THE DISTRIBUTION AND DIVERSITY OF TEXAS

VERTEBRATES: AN ECOREGION PERSPECTIVE

by

ERIC ALLEN HOLT, B.S.

A THESIS

IN

WILDLIFE SCIENCE

Submitted to the Graduate Faculty of Texas Tech University in

Partial Fulfillment of the Requirements for

the Degree of

MASTER OF SCIENCE

AoDroved

/ December, 1999

Bos / ' • • " • • ^ . . ACKNOWLEDGEMENTS I ->

:vif ^ tin This work would not have been possible if it were not for the financial

^ support of many entities. More importantly, the employees of these entities SI/ I

provided unmeasurable moral and technical support. With this in mind, I wish to

thank the Texas Cooperative Fish and Wildlife Research Unit, the National Gap

Analysis Program, the US Geological Survey. Texas Tech University, the College

of Agricultural Sciences and Natural Resources, and the Department of Range,

Wildlife, and Fisheries Management. On a more personal level, a hearty thanks

is due to every person that I worked with on the Texas Gap Analysis Project. Not

a day went by that the conversations within this lab did not improve my

knowledge of the application of computers for managing natural resources. I

also thank Dr. Nick Parker for giving me, first, the opportunity, and second, the

freedom to work on this thesis. Unlike many thesis projects, I had the unique

opportunity to have every available resource available to complete my research

in the manner I wished. Dr. Parker is to thank for this. I also thank my other two

committee members. Dr. Mark Wallace and Dr. Mark McGinley, for their

guidance during the growth of this project and for their overall improvement of

this thesis. I also thank my good friend Dr. James Mueller. Jim informed me of

this graduate school opportunity and provided invaluable support during all steps

of this thesis's creation.

My final "thank you" is to my family. My mom and dad always encouraged

and supported my educational pursuits, even when it appeared as if I really was

not pursuing anything more than "hanging-out." I hope this thesis is a reward to

them for the many hours they spent helping me with my homework, editing my

papers, and preaching the importance of a college education. During this

project, my wife watched our two children, did most of the housework, took night

classes, worked nights, and gave birth to our third, and yes, last, child. Tammy is

a great lady and has supported everything (well, most everything) I have ever

done, even bringing her from the Rocky Mountains to the Texas High Plains.

Although Clifford Ahimas Holt, my father, was an architect by profession

and an artist by nature, he was also a naturalist and lover of the outdoors. My

father died when I was 8 years old. During the past 24 years, I have thought of

him every day. Most of these thoughts have been on lamenting on how much his

presence in my life would have improved me as a man, a father, and a biologist.

This thesis is dedicated to the memory of my father.

Ill

TABLE OF CONTENTS

ACKNOWLEDGEMENTS i

LIST OF TABLES v

LIST OF FIGURES vii

LIST OF ACRONYMS ix

CHAPTER

1. INTRODUCTION 1

1.1 Introductory Comments 1

1.2 Justification 3

1.3 Objectives 4

2. LITERATURE REVIEW 8

2.1 Biodiversity 8

2.2 Reserve Selection 9

2.3 Ecosystem Management 11

2.4 The Ecoregions of Texas 13

2.5 Large-Scale Studies on Texas' Vertebrates 15

2.6 Theories of Biodiversity 16

3. METHODOLOGY 22

3.1 Species List 22

3.2 Objective 1 25

3.3 Objective 2 27

3.4 Statistics 31

4. RESULTS 33

4.1 Species List 33

4.2 Objective 1 34

4.3 Objective 2 41

5. DISCUSSION 49

IV

LITERATURE CITED 56

APPENDICES

A. SPECIES PRESENCE VERSUS ABSENCE MATRICES FOR THE TERRESTRIAL VERTEBRATES LIVING IN THE MAJOR ECOREGIONS OF TEXAS 64

B. COEFFICIENT OF COMMUNITY VALUES FOR EACH PAIR OF TEXAS ECOREGIONS ACROSS EACH TERRESTRIAL VERTEBRATE GLASS AND ACROSS ALL TERRESTRIAL VERTEBRATES 82

C. SCATTER PLOTS INDICATING THE RELATIONSHIP BETWEEN HABITAT, SPATIAL. AND CLIMATIC VARIABLES AND THE SPECIES RICHNESS OF THE MAJOR ECOREGIONS OF TEXAS 87

LIST OF TABLES

1.1. Description of ecoregion-specific habitat variables used to predict the species richness of Texas ecoregions using simple linear regression 6

1.2. Description of ecoregion-specific spatial variables used to predict the species richness of Texas ecoregions using simple linear regression 6

1.3. Description of ecoregion-specific climatic variables used to predict the species richness of Texas ecoregions using simple linear regression 7

4.1. Species richness values for terrestrial vertebrates living in the major ecoregions of Texas 34

4.2. Number of species living in the major ecoregions of Texas that are listed on either state or federal threatened and endangered species lists 35

4.3. CGRS, CGI, and EE values for the vertebrate communities living in the major ecoregions of Texas 37

4.4. CGRS, CGI. and EE values for the vertebrate class-level communities living in the major ecoregions of Texas 40

4.5. The percent of each ecoregion currently being managed for the long-term protection of biodiversity 41

4.6. Descriptive characteristics of the major ecoregions of Texas 43

4.7. Significant (p < 0.05) simple-linear regression models predictingthe species richness of ecoregions for vertebrate classes based on habitat variables 44

4.8. Significant (p < 0.05) simple-linear regression models predicting the species richness of ecoregions for vertebrate classes based on spatial variables 45

4.9. Significant (p < 0.05) simple-linear regression models predicting the species richness of ecoregions for vertebrate classes based on climatic variables 47

VI

A . I . Species presence (Y) versus absence matrix for the 77 amphibian species living in the major ecoregions of Texas 65

A.2. Species presence (Y) versus absence matrix for the 500 avian species living in the ecoregions of Texas and the 5 extinct avian species that once lived in Texas ecoregions 67

A,3. Species presence (Y) versus absence matrix for the 164 mammalian species living in the ecoregions of Texas and the 8 extinct mammals that once lived in Texas ecoregions 76

A.4. Species presence (Y) versus absence matrix for the 174 reptilian species living in the ecoregions of Texas 79

B.I . Coefficient of community values for each pair of Texas ecoregions across each terrestrial vertebrate class and across all terrestrial vertebrates 83

VII

LIST OF FIGURES

2.1. The ecoregions of Texas (Bailey et al. 1994); the 13 major ecoregions are labeled 14

3.1. The location of weather stations found in the major ecoregions of Texas 30

4.1 . The location of biodiversity reserves among the major ecoregions of Texas 39

C.I . Scatter plots indicating the relationship between habitat variables and the species richness of the major ecoregions of Texas 88

C.2. Scatter plots indicating the relationship between spatial variables and the species richness of the major ecoregions of Texas 94

G.3. Scatter plots indicating the relationship between climatic variables and the species richness of the major ecoregions of Texas 102

VIII

LIST OF ACRONYMS

BAR Basin and Range

BLP Blackland Prairies

CGI Coefficient of Community Index

CGRS Coefficient of Community Rank Sum

CGP Central Gulf Prairies and Marshes

CTP Gross Timbers and Prairie

DEM Digital Elevation Model

EE Ecoregion Endemic

EGP Eastern Gulf Prairies and Marshes

EWP Edwards Plateau

GAP Gap Analysis Program

GIS Geographic Information System

MGP Mid-Coastal Plains

NM-GAP New Mexico Gap Analysis Project

OWP Oak Woods and Prairies

RGP Rio Grande Plain

RLP Rolling Plains

SGP Southern Gulf Prairies and Marshes

SKP Stockton Plateau

THP Texas High Plains

TPWD Texas Parks and Wildlife Department

TX-GAP Texas Gap Analysis Project

USGS US Geological Survey

IX

CHAPTER 1

INTRODUCTION

1.1 Introductory Comments

There is an urgent concem among the scientific community and the

general public concerning the loss of species on earth. This concern is fueled by

the thoughts of many (e.g., Ehriich 1988, Huston 1994, Noss and Peters 1995,

Wilson 1989,1992) who suggest that current and projected rates of extinction

are abnormally high and that extinction rates are associated with the impacts of

an exploding population of humans (see Soule 1991 and Cohen 1995). This

concern has led to the addition of the term biodiversity (a contraction of biological

diversity) to the English language (Huston 1994). The importance of conserving

biodiversity in the face of human actions that fragment, homogenize, and destroy

ecosystems has led to a woridwide increase in study devoted to the relatively

new field of conservation biology: "The branch of the biological sciences

concerned with the planning and management of natural resources, and

especially with the maintenance of the balance of nature, the diversity of species

and genetic material, and natural evolutionary change" (Academic Press 1999).

In the United States, the National Gap Analysis Program (GAP) of the

U.S. Geological Survey (USGS) is assessing the biodiversity of the nation. Scott

et al. (1993) described the Gap Analysis process in detail, and a complete

description of GAP methodologies and guidelines is described by the Gap

Analysis Program (1998). The following GAP synopsis is possible due to

information I obtained from these sources and from being a member of the Texas

Gap Analysis project (TX-GAP). In general, GAP seeks to identify "gaps" in the

representation of biodiversity within the current network of lands managed

primarily for native species and natural ecosystems (e.g.. State and National

Parks, Wildlife Management Areas, National Wildlife Refuges, and Wilderness

Areas) in the U.S. Throughout this document, these types of lands are

conveniently grouped and referred to as biodiversity reserves. The actual

1

identification of "gaps" is done within the context of a Geographic Information

System (GIS)(i.e., a computer system, including peripherals and software, that is

used to store, manipulate, and analyze geospatial data). Within the GIS,

geographically referenced digital maps, hereafter referred to as GIS coverages,

or simply coverages, representing the predicted location of flora and fauna are

overiaid in order to identify spedes "hot spots." The locations of "hot spots" are

then compared to coverages of existing biodiversity reserves and "gaps" are

Identified. Once identified, "gaps" can be filled by establishing new reserves or

by changing land-use practices.

As mentioned, one of the three main types of coverages required in a Gap

Analysis project is one representing the predicted geographic distribution of

animals. As a starting point, current GAP efforts focus on assessing the effort

afforded to the protection of terrestrial-vertebrate diversity (Gsuti and Grist 1998).

Thus, TX-GAP is creating predicted distribution coverages for the terrestrial

vertebrates breeding in, and native to. Texas. Gsuti and Grist (1998) described in

detail the process of creating these coverages. In simplest terms, these

coverages are created by identifying the habitat, within a species' expected

geographic range, in which the species is expected to be found. TX-GAP is

using range maps from field guides to represent the expected geographic ranges

of Texas veilebrates. Although these course range maps alone are not

adequate for GAP because they overestimate the distribution of spedes by

induding habitats in which the animal is not found (Scott et al. 1993), they are

suitable for identifying which spedes live in large-scale geographic areas such as

ecoregions. Ecoregions, in turn, like the more commonly used non-natural land

units (e.g., counties, states, arbitrary grid cells), can serve as the unit of measure

for describing distribution and diversity patterns of wildlife.

1.2 Justification

Due to its large size, Texas is rich in environmental diversity. This

diversity in dimate, vegetation, and geography is evident by the number of

ecoregions in the state. Bailey et al. (1994) divide Texas and California into 18

section-level ecoregions (described in greater detail in the literature review),

more than any of the other contiguous Unite States. Given this number of

ecoregions, Texas is naturally home to many wildlife spedes. The Texas Parks

and Wildlife Department (1997) recognized 1,039 separate species of terrestrial

vertebrates (i.e., amphibians, birds, mammals, and reptiles) as "living" in Texas.

Due to the number of ecoregions, the environmental diversity, and the large

number of resident vertebrates, Texas is well suited for describing the distribution

and diversity of vertebrates while using ecoregions as the unit of measure.

In addition to providing general descriptive information on the ranges of

Texas vertebrates, identifying what spedes live in each ecoregion can t>e an

important tool for conservation biologists to site new biodiversity reserves.

During this process, it is logical to first identify the ecoregion(s) in which a new

reserve(s) would be most benefidal. Once identified, smaller-scale information

can then be used to decide where within the ecoregion to actually place the

reserve(s).

Attributing ecoregions with species-presence data can also assist in

answering the question, "Why do more species live in one area than another?"

Several theories have been proposed to answer this question (Gurrie 1991,

Huston 1994, Pianka 1966, Rohde 1992). Gume (1991) suggested that these

theories can be grouped under 8 general headings: climate, climatic variability,

habitat heterogeneity, history, energy, competition, predation, and disturbance.

Most of the work on which these theories are based was done by first identifying

a set of land units. The species richness of each land unit (i.e., the unit of

measure) was then determined and values for various physical, environmental,

and biological variables were assigned to each unit. Relationships between the

chosen variables and species richness were then identified. It appears that the

nrjost common units of measure for this type of work were islands, continents, or

cells within some arbitrary grid.

Although the statewide distributions of Texas' vertebrates have been

studied (e.g., Owen 1988,1990; Owen and Dixon 1989; Rogers 1976; Ward

1990. 1994; Webb 1950), there has not been a single comprehensive study to

evaluate the distribution of birds, reptiles, amphibians, and mammals. In

addition, no studies have used GIS or evaluated species distributions using

geographic areas of the same relative size as Bailey's (1994) section-level

ecoregions as the unit of measure. GIS technology allows for the efficient

organization and analysis of geospatial data and ecoregions serve as convenient

natural-geographic units for evaluating biodiversity at large scales. Large-scale

geographic investigation of biological data is lacking (Root and Schneider 1993),

and many (e.g., Scott et al. 1987) suggest that this type of work is needed to

ensure the maintenance of biological diversity woridwide.

1.3 Objectives

One goal of this thesis was to provide information on the distribution and

diversity of Texas vertebrates in a manner that can be used by decision-makers

to assist in locating future biodiversity reserves. My objectives were to use the

ecoregion as the unit of measure for describing the distribution and diversity of

Texas vertebrates by determining:

1. which spedes live in each ecoregion,

2. the spedes richness for each ecoregion,

3. the number of threatened and endangered species living in each

ecoregion,

4. coefficient of community values, as a measure of uniqueness, for

each ecoregion pair.

5. the number of spedes living in each ecoregion that are found

only in that ecoregion, and

6. the current amount of land in each ecoregion currentiy managed

by state and federal agendes for the protection of biodiversity.

In addition, several theories induding island biogeography, habitat

heterogeneity, dimatic stability, productivity, and latitude have been proposed to

explain why more species live in one area than another. A second goal of my

research was to assess how well these theories explained vertebrate diversity in

Texas. Specifically. I tested the following null hypothesis:

Ho: The number of vertebrate spedes living in the ecoregions of Texas is

not related to the diversity of habitats found within ecoregions, the

spatial location of ecoregions, or dimatic factors influendng the

ecology of ecoregions.

To test this overall null hypothesis I used simple linear regression to evaluate the

relationship between habitat (Table 1.1), location (Table 1.2), and climate (Table

1.3) and the variation in vertebrate richness among the ecoregions of Texas.

K'' \

^v./

Table 1.1. Description of ecoregion-specific habitat variables used to predict the species richness of Texas ecoregions using simple linear regression.

Variable code Description

POLYAREA

TOPOINDX

ELEVDIF

VEGTYPE

SOILCLAS

SOILGOMP

SOILSIZE

SOILTEXT

Planar area

Percent change from planar area to surface area

Difference between the highest and lowest elevation

Number of vegetation types

Number of soil taxonomic classifications

Number of soil component names

Number of soil particle size dassifications

Number of soil surface texture dassifications

Table 1.2. Description of ecoregion-specific spatial variables used to predict the species richness of Texas ecoregions using simple linear regression.

Variable Code Description

EMIN

EMAX

EGENT

NMIN

NMAX

NGENT

minimum eastern extent (i.e., western extent)

maximum eastern extent

the difference t>etween EMAX and EMIN

minimum northern extent (i.e., southern extent)

maximum northern extent

the difference between NMAX and NGENT

Table 1.3. Description of ecoregion-specific climatic variables used to predict the species richness of Texas ecoregions using simple linear regression.

Variable code Description

MAXMIN

MAXMAX

MAXVAR

MAXDIF

MAXANN

MINMIN

MINMAX

MINVAR

MINDIF

MINANN

MXMNDIF

ANNDIF

PRGPMAX

PRCPMIN

PRGPDIF

PRCPVAR

PRGPANN

mean daily maximum temperature for the coolest month

mean daily maximum temperature for the hottest month

the variance of maximum monthly temperature values

the difference between MAXMIN and MAXMAX

mean monthly maximum temperature

mean daily minimum temperature for the coolest month

mean daily minimum temperature for the hottest month

the variance of minimum monthly temperature values

the difference between MINMIN and MINMAX

mean monthly minimum temperature

difference between MAXMAX and MINMIN

difference between MAXANN and MINANN

mean monthly predpitation for the wettest month

mean monthly predpitation for the driest month

the difference between PRGPMAX and PRCPMIN

the variance of total monthly precipitation values .

mean annual predpitation

7

CHAPTER 2

LITERATURE REVIEW

2.1 Biodiversity

The meaning of the term biodiversity has been described differentiy by

several people. After reviewing 85 definitions and related literature, DeLong

(1996:745) conduded that: "Biodiversity is an attribute of a site or area that

consists of the variety within and among biotic communities, whether influenced

by humans or not, at any spatial scale from microsites and habitat patches to the

entire biosphere." Noss and Gooperrider (1994:5) described biodiversity as "the

variety of life and its processes; it indudes the variety of living organisms, the

genetic differences among them, the communities and ecosystems in which they

occur, and the ecological and evolutionary processes that keep them functioning,

yet ever changing and adapting." Perhaps Huston (1994:1) summarizes the

concept of biodiversity best when he stated that "in all its manifestations,

(biodiversity) is an essential component of the quality of human existence,

summarized in the andent aphorism: 'variety is the spice of life.'"

Huston (1994:65) stated that the concept of diversity has two primary

statistical components: (1) the number of different objects (species richness) and

(2) the relative anxDunt of each different type of object (evenness). Several

different indices of biodiversity have also been suggested. These indices differ in

the assumptions made about the "evenness" of species, in their sensitivity to

different types of change in community structure, and in their degree of

independence of sample size (Peet 1974, Pielou 1975). Two statistics commonly

used by ecologists that are sensitive to changes in both the number of species

and to changes in the distribution of individuals among the species present are

the Simpson's Index (Simpson 1949) and the Shannon-Weaver Function

(Shannon and Weaver 1949). Both of these methods use species richness and

evenness to establish an index.

8

species richness alone, however, also serves as a useful index of

biodiversity. The obvious disadvantage of using species richness alone is that by

not accounting for the relative abundance of each species, each spedes Is

weighted equally regardless of its density and, thus, a species with only one

individual representative carries the same weight as a species with 1,000,000

individuals. At the same time, however, species richness values have been

shown to be highly correlated with diversity indices that also incorporate relative

abundance (Gonnell 1978, Brown and Gibson 1983).

In addition to having various methods for measuring diversity, diversity

can also be measured at any scale. Whittaker (1960) suggested that patterns of

diversity can be measured at three spatial scales: within-habitat (alpha) diversity,

between-habitat (beta) diversity, and geographic (gamma) diversity. At the

geographic scale it is logistically prohibitive to obtain abundance data and, thus,

species richness is typically the selected method (e.g., GAP). Rather than

conducting a field-based study, at this scale, species richness data can be

acquired through review of the literature and accumulation of data from local data

sets (e.g., Scott et al. 1987).

2.2 Reserve Selection

Throughout history, man has set aside parcels of land for the protection of

natural resources and the propagation of wildlife. On March 1, 1872, the US

Congress created Yellowstone National Park as the first national park in the

worid. Among its many goals, Yellowstone was "dedicated and set apart...for the

preservation, from injury or spoliation, of all timber, mineral deposits, natural

curiosities, or wonders... and their retention in their natural condition" (National

Park Service 1999). The US National Park Service is now responsible for

managing > 300 national parks. These and many other federally-, privately-, and

state-managed nature reserves have played a very important role in protecting

the biodiversity of North America. In terms of creating new reserves, the actual

placement is ultimately dependent upon the objective of the reserve. Only after

the objectives of the reserve are cleariy stated can parcels of land be identified

that will meet the objectives. Obtaining the desired land is then in turn

dependent upon budgets and the ability to secure the land and to enact the

necessary legislation. Gaughley and Gunn (1996) provide an excellent

description of the issues that should be considered when designing a new

biodiversity reserve. Some of the issues discussed in this chapter are reserve

size, number, shape, and how reserves can be connected. Debate over and the

study of these issues assisted in the rapid development of conservation biology.

The following is a synopsis of these issues as presented by Gaughley and Gunn

(1996:311-340).

The required size of a reserve is determined by what it is supposed to

conserve; a reserve aimed at conserving grizzly t)ears will be larger than one

designed to conserve butterflies. The authors suggest using viability analysis to

estimate the appropriate area of a reserve. Viability analysis yields an estimate

of the "mean population size necessary to retain that species at designated

levels of probability and time. The estimate, divided by the average density of

the spedes in that environment, returns the minimum size of a reserve" (p. 318).

Once the amount of land area needed is determined, the problem is to determine

whether a single large reserve should be established or if the area should be

partitioned into several small reserves. The answer to this question depends on

"the difference between the extinction probabilities of a small and a large

population, the number of populations, the correlation in year-to-year fluctuation

of the environments of the populations, and the probability of recolonization of a

patch emptied by local extinction" (p. 319).

