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Interdisciplinary research and education for ecosystems management
0147-x001 79 0501-0043SO? 00 ,I
INTERDISCIPLINARY RESEARCH AND EDUCATION FOR ECOSYSTEMS MANAGEMENT* R. RAJAGOPAL The School
of Forestry
and Environmental Studies, NC 27706, U.S.A.
Duke University,
Durham.
Abstract-Contemporary problems in resource and environmental management often cut across disciplinary and other man-made boundaries. Recently several governmental. educational, and research institutions have come up with a variety of organizational plans to address environmental problems that are interdisciplinary in nature. Within the scope of many of these plans. systems analysis has been considered a potential framework in which varied disciplinary efforts can be integrated and unified. This paper discusses the scope and limitation of systems analysis as such an integrative tool. In particular, the roles of objectives, orchestration, synthesis, and implementation in the solution of resource and environmental problems are discussed in detail. Finally, an integrated curriculum for ecosystems management and a set of recommendations to improve the organizational efficiency and the design of interdisciplinary research efforts are also provided.
INTRODUCTION IN THE sixties and the early seventies a score of environmental problems emerged in the national scenes of technologically developed countries of the world. For example, the impact of residuals, both toxic and ndn-toxic, on the environment became a major issue for quite some time-reaching a peak in the late sixties. Recently several governmental institutions have expressed the need to better understand the impact of key transportation, energy, and other major public service facilities on the environment. Traditionally, institutions have dealt with resource and environmental programs along disciplinary lines of land, water, air, timber, wildlife, etc. Often such narrow single resource considerations have led to crisis situations and politically and/or economically expedient solutions have been sought and implemented. Having encountered such crisis situations, many units of government, educational institutions, research organizations, and even a few single or small group of individuals have come up with organizational plans to address multiple resource problems. These plans have included the creation of:
Disciplinary divisions within a department natural resources, environmental protection,
of the etc.
environment,
ecology,
A multi-disciplinary research team brought together by skilful administrative fiat to address various disciplinary components of an inter-disciplinary problem. Often the research results of these efforts are integrated by a common binder of the final report [l]. An interdisciplinary department formed trained in one or more disciplines [2].
by several
faculty
and
researchers
A research team consisting of a lone wolf with an extended multi-disciplinary training or a pack of 2-5 wolves who have perfected the art of organic interdisciplinary research through professional competence, personal friendship, mutual respect, and a high tolerance for differing views. Within the scope of many of these plans, systems analysis (mathematical modeling) has been considered as a framework to integrate and unify varied disciplinary efforts. In recent years multi-disciplinary research endeavors such as the Sea-Grant program, * An earlier version of this paper CA, May, 1977.
was presented
at the TlMSjORSA
43
Joint National
Meeting,
San Francisco,
R. RAJAGOPAL
14
the International Biological Program, and the Research Applied to National Needs (RANN of NSF) program, among others, have been undertaken through various federally-supported grants. Under these programs, many talented scientists from varied disciplines such as ecology, engineering, mathematics, chemistry, economics, statistics, computer science, geography, hydrology and forestry have been brought together in an attempt to understand and plan for the management of large scale human engineered ecosystems. A few teams headed by Mar [3]. Mitchell c’t ~1.[4], and Kramer rt ul. [S] have also been supported to evaluate multi-disciplinary research efforts (RANN. BIOME, IBP) and suggest possible ways to improve their effectiveness. Unfortunately, the diverse background of the participants with their specialized disciplinary training has led to serious obstacles in the development of unified approaches to the description, analysis and documentation of such research efforts. In particular, whenever analytical methodologies have formed the basis of such efforts. attempts to compare and interrelate the research findings of different individuals have met with a variety of problems 161. As O’Neill[7], Wolman [I] and Hare [S] have pointed out, limitations occurred not only due to the differences in analytical and information processing methodologies but also due to such factors as training, peer pressure, rewards and incentives, and jurisdictional and administrative constraints surrounding interdisciplinary research and educational efforts. Also due to several years of specialized training. it is perhaps difficult for disciplinarians to accommodate and understand broad based interdisciplinary goals and objectives.
