4 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Conservation Elements

Conservation elements represent ecological communities 

and species that are of management concern in the region. The 

emphasis on ecological communities is based on the premise that intact and functioning ecological systems are more 

resistant and resilient to change agents, including both natural 

and human stressors (Noss, 1987; Poiani and others, 2000; 

Parrish and others, 2003). Because it is not feasible to manage 

or monitor all species individually, the protection of intact ecological communities may serve as a safety net for species not 

addressed specifically by the REA. Species or species assemblages of management concern not adequately addressed at the 

community level may be evaluated as conservation elements.

Key Ecological Attributes

Key ecological attributes can be defined as a limited 

number of biological characteristics, ecological processes, and 

interactions with the physical environment that are especially 

pivotal and influence the long-term persistence of communities and species (Parrish and others, 2003). The attributes can 

include both the biological and physical environments and the 

ecological processes that collectively regulate (1) the occurrence, such as distribution and abundance, of communities and 

habitats; (2) landscape structure, such as size and connectivity 

of patches; and (3) landscape dynamics, such as natural disturbances including fire and drought.

Change Agents

The REA identifies and assesses the primary factors, or 

change agents, that currently affect or are likely to affect the 

condition of species and communities in the future. Minimally, 

the change agents to be evaluated for the entire ecoregion 

include

• development (agriculture, urban areas, roads, dams, 

diversions, energy and minerals),

• fire,

• invasive species, and

• climate change.

Overview of the Rapid Ecoregional 

Assessment Process

The REA is guided by a Management Team, Technical 

Team, and advisors consisting of BLM managers, partner 

agencies, and technical specialists representing the ecoregion (hereafter referred to as stakeholders) (Assal and others, 

2015). The stakeholders are responsible for ensuring that 

management needs and conservation priorities are addressed 

by identifying management questions, conservation elements, 

and change agents. The stakeholders also provide technical 

reviews and feedback on all reports and derived datasets.

An REA entails a two-phase process. In the pre-assessment 

phase, the lists of priority management questions, conservation 

elements, and change agents are developed and finalized by the 

stakeholders. The pre-assessment report documents the process 

and justification used to identify management questions and 

conservation elements, and it provides background information on all conservation elements (Assal and others, 2015). The 

assessment phase includes compilation, synthesis, analysis, and 

documentation of datasets to address management questions and 

completion of the ecoregional assessment. It also summarizes 

the management questions that cannot be addressed because of 

data gaps and limitations.

Southern Great Plains Rapid 

Ecoregional Assessment

Project Area

The BLM partnered with the Great Plains Landscape 

Conservation Cooperative (GPLCC) to ensure that the results 

of the Southern Great Plains REA provide information useful in addressing regional management issues identified by 

a diverse set of stakeholders representing both the REA and 

the GPLCC. The region covered by the Southern Great Plains 

REA (hereafter referred to as Southern Great Plains) was 

expanded from the original project area (Assal and others, 

2015) to include the maximum area covered by the GPLCC 

buffered boundary (Manier, 2011), four Level-III ecoregions—

High Plains, Central Great Plains, Southwestern Tablelands, 

and Nebraska Sand Hills (Omernik, 1987)—and an adjacent 

buffer delineated by fifth-level watersheds intersecting the 

combined ecoregion boundary (fig. 1–1). The Southern Great 

Plains (SGP) encompass 961,105 square kilometers (km2) 

(371,085 square miles [mi2]) and include portions of eight 

states: Colorado, Kansas, Nebraska, New Mexico, Oklahoma, 

South Dakota, Texas, and Wyoming (fig. 1–1). The area covered by the Southern Great Plains REA is the largest of all the 

REAs (Bureau of Land Management, 2016).

Most of the surficial land in the SGP is privately owned 

(fig. 1–2). Collectively, State, Federal, and tribal lands cover 

less than 9 percent of the SGP. Additionally, the BLM is 

responsible for managing subsurface rights to the development 

of mineral resources that are publically held, also known as 

split estate. The SGP overlaps ten BLM field office jurisdictions: Carlsbad, Casper, Newcastle, Oklahoma, Rawlins, Rio 

Puerco, Roswell, Royal Gorge, Socorro, and Taos. Most BLM 

lands within the SGP are located in New Mexico.
Chapter 1. Introduction and Overview 5

Figure 1–1. Southern Great Plains Rapid Ecoregional Assessment boundary. Level-III ecoregions (Omernik, 1987) and the 

Great Plains Landscape Conservation Cooperative boundary are shown.
6 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Figure 1–2. Land ownership and jurisdictions for the Southern Great Plains.
Chapter 1. Introduction and Overview 7

Management Questions

The management questions developed by the stakeholders were organized into two general themes: core and integrated. Core management questions were tailored for each 

community and species to evaluate the potential landscapelevel effects of change agents. Integrated management questions synthesize the results of the core management questions 

to provide an overall evaluation of the landscape-level condition of each conservation element.

Conservation Elements

Ecological Communities

Six major ecological communities (hereafter referred to 

as communities) were selected as conservation elements for 

the Southern Great Plains REA (table 1–1, fig. 1–3) (Assal and 

others, 2015): three terrestrial and three aquatic. The terrestrial 

communities selected are mixed-grass prairie, shortgrass prairie, 

and sand prairie. Although not initially identified as a conservation element, we also addressed management questions for all 

grasslands collectively, which include the three grassland conservation elements and the remaining grassland types present 

in the SGP: tallgrass, northwest mixed-grass, and cool-season 

bunchgrass prairies; foothill and saline grasslands; and semidesert grassland and shrubland. The aquatic communities selected 

are riparian and nonplaya wetlands, playa wetlands and saline 

lakes, and prairie streams and rivers.

Species and Assemblages

A preliminary list of priority species and species assemblages was developed during the pre-assessment phase by the 

stakeholders (Assal and others, 2015). Thirteen species and 

species assemblages were included in the final list of conservation elements: freshwater mussel assemblage, Arkansas River 

shiner (Notropis girardi), ferruginous hawk (Buteo regalis), 

lesser prairie chicken (Tympanuchus pallidicinctus), snowy 

plover (Charadrius nivosus), mountain plover (Charadrius 

montanus), long-billed curlew (Numenius americanus), 

interior least tern (Sternula antillarum athalassos), burrowing 

owl (Athene cunicularia hypugaea), black-tailed prairie dog 

(Cynomys ludovicianus), bat assemblage, swift fox (Vulpes 

velox), and mule deer (Odocoileus hemionus). The ecoregional 

assessments of species and assemblages will be addressed in 

the second volume of the REA report.

Change Agents

We evaluated the four primary change agents required 

for the REA (development, fire, invasive species, and climate 

change). Fire and climate (such as drought) are inherent drivers of ecosystem dynamics in the SGP, but fire and climatic 

regimes may be influenced by human activities (Rowland 

and Leu, 2011). Human alteration of disturbance regimes, 

in turn, can lead to habitat loss and other negative effects on 

species and species assemblages. We refer to natural drivers of landscape dynamics (such as fire and drought) as key 

ecological attributes and human influences (such as development, altered fire regimes, invasive species, and anthropogenic climate change) on communities and wildlife habitats 

as change agents. We initially considered grazing as a change 

agent, based on input from the stakeholders, but limited data 

availability precluded a regional assessment (see chapter 11, 

“Data Gaps, Limitations, and Uncertainty”); grazing is best 

addressed through local-level data (Assal and others, 2015).

Reports and Organization

Pre-Assessment Report

The pre-assessment report (Assal and others, 2015) 

lists the management questions, conservation elements, and 

change agents selected by the REA stakeholders. The report 

documents the process used to select these REA components 

for the SGP. Background information is provided on the key 

ecological attributes and change agents for each conservation 

element. The background information includes a narrative, an 

ecological conceptual model that portrays some of the potential primary interactions and feedbacks among change agents, 

and tables that summarize potential key ecological attributes 

System Ecological communities1

Percent of the 

Southern Great Plains 

area2

Terrestrial Mixed-grass prairie3 11.2

Shortgrass prairie 15.0

Sand prairie 12.3

Other grasslands4 9.7

Aquatic Riparian and nonplaya wetlands

Playa wetlands and saline lakes

Prairie streams and rivers

5.2

0.4

Not applicable5

1

Open water (lakes and reservoirs) and shrubland, woodland, and forest 

communities were mapped but not evaluated as conservation elements. Open 

water accounts for 0.7 percent, and shrubland, woodland, and forests account for 

10.6 percent of the Southern Great Plains.

2

Developed lands and cropland account for 34.9 percent of the Southern Great 

Plains.

3

Includes midgrass prairie.

4

Other grasslands include tallgrass, northwest mixed-grass, and cool-season 

bunchgrass prairies; foothill and saline grasslands; and semidesert grassland and 

shrubland.

5

Prairie streams and rivers are linear features and consequently were excluded 

from areal calculations.

Table 1–1. Ecological communities evaluated as conservation 

elements for the Southern Great Plains Rapid Ecoregional 

Assessment.
8 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Figure 1–3. Distribution of ecological communities in the Southern Great Plains as well as areas dominated by agricultural 

cropland and other development. The “other grasslands” community includes tallgrass, northwest mixed-grass, and coolseason bunchgrass prairies; foothill and saline grasslands; and semidesert grassland and shrubland. Sparsely vegetated 

areas and the community “prairie streams and rivers” are not depicted. Modified from Reese and others (2016).
Chapter 1. Introduction and Overview 9

and change agents. The conceptual models and tables were 

intended to highlight factors relevant to the REA and are not 

an exhaustive synthesis of all factors important to a species 

or community. Not all key ecological attributes and change 

agents could be addressed because of data and time limitations 

(see chapter 11, “Data Gaps, Limitations, and Uncertainty”).

Rapid Ecoregional Assessment Reports

The Southern Great Plains REA is summarized in two 

volumes. Volume I (this volume) provides background information, methods, and summaries for all the ecological communities evaluated for the REA. Volume II will address the 13 

species and species assemblages evaluated for the REA.

Volume I Organization

Chapter 1, “Introduction and Overview,” provides 

an overview of the BLM’s REA program and the required 

REA components, as well as a summary of the management 

questions, conservation elements, and change agents for the 

Southern Great Plains REA. Chapter 2, “Methods Overview,” 

describes the standard methodologies used to assess the core 

and integrated management questions for conservation elements. This chapter is intended for semitechnical audiences 

and provides an overview of the approach used for both volumes. Additional technical details are provided in appendix A. 

Chapter 3, “Change Agents,” addresses management questions 

for the four primary change agents. The results presented in 

this chapter were used to assess potential effects on conservation elements in both volumes. Chapters 4–10 address the 

seven ecological communities. These chapters have a consistent format and are organized with respect to the management 

questions. Each chapter includes the following information.

• A brief narrative overview that highlights information 

provided in the pre-assessment report.

• Summary tables for each conservation element (additional details on the indicators are provided in chapter 2 and appendix A):

1. The indicators used to evaluate the key ecological 

attributes.

2. The indicators used to evaluate change agents.

3. The ranking factors used for evaluating overall 

landscape-level rank.

4. The management questions addressed.

• Maps representing the derived datasets associated with 

each management question.

• Summary information that highlights a few key findings for each conservation element.

Chapter 11, “Data Gaps, Limitations, and Uncertainty,” 

provides an overview of issues associated with the datasets, 

analyses, and models used to evaluate change agents and the 

ecological communities. This chapter also summarizes general 

management question themes that could not be addressed 

at the ecoregional level because of data gaps or limitations. 

Appendix A provides more technical details not included in 

the methods overview and a list of all source datasets used for 

the report.

Accessing the Rapid Ecoregional 

Assessment Datasets

All source and derived datasets for the REAs are served 

online at the BLM Landscape Approach Data Portal 

(https://landscape.blm.gov/geoportal/catalog/main/home.page).

References Cited

Assal, T.J., Melcher, C.P., and Carr, N.B., eds., 2015, 

Southern Great Plains Rapid Ecoregional Assessment—

Pre-assessment report: U.S. Geological Survey Open-File 

Report 2015−1003, 284 p., accessed September 2015 at 

https://doi.org/10.3133/ofr20151003.

Bureau of Land Management, 2016, The BLM’s landscape 

approach for managing public lands: Bureau of Land Management website, accessed October 2016 at http://www.blm.gov/

wo/st/en/prog/more/Landscape_Approach.html.

Carter, S.K., Carr, N.B., Miller, K.H., and Wood, D.J.A., eds., 

2017, Multiscale guidance and tools for implementing a 

landscape approach to resource management in the Bureau 

of Land Management: U.S. Geological Survey OpenFile Report 2016–1207, 79 p., accessed January 2017 at 

https://doi.org/10.3133/ofr20161207.

Clement, J.P., Belin, A.d’A., Bean, M.J., Boling, T.A., and 

Lyons, J.R., 2014, A strategy for improving the mitigation 

policies and practices of the Department of the Interior—A 

report to the Secretary of the Interior from the Energy and 

Climate Change Task Force: U.S. Department of the Interior, 

Energy and Climate Change Task Force, 25 p., accessed 

October 12, 2016, at http://www.doi.gov/news/upload/

Mitigation-Report-to-the-Secretary_FINAL_04_08_14.pdf.

Hanser, S.E., Leu, Matthias, Knick, S.T., and Aldridge, 

C.L., eds., 2011, Sagebrush ecosystem conservation and 

management—Ecoregional assessment tools and models for 

the Wyoming Basins: Lawrence, Kans., Allen Press, 409 p.
10 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Manier, Daniel, 2011, Great Plains Landscape Conservation 

Cooperative boundary, buffered 10km: U.S. Geological Survey 

dataset, accessed April 25, 2016, at https://www.sciencebase.

gov/catalog/item/5100263ee4b0ca65fed0d344.

Noss, R.F., 1987, From plant communities to landscapes in 

conservation inventories—A look at the Nature Conservancy (USA): Biological Conservation, v. 41, p. 11–37.

Omernik, J.M., 1987, Ecoregions of the conterminous United 

States: Annals of the Association of American Geographers, 

v. 77, p. 118−125, map scale 1:7,500,000.

Parrish, J.E., Braun, D.P., and Unnasch, R.S., 2003, Are we 

conserving what we say we are?—Measuring ecological integrity within protected areas: BioScience, v. 53, 

p. 851–860.

Poiani, K.A., Richter, B.D., Anderson, M.G., and Richter, 

H.E., 2000, Biodiversity conservation at multiple scales—

Functional sites, landscapes, and networks: BioScience, 

v. 50, p. 133–146.

Reese, G.C., Manier, D.J., Carr, N.B., Callan, Ramana, Leinwand, I.I.F., Assal, T.J., Burris, Lucy, and Ignizio, D.A., 2016, 

Estimated historical distribution of grassland communities of 

the Southern Great Plains: U.S. Geological Survey Open-File 

Report 2016–1184, 13 p., accessed December 12, 2016, at 

https://doi.org/10.3133/ofr20161184.

Rowland, M.M., and Leu, Matthias, 2011, Study area description, 

in Hanser, S.E., Leu, Matthias, Knick, S.T., and Aldridge, C.L., 

eds, Sagebrush ecosystem conservation and management— 

Ecoregional assessment tools and models for the Wyoming 

Basins: Lawrence, Kans., Allen Press, p. 10–45.

Wood, J.A., Carter, S.K., Litshert, S.E., and Carr, N.B., 2017, 

Including broad-scale indicators in multiscale natural resource 

monitoring and assessment programs in the Bureau of Land 

Management, chap. 3 of Carter, S.K., Carr, N.B., Miller, K.H., 

and Wood, D.J.A., eds., Multiscale guidance and tools for 

implementing a landscape approach to resource management 

in the Bureau of Land Management: U.S. Geological Survey 

Open-File Report 2016–1207, p. 15–37, accessed January 2017 

at https://doi.org/10.3133/ofr20161207.
Chapter 2. Methods Overview

Assessment Framework

Management questions form the foundation for the 

assessment framework of the Southern Great Plains Rapid 

Ecoregional Assessment (REA). Core management questions 

relate to the key ecological attributes and change agents for 

each conservation element. The integrated management question synthesizes information from core management questions 

to provide an overall landscape-level rank for each conservation element. The overall landscape-level ranks can be used 

to identify the largest intact (least developed) areas across 

the entire distribution of each conservation element, which is 

one application of REA datasets (Carr and others, 2017). The 

chapters for each conservation element provide a list of all the 

management questions addressed, and the results (maps and 

graphs) are organized by management question. The management questions can be organized into the following themes.

Core Management Questions

• Where is the conservation element, and what and 

where are its key ecological attributes?

• What and where are the change agents that potentially 

affect the conservation element?

• How do the change agents affect the key ecological 

attributes of the conservation element?

Integrated Management Question

• Where are the areas with the highest overall landscapelevel ranks?

Scale of Analysis for Management Questions

One objective of the REAs is to facilitate evaluation of 

the cumulative effects of change agents at broad spatial scales 

(Carter and others, 2017). Scale is an important consideration 

for conducting REAs because the resolution or precision of the 

source data, the resolution of reporting units, and the spatial 

extent over which data are analyzed may affect the results and 

conclusions. Additionally, a single scale may be sufficient to 

summarize information for a particular management question, 

but multiple scales may be needed to summarize patterns that 

vary across spatial scales (Carr and others, 2017; Carter and 

others, 2017). We selected analysis scales based on Carr and 

Melcher (2015) and Carr and others (2017).

We defined three nested levels for evaluating conservation elements in the REA. For terrestrial conservation 

elements, the three levels are the native resolution of the 

datasets, landscape, and regional. The native resolution of 

the source datasets was 900 square meters (1,076 square 

yards) (LANDFIRE, 2012; Reese and others, 2016). Landscape level corresponds to scales used for most analyses of 

terrestrial conservation elements (for example, moving windows with a 2.5-kilometer [km; 1.55-miles (mi)] radius), and 

the regional level corresponds to patterns that span the entire 

Southern Great Plains (SGP) (see chapter 1, “Introduction 

and Overview,” for a description of the SGP boundary). 

For aquatic conservation elements, we used three nested 

watershed levels: catchment, sixth-level watershed, and 

fifth-level watershed (U.S. Environmental Protection Agency 

and U.S. Geological Survey, 2012; U.S. Geological Survey 

and U.S. Department of Agriculture, Natural Resources 

Conservation Service, 2013). Core management questions were addressed using the native resolution of the data 

and summarized using 2.5-km moving windows or sixthlevel watersheds. Integrated management questions were 

addressed by creating relative ranks for the entire SGP using 

5-km (3.1-mi) moving windows for terrestrial conservation 

elements or fifth-level watersheds for aquatic conservation 

elements. A description of the methods, including a description of the analysis scales, is organized by each management 

question in the “Management Questions” section.

Baseline Distributions for Evaluating 

Change Agents

To evaluate the current conditions of the SGP, we 

mapped the baseline distribution of each conservation 

element. Baseline distributions for terrestrial (grassland) 

and aquatic communities were determined differently 

depending on the availability of data on reference conditions (prior to EuroAmerican settlement). For grassland 

communities, we used the estimated distribution of historical grasslands (Reese and others, 2016) as reference conditions and for deriving the baseline distribution of grasslands 

in the SGP. In the case of aquatic communities, the lack 

of regional information on reference conditions limits our 

ability to fully quantify the direct effects of past alteration. 

Creation and modification of wetlands by agricultural activities further confounds this problem. The methods used to 

map baseline distributions are described in the “Management 

Questions” section.
12 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

The baseline distribution of each conservation element 

was used to evaluate the potential effects of change agents. The 

change agents evaluated varied among conservation elements 

depending on the core management questions and the availability 

of data and published models.

• Development.—We evaluated the broad-scale cumulative effects of existing development for all conservation 

elements by using the terrestrial development index and 

the aquatic development index.

• Fire.—Recent fire occurrence (1984–2014) was evaluated for the entire SGP and summarized by ecological 

community.

• Invasive species.—Available information and models are 

limited for most invasive species, so we focused on the 

presence of two woody species in grasslands (honey mesquite [Prosopis glandulosa] and eastern redcedar [Juniperus virginiana]) and the presence of and habitat suitability 

for two woody species in riparian areas (Russian olive 

[Elaeagnus angustifolia] and tamarisk [Tamarix spp.]).

• Climate change.—We summarized projected changes 

in temperature and precipitation for climate change 

scenarios for the entire SGP and the potential effects 

on grassland communities.

The management questions and results for the change 

agents across the entire SGP are summarized in chapter 3, 

“Change Agents.” The methods used to quantify the change 

agents are described for the “Management Questions” section. 

The data sources used for all change agents are summarized in 

appendix A (table A−1).

Management Questions

Core Management Questions for Evaluating 

Current and Future Change Agents

Where does existing development pose the greatest threats to 

terrestrial communities, and where are the large, relatively 

undeveloped areas? How do development levels vary by development types in terrestrial communities?

The terrestrial development index (TDI) quantifies the 

cumulative effects of development (agricultural croplands, 

urban areas, transportation [roads, railroads], and energy and 

minerals) at the landscape level. We quantified each development type (fig. 2–1) by using a common metric, the area 

Figure 2–1. Overview of the process for deriving the terrestrial development index for the Southern Great Plains 

Rapid Ecoregional Assessment. (km, kilometer; mi, mile)
Chapter 2. Methods Overview 13

of the surface disturbance footprint (hereafter referred to as 

footprint), and combined individual development types into an 

overall footprint area. The TDI was derived from the percent 

of the overall footprint area in a circular moving window with 

a radius of 2.5 km (1.55 mi). The size of the moving window 

was selected to capture the cumulative effects of development 

that cannot be determined at much smaller scales (for additional justification for analysis scales and methods, see Carr 

and Melcher [2015] and Carr and others [2017]).

TDI scores range from 0 to 100 percent and were divided 

into seven classes for visualization and analysis purposes 

(figs. 3–1 and 3–2). To identify areas with the least development, we defined relatively undeveloped areas as having a 

TDI score ≤2 percent, which represents approximately 10 to 

30 percent of each grassland community. See the “Terrestrial 

Development Index” section in appendix A for additional 

detail on the methods used to calculate the TDI and select the 

breakpoints between classes. The continuous TDI scores can 

be partitioned into alternative classes to address other management questions. We also calculated TDI by development type 

(figs. 3–3 and 3–4).

Where does existing development pose the greatest threats 

to aquatic communities, and where are the large, relatively 

undeveloped areas? How do development levels vary by 

development type in aquatic communities?

We calculated an overall aquatic development index 

(ADI) with inputs used for calculating the TDI and additional aquatic development variables (fig. 2–2, table A–1). 

The ADI is adapted from Annis and others (2010), Fore 

and others (2014), and Carr and Melcher (2015). We used 

the footprint from agricultural croplands, urban areas, 

transportation (roads, railroads), and energy and minerals 

development as input variables that can affect sedimentation rate, flow regime, and water quality (table A–2). The 

additional input variables relate to water use and flow (dams, 

diversions, and road and railroad stream crossings) (table 

A–1), but they can also affect sedimentation rate and riparian vegetation occurrence (table A–2). All development 

variables were quantified at the local catchment level (local 

ADI, fig. A–3A) and by the upstream contributing area for 

each catchment (upstream ADI, fig. A–3B). Catchments were 

Figure 2–2. Overview of the process for deriving the aquatic development index for the Southern Great Plains 

Rapid Ecoregional Assessment. The aquatic development index (ADI) is quantified for the local catchment (local 

ADI), the upstream contributing area (upstream ADI), and the overall ADI (combined local and upstream ADI).
14 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

defined according to the National Hydrography Dataset Plus, 

version 2.1 (U.S. Environmental Protection Agency and U.S. 

Geological Survey, 2012).

ADI scores range from 0 to 100. Scores less than 50 were 

divided into six classes using equal breakpoints, and scores 

greater than 50 were combined into a seventh class (see Carr 

and Melcher [2015] for justification for ADI classes). To 

identify areas with the least development, we define relatively 

undeveloped areas as having ADI and local ADI scores ≤20. 

We summarized ADI for catchments (native resolution of 

the ADI) and averaged the catchment-level ADI scores by 

sixth-level watershed (fig. 3–5). ADI was also calculated by 

development type (fig. 3–7).

What is the size and distribution of fires since 1984 overall 

and by ecological community?

Because information on fire occurrence is limited, it is 

difficult to evaluate how fire suppression has altered communities and habitats in the SGP, which was a priority management 

question (see Assal and others [2015] for additional details on 

the historical fire regimes). To evaluate fire, we compiled data 

on recent fire occurrences (1984–2014) for the entire REA 

project area (table A−1). Fire perimeters that extended beyond 

the project boundary were included. We summarized the total 

area burned annually (figs. 3−8 and 3−9) and the total area 

burned for each ecological community (table 3−3).

What is the distribution of potentially altered vegetation?

We mapped the potential for the presence of altered vegetation using LANDFIRE (2012) Existing Vegetation Types 

(EVTs) (table A−5) that represent invasive herbaceous plants, 

urban vegetation, pasturelands, and ruderal vegetation (composition altered by human activities) (fig. 3−10).

How do contemporary patterns of temperature and precipitation compare to that projected for relatively hot-dry and 

warm-wet climate scenarios in 2030 and 2060?

