Analysis of Rhyolite Canyon Watershed, Chiricahua National Monument, Arizona : a Prototype Study to Improve the Efficiency of Natural Resource Decision-Making

Home , Quercus arizonica

Analysis of Rhyolite Canyon watershed, Chiricahua National Monument, Arizona : a prototype study to improve the efficiency of natural resource decision-making

Item Type Thesis-Reproduction (electronic); text

Authors Frondorf, Anne Fenton, 1951-

Publisher The University of Arizona.

Rights Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.

Download date 24/09/2021 08:28:46

Link to Item http://hdl.handle.net/10150/191659 ANALYSIS OF RHYOLITE CANYON WATERSHED, CHIRICAHUA

NATIONAL MONUMENT, ARIZONA: A PROTOTYPE STUDY TO

IMPROVE THE EFFICIENCY OF NATURAL RESOURCE DECISION-MAKING

Anne Fenton Frondorf

A Thesis Submitted to the Faculty of the

SCHOOL OF RENEWABLE NATURAL RESOURCES

In Partial Fulfillment of the Requirements For the Degree of

MASTER OF LANDSCAPE ARCHITECTURE WITH A MAJOR IN LANDSCAPE ARCHITECTURE

In the Graduate College

THE UNIVERSITY OF ARIZONA

1 9 7 7 STATEMENT BY AUTHOR

This thesis has been submitted in partial fulfillment of requirements for an advanced degree at The University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library.

Brief quotations from this thesis are allowable without special permission, provided that accurate acknowledgment of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his judg- ment the proposed use of the material is in the interests of scholar- ship. In all other instances, however, permission must be obtained from the author.

SIGNED:

APPROVAL BY THESIS DIRECTOR

This thesis has been approved on the date shown below:

M. M. McCARTHY Assistant Professor f Landscape Architecture ACKNOWLEDGMENTS

Research for this project was funded through National Park

Service grant PX 810060108. The efforts of Warren F. Steenbergh of the Cooperative National Park Service Resources Study Unit on behalf of this project are sincerely appreciated.

This thesis would not have even been begun, let alone completed, without the direction and encouragement of Michael

McCarthy. Thanks for everything, Mike.

To Jon Rodiek and Lewis Albert goes my appreciation for their advice and support throughout this project.

The technical assistance of Michael Bell, Harry Goforth,

William Murray, William Rasmussen, and Eric Roberts is gratefully acknowledged. TABLE OF CONTENTS

Page

. LIST OF ILLUSTRATIONS vi

LIST OF TABLES vii

ABSTRACT viii

1. OBJECTIVES

2. STUDY AREA 4

Chiricahua National Monument 4 . Use of a Prototype Area for Methodology Development . • • 5 Rhyolite Canyon Watershed 5

3. METHODOLOGY 6

Preliminary Listing of Principal Properties and Processes 6 Data-Availability Matrix 6 Sampling Scheme 9 Data Collection and Standardization 11 Multiple Cluster Analyses 13 Dendrogram Analysis 14 Data Entry 18

4. PRODUCTS 21

5. FUTURE NEEDS 28

Application of the Methodology to the Entire Monument . . 28 Preparation of a Working Manual for the Manager 29 Simulation Models 30

6. CONCLUSIONS 31

Applicability to the Manager 31 Innovations Toward Cost/Time Efficiency 33 Importance of the Researcher-Manager Relationship 33

iv V

TABLE OF CONTENTS - -Continued

Page

APPENDIX A: PRELIMINARY LISTING OF NATURAL RESOURCE PROPERTIES AND PROCESSES 35

Air 35 Water 35 Land 36 Flora 37 Fauna 38 Process 39

APPENDIX B: DATA COLLECTION/STANDARDIZATION FORMS 40

APPENDIX C: SPECIES LISTS 66

Plant Species 66 Resident Birds 66 Winter Birds 68 Sumner Birds 68 Mammals 69 Bats 70 Amphibians, Turtles, Lizards 71 Venomous Snakes 71 Non-Venomous Snakes 72

APPENDIX D: NINE FUNCTIONAL GROUPS AND GROUP COMBINATIONS . . 73

Group 1 73 Group 2 75 Group 3 76 Group 4 78 Group 5 79 Group 6 81 Group 7 82 Group 8 84 Group 9 85 Groups 1 and 2 87 Groups 1, 2, and 3 87 Groups 4 and 5 88 Groups 1, 2, 3, 4, and 5 89 Groups 6 and 7 90 Groups 1, 2, 3, 4, 5, 6, and 7 90 Groups 8 and 9 91