SYSTEMS
ANALYSIS
FOR
PROBLEM
SOLVING
A system can be defined as a collection of interacting components with each component described by one or more interacting attributes or properties. One type of a system is an ecosystem. defined by Dale [9] as a special case in which at least one component is classed as living. In general. analysis of ecosystems can be viewed as relating to all of the following areas:
. . production transfer
and movement
of biomass
or flow of energy
bio-geo-chemical . . biogeographic matter in space. a valuation populations. . a combination
between
in nature.
components
cycles or flows in ecological patterns
and
(often economic)
distributions
process
leading
of ecological
systems.
systems.
of genetic
material
to monetary
and
living
flows in human
of some or all of the above.
Systems analytic studies of productivity followed by bio-energetic flows in ecosystems have dominated the attention of ecologists (especially those with biological training) over the last two decades. Ecosystems analysis can be further developed into a broad study and action area to include the socio-economic, political, and legal ramifications of the societal system within which we live. Also, in addition to the traditional reductionist approaches to scientific problem solving, another approach is needed. One needs to consider ecological problems in a macroscopic and holistic framework in order to understand systems as interacting components that form functional wholes [lo]. This paper will address interdisciplinary research in ecosystems analysis with a clear mission, implying the existence of a definite objective or orientation in solving problems [I 11. In particular, the scope and limitations of systems analysis as an integrative framework will be discussed in detail. Explorative and speculative research in an interdisciplinary context, however important, are not considered in this paper. Within the scope of a mission orientation, it is further assumed that ecosystems will be analyzed essentially
Interdisciplinary
research
45
and education
Problem identification, formulation of mcosurablc objectives (quantitative ond qualitative)
-
t Orchestration of o set of measurable environmental voriobles within an onolyttc framework (quantitotlve ond quolitotive)
,
t Implementation within existing or modified institutional arrangements
Fig. 1. Necessary
components
of a resource
and environmental
management
program
for purposes of management, prediction, and/or control, including occasional cases of non-intervention as in forest fires, insect defoliation, etc. In short, only those aspects of ecosystems analysis that provide direct support to natural resource and environmental management in a human context will be considered. Resource and environmental management in a general context can be viewed as management of people, material, and technology within a set of interrelating constraints (ecologic, economic, legal, social, etc). in order to meet certain predefined objectives. In general such a management process can be grouped into three major activities: . . . Problem
definition
. . . Generation models having cepts.
in terms of clearly
defined
and analysis of alternatives measurable input and output
. . * Selection and implementation range of alternatives as limited and legal constraints.
objectives
in terms variables
and constraints.
of a broad range of and synthesizing con-
of solutions developed within the above by socio-political, economic, institutional,
The above three activities are highly interrelated (Fig. 1). In order to define meaningful problems, one needs to know the availability and limitations of models and related measurement concepts as well as the feasibility of implementing the generated solutions.
ECOSYSTEM
COMPREHENSION
Objectives
Contemporary problems in resource and environmental management are perceived in different form and scope by different individuals. They are expressed as problems of production, allocation, technology, ecology, economics, ethics, information, institutions, law, or some combination of these factors. The actual perspective of a particular problem at hand depends heavily on the point of view held by the observer, a group of observers, the affected organization, or the agency managing the resource in question. This perspective eventually leads to the definition of objectives and criteria for the evaluation of management alternatives. For example, an objective within an ecological perspective, such as the preservation of specific flora or fauna, can be defined as a
R. RAJAGOPAI
Single Management less
Fig.