To evaluate the potential for changes in mean annual 

temperature and precipitation, we used output from general 

circulation models to compare contemporary climatic conditions (1981–2010) to relatively hot-dry and warm-wet climate 

scenarios (figs. 3−11 and 3−12). Contemporary climatic conditions (1981–2010) were derived from monthly data (Maurer 

and others, 2002). We used mean annual temperature and 

precipitation from general circulation models corresponding to 

a hot-dry scenario (ACCESS1.0 [table A–1]) and a warm-wet 

scenario (CESM1(BGC) [table A–1]) (figs. 3–11 and 3–12). 

Two future time periods were evaluated: 2016–2045 represented by 2030 and 2046–2075 represented by 2060.

Core Management Questions for Evaluating 

Terrestrial Communities

What are the baseline distributions of grassland communities, 

and where have they been altered by development?

We used the estimated historical distribution of grasslands 

(hereafter referred to as historical grasslands) to map the grassland communities prior to conversion by development (Callan 

and others, 2016; Reese and others, 2016). Historical grassland 

cells were classified as “converted” using the footprint from the 

terrestrial development index. We removed historical grassland 

cells currently classified as a baseline aquatic community. In 

addition, we used LANDFIRE EVT to update the historical 

grassland map (see “Terrestrial Community Baseline Distribution Mapping” section in appendix A).

We mapped each of the three grassland conservation 

elements individually (mixed-grass, fig. 5−1; shortgrass, fig. 6−1; 

and sand prairies, fig. 7−1). To address management questions 

that were most appropriate for all grasslands in the SGP, we combined all grassland communities: mixed-grass, shortgrass, sand, 

tallgrass, northwest mixed-grass, and cool-season bunchgrass 

prairies; foothill and saline grasslands; and semidesert grassland 

and shrubland.

Where does existing development pose the greatest threat to 

grassland communities, and where are the large, relatively 

undeveloped areas?

To evaluate the cumulative effects of development, 

we overlaid the TDI (fig. 3–1) on the baseline distribution map 

for each grassland community (figs. 4−3, 5−2, 6−2, 7−3).

Where has development fragmented grassland communities?

We evaluated the fragmenting effects of development for 

each grassland community in two ways (figs. 4−5, 5−4, 6−4, 

7−5). First, we compared patch sizes of the historical and baseline 

distributions. Second, we evaluated patch sizes of relatively 

undeveloped areas. Because the estimated distribution of historical grasslands is highly connected across the SGP (fig. 4−1), the 

grassland patch-size map displays patch-size distributions for the 

baseline distribution compared to relatively undeveloped grasslands (fig. 4−6). In contrast, maps for the other three grassland 

types display patch sizes for the estimated historical and baseline 

distributions (figs. 5−5, 6−5, 7−6). Patch sizes for historical, 

baseline, and relatively undeveloped conditions are summarized 

graphically for each community (figs. 5−4, 6−4, 7−5).

Where are potential areas of honey mesquite and eastern 

redcedar expansion?

Prior to EuroAmerican settlement, honey mesquite 

occurred as far north as Oklahoma but has since expanded, 

especially in the semidesert grassland and shrubland community. The distribution of eastern redcedar has been increasing 

in the SGP. To map the potential distribution of honey mesquite and eastern redcedar (fig. 4−7), we compiled occurrence 

data (table A−1) and included all LANDFIRE EVTs that 

indicated the possible occurrence of mesquite (table A−5).

Where are grasslands vulnerable to projected climate change?

To evaluate the potential effects of climate change on 

grasslands, we used available models of bioclimatic conditions (also called bioclimatic envelopes) suitable for Great 
Chapter 2. Methods Overview 15

Plains grassland communities (excluding semidesert grassland 

and shrubland) developed by Rehfeldt and others (2012). An 

ensemble bioclimatic model derived using three bioclimatic 

models (each using two greenhouse gas emissions scenarios) 

(Rehfeldt and other, 2012) was used to map the potential 

distribution of grasslands for two time periods: 2026−2035 

(2030) and 2056−2065 (2060). The resulting bioclimatic 

envelope maps show the potential for change and indicate 

how the bioclimatic conditions conducive for grasslands 

could shift; thus, they indicate potential vulnerabilities for the 

climate scenarios evaluated (fig. 4–8). The modeled bioclimatic envelope for contemporary climatic conditions generally 

corresponds to the distribution of grassland communities in 

the SGP (excluding semidesert grassland and shrubland), but 

it shows a much broader spatial representation of areas where 

grasslands could potentially occur than was observed for the 

baseline distribution. Local conditions, biotic interactions, and 

legacies of past disturbance, among other factors, can affect 

the distribution of grasslands within otherwise potentially suitable bioclimatic conditions.

Core Management Questions for Evaluating 

Aquatic Communities

What is the baseline distribution of the aquatic communities?

Distribution maps for aquatic communities were derived 

from LANDFIRE EVTs or published datasets (table A−4; 

figs. 8–1, 9–1, 9–2, 10–1). All aquatic community maps were 

summarized by sixth-level watersheds (also referred to as 

12-digit hydrologic units in the National Watershed Boundary 

Dataset [U.S. Geological Survey and U.S. Department of 

Agriculture, Natural Resources Conservation Service, 2013]) 

(figs. 8–2, 9–3, 10–2, 10–3). The area of riparian and nonplaya wetlands (hereafter referred to as wetlands) and of playa 

wetlands (hereafter referred to as playas) and saline lakes was 

summarized as percentage of sixth-level watershed (total area 

of the community type divided by watershed area). We summarized the total stream length by sixth-level watershed for 

perennial and intermittent stream lengths separately.

Where does existing development pose the greatest threat 

to aquatic communities, and where are the large, relatively 

undeveloped areas?

We assessed development levels for riparian and nonplaya wetlands and for prairie streams and rivers (hereafter 

referred to as prairie streams) based on the ADI score. For 

riparian and nonplaya wetlands, area-weighted scores were 

derived using the catchment ADI score and the total area of 

the aquatic community in that catchment. For streams, we 

used stream-length weighted scores (figs. 10–4, 10–5). The 

weighted ADI score was used to calculate the mean ADI score 

by community for each sixth-level watershed containing the 

community (fig. 8–3).

For playas and saline lakes, we used only the local ADI 

scores to assess development levels and did not include the 

upstream ADI component because these systems are primarily fed by precipitation and runoff from the surrounding 

catchment. Upstream catchment inputs are assumed to have 

relatively little, if any, effect on the local catchment. We calculated a local ADI score for each playa and saline lake, which 

was then used to calculate the area-weighted mean local ADI 

score for each sixth-level watershed (figs. 10–4, 10–5).

Where has development fragmented aquatic communities?

The calculation of patch sizes was determined differently 

for each aquatic community (figs. 8–5, 9–7A, 9–7B, 10–7). 

Patch size for riparian and nonplaya wetlands was calculated by 

using the area of contiguous cells of either riparian or nonplaya 

wetlands. Mapped area (defined by polygons) was used to 

calculate playa and saline lake patch size. For perennial prairie 

streams, patch size was calculated by stream-segment length for 

both the baseline distribution and relatively undeveloped conditions. Differences in stream-segment length between the baseline distribution and relatively undeveloped areas were used as 

an index of fragmentation. For perennial streams, the aggregate 

count of dams and potential barriers (points of diversion and 

road and railroad stream crossings) (table A–1) was summarized 

by sixth-level watershed to evaluate where development may 

have altered flow regime, fragmented streams, and decreased 

structural connectivity (fig. 10–8).

Where are Russian olive and tamarisk present, and where 

are riparian areas suitable for their expansion?

We compiled and mapped occurrences of Russian olive and 

tamarisk (table A–1; figs. 8–6, 8–7). We used available models 

of habitat suitability for Russian olive and tamarisk developed 

for the western United States to identify areas of suitable habitat (Jarnevich and others, 2011; Jarnevich and Reynolds, 2011; 

Carr and Melcher, 2015). See the “Russian Olive and Tamarisk” 

section in appendix A for additional details.

Integrated Management Question for Evaluating 

Terrestrial and Aquatic Communities

Where are the areas with the highest overall landscapelevel ranks?

The integrated management questions synthesize the 

results from core management questions. We used the amount 

and distribution of each conservation element, which is a key 

ecological attribute, for summarizing landscape-level area (or 

density for streams). We used the TDI or ADI for each conservation element, which is a change agent, for summarizing 

landscape-level development. These two landscape-level ranking factors were used because they were consistently available 

for all conservation elements. Landscape-level area (density) 

and landscape-level development were ranked by conservation 

element for the entire SGP, and these ranks were combined 

into an overall landscape-level rank (fig. 2–3).
16 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

We used the baseline distributions of each conservation 

element to summarize landscape-level area (density). For terrestrial conservation elements, we summarized landscape-level 

area as a percentage of a moving window with a 5-km (3.1-mi) 

radius. For riparian and nonplaya wetlands and playas and saline 

lakes, we summarized landscape-level area as a percentage of 

fifth-level watershed area. For streams, we calculated landscapelevel density using stream length divided by watershed area, 

also referred to as drainage density (Horton, 1932).

To calculate landscape-level development using TDI or 

ADI, we used the same methods described for the core management questions, but the scores were summarized using larger 

analysis units. For terrestrial communities, we summarized 

mean TDI using a moving window with a 5-km (3.1-mi) radius. 

For aquatic communities, we summarized ADI (or local ADI for 

playas and saline lakes) by fifth-level watershed.

The ranking breakpoints for landscape-level area (density) 

for terrestrial and aquatic communities were determined by 

partitioning the data into three equal subsets (with adjustments 

for highly skewed distributions; see appendix A). The ranking breakpoints for TDI and ADI scores were standardized and 

applied consistently for each community (TDI: 0–2, >2–10, >10; 

local ADI and ADI: 0–20, >20–40, >40). Because the ranking 

breakpoints for landscape-level area are calculated for each conservation element, the ranks are not equivalent across conservation elements. Rather, ranks are relative and provide a synthetic 

regional summary of the geospatial datasets developed to address 

core management questions for each conservation element.

The highest overall landscape-level rank represents 

locations with the largest area (or density) of the conservation element and the lowest development levels. The lowest 

landscape-level rank represents locations with the smallest 

area (or density) of the conservation element and the highest 

development levels. Additional possible combinations were 

used to represent a gradient in landscape-level ranks (fig. 2–3). 

Because rankings are sensitive to the input data and criteria 

used to develop the ranking thresholds, they are not intended 

as standalone maps. However, they are useful for comparing 

rankings among areas in the SGP when used in conjunction 

with more detailed geospatial data summarized for core management questions.

Summary

The overall process model used to conduct the assessments of conservation elements is summarized in figure 2–4. 

Core and integrated management questions form the foundation for the assessment framework. Core management 

questions address the key ecological attributes and change 

agents that were evaluated for each conservation element. 

Integrated management questions synthesize information 

from the primary core management questions, focusing on the 

landscape-level area (or density) of the conservation element 

and landscape-level development.

Figure 2–3. Overall landscape-level ranks for the Southern Great Plains Rapid Ecoregional Assessment. The 

“very high” landscape rank represents locations with the largest landscape-level area and lowest landscapelevel development. The “very low” landscape rank represents locations with the smallest landscape-level area 

and highest landscape-level development. Other possible combinations of ranks are shown.
Chapter 2. Methods Overview 17

Figure 2–4. Overview of the process used to address the core and integrated management questions for each 

conservation element. Dotted lines indicate analyses (white boxes) performed on derived maps.

References Cited

Annis, G.M., Sowa, S.P., Diamond, D.D., Combes, M.D., 

Doisy, K.E., Garringer, A.J., and Hanberry, Phillip, 2010, 

Developing synoptic human threat indices for assessing the 

ecological integrity of freshwater ecosystems in EPA Region 

7, final report: Columbia, Mo., University of Missouri, 

School of Natural Resources, 192 p.

Assal, T.J., Melcher, C.P., and Carr, N.B., eds., 2015, Southern 

Great Plains Rapid Ecoregional Assessment—Pre-assessment 

report: U.S. Geological Survey Open-File Report 2015–1003, 

284 p., accessed September 2015 at https://doi.org/10.3133/

ofr20151003.

Callan, Ramana, Leinwand, I.I.F., Reese, G.C., Assal, T.J., 

Manier, D.J., Carr, N.B., Burris, Lucy, and Ignizio, D.A., 2016, 

Estimated historical distribution of grassland communities of 

the Southern Great Plains: U.S. Geological Survey data release,

accessed November 14, 2016, at https://doi.org/10.5066/

F71Z42J3.

Carr, N.B., Leinwand, I.F., and Wood, D.J.A., 2017, A 

multiscale index of landscape intactness for management 

of public lands, chap. 5 of Carter, S.K., Carr, N.B., Miller, 

K.H., and Wood, D.J.A., eds., Multiscale guidance and tools 

for implementing a landscape approach to resource management in the Bureau of Land Management: U.S. Geological 

Survey Open-File Report 2016–1207, p. 55–74, accessed 

January 2017 at https://doi.org/10.3133/ofr20161207.

Carr, N.B., and Melcher, C.P., eds., 2015, Wyoming Basin 

Rapid Ecoregional Assessment: U.S. Geological Survey 

Open-File Report 2015–1155, 896 p., accessed September 2015 at https://doi.org/10.3133/ofr20151155.

Carter, S.K., Carr, N.B., Miller, K.H., and Wood, D.J.A., eds., 

2017, Multiscale guidance and tools for implementing a 

landscape approach to resource management in the Bureau 

of Land Management: U.S. Geological Survey OpenFile Report 2016–1207, 79 p., accessed January 2017 at 

https://doi.org/10.3133/ofr20161207.
18 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Fore, J.D., Sowa, S.P., Galat, D.L., Annis, G.M., Diamond, 

D.D., and Rewa, Charles, 2014, Riverine threat indices to 

assess watershed condition and identify primary management capacity of agriculture natural resource management 

agencies: Environmental Management, v. 53, p. 567–582.

Horton, R.E., 1932, Drainage-basin characteristics: Transactions American Geophysical Union, v. 13, p. 350–361.

Jarnevich, C.S., Evangelista, Paul, Stohlgren, T.J., and 

Morisette, Jeffery, 2011, Improving national-scale invasion 

maps—Tamarisk in the western United States: Western 

North American Naturalist, v. 71, p. 164–175.

Jarnevich, C.S., and Reynolds, L.V., 2011, Challenges of 

predicting the potential distribution of a slow-spreading 

invader—A habitat suitability map for an invasive riparian 

tree: Biological Invasions, v. 13, p. 153–163.

LANDFIRE, 2012, Existing Vegetation Type (ver. 1.3.0): 

U.S. Forest Service and U.S. Department of the Interior, 

accessed December 29, 2014, at https://landfire.gov/

NationalProductDescriptions21.php.

Maurer, E.P., Wood, A.W., Adam, J.C., Lettenmaier, D.P., and 

Nijssen, B., 2002, A long-term hydrologically based dataset 

of land surface fluxes and states for the conterminous United 

States: Journal of Climate, v. 15, no. 22, p. 3237–3251.

Reese, G.C., Manier, D.J., Carr, N.B., Callan, Ramana, 

Leinwand, I.I.F., Assal, T.J., Burris, Lucy, and Ignizio, D.A., 

2016, Estimated historical distribution of grassland communities of the Southern Great Plains: U.S. Geological Survey 

Open-File Report 2016–1184, 13 p., accessed December 12, 

2016, at https://doi.org/10.3133/ofr20161184.

Rehfeldt, G.E., Crookston, N.L., Sáenz-Romero, Cuauhtémoc, 

and Campbell, E.M., 2012, North American vegetation 

model for land-use planning in a changing climate—A solution to large classification problems: Ecological Applications, v. 22, no. 1, p. 119–141.

U.S. Environmental Protection Agency and U.S. Geological 

Survey, 2012, NHDPlus version 2.1 [vector digital data, 

raster digital data, tabular digital data]: U.S. Environmental 

Protection Agency and U.S. Geological Survey, accessed 

January 25, 2015, at http://www.horizon-systems.com/

NHDPlus/NHDPlusV2_data.php.

U.S. Geological Survey and U.S. Department of Agriculture, 

Natural Resources Conservation Service, 2013, Federal 

standards and procedures for the National Watershed 

Boundary Dataset (WBD) (4th ed.): U.S. Geological 

Survey Techniques and Methods, book 11, chap. A3, 63 p., 

accessed January 18, 2017, at http://pubs.usgs.gov/tm/

tm11a3/.
Chapter 3. Change Agents

Introduction

The Homestead Act of 1862 encouraged 1.5 million people 

to settle in the Great Plains. Large tracts of native grassland were 

subsequently converted to cultivated agriculture in the eastern 

Great Plains and used as pasturelands in the western Great Plains 

(Samson and others, 2004). Since the 1950s, the total population 

has increased despite declines in the rural areas (Wilson, 2009). 

Today the major population centers are found along the eastern 

and western boundaries of the region.

In the Great Plains, the total area in cultivation and pasturelands peaked during the mid-20th century and remained relatively stable through the 1990s (Gutman and others, 2005). By 

the 1940s, advanced pumping technology and the availability of 

rural electric power encouraged the use of groundwater for crop 

irrigation (Hart, 2008). The largest source of groundwater in the 

region is the Ogallala aquifer, which has been severely depleted 

by extensive pumping for irrigation (Nativ and Smith, 1987). 

Agriculture remains the dominant land use in the Southern Great 

Plains (SGP). Livestock grazing is the main agricultural activity 

of the shortgrass prairie, whereas cultivation of wheat is the dominant land use in areas that were historically mixed-grass prairie 

(Hart, 2008; National Park Service, 2008). Minerals and energy 

extraction are important economic drivers in the region (National 

Park Service, 2008), and wind development has accelerated in 

recent years. Additional background information can be found in 

the SGP pre-assessment report (Assal and others, 2015).

Change Agents and Management 

Questions

The change agents evaluated are development (agricultural 

croplands, urban areas, transportation, and energy and minerals), fire, invasive species, and climate change (table 3–1). In this 

chapter, we summarize the overall patterns and general effects of 

each change agent across the entire SGP. The core management 

questions are listed in table 3–2. Additional management questions for each change agent are addressed by ecological community in chapters 4−10.

Change agents Variables Indicators1

Development Terrestrial development index (TDI) TDI scores overall and by development classes

Aquatic development index (ADI) ADI and local ADI scores overall and by development classes 

Fire Fire occurrence Fire size overall and by community types

Invasive species Potentially altered vegetation Existing Vegetation Types (LANDFIRE, 2012a) indicating potential for 

altered species composition. 

Occurrence and potential for woody species expansion See chapter 4, “Grasslands”; chapter 8, “Riparian and Nonplaya Wetlands”

Climate change Projected temperature and precipitation Contemporary climate (1981–2010) and projected temperature and 

precipitation for relatively hot-dry and warm-wet climate scenarios 

(2016–2045; 2046–2075)

Potential grassland change as a function of projected 

temperature and precipitation

See chapter 4, “Grasslands”

1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

Table 3–1. Change agents and associated indicators used to address management questions for the Southern Great Plains Rapid 

Ecoregional Assessment.

Core management questions Results

Where does existing development pose the greatest threats to terrestrial communities, and where are the 

large, relatively undeveloped areas?

Figures 3−1 and 3−2

How do development levels vary by development types in terrestrial communities? Figures 3−3 and 3−4

Where does existing development pose the greatest threat to aquatic communities, and where are the large, 

relatively undeveloped areas?

Figures 3−5 and 3−6

How do development levels vary by development types in aquatic communities? Figure 3−7

What is the size and distribution of fires since 1984 overall and by ecological community? Figures 3−8 and 3−9; table 3−3

What is the distribution of potentially altered vegetation? Figure 3−10

How do contemporary patterns of temperature and precipitation compare to that projected for relatively 

hot-dry and warm-wet climate scenarios?

Figures 3−11 and 3−12

Table 3–2. Management questions addressed for change agents for the Southern Great Plains Rapid Ecoregional Assessment.
20 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Management Questions and Results

Where does existing development pose the greatest threat to terrestrial communities, and where are the large, relatively undeveloped areas (figs. 3–1 and 3–2)?

Figure 3–1. Terrestrial development index (TDI) in the Southern Great Plains. Relatively undeveloped areas were defined 

as having TDI scores ≤2 percent.
Chapter 3. Change Agents 21

Figure 3–2. Total area in each terrestrial development index class in the Southern Great Plains.
22 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

How do development levels vary by development types in terrestrial communities (figs. 3−3 and 3−4)?

Figure 3–3. Terrestrial development index for (A) agricultural croplands and (B) urban areas in the Southern Great Plains.
Chapter 3. Change Agents 23

Figure 3–4. Terrestrial development index for (A) transportation and (B) energy and minerals development in the Southern 

Great Plains.
24 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Where does existing development pose the greatest threat to aquatic communities, and where are the large, relatively undeveloped areas (figs. 3–5 and 3–6)?

Figure 3–5. Aquatic development index (ADI) in the Southern Great Plains summarized by (A) catchment (native resolution 

of index) and (B) sixth-level watershed. Relatively undeveloped areas were defined as having ADI scores ≤20.
Chapter 3. Change Agents 25

Figure 3–6. Total area in aquatic development index classes in the Southern Great Plains.

Figure 3–7. Aquatic development index for three classes of development in the Southern Great Plains, summarized by 

catchment. A, Agricultural cropland, urban, and transportation development. B, Energy and minerals development. C, Road 

and railroad stream crossings, dams, and diversions.

How do development levels vary by development types in aquatic communities (fig. 3−7)?
26 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

What is the size and distribution of fires since 1984 overall and by ecological community (figs. 3–8 and 3–9; table 3–3)?

Figure 3–8. Fire perimeters in the Southern Great Plains, 1984–2014. Minimum mapping acreage for a fire is 405 hectares 

(1,000 acres), although some smaller fires may be mapped.
Chapter 3. Change Agents 27

Figure 3–9. Area burned annually in the Southern Great Plains, 1984–2014.

Ecological communities

Area burned

by fire,

in ha1

Percent of all

communities 

burned by fire

Community area,

in ha

Percent of

community area

burned by fire

Riparian and nonplaya wetlands 187,784 5.00 5,021,043 3.74

Playa wetlands 2,031 0.05 331,337 0.61

Mixed-grass prairie 759,653 20.22 10,735,740 7.08

Shortgrass prairie 758,237 20.19 14,404,627 5.26

Sand prairie 640,688 17.06 11,849,413 5.41

Other grasslands2 826,922 22.01 9,183,646 9.00

Shrubland, woodland, and forest 581,147 15.47 10,211,464 5.69

All communities3 3,756,462 100.00 61,737,270 6.08 1

Includes fires 405 ha (1,000 acres) or larger, although some smaller fires may be included. Excludes within-year repeat burns.

2

“Other grasslands” includes foothill grassland, northwest mixed-grass prairie, saline grassland, semidesert grassland and shrubland, tallgrass prairie, 

and cool-season bunchgrass prairie.

3

Fires in sparsely vegetated areas, low-intensity developed areas, and developed areas (totaling 374,800 ha) were not included.

Table 3–3. Area of ecological communities burned in the Southern Great Plains, 1984–2014.

[See appendix A for data source. ha, hectare]
28 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

What is the distribution of potentially altered vegetation (fig. 3−10)?

Figure 3–10. Potentially altered vegetation in the Southern Great Plains.
Chapter 3. Change Agents 29

How do contemporary patterns of temperature and precipitation compare to those projected for relatively hot-dry and warm-wet 

climate scenarios (figs. 3–11 and 3–12)?

Figure 3–11. Average annual temperatures in the Southern Great Plains for (A) 1981−2010, a hot and dry scenario for 

(B) 2016−2045 and (C) 2046−2075, and a warm and wet scenario for (D) 2016−2045 and (E) 2046−2075.
30 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Figure 3–12. Average annual precipitation in the Southern Great Plains for (A) 1981−2010, a hot and dry scenario for 

(B) 2016−2045 and (C) 2046−2075, and a warm and wet scenario for (D) 2016−2045 and (E) 2046−2075.
Chapter 3. Change Agents 31

Summary

• Terrestrial and aquatic development is greatest in 

the eastern half of the SGP. Agricultural croplands, 

energy development, road densities, dams, and 

diversions are more concentrated in the eastern 

portions of the SGP. Development is lower in the 

western portion where shortgrass prairie and intermittent streams are the dominant terrestrial and aquatic 

community types.

• Data on fire size and severity are limited, but the 

largest fires since 1984 occurred during severe 

widespread drought in 2006 and 2011. Of the area 

burned in 2006, greater than 68 percent occurred 

in either mixed-grass, shortgrass, or sand prairie 

(fig. 3–8). In 2011, nearly 60 percent of burned 

areas occurred across those three communities. 

Approximately 4 percent of the SPG burned over 

the past 30 years, which is quite limited compared 

to the amount of area that would have burned prior 

to EuroAmerican settlement. Historical fire regimes 

are poorly understood, but generally the fire return 

interval was less than 50 years, depending on the 

community type. Estimated fire return intervals 

were the shortest in the Nebraska Sand Hills and 

tallgrass prairie (0–10 years) and mixed-grass prairie (11–15 years) and longer in shortgrass prairie 

(21–50 years) (LANDFIRE, 2012b).