LIST OF REFERENCES 93 LIST OF ILLUSTRATIONS

Figure Page

1. Methodology Flow Chart 7

2. Systematic Unaligned Sampling Technique 10

3. Location of Sample Sites within Study Area 12

4. Cluster Analysis 107-21 and Group 1 15

5. Groups 2, 3, 4, 5 16

6. Groups 6, 7, 8, 9 17

7. Data Expansion Chart 19

8. Base Maps of Elevation, Orientation/ Drainage, and Vegetation Density 23

9. Apache Fox Squirrel Habitats 24

10. Rufous-Crowned Sparrow Habitats 25

11. Group 6 Locations within Study Area 26

12. Group 8 Locations within Study Area 27

vi LIST OF TABLES

Table Page

1. Data-Availability Matrix 8

vii ABSTRACT

Successful and meaningful natural resource planning and decision-making should be based upon a sound understanding of the relevant resource parameters and their functional interrelationships. Such a baseline understanding is the foundation on which any predictive-type planning efforts will be built.

Formation of a computerized natural resource information system for a major watershed of the Chiricahua National Monument has involved the use of data-intensive spatial sampling techniques; multiple, simultaneous hierarchical cluster analyses; and computer graphics in an attempt to provide Park Service management personnel with a functional, accessible, and dynamic information base. The total methodological package, which has been geared toward operationality and economy, is herein described. Study output includes two- and three-dimensional computer displays of resource properties and processes. Possible future applications of the methodology, to the entire Monument or to other natural resource areas, are discussed.

viii CHAPTER 1

OBJECTIVES

In the process of preparing a Resource Management Plan (Chiricahua

Staff 1977) and establishing a Resource Monitoring System (Moir 1974) the management staff at Chiricahua National Monument recognized a need for a capability which would allow them to predict the consequences of certain management decisions on the resource base. While implementation of the monitoring system could help managers to adequately analyze past impacts and to assess present use levels, any long-range management programs require some type of predictive capacity as to future uses and their respective impacts. It was in the hope of eventually reaching this predictive level that Monument management originally contracted the

School of Renewable Natural Resources at The University of Arizona, thus initiating the research upon which this report is based.

Ideally, such a predictive capacity would involve a series or hierarchy of predictive-type or simulation models, operating on a variety of scales, through which could be channeled not only ecological or natural resource parameters, but also relevant socio-economic and socio-political factors, all of which play a part in land use management and planning. Such a working system of models would permit the manager or decision-maker to examine the outcome of certain actions or series of actions before any such actions have actually been implemented and to select the best alternative for the circumstances.

1 2

Before such relatively sophisticated predictive or simulative

systems can be realized, it is necessary to establish a working under-

standing of the natural resources through the completion of a baseline

resource inventory. This baseline analysis produces the needed

information upon which the alternative impacts may be superimposed. It

was thus decided that the first phase of this research project would be

directed toward the development of such a data base for a prototype

area within the Monument and that development and testing of appropriate

simulation models would follow, at some future time, the completion of

the data base/information system for the entire Monument.

Even before the development and use of the simulation models,

the information system can be of immediate value to the manager through

its use in the production of computer graphics. These graphics can

display the resource parameters in two- or three-dimensional fashion,

using a variety of formats. Such displays, produced as maps or perspective relationships, could involve weighted or non-weighted

combinations of parameters to produce new levels of information. If

such output were available to the manager it could be used as reference material in making daily management decisions, to locate certain

features or species within the Monument, to familiarize new staff members with the resource base, or in any one of a variety of other ways.

While the information system is, at best, only an abstraction of the real world, it will be used to make decisions in the real world and thus is only valuable when it can closely approximate nature. The parameters which comprise the natural ecosystems with which we will be 3

dealing are not merely frozen, isolated variables, but a series of

complex and dynamic relationships between properties. These relation-

ships can be referred to as resource processes. It was a primary goal

of this study to go beyond the static resource property inventory

approach and to develop a dynamic information system that acknowledged

the process nature of the ecosystem and examined the functional

interrelationships of resource properties into processes.

With this basic, process-oriented philosophy in mind, a

methodology was developed in order to meet the following objectives:

A) Formation of a data-intensive, computerized information

system which would contain viable information on the natural resource properties and processes operative within the Monument.