2. Components
transient,
outhortty ~__ of
a
small
adustrtol
are anal~red for the express purpose oh~ectlves.
scale system
of meeting
the overall
management
combination of several factors such as stability. diversity. complexity, productivity, net energy How. etc. Objectives defined in various forms of equity, such as income re-distribution and access to facilities. are common in the socio-political. economic and legal arenas. Finally. the objectives defined in terms of economic efficiency, such as the capital investments of major industrial and business organizations, frequently involve aspects of net monetary returns, benefit-cost analyses. and cost-effectiveness procedures. . Closely associated with the above definition of objectives is our ability to measure, synthesize, and evaluate alternatives in order to choose and recommend implementable solutions. The fields of space exploration, initially and industrial production, subsequently, have pioneered new applications of management techniques for mission-oriented projects. Such undertakings are characterized by a clear set of management objective(s) in terms of space. time and money. Project components are disaggregated from the whole and analyzed for the express purpose of meeting the overall management goals and objectives (Fig. 3). Moreover, precise computational and quantitative tools are utilized for planning, scheduling, staging, developing, implementing, and evaluating the proposed actions. Many of these tools did not exist a generation ago. and few have been effectively incorporated into resource management planning. In the management of resources and the environment. especially those that are under the jurisdictions of multiplc public authorities, definitions of objectives are often conflicting and fuzzy. Components may be studied in sufficient detail within their own perspective (Fig. 3). but solutions that are generated for implementation often require subsequent clarification or adjustment for existing legal and institutional arrangements. We are also painfully made aware that modification of such arrangements through litigation, reorganization, public hearing, education. etc. is tedious, time consuming and slow. Therefore, in order to understand multi-authorities resource management problems, it is necessary that we define the problems in meaningful terms of measurable (quantitative and qualitative) objectives. Obviously, objectives can be better defined on the basis of our knowledge of synthesizing concepts and the nature of institutions that help facilitate implementation. Orchrstrutim
Given our predicament (or fortune) that symbolic (language. numbers and pictures) information dominates the decision making process, it is necessary that we understand various measurement concepts and associated synthetic and analytic techniques. Definition of measures, including the units of measurement, and the method of synthesis of such measurements are basic to alternative analysis in management. Because of precision, interpretation, and repeatability, certain quantifiable terms such as dry weight (kg). energy (kcal), BOD (ppm), DO (ppm), particulates (ppm), volume (m3), income
Interdisciplinary
research and education
Multiple
highly
Monagement transient,
47
authorities of a large scale aquatic resource system
interactions ,\
I ‘x,
Fig. 3. Components
_/’
are studied in their own perspective.
($), visitor days (No. of people-days) and the like are given more weight in the analysis than social, aesthetic and other intangibles. As Saint Exupery [ 121 reminds us: I have told you these details of asteroid B612 and I have given you its number because of grownups. Grownups love numbers. When you speak to them of a new friend, they never inquire about essentials. They never say to you: “What is the sound of his voice? What are his favorite games? Does he collect butterflies?” They ask you: “How old is he? How many brothers does he have? How much does he weigh? How much does his father make?” Only then do they think they know him. In the process of extracting information from an ecosystem and coping with the limitations of time, effort and money, several attributes are transformed into alphabetical, numerical or pictorial codes. However, such a coding scheme both creates and destroys information. In order to completely describe an ecosystem, infinite classifications are necessary, but all our coding schemes are finite. Thus, coded data loses most of the information not contained in the category into which it is placed. The emergence of the modern electronic computer has widened the frame of reference in which ecosystems can be analyzed with the following two approaches being the extremes : . . . Simple models of systems along with experience, judgment,
and intuition
are sufficient to obtain insight into ecosystem functions. . . . Since powerful information
storage and retrieval systems exist, as many attributes as possible should be coded. The larger the number of codes, the greater is the amount of information and hence better is the insight.
In addition to inherent problems of information coding, analytical approaches are also susceptible to various kinds of information processing errors. Errors due to interpretation, sampling, instrumentation, and truncation and roundoff in computers are especially common. There is an urgent need tb develop cost-effective measurement and experimental concepts for ecosystem analysis as a function of the number and location of sampling sites, frequency of sampling, precision and type of instrumentation, number of parameters, and modeling methodology. Such ideas have also been expressed under the topics of fidelity, complexity and error propagation in ecosystem models [9, 13, 141.