The next question is then the shape of the reserve? The authors discuss

two contradictory options: circular vs. long and narrow. As suggested by

Diamond (1975): "If the reserve is too elongate or had dead-end peninsulas,

dispersal to outiying parts of the reserve from more central parts may be

sufficientiy low to perpetuate local extinctions by island-like effects" (p. 129). In

addition to minimizing within-reserve dispersal distances, circle reserves, by

10

virtue of minimized "edge." also minimize the effect of external influences on the

reserve. On the other hand, long narrow reserves have the opportunity to

indude a greater diversity of habitats and thus may hold more species.

However, under this design, the area of each habitat type may be so small, that it

is unable to support a healthy population of many species.

Perhaps the nruDst important, and often overiooked, aspect of reserve

design is how well the new reserve will be connected with current and future

reserves. Ensuring that corridors exist tjetween reserves allows for gene flow

between reserves, encourages metapopulation dynamics whereby a declining

population in one reserve might be rescued by dispersal from another, and

increases the effective size of the component populations. At the same time,

however, it has also t>een warned that corridors can help spread disease and

fire, and increase exposure to unauthorized hunting, predation, and competition

with domestic animals. Possible alternatives to using corridors are translocation

of individuals and artifidal insemination.

2.3 Ecosystem Management

As mentioned, analysis of biodiversity can be conducted at any spatial

scale from one's back yard, to a political unit (e.g., county, state, country), to an

ecosystem, to a continent, to a planet. The recent awareness regarding the

importance of understanding and sampling at varying spatial scales is evident in

the recent literature. Root and Schneider (1993) suggest that ecological studies

conducted at large scales are lacking and that these types of studies are needed

and can indicate which smaller-scale studies are most likely to help assess the

ecological implication of global changes and help to design conservation

measures in response to these changes. Scott et al. (1987) stated that the battle

for species preservation is fought at six levels: landscape, ecosystem,

community, species, population, and individual. They also suggested that the

11

management costs per species increases and the probability of successful

recovery decreases as conservation actions are focused on lower levels of the

hierarchy.

Ecosystem management is one concept that incorporates large-scale

investigation and has received generous attention over the past 2 decades

(Czech and Krausman 1997). Odum (1983:13) defined an ecosystem as "any

unit that includes all the organisms that function together in a given area

interacting with the physical environment so that a flow of energy leads to cleariy

defined biotic structures and cycling of materials between living and nonliving

parts." Bailey (1996) described the scale of ecosystem units in a hierarchical

classification where the smallest ecosystems are referred to as sites or

microecosystems. Linked sites are in turn referred to as landscape mosaics or

mesoecosystems. These landscapes are then connected to form larger units

called ecoregions or macroecosystems. The U.S. Forest Service, in response to

adopting a policy of ecosystem management (McNab and Avers 1994), further

subdivided these three dassifications by dividing ecoregions into domains,

divisions, and provinces; landscape mosaics Into sections, subsections, and

landtype associations; and sites into landtypes and landtype phases (EGOMAP

1993).

Like the term biodiversity, there is some debate over the definition of, and

rationale for, ecosystem management (see Czech and Krausman 1997). Bailey

(1996:4) stated that "an ecosystem approach to land evaluation stresses the

interrelationships among components rather than treating each one as a

separate characteristic of the landscape." Although a single definition for

ecosystem management will never be accepted by all. and as Czech and

Krausman (1997:671) suggest, the term really "requires no definition," the

fundamental idea behind the plan was perhaps best summarized by Sparks

(1995:170) "as working with the natural driving forces and variability in these

ecosystems with the goal of maintaining or recovering biological integrity."

12

2.4 The Ecoregions of Texas

As summarized by Blair (1950), the first attempt at classifying Texas into

environmental regions was when Bailey (1905) mapped the "life zones" of Texas.

Blair concluded that V. Bailey's (not to be confused with R. Bailey's ecoregions)

system was not satisfactory because it was based largely on temperature and

ignored other ecological factors. Under V. Bailey's dassification. the lower Rio

Grande Valley, eastem Panhandle, and the deserts of the Trans-Pecos were all

placed in the same "life zone." Dice (1943) divided North America into "biotic

provinces" which were largely based upon vegetation types but also considered

ecological dimax, flora, fauna, dimate, physiography, and soil if data existed. In

an attempt to improve on Dice's continental map, Blair (1950) used recent data

and subjectively (as opposed to quantitatively) incorporated topographic features,

climate, vegetation types, and the distribution of non-bird terrestrial vertebrates to

delineate the "biotic provinces" of Texas. His work has strongly influenced the

way ecologists and biogeographers have viewed the biota of Texas (Ward et al.

1990).

In 1994, R. Bailey et al. (1994) published the map Ecoregions and

Subregions of the United States. This map delineated the boundaries of the

ecosystems of the United States at the domain, division, province, and section

levels. Domains, the highest level in the hierarchy, are identified on the basis of

broad climatic similarity (EGOMAP 1993, Bailey 1996). Domains are further

subdivided, again on the basis of dimate criteria, into divisions. Then, based on

the dimax plant formation that geographically dominates the upland areas,

divisions are subdivided into provinces, and provinces are further subdivided into

sections on the basis of differences in the composition of the climax vegetation.

Under this scheme, Texas is divided into 18 ecoregions, 4 of which extend into

Mexico. Of the U.S. portions of these ecoregions, 9 are more or less completely

contained within the boundaries of Texas, 1 is 80% contained, 3 are about 50%

contained, and the proportions in Texas of 5 ecoregions are so small that they

can not be considered as "major ecoregions of Texas" (Figure 2.1).

13

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14

2.5 Large-Scale Studies of Texas' Vertebrates

The first study that quantitatively analyzed state-wide wildlife distributions

in Texas was when Webb (1950) used mammalian distribution patterns to define

four major biotic communities in Texas and Oklahoma: the eastern forest

community, the Rio Grande Plain community of south Texas, the Trans-Pecos

community of west Texas, and the High Plains community of both states'

panhandles. These biogeographic regions were identified by first creating a

species list for systematically placed sample points located throughout the states.

Sample points were located 100 miles apart and species-presence data was

calculated by overiaying this sample-point grid, drawn on tracing paper, on the

range maps for the few groups of mammals having "accurate" maps at'that time.

Similarity values for each pair of adjacent points (number similar/total number)

were determined, similar values were connected with contour lines, and regions

with high values (>75) were considered separate communities.

Rogers (1976) divided Texas into 63 geographical units based on county

boundaries. Each unit was then assigned a value for the species richness of

amphibians and reptiles. In addition, dimatic data were assigned to each region

based on data collected from a single weather station in each region. Based on

these data, he found that spedes richness of Texas amphibians and reptiles

were highly correlated with several components of the physical environment.

Some of the factors that had positive con-elations with the richness of at least

groups of amphibians and reptiles were topography, mean annual precipitation,

mean annual temperature, and growing season. Altitude was found to have a

negative correlation with richness for all groups.

Owen (1988, 1990) and Owen and Dixon (1989) evaluated various

aspects of the distribution of Texas' vertebrates. In order to have a reference

system from which to record presence or absence data for each species

considered in each study, the authors drew a grid consisting of 189 cells

representing 63.9 km on each side onto a mylar sheet. The sheet was then

overiaid on range maps, maps of museum records (i.e., locality data), and other

15

locality records to assign presence or absence data to each cell for rodent and \ 3

carnivore spedes (Owen 1988), reptile and amphibian species (Owen and Dixon j

1989), and mammalian species (Owen 1990). Using quadrates of equal size j m

"greatiy reduced the problem of area effects as a confounding variable in the j

statistical analysis" (Owen 1990:1824-1825). »

Ward et al. (1990,1994) evaluated reptile distributions in Texas. In

contrast to the grid concept used by Owen (1988,1990) and Owen and Dixon

(1989), Ward et al. (1990) chose to use counties as the unit of measure because

many of the sources of distribution records were not specific beyond county of

occurrence. Owen and Dixon (1989) also recognized this problem and, thus,

used their "knowledge of the ecology of the species" to assign a species as

present or absent to a grid cell when a county for which a species was known to

exist bisected their grid cell.

Hierarchical duster analysis was used by Owen and Dixon (1989) to

define herpetofaunal regions of Texas and by Owen (1990) to define mammalian

regions of Texas. In both cases the authors conduded that regions identified by

cluster analysis were complex and essentially continuous with each other. Using

a different duster analysis technique. Ward et al. (1990) identified reptilian

regions of Texas. In contrast to Owen and Dixon (1989) and Owen (1990), Ward

et al. (1990) identified discrete faunal regions, similar to those descritjed by Blair

(1950), with complex zones of transition t)etween regions.

2.6 Theories of Biodiversity

Several theories have been proposed to explain patterns of species

diversity (see Pianka 1966, Rohde 1992). The studies behind these theories,

abundant testing of these theories, and extensive debate over the validity of

these theories have led to several artides and books on the topic; yet there is still

no consensus on why some areas have more species than others. It is clear,

however, that there is no one factor that can explain this question. Following is a

review of the literature concerning this topic as it relates to the variables I have

16

chosen to meet my second objective. By no means do I attempt to review all

literature related to these theories.

One of the nnost productive ecological models addressing patterns of

species numbers is the equilibrium theory of island biogeography (Wright 1983)

proposed by MacArthur and Wilson (1963,1967). This theory is based upon the

regulation of spedes diversity as a dynamic process where immigration opposes

extinction and where the primary factors affecting insular immigration and

extinction rates are area and isolation (Wright 1983). Of importance to this

thesis, one of the observations made by MacArthur and Wilson (1967), is that

islands of larger area should support more species than smaller islands. A

similar condusion was also reached by Preston (1962) who went through great

effort to mathematically explain a spedes-area curve.

Recognizing this spedes-area theory. Wright (1983) points out that area

itself usually has no direct effect on organisms but rather that area is a secondary

correlate which measures more proximate factors. Two such factors are a

greater amount of total habitat thus capable of supporting larger populations and

a greater variety of habitats thus capable of supporting a variety of species

(Wright 1983, MacArthur and Wilson 1967, Connor and McCoy 1979). This

spatial heterogeneity theory daims that areas that have a physical environment

that is more heterogeneous and complex will support a more complex and

diverse animal community (Pianka 1966). The number of factors that contribute

to spatial heterogeneity is infinite (Huston 1994).

In terms of topography. Simpson (1964) drew mammalian species-

richness contours on a map of North America by assigning species-presence

values to grid cells measuring 150 miles on each side. His work revealed that

the highest mammalian-species richness for both western and eastem North

American occurred predsely in the same quadrat as the highest mountains and

maximum relief. Using grid cells measuring 100 miles on each side. Kiester

(1971) found that amphibian density is also correlated with mountain regions, yet

reptile density is negatively correlated with topographic diversity. In Texas,

17

Rogers (1976:26) found the richness of several genera of reptiles and %

amphibians to increase significantiy with increasing topographic relief. He |

concluded that this result was "probably because of the greater number of habitat

types that occur in uneven terrain versus flat terrain."

MacArthur and MacArthur (1961:597) studied bird-species richness on a

series of study sites and found that "although plant spedes diversity alone is a

good predictor of bird spedes diversity, it is because plant species diversity is

high when foliage height diversity is high." Gurrie (1991) investigated 5 factors

hypothesized to influence species richness. In his work he divided North

America north of Mexico into 336 quadrates and assigned each quadrat a value

for the number of trees, birds, mammals, amphibians, and reptiles. When

comparing vertebrate richness to tree richness, he found that only amphibian

richness showed a dear monotonic relationship with tree richness.

For most groups of terrestrial plants and animals, diversity is lowest near

the poles and increases towards the tropics (Huston 1994). This latitudinal

gradient was the first pattern that attracted the sdentific community to species

diversity (Huston 1994). Although this obvious pattern exists, it is also

recognized that species richness is not determined by latitude itself but rather

depends on other factors that co-vary imperfectly with latitude (Gurrie 1991).

Most of these proposed factors are themselves a function of climate.

Klopfer (1959:337) proposes that a stable environment "where seasonal

environmental fluctuations, as temperature, rainfall, wind force, are minimal"

enhances faunal diversity. This condusion is based upon the assumption that

where environmental fluctuations are minimal, the type of habitat and food that

are available remain fairiy constant and, thus, allows for narrower niches which

can be filled by more species. Klopfer and MacArthur (1961) concluded that the

major factor causing increased number of bird species in the tropics was not

complexity of habitat or increased specialization, but an increase in the similarity

of coexisting species. Thus areas having stable environments allow for narrower

niches and for greater niche overiap. Theories of increased niche overiap as a

18

function of dimate are difficult to distinguish between theories of niche overiap

based upon competition (Pianka 1966).

Gonnell and Orias (1964) conduded that although some niches are

determined by physical variations in the environment, most are a function of

various interactions between organisms. Their hypothesis explaining species

diversity is that with greater environmental stability, less energy is required for

regulatory functions and, thus, more energy is left for growth and reproduction.

Wright (1983) condudes that energy, in the form of resources, is the fundamental

parameter behind both factors (i.e., greater total amount of habitat and greater

variety of habitats) explaining the spedes-area theory. Wright's species-energy

nrjodel is analogous to the MacArthur-Wilson model; "if the island as a whole

produces littie energy that is available to the spedes in question, the spedes

population will be small, and the extinction rate on the island will be high. On the

other hand, islands with large amounts of available energy will support large

populations of all spedes, and so will have lower extinction rates" (Wright

1983:498).

Energy available to animals consists of the production of food items that

can be induded in their diets and should ideally be measured in units of energy

per unit time, e.g., joules per year (Wright 1983). However, as Wright (1983:501)

mentions, "any relative measure of available energy can serve, as long as it

bears a consistent proportionality to available energy." In Wright's work he used

total actual evapotranspiration to produce a measure of energy available to

plants and total net primary production as a measure of energy available to birds.

Primary production was estimated by multiplying the average per-unit-area net

primary productivity on the island by the area of the island. Gurrie (1991)

estimated primary productivity using the following model presented by Lieth

(1975),

P = 3000 [i.e-oooo^95(E-20)j

19

where P is the annual net primary productivity (g/m^), E is the annual actual 3

evapotranspiration (mm), and e is the base of natural logarithms.

As described eariier, Gurrie (1991) assigned species richness values for trees

and vertebrates to grid-cells overiaid on North America. In addition, 21

descriptors of the environment were determined for each cell. His analysis

revealed that the three strongest correlates of spedes richness are potential

evapotranspiration, solar radiation, and mean annual temperature, all of which,

he concludes, reflect aspects of the regional energy balance. Gurrie used

Budyko's (1974) model, which incorporates solar radiation plus adjective

energy fluxes, to estimate potential evapotranspiration. Potential

evapotranspiration represents the maximum amount of water that would be

lost by evaporation from surfaces and transpiration of plant leaves when

evapotranspiration is not limited by water availability (Huston 1994).

In Texas, Owen and Dixon (1989) evaluated patterns of reptile and amphibian

species richness based upon 15 environmental variables. Their work showed

that herpetofaunal species distributions in Texas differentiate along a dominant

east-west gradient of decreasing predpitation and productivity and along a

south-north gradient of decreasing mean annual temperature, increasing rigor

of winter cold, and increasing seasonality of temperature. More specifically,

their work revealed that in Texas, amphibians and turtles appear to be highly

dependent upon predpitation, an abiotic factor. In contrast, snakes and lizards

appear to be more dependent upon habitat structural complexity, a mainly

biotic factor.

Owen (1990) analyzed species-richness patterns of Texas mammals. His

work revealed that mammalian species distributions differentiate along two

major dines: a dominant east-to-west gradient of decreasing precipitation and

productivity and a south-to-north gradient of decreasing mean annual

temperature, increasing winter cold, and increasing seasonal variation of

temperature. More specifically, he conduded that his work did not support

either the productivity or stability hypotheses of species richness.

20

Owen (1988) compared rodent and carnivore species diversity with

estimates of net above ground-primary productivity. His work revealed that

rodent diversity in Texas was highest at low productivity levels and declined as

productivity increased. In contrast, Texas carnivores showed an initial increase

with increased productivity, a peak at intermediate levels of productivity, and a

decline at higher levels of productivity. This pattern is like that proposed by

Tilman (1982) to predict the number of plant species that can coexist

competitively on a limited resource base.

Brown (1973) studied seed-eating rodent fauna on sand dunes of eastern

California, Nevada, and westem Utah. He found that species diversity was most

closely correlated with the predictable amount of annual rainfall, which he

concluded should be an accurate estimate of the size and predictability of the

annual seed crop. Also studying rodents in an arid environment, Abramsky and

Rosenzweig (1984) used predpitation to reflect productivity. Their results, like

Owen (1988) above, also agree with the model proposed by Tilman (1982).

21

CHAPTER 3

METHODOLOGY

3.1 Spedes List

The first step in meeting my objectives was to create a list of the terrestrial

vertebrates living in the major ecoregions of Texas. Developing this species list

was a two-step process. I first developed a base list consisting of the native

terrestrial-vertebrates living in Texas. I then induded additional spedes that do

not occur in Texas but that are found in the New Mexico portion of the Basin and

Range and Texas High Plains ecoregions; the Oklahoma portion of the Texas

High Plains, Gross Timbers and Prairie, Oak Woods and Prairie, and Mid Coastal

Plains ecoregions; and the Arkansas and Louisiana portion of the Mid Coastal

Plains ecoregion (Figure 2.1).

To develop the Texas base list I started with the list of 986 terrestrial

vertebrates considered by the Texas Parks and Wildlife Department (1997) as

being native to Texas. I reduced this list to a total of 815 species for Objective 1

and 829 spedes for Objective 2 following the rules and exceptions described

below:

Since the goal of my first objective was to provide information that would

be useful for protecting biodiversity in Texas, those species for which protection

efforts are too late (i.e., the spedes is extinct or extirpated from the ecoregions)

were not induded. My species list, thus, does not include 12 species that are

considered by TPWD (1997) as now absent from Texas (Appendix A). However,

both the mountain sheep (Ows canadensis) and elk (Cen/us elaphus) are

induded because of their presence in the New Mexico portion of the Basin and

Range ecoregion. Also, the black bear (Ursus americana), which was once

found in several ecoregions in Texas, is now only considered for Objective 1 as a

member of the Basin and Range community. In addition, my list does not indude

the Eskimo curiew (Numenius borealis) which is currentiy not considered extinct

by TPWD (1997) but which has not been seen in Texas since May 1987 and is

22

thought to be very dose to extinction (Campbell 1995). Since the goal of

Objective 2 was to identify significant relationship between attributes of an

ecoregion and the number of spedes living there, I chose to indude extinct and

extirpated spedes since the ecoregion attributes being measured are likely the

same now as when they once occurred In these regions.

Other deletions from the TPWD (1997) list induded those birds considered

as accidental (out of range and not expected yeariy), presumptive (accepted

sight records, but no specimen, photograph, or recording), hypothetical (report of

merit exist but not documented), or historical (records in the literature but no

existing spedmens or photographs). These species were not deleted if they were

protected under either the federal or the Texas threatened and endangered

species lists (as documented by TPWD 1997) or if the species was considered

by Kutak (1998) as having nested in Texas in the past 30 years. Also, due to

nomenclature changes that occurred after the avian range maps that were used

to attribute ecoregions with presence or absence values for birds were created

(see Section 3.2), the spotted towhee (Pipilo maculatus) and the eastem towhee

(P. erythrophthalmus) were grouped as the former rufous-sided towhee (P.

erythrophthalmus). The bullock's oriole (Icterus bullockii) and Baltimore oriole (/.

galbula) were grouped as the former northern oriole (formerly /. galbula). The

blue-headed vireo (Vireo solitarius), Gassin's vireo (V. cassinii), and plumbeous

vireo (V. plumbeus) were grouped as the former solitary vireo (V. solitarius).

Finally, the red-naped sapsucker (Sphyrapicus nuchalis) was included as the

yellow-bellied sapsucker (S. varius).

For reptiles, the following changes regarding snakes were made to TPWD

(1997) following Tennant (1998). The blackhood (Tantilla cucullata) and Devil's

River blackhead (7. diabola) snakes were recognized as the single blackhood

snake (T. rubra). The Ruthven's whipsnake (formerly Masticophis schotti

ruthveni) was recognized as M. ruthveni, and the Schott's whipsnake (formerly

M. s. schotti) was recognized as M. schotti. The Texas' scariet snake (formerly

Cemophora lineri) was recognized as C. coccinea lineri. The Western

23

yellowbelly racer (Coluber mormon) was not recognized. Also, following Garret

and Barker (1994), the Southern redback salamander (Plethodon serratus) was

added.

For mammals the following 5 bats were initially not included because their

occurrence in Texas was based on only one spedmen, and their range was thus

not well known (Davis and Schmidly 1994): the Mexican long-tongued bat

(Choeronycteris mexicana), the northern myotis (Myotis septentrionalis), the little

brown myotis (Myotis lucifugus), the Western red bat (Lasiurus blossevillii), and

the hairy-legged vampire (Diphylla ecaudata). However, by comparing hard

copies of predicted distribution maps created by New Mexico GAP (Thompson et

al. 1996) to a hard copy of the Basin Range ecoregion in New Mexico, I

discovered that the littie brown myotis, the Mexican long-tongued bat, and the

Western red bat were present in at least the New Mexico portion of the Basin and

Range ecoregion and these spedes were, thus, induded in the study.

The neighboring states of New Mexico (Thompson et al. 1996) and

Arkansas (Smith et al. 1998) have completed vertebrate distribution maps in

support of GAP. Based on ocular examination of these maps, for step 2 of the

species list creation, I induded 72 additional species based on their presence in

the New Mexico portions of the Basin and Range and Texas High Plains

ecoregions, and 11 spedes were induded based on their presence in the

Arkansas portion of the Mid Coastal Plains ecoregion. Additional migratory birds

in Arkansas were not considered because maps for these species did not exist.

Like TX-GAP, Oklahoma GAP had not yet mapped predicted distributions for

their vertebrates. Thus range maps from Black and Sievert (1989), Gaire et al.