B) Production of two- and three-dimensional computer graphics which display the important resource properties and processes in such a way so as to be of direct use by the resource manager on a day-to-day basis.

C) Use of the information system in future studies as the

foundation for a series of predictive-type, computer-assisted models which could be utilized by managers to understand the conditional impacts of various management and planning decisions.

As shall be seen, a primary orientation throughout the study was to keep total costs, man-hours, and computer time at a minimum, while sacrificing neither accuracy nor relevance to management. CHAPTER 2

STUDY AREA

The biological and physical complexity of Chiricahua National

Monument makes it a rich and unique area of study for the natural resource planner as well as the ecologist or naturalist.

Chiricahua National Monument

Located in Cochise County, in the southeastern corner of

Arizona about 100 miles from Tucson, Chiricahua National Monument covers a total area of 10,645 acres. Elevation ranges from around 5200 feet to over 7200 feet. Major vegetation types encountered range, as would be expected with such an elevational spread, from desert grassland through chaparral or oak woodland to coniferous forests.

As part of the basin and range topographic system which characterizes southern Arizona, the Chiricahua Mountains exist as a virtual island amidst a sea of dry desert grassland. This evolutionary isolation, in conjunction with the proximity of Mexico and its tropical influences, as well as the physical complexity offered by the myriad of volcanic rock columns, has resulted in a set of fairly unique biogeo- graphic implications. Diversity of plant and animal species is high and there are several rare or unique species present.

The uniqueness of the area in offering such a wide assortment of species and features in a fairly limited area has attracted scores 5 of biological, ecological, and geological researchers over the years and, consequently, there is a relative abundance of studies and reports already available on natural and physical features.

Use of a Prototype Area for Methodology Development

It was initially decided that the total methodology, involving spatial sampling techniques and multiple cluster analyses, would first be developed and applied to a prototype area within the Monument. This decision was made on the assumption that if the methodology were successful on the smaller scale, it would then be a relatively simple matter to transfer findings to the entire Monument. That is, in this prototype phase, a major part of the time and money involved was spent on intensive data collection and the determination of key relationships between resource parameters.

Once these relationships have been clearly understood and represented, transfer of the methodology to the entire Monument would only require a small (2-3%) preliminary sample to verify that the same relationships hold true in the larger system. Following such verification, input of data on a spatial basis can begin.

Rhyolite Canyon Watershed

Rhyolite Canyon Watershed, chosen as the prototype area, covers approximately 2700 acres, thus comprising more than one-fourth of the total area of the Monument. The majority of the visitor trails are contained within Rhyolite Canyon. Elevation range and distribution of major vegetation types are representative of the entire Monument. CHAPTER 3

METHODOLOGY

Although individual steps of the total methodology may have been applied in previous studies, it is believed that the total methodology, which has been developed with its eventual transfer to the rest of the Monument and to other natural resource areas in mind, represents a unique approach to the production of a natural resource data base. A concise display of the total methodology may be seen in

Fig. 1.

Preliminary Listing of Principal Properties and Processes

A preliminary listing of the principal natural resource properties and processes; including land, air, water, floral, and faunal systems, was used as the basis for review of relevant existing sources of data and decisions as to additional types of data which would be collected during field work. This preliminary list may be seen in Appendix A.

Data-Availability Matrix

Working from the preliminary listing of resource properties and processes, a data-availability matrix was compiled. Table 1 displays the 22 individual data types which were collected from existing sources. Source material is also noted.

6 7

ty%

Fr-1

TABLE 1. Data-Availability Matrix

DATA TYPE SOURCE

Land

Elevation U.S.D.I. 1968 Slope U.S.D.I. 1968 Orientation U.S.D.I. 1968 Geology Enlows 1955, Fernandez and Enlows 1966, Sabins 1957

Flora

Vegetation Type Moir 1974, Roseberry and Dole 1939 Pattern Diversity Roseberry and Dole 1939 Edge Diversity Roseberry and Dole 1939 Fragility Murray 1976 Replaceability Murray 1976