• Data on invasive plant species are limited, but the best 

data and models for the SGP indicate the widespread 

potential for altered vegetation.

• There are two major climate gradients in the SGP: 

a west-east gradient of increasing precipitation and 

northwest-southwest gradient of increasing temperature. For both the hot-dry and warm-wet climate 

scenarios, temperatures were projected to increase 

throughout the SGP. Precipitation was projected 

to decrease in the southern portion of the region, 

particularly for the hot-dry scenario. This pattern contributes to the potential for changes in the distribution of SGP grasslands, as projected by other climate 

change models (Rehfeldt and others, 2012; chapter 4, 

“Grasslands”).

References Cited

Assal, T.J., Melcher, C.P., and Carr, N.B., eds., 2015, 

Southern Great Plains Rapid Ecoregional Assessment—

Pre-assessment report: U.S. Geological Survey Open-File 

Report 2015–1003, 284 p., accessed September 2015 at 

https://doi.org/10.3133/ofr20151003.

Gutman, M.P., Parton, W.J., Cunfer, Geoff, and Burke, I.C., 

2005, Population and environment in the U.S. Great Plains, 

in Entwisle, Barbara, and Stern, P.C., eds., Population, land 

use, and environment—Research directions: Washington, 

D.C., The National Academies Press, p. 84–105.

Hart, R.H., 2008, Land-use history on the shortgrass steppe, 

in Lauenroth, W.K., and Burke, I.C., eds., Ecology of the 

shortgrass steppe: Oxford, United Kingdom, Oxford University Press, p. 132–180.

LANDFIRE, 2012a, Existing Vegetation Type (ver. 1.3.0): 

U.S. Forest Service and U.S. Department of the Interior, 

accessed December 29, 2014, at https://landfire.gov/

NationalProductDescriptions21.php.

LANDFIRE, 2012b, Mean fire return interval (ver. 

1.1.3): U.S. Geological Survey, Wildland Fire Science, 

Earth Resources Observation and Science Center, 

accessed March 4, 2015, at https://www.landfire.gov/

NationalProductDescriptions13.php.

National Park Service, 2008, Southern Plains Network vital 

signs monitoring plan: National Park Service, Natural 

Resource Technical Report NPS/SOPN/NRR–2008/028, 

132 p.

Nativ, Ronit, and Smith, D.A., 1987, Hydrogeology and 

geochemistry of the Ogallala aquifer, southern High Plains: 

Journal of Hydrology, v. 91, p. 217–253.

Rehfeldt, G.E., Crookston, N.L., Sáenz-Romero, Cuauhtémoc, 

and Campbell, E.M., 2012, North American vegetation 

model for land-use planning in a changing climate—A solution to large classification problems: Ecological Applications, v. 22, no. 1, p. 119–141.

Samson, F.B., Knopf, F.L., and Ostlie, W.R., 2004, Great 

Plains ecosystems—Past, present, and future: Wildlife 

Society Bulletin, v. 32, no. 1, p. 6–15.

Wilson, S.G., 2009, Population dynamics of the Great 

Plains—1950 to 2007: U.S. Census Bureau report 

P25–1137, 19 p., accessed January 13, 2014, at 

http://www.census.gov/prod/2009pubs/p25-1137.pdf.

Chapter 4. Grasslands

Introduction

Grasslands are the dominant vegetation of the Southern 

Great Plains (SGP), but they have been altered and fragmented 

by conversion to cropland, fire exclusion, loss of native 

herbivores, altered grazing regimes, energy development, and 

spread of invasive species (Conner and others, 2001). Consequently, the grasslands of the Great Plains are considered 

among the most threatened ecosystems in the world (Glaser, 

2012). The SGP comprises a mosaic of grassland types characterized by distinct plant species assemblages and structures 

(Samson and Knopf, 1994; Shiflet, 1994). In general, the 

distribution and structure of SGP grasslands have been shaped 

primarily by relatively high average summer temperatures, 

persistent wind, and highly variable moisture regimes. These 

conditions often create annual summer drought that favors 

perennial warm-season grasses and forbs over woody vegetation (Samson and Knopf, 1994). An increasing gradient 

in precipitation from west to east contributes to a gradual 

transition from predominantly shortgrass prairie in the west, to 

mixed-grass prairie in the middle and east, to tallgrass prairie 

in the extreme east-central region of the SGP (see chapter 6, 

“Shortgrass Prairie,” and chapter 5, “Mixed-Grass Prairie”). 

The transition zones are dynamic and can shift in response to 

climatic variation, especially prolonged drought (Weaver and 

others, 1996; Knight and others, 2014). Sand prairie, another 

major SGP grassland type, predominantly occurs in Nebraska 

and otherwise is widely scattered throughout much of the SGP 

(see chapter 7, “Sand Prairie”).

Additional types of grassland communities with more 

restricted distributions in the SGP include northwest mixedgrass prairie and cool-season bunchgrass prairie in the 

northwest, foothill grassland in west-central areas, saline 

grassland in east-central Colorado, semidesert grassland 

and shrubland in the southwest, and midgrass prairie in the 

southeast. Although both mixed-grass and midgrass prairies 

are characterized by an average vertical structure of medium 

heights, mixed-grass prairie occurs in highly variable growing conditions that lead to a shifting mosaic of shorter to taller 

grasses; this may include sod- and bunch-forming grasses as 

well as warm- and cool-season grasses (Bragg and Steuter, 

1996). In contrast, midgrass prairie is dominated primarily 

by mid-height warm season bunchgrasses, a function of the 

longer growing season and warmer average temperatures in 

the southeastern SGP. We included midgrass prairie in the 

mixed-grass prairie community.

Historically, fire, herbivory, other animal activities 

(trampling, wallowing, and burrowing), and drought were 

the primary agents of natural disturbances in SGP grasslands. 

Tornados, deep snow drifts, deep freezes, floods, and other 

weather events also influenced grassland dynamics (Mutel and 

Emerick, 1984). Change agents strongly affecting the composition and structure of SGP grasslands since EuroAmerican 

settlement include agricultural conversion, livestock grazing, 

loss of native herbivores, and altered fire regimes (Chaney 

and others, 1990; Weaver and others, 1996; Derner and others, 

2009). For example, different grazing patterns between migratory bison and domestic livestock have shifted the grazing 

regime from intermittent and intensive to more continuous and 

consistent grazing (Weaver and others, 1996). More recently, 

energy and urban development and invasive plant species are 

also altering SGP grasslands (Engle and others, 1996; Glaser, 

2012). Additional background information can be found in the 

SGP pre-assessment report (Assal and others, 2015).

Rapid Ecoregional Assessment 

Components Evaluated for Grasslands

The key ecological attributes and change agents addressed 

by core management questions for grasslands include amount 

and distribution, landscape structure (patch size), development, 

invasive woody species, and climate change (tables 4−1 and 

4−2). Fire occurrence and potentially altered vegetation (including invasive herbaceous plants) were evaluated for the entire 

SGP (see chapter 3, “Change Agents”). Overall landscape-level 

ranking variables are summarized in table 4−3. The core and 

integrated management questions are listed in table 4−4.

Rocky Mountain Arsenal, Colorado. Photograph by Rich Keen, 

DPRA (Creative Commons Attribution 2.0 Generic).
34 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Attributes Variables Indicators1

Development Terrestrial development index Percentage of all grasslands in seven development classes based on a 2.5-km 

(1.55-mi) moving window

Index of fragmentation Patch sizes for relatively undeveloped2 grasslands

Invasive species Potential for woody species expansion Occurrence of honey mesquite and eastern redcedar 

Climate change Potential grassland change as a function 

of projected temperature and precipitation

Potential distribution of bioclimatic envelope conducive to grasslands in 2030 

and 20603

1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

2

Terrestrial development index score less than or equal to 2 percent.

3

Bioclimatic envelope represents the climatic conditions conducive for grasslands as modeled by Rehfeldt and others (2012).

Table 4–2. Anthropogenic change agents and associated indicators used to address core management questions for grasslands for 

the Southern Great Plains Rapid Ecoregional Assessment.

[km, kilometer; mi, mile]

Landscape-level 

variables1 Description

Relative rank2

Lowest Medium Highest

Area Percentage of 5-km-radius (3.11-mi) moving window classified as 

baseline grasslands

>0–57.2 >57.2–79.8 >79.8

Development Mean terrestrial development index score for baseline grasslands within 

a 5-km-radius (3.11-mi) moving window

0–2 >2–10 >10

1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

2

Ranking breakpoints for area of grasslands were determined from equal subsets of the data. Ranking breakpoints for terrestrial development index scores 

were standardized for all terrestrial conservation elements.

Table 4–3. Landscape-level variables used to address the integrated management question for grasslands. Ranks for landscape-level 

area and development were combined into an overall landscape-level rank for the Southern Great Plains Rapid Ecoregional Assessment.

[>, greater than; km, kilometer; mi, mile]

Core management questions1 Results

What are the estimated historical and baseline distributions of grasslands? Figures 4−1 and 4−2

Where does existing development pose the greatest threat to grasslands, and where are the large, relatively undeveloped areas? Figures 4−3 and 4−4

How has development fragmented grasslands? Figures 4−5 and 4−6

Where are grasslands potentially vulnerable to honey mesquite and eastern redcedar expansion? Figure 4−7

Where are grasslands potentially vulnerable to projected climate change? Figure 4−8

Integrated management question2 Results

Where are the grasslands with the highest overall landscape-level rank? Figure 4−9 1

See chapter 11, “Data Gaps, Limitations, and Uncertainty,” for management questions that could not be addressed.

2

See table 4−3.

Table 4–4. Management questions addressed for grasslands for the Southern Great Plains Rapid Ecoregional Assessment.

Attributes Variables Indicators1

Amount and distribution Total area Estimated historical and baseline distributions2 of all grassland types

Landscape structure Patch size Patch sizes for estimated historical and baseline distributions of all grassland types

Landscape dynamics Fire occurrence See chapter 3, “Change Agents” 1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

2

Baseline distribution is determined by the most current regional data available.

Table 4–1. Key ecological attributes and associated indicators used to address core management questions for grasslands for the 

Southern Great Plains Rapid Ecoregional Assessment.
Chapter 4. Grasslands 35

Management Questions and Results

What are the estimated historical and baseline distributions of grasslands (figs. 4–1 and 4–2)?

Figure 4–1. Estimated historical distribution of grasslands in the Southern Great Plains. The “other grasslands” 

community includes tallgrass, northwest mixed-grass, and cool-season bunchgrass prairies; foothill and saline grasslands; 

and semidesert grassland and shrubland.
36 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Figure 4–2. Baseline distribution of grasslands in the Southern Great Plains. The “other grasslands” community includes 

tallgrass, northwest mixed-grass, and cool-season bunchgrass prairies; foothill and saline grasslands; and semidesert 

grassland and shrubland.
Chapter 4. Grasslands 37

Where does existing development pose the greatest threat to grasslands, and where are the large, relatively undeveloped areas 

(figs. 4–3 and 4–4)?

Figure 4–3. Terrestrial development index for baseline grasslands in the Southern Great Plains.
38 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

How has development fragmented grasslands (figs. 4–5 and 4–6)?

Figure 4–4. Area of baseline grasslands by terrestrial development index class in the Southern Great Plains.

Figure 4–5. Area of grasslands in the Southern Great Plains as a function of patch size for historical, baseline, and 

relatively undeveloped conditions (terrestrial development index score ≤2 percent). Historically, 739,700 km2 (285,600 

mi2) of grasslands occurred in patches larger than 5,000 km2 (1,931 mi2). (km2, square kilometer; mi2, square mile)
Chapter 4. Grasslands 39

Figure 4–6. Patch size of grasslands in the Southern Great Plains. A, Baseline distribution. B, Relatively undeveloped 

areas (terrestrial development index score ≤2 percent).
40 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Where are grasslands potentially vulnerable to honey mesquite and eastern redcedar expansion (fig. 4–7)?

Figure 4–7. Occurrences of (A) honey mesquite, (B) eastern redcedar, and (C) either species, summarized by sixth-level 

watershed, in the Southern Great Plains. Cross-hatching represents areas with greater than 1 percent cover of honey 

mesquite and (or) eastern redcedar.
Chapter 4. Grasslands 41

Figure 4–8. Potential changes in grasslands distribution as projected by climate change scenarios in the Southern Great 

Plains. The potential distribution is based on the bioclimatic envelope for Great Plains grassland developed by Rehfeldt and 

others (2012) for (A) 2030 and (B) 2060.

Where are grasslands potentially vulnerable to projected climate change (fig. 4–8)?
42 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Figure 4–9. Landscape-level summaries for grasslands in the Southern Great Plains. Overall landscape-level rank (C) is 

derived from (A) landscape-level area and (B) landscape-level development, summarized by a 5-kilometer-radius (3.11-mileradius) moving window (see table 4–3). Highest overall landscape-level rank corresponds to the largest landscape-level area 

and the lowest landscape-level development. Lowest overall landscape-level rank corresponds to the smallest landscapelevel area and highest landscape-level development. Landscape-level ranks are not intended as standalone summaries and 

are best interpreted in conjunction with the geospatial datasets used to address core management questions.

Where are the grasslands with the highest overall landscape-level rank (fig. 4–9)?
Chapter 4. Grasslands 43

Summary

• Grasslands were once widely distributed and connected, totaling an estimated 84 percent of the SGP 

(fig. 4–1). Historically, the more than 809,000 square 

kilometers (km2; 312,357 square miles [mi2]) of SGP 

grassland comprised a mosaic of shortgrass prairie, 

mixed-grass prairie, and other grasslands (cool-season 

bunchgrass prairie, foothill grassland, northwest 

mixed-grass prairie, saline grassland, sand prairie, 

semidesert grassland and shrubland, and tallgrass 

prairie).

• Within the SGP, approximately 43 percent of the 

estimated historical distribution of grasslands has 

been converted by development. Grasslands currently account for approximately 48 percent of the 

SGP. Mixed-grass prairie largely occurs in the eastern portions, sand prairie is scattered throughout but 

concentrated in the Nebraska Sand Hills at the northern extent, and shortgrass prairie is primarily in the 

western potions (fig. 4–2). Agricultural conversion is 

the dominant land use affecting grasslands. Because 

of data limitations, grasslands in the Conservation 

Reserve Program were not evaluated as a part of the 

Southern Great Plains Rapid Ecoregional Assessment 

(REA); Conservation Reserve Program lands may 

represent additional grasslands that were not mapped 

as grasslands for the REAs.

• Nearly one-third of remaining grasslands is relatively 

undeveloped (terrestrial development index [TDI] 

score ≤2 percent) (figs. 4–3 and 4–4), but approximately 18 percent has very high levels of development 

(TDI score >35 percent). Development has greatly 

fragmented and decreased connectivity throughout 

much of the SGP. Historically, the majority of grasslands occurred in very large patches on the landscape, 

but currently, the largest grassland patches occur in 

shortgrass prairie and in the Nebraska Sand Hills (fig. 

4–6A). Most patches of relatively undeveloped grasslands are <1,000 km2

 (386 mi2) (fig. 4–6B).

• Watersheds with honey mesquite and eastern redcedar 

are broadly distributed across the southern and eastern 

portions of the SGP (fig. 4–7). Eastern redcedar is 

expanding westward, and widespread occurrence in 

tallgrass prairie directly east of the SGP indicates the 

potential for additional expansion. Honey mesquite 

is expanding both within and outside of its historical 

range in southern portions of the SGP.

• Bioclimatic conditions conducive to grasslands 

(excluding semidesert grassland and shrubland) are 

projected to remain throughout much of the SGP 

through at least 2060, indicating low vulnerability to 

the climate change scenarios evaluated. The greatest 

vulnerability to climate change is projected to occur 

in the southern portions of the SGP, where conditions 

suitable for semidesert grassland and shrubland are 

projected to expand northward (fig. 4–8). In addition, 

expansion of honey mesquite may be favored by projected climate changes.

• The greatest overall landscape-level ranks (where 

landscape-level area is largest and landscape-level 

development is lowest) occur in the western portion of 

the SGP and northern Nebraska (fig. 4–9C). Because a 

considerable proportion of the grasslands are grazed by 

livestock, additional local-level information on grazing 

is necessary to evaluate conditions in these areas.

References Cited

Assal, T.J., Melcher, C.P., and Carr, N.B., eds., 2015, 

Southern Great Plains Rapid Ecoregional Assessment—

Pre-assessment report: U.S. Geological Survey Open-File 

Report 2015–1003, 284 p., accessed September 2015 at 

https://doi.org/10.3133/ofr20151003.

Bragg, T.B., and Steuter, A.A., 1996, Prairie ecology—The 

mixed prairie, in Samson, F.B., and Knopf, F.L., eds., 

Prairie conservation—Preserving North America’s most 

endangered ecosystem: Washington, D.C., Island Press, 

p. 53–66.

Chaney, Ed; Elmore, Wayne; and Platts, W.S., 1990, Livestock 

grazing on western riparian areas: U.S. Environmental Protection Agency, prepared by Northwest Resource Information Center, Inc., Eagle, Idaho, 45 p.

Conner, Richard; Seidl, Andrew; Van Tassell, Larry; and 

Wilkins, Neal, 2001, United States grasslands and related 

resources—An economic and biological trends assessment: 

College Station, Tex., Texas A&M Institute of Renewable 

Natural Resources, 154 p., accessed September 18, 2016, 

at https://irnr.tamu.edu/publications/research-reports/2001/

united-states-grasslands-and-related-resources-an-economic-and-biological-trends-assessment.

Derner, J.D., Lauenroth, W.K., Stapp, Paul, and Augustine, 

D.J., 2009, Livestock as ecosystem engineers for grassland 

bird habitat in the western Great Plains of North America: 

Rangeland Ecology and Management, v. 62, p. 111−118.
44 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Engle, D.M., Bidwell, T.G., and Moseley, M.E., 1996, Invasion of Oklahoma rangelands and forests by eastern red 

cedar and ashe juniper: Stillwater, Okla., Oklahoma State 

University, Cooperative Extension Service Circular E–947, 

12 p.

Glaser, Aviva, ed., 2012, America’s grasslands conference—Status, threats, and opportunities. The first Biennial 

Conference on the Conservation of America’s Grasslands, 

Sioux Falls, S. Dak., August 14–17, Proceedings: National 

Wildlife Federation and South Dakota State University, 

92 p., accessed September 18, 2016, at https://www.nwf.

org/pdf/Policy-Solutions/Americas%20Grasslands%20

Conference%20Proceedings061312.pdf.

Knight, D.H., Jones, G.P., Reiners, W.A., and Romme, W.H., 

2014, Grasslands, chap. 6 of Mountains and plains—The 

ecology of Wyoming landscapes (2d ed.): New Haven, 

Conn., Yale University Press, p. 83–108.

Mutel, C.F., and Emerick, J.C., 1984, From grassland to 

glacier—The natural history of Colorado: Boulder, Colo., 

Johnson Books, 238 p.

Rehfeldt, G.E., Crookston, N.L., Sáenz-Romero, Cuauhtémoc, 

and Campbell, E.M., 2012, North American vegetation 

model for land-use planning in a changing climate—A solution to large classification problems: Ecological Applications, v. 22, no. 1, p. 119–141.

Samson, F.B., and Knopf, F.L., 1994, Prairie conservation in 

North America: BioScience, v. 44, p. 418–421.

Shiflet, T.N., ed., 1994, Rangeland cover types of the United 

States: Denver, Colo., Society for Range Management, 152 p.

Weaver, T., Payson, E.M., and Gustafson, D.L., 1996, Prairie ecology—The shortgrass prairie, in Samson, F.B., and 

Knopf, F.L., eds., Prairie conservation—Preserving North 

America’s most endangered ecosystem: Washington, D.C., 

Island Press, p. 67–76.
Chapter 5. Mixed-Grass Prairie

Introduction

In the Southern Great Plains (SGP), mixed-grass prairie 

(including midgrass prairie) primarily occurs from southcentral Nebraska to north-central Texas. This community 

forms a transition zone between shortgrass prairie to the west 

and tallgrass prairie to the east. The boundaries between grassland communities can shift in response to the region’s highly 

variable cycles of drought and wetter periods (Samson and 

Knopf, 1996; Knight and others, 2014). Codominant plants 

in mixed-grass prairie include blue (Bouteloua gracilis) and 

sideoats (Bouteloua curtipendula) gramas, western wheatgrass 

(Pascopyrum smithii), and little (Schizachyrium scoparium) 

and big (Andropogon gerardii) bluestems. Forbs may represent up to 25 percent of the annual biomass and contribute to 

overall plant diversity (Bragg and Steuter, 1996).

Historically, primary disturbances in mixed-grass prairie 

were fire and herbivory, which removed fine fuels, suppressed 

woody species, and recycled nutrients (Bragg and Steuter, 

1996). Other animal activities, including burrowing and trampling, and local site factors such as soil type and topographic 

features also affected patchiness and community diversity 

(Limb and others, 2011; Winter and others, 2011). The extent 

and frequency of these factors, as well as their interactive 

effects, contributed to the patchy vegetation structure and 

shifting dynamics of mixed-grass prairie. Periods of drought 

and wetter periods also contribute to patch dynamics. Tall 

grasses and less drought-tolerant mid-height grasses typically 

dominate in mesic conditions, but short grasses and droughttolerant mid-height grasses may become dominant during drier 

conditions and drought (Bragg and Steuter, 1996). Trees and 

shrubs may become dominant where landscape features reduce 

fire frequency, preclude large herbivores, and promote greater 

moisture availability (Bragg and Steuter, 1996). In turn, the 

patchy nature of the mixed-grass prairie historically supported 

a diversity of wildlife, including six species evaluated in the 

SGP Rapid Ecoregional Assessment: ferruginous hawk, lesser 

prairie chicken, burrowing owl, black-tailed prairie dog, swift 

fox, and mule deer.

Change agents in the mixed-grass prairie include agriculture and altered fire and herbivory disturbance regimes 

(Brudvig and others, 2007; Winter and others, 2011). Irrigation for agriculture has led to redistribution of surface water 

and decreased groundwater levels. Introductions of nonnative 

species and fire suppression, which can lead to expansion 

of honey mesquite in the southern portion of the SGP, have 

altered plant communities (Ansley and others, 2001). More 

recently, energy development, urbanization, development of 

extensive transportation corridors, and associated invasions or 

expansions of exotic species have further altered the structure 

and dynamics of mixed-grass prairie (Bragg and Steuter, 1996; 

Ansley and others, 2001). Additional background information 

can be found in the SGP pre-assessment report (Assal and 

others, 2015).

Rapid Ecoregional Assessment 

Components Evaluated for 

Mixed-Grass Prairie

The key ecological attributes and change agents addressed 

by core management questions for mixed-grass prairie include 

amount and distribution, landscape structure (patch size), and 

development (tables 5−1 and 5−2). Invasive woody species 

and climate change were evaluated for all grassland types 

(see chapter 4, “Grasslands”). Fire occurrence and potentially 

altered vegetation (including invasive herbaceous plants) were 

evaluated for the entire SGP (see chapter 3, “Change Agents”). 

Overall landscape-level ranking variables are summarized in 

table 5−3. The core and integrated management questions are 

listed in table 5−4.

Kirwin National Wildlife Refuge, Kansas. Photograph by 

Tony Ifland, U.S. Fish and Wildlife Service (Creative Commons 

Attribution 2.0 Generic).
46 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Change agents Variables Indicators1

Development Terrestrial development index Percentage of mixed-grass prairie in seven development classes based on a 2.5-km 

(1.55-mi) moving window

Index of fragmentation Patch sizes for relatively undeveloped2 mixed-grass prairie

Invasive species Potential for woody species expansion See chapter 4, “Grasslands”

Climate change Potential distribution of grasslands See chapter 4, “Grasslands” 1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

2

Terrestrial development index score less than or equal to 2 percent.

Landscapelevel variables1 Description Relative rank2

Lowest Medium Highest

Area Percentage of 5-km-radius (3.11-mi) moving window classified as baseline mixedgrass prairie>0–31.9 >31.9–50.6 >50.6

Development Mean terrestrial development index score for baseline mixed-grass prairie within a 

5-km-radius (3.11-mi) moving window

0–2 >2–10 >10

1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

2

Ranking breakpoints for area of mixed-grass prairie were determined from equal subsets of the data. Ranking breakpoints for terrestrial development index 

scores were standardized for all terrestrial conservation elements.

Core management questions1 Results

What is the baseline distribution of mixed-grass prairie, and where has it been converted by development? Figure 5−1

Where does existing development pose the greatest threat to mixed-grass prairie, and where are the large, relatively 

undeveloped areas?

Figures 5−2 and 5−3

How has development fragmented mixed-grass prairie? Figures 5−4 and 5−5

Integrated management question2 Results

Where are the mixed-grass prairies with the highest overall landscape-level rank? Figure 5−6 1

See chapter 11, “Data Gaps, Limitations, and Uncertainty,” for management questions that could not be addressed.

2

See table 5−3.

