CLIMATE

Carbon

Sequestration

Greenhouse Gas

Regulation

BIODIVERSITY

Fish and Wildlife

Pollinators

Native Plants

WATER QUALITY

Sediment Retention

Chemical Remediation

Nutrient Regulation

WATER SUPPLY

Aquifer Recharge

Flood Control

Drinking Water

U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2016–1006

Prepared in cooperation with the U.S. Department of Agriculture Natural Resources 

Conservation Service and Farm Service Agency

The Integrated Landscape Modeling Partnership—Current 

Status and Future Directions
Cover photograph. Potential ecosystem services provided by a cropland wetland located near 

Jamestown, North Dakota (photograph by David Mushet, U.S. Geological Survey, July 28, 2004).
The Integrated Landscape Modeling 

Partnership—Current Status and Future 

Directions

By David M. Mushet

Prepared in cooperation with the U.S. Department of Agriculture Natural 

Resources Conservation Service and Farm Service Agency

Open-File Report 2016–1006

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Director

U.S. Geological Survey, Reston, Virginia: 2016

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Suggested citation:

Mushet, D.M., 2016, The integrated landscape modeling partnership—Current status and future directions: U.S. Geological Survey Open-File Report 2016–1006, 59 p., http://dx.doi.org/10.3133/ofr20161006.

ISSN 2331-1258 (online)
iii

Contents

Acknowledgments......................................................................................................................................viii

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................1

Background.....................................................................................................................................................1

History of the Integrated Landscape Modeling Partnership.........................................................2

Regional Assessments and Data Availability for Integrated Landscape Modeling ..................4

InVEST Modeling............................................................................................................................................5

Amphibian Habitat ................................................................................................................................5

Carbon Storage .....................................................................................................................................7

Plant Communities................................................................................................................................7

Pollination Services..............................................................................................................................9

Waterfowl and Grassland Bird Habitat...........................................................................................10

Use of InVEST in Other CEAP–Wetlands Regions.........................................................................12

APEX Modeling.............................................................................................................................................12

Overview of Modeling Progress.......................................................................................................15

Elevation Data......................................................................................................................................15

Processing Elevation Data for Input to APEX .......................................................................18

Soils.......................................................................................................................................................20

Climate..................................................................................................................................................20

Implementing WinAPEX for the Prairie Pothole Region...............................................................21

Stream Parameters ...................................................................................................................21

Reservoir Feature.......................................................................................................................21

Setting Management Variables...............................................................................................21

Output....................................................................................................................................................21

Conclusions from Preliminary APEX Modeling..............................................................................22

Other Related Modeling..............................................................................................................................23

STELLATM Dynamic Systems Modeling...........................................................................................23

Development of Predictive Algorithms ...........................................................................................23

Summary........................................................................................................................................................25

References Cited..........................................................................................................................................25

Appendix 1. Memorandum of Understanding......................................................................................30

Appendix 2. Completed Integrated Valuation of Ecosystem Services and Tradeoffs Threat

and Susceptibility Tables used in Evaluations of Amphibian Habitat in the Prairie

Pothole Region of the United States ...........................................................................................37

Appendix 3. Vegetation (Living and Dead) and Soil (Organic and Inorganic) Carbon Pool

Estimates in Megagrams per Hectare for the Northern and Northwestern Glaciated

Plains Ecoregions...........................................................................................................................39

Appendix 4. Completed Integrated Valuation of Ecosystem Services and Tradeoffs Threat

and Susceptibility Tables used in Evaluations of Native Plant Communities in the

Prairie Pothole Region of the United States ..............................................................................41

Appendix 5. A Geographic Information System Tool to Project Managed and Wild Bees

on Any Landscape..........................................................................................................................43

Appendix 6. Floral Resource Values for Land-Cover Types used in Integrated Valuation of

Ecosystem Services and Tradeoffs Managed Bee Model Runs............................................56
iv

Appendix 7. Select Subarea Settings Used in Agricultural Policy/Environmental Extender

Runs..................................................................................................................................................58

Appendix 8. Select Control File Settings Used in Agricultural Policy/Environmental

Extender Runs.................................................................................................................................59

Figures

1. Map showing physiographic regions of the United States to be evaluated under

the wetlands component of the Conservation Effects Assessment Project ......................4

2. Maps showing amphibian habitat quality of the Prairie Pothole Region of the

United States under current Conservation Reserve Program enrollment

conditions, a scenario in which 100 percent of CRP grasslands are converted to

cropland, and difference between 2012 conditions and 100 percent CRP

conversion scenario.....................................................................................................................6

3. Maps showing carbon stores of the Prairie Pothole Region of the United States

under current Conservation Reserve Program enrollment conditions, a scenario

in which 100 percent of CRP grasslands are converted to cropland, and

difference between 2012 conditions and 100 percent CRP conversion scenario .............8

4. Maps showing native plant floristic quality of prairie pothole wetlands of the

United States under current Conservation Reserve Program enrollment

conditions, a scenario in which 100 percent of CRP grasslands are converted to

cropland, and difference between 2012 conditions and 100 percent CRP

conversion scenario...................................................................................................................11

5. Maps showing managed bee floral resources quality of the Prairie Pothole

Region of the United States under current Conservation Reserve Program

enrollment conditions, a scenario in which 100 percent of CRP grasslands are

converted to cropland, and difference between 2012 conditions and 100 percent

CRP conversion scenario ..........................................................................................................13

6. Graphs showing population trends of gadwall and northern pintail, 1955 to 2014..........14

7. Graph showing North American breeding bird survey population index for the

grasshopper sparrow in the central Mixed-Grass Prairie region, 1967 to 2012...............15

8. Diagram showing ArcGIS Desktop Model Builder diagram of the automated

workflow developed to select the lowest elevation sink in each basin and create

a single feature class of all such sinks ...................................................................................17

9. Two schematic diagrams of alternate subarea configurations for a wetland basin

in the Agricultural Policy/Environmental Extender model that could flood

subareas 1 and 2, and also allow overflow to the adjacent basin .....................................18

10. Configuration of the six subareas being used in the Agricultural

Policy/Environmental Extender model of the basin for wetland T1....................................19

11. Graph showing results from a STELLATM systems model simulation of water

levels in a prairie pothole region wetland over a 24-year period.......................................24
v

Tables

1. Area of amphibian habitat in the Prairie Pothole region of the United States with

a habitat quality rating greater than or equal to 0.8 under scenarios representing

the conversion of various percentages of Conservation Reserve Program

grasslands to croplands ..............................................................................................................5

2. Results of Integrated Valuation of Ecosystem Services and Tradeoffs carbon

model runs for the Prairie Pothole region of the United States under scenarios

representing the conversion of various percentages of Conservation Reserve

Program grasslands to croplands..............................................................................................7

3. Carbon dioxide released into the atmosphere under scenarios representing the

conversion of various percentages of Conservation Reserve Program grasslands

to croplands...................................................................................................................................9

4. Societal costs of increased levels of atmospheric carbon dioxide due to carbon

releases in the Prairie Pothole Region of the United States under scenarios

representing the conversion of various percentages of Conservation Reserve

Program grasslands to croplands..............................................................................................9

5. Wetland area in the Prairie Pothole region of the United States with native

plant-community floristic quality ratings greater than 0.5...................................................10

6. Area of the Prairie Pothole region of the United States with a floral resources

quality score greater than 0.25 under scenarios representing the conversion of

various percentages of Conservation Reserve Program grasslands to croplands.........12

7. Algorithms developed by the CEAP–Wetlands High Plains regional assessment

team for predicting amphibian species richness and probability of individual

species being present in High Plains playa wetlands..........................................................23

Appendix Tables

2–1. Integrated Valuation of Ecosystem Service and Tradeoffs threat table for

amphibian habitat model ...........................................................................................................37

2–2. Integrated Valuation of Ecosystem Service and Tradeoffs susceptibility table for

amphibian habitat model ...........................................................................................................37

3–1. Vegetation and soil carbon pool estimates in megagrams per hectare for the

Northwestern and Northern Glaciated Plains ecoregions..................................................39

4–1. Integrated Valuation of Ecosystem Services and Tradeoffs threat table for plant

community model........................................................................................................................41

4–2. Integrated Valuation of Ecosystem Services and Tradeoffs susceptibility table for

plant community model..............................................................................................................41

6–1. Floral resource values for land-cover types used in Integrated Valuation of

Ecosystem Services and Tradeoffs managed bee model runs...........................................56

7–1. Select subarea settings used in Agricultural Policy/Environmental Extender runs .......58

8–1. Select control file settings used in Agricultural Policy/Environmental Extender runs...59
vi

Conversion Factors

[International System of Units to Inch/Pound]

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in)

millimeter (mm) 0.03937 inch (in)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2) 0.0002471 acre 

hectare (ha) 2.471 acre

Volume

liter (L) 33.82 ounce, fluid (fl. oz)

Flow Rate

meter per minute (m/m) 3.281 foot per minute (ft/min) 

millimeters per hour (mm/h) 0.03937 inches per hour (in/h)

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

megagram (Mg) 0.9842 ton, long (2,240 lb)

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.

Supplemental Information

Note to USGS users: Use of hectare (ha) as an alternative name for square hectometer (hm2) is 

restricted to the measurement of small land or water areas. Use of liter (L) as a special name 

for cubic decimeter (dm3) is restricted to the measurement of liquids and gases. No prefix other 

than milli should be used with liter. Metric ton (t) as a name for megagram (Mg) should be 

restricted to commercial usage, and no prefixes should be used with it.
vii

Abbreviations

APEX Agricultural Policy/Environmental Extender

ARIES Artificial Intelligence for Ecosystem Services

CEAP Conservation Effects Assessment Project

CHD channel depth

CHL channel length

CHN channel roughness (Manning’s N)

CHS channel slope

CLSA Cottonwood Lake study area

CO2 carbon dioxide

CRP Conservation Reserve Program

DEM digital elevation model

DES Des Moines Lobe

DOI U.S. Department of the Interior

FSA Farm Service Agency

GIS geographic information system

GPS global positioning system

ha hectare

ILM integrated landscape modeling

ILMRI Integrated Landscape Monitoring Research Initiative

InVEST Integrated Valuation of Ecosystem Services and Tradeoffs

LAP Lake Agassiz Plain

lidar light detection and ranging

METSTAT Meteorological/Statistical solar radiation model

Mg megagrams

mm millimeters

mm/h millimeters per hour

m/m meters per minute

NGP Northern Glaciated Plains

NLCD National Land Cover Database

NRCS Natural Resources Conservation Service

NSRDB National Solar Radiation Database

NWGP Northwestern Glaciated Plains

NWI National Wetlands Inventory

PPR Prairie Pothole Region
viii

RCHC crop management channel factor

RCHK erodibility channel factor

RCHL channel routing reach length

RFPW buffer/floodplain width

RSBD bulk density of sediments in reservoir

RSDP days required for reservoir to return to normal conditions

RSHC reservoir hydraulic conductivity

RSRR reservoir return rate

RSYN normal sediment concentration in reservoir

RSYS initial sediment concentration in reservoir

SSURGO Soil Survey Geographic Database

USDA U.S. Department of Agriculture

USGS U.S. Geological Survey

USLE Universal Soil Loss Equation

winAPEX APEX for Windows®

Acknowledgments

External funding for the ILM Partnership was provided by the Conservation Effects Assessment 

Project of the U.S. Department of Agriculture’s Natural Resources Conservation Service and the 

Monitoring, Assessment and Evaluation Program of the U.S. Department of Agriculture’s Farm 

Service Agency.
The Integrated Landscape Modeling Partnership—Current 

Status and Future Directions

By David M. Mushet

Abstract

The Integrated Landscape Modeling (ILM) partnership 

is an effort by the U.S. Geological Survey (USGS) and U.S. 

Department of Agriculture (USDA) to identify, evaluate, and 

develop models to quantify services derived from ecosystems, 

with a focus on wetland ecosystems and conservation effects. 

The ILM partnership uses the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) modeling platform to 

facilitate regional quantifications of ecosystem services under 

various scenarios of land-cover change that are representative 

of differing conservation program and practice implementation scenarios. To date, the ILM InVEST partnership has 

resulted in capabilities to quantify carbon stores, amphibian 

habitat, plant-community diversity, and pollination services. 

Work to include waterfowl and grassland bird habitat quality 

is in progress. Initial InVEST modeling has been focused on 

the Prairie Pothole Region (PPR) of the United States; future 

efforts might encompass other regions as data availability and 

knowledge increase as to how functions affecting ecosystem 

services differ among regions.

The ILM partnership is also developing the capability for field-scale process-based modeling of depressional 

wetland ecosystems using the Agricultural Policy/Environmental Extender (APEX) model. Progress was made towards 

the development of techniques to use the APEX model for 

closed-basin depressional wetlands of the PPR, in addition to 

the open systems that the model was originally designed to 

simulate. The ILM partnership has matured to the stage where 

effects of conservation programs and practices on multiple 

ecosystem services can now be simulated in selected areas. 

Future work might include the continued development of 

modeling capabilities, as well as development and evaluation of differing conservation program and practice scenarios 

of interest to partner agencies including the USDA’s Farm 

Service Agency (FSA) and Natural Resources Conservation 

Service (NRCS). When combined, the ecosystem services 

modeling capabilities of InVEST and the process-based 

abilities of the APEX model should provide complementary 

information needed to meet USDA and the Department of the 

Interior information needs.

Introduction

Ecosystems provide multiple provisioning, regulating, 

cultural, and supporting services important to societies across 

the globe (Millennium Ecosystem Assessment, 2005). Provisioning services are services that directly benefit a society by 

providing food, fuel, water, and other valued products. Regulating services benefit societies through their effects on events 

such as flooding, pesticide transport, and disease spread. 

Cultural services are the spiritual, religious, educational, recreational, and other, often nonmaterial, benefits received from 

having intact ecosystems on a landscape. Finally, supporting services are ecosystem functions and processes, such as 

nutrient cycling, soil formation, and primary production, that 

support all previously mentioned provisioning, regulating, and 

cultural services. Thus, supporting services are analogous to 

the functions and processes typically associated with wetlands and other ecosystems (Euliss and others, 2013, p. 187). 

Given not only the global importance of ecosystem services 

to societies and the vulnerability of natural ecosystems to 

threats associated with changing land uses and management 

practices, but also the potential to enhance services through 

targeted implementation of conservation programs and practices, there is great value in developing tools to facilitate the 

quantification of ecosystem services under differing land-use 

and land-management scenarios. In this report, we provide 

information on the status and potential future directions of 

a U.S. Geological Survey (USGS) and U.S. Department of 

Agriculture (USDA) partnership to develop ecosystem service 

quantification tools and to use these tools to evaluate effects of 

conservation programs and practices on ecosystem services of 

value to society.

Background

Integrated Landscape Modeling (ILM), as being used 

within this USGS–USDA partnership, is (1) the identification, 

evaluation, and development of models that quantify all types 

of ecosystem services; and (2) the integration of model outputs 
2 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

in a manner that facilitates the formation of a holistic vision of 

the effects of land use and land management on multiple services provided by ecosystems across a given landscape. The 

overarching vision of the ILM partnership (Euliss and others, 

2010) called for a modeling system that would provide for the 

simultaneous quantification of multiple ecosystem services 

and that also could use monitoring data that would facilitate 

model adaptation to increasingly robust datasets. Thus, part of 

the “integration” described by Euliss and others (2010) was 

the linking of ecosystem models with monitoring. The result 

would be an adaptive modeling system fused to a monitoring network capable of providing simultaneous forecasts of 

diverse ecosystem services for use in identifying management 

and policy options for sustainable management of ecosystems. Euliss and others (2010, p. 7762) correctly described 

the development of such a system as a “daunting” task but 

stated that it was possible within current (2010) technical 

capabilities.

History of the Integrated Landscape Modeling 

Partnership

In response to information needs related to Federal 

wetland restoration programs in the Prairie Pothole Region 

(PPR) of the United States, an extensive survey of restored 

prairie pothole wetlands and adjacent uplands was completed 

by USGS in 2004 to facilitate quantifications of ecosystem 

services derived from U.S. Department of the Interior (DOI) 

and USDA conservation programs (Gleason and others, 2008). 

Funding for the survey was provided by the USDA Farm Service Agency (FSA) through their Monitoring, Assessment, and 

Evaluation Program; USDA Natural Resources Conservation 

Service (NRCS) through the Conservation Effects Assessment 

Project (CEAP); and USGS. In general, agencies had similar 

research interests and information needs, but no single agency 

had sufficient funds to evaluate the array of wetland functions 

affecting the diverse ecological services provided by these 

wetland ecosystems; therefore, recognizing the importance 

of quantifying concurrent ecosystem services, the partner 

agencies designed a study to evaluate a broad suite of wetland functions across a larger spatial gradient. Data from this 

survey were analyzed in a published report that summarizes 

environmental changes in the PPR attributable to DOI and 

USDA conservation programs (Gleason and others, 2008).

Both DOI and USDA seek to quantify benefits not only 

of federally funded practices and programs but also of all 

wetland conservation practices, whether funded in whole or 

part by Federal agencies, States, local municipalities, or the 

landowners themselves; however, information collected as part 

of the 2004 survey (Gleason and others, 2008) provided only 

a “point-in-time” estimate of the environmental benefits, and 

only for a discrete set of conservation practices. To overcome 

such limitations, the ILM partnership was formed to develop 

cost-effective, standardized, and efficient techniques to quantify ecosystem services within a framework that would allow 

for the estimation of future ecological changes attributable to 

multiple and diverse conservation programs and practices.