4x
R. RAJAGOPAI
The potential of computer models, display techniques and gaming simulations in environmental planning and resource management far exceeds those actually being used [S]. Incredibly sophisticated information storage, retrieval and display techniques already exist. But comparable efficiency is not achieved in resynthesizing, or adapting to real situations, the output of this stored information. A selected study of several RANN modeling efforts by Mar [3] indicates that multidisciplinary teams have difficulty in defining, orchestrating, validating, and documenting environmental modeling efforts. In particular, each new effort also fails to benefit from previous research experiences. Kramer et al. [S], in an effort to evaluate the U.S. IBP. report that one of the troublesome aspects of the program was the storage and retrieval of voluminous data for future users. Due to the diversity of background of the people involved, there is no specific framework for the development, description, and documentation of models. Also. the scope and nature of some of the models vary in the following important areas: Methodology: Variations in methodology tical to simple and descriptive.
range from rigorous
‘Time-Scale: Long-range planning or management short-range or time independent type. Resolution and Complexity: Models of parameters to be measured.
with
varying
models
and mathema-
versus
confidences
Linkages: Models which are closely linked in terms of inputs of other models and those that are studied independently.
and
those
of
number
or outputs
Due to the synergetic nature of ecosystems and the enormity of information content, it is almost impossible to know all the mechanisms operating at any one interval of time. Also, due to our continuous resource needs, frequent decisions have to be made affecting these systems. Therefore. it is necessary to study systems considering available information rather than to wait for more information which would be available at a future date. Also. every component modeling effort could have considerable merit in its own right: however. it is in their total interrelationship with one another that effective resource management principles can meet interdisciplinary research ob.jectives. Input-output analysis, mathematical programming, algebraic and probabilistic methods of systems analysis, econometric modeling, land-use analysis, delphi exercises, and system dynamics are some of the major synthesizing techniques that have been successfully applied to resolve resource and environmental management problems. In particular, Patten [ 151, Watt [ 161, Linstone and Turoff [17], Russell [IS]. and Ott [ 191 have brought together several applications of such synthesizing techniques in ecosystems analysis, environmental modeling. and governmental planning.
In order to stage successful interdisciplinary research efforts there is an urgent need to develop flexible institutional arrangements to facilitate and promote healthy interactions between participants on a research team. In academic institutions, perhaps it might also be beneficial to reward faculty for implementation of models and concepts in the real world in addition to the usual reward of recognition by the publication of scholarly writings in disciplinary journals. Some common reasons given by administrators. managers, and policy makers for non-implementation of research modeling efforts are: the models are too gross or general to adequately represent ena modeled and detect variations in alternative policies [20].
the phenom-
Interdisciplinary
. the models are too detailed alternative policies [20]. . . the models of the problem
do not include at hand.
research
and education
and complicated
socio-economic,
. . . the models do not include the technical, tations of the implementing agency.
to comprehend
political,
financial,
and evaluate
and
legal
and administrative
realities
limi-
In dealing with the development and mxnagement of natural resources and land, and the shaping of regulation and policy associated with their use, a variety of forces will come into play. Eventually it is the community itself, through existing or modified institutional framework, that will have to come to grips with the issues in resource and environmental management. Therefore understanding the functioning of a community in terms of its socio-political, legal, administrative, and planning institutions will be an asset in defining problems meaningfully for appropriate analysis. Hence. it is strongly advocated that managers, decision-makers, and planners from appropriate agencies be sought from the very beginning and made active participants in large scale interdisciplinary research efforts. Such a process will increase the chances of implementation of research findings. In certain specific cases, where there has been successful man-computer problem solving, Sackman [21] reports the existence of a compatible team with the division of labor between a mission-oriented leader who is confident cf this importance and a well trained computer specialist who prefers to work alone. Also, he relates problem success to senior project leaders who tend to obtain major problem insight alone, instead of as a member of a team.