Table 5–2. Anthropogenic change agents and associated indicators used to address core management questions for mixed-grass 

prairie for the Southern Great Plains Rapid Ecoregional Assessment.

[km, kilometer; mi, mile]

Table 5–3. Landscape-level variables used to address the integrated management question for mixed-grass prairie. Ranks for 

landscape-level area and development were combined into an overall landscape-level rank for the Southern Great Plains Rapid 

Ecoregional Assessment.

[>, greater than; km, kilometer; mi, mile]

Table 5–4. Management questions addressed for mixed-grass prairie for the Southern Great Plains Rapid Ecoregional Assessment.

Attributes Variables Indicators1

Amount and distribution Total area Estimated historical and baseline distributions2 of mixed-grass prairie

Landscape structure Patch size Patch sizes for estimated historical and baseline distributions of mixed-grass prairie

Landscape dynamics Fire occurrence See chapter 3, “Change Agents” 1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

2

Baseline distribution is determined by the most current regional data available.

Table 5–1. Key ecological attributes and associated indicators used to address core management questions for mixed-grass prairie 

for the Southern Great Plains Rapid Ecoregional Assessment.
Chapter 5. Mixed-Grass Prairie 47

Management Questions and Results

What is the baseline distribution of mixed-grass prairie, and where has it been converted by development (fig. 5–1)?

Figure 5–1. Baseline and converted historical distributions of mixed-grass prairie in the Southern Great Plains.
48 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Where does existing development pose the greatest threat to mixed-grass prairie, and where are the large, relatively undeveloped 

areas (figs. 5–2 and 5–3)?

Figure 5–2. Terrestrial development index for baseline mixed-grass prairie in the Southern Great Plains.
Chapter 5. Mixed-Grass Prairie 49

Figure 5–3. Area of baseline mixed-grass prairie by terrestrial development index class in the Southern Great Plains.

Figure 5–4. Area of mixed-grass prairie in the Southern Great Plains as a function of patch size for historical, 

baseline, and relatively undeveloped conditions (terrestrial development index score ≤2 percent).

How has development fragmented mixed-grass prairie (figs. 5–4 and 5–5)?
50 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Figure 5–5. Patch size of mixed-grass prairie in the Southern Great Plains. A, Estimated historical distribution. B, Baseline 

distribution.
Chapter 5. Mixed-Grass Prairie 51

Where are the mixed-grass prairies with the highest overall landscape-level rank (fig. 5–6)?

Figure 5–6. Landscape-level summaries for mixed-grass prairie in the Southern Great Plains. Overall landscape-level 

rank (C) is derived from (A) landscape-level area and (B) landscape-level development, summarized by a 5-kilometerradius (3.11-mile-radius) moving window (see table 5–3). Highest overall landscape-level rank corresponds to the largest 

landscape-level area and the lowest landscape-level development. Lowest overall landscape-level rank corresponds 

to the smallest landscape-level area and highest landscape-level development. Landscape-level ranks are not intended 

as standalone summaries and are best interpreted in conjunction with the geospatial datasets used to address core 

management questions.
52 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Summary

• Historically, mixed-grass prairie covered more 

than 20 percent of the SGP and formed a heterogeneous mosaic with shortgrass and tallgrass prairie 

(fig. 5−1).

• Agricultural and other development have converted 

much of the mixed-grass prairie. Approximately 

107,350 square kilometers (km2; 41,448 square 

miles [mi2]) remain, representing less than 

55 percent of the estimated historical distribution 

(fig. 5−1).

• Only 9.5 percent of mixed-grass prairie is relatively undeveloped (terrestrial development index 

[TDI] score ≤2 percent), and approximately 

14 percent has low development levels (TDI 

scores 2−5 percent) (figs. 5−2 and 5−3). More than 

27 percent has very high levels of development (TDI 

scores >35 percent).

• Development has greatly fragmented and decreased 

connectivity of mixed-grass prairie. Nearly 59 percent 

of the estimated historical mixed-grass prairie occurred 

in patches >1,000 km2 (386 mi2), whereas most remaining patches are <10 km2

 (4 mi2) (figs. 5−4 and 5−5). 

The largest patches remaining are in Nebraska and 

Texas (fig. 5−5).

• Most of the remaining mixed-grass prairie (fig. 5–6A) 

occurs in areas with high development levels 

(fig. 5–6B). The locations surrounded by the largest 

amounts of mixed-grass prairie and lowest amounts of 

development (highest overall landscape-level ranks) 

are relatively small and are scattered throughout the 

SGP (fig. 5–6C).

References Cited

Ansley, R.J., Wu, X.B., and Kramp, B.A., 2001, Observation—Long-term increases in mesquite canopy cover in a 

north Texas savanna: Journal of Range Management, v. 54, 

p. 171−176.

Assal, T.J., Melcher, C.P., and Carr, N.B., eds., 2015, 

Southern Great Plains Rapid Ecoregional Assessment—

Pre-assessment report: U.S. Geological Survey Open-File 

Report 2015–1003, 284 p., accessed September 2015 at 

https://doi.org/10.3133/ofr20151003.

Bragg, T.B., and Steuter, A.A., 1996, Prairie ecology—The 

mixed prairie, in Samson, F.B., and Knopf, F.L., eds., Prairie conservation—Preserving North America’s most endangered ecosystem: Washington, D.C., Island Press, p. 53−66.

Brudvig, L.A., Mabry, C.M., Miller, J.R., and Walker, T.A., 

2007, Evaluation of central North American prairie management based on species diversity, life form, and individual species metrics: Conservation Biology, v. 21, no. 3, 

p. 864−874.

Knight, D.H., Jones, G.P., Reiners, W.A., and Romme, W.H., 

2014, Grasslands, chap. 6 of Mountains and plains—The 

ecology of Wyoming landscapes (2d ed.): New Haven, 

Conn., Yale University Press, p. 83–108.

Limb, R.F., Fuhlendorf, S.D., Engle, D.M., Weir, J.R., Elmore, 

R.D., and Bidwell, T.G., 2011, Pyric-herbivory and cattle 

performance in grassland ecosystems: Rangeland Ecology 

and Management, v. 64, p. 659−663.

Samson, F.B., and Knopf, F.L., eds., 1996, Prairie conservation—Preserving North America’s most endangered 

ecosystem: Washington, D.C., Island Press, 339 p.

Winter, S.L., Fuhlendorf, S.D., Goad, C.L., Davis, C.A., and 

Hickman, K.R., 2011, Topoedaphic variability and patch 

burning in sand sagebrush shrubland: Rangeland Ecology 

and Management, v. 64, p. 633−640.
Chapter 6. Shortgrass Prairie

Introduction

In the Southern Great Plains (SGP), shortgrass prairie 

occurs in southeastern Wyoming and southwestern Nebraska, 

eastern Colorado and New Mexico, the western counties of 

Kansas, and the panhandles and rolling plains of Oklahoma 

and Texas. The codominant plants are blue grama (Bouteloua 

gracilis) and buffalograss (Bouteloua dactyloides), which 

characterize this community’s short vertical structure. Historically, fire exerted a strong influence on the landscape structure 

and dynamics of shortgrass prairie (Ford and McPherson, 

1996; Brockway and others, 2002) by setting back seral stages 

and releasing nutrients. Although poorly understood, fires 

may have occurred every 2–30 years, with longer fire-return 

intervals expected where fuels had less continuity (Knight and 

others, 2014). Other influential disturbances include drought, 

herbivory, and other activities of animals. After disturbances, 

there may be pulses of vegetative growth during wet years 

and after influxes of nutrients. Other important influences on 

landscape struture and dynamics of shortgrass prairie include 

heterogeneity in soil type and topography (Weaver and others, 

1996; Limb and others, 2009; Knight and others, 2014). This 

combination of factors that influences shortgrass structure and 

dynamics creates a shifting mosaic that supports a rich diversity of plant and animal species (Ricketts and others, 1999), 

including five species addressed in the SGP Rapid Ecoregional 

Assessment: ferruginous hawk, lesser prairie chicken, longbilled curlew, burrowing owl, and swift fox.

Agriculture and livestock grazing are dominant land 

uses in the shortgrass prairie. High-intensity grazing practices, 

intensified crop rotations, and changes to the Conservation 

Reserve Program continue to alter the structure and dynamics of shortgrass landscapes. Recent expansion of wind, oil, 

and gas energy development are contributing to the direct 

habitat loss and fragmentation of shortgrass prairie. These 

human activities also may alter natural disturbance regimes, 

including fire and herbivory. Although honey mesquite 

historically occurred in the southernmost regions of the SGP, 

it has expanded and increased in abundance, presumably as 

a result of fire and livestock grazing (Dick-Peddie, 1993; 

Ansley and others, 2001). Additional background information 

can be found in the SGP pre-assessment report (Assal and 

others, 2015).

Rapid Ecoregional Assessment 

Components Evaluated for 

Shortgrass Prairie

The key ecological attributes and change agents 

addressed by core management questions for shortgrass prairie 

include amount and distribution, landscape structure (patch 

size), and development (tables 6−1 and 6−2). Invasive woody 

species and climate change were evaluated for all grassland 

types (see chapter 4, “Grasslands”). Fire occurrence and 

potentially altered vegetation (including invasive herbaceous 

plants) were evaluated for the entire SGP (see chapter 3, 

“Change Agents”). Overall landscape-level ranking variables 

are summarized in table 6−3. The core and integrated management questions are listed in table 6−4.

Pawnee Buttes, Colorado. Photograph by Natasha Carr, 

U.S. Geological Survey.
54 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Core management questions1 Results

What is the baseline distribution of shortgrass prairie, and where has it been converted by development? Figure 6−1

Where does existing development pose the greatest threat to shortgrass prairie, and where are the large, relatively 

undeveloped areas?

Figures 6−2 and 6−3

How has development fragmented shortgrass prairie? Figures 6−4 and 6−5

Integrated management question2 Results

Where are the shortgrass prairies with the highest overall landscape-level rank? Figure 6−6 1

See chapter 11, “Data Gaps, Limitations, and Uncertainty,” for management questions that could not be addressed.

2

See table 6−3.

Table 6–4. Management questions addressed for shortgrass prairie for the Southern Great Plains Rapid Ecoregional Assessment.

Table 6–2. Anthropogenic change agents and associated indicators used to address core management questions for shortgrass 

prairie for the Southern Great Plains Rapid Ecoregional Assessment.

[km, kilometer; mi, mile]

Change agents Variables Indicators1

Development Terrestrial development index Percentage of shortgrass prairie in seven development classes based on a 2.5-km 

(1.55-mi) moving window

Index of fragmentation Patch sizes for relatively undeveloped2 shortgrass prairie

Invasive species Potential for woody species expansion See chapter 4, “Grasslands”

Climate change Potential distribution of grasslands See chapter 4, “Grasslands” 1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

2

Terrestrial development index score less than or equal to 2 percent.

Landscapelevel variables1 Description Relative rank2

Lowest Medium Highest

Area Percentage of 5-km-radius (3.11-mi) moving window classified as baseline shortgrass prairie >0–43.2 >43.2–68.4 >68.4

Development Mean terrestrial development index score for baseline shortgrass prairie within a 5-km-radius 

(3.11-mi) moving window

0–2 2–10 >10

1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

2

Ranking breakpoints for area of shortgrass prairie were determined from equal subsets of the data. Ranking breakpoints for terrestrial development index 

scores were standardized for all terrestrial conservation elements.

Table 6–3. Landscape-level variables used to address the integrated management question for shortgrass prairie. Ranks for landscape-level 

area and development were combined into an overall landscape-level rank for the Southern Great Plains Rapid Ecoregional Assessment.

[>, greater than; km, kilometer; mi, mile]

Table 6–1. Key ecological attributes and associated indicators used to address core management questions for shortgrass prairie for 

the Southern Great Plains Rapid Ecoregional Assessment.

Attributes Variables Indicators1

Amount and distribution Total area Estimated historical and baseline distributions2 of shortgrass prairie

Landscape structure Patch size Patch sizes for estimated historical and baseline distributions of shortgrass prairie

Landscape dynamics Fire occurrence See chapter 3, “Change Agents” 1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used. 

2

Baseline distribution is determined by the most current regional data available. 
Chapter 6. Shortgrass Prairie 55

Management Questions and Results

What is the baseline distribution of shortgrass prairie, and where has it been converted by development (fig. 6–1)?

Figure 6–1. Baseline and converted historical distributions of shortgrass prairie in the Southern Great Plains.
56 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Where does existing development pose the greatest threat to shortgrass prairie, and where are the large, relatively undeveloped 

areas (figs. 6–2 and 6–3)?

Figure 6–2. Terrestrial development index for baseline shortgrass prairie in the Southern Great Plains.
Chapter 6. Shortgrass Prairie 57

How has development fragmented shortgrass prairie (figs. 6–4 and 6–5)?

Figure 6–3. Area of baseline shortgrass prairie by terrestrial development index class in the Southern Great Plains.

Figure 6–4. Area of shortgrass prairie in the Southern Great Plains as a function of patch size for historical, baseline, 

and relatively undeveloped conditions (terrestrial development index score ≤2 percent).
58 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Figure 6–5. Patch size of shortgrass prairie in the Southern Great Plains. A, Estimated historical distribution. B, Baseline 

distribution.
Chapter 6. Shortgrass Prairie 59

Figure 6–6. Landscape-level summaries for shortgrass prairie in the Southern Great Plains. Overall landscape-level 

rank (C) is derived from (A) landscape-level area and (B) landscape-level development, summarized by a 5-kilometerradius (3.11-mile-radius) moving window (see table 6–3). Highest overall landscape-level rank corresponds to the largest 

landscape-level area and the lowest landscape-level development. Lowest overall landscape-level rank corresponds 

to the smallest landscape-level area and highest landscape-level development. Landscape-level ranks are not intended 

as standalone summaries and are best interpreted in conjunction with the geospatial datasets used to address core 

management questions.

Where are the shortgrass prairies with the highest overall landscape-level rank (fig. 6–6)?
60 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Summary

• Historically, shortgrass prairie occupied more than 

27 percent of the SGP, mostly in a large, relatively 

continuous band from southeast Wyoming to central 

Texas (fig. 6−1).

• Agricultural and other development have converted 

more than 44 percent of shortgrass prairie. Approximately 144,045 square kilometers (km2

; 55,616 square 

miles [mi2]) of shortgrass prairie remain (fig. 6−1).

• Approximately 40 percent of the remaining shortgrass 

prairie is relatively undeveloped (terrestrial development 

index [TDI] score ≤2 percent), and nearly 16 percent 

has low development levels (TDI scores 2−5 percent). 

Approximately 18 percent has very high development 

levels (TDI score >35 percent) (figs. 6−2 and 6−3).

• Development has greatly fragmented shortgrass prairie. 

Approximately 67 percent of the historical shortgrass 

prairie occurred in patches >15,000 km2 (5,792 mi2), 

whereas 61 percent currently occurs in patches <10 km2

(4 mi2), and more than 91 percent of the total area is in 

patches <100 km2 (39 mi2) (figs. 6−4 and 6−5).

• Conversion to agriculture and other development is lower 

in the western shortgrass prairie than along its eastern 

boundary, where it forms a mosaic with mixed-grass prairie. Consequently, the highest landscape-level ranks occur 

in eastern Colorado and New Mexico (fig. 6–6C).

References Cited

Ansley, R.J., Wu, X.B., and Kramp, B.A., 2001, Observation—Long-term increases in mesquite canopy cover in a 

north Texas savanna: Journal of Range Management, v. 54, 

p. 171−176.

Assal, T.J., Melcher, C.P., and Carr, N.B., eds., 2015, 

Southern Great Plains Rapid Ecoregional Assessment—

Pre-assessment report: U.S. Geological Survey Open-File 

Report 2015–1003, 284 p., accessed September 2015 at 

https://doi.org/10.3133/ofr20151003.

Brockway, D.G., Gatewood, R.G., and Paris, R.B., 2002, 

Restoring fire as an ecological process in shortgrass prairie 

ecosystems—Initial effects of prescribed burning during the 

dormant and growing seasons: Journal of Environmental 

Management, v. 65, p. 135–152.

Dick-Peddie, W.A., 1993, New Mexico vegetation—Past, 

present, and future: Albuquerque, N. Mex., University of 

New Mexico Press, 244 p.

Ford, P.L., and McPherson, G.R., 1996, Ecology of fire in 

shortgrass prairie of the Southern Great Plains, in Finch, 

D.M., ed., Ecosystem disturbance and wildlife conservation in western grasslands—A symposium proceedings, Albuquerque, N. Mex., September 22–26, 1994: 

Fort Collins, Colo., U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Forest and Range Experiment Station, General Technical Report RM–GTR–285, 

p. 20–39.

Knight, D.H., Jones, G.P., Reiners, W.A., and Romme, W.H., 

2014, Mountains and plains—The ecology of Wyoming 

landscapes (2d ed.): New Haven, Conn., Yale University 

Press, 404 p.

Limb, R.F., Fuhlendorf, S.D., and Townsend, D.E., 2009, 

Heterogeneity of thermal extremes—Driven by disturbance 

of inherent in the landscape: Environmental Management, 

v. 43, p. 100–106.

Ricketts T.H.; Dinerstein, Eric; Olson, D.M.; Louks, C.J.; 

Eichbaum, William; DellaSala, D.A.; Kavanaugh, Kevin; 

Hedao, Prashant; Hurley, Patrick; Carney, Karen; Abell, 

Robin; and Walters, Steven, 1999, Terrestrial ecoregions of 

North America—A conservation assessment: Washington, 

D.C., Island Press, 508 p.

Weaver, T., Payson, E.M., and Gustafson, D.L., 1996, Prairie ecology—The shortgrass prairie, in Samson, F.B., and 

Knopf, F.L., eds., Prairie conservation—Preserving North 

America’s most endangered ecosystem: Washington, D.C., 

Island Press, p. 67–76.
Chapter 7. Sand Prairie

Introduction

In the Southern Great Plains (SGP), sand prairie encompasses three plant associations: the Nebraska Sand Hills, 

sand sagebrush (Artemisia filifolia), and sand shinnery oak 

(Quercus havardii), a thicket-forming deciduous shrub. The 

sand hills are broadly distributed across the large dunes of 

north-central Nebraska and are characterized by a mix of 

mid-height and tall grasses (Weaver and Albertson, 1956). 

Outside of the sand hills, sand sagebrush occurs throughout 

much of the sand prairie, whereas sand shinnery oak (hereafter referred to as shinnery oak) occurs primarily south of 

Colorado and Kansas (Peterson and Boyd, 1998). Both sand 

sagebrush and shinnery oak occur on deep, sandy soils, often 

on small lunettes and sandy deposits broadly associated with 

paleo floodplains (Whitman and Barker, 1994; Peterson and 

Boyd, 1998; Colorado Natural Heritage Program, 2013). Sand 

sagebrush typically grows less than 1 meter (3 feet) high and 

accounts for up to 50 percent of the cover, whereas shinnery oak typically grows less than 2 meters (6 feet) high and 

accounts for up to 100 percent of the cover. The understory 

of sand sagebrush is generally dominated by short grasses, 

although mid-height grasses may dominate where conditions 

are more mesic (Weaver and Albertson, 1956). Shinnery oak 

is generally codominant with mid-height and tall grasses that 

may overtop the oak (Peterson and Boyd, 1998). Both species, which often co-occur, support biotic communities that 

differ from those of surrounding grasslands. In particular, 

they provide essential habitat for the dunes sagebrush lizard 

(Sceloporus arenicolus), western massasauga (Sistrurus 

catenatus tergeminus), and lesser prairie chicken, all of which 

are species of special concern in the SGP.

Historically, the primary disturbances in the sand prairie 

were fire, herbivory and other animal activities, interactions 

between fire and grazing, drought, and wind (Stubbendieck 

and others, 1989; Peterson and Boyd, 1998; Winter and others, 

2012). Fire and grazing promote increased productivity of 

sand prairie by removing litter and recycling nutrients. Where 

dunes are poorly vegetated, blowouts created by the interactive effects of grazing animals and wind erosion can occur 

(Stubbendieck and others, 1989), providing unique habitats 

for many rare species (for example, blowout penstemon 

[Penstemon haydenii]). It was once generally assumed that 

shinnery oak had recently expanded at the expense of grasslands, which led to decades of herbicide treatments and 

other management actions to kill or discourage shinnery oak. 

Although reducing shinnery oak cover can stimulate grass 

production in the short term, eradication may destabilize sandy 

soils. Furthermore, recent research indicates that shinnery 

oak systems have not expanded (Bragg and Steuter, 1996). 

Although shinnery oak responds well to disturbances that 

leave the roots largely intact, it does not readily expand into 

or recolonize recently disturbed areas. Altered fire regimes, 

energy development, urbanization, agricultural conversion, 

and certain livestock management practices have fragmented 

and contributed to the direct loss of sand prairie. Livestock 

grazing practices and loss of native herbivores often lead to 

shifts in community composition, such as increased shrub 

cover and decreased grass production (Colorado Natural 

Heritage Program, 2013). Additional background information 

can be found in the SGP pre-assessment report (Assal and 

others, 2015).

Rapid Ecoregional Assessment 

Components Evaluated for Sand Prairie

The key ecological attributes and change agents 

addressed by core management questions for sand prairie 

include amount and distribution, landscape structure (patch 

size), and development (tables 7−1 and 7−2). Invasive woody 

species and climate change were evaluated for all grasslands 

types (see chapter 4, “Grasslands”). Fire occurrence and 

potentially altered vegetation (including invasive herbaceous 

plants) were evaluated for the entire SGP (see chapter 3, 

“Change Agents”). Overall landscape-level ranking variables 

are summarized in table 7−3. The core and integrated management questions are listed in table 7−4.

Sandhills, Nebraska. Photograph by Melvin Nenneman, U.S. Fish 

and Wildlife Service (Creative Commons Attribution 2.0 Generic).
62 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Change agents Variables Indicators1

Development Terrestrial development index Percentage of sand prairie in seven development classes based on a 2.5-km 

(1.55-mi) moving window

Index of fragmentation Patch sizes for relatively undeveloped2 sand prairie 

Invasive species Potential for woody species expansion See chapter 4, “Grasslands”

Climate change Potential distribution of grasslands See chapter 4, “Grasslands” 1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

2

Terrestrial development index score less than or equal to 2 percent.

Landscapelevel variables1 Description Relative rank2

Lowest Medium Highest

Area Percentage of 5-km-radius (3.11-mi) moving window classified as baseline sand prairie >0–43.5 >43.5–73.5 >73.5

Development Mean terrestrial development index score for baseline sand prairie within a 5-km-radius 

(3.11-mi) moving window

0–2 >2–10 >10

1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

2

Ranking breakpoints for area of sand prairie were determined from equal subsets of the data. Ranking breakpoints for terrestrial development index scores 

were standardized for all terrestrial conservation elements.

Table 7–2. Anthropogenic change agents and associated indicators used to address core management questions for sand prairie 

for the Southern Great Plains Rapid Ecoregional Assessment.

[km, kilometer; mi, mile]

Table 7–3. Landscape-level variables used to address the integrated management question for sand prairie. Ranks for landscapelevel area and development were combined into an overall landscape-level rank for the Southern Great Plains Rapid Ecoregional 

Assessment.

[>, greater than; km, kilometer; mi, mile]

Core management questions1 Results

What is the baseline distribution of sand prairie, and where has it been converted by development? Figure 7−1

What are the baseline distributions of sand sagebrush and shinnery oak in the sand prairie community? Figure 7−2

Where does existing development pose the greatest threat to sand prairie, and where are the large, relatively undeveloped areas? Figures 7−3 and 7−4

How has development fragmented sand prairie? Figures 7−5 and 7−6

Integrated management question2 Results

Where are the sand prairies with the highest overall landscape-level rank? Figure 7−7 1

See chapter 11, “Data Gaps, Limitations, and Uncertainty,” for management questions that could not be addressed.

2

See table 7−3.

Table 7–4. Management questions addressed for sand prairie for the Southern Great Plains Rapid Ecoregional Assessment.

Attributes Variables Indicators1

Amount and distribution Total area Estimated historical and baseline distributions2 of sand prairie

Landscape structure Patch size Patch sizes for estimated historical and baseline distributions of sand prairie

Landscape dynamics Fire occurrence See chapter 3, “Change Agents” 1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

2

Baseline distribution is determined by the most current regional data available.

Table 7–1. Key ecological attributes and associated indicators used to address core management questions for sand prairie for the 

Southern Great Plains Rapid Ecoregional Assessment.
Chapter 7. Sand Prairie 63

Management Questions and Results

What is the baseline distribution of sand prairie, and where has it been converted by development (fig. 7–1)?

Figure 7–1. Baseline and converted historical distributions of sand prairie in the Southern Great Plains.
64 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Figure 7–2. Baseline distributions of sand sagebrush and shinnery oak within the sand prairie community in the Southern 

Great Plains.

What are the baseline distributions of sand sagebrush and shinnery oak in the sand prairie community (fig. 7−2)?
Chapter 7. Sand Prairie 65

Figure 7–3. Terrestrial development index for baseline sand prairie in the Southern Great Plains.

