Management of Cowbird Traps on the

Landscape: an Individual-based

Modeling Approach for Fort Hood,

Texas

by

Ann-Marie Trame, Steven J. Harper, and James Westervelt

The presence of brown-headed cowbirds

(BHCO) affects local songbird populations,

including breeding populations of black-capped

vireos (BCVI) and golden-cheeked warblers

(GCWA), across large distances on Fort Hood,

Texas. Due to the impact of BHCO parasitism

on these two endangered species, Fort Hood

personnel have conducted a BHCO control

program since 1991, which includes livetrapping and shooting individual BHCO

throughout the breeding season. The objective

of this study was to develop a model that

simulates BHCO breeding and feeding habitats

across the Fort Hood landscape, daily BHCO

movement behavior, and the placement and

management of BHCO traps. The model

generates predictions of sites most likely to

receive frequent visitation by feeding BHCOs,

and compares the capture success among

different trapping strategies. Output suggests

that the model is fairly stable to variation in

several input variables, and the model

accurately captures current knowledge about

BHCO movement and locations on Fort Hood.

Ongoing efforts will include improved mapping

of nearby feeding habitat across the installation

boundary, and validation of BHCO visitation

output.

US Army Corps

of Engineers

Construction Engineering

Research Laboratories

USACERL Technical Report 98/121

September 1998

Approved for public release; distribution is unlimited.
USACERL TR 98/121 iii

Foreword

This study was conducted for III Corps and Fort Hood under Military

Interdepartmental Purchase Request (MIPR) Numbers 7DCER00399 and

8DCER00242; Work Units “Brown-headed Cowbird Simulation Model” and

“Improvement/Validation of the Individual Cowbird Behavior Model (ICBM).”

The technical monitor was John Cornelius, Fort Hood Endangered Species

Branch.

The work was performed by the Natural Resource Assessment and Management

Division (LL-N) of the Land Management Laboratory (LL), U.S. Army

Construction Engineering Research Laboratories (USACERL). The USACERL

principal investigator was Ann-Marie Trame; Jim Westervelt was Assistant

Investigator. Steven J. Harper was a Research Fellow with the Oak Ridge

Associated Universities post-graduate program. Dr. Harold E. Balbach is Acting

Chief, CECER-LL-N; and Dr. John T. Bandy is Operations Chief, CECER-LL.

The USACERL technical editor was Gloria J. Wienke, Technical Information

Team.

COL James A. Walter is Commander and Dr. Michael J. O’Connor is Director of

USACERL.
iv USACERL TR 98/121

Contents

SF 298......................................................................................................Error! Bookmark not defined.

Foreword ...............................................................................................................................................iii

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

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

Objectives............................................................................................................................3

Approach.............................................................................................................................3

Scope ..................................................................................................................................4

Mode of Technology Transfer...............................................................................................4

2 Modeling Approach...................................................................................................................... 5

Introduction to GIS ..............................................................................................................5

Introduction to the Simulation Modeling Environment.........................................................6

3 Cowbird Behavior Component................................................................................................... 9

Input Development ..............................................................................................................9

Swarm Model ....................................................................................................................15

4 Cowbird Trapping Component ................................................................................................. 21

GIS Input ...........................................................................................................................21

Swarm Model ....................................................................................................................23

5 Application and Results............................................................................................................ 26

Cattle Distances Moved ....................................................................................................26

Cowbird Distances Moved.................................................................................................26

Cumulative Visitation Patterns ..........................................................................................28

Comparison of Trapping Strategies...................................................................................29
USACERL TR 98/121 v

6 Conclusions and Future Application...................................................................................... 32

List of Abbreviations......................................................................................................................... 33

References.......................................................................................................................................... 34

Appendix A: Input to Simulation.................................................................................................... 37

Appendix B: Output and Statistics................................................................................................ 41

Appendix C: Density Output........................................................................................................... 42

Appendix D: Rule Output ................................................................................................................ 47

Distribution
vi USACERL TR 98/121

List of Figures and Table

Figures

1 An overview of the ICBM model structure........................................................6

2 User dialog boxes available through the Observer Swarm component of the

ICBM ..................................................................................................................................8

3 GRASS map depicting grassland and non-grassland cells classified from 1996

Landstat imagery using a neural network approach.....................................................10

4 Mathematical relationships used to calculate grazing quality of Feeding Area in

the ICBM. .........................................................................................................................13

5 Example depiction of initial location of corrals (circles) and cattle herds (small

squares). ..........................................................................................................................18

6 Example output showing the cumulative number of cattle herds moving the

equivalent of 1,2, or more than 2 cells (each cell = feeding area, 56.25 ha in size)

on a daily basis ................................................................................................................18

7 Example output showing the cumulative numbers of BHCO caught in all traps

throughout a simulation run .............................................................................................21

8 Example depiction of 24 BHCO traps placed onto the land of Fort Hood

according to the 24 sites most likely to have BHCO visitation.........................................22

9 Example depiction of corrals, cattle herds, individual BHCOs, and traps. ................24

10 Percent of cattle herds moving across 0,1,2, or 3 feeding areas on a daily

basis.................................................................................................................................26

11 Mean daily searching distances and mean final distances between BHCO

breeding sites and afternoon feeding sites, as output from ICBM simulation and as

measured by telemetry studies on Fort Hood..................................................................27

12 Number of BHCO removed from Fort Hood land in the ICBM simulation

exercise comparing trap movement intervals and relocation distance restrictions..........31

13 Numbers of BHCO removed from Fort Hood land in the ICBM simulation

exercise comparing different numbers of Hybrid and Mega traps ...................................31

Table

1 Comparison of final, straight-line distances between breeding and feeding

sites on a daily basis........................................................................................................27
USACERL TR 98/121 1

1 Introduction

Background

The presence of brown-headed cowbirds (BHCO; Molothrus ater) affects local

songbird populations across large distances. The BHCO is an obligate brood

parasite; females lay their eggs in the nests of other species. Over 200 species of

birds are known to be parasitized by BHCOs (Friedmann and Kiff 1985),

including two endangered species that breed on Fort Hood, Texas: the blackcapped vireo (BCVI; Vireo atricapillus) and the golden-cheeked warbler (GCWA;

Dendroica chrysoparia). A parasitic strategy allows the BHCO to use breeding

sites that are not closely coupled with their foraging requirements; since they do

not have a nest or offspring to protect, their daily movements can be extensive

(up to 7 km/day; Rothstein, Verner, and Stevens 1984). The spatial arrangement

of feeding areas and breeding habitats on the landscape strongly influences the

population and breeding ecology of the BHCO. Agricultural land use, especially

feedlots and overgrazed grasslands, provides ideal foraging habitats for BHCOs.

Research has found higher parasitism rates in landscapes with forest openings,

clearcuts, small tracts of forests, and edges, compared to landscapes of large,

continuous forest tracts, especially if foraging habitat is abundant (Gates and

Gysel 1978, Brittingham and Temple 1983, Robinson et al. in press). The

landscape of Fort Hood supports large numbers of BHCOs, although the

breeding population has not been counted.

In many cases, host adults raise the BHCO young instead of their own, so brood

parasitism can have a substantial impact on host populations (May and

Robinson 1985). The BHCO parasitizes both the BCVI and GCWA, but the BCVI

seems particularly vulnerable. The incubation period for BCVI is 14 to 17 days

(Graber 1961), compared to an 11-day period in the BHCO (Friedmann 1963).

This gives the BHCO a developmental advantage over the smaller BCVI young,

so BCVI rarely fledge from nests that also contain BHCOs. The only defense

demonstrated by BCVIs is nest desertion, which is much more common in

parasitized nests than in unparasitized nests. This strategy leads to lower nest

success in parasitized nests (Hayden et al. in press). After nest desertion, BCVIs

often renest, but the overall low productivity due to BHCO parasitism has been

identified as a major reason for the endangered status of the species (U. S. Fish

and Wildlife Service [USFWS] 1991). With the expansion of BHCO foraging
2 USACERL TR 98/121

habitat over the past century, an increase in BHCO populations has been

documented (Mayfield 1965; Brittingham and Temple 1983). This increase has

been implicated in the decline of a large number of passerines (Robinson and

Wilcove 1994; Robinson et al. 1995).