Fauna

Resident Birds Jackson 1970, Monson 1975 Winter Birds Monson 1975, Westcott 1963 Summer Birds Ligon and Balda 1968, Tanner and Hardy 1958 Rare/Threatened Birds U,S.D.I. 1974 Mammals Cahalane 1939, Cockrum and Justice 1958, Lanning 1974a, Maza 1965 Bats Cockrum and Ordway 1959, Lanning 1974a Rare/Threatened Mammals U.S.D.I. 1974, Lanning 1974a, 1974b Unique Mammals Lanning 1974b, Yoder 1958 Snakes/Venomous Jackson 1970, Lunsford 1974 Snakes/Non-Venomous Jackson 1970, Lunsford 1974 Amphibians, Lizards and Turtles Jackson 1970, Sipes 1975 Beetles Linsley, Knull, and Statham 1961 Spiders Jung and Roth 1974 9

The "negative" of the data-availability matrix becomes a

display of the data types which were not readily available from

existing sources. This display served then as the basis for decisions

as to which additional data types would be collected during on-site

examination. The decisions as to which additional data types to collect were based primarily on the need to obtain as much useful and unique

data as possible, while working within the limitations of available manpower and time. The three data types which were eventually collected

on-site include:

11 Diversity of Individual Floral Species 21 Diversity of Vertical Layers (Floral) 3) Canopy (Plant Density),

Sampling Scheme

A 10% systematic unaligned sample, applied to the study area,

resulted in the location of 107 one-hectare sample sites. The system-

atic unaligned sampling strategy has been shown in previous studies

(Berry 1962, Cochran 1953, Holmes 1967, Keyes and Basoglu 1973, Morrison

1970, Quenouille 1949) to generally result in greater statistical precision when compared to other sampling schema; including stratified random unaligned, random unaligned, and systematic aligned.

Fig. 2 demonstrates how points are located in the systematic unaligned sample design. The study area is first divided into regular sub-areas. Two sets of random numbers are selected, one set each for the x and y directions. These numbers represent distances to be measured off in the x or y direction in a particular sub-area. The value x applies to all sub-areas in the first row. Value y applies 1 1 1 0

1 I I Y2 Y1i I I I

--X1 ---

I I I I Y2

Y1 - - - - x2 -- -') I n 1 - - - X2 - --,)

Fig. 2. Systematic Unaligned Sampling Technique 11

to all sub-areas in the second row, y 2 to all sub-areas in the second

column and so on. The intersection of the respective x and y distances within each sub-area specifies the location of the sample point. Each point is then taken as the center of a one-hectare sample site. Fig. 3

shows the distribution of the 107 sample sites within the study area.

Data Collection and Standardization

The 22 data types drawn from existing sources were collected

from each of the 107 sample sites. The extreme topographic complexity of Rhyolite Canyon and the entire Monument rendered 53 of the sample sites Inaccessible to on-site examination. The three on-site data types were collected from the remaining 54 sample sites. During the on-site collection efforts, photographs were taken in the north, south, east and west directions at each sample site visited. These photos were used for ground-truth verifications and could also be used in future studies to assess levels of change in the landscape.

All collected data were standardized into a numerical system whereby each level within a particular data type is represented by a unique integer. It is these integers which are then entered into the computer during the cluster analyses. Appendix B contains the data collection and standardization sheets, one for each data type. Species referred to by abbreviations in the floral and faunal data types may be found in the species lists, Appendix C. In the cluster analyses each data type represents a single "variable," while each sample site

-represents an "individual." 12

RHYOLITE CANYON WATERSHED CHIRICAHUA NATIONAL MONUMENT, ARIZONA SCHOOL OF RENEWABLE NATURAL RESOURCES UNIVERSITY OF ARIZONA SCALE 1:12,000 NORTH

Fig. 3. Location of Sample Sites within Study Area 13

Multiple Cluster Analyses

Application of polythetic agglomerative hierarchical cluster analyses to the collected data allows for better understanding of the key interrelationships and functional groupings of the parameters

(variables) under examination. The polythetic agglomerative strategy of clustering (Clifford and Stephenson 1975, Hughes and Thomas 1971,

Lance and Williams 1967, Pritchard and Anderson 1971) joins individuals or groups of individuals together (agglomerative) on the basis of their similarity among several variables (polythetic). Results are displayed in the form of dendrograms in which individuals are arrayed along the horizontal axis and similarity decreases going up the vertical axis.

Thus, the "most similar" individuals are those which are joined closest to the bottom.

The clustering program CLUSTAN (Wishart 1969) was chosen for use in the cluster analyses. CLUSTAN actually represents a suite of programs from which the following programs were selected for use during this section of the study:

Program FILE: Creates data file from raw data for CORREL.