(1989), Sievert and Sievert (1993), and Wood and Schnell (1984), were used to

identify additional amphibians, mammals, reptiles, and birds, respectively,

occurring in the Oklahoma portions of the Texas High Plains, Gross Timbers and

Prairie, Oak Woods and Prairie, and Mid Coastal Plains ecoregions. This

process resulted in the addition of 10 species.

24

%

9

3.2 Objective 1

As stated eariier, the first objective of this thesis was to use the ecoregion

as the unit of measure for describing the distribution and diversity of Texas

vertebrates. The goal of this objective was to provide information that could be

used by dedsion-makers to assist in locating future biodiversity reserves in

Texas. The first step in meeting this objective was to develop species-presence

versus spedes-absence matrices for the major ecoregions of Texas. This was

accomplished by using the GIS to overiay a coverage of Texas ecoregions with

the range extents of each of the vertebrates in my species list. A spedes was

identified as present in a particular ecoregion if its range extent intersected the

boundaries of the particular ecoregion (Appendix A).

For an ecoregion map, I chose the ecoregion scheme delineated by Bailey

et al. (1994). This map is used by several private and public agencies and is

available on the Internet as a GIS coverage. From this nation-wide coverage, I

used the GIS to create a coverage representing the 13 major ecoregions of

Texas (Figure 2.1). This coverage served as the base map for which I assigned

ecoregion-spedfic values for the various variables investigated for this thesis.

Since range maps for the terrestrial vertebrates of Texas were not available as

GIS coverages, other TX-GAP personnel and I digitized range maps from the

following field guides: The Mammals of Texas (Davis and Schmidly 1994), A

Field Guide: Birds of Texas (Rappole and Blacklock 1994), A Field Guide to

Reptiles and Amphibians of Texas (Garret and Baker 1994), and A Field Guide to

Texas Snakes (Tennant 1998). These field guides were suggested by

recognized vertebrate experts and were the most recent Texas-specific field

guides available.

I used the presence versus absence matrices to determine the species

richness of each ecoregion. I determined the uniqueness of the vertebrate

community of each ecoregion by calculating coefficient of community values for

each ecoregion pair. Determining coefficient of community values is a common

method used to compare species composition between areas where only species

25

richness is known (Brower and Zar 1984). I calculated coefficient of community

values using the formula proposed by Sorensen (1948):

CG5 = 2c/si + S2,

in which c is the number of spedes common to two areas, and s^ and S2 are the

total number of species in communities 1 and 2, respectively. The result of a

coefficient of community calculation is a value between 0-100% where increasing

values indicate a greater community similarity.

Coefficient of community values were evaluated in two ways. I calculated

a coeffident of community Index (CGI) that represented the frequency of

occurrence for each ecoregion within the 16 (approx. 20%) most unique

ecoregion pairs. I also ranked the coefficient of community values from low to

high; I then assigned the members of each ecoregion pair a value from 1 to 78,

respectively. For each ecoregion, I calculated the total rank sum (CGRS) as the

sum of all rank values (i.e., 1-78) for each ecoregion. Ecoregions with higher

CGI or CGRS values had more unique vertebrate communities. The number of

vertebrates found in each ecoregion that were unique to only one ecoregion were

identified as ecoregion endemics (EE).

Each species was assigned a state and a federal listing value based on

threatened and endangered spedes lists maintained by the Louisiana

Department of Fisheries and Wildlife (1995), New Mexico Department of Game

and Fish (1997,1998), Oklahoma Department of Wildlife and Conservation

(1998), Texas Parks and Wildlife Department (1997), and the U.S. Fish and

Wildlife Service (1998). I determined the amount of land being managed for the

long-term protection of biodiversity within each ecoregion by using the GIS to

overiay coverages of the lands being managed by state and federal agencies

with the ecoregion coverage. I downloaded coverages for federally managed

lands from the Intemet (http://www.tnris.state.tx.us/DigitalDataydata_cat.htm),

and I received the coverage for lands managed by the TPWD from the TPWD

26

GIS Lab, Austin, Texas, USA. Coverages for state lands outside of Texas and

for privately-owned lands directiy managed for the long-term protection of

biodiversity (e.g.. Nature Conservancy Preserves, Fish and Wildlife Service

easements) were not available.

3.3 Objective 2

My second objective was to test whether the number of vertebrate species

living in the ecoregions of Texas is related to the diversity of habitats found within

ecoregions, the spatial location of ecoregions, or climatic factors influencing the

ecology of ecoregions. To meet this objective I used simple linear regression to

identify signiflcant relationships between independent variables and species

richness, at varying taxonomic levels, for the 13 major ecoregions of Texas. The

following is a detailed description of the predictor variables that I assessed.

POLYAREA

TOPOINDX

Since a GIS coverage is simply a geographically referenced

digital map, ecoregion size was already an attribute of Bailey's

(1994) ecoregion coverage. However, because this coverage

did not indude areas in Mexico, the total size of the three

ecoregions shared with Mexico could not be calculated.

POLYAREA is reported as km^.

To assign each ecoregion a value for topographic relief, I used

the GIS to create digital elevation models (DEM) for each

ecoregion. DEMs are digital records of elevation for regulariy-

spaced horizontal ground locations and are created from USGS

quadrangle maps (USGS 1990). State DEMs were downloaded

from the Internet (http://edcwww.cr.usgs.gov/doc/edchome/

ndcdb/ndcdb.html) and the GIS was used to create single DEM

coverages for each ecoregion. From these three-dimensional

topographic maps, I used the GIS to determine the total land

surface-area per ecoregion. I then calculated the difference

27

ELEVDIF

VEGTYPE

between POLYAREA and surface area and recorded

TOPOINDX as the percent increase. Under this scheme, a value

of 0% represents a completely flat ecoregion, and larger values

represent increasing topographic relief.

As another measure of topographic relief, I again used the DEMs

for each ecoregion in order to calculate the difference between

the highest and lowest elevation within each ecoregion.

ELEVDIF is reported in meters.

I determined the number of vegetation types per ecoregion using

the vegetation map created by McMahan et al. (1984). I chose

this map because it was the most recent statewide dassification

and because it was available as a GIS coverage on the Internet

(http://www.tpwd.state-tx.us/admin/gis/download-htm).

Unfortunately since there was not a single vegetation map for

Texas and neighboring states, I was only able to identify the

minimum number of vegetation types per ecoregion for the six

ecoregions not contained more or less entirely within Texas.

However since ecoregions are in part delineated based on

vegetation, the number of vegetation types known in a portion of

an ecoregion may be a good estimate of the total number in the

whole ecoregion.

I used the GIS, the ecoregion coverage, and data provided by the U.S.

Department of Agriculture's State Soil Geographic (STATSGO) database to

identify soil attributes for each ecoregion. The STATSGO database is one of

three national soil geographic data bases established by the Natural Resources

Conservation Service, formeriy the Soil Conservation Service, and was designed

primarily for regional assessment (U.S. Department of Agriculture 1994).

Although STATSGO data were available on the Intemet (http://www.ftw.nrcs.

usda.gov/stat_data.html) as national GIS coverages, they were not available for

28

the portions of the three ecoregions that extend into Mexico; thus, soil values are

probably underestimated for these ecoregions.

SOILCLAS

SOILGOMP

SOILSIZE

SOILTEXT

The number of soil taxonomic classifications (e.g., aquic

haploborolls, fine, mixed; typic paleorthids, loamy, mixed,

thermic, shallow; etc.) found in each ecoregion.

The number of soil component names (e.g., Apache, Milner,

Pirodel, etc.) found in each ecoregion.

The number of soil partide size dassifications (e.g., sandy,

loamy, ashy, etc.) found in each ecoregion.

The number of soil surface texture classifications (e.g., clay,

sand, bouldery, etc.) found in each ecoregion.

•.51

I obtained values for dimatic variables based on data collected by the

National Climatic Data Center and summarized and supplied in digital format by

Earthlnfo (1996). This database contained daily dimatic data and spatial

coordinates for hundreds weather stations throughout the U.S. portions of the

major ecoregions of Texas. The period of data collection at a single station

ranged from 1 to 100 years. In an effort to eliminate this potential bias, I only

used data collected from stations which recorded data for at least 25 years

during the period 1942 -1991 . Once the station sample was identified, I used

the GIS to assign each of the 1300 stations an ecoregion of occurrence (Figure

3.1). These data were then organized into three data sets: (1) average daily

minimum temperature for each month, per station, per year, (2) average daily

maximum temperature for each month, per station, per year, and (3) total

monthly precipitation, per station, per year.

29

100 200 300 400 500 600 TOO 800 Kkxrvsters

V'

Figure 3.1. Location of weather stations found in the major ecoregions of Texas having > 25 years of temperature and/or precipitation data during the period 1942 -1991 .

I calculated monthly minimum and monthly maximum temperature values

for each ecoregion by averaging the annual monthly values across all years. The

resulting data set contained a single monthly value for each ecoregion, based on

all the stations in that ecoregion, for both the 50-year average daily maximum

and minimum temperature. Earthlnfo (1996) reported the monthly value for

precipitation as the total, rather than daily average, monthly precipitation per

station per year. I averaged these monthly values across all years per ecoregion

to obtain the 50-year monthly average precipitation per ecoregion. Table 1.3 lists

and describes the climatic variables measured. Temperature and precipitation

30

values are presented in degrees Fahrenheit and inches, respectively, because

the data used in this analysis were collected in these units, and these units may

be of more use to potential end users.

I identified the spatial location of each ecoregion using the GIS to

determine the furthest western, eastern, southern, and northem coordinates for

each ecoregion. I assigned coordinates using the Universal Transverse Mercator

(UTM) projection, and, thus, coordinates were recorded as meters in UTM zone

14. Table 1.2 lists and describes the spatial variables measured.

All GIS analysis for this study was completed on a Microsoft Windows NT

(Microsoft 1996) personal computer using Environmental Systems Research

Institute's software packages ArcView GIS (Environmental Systems Research

Institute 1999a) and ARC/INFO (Environmental Systems Research Institute

1999b). Non-GIS data were stored, managed, and manipulated using Microsoft's

(Microsoft 1997) Excel spreadsheet and Access database software; word-

processing was completed using Word.

3.4 Statistics

I used simple linear regression to identify significant relationships between

the 31 predictor variables and vertebrate species richness for the 13 major

ecoregions of Texas (Objective 2). I assessed both total vertebrate species

richness and species richness for each of the four vertebrate classes (i.e.

amphibians, birds, reptiles, and mammals). Multiple regression was not used

because of insuffident sample size (n = 13; see StatSoft 1998b: 1646). Zar

(1996:325) described five assumptions that must be met when conducting

regression analysis: (1) for each value of X there exists in the population a

normal distribution of Y values, (2) homogeneity of variances, (3) the relationship

is linear, (4) values of Y are random and independent, and (5) error-free

measurements of X.

.«

31

I examined scatterplots of the original data and the residuals to assess

compliance with the assumptions of normality, homogeneous variances, and

linearity. I also tested the assumption of normality using the Shapiro-Wilk test

and identified outiiers using Cook's and Mahalanobis' distances. Zar (1996:325-

326) warns that although simple linear regression is robust to at least some of

the underiying assumptions, outiiers (i.e., "a recorded measurement that lies very

much apart from the trend of the bulk of the data") will cause violations of

normality and honxjgenous variances. When outiiers were identified, the model

was reanalyzed with the outiier removed to determine if the model was still

significant. It is assumed that the values of Y are random and independent and

that the values of X are without error. I computed all descriptive statistics and

statistical tests using STATISTIGA (StatSoft 1998a) and used a = 0.05 to

determine if departures from the null hypothesis were significant. Means are

reported ± 1 SE.

• < ? •

'^

32

CHAPTER 4

RESULTS

4.1 Spedes List

I identified 920 terrestrial vertebrates native to the major ecoregions of

Texas. Appendix A lists all the spedes induded in this study, their taxonomic

classification, and the ecoregions in which they are found. Of these, 12 are no

longer found within these ecoregions and 151 are not considered as permanent

residents t)ecause they do not nest within the study. The species making up this

diverse group of vertebrates belong to 4 taxonomic classes, 31 orders, and 120

families. The dass aves (n = 505) contains far more species than either reptilia

(n = 174), mammalia (n = 165), or amphibia (n = 77). Within the birds, neariy half

the species (n = 251) belong to the order Passeriformes, all but 32 of the reptiles

belong to the order Squamata, and 79 of the mammals belong to the order

Rodentia. In fact, greater than half of all the vertebrates living in these

ecoregions belong to these three orders.

4.2 Objective 1

Current vertebrate species richness (n = 908) across the ecoregions of

Texas ranged from 476 species on the Texas High Plains to 625 species on the

Rio Grande Plain (Table 4.1 , x = 532 ± 47). In comparison to its two closest

species-rich rivals, the Rio Grande Plain had only 18 more species than the

Basin and Range but had 62 more spedes than the Southern Gulf Prairie. In

terms of the least species-rich dass (n = 77), the Texas High Plain and the

Stockton Plateau shared the fewest number of amphibian species (n = 18), while

the Oak Woods and Prairies and Mid-coastal Plains had the most (n = 43, x =

31 ± 9). For mammals (n = 157) and reptiles (n = 174), the Eastern Gulf Prairies

had the fewest of each (n = 51 and 67, respectively), while the Basin and Range

had the most (n = 118 and 104 species, x =73 ±16.61 and x =84 ±10.93,

respectively). Within the study area, I found that more terrestrial vertebrates

33

belonged to the taxonomic class aves (n = 500) than the other 3 classes

combined. Within this dass, the least rich ecoregion was the Rolling Plains (n -

295), and the most rich was the Rio Grande Plain (n = 409, x = 344 ± 37).

These data suggest under a "most bang for the buck" scenario, either the Rio

Grande Plain or the Basin and Range ecoregions should be considered for

placement of a new biodiversity reserve, or network of reserves. However a

reserve placed in either of these ecoregions, espedally the Basin and Range,

would likely not protect as many amphibians as one placed in the Mid-coastal

Plains or the Oak-woods and Prairies.

Table 4.1. Spedes richness values for terrestrial vertebrates living in the major ecoregions of Texas.

Ecoregion

Texas High Plains

Rolling Plains

Stockton Plateau

East. Gutf Prairies and Marshes

Mid Coastal Plains

Eciwards Plateau

Blackland Prairies

Cross Timt)ers and Prairie

Oak W(X)ds and Prairies

Cent. Gulf Prairies and Marshes

South. Gutf Prairies and Marshes

Basin and Range

Rio Grande Plain

Code

THP

RLP

SKP

EGP

MOP

EWP

BLP

CTP

OWP

CGP

SGP

BAR

RGP

Amphibia

°18

21

"18

29

'43

34

40

36

'43

33

29

23

34

Aves

305

"295

313

350

316

303

337

346

360

382

396

362

'409

Mammalia

82

82

80

"51

63

74

62

64

67

64

62

'118

80

Reptilia

71

85

81

"67

76

93

86

82

88

81

76

'104

102

Total

476

483

492

497

498

504

525

528

558

560

563

607

625

The ecoregion(s) with the highest number of species belonging to the dass. "The ecoregion(s) with the lowest number of species belonging to the dass.

34

There are 113 spedes listed as either threatened or endangered under

either state or federal threatened and endangered species lists living in the major

ecoregions of Texas (Table 4.2). This figure represents 20.11 % (35/174) of the

reptiles, 19.48% (15/77) of the amphibians. 10.83% (17/157) of the mammals,

and 9.20% (46/500) of the birds. The mean number of species listed per

ecoregion is x = 35 ± 14 spedes. The Texas High Plains, which had the lowest

species richness, also had the fewest threatened or endangered species (n =

17). The Basin and Range, which had the second highest species richness, had

the most listed spedes (n = 62) while the Rio Grande Plain, which had the

highest spedes richness, had the second most listed species (n = 56). Based on

total vertebrate richness and total number of listed spedes, both the Basin and

Range and Rio Grande Plain should be considered for placement of a new

reserve.

Table 4.2. Number of spedes living in the major ecoregions of Texas that are listed on either state or federal threatened and endangered spedes lists.

Ecoregion

Texas High Plains

Mid Coastal Plains

Blackland Prairies

Cross Timbers and Prairie

Rolling Plains

Stockton Plateau

Edwards Plateau

Oak Woods and Prairies

East. Guff Prairies and Marshes

Cent. Gulf Prairies and Marshes

South. Gulf Prairies and Marshes

Rio Grande Plain

Basin and Range

Not

Listed

459

473

499

501

456

463

474

526

462

519

511

569

545

Listed as

endangered

6

9

12

12

8

8

10

11

14

16

16

17

18

Listed as

threatened

11

16

14

15

19

21

20

21

21

25

36

39

44

Total

Listed

17

25

26

27

27

29

30

32

35

41

52

56

62

Percent

Listed

3.57

5.02

4.95

5.11

5.59

5.89

5.95

5.73

7.04

7.32

9.24

8.96

10.21

35

Coefficient of community analysis was used to evaluate the similarity

between the vertebrate communities of each ecoregion pair. As described in

Section 3.2, a coeffident of community value ranges from 0 to 100; the lower a

coefficient of community value, the more unique the two communities.

Determining all possible coeffident of community values for 13 ecoregions

required 78 pair-wise calculations (Appendix B). Based on this analysis, it was

obvious that the Basin and Range ecoregion had the most unique vertebrate

community (Table 4.3). This ecoregion had the highest CGRS, CGI, and EE

values. Since this ecoregion is an exterior ecoregion (i.e., lies at the periphery of

the ecoregion group and only shares a small portion of its borders with other

ecoregions within the group), these results are not unexpected. In the same

manner, the Mid Coastal Plains and the Texas High Plain, which could also be

considered as exterior ecoregions, have the sixth and third highest CGRS values,

share the third and second highest CGI values, and have the second and share

the fifth highest number of ecoregion endemics, respectively. Thus, although

reserves placed in any of these three ecoregions, especially the Basin and

Range, would likely protect spedes that would not be protected by placing a new

reserve in another ecoregion, the spedes that make these vertebrate

communities unique from the other communities in this study, may be well

protected in ecoregions not induded in this study.

Identifying the ecoregion that had the most unique vertebrate community

among the interior ecoregions (i.e., ecoregions that are more or less completely

surrounded by other ecoregions included in this study) was not obvious. I I i

Although the Rio Grande Plain and the Edwards Plateau each had more i

ecoregion endemics than most other ecoregions, they had very low CGRS and

CGI values; and, although the Stockton Plateau had no ecoregions endemics, it

did have the highest CGRS and shared the second highest CGI of the interior

ecoregions. The Eastern Gulf Prairies had the highest CGI of the interior

36

ecoregions. Perhaps the only obvious result concerning the interior ecoregions

was the lack of uniqueness of the vertebrate communities residing in three

adjacent ecoregions belonging to the Humid Temperate domain: Gross Timbers

and Prairies, Oak Woods and Prairies, and Black Land Prairies.

Table 4.3. CGRS, CGI, and EE values for the vertebrate communities living in the major ecoregions of Texas.

Ecoregion

Basin and Range

Stockton Plateau

Texas High Plains Plains

Eastem Gulf Prairies and Marshes

Southem Gulf Prairies and Marshes

Mid Coastal Plains

Rolling Plains

Rio Grande Plain

Edwards Plateau

Central Gulf Prairies and Marshes

Cross Timbers and Prairie

Oak Woods and Prairies

Blackland Prairies

Code

BAR

SKP

THP

EGP

SGP

RLP

MGP

RGP

EWP

CGP

CTP

OWP

BLP

CGRS

744

566

553

493

491

484

484

451

421

394

370

359

352

CGI

8

3

4

4

2

3

1

1

0

3

1

1

1

EE

107

0

2

0

0

13

0

5

4

0

2

0

2

At the taxonomic class level, it was again obvious that the Basin and

Range ecoregion had the most unique vertebrate community. This ecoregions

had the highest CGRS, CGI, and EE values across all taxonomic classes (Table

4.4). With the exception of reptiles, the Stockton Plateau had the second highest

CGRS across the remaining classes; however, this ecoregion had no ecoregion

endemics. The Texas High Plains had the second highest CGRS and shared the

37

second highest CGI for reptiles. The dass aves had the most ecoregion

endemics with 42 coming from 3 ecoregions. Mammals had 39 ecoregion

endemics across 2 ecoregions while reptiles had 33 ecoregion endemics across

4 ecoregions. With only 16 ecoregion endemics, amphibians had the fewest;

however, this class had more ecoregions with endemic spedes than any other

dass (n = 5). The CGRS values for the Rio Grande Plain, Oak Woods and

Prairies, Central Gulf Prairies and Marshes, and Gross Timbers and Prairies

were in the lowest 5 across all taxonomic classes.

Current biodiversity protection is limited (Figure 4.1 and Table 4.5). The

mean proportion of land in each ecoregion managed by state and federal

agendes (i.e., biodiversity reserves) was only x = 4.57% ± 5.78%. The

ecoregion with the least long-term biodiversity protection was the Rolling Plains

(0.21%); however, with 4 other ecoregions also having less than 1 % and 3 others

having less that 3% of their land in biodiversity reserves, it is difficult to condude

which ecoregion is least protected. The ecoregion with the most protection was

the Basin and Range (19.64%). The only ecoregions that appeared to be

relatively well protected were the Basin and Range, the Mid Coastal Plains, and

the 3 Gulf Prairies and Marshes ecoregions.

38

39

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40

Table 4.5. The percent of each ecoregion currentiy being managed for the long-term protection of biodiversity.