Due to the impact of BHCO parasitism on endangered species, Fort Hood

personnel have conducted a BHCO control program since 1991 (reviewed by

Hayden et al. in press). Experience demonstrated that trapping female BHCOs

from endangered species nesting habitat was not as successful as trapping in

BHCO feeding areas, especially in areas frequented by cattle. Increased

understanding of large-scale BHCO movements has improved capture rates in

the control program. Currently, a BHCO telemetry project, led by The Nature

Conservancy of Texas, is underway on Fort Hood (Cook, Koloszar, and Goering

1995).

Computer-based landscape simulation modeling can be used to evaluate the

effectiveness of alternative land management strategies. This report documents

the design and development of a simulation model that focuses on the behavior

of the BHCO. The Individual Cowbird Behavior Model (ICBM) simulates a

landscape of BHCO breeding and foraging habitats, cattle herd movements,

BHCO movements, and the placement and management of BHCO traps, over the

course of one breeding season (April, May, and June). The input variables and

trapping strategies can be altered between different runs, and the resulting

outcomes compared, to assess which trap management strategies lead to the

highest rates of BHCO removal.

The ecology and management of BHCOs is well-suited to landscape simulation

since (1) the BHCO responds to landscape level patterns in foraging and

breeding habitats, and affects passerine populations across large areas, and (2)

management of BHCOs is best accomplished by targeting control across the

landscape. The 87,890 hectare landscape of Fort Hood has been influenced by

the presence of human activity and livestock grazing over the past century.

Cattle grazing is currently an important consideration in the ecology of the

BHCO and endangered species management. Landscape features of Fort Hood,

combined with an understanding of BHCO behavior, have been captured in a

simulation model to identify trapping practices most likely to maximize removal

of female BHCO that breed within endangered species areas.
USACERL TR 98/121 3

Objectives

The objectives of the study were to: (1) develop the ICBM in consultation with

Fort Hood endangered species biologists and with ecologists from The Nature

Conservancy on Fort Hood and (2) apply it in order to compare the efficacy of

various BHCO trapping strategies through simulation modeling. To accomplish

this, researchers summarized and incorporated available information about the

Fort Hood landscape and knowledge of BHCO movement behavior into an

individual-based model. The modeling objectives were (1) to develop a model

structure that captured the movement dynamics of female BHCOs on Fort Hood,

and (2) to capture information about the landscape, cattle, and BHCOs, as

available from input maps and literature review.

Approach

This work evolved from development of the Fort Hood Avian Simulation Model

(FHASM; Trame et al. 1997), a simulation tool for evaluating changes in BCVI

and GCWA habitat and populations. According to FHASM simulations, the most

effective management activity for increasing the fecundity of the BCVI was to

trap BHCOs. This suggested that development of a relatively simple model

focusing on the decisions of individual female BHCOs might improve BHCO

trapping and BCVI reproduction.

Development of the ICBM began with an assessment of known and available

information on female BHCO behavior. Literature on individual modeling

approaches, BHCO biology, cattle movement patterns, and predator-prey

relationships was reviewed. The results of the literature review revealed the

important processes that formed the structure of the model. The model was built

in two stages:

1. using Geographic Information Systems (GIS) analysis, in Geographic

Resources Analysis Support System (GRASS) 4.2, to capture spatially explicit

habitat characteristics and initial locations of cattle, BHCOs, and traps, and

2. using an individual simulation modeling environment to program dynamic

interactions among the cattle, BHCOs, and traps.
4 USACERL TR 98/121

Scope

The ICBM simulates the landscape of Fort Hood and all adjacent areas within 7

km of the installation boundary. The ICBM simulates changes occurring during

the months of April, May, and June, the breeding season of the endangered

species of concern, and incorporates a time-step of 1-day intervals. The model

includes BHCO breeding and feeding habitat types; habitat variables affecting

cattle forage behavior; the movements and interactions between habitat types,

cattle, and BHCOs; the occurrence and management of BHCO traps; and the

trapping of BHCOs through time. Behavior of cattle and BHCOs is captured at a

750-m X 750-m resolution, representing approximately 56 hectare-sized sections

of land. This scale is consistent with data on cattle behavior and estimates of

BHCO breeding territory size (Bailey, Walker, and Rittenhouse 1990; Cook,

Koloszar, and Goering 1995).

Mode of Technology Transfer

The product of this work effort (including a summary of the results of

comparative modeling simulation runs) will be transferred to the Fort Hood

Endangered Species Branch. The relative efficacy of various trapping strategies

(e.g., how many traps to use, which types of traps, how often to move them, etc.),

in capturing female BHCOs with potential for parasitizing endangered species,

will be reported.

The ICBM is available for further development. The occurrence and behavior of

cattle outside the installation boundary will be improved in Fiscal Year 98

(FY98). A World Wide Web (WWW) user interface for running the ICBM and

retrieving output will also be developed during FY98. Other development

possibilities include applying simulated cattle or BHCO occurrences to other

management concerns or extending the model to other locations at which BHCO

or livestock behavior is a management consideration.

This report is available on the USACERL web page at http://www.cecer.army.mil
USACERL TR 98/121 5

2 Modeling Approach

Conceptually, the ICBM consists of two components: the BHCO behavior

component and the BHCO trapping component. The behavior component

generates maps of cumulative visitation by cattle and BHCOs, providing a

foundation for the trapping component. The trapping component simulates

management decisions about trap placement and trap movement rules, and

monitors captures of BHCO in traps. Both components were developed using a

combination of GIS analyses/input, and dynamic modeling within the simulation

environment. Development and execution of the ICBM model was accomplished

on machines running versions of the UNIX operating system: Solaris 5.0 on a

Sun UltraSparc computer and Redhat Linux running on an IBM-compatible PC.

Introduction to GIS

The researchers used GRASS to perform a number of GIS data development and

analysis steps. GRASS commands were combined with standard UNIX

commands in shell scripts to create efficient analysis steps. Various digital maps

in a pre-existing Fort Hood database were analyzed to evaluate habitat for

cattle, BHCO, GCWA, and BCVI. Input maps included streams, roads, and the

installation boundary. Analysis steps generated habitat quality index maps for

cattle and BHCOs. The GRASS GIS was also used to generate scenarios

reflecting random placement of varying densities of birds and cattle corrals.

The ICBM is controlled though an integrated set of UNIX shell scripts that

process user options through GIS analyses, and ultimately generate simulation

model input files. Once the initial state of the landscape is defined through GIS

analyses, the simulation component of ICBM, developed in Swarm (a dynamic

simulation modeling environment) is initialized and run. Details of this process

are described below.

GRASS is a public-domain GIS, which allowed direct access to GRASS data

layers during the Swarm ICBM simulation. During Swarm simulation runs,

GRASS raster maps were read and written. Once written, the output maps were

then analyzed and displayed using the GIS software.
6 USACERL TR 98/121

Introduction to the Simulation Modeling Environment

For this project, the researchers adapted the Swarm dynamic simulation

modeling environment, which is a programming environment for modeling

individual, discrete entities and events (Santa Fe Institute). The object-oriented

modeling approach simulates independent entities (“agents”) interacting via

discrete events. This simulation uses agents to represent individual organisms.