Program CORREL: Creates similarity matrix. Squared Euclidean Distance (code 1) was selected as the similarity coefficient option.

Program HIERAR: Computes hierarchic fusion from the similarity matrix. Ward's Method (error sum, code 6) was chosen as the hierarchic fusion option.

Program RESULT: Prints selected data file values and computes cluster diagnostics.

Program PLINK: Provides graph plotter routine for preparation of the dendrogran. 14

All computer work was done on the CDC 6400 and DEC-10

computers and the Calcomo Plotter at the University of Arizona

Computer Center.

Separate cluster analyses were performed on various combina-

tions of 14, 21, or 24 variables (data types) and 33, 54, or 107

individuals (sample sites). The decision to undertake such multiple

cluster analyses was based on the assumption that validity of cluster

results would be more firmly demonstrated if the same clustering

pattern were revealed in all resulting dendrograms. The same pattern

of clustering as can be seen in the 107-21 (107 individuals, 21

variables) dendrogram (Fig. 4, top) was seen in all of the other

dendrograms produced.

Dendro gram Analysis

In-depth analysis of all resulting dendrograms revealed the

existence of nine functional groups or "ecosystem types" within the

study area. These groups are arrayed across the horizontal axis of

dendrogram 107-21 and the delineation of the parameters (data types) which characterize each group or combination of groups may be found in

Appendix D. As these groups represent combinations of natural resource properties, they could be thought of as one indication of the operative

resource processes in the study area.

Graphic representations of the major characteristics of each

group may be seen in Figs. 4, 5, and 6. These representations are

"model" descriptions of the nine systems, in terms of their respective

floral, faunal, and land surface features. 15

VIIOZIEN JLW$611101 1910211V JO AMMAN S3OEN1OS311 1111111111N MUM JO 100113S S3311110S31111111111VNINWA311311.40100H2S MEIN '.U1311111NOW MINOLLVII DME1110111 MOWN mom 031131/1121 NUM mauls BISE1313111ŒUR13 11110AMI sivnamiami 12-L01 MINIM EISM3 12-L01 dflOEIS

4=. CC CD

D. CD 0 Ps CD D. = CO CC CD D. 0 0 la 0 CC CD CO = 0 - CC CO c'd

rj ›.1 ni

D. C*) CD dt CC 4,, a CD a CD 0

B •71, ea tr) • n••1 GLI 16

to_tozoitio ABSIGIUNII Iowan Jo =moon ntRUIN 3121L51311311 JO 100KG SDAIOS711 nyttnNN MIMI 100KIS liana( mom nnovolgoo t910211111aMpW MOWN Vflitt3ItIN 01611111141KW1113 31110111t1 NOANIt3 Uinta 2-101 t 4089 a-hot 's dI10119 I

, ard„, ;;)Ei'

4:11077-1."-i" ).*-4.1. I .'\\ Y. 4 Il I §1 v 5 =12 ,...., -,=3.3 1

11140211111 LUSIEIAN1 n 1111021111 JO OMANI SKSIOS311 warm 118111131611 10 10010S =M3I 1111111191 IMAM JO 100113S MOON 1/114=1113 VIC= 'mom mount 11111frAlla 031SHI1JMN0UW3 311101101 OMEN Ii0A111f3 111111LIN 12-L0I 7 anon dI10419

• - 17

IINOZ14111 S3311110EIV nriffulni 319 %.vm Al WOE 231110511MME0 VOWS IIMOR1=0111•0 41 ,10111/110 323rMICI WC= '11131010M VONVJOIC) MUM KUM Baum IZ -GM Z CON Wall 8 MOW

— -

VIEZ1111 lftlaZIN SKIMOSMI MUM 7181113E31 30 *miss S3111110S311 TV41111111111MILielli JO WOE maw 'mum nrNOWill RPM 'DEMON WHOM minim SEEM 110ÂNIM 111101A =SHUN KUM 31110.U10 le-Lill 9 dI1089 kt„ 12-ZIII 8 d110119 18

The cluster analysis, through interpretation of the resulting

dendrograms, allows for the selective reduction of data types which

need be actually entered into the computer and stored as files.