Ecoregion

Basin and Range

Southem Gulf Prairies and Marshes

Eastem Gulf Prairies and Marshes

Mid Coastal Plains

Central Gulf Prairies and Marshes

Texas High Plains

Edwards Plateau

Stockton Plateau

Oak Woods and Prairies

Gross Timbers and Prairie

Rio Grande Plain

Blackland Prairies

Rolling Plains

Total

Ecoregion

Area (Ha)

19,091,846

662,743

562,313

733,270

2,316,495

12,569,412

5,665,966

3,185,404

5,679,029

9,302,726

7,682,649

3,416,316

10,070,694

80,938,863

Reserve

Area (Ha)

3,749,170

76,983

46,345

9,833,409

91,611

318,781

116,274

33,847

44,965

71,937

47.372

18,651

21,215

14,470,560

Percent in

Reserve

19.64

11.62

8.24

7.46

3.95

2.54

2.05

1.06

0.79

0.77

0.62

0.55

0.21

17.88

4.3 Objective 2

Ecologists have long tried to answer the question, "Why do more species

live in one area than another?" Objective 2 of this thesis was to assist in

understanding this question by evaluating the relationship between several

habitat (Table 1.1), spatial (Table 1.2), and dimatic (Table 1.3) variables and the

species richness of Texas ecoregions. This type of analysis was only possible

due to the large number of diverse ecoregions in Texas (Table 4.6). As

discussed eariier, Bailey et al. (1994) located 18 section-level ecoregions in

Texas. Of these, the majority of 13 are found within the state (Figure 2.1).

Based on the analysis described in Section 3.3, the largest (190,918 km ) and

41

driest (PRGPANN = 12.10 inches) of these 13 ecoregions was the Basin and

Range, and the smallest (5,736 km^) and wettest (PRGPANN = 53.84 inches)

was the Eastem Gulf Prairies and Marshes. The Texas High Plains was the

coolest ecoregion (MINANN = 43.34 F°), while the Rio Grande Plain was the

hottest (MAXANN = 83.79 F°). The Texas High Plains also had the fewest soil (n

= 15) and vegetation types (n = 6), while the Basin and Range had the most soil

types (n = 131), and the Central Gulf Prairies and Marshes had the most

vegetation types (n = 22). The Basin and Range had the largest range in

elevation (3,140 m) and the Texas High Plains the least (30 m).

For habitat characteristics, I found 4 significant (P < 0.05) relationships

(Table 4.7) between habitat variables (see Table 1.1) and the species richness of

Texas ecoregions. Reptile richness was positively related (P = 0.048) to the

number of vegetation types. Amphibian richness was negatively related to range

of elevation; however, this relationship was only significant (P = 0.016) with the

removal of the Basin and Range outiier value. Mammal richness was positively

related to both the number of vegetation types and the range of elevation; these

relations were significant with (P = 0.003 and P < 0.001, respectively) or without

(P = 0.005 and P < 0.001, respectively) the Basin and Range outiier in the model.

I found 6 models that were only significant with indusion of a Basin and Range

outiier. Mammalian richness was positively related to the number of soil

classifications (P = 0.049), number of soil textures (P < 0.001), TOPOINDX (P <

0.001), and planar area (P = 0.001) when the Basin and Range outiier was

induded. Similariy, reptilian richness was positively related to both SOILTEXT (P

= 0.012), and TOPOINDX (P = 0.040) with the indusion of the outiier.

42

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43

Table 4.7. Significant (P < 0.05) simple-linear regression models predicting the species richness of ecoregions for vertebrate dasses based on habitat variables. Predictor variables are defined in Table 1.

Vertebrate

dass

Amphibians^

Mammals^

Mammals'

Mammals^

Mammals'^

Mammals^

Mammals^

Mammals'

Mammals^

Reptiles

Reptiles^

Reptiles^

Predictor

variable

ELEVDIF

VEGTYPE

VEGTYPE

SOILCLASS

SOILTEXT

TOPOINDX

ELEVDIF

ELEVDIF

POLYAREA

VEGTYPE

SOILTEXT

TOPOINDX

Adjusted

r

0.400

0.524

0.513

0.244

0.670

0.631

0.875

0.659

0.610

0.248

0.403

0.269

P

0.016

0.003

0.005

0.049

< 0.001

< 0.001

< 0.001

<0.001

0.001

0.048

0.012

0.040

Model

38.1 -0.0139y

36.1 + 2.98y

46.7 + 1.98y

59.2+ 0.269y

47.0 + 1.64y

67.0 + 118y

62.6 + 0.0200y

61.8 + 0.0219y

57.2 + 2.73e-6y

65.9 + 1.34y

69.7 + 0.802y

80.1 + 50.7y

Figure

4a

4b

4c

4d

4e

4f

4g

4h

41

4j

4k

41

' Basin and Range outiier excluded from the model. ^ Model contains a Basin and Range outiier. ^ Model is not significant when the Basin and Range outiier is removed.

For spatial characteristics, I found 5 significant (P < 0.05) relationships

(Table 4.8) between spatial variables (see Table 1.2) and the species richness of

Texas ecoregions. Amphibian richness was positively related (P = 0.001) to the

eastern extent of ecoregions, and vertebrate richness was negatively related (P =

0.010) to the southern extent of ecoregions. Bird richness was negatively related

to both the southern (P = 0.002) and northem (P = 0.025) extents of ecoregion,

as well as to the center of these two extremes (P = 0.008). On the other hand,

mammalian richness was negatively related to the western extent, eastem

extent, and center; these relationships were significant with (P < 0.001 for all

three variables) or without (P < 0.001, P = 0.002, and P < 0.001 respectively)

inclusion of the Basin and Range outiiers. Amphibian richness was positively

related to both the western extent and the west-east center of ecoregions; these 44

relationships were significant with [P= 0.019 (assumption of normality violated)

and P = 0.007, respectively) or without (P = 0.011 and 0.004, respectively)

indusion of the basin and range outiier. I found 1 spatial model that was only

significant with indusion of a Basin and Range outiier: reptile richness was

related (P = 0.042) to the western extent of ecoregions.

Table 4.8. Significant (P < 0.05) simple-linear regression models predicting the species richness of ecoregions for vertebrate dasses based on spatial variables. Predictor variables are defined in Table 1.

Vertebrate

Class

Amphibians'^''

Amphibians'

Amphibians

Amphibians^

Amphibians'

Birds

Birds

Birds

Mammals^

Mammals'

Mammals^

Mammals'

Mammals^

Mammals'

Reptiles^

All

Predictor

variable

EMIN

EMIN

EMAX

ECENT

ECENT

NMIN

NMAX

NCENT

EMIN

EMIN

EMAX

EMAX

ECENT

ECENT

EMIN

NMIN

Adjusted

r

0.350

0.445

0.585

0.459

0.538

0.550

0.323

0.446

0.934

0.832

0.687

0.594

0.867

0.746

0.262

0.415

P

0.019

0.011

0.001

0.007

0.004

0.002

0.025

0.008

< 0.001

< 0.001

< 0.001

0.002

< 0.001

< 0.001

0.042

0.010

Model

25.3 + 140€-5y

20.1 + 2.37e-5y

12.2 + 2.59e-5y

20.2 + 1.91e-5y

14.3 + 2.78e-5y

818- 1.47e-4y

621 - 7.58e-5y

720-1.09e-4y

93.5-4.39e-5y

91.3-3.98e-5y

117-5.69e-5y

102-3.90e-5y

105-5.17e-5y

97.6-4.10e-5y

90.2-1.56e-5y

1.08e3-1.69e-4y

Figure

5a

5b

5c

5d

5e

5f

5g

5h

51

5j

5k

51

5m

5n

5o

5p

' Basin and Range value excluded from the model. ^ Model contains a Basin and Range outiier. ^ Model is not significant when the Basin and Range outiier is removed. "* Assumption of normality violated.

45

s

For climatic characteristics, I found 19 significant (P < 0.05) relationships

(Table 4.9) between climatic variables (see Table 1.3) and the species richness

of Texas ecoregions. Amphibian richness was positively related (P = 0.007) to

PRGPMAX. Bird richness was positively related to both MAXMIN (P = 0.011)

and MINMIN (P = 0.006) and to both MAXANN (P = 0.029) and MINANN (P =

0.019). Bird richness was negatively related to both MAXDIF (P= 0.011) and

MINDIF (P = 0.001) and to both MAXVAR (P = 0.010) and MINVAR. Bird

richness was also negatively related (P = 0.008) to MXMNDIF. Bird richness was

related to ANNDIF (P= 0.009); however, this relationship was only significant

when the Basin and Range outiier was renrKDved from the model. Similariy,

vertebrate richness was positively related to both MAXMIN (P = 0.016) and

MAXANN (P= 0.009) and negatively related to both MINDIF (P= 0.013) and

MINVAR (P= 0,012); however, these relationship were only significant with the

removal of the Basin and Range outiier.

Mammalian richness was negatively related to PRCPMIN, PRGPMAX,

and PRGPANN; these relationships were significant with (P = 0.003, P < 0.001,

and P < 0.001, respectively) or without (P = 0.001. P < 0.001, and P < 0.001.

respectively) indusion of the Basin and Range outiiers. Mammal richness was

positively related to ANNDIF with (P < 0.001) or without (P = 0.004) the Basin

and Range outiier. Reptile richness was positively related to MAXMAX with (P =

0.043) or without (P < 0.001) the Basin and Range outiier. Amphibian richness

was related to PRCPMIN and PRGPANN; these nr»odels were significant with (P -

= 0.005 and P = 0.009, respectively) or without (P < 0.001 for both) the indusion |

of the Eastern Gulf Prairies and Marshes outlier. I also found a relationship j

between mammalian richness and both MINMAX (P = 0.005) and MINANN (P =

0.024); these models were only significant with the removal of the Basin and

Range outiier.

46

^

Table 4.9. Significant (P < 0.05) simple-linear regression models predicting the species richness of ecoregions for vertebrate dasses based on dimatic variables. Predictor variables are defined in Table 1.1.

Vertebrate

dass

Amphibians*

Amphibians^

Amphibians

Amphibians'*

Amphibians^

Birds

Birds

Birds

Birds

Birds

Birds

Birds

Birds

Birds

Birds'

Mammals^

Mammals'

Mammals^

Mammals'

Mammals^

Mammals'

Mammals^

Predictor

variable

PRCPMIN

PRCPMIN

PRGPMAX

PRGPANN

PRGPANN

MAXMIN

MAXANN

MAXDIF

MAXVAR

MINMIN

MINANN

MINDIF

MINVAR

MXMNDIF

ANNDIF

PRCPMIN

PRCPMIN

PRGPMAX

PRGPMAX

PRGPANN

PRGPANN

MINMAX

Adjusted

r

0485

0.731

0.457

0.424

0.695

0.413

0.308

0409

0.421

0.465

0.352

0.584

0.584

0.440

0.463

0.531

0.618

0.787

0.823

0.673

0.736

0.476

P

0.005

< 0.001

0.007

0.009

< 0.001

0.011

0.029

0.011

0.010

0.006

0.019

0.001

0.001

0.008

0.009

0.003

0.001

< 0.001

< 0.001

< 0.001

< 0.001

0.005

Model

20.1 +6.88y

17.5 + 941y

8.45 + 5.29y

15.7 + 0.478y

10.3 + 0.695y

174 + 5.56y

-298 + 8.23y

537 - 5.40y

445 - 0.590y

215 + 3.69y

125 + 4.07y

587 - 6.70y

477-0.718y

551 - 348y

530 - 7.96y

99.0-14.7y

89.1 -10.2y

134-13.7y

117-10.1y

114-1.19y

100-0.847y

350 - 3.84y

Figure 6

6a i 6b 1 6c 1 6d 1 6e ^

1 6g 1 6h 1 61

6]

6k

61

6m

6n

6o

6p

6q

6r

6s

6t

6u

6v

47

Table 4.9. Continued.

Vertebrate

Glass

Mammals"^

Mammals^

Mammals^

Reptiles^'^

Reptiles^

All^

All '

All^

All^

Predictor

variable

MINANN

ANNDIF

ANNDIF

MAXMAX

MAXMAX

MAXMIN

MAXANN

MINDIF

MINVAR

Adjusted

^

0.329

0.643

0.542

0.260

0.653

0.400

0.463

0.422

0.430

P

0.024

< 0.001

0.004

0.043

< 0.001

0.016

0.009

0.013

0.012

Model

180-1.93y

-13.6 + 3.76y

13.7 + 2.51y

-246 + 3.49y

-328 + 4.34y

157 + 6.30y

-351 + 11.3y

770 - 6.69y

662 - 0.722y

Figure

6w

6x

6y

6z

6aa

6ab

6ac

Gad

6ae

' Basin and Range value excluded from the model. ^ Model contains a Basin and Range outiier. ^ Model is not significant when the Basin and Range outiier is removed. ^ Model contains an Eastem Gulf Prairies and Marshes outiier. ^ Eastem Gulf Prairies and Marshes outiier excluded from the model. ^ Assumption of normality violated.

48

CHAPTER 5

DISCUSSION

Bailey et al. (1994) divided the 48 contiguous states into 163 ecoregions.

Of these, at least half of the area of 13 ecoregions are located within Texas.

These 13 ecoregions encompass 809,389 km^ (> 10% of the total area of all

lower-48 ecoregions) of diverse wildlife habitat. There are currentiy 908

terrestrial vertebrates known to be native to and living within these habitats. An

additional 12 terrestrial vertebrates are known to have lived in these ecoregion in

recent time, but are now considered extinct or extirpated, and 113 species are

currentiy considered by state and or federal agencies as being in danger of

becoming extinct within at least these ecoregions. To ensure that that these and

other species do not reach this fate, it is important that their habitats are

adequately protected within a network of biodiversity reserves throughout the

area. In dedding where to establish a new reserve(s), it is logical to first identify

the ecoregion where it would be most benefidal and then to use smaller scale

data to identify exactly where within the ecoregion to locate the reserve(s).

Some information that should be considered when choosing an ecoregion for

reserve establishment is vertebrate richness of the ecoregion, the relative

uniqueness of the vertebrate community living in each ecoregion, the number of

vertebrates currentiy listed on either state or federal endangered spedes lists,

and the current amount of land in each ecoregion currentiy being managed for

the long-term protection of biodiversity.

Neariy 70% (625) of all the vertebrates found within the major ecoregions

of Texas are found in the Rio Grande Plain. However, with the second highest

species richness, the Basin and Range had only 18 fewer species (2% of all

species) than the Rio Grande Plain. Conversely, the third richest ecoregion, the

Southern Gulf Prairies and Marshes, is well separated from both the Rio Grande

Plain, with 62 fewer species, and from the Basin and Range with 44 fewer

species. In addition, at the taxonomic class level, the Basin and Range had

49

nxDre mammals and reptiles than any other ecoregion, and the Rio Grande plain

had the nrxDst birds. The Mid Coastal Plains and the Oak Woods and Prairies

had the nrrast amphibians. In addition, since the full extent of the Basin and

Range and the Rio Grande Plain is not known (portions of these ecoregions exist

in Mexico outside the scope of this study), it is likely that the species richness for

tx)th these ecoregions is actually higher. There is a direct correlation between

species richness and the number of listed spedes within each ecoregion. The

Basin and Range, Rio Grande Plain, and the Southem Gulf Prairies and Marshes

each have > 50 (9-10 % of their species) species listed as threatened or

endangered under either state or federal threatened or endangered species lists,

well PTHDre than any other ecoregion.

In addition to being the second richest ecoregion, coefficient of community

analysis indicates that the Basin and Range also had the most unique vertebrate

community across all dasses and contains the highest number of spedes that

are found in only one ecoregion (i.e., ecoregion endemics). The vertebrate

community of the Rio Grande Plain, on the other hand, is relatively non-unique.

Perhaps surprisingly, the Texas High Plains and the Stockton Plateau, 2 of the 3

least rich ecoregions and 2 ecoregions which together only contain 2 ecoregion

endemics, have very unique vertebrate communities.

In terms of current long-term biodiversity protection, 18% of the lands

within this study area are managed by state and federal agencies. This number,

however, is deceivingly inflated due solely to amount of land in reserve within the

New Mexico portion of the Basin and Range. In fact, of all 13 ecoregions, 9

actually have < 4% of their lands being managed by state and federal agencies.

It is, however, recognized that some privately-owned lands are currentiy

protecting biodiversity at some level by virtue of their large size and lack of

human activities (e.g., game and cattle ranches), but since these lands to not

have long-term biodiversity management plans, as there is with most state and

federally managed lands, there is no guarantee as to the future use of the land.

It is thus clear that biodiversity within the major ecoregions of Texas is not well

50

protected, and additional reserves must be placed in all ecoregions. Which

ecoregion should have priority is not evident. Because it had the second highest

species richness, the most unique vertebrate community, and the most number

of threatened and endangered spedes, the Basin and Range would seem to be

an ecoregion that would demand many biodiversity reserves; however, due to

current protection efforts with in this ecoregion, a reserve placed elsewhere

would likely be more benefidal. Similariy, the Rio Grande Plain which had the

highest spedes richness and the second most threatened and endangered

species, is not well protected; however, because this vertebrate community is not

unique, the spedes occurring here are perhaps well protected within other

ecoregions. In contrast, the Texas High Plain and the Stockton Plateau both

have unique vertebrate communities, and these spedes are only being protected

on 1-3% of the land within each ecoregion. However, these two ecoregions have

among the lowest spedes richness values and contain relatively few threatened

and endangered spedes.

In addition to creating reserves to protect long-term biodiversity, it is also

useful to understand what it is about a given ecoregion that determines the

number of species that can live there. I used simple linear regression to evaluate

the relationship between vertebrate richness per ecoregion and 31 ecoregion-

specific variables (see Tables 1.1, 1.2, and 1.3). Of these, only 5 predicted the

total number of vertebrates found in the major ecoregions of Texas. These

variables were, however, much more successful at predicting the number of

vertebrates found in these ecoregions that belong to a particular taxonomic class.

This analysis revealed that mammalian (n = 15) and avian (n = 13) richness were

related to more variables than either amphibians (n = 7) or reptiles (n = 5). With

the exception of bird richness not being related to any habitat variables, at least

one of each variable type (i.e., habitat, spatial, and climatic) predicted the

number of vertebrates found in each ecoregion at the taxonomic class level.

More specifically, in terms of the spatial location of ecoregions, the

southern extent of ecoregions was negatively related to total vertebrate richness;

51

however, neither the northern extent nor the north-south median were related.

This relationship across all vertebrates is heavily influenced by the relatively

large number of birds found in all ecoregions. At the taxonomic class level, bird

richness was negatively related to all northem variables, mammalian richness

was negatively related to all eastern variables, and amphibian richness was

positively related to all eastem variables. Reptile richness was negatively related

to the western extent of ecoregions; however this model was not significant when

the Basin and Range outiier was renrjoved. Basin and Range outiiers were

commonplace throughout this study. As discussed in Sections 4.2 and 4.3, the

Basin and Range ecoregion is the nx^st unique of all the Texas ecoregions. It

was thus not unexpected that habitat, dimatic, and spatial values for this

ecoregion would appear as outiiers during statistical analysis. However, it is

important to note that it is likely these values would not be considered outiiers if

neighboring ecoregions similar to the Basin and Range were induded in the

study and, thus, the dependent variables containing the outiier may actually be a

valid predictor of spedes richness, especially in those cases where the model

was still significant after removing the outiier.

The number of amphibians, mammals, and reptiles found in Texas

ecoregions were each related to at least one habitat variable. However, only the

nxjdel predicting reptile richness based on number of vegetation types lacked a

Basin and Range outiier. The number of vegetation types in an ecoregion also

predicted mammalian richness, with or without the outiier, but did not predict

amphibian, avian, or total vertebrate richness. Two of the four soil variables

predicted mammalian richness, and one predicted reptile richness, however

these models were only significant when the Basin and Range outiier was

induded. Both topographic variables predicted mammalian richness, but only the

difference between the highest and lowest elevations (ELEVDIF) was significant

when the outiier was removed. On the other hand, the percent change from

planar to surface area (TOPOINDX) only predicted reptile richness when the

52

outlier was included, and ELEVDIF also only predicted amphibian richness when

the outiier was renxDved.

In terms of temperature variables, avian richness was related to 10 of the

12 variables. Avian richness was positively related to txDth the mean daily

maximum and minimum temperatures for the coolest month. Avian richness was

also positively related to both the mean monthly maximum and mean monthly

minimum temperatures. On the other hand, avian richness was negatively

related to all variables that measured the anxjunt of temperature variation within

an ecoregion. With the Basin and Range outiier included in the model, overall

vertebrate richness was not related to any dimatic variables. However, when the

outlier was renrKDved, vertebrate richness was positively related to maximum

temperature and negatively related to the variation in temperature. Reptile

richness was positively related to the maximum daily temperature for the hottest

month but was not related to any other dimatic variables. There were no

relationships between amphibian richness and temperature. In terms of

predpitation, however, I found that amphibian richness was positively related

with, or without, the Eastern Gulf Prairies and Marshes outiier and mammalian

richness was negatively related with, or without, the Basin and Range outiier, to

both mean monthly and mean annual predpitation. Neither birds, reptiles, nor all

vertebrates were related to any of the precipitation variables and the variation in

monthly predpitation was not related to species richness at any taxonomic class.

So, why do nrrare species live in one area than another? For the

ecoregions of Texas, I found that more vertebrates were found in those

ecoregions that extend further south, regardless of their northem extent. At the

class level, bird richness increased along a north to south gradient, mammal

richness increased along an east to west gradient, and amphibian richness

increased along a west to east gradient. Thus the spatial location of an

ecoregion was a good predictor of vertebrate richness. However, spatial location

itself is probably not that important, but rather, the spatial location is likely

53

con-elated to other environmental variables that are the actual reason for why

more species are found in one ecoregion than another.