In 2006, the USGS initiated a separate Integrated 

Landscape Monitoring Research Initiative (ILMRI). The 

overarching goals of the ILMRI were to observe, understand, 

and predict change in environmental services as the landscape 

changed and to evaluate the implications on natural resources 

at multiple spatial and temporal scales in order to address priority natural resource management and policy issues. The program sought to be responsive to management needs; to operate 

as collaborative multidisciplinary and multiagency teams; to 

remain driven by questions that bridge management and science; to seek results and approaches that were transferable and 

interoperable; to leverage existing knowledge, information, 

programs, and capabilities; and to provide approaches and 

tools that were feasible, practical, achievable, and cost effective. The USGS ILMRI teams were organized and focused 

on pilot projects in the PPR, Lower Mississippi Valley, Great 

Basin, and Puget Sound, respectively, with the goal of increasing knowledge of ecological processes with these four focal 

regions. The ILMRI operated from 2006 to 2009 exploring 

development of conceptual and other models, with one important outcome being the establishment of a Memorandum of 

Understanding between partner agencies that also included the 

U.S. Fish and Wildlife Service (appendix 1).

During the period that the USGS ILMRI was in operation, assessment efforts similar to the 2004 extensive survey 

of PPR wetlands (Gleason and others, 2008) were initiated 

in other regions of the United States as part of the Wetlands 

Component of the National USDA CEAP effort (CEAP–

Wetlands). The CEAP–Wetlands effort and the ILMRI had 

complementary goals, with CEAP–Wetlands being focused 

more specifically on working agricultural lands and the effects 

of USDA conservation programs and practices. Although 

the regions used in the ILMRI and CEAP–Wetlands efforts 

were largely unique, two regions (PPR and lower Mississippi 

Alluvial Valley) were common between efforts. When the 

ILMRI ended in 2009, the ILM moniker was assimilated into 

CEAP–Wetlands and model development continued. As this 

change occurred, funding from the FSA Monitoring, Assessment, and Evaluation Program continued to be instrumental. 

The primary focal area for the ILM partnership remained in 

the PPR but with the goal of expanding to other CEAP–Wetlands regions in the future as modeling capabilities matured 

and as data from other regional assessments became available. 

A number of recent studies addressing wetland functions and 

services have been completed by other regional teams, including the mid-Atlantic (McFarland and others, in press; Kluber 

and others, 2014; Yepsen and others, 2014; Fenstermacher and 

others, 2014), the High Plains (O’Connell and others, 2012; 

Venne and others, 2012; Daniel and others, 2014; Beas and 

Smith, 2015; Daniel and others, 2015a,b), and the California 

Central Valley (Duffy and others, 2011). These studies will 

provide the background data necessary to initiate expansion of 

the ILM into these regions and facilitate development of other 

ecosystem services modeling capabilities, such as nutrient 
Background 3

regulation and hydrologic flow modeling (Denver and others, 

2014; Hunt and others, 2014; McDonough and others, 2014; 

Ator and others, 2013), and the techniques necessary to predict 

the provision of services (Huang and others, 2014; Lang and 

others, 2013).

The ILM partnership originally focused on developing a 

single, all-encompassing modeling system that would simulate wetland and landscape process across a given region and 

provide quantitative output of multiple services in response 

to changing land uses, climate change, management, or other 

scenarios. Development of this “EcoServ” modeling system 

was led by the USGS in Sioux Falls, South Dakota. As with 

the broad ILM partnership, the vision of the EcoServ model 

was ambitious. At its core, the modeling system needed to 

simulate numerous complex hydrologic, geochemical, and 

biotic processes that shape and affect not just wetland ecosystems but also the surrounding terrestrial ecosystem processes 

that ultimately lead to the provisioning of services. Also, the 

system was designed to be openly shared and accessed over 

the internet through the use of distributed geographic information system (GIS) techniques and open geospatial standards 

(Feng and others, 2011).

The data initially collected during the CEAP–Wetlands 

regional assessments surveys did not encompass all variables 

needed to facilitate EcoServ modeling efforts, nor did the sampling approaches provide the temporal resolution needed to 

support development and parameterization of the envisioned 

modeling system. Due to these limitations, EcoServ model 

development began by focusing on the USGS’s Cottonwood 

Lake Study Area in Stutsman County, North Dakota. The 

Cottonwood Lake Study Area is a long-term (several decades) 

ecosystem-monitoring site with detailed hydrologic, chemical, 

and biotic data for multiple wetland basins within an interconnected wetland complex (Winter, 2003).

Substantial progress was made in 2009 and early 2010 

in developing the EcoServ modeling system so that it could 

accurately simulate water levels and changes in vegetative 

zones in wetlands of the Cottonwood Lake Study Area wetlands in response to changes in climate. Although much was 

learned from the effort, it was unclear how the system could 

be expanded beyond the Cottonwood Lake Study Area to other 

areas where the detailed data needed to calibrate and validate 

the model were not available. The modeling system was operating at a level beyond the current range of knowledge and 

data availability; furthermore, the funding levels required to 

expand its operability and adaptive capabilities to allow linkage to an as yet undefined monitoring system were not available within the ILM partnership. In mid-2010, a joint decision 

by the partner agencies shifted the available resources to focus 

on evaluating more recently available decision-support tools 

that could facilitate assessments of ecosystem services.

The two most promising open-source ecosystem services decision-support tools are the Integrated Valuation of 

Ecosystem Services and Tradeoffs (InVEST; Natural Capital 

Project, 2013) and the Artificial Intelligence for Ecosystem 

Services (ARIES; Villa and others, 2014) models (Vigerstol 

and Aukema, 2011). The InVEST suite of desktop tools is able 

to model and map ecosystem services and quantify landscapelevel patterns and changes in ecosystem services that result 

from land-cover or climate changes. By contrast, ARIES is 

a web-based modeling tool that allows for the evaluation of 

trade-offs between ecosystem services. Key to the ARIES 

system is its focus on the transfer of services from where they 

are produced to where benefits occur. Ultimately, InVEST 

was selected as the most appropriate modeling system to use 

within the ILM partnership. The decision was primarily due 

to a lack of ability to model habitat-provisioning services 

within ARIES; quantifying benefits of conservation programs and practices to wildlife populations and other biota 

was considered essential to meet overall ecosystem-services 

quantification goals. However, two advantages of the ARIES 

tool are (1) its use of agent-based models to quantify the flow 

of ecosystem services between areas where the services are 

produced and areas where human benefactors are located, 

thereby separating actual as opposed to theoretical provisioning of services; and (2) the system’s ability to quantify uncertainties through the use of Bayesian network modeling and 

Monte Carlo simulation. Thus, if habitat-provisioning services 

become a part of the future modeling capabilities of ARIES, a 

second look at this powerful ecosystem services modeling tool 

may be warranted.

From 2011 to 2015, the ILM partnership has developed 

InVEST modeling to quantify a variety of services generated 

on a landscape under various land-cover scenarios. Partners 

can inform these scenarios so that they provide a representation of differing conservation programs and practices. In the 

examples described herein, model testing for the PPR region 

has focused on identifying changes in services under scenarios 

representing land-cover change that would result from varying 

percentages of Conservation Reserve Program (CRP) grassland contracts expiring and being returned to crop production; 

however, the need to relate ecosystem service quantifications to 

process-based models at field scales is still recognized. Thus, 

in 2013–14 the ILM partnership began exploring the potential 

of the Agricultural Policy/Environmental Extender (APEX) 

model. The APEX model facilitates biophysical process-based 

simulations of small basins. Considerable progress was made 

towards developing techniques to model closed-basin depressional wetlands of the PPR, expanding beyond the open-flow 

systems that the model was originally designed to simulate. 

APEX modeling is ongoing and is currently being led by the 

CEAP Modeling Team in Temple, Texas. When combined, the 

ecosystem services modeling capabilities of InVEST and the 

process-based abilities of APEX should provide complementary information needed to meet USDA and DOI information 

needs.
4 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

Regional Assessments and Data Availability for 

Integrated Landscape Modeling

The primary source of data available, for model development and validation, has come from seven physiographic 

regions of the United States, Glacial Interior Plains, Prairie 

Pothole Region, Gulf Atlantic Coastal Flats, High Plains, Mississippi Alluvial Valley, California Central Valley, and GulfAtlantic Rolling Plains, corresponding to the areas covered 

under CEAP–Wetlands regional assessments (fig. 1). Within 

these regions, detailed data on wetlands and surrounding 

upland ecosystems were collected. As previously mentioned, 

the first regional assessment was completed in the PPR in 

2004; it was built on an earlier 1997 assessment that only 

included seasonal and semi-permanent wetlands (Gleason and 

others, 2008). Many of the wetland catchments surveyed in 

1997 were resampled in the 2004 assessment, and additional 

wetlands were added to include temporary wetlands within the 

sample population. The overall sample design used in the 2004 

PPR regional assessment (Gleason and others, 2008) was used 

as the initial framework for sampling in the other regions. Key 

to the design was the sampling of wetlands across a disturbance 

gradient spanning drained wetlands within actively farmed 

croplands, wetlands within idle croplands that had been planted 

to perennial cover (for example, CRP grasslands, WRP easements), and wetlands within noncropped native grasslands.

Efforts were made to standardize sampling techniques 

across assessment regions as each region began its surveys, 

but numerous adjustments were needed to accommodate the 

unique environmental characteristics and scientific interests 

and capabilities in each 

region. Thus, available data 

and sampling techniques 

varied considerably among 

regions. Sampling designs 

and wetland types were most 

similar between the PPR and 

High Plains (potholes and 

playas, respectively), whereas 

in other regions the wetland 

systems and sampling approaches differed in many details, 

apart from a common theme of sampling across a disturbance 

gradient. This regional variation in techniques, variables 

measured, and ecosystem services evaluated complicated the 

ability to develop a shared modeling system for quantifying 

ecosystem services across regions (as in the EcoServ model); 

however, the sampling-method variability reflects the distinctive differences among regional wetland systems.

A comparison of the PPR and lower Mississippi Alluvial Valley regions indicates these differences. An important 

ecosystem service in the PPR is the provisioning of waterfowl 

nesting habitat needed to support reproduction in summer; 

however, in the lower Mississippi Alluvial Valley, the most 

important supporting service for waterfowl is the production 

of food resources needed to support overwintering adults 

(Faulkner and others, 2011). Waterfowl modules of an ecosystem model would likely be very different between these two 

regions. Other ecological factors will differ among regions. 

Frozen soils during winter have a major effect on wetland 

hydrology in northern regions such as the PPR, whereas 

in other regions frozen soils do not need to be considered. 

Amphibian habitat models in species-poor areas such as the 

PPR would differ from models in amphibian-rich hotspots, 

such as the Mid-Atlantic States (Mitchell, in press). Quantifying floodwater storage in the PPR is based on a spill-point 

elevation, whereas in the High Plains it is based on hydric-soil 

delineations. Although a challenge to model development, this 

diversity of ecological variables is an integral aspect of how 

wetland ecosystem services are provisioned across different 

geographic regions. Thus, although the ILM was originally 

envisioned as a modeling system that would quantify services 

Wetland regions from U.S. Department of Agriculture, Natural Resources Conservation Service, 2008

Elevation data from U.S. Geological Survey Digital Elevation Model data

0 250 500 MILES

0 250 500 KILOMETERS

Glaciated Interior plains

Pacific Mountain 

Prairie Pothole Region

Gulf-Atlantic Coastal Flats

Appalachian Highlands

Piedmont

High Plains 

Mississippi Alluvial Valley

Central Valley

Gulf Atlantic Rolling Plain

Central Plains

Wetlands component 

assessment regions

EXPLANATION

CANADA

UNITED STATES

Figure 1. Physiographic 

regions of the United States 

to be evaluated under the 

wetlands component of 

the Conservation Effects 

Assessment Project (CEAP–

Wetlands). Data collection 

efforts have occurred in 7 of 

the 11 regions: Glacial Interior 

Plains (1), Prairie Pothole 

Region (3), Gulf Atlantic Coastal 

Flats (4), High Plains (7), 

Mississippi Alluvial Valley (8), 

California Central Valley (9) and 

Gulf-Atlantic Rolling Plains (10).
InVEST Modeling 5 n

across all physiographic regions of interest, it has, out of 

necessity, focused on the PPR for initial model development. 

In the future, the partnership hopes to expand the effort to 

encompass other regions as data availability and knowledge 

increases relative to how systems function uniquely among 

regions. The following sections describe the results of initial 

modeling efforts for selected ecosystem services using the 

InVEST and APEX tools.

InVEST Modeling

The ILM partnership is utilizing the InVEST modeling 

framework to quantify services provided by wetlands and surrounding ecosystems. One advantage of the InVEST system is 

the ability to inform the model with expert knowledge when 

quantitative information is lacking, which has been an asset in 

augmenting the data collected by the CEAP–Wetlands regional 

assessments. To date, the ILM partnership’s InVEST modeling effort has been able to quantify carbon stores, pollination 

services, and multiple plant and wildlife habitat services (for 

example, Mushet and others, 2014) provided by PPR ecosystems under various land-use and conservation scenarios.

Amphibian Habitat

Provision of habitat for sensitive wildlife species is an 

important ecosystem service provided by wetlands in the PPR 

and elsewhere. Initial tests of the InVEST model from 2007 to 

2012 focused on quantifying amphibian habitats under various scenarios of conversion of CRP grasslands to croplands. 

Because their life cycles depend on both wetland and terrestrial habitats, amphibians are a prime example of the view of 

the ILM partnership that aquatic and terrestrial ecosystems are 

inextricably linked. Thus, the relevant surveys under CEAP–

Wetlands and other programs have sampled inclusively from 

the wetland center to the surrounding upland divides that separate wetland catchments (Gleason and others, 2008). InVEST 

modeling for amphibian habitat involves quantifying habitats 

and assessing threats to those habitats to derive an overall 

rating of amphibian habitat quantity and quality. Results from 

the amphibian habitat modeling scenarios have been published in Mushet and others (2014). Values used to populate 

the InVEST threat and susceptibility tables are presented in 

appendix 2, and detailed notes on developing land-cover layers used in our analyses can be accessed in the online supplemental materials for Mushet and others (2014).

The amphibian habitat model demonstrated the utility 

of the InVEST system to quantify ecosystem services across 

large regions of the landscape. We divided the PPR into four 

ecoregions (Northern Glaciated Plains, Northwestern Glaciated Plains, Lake Agassiz Plain, and Des Moines Lobe) following Omernik (1987), which allowed changes in amphibian 

habitat to be evaluated on an ecoregion-specific basis or across 

the entire area modeled (table 1). Land-cover maps developed to simulate land-cover changes representative of varying percentages of CRP grasslands being converted back to 

croplands (for example, corn, soybeans) were used for model 

runs. The scenario layers created represented current (2012) 

conditions and conversion scenarios of 10, 25, 50, 75, and 

100 percent. By running a 100-percent conversion scenario, 

the overall effects of a particular program or practice, in this 

case the CRP, can be evaluated. For example, 26 percent of the 

amphibian habitat availability on the PPR landscape in 2012 

was determined to be a direct result of CRP grasslands under 

the 100-percent conversion scenario (table 1). An alternative 

way of expressing model results is that the conversion of all 

CRP grassland back to crop production in the modeled region 

would result in the loss of approximately 2.2 million hectares 

(ha) of potential habitat suitable for amphibians.

One of the key features of the InVEST model is the 

ability to produce maps that provide a visualization of how 

ecosystem services are distributed across a landscape (for 

example, fig. 2). Visualization aids in efforts to communicate 

the benefits of conservation programs and practices to a wider 

audience, including partners, managers, policy makers, and 

other interested parties.

Table 1. Area (hectare) of amphibian habitat in the Prairie Pothole region of the United States (excluding Montana) with a habitat 

quality rating greater than or equal to 0.8 under scenarios representing the conversion of various percentages of Conservation Reserve 

Program (CRP) grasslands to croplands. All losses are relative to amphibian habitat available in 2012.

[From Mushet and others (2014). NGP, Northern Glaciated Plains; NWGP, Northwestern Glaciated Plains; LAP, Lake Agassiz Plain; DML, Des Moines Lobe; 

%, percent; CRP, Conservation Reserve Program]

Scenario Amphibian habitat in hectares (percent loss)

NGP NWGP LAP DML Total

Baseline (2012) 1,103,000 1,300,000 451,000 88,000 2,942,000

-10% CRP 1,053,000 (-5%) 1,265,000 (-3%) 436,000 (-3%) 85,000 (-3%) 2,840,000 (-3%)

-25% CRP 974,000 (-12%) 1,216,000 (-6%) 415,000 (-8%) 81,000 (-8%) 2,687,000 (-9%)

-50% CRP 871,000 (-21%) 1,136,000 (-13%) 383,000 (-15%) 77,000 (-12%) 2,468,000 (-16%)

-75% CRP 800,000 (-27%) 1,077,000 (-17%) 361,000 (-20%) 73,000 (-17%) 2,311,000 (-21%)

-100% CRP 751,000 (-32%) 1,030,000 (-21%) 339,000 (-25%) 70,000 (-20%) 2,190,000 (-26%)
6 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

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A B

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Change in habitat quality

High 

Low 

Habitat quality rating

0.46 to 1

0.15 to 0.46

0.015 to 0.15

0 to 0.015

Ecoregion boundary

EXPLANATION

Figure 2. Amphibian habitat quality of the Prairie Pothole Region of the United States under A, current (2012) Conservation Reserve 

Program (CRP) enrollment conditions; B, a scenario in which 100 percent of CRP grasslands are converted to cropland (modified from 

Mushet and others, 2014); and C, difference between 2012 conditions and 100 percent CRP conversion scenario.
InVEST Modeling 7 n

Carbon Storage

The addition or removal of greenhouse gases such as 

carbon dioxide (CO2) from the atmosphere is a regulating 

service provided by ecosystems, with resultant effects on 

the Earth’s climate. Ecosystems store carbon in four primary 

pools: (1) aboveground living biomass, (2) aboveground dead 

biomass, (3) belowground living biomass (that is, living root 

systems) and (4) soil organic matter. When carbon in any of 

these pools is released, it primarily enters the atmosphere as 

CO2. Thus, by tracking carbon pools, CO2 sequestration or 

exportation to the atmosphere can be quantified. The InVEST 

model quantifies carbon stores in each of the primary pools 

(Natural Capital Project, 2015). We used data from the PPR 

regional assessment (Gleason and others, 2008) to parameterize the InVEST carbon-storage model for each of the four 

ecoregions (that is, Northern Glaciated Plains, Northwestern 

Glaciated Plains, Lake Agassiz Plain, and Des Moines Lobe) 

of the PPR. The PPR regional assessment revealed that carbon 

stores in various pools and land-cover types differed among 

ecoregions (Gleason and others, 2008), so each region was 

parameterized separately (appendix 3). Aboveground biomass for all crop types was set to zero because they generally 

recycle carbon too quickly to be a source of long-term storage. 