Dendrogram interpretation revealed elevation, slope, vegetation type,

and vegetation density to be key or "indicator" parameters or data

types within the study area. That is, all remaining data types were

shown by the clustering to be linked in various ways to these four

indicator data types. Thus, by only entering and storing these four

parameters, the researcher realizes an enormous saving in costs and

time, while sacrificing neither accuracy nor specificity. The explicit

input of the indicator data leads to the implicit input of all

remaining data, through understanding of the functional interrelation-

ships revealed through clustering. Fig. 7 demonstrates the

relationships between the indicator data and all remaining data. Note

that data is available down to the level of individual species. Entire

listings of species have been eliminated here to conserve space.

Data Entry

Indicator data were entered into the computer for the entire study area using a digitizer which automatically registers the x, y coordinates of a selected point and transmits these to the computer for data storage.

In actuality, only elevation, vegetation type, and vegetation density were entered; as a separate program, SLOPE, was used which derives slope and aspect values from an elevation matrix. 19

g g tir, 55m; n

-- 20

Elevation was digitized directly off the contoured base map.

Vegetation density was transferred from aerial photos to the base map using a zoom-transfer scope and then was digitized in. Vegetation type was transferred from a previously-existing vegetation map (Roseberry and Dole 1939), which had been first verified with aerial photos, to the base map and then digitized in. CHAPTER 4

PRODUCTS

With the indicator data files assembled, computer graphics were produced which display data types or various weighted or non- weighted combinations of data in a variety of formats. Existing

computer software (Durfee 1974, Laboratory for Computer Graphics and Spatial Analysis 1975a, 1975b) was used as much as possible, in order to conserve money and time.

Past information system studies have often invested large amounts of both time and money in an attempt to design new software

systems to handle problems this study has shown can be quite adequately

dealt with by existing programs. This is not to say that all software

development related to studies of this genre should cease. On the

contrary, it is hoped that development of new and better computerized

systems for the manipulation and display of information on a spatial basis will continue. But in a study of the type being discussed here, where Park Service management has specifically requested a usable system to be ready within a limited amount of time, it would seem of primary importance to get the system up and running in as inexpensive a manner as possible and this is where existing software is an advantage.

Display formats ranging from two-dimensional printer maps or two-dimensional plotter maps to three-dimensional perspective relationships can be utilized according to which technique best

21 22 satisfies the manager's current needs. Fig. 8 displays some of these formats in base maps of the indicator data types: elevation,

orientation/drainage, and vegetation density.

Figs. 9, 10, 11, and 12 are examples of composite mapping, in which particular combinations of data are used to portray a more complex resource process or property. Fig. 9 demonstrates the location, within the study area, of habitats of the Apache Fox Squirrel, a species unique within the United States to the Chiricahua Mountains. Fig. 10 displays habitats of the Rufous-Crowned Sparrow. Fig. 11 portrays the location, within the study area, of communities classified as Group 6 in the cluster analyses. Fig. 12 displays Group 8 locations. 2 3

CO 24

CS1 25

'' ill» IA li 111/0 6

////60 i ' "N'h 14I i,» lifiqiiii/IP , i fililp' Z-1 — , b r i — .'----1'17 ,711`I 011 ;11,1/i114§/01/ „Lriigl i; ill — —- 7, • ------. -- /, 'ill 7/

, 11:Z7=La==:*/./11pV 26 27

- CHAPTER 5

FUTURE NEEDS

As the Rhyolite Canyon project represents a prototype

investigation, of major importance is the utilization of the results of

this investigation in future projects and the incorporation of collected

information into a total package designed to assist resource managers on

both a daily and long-range planning level.

Application of the Methodoloqy to the Entire Monument

Transfer of the methodology described here to the rest of the

Monument and its immediate surrounding area represents the next logical

step toward development of a total information system/simulation

modeling package. As has been mentioned previously, the success of the

methodology, as applied to the prototype area, in revealing the

functional groupings and the essential or indicator data types means

that application to the entire Monument should proceed with relatively

less cost and effort. A 2-3% preliminary sample, applied to the

remaining portion of the Monument plus those contiguous areas which would be included within a natural boundary (such as a watershed),

can reveal if the same functional relationships and indicator parameters

are valid for the larger area. If they are valid, data entry can begin

immediately. If parameters exist in the larger area that can not be

28 29 accounted for by the prototype study, some additional data collection and cluster analyses will be undertaken before the final data entry.

When the essential data have been entered for the rest of the Monument a complete and functional data base will exist for computer map production and predictive modeling.