The total number of vertebrates living in the ecoregions of Texas was not

related to the diversity of habitats found in these ecoregions. However, the

number of vegetation types found in an ecoregion was positively related to both

mammalian and reptile richness while increasing topographic relief was also

positively related to mammalian richness. The remaining significant habitat

nxDdels were only significant if the Basin and Range outiier was either included or

renrKDved. No habitat variables predicted either avian or total vertebrate richness,

and only when an outiier was removed, did any variable predict amphibian

richness. This study also revealed that avian richness was higher in those

ecoregions having milder winters, those ecoregions having overall higher

temperatures, and those ecoregions having more stable temperature patterns.

Extreme summer heat was also a good predictor of reptile richness. In terms of

predpitation, increased wetness coindded with an increase in amphibian

richness and a decrease in mammalian richness.

Although a single spedfic statistical test of my null hypothesis that the

vertebrate spedes richness of Texas ecoregions is not related to the spatial

location of ecoregions, the diversity of habitats within ecoregions, or dimatic

factors, the several single variable tests I completed did reveal that these factors

are indeed related to spedes richness. With, or without, an outiier at least one

spatial variable predicted the spedes richness of both all vertebrates and of just

amphibians, birds, and mammals. Several habitat variables, some including a

Basin and Range outiier, predicted both mammalian and reptilian richness.

Temperature was a good predictor of avian richness, precipitation was a good

predictor of amphibian richness, and txDth dimatic measures were good

predictors of mammalian richness.

In terms of identifying the location of future biodiversity reserves, I feel that

the methodologies I presented in this thesis are essential first step in the decision

process. This type of ecoregional analysis provides a framework within which

54

other variables can be evaluated and in which smaller scale studies can be

completed. Perhaps if this type of work were completed for all the ecoregions of

US. we would have a better handle on how to assist in managing the biodiversity

resources of the worid. In terms of understanding why more species live in one

area than another, it is obvious from my work and the work of other, that the

reason why spedes richness varies across the landscape is not related to a

single variable, and also that different groups of animals respond differentiy to

different variables. In general, my work on this topic has revealed the need to

complete this type of analysis at at least a national level. For if these same data

were collected for the 164 ecoregions of the contiguous states, many more

statistical models could be evaluated.

55

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62 i

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/ . I

63

APPENDIX A

SPECIES PRESENCE VERSUS ABSENCE MATRICES FOR

THE TERRESTRIAL VERTEBRATES LIVING IN THE

MAJOR ECOREGIONS OF TEXAS

i

64

Table A. I . Species presence (Y) versus absence matrix for the 77 amphibian species living in the major ecoregions of Texas.

Scientific Name Achs crepitans Ambystoma annulatum Ambystoma maculatum Ambystoma opacum Ambystoma talpoideum Ambysttxna texanum Ambystoma tigrinum Amphiuma tridactylum Bufo alvarius' Bufo amehcanus Bufo txignatus Bufo debilis Bufo houstonensis^ Bufo mahnus Bufo microscaphus Bufo punctatus Bufo speciosus Bufo vallkxps Bufo woodhousii Desmognathus auriculatus Desmognathus brimleyorum Desmognathus fuscus Eleutttemdactylus augusti Eurycea latians' Eurytxa longicauda Eurycea multiplicata Eurycea nana^ Eurycea neotenes Eurycea ptemphila Eurycea quadridiqitata Eurycea rattibuni Eurycea robusta^ Eurycea sosorum^ Eurycea tridentifera' Eurycea troglodytes Gastrophryne carolinensis Gastnophryne olivacea^ Hemidactylium scutatum Hyla arenicolor Hyia avNOca Hyla chrysoscelis Hyla cinerea Hyla squirella Hyla versicolor Hypopachus vaholosus^ Leptodactylus labialis^ Necturus beyeri Necturus maculosus Notophthalmus meridionalis^ Notophthalmus viridescens Plethodon albagula Plethodon serratus Pseudacris clarkii Pseudacris crucifer Pseudacris streckeri Pseudacris triseriata Rana areolata

MOP Y Y Y Y Y Y Y Y

Y

Y

Y Y Y Y

Y

Y Y Y

Y Y Y Y

Y Y

Y Y Y Y Y

Y

Y Y Y Y Y

EGP Y

Y

Y Y Y

Y Y Y

Y

Y Y Y

Y Y Y Y

Y Y

Y Y Y Y Y Y Y Y

CTP Y

Y Y

Y Y

Y Y Y

Y Y Y Y

Y

Y

Y Y

Y Y

Y

Y

Y

Y Y Y Y Y

BLP Y

Y

Y Y

Y

Y Y

Y Y Y Y Y

Y

Y Y

Y Y

Y

Y

Y

Y Y Y Y Y

OWP Y

Y Y Y Y Y Y

Y

Y Y

Y Y Y Y Y

Y

Y Y Y Y Y Y

Y

Y Y

Y Y Y Y

Y

Y

Y Y Y Y Y

CGP Y

Y

Y Y Y

Y Y

Y Y Y Y

Y

Y Y Y

Y Y Y Y

Y Y Y

Y Y

Y Y Y Y Y

THP Y

Y

Y Y

Y Y

Y

Y

Y

Y

Y

Y

Y

Y Y

RLP Y

Y

Y Y

Y Y Y Y

Y

Y

Y

Y

Y

EWP Y

Y Y

Y

Y Y Y Y

Y Y

Y

Y Y

Y Y

Y

Y

Y

RGP Y

Y Y

Y

Y

Y Y Y Y

Y

Y Y Y

Y

Y Y Y Y

Y Y

Y Y Y

Y

Y

Y Y Y

SGP Y

Y Y

Y

Y Y Y

Y Y

Y Y Y Y Y Y

Y

Y

Y Y Y

BAR Y

Y

Y

Y Y

Y Y Y

Y

Y

Y

Y

Y

Y

SKP Y

Y

Y

Y Y Y Y

Y

Y

Y

X

3

65

Table A.1. Continued.

Scientific Name Rana beriandieri Rana blairi Rana catesbeiana Rana f:hiricahuensis Rana clamitans Rana grylio Rana palustris Rana pipiens Rana sphenocephala Rana yavapaiensis^ Rhinophrynus dorsalis^ Scaphiopus bombifrons Scaphiopus couchii Scaphiopus hurterii Scaphiopus multiplicatus Siren intermedia Smilisca baudinii' Syrrhophus cystignathoides Synrtiophus guttilatus Synhophus mamockii

MOP

Y

Y Y Y

Y

Y

EGP

Y

Y Y Y

Y

Y

Y

CTP Y Y Y

Y

Y

Y

Y Y Y Y Y

BLP Y

Y

Y

Y

Y

Y Y

Y

Y

OWP Y

Y

Y

Y

Y

Y Y

Y

CGP Y

Y

Y

Y

Y

Y Y

Y

THP Y Y Y

Y Y

Y Y

RLP Y Y Y

Y Y

Y

Y

EWP Y Y Y

Y

Y

Y Y Y

Y

RGP Y

Y

Y

Y

Y Y Y Y

Y Y Y

Y

SGP Y

Y

Y

Y Y Y

Y Y Y

BAR Y Y Y Y

Y

Y

Y Y

Y Y

Y

SKP Y

Y

Y Y

Y

Y

^ Listed on at least the Louisiana. New Mexico. Oklahoma. Texas, or federal Threatened and Endangered Spedes list.

66

Table A.2. Spedes presence (Y) versus absence matrix for the 500 avian species living in the ecoregions of Texas and the 5 extinct avian spedes that once lived in Texas ecoregions.

Scaentrfic Name AcdpHer coopehi AcdpHer gentiis AcdpHer striatus Actiis macularia Aochmophorus darkif' Aechmophorus txxidentalis^ AegoSus acadicus Aeronautes saxatalis Agelaius phoeniceus AkmphUa aestis/alis^ Aknophia botterii' Aimophila cassinii AimophHa ruficeps Abe sponsa Afaia ajaja Amaziia vioOceps' Amaziha yucatanensis Amazona viridigenalis Ammodramus bairdit ^ Ammodramus henslowii Ammocbamus leconteif' Ammockamus marHimus Ammodramus nelson f' Ammodramus savannarum Amphispiza t>eui' Amphispiza bUneata Anas acuta Anas americana^ Anas c^ypeata Anas crecca' Anas cyanoptera Anas discors Anas fuh/igula Anas piatyrhynchos Anas strepera Anhinga anhinga Anser albifrons Anthus rubescens^ Anthus spragueii Aphekxxima caHfomica Aphekxoma uttramarina Aquia chrysaettis Aratinga hokxhkxa ArchHochus alexandri ArchicKhus colubris Ardea herodias Arenaria mterpres Arremonops rufMrgatus Ask) flammeus^ Asio otus Asturina nitida^ Auriparus flaviceps Aythya afTinis Aythya americana Aythya coUaris^ Aythya marUa^ Aythya valisineria^ Baeoiophus hdgwayi

MOP Y

Y Y Y Y

Y

Y Y

Y Y

Y Y

Y Y

Y Y Y Y Y Y Y Y Y Y Y Y Y

Y

Y Y Y

Y Y

Y Y Y Y Y

EGP Y

Y Y Y Y

Y

Y Y

Y Y

Y Y Y Y Y

Y Y Y Y Y Y Y Y Y Y Y Y Y

Y

Y Y Y

Y Y

Y Y Y Y Y

CTP

Y Y Y Y Y Y

Y

Y Y

Y Y Y Y

Y Y

Y Y

Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Y

Y Y Y Y

Y Y

Y Y Y Y Y

BLP Y

Y Y Y Y

Y

Y Y

Y

Y Y

Y Y

Y Y

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Y

Y Y Y Y

Y Y

Y Y Y Y Y Y

OWP

Y

Y Y Y Y

Y

Y Y

Y

Y Y

Y Y

Y Y

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Y

Y Y Y Y Y Y Y

>- >

•

Y Y Y Y

CGP Y

Y Y Y Y

Y

Y Y Y Y

Y Y

Y

Y Y Y Y Y

Y Y Y Y Y Y Y Y Y Y Y Y Y

Y

Y Y Y Y Y Y Y

Y Y Y Y Y Y

THP Y

Y Y Y Y

Y Y Y

Y Y Y

Y

Y Y Y Y Y Y Y Y Y

Y Y

Y Y Y

Y

Y

Y Y Y Y

Y Y

Y Y Y Y Y Y

RLP Y

Y Y Y Y

Y

Y

Y Y Y

Y

Y Y Y Y Y Y Y Y Y

Y Y

Y Y Y

Y

Y

Y Y Y Y

Y Y

>- >-

Y Y Y Y

EWP

Y

Y Y Y Y

Y

Y

Y Y Y

Y

Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Y

Y

Y Y Y Y Y Y Y

Y Y Y Y Y Y

RGP Y

Y Y Y Y

Y

Y Y

Y Y

Y Y

Y Y Y Y

Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Y

Y

Y Y Y Y Y Y Y Y

Y Y Y

Y Y Y Y

SGP Y

Y Y Y Y

Y

Y Y

Y Y

Y Y

Y Y Y Y

Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Y

Y Y Y Y Y Y Y Y

Y Y Y

Y Y Y Y

BAR

Y Y Y Y Y Y

Y Y Y

Y Y Y

Y

Y

Y Y Y Y Y Y Y Y Y

Y Y

Y Y Y

Y Y

Y

Y Y Y Y

Y Y

Y Y Y Y Y Y

Y

SKP

Y

Y Y Y Y

Y Y Y

Y Y Y

Y

Y Y Y Y Y Y Y Y Y

Y Y Y Y Y Y

Y

Y

Y Y Y Y Y Y Y

Y Y Y Y Y Y

67

Table A.2. Continued.

Scientific Name Baeoiophus wollweberi Bartramia longicauda Bombycilla cedrorum' Botaurus lentiginosus Branta canadensis' Bubo virginianus Bubukus ibis Bucephala albeola' Bucephala clangula' Buteo albicaudatus^ Buteo albonotatus^ Buteo jamaicensis Buteo lagopus' Buteo lineatus Buteo platypterus Buteo regalis Buteo swainsoni Buteogallus anthracinus^ Butorides vtescens Cairina mochata' Calamospiza melanocorys Calcarius lapponicus' Cakarius mccownii' Calcarius omatus' Calcarius pictus' Calidris alba' Calidris alpina' Calidris bairdii' Calidris canutus' Calidris fuscicollis' Calidris himantopus' Calidris maurf' Calidris melanotos' Calidris minutilla' Calidris pusilla' Callipepla gambelii Callipepla squamata Cahnectris diomedea' Calothorax ludfer* Calypte anna Caiypte costae^ Campephilus principalis^ Camptostoma imberbe^ Campylorhynchus brunneicapillus Caprimulgus carolinensis Caprimulgus ridgwayi^ Caprimulgus vociferus Cardellina rubrifrons Cardinalis cardinalis Cardinalis sinuatus Carduelis pinus Carduelis psaltria Carduelis tristis Carpcxiacus cassinii' Carpodacus mexicanus Carpodacus purpureus' Casmerodius alb us Cathartes aura Catharus fuscescens' Catharus guttatus Catharus minimus

MCP

Y Y Y Y Y Y Y Y Y

Y Y Y Y Y Y

Y

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Y

Y

Y

Y

Y

Y Y Y

Y Y Y Y Y Y Y

EGP

Y Y Y Y Y Y Y Y Y

Y Y Y Y Y Y

Y

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Y

Y

Y

Y

Y

Y Y Y

Y Y Y Y Y Y Y

CTP

Y Y Y Y Y Y Y Y

Y Y Y Y Y Y

Y

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Y

Y

Y

Y

Y

Y Y Y

Y Y Y Y Y Y Y

BLP

Y Y Y Y Y Y Y Y

Y Y Y Y Y Y

Y

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Y

Y

Y

Y

Y Y Y Y Y

Y Y Y Y Y Y Y

OWP

Y Y Y Y Y Y Y Y Y

Y Y Y Y Y Y

Y

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Y

Y

Y

Y

Y

Y Y Y Y Y

Y Y Y Y Y Y Y

CGP

Y Y Y Y Y Y Y Y Y

Y Y Y Y Y Y

Y

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Y Y

Y

Y

Y

Y

Y Y Y Y Y

Y Y Y Y Y Y Y

THP

Y Y Y Y Y Y Y Y

Y Y Y Y Y Y Y

Y

Y Y Y Y

Y Y Y Y Y Y Y Y Y Y

Y

Y

Y Y Y Y Y Y Y Y Y Y

Y

RLP

Y Y Y Y Y Y Y Y

Y Y Y Y Y Y Y

Y

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Y

Y

Y Y Y Y Y Y Y Y Y Y

Y

EWP

Y Y Y Y Y Y Y Y

Y Y Y Y Y Y Y

Y

Y Y Y Y

Y Y Y Y Y Y Y Y Y Y

Y

Y

Y

Y

Y Y Y Y Y

Y Y Y Y

Y

RGP

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Y Y

Y Y Y Y Y Y Y Y Y Y

Y Y

Y

Y Y

Y

Y

Y Y Y Y Y

Y Y Y Y Y Y Y

SGP

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Y

Y Y

Y Y Y Y Y Y Y Y Y Y

Y Y

Y

Y Y

Y

Y

Y Y Y Y Y

Y Y Y Y Y Y Y

BAR Y Y Y Y Y Y Y Y Y

Y Y Y Y Y Y Y Y Y

Y

Y Y

Y Y Y Y Y Y Y Y Y Y Y Y

Y Y Y

Y Y

Y Y Y Y Y Y Y Y Y Y Y Y Y

Y

SKP

Y Y Y Y Y Y Y Y

Y Y Y Y Y Y Y Y Y

Y

Y Y

Y Y Y Y Y Y Y Y Y Y

Y

Y

Y

Y

Y

Y Y Y Y Y Y Y Y Y Y

Y

•9

68

Table A.2. Continued.

Scientific Name Catharus ustulatus'' Catherpes mexicanus Catoptrophorus semipalmatus Certhia americana Ceryle akyon Ceryle torquata Chaetura pelagka Charadrius alexandrinus Charadrius melodus^' Charadrius montanus Charadrius semipalmatus' Charadrius vociferus Charadrius wilsonia Chen (xerulescens' Chen rossii' Chlidonias niger' Chlonxeryle americana Chondestes grammacus Chondrohierax uncinatus Chordeiles acutipennis Cttordeiles minor Cinclus mexkanus Circus cyaneus Cistottiorus palustris Cistothorus platensis' Clangula hyemalis' Ccxxxithraustes vespertinus' Coccyzus americanus Coccyzus erythmpthalmus' Colaptes auratus Colinus virginianus Columba fasciata Columba flavirostris Columbine passerine^ Contopus cooperi Contopus pertinax Contopus sordidulus Contopus virens Conuropsis carolinensis Coragyps atratus Corvus brachyrhynchos Corvus corax Corvus cryptoleucus Corvus imparatus Corvus ossifragus Cotumkops noveboratxnsis Crotophaga sukirostris CyanocUta cristate Cyanocitta stelleri Cyanocorax morio Cyanocorax yncas Cygnus columbianus Cynanthus latirostris^ Cypseloides niger Cyrtonyx montezumae Dendrocygna autumnalis Dendrocygna bkolor Dendroka caerulescens Dendroka castanea Dendroka cerulea

MCP Y

Y

Y Y

Y Y Y Y Y Y

Y

Y

Y

Y

Y Y Y

Y

Y Y Y Y

Y Y

Y Y Y Y

Y Y

Y Y

Y

Y Y Y Y

EGP Y

Y

Y Y

Y Y Y Y Y Y Y Y Y Y

Y

Y Y

Y Y Y Y Y

Y Y Y Y

Y Y

Y

Y Y

Y Y

Y Y

Y

Y Y Y Y

CTP Y Y Y

Y Y

Y Y Y Y Y Y

Y Y Y

Y

Y

Y Y Y Y Y

Y Y Y Y

Y Y

Y

Y Y

Y

Y

Y Y

Y

Y Y Y

BLP Y Y Y

Y Y

Y Y Y Y Y Y

Y

Y Y Y

Y Y

Y Y Y

Y

Y Y Y Y

Y Y

Y Y Y Y

Y

Y

Y

Y Y Y

OWP Y Y Y

Y Y

Y Y Y Y Y Y

Y Y Y Y Y

Y Y

Y Y Y Y Y

Y Y Y Y

Y Y

Y Y Y Y

Y Y

Y Y

Y

Y Y Y Y Y

CGP Y

Y

Y Y

Y Y Y

Y Y Y Y Y Y Y Y

Y Y

Y Y Y Y Y

Y Y Y Y

Y Y

Y

Y Y

Y Y

Y Y

Y Y

Y Y Y Y Y

THP Y Y Y

Y Y

Y Y

Y Y

Y Y Y

Y

Y Y

Y Y Y

Y

Y

Y Y Y

Y Y

Y Y

Y Y Y Y

Y Y

Y

RLP Y Y Y

Y Y

Y Y

Y Y

Y

Y Y Y

Y Y

Y Y Y

Y

Y

Y Y

Y Y

Y Y

Y Y Y Y

Y

Y

Y

EWP Y Y Y

Y Y

Y Y

Y Y

Y

Y Y Y

Y Y

Y Y Y

Y

Y

Y Y

Y Y

Y Y

Y Y Y Y

Y Y

Y

Y

Y

RGP Y Y Y

Y Y Y Y Y Y

Y Y Y Y Y Y Y Y Y Y Y

Y Y Y Y Y

Y Y Y Y

Y Y Y

Y Y

Y Y

Y Y

Y

Y Y

Y Y

Y

v/ Y Y Y Y Y Y

SGP Y

Y

Y Y Y Y Y Y

Y Y Y Y Y Y Y Y

Y Y

Y Y Y Y

Y Y Y Y

Y Y Y

Y Y

Y

Y Y

Y

Y Y

: -<

-<

T

Y Y Y Y Y

BAR Y Y Y

Y Y

Y

Y Y Y

Y Y Y Y Y

Y Y Y Y Y Y

Y

Y

Y Y Y

Y Y Y Y Y

Y Y Y Y

Y Y Y

Y >->

->-

SKP Y Y Y

Y Y

Y Y

Y Y

Y

Y Y Y

Y Y

Y Y Y

Y

Y

Y Y Y

Y Y

Y Y

Y

Y Y

Y

Y

Y

Y

f"^

69

Table A.2. Continued.