Data from Gleason and others (2008) were insufficient to 

provide accurate carbon pool estimates by land-cover type 

for the Lake Agassiz Plain and Des Moines Lobe ecoregions; 

therefore, carbon pools estimates from the Northern Glaciated 

Plains were used as surrogates for these regions in our model 

runs.

Land-cover maps for the effects of CRP conversion 

scenarios on carbon stores were developed following the 

same procedures used for amphibian habitat runs with one 

modification: for carbon modeling, wetlands embedded within 

grassland were uniquely identified so that a different carbon 

pool estimate could be assigned to these wetlands (see cover 

code 600 “Wetland within Grassland” in appendix 3). Results 

from the InVEST carbon model runs are provided in table 2, 

indicating a potential loss of over 11 million megagrams 

(Mg) of carbon storage across the PPR under the 100-percent 

conversion scenario. Although use of the model was tested 

under CRP loss scenarios, other scenarios representing various 

conservation programs and practices can be run if appropriate 

land-cover maps are available and quantifications of carbon 

pools are available by program or practice type. As with 

amphibian habitat, the InVEST modeling system provides a 

visualization of carbon storage changes under each modeled 

scenario (fig. 3).

The value of sequestering a ton of carbon in any of the 

aboveground or belowground pools is equal to the social damage that is avoided by keeping that carbon from being released 

to the atmosphere. Since societal costs of releasing carbon 

to the atmosphere are generally quantified in terms of CO2, 

we converted carbon losses to megagrams of CO2 released 

(table 3). When carbon is released into the atmosphere as 

CO2, two oxygen atoms are added to the carbon atom thereby 

increasing its molecular weight. Thus, for every gram of 

carbon released, 3.66 grams of CO2 results. Using the central 

value of estimated societal costs of carbon releases (that is, 

$43.00 per megagram of CO2) provided by the U.S. Government Working Group on the Social Costs of CO2

 (2013), damages associated with carbon losses to the atmosphere under 

each CRP loss scenario were monetized (table 4). If all CRP 

in the U.S. portion of the PPR is converted to crop production, societal costs in excess of 1.8 billion U.S. dollars will be 

realized.

Plant Communities

Maintaining the ecological integrity of native plant communities is an important component of maintaining diversity 

and habitat quality in the PPR. To quantify plant-community 

integrity, the PPR CEAP–Wetlands Regional Assessment 

quantified differences in native and nonnative species richness and floristic quality (using a floristic quality index; Swink 

and Wilhelm, 1994) of plant species among differing land-use 

treatments (Gleason and others, 2008). The floristic quality 

index (a measure of similarity to the composition of native 

Table 2. Results of Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) carbon model runs for the Prairie Pothole 

region of the United States (excluding Montana) under scenarios representing the conversion of various percentages of Conservation 

Reserve Program (CRP) grasslands to croplands. All losses are relative to estimated carbon stores in 2011.

[From Mushet and others (2014). NGP, Northern Glaciated Plains; NWGP, Northwestern Glaciated Plains; LAP, Lake Agassiz Plain; DML, Des Moines Lobe; 

%, percent; CRP, Conservation Reserve Program]

Scenario Change in carbon stores (milligrams)

NGP NWGP LAP DML Total

Baseline (2011) 0 0 0 0 0

-10% CRP -604,000 -267,000 -190,000 -107,000 -1,169,000

-25% CRP -1,531,000 -681,000 -438,000 -276,000 -2,926,000

- 50% CRP -3,058,000 -1,347,000 -860,000 -549,000 -5,814,000

-75% CRP -4,563,000 -1,941,000 -1,313,000 -821,000 -8,637,000

-100% CRP -6,041,000 -2,666,000 -1,765,000 -1,104,000 -11,576,000
8 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

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Change in amount of carbon

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Amount of carbon, in metric tons

13.5 to 15

11.5 to 13.5

1 to 11.5

0

Ecoregion boundary

EXPLANATION

Figure 3. Carbon stores of the Prairie Pothole Region of the United States under A, current (2012) Conservation Reserve Program 

(CRP) enrollment conditions; B, a scenario in which 100 percent of CRP grasslands are converted to cropland; and C, difference 

between 2012 conditions and 100 percent CRP conversion scenario.
InVEST Modeling 9 n

Table 3. Carbon dioxide released into the atmosphere (megagrams) under scenarios representing the conversion of various 

percentages of Conservation Reserve Program (CRP) grasslands to croplands. All losses are relative to carbon stores in 2011.

[From Mushet and others (2014). NGP, Northern Glaciated Plains; NWGP, Northwestern Glaciated Plains; LAP, Lake Agassiz Plain; DML, Des Moines Lobe; 

%, percent; CRP, Conservation Reserve Program]

Scenario Carbon dioxide released to atmosphere (megagrams)

NGP NWGP LAP DML Total

-10% CRP 2,215,000 978,000 697,000 393,000 4,282,000

- 25% CRP 5,609,000 2,495,000 1,605,000 1,011,000 10,720,000

- 50% CRP 11,205,000 4,935,000 3,153,000 2,011,000 21,303,000

-75% CRP 16,719,000 7,113,000 4,810,000 3,007,000 31,649,000

-100% CRP 22,136,000 9,769,000 6,467,000 4,045,000 42,417,000

Table 4. Societal costs of increased levels of atmospheric carbon dioxide due to carbon releases in the Prairie Pothole Region of the 

United States under scenarios representing the conversion of various percentages of Conservation Reserve Program (CRP) grasslands 

to croplands.

[From Mushet and others (2014). NGP, Northern Glaciated Plains; NWGP, Northwestern Glaciated Plains; LAP, Lake Agassiz Plain; DML, Des Moines Lobe; 

%, percent; CRP, Conservation Reserve Program. Estimates are based on a societal cost of carbon dioxide of $43 (in 2007 dollars) per megagrams (U.S. Government Working Group on the Social Costs of CO2

, 2013)]

Scenario Societal costs of carbon dioxide released (in 2007 dollars)

NGP NWGP LAP DML Total

-10% CRP $95,242,000 $42,056,000 $29,951,000 $16,884,000 $184,133,000

- 25% CRP $241,166,000 $107,299,000 $69,005,000 $43,479,000 $460,950,000

- 50% CRP $481,804,000 $212,210,000 $135,558,000 $86,470,000 $916,043,000

-75% CRP $718,926,000 $305,866,000 $206,809,000 $129,315,000 $1,360,917,000

-100% CRP $951,864,000 $420,049,000 $278,067,000 $173,956,000 $1,823,936,000

plant communities) provided the best separation of community 

composition by land use (Gleason and others, 2008). Therefore, this variable was used as the basis for the InVEST plantcommunity quality model, where floral quality index values 

greater than (>) 0.5 (on a scale of 0 to 1) represent adequate 

to good resemblance to native communities and lower values 

indicate greater presence of nonnative or atypical plant species. We used the same CRP grassland conversion scenarios 

(that is, baseline of 2012, 10-percent conversion, 25-percent 

conversion, 50-percent conversion, 75-percent conversion, 

and 100-percent conversion) in our model runs of plant-community quality. Values used to populate InVEST threat and 

susceptibility tables are provided in appendix 4. Land-cover 

layers were the same as those used in the amphibian habitat 

model. One difference between the InVEST plant-community 

modeling and the quantifications of other services is that the 

available plant-community data do not extend past the wetland 

edge into the upland. Thus, the variable measured is floristic 

quality of the wetland community rather than the overall plant 

community of a region. Results of our plant-community model 

runs are provided in table 5 and figure 4. In all cases, floristic 

quality of wetland plant communities declines under scenarios 

of decreasing CRP on the PPR landscape.

Pollination Services

Managed honeybee (Apis mellifera) colonies are ubiquitous across most of the United States and provide crop-pollination services valued in excess of $15 billion U.S. dollars 

annually (Calderone, 2012). The InVEST pollination model 

focuses on native pollinator species, but the ILM partnership wanted the capability to quantify pollination services 

as related to the vitally important nonnative honeybees. In 

the PPR, honeybee colonies are placed on the landscape in 

early spring. Throughout the spring, summer, and fall they 

pollinate crops and produce honey that is harvested and sold 

as a commodity. Most importantly, this spring to fall period 

is when honeybee colony strength and health is built, maintained, and in some cases lost. Following the honey production 

season, most bee colonies are shipped to warmer regions (for 

example, in California, Florida, Texas) where they pollinate 

crops including almonds and oranges. It is essential that the 

honeybee colonies be healthy enough to survive transport 

across the United States; thus, building colony strength and 

health is important during the spring to fall honey-production 

season. Quantifying the ability of a region to support honeybees in terms of enhancing colony strength and improving 
10 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

Table 5. Wetland area in the Prairie Pothole region of the United States (excluding Montana) with native plant-community floristic 

quality ratings greater than 0.5 (0 to 1 scale, with 1 being greatest floristic quality). Model run outputs are for scenarios representing the 

conversion of various percentages of Conservation Reserve Program (CRP) grassland to cropland. All losses are relative to 2012 quality 

ratings.

[From Mushet and others (2014). NGP, Northern Glaciated Plains; NWGP, Northwestern Glaciated Plains; LAP, Lake Agassiz Plain; DML, Des Moines Lobe; 

%, percent; CRP, Conservation Reserve Program]

Scenario Floristic quality

NGP NWGP LAP DML Total

Baseline (2012) 1,010,705 675,987 214,343 184,786 2,085,821

-10% CRP 996,971 667,861 210,135 183,536 2,058,503

- 25% CRP 974,440 654,745 203,371 181,518 2,014,074

- 50% CRP 937,157 632,736 191,886 178,192 1,939,971

-75% CRP 900,837 610,609 181,906 174,214 1,867,566

-100% CRP 869,310 592,264 171,456 171,919 1,804,949

honeybee health is highly desirable. Gallant and others (2014) 

constructed a preliminary model for mapping honeybee landscape suitability; however, to facilitate modeling of managed 

bee habitat in a manner compatible with InVEST, the ILM 

partnership teamed with Drs. Eric Lonsdorf and Amélie Davis 

to develop a managed-bee model for the InVEST platform. 

Dr. Lonsdorf was one of the original developers of the nativebee pollination model in InVEST (Lonsdorf and others, 2011).

The new InVEST managed-bee model (see appendix 5 

for the model’s manual) uses several inputs to estimate floral 

resource suitability. First, a land-cover dataset is input as a raster 

file. The land-cover-class pixels are given values of 0 to 1 based 

on estimates of their floral resource quality, where zero represents the least suitable land cover for bees and 1 represents the 

greatest suitable land cover for bees. This creates a suitability 

map for floral resources at each pixel. Because bees forage over 

some distance, the suitability map is then integrated over a foraging radius. The floral resources for the pixels surrounding the 

center pixel are weighted higher, because they represent easier 

resources for the bees to obtain, and weights decrease exponentially as the distance from the center pixel increases. The model 

result is a suitability map (ranging from 0 to 1) for managed 

bees within the source land-cover extent, which can be thought 

of as a foraging distance-weighted floral quality map.

The model also requires floral resource quality estimates 

and a kernel distance script. Expert opinions from bee specialists at the USGS were used to determine the suitability 

of land-cover types with respect to pollen and nectar content 

for honeybees. The differences in land cover represent the 

expected availability of floral resources for bees. The suitability tool produces outputs for three seasons (spring, summer, fall) of floral resource suitability, as well as an overall 

weighted suitability that combines all floral seasons based on 

user provided weights (appendix 6). These weights are chosen 

to represent the relative importance of each season for the provision of resources to the managed bees. The suitability values 

vary seasonally because summer generally provides the most 

suitable floral resources for managed bees.

To demonstrate the utility of this newly developed module, we used it to quantify managed bee floral quality for the 

PPR (table 6, fig. 5), using the same land-cover conversion 

scenarios evaluated in the other InVEST model runs. In the 

PPR, most CRP program land that is returned to production is 

planted to corn, soybeans, or a small grain crop. These crop 

types provide little pollen or nectar that is beneficial to honeybees, so similarly low floral availability values were assigned 

to these converted CRP lands. One factor that we were unable 

to account for in our model parameterization was the variable 

quality of CRP grasslands. Whereas some CRP grasslands are 

plentiful in forbs of high value to honeybees and other pollinators, others consist almost exclusively of lesser-quality grass 

species of little value to pollinators. The spatial data currently 

available are inadequate to reliably identify the quality of 

various CRP grasslands with respect to pollinators. Thus, these 

conservation grasslands were assigned a floral availability 

value in the middle of the spectrum. Unlike most croplands, 

the nectar and pollen values of grasslands were held constant 

across all three seasons to reflect the fact that the forb component of grasslands typically consists of a mixture of species 

with varying seasonal flowering periods, whereas cropland 

generally consists of monocultures that flower during very 

specific and limited periods.

Waterfowl and Grassland Bird Habitat

The PPR supports a myriad of waterfowl and grassland 

bird species and is important to the maintenance of their 

populations. The ILM team is currently working to include 

several bird habitat models in the PPR InVEST models. Given 

the wetland-centric focus of CEAP–Wetlands, we are initially focusing on two waterfowl species, the gadwall (Anas 

strepera) and the northern pintail (Anas acuta). They were 

selected for study because population survey data reveals 

opposing trends for these two species during the period of 

record (fig. 6). Gadwalls have responded very positively to 
InVEST Modeling 11 n

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EXPLANATION

0 to 0.2

0.2 to 0.4

0.4 to 0.6

0.6 to 0.8

0.8 to 1

0

0 to 0.1

0.1 to 0.2

0.2 to 0.4

0.4 to 0.7

Figure 4. Native plant floristic quality of prairie pothole wetlands of the United States under A, current (2012) Conservation Reserve 

Program (CRP) enrollment conditions; B, a scenario in which 100 percent of CRP grasslands are converted to cropland; and C, 

difference between 2012 conditions and 100 percent CRP conversion scenario.
12 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

Table 6. Area (hectares) of the Prairie Pothole region of the United States (excluding Montana) with a floral resources quality score 

greater than 0.25 (on a scale of 0 to 1) under scenarios representing the conversion of various percentages of Conservation Reserve 

Program (CRP) grasslands to croplands.

[From Mushet and others (2014). NGP, Northern Glaciated Plains; NWGP, Northwestern Glaciated Plains; LAP, Lake Agassiz Plain; DML, Des Moines Lobe; 

%, percent; CRP, Conservation Reserve Program]

Scenario Area in hectares with floral resources quality score greater than 0.25 (percent loss)

NGP NWGP LAP DML Total

Baseline (2012) 7,485,000 5,076,000 1,870,000 718,000 15,148,000

-10% CRP 7,274,000 (-2.8%) 4,905,000 (-3.4%) 1,842,000 (-1.5%) 710,000 (-1.1%) 14,730,000 (-2.8%)

-25% CRP 6,965,000 (-7.0%) 4,661,000 (-8.2%) 1,796,000 (-4.0%) 700,000 (-2.5%) 14,121,000 (-6.8%)

-50% CRP 6,466,000 (-13.6%) 4,257,000 (-16.1%) 1,733,000 (-7.3%) 682,000 (-4.9%) 13,138,000 (-13.3%)

-75% CRP 6,056,000 (-19.1%) 3,923,000 (-22.7%) 1,678,000 (-10.3%) 666,000 (-7.2%) 12,323,000 (-18.7%)

-100% CRP 5,737,000 (-23.4%) 3,670,000 (-27.7%) 1,630,000 (-12.3%) 651,000 (-9.3%) 11,688,000 (-22.8%)

recent (1993 to 2015) increases in precipitation and water 

availability across the region, whereas northern pintails have 

shown marked declines. Both species feed on aquatic invertebrates and underwater aquatic plants in shallow marshes. 

Gadwalls seek out tall, densely vegetated areas for nesting, 

and they usually nest within 90 meters (m) of water (Belrose, 

1980). Northern pintails use similar wetland habitats for 

feeding and also nest within 90 m of water, but they generally select upland nesting areas that are open with short, 

sparse vegetation, such as cropland stubble before fields are 

planted in the spring (Belrose, 1980). This tendency to nest 

in croplands reduces pintail nest success. Including these 

two species in our InVEST simulations will provide a good 

contrast between two species with differing nesting-habitat 

requirements.