Simulations consist of groups of many interacting agents. For example, an

ecosystem simulation could consist of agents representing individual predators

and their prey. The fundamental component that organizes the agents of a

Swarm model is an object called a “swarm.” A swarm is a collection of agents

associated with activity schedules. For example, a swarm could be a collection of

15 predators, 50 prey, and a simple activity schedule: the prey hiding and the

predators eating the prey. An activity schedule is a sequence of actions to be

performed. Swarm objects can also be swarms, which provides a hierarchical

structure to the model (Figure 1).

Figure 1. An overview of the ICBM model structure.
USACERL TR 98/121 7

The primary swarm in the ICBM is the Observer Swarm. It provides a map

display, several point-and-click dialog boxes that allow the user to alter initial

parameter settings (Figure 2; Appendix A), and dynamic output during

simulations runs (see Appendix B for details about output). The main

simulation is captured inside the Observer Swarm as the Model Swarm.

The Model Swarm is composed of object classes that represent the difference

ecological entities on Fort Hood. The objects representing each cattle herd and

individual BHCO model the behavior of the organisms based on their

characteristics and their locations on the map. Each of the individual cattle

herds or BHCOs accumulates its own unique history throughout the simulation.

Other objects represent feeding areas, corrals, and traps, each of which is

characterized by (x,y) coordinates in the input files. An object called the “region

manager” maintains spatial information about each object, and about the spatial

relationships between objects. These properties are maintained throughout the

simulation. As the objects interact, they can query and use information about

other objects.
8 USACERL TR 98/121

Figure 2. User dialog boxes available through the Observer Swarm component of the ICBM.
USACERL TR 98/121 9

3 Cowbird Behavior Component

Input Development

Bands from April 1996 Landsat TM (thematic mapping) imagery were used to

generate a map containing unsupervised classification of vegetation types. This

map was then categorized into BHCO breeding habitat types and cattle grazing

habitat types. Additional GIS analyses provided the following inputs to the

dynamic simulation model: locations of female BHCOs in breeding territories,

locations of potential cattle grazing areas and their associated grazing quality

values, and present-day corral locations. Details of the analyses are provided

below. The Swarm modeling environment is not map-based. Spatial inputs were

provided to the dynamic Swarm model as lists of (x, y) coordinates with

associated identities and characteristics. Swarm provides a tool for keeping

track of the characteristics, histories, and interactions of many individual

entities, which may include their site coordinates. Thus, model output can be

mapped. For this reason, Swarm was linked to GRASS 4.2 to display a

background image during simulation, and to visualize and process spatial

output.

Evaluation of Habitat Quality

The landscapes of Fort Hood and adjacent lands within 7 km were represented

in a GRASS map of two vegetation types known to influence BHCO behavior

(Figure 3). The two vegetation categories, grassland and non-grassland, were

determined from April 1996 Landsat TM imagery through neural network

classification.* The neural network was trained with 152 Land Condition Trend

Analysis (LCTA) points from 1995. Ninety-one points were known to be

grassland communities; 61 points were known to be non-grassland communities,

based on the PCC (Plant Community Classification) methodology (Tazik et al.

 

* Results of two neural network calculations were combined to create the final vegetation map. Only cells classified

as grassland in both calculations were considered grasslands in the ICBM. Settings for the two calculations were:

percent trained: 0.75, 0.75; learn: 0.20, 0.60; momentum: 0.90, 0.50; epochs: 100, 100; iterations: 100, 1000.

The error for the two calculations was: 0.0979 and 0.0094.
10 USACERL TR 98/121

1992). The output consisted of grassland and non-grassland cells at 50-meter

(m) resolution.

The ICBM simulation cell size was 750-m X 750-m, so there were 225 smaller

(50-m) cells within each ICBM cell on the background habitat map. To maintain

as much information as possible at the larger cell size, the smaller imagery cells

were summarized using GRASS; those summary values were associated with the

larger cells. Each 750-m cell had a value (range: 0-225) for the number of 50-m

cells classified as grassland, and a value (range: 0-225) for the number of 50-m

cells classified as non-grassland. These values were important in determining

habitat quality for both the BHCO and cattle herds. Each ICBM cell had

associated values for landscape context as well. The 50-m grassland cells were

processed into contiguous patches using GRASS. Each 750-m cell had a value

(range: 0-17) for the number of unique patches, which represented the

fragmentation of grasslands at that scale (i.e., larger values resulted from

greater fragmentation). This value was important in determining grazing

quality of the cell for cattle. The mean distance of all 50-m non-grassland cells

from their nearest grassland neighbors was also calculated for each 750-m cell.

This value (range: 1-42, with each number representing an additional 50-m in

distance) represented an index of edge habitat in each larger cell. This value

was important in determining the BHCO breeding habitat quality of the cell.

Hereafter, “ICBM cells” refers to the 750-m cells; “smaller cells” refers to the 50-

m cells.

Figure 3. GRASS map depicting grassland and non-grassland cells classified from 1996

Landstat imagery using a neural network approach.

Key

White = grassland

Light gray = non-grassland

Medium gray = water

Dark grey = urban areas
USACERL TR 98/121 11

Cowbird Choice of Breeding Habitat

The ICBM was designed to assess trapping strategies for removal of female

BHCOs with the potential to parasitize the BCVI and the GCWA. For this

reason, breeding BHCOs were placed into ICBM cells consisting of at least 70

(out of 225) smaller cells of non-grassland vegetation; this was found to be the

lower range of non-grassland cover in endangered species restricted areas (of

both species). Three levels of BHCO breeding habitat quality were incorporated

into the ICBM. Breeding BHCOs have been shown to prefer host nests within

100 m of the boundary between forested areas and grasslands (Brittingham and

Temple 1983). ICBM cells with a value less than or equal to 100 m for mean

distance between non-grassland and grassland cells were considered high quality

due to a high edge component. ICBM cells with a value greater than 300 m for

mean distance between non-grassland and grassland cells were considered low

quality (Brittingham and Temple 1983), while intermediate mean distance

values were considered of intermediate quality. Female BHCOs were randomly

allocated to these cells based on relative habitat quality. The weighting of the

three habitat types was: low quality cells = 1.0; intermediate quality cells = 2.3,

and the highest quality cells = 3.6. These values were based on Brittingham and

Temple’s (1983) results that the proportion of nests parasitized in edge habitats

is 3.6 times higher than the proportion of nests parasitized in non-edge habitats

(65% vs. 18%). These weights can be altered by the user through the Observer

Swarm interface.

The model’s Cowbird Object was populated with a GIS input map (see Appendix

A, input script “3) Initial BHCO Locations”) of a user-defined, initial number of

individuals,* which were placed in breeding territories during ICBM

initialization. Breeding territory locations of individuals were static thereafter.

Each ICBM cell was 56.25 ha. This value approximated the median (50 ha) and

mean (65 ha) size of BHCO breeding territories from 1995 telemetry studies on

Fort Hood (Cook, Koloszar, and Goering 1995). Thus, each ICBM cell

represented the breeding territory of one female.

 

* The user defines the proportion of total possible number of female BHCOs, based on the number of ICBM cells that

are potential breeding habitat. If the number of BHCOs is the maximum (= 1.0), all cells with more than 70 smaller

non-grassland cells become occupied; at lower proportions, high quality cells are most likely to be occupied,

according to their relative weights.
12 USACERL TR 98/121

Cowbird Feeding Habitat Preferences

Choice of afternoon feeding habitat by BHCOs is strongly influenced by the

presence of cattle on short grass range (J. Cornelius, Wildlife Biologist,

Endangered Species Branch, Fort Hood, TX, professional discussion, 14 May

1997 [hereafter referred to as J. Cornelius 14 May 1997]; T. Cook, The Nature

Conservancy of Texas, Fort Hood, TX, professional discussion, 14 May 1997

[hereafter referred to as T. Cook 14 May 1997]). Telemetry studies of BHCO on

Fort Hood, conducted during the breeding seasons of 1995 and 1996, revealed

that more than 90% of the afternoon sightings of feeding BHCOs were of birds in

the presence of at least 30 cattle (T. Cook 14 May 1997). Because of this

relationship, the presence of cattle strongly influenced the feeding locations

selected by BHCOs in the ICBM; it was crucial to understand and predict cattle

movement. The ICBM determined the location of cattle herds (30 or more

individuals) on a daily time step, based on their expected behavior for a majority

of time in the afternoon (grazing, resting, and drinking combined), since this is

the period of time when feeding by BHCOs is strongly associated with cattle.