Where does existing development pose the greatest threat to sand prairie, and where are the large, relatively undeveloped areas 

(figs. 7–3 and 7–4)?
66 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Figure 7–4. Area of baseline sand prairie by terrestrial development index class in the Southern Great Plains.

Figure 7–5. Area of sand prairie in the Southern Great Plains as a function of patch size for historical, baseline, 

and relatively undeveloped conditions (terrestrial development index score ≤2 percent).

How has development fragmented sand prairie (figs. 7–5 and 7–6)?
Chapter 7. Sand Prairie 67

Figure 7–6. Patch size of sand prairie in the Southern Great Plains. A, Estimated historical distribution. B, Baseline 

distribution.
68 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Figure 7–7. Landscape-level summaries for sand prairie in the Southern Great Plains. Overall landscape-level rank 

(C) is derived from (A) landscape-level area and (B) landscape-level development, summarized by a 5-kilometer-radius 

(3.11-mile-radius) moving window (see table 7–3). Highest overall landscape-level rank corresponds to the largest 

landscape-level area and the lowest landscape-level development. Lowest overall landscape-level rank corresponds 

to the smallest landscape-level area and highest landscape-level development. Landscape-level ranks are not intended 

as standalone summaries and are best interpreted in conjunction with the geospatial datasets used to address core 

management questions.

Where are the sand prairies with the highest overall landscape-level rank (fig. 7–7)?
Chapter 7. Sand Prairie 69

Summary

• The current distribution of sand prairie covers less than 

13 percent of the SGP, which represents a 30 percent 

reduction in area from the estimated historical distribution (fig. 7–1). In addition, removal of shinnery oak 

has altered the structure of sand prairie in many areas 

(Bragg and Steuter, 1996).

• Currently, over 40 percent of sand prairie is relatively 

undeveloped (terrestrial development index [TDI] score 

≤2 percent), and 15 percent has low development levels 

(TDI scores 2–5 percent) (figs. 7–3 and 7–4).

• Development has greatly fragmented sand prairie. 

More than 62 percent of estimated historical sand prairie 

occurred in patches >1,000 square kilometers (km2; 

386 square miles [mi2]), and large, widely distributed 

contiguous patches occurred in Nebraska, eastern 

New Mexico, and western Texas. Only 4 percent of 

remaining patches exceed 1,000 km2 (386 mi2), and 

more than 75 percent is in patches <100 km2 (39 mi2) 

(figs. 7–5 and 7–6).

• The areas of sand prairie with the highest overall 

landscape-level ranks (where landscape-level area is 

largest and landscape-level development is lowest) are in 

the Nebraska Sand Hills (fig. 7–7). The patches of sand 

sagebrush and shinnery oak south of the sand hills are 

relatively small, but there are many scattered areas that 

have high overall landscape-level ranks.

References Cited

Assal, T.J., Melcher, C.P., and Carr, N.B., eds., 2015, 

Southern Great Plains Rapid Ecoregional Assessment—

Pre-assessment report: U.S. Geological Survey Open-File 

Report 2015−1003, 284 p., accessed September 2015 at 

https://doi.org/10.3133/ofr20151003.

Bragg, T.B., and Steuter, A.A., 1996, Prairie ecology—The 

mixed prairie, in Samson, F.B., and Knopf, F.L., eds., 

Prairie conservation—Preserving North America’s most 

endangered ecosystem: Washington, D.C., Island Press, 

p. 53–66.

Colorado Natural Heritage Program, 2013, Western Great 

Plains sandhill shrubland, in Ecological systems of Colorado: Colorado Natural Heritage Program website, accessed 

October 16, 2015, at http://www.cnhp.colostate.edu/

download/projects/eco_systems/eco_systems.asp.

Peterson, R.S., and Boyd, C.S., 1998, Ecology and management of sand shinnery communities—A literature 

review: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station, General Technical 

Report RMRS–GTR–16, 44 p.

Stubbendieck, James, Flessner, T.R., and Weedon, Ronald, 

1989, Blowouts in the Nebraska Sandhills—The habitat 

of Penstemon haydenii, in Bragg, T.B., and Stubbendieck, 

James, eds., Prairie pioneers—Ecology, history and culture. 

Proceedings of the eleventh North American Prairie Conference, Lincoln, Nebr., August 7−11, 1988: Lincoln, Nebr., 

University of Nebraska Printing, accessed October 16, 

2015, at http://digitalcommons.unl.edu/napcproceedings/3/.

Weaver, J.E., and Albertson, F.W., 1956, Grasslands of the 

Great Plains—Their nature and use: Lincoln, Nebr., Johnsen 

Publishing Company, 395 p.

Whitman, W.C., and Barker, W.T., 1994, Rangeland cover 

types of the northern Great Plains, in Shiflet, T.N., ed., 

Rangeland cover types of the United States: Denver, Colo., 

Society for Range Management, p. 69–84.

Winter, S.L., Fuhlendorf, S.D., Goad, C.L., Davis, C.A., 

Hickman, K.R., and Leslie, D.M., Jr., 2012, Restoration of 

the fire-grazing interaction in Artemisia filifolia shrubland: 

Journal of Applied Ecology, v. 49, no. 1, p. 242−250.

Chapter 8. Riparian and Nonplaya Wetlands

Introduction

Riparian and nonplaya wetlands (hereafter referred to 

as wetlands) are transition zones between aquatic and terrestrial ecosystems and are shaped by processes that affect 

both ecosystems (Culver, 2014). Riparian areas are strongly 

influenced by fluvial dynamics including periodic flooding and desiccation. Floods can scour vegetation and alter 

channel morphology (Naiman and Decamps, 1997). In the 

Southern Great Plains (SGP), wetlands occur where the water 

table is at or near the land surface and may be inundated 

permanently or intermittently. There are both recharge and 

discharge wetlands, including playa wetlands (addressed in 

chapter 9, “Playa Wetlands and Saline Lakes”), shallow ponds, 

marshes, and wet meadows (Haukos and Smith, 2003; Culver, 

2014; see table A−5 in appendix A). Woody riparian species 

include cottonwood (Populus spp.) and willow (Salix spp.). 

Wetlands may be dominated by herbaceous wetland obligates 

or facultative species, depending on the timing and duration 

of inundation. Many riparian plants are adapted to rapidly 

resprout or recolonize recently disturbed areas (Naiman and 

Decamps, 1997). Riparian and wetland communities may also 

be influenced by disturbance from fire, wind, and herbivory 

(Gregory and others, 1991; Naiman and Decamps, 1997; 

Mitsch and Gosselink, 2000). Riparian and wetland communities provide nesting, feeding, or roosting habitats for several 

species of management concern evaluated for the SGP Rapid 

Ecoregional Assessment: long-billed curlew, snowy plover, 

interior least tern, and bats.

Change agents affecting SGP riparian and nonplaya wetlands include agriculture, dams and water diversions, unsuitable 

grazing practices, and invasive species (Poff and others, 2011). 

Dams, ditches, road crossings, and energy development can alter 

flow regimes and sedimentation. Agricultural and infrastructural 

development can lead to sedimentation, contamination, and 

groundwater depletion (Smith and others, 2008; Brinson and 

Eckles, 2011). Redistributions of water can decrease, create, or 

expand riparian and wetland vegetation (Crifasi, 2005; Wiener 

and others, 2008). Inundation following reservoir construction 

kills riparian and wetland vegetation, but it can also promote 

establishment of riparian and wetland communities on expansive 

reservoir deltas (Volke and others, 2015) depending on the time 

since construction. For example, dams can result in channel narrowing that may initially facilitate establishment of cottonwood 

and willow (Friedman and others, 1998). Over the long-term, 

however, decreased seasonal flooding can inhibit riparian vegetation regeneration (Friedman and others, 1997). Altered flows and 

irrigation ditches facilitate invasion by invasive species (Uowolo 

and others, 2005; Merritt and Poff, 2010). Fire suppression may 

favor woody plants and promote an increase in fire severity 

by allowing fuels to accumulate (Dwire and Kauffman, 2003). 

Finally, climate change could alter hydrological regimes and 

water temperatures (Bisson and others, 2003; Barnett and others, 

2008; Matthews, 2008; Polley and others, 2013). Additional 

background information can be found in the SGP pre-assessment 

report (Assal and others, 2015).

Rapid Ecoregional Assessment 

Components Evaluated for Riparian 

and Nonplaya Wetlands

The key ecological attributes and change agents 

addressed by core management questions for riparian and 

wetland communities include amount and distribution, landscape structure (patch size), development, and invasive woody 

species (tables 8−1 and 8−2). Connectivity was addressed for 

prairie streams and rivers (see chapter 10, “Prairie Streams 

and Rivers”). Fire occurrence, potentially altered vegetation 

(including invasive herbaceous plants), and climate change 

were evaluated for the entire SGP (see chapter 3, “Change 

Agents”). Overall landscape-level ranking variables are summarized in table 8−3. The core and integrated management 

questions are listed in table 8−4.

Spotted Tail Wetlands Complex, Nebraska. Photograph by 

Miruh Hamend, Playa Lakes Joint Venture (used with permission).
72 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Change agents Variables Indicators1,2

Development Aquatic development index (ADI) Percentage of riparian and nonplaya wetlands in seven 

development classes

Index of fragmentation Patch sizes for relatively undeveloped3 riparian and nonplaya 

wetlands

Barriers affecting patch size and structural connectivity 

of riparian communities

See chapter 10, “Prairie Streams and Rivers”

Invasive species Occurrence and potential for woody species expansion Occurrence of Russian olive and tamarisk

Probability of suitable habitat for Russian olive and tamarisk 1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

2

Summarized by sixth-level watershed.

3

Relatively undeveloped: aquatic development index less than or equal to 20.

Table 8–2. Anthropogenic change agents and associated indicators used to address core management questions for riparian and 

nonplaya wetlands for the Southern Great Plains Rapid Ecoregional Assessment.

Landscapelevel variables1 Description Relative rank2

Lowest Medium Highest

Area Area of baseline riparian and nonplaya wetlands as a percentage of fifth-level watershed area 0–<2.95 2.95–<6.15 ≥6.15

Development Mean aquatic development index score for baseline riparian and nonplaya wetlands, 

summarized by fifth-level watershed

0–20 >20–40 >40

1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

2

Ranking breakpoints for area of riparian and nonplaya wetlands determined from equal subsets of the data (with adjustments for highly skewed distributions). Ranking breakpoints for aquatic development index scores were standardized for all aquatic conservation elements.

Core management questions1 Results

What is the baseline distribution of riparian and nonplaya wetlands? Figures 8−1 and 8–2

Where does existing development pose the greatest threat to riparian and nonplaya wetlands, and where are the large, 

relatively undeveloped areas?

Figures 8−3 and 8−4

How has development fragmented riparian and nonplaya wetlands? Figure 8−5

Where are Russian olive and tamarisk present, and where are riparian suitable for their expansion? Figures 8−6 and 8−7

Integrated management question2 Results

Where are the watersheds with the highest overall landscape-level rank for riparian and nonplaya wetlands? Figure 8−8 1

See chapter 11, “Data Gaps, Limitations, and Uncertainty,” for management questions that could not be addressed.

2

See table 8−3.

Table 8–3. Landscape-level variables used to address the integrated management question for riparian and nonplaya wetlands. Ranks 

for landscape-level area and development were combined into an overall landscape-level rank for the Southern Great Plains Rapid 

Ecoregional Assessment.

[<, less than; >, greater than]

Table 8–4. Management questions addressed for riparian and nonplaya wetlands for the Southern Great Plains Rapid Ecoregional 

Assessment.

Attributes Variables Indicators1

Amount and distribution Total area Baseline distribution of riparian and nonplaya wetlands2 and area of riparian and nonplaya 

wetlands as a percentage of sixth-level watershed

Landscape structure Patch size Patch sizes for baseline riparian and nonplaya wetlands

Landscape dynamics Fire occurrence See chapter 3, “Change Agents” 1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

2

Baseline distribution is determined by the most current regional data available.

Table 8–1. Key ecological attributes and associated indicators used to address core management questions for riparian and nonplaya 

wetlands for the Southern Great Plains Rapid Ecoregional Assessment.
Chapter 8. Riparian and Nonplaya Wetlands 73

Management Questions and Results

What is the baseline distribution of riparian and nonplaya wetlands (figs. 8–1 and 8–2)?

Figure 8–1. Baseline distribution of riparian and nonplaya wetlands in the Southern Great Plains.
74 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Figure 8–2. Percentage of each sixth-level watershed classified as riparian and nonplaya wetlands in the Southern Great Plains.
Chapter 8. Riparian and Nonplaya Wetlands 75

Figure 8–3. Aquatic development index for baseline riparian and nonplaya wetlands, summarized by sixth-level 

watershed, in the Southern Great Plains.

Where does existing development pose the greatest threat to riparian and nonplaya wetlands, and where are the large, relatively 

undeveloped areas (figs. 8–3 and 8–4)?
76 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Figure 8–4. Area of baseline riparian and nonplaya wetlands by aquatic development index class in the Southern 

Great Plains.

Figure 8–5. Area of riparian and nonplaya wetlands in the Southern Great Plains as a function of patch size for 

baseline and relatively undeveloped conditions (aquatic development index score ≤20).

How has development fragmented riparian and nonplaya wetlands (fig. 8–5)?
Chapter 8. Riparian and Nonplaya Wetlands 77

Where are Russian olive and tamarisk present, and where are riparian areas suitable for their expansion? (figs. 8–6 and 8–7)?

Figure 8–6. Probability of suitable habitat and reported occurrences for Russian olive, summarized by sixth-level watershed, 

in the Southern Great Plains. Suitable habitat is generally limited to riparian areas within each watershed.
78 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Figure 8–7. Probability of suitable habitat and reported occurrences for tamarisk, summarized by sixth-level watershed, 

in the Southern Great Plains. Suitable habitat is generally limited to riparian areas within each watershed.
Chapter 8. Riparian and Nonplaya Wetlands 79

Where are the watersheds with the highest overall landscape-level rank for riparian and nonplaya wetlands (fig. 8–8)?

Figure 8–8. Landscape-level summaries for riparian and nonplaya wetlands in the Southern Great Plains. Overall 

landscape-level rank (C) is derived from (A) landscape-level area, and (B) landscape-level development, summarized by 

fifth-level watershed (see table 8−3). Highest overall landscape-level rank corresponds to the largest landscape-level area 

and the lowest landscape-level development. Lowest overall landscape-level rank corresponds to the smallest landscapelevel area and highest landscape-level development. Landscape-level ranks are not intended as standalone summaries and 

are best interpreted in conjunction with the geospatial datasets used to address core management questions.
80 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Summary

• The surface area of riparian and nonplaya wetlands 

is approximately 50,210 square kilometers (km2; 

19,390 square miles [mi2]), representing 5.2 percent 

of the SGP. Watersheds with the greatest riparian and 

wetland area occur in the northern and eastern portions of the SGP, especially in the Nebraska Sand Hills, 

where there is a greater density of perennial streams 

compared to the western and southwestern portions of 

the SGP, where intermittent streams are more common 

(figs. 8−2, 10−2, and 10−3).

• About 20 percent of riparian and wetland areas are 

relatively undeveloped (aquatic development index 

[ADI] score ≤20), and about 49 percent have moderate 

to high development (ADI score >40), primarily in the 

eastern portions of the SGP (figs. 8−3 and 8−4).

• Riparian and wetland areas are naturally small, and the 

distribution of patch sizes is similar among all development levels, but relatively undeveloped areas make up 

a fraction of the total baseline distribution (fig. 8−5). 

There are complex interactions between natural 

dynamics and altered hydrological regimes that can 

both create and destroy these communities (Friedman 

and others, 1997).

• Russian olive and tamarisk occur in many riparian 

areas of the SGP (figs. 8−6 and 8−7). Models indicate 

that conditions suitable for expansion of these nonnative species are widespread in the SGP. Comprehensive surveys are needed to improve habitat suitability 

models for these invasive plants (see chapter 11, “Data 

Gaps, Limitations, and Uncertainty”).

• Most of the watersheds with the highest overall 

landscape-level ranks occur in the northeastern, 

southwestern, and southeastern portions of the SGP 

(fig. 8−8C).

References Cited

Assal, T.J., Melcher, C.P., and Carr, N.B., eds., 2015, 

Southern Great Plains Rapid Ecoregional Assessment—

Pre-assessment report: U.S. Geological Survey Open-File 

Report 2015−1003, 284 p., accessed September 2015 at 

https://doi.org/10.3133/ofr20151003.

Barnett, T.P., Pierce, D.W., Hidalgo, H.G., Bonfils, Celine, 

Santer, B.D., Das, Tapash, Bala, Govindasamy, Wood, A.W., 

Nozawa, Toru, Mirin, A.A., Cayan, D.R., and Dettinger, 

M.D., 2008, Human-induced changes in the hydrology of 

the western United States: Science, v. 319, p. 1080–1083.

Bisson, P.A., Rieman, B.E., Luce, Charlie, Hessburg, P.F., Lee, 

D.C., Kershner, J.L., Reeves, G.H., and Gresswell, R.E., 

2003, Fire and aquatic ecosystems of the western USA—

Current knowledge and key questions: Forest Ecology and 

Management, v. 178, p. 213–229.

Brinson, M.M., and Eckles, S.D., 2011, U.S. Department 

of Agriculture conservation program and practice effects 

on wetland ecosystem services—A synthesis: Ecological 

Applications, v. 21, p. S116–S127.

Crifasi, R.R., 2005, Reflections on a stock pond—Are anthropogenically derived freshwater ecosystems natural, artificial, or something else?: Environmental Management, v. 36, 

p. 625–639.

Culver, D.R., 2014, Common wetland plants of Colorado’s 

eastern plains—A pocket guide: U.S. Environmental Protection 

Agency, prepared by Colorado Natural Heritage Program, 

Fort Collins, Colo., 154 p., accessed June 2017 at 

http://www.cnhp.colostate.edu/cwic/ident/fieldGuideEP.aspx.

Dwire, K.A., and Kauffman, J.B., 2003, Fire and riparian ecosystems in landscapes of the western USA: Forest Ecology 

and Management, v. 178, p. 61–74.

Friedman, J.M., Osterkamp, W.R., Scott, M.L., and Auble, 

G.T., 1998, Downstream effects of dams—Regional patterns in the Great Plains: Wetlands, v. 18, p. 619–633.

Friedman, J.M., Scott, M.L., and Auble, G.T., 1997, Water 

management and cottonwood forest dynamics along prairie 

streams, in Knopf, F.L., and Samson, F.B., eds., Ecology 

and conservation of Great Plains vertebrates: New York, 

Springer, p. 49–71.

Gregory, S.V., Swanson, F.J., McKee, W.A., and Cummins, 

K.W., 1991, An ecosystem perspective of riparian zones: 

BioScience, v. 41, p. 540–551.

Haukos, D.A., and Smith, L.M., 2003, Past and future impacts 

of wetland regulation on playa ecology in the Southern 

Great Plains: Wetlands, v. 23, p. 577–589.

Matthews, J.H., 2008, Anthropogenic climate change in the 

Playa Lakes Joint Venture Region—Understanding impacts, 

discerning trends, and developing responses: Playa Lakes 

Joint Venture, prepared by World Wildlife Fund, Corvallis, 

Oreg., 40 p., accessed February 2014 at http://www.pljv.org/

documents/science/PLJV_climate_change_review.pdf.

Merritt, D.M., and Poff, N.L., 2010, Shifting dominance of 

riparian Populus and Tamarix along gradients of flow alteration in western North American rivers: Ecological Applications, v. 20, p. 135–152.

Mitsch, W.J., and Gosselink, J.G., 2000, Wetlands (3d ed.): 

New York, John Wiley and Sons, 938 p.

Naiman, R.J., and Decamps, Henri, 1997, The ecology of 

interfaces—Riparian zones: Annual Review of Ecology and 

Systematics, v. 28, p. 621–658.
Chapter 8. Riparian and Nonplaya Wetlands 81

Poff, Boris, Koestner, K.A., Neary, D.G., and Henderson, Victoria, 2011, Threats to riparian ecosystems in western North 

America—An analysis of existing literature: Journal of the 

American Water Resources Association, v. 47, p. 1241–1254.

Polley, H.W., Briske, D.D., Morgan, J.A., Wolter, Klaus, Bailey, 

D.W., and Brown, J.R., 2013, Climate change and North 

American rangelands—Trends, projections, and implications: 

Rangeland Ecology and Management, v. 66, p. 493–511.

Smith, L.M., Euliss, N.H., Jr., Wilcox, D.A., and Brinson, 

M.M., 2008, Application of a geomorphic and temporal perspective to wetland management in North America: Wetlands, 

v. 28, p. 563–577.

Uowolo, A.L., Binkley, Dan, and Adair, E.C., 2005, Plant 

diversity in riparian forests in northwest Colorado—Effects 

of time and river regulation: Forest Ecology and Management, v. 218, p. 107–114.

Volke, M.A., Scott, M.L., Carter Johnson, W., and Dixon, 

M.D., 2015, The ecological significance of emerging deltas 

in regulated rivers: Bioscience, v. 65, p. 598–611.

Wiener, J.D., Dwire, K.A., Skagen, S.K., Crifasi, R.R., and 

Yates, David, 2008, Riparian ecosystem consequences of 

water redistribution along the Colorado Front Range: Water 

Resources Impact, v. 10, p. 18–21.

Chapter 9. Playa Wetlands and Saline Lakes

Introduction

In the Southern Great Plains (SGP), playa wetlands 

(hereafter referred to as playas) and saline lakes are isolated, 

depressional wetlands (Tiner, 2003). Playas are small, shallow, 

freshwater recharge wetlands located at the low points of small 

watersheds in relatively level terrain. They fill during heavy 

storms, which occur erratically. An estimated 66,000 to 80,000 

playas are largely distributed in association with the Ogallala 

aquifer (Gurdak and Roe, 2009; Playa Lakes Joint Venture, 

2014). Playa density and size increase from southwest to 

northeast along a gradient of increasing average precipitation, 

although soil texture also influences playa density (Smith, 

2003). Saline lakes, which are typically larger and deeper than 

playas, are discharge wetlands in eroded terrain or the low 

points of paleolakes. Saline lakes occur primarily in northwestern Texas and northeastern to east-central New Mexico 

(Hall, 2001; Allen, 2005). Playas and saline lakes provide 

crucial wildlife habitats, especially for migratory waterbirds 

(Haukos and Smith, 1994; Andrei and others, 2008). Snowy 

plover, long-billed curlew, interior least tern, and bats are 

species assessed by the SGP Rapid Ecoregional Assessment 

that use playas or saline lakes.

Much is unknown about the processes that historically influenced playa and saline lake communities, but they 

included fire, herbivory, and drought (Smith, 2003). Although 

characterized by frequent, low-intensity fires, more intense 

fires may have occurred after pulses of vegetative growth 

followed by severe drought (Samson and Knopf, 1996; 

Smith, 2003). Since European settlement, human influences 

including fire suppression, grazing practices, invasive species, agricultural conversion, and groundwater withdrawal 

in surrounding watersheds have contributed to habitat fragmentation, altered hydrology, and increased accumulated 

sediments in basins (Smith, 2003; Burris and Skagen, 2013). 

Land uses surrounding playas, particularly cultivation of corn 

and wheat, can reduce inundation frequency, increase sedimentation, and alter hydroperiods compared to playas located 

in grasslands or pasture (Tsai and others, 2007; Collins and 

others, 2014). Intensive energy development, urbanization, 

and livestock may result in nonpoint-source contamination 

(Irwin and others, 1996). Transmission lines, roads, and wind 

energy development can fragment wetland complexes and can 

create collision hazards for bats and birds that may congregate at playas and lakes (Stewart and others, 2005; Horn and 

others, 2008). Invasive plant species and diseases promoted 

by altered hydrological regimes and contamination further 

alter and degrade ecological processes and wildlife habitats 

(Friend, 1982; Smith, 2003). Additional background information can be found in the SGP pre-assessment report (Assal and 

others, 2015).

Rapid Ecoregional Assessment 

Components Evaluated for Playa 

Wetlands and Saline Lakes

The key ecological attributes and change agents 

addressed by core management questions for playas and 

saline lakes include amount and distribution, landscape structure (patch size), and development (tables 9−1 and 9−2). Fire 

occurrence, potentially altered vegetation (including invasive 

herbaceous plants), and climate change were evaluated for 

the entire SGP (see chapter 3, “Change Agents”). Overall landscape-level variables are summarized in table 9−3. 

The core and integrated management questions are listed in 

table 9−4.

Playa wetland, eastern Colorado. Photograph by Anne Bartuszevige, 

Playa Lakes Joint Venture (used with permission).
84 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Landscapelevel variables1 Description Relative rank2

Lowest Medium Highest

Area Area of baseline playas and saline lakes as a percentage of fifth-level watershed area 0–<0.07 0.07–<0.36 ≥0.36

Development Mean local aquatic development index score for baseline playas and saline lakes, 

summarized by fifth-level watershed

0–20 >20–40 >40

1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

2

Ranking breakpoints for area of playa wetlands and saline lakes were determined from equal subsets of the data. Ranking breakpoints for the aquatic development 

index were standardized for all aquatic conservation elements.