AN
INTEGRATED
CURRICULUM
FOR
ECOSYSTEMS
MANAGEMENT
In order to partially overcome some of the above-mentioned problems, at least a modest effort needs to be launched wherein quantitative concepts (including modeling) arc couched within ecological or resource management contexts so as to be palatable, comprehendable, and retainable by students, scientists. and practitioners of resource and environmental management. Undergraduate students in the natural sciences take introductory courses in calculus and computer science during their freshman and/or sophomore years in college. In their junior or senior year, or in some cases the first year of graduate work, fields of major study become somewhat more crystallized. For example, resource (forestry, fisheries, hydrology, wildlife, land, etc.) ecology, management, planning, policy, and environmental studies are some of the areas in which natural science (as well as a few social science) students are increasingly declaring majors. However, the majority of such students do not retain the contents of their freshman year calculus and computer-related coursework. This lack of retention can be partially attributed to the general framework in which natural science (and social science) courses are taught. In particular, concepts in binary logic and drill exercises in differentiation and integration are always introduced and almost never tied to real-life scenarios. By the senior year or in the first year of graduate work, many of these students often must meet certain program requirements in biometry, calculus, computer science, quantitative ecology, resource management, etc. by taking a series of independent “methods” courses. But because of the inherent conceptual nature of analytical methodologies, along with the prevailing bias of students in the natural and social sciences that he or she cannot do mathematics, these quantitative program supplements often fail to fully relate to the applications of resource management. This problem is frequently heightened by the presence of life science faculty themselves, many of whom have not been exposed or have an aversion to analytical methodologies. In addition, the synthetic quantitative courses are offered primarily for mathematics, physics, computer science and engineering majors. Consequently, life I’S 4 I~ I>
50
R. RAJAGOPAI
science majors aspiring to become resource managers are faced with an almost insurmountable problem of integrating subject matter which has been packaged for other users. Currently computer based algorithms and procedures are extensively used in the analysis of several resource and environmental management problems. Such procedures are readily available to users through the libraries of computer centers and software vendors. In order to fully appreciate the existence of such computational capabilities and to realize their benefits, the resource manager. students and researchers must understand the assumptions and the methods behind the development and application of algorithms and procedures. Eficient time saving algorithms about in the field of operations research and systems analysis, especially in the areas of linear programming, network analysis, and simulation. Knowledge of these algorithms, coupled with the understanding of the information storage and retrieval capability of the computer, releases the student, the scientist. or the manager from the constraints of problem size and thus enables him to devote more time to problem formulation and consideration of underlying assumptions. It is not advocated here that all students, researchers, and managers learn and practice the mechanics of problem solving through the use of algorithms and procedures but they should spend at least sufficient time to understand the concepts behind such procedures. A common erroneous assumption is that a student or a manager does not have to understand algorithms or procedures but only to learn how to use computer outputs. This fallacy is clearly exposed by Ackoff [22] who states that the manager should actively participate in the design of the management information system that is supposed to serve him so that he can evaluate its performance against projections. Those managers who do not invest enough time in this activity will probably not use the system well and may even become a victim of its abuse. It is clear that within a university setting, teaching computational ideas, statistical concepts, design of experiments, calculus, quantitative ecology. mathematical modeling. etc. as discrete courseware has been of marginal value to most students. It is thercforc necessary to synthesize and synchronize the contents of such courses within the context of specific resource management and decision making problems. This can be done by selecting a number of current management problems in forestry-, energy-. water-, air-. and land-related resources and developing Management Information Systems that can be used as tools in the decision making process. Development of such information systems can be progressively accomplished with the help of concepts that are already being taught in introductory and