Scientific Name Dendroka chrysoparia' Dendroka coronata Dendroka distxior Dendroka dominka Dendroka fusca' Dendroka graciae Dendroka magnolia' Dendroka nigrescens' Dendroka cxxidentalis' Dendroka palmarum' Dendroka pensylvanka' Dendroka petechia Dendroka pinus Dendroka striata' Dendroka tigrina' Dendroka townsendi' Dendroka virens' Dolkhonyx oryzivorus' Dryocopus pileatus Dumetella carolinensis Ectopistes migratorius' Egretta caerulea Egretta rufescens^ Egretta thula Egretta tricolor Elanoides forTcatus^ Elanus leucurus Empidonax ahorum' Empidonax flaviventris Empidonax hammondii' Empidonax minimus' Empidonax oberholseri' Empidonax occidentalis Empidonax traiWu ' Empidonax virescens Empidonax wrightii Eremophila alpestris Eudocimus albus Eugenes fulgens Euphagus carolinus Euphagus cyanocephalus Fako columbarius Fako femoralis^ Fako mexicanus Fako peregrinus Fako span/erius Fregata magnifcens Fulka americana Gallinago gallinago' Gallinula chloropus Gavia immer' Gecxxxxyx califomianus Geothlypis polkephala Geothtypis trkhas Glaucidium brasilianum Glaucidium gnoma Grus amerkana Grus canadensis Guiraca caerulea Gymnorhinus cyanocephalus Haematopus palliatus

MCP

Y Y Y Y

Y Y

Y Y Y Y Y

Y Y Y Y

Y

Y Y

Y Y Y

Y

Y Y

Y Y

Y Y Y

Y Y Y

Y Y Y Y Y

Y

Y Y

EGP

Y Y Y Y

Y Y

Y Y Y Y Y Y

Y Y Y Y

Y Y Y Y Y Y Y Y

Y

Y Y

Y Y

Y Y Y

Y Y Y Y Y Y Y Y Y

Y

Y Y

Y

CTP Y Y Y Y Y

Y Y

Y Y Y Y Y

Y Y Y Y

Y

Y Y

Y Y Y

Y

Y Y

Y Y

Y Y Y

Y Y Y Y Y Y Y Y Y

Y

Y Y Y

BLP Y Y Y Y Y

Y Y

Y Y Y Y Y

Y Y Y Y

Y

Y Y

Y Y Y

Y

Y Y

Y Y

Y Y Y

Y Y Y

Y Y Y Y Y

Y

Y Y Y

OWP

Y Y Y Y

Y Y

Y Y Y Y Y Y

Y Y Y Y

Y

Y Y

Y Y Y

Y

Y Y

Y Y

Y Y Y

Y Y Y

Y Y Y Y Y

Y

Y Y Y

CGP

Y Y Y Y

Y Y

Y Y Y Y Y Y

Y Y Y Y

Y Y Y Y Y Y Y Y

Y

Y Y

Y Y

Y Y Y

Y Y Y Y Y Y Y Y Y

Y

Y Y Y

Y

THP

Y

Y

Y

Y

Y

Y

Y

Y

Y Y Y Y Y

Y Y

Y Y Y Y Y Y

Y Y Y Y Y

Y

Y Y Y

RLP

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y Y

Y Y Y

Y Y Y Y Y

Y

Y Y Y Y

EWP Y Y

Y

Y

Y Y

Y

Y

Y

Y

Y

Y Y

Y

Y Y

Y Y Y

Y Y Y Y Y

Y

Y Y Y

RGP Y Y Y Y Y

Y Y

Y Y Y Y Y Y

Y Y Y Y Y Y Y Y Y Y Y Y Y

Y

Y Y

Y Y

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Y Y

Y

SGP

Y Y Y Y

Y Y

Y Y Y Y Y Y

Y Y

Y

Y Y Y Y Y Y Y Y

Y

Y Y

Y Y

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Y Y

v/ Y

BAR

Y

Y

Y Y

Y

Y

Y

Y

Y

Y

Y Y Y Y Y

Y Y

Y

Y Y Y Y Y Y

Y Y Y Y Y

Y

Y

Y Y Y

SKP Y Y

Y

Y Y

Y

Y

Y

Y

Y

Y

Y Y Y

Y

Y

Y Y Y Y Y Y

Y Y Y Y Y

Y

Y Y Y

70

Table A.2. Continued.

Scientific Name MCP EGP CTP BLP OWP CGP THP RLP EWP RGP SGP BAR SKP Haliaeetus leutxxephalus Helmiheros vermivorus Himantopus mexkanus Hirundo rustka Hykxharis leucotis^ Hylocichia musteline kteria virens Icterus cucullatus Icterus galbula Icterus graduacauda Icterus gularis ktenjs parisorum Icterus spurius ktinia mississippiensis beobrychus exilis Jacana spinosa Junco hyemalis Junco phaeonotus^ Lampomis clemenciae Lanius excubitor' Lanius ludovkianus Larus argentatus' Larus atrkilla Larus delawarensis' l^rus fuscus' Larus hyperboreus' Larus Philadelphia' Larus pipixcan' Laterallus jamakensis Leptotila verreauxi Limnodromus griseus' Limnodromus stxilopaceus^ Limnothtypis swainsonii Limosa fedoa' Limosa haemastica' Lophodytes cucullatus Loxia curvirostra Melanerpes aurifrons Melanerpes carolinus Melanerpes erythrocephalus Melanerpes formkrvorus Melanerpes lewis' Melanerpes uropygialis^ Melanitta fusca Melanitta nigra' Melanitta perspkillata' Meleagris gallopavo Melospiza georgiana' Melospiza lincolnil' Mekspiza melodia' Mergus merganser' Mergus senator Mkrathene whitneyi Mimus polyglottos Mnktitta varia Mokthrus aeneus Motothrus ater Morus bassanus Myadestes townsendi Mycteria amerkana

Y Y Y Y

Y Y

Y

Y Y Y

Y

Y Y Y Y

Y Y Y

Y Y Y Y Y Y Y

Y Y

Y

Y Y Y Y Y Y

Y Y

Y

Y

Y Y Y Y

Y Y

Y

Y Y Y

Y

Y Y Y >

->•>

• Y Y Y

Y Y Y Y Y Y Y

Y Y

Y Y Y Y Y Y Y Y Y

Y Y

Y Y

Y

Y Y Y Y

Y Y

Y

Y Y Y

Y

Y Y Y Y Y

Y Y Y

Y Y Y Y Y Y Y Y Y Y

Y

Y

Y Y Y Y Y Y Y

Y Y

Y

Y Y

Y Y Y Y

Y Y

Y

Y Y Y

Y

Y Y Y Y

Y Y Y

Y Y Y Y Y Y Y Y Y Y

Y

Y Y Y Y Y Y

Y Y Y Y

Y Y

Y Y Y Y

Y Y

Y

Y Y Y Y Y

Y Y Y

-<-<

-<

Y Y Y

Y Y Y Y Y Y Y Y Y Y

>•>

->

-

Y Y Y Y Y Y

Y Y Y Y

Y Y

Y Y Y Y

Y Y

Y

Y Y Y Y Y

Y Y Y Y Y Y Y Y Y

Y Y Y Y Y Y Y Y Y Y

>->

->•

Y Y Y Y Y Y

Y Y Y Y Y

Y

Y

Y Y

Y

Y

Y Y Y Y

Y

Y Y Y

Y

Y

Y Y

Y Y Y Y Y Y Y

Y

Y

Y Y Y Y Y Y

Y Y

Y

Y

Y

Y Y

Y

Y

Y Y Y Y

Y

Y Y Y

Y

Y

Y Y

Y Y Y Y Y

Y

Y

Y Y Y Y Y Y

Y Y

Y

Y

Y

Y Y

Y

Y

Y Y Y Y

Y

Y Y

Y

Y Y

Y Y

Y Y Y Y Y

Y

Y

Y

Y Y Y Y Y Y

Y Y Y Y

Y Y

Y Y Y Y

Y Y Y

-<-<

-<

Y Y Y Y Y

Y Y Y

<<

<

Y Y Y Y Y Y Y Y Y Y Y Y Y Y

>->

->

•

Y Y Y Y Y Y

Y Y Y Y Y Y Y

Y Y Y Y

Y Y Y Y Y Y

Y Y Y Y Y

Y Y Y

-<-<

-<

Y Y Y Y Y Y Y Y Y Y Y Y

Y •< <

<

Y Y Y Y Y Y

Y Y Y Y Y Y Y

Y

Y Y Y

-< -

<

Y

Y Y Y Y

Y Y Y Y Y Y

Y

Y Y

Y Y

Y Y Y Y

>- >

->->

-

Y Y Y Y Y Y Y Y Y Y Y

Y

Y Y

Y Y Y

Y Y Y Y

Y

Y Y

Y

Y

Y Y

Y Y Y Y Y

Y

Y Y Y Y Y >- >-

Y Y Y Y

Y

71

Table A.2. Continued.

Scaentific Name MCP EGP CTP BLP OWP CGP THP RLP EWP RGP SGP BAR SKP Myiarchus cinerascens Myiarchus crinHus Myiarchus tuberculifer Myiarchus tyrannulus Mykt>orus pictus Nomonyx dominkus Nucifraga Columbiana Numenius americanus Numenius tiorealis^ Numenius phaeopus' Nyctanassa violacea Nyctkorax nyctkorax Nyctidromus albkollis Oceancxiroma castro' Oporomis formosus Oporomis Philadelphia' Oporomis tolmiei^ Oreoscoptes montanus' Ortalis vetula Otus asio Otus flammeolus Otus kennkottii otus trkhopsis^ Oxyura jamakensis Pachyramphus aglaiae' Pandkn haliaetus Parabuteo unkinctus Parula americana Parula pHiayumi^ Parus tiicolor Passerculus sandwkhensis' Passeralla iliaca Passerine amoena Passerine ciris Passerine cyanea Passerine versicolor^ Pelecanus erythrorhynchos Pelecanus ocxklentalis^ PetroctieMon fufva Petrochelidon pyrrhonota Peucedramus taeniatus Phainopepla nitens Phalacrocorax auritus Phalacrocorax brasilianus^ Phalaenoptilus nuttellii Phalaropus lobatus' Phalaropus trkolor Pheuctkus ludovkianus' Pheuctkus melancxephalus Pica pka Picoides borealis^ Pkokjes pubescens Picokies scalaris Pkokies strkklandi Pkokies tridactylus Pkoides villosus Pipilo aberti Pipilo chlorurus Pipilo erythrophthalmus

Y

Y Y Y Y Y

Y Y

Y

Y

Y

Y

Y Y

Y Y Y Y

Y

Y

Y Y

Y Y Y Y

Y Y

Y

Y Y

Y

Y

Y Y Y Y Y

Y Y Y

Y

Y

Y

Y

Y Y

Y Y Y Y

< <

Y

Y Y

Y Y Y Y

Y Y

Y

Y Y

Y Y

Y Y Y Y Y Y

Y Y Y Y

Y

Y

Y Y Y

Y Y

Y Y Y Y

Y Y

Y

Y Y Y Y Y Y Y

Y Y Y Y

Y

Y Y

Y Y

Y Y Y Y Y

Y Y Y Y

Y

Y

Y Y Y

Y Y

Y Y Y Y

Y

Y

Y Y Y Y Y Y Y Y

Y Y

Y

Y Y

Y Y

Y

Y Y Y Y Y Y

Y Y Y Y

Y

Y

Y Y Y

Y Y

Y Y Y Y

Y

Y Y

Y Y Y Y Y Y Y Y

Y Y Y

Y

Y Y

Y Y

Y

Y

Y Y Y Y Y Y Y Y Y Y Y

Y

Y

Y Y Y

Y Y

Y Y Y Y

Y Y Y Y

Y Y Y Y Y Y Y Y

Y Y Y

Y

Y Y

Y Y

Y

Y Y Y

Y Y

Y Y

Y

Y Y

Y Y

Y Y Y Y

Y

Y

Y Y

Y Y Y Y Y

Y Y

Y

Y Y

Y Y

Y Y Y Y Y

Y Y

Y

Y

Y Y

Y Y

Y Y Y Y

Y

Y Y

Y Y Y Y Y Y

Y

Y Y

Y

Y Y

Y Y

Y Y Y Y Y

Y Y

Y

Y

Y Y Y

Y Y

Y Y Y Y

Y

Y Y

Y Y Y Y Y Y

Y

Y Y

Y

Y Y

Y

< <

<

Y Y Y Y

-<-<

-<

Y Y Y Y Y Y

Y Y Y Y Y Y Y Y

Y Y Y Y Y Y Y Y Y

Y Y Y Y Y Y Y Y

Y Y

Y

Y Y

Y >-

>-

>-

Y Y Y Y >

->•>

-

Y Y Y Y Y Y

Y Y Y Y Y Y Y Y

Y Y Y Y Y Y Y Y Y

Y Y Y Y Y Y Y Y

Y Y

Y

Y Y

Y

Y Y Y

Y Y

Y Y Y

Y Y

> >>>

Y

Y Y

Y Y

Y Y Y Y Y Y

Y Y Y Y Y Y Y Y Y Y Y

Y Y Y Y Y Y Y Y

Y Y

Y

Y

Y Y Y

Y Y

Y

Y

Y Y Y

Y Y

Y Y Y >- >-

Y

Y Y

Y Y Y Y Y Y

Y

Y Y

Y

Y Y

72

Table A.2. Continued.

Scientific Name Pipilo fuscus Piranga flava Piranga ludovkiana Piranga olivacee' Piranga rubra Pitangus sulphuratus Plegadis chihi' Plegadis fakinellus' Pluvialis dominke' Pluvialis squatarola' Ptxtkeps auritus' Podkeps nigrkollis Pcxiilymbus podkeps Poecile atrkapillus Poecile cerolinensis Poecile gamtieli Poecile sdateri Polkjptila caerulea Polkptile melanura Polyborus plancus Ptxiecetes gramineus Porphyrula martinka Porzana Carolina' Progne subis Protonotaria citrea Psairiparus minimus Puffinus Iherminieri' Pyrocephalus rubinus Quiscalus major Quiscalus mexkanus Quiscalus quiscula Rallus elegens Rallus limkola' Rallus kingirostris Recurvirostra amerkana Regulus calendula' Regulus satrapa' Riparia riperia Rynf:t}ops niger Salpinctes obsoletus Sayomis nigricans Sayomis phoebe Sayomis saya Scardafella inca Scotopax minor Seiurus aurocapillus' Seiurus motacilla Seiurus noveboracensis' Selasphorus platytxrcus Selasphorus rufus' Setophaga rutkilla Sialia currucokies' Sialia mexkana Sialia sialis Sitta canadensis' Sitta carolinensis Sitta pusilla Sitta pygmaea Speotyto cunkularia Sphyrapkus thyrokieus Sphyrapkus varius Spiza amerkana Spizella arborea

MCP

Y Y Y

Y

Y Y Y Y Y

Y

Y

Y Y Y Y Y Y

Y

Y Y Y Y

Y Y Y Y

Y Y Y Y Y Y Y

Y Y

Y Y Y Y

Y

Y Y Y

EGP

Y Y Y

Y Y Y Y Y Y Y

Y

Y

Y Y Y Y Y Y

Y Y Y Y Y Y Y Y Y Y Y Y Y

Y Y Y Y Y Y Y

Y Y

Y Y Y Y

Y

Y Y Y

CTP Y

Y Y Y

Y

Y Y Y Y Y

Y

Y

Y Y Y Y Y Y

Y

Y Y Y Y

Y Y Y Y

Y

Y Y Y Y Y Y Y

Y Y Y

Y Y Y Y

Y

Y Y Y

BLP

Y Y Y

Y

Y Y Y Y Y

Y

Y

Y Y Y Y Y Y

Y

Y Y Y Y

Y Y Y Y

Y Y Y Y Y Y Y Y Y

Y Y Y

Y Y Y

Y

Y Y Y

OWP

Y Y Y

Y

Y Y Y Y Y

Y

Y

Y Y Y Y Y Y

Y

Y Y Y Y

Y Y Y Y

Y Y Y Y Y Y Y Y Y

Y Y Y

Y Y Y Y

Y

Y Y Y

CGP

Y Y Y

Y Y Y Y Y Y Y

Y

Y

Y Y Y Y Y Y

Y Y Y Y Y Y Y Y Y Y Y Y Y

Y Y Y Y Y Y Y Y Y Y Y

Y Y Y Y

Y

Y Y Y

THP Y

J

Y

Y

Y

Y Y Y Y Y Y Y Y

Y

Y

Y Y

Y

Y

Y Y

Y

Y Y Y Y

Y Y Y Y Y

Y

Y Y Y Y Y Y Y Y Y

Y Y Y Y Y

RLP Y

Y

Y

Y

Y Y Y Y Y

Y

Y

Y Y

Y Y

Y

Y

Y Y

Y

Y Y Y Y

Y Y Y Y Y

Y

Y Y Y Y Y Y Y

Y Y Y Y Y

EWP Y

Y

Y

Y

Y Y Y Y Y

Y

Y

Y Y

Y Y

Y

Y

Y Y

Y

Y Y Y Y

Y Y Y Y Y

Y

Y Y Y Y Y Y Y

Y Y Y Y Y

RGP

Y Y Y Y Y

Y Y Y Y Y

Y

Y Y Y Y Y Y Y Y

Y Y

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Y

Y Y Y

SGP

Y Y Y Y Y

Y Y Y Y Y

Y

Y

Y Y Y Y Y Y

Y Y

Y

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Y Y Y

Y

Y Y Y

BAR Y Y Y

Y

Y

Y Y Y Y Y

Y Y Y Y Y Y

Y Y

Y

Y

Y Y

Y

Y Y Y Y

Y Y Y Y Y

Y Y Y Y Y Y Y Y Y

Y Y Y Y Y Y

SKP Y

Y

Y

Y

Y Y Y Y Y

Y

Y Y Y Y

Y Y

Y

Y

Y

Y

Y Y Y Y

Y Y Y Y Y

Y Y Y Y Y Y Y Y

Y Y Y Y Y

73

Table A.2. Continued.

Scaentific Name Spizella atrogularis Spizella breweri Spizella pallkja' Spizella passerine Spizella pusilla Sporophila torqueola Stelgidopteryx serripennis Stellula calliope' Stercorarius parasikus' Stercorarius pomarinus' Sterne anaethetus' Sterne antillarum' Sterna caspia Sterne forsteri Sterna fuscata^ Sterna hirundo Sterne maxima Sterna nitotka Sterna sandvicensis Strix occkientalis^ Strix varia Stumelle magna Stumella neglecta Sula dactylatra' Tachybaptus dominkus Tachycinete bkolor Techycineta thalassina Thryomanes bewkkii Thryothorus ludovkianus Toxostoma t>endirei Toxosiome curvirostre Toxostoma dorsale Toxostoma kingirostre Toxostoma rufum Tringa flavipes' Tringa melanoleuca Tringa solitaria' Trogkdytes aedon Trogkdytes tmgkxiytes Trogon elegens^ Tryngites subrufcollis Turdus grayi Turdus migratorius Tympanuchus cupkio Tympanuchus palMkinctus Tyrannus couchii Tyrannus crassirtystris' Tyrannus dominkensis Tyrannus forikatus Tyrannus melancholkus Tyrannus tyrannus Tyrannus vertkelis Tyrannus vociferans Tyto alba Vermivora bachmanii Vermivora celata Vermivora chrysoptera Vermivora crissalis Vermivora luciae Vermivora peregrine Vermivore pinus

MCP

Y Y Y

Y

Y Y Y

Y

Y Y Y

Y

Y Y

Y Y Y Y Y Y

Y

Y

Y

Y Y

Y Y Y Y

Y Y

EGP

Y Y Y

Y

Y Y Y Y Y Y Y Y Y Y Y

Y Y Y Y

Y

Y Y

Y Y Y Y Y Y

Y

Y Y

Y Y

Y Y

Y

Y Y

Y Y

CTP

Y Y Y

Y Y

Y Y Y

Y

Y Y Y

Y

Y Y

Y

Y Y Y Y Y Y

Y

Y

Y

Y Y

Y

Y Y

Y Y

BLP

Y Y Y

Y Y

Y Y Y

Y

Y Y Y

Y

Y Y

Y

Y Y Y Y Y Y

Y

Y

Y

Y Y

Y

Y Y

Y Y

OWP

Y Y Y

Y Y

Y Y Y

Y

Y Y Y

Y

Y Y

Y

Y Y Y Y Y Y Y

Y

Y Y

Y

Y

Y Y

Y

Y Y

Y Y

CGP

Y Y Y

Y

Y Y Y Y Y Y Y Y Y Y Y

Y Y Y Y Y Y

Y Y

Y

Y Y Y Y Y Y Y

Y

Y Y

Y

Y Y

Y Y

Y

Y Y

Y Y

THP Y Y Y Y Y

Y Y

Y

Y Y

Y

Y Y

Y Y

Y Y Y Y Y Y

Y

Y

Y

Y

Y Y Y Y

Y

Y

RLP

Y Y Y Y

Y Y

Y

Y Y Y

Y

Y Y

Y Y

Y Y Y Y Y Y

Y

Y

Y

Y

Y Y Y Y

Y

EWP

Y Y Y

Y Y

Y

Y

Y Y Y

Y

Y Y

Y

Y Y Y Y Y Y Y

Y

Y

Y

Y Y

Y

Y

RGP

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

Y Y Y Y Y Y

Y Y

Y

Y Y Y Y Y Y Y

Y V/ Y Y

Y

Y v/ Y Y Y

Y

Y Y

Y Y

SGP

Y Y Y Y Y

Y Y Y Y Y Y Y Y Y Y Y

Y Y Y Y Y Y

Y Y

Y

Y Y Y Y Y Y Y

Y v/ Y Y

Y

Y

Y Y Y Y

Y

Y Y

Y Y

BAR Y Y Y Y Y

Y Y

Y

Y

Y

Y Y

Y Y Y Y Y Y Y

Y Y Y Y Y Y v/ Y Y

Y

Y

Y

Y

Y Y vy Y Y

Y

v / Y vy Y Y

SKP Y Y Y Y Y

Y Y

Y

Y Y

Y Y

Y Y

Y Y Y Y Y Y Y Y Y

Y

Y

Y

Y Y vy Y Y

Y

74

Table A.2. Continued.