To present a holistic view of modeling services across the 

diverse PPR landscape, we also will be developing a grassland-birds module for the grasshopper sparrow (Ammodramus 

savannarum). This species is a grassland specialist that breeds 

in a variety of habitats, from native prairies to human-created 

grasslands such as CRP fields. Population trends of this species reveal a general decline in numbers across much of the 

PPR (fig. 7). Key to the maintenance of this species on a landscape is the availability of large, contiguous areas of grassland 

of intermediate height with moderately deep litter and low 

shrub density. Specific values of vegetative structure preferred 

by this species for nesting habitat, patch-size requirements, 

and other measures of landscape-level requirements will be 

incorporated into an InVEST model.

Use of InVEST in Other CEAP–Wetlands Regions

To date, use of the InVEST modeling system has been 

focused primarily on scenarios run in the PPR; however, a 

working group was formed to develop a general plant-community conceptual model that could assist in development of 

InVEST plant-community models across regions. The conceptual model was recently published in a CEAP Science Note 

(Conservation Effects Assessment Project, Wetlands Plant 

Working Group, 2015). The High Plains regional assessment team has explored, to a limited degree, use of InVEST 

to quantify plant-diversity metrics in playa wetlands. The 

California Central Valley regional assessments team has used 

InVEST to model breeding habitats of waterfowl, shorebirds, 

and upland birds This team is also working to include amphibian habitat quality, carbon storage, and native pollinator habitat quality in their InVEST modeling capabilities.

APEX Modeling

An important area of research of the ILM partnership 

involves the use of the Agricultural Policy/Environmental Extender (APEX) model. APEX is a processes-based, 

field-scale model that facilitates environmental analyses of 

landscapes and drainage basins. Although APEX provides 

the water and sediment outputs needed to facilitate quantifications of ecosystem services, it was designed for flowing 

water (that is, stream) systems without specific capabilities 

or guidelines for use in depressional wetland systems such 

as those that dominate the PPR and other parts of the United 

States (for example, High Plains, portions of the mid-Atlantic, California Central Valley). From 2012 to 2014, the ILM 

Partnership, led by USGS, initiated work to develop a methodology for adapting APEX to depressional settings where 

wetlands are not typically connected by well-developed 

stream networks. Because this is a substantially new extension of the APEX modeling system, a detailed description of 

model development and progress are provided below. Continued development of the APEX model for use in depressional 

systems will be led in the future by the CEAP Modeling Team 

in Temple, Tex.
APEX Modeling 13

0 250 500 KILOMETERS

0 250 500 MILES

100°W 90°W

50°N

45°N

40°N

100°W 90°W

50°N

45°N

40°N

100°W 90°W

50°N

45°N

40°N

IOWA

MINNESOTA

NORTH

DAKOTA

SOUTH

DAKOTA

NEBRASKA

IOWA

MINNESOTA

NORTH

DAKOTA

SOUTH

DAKOTA

NEBRASKA

IOWA

MINNESOTA

NORTH

DAKOTA

SOUTH

DAKOTA

NEBRASKA

Base from U.S. Geological Survey 

digital data, 2005, 1:1,000,000

Albers Equal-Area Conic projection

Standard parallels 29°30’ and 45°30’N

Central meridian 96°00’W

Ecoregion boundaries from Omernik, 1987

A B

C

Change in floral resources

High 

Low 

Floral resources quality rating

0.25 to 0.64

0.22 to 0.25

0.15 to 0.22

0 to 0.15

Ecoregion boundary

EXPLANATION

Figure 5. Managed bee floral resources quality of the Prairie Pothole Region of the United States under A, current (2012) 

Conservation Reserve Program (CRP) enrollment conditions; B, a scenario in which 100 percent of CRP grasslands are converted to 

cropland; and C, difference between 2012 conditions and 100 percent CRP conversion scenario.
14 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

Breeding population, in thousands

500

2,500

4,500

6,500

8,500

10,500

12,500

1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010 2014

A. Gadwall (Anas strepera)

B. Northern pintail

Year

Figure 6. Population trends of A, gadwall (Anas strepera) and B, northern pintail (Anas acuta), 1955 to 2014 (Zimpfer and 

others, 2014).
APEX Modeling 15

Year

10

15

20

25

30

35

40

1967 1971 1975 1979 1983 1987 1991 1995 1999 2003 2007 2011

Population index

Figure 7. North American breeding bird survey population index for the grasshopper sparrow (Ammodramus savannarum) in 

the central Mixed-Grass Prairie region, 1967 to 2012 (Sauer and others, 2014).

Overview of Modeling Progress

The USGS simulated observed changes in water-surface 

elevations of depressional wetlands at the Cottonwood Lake 

Study Area (CLSA) from 1982 to 2006. The CLSA has been a 

wetlands research and monitoring site since its purchase by the 

U.S. Fish and Wildlife Service in 1962; it also is a USGS wetland ecosystem monitoring site (Winter, 2003). The 25-year 

simulation period represents years for which needed meteorological data were available for CLSA. Additional data used for 

the simulation include a high-resolution, light detection and 

ranging (lidar)-based digital elevation model (DEM) and soils 

data from the Soils Survey Geographic database (SSURGO; 

Soil Survey Staff, 2013).

Elevation Data

The basic elevation data used to describe the study 

basin for the APEX model was a bare earth DEM with a 

0.25-square-meter (m2

) cell size. The DEM was based primarily on airborne lidar data acquired by Fugro Horizons of Rapid 

City, S. Dak., and made into a DEM by USGS personnel using 

the ArcGIS extension LP360 (QCoherent Software, 2013). 

Additional DEM processing to incorporate the bathymetry of 

study wetlands was done using survey-grade global positioning system (GPS) data collected by soundings of the bottom 

of each wetland. Through these steps, a continuous elevation 

model representing both the surrounding uplands and wetland 

basins was produced for the site.

Including the bathymetry of wetlands was essential to 

this approach. First, it allowed runoff estimates to be translated directly from volume to water depth. Knowing how 

water depth changes over time is critical to classifying and 

quantifying ecosystem services that accrue from depressional 

wetlands. Additionally, water depth can be converted directly 

to water-surface elevation with a georeferenced DEM, thereby 

facilitating use of historical water-surface elevation data to 

calibrate and validate hydrologic models. Finally, bathymetric 

information makes it possible to know water depths throughout the wetland on a spatially explicit basis, which is fundamental to understanding the distribution and extent of wetland 

vegetation, as well as depth-dependent physical and chemical 

processes.

The importance of using a high-resolution DEM for 

hydrologic modeling is heightened for the depressional wetland basins of the PPR and High Plains because of the low 

topographical relief in these areas. This low relief diminishes 

the utility of coarser scale DEMs, such as the standard 10-m 

or 30-m DEMs, because the aggregation of elevation data into 

these relatively large raster cells creates ambiguity in analyses, 

such as defining catchment boundaries. Conversely, finer resolution elevation data, such as those acquired by lidar, represent 

the land surface more precisely (finer spatial resolution) and 
16 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

typically have greater vertical accuracy. These qualities make 

it possible to use tools in GIS to depict important hydrologic 

features with greater accuracy, like the position of catchment boundaries. In our approach, considerable importance is 

placed on representing the topography of the site accurately 

because this is perhaps the most critical element to the hydrologic simulation of a basin (Wechsler, 2007).

To model the bathymetry of a wetland from individual 

GPS data points, elevation values were interpolated between 

the GPS points and the shoreline to build a continuous raster 

surface. The lidar-based DEM of the shoreline and uplands 

was incorporated by first converting this part of the elevation 

model from raster cells to points, which allowed the slope of 

the land surface to be used in the interpolation. The GIS workflow and settings for this process were developed empirically, 

exploring different types of interpolation and quantitatively 

comparing the results of each. Initial efforts employed “kriging,” a geostatistical analysis technique available in ArcGIS 

Desktop. Despite multiple iterations and different settings 

for parameters, the kriging approach consistently produced 

undesirable artifacts in the modeled surface, such as repeating 

ridges and ledges. Still using ArcGIS, the wetland bathymetry was modeled with both “inverse distance weighting” and 

“radial basis” functions, but the resulting surfaces were not 

satisfactory. The tool that created the best bathymetric surface 

from the survey points was “splining.” This approach connects 

the narrow band of points generated from the shoreline, as 

described above, to the individual GPS points on the wetland 

bottom to produce a continuous raster surface that minimizes 

the curvature between points to produce a realistic representation of the basin.

Once wetland bathymetry was successfully interpolated 

and joined with the DEM of the land surface, much of the 

remaining GIS work was accomplished using tools that are 

part of Arc Hydro, an ArcGIS extension that can also be used 

as a temporal and spatial modeling platform. Our technique 

exploited only a few of the tools of Arc Hydro, using these 

tools to condition and analyze the DEM. Nevertheless, the features created in this step were essential and were used either 

as direct input for the APEX model of this site or as potential 

future input to APEX. For example, user-specified criteria 

direct Arc Hydro to generate basin boundaries, which we used 

in APEX to measure basin and subarea extent, along with 

corresponding slopes. Arc Hydro can also be used to delineate 

the location of streams, a feature that we did not use in APEX, 

but that could be incorporated if needed to improve model 

performance for other sites.

The first task we undertook to extract this information 

from the DEM was conditioning it for hydrologic analysis by 

eliminating sinks, which are those raster cells with a lower 

elevation than surrounding cells, and which therefore have 

an undefined flow direction. Flow direction must be defined 

for the entire DEM because the algorithms Arc Hydro uses to 

complete subsequent automated steps that produce GIS layers 

used in APEX rely on each cell having a definite flow direction. Arc Hydro eliminates a sink from the DEM by digitally 

“filling” the lower elevation cell (or cells) to a level that is 

higher than at least one of the adjacent cells, thus allowing the flow direction to be determined for the cell. This is a 

relatively straightforward task to accomplish if water is to be 

routed through a basin with a typical dendritic stream network 

because even before sinks are eliminated the DEM intrinsically has a single low point where flow accumulates (that is, 

the most downstream point in the stream network); however, 

in landscapes with depressional wetlands there is no single 

stream network or low point to where water can be routed, 

but rather there are multiple wetlands in adjacent basins into 

which water must be directed. This landscape complexity 

makes the process of eliminating sinks and defining flow 

direction with an automated technique difficult.

In the detailed guidance for how Arc Hydro can be 

applied to analyze landscapes with depressional wetlands, 

such terrain is referred to as “deranged” (Djokic, 2008), which 

is the geomorphological term for a nondendritic drainage 

pattern. Although Arc Hydro is powerful for hydrological analysis, it cannot automate the elimination of sinks for 

such drainage patterns without using pre-existing GIS layers 

to identify individual wetland basins; these GIS layers are 

generally not available for the PPR. Nonetheless, being able to 

automate this task is essential given the substantial number of 

hydrologic sinks in these areas; for example, in a single DEM 

representing the 800 ha around CLSA, there were 246,135 

sinks. Hence, we sought to increase the functionality of this 

approach by developing an algorithm and automated GIS 

workflow that works with Arc Hydro to objectively identify 

and delineate the lowest elevation point in each wetland basin 

in a DEM, which are the points Arc Hydro must reference to 

automatically fill sinks correctly. This was done successfully 

using ArcGIS Desktop Model Builder.

This model construct (fig. 8) compares the location 

and elevation of several Arc-Hydro-generated GIS layers to 

specify the exact raster cell in each wetland basin to which 

water should flow. Accordingly, the output is a feature class 

that identifies the lowest elevation cell in each basin. The output is then used to assign each of these cells a value of “1” for 

the “IsSink” field, which prevents these cells from being filled 

when using the “Fill Sinks” function of Arc Hydro.

On our test DEM of approximately 800 ha, this process 

automated the identification and hydrologic conditioning of 

79 wetland basins. To check these results, basins that our 

process identified were compared with those in the National 

Wetlands Inventory (NWI), an extensive wetland database 

developed by the U.S. Fish and Wildlife Service (2015). This 

revealed that along with each of the NWI wetlands in these 

800 ha that were successfully identified by our automated GIS 

process, 8 additional wetlands were also delineated. Each of 

these additional wetlands was verified as being actual wetland 

using field observations.
Iterates through all polygons in True_sinks (larger polygons) by HydroID

Selects the SinkPolys within the True_sinks

Finds the minimum value

Figure 8. ArcGIS Desktop Model Builder diagram of the automated workflow developed to select the lowest elevation sink in each basin and create such sinks.
18 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

Processing Elevation Data for Input to APEX

The primary decision we faced in building an APEX 

model was how to delineate subareas within a basin. There 

are two basic considerations that needed to be taken into 

account. First, each subarea should be relatively homogenous 

with respect to slope, soils, and land cover. Correspondingly, 

important differences in these same elements across the basin 

should be recognized by creating unique subareas based on 

these criteria. The second consideration was how to delineate 

subareas in a way that would allow the reservoir feature of 

APEX to best represent the dynamic changes in pool volume, 

areal extent of inundation, and water-surface elevation of the 

wetland. For example, if the APEX reservoir feature floods 

one adjacent subarea during wet periods, then the flood-prone 

part of the basin should have two subareas (fig. 9A). In addition, wetland basins that can overflow during wet periods need 

to have subareas built so that water can actually be routed 

through the overflow spillway to the next downstream basin. 

This may require, for example, that the subarea with the 

overflowing reservoir extend to the point on the catchment 

divide where overflow occurs, which may require an irregularly shaped subarea to maintain within-subarea homogeneity 

(fig. 9B).

The subareas created for modeling the basin of wetland 

T1, one of 16 wetlands in the CLSA, are shown on a shaded 

DEM with the water surface visible (fig. 10). The basin 

boundary was constructed based on the modeled bathymetric 

surface, which is not shown. The location of subareas was 

determined based on slope measurements made in a GIS. Once 

basins and subareas are constructed, these processed elevation 

data are queried through ArcGIS Desktop functions that reside 

either in Arc Toolbox or as basic functions in the toolbar. 

Values extracted from this elevation dataset include area, mean 

slope, and slope length. These values are stored in a spreadsheet for arithmetic processing, reference, and subsequent 

input to APEX. Also note in figure 10 that the wetland pool of 

the basin described, T1, has merged with the pool of the basin 

immediately to the west, P1. This is a common occurrence for 

many wetlands during wet periods and is important to address 

from both a hydrological modeling perspective, as well as for 

biological modeling. In drier conditions, as the water surface 

drops, these same pools separate.

As described previously, high-resolution DEMs are desirable for accuracy and overall utility in modeling the hydrology of low relief landscapes such as the PPR; however, some 

error is still inherent in using such models (Wechsler, 2007), 

and one example is the extraction of slope values, which may 

vary with the grid size of the DEM (Zhang and Montgomery, 

1994; Zhang and others, 1999). Moreover, the documentation 

for APEX does not specifically address the use of DEMs to 

derive important topographic parameters but rather instructs 

the user to do an analysis based on elevation contour lines. 

In this case, the APEX parameter is Average Upland Slope 

(STP). Hence, to develop a reliable and consistent method to 

estimate average slope for each subarea, results from three 

different techniques were compared: (1) the Contour-Length 

EXPLANATION

SA 4

SA 5

SA 3

SA 1

SA 6

SA 7

SA 8

SA 9

SA 10

Wetland

reservoir

Principal spillway

Emergency spillway

SA 4

SA 5

SA 3

SA 1

SA 6

SA 7

SA 8

SA 9

SA 10

Wetland

reservoir

Principal spillway

Emergency spillway

SA 2 SA 2

SA 3 Subarea number

A B

Figure 9. Two schematic diagrams of alternate subarea configurations for a wetland basin in the Agricultural Policy/

Environmental Extender (APEX) model that could flood subareas 1 and 2, and also allow overflow to the adjacent basin. 

Spillway locations refer to reservoir-related variables of APEX. A, a basin with a relatively large subarea 1 where the wetland 

reservoir is located; and B, the same basin with a small subarea 1 that extends to the catchment boundary by way of a narrow 

strip, which would be suitable if the higher elevation areas that flood are substantially different in slope or soil type than the 

area immediately surrounding the wetland.
APEX Modeling 19

Subarea boundary

Subarea number

Wetland identifier

EXPLANATION

0 60 90 120 METERS

0 200 300

30

100 400 FEET

SA3

SA5

SA4

SA6

SA2

P1

SA1

T1

SA3

T1

N

Figure 10. Configuration of the six subareas being used in the Agricultural Policy/Environmental Extender (APEX) model of the 

basin for wetland T1 (SA1–SA6). 
20 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

Method referred to in the APEX User’s Manual (Steglich 

and Williams, 2013) and described by Williams and Berndt 

(1977), (2) a raster-based analysis of the existing DEM using 

a zonal statistics analysis of a slope raster, and (3) a rasterbased analysis of the DEM with larger raster cell sizes derived 

by aggregating the original 0.25-m2 cells into 9-m2 and 16-m2

raster cells followed by a zonal statistics analysis. In all cases, 

the same 0.25-m2 DEM was the initial data source, and all 

analyses were done in the ArcGIS environment. Results of 

the three techniques were comparable for all but the steepest slopes, which occur in subarea 2. The average slope in 

subarea 2 was estimated to be 24–25 percent using the zonal 

statistics techniques but only 19 percent using the ContourLength Method. Our preference was to use zonal statistics on a 

slope raster made from the original DEM because it provided a 

more direct workflow and also introduced a minimum amount 

of data transformation.

A second topographic parameter for APEX input 

extracted from the DEM was Average Upland Slope Length 

(SPLG). Values for SPLG were derived by visual inspection of 

slope aspect values for each subarea to obtain a representative 

slope length parallel to the predominant slope aspect.