Preparation of a Working Manual for the7

A complete and operational computer data base could be used in the preparation of a manual or "workbook" for the manager and containing a concise, relatively non-technical description of the capabilities of the system; lists and descriptions of available computer software which the manager could request be applied to a specific problem; and a series of two-dimensional printer maps which displayed, on a spatial basis, the resource properties and processes down to the level of individual species. Such printer maps are relatively inexpensive to produce and would afford to the manager the opportunity to visually assess relationships between resource parameters within a matter of moments when making management or planning decisions.

It is hoped that, with such a manual in hand, the management staff would be Able to deal with most questions answerable by the information system without having to directly contact the researchers at the University. More complex computer graphics such as those involving weighted combinations of data would, of course, still need to be prepared by the researcher on an individual basis in reply to a specific question from management personnel. But, on the whole, proper 30 exploitation of such a manual would put the reins of the information system into the hands of the management staff.

Simulation Models

After the total data base has been assembled for the entire

Monument, work can begin on organizing a series of simulation models for use in predicting the consequences of certain land use decisions on the resource base. The simulation modeling project would consist of two phases. The first phase would essentially involve the review of existing land use simulation models and the selection of those deemed most applicable to the Chiricahua system. A listing of these appropriate models, including descriptions of their capabilities and suggestions as to their utility for the manager, would be provided to the management staff to be used as a reference tool.

The second phase of the modeling project would involve the actual application of selected models to real-life management situations.

As has been mentioned before, these models should cover a range of factors, including ecological factors such as fire control, erosion, or streamflow; as well as relevant socio-political or socio-economic topics. CHAPTER 6

CONCLUSIONS

The success of this project rests on how well the original

objectives of creating an operational and dynamic information system

which is responsive to managerial demands while keeping costs and time

to a minimum have been realized. The value of the contact which has

been initiated here between the Park Service management and administra-

tive personnel and the university researcher can also be seen as adding

to the success of the project as a whole.

icabi1ity to the Manaqer

The development of an information system of this sort is meaningless if such a system cannot be of direct use by management personnel in making real-life resource planning or management decisions.

The National Park Service is generally charged with both conserving

(or preserving) the natural resources under its control and offering these resources to the general public for their enjoyment and enlighten- ment. It is believed the Rhyolite Canyon System (and the total

Chiricahua system when it is eventually completed) can be of assistance to Park Service managers and decision-makers in both areas.

Some of the applications of the data base as related to resource conservation or management have already been discussed.

31 32

Utilization of the computer makes it possible to manipulate, combine,

and display on the order of several hundred individual types of

natural resource information; much more than the individual, no matter

how familiar with the area, could ever deal with at one time. Control

of such a large amount of data, down to the level of individual species,

can be of use to the manager in such areas as wildlife management;

fire management; or the addition, enlargement, and maintenance of

facilities.

In wildlife management, computer displays of all locations

within the study area which satisfy a certain wildlife species' habitat

requirements are available.

Composite mapping allows the manager to see areas where slope,

elevation, aspect, vegetation type, and vegetation density are such that

a fire would be more difficult to contain than in other areas. Ideally,

weighted composites could be fed into a fire simulation model and would

allow the manager to predict the consequences of a certain type of fire

occurring anywhere within the study area.

If roads, trails, or campgrounds need to be added, enlarged, or

just maintained; computer graphics can be used to show managers where such activity would be most/least desirable in terms of its impact upon plant and animal habitats.

The information system can also help management with its responsibility to the public. The impact of the graphic output from this study constitutes an important part of its success. It might even be possible to have a slide-tape presentation available in the visitor 33 center which would explain some of the functional results of the study in a non-technical manner, so as to provide visitors with a slightly more in-depth view of the environment they are about to see and enjoy.

Such a presentation would also give visitors a chance to begin to see how Park Service management and planning decisions are made.

On another level, computer displays of habitats of particular wildlife or plant species could be of value to certain special interest groups or scientific researchers. Such maps could be produced easily and with relatively little expense.

Innovations Toward Cost/Time Efficiency

A primary motive force behind this project has been the drive to produce an accessible information system which was responsive to

Park Service needs while, at the same time, representing a model of cost/time efficiency in total program design.

Use of the prototype area for methodology development and testing, application of data-intensive spatial sampling techniques in conjunction with multiple cluster analysis procedures, and reliance, wherever possible, on existing software components have resulted in a significant limitation of total costs and man-hours as compared to studies of this type in the past.