intermediate biometry, calculus. life science and computational courses. There may be some loss in rigor from current discipIinary course offerings, but on the other hand, we are reasonably sure to gain substantially in the areas of relevance, retention, timeliness, and exposure of our students to interdisciplinary real world contexts. In fact it might also be feasible and advantageous [23] to train a disciplinarian in another discipline, for example, an ecologist in engineering and vice versa. It is neither suggested that one should duplicate excellent text materials that already exist in biometry, calculus, or computer science for life science or resource management majors, nor compile a set of Ph.D. dissertations or research reports that are beyond the grasp of many seniors, beginning graduate students and professionals. We should clearly synthesize and synchronize the contents of introductory and intermediate quantitative science concepts within a set of contemporary resource management problems. In recent years, universities and public and private agencies have all voiced a need for continuing education for their employees. The need is particularly critical for those concerned with natural resources. Foresters, wildlife managers, hydrologists, land LISL’ planners and a host of other professionals are finding it increasingly difficult to cope with the day-to-day management problems with which they are faced. Updating the skills once learned and the acquisition of new skills in an integrated holistic context are cited widely [24] as some of the needs of these practicing professionals. If continuing
Interdisciplinary
research
and education
51
education of the practicing professionals is one of the great challenges of higher education today, as is commonly believed, then it is imperative to develop,innovative experimental courseware and to explore alternative delivery patterns to reach as many of these professionals as possible. OBSERVATIONS
AND
RECOMMENDATIONS
Ecosystems analysis in an interdisciplinary framework is a new and an emerging field of study. Several researchers have reported substantial gain and understanding of the structure and function of ecosystems through various multi-disciplinary research efforts. The following observations and recommendations are provided with a hope of increasing the benefits from such understandings in the planning and management of natural and environmental resources: . . In addition to studying the dynamics of biomass, energy, bio-geo-chemicals, genetic matter, and populations, ecosystem studies should expand to include the flow of money (quality of life, welfare, etc.) in human populations with reference to socio-political, legal, and cultural constraints. . . Success of systems analytic studies in ecology for the current state of the art depends on our ability to define measurable and comparable objectives. It is urgent that we develop measurement or valuation concepts and qualitative modes of analysis to include intangibles in the evaluation of management alternatives. A potential area for further research is the study of problems with multiple objectives measured under different frames of references (goal programming, [25-81). systems often depends on the . . . Success or failure of human engineered efficiency of pre-planning activities. Fund granting agencies should encourage or even support pre-planning activities in the realm of proposal writing. A well thought out and pre-planned proposal would have more chances of completion and implementation (use) than one hastily put together to meet deadline requirements. (number of . . Fidelity in ecosystem research design in terms of complexity parameters to measure), error propagation, and information content with reference to a clearly defined objective is one of the areas of research that need major consideration. Lack of understanding in this area often results in consultation with a statistician at the end of a data collection endeavor. . . Currently it is a common practice to develop component models first and eventually give consideration to system synchronization and synthesis. Such a practice often leads to different chapters in a text book or a research report without continuity or linkage. System and linkage considerations should play a dominant role throughout mission oriented research efforts. . . There is need to understand and perceive processes that go on within the minds of individual disciplinarians with reference to multi-disciplinary research goals and objectives. Further research is necessary to verify the hypothesis that problem insight is an individual activity whereas successful implementation of programs is a group activity. . Motivational structure of individual scientists, organizational structure for team activity, and institutional arrangements for incentives, rewards, and recognition need substantial consideration for successful implementation of multi-disciplinary research efforts. . . Planners and managers from appropriate resource and environmental agencies should be made a part of large scale multi-disciplinary research efforts for successful implementation.