Soils

Soils data are input to the APEX model primarily through 

tables in a Microsoft® Access® database. A version of this 

database specific to the State of North Dakota was provided 

by the USDA Grassland, Soil and Water Research Laboratory 

in Temple, Tex., and was the starting point for representing 

the soils present at the study site. Further modification of this 

database was then necessary prior to running APEX so that the 

database associated the proper soil with the county where the 

study site is located, and so each of these soils had the most 

recent and comprehensive values available. Guidance for making these modifications was obtained in the form of documentation (Steglich and Francis, 2008) and specific input from 

individuals at the USDA Grassland, Soil and Water Research 

Laboratory.

Although the soils mapped to the study site were included 

in the North Dakota version of the APEX database, not all of 

these soils were associated in that database with the county 

where the study site is located, which prevented them from 

being accessed and used in the WinAPEX interface. To make 

these soils accessible, the tables SOILS_LIST and SOILS_

DATA were modified by duplicating existing rows from the 

same table that show the desired soils and soil layers, and 

then replacing the value in the Federal Information Processing 

Standard (FIPS) code field of these new rows with the value 

that associates the data with the county where the study site 

is located. The correct FIPS code to use for this was in the 

table COUNTIES, and once this was updated the desired soil 

could be viewed and selected when creating or editing subareas in the WinAPEX interface. With respect to which soils 

were mapped to and identified at the study site, it is worth 

mentioning that the SSURGO data used for this purpose (Soil 

Survey Staff, 2013) were resurveyed for North Dakota after 

this study began. This has resulted in a few soil name changes, 

an entirely new series of map unit symbols, and an extra level 

of care being required to assign the correct chemical and 

physical soil properties to each subarea.

An example of the ongoing refinement of the APEX 

platform that has informed our application of the model was 

the use of updated soil parameter values provided to us by the 

CEAP Modeling Team. These new values for certain parameters replaced the existing ones in the soils data tables with 

the most recent data available from the NRCS Soil Survey 

Laboratory in Lincoln, Nebraska.

Climate

Historical climate data measured at the CLSA were used 

for this phase of the APEX modeling effort because the intent 

was to calibrate and validate the model based on observed 

CLSA water-surface elevations. Parameters measured at CLSA 

include daily temperature maxima and minima and daily precipitation; however, there were substantial gaps in this record, 

and missing values were estimated using data from nearby 

weather stations. For precipitation, a weighted average of the 

closest weather stations was developed by USGS researchers 

in Denver to maximize agreement among CLSA values. Missing temperature values were filled with unweighted values 

directly from the nearby weather stations, so that the record 

from the station with the best agreement with CLSA values 

was used first, and any remaining missing values were filled 

using the record from the station with the second best agreement with CLSA values. The data used to fill missing values 

for temperature and precipitation were from the National 

Oceanic and Atmospheric Administration (NOAA; National 

Oceanic and Atmospheric Administration, 2013) and the North 

Dakota Agricultural Weather Network (2013).

Measured solar radiation data were not available for the 

period of this simulation (1982–2006), but we did identify 

a source of modeled solar radiation data, which was used to 

compile daily values. This data source is the National Solar 

Radiation Database (NSRDB; National Solar Radiation Database, 2013; Wilcox, 2007), which has modeled solar radiation 

from 1961 to 2010. Values are based on the METSTAT model 

(Maxwell, 1998), which utilizes observed cloud cover data 

and measured radiation at discrete locations. In using this 

database for solar radiation data, several different radiation 

parameters, including direct normal, diffuse horizontal, and 

global radiation, were assessed. The solar radiation input to 

APEX used was the sum of direct normal and diffuse horizontal radiation. An additional source of modeled solar radiation 

data that offers even better agreement with ground measurements is the SUNY model (Perez and others, 2002), but this 

was not used for this study because the time period covered 

by the SUNY model is only the latter part of the simulation 

period.
APEX Modeling 21

Additional climate data for APEX input were provided by 

the CEAP Modeling Team from a database of time series climate data assembled for individual weather stations throughout the United States. For this simulation, parameter values 

from this database were accessed for mean daily relative 

humidity, wind velocity, and solar radiation that correspond to 

conditions at Pettibone, N. Dak., which was the nearest station 

to the study site.

Implementing WinAPEX for the Prairie Pothole 

Region

For this project, we used WinAPEX0806, version 1.0.0.0, 

created April 4, 2013. The APEX engine was 0806, created 

April 22, 2013. We ran WinAPEX in the Windows® 7 operating system.

Stream Parameters

We sought to minimize the effect that in-channel processes have on sediment movement or flow routing in this 

model because there are no streams at the study site. Hence, 

the recommendation in the WinAPEX dialog box to set the 

variables channel length (CHL), depth (CHD), slope (CHS), 

and roughness (Manning’s N; CHN) equal to zero for basins 

less than 20 hectares (Steglich and Williams, 2013) were 

followed. To set values for the parameter RCHL (Length of 

Routing Reach, in kilometers) there are a few rules in the 

APEX user’s manual (Steglich and Williams, 2013) based 

on (1) the value of the CHL parameter (distance from outlet 

to most distant point in subarea, in kilometers), which has its 

own guidance as described above; and (2) whether or not the 

subarea is EXTREME (does not receive water from another 

subarea) or DOWNSTREAM (does receive water from 

another subarea). Specifically, the rules for setting RCHL 

state that if the subarea being described is EXTREME, then 

the RCHL value must equal the CHL value, and conversely 

if the subarea being described is DOWNSTREAM, then the 

RCHL value cannot equal the CHL value. Accordingly, we set 

RCHL to 0 for subareas 1, 2, 3, and 4 (see fig. 10), whereas for 

subareas 5 and 6 we left RCHL set to 0.025 kilometer (km), 

which is the default value. This value seems conservative for 

subarea 5, which is relatively large, but it should help minimize the effect of in-channel processes related to sediment 

transport. Given the absence of any streams, it may be useful 

to reduce this value further.

The parameters RCHC (USLE Crop Management 

Channel Factor) and RCHK (USLE Erodibility Channel 

Factor) were set to 0.0001 to prevent the channel from being 

a sediment source; however, it will be important to discover 

if sediment eroded from the upland will still be routed to the 

reservoir with these settings. The parameters RFPW (Buffer/

Floodplain Width, in meters) and RFPL (Buffer/Floodplain 

Length, in kilometers) were both set to 0 because there was no 

flood plain; it would be preferable to treat any surface runoff 

as overland flow throughout the study area. Additional values 

used for subareas are shown in appendix 7, and control file 

values are shown in appendix 8.

Reservoir Feature

The inclusion of a functional APEX reservoir is important 

to successfully model the hydrology of depressional wetland 

basins. The primary processes we intended to address by treating the wetland as a reservoir was (1) estimating evaporation 

losses from the wetland, (2) routing water out of the basin if 

there is a true outlet for overflow, (3) predicting the timing and 

magnitude of changes in wetland water depth, and (4) quantifying the areal extent of the water surface. The reservoir 

feature was not included in our functioning APEX model, but 

it was used in an earlier nonfunctioning version. We believe 

that inclusion of this feature in our initial model prevented 

the model from functioning. The guidance received from the 

CEAP Modeling Team indicated the lack of functionality with 

this routine was potentially due to using values of zero for 

several sediment-related parameters of the reservoir feature 

(that is, reservoir return rate (RSRR), initial sediment concentration in reservoir (RSYS), normal sediment concentration in 

reservoir(RSYN), reservoir hydraulic conductivity (RSHC), 

days required to return to normal concentrations (RSDP), and 

bulk density of sediments in reservoir (RSBD).

Setting Management Variables

In management data, the study site was represented as a 

mix of 50 percent little bluestem grass (Schizachyrium scoparium) and 50 percent smooth bromegrass (Bromus inermis). 

These are considered crops in APEX, so an associated “budget” must be created. In our budget “1 to 4 Perennial and(or) 

Annual Crop(s),” with the additional settings of 2 crops (No 

Till and Dryland) for 50 years was used. After entering the 

above settings, the option to automatically add operations for 

the remaining years was chosen. At this point in the setup procedure, hauling operations needed to be specified. WinAPEX 

forces the user to use a hauling operation, which is clearly not 

appropriate for areas that are not cut such as the CLSA. To 

minimize the effect of this, we chose to haul with the smallest 

vehicle in the list, an All-Terrain vehicle, on June 1 of each 

year.

Output

The model output discussed here is subarea-level output. 

Although APEX ran to completion for the 25-year simulation, 

considerable refinement is still required to produce representative results. Our collaborative efforts to date have produced 

encouraging results, and communication with the CEAP 

Modeling Team has been highly productive. The model is not 

currently set up to route water from all subareas into the wetland, but inspection of subarea-specific runoff alone indicates 
22 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

that two parts of the construct may need improvement: (1) not 

enough runoff is being produced by any of the subareas to 

supply enough water to the wetland to maintain the watersurface elevations at the levels that have been observed; and 

(2) daily runoff values are almost identical across all subareas, 

which is unexpected due to the marked differences in average 

slope. The first issue of insufficient runoff recurred throughout 

the simulation—even in years that had substantial snowpack 

and are known to have contributed greatly to the seasonal 

springtime increase in wetland pool volume. Further inspection of other output parameters should show where water is 

going, and it is likely infiltrating to the subsurface. If water is 

infiltrating to the subsurface, this could still be representative 

of the hydrology at the site if the water is moving laterally as 

return flow to the wetland; however, if the water is infiltrating to recharge deep groundwater, then this model behavior 

needs to be modified. An interesting feature of the glacial till 

soil in the PPR that may or may not be captured by APEX is 

a combination of low vertical conductivity and high lateral 

conductivity, which tends to allow water to migrate as shallow 

subsurface flow from the uplands to supply wetlands during 

spring snowmelt.

The second concern is that daily runoff amounts are 

the same across subareas despite differences in slope, which 

was unexpected because the primary reason that subareas are 

delineated in the present configuration is to honor the differences in average slope across the study site, with the intent of 

capturing corresponding variation in hydrological processes 

(particularly runoff in this example). Two possible explanations for the lack of variation across subareas were considered: (1) measured slope differences between subareas are not 

large enough to create measureable differences in runoff; and 

(2) runoff differences between subareas actually do exist, but 

APEX does not consider the subprocesses that cause differences in runoff. Subprocesses that might create such differences include redistribution of snowpack amounts due to 

wind, or dissimilar snowmelt rates across the site due to slope 

aspect and solar radiation. For example, more windblown 

snow is thought to accumulate in uplands where grass is present than where shorter stubble from row crops is the primary 

groundcover. Investigating such phenomena with this combination of hydrological modeling and the long-term record for 

the study site may present opportunities for advancement in 

several areas.

To further explore the source of springtime runoff, the 

total snowfall for each winter (estimated as the total of daily 

precipitation amounts from November 1 to April 1) was 

compared to the modeled daily snow-water equivalent in 

the snowpack during the same year. In several years of the 

simulation, modeled snow-water equivalent during the winter 

reached a maximum less than or equal to 10 percent of the 

total winter precipitation, whereas in other years the peak 

modeled snow-water equivalent for the winter was more than 

50 percent of total winter precipitation. Some of the deviation 

of modeled snow-water equivalent from total precipitation is 

likely due to temperature variations among years that resulted 

in varying amounts of precipitation contributing to snowpack. 

Yearly variation in the amount of total precipitation contributing to snow-water equivalent of the snowpack available to 

influence spring runoff provides an opportunity to improve 

model estimates of a critical hydrologic input to this system. 

We suspect the sources of this year-to-year variability in the 

relation of total winter precipitation to modeled snow-water 

equivalent are discernible through inspection of the output 

values for related parameters; therefore, output for each of the 

25 years likely has the potential to improve our understanding 

of prairie hydrology and model behavior. For example, there 

are S-curve parameters worth considering (that is, SCRP1[16] 

and SCRP2[16]) as a potential source of the disparity between 

total precipitation and snow-water equivalent across years. 

These parameters help define the Snowmelt function in APEX 

(Steglich and Williams, 2013).

Conclusions from Preliminary APEX Modeling

The ability to apply APEX to model the hydrology of 

depressional wetlands using the WinAPEX interface has been 

demonstrated. The primary challenge is setting up the model 

in a way that facilitates the basin-scale approach by aggregating the areal extent effectively. GIS tools have been applied 

to successfully exploit high-resolution elevation data. Specifically, we have developed automated, objective steps that 

manage the large amount of geospatial information and the 

requirement to hydrologically condition the DEM in order to 

take advantage of the valuable detail in these data. Based on 

our experience with the algorithms used by the GIS extension 

Arc Hydro, such high-resolution data are extremely desirable 

input for APEX models of depressional wetlands. We have 

extended this raster dataset to incorporate another important 

source of elevation data, bathymetric survey points. The 

resulting elevation model robustly describes the study site 

and is a basis for providing detailed input to APEX, as well as 

allowing comprehensive hydrological and ecological interpretation of APEX output.

Data input and parameter settings for the APEX platform 

were studied extensively for their application to the current 

study site, and the potential for more in-depth consideration 

of these settings now exists in the context of calibrating and 

validating APEX output. The features of the model that are of 

particular interest are using the reservoir routine to describe 

wetland hydroperiod and verifying the appropriate parameter 

values for stream-related variables. One important process 

in the natural system where depressional wetlands occur in 

the PPR is aeolian transport and redeposition of snow, which 

is not currently addressed in APEX. The role of this process 

could, however, be investigated through simulations using 

historical data.
Other Related Modeling 23

Other Related Modeling

Although modeling work under the ILM partnership is 

currently focused on use of InVEST for regional quantifications and APEX for more detailed process-based modeling, 

other modeling efforts are relevant to the partnership’s overall 

goals of quantifying ecosystem service effects of conservation 

programs and practices.

STELLATM Dynamic Systems Modeling

The USGS ILM Team is working to develop a sitespecific model of wetlands at the CLSA. This effort is funded 

through the USGS Climate and Land-Use Change Mission 

Area and is generating information and products that directly 

benefit the overall ILM modeling effort. The CLSA wetlands 

model is being developed using STELLATM dynamic simulation modeling software (Richmond, 2004) and will tie hydrologic and geochemical models to biotic response variables (for 

example, plants, amphibians, invertebrates, and birds). The 

base hydrologic model for this effort was completed in 2014. 

The hydrologic model accurately simulates seasonal water 

dynamics in closed-basin wetlands. Detailed water-level data 

from the CLSA was used for model calibration and validation 

(fig. 11). The goal is to work towards incorporating yearly 

variation in snowmelt runoff into the model, as spill and fill 

dynamics connect multiple wetlands into a wetland complex. Additional phases of this modeling effort will focus on 

integrating geochemical cycling with the hydrologic dynamics and quantification of biotic responses. Information gained 

from this localized modeling effort will be used to inform the 

overall ILM modeling efforts using InVEST and APEX.

Development of Predictive Algorithms

As part of the CEAP–Wetlands effort, algorithms 

facilitating the prediction of specific variables related to the 

provisioning of ecosystem services have been developed. To 

date, algorithm development has been limited primarily to the 

CEAP–Wetlands High Plains regional assessment team’s work 

for playa wetlands. An example of algorithms developed by 

the High Plains team is shown in table 7. These algorithms 

predict amphibian species richness and the probability that a 

particular species will be present in any given playa wetland. 

Other algorithms developed by the CEAP–Wetlands High 

Plains regional assessment team allow for the quantification 

of floodwater storage, sedimentation, contaminant amelioration, and several plant species richness and vegetation cover 

Table 7. Algorithms developed by the CEAP–Wetlands High Plains regional assessment team for predicting amphibian species 

richness and probability of individual species being present in High Plains playa wetlands.

Predicted variable Algorithm (transformed models)

Total species richness exp(0.0016115*hydroperiod-0.0020619*watershed area to playa area ratio+1.0669053)

Bufo species richness exp(0.0017388*hydroperiod-0.2742842)

Lithobates species richness exp(0.003747*hydroperiod-0.039586*watershed area to playa area ratio-0.651161)

Spea presence exp(-0.003407*Conservation Reserve Program land+2.661548)/(1+exp[-0.003407*Conservation 

Reserve Program land+2.661548]) 

Anaxyrus cognatus presence exp(0.013830*hydroperiod-0.010758)/(1+exp[0.013830*hydroperiod-0.010758])

Anaxyrus woodhousii presence exp(0.009332*cropland-0.009411*grassland-3.716480)/(1+exp[0.009332*cropland0.009411*grassland-3.716480])

Anaxyrus debilis presence exp(-0.05487*Conservation Reserve Program land+0.02132*grassland-3.53401)/(1+exp[-0.05487*

Conservation Reserve Program land+0.02132*grassland-3.53401])

Pseudacris clarkii presence exp(0.007991*hydroperiod-0.007087*grassland-0.325353)/(1+exp[0.007991*hydroperiod0.007087*grassland-0.325353])

Acris crepitans presence exp(-0.12349*watershed area to playa area ratio-0.74235)/(1+exp[-0.12349*watershed area to playa 

area ratio-0.74235])

Gastrophryne olivacea presence exp(-0.006338*cropland-1.032310)/(1+exp[-0.006338*cropland-1.032310])

Lithobates blairi presence exp(0.005388*hydroperiod-0.050825*watershed area to playa area ratio-0.185191)/

[1+exp(0.005388*hydroperiod-0.050825*watershed area to playa area ratio-0.185191])

Lithobates catesbeianus presence exp(0.012439*hydroperiod-0.156494*watershed area to playa area ratio-2.719996)/

[1+exp(0.012439*hydroperiod-0.156494*watershed area to playa area ratio-2.719996]))

Ambystoma tigrinum presence exp(0.003649*watershed area-1.325456)/(1+exp[0.003649*watershed area-1.325456])
EXPLANATION

559.0559.5560.0560.5561.0561.5562.0562.5563.0

5/19/1982

5/19/1983

5/18/1984

5/18/1985

5/18/1986

5/18/1987

5/17/1988

5/17/1989

5/17/1990

5/17/1991

5/16/1992

5/16/1993

5/16/1994

5/16/1995

5/15/1996

5/15/1997

5/15/1998

5/15/1999

5/14/2000

5/14/2001

5/14/2002

Stage (meters above mean sea level)

Date

Runoff*10-4 (cubic meters)

Stage-0.36 (staff gage datum)P6 elevation (meters)-newDaily precipitationSnowmelt volume to pool

. Modeled wa

hat currently are not a

Figure 11. Results from a STELLATM systems model simulation of water levels in a prairie pothole region wetland over a 24-year periodadjusted to match measured levels in the wetland at the beginning of each year to account for differences in snowmelt runoff t
References Cited 25

metrics for playa wetlands (Smith and others, 2015). Although 

the High Plains team has demonstrated success in developing 

algorithms specific to playa wetlands, development of similar algorithms for other CEAP–Wetlands assessment regions 

has been hampered, largely due to great year-to-year variability and a lack of data at a temporal resolution sufficient to 

adequately account for this variability. As an example, in the 

PPR an attempt was made to develop amphibian occurrence 

algorithms for prairie pothole wetlands; however, this effort 

was unsuccessful due to limited sample size and great variability between the 2 years for which wetland data were available 

(that is, the year was identified as a important variable, effectively requiring that a separate algorithm be developed for 

each year; Balas and others, 2012). Additionally, PPR regional 

assessment amphibian data were only available for a single 

wetland type, seasonal wetlands, out of the multiple wetland 

types present in the PPR. Although the development of algorithms to predict specific variables of interest is a worthwhile 

effort, additional data collection efforts will likely be needed 

to facilitate the development of robust algorithms in regions 

outside of the High Plains.