Core management questions1 Results

What are the baseline distributions of playas and saline lakes? Figures 9−1 to 9−3

Where does existing development pose the greatest threat to playas and saline lakes, and where are the large, relatively 

undeveloped areas?

Figures 9−4 to 9−6

How has development fragmented playas and saline lakes? Figures 9−7

Integrated management question2 Results

Where are the watersheds with the highest overall landscape-level rank for playas and saline lakes? Figure 9−8 1

See chapter 11, “Data Gaps, Limitations, and Uncertainty,” for management questions that could not be addressed.

2

See table 9−3.

Table 9–3. Landscape-level variables used to address the integrated management question for playa wetlands and saline lakes. Ranks 

for landscape-level area and development were combined into an overall landscape-level rank for the Southern Great Plains Rapid 

Ecoregional Assessment.

[<, less than; >, greater than]

Table 9–4. Management questions addressed for playa wetlands and saline lakes for the Southern Great Plains Rapid Ecoregional 

Assessment.

Change agents Variables Indicators1

Development Local aquatic development index (ADI) Percentage of playas and saline lakes in seven development classes2

Index of fragmentation Patch sizes for relatively undeveloped3 playas and saline lakes 1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

2

Summarized by sixth-level watershed.

3

Relatively undeveloped: local aquatic development index less than or equal to 20.

Table 9–2. Anthropogenic change agents and associated indicators used to address core management questions for playa wetlands 

and saline lakes for the Southern Great Plains Rapid Ecoregional Assessment.

Attributes Variables Indicators1

Amount and distribution Total area Baseline distributions2 of playas and saline lakes; and area of playas as a percentage 

of sixth-level watershed areas

Landscape structure Patch size Patch sizes for baseline playas and saline lakes

Landscape dynamics Fire occurrence See chapter 3, “Change Agents” 1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

2

Baseline distribution is determined by the most current regional data available.

Table 9–1. Key ecological attributes and associated indicators used to address core management questions for playa wetlands and 

saline lakes for the Southern Great Plains Rapid Ecoregional Assessment.
Chapter 9. Playa Wetlands and Saline Lakes 85

Management Questions and Results

What are the baseline distributions of playa wetlands and saline lakes (fig. 9–1 to 9–3)?

Figure 9–1. Baseline distribution of playa wetlands in the Southern Great Plains. Data on the occurrence of playa wetlands 

in Wyoming were not available.
86 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Figure 9–2. Baseline distribution of saline lakes in the Southern Great Plains.
Chapter 9. Playa Wetlands and Saline Lakes 87

Figure 9–3. Area of baseline playa wetlands as a percentage of sixth-level watershed in the Southern Great Plains. Data 

on the occurrence of playa wetlands in Wyoming were not available.
88 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Where does existing development pose the greatest threat to playa wetlands and saline lakes, and where are the large, relatively 

undeveloped areas (figs. 9–4 to 9–6)?

Figure 9–4. Local aquatic development index for baseline playa wetlands, summarized by sixth-level watershed, in the 

Southern Great Plains. Data on the occurrence of playa wetlands in Wyoming were not available.
Chapter 9. Playa Wetlands and Saline Lakes 89

Figure 9–5. Local aquatic development index for baseline saline lakes, summarized by sixth-level watershed, in the Southern 

Great Plains.
90 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Figure 9–6. Area of (A) playa wetlands and (B) saline lakes by local aquatic development index class in the 

Southern Great Plains. Data on the occurrence of playa wetlands in Wyoming were not available.
Chapter 9. Playa Wetlands and Saline Lakes 91

How has development fragmented playa wetlands and saline lakes (fig. 9–7)?

Figure 9–7. Area of (A) playa wetlands and (B) saline lakes in the Southern Great Plains as a function of patch size for 

baseline and relatively undeveloped conditions (local aquatic development index score ≤20). Data on the occurrence of 

playa wetlands in Wyoming were not available.
92 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Where are the watersheds with the highest overall landscape-level rank for playa wetlands and saline lakes (fig. 9–8)?

Figure 9–8. Landscape-level summaries for playa wetlands and saline lakes in the Southern Great Plains. Overall 

landscape-level rank (C) is derived from (A) landscape-level area and (B) landscape-level development, summarized by 

fifth-level watershed (see table 9–3). Highest overall landscape-level rank corresponds to the largest landscape-level 

area and the lowest landscape-level development. Lowest overall landscape-level rank corresponds to the smallest 

landscape-level area and highest landscape-level development. Landscape-level ranks are not intended as standalone 

summaries and are best interpreted in conjunction with the geospatial datasets used to address core management 

questions. Data on the occurrence of playa wetlands in Wyoming were not available.
Chapter 9. Playa Wetlands and Saline Lakes 93

Summary

• There are approximately 97,000 mapped playas in the 

SGP, distributed from the Nebraska Rainwater Basin 

through the Texas panhandle (figs. 9−1 and 9−3). Total 

surface area is approximately 3,314 square kilometers 

(km2; 1,279 square miles [mi2]) or 0.3 percent of the 

SGP. There are 105 mapped saline lakes in the SGP (51 

of which are named), primarily in the Texas panhandle 

and adjacent portions of New Mexico (fig. 9−2). The 

number of saline lakes depends on whether adjacent 

waterbodies separated by roads, dikes, or other barriers 

are considered a single lake or separate lakes. Because 

we lack information on the hydrological connectivity 

among adjacent saline lakes, we retained each lake as 

a separate polygon. Total surface area is approximately 

126 km2 (48 mi2).

• Only 3.4 percent of playas are relatively undeveloped 

(local aquatic development index [local ADI] score 

≤20), and 70 percent of playas have experienced moderate to high levels of development (local ADI score 

>40) (fig. 9−6A). Most relatively undeveloped playas 

occur in shortgrass prairie (fig. 9−4).

• Only 9 percent of saline lakes are relatively undeveloped, and 48 percent of saline lakes have experienced 

moderate to high levels of development (fig. 9−6B).

• The average area of playas is 0.03 km2 (0.01 mi2), and 

the largest playa is 9.5 km2 (3.7 mi2) (fig. 9−7). The 

average area of saline lakes is 1.2 km2 (0.5 mi2) and the 

largest is 22.5 km2 (8.7 mi2).

• Because we lack information on the historical distribution of playas and saline lakes across the entire SGP, 

we could not evaluate how development has decreased 

their occurrence. In addition, because many playas are 

located in agricultural settings, local information is 

needed to evaluate the ecological condition of playas.

• Playas in watersheds with high overall landscapelevel rank occurred in the Rainwater Basin, eastern 

portions of Colorado and New Mexico, and the Texas 

Panhandle. This area has some of the highest concentrations of playas in the ecoregion despite limited 

surface water (fig. 9−8). There were no watersheds 

with both the highest landscape-level area and the 

lowest landscape-level development; consequently, 

there were no watersheds in the highest overall 

landscape-level rank.

References Cited

Allen, B.L., 2005, New Mexico’s ice ages: New Mexico 

Museum of Natural History and Science Bulletin, v. 28, 

p. 107–114.

Andrei, A.E., Smith, L.E., Haukos, D.A., and Surles, J.G., 

2008, Habitat use by migrant shorebirds in saline lakes of 

the Southern Great Plains: Journal of Wildlife Management, 

v. 72, no. 1, p. 246–253.

Assal, T.J., Melcher, C.P., and Carr, N.B., eds., 2015, Southern 

Great Plains Rapid Ecoregional Assessment—Pre-assessment 

report: U.S. Geological Survey Open-File Report 2015−1003, 

284 p., accessed September 2015 at https://doi.org/10.3133/

ofr20151003.

Burris, Lucy, and Skagen, S.K., 2013, Modeling sediment 

accumulation in North American playa wetlands in response 

to climate change, 1940–2100: Climatic Change, v. 117, 

no. 1–2, p. 69–83.

Collins, S.D., Heinntzman, L.J., Starr, S.M., Wright, C.K., 

Henebry, G.M., and McIntyre, N.E., 2014, Hydrological 

dynamics of temporary wetlands in the Southern Great 

Plains as a function of surrounding land use: Journal of 

Arid Environments, v. 109, p. 6–14.

Friend, Milton, 1982, Playas and wildlife disease: Madison, 

Wis., U.S. Fish and Wildlife Service, 93 p.

Gurdak, J.J., and Roe, C.D., 2009, Recharge rates and 

chemistry beneath playas of the High Plains aquifer—A 

literature review and synthesis: U.S. Geological Survey 

Circular 1333, 39 p., accessed September 30, 2013, at 

http://pubs.usgs.gov/circ/1333/.

Hall, S.A., 2001, Geochronology and paleoenvironments of 

the glacial-age Tahoka Formation, Texas and New Mexico: 

New Mexico Geology, v. 23, no. 3, p. 71–77.

Haukos, D.A., and Smith, L.M., 1994, Importance of playa 

wetlands to biodiversity of the southern High Plains: 

Landscape and Urban Planning, v. 28, p. 83–98.

Horn, J.W., Arnett, E.B., and Kunz, T.H., 2008, Behavioral 

responses of bats to operating wind turbines: Journal of 

Wildlife Management, v. 72, no. 1, p. 123–132.

Irwin, R.J.; Connor, P.J.; Baker, Denise; Dodson, Susan; 

and Littlefield, C.D., 1996, Playa lakes of the Texas High 

Plains—A contaminants survey and assessment of biological integrity: Arlington, Tex., U.S. Fish and Wildlife 

Service, Ecological Services Field Office, 94 p. [Also available at http://www.fws.gov/southwest/es/arlingtontexas/pdf/

Playa_Lakes_1996.pdf.]
94 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Playa Lakes Joint Venture, 2014, Maps of probable playa 

locations: Playa Lakes Joint Venture, accessed November 2013 at http://pljv.org/for-habitat-partners/mapsand-data/maps-of-probable-playas/ [shapefile] and http://

www.pljv.org/PPv4_MapBook/PPv4_release_notes.pdf

[release notes].

Samson, F.B., and Knopf, F.L., eds., 1996, Prairie conservation—Preserving North America’s most endangered ecosystem: Washington, D.C., Island Press, 339 p.

Smith, L.M., 2003, Playas of the Great Plains: Austin, Tex., 

University of Texas Press, 257 p.

Stewart, G.B., Pullin, A.S., and Coles, C.F., 2005, Effects of wind 

turbines on bird abundance: Collaboration for Environmental 

Evidence, CEE review 04–002 (SR4), accessed November 2013 at http://www.environmentalevidence.org/SR4.html.

Tiner, R.W., 2003, Geographically isolated wetlands of the 

United States: Wetlands, v. 23, no. 3, p. 494–516.

Tsai, J.-T., Venne, L.S., McMurry, S.T., and Smith, L.M., 

2007, Influences of land use and wetland characteristics on 

water loss rates and hydroperiods of playas in the southern 

High Plains, USA: Wetlands, v. 27, p. 683–692.
Chapter 10. Prairie Streams and Rivers

Introduction

Prairie streams and rivers of the Southern Great Plains 

(SGP) vary widely in geomorphology, streamflow pattern, and 

biota (Poff and Ward, 1989; Dodds and others, 2004; Costigan, 

2013). Historically, prairie streams were dynamic and alternated between flooding and drying. Intermittent streamflow 

is characteristic of many streams throughout the SGP (Dodds 

and others, 2004). Perennial prairie streams in the SGP include 

rivers, which are simply large streams. Streamflow dynamics are influenced by snowmelt in the west and by convective 

storms in the central and eastern regions. Climatic dynamics 

result in high variation and low predictability of streamflow 

(Poff and Ward, 1989). Many aquatic species are adapted 

to the intermittent nature of prairie streams and can rapidly 

recolonize previously desiccated reaches. The rate of recovery from desiccation is influenced by the distance to source 

population and the disturbance intensity and frequency (Dodds 

and others, 2004).

Primary anthropogenic change agents affecting prairie 

streams include agriculture, energy development, and urbanization (Dodds and others, 2004). Hydrological regimes are altered 

by dams, water diversions, and pumping groundwater to the 

surface, which can fragment streams and alter the persistence of 

perennial-pool refugia and underlying aquifers (Falke and others, 2011; Costigan and Daniels, 2012). Erosion from surrounding croplands, intensive livestock grazing along embankments, 

and channelization of streams and rivers can degrade water 

quality through sedimentation and contamination. Energy 

development can lead to depletion and contamination of surface 

water and groundwater, particularly where hydraulic fracturing 

is prevalent. Transportation corridors and road crossings can 

also fragment streams and rivers. Although the historical role 

of fire in the SGP is poorly understood, active fire suppression 

and intensive grazing can diminish fire frequency, which can 

increase fire severity and concomitant sedimentation of streams. 

Invasive species that can affect stream hydrology and function 

include woody riparian species and the escape of nonnative 

fishes from artificial impoundments into streams (Schrank and 

others, 2001; Falke and Gido, 2006). Finally, changing climatic 

conditions could affect the dynamics and function of prairie 

streams (Falke and others, 2011).

The cumulative effects of natural and anthropogenic 

disturbances can decrease habitat for aquatic species, isolate 

populations, and eliminate refugia. For example, large-river 

minnow species such as the Arkansas River shiner are very 

sensitive to altered flows and habitat fragmentation (Luttrell and 

others, 1999; Perkin and others, 2010; Perkin and Gido, 2012). 

In addition, functional connectivity for aquatic species (in the 

sense of Perkin and Guido, 2012) can be decreased by poorly 

designed or inadequately maintained road and railroad stream 

crossings (Warren and Pardew, 1998; Ottburg and Blank, 2015). 

Additional background information can be found in the SGP 

pre-assessment report (Assal and others, 2015).

Rapid Ecoregional Assessment 

Components Evaluated for Prairie 

Streams and Rivers

The key ecological attributes and change agents addressed 

by core management questions for prairie streams and rivers 

include amount and distribution, landscape structure (patch 

size, connectivity), and development (tables 10−1 and 10−2). 

Fire occurrence and climate change were evaluated for the 

entire SGP (see chapter 3, “Change Agents”), and invasive 

woody species were evaluated for riparian areas (see chapter 8, 

“Riparian and Nonplaya Wetlands”). Overall landscape-level 

ranking variables are summarized in table 10−3. The core and 

integrated management questions are listed in table 10−4.

Platte River, Nebraska. Photograph by Larry Crist, U.S. Fish and 

Wildlife Service (Creative Commons Attribution 2.0 Generic).
96 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Change agents Variables Indicators1

Development Aquatic development index (ADI) ADI scores for streams

Index of fragmentation Segment length of perennial streams partitioned by dams that are relatively undeveloped2

Barriers affecting patch size and 

structural connectivity

Number of dams and potential barriers (points of diversion and road and railroad 

stream crossings)in perennial streams, summarized by sixth-level watershed

Fire Fire occurrence See chapter 3, “Change Agents”

Invasive species Occurrence and potential for woody 

species expansion

See chapter 8, “Riparian Areas and Nonplaya Wetlands”

1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

2

Relatively undeveloped: aquatic development index less than or equal to 20.

Landscapelevel variables1 Description Relative rank2

Lowest Medium Highest

Density3 Ratio of perennial stream length (kilometers) to watershed area 

(square kilometers), by fifth-level watershed

0.004–<0.391 0.391−<0.662 ≥0.662

Development Mean aquatic development index score for perennial and intermittent 

streams, summarized by fifth-level watershed

0–20 >20−40 >40

1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

2

Ranking breakpoints for landscape-level densities were determined from equal subsets of the data. Ranking breakpoints for the aquatic development index 

were standardized for all aquatic conservation elements.

3

Density refers to drainage density of streams (Horton, 1932).

Core management questions1 Results

What is the current distribution of prairie streams and rivers? Figures 10−1 to 10−3

Where does existing development pose the greatest threat to prairie streams and rivers, and where are the large, relatively 

undeveloped areas?

Figures 10−4 to 10−6

How has development fragmented prairie streams and rivers, and decreased structural connectivity? Figures 10−7 and 10−8

Integrated management question2 Results

Where are the watersheds with the highest overall landscape-level rank for prairie streams and rivers? Figure 10−9 1

See chapter 11, “Data Gaps, Limitations, and Uncertainty,” for management questions that could not be addressed.

2

See table 10−3.

Table 10–2. Anthropogenic change agents and associated indicators used to address core management questions for prairie streams 

and rivers for the Southern Great Plains Rapid Ecoregional Assessment.

Table 10–3. Landscape-level variables used to address the integrated management question for prairie streams and rivers. Ranks 

for landscape-level density and development were combined into an overall landscape-level rank for the Southern Great Plains Rapid 

Ecoregional Assessment.

[<, less than; >, greater than]

Table 10–4. Management questions addressed for prairie streams and rivers for the Southern Great Plains Rapid Ecoregional 

Assessment.

Attributes Variables Indicators1

Amount and distribution Total length Baseline distribution2 and total length of streams3

Landscape structure Patch size Segment length of baseline perennial stream segments3

Landscape dynamics Fire occurrence See chapter 3, “Change Agents” 1

See chapter 2, “Methods Overview,” and appendix A for methods and datasets used.

2

Baseline distribution is determined by the most current regional data available.

3

Summarized by sixth-level watershed.

Table 10–1. Key ecological attributes and associated indicators used to address core management questions for prairie streams and 

rivers for the Southern Great Plains Rapid Ecoregional Assessment.
Chapter 10. Prairie Streams and Rivers 97

Figure 10–1. Baseline distribution of prairie streams and rivers by hydroperiod (perennial or intermittent) in the Southern 

Great Plains.

Management Questions and Results

What is the current distribution of prairie streams and rivers (figs. 10–1 to 10–3)?
98 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Figure 10–2. Total length of perennial streams, by sixth-level watershed, in the Southern Great Plains.
Chapter 10. Prairie Streams and Rivers 99

Figure 10–3. Total length of intermittent streams, by sixth-level watershed, in the Southern Great Plains.
100 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Where does existing development pose the greatest threat to prairie streams and rivers, and where are the large, relatively undeveloped areas (figs. 10–4 to 10–6)?

Figure 10–4. Aquatic development index for baseline perennial streams in the Southern Great Plains.
Chapter 10. Prairie Streams and Rivers 101

Figure 10–5. Aquatic development index for baseline intermittent streams in the Southern Great Plains.
102 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Figure 10–6. Length of prairie streams and rivers by aquatic development index class in the Southern Great Plains. 

A, Perennial streams. B, Intermittent streams.
Chapter 10. Prairie Streams and Rivers 103

How has development fragmented prairie streams and rivers, and decreased structural connectivity (figs. 10–7 and 10–8)?

Figure 10–7. Total length of perennial streams in the Southern Great Plains as a function of stream-segment size 

class for baseline and relatively undeveloped conditions (aquatic development index score ≤20).
104 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Figure 10–8. Potential barriers (combined dams, diversions, and road and railroad stream crossings) in perennial streams, 

summarized by sixth-level watershed, in the Southern Great Plains.
Chapter 10. Prairie Streams and Rivers 105

Figure 10–9. Landscape-level summaries for prairie streams and rivers in the Southern Great Plains. Landscape-level 

rank (C ) is derived from (A) landscape-level density and (B) landscape-level development, summarized by fifth-level 

watershed (table 10−3). Highest overall landscape-level rank corresponds to the highest landscape-level density and the 

lowest landscape-level development. Lowest overall landscape-level rank corresponds to the lowest landscape-level 

density and highest development levels. Landscape-level ranks are not intended as standalone summaries and are best 

interpreted in conjunction with the geospatial datasets used to address core management questions.

Where are the watersheds with the highest overall landscape-level rank for prairie streams and rivers (fig. 10–9)?
106 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Summary

• In the SGP, 85 percent of prairie streams are intermittent. Perennial streams are concentrated in the narrow 

band of western uplands and throughout the eastern 

portions of the SGP (figs. 10−1 to 10−3). Intermittent 

streams reach their highest densities in the eastern 

portion of the SGP, but they are also common in the 

western and southeastern portions.

• Only 3.5 percent of perennial streams are relatively 

undeveloped (aquatic development index [ADI] score 

≤20), whereas 31 percent of intermittent streams are 

relatively undeveloped (figs. 10−4 to 10−6). More 

than 71 percent of perennial streams have experienced 

significant development (ADI score >50).

• Dams have fragmented perennial streams such that the 

longest perennial stream segment within a sixth-level 

watershed is 184 kilometers (km; 114 miles [mi]) and 

mean segment length is 17 km (10.5 mi) (figs. 10−7 

and 10−8). Flow has been altered by dams, diversions, 

and road and railroad stream crossings in 84 percent 

of sixth-level watersheds containing perennial streams 

(fig. 10−8).

• Stream segments under baseline conditions generally 

exceed 500 km (311 mi), but all of the relatively undeveloped streams are less than 500 km long (fig. 10−7).

• In general, fifth-level watersheds with the highest 

landscape-level density (fig. 10−9A) also have experienced high levels of development, leading to generally 

lower overall landscape-level rank for much of the central and southern portions of the ecoregion (fig. 10−9C).

References Cited

Assal, T.J., Melcher, C.P., and Carr, N.B., eds., 2015, Southern 

Great Plains Rapid Ecoregional Assessment—Pre-assessment 

report: U.S. Geological Survey Open-File Report 2015−1003, 

284 p., accessed September 2015 at https://doi.org/10.3133/

ofr20151003.

Costigan, K.H., 2013, Hydrology and geomorphology of 

select Great Plains rivers: Manhattan, Kans., Kansas State 

University, Ph.D. dissertation, 114 p.

Costigan, K.H., and Daniels, M.D., 2012, Damming the 

prairie—Human alteration of Great Plains river regimes: 

Journal of Hydrology, v. 444–445, p. 90–99.

Dodds, W.K., Gido, Keith, Whiles, M.R., Fritz, K.M., and 

Matthews, W.J., 2004, Life on the edge—The ecology of 

Great Plains prairie streams: BioScience, v. 54, p. 205–216.

Falke, J.A.; Fausch, K.D.; Magelky, Robin; Aldred, Angela; 

Durnford, D.S.; Riley, L.K.; and Oad, Ramchand, 2011, 

The role of groundwater pumping and drought in shaping ecological futures for stream fishes in a dryland river 

basin of the western Great Plains, USA: Ecohydrology, v. 4, 

p. 682–697.

Falke, J.A., and Gido, K.B., 2006, Effects of reservoir connectivity on stream fish assemblages in the Great Plains: 

Canadian Journal of Fisheries and Aquatic Sciences, v. 63, 

p. 480–493.

Horton, R.E., 1932, Drainage-basin characteristics: Transactions 

American Geophysical Union, v. 13, p. 350–361.

Luttrell, G.R., Echelle, A.A., Fisher, W.L., and Eisenhour, 

D.J., 1999, Declining status of two species of the 

Macrhybopsis aestivalis complex (Teleostei: Cyprinidae) in 

the Arkansas River Basin and related effects of reservoirs as 

barriers to dispersal: Copeia, v. 1999, no. 4, p. 981–989.

Ottberg, Fabrice, and Blank, Matt, 2015, Solutions to the 

impacts of roads and other barriers on fish and fish habitat, 

in van dee Ree, Rodney, Smith, D.J., and Grilo, Clara, eds., 

Handbook of road ecology: John Wiley & Sons, p. 364–372.

Perkin, J.S., and Gido, K.B., 2012, Fragmentation alters 

stream fish community structure in dendritic ecological 

networks: Ecological Applications, v. 22, p. 2176–2187.

Perkin, J.S., Gido, K.B., Johnson, Eric, and Tabor, V.M., 2010, 

Consequences of stream fragmentation and climate change 

for rare Great Plains Fishes: Great Plains Landscape Conservation Cooperative, prepared by Kansas State University, 

35 p., accessed February 18, 2014, at https://lccnetwork.org/

project/consequences-stream-fragmentation-and-climatechange-rare-great-plains-fishes.

Poff, N.L., and Ward, J.V., 1989, Implication of streamflow 

variability and predictability for lotic community structure—

A regional analysis of streamflow patterns: Canadian Journal 

of Fisheries and Aquatic Sciences, v. 46, p. 1805–1818.

Schrank, S.J., Guy, C.S., Whiles, M.R., and Brock, B.L., 2001, 

Influence of instream and landscape-level factors on the 

distribution of Topeka shiners Notropis topeka in Kansas 

streams: Copeia, v. 2001, no. 2, p. 413–421.

Warren, M.L., Jr., and Pardew, M.G., 1998, Road crossings as 

barriers to small-stream fish movement: Transactions of the 

American Fisheries Society, v. 127, p. 637–644.
Chapter 11. Data Gaps, Limitations, and Uncertainty

Data Gaps, Limitations, and Uncertainty

Overview

Rapid Ecoregional Assessments (REAs) summarize information at spatial extents that provide a broader context for local 

management decisions, but they often lack the spatial resolution that provides information on local ecological conditions. 

Regional datasets also have spatial inaccuracies that limit their 

usefulness at scales approaching the resolution of the datasets. 

For example, 30×30-meter-resolution vegetation maps are best 

used to summarize vegetation patterns at much larger spatial 

extents, such as 1 kilometer (0.6 mile) (LANDFIRE, 2012). 