Livestock pastures and corrals located on neighboring lands (within 7 km of the

installation boundary) may have influenced the behavior of BHCOs on Fort

Hood, since birds that breed on post may have flown over the fenceline to feed on

private lands nearby. The ICBM included livestock, and BHCO feeding

decisions, on private land.

Cattle Grazing Habitat Preferences

Prior to federal ownership in 1942, the land of Fort Hood was divided into

private cattle ranches; this history influences grazing management decisions

today. Currently, the Central Texas Cattlemen’s Association holds a permit to

graze 3500 animal units (AU) on the Fort Hood open range. At any given time,

cattle are not evenly distributed across the landscape. They prefer to rest and

feed on elevated, breezy, flat areas with trees that provide shade and a nearby

water supply (G. Eckrich, Cowbird Control Technician, Fort Hood, professional

discussion, 25 June 1996 hereafter referred to as G. Eckrich 25 June 1996).

Cattlemen maintain corrals and provide supplemental hay and salt blocks at

former ranch and homestead sites, so cattle tend to concentrate in these

locations (J.Cornelius 14 May 1997). These factors contributed to the habitat

quality of grazing areas in the ICBM.

Abiotic factors, including the location of water, are recognized as primary

determinants of cattle movement over large areas (Senft et al. 1987, Coughenour

1991). The importance of water to cattle feeding behavior has been recognized
USACERL TR 98/121 13

for decades. Valentine (1947) recommended that stocking rates be adjusted for

distance to water, since vegetation far from water was underused compared to

vegetation near water. The influence of distance to water on cattle grazing also

was found to be highly significant by numerous other researchers (Cook 1966,

Stafford Smith 1988, Senft, Rittenhouse, and Woodmansee 1983). Other factors

may influence the relationship between water location and grazing preferences,

such as time of year, breed and age of livestock, grazing system, temporary

availability of outlying water (ephemeral sources), snow, succulent forage,

salting locations, presence of roads or trails, resistance of forage to grazing and

trampling, and topography (Valentine 1947, Stafford Smith 1988). However,

because the effects of these factors have not been mapped nor measured for the

landscape of Fort Hood, they were not included in the ICBM.

Figure 4. Mathematical relationships used to calculate grazing quality of Feeding Area in the

ICBM.
14 USACERL TR 98/121

The quality of habitat for grazing by cattle was calculated using four factors:

number of smaller cells designated as grassland within each ICBM cell, number

of patches of grassland within each ICBM cell (an index of fragmentation),

distance to nearest permanent source of water, and distance to nearest corral

(see Figure 4). Habitat quality was represented with a value from 0 to 1 for each

of these factors, and the product of these was used as the measure of grazing

habitat quality. While each factor currently contributes equally to the final

value, individual factors could be weighted to alter their relative importance.

The resulting GIS map of grazing quality was used to initialize the Feeding Area

Object in the Swarm dynamic model.

For this work, the authors developed mathematical relationships to describe how

the quality of the grazing habitat was influenced by each of the four components.

For distance to water, we used a negative exponential equation after the

approach documented in Senft, Rittenhouse, and Woodmansee (1983) and

Stafford Smith (1988). We modified this relationship such that at distances of 0,

1, and 2 cells (0 to 1.50 km) away from water, habitat quality was maximized

(value = 1), but after that point, habitat quality declined. Habitat quality

reached an extremely low value at a distance of 24 cells (18 km; Figure 4). This

relationship resulted in most cattle (96.4%) remaining within 5 cells of water (5

cells = 3.75 km), consistent with results of other researchers (Valentine 1947,

Hodder and Low 1978, Squires 1982, Holechek, Pieper, and Herbel 1989).

No quantitative information was available to describe cattle foraging response to

location of supplemental feeding, although it is believed to be significant

(McDougald, Frost, and Jones 1989). Personnel at Fort Hood believe the

majority of cattle remain within 2 km of corrals most of the time. The effect of

distance to corrals on cattle grazing habitat quality was characterized using a

negative exponential equation. Again, within a distance of 0, 1, or 2 cells (up to

1.5 km), the habitat quality remained at a maximum, and then declined

exponentially (Figure 4). Although we had no information regarding the

relationship between corrals and livestock movement on nearby private lands,

we assumed that the same mathematical relationship applied.

The effects of vegetation type and quality are less clear. Work in Australia

showed that vegetation type influenced movements; cattle went farther from

water to stay in preferred vegetation (Hodder and Low 1978). However, another

study suggested that cattle spent time on different vegetation types in proportion

to the area represented (neither seeking nor avoiding different areas). Dwyer

(1961) compared the proportion of three Oklahoma vegetation types, based on

soil type, to cattle occurrence: (1) loamy prairie made up 70% of the study site;

cattle spent 77.1% of their grazing time there; (2) shallow soils covered 12% of
USACERL TR 98/121 15

the pasture; cattle occurred on these areas 8.3% of the time; and (3) rocky breaks

were 18% of the pasture area; cattle spent 14.6% of their grazing time there. In

the ICBM, grazing habitat quality was affected by both amount of grassland and

fragmentation of grassland. Quality increased linearly from 0 to 1 as the

number of smaller grassland cells increased from 1 to 112, out of a maximum of

225 (up to 50% grass cover). ICBM cells containing 112 to 225 smaller grassland

cells (over 50% grass cover) were assigned a maximal quality value of 1 (Figure

4). Greater habitat quality values were assigned to areas of contiguous

grassland compared with areas containing numerous disjunct patches of

grassland. A habitat quality value of 1 was assigned to ICBM cells with only 1 or

2 patches of grass; this value decreased linearly as the number of unique

patches (fragmentation) increased. ICBM cells with 10 or more grassland

patches were assigned a quality value of 0 (Figure 4).

Swarm Model

Cattle Movement

Cattle movement in the ICBM was largely based on day-to-day movement

patterns measured by Bailey, Walker, and Rittenhouse (1990) in a 248-ha Texas

pasture of fairly homogenous forage. The pasture was initially divided into 63

units, but the final analysis grouped the units into 5 large sections, to calculate

transition probabilities among different areas on a daily time scale.* Their

observations suggested that location early in the morning is a good indication of

24-hr grazing locations at this spatial scale. This was corroborated by Low,

Dudzinski, and Muller (1981), who found that at daybreak, most cattle were

found in the plant community in which they spent most of their grazing time.

For the ICBM, we assumed that morning location results are a good predictor of

cattle location during the afternoon feeding period of BHCOs on Fort Hood. The

majority of the cattle in Bailey, Walker, and Rittenhouse’s (1990) study remained

in the same pasture section two mornings in a row in 29.4% of the observation

sets during March-May-June observations. This is very close to the rate at

which they moved to neighboring areas or more distant areas (35.3% and 29.4%

of the time, respectively). They moved between farthest areas only once out of 17

times (about 5.9% of the time). Bailey, Walker, and Rittenhouse concluded that

cattle display a “win-switch” strategy when foraging. Rather than stay in a

 

* The average area of the five sections was 49.6 ha, approximated in the ICBM by a cell size of 56.25 ha.
16 USACERL TR 98/121

productive area until the value drops, as predicted by optimal foraging theory

(Charnov 1976), they switch grazing areas before forage quality drops. This may

allow for increased replenishment between foraging bouts (but see Squires 1982

for different results from Australia). In addition, cattle may use spatial memory

(Bailey 1995, Bailey et al. 1996) to avoid recently grazed areas, which would lead

to the low community selectivity seen in this and other studies (e.g., Bailey

1995). Behavioral research has shown that cattle recall locations of food

depletion for at least 8 days (Bailey et al. 1996). Such mechanisms may be

important in homogenous environments; studies in heterogenous landscapes

may show different patterns arising from alternative mechanisms (Bailey,

Walker, and Rittenhouse 1990). For the ICBM, we assumed that forage quality

across feeding areas is relatively homogenous.