Summary

The Integrated Landscape Modeling (ILM) partnership 

is an effort by the U.S. Geological Survey (USGS) and U.S. 

Department of Agriculture (USDA) to identify, evaluate, and 

develop models to quantify services derived from ecosystems, with a focus on wetland ecosystems and conservation 

effects. The ILM partnership has matured to the stage where 

effects of conservation programs and practices on multiple 

ecosystem services can now be simulated in selected areas. 

The primary tool being used by the partnership to quantify 

the ecosystem services provided by wetlands and surrounding upland ecosystem is the InVEST modeling platform. The 

model testing phase of this effort has focused primarily on 

the PPR. Services quantified to date include carbon stores, 

amphibian habitat, plant-community biodiversity and pollination services. Work to include waterfowl and grassland bird 

habitat quality in InVEST modeling capabilities for the PPR is 

in progress. The ILM partnership plans to continue to develop 

and use InVEST’s modeling capabilities to provide regional 

projections of ecosystem services under varying scenarios of 

interest to USDA (for example, to meet reporting needs of 

CEAP–Wetlands, Croplands and Grazing Lands; and to facilitate efforts of the FSA Monitoring, Assessment and Evaluation 

Program). Additionally, work will broaden to develop and 

test InVEST modeling capabilities beyond the PPR in other 

regions of the United States.

The ILM partnership is also developing the capability 

to facilitate process-based modeling of depressional wetland 

ecosystems using the APEX model. Work to date has focused 

on developing techniques to use this model in closed-basin 

depressional wetlands in the PPR in addition to the open 

systems the model was originally designed to simulate. The 

CEAP Modeling Team in Temple, Tex., is currently leading 

APEX modeling work.

Future work for the ILM partnership could also include 

the exploration of current (for example, National Resources 

Inventory) and potential future monitoring frameworks to 

begin including model adaptation capabilities into ILM 

partnership efforts. The primary goal guiding future work will 

be to provide improved information on ecosystem services 

to USDA and DOI in support of reporting needs, to facilitate 

evaluations of conservation programs and practices, and to 

facilitate management of the Nation’s ecosystems and the 

services they provide to society.

References Cited

Ator, S.W., Denver, J.M., LaMotte, A.E., and Sekellick, 

A.J., 2013, A regional classification of the effectiveness 

of depressional wetlands at mitigating nitrogen transport 

to surface waters in the Northern Atlantic Coastal Plain: 

U.S. Geological Survey Scientific Investigations Report 

2012–5266, 23 p.

Balas, C.J., Euliss, N.H., Jr., and Mushet, D.M., 2012, Influence of conservation programs on amphibians in the prairie 

pothole region: Wetlands, v. 32, p. 333–345.

Beas, B.J., and Smith, L.M., 2015, Amphibian community 

responses to playa restorations in the Rainwater Basin: 

Wetlands, v. 34, p. 1247–1253.

Bellrose, F.C., 1980, Ducks, geese and swans of North 

America, Stackpole Books, Harrisburg, PA, 540 p.

Calderone, N.W., 2012, Insect pollinated crops, insect pollinators and US agriculture—Trend analysis of aggregate 

data for the period 1992–2009: PLoS One, v. 7, e37235, 

27 p., accessed DATE, at http://journals.plos.org/plosone/

article?id=10.1371/journal.pone.0037235.

Conservation Effects Assessment Project, Wetlands Plant 

Working Group, 2015, Assessing the effects of wetland 

practices in agricultural landscapes—A conceptual model 

for wetland plant diversity: Washington, D.C., CEAP 

Science Note, U.S. Department of Agriculture, Natural 

Resources Conservation Service, Conservation Effects 

Assessment Project, 5 p., accessed December 28, 2015, at 

http://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/

nrcseprd401829.pdf.

Daniel, D.W., Smith, L.M., Belden, J.B., McMurry, S.T., and 

Swain, S., 2015a, Effects of land-use change and fungicide 

application on soil respiration in playa wetlands and adjacent uplands of the U.S. High Plains: Science of the Total 

Environment, v. 514, p. 290–297.
26 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

Daniel, D.W., Smith, L.M., Haukos, D.A., Johnson, L.A., and 

McMurry, S.T., 2014, Land use and Conservation Reserve 

Program Effects on the persistence of playa wetlands in the 

High Plains: Environmental Science & Technology, v. 48, 

p. 4282–4288.

Daniel, D.W., Smith, L.M., and McMurry, S.T., 2015b, Land 

use effects on sedimentation and water storage volumes in 

playas of the rainwater basin of Nebraska: Land Use Policy, 

v. 42, p. 426–431.

Denver, J., Ator, S., Lang, M., Fisher, T., Gustafson, A., Fox, 

R., Clune, J., and McCarty, G., 2014, Nitrate fate and 

transport through current and former depressional wetlands 

in an agricultural landscape, Choptank Watershed, Maryland, USA: Journal of Soil and Water Conservation, v. 69, 

p. 1–16.

Djokic, D., 2008, Comprehensive terrain preprocessing using 

Arc Hydro tools: Redlands, California, Esri, 61 p.

Duffy, W.G., Kahara, S.N., and Records, R.M., eds., 2011, 

Conservation effects assessment project—Wetlands assessment in California’s Central Valley and Upper Klamath 

River Basin: U.S. Geological Survey Open-File Report 

2011–1290, 128 p.

Euliss, N.H., Jr., Brinson, M.M., Mushet, D.M., Smith, L.M., 

Conner, W.H., Burkett, V.R., Wilcox, D.A., Hester, M.H., 

and Zheng, H., 2013, Ecosystem services—Developing sustainable management paradigms based on wetland functions 

and processes, in Anderson, J.T., and Davis, C.A., eds., 

Wetland techniques, Volume 3—Applications and management: New York, Springer, p. 181–227.

Euliss, N.H., Jr., Smith, L.M., Liu, S., Feng, M., Mushet, 

D.M., Auch, R.F., and Loveland, T.R., 2010, The need for 

simultaneous evaluation of ecosystem services and landuse change: Environmental Science and Technology, v. 44, 

p. 7761–7763.

Faulkner, S., Barrow, W., Jr., Keeland, B., Walls, S., and Telesco, D., 2011, Effects of conservation practices on wetland 

ecosystem services in the Mississippi Alluvial Valley,: 

Ecological Applications, v. 21, p. S31–S48.

Feng, M., Liu, S., Euliss, N.H., Jr., Young, C., and Mushet, 

D.M., 2011, Prototyping an online wetland ecosystem services model using open model sharing standards: Environmental Modeling & Software, v. 26, p. 458–468.

Fenstermacher, D., Rabenhorst, M., Lang, M., McCarty, G., 

and Needelman, B., 2014, Distribution, morphometry, and 

land use of Delmarva bays: Wetlands, v. 34. p. 1219–1228.

Gallant, A.L., Euliss, N.H., Jr., and Browning, Z., 2014, Mapping Large-Area Landscape Suitability for Honey Bees 

to Assess the Influence of Land-Use Change on Sustainability of National Pollination Services: PLoS ONE v. 9, 

p. e99268. doi:10.1371/journal.pone.0099268.

Gleason, R.A., Laubhan, M.K., and Euliss, N.H., Jr., eds., 

2008, Ecosystem services derived from wetland conservation practices in the United States Prairie Pothole Region 

with an emphasis on the U.S. Department of Agriculture 

Conservation and Wetlands Reserve Programs, U.S. Geological Survey Professional Paper 1745, 58 p.

Huang, C., Peng, Y., Lang, M., Yeo, I., and McCarty, G., 2014, 

Wetland inundation mapping and change monitoring using 

Landsat and airborne LiDAR data: Remote Sensing of Environment, v.141, p. 231–242.

Hunt, P., Miller, J., Ducey, T., Lang, M., Szogi, A., and 

McCarty, G., 2014, Denitrification in natural, restored, and 

converted wetlands of the Delmarva Region of the US: 

Ecological Engineering, v. 71, p. 438–447.

Interagency Working Group on Social Cost of Carbon, 2013, 

Technical update of the social cost of carbon regulatory 

impact analysis: accessed August 5, 2015, at https://www.

whitehouse.gov/sites/default/files/omb/inforeg/social_cost_

of_carbon_for_ria_2013_update.pdf.

Kluber, L., Miller, J., Hunt, P., Ducey, T., and Lang, M., 2014, 

Impact of mid-Atlantic wetland conversion and restoration 

on greenhouse gas emissions and soil microbial communities: Applied Soil Ecology, v. 76, p. 87–94.

Lang, M., McCarty, G., and Oesterling, R., 2013, Topographic 

metrics for improved mapping of forested wetlands: Wetlands, v. 33, p. 141–155.

Lonsdorf, E., Ricketts, T., Kremen, C., Winfree, R., Greenleaf, S., and Williams, N., 2011, Crop pollination services, 

chap. 10 of Kareiva, P., Tallis, H., Ricketts, T.H., Daily, 

G.C., and Polasky, S., eds., Natural capital—Theory and 

practice of mapping ecosystem services: Oxford, New York, 

Oxford University Press, p. 168–187.

Maxwell, E.L., 1998, METSTAT—The solar radiation model 

used in the production of the National Solar Radiation Database (NSRDB): Solar Energy, v. 62, p. 263–279.

McDonough, O., Lang, M., Hosen, J., and Palmer, M., 2014, 

Surface hydrologic connectivity between Delmarva Bay 

wetlands and nearby streams along a gradient of agricultural 

alteration: Wetlands, v.35, p. 41–53.
References Cited 27

McFarland, E., LaForgia, M., Yepsen, M., Whigham, D., Baldwin, A., and Lang, M., in press, Plant biomass and nutrients (C, N, and P) in natural, restored, and prior converted 

depressional wetlands in the mid-Atlantic Coastal Plain: 

U.S. Folia Geobotantica.

Millennium Ecosystem Assessment, 2005, Ecosystems and 

human well-being—Wetlands and water synthesis: Washington, D.C., World Resources Institute, 80 p.

Mitchell, J., in press, Amphibian use of restored wetlands in 

agricultural landscapes in the mid-Atlantic Region, USA: 

Journal of Fish and Wildlife Management.

Mushet, D.M., Neau, J.L., and Euliss, N.H., Jr., 2014, Modeling effects of conservation grassland losses on amphibian 

habitat: Biological Conservation, v. 174, p. 93–100.

National Oceanic and Atmospheric Administration, 2013, 

National Climatic Data Center: accessed August 10, 2013, 

at http://www.ncdc.noaa.gov/.

National Solar Radiation Data Base, 2013, Data sets: accessed 

December 10, 2013, at http://rredc.nrel.gov/solar/old_data/

nsrdb/.

Natural Capital Project, 2013, InVEST Version 2.4.5.: 

accessed December 18, 2015, at http://www.naturalcapitalproject.org/invest.

Natural Capital Project, 2015, Carbon storage and sequestration: climate regulation: accessed December 18, 2015, at 

http://data.naturalcapitalproject.org/nightly-build/investusers-guide/html/carbonstorage.html#summary.

North Dakota Agricultural Weather Network, 2013, NDAWN 

Center: accessed December 5, 2013, at http://ndawn.ndsu.

nodak.edu/.

O’Connell, J.L., Johnson, L.A., Smith, L.M., McMurry, S.T., 

and Haukos, D.A., 2012, Influence of land-use and conservation programs on wetland plant communities of the semiarid United States Great Plains: Biological Conservation, 

v. 146, p. 108–115.

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

States: Annals of the Association of American Geographers, 

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

Perez, R., Ineichen, P., Moore, K., Kmiecik, M., Chain, C., 

George, R., and Vignola, F., 2002, A new operational model 

for satellite-derived irradiances—Description and validation: Solar Energy, v. 73, p. 307–317.

QCoherent Software, 2013, Getting started with LP360: 

Colorado Springs, Colorado, QCoherent Software, 13 p., 

accessed December 10, 2013, at www.qcoherent.com/products/docs/lp360_gettingstarted.pdf.

Richmond, B., 2004, An introduction to systems thinking—A 

STELLA software guide book: Lebanon, New Hampshire, 

Isee Systems, Inc., 165 p.

Sauer, J.R., Hines, J.E., Fallon, J.E., Pardieck, K.L., 

Ziolkowski, D.J., Jr., and Link, W.W., 2014, The North 

American breeding bird survey—Results and analysis 1966–2013, Version 01.30.2015: Laurel, Maryland, 

U.S. Geological Survey, Patuxent Wildlife Research Center, 

accessed December 28, 2015, at http://www.mbr-pwrc.usgs.

gov/bbs/bbs.html.

Smith, L.M., Effland, W.R., Behrman, K.D., and Johnson, 

M.V., 2015, Assessing the effects of USDA conservation 

programs in ecosystem services provided by wetlands. 

National Wetlands Newsletter, v. 37, p. 10–14. 

Soil Survey Staff, 2013, Soil Survey Geographic (SSURGO) 

Database for Stutsman County, North Dakota: Natural 

Resources Conservation Service, United States Department 

of Agriculture, accessed December 18, 2015, at http://www.

nrcs.usda.gov/wps/portal/nrcs/main/soils/survey/geo/.

Steglich, E.M., and Francis, L., 2008, Creating a WinEPIC/

CroPMan/WinAPEX database and related files for your area 

using the Texas central database: Temple, Texas, Blackland 

Research and Extension Center, BREC Report 2008–15, 

22 p.

Steglich, E.M., and Williams, J.R., 2013, Agricultural Policy/

Environmental eXtender Model user’s manual version 

0806: Temple, Texas, Blackland Research and Extension 

Center, 228 p.

Swink, F., and Wilhelm, G., 1994, Floristic quality assessment, 

in Swink, F., and Wilhelm, G., eds., Plants of the Chicago 

region: Indianapolis, Indiana, Indiana Academy of Science, 

p. 14–18.

U.S. Fish and Wildlife Service, 2015, National Wetlands 

Inventory, accessed July 12, 2015, at http://www.fws.gov/

wetlands/index.html.

Venne, L.S., Tsai, J.S., Cox, S.B., Smith, L.M., and McMurry, 

S.T., 2012, Amphibian community richness in cropland and 

grassland playas in the Southern High Plains, USA: Wetlands, v. 32, p. 619–629.

Vigerstol, K.L., and Aukema, J.E., 2011, A comparison of 

tools for modeling freshwater ecosystem services: Journal 

of Environmental Management, v. 92, p. 2403–2409.

Villa, F., Bagstad, K.J., Voigt, B., Johnson, G.W., Portela, R., 

Horzak, M., and Batker, D., 2014, A methodology for adaptable and robust ecosystem services assessments: PLoS One, 

v. 9, e91001, 18 p.
28 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

Wechsler, S.P., 2007, Uncertainties associated with digital 

elevation models for hydrologic applications—A review: 

Hydrology and Earth System Sciences, v. 11, p.1481–

1500.

Wilcox, S., 2007, National Solar Radiation Database 1991–

2005 update—User’s manual: Golden, Colorado, National 

Renewal Energy Laboratory, 462 p.

Williams, J.R., and Berndt, H.D., 1977, Sediment yield 

predictions based on watershed hydrology: Transactions 

of the American Society of Agricultural Engineers, v. 20, 

p. 1100–1104.

Winter, T.C., 2003, Hydrological, chemical, and biological 

characteristics of a prairie pothole wetland complex under 

highly variable climate conditions—The Cottonwood Lake 

area, east-central North Dakota: U.S. Geological Survey 

Professional Paper 1675, 109 p.

Yepsen, M., Baldwin, A., Whigham, D., McFarland, E., 

LaForgia, M., and Lang, M., 2014, Agricultural wetland restorations achieve diverse native wetland plant communities 

but differ from undisturbed wetlands: Agriculture, Ecosystems, and Environment, v. 197, p. 11–20.