Scientific Name Vermivora ruficapilla'' Vermivora virginiae Vireo atrkapillus^ Vireo tiellii^ Vkeo flavifrons Vireo flavoviridis Vireo gilvus Vireo griseus Vkeo huttoni Vireo olivec^eus Vireo philadelphkus' Vkeo solitarius Vkeo vkinkir^ Wilsonia t^nadensis' Wilsonia citrine Wilsonia pusilla' Xanthocephalus xanthocephalus Zenekia asietka Zenakia macroura Zonotrkhia albkxillis' Zonotrichia leutxiphrys' Zonotrkhia querula'

MCP Y

Y Y

Y Y

Y Y Y

Y Y Y Y

Y Y Y Y

EGP Y

Y Y

Y Y

Y Y Y

Y Y Y Y

Y Y Y Y

CTP Y

Y Y Y

Y Y

Y Y Y

Y Y Y Y

Y Y Y Y

BLP Y

Y Y

Y Y

Y Y Y

Y Y Y Y

Y Y Y Y

OWP Y

Y Y

Y Y

Y Y Y

Y Y Y Y

Y Y Y Y Y

CGP Y

Y Y

Y Y

Y Y Y

Y Y Y Y

Y Y Y Y Y

THP Y

Y Y

Y Y

Y

Y

Y Y

Y Y Y Y

RLP Y

Y Y Y

Y Y

Y

Y

Y Y

Y Y Y Y

EWP Y

Y Y Y

Y Y

Y

Y

Y Y

Y Y Y Y Y

RGP Y

Y Y Y Y Y

Y Y Y

Y Y Y Y

Y Y Y Y Y

SGP Y

Y Y Y Y Y

Y Y Y

Y Y Y Y

Y Y Y Y Y

BAR Y Y Y Y Y

Y Y Y Y

Y Y

Y Y

Y Y Y Y Y

SKP Y

Y Y Y

Y Y Y Y

Y Y

Y Y

Y Y Y Y Y

^ Listed on at least the Louisiana. New Mexico. Oklahoma, Texas, or federal Threatened and Endangered Species list.

^ Species is no longer found in any of the major ecoregion of Texas (i.e., extinct or extirpated).

^ Species has not nested in Texas since at least 1930.

75

Table A.3. Spedes presence (Y) versus absence matrix for the 164 mammalian species living in the ecoregions of Texas and the 8 extinct mammals that once lived in Texas ecoregions.

Scaentific Name Ammospermophilus hairisii Amnxispermophilus interpres Antilocapra americana Antrozous palMus Bakmys taykri Bassariscus astutus Blarine cerolinensis Blerine hykphaga Bos bison^ Cants latrans Canis lupus^ Canis rufus^ Castor canedensis Cervus elephus Cheetodipus baileyi Chaetodipus hispidus Cheetodipus intermedius Cheetodipus nelsoni Chaetodipus penkillatus Ctioeronycteris mexkena Conepatus leuconotus Conepetus mesoleucus Corynorhinus rafinesquii' Corynorhinus townsendii Cratogeomys castanops Cryptotis parva^ Cynomys gunnisoni Cynomys ludovkianus Dasypus novemcinctus DkJelphis virginiana Dipodomys compactus Dipodomys elator^ Dipodomys meniemi Dipodomys ordii Dipodomys spectebilis Epteskus fuscus Erethizon dorsatum Euderma maculatum Eumops perotis Felis concokx Felis pardalis^ Felis wiedii Felis yaguarondi Geomys arenarius Geomys attwateri Geomys brevkeps Geomys burserius Geomys knoxjonesi Geomys personatus Geomys texensis Glaucomys volans Idknycteris phyllotis Lasionycteris noctivagans Lasiurus blossevillii Lasiurus borealis Lasiurus cinereus Lasiurus ega Lasiurus intermedius Lasiurus seminolus

MCP

Y Y Y Y

Y

Y Y

Y

Y Y

Y

Y Y

Y

Y

Y

Y

Y

Y Y

Y Y

EGP

Y Y Y

Y

Y Y

Y Y

Y

Y Y

Y

Y Y

Y

Y

Y

Y Y

Y Y

CTP

Y Y Y Y Y Y Y Y Y

Y

Y

Y

Y Y Y

Y

Y

Y Y

Y

Y

Y

Y

Y Y

BLP

Y Y Y Y

Y

Y Y

Y

Y

Y

Y Y Y

Y Y

Y

Y Y Y

Y

Y

Y Y

Y

OWP

Y Y Y Y

Y

Y Y

Y

Y Y

Y

Y Y Y

Y

Y Y

Y Y

Y

Y

Y Y

Y Y

CGP

Y Y Y Y

Y

Y Y

Y

Y Y Y

Y

Y Y Y

Y

Y Y

Y Y

Y

Y

Y Y

Y Y

THP

Y

Y Y Y Y

Y Y Y

Y

Y Y Y Y

Y

Y Y Y

Y Y Y

Y Y Y Y Y Y

Y

Y Y

Y

Y Y

RLP

Y

Y Y Y Y

Y Y Y Y Y

Y

Y Y

Y

Y Y Y

Y Y Y

Y Y Y Y Y Y

Y Y

Y

v y

Y

Y

Y Y

EWP

Y Y Y

Y Y Y Y Y

Y

Y

Y

Y

Y Y Y Y

Y

Y Y

Y Y

Y

v y

Y

Y

Y

Y

Y Y

Y

RGP

Y Y Y

Y Y Y Y

Y

Y

Y Y

Y Y

Y Y Y Y

Y Y

Y Y

Y Y vy Y Y

Y

Y Y

v y

Y

Y Y Y v y Y

SGP

Y Y Y

Y

Y

Y Y

Y

Y

Y Y

Y Y Y

Y

Y Y

Y

Y

Y

v y

Y

Y Y Y vy Y

BAR Y Y

Y Y Y Y

Y Y Y

Y Y Y Y Y Y Y Y

Y

Y Y

Y Y Y Y

Y Y vy Y Y Y Y v y Y Y Y

Y

>• >-

:-<

-«:-

<

Y

Y Y

SKP

Y

Y Y Y Y

Y Y Y Y Y

Y Y Y Y

Y

Y Y Y

v y

Y Y Y

Y Y Y

Y

Y v y

Y Y

Y

Y

Y Y

76

Table A.3. Continued.

Scientific Name Leptonycteris curasoae' Leptonycteris nivalis^ Lepus califomkus Lepus callotis^ Lkmys rroratus Lutra canadensis Lynx rufus Marmota monax Mephitis macroura Mephitis mephitis Mkrotus kjngkaudus Mkrotus mexkanus Mkrotus ochrogaster Mkrotus pinetorum Mormoops megelophylla Mustele frenata Mustela nigripes^ Mustele vison Myotis aurkulus Myotis austroriparius Myotis califomkus Myotis ciiiolabrum Myotis evotis Myotis keenii Myotis kjcifugus Myotis septentrionelis Myotis thysanodes Myotis velifer Myotis volans Myotis yumanensis Nasua nerica^ Neotoma albigula Neotoma fhridana Neotoma mexkana Neotoma mkropus Neotome stephensi Notksorex crawfordi Nyctkeius humeralis Nyctinomops femorosaccus Nyctinomops macrotis Ochrotomys nuttalli Odcxxiileus hemkjnus Odcxxiileus virginienus Ondatra zi}ethkus Onyt^homys arenkola Onychomys leucoaaster Oryzomys couesi Oryzomys pelustris Ovis f^anadensis^ Panthera onca^ Perognethus flevescens Perognathus flavus Perognethus meniemi Peromyscus attwateri Pemmyscus boylii Peromyscus eremkus Pemmyscus gossypinus Peromyscus leucopus Peromyscus meniculatus Peromyscus nesutus Peromyscus pectoralis Pemmyscus truei^

MCP

Y

Y Y Y

Y

Y Y

Y

Y

Y

Y Y Y

Y

Y Y

Y Y

Y Y

Y

Y Y Y

EGP

Y

Y Y

Y

Y

Y

Y

Y

Y

Y

Y Y

Y Y

Y

Y Y Y

CTP

Y

Y Y

Y

Y Y

Y

Y

Y

Y

Y

Y Y

Y

Y Y

Y

Y

Y Y

Y Y

Y

BLP

Y

Y Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y Y

Y

Y Y

Y Y Y

Y

OWP

Y

Y Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y Y

Y Y

Y Y

Y

Y

Y

Y Y Y

CGP

Y

Y Y

Y

Y

Y

Y

Y

Y

Y

Y Y

Y

Y Y

Y

Y

Y

Y Y Y

THP

Y

Y Y

Y

Y

Y Y

Y

Y

Y

Y

Y Y

Y

Y

Y Y Y Y Y

Y Y Y Y Y

Y Y Y Y Y

RLP

Y

Y Y

Y

Y

Y Y Y

Y

Y

Y Y Y

Y

Y Y

Y

Y Y Y Y Y

Y

Y Y Y

Y Y

Y Y

EWP

Y

Y Y

Y

Y Y Y

Y

Y

Y Y Y

Y

Y Y

Y

Y

Y

Y Y

Y Y

Y

RGP

Y

Y Y Y

Y

Y Y

Y

Y

Y Y Y Y

Y

Y Y

Y

Y

Y Y Y

Y

Y Y

Y

Y Y

Y

SGP

Y

Y Y Y

Y

Y Y

Y

Y Y

Y

Y Y

Y

Y

Y Y Y

Y

Y Y

BAR Y Y Y Y

Y

Y Y Y Y

Y Y Y

Y

Y Y Y

Y

Y Y Y Y Y Y

Y Y Y Y

Y Y

Y Y Y Y Y

Y Y Y Y Y Y Y Y

Y Y Y Y Y

SKP

Y

Y

Y Y

Y Y

Y

Y Y

Y Y Y Y

Y

Y Y

Y

Y Y Y Y Y

Y

Y Y Y Y Y

Y Y Y Y

77

Table A.3. Continued.

Scientific Name Pipistrellus hesperus Pipistrellus subflavus Procyon totor Reithmdfintomys fulvescens Reihrodontomys humulis Re'thmdontomys megaktis Reihrodontomys montanus Scakpus aquatkus Sdunjs aberti Sdunjs camlinensis Sdurus niger Sigmodon fuh/iventer Sigmodon hispkius Signndon ochrognethus Sorex erizonae^ Sorex kingimstris Sorex montkolus Spermophilus leteralis Spermophilus mexkanus Spermophilus spikisome Spermophilus tridecemlineetus Spermophilus variegatus Spitogale gracilis Spikigale putorius Sylvilegus aquatkus Sylvilagus audubonii SylvHegus floridanus Synaptomys cooperi Tederida bmsiliensis Tamias canipes Tamias cherekollis Tamias dorsalis Tamies quedrivittatus^ Tamias striatus Tamiesdurus hudsonkus Taxkea taxus Tayassu tajacu Thomomys bottee Thomomys umbrinus^ Urocyon cinemoargenteus Ursos arctos^ Ursus amerkene^ Ursus americanus Vulpes velox

MCP

Y Y Y

Y

Y Y

Y Y

Y

Y

Y Y

Y Y Y

Y

Y

Y

Y

EGP

Y Y Y

Y

Y

Y Y

Y

Y Y

Y

Y

Y

Y

CTP

Y Y Y

Y

Y Y

Y Y

Y

Y Y Y

Y

Y Y

Y

Y

Y

Y

BLP

Y Y Y

Y

Y Y

Y Y

Y

Y

Y

Y

Y Y

Y

Y

Y

Y

OWP

Y Y Y

Y

Y Y

Y Y

Y

Y Y Y

Y Y

Y

Y

Y Y

Y

Y

CGP

Y Y Y

Y

Y

Y Y

Y

Y Y Y

Y Y

Y

Y

Y Y

Y

Y

THP Y Y Y Y

Y Y Y

Y

Y

Y Y Y

Y Y Y

Y Y

Y

Y Y Y

Y

Y

RLP Y Y Y Y

Y Y Y

Y

Y

Y Y Y

Y Y Y

Y Y

Y

Y Y Y

Y

Y

Y

EWP Y 1

Y Y Y

Y

Y Y

Y Y

Y

Y Y Y

Y Y Y Y Y Y

Y

Y Y Y

Y

Y

Y

RGP Y Y Y Y

Y Y

Y Y

Y

Y Y Y

Y Y Y Y Y Y

Y

Y Y Y

Y

Y

SGP

Y Y Y

Y

Y

Y

Y Y Y

Y Y Y Y

Y

Y Y

Y

BAR Y

Y Y

Y Y

Y

Y Y Y Y

Y Y Y Y Y

Y Y Y

Y Y

Y Y Y Y Y

Y Y Y Y Y Y Y

Y Y

SKP Y Y Y Y

Y Y

Y

Y

Y Y

Y Y

Y Y

Y

Y Y Y

Y

Y

Y

''" Listed on at least the Louisiana, New Mexico, Oklahoma, Texas, or federal Threatened and Endangered Species list.

^ Species is no longer found in any of the major ecoregion of Texas (i.e., extinct or extirpated).

78

Table A.4. Species presence (Y) versus absence matrix for the 174 reptilian species living in the ecoregions of Texas.

Scientific Name MCP EGP CTP BLP OWP CGP THP RLP EWP RGP SGP BAR SKP Agkistmdon contortrix Y Y Y Y Y Y Y Y Y Y Y Y Y Agkistmdon piscivorus Y Y Y Y Y Y Y Y Y Y Y Y Alligator mississippiensis Y Y Y Y Y Y Y Y Y Anolis carolinensis Y Y Y Y Y Y Y Y Y Apatonemutka Y Y Y Y Y Y Y Y Y Apahne spinifera Y Y Y Y Y Y Y Y Y Y Y Y Y Arizona elegans Y Y Y Y Y Y Y Y Y Y Y Y Callisaums draconokjes ' Caretta caretta^ Y Y Y Y Carphophis amoenus Y Y Y Cemophora ccjccinea' Y Y Y Y Y Y Y Y Chektnia mydas^ Y Y ^ w vz v Chelydra serpentina Y Y Y Y Y Y Y Y Y Y Y Y Y Chrysemys pkta Y Y Y CnemkJophorus burtf Cnemkkiphorus d'lxoni' CnemkJophorus exsanguis Y Cnemkiophorus ftogellkaudus

Deircxhelys retkuleria Y Y Dermochelys coriacea Y

Eumeces fasciatus Eumeces laticeps Fumeces multivirgatus

Y Y Y Y Y Y Y Y Y Y Y Y

Y Y Y Y Y Y Y Y

CnemkJophorus gularis Y Y Y Y Y Y Y Y T T ^ Cnemkhphorus hometus Y Y CnemkJophorus laredoensis Y CnemkJophorus neomexkanus Y CnemkJophorus septemvittatus . W W ^ X / V V Y Y CnemkJophorus sexlineetus Y Y Y Y Y Y Y Y Y Y Y Y Y

CnemkJophorus tesseletus v Y Y Y Y CnemkJophorus tigris Y ^ CnemkJophorus uniparens Y CnemkJophorus vekx Y Y Y Y Y Y Coleonyx brev'ts Y Coleonyx retkuletus^ Y Coteonyx var»ga/us ^ / v v Y Y Y Y Y Y Y Y Y Y Coluber constrictor ^ Y Y Y Y Y T T I Y Y

Conkphenes imperialis Y Y Y Y Y Y Cop/K>saurus texanus ^ v v Y Y Y Y Y Y Y Y Y Y Crtrfa/us afrox ^ J Y Y Y Y Y Y Y Y Y

Cmtalus homdus Y Y T T ' Y Y Y Y Y Cmtalus lepkJus Y Y Y Y Y Y Cmtalus mokissus Y Y Y Cmtalus scutulatus Y Y Y Y Y Cmtalus viridis Y Y Cmtalus willardi^ V Y Y Y Y Y Y Y Y Cmtaphytus collaris Y Y Y Y ^ ^ Cmtaphytus retkulatus' v, y Y Y Y Y

Y Y Y Y Y Y Y Y Y Y Y

Diadophis punctatus ^ ^ ^ Y Y Y Y Y Y Y Drymarchon corals ^ Y Y Drymobius margaritiferus Y Y Y Y Y Elaphebeirdi ^ V Y Y Y Y Y Y Y Y Y Y Y

Elaphe guttata ^ V Y Y Y Y Y Y Y Y Y Elaphe obsolete Y Y T I Y Y Y Y Y Y Elaphe subocularis Y Elgaria kingii ^ Y Y Y Eretmochelys imbricata Y v Y Eumeces anthracinus Y '*' w 3

79

Table A.4. Continued

Scientific Name MCP EGP CTP BLP OWP CGP THP RLP EWP RGP SGP BAR SKP_ Eumeces obsoletus Y Y Y Y Y Y Y Y Y Y Eumeces septentrionalis Y Y Y Y Y Y Y Y Y Y Eumeces tetragrammus^ Y Y Y Y Y Y Y Farancia abacum Y Y Y Y Y Y Y Y Fkimia streckeri Gamtielia wislizenii Gerrhonotus kfemelis

Y Y Y Y

Y Y Y Y

Gopherus beriandieri^ Y Y Y Y Y Graptemys caglei Y Y Y T Graptemys geogrephka Y Gmptemys ouechitensis Y Y Y Y Y Y Graptemys Y Y Y Y Y Y Y Y pseudfigeogmphka Graptemys versa Y Y Y " v Y Y Gyakpkn canum Y Y Y HekxJerma suspectum v, x/ v v Y Hetemdon naskus Y Y Y Y Y Y Y Y Y Y Y Y Y Hetemdon platiriiinos Y Y Y Y Y Y Y Y Y Y Y ^ Holbmokia lacerate Y "^ 1 y Holbmokia maculata Y Y ^ w Holbmokia pmpinqua Y Y Y Holbmokia subcaudalis v. w v v v Y Y

vy v v v Y Y Y Y Y T T '

Hypsigtene torqueta Y W J V Y Y Y Y Y Y Y Y K/hosfomon flevescens Y Y Y T i > ^ Kffios^emon hutipes Y

Y Y Y Y Y Y Y

Kinostemon sonoriense Y Y Y Kinosfemon subrubrum Y Y Y Y Y Y ^ Y Y Lampmpeltis elteme V Y Y Y Y Y Y Y Lempmpeltis celligester " ^ ^ ^ Y Y Y Y Y Y Y Y Y Y Lampmpeltis getula Y Y Y Y T T ^ Lampmpeltis pymmelene X , V Y Y Y Y Y Y Y Y Y Y Lempmpeltis triengulum Y Y Y Y T T Y Y LepkJochelys kempii^ Y Y Y Leptodeire septentrionelis^ V Y Y Y Y Y Y Y Y Y Y

Leptotyphtops dukis Y Y Y Y Y Y Y Y Leptotyphtops humilis^ Y Y ^ Lkxhkimphis vemelis^ ^ ^ "" v Y Y Y Macroctemys temminckii Y Y Y Y Y ^ /Wa/iac/emys tenepin Y Y Y Mestkophis bilineetus v Y Y Y Y Y Y Y Y Y Y Mestkophis flagellum Y Y Y Y Y Y Mastkxiphis ruthveni Y Y Y Y Y Mestkophis schotti Y Y Y Y Y Y Y Y Y Mestkophis teenietus Y Y Y MkrumkJes euryxanthus Y Y Y Y Y Y Y Y Y Y Mkrurus fulvius Y Y Y ^ Y Nerodie clericii Y Y Y Y Y Y Nemdie cydopkn ^ ^ V Y Y Y Y Y Y Y Y Y Y Y

Nemdia erythrogester ^ V Y Y Y Y Y Y Y Nerodie fescieta ^ y Y Y Nerodie harteri^ Y Y Y Nemdia paucknaculata' Y Y Y Y Y Y Y Y Y Y Y Nemdia rhombifer ^ ^ w Nemdia sipedon Y Y Y Y Y Y Y Y Y Y

Opheodrys eestivus ^ ^ v y Y V Y Y Y Y Ophisaurusattenuatus^ ^ ^ Y Y Y Y Y Y Y Y Y Y V Phrynosoma comutum Y Y Y Y ^ Y Phrynosoma douglesi Y Y Y > Phrynosoma modestum Y Phrynosoma solam Y Y Y Y Y Y Y Y Y Y >

Pituophis cetenifer ^ Y Pituophis ruthveni^ Y Y ^

80

Table A.4. Continued.

Scientific Name Pseudemys concinna PseufJemys gorzugi* Pseudemys texana Regina grahamii Regine rigkJe Regina septerrrvHteta Rhinocheilus lecxtntei Selvadora desertkola SalvatJora grahamiae Scekporus clarkii Scekpoms gracksus Scekporus grammkus Scekporus jarmvii Scekporus magister Scekpoms meniemi Scekporus oTiveceus Scekporus poinsettii Scekpoms scelaris^ Scekpoms serrifer Scekpoms undulatus Scekporus variabilis Scekpoms vrgatus Sdncella lateralis Sentkolis triespis^ Sistmms catenetus Sistrurus miliarius Sonora semiannulata Stemothems t^rinetus Stemothems odomtus Storerie dekayi Stomria occipiomaculeta Tantilla atrkeps Tantilla gracilis Tantilla hobertsmUhi Tantilla nigrkeps Tantilla mbra' Tantilla yaquia Terrepene cemlina Terrepene omata Thamnophis cyrtopsis Themnophis elegens Thamnophis eques^ Thamnophis mamianus Thamnophis pmximus^ Themnophis radix Thamnophis mfipunctatus^ Thamnophis srtalis Trachemys gaigeae Tmchemys scripta Trimorphodon biscutatus' TmpkJcKlonkn lineetum Urosaums omatus Uta stansburiana Vkginia striatula Virginia veleriae

MCP EGP CTP BLP ~~Y Y Y Y ~

OWP CGP THP RLP Y

EWP RGP SGP BAR SKP

Y Y Y Y

Y Y Y Y Y Y

Y Y

Y

Y

Y

Y Y

Y Y

Y Y Y Y Y Y Y

Y Y

Y

Y

Y

Y Y

Y

Y

Y Y Y Y Y Y Y

Y

Y

Y Y

Y Y

Y

Y

Y Y

Y Y

Y Y Y

Y

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Y Y

Y Y Y Y Y Y Y

Y

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'^ Listed on at least the Louisiana, New Threatened and Endangered Species

Mexico, Oklahoma, Texas, or federal lisL

Y

Y

Y Y Y

Y Y Y

Y

Y

Y

Y Y Y Y

Y Y

Y Y

Y Y Y Y Y Y

81

APPENDIX B

COEFFICIENT OF COMMUNITY VALUES FOR EACH PAIR OF TEXAS

ECOREGIONS ACROSS EACH TERRESTRIAL VERTEBRATE GLASS

AND ACROSS ALL TERRESTRIAL VERTEBRATES

82

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86

APPENDIX G

SCATTER PLOTS INDICATING THE RELATIONSHIP BETWEEN

HABITAT, SPATIAL, AND CLIMATIC VARIABLES AND THE

SPECIES RICHNESS OF THE MAJOR

ECOREGIONS OF TEXAS

87

ELEVDIF vs. AMPHIBIA

AJ\ PHIB1A 38.066-.0139-ELEVDIF

Oxrelalion: r -.6745

< m I CL

1400 1600

VEGTYPE vs. MAMMALIA

MAMMALIA 36.075 + 2.9773 * VEGTYPE

Correlaticn: r .75061

VEGTYPE

(b)

Figure 0.1. Significant (P < 0.05) simple-linear regression models predicting the species richness of ecoregions for vertebrate classes based on habitat variables. Predictor variables are defined in Table 1.1 and P - values are listed in Table 4.7.