The ICBM initialized 30 corrals within the installation boundary at locations

specified by Fort Hood personnel (G. Eckrich, June 1997; see Appendix A, input

script “5) Corral Locations”). The average density of corrals on the Fort Hood

landscape was calculated. The same density of corrals was randomly placed on

the landscape outside the installation boundary, to allow for the influence of

agricultural lands nearby. This process resulted in 86 corrals being placed in

random positions off-post. The Map Manager registered each corral location and

assigned a specified number of cattle herds (representing about 30 cattle each) to

each corral. To match current grazing policy on Fort Hood, 4 cow herds were

associated with each corral, for a total of 3,600 cattle in 120 unique herds on the

Fort Hood landscape, and a total of 10,320 cattle in 344 unique herds offpost.

Each Cow Herd Object identified the feeding areas within a fixed distance (3.75

km) from its associated corral, and created cow-memory objects for each (see

Appendix A, input script “4) Initial Cattle Grazing Quality”). To minimize

computational overhead during simulations, cow herds only considered moving

to these feeding areas. Although cattle were placed on both sides of the

installation boundary, movement did not occur across the border (which is

fenced).

During simulation, each Cow Herd Object chose which feeding area to occupy

each day (Figure 5). The choice was based on a calculated attractiveness value

for each associated Feeding Area, composed of three factors: (1) its grazing

quality (see Cattle Grazing Habitat Preferences), (2) time since first occupied

by the herd, and (3) distance from the object’s current location.

When a cow herd visited a Feeding Area, it was registered with the Feeding Area

Object and the Cow Herd Object itself. Following this initial visit, the grazing

quality of the cell was adjusted to match observed behavior that cattle rarely

occupied the same feeding area for more than 1 or 2 days, and avoided areas that
USACERL TR 98/121 17

they recently visited (Bailey, Walker, and Rittenhouse 1990). The value of the

visited Feeding Area decreases daily through the 5th day following initial

visitation. By that day the probability of re-visiting dropped to zero. The value

of the Feeding Area increased over the next 8 days. No reduction in value was

created due to recent occupation by a different herd, since avoidance is thought

to be based on memory of the cattle’s own occupation, not environmental cues.

When a cow herd visited a Feeding Area, the Cow Herd Object recorded the

herd’s location. This influenced the choice of feeding area during the next time

step, since cattle are more likely to move to adjacent or nearby feeding areas,

rather than distant areas. Attractiveness was not reduced for the Feeding Area

occupied, nor for immediately adjacent Feeding Areas, but the value declined in

a linear fashion for Feeding Areas at distances of 2, 3, and 4 cells. Beyond

distances of 4 cells, the attractiveness of a Feeding Area dropped to zero.

Each of these three attractiveness factors (grazing quality, distance from the

previous herd location, and time since first occupied) was represented by values

ranging from 0 to 1, which were multiplied together to calculate the overall

attractiveness value. At t = 1, each of the cattle herds moved to the first

randomly selected Feeding Area. For each time step of the simulation, the

process was repeated. Percent of cattle herds moving 0, 1, 2, and 3-or-more cells

distance is output in a moving strip chart at each time step (Figure 6). Average

distances moved by the ICBM cattle herds was captured and compared to values

reported in the literature (in Bailey, Walker, and Rittenhouse 1990), as one form

of model validation. This comparison is provided in the chapter titled

Application and Results.
18 USACERL TR 98/121

Figure 5. Example depiction of initial location of corrals (circles) and cattle herds (small

squares).

Figure 6. Example output showing the cumulative number of cattle herds moving the equivalent

of 1,2, or more than 2 cells (each cell = feeding area, 56.25 ha in size) on a daily basis.
USACERL TR 98/121 19

Cowbird Movement

Daily feeding area movement patterns of BHCOs have not been reported in the

literature to date. Four algorithms (rules) were evaluated to model BHCO

movement on a daily basis among Feeding Area Objects; each one assumed a

different level of knowledge about the landscape. In all cases, BHCO foraging

locations were based on the presence of a cow herd.

In rule 0, no previous knowledge was assumed. Each day, each BHCO moved

randomly from its breeding site to the nearest Feeding Area, and then to the

next nearest Feeding Area, until a Feeding Area occupied by cattle was

encountered.

In rule 1, decisions were made based first on memory of previously-used areas,

and then based on searches for the next nearest Feeding Areas occupied by

cattle. For each time step, each female was placed into a Feeding Area according

to the following sequential rules:

a. If the Feeding Area it last occupied again was occupied by cattle, then it

remained there for the day.

b. If that Feeding Area did not contain cattle, but the second-to-last-visited

Feeding Area was occupied by cattle, then it settled into this second location.

c. If neither of those Feeding Areas were occupied by cattle, it checked the

third-to-last-used Feeding Area, and stayed there if cattle were present.

d. If none of the three previous locations were occupied by cattle, the bird

searched other potential Feeding Areas by randomly visiting the next nearest

(but as-yet unvisited) Feeding Area, until one occupied by cattle was

encountered.

Rule 2 was identical to rule 1, except that BHCOs were capable of using memory

to assess the Feeding Areas located between those last visited. If a Feeding Area

contained cattle and was found between any of the previously-used Feeding

Areas, the BHCO would select that as its feeding site. All Feeding Areas within

1000 meters of the straight-line path between previously-visited sites became

potential feeding sites under rule 2. This “perception distance” could be modified

by the model user.
20 USACERL TR 98/121

Lastly, rule 3 assumed omniscient BHCOs; during each time step, each female

traveled directly to the Feeding Area nearest to its breeding site that contained

cattle.

To evaluate the results of these four alternatives, 100 runs were conducted using

each movement rule. In all runs, the number of BHCOs initialized was held

constant, at 25% of saturation densities. Corrals were placed outside the

installation boundary, at randomly selected locations, but matching the density

of corrals on-post. Each run consisted of 100 time steps.

For each of the four movement rules, the average distances moved each day

while en route to feeding sites, and the average straight-line distances between

breeding sites to feeding sites were calculated. The latter were compared to

available Fort Hood telemetry data (in Cook, Koloszar, and Goering 1995).

Details of this comparison are reported in the chapter titled Application and

Results. The results indicate that rule 2 provides acceptable patterns, while

also being logically defensible. Thus, we chose rule 2 for subsequent simulations.

Output from the BHCO behavior component was converted into two maps: one

reflecting the cumulative number of visits by cattle herds over the 100-day run,

and one reflecting the cumulative number of BHCO visits over the 100-day run,

for each Feeding Area. A limited sensitivity analysis was conducted to assess the

importance of off-post corral locations and densities, and the importance of

initial BHCO densities, on these cumulative cattle and BHCO visitation maps

(see chapter titled Application and Results). The map of cumulative BHCO

visitation was used as a primary input into the BHCO trapping component.
USACERL TR 98/121 21

4 Cowbird Trapping Component

The output of the BHCO behavior component was mapped as cumulative

visitation counts of both cattle herds and BHCOs to each Feeding Area. This

spatial information, combined with additional trap placement rules, was used as

input to the BHCO trapping component of the ICBM. This component simulated

management decisions about trap numbers, trap types, trap placement, and

schedules for moving traps throughout the breeding season. Some of these

decisions were captured through GIS analysis, and provided as input to Swarm,

while other decisions were modeled dynamically within Swarm. The output of

the BHCO trapping component was cumulative numbers of female BHCOs

caught in all traps (Figure 7).