Zimpfer, N.L., Rhodes, W.E., Silverman, E.D., Zimmerman, 

G.S., and Richkus, K.D., Trends in duck breeding populations, 1955–2014: Laurel, Maryland, U.S. Fish and Wildlife 

Services, Division of Migratory Birds, 23 p.

Zhang, W., and Montgomery, D., 1994, Digital elevation model 

grid size, landscape representation, and hydrologic simulations: Water Resources Research, v. 30, p. 1019–1028.

Zhang, X., Drake, N.A., Wainwright, J., and Mulligan, M., 1999, 

Comparison of slope estimates from low resolution DEMs—

Scaling issues and a fractal method for their solution: Earth 

Surface Processes and Landforms, v. 24, p. 763–779.
Appendixes 1–8
30 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

Appendix 1. Memorandum of Understanding
Appendixes 1–8 31
32 The Integrated Landscape Modeling Partnership—Current Status and Future Directions
Appendixes 1–8 33
34 The Integrated Landscape Modeling Partnership—Current Status and Future Directions
Appendixes 1–8 35
36 The Integrated Landscape Modeling Partnership—Current Status and Future Directions
Appendixes 1–8 37

Appendix 2. Completed Integrated Valuation of Ecosystem Services and 

Tradeoffs (InVEST) Threat and Susceptibility Tables used in Evaluations 

of Amphibian Habitat in the Prairie Pothole Region of the United States 

(reproduced from Mushet and others, 2014)

Table 2–1. Integrated Valuation of Ecosystem Service and Tradeoffs (InVEST) threat table for amphibian habitat model.

[From Mushet and others, 2014]

MAX_DIST WEIGHT DECAY Threat

1.0 0.6 1 Croplands

0.5 0.2 1 Permanent hydroperiod

0.1 0.1 1 Temporary hydroperiod

0.1 0.1 1 Isolation

Table 2–2. Integrated Valuation of Ecosystem Service and Tradeoffs (InVEST) susceptibility table for amphibian habitat model.

[From Mushet and others, 2014. CRP, Conservation Reserve Program; NLCD, National Land Cover Data]

Code Cover type HABITAT L_PERM L_TEMP L_ISO L_CROP

1 Corn 0.0 0.0 0.0 0.0 0.0

4 Sorghum 0.0 0.0 0.0 0.0 0.0

5 Soybeans 0.0 0.0 0.0 0.0 0.0

6 Sunflowers 0.0 0.0 0.0 0.0 0.0

12 Sweet corn 0.0 0.0 0.0 0.0 0.0

13 Ornamental/popcorn 0.0 0.0 0.0 0.0 0.0

21 Barley 0.0 0.0 0.0 0.0 0.0

22 Durum wheat 0.0 0.0 0.0 0.0 0.0

23 Spring wheat 0.0 0.0 0.0 0.0 0.0

24 Winter wheat 0.0 0.0 0.0 0.0 0.0

25 Other small grains 0.0 0.0 0.0 0.0 0.0

26 Wheat/soybeans double crop 0.0 0.0 0.0 0.0 0.0

27 Rye 0.0 0.0 0.0 0.0 0.0

28 Oats 0.0 0.0 0.0 0.0 0.0

29 Millet 0.0 0.0 0.0 0.0 0.0

31 Canola 0.0 0.0 0.0 0.0 0.0

32 Flaxseed 0.0 0.0 0.0 0.0 0.0

33 Safflower 0.0 0.0 0.0 0.0 0.0

34 Rape seed 0.0 0.0 0.0 0.0 0.0

35 Mustard 0.0 0.0 0.0 0.0 0.0

36 Alfalfa 0.0 0.0 0.0 0.0 0.0

37 Other hays 0.0 0.0 0.0 0.0 0.0

38 Camelina 0.0 0.0 0.0 0.0 0.0

39 Seed crop 0.0 0.0 0.0 0.0 0.0

41 Sugarbeets 0.0 0.0 0.0 0.0 0.0

42 Dry beans 0.0 0.0 0.0 0.0 0.0

43 Potatoes 0.0 0.0 0.0 0.0 0.0

44 Other crops 0.0 0.0 0.0 0.0 0.0

47 Miscellaneous vegetables/fruits 0.0 0.0 0.0 0.0 0.0

52 Lentils 0.0 0.0 0.0 0.0 0.0

53 Peas 0.0 0.0 0.0 0.0 0.0

57 Herbs 0.0 0.0 0.0 0.0 0.0

58 Clover/wildflowers 0.0 0.0 0.0 0.0 0.0

59 Seed/sod grass 0.0 0.0 0.0 0.0 0.0
38 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

Table 2–2. Integrated Valuation of Ecosystem Service and Tradeoffs (InVEST) susceptibility table for amphibian habitat model.—

Continued

[From Mushet and others, 2014. CRP, Conservation Reserve Program; NLCD, National Land Cover Data]

Code Cover type HABITAT L_PERM L_TEMP L_ISO L_CROP

60 Switchgrass 0.0 0.0 0.0 0.0 0.0

61 Fallow/idle cropland 0.0 0.0 0.0 0.0 0.0

62 Pasture/range/CRP/nonagricultural 0.0 0.0 0.0 0.0 0.0

63 Woodland 0.0 0.0 0.0 0.0 0.0

64 Shrubland 0.0 0.0 0.0 0.0 0.0

65 Barren 0.0 0.0 0.0 0.0 0.0

68 Apples 0.0 0.0 0.0 0.0 0.0

70 Christmas trees 0.0 0.0 0.0 0.0 0.0

82 Urban/developed 0.0 0.0 0.0 0.0 0.0

83 Water 0.0 0.0 0.0 0.0 0.0

87 Wetlands 1.0 0.5 0.5 1.0 1.0

88 Nonagricultural/undefined 0.0 0.0 0.0 0.0 0.0

111 NLCD-open water 0.0 0.0 0.0 0.0 0.0

121 NLCD-open space 0.0 0.0 0.0 0.0 0.0

122 NLCD-developed/low intensity 0.0 0.0 0.0 0.0 0.0

123 NLCD-developed/medium intensity 0.0 0.0 0.0 0.0 0.0

124 NLCD-developed/high intensity 0.0 0.0 0.0 0.0 0.0

131 NLCD-barren 0.0 0.0 0.0 0.0 0.0

141 NLCD-deciduous forest 0.0 0.0 0.0 0.0 0.0

142 NLCD-evergreen forest 0.0 0.0 0.0 0.0 0.0

143 NLCD-mixed forest 0.0 0.0 0.0 0.0 0.0

152 NLCD-shrubland 0.0 0.0 0.0 0.0 0.0

171 NLCD-grassland herbaceous 0.0 0.0 0.0 0.0 0.0

181 NLCD-pasture hayland 0.0 0.0 0.0 0.0 0.0

182 NLCD-croplands 0.0 0.0 0.0 0.0 0.0

190 NLCD-woody wetland 1.0 0.5 0.5 1.0 1.0

195 NLCD-herbaceous wetland 1.0 0.5 0.5 1.0 1.0

205 Triticale 0.0 0.0 0.0 0.0 0.0

224 Vetch 0.0 0.0 0.0 0.0 0.0

225 Double crop winter wheat/corn 0.0 0.0 0.0 0.0 0.0

229 Pumpkins 0.0 0.0 0.0 0.0 0.0

236 Double crop winter wheat/sorghum 0.0 0.0 0.0 0.0 0.0

240 Double crop soybeans/oats 0.0 0.0 0.0 0.0 0.0

241 Double crop corn/soybeans 0.0 0.0 0.0 0.0 0.0

246 Radishes 0.0 0.0 0.0 0.0 0.0

401 Temporary wetland 1.0 0.5 0.5 1.0 1.0

402 Seasonal wetland 1.0 0.5 0.5 1.0 1.0

403 Semipermanent wetland 1.0 0.5 0.5 1.0 1.0

404 Permanent wetlands 0.0 0.0 0.0 0.0 0.0

405 Lacustrine 0.0 0.0 0.0 0.0 0.0

406 Riverine 0.0 0.0 0.0 0.0 0.0

407 Artificially flooded wetland 1.0 0.5 0.5 1.0 1.0

408 Wetland 160-meter buffer 1.0 0.5 0.5 1.0 1.0
Appendixes 1–8 39

Appendix 3. Vegetation (Living and Dead) and Soil (Organic and Inorganic) 

Carbon Pool Estimates in Megagrams per Hectare for the Northern and 

Northwestern Glaciated Plains Ecoregions

Table 3–1. Vegetation (living and dead) and soil (organic and inorganic) carbon pool estimates in megagrams per hectare for the 

Northwestern and Northern Glaciated Plains ecoregions.

[CRP, Conservation Reserve Program; NLCD, National Land Cover Data]

Northwestern Glaciated Plains Northern Glaciated Plains

Code Cover type Vegetation Soil Vegetation Soil

1 Corn 0.00 48.05 0.00 46.19

4 Sorghum 0.00 48.05 0.00 46.19

5 Soybeans 0.00 48.05 0.00 46.19

6 Sunflowers 0.00 48.05 0.00 46.19

12 Sweet corn 0.00 48.05 0.00 46.19

13 Ornamental/pop corn 0.00 48.05 0.00 46.19

21 Barley 0.00 48.05 0.00 46.19

22 Durum wheat 0.00 48.05 0.00 46.19

23 Spring wheat 0.00 48.05 0.00 46.19

24 Winter wheat 0.00 48.05 0.00 46.19

25 Other small grains 0.00 48.05 0.00 46.19

26 Wheat/soybeans double crop 0.00 48.05 0.00 46.19

27 Rye 0.00 48.05 0.00 46.19

28 Oats 0.00 48.05 0.00 46.19

29 Millet 0.00 48.05 0.00 46.19

31 Canola 0.00 48.05 0.00 46.19

32 Flaxseed 0.00 48.05 0.00 46.19

33 Safflower 0.00 48.05 0.00 46.19

34 Rape seed 0.00 48.05 0.00 46.19

35 Mustard 0.00 48.05 0.00 46.19

36 Alfalfa 1.54 53.05 1.88 51.19

37 Other hays 1.54 53.05 1.88 51.19

38 Camelina 0.00 48.05 0.00 46.19

39 Seed crop 0.00 48.05 0.00 46.19

41 Sugarbeets 0.00 48.05 0.00 46.19

42 Dry beans 0.00 48.05 0.00 46.19

43 Potatoes 0.00 48.05 0.00 46.19

44 Other crops 0.00 48.05 0.00 46.19

47 Miscellaneous vegetables/fruits 0.00 48.05 0.00 46.19

52 Lentils 0.00 48.05 0.00 46.19

53 Peas 0.00 48.05 0.00 46.19

57 Herbs 0.00 48.05 0.00 46.19

58 Clover/wildflowers 0.00 48.05 0.00 46.19

59 Seed/sod grass 0.00 48.05 0.00 46.19
40 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

Table 3–1. Vegetation (living and dead) and soil (organic and inorganic) carbon pool estimates in megagrams per hectare for the 

Northwestern and Northern Glaciated Plains ecoregions.—Continued

[CRP, Conservation Reserve Program; NLCD, National Land Cover Data]

Code

60

Cover type

Switchgrass

Northwestern Glaciated Plains

Vegetation Soil

1.54 53.05

Northern Glaciated Plains

Vegetation Soil

1.88 51.19

61 Fallow/idle cropland 0.00 48.05 0.00 46.19

62 Pasture/range/CRP/nonagricultural 1.54 53.05 1.88 51.19

64 Shrubland 0.00 0.00 0.00 0.00

87 Wetlands 0.68 53.13 1.71 53.56

171 NLCD-grassland herbaceous 1.54 53.05 1.88 51.19

181 NLCD-pasture hayland 1.54 53.05 1.88 51.19

182 NLCD-croplands 0.00 48.05 0.00 46.19

190 NLCD-woody wetland 0.68 53.13 1.71 53.56

195 NLCD-herbaceous wetland 0.68 53.13 1.71 53.56

205 Triticale 0.00 48.05 0.00 46.19

224 Vetch 0.00 48.05 0.00 46.19

225 Double crop winter wheat/corn 0.00 48.05 0.00 46.19

229 Pumpkins 0.00 48.05 0.00 46.19

236 Double crop winter wheat/sorghum 0.00 48.05 0.00 46.19

240 Double crop soybeans/oats 0.00 48.05 0.00 46.19

241 Double crop corn/soybeans 0.00 48.05 0.00 46.19

246 Radishes 0.00 48.05 0.00 46.19

401 Temporary wetland 0.68 53.13 1.71 53.56

402 Seasonal wetland 0.68 53.13 1.71 53.56

403 Semipermanent wetland 0.68 53.13 1.71 53.56

407 Artificially flooded wetland 0.68 53.13 1.71 53.56

408 Wetland 150-meter buffer 1.54 53.05 1.88 51.19

600 Wetland within grassland 1.81 58.13 2.21 58.56
Appendixes 1–8 41

Appendix 4. Completed Integrated Valuation of Ecosystem Services and 

Tradeoffs (InVEST) Threat and Susceptibility Tables used in Evaluations of Native 

Plant Communities in the Prairie Pothole Region of the United States

Table 4–1. Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) threat table for plant community model.

[CRP, Conservation Reserve Program]

MAX_DIST WEIGHT DECAY Threat

0.5 0.5 1 CRP Grasslands

0.5 1.0 1 Croplands 

0.5 0.5 1 Isolation

Table 4–2. Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) susceptibility table for plant community model.

[CRP, Conservation Reserve Program; NLCD, National Land Cover Data]

Code Cover type HABITAT L_PERM L_TEMP L_CROP

1 Corn 0 0 0 0

4 Sorghum 0 0 0 0

5 Soybeans 0 0 0 0

6 Sunflowers 0 0 0 0

12 Sweet corn 0 0 0 0

13 Ornamental/pop corn 0 0 0 0

21 Barley 0 0 0 0

22 Durum wheat 0 0 0 0

23 Spring wheat 0 0 0 0

24 Winter wheat 0 0 0 0

25 Other small grains 0 0 0 0

26 Wheat/soybeans double crop 0 0 0 0

27 Rye 0 0 0 0

28 Oats 0 0 0 0

29 Millet 0 0 0 0

31 Canola 0 0 0 0

32 Flaxseed 0 0 0 0

33 Safflower 0 0 0 0

34 Rape seed 0 0 0 0

35 Mustard 0 0 0 0

36 Alfalfa 0 0 0 0

37 Other hays 0 0 0 0

38 Camelina 0 0 0 0

39 Seed crop 0 0 0 0

41 Sugarbeets 0 0 0 0

42 Dry beans 0 0 0 0

43 Potatoes 0 0 0 0

44 Other crops 0 0 0 0

47 Miscellaneous vegetables/fruits 0 0 0 0

52 Lentils 0 0 0 0

53 Peas 0 0 0 0

57 Herbs 0 0 0 0

58 Clover/wildflowers 0 0 0 0

59 Seed/sod grass 0 0 0 0

60 Switchgrass 0 0 0 0

61 Fallow/idle cropland 0 0 0 0
42 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

Table 4–2. Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) susceptibility table for plant community model.—

Continued

[CRP, Conservation Reserve Program; NLCD, National Land Cover Data]

Code Cover type HABITAT L_PERM L_TEMP L_CROP

62 Pasture/range/CRP/nonagricultural 0 0 0 0

63 Woodland 0 0 0 0

64 Shrubland 0 0 0 0

65 Barren 0 0 0 0

68 Apples 0 0 0 0

69 Grapes 0 0 0 0

70 Christmas trees 0 0 0 0

82 Urban/developed 0 0 0 0

83 Water 0 0 0 0

87 Wetlands 0 0 0 0

88 Nonagricultural/undefined 0 0 0 0

111 NLCD-open water 0 0 0 0

121 NLCD-open space 0 0 0 0

122 NLCD-developed/low intensity 0 0 0 0

123 NLCD-developed/medium intensity 0 0 0 0

124 NLCD-developed/high intensity 0 0 0 0

131 NLCD-barren 0 0 0 0

141 NLCD-deciduous forest 0 0 0 0

142 NLCD-evergreen forest 0 0 0 0

143 NLCD-mixed forest 0 0 0 0

152 NLCD-shrubland 0 0 0 0

171 NLCD-grassland herbaceous 0 0 0 0

175 CRP 0 0 0 0

181 NLCD-pasture hayland 0 0 0 0

182 NLCD-croplands 0 0 0 0

190 NLCD-woody wetland 0 0 0 0

195 NLCD-herbaceous wetland 0 0 0 0

205 Triticale 0 0 0 0

206 Carrots 0 0 0 0

222 Squash 0 0 0 0

224 Vetch 0 0 0 0

225 Double crop winter wheat/corn 0 0 0 0

229 Pumpkins 0 0 0 0

236 Double crop winter wheat/sorghum 0 0 0 0

240 Double crop soybeans/oats 0 0 0 0

241 Double crop corn/soybeans 0 0 0 0

246 Radishes 0 0 0 0

254 Double crop barley/soybeans 0 0 0 0

401 Temporary wetland 1 1 1 1

402 Seasonal wetland 1 1 1 1

403 Semipermanent wetland 1 1 1 1

404 Permanent wetlands 0 0 0 0
Appendixes 1–8 43

Appendix 5. A Geographic Information System Tool to Project Managed and 

Wild Bees on Any Landscape

Developed by Eric Lonsdorf and Amélie Davis1

Overview

The goals of the tool are to assist in identifying high quality habitat for native and managed bees and to improve the ability 

to anticipate consequences of land-use and land-cover change for bee populations. The tool models native and managed bees’ 

habitat suitability for any land cover. Managed bees and wild bees are modeled separately but each model outputs a spatially 

explicit map of pollination services. The scores do not factor in weather, insecticides, or other factors that might affect bees, but 

instead use expert values on suitability of crops and other land cover to provide floral resources to managed bees and floral and 

nesting resources to wild bees. Also taken into consideration are foraging ranges of bees, seasonality, and bee guilds.