R. RAJAGOPAL
. For a variety of reasons, largely related to sequencing, disciplinary content. and relevance of college courseware, the environmental and resource management students, scientists, and practitioners under-utilize analytical decision making tools. It is necessary to design, develop, test, and evaluate courseware that will synthesize and synchronize a wide range of mensurational, computational. and analytical tools in a resource management framework. It is then necessary to disseminate such material to advanced undergraduate and first year graduate students, college teachers, scientists, and practicing professionals in resource and environmental management. Such interdisciplinary courseware development and dissemination will magnify the understanding of analytical tools and promote their acceptance and subsequent use in resource and environmental problem solving. REFERENCES research in the Univcrsitj setting. E~7r.i~ I. Mar. B. W.. Newell. W. T. and Saxberg. B. 0. interdisciplinary SC,;. Terilno/. IO, 650-653 (1976). Erlucrr~ion: A Conriming E.upwinwm. Scirwc. 198, XOG-804 (I Y77). 2. Wolman. M. Ci. Intrrdisciplinury resources and environmental rlmulation model\ 3. Mar. B. W. Problems cncounterrd in multidiscipltnary development. J. Erwir. .hlqmt. 2, 83. 100 (1974). 4 Mitchell. Rodger tat ui. An evaluation of three hiome programs. Scirrwc: 192. 859-865 (1976). 5. Kramer, Paul YI rri. An evaluation of the int~rnation~ll biological program. Committee to evaluate the ISP. National Academv of Sciences. National Technical Information Service. PB 253 15X (1976). for non-point pollution. Prw. of’ C’IPS- -1C21 6. Rajagopal. R. Informaiion processing and communication Pm$c Reyioikd S!‘atp. pp. 45-57 (1974). Mode/iu/. of large-scale environmental modeling projects. In Erdoyicrrl 1. O’Neill. R. V. Management Clifford S. Russell (Ed.) Resources for the Future. Washington, DC (1975). b. Hare. F. K. How should we treat environment? Scic,,~cc. 167, 352- 355 (19701. 9. Dale, M. B. Systems analysis and ecology. Ecoolog,r. 51, 2 -16 (19701. I (L Odum. E. P. The emergence of ecology as a new integrative dis~ipI~ne. Scicncr. 195. 178% I X3 (19771. wirh u Mirsim. American Society of Agronomy. Special Puhiication I I. Nelson. W. L. Preface to Resrarth Number 14. (1969). to Gcwrul .S~sfotr.s Thirkirly. Wiley-Interscicnce. New York (I Y7.5). I? Weinberg. G. M. A/t Inrroducrio/l 13. O’Neill. R. V. Error analysis of ecological models. In /?oc. o/ rllr 7Iird iVuriow/ Synrp. on Rur’iowo/o~/~~. D. .I. Nelson (Ed.), pp. 89%907 (1971). 14. Slnhodkin. I_. B. Comments from a Biologist to a Mathematiczan. In Ecnsrstc,nt A~rrr/~.*iz trnci I’~~~~~icri~~rr. S A. Lcvin [Ed.). SIAM Institute for Mathematics and Society (lY75). Press. N Y. ctrtti Sil?iz~~~j~i~)}l irr Ecr&q~.. GoIs I. 1. 3 and 4. Academic 15. Pattrn. H. C. S~.~te+ns .4n~i1+ 11971. ‘77. ‘75. ‘76). I h. Watt. K. E. F. S~.str/ns A,~ulJxis i,t E~j/aqy. Academic Press. New York (I 966). I 7. Linstone. H. A. and Turoff, M. T/w Delphi M~~thotl. Addtson-Wesley. MA (1975). Resources for the Future, Washington. DC ( 1975). IX Russell, C S. (Ed.). Ecoloqic~ul Motldiny. hftddiry md Sirnuhtiort. EPA 600;9-76-01 h I’). Ott. W. R. (Ed.), Pro<,. offhe EPA C~orfcvwzw m Etwrrtmnunr~rl ( 1976). 20. Grecnherger, M., Crenson. M. A. and Crissey. 3. L. M&r/r irt ihr Pdict. Procc~ss. Russell Sage Foundation. New York. (1976). Sackman. H. Stages of real-world problem solving with and without computers. Pvrst,. I$ CIPS-rfCZ,f Ptrc~if~c Raqiwtrl Swp. pp. 345-358 (19741. I&off, R. L. Management misinformation systems. Mqmt Sci. (Application Series), 14, B147 -156 (1967). Werner. P. .4. and Goodman, E. II. New academic training for ecologists and engineers. rlIB.S Erluccrrrrrr7 K~,r.int-. 3, Dec. (1974). Lynn. W. R. Engineering and society programs in engineering cducatmn. Scicnct: 195, 1% IS5 (19771. Bell. E. F. Mathematical programming in forestry. .I. For. 75, 317 319 (1977). Schuler. A. T.. Webster. ii. H. and Meadows, J, C. C&xi/ P~~~~~~~~~I??z~~~~~ irl F<>re,>i ‘~~~~z~r~~~,??~~ff~. .1. For. 75, 33% 324 (1977). Dane. C. W.. Meador. N. C. and White. J. B. C;rx!/ Prrqpwnmim/ in I,mr/ L’se Pltrminy, .I