Importance of the Researcher-Mana er Relationship

This system is now fully operational and is producing pertinent computer graphics in response to questions from the Monument management staff. The links thus established between the researcher at the 34 university and the manager in the field are important and should not be overlooked. The information system in no way replaces the manager and his staff and indeed is only viable if contact between those in the field and those at the university is continued.

The researcher offers to the manager his expertise in terms of data manipulation, analysis, long-range project design, and exploitation of the computer. While the manager and his staff can offer to the researcher their in-depth understanding of and feeling for the resource, daily contact with the resource allow them to spot changes and trends relatively easily.

For example, Monument personnel are currently involved in monitoring the status of the resource base through the Resource

Monitoring System which was established in 1974 at Chiricahua. As changes or trends in resource processes become evident through such monitoring, this data can be relayed to the researcher and the computer.

Further analysis will indicate how such data can be used to update and modify the information system. By thus working together the researcher and manager can continue to improve the information system and insure that it remains dynamic and open. APPENDIX A

PRELIMINARY LISTING OF NATURAL RESOURCE PROPERTIES AND PROCESSES

Air

I Quality

a. Sulfur Oxides b. Carbon Monoxide c. Hydrocarbons d. Photo Oxidants e. Oxides of Nitrogen f. Particulates

II Climate

a. Macro 1. Temperature 2. Precipitation b. Micro 1. Temperature 2. Precipitation

III Currents

a. Macro b. Micro

Water

I Surface

a. Quality 1. Temperature 2. Phosphorous 3. Nitrogen 4. Suspended Solids 5. Bacteria 6. Toxic Metals 7. pH 8. Turbidity 9. Biological Oxygen Demand 10. Dissolved Oxygen

35 36 b. Watershed Order c. Runoff d. Types 1. Streams 2. Lakes-Reservoirs 3. Bogs 4. Intermittent Streams 5. Ditches 6. Ponds 7. Flood Plains 8. Unique Features

II Ground

a. Quality b. Depth to Table c. Aquifer Recharge Areas d. Quantity

III Processes

a. Floods b. Erosion c. Deposition

Land

I Topography

a. Slope b. Elevations c. Drainage Patterns d. Landslide Areas e. Orientation f. Response Units g. Unique Features

II Geologic Features

a. Minerals b. Tectonic Information 1. Earthquakes 2. Faults/folds 3. Volcanos 37 c. Bedrock Information d. Extrusive Activity e. Unique Features

III Soils

a. Types and Series b. pH c. Depth d. Texture e. Structure f. Parent Material g. Permeability h. Stability i. Storage Capacity j. Cation Exchange Capacity k. Compactability 1. Solution m. Fertility n. Expansive Potential o. Erodability

IV Force Fields Background Radiation

V Solar Orientation

Flora

I Synecology

a. Overstory b. Understory c. Ground Cover d. Aquatic e. Distribution Patterns f. Ecological Communities 1. Unique Communities g. Stability h. Productivity

II Autecology

a. Assimilative Capacity b. Unique Species c. Endangered Species d. Disease Vectors 38 Fauna

I Large Mammals

a. Habitats b. Niches c. Seasonal Migrations-Range d. Behavioral Units e. Disease/Pests

II Small Mammals

a. Habitats 1. Horizontal 2. Vertical b. Niches c. Seasonal Migrations d. Behavioral Units e. Diseases/Pests

III Reptiles/Amphibians/Fish

a. Habitats b. Niches c. Seasonal Migrations d. Behavioral Units e. Diseases/Pests

IV Birds/Insects

a. Habitats 1. Horizontal 2. Vertical b. Niches c. Seasonal Migrations d. Behavioral Units e. Diseases/Pests

V Unique Species

VI Endangered Species

VII Stability

VIII Assimilative Capacity 39 Process

I Plant Succession

II Animal Succession

III Eutrophication

IV Trophic Levels APPENDIX B

DATA COLLECTION/STANDARDIZATION FORMS

41 DATA TYPE: ELEVATION (FEET)

LEGEND: 1 5375 - 5475 2 5476 - 5575 3 5576 - 5675 4 5676 - 5775 5 5776 - 5875 6 5876 - 5975 7 5976 - 6075 8 6076 - 6175 9 6176 - 6275 10 6276 - 6375 11 6376 - 6475 12 6476 - 6575 13 6576 - 6675 14 6676 - 6775 15 6776 - 6875 16 6876 - 6975 17 6976 - 7075 18 7076 - 7175