More detailed information is often available locally that is not 

available for broad geographic extents, such as ecoregions. In 

addition, local datasets may be updated more frequently than 

regional datasets. Although local assessments can have greater 

accuracy and precision than REA datasets, they may not be at a 

sufficiently broad extent to capture landscape-level ecological 

attributes (such as landscape structure) and patterns necessary 

to evaluate cumulative effects. Because of these limitations, 

landscape- and local-level data can complement each other, and 

their limitations can be minimized by using both scales of data 

for informing planning and management decisions. The use of 

multiscale data necessitates an understanding of the limitations 

and assumptions inherent to each dataset scale.

The spatial extent of the datasets ideally covers the full 

spatial extent of the Southern Great Plains (SGP) to avoid 

introducing spatial bias created by combining datasets that 

may vary in methods, accuracy, and completeness. For example, some datasets were only available at a state level (such as 

water diversions), and differences across state lines may result 

from differences among source datasets.

REAs synthesize and summarize datasets to assess change 

agents and their potential effects on species habitats and ecological communities. However, we generally lack data on how 

most species respond to change agents along an intensity gradient. For example, many species have been documented to avoid 

high-intensity energy development, but the intensity level or 

threshold at which a species is no longer negatively affected by 

energy development is poorly understood for most species and 

varies among species. Consequently, the potential risks posed 

by change agents are best viewed along an intensity gradient, 

with greater confidence in the potential risk from change agents 

at the highest intensity levels (for example, terrestrial development index [TDI] scores >35 percent) and the lowest risks in 

relatively undeveloped areas (TDI scores ≤2 percent).

In this chapter, we summarize the data gaps, limitations, 

and uncertainty for source and derived datasets for the Southern Great Plains REA. We also summarize the general themes 

of management questions identified in the pre-assessment 

phase of this REA (Assal and others, 2015) that could not be 

addressed because of data limitations.

Change Agents

Development

The surface disturbance footprint used to generate the 

TDI was compiled from a variety of sources that vary in 

spatial accuracy (table A–1). Spatial inaccuracies in mapping 

the development variables (agricultural croplands, urban areas, 

roads, and energy and minerals) can affect the TDI score. At 

broad spatial extents, deriving an index from moving windows 

can minimize the effects of local inaccuracies by averaging 

the area of the surface disturbance footprint over larger scales 

(Theobald, 2007). National road datasets (table A–1) often 

lack the precision, accuracy, and detail of datasets available at 

smaller spatial extents, such as a digitized road layer developed at the state level (O’Donnell and others, 2014). 

The aquatic development index (ADI) uses catchmentscale mapping units from the National Hydrography Dataset 

Plus, version 2.1 (U.S. Environmental Protection Agency 

and U.S. Geological Survey, 2012), which is derived from a 

30×30-meter-resolution digital elevation model. In the relatively 

flat terrain prevalent across much of the Southern Great Plains 

(SGP), this resolution is insufficient to detect centimeter-scale 

variations in elevation that can affect hydrologic flow, resulting in poorly delineated catchments and catchments that span 

the boundaries of adjacent sixth-level watersheds. Improved 

elevation models (particularly based on lidar [light detection 

and ranging]) can help to improve catchment definitions. Many 

fine-scale inaccuracies are minimized by using ADI scores summarized by sixth- and fifth-level watersheds.

There were some limitations for the datasets used as input 

variables in the ADI (table A–1). Surface and ground diversion datasets were only available at a state level, which can 

introduce discrepancies across state boundaries. Most state 

datasets included information on water rights, but the status 

(for example, licensed, permitted, abandoned) was not consistently available, so we assumed that all water rights were 

active. Water diversion datasets generally lack information 

on size or water volumes diverted. Although many state-level 

datasets include precise location data (such as latitude and longitude coordinates), others are generalized to the Public Land 
108 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Survey System section (in those cases, all diversions were 

assumed to be located at the center of the section). In addition, 

the most recent available state-level datasets varied by year 

(2007–2016) and update frequency.

National-level datasets on the location of dams and road 

and railroad crossing were used. The datasets used to map 

the location of dams (table A–1) included dams ranging in 

storage area from 0 (or unrecorded) to 30,220,260,000 cubic 

meters (24,500,000 acre-feet). Although dam size can affect 

the degree of flow alteration, dams of any size can alter flow 

and restrict fish movement. In addition, off-stream dams can 

reduce overland flows from intermittent drainages. Road and 

railroad stream crossings also vary in their capacity to reduce 

fish movement (Ottberg and Blank, 2015). Consequently, 

we could not factor in the size, degree of flow alteration, or 

potential for inhibiting fish movement in the ADI. Despite 

such limitations, we found that the ADI is a relatively good 

predictor of invertebrate and sensitive fish diversity and can 

serve as a useful index for identifying relatively undeveloped 

aquatic systems (Carr and Melcher, 2015).

Fire

Prior to the 1980s, there was limited availability of 

regional fire occurrence data. In addition, there is incomplete 

understanding of historical fire regimes in the SGP because 

evidence of burning does not persist as long for grasslands and 

shrublands as it does for forests. The Monitoring Trends in 

Burn Severity perimeter dataset (table A–1) used for mapping 

fire occurrence is derived from satellite imagery and may not 

capture low-severity fires smaller than the minimum mapping 

unit (405 hectares; 1,000 acres) or burns that have had time 

to revegetate between the Landsat imaging events used to 

map burns.

Invasive Species

Survey data on nonnative and invasive species are 

lacking for many regions of the SGP. Survey data are often 

biased by proximity to roads. Because of the limited amount 

of occurrence data, we mapped potentially altered vegetation by using several LANDFIRE Existing Vegetation Types 

(EVTs) (table A–5) that may represent a continuum of altered 

vegetation ranging from highly altered urban landscaping to 

pasturelands that may be similar in vegetative composition to 

native grasslands.

Because LANDFIRE EVTs (table A–5) do not accurately 

map introduced woody riparian species (Carr and Melcher, 

2015), we used published occurrence data for these species 

(Jarnevich and others, 2011; Jarnevich and Reynolds, 2011) 

to model habitat suitability. Although this published dataset 

was the best available across the SGP, our modeled thresholds 

were statistically weak. As a consequence, the models may 

under- or over-represent suitable habitat for Russian olive and 

tamarisk. Spot inspection of aerial imagery indicated considerable variation in vegetative cover and potential misclassification of invasive species EVTs.

Climate Change

Projecting potential changes in temperature and 

precipitation poses many challenges, and there is considerable 

uncertainty in the projected results of climate change models. 

Available global climate models produce a broad range of 

potential climate scenarios, particularly for precipitation. The 

likelihood of any specific scenario occurring is unknown. 

Furthermore, the bioclimatic envelope models used to predict 

the potential distribution of species for different climate 

scenarios do not include biotic interactions, the effects of 

increasing CO2 on evapotranspiration, interactions with other 

change agents, and other factors that can influence where 

species may occur. However, evaluating the potential for change 

in several climate scenarios can help managers to consider areas 

of the SGP that may be most vulnerable to changing climates 

and to identify management actions that may enhance system 

resilience to climate change and other change agents.

Ecological Community Distributions

Grassland Communities

We conducted a preliminary review and analysis of the suitability of using existing landcover datasets to map the historical 

and baseline grassland communities for the REA project area 

(Reese and others, 2016). Because of the limitations in these 

datasets, the REA stakeholders suggested using datasets from 

the Natural Resources Conservation Service (NRCS) to map the 

potential historical and current distribution of native grasslands 

for the REA (Callan and others, 2016; Reese and others, 2016). 

The inaccuracies and spatial resolution of the NRCS datasets 

may influence the local-level accuracy of the estimated historical 

grasslands datasets, but the datasets nevertheless provide a basis 

for evaluating broad-scale changes in grassland distributions.

Riparian Areas and Nonplaya Wetlands

Available datasets for delineating riparian areas and 

nonplaya wetlands across the SGP were limited. Data from the 

National Wetlands Inventory (U.S. Fish and Wildlife Service) 

varied considerably in wetland density and completeness 

across state lines. We used LANDFIRE EVTs (table A–5) 

because they provide more consistency across the SGP, but 

they also contain many fine-scale errors in vegetation classification, particularly for introduced riparian vegetation. Regionwide information on the historical distribution of aquatic 

communities was not available for the SGP.
Chapter 11. Data Gaps, Limitations, and Uncertainty 109

Playa Wetlands and Saline Lakes

We used the most comprehensive playa datasets, which 

were compiled from several different sources (table A–4), 

but data were not available for Wyoming. Because playas 

modified by cultivation are generally not mapped, we were 

unable to distinguish between altered and functional playas. 

Field verification is the most effective method for establishing playa condition, but few of the roughly 90,000 recorded 

playas have been surveyed. The perimeters of both playas 

and saline lakes can vary between years, leading to variation among datasets due to timing of source imagery. In 

addition, both saline lakes and playa wetlands are sometimes divided by roads and other barriers, making counts 

potentially inconsistent. Because we were unable to assess 

hydrological connectivity, we delineated each saline lake 

divided by anthropogenic barriers as a separate waterbody, 

which may lead to higher counts compared to the number 

of named saline lakes.

Prairie Streams and Rivers

We used the National Hydrography Dataset to map prairie streams and rivers (table A–4). At 1:100,000 (medium) 

resolution, ephemeral streams were poorly mapped. Consequently, we have likely underestimated the total stream 

length for the SGP. Although altered flow (both annual flow 

and instream storage) is a crucial hydrological function, 

assessing flow alteration was beyond the scope of the REA, 

which relies on published models (such as Fore and others, 2014) that do not include altered flow. Annual flow was 

retained and included with the data delivery for possible 

future use.

Patch Size and Connectivity

Although landscapes are clearly patchy, defining a patch 

depends on the species or response variable in question, as 

well as the scale of analysis (Kotliar and Wiens, 1990). This 

is partly due to differences among species in their responses 

to spatial heterogeneity in the environment, which can vary 

across spatial and temporal scales. Similarly, functional connectivity refers to how landscape structure affects the movement of organisms and depends on a species’ response to 

structural connectivity of its habitat (Wiens, 2002). Although 

methods for evaluating functional connectivity are available 

(such as Compton and others, 2007; Beier and others, 2011; 

Cushman and others, 2013), the short time frame of the REA 

was not sufficient to develop functional connectivity models 

for each species.

The REA includes landscape-level analyses that reflect 

broad-scale structural connectivity. TDI and ADI maps display 

relatively undeveloped areas, which are assumed to promote 

organism movements, and relatively developed areas, which 

may inhibit movements. Patch-size and landscape-level area 

maps show the size and spatial patterning of patches; larger 

and clustered patches may facilitate movements. Smaller, 

dispersed patches may serve as stepping stones to enable 

movements across otherwise unsuitable areas. Collectively, 

development, patch size, landscape-level area, and overall 

landscape-level rank provide indexes of fragmentation and 

structural connectivity across a range of scales.

Management Questions Not Addressed

There were several general themes of management questions identified for the Southern Great Plains REA (Assal and 

others, 2015, table B–1) that could not be addressed because 

of data gaps or limitations.

Altered Fire Regimes

•	 What are the effects of fire on communities and wildlife habitats and forage?

•	 How have wildlife habitats and communities been 

degraded (presence of invasive woody species, loss 

of shinnery oak) because of improper fire management, including lack of fire?

•	 Where has ecological conversion of communities 

occurred as a result of fire exclusion?

•	 Where can prescribed fire be reintroduced to maintain 

shortgrass prairie?

•	 Where has erosion increased because of increased fire 

frequency or intensity?

Shrub Dynamics

•	 Where are shinnery oak stands and sand sagebrush 

altered by herbicide application and inappropriate 

grazing practices?

•	 Where does shinnery oak control overlap with the 

distribution of lesser prairie chicken?

•	 Where are monotypic stands of shinnery oak outside of 

the range of natural variation (altered composition of 

the grassland, forb, and shinnery oak mosaic)?

•	 Where are nonnative woody shrubs expanding into 

native shrublands?

•	 How can we manage eastern redcedar (mostly privately 

owned), mesquite, and one-seeded juniper (Juniperus 

monosperma)?
110 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Terrestrial Development

•	 How has development altered air quality?

•	 How has oil and gas development affected ferruginous 

hawk nesting?

Aquatic Development

•	 How does water withdrawal (including reduced flow, 

water quality, dewatering, impoundments, and seasonal dynamics) affect aquatic species (freshwater 

mussels, fish communities, and fish hosts)?

•	 How does altered flow regime, channelization, and 

inundation of habitat affect birds (especially the habitat for the interior least tern and its forage fish species 

and playa lakes for the snowy plover)?

•	 How does aquatic development affect water quality and 

salinity changes?

•	 How are successional changes in lakes and reservoirs 

(siltation) affecting habitat heterogeneity?

•	 How are aquatic communities (rivers, streams, riparian 

areas, nonplaya wetlands, playas, and saline lakes) 

being affected by water development?

•	 Where are toxic algae blooms occurring in lakes and 

reservoirs?

•	 How is water demand contributing to ecological 

changes in areas experiencing prolonged drought?

•	 What are the expected ecological communities after 

currently irrigated areas are abandoned because of 

aquifer depletion?

Invasive Species

•	 How are invasive species affecting populations and 

habitats of native species?

•	 What areas are potentially at risk from invasive species 

expansion?

•	 What is the distribution of invasive species?

Conservation Reserve Program

•	 What are the most beneficial native seed mixes for wildlife used on Conservation Reserve Program lands?

•	 What are the potential consequences of removing 

Conservation Reserve Program lands from the program, especially playa wetlands?

•	 What Conservation Reserve Program lands have high 

value for wildlife?

Climate Change

•	 What is the phenology of grasslands for changing 

climates?

•	 How could flooding or drying projected by climate 

change scenarios affect playa wetlands (including 

as habitat for conservation elements)?

•	 How will climate change affect livestock practices 

on the landscape?

•	 How will drought affect conservation elements?

•	 How will climate change affect water demand, and 

what are the concomitant effects on conservation 

elements?

•	 What are the potential hydrological changes related 

to and the effects of changing climates?

Poisoning/Herbicides/Pesticides

•	 How does poisoning intended for coyotes inadvertently 

affect swift fox?

•	 How is the dune sagebrush lizard affected by herbicides?

•	 How does pesticide use in agricultural croplands affect 

forage for wildlife?

•	 How does chemical contamination affect the interior 

least tern’s prey base and burrowing owls?

•	 How does control of prairie dogs affect ferruginous 

hawks?

Grazing

•	 What are the effects of grazing on shortgrass prairie, 

riparian areas, sand sagebrush, and shinnery oak?

•	 How does grazing affect wildlife species (especially 

the lesser prairie chicken)?

•	 What are the effects of the loss of native grazing species?

Recreation

•	 How are human activities, including recreation, affecting nesting birds?
Chapter 11. Data Gaps, Limitations, and Uncertainty 111

References Cited

Assal, T.J., Melcher, C.P., and Carr, N.B., eds., 2015, 

Southern Great Plains Rapid Ecoregional Assessment—

Pre-assessment report: U.S. Geological Survey Open-File 

Report 2015–1003, 284 p., accessed September 2015 at

https://doi.org/10.3133/ofr20151003.

Beier, Paul; Spencer, Wayne; Baldwin, R.F.; and McRae, B.H., 

2011, Toward best practices for developing regional connectivity maps: Conservation Biology, v. 25, no. 5, p. 879−892.

Callan, Ramana, Leinwand, I.I.F., Reese, G.C., Assal, T.J., 

Manier, D.J., Carr, N.B., Burris, Lucy, and Ignizio, D.A., 

2016, Estimated historical distribution of grassland communities of the Southern Great Plains: U.S. Geological 

Survey data release, accessed November 14, 2016, at

https://doi.org/10.5066/F71Z42J3.

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Rapid Ecoregional Assessment: U.S. Geological Survey 

Open-File Report 2015–1155, 896 p., accessed September 2015 at https://doi.org/10.3133/ofr20151155.

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Gamble, L.R., 2007, A resistant-kernel model of connectivity for amphibians that breed in vernal pools: Conservation 

Biology, v. 21, no. 3, p. 788−799.

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Conservation, v. 22, p. 2583−2605.

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predicting the potential distribution of a slow-spreading 

invader—A habitat suitability map for an invasive riparian 

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study of heterogeneity: Oikos, v. 59, p. 253−260.

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Appendix A. Methodological Details for Derived Datasets

Introduction

An overview of the assessment framework for the 

Southern Great Plains Rapid Ecoregional Assessment (REA) 

is provided in chapter 2, “Methods Overview.” This appendix 

provides additional details on the methods and source datasets (source datasets are listed in the “Data Sources” section 

of this appendix). All analyses were performed using ArcGIS 

ModelBuilder tools or Python scripts (Environmental Systems 

Research Institute, 2014; Python Software Foundation, 2014). 

Additional information for REAs, including metadata, as 

well as geographic information system tools and scripts, are 

served online at the BLM Landscape Approach Data Portal 

(https://landscape.blm.gov/geoportal/catalog/main/home.page).

Change Agents

Four change agents were evaluated for the Southern Great 

Plains REA: development, fire, invasive species, and climate 

change (see chapter 2 “Methods Overview”). The source datasets for all change agents are provided in table A−1.

Development

Terrestrial Development Index

The terrestrial development index (TDI) uses methods 

similar to those described in Carr and Melcher (2015) and 

Carr and others (2017). We quantified the surface disturbance 

footprint (hereafter referred to as footprint) from development 

(agricultural croplands, urban areas, roads, railroads, and energy 

and minerals) by using datasets listed in table A−1. All linear 

features (roads, railroads, pipelines, utility lines) and points (oil 

and gas wells, wind turbines) were buffered using the distances 

in Carr and others (2017). Buffered footprints and polygonal 

data (solar arrays, mines) were rasterized (converted to a grid) 

and assigned a value of 1. The 30×30-meter (m) footprint was 

created by summing all component datasets, and the maximum disturbance value of the composite data layer was set 

at 100 percent. The TDI score was derived by summing the 

footprint within a 2.5-kilometer-radius (1.6-mile-radius) moving 

window using the ArcGIS focal neighborhood statistics function 

(Environmental Systems Research Institute, 2014).

TDI scores range from 0 to 100 percent. For visualization 

and analysis purposes, we divided the TDI scores into seven 

classes by using 10 approximately equal subsets of the data. 

Because a primary objective of the REA is to identify relatively 

intact areas, we retained the four classes with the lowest scores 

and combined the remaining classes with the highest scores into 

three classes (fig. A–1). At 2.5 kilometers (km), TDI scores of 

<1 percent represent areas with very low levels of development 

(such as a few roads or oil and gas wells). Scores between 1 and 

5 percent often represent areas with low to medium development, including low densities of roads or medium densities of 

oil and gas wells (for example, 4–8 wells per section), whereas 

TDI scores >5 percent represent medium to high levels of 

development, including large, relatively high-density oil and 

gas fields, surface mines, agricultural fields, centers of urban 

development, and major highways (figs. 3–1 and A–1).

Aquatic Development Index

The aquatic development index (ADI) is modified from 

similar methods (Annis and others, 2010; Fore and others, 2014; 

Carr and Melcher, 2015). Table A–2 summarizes the variables 

and metrics used as indicators for ecological attributes associated 

with streams and rivers. Table A–1 summarizes the data sources 

for each variable. Variables and metrics can address more than 

one ecological attribute (table A–2). For example, structures that 

alter connectivity (roads, dams, and water diversions) can also 

alter flow or sedimentation regimes. We used the footprints from 

the terrestrial development variables to address multiple ecological attributes (table A–2). Stream crossings (roads and railroads),

water diversions, and dams were used as indicators of altered 

flow regimes, sedimentation, and connectivity.

ADI quantifies development at three hydrologically 

defined scales: (1) local catchment for a given stream segment, 

(2) upstream contributing area for the local catchment, and 

(3) the total drainage representing the combined local catchment 

and the full upstream contributing area (fig. A–2). We included 

the upstream contributing area for all catchments, including portions of river drainages that extended beyond the SGP boundary 

(specifically, the upper and lower Missouri River; the Arkansas, 

Red, and White Rivers; Texas rivers; and the Rio Grande). 

Catchments, flow lines for perennial and intermittent streams, and 

processing tools were obtained from the National Hydrography 

Dataset (NHD) Plus, Version 2.1 (U.S. Environmental Protection 

Agency and U.S. Geological Survey, 2012). We used the 

Catchment Attribute Allocation and Accumulation Tool, version 2 

(CA3TV2) to aggregate development variables in the total drainage for each local catchment. To obtain upstream values exclusive 

of the local catchment, we subtracted local catchment values 

from the total drainage. Development variables for nondraining 

areas (sinks), which were not addressed by CA3TV2, were also 

included. Very small catchments less than 3,600 square meters 

(38,750 square feet) were combined with the adjacent catchment with which they shared the longest boundary. Development 

variables (counts, lengths, and areas) for local, upstream, and total 

drainage were converted to densities (number of points, area, or 

length per catchment area).
114 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Change

agent Variable Data type Data sources1

Development Transportation Roads and railroads TIGER; U.S. Census Bureau (2010)

Energy Oil and gas wells IHS, Inc. (2014)

Solar arrays Surface area of solar arrays; Carr and others (2016)

Wind turbines Onshore industrial wind turbine locations; Diffendorfer and others (2015)

Oil and gas pipelines National Pipeline Mapping System; U.S. Department of Transportation (2014)

Utility and service lines TIGER; U.S. Census Bureau (2010)

Minerals Surface mines and quarries Level 3 data; National Gap Analysis Program (2010)

Agriculture Cultivated croplands Cultivated crop layer; National Agricultural Statistics Service (2014)

Urban Urban impervious surface National Land Cover Database; U.S. Geological Survey (2014)

Water Dams National Anthropogenic Barrier Dataset; Ostroff and others (2013)

National Inventory of Dams; U.S. Army Corps of Engineers (2015)

Surface and ground 

diversions

Colorado Division of Water Resources (2016a, b), Greenwood Mapping 

(2007), Kansas Geological Survey (2016a, b), Nebraska Department of 

Natural Resources (2016a, b), New Mexico Office of the State Engineer 

(2015), Oklahoma Water Resources Board (2016), South Dakota 

Department of Environment and Natural Resources (2016), Texas 

Commission on Environmental Quality (2015), Texas Water Development 

Board (2016), Wyoming State Engineer’s Office (2016)

Road and railroad 

crossings

TIGER; U.S. Census Bureau (2010)

National Hydrography Dataset Plus; U.S. Environmental Protection Agency 

and U.S. Geological Survey (2012)

Fire Fire occurrence Fire polygons Monitoring Trends in Burn Severity; U.S. Department of Agriculture and 

U.S. Geological Survey (2016)

Invasive species Mesquite Vegetation types Kansas Biological Survey (2016)

LANDFIRE Existing Vegetation Types; LANDFIRE (2012)

Eastern redcedar Occurrence Global Biodiversity Information Facility (2016), Kansas Biological Survey 

(2016), Natural Resources Conservation Service (2015), Texas Parks and 

Wildlife Department (2014)

Tamarisk Occurrence Jarnevich and others (2011)

Russian olive Occurrence Jarnevich and Reynolds (2011)

Potentially altered 

vegetation

Land cover types LANDFIRE Existing Vegetation Types; LANDFIRE (2012)

Climate change Temperature 

and precipitation

Historical reference period Maurer and others (2002)

Hot and dry scenario ACCESS1.0; Collier and Uhe (2012)

Warm and wet scenario CESM1(BGC); Neale and others (2012)

Community 

distributions

Bioclimatic envelope 

vegetation models

Rehfeldt and others (2012)

1

Full references for datasets are provided in “Data Sources” section.

Table A–1. Data sources used to quantify change agents for the Southern Great Plains Rapid Ecoregional Assessment. Variables and 

data types are provided.

[TIGER, Topologically Integrated Geographic Encoding and Referencing; ACCESS1.0, Australian Community Climate and Earth System Simulator; 

CESM1(BGC), Community Earth System Model, version 1–Biogeochemistry]
Appendix A. Methodological Details for Derived Datasets 115

Figure A–1. Terrestrial development index (TDI) score and the contributing footprint from development for two landscapes 

in the Southern Great Plains. Each location is depicted by a colored square on the inset map. The TDI, derived from the 

percent of the footprint within a 2.5-kilometer-radius (1.6-mile-radius) moving window (red dashed circles), is depicted. 

A, A mixed oil and gas field and agricultural landscape in northern Colorado. B, The edge of an agricultural landscape in 

northern New Mexico.

Variable Metric

Ecological attribute

Flow

regime

Sedimentation

regime

Riparian

zone

ConnectivityWater

quality

Surface disturbance Variables in the terrestrial development index (km2) per catchment 

area (km2)

X X X X

Road crossings Number of road and railroad crossings per catchment area (km2) X X X

Water use Number of dams per catchment area (km2) X X X X

Number of water diversions per catchment area (km2) X X

Table A–2. Relations between component variables and metrics for the overall aquatic development index and key ecological attributes 

for prairie streams and rivers.