GIS Input

The output of the BHCO behavior model, mapped as cumulative visitation

counts of both cattle herds and BHCOs to each Feeding Area, was used in

combination with trap placement rules to generate a Swarm input file of trap

sites. The model can reflect strategies such as dispersing traps evenly across the

installation, or encircling the installation boundary. Trap sites can be

constrained to areas near paved roads, and traps can be placed on either side of

the installation boundary. After GIS processing, the resulting ACSII file of (x,y)

coordinates was input into Swarm (Figure 8).

Figure 7. Example output showing the cumulative numbers of BHCO

caught in all traps throughout a simulation run.
22 USACERL TR 98/121

Figure 8. Example depiction of 24 BHCO traps placed onto the land of Fort Hood according to

the 24 sites most likely to have BHCO visitation.

Two files are input to the trapping component. The first file indicates the total

number of Mega and Hybrid traps, and the capture efficiency of each type of

trap. Capture efficiency is a value (described in Hayden et al. In press) between

0 and 1 indicating the proportion of birds visiting a Feeding Area that become

trapped daily. Since we do not know the probability of a female entering a trap,

we fixed a value for all traps of a given type (i.e., one capture efficiency for Mega

traps and one capture efficiency for Hybrid traps). The relative capture

efficiency of each type of trap is more important than the actual value, since the

actual number of females on the landscape is unknown, and the actual number

“caught” in the simulations is not predictive except in a relative sense when

comparing among different scenarios.

KEY

Small closed red = Hybrid trap

Large closed red = Mega trap

Open red = potential sites for move
USACERL TR 98/121 23

The second file includes the following information:

• Site coordinates (x,y) for trap locations.

• Whether that location is initialized with the trap present or whether it is

provided as a potential trap site for possible Hybrid trap placement during

simulation.

• Identity of trap type (Mega or Hybrid). All potential sites are characterized

as “Hybrid.”

• Relative level of expected cattle or BHCO visitation, based on the cumulative

visitation map output from the BHCO behavior component.

• Frequency at which trap is moved to a new location (measured in time step

units, only relevant for Hybrid trap types) can be given a unique value for

each trap, instead of managing all Hybrid traps on the same movement

schedule.

Swarm Model

The Trap Object kept track of the information in the two input files, as well as

additional trap movement rules set through the Swarm user interface. Mega

traps were placed on the landscape through the input files, and did not move.

Hybrid traps were initialized onto the landscape through the input files, and

then were controlled through several movement rules options (Figure 9). The

following possibilities for timing of trap movement were simulated:

1. No movement of any traps,

2. A fixed schedule of movement (in time-step units),

3. Trap movement triggered by the capture rate dropping below a user-provided

threshold, and

4. Trap movement triggered by a drop in capture rate, but only after a minimal

period of time has passed (e.g., even if capture rates are very low, the trap

cannot be moved any more frequently than a specified time interval).
24 USACERL TR 98/121

Figure 9. Example depiction of corrals, cattle herds, individual BHCOs, and traps.

Hybrid traps moved according to one or more of the following placement rules:

1. Each trap was placed into the site having the next-highest expected BHCO

visitation, as recorded in the input file, but which had not yet had a trap

placed in it,

2. An option that increased the distance between subsequent trapping locations.

This was accomplished by each potential trap location recognizing the

nearest current trap site (in its “neighborhood”), and then each trap being

moved to the farthest potential location among these “neighborhood” choices,

and

KEY

Corrals = green circles

Cattle herds = brown squares

Traps = red circles

Individual territory, BHCO = black dot; line connects

daily feeding territory with breeding territory
USACERL TR 98/121 25

3. An option that moved each trap into the site having the next-highest

expected BHCO visitations, but only if the site was located a minimal

distance from the current site. This minimal distance moved can be changed

through the Swarm user interface.

The number of female BHCOs captured in each trap was counted and the

cumulative total number trapped was output in a moving strip chart during

simulation.
26 USACERL TR 98/121

5 Application and Results

Cattle Distances Moved

ICBM output of daily cattle movement distances was compared to literature

values, which served as a validation exercise for the cattle movement algorithm

in the model. Values of distances moved by each cattle herd during a simulation

were captured in an ASCII output file and then compared to values reported by

Bailey, Walker, and Rittenhouse (1990). Results of ICBM simulation

approximated reported values, although the model over-predicted movements

across three cells and underpredicted the percent of cattle remaining in the same

cell for 2 days in a row (Figure 10).

Cowbird Distances Moved

To evaluate the 4 possible BHCO movement rules, 100 runs were conducted

using each rule, and output was compared to 1996 telemetry data (Cook,

Koloszar, and Goering 1996). In all runs, the number of BHCOs initialized was

Figure 10. Percent of cattle herds moving across 0,1,2, or 3 feeding areas on a daily basis.
USACERL TR 98/121 27

held constant, at 25% of saturation densities (n=312). Corrals were placed

outside the installation boundary, at randomly selected locations, but matching

the density of corrals on-post, for a total of 344 corrals off-post. Each run

consisted of 100 time-steps.

For each of the four movement rules, the average distance moved each day while

en route to feeding sites, and the average straight-line distance between

breeding sites to feeding sites were calculated, and the latter compared to

available Fort Hood telemetry data (in Cook, Koloszar, and Goering 1996; Figure

11). Results indicate that rule 2 provided acceptable patterns, and also was

logically defensible. Thus, we chose rule 2 for simulations performed for

comparison of various trapping scenarios.

Additionally, more detailed distance data from a single run were compared to

telemetry data (Table 1). Simulated mean and median distances moved were

similar to those measured through telemetry studies on Fort Hood.

Figure 11. Mean daily searching distances and mean final distances between BHCO

breeding sites and afternoon feeding sites, as output from ICBM simulation and as

measured by telemetry studies on Fort Hood (Cook, Kolozar, and Goering, 1996).

Table 1. Comparison of final, straight-line distances (d) between breeding and feeding sites on

a daily basis.

Mean d (km) Median d (km) Min Max

ICBM, All Birds 3.08 2.37 0 9.0

ICBM, Birds breeding in

BCVI habitat

3.54 3.09 0 9.0

Telemetry, Birds breeding

in BCVI habitat

2.68* 2.74* 0.1 13.1
28 USACERL TR 98/121

* Values are averaged from the daily movements of 7 individuals

Cumulative Visitation Patterns

Sensitivity analysis was conducted to assess (1) the influence of BHCO

initialization densities and off-post corral densities, and (2) the influence of the

four BHCO movement rules, on cumulative visitation output from the behavior

component. To evaluate the importance of off-post corral densities to cattle

movement, 100 runs were conducted with off-post corrals placed in random sites

at 50%, 100%, and 150% the density of on-post corrals. Results indicated that

variation in off-post corral densities did not significantly affect the cumulative

visitation of cattle herds (Appendix C, Figure C1). To evaluate the importance of

BHCO densities and off-post corral densities on BHCO movement, 100 runs were

conducted for each of the following twelve combinations of settings (for a total of

1200 runs):

• BHCO movement rule 2 for all runs,

• BHCO initialization densities equivalent to 25%, 50%, 75%, or 100%

saturation densities, with random, static breeding locations, and

• Off-post corral densities equivalent to 50%, 100%, or 150% on-post densities,

with random locations off-post.

Results suggest that neither initialization densities of BHCO nor off-post corral

densities qualitatively affect the cumulative visitation patterns of BHCOs

(Appendix C, Figures C2-C5). However, consistent differences do appear

between visitation patterns of BHCOs breeding in BCVI and GCWA habitats,

and between these BHCOs and all BHCOs on post. These results suggest that

certain sites with high BHCO visitation may be more likely to contain

individuals that threatened BCVI or GCWA populations, and trapping could be

focused on these areas for maximum benefit to the two listed species.