We describe the input files for the managed and wild bee models, how to load the toolbox in ArcGIS, how to run the tool, 

and how to interpret the output files. Although the tool should be fairly straightforward to use, guidance from a person familiar 

with the use and application of ArcGIS is recommended for those less comfortable. As such, we assume some working knowledge of ArcGIS and access to the necessary input files. Lastly, we provide example input files for the user to run the model with 

and familiarize themselves with the format of the input files, as well as see what the output files look like.

NOTE: This tool was written and debugged for ArcGIS 10.2.

Input Files for Managed Bee Model

Prior to loading the tool, it is important to first make sure you have the necessary input files. There are three files that you 

need to run the managed bee suitability tool: a raster land cover map, floral suitability estimates, and a kernel distance file. It is 

recommended that you keep all input files within one folder, in order to easily locate them while running the tool.

Land-cover file.—The land-cover file should be a raster and can be updated using a layer symbology to give coded colors to 

the various land classes. This file will contain the various land-use and land-cover types within a given area. The tool will output 

the same area as this input land-cover file, but with values corresponding to seasonal and overall suitability of the land cover for 

managed or wild bees. The file should be a raster GRID, geotiff, or other raster file type.

In the example folder (named BeeModelsUSGS\Example_Input_Files\) the land-cover file is named lulc. It is in GRID 

format and has a 40- by 40-meter (m) spatial resolution.

Floral suitability codes.—This file will be used to input the land-use and land-cover suitability values for the different floral 

seasons. Each land cover is assigned a suitability value ranging from zero to one that specifies how much that land cover provides floral resources for bees. A score of zero indicates bees cannot use any floral resources on that landscape, or that there are 

no floral resources on that landscape. A score of one indicates this land cover provides the best floral resources. This can be done 

for up to three seasons because floral output for different land covers can vary by season. The seasons have designated weights 

in the tool. This file should be in .csv format.

1

USGS Cooperative Agreement # G13AC00378.
44 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

In the example folder, this file is named example_model_input_managedBees.xls. Each land cover has its own row. The 

spreadsheet must have five columns and a header row. If you are not using some of the seasons, keep the column and numbers in 

it (set them all to ones or zeros). Here is a screenshot of how the first few rows are set up:

The user then saves the worksheet as a .csv. In the example, we have saved this file as example_model_input_floral.csv. Of 

course you can work directly in the .csv format if you wish.

Distance kernel.—This file mimics the foraging distance necessary to predict suitability for bees. The kernel weights are 

applied to the surrounding land-cover pixels based on how far they are from the center pixel, with closer cells weighted more 

strongly. This file should be in text format.

In the example folder, you will find three different kernel choices. These kernels were made for a raster with 40- by 

40-m pixels and are mimicking foraging ranges of 1.3-, 2.5-, and 5-kilometer (km) radii. These example kernels are named 

kernel_1_3kmradius.txt, kernel_2_5kmradius.txt, and kernel_5kmradius.txt, respectively. If the spatial resolution of your landcover raster is different than 40 by 40 meters, you need to make new neighborhood kernels. Instructions on how to make this 

kernel file can be accessed at http://resources.arcgis.com/en/help/main/10.2/index.html#//005m0000006q000000 or by searching 

the ArcGIS help for the neighborhood weight object “NbrWeight.” If you want larger foraging ranges you will also need to make 

new neighborhood kernels but note that the larger the radius, the longer the model takes to run.

Input Files for Wild Bee Model

The input files are essentially the same as the managed bee tool for the wild bee model; however, there is one extra input 

file that is needed. The four files that you need to run the managed bee suitability tool include a raster land-cover map, floral 

suitability estimates, a kernel distance file, and nesting suitability estimates (that is, the extra input file).

Land-cover file.—This file will be the same as the managed bee raster land-cover file.

Floral suitability codes.—This file will be the same as the managed bee floral suitability values.

Distance kernel.—This file will be the same as the managed bee distance kernel file.

Nesting suitability file.—This will be a .csv document, detailing nesting suitability for a variety of guilds. For example, it is 

possible to include nesting suitability values for up to four nesting types such as ground, cavity, stem, and wood nesting bees.
Appendixes 1–8 45

In the example folder, this file is named example_model_input_wildBees.xls. Each land cover has its own row. The spreadsheet must have six columns and a header row. If you are not using some of the guilds, keep the column and numbers in it (set 

them all to ones or zeros). Here is a screenshot of how the first few rows are set up:

The user then saves the worksheet as a .csv. In the example, we have saved this file as example_model_input_nesting.csv. 

Of course you can work directly in the .csv format if you wish.

Loading the Tool in GIS

We have designed the tool to be used like other tools in ArcGIS and so it is part of a custom-made toolbox. We recommend 

placing the toolbox that came from the zipped folder in your ArcGIS folder within your “Documents” folder. Please make sure 

that the python files, “ManagedBeeSuitabilityModel.py,” and “WildBeeSuitabilityModel.py” are also with the toolbox. For 

example, on my computer the files are located in “C:\Users\Documents\ArcGIS\Toolbox_and_Python_Code\USGS_ Bee_Models.tbx.” Once you have done this, you need to open ArcGIS 10.2 and add the toolbox to the other available toolboxes. 

Following the help file for ArcGIS:
46 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

Then, you will be asked to navigate to the location of the custom toolbox, and you should be able to locate the toolbox, as 

shown below. Once you highlight the toolbox, click open and the toolbox should now show up in the list of tools available to 

you.

Your new toolbox set should look something like this with “USGS_Bee_Models” now included:

IMPORTANT: Once you’ve added the tool, you should check to make sure that the tool references the correct location for the 

python code that runs the analysis. To do this, right click on the “Managed Bee Suitability Model” icon, and select “Properties.” 

Then select the “Source” tab. Locate the script file in the same place as the toolbox and click “OK.” Do the same for the “Wild 

Bee Suitability Model.”
Appendixes 1–8 47

Loading the Input Files

Prior to running the tool, we advise adding the necessary input layers to the Table of Contents. Here are instructions to do 

so from ArcGIS:

In the “Example_Input_Files” folder you will find example input files necessary to run the tool. You can select your landuse and land-cover (lulc) file and load it into ArcMap. We show here how to load the example lulc file we provided with the tool 

(it is located in the “...\BeeModels_USGS\Example_Input_Files” folder).
48 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

The example land-cover raster loaded into ArcMap should look something like this:

After you have loaded your tool and have your input files ready, create an output folder in an easily accessible location in 

order to have your model outputs go to the same place once you run the model.

Running the Managed Bee Tool

Once you have added the necessary lulc layer, you are ready to use the tool. Expand the toolbox and double-click on the 

script labeled “Managed Bee Suitability Model.” First, you must tell the tool where to locate the input files. By navigating with 

the folder button, you can locate each file within your Table of Contents in ArcMap.
Appendixes 1–8 49

Here is a screen shot and the description of the additional information is described below it:

Seasonal Weights for Suitability.—Apart from the input files, other user inputs include specifying the seasonal weights and 

names for an output folder and an output file. These values will already be filled in once you open the tool but they are just suggestions. In this case, Season 2 (summer) receives the highest weight of 0.5, because floral resources are most valuable to bees 

during this time, followed by Season 1 (spring) at 0.3 and Season 3 (fall) at 0.2. You can modify these according to the importance of the floral blooms to bees in your study region but the values for the weighted seasons must total to 1. Season 1weight 

must always have a value greater than zero (up to one).

Designating the output files.—The user must have an output folder where intermediary files created by the model will be 

saved as well as the final output file. Also, make sure to update the “Final output raster name” each time you run the model. 

Once you have directed the tool to the location of the necessary input files and provided the additional three inputs required, 

click “OK” to run the analysis.
50 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

You should see the analysis running, and assuming it ran successfully, the dialogue box should look something like this:

Viewing the Output Files

The tool creates several output layers to the output folder location. To view them, you’ll need to add them to the ArcMap 

document as you did the input layer. Below is an example of the output layers when the tool is run with the example files:
Appendixes 1–8 51

ManagedBeeSuitability.tif.—This file depicts the overall habitat suitability for managed bees (for example combining the 

three seasonal outputs using the user specified weights). Higher values indicate higher suitability. Below is an example output of 

the ManagedBeeSuitability.tif:

ManagedBeeSuitability _Season1.—This file depicts the floral resource suitability for managed bees in the spring. Below is 

an example output of the ManagedBeeSuitability_Season1.tif:
52 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

ManagedBeeSuitability _Season2.—This file depicts the floral resource suitability for managed bees in the summer. Below 

is an example output of the ManagedBeeSuitability_Season2.tif:

ManagedBeeSuitability_Season3.tif.—This file depicts the floral resource suitability for managed bees in the fall. Below is 

an example output of the ManagedBeeSuitability_Season3.tif:
Appendixes 1–8 53

Running the Wild Bee Tool

The Wild Bee Suitability Tool has the same inputs as the Managed Bee Suitability Tool. The only additional inputs are the 

following:

Nesting suitability codes.—This file will be used to input the land-use and land-cover suitability values for the different 

guilds. This file should be in .csv format and is similar to the Floral Nesting Suitability Codes file except that each land use and 

land cover is rated as a function of how good of a nesting ground it provides for up to four different bee guilds.

Weights for guilds.—The user needs to specify weights for each guild. These values will already be filled in once you open 

the tool. You may decide to use just one, two, three, or up to four different guilds. If you do not wish to use that guild, enter a 

weight of zero. The weights represent the proportion of bees in each of these guilds that you would expect to find in your study 

region. In the example, all four guilds are represented equally in this study region and are thus assigned a weight of 0.25. The 

values for the weighted guilds must total to 1. Guild A weight must always have a value greater than zero (up to one).

Here is an example of the inputs for the Wild Bee Suitability Model:
54 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

You should see the analysis running, and assuming it ran successfully, the dialogue box should look something like this:
Appendixes 1–8 55

Viewing the Output Files

The tool creates one output layer to the output folder location. To view it, you will need to add it to the ArcMap document 

as you did the input layer.

WildBeeSuitability.tif.—This file depicts the overall habitat suitability for wild bees. Higher values indicate higher 

suitability.
56 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

Appendix 6. Floral Resource Values for Land-Cover Types used in Integrated 

Valuation of Ecosystem Services and Tradeoffs (InVEST) Managed Bee Model 

Runs

Table 6–1. Floral resource values for land-cover types used in Integrated Valuation of Ecosystem 

Services and Tradeoffs (InVEST) managed bee model runs.

[NLCD, National Land Cover Data]

Code Land cover Season 1 Season 2 Season 3

1 Corn 0.00 0.25 0.00

4 Sorghum 0.00 0.00 0.00

5 Soybeans 0.00 0.25 0.00

6 Sunflowers 0.00 1.00 0.00

12 Sweet corn 0.00 0.25 0.00

13 Ornamental/pop corn 0.00 0.25 0.00

21 Barley 0.00 0.00 0.00

22 Durum wheat 0.00 0.00 0.00

23 Spring wheat 0.00 0.00 0.00

24 Winter wheat 0.00 0.00 0.00

25 Other small grains 0.00 0.00 0.00

26 Wheat/soybeans double crop 0.00 0.25 0.00

27 Rye 0.00 0.00 0.00

28 Oats 0.00 0.00 0.00

29 Millet 0.00 0.00 0.00

31 Canola 0.00 1.00 0.00

32 Flaxseed 0.00 1.00 0.00

33 Safflower 0.00 1.00 0.00

34 Rape seed 0.00 1.00 0.00

35 Mustard 0.50 1.00 0.25

36 Alfalfa 0.00 1.00 0.50

37 Other hays 0.00 0.75 0.50

38 Camelina 0.00 1.00 0.00

39 Seed crop 0.25 0.25 0.25

41 Sugarbeets 0.00 0.00 0.00

42 Dry beans 0.00 0.25 0.00

43 Potatoes 0.00 0.00 0.00

44 Other crops 0.00 0.00 0.00

47 Miscellaneous vegetables/fruits 0.25 0.25 0.25

52 Lentils 0.00 0.00 0.00

53 Peas 0.00 0.75 0.00

57 Herbs 0.00 1.00 0.75

58 Clover/wildflowers 0.75 1.00 0.50

59 Seed/sod grass 0.00 0.00 0.00

60 Switchgrass 0.00 0.00 0.00

61 Fallow/idle cropland 0.50 0.50 0.50

68 Apples 1.00 0.00 0.00

69 Grapes 0.00 0.00 0.00
Appendixes 1–8 57

Table 6–1. Floral resource values for land-cover types used in Integrated Valuation of Ecosystem 

Services and Tradeoffs (InVEST) managed bee model runs.—Continued

[NLCD, National Land Cover Data]

Code Land cover Season 1 Season 2 Season 3

87 Wetlands 0.25 0.50 0.25

111 NLCD-open water 0.00 0.00 0.00

121 NLCD-open space 0.50 0.50 0.50

122 NLCD-developed/low intensity 0.50 0.50 0.50

123 NLCD-developed/medium intensity 0.25 0.25 0.25

124 NLCD-developed/high intensity 0.00 0.00 0.00

131 NLCD-barren 0.00 0.00 0.00

141 NLCD-deciduous forest 0.75 0.25 0.00

142 NLCD-evergreen forest 0.00 0.00 0.00

143 NLCD-mixed forest 0.75 0.25 0.00

152 NLCD-shrubland 0.25 0.25 0.25

171 NLCD-grassland herbaceous 0.25 0.25 0.25

190 NLCD-woody wetland 0.50 0.50 0.25

195 NLCD-herbaceous wetland 1.00 0.75 0.50

205 Triticale 0.00 0.00 0.00

206 Carrots 0.00 0.00 0.00

222 Squash 0.00 1.00 0.00

224 Vetch 0.00 0.75 0.25

225 Double crop winter wheat/corn 0.00 0.25 0.00

229 Pumpkins 0.00 1.00 0.00

236 Double crop winter wheat/sorghum 0.00 0.00 0.00

240 Double crop soybeans/oats 0.00 0.25 0.00

241 Double crop corn/soybeans 0.00 0.25 0.00

246 Radishes 0.00 0.00 0.00

254 Double crop barley/soybean 0.00 0.25 0.00

401 Temporary wetland 0.25 0.25 0.25

402 Seasonal wetland 0.25 0.25 0.25

403 Semipermanent wetland 0.50 0.50 0.25

404 Permanent wetlands 0.50 0.50 0.25

405 Lacustrine 0.25 0.25 0.25

406 Riverine 0.25 0.25 0.25

407 Artificially flooded wetland 0.00 0.00 0.00

408 Wetland 160-meter buffer 0.25 0.25 0.25
58 The Integrated Landscape Modeling Partnership—Current Status and Future Directions

Appendix 7. Select Subarea Settings Used in Agricultural Policy/

Environmental Extender (APEX) Runs

Table 7–1. Select subarea settings used in Agricultural Policy/Environmental Extender (APEX) runs.

[USLE, Universal Soil Loss Equation]

Variable Definition Value Comment

STDO Standing dead crop residue 0

AZM Azimuth orientation of land slope 0

CHL Distance from outlet to most distant point on watershed 

(channel length)

0

CHD Channel depth 0

CHS Mainstream channel slope 0

CHN Manning’s N for channel 0

UPN Manning’s N for upland 0.17 shortgrass prairie/dense grass.

FFPQ Fraction of flood-plain flow 0 no filter strip.

RCHL Channel length of routing reach 0 for EXTREME subareas.

RCHL Channel length of routing reach 0.025 for DOWNSTREAM subareas (kilometer).

RCHD Channel depth of routing reach 0

RCBW Bottom width of channel of routing reach 0

RCTW Top width of channel of routing reach 0

RCHS Channel slope of routing reach 0

RCHN Channel Manning’s N of routing reach 0

RCHC USLE crop management channel (C)factor 0.0001

RCHK USLE erodibility channel (K)factor 0.0001

RFPW Reach flood-plain width 0

RFPL Flood-plain length 0

PEC Erosion control practice factor 0.7
Appendixes 1–8 59

Appendix 8. Select Control File Settings Used in Agricultural Policy/

Environmental Extender (APEX) Runs

Table 8–1. Select control file settings used in Agricultural Policy/Environmental Extender (APEX) runs.

[m/m, meter per minute; mm/h, millimeter per hour; mm, millimeter]

Variable Definition Value Comment

IET Potential evapotranspiration equation code 4

ISCN Stochastic CN estimator code 0

ITYP Peak rate estimate code 3

ISTA Static soil code 0

IHUS Automatic heat unit scheduling 0

NVCNO Non-varying CN-CN2 used 4

QG Channel capacity flow rate 1 to force flood-plain flow.

QCF Exponent in watershed area flow rate equation 0.5

CHSO Average upland slope (m/m) in watershed 0.099 average of upland subarea slope.

BWD Channel bottom width/depth, in m/m 5

FCW Flood-plain width/channel width, in m/m 10

FPSC Flood-plain saturated hydraulic conductivity, in mm/h 1 unable to find good data for this.

GWSO Maximum ground water storage, in mm 50

RFTO Ground water residence time, in days 0

DIAM Soil particle diameter 50 microns.
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Mushet—The Integrated Landscape Modeling Partnership—Current Status and Future Directions—Open-File Report 2016–1006

ISSN 2331-1258 (online)

http://dx.doi.org/10.3133/ofr20161006