[km2, square kilometers]
116 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

For each catchment, we calculated local, upstream, and 

overall ADI. The ADI combines the ranked and scaled variable 

scores for the development variables in table A–2. We did not 

weight factors because weighting has limited effects on the ADI 

score while introducing unnecessary assumptions (Paukert and 

others, 2011; Fore and others 2014). The local ADI was derived 

from the local catchment densities, whereas upstream ADI was 

derived from upstream contributing area densities (exclusive 

of the local contribution). The overall ADI by catchment was 

derived by using the total drainage densities for each catchment. 

The density values for each variable were ranked and rescaled 

from 0 to 100, derived from a script written in R (R Core Team, 

2016). The ranked and rescaled variables were summed by 

catchment and normalized from 0 to 100 at each scale, representing local ADI, upstream ADI, and overall ADI (figs. A–3, 3–5A,

A–4). The catchment-level ADI scores were aggregated by sixthlevel watershed (fig. 3–5B) for core management questions and 

by fifth-level watershed for integrated management questions.

Invasive Species

Honey Mesquite and Eastern Redcedar

We compiled occurrence data for mesquite and eastern 

redcedar (table A–1) and mapped occurrences by sixth-level 

watersheds (fig. 4–7). To map potential mesquite occurrence, 

we summarized the percent of cells (30×30-m resolution) 

in each watershed containing the LANDFIRE Existing 

Vegetation Type (EVT) that indicates the presence of mesquite 

(table A–5). To map eastern redcedar, we included all watersheds with eastern redcedar occurrences. We also included 

watersheds containing cells with >1 percent cover of honey 

mesquite and (or) eastern redcedar by using data from the 

Kansas Biological Survey (table A–1).

Russian Olive and Tamarisk

We used the same approach to model Russian olive and 

tamarisk habitat suitability as was used for the Wyoming Basin 

REA (Carr and Melcher, 2015) and the western United States 

(Jarnevich and others, 2011, Jarnevich and Reynolds, 2011, 

Morisette and others, 2013).

The nine bioclimatic variables and distance to water used 

for the distribution models, and the percent contribution to the 

model for each variable, are listed in table A–3. Bioclimatic 

variables were derived following O’Donnell and Ignizio (2012) 

by using Parameter-elevation Regressions on Independent Slopes 

Model (PRISM) climate averages for 1980–2009. To identify the 

threshold for classifying suitable habitat conditions, we identified sixth-level watersheds that contained either Russian olive or 

tamarisk, and the thresholds were established where the distribution departed from uniform; a probability threshold of 0.0862 was 

used for Russian olive and 0.25 for tamarisk (figs. 8–6 and 8–7).

Figure A–2. Diagram of the local and upstream contributing areas for a local catchment. Development variables 

were summarized for local, upstream, and total drainages, which were used to calculate the local, upstream, and 

overall aquatic development index.
Appendix A. Methodological Details for Derived Datasets 117

Figure A–3. Components of the aquatic development index (ADI) for the Southern Great Plains, summarized by catchment.

A, Local ADI. B, Upstream ADI.
118 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Ecological Communities

Terrestrial Community Baseline 

Distribution Mapping

The estimated historical grasslands map (Callan and others, 

2016; Reese and others, 2016), which compiled data from the 

Natural Resources Conservation Service (2015) and LANDFIRE 

Biophysical Settings (BpS) (LANDFIRE, 2010), was used as the 

foundation for mapping baseline grassland communities. Ordered 

processing steps for mapping current vegetation and baseline 

grassland distributions are included in the metadata associated 

with the Current Vegetation and Land Cover of the Southern 

Great Plains raster. We replaced the historical “shrubland, 

woodland, and forest” class with LANDFIRE EVT (table A–5) 

except those areas currently classified as ruderal, pastureland, 

developed, urban vegetation, or invasive herbaceous vegetation 

(fig. 3–10), where we retained the estimated historical class. To 

account for grasslands converted by development (for example, 

figs. 5–1, 6–1, 7–1), we overlaid the development footprint (see 

the “Terrestrial Development Index” section). Based on the 

development footprint, approximately 43 percent of historical 

grasslands has been converted. We also overlaid riparian and 

wetlands (fig. 8–1), open water, and “shrubland, woodland, and 

forest” (fig. 1–3) based on LANDFIRE EVT (table A–5) as well 

as other aquatic communities (figs. 9–1, 9–2, 10–1). Areas where 

the estimated historical distribution of “shrubland, woodland, and 

forest” is currently classified as grassland by LANDFIRE EVT 

were reclassified using the cover types listed in table A–5.

Aquatic Community Baseline 

Distribution Mapping

To map the distribution of riparian areas and nonplaya 

wetlands, we compiled LANDFIRE EVTs and the estimated 

historical distribution indicating mesic soils and riparian areas 

(Callan and others, 2016; Reese and others, 2016). Collectively, 

the two datasets best captured riparian areas and nonplaya wetlands visible in aerial imagery. We classified EVTs corresponding 

to bottomland, floodplain, marsh, meadow, ravine, riparian areas, 

swamp, or wetlands to map riparian and wetland areas as indicated in table A–5. Any areas coincident with playas, saline lakes, 

or the agricultural footprint were not included as riparian areas or 

nonplaya wetlands. For additional details see previous section.

We used the datasets in table A–4 to map the distribution of playa wetlands and saline lakes. Data from Texas 

Ecological Mapping Systems (Texas Parks and Wildlife 

Department, 2014) were used to map playas in Reagan and 

Upton Counties in south Texas. Published maps (Reeves and 

Reeves, 1996; Andrei and others, 2006) were used for name 

Figure A–4. Total area in aquatic development index (ADI) classes in the Southern Great Plains. A, Local 

ADI. B, Upstream ADI. The area of upstream catchments summarized in panel B covers 50 percent of the 

Southern Great Plains (see fig. A−3B).
Appendix A. Methodological Details for Derived Datasets 119

attribution and verification of saline lakes. Any playa wetlands 

intersecting saline lakes were excluded from the final playa 

wetland dataset. Playa wetland data were not available in 

Wyoming, so that area was excluded from analysis. 

The baseline distributions of prairie streams and rivers 

were derived from NHD flow lines (table A–4) and partitioned 

by intermittent and perennial hydroperiods. Intermittent streams 

were mapped by using intermittent and ephemeral flow lines. A 

preliminary perennial streams dataset was created from all flow 

lines with perennial or unassigned hydroperiod values. Connector 

and artificial path flow lines intersecting perennial streams were 

merged with the preliminary perennial streams. Pipelines, canals, 

and conduits were excluded from the stream datasets.

Landscape Structure

Patch Size

We summarized the area by patch size class (or stream 

fragment length) for all ecological communities for baseline 

and relatively undeveloped conditions. For all communities except prairie streams, we used the RegionGroup tool in 

ArcGIS (Environmental Systems Research Institute, 2014) 

with tool parameters of eight neighboring cells that used a 

search radius for cells of the same value. For prairie streams, 

we derived stream-segment length of perennial streams by 

using stream partitioning as indicated by the presence of dams. 

Model variables Percent contribution to the model1

Russian olive Tamarisk

Mean diurnal range (mean of monthly [max temp – min temp]) 7.158 5.537

Temperature seasonality 3.991 4.841

Maximum temperature of warmest month 32.472 27.195

Minimum temperature of coldest month 34.334 38.378

Mean temperature of wettest quarter 9.325 7.366

Precipitation of driest month 2.217 0.207

Precipitation seasonality (coefficient of variation) Not applicable 0.109

Precipitation of warmest quarter 5.347 13.244

Precipitation of coldest quarter 1.292 0.037

Distance to water 3.864 3.085 1

Calculated by permuting the values at presence and background locations and calculating the change in the area under a receiver operating characteristic curve.

Appendix A–3. Variables used in the Russian olive and tamarisk distribution models, including values for their percent contribution to 

the model based on climate data for 1980−2009.

[max temp, maximum temperature; min temp, minimum temperature]

Ecological community 

and other cover types

Distribution

type Data sources

Terrestrial communities Historical Callan and others (2016)

Baseline1 Callan and others (2016)

LANDFIRE Existing Vegetation Types; LANDFIRE (2012)

LANDFIRE Biophysical Settings; LANDFIRE (2010)

 Riparian and 

nonplaya wetlands

Baseline Callan and others (2016)

LANDFIRE Existing Vegetation Types; LANDFIRE (2012)

Playa wetlands and 

saline lakes

Baseline NHDPlus—water bodies; U.S. Environmental Protection Agency and U.S. Geological Survey (2012)

Playa Lakes and Rainwater Basin Joint Ventures (2016), Playa Lakes Joint Venture (2014), 

Texas Parks and Wildlife Department (2014), Texas Tech University Center for Geospatial 

Technology (2014)

Prairie rivers and stream Baseline NHDPlus—flow lines; U.S. Environmental Protection Agency and U.S. Geological Survey (2012)

Open water Baseline LANDFIRE Existing Vegetation Types; LANDFIRE (2012)

Potentially altered vegetation Baseline LANDFIRE Existing Vegetation Types; LANDFIRE (2012)

1

Datasets developed for the Southern Great Plains were also used in mapping baseline grassland communities, including all aquatic ecological communities 

and the footprint from development.

Appendix A–4. Data sources used to map the distribution of baseline ecological communities for the Southern Great Plains Rapid 

Ecoregional Assessment.

[NHD, National Hydrology Dataset]
120 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Cover type1 LANDFIRE

identifier

LANDFIRE

Existing Vegetation Type

Mixed-grass prairie 3132 Central Mixedgrass Prairie Grassland

3141 Northwestern Great Plains Mixedgrass Prairie

Shortgrass prairie 3149 Western Great Plains Shortgrass Prairie

Sand prairie 3148 Western Great Plains Sand Prairie Grassland

3212 Western Great Plains Sandhill Grassland

3093 Southern Colorado Plateau Sand Shrubland

3094 Western Great Plains Sandhill Shrubland

3209 Western Great Plains Sand Prairie Shrubland

Tallgrass prairie 3150 Western Great Plains Tallgrass Prairie

3412 North-Central Interior Sand and Gravel Tallgrass Prairie

3421 Central Tallgrass Prairie

3423 Southeastern Great Plains Tallgrass Prairie

Semidesert grassland 

and shrubland

3066 Inter-Mountain Basins Mat Saltbush Shrubland

3074 Chihuahuan Creosotebush Desert Scrub2

3075 Chihuahuan Mixed Salt Desert Scrub2

3077 Chihuahuan Succulent Desert Scrub

3081 Inter-Mountain Basins Mixed Salt Desert Scrub

3109 Sonoran Paloverde-Mixed Cacti Desert Scrub

3503 Chihuahuan Sandy Plains Semi-Desert Grassland2

3076 Inter-Mountain Basins Semi-Desert Grassland2

3111 Chihuahuan Loamy Plains Desert Grassland2

3121 Chihuahuan Stabilized Coppice Dune and Sand Flat Scrub2

3204 Western Great Plains Mesquite Woodland2

3256 Apacherian-Chihuahuan Semi-Desert Shrubland

3122 Western Great Plains Mesquite Shrubland2

3127 Apacherian-Chihuahuan Semi-Desert Grassland2

3100 Inter-Mountain Basins Semi-Desert Shrub-Steppe2

3503 Chihuahuan Mixed Desert and Thornscrub2

Appendix A–5. Cover types classified from LANDFIRE Existing Vegetation Types (EVTs) for the Southern Great Plains Rapid 

Ecoregional Assessment. The cover types were used in conjunction with the estimated historical grassland distribution (Callan and 

others, 2016) to map baseline grassland communities, baseline riparian and nonplaya wetlands, open water, and potentially altered 

vegetation (table A–4).

[Existing Vegetation Types from LANDFIRE (2016)]

Dams within 30 m of a perennial stream were defined as creating a flow discontinuity. Fragment lengths were summarized 

by stream-segment classes for baseline and relatively undeveloped conditions.

Landscape-Level Area Ranking Breakpoints: 

Calculating Equal Subsets of the Data

The methods for summarizing overall landscape-level 

ranks are described in chapter 2. Here we provide additional 

details on how ranking breakpoints (hereafter referred to 

as breakpoints) for landscape-level area were determined. 

The distribution of percentages of the 5-km-radius moving 

window occupied by each community type was used to select 

the landscape-level area breakpoints for grassland communities. The distribution of percentages of fifth-level watersheds occupied by each community type was used to select 

the landscape-level area breakpoints for riparian areas and 

nonplaya wetlands and for playas and saline lakes. To prevent 

strongly skewed distributions of percentages from excessive 

influence on the breakpoints in aquatic communities, we tested 

for skewness by determining if the observed distribution 

deviated from an expected exponential distribution, in which 

case we established a skew threshold. We then partitioned the 

values above the threshold into three equal subsets of the data 

(terciles) by using ascending sorted values derived from the 

Numpy library “percentile” function (Jones and others, 2001; 

SciPy, 2014). Values below the skew threshold were assigned 

to the lowest ranked subset.

Landscape-level density for prairie streams and rivers 

was determined by combining perennial and intermittent 

stream length in each fifth-level watershed containing either 

or both stream types. The density was calculated as the total 

stream length divided by watershed area for each watershed. To minimize the effects of mapping errors, any watersheds with less than 30 m (98.4 feet) of aggregated stream 

length were mapped as “below threshold” and excluded. 

The remaining stream densities were sorted and partitioned 

into three equal subsets using ascending order sorted values 

derived from the Numpy library “percentile” function (Jones 

and others, 2001; SciPy, 2014).
Appendix A. Methodological Details for Derived Datasets 121

Cover type1 LANDFIRE

identifier

LANDFIRE

Existing Vegetation Type

Foothill grassland 3139 Northern Rocky Mountain Lower Montane-Foothill-Valley Grassland

3140 Northern Rocky Mountain Subalpine-Upper Montane Grassland

3144 Rocky Mountain Alpine Turf

3146 Southern Rocky Mountain Montane-Subalpine Grassland

3147 Western Great Plains Foothill and Piedmont Grassland

Evergreen shrubland 

and woodland

3101 Madrean Oriental Chaparral

3023 Madrean Encinal

3410 Llano Uplift Acidic Forest and Woodland

3024 Madrean Lower Montane Pine-Oak Forest and Woodland

3104 Mogollon Chaparral

3108 Sonora-Mojave Semi-Desert Chaparral

3393 Edwards Plateau Limestone Shrubland

3559 Edwards Plateau Limestone Savanna

3383 Edwards Plateau Limestone Woodland

3561 Llano Uplift Acidic Herbaceous Glade

3115 Inter-Mountain Basins Juniper Savanna

3116 Madrean Juniper Savanna

3119 Southern Rocky Mountain Juniper Woodland and Savanna

3049 Rocky Mountain Foothill Limber Pine-Juniper Woodland

3057 Rocky Mountain Subalpine-Montane Limber-Bristlecone Pine Woodland

3558 Edwards Plateau Limestone Grassland

3016 Colorado Plateau Pinyon-Juniper Woodland

3025 Madrean Pinyon-Juniper Woodland

3059 Southern Rocky Mountain Pinyon-Juniper Woodland

Upland shrubland 3086 Rocky Mountain Lower Montane-Foothill Shrubland

3106 Northern Rocky Mountain Montane-Foothill Deciduous Shrubland

3107 Rocky Mountain Gambel Oak-Mixed Montane Shrubland

3217 Quercus gambelii Shrubland Alliance

3153 Inter-Mountain Basins Greasewood Flat

3213 Quercus havardii Shrubland Alliance

3192 Recently Logged-Shrub Cover

3062 Inter-Mountain Basins Curl-leaf Mountain Mahogany Woodland

3250 Inter-Mountain Basins Curl-leaf Mountain Mahogany Shrubland

3095 Apacherian-Chihuahuan Mesquite Upland Scrub2

3210 Coleogyne ramosissima Shrubland Alliance

3064 Colorado Plateau Mixed Low Sagebrush Shrubland

3072 Wyoming Basins Dwarf Sagebrush Shrubland and Steppe

3080 Inter-Mountain Basins Big Sagebrush Shrubland

3085 Northwestern Great Plains Shrubland

3125 Inter-Mountain Basins Big Sagebrush Steppe

3126 Inter-Mountain Basins Montane Sagebrush Steppe

3220 Artemisia tridentata ssp. vaseyana Shrubland Alliance

3207 Central Mixedgrass Prairie Shrubland

3070 Rocky Mountain Alpine Dwarf-Shrubland

3169 Northern Rocky Mountain Subalpine Deciduous Shrubland

Woodland and forest 3011 Rocky Mountain Aspen Forest and Woodland

3050 Rocky Mountain Lodgepole Pine Forest

3051 Southern Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest and Woodland

3055 Rocky Mountain Subalpine Dry-Mesic Spruce-Fir Forest and Woodland

3166 Middle Rocky Mountain Montane Douglas-fir Forest and Woodland

3208 Abies concolor Forest Alliance

3048 Northwestern Great Plains Highland White Spruce Woodland

3304 Ozark-Ouachita Dry-Mesic Oak Forest

3314 North-Central Interior Maple-Basswood Forest

3334 Ozark-Ouachita Mesic Hardwood Forest

2141 East-Central Texas Plains Post Oak Savanna and Woodland

Appendix A–5. Cover types classified from LANDFIRE Existing Vegetation Types (EVTs) for the Southern Great Plains Rapid 

Ecoregional Assessment. The cover types were used in conjunction with the estimated historical grassland distribution (Callan and 

others, 2016) to map baseline grassland communities, baseline riparian and nonplaya wetlands, open water, and potentially altered 

vegetation (table A–4).—Continued

[Existing Vegetation Types from LANDFIRE (2016)]
122 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Cover type1 LANDFIRE

identifier

LANDFIRE

Existing Vegetation Type

Woodland and forest

—Continued

2146 Edwards Plateau Mesic Canyon

2147 Edwards Plateau Dry-Mesic Slope Forest and Woodland

2148 Southern Rocky Mountain Mesic Montane Mixed Conifer Forest and Woodland

2149 Inter-Mountain Basins Aspen-Mixed Conifer Forest and Woodland

2181 Western Great Plains Dry Bur Oak Forest and Woodland

2182 Crosstimbers Oak Forest and Woodland

2183 North-Central Interior Dry-Mesic Oak Forest and Woodland

2195 Southern Rocky Mountain Ponderosa Pine Woodland

63 Southern Rocky Mountain Ponderosa Pine Savanna

64 Northwestern Great Plains-Black Hills Ponderosa Pine Woodland and Savanna

65 Northern Rocky Mountain Subalpine Woodland and Parkland

Riparian and 

nonplaya wetlands

3180 Introduced Riparian Forest and Woodland

3259 Introduced Riparian Shrubland

3012 Rocky Mountain Bigtooth Maple Ravine Woodland

3056 Rocky Mountain Subalpine Mesic-Wet Spruce-Fir Forest and Woodland

3154 Inter-Mountain Basins Montane Riparian Forest and Woodland

3155 North American Warm Desert Riparian Forest and Woodland

3159 Rocky Mountain Montane Riparian Forest and Woodland

3160 Rocky Mountain Subalpine/Upper Montane Riparian Forest and Woodland

3162 Western Great Plains Floodplain Forest and Woodland

3251 Rocky Mountain Montane Riparian Shrubland

3252 Rocky Mountain Subalpine/Upper Montane Riparian Shrubland

3253 Western Great Plains Floodplain Shrubland

3275 Central Interior and Appalachian Floodplain Shrubland

3319 Central Interior and Appalachian Riparian Shrubland

3385 Western Great Plains Wooded Draw and Ravine

3469 Eastern Great Plains Floodplain Woodland

3471 Central Interior and Appalachian Floodplain Forest

3472 Central Interior and Appalachian Riparian Forest

3525 Edwards Plateau Riparian Woodland

3563 Edwards Plateau Riparian Shrubland

3254 Western Great Plains Floodplain Herbaceous

3145 Rocky Mountain Subalpine-Montane Mesic Meadow

3164 Rocky Mountain Wetland-Herbaceous

3258 North American Warm Desert Riparian Herbaceous

3273 Eastern Great Plains Floodplain Herbaceous

3274 Central Interior and Appalachian Floodplain Herbaceous

3300 Central Interior and Appalachian Riparian Herbaceous

3462 West Gulf Coastal Plain Seepage Swamp and Baygall

3488 Eastern Great Plains Wet Meadow-Prairie-Marsh

3495 Western Great Plains Depressional Wetland Systems

Sparsely vegetated 3293 Snow-Ice

3294 Barren

3001 Inter-Mountain Basins Sparsely Vegetated Systems

3004 North American Warm Desert Sparsely Vegetated Systems

3006 Rocky Mountain Alpine/Montane Sparsely Vegetated Systems

3007 Western Great Plains Sparsely Vegetated Systems

3218 North American Warm Desert Sparsely Vegetated Systems II

3219 Inter-Mountain Basins Sparsely Vegetated Systems II

3222 Rocky Mountain Alpine/Montane Sparsely Vegetated Systems II

Open water 3292 Open Water

Appendix A–5. Cover types classified from LANDFIRE Existing Vegetation Types (EVTs) for the Southern Great Plains Rapid 

Ecoregional Assessment. The cover types were used in conjunction with the estimated historical grassland distribution (Callan and 

others, 2016) to map baseline grassland communities, baseline riparian and nonplaya wetlands, open water, and potentially altered 

vegetation (table A–4).—Continued

[Existing Vegetation Types from LANDFIRE (2016)]
Appendix A. Methodological Details for Derived Datasets 123

Cover type1 LANDFIRE

identifier

LANDFIRE

Existing Vegetation Type

Ruderal 3528 Ruderal Upland Shrubland

3529 Ruderal Upland Herbaceous

3531 Ruderal Upland Forest

3532 Ruderal Forest-Northern and Central Hardwood and Conifer

3533 Ruderal Forest-Southeast Hardwood and Conifer

3945 Western Warm Temperate Undeveloped Ruderal Deciduous Forest

3946 Western Warm Temperate Undeveloped Ruderal Evergreen Forest

3947 Western Warm Temperate Undeveloped Ruderal Mixed Forest

3948 Western Warm Temperate Undeveloped Ruderal Shrubland

3949 Western Warm Temperate Undeveloped Ruderal Grassland

3950 Eastern Cool Temperate Undeveloped Ruderal Deciduous Forest

3951 Eastern Cool Temperate Undeveloped Ruderal Evergreen Forest

3952 Eastern Cool Temperate Undeveloped Ruderal Mixed Forest

3953 Eastern Cool Temperate Undeveloped Ruderal Shrubland

3954 Eastern Cool Temperate Undeveloped Ruderal Grassland

Pastureland 3977 Eastern Cool Temperate Pasture and Hayland

3539 Modified/Managed Northern Tallgrass Grassland

3540 Modified/Managed Southern Tallgrass Grassland

3564 Modified/Managed Southern Tallgrass Shrubland

3967 Western Cool Temperate Pasture and Hayland

3987 Western Warm Temperate Pasture and Hayland

Urban vegetation 3296 Low-intensity Development

3905 Eastern Cool Temperate Urban Deciduous Forest

3900 Western Cool Temperate Urban Deciduous Forest

3901 Western Cool Temperate Urban Evergreen Forest

3902 Western Cool Temperate Urban Mixed Forest

3910 Western Warm Temperate Urban Deciduous Forest

3911 Western Warm Temperate Urban Evergreen Forest

3912 Western Warm Temperate Urban Mixed Forest

3906 Eastern Cool Temperate Urban Evergreen Forest

3907 Eastern Cool Temperate Urban Mixed Forest

3903 Western Cool Temperate Urban Herbaceous

3913 Western Warm Temperate Urban Herbaceous

3908 Eastern Cool Temperate Urban Herbaceous

3904 Western Cool Temperate Urban Shrubland

3914 Western Warm Temperate Urban Shrubland

Invasive herbaceous plants 3182 Introduced Upland Vegetation-Perennial Grassland and Forbland

3181 Introduced Upland Vegetation-Annual Grassland

3183 Introduced Upland Vegetation-Annual and Biennial Forbland 1

LANDFIRE EVTs classified as invasive herbaceous plants, urban vegetation, pastureland, and ruderal cover types were used to map potentially altered 

vegetation (fig. 3–10).

2

Used to map potential for mesquite expansion in fig. 4–7.

Appendix A–5. Cover types classified from LANDFIRE Existing Vegetation Types (EVTs) for the Southern Great Plains Rapid 

Ecoregional Assessment. The cover types were used in conjunction with the estimated historical grassland distribution (Callan and 

others, 2016) to map baseline grassland communities, baseline riparian and nonplaya wetlands, open water, and potentially altered 

vegetation (table A–4).—Continued

[Existing Vegetation Types from LANDFIRE (2016)]
124 Southern Great Plains Rapid Ecoregional Assessment—Volume 1. Ecological Communities

Disclaimers

Maps provided within this document are not for navigational use. 

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For more information concerning this publication, contact:

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Reese and others—Southern Great Plains Rapid Ecoregional Assessment—Volume I. Ecological Communities—Open-File Report 2017–1100

ISSN 0196-1497 (print)

ISSN 2331-1258 (online)

https://doi.org/10.3133/ofr20171100