The second analysis evaluated the influence of the four BHCO movement rules

on cumulative BHCO visitation. These simulations were conducted using BHCO

initialization densities of 25% saturation levels, and equal densities of corrals onand off-post. Movement rule 0 (search next nearest areas until cattle are

located) tends to produce more sites with high visitation, whereas rules including

memory (rule 1 and rule 2) tend to decrease the number of “hot spots.” In all
USACERL TR 98/121 29

cases, overall patterns of hot spots and even infrequently visited sites are similar

(Appendix D, Figures D1-D3).

Regardless of movement rule, most birds breeding in BCVI habitat do not select

off-post feeding areas, except along the boundary of West Fort Hood (Figure D2).

Current ICBM simulations indicate that BHCOs with the potential to parasitize

GCWA may be more likely to feed across the installation boundary (Figure D3).

Access to better information about livestock distributions on private adjoining

lands will allow further evaluation of the importance of nearby foraging habitat

to BHCOs on Fort Hood.

Comparison of Trapping Strategies

The relative effectiveness of varying proportions of Hybrid and Mega traps and

different fixed schedules for trap movement were compared through ICBM

simulation. For all runs,

• BHCOs were initialized at 25% saturation densities,

• Off-post corral density was equal to on-post density,

• BHCO movement rule 2 was used, and

• Traps were allowed to be placed outside installation boundaries.

Trap placement was restricted to within 750 m of a road (“road” was defined by

categories 1-5 in the GRASS “roads_50” map). Trapping efficiencies were set as

follows: Mega traps = 0.3020; Hybrid traps = 0.1836. These values were

generated by calculating the relative capture rates of 1995 Mega traps and 1995

Hybrid traps from the raw Fort Hood data, assuming that visitation to the traps

was equivalent. These actual capture (and trap efficiency) rates were then

increased by the same proportion to allow reasonable capture rates during ICBM

simulation, considering the numbers of females simulated by the model. It is

only the relative value of the trap efficiencies that is important during

simulation, since BHCO population levels are arbitrarily set upon initialization,

and only the relative success of different trapping strategies is intended to be

used by land managers.

The first exercise used 12 Hybrid and 12 Mega traps; the Hybrid traps were

moved to new locations using 3 different schedules: every 7 days, every 28 days,

and every 56 days. Additionally, a minimum distance restriction was applied to
30 USACERL TR 98/121

control placement of Hybrids in the new locations. These distance restrictions

included: 0 km (no restriction to placement), 1 km, 2 km, 3 km, and 4 km. Mean

total numbers of BHCOs (+/- SD) trapped were calculated for each combination

of trapping strategies. Neither the trap movement interval nor the relocation

distance appear to affect overall capture success within the parameters used in

this exercise (Figure 12).

A second exercise compared varying numbers of Hybrid and Mega traps. For

these runs, Hybrid traps were moved at a fixed interval of 28 days; there were no

distance restrictions on relocation decisions. All other rules and parameters

were the same as in the first exercise. As one setting for this exercise, the actual

numbers of traps used on Fort Hood during May, June, and July, 1995 (i.e., 8

Mega and 36 Hybrid), were used. Furthermore, to better reflect recent trapping

practices on Fort Hood, these 36 Hybrid traps were not moved during the

simulation. Mean total numbers (+/- 1 SD) BHCOs trapped were calculated for

each combination of trapping strategies. The use of Mega traps had a larger

influence on total BHCO captured compared to an equivalent number of Hybrid

traps. Recent (1995) trapping practices on Fort Hood were found to have

intermediate capture success compared to the other trapping regimes simulated

in this exercise (Figure 13).
USACERL TR 98/121 31

Figure 12. Number of BHCO removed from Fort Hood land in the ICBM simulation exercise

comparing trap movement intervals and relocation distance restrictions.

Figure 13. Numbers of BHCO removed from Fort Hood land in the ICBM simulation exercise

comparing different numbers of Hybrid and Mega traps.

Striped bars = movement interval of 7 days

White bars = movement interval of 28

days

Grey bars = movement interval of 56

days

**This scenario was intended to evaluate actual

practices on Ft. Hood

using 8 Mega traps and 36

Hybrid traps.

Striped bars = 0 Hybrid

traps

White bars = 6 Hybrid

traps

Grey bars = 12 Hybrid

traps

Ten runs of each

scenario were conducted for 312 BHCOs

over 100 days.

Ten runs of each

scenario were conducted for 312 BHCO over

100 days.
32 USACERL TR 98/121

6 Conclusions and Future Application

The ICBM demonstrates that individual-based modeling technology is applicable

to landscape-scale wildlife management concerns, such as the management of

BHCOs across a large military installation. Even without full knowledge about

the factors that influence BHCO and cattle movement, initial ICBM output

compares well to data collected by field biologists. In addition, early output and

predictions were reviewed by Fort Hood Endangered Species Branch personnel,

who stated that the model appears reasonable, and holds promise for application

on landscapes with no previous trapping work (J. Cornelius; G. Eckrich; J.

Koloszar, Fort Hood endangered species technician, 16 December 1997). Field

work planned for 1998 will improve model input of livestock occurrence outside

the boundary of the installation. Additionally, data with which to validate cattle

and BHCO movement decisions will be gathered. The potential exists for

application of the ICBM by other land management agencies charged with

conservation of GCWA and BCVI breeding populations.
USACERL TR 98/121 33

List of Abbreviations

AU animal units

BCVI black-capped vireo

BHCO brown-headed cowbird

FHASM The Fort Hood Avian Simulation Model

GCWA golden-cheeked warbler

ICBM Individual Cowbird Behavior Model

USACERL United States Army Construction Engineering Research

Laboratories

USDA United States Department of Agriculture

USFWS United States Fish and Wildlife Service
34 USACERL TR 98/121

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553- 557.
36 USACERL TR 98/121

Senft, R.L., M.B. Coughenour, D.W. Bailey, L.R. Rittenhouse, O.E. Sala, and D.M. Swift, “Large

Herbivore Foraging and Ecological Hierarchies,” Bioscience vol 37, no.11 (1987), pp. 789-799.

Squires, V.R., “Behavior of Free-Ranging Livestock on Native Grasslands and Shrublands,”

Tropical Grasslands vol 16, no. 4 (1982), pp. 161- 170.

Stafford Smith, M.S., Modeling: Three Approaches to Predicting How Herbivore Impact is

Distributed in Rangelands. New Mexico State University Agricultural Experiment Station

Research Report 628 (1988).

Tazik, D.J., S.D. Warren, V.E. Diersing, R.B. Shaw, R.J. Brozka, C.A. Bagley, and W.R. Whitworth,

U.S. Army Land Condition-Trend Analysis (LCTA) Plot Inventory Field Methods, U.S. Army

Construction Engineering Research Laboratories (USACERL) Technical Report N92/03/ADA247931, (U.S. Army Construction Engineering Research Laboratories [USACERL],

February 1992).

Trame, A., S.J. Harper, J. Aycrigg, and J. Westervelt, The Fort Hood Avian Simulation Model: A

Dynamic Model of Ecological Influences on Two Endangered Species, USACERL Technical

Report 97/88 (USACERL, February 1997).

United States Fish and Wildlife Service (USFWS), Black-capped Vireo (Vireo atricapillus) Recovery

Plan (USFWS, Austin, TX, 1991).