88

VEGTYPE vs. MAMMALIA

MAMMALIA 46.633+1.9768 * VEGTYPE

CoTTBiation: r .74629

90

85

60

75

< 70

65

60

55

50

•

•

^ ««• *"

O

o

o o

^ ^ ^ ^ ^-^

^ o

•y

• y

^ ^ y ^y^

_ ^ - ' ' **

O

^ ^ - ^ ^ 0

^ ^ *

^ ""

12 16

VEGTYPE

20 24

(c)

SOILCLAS vs. MAMMALIA

MAMMALIA 59.161 + .26907 * SOILCLAS

CometeCicn: r .55430

20 40 60 80

SOILCLAS

100 120 140

(d)

F i g u r e d . Continued.

89

SOILTEXT vs. MAMMALIA

MAMMALIA 47.015 -• 1.6398 * SOILTEXT

Correiation: r .83491

< 2

TOPOINDX vs. MAMMALIA

MAMMALIA 67.039 -t- 118.28 * TOPOINDX

Ckimelation: r .81327

l O U

120

110

100

90

60

70

60

50

An

y ^ * ^ ^ ^ y O ^ - - ^

y ^ ^ ' ^ • ^ ^ ^.^y^

y ^^^-^^

y ^.^^^ ^ ^y^'^ " " " y ^ , — — —

y ^,y^ _ -» ^ ^ .y"^ - • '

y _^y^ -^ — y _^y^ _ »*

OCP ^-^ ^ y ^ . - - - '

- ' ^ ^ P - - - ' — - "' *^ •* ^ '''''' ^

/ - ^ ' O ^ y

^^ o

-0.05 0.05 0.15 0.25

TOPOINDX

0.35 0.45 0.55

(f)

F i g u r e d . Continued.

90

ELEVDIF vs. MAMMALIA

MAMMALIA 62.590 + .02002 * ELEVDIF

Cometatican: r .94088

130

< 2

-500 500 1000 1500 2000 2500 3000

ELEVDIF

3500

(9)

ELEVDIF vs. MAMMALIA

MAMMALIA 61.807-»• .02193 * ELEVDIF

Cxarrelation: r .83081

-200 200 400 600 800

ELEVDIF

1000 1200 1400 1600

(h)

Figure d . Continued.

91

POLYAREA vs. MAMMALIA

MAMMALIA 57.231 + .00000 ' POLYAREA

Correlaiion: r .80161

_i <

2.2e7

(i)

110

VEGTYPE vs. REPTILIA

REPTIUA 65.923 •*• 1.3429 * VEGTYPE

Comelation: r .55759

F i g u r e d . Continued.

0)

92

SOILTEXT vs. REPTILIA

REPTILIA 69.743 + .80233 * SOILTEXT

C:orre<aticyi: r .67279

(k)

TOPOINDX vs. REPTILIA

REPTILIA 80.094 + 50.687 ' TOPOINDX

C^Drrelation: r .57398

110

-0.05 0.05 0.15 0.25

TOPOINDX

0.35

(I)

F i g u r e d . Continued.

0.45 0.55

93

< m X CL

EMIN vs. AMPHIBIA

AMPHIBIA 25.309 + .00001 - EMIN

Ckxretation: r .63599

8e5 1e6

(a)

EMIN vs. AMPHIBIA

AMPHIBIA 20.098 + .00002 * EMIN

C^orreJation: r .70414

1e6

EMIN

(b)

Figure G.2. Significant (P < 0.05) simple-linear regression models predicting the species richness of ecoregions for vertebrate classes based on spatial variables. Predictor variables are defined in Table 1.1 and P- values are listed in Table 4.8.

94

EMAX vs. AMPHIBIA

AMPHIBIA 1Z237-t-.00003-EMAX

CocT^atkn: r .78704

< m X

1.3e6

< m X Q.

ECENTER vs. AMPHIBIA

AMPHIBIA 20.198-t-.00002-ECENTER

Ckjrrelation: r .71024

^ e 5 -2e5 2e5 4e5 6e5

ECENTER

8e5 1e6 1.2e6

(d)

Figure C.2. Continued.

95

ECENTER vs. AMPHIBIA

AMPHIBIA 14.254-»• .00003 * ECENTER

Ckxrelation: r .76161

m X Q.

40

34

28

22

l f i

y

y ^ '

O ^^ 3^^ y ^y^^

O . ^ ^ y - ^ y ^y"^

y ^ y ^ ^^^

' " 0 ^ ^

^ - - ^ ^ 0 ^ - ^ ^ ^ ^ ^ - . ^ - ^ .^^ ^ •* •* ^^^ —• • • . X " ^ ^

- - - ' ^ y ^ 0 - ^ " O — -» ^-^^ ^

• * ^ ^ ^ ^ • *

^y'^ •» ^^y^ ^

^^y^ y ^y^ y

^ y ^ y

^ ^ Oy^ ^y^ y

y ^ CO , --^

1e5 3e5 5e5 7e5

ECENTER

(e)

9e5 1.1e6

NMIN vs. BIRDS BIRDS 817.68-.0001 * NMIN

Correlation: r -.7665

280 2.8e6 2.9e6 3e6 3.1e6

Figure C.2. Continued.

3.2e6 3.3€6

NMIN

3.4€6 3.5e6 3.6e6

(f)

96

NMAX vs. BIRDS

BIRDS 620.73 - .0001 * NMAX

Ckxrelation; r -.6158

4.2e6

420

400

380

360 w o Q:

5 340

320

300

(g)

NCENTER vs. BIRDS

BIRDS 719.71 .0001 ' NCENTER

C jmelation: r -.7015

280

^^^^^>>^ ^ " - ^ o o

- -. ^ ^ - < L ^ - -.

. . , .

2.9e6 3.1e6 3.3e6 3.5€6

NCENTER

3.7e6 3.9e6

(h)

Figure G.2. Continued.

97

EMIN vs. MAMS

MAMS 93.452-.0000-EMIN

Correlation: r -.9692

2

130

120

110

100

90

80

70

60

50

40

S i ^ » . . . . .

^ - .o "^ - ^ ^ ^ " -

'• ^ " ^ ^ ^^^^ ' *" - ^"^v. -• * ^ ^ _ 'V.

*• ^ N w •» ^ - ^ ^ ^ N ^ " ^

^ ^ ^ ^ f c . * .

^ ^ ^ v T -" C^^^**"

** ^^^ • ** '"'b^oS^. o

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^ ^ ^ ^ ^ .

- > - ^ ^ -•V ^ ^

>. **

-8e5 -6e5 -4e5 -2e5 0 2e6 4e5 6e5 8e5 1e6

EMIN

(i)

EMIN vs. MAMS

MAMS 91.263-.0000-EMIN

Correlation: r -.9206

1e6

Figure G.2. Continued.

(j)

98

S

EMAX vs. MAMS

MAMS 117.07 - .0001 * EMAX

Oxrelation: r -.8442

130

120

110

100

90

80

70

60

50

40

" x 0 »*

>» • >

. x ^ ^ ^ ^. ^*'x,^^ ^

^ ' * ^ > „ ^ «». ^x>^^ ^

^ " V ^ ^ X ^ ^ . ^ ^ .w

- ^ ^ " " > . ^ •*» * • » . ^ x > ^ •>.

* * • * ^ ^ ^ f c . ^

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* > • *** , ^ ^ ^ ^ ^ ^ ^

^ ^ ^ ^ N ^ ^ * * ^ ^ « l ^ ^

^ ^ ^ ^ ^ ^

1o5 3e5 5e5 7e5

EMAX

9e5 1.1e6 1.3e6

(k)

EMAX vs. MAMS

MAMS 101.81-.0000-EMAX

Correlation: r -.7945

CO

1.3e6

(I)

Figure G.2. Continued.

99

ECENTER vs. MAMS MAMS 104.95-.0001-ECENTER

OarreJation: r -.9369

130

120

110

100

90 CO

2

< 80

70

60

50

40

>.

^ ^ ^ ^ -^ V ^ N

^ " ^ ^ ^ X ^ " V ^ X.

^ ^ V . ^ • ^ - . ^ — " ^

*» ^v. ^ ^v. ^ • • ^ N s ^ N (

** •^^ ^"*V^ ** X ^ ^ X . '^

** ^ V ^ *• - ^ ^ V ^ X

*** ^ ***w r^

" ^v^ *• •N ^ S ^ •*. ^ ^K^^ •*• •» ^ S ^ -»

^ ^ ^ w ** •S ^ V ^ **

"* v *• "N ^ S - ^ *»

S ^^^w^ *v ^^N^^

>. ^ * V . *»

-4e5 -2e5 - 0 2e5 4e5 6e5 8e5 1e6 1.2e6

ECENTER

(m)

ECENTER vs. MAMS MAMS 97.606 - .0000 * ECENTER

Comelation: r -.8770

l. leS

(n)

Figure C.2. Continued.

100

EMIN vs. REPS

REPS 90.166-.0000-EMIN

Correlation: r -.5690

110

100

CO CL U CC

90

80

70

60

o

•

.

.

•s ^ *s

o %•

,„^^ ** ^ * * ^ " » « - ^ "^ ^^"^^^ *% ^ ^ • • ^ f c ^ ^ * < •

^ ^ ^ _ ^ - . o ^ ^ ' * « ' f c ^ * *

^ • " " ^ l . , , ^ ^ ^ .

—-^ - - o - ^

*• ^ ''** *, _ ^• - .0 o ^ - ^

• > .

>» O ^ ^ Q.

>. X

-«e5 -6e5 -4e5 -2e5 2e5 4e5 6e5 8e5 1e6

EMIN

(o)

NMIN vs. ALL

ALL 1078.5-.0002-NMIN

Correlation: r -.6813

<

660 fv

620

580

540

500

460

• *

-.» ! V

" 1 O - s

% X _ -s o

X ^ "Xn,,,,^^ X

^ ^ * « 1 « » , ^ ^ ^ " X

^ ^ * " « l l m n ^ ^ ^ ^

^^^^^^ ^ ^^^^^^ ^ ^ * * ^ ^ n ^ "X

^ ' ^ ' ^ ^ i * , ^ ^ ^ ^ * * *

O ^ ^ " " ^ v ^ O 0 " ^ -, ^^ . ^.

" - * — — - . ^ " ^ • ^ " ^ ^ ' * * ' " " -

* " - - . ^ ^ ' • ^ ' * - > ^ • ^ ^ * ^ * ^ . w ^

«o ^"^ -^ "--x ° ^ ^ " - ^ •»

*>

2.8e6 2.9e6 3e6 3.1e6 3.2e6 3.3e6 3.4€6

NMIN

3.5e6 3.6€6

(P)

Figure C.2. Continued.

101

m X

PRCPMIN vs. AMPHIBIA

AMPHIBIA = 20.104 + 6.8786 * PRCPMIN

Correlation: r = .72648

46

40

34 < m X a 2 28 <

22

16 0.2

PRCPMIN vs. AMPHIBIA

AMPHIBIA = 17.484 + 9.4114 * PRCPMIN

Correiation: r = .86934

o ^ ^

p o

o

o ^

o

• o

... .

o

o o .

o

y ^ O

'

•

0.8 1.4 2.0

PRCPMIN

2.6 3.2

(b)

Figure G.3. Significant (P < 0.05) simple-linear regression models predicting the species richness of ecoregions for vertebrate classes based on climatic variables. Predictor variables are defined in Table 1.1 and P - values are listed in Table 4.9.

102

m X Q.

PRCPMAXvs. AMPHIBIA

AMPHIBIA = 8.4463 -•• 5.2882 * PRCPMAX

Corretation: r = .70853

1.5 2.0 2.5 3.0 3.5 4.0

PRCPMAX

4.5 5.0 5.5

(c)

< m X CL

46

40

34

28

22

16

PRCPANN vs. AMPHIBIA

AMPHIBIA = 15.694 + .47842 * PRCPANN

ComaJation: r = .68734

Oy

GH

O

6

O

O

o

o

o

o

o

•

15 25 35

PRCPANN

45 55 65

Figure G.3. Continued. (d)

103

46

CO

X Q.

50

PRCPANN vs. AMPHIBIA

AMPHIBIA = 10.259 + .69545 - PRCPANN

Correlation: r = .85027

52 54 56 58 60

MAXMIN

66 68

(f)

Figure G.3. Continued.

104

420

420

MAXA^^J vs. AVES

AVES = -297.7 •*• 8.2334 ' MAXANN

Correction: r = .60468

(g)

MAXD(F vs. AVES

AVES = 536.71 - 5.398 * MAXDIF

CorreiatJon: r =-.6768

(h)

Figure C.3. Continued.

105

420

400

380

360 CO

ai < 340

320

300

280

MAXVAR vs. AVES

AVES = 445.00 - .5896 ' MAXVAR

Correction: r =-.6852

^ ^

, .

•

0

^ - . o

o

.

,.^. ' o .

o o

o

^•"-^ o

o o

o

•

•

o

^ ^ ^

80 100 120 140 160 180 200 220 240

MAXVAR

260

420

400

380

360 to LU < 340

320

300

280 20

Figure C.3. Continued.

(i)

MINMIN vs. AVES

AVES = 215.17 + 3.6916 ' MINMIN

Corrotation: r = .71379

•

•

•

^ y ^

0

0

o

^^^^o

6 o

o

o ^

o

Oy

o

o ^^^

•

•

•

26 32 38

MINMIN

(j)

44 50

106

MINANN vs. AVES

AVES = 124.65 -•• 4.0723 ' MINANN

Correction: r = .63729

(k)

MINDIF vs. AVES

AVES = 586.93 - 6.700 * MINDIF

Correlation: r = -.7863

(I)

Figure G.3. Continued.

107

420

400

380

360 CO

at

< 340

320

300

280 100

MINVAR vs. AVES

AVES = 477.06 - .7181 * MINVAR

Correction; r = -.7861

120

- N

^ ^ ^ ^ v ^

• •

•

•

o

^ ^ ^ ^

...

0

" ^ v ^ ••

,

o

o

o •

o

•

•

o

.^.^

o ^^^^--v^

140 160 180

MINVAR

200 220 240 260

(m)

42

MXMfOIF vs. AVES

AVES = 551.49 - 3.482 " MXMNDIF

Correction: r = -.6977

48 54 60

MXMNDIF

66 72

Figure G.3. Continued.

(n)

108

ANNDIFF vs. AVES

AVES = 529.65 - 7.964 * ANNDIFF

CooBlation: r = -.7154

(o)

< 2

0.0 0.6

PRCPMIN vs. MAMMALIA

MAMMAUA = 99.039 - 14.65 * PRCPMIN

Correlation: r = -.7550

1.2 1.8

PRCPMIN

3.0 3.6

(P)

Figure G.3. Continued.

109

PRCPMIN vs. MAMMALIA

MAMMALIA = 89.131 -10.22 - PRCPMIN

Correlation: r = -.8079

1.4 1.8

PRCPMIN

3.4

~^-. Regression 95% coofid.

(q)

130

120

110

100

5 90

2 80

70

60

50

Art

••-. o

" •-..

PRCPMAX vs. MAMMALIA

MAMMALIA = 134^8 - 13.72 * PRCPMAX

CorrefatJon: r = -.8969

o o •••.. o .

o"

Or, •••-•-. o o

••.o

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0

PRCPMAX

o . Regression 95% confid.

Figure G.3. Continued.

(r)

110

90

85

80

75

70

65

60

55

50 2.4 3.0

PRCPMAX vs. MAMMALIA

MAMMAUA = 116.53 - 10.05 * PRCPMAX

CcxreCtion: r = -.9158

•-. . o o •••.. "• ,, o

" ' • ' • . • • - . " " - .

" • • • . ' • - P "" • .

• • . . • . .

• • - . o • • • • .

• - . o • • • . b-. •-... o. " • • . o •• • - .

'. _ '..

o • - . _

3.6 4.2

PRCPMAX

4.8 5.4 6.0

(s)

PRCPANN vs. MAMMALIA

MAMMALIA = 113.96 - 1.194 - PRCPANN

Ccxrelation: r = -.8366

15 25 35

PRCPANN

45

(t) Figure G.3. Continued

55 65

111

90

85

80 }

75

70

65

60

55

50

PRCPANN vs. MAMMALIA

MAMMALIA = 100.36 - .8466 " PRCPANN

Ccjrretabon: r = -.8720

' " . •

:•,

•

• •

•,p cix , " - .o

• , > • , * •

•, "•.

' • . *

" • • • - . " • • - .

"o.

o

' • • • . .

"•-. °

0 "••.

* ,

o ' • • • - .

o *•-.

' ' . ^ • • • .

""•.

" •

•-. ,

*.. ^

o

. ,

• • - . ,

• • - .

15 20 25 30 35 40

PRCPANN

45 50 55 60

62

(u)

MINMAX vs. MAMMALIA

MAMMALIA 349.75 - 3.836 * MINMAX

Correction: r -.7210

MA

LIA

^

1 J U

120

110

100

90

80

70

60

50

An

• - .

o "•• _

" • • • - . " • • .

'• .

• • • . , o • • • • - .

o.. - • - . o

o - . . " • • - . ,

64 66 68 70

MINMAX

72 74 76

(V)

Figure C.3. Continued.

78

112

130

120

110

100

^ 90

2 80

70

60

50

40 40 44

MINANN vs. MAMMALIA

MAMMAUA = 180.47 - 1.925 ' MINANN

Correlation: r = -.6201

' • • - . o • - .

o •• - . . '"--o.. . o

" Q . .

o • . . • • - . . o " ' " • . ' • • - - .

48 52 56

MINANN

(w)

60 64 68

16 18

ANNDIFF vs. MAMMALIA

MAMMALIA = -13.62 * 3.7556 * ANNDIFF

Correlalion: r = .82013

MA

LIA

2

IJU

120

110

100

90

80

70

60

50

An

•

. . - • • • • 6 "

o.. - •

. - • '

. . • • • " ' ° . . - - " o .-• o

o ..

•'o

_ . - • •

^ - • '

..."

_-"'

..-•

.-•O"'

0 . *

*

. • • ' '

- - • • • • "

20 22 24

ANNDIFF

26 28

(X)

Figure C.3. Continued.

30 32

113

ANNDIFF vs. MAMMALIA

MAMMALIA = 13.680 * 2.5091 ' ANNDIFF

Correlation: r = .76376

(y)

110

100

90

OL Ol tn 80

70

60 91.5 92.5

MAXMAX vs. REPTILIA

REPTILIA = -245.8 > 3.4908 - MAXMAX

Correlation: r = .56707

0 o

. . - - •

. . - • ' ' b o . • • • ' • " * '

. • " , • * o . . - ' '

93.5 94.5 95.5

MAXMAX

96.5 97.5 98.5

Figure G.3. Continued.

(z)

114

105

100

95

90

RE

PTI

LIA

85

80

75

70

65

60 91 92.5

MAXMAX vs. REPTILIA

REPTILIA = -328.2 * 4.3413 - MAXMAX

Correction: r = .82717

.a'

. • * • ' ' * ' * - " ' '

o . . . • ' • ' . . . • • • "

. - - • ' o . . - • • • . . . . - • • • • • "

. . • • • • " " . • - - ' " . • • ' '

o . - - • ' b .-• ' . . '*' -•*'

93.5 94.5 95.5

MAXMAX

96.5 97.5 98.5

660

620

580

CO

cc ^ 540

500

460

(aa)

MAXMIN vs. VERTEBRA

VERTEBRA = 156.75 -»• 6.3049 * MAXMIN

Correlation: r= .67412

• ' . ' *

^ o .... '

. - • • ' ' . - - o ' "

. . - • • • • • ' . . . • • • • • "

o . . • •

_ . . - - • ' b . - • • '

_ _ » • * . . . — • fc • . ^ —

50 52 54 56 58 60

MAXMIN

62 64 66 68

(ab) Figure C.3. Continued.

115

660

MAXANN vs. VERTEBRA

VERTEBRA = -351.1 + 11.259 * MAXANN

Correction: r=.71551

78 80

MAXANN

(ac)

86

660

MINDIF vs. VERTEBRA

VERTEBRA = 770.35 - 6.692 - MINDIF

Correlation: r = -.6892

Figure G.3. Continued.

116

660

620

580

m HI cc ^ 540

500

460

MINVAR vs. VERTEBRA

VERTEBRA = 661.52 - .7223 * MINVAR

Correlation: r = -.6937

' • • - .

" » . ' ' • -

o

0

' • • - .

*•.,

- Q

' • • .

' " .

o

' •o. .

' " ' • ,

* • . ,

" " • • •

' - • % .

o

• • .

' " - . _ . o

.

' - . ,

o

'a- • . .

\

" • - . .

100 120 140

Figure G.3. Continued.

160 180 200

MINVAR

(ae)

220 240 260

117