Valentine, K.A., “Distance from Water as a Factor in Grazing Capacity of Rangeland,” Journal of

Forestry, vol 45 (1947), pp. 749-754.
USACERL TR 98/121 37

Appendix A: Input to Simulation

User Interface

maxHerdDistFromCorral : The maximum distance, in meters, that a herd is

allowed to move away from its associated corral. Range: 750- no max. Current

setting: 4000

herdVisitMapSaveFreq : Frequency (in time steps) that a map of cumulative

cattle herd visitation is output from the BHCO behavior component. Range: 1-

100. Current setting: 100

birdVisitMapSaveFreq : Frequency (in time steps) that a map of cumulative

total BHCO visitation is output from the BHCO behavior component. Range: 1-

100. Current setting: 100

doBirds : a 0/1 switch that includes birds in the ICBM simulation. Current

setting: 1 (yes)

maxBreedFeedDist : The maximum distance, in meters, that a BHCO is allowed

to search for and select a feeding site with respect to its breeding site. Range:

750- no max. Current setting: 9000.

traceBirdFlights : a 0/ 1 switch that allows the display to show the pathways of

all BHCOs as they search for feeding sites. Current setting: 0

birdMovementRule : User designates which of the 4 BHCO movement rules

should be used. Range: 0-3. Current setting: 2.

birdViewThresh : Distance to either side, in meters, that a BHCO can search for

cattle herds while travelling en route to previously-used Feeding Areas during

movement rules 1 and 2. Range: 0- no max. Current setting: 1000.

doTraps : a 0/1 switch that includes traps in the ICBM simulation. Current

setting: 1 (yes)
38 USACERL TR 98/121

moveFreq : If the ASCII BHCO trapping component input file does not specify

unique values for trap movement intervals for each mobile trap (e.g., the column

= 0 for all traps) then this parameter creates a fixed movement interval for all

mobile traps, in time- steps. Range 1-100. Current Setting: 28.

minAvgDailyTrapRate : If the ASCII BHCO trapping component input file does

not specify unique values for trap movement intervals for each mobile trap (e.g.,

the column = 0 for all traps), and the moveFreq parameter equals 0, then this

parameter specifies the threshold daily capture rate at which mobile traps are

moved. Otherwise, it is ignored, and a fixed movement schedule is used. Range:

0- no max. Current setting: N/A.

visitFreq : Interval, in time steps, at which trap managers visit traps and could

possibly move mobile traps. If this parameter > 1, then trap movement activity

will only occur during time steps when traps are visited as well as when other

criteria are met. Range: 1-100. Current setting: 1.

minTrapResidence : The minimum length of time for which mobile traps must

remain in a given location (to be used with the minimum capture rate trigger for

trap movement, so that traps are not moved every day, or some other

unrealistically frequent interval). Range: 0-100. Current setting: 7.

maximizeTrapDist : 0/ 1 switch to consider all possible trapping locations within

the vicinity of current or past trap locations, and then relocate each trap into the

farthest possible choice, to maximize distance between past and new trap

locations. Current setting: 0

trapSpacingDist : Sets the minimum distance (m) between past and new trap

locations when mobile traps are relocated. Range: 0- no max. Current setting:

N/A.

Input Files

1) Simulation Setup: setup.model

This file captures the user input from the Model Swarm dialog box shown in

Figure 2.

BHCO movement rules:

0 = birds fly to next-nearest Feeding Area sequentially, until cattle herd is

located
USACERL TR 98/121 39

1 = birds fly from breeding site to last three feeding sites in reverse order, then if

no cattle have been located, start searching as in Rule 0.

2 = birds fly to last three feeding sites, and can stop at in-between points if cattle

are present. If no cattle have been located, search as in Rule 0.

3 = Fly directly from breeding site to nearest Feeding Area with cattle.

2) Simulation Observer: setup.observer

This file captures the user input from the Observer Swarm dialog box shown in

Figure 2.

3) Initial BHCO Locations (Breeding Locations): xy_cowbirds

Easting Northing Area_type

Area_type categories:

1 = breed off- post (currently not used)

2 = breed in protected BCVI habitat

3 = breed in protected GCWA habitat

4 = breed on post but not in protected TES habitat

EXAMPLE:

613745 3472255 4

4) Inital Cattle Grazing Quality: xy_herdsuit

Easting Northing suitability on post = 1/ off post = 0

EXAMPLE:

614495 3473005 0.0442 1
40 USACERL TR 98/121

5) Corral Locations: xy_corrals

Easting Northing Number of herds on post = 1/ off post =0

EXAMPLE:

597995 3480505 4 0

6) Overview of Traps Used: traps

type number fixed(1)/ mobile (0) efficiency

EXAMPLE:

Hybrid 12 0 0.1836

Mega 12 1 0.3020

7) Individual Trap Information: xy_traps

Easting Northing Predicted Visits Days Init/Future (1/0) mobile/fixed

Definitions:

Predicted Visits = predicted number of BHCO visits at that site based on output

of the BHCO behavior component

Days = fixed number of days that mobile traps will stay in any location (can be

set differently for each individual trap)

Init/ Future = 1 means that this site will be initialized with a trap present

0 means that this site will be a possible future location for mobile

traps

EXAMPLE:

606995 3458005 254 0 1 fixed

608495 3452005 98 0 1 fixed

609245 3452005 148 28 1 mobile

609245 3452755 50 28 0 mobile
USACERL TR 98/121 41

Appendix B: Output and Statistics

Cattle Herd Movement:

Timestep Name Number Northing Easting Move Distance

BHCO Movement:

Timestep Name Number Northing Easting Breed-Feed Dist Travel-

(searching) Distance Bird Type (0-4)

Trap Locations and Capture Rates:

Timestep Name Number Northing Easting Trap Type BirdVisits

BirdCaptures Avg Daily Capture Rate

Cattle Herd Stats:

AvgDist (moved) Standard Deviation (dist moved)

BHCO Statistics:

AvgDistanceMoved Standard DeviationMoved AvgDistanceTravel (searched)

Standard DeviationTravel (distance searched)

Trap Statistics:

Total Captures
42 USACERL TR 98/121

Appendix C: Density Output*

 Figure C1. Cumulative visitation counts for cattle herds over 100 runs of 100 time steps each,

varying with three levels of off-post corral densities (50%, 100%, and 150% of on-post density).

* The intensity of the shading indicates higher cumulative visitation; the upper

value is shown in the legend for each map. Note the different absolute scale for

each map.

100% 150%

50%
USACERL TR 98/121 43

 50%

 100%

 150%

Figure C2. Cumulative visitation counts for all BHCOs over 100 runs of 100 time steps each, at

25% saturation levels of BHCOs, and three levels of off-post corral densities.
44 USACERL TR 98/121

 50%

 100%

 150%

Figure C3. Cumulative visitation counts for all BHCOs over 100 runs of 100 time steps each, at

50% saturation levels of BHCOs, and three levels of off-post corral densities.
USACERL TR 98/121 45

 50%

 100%

 150%

Figure C4. Cumulative visitation counts for all BHCOs over 100 runs of 100 time steps each, at

75% saturation levels of BHCOs, and three levels of off-post corral densities.
46 USACERL TR 98/121

 50%

 100%

 150%

Figure C5. Cumulative visitation counts for all BHCOs over 100 runs of 100 time steps each, at

100% saturation levels of BHCOs, and three levels of off-post corral densities.
USACERL TR 98/121 47

Appendix D: Rule Output

Rule 0 Rule 1

 

Rule 2 Rule 3

 Figure D1. Cumulative visitation counts for all BHCOs over 100 runs of 100 time steps each, at

25% saturation levels of BHCOs, comparing the effects of four BHCO movement rules.
48 USACERL TR 98/121

Rule 0 Rule 1

 

Rule 2 Rule 3

 

Figure D2. Cumulative visitation counts for BHCOs that breed in designated BCVI habitat, over

100 runs of 100 time steps each, at 25% saturation levels of BHCOs, comparing the effects of

four BHCO movement rules.
USACERL TR 98/121 49

Rule 0 Rule 1

 

Rule 2 Rule 3

 

Figure D3. Cumulative visitation counts for BHCOs that breed in designated GCWA habitat, over

100 runs of 100 time steps each, at 25% saturation levels of BHCOs, comparing the effects of

four BHCO movement rules.
50 USACERL TR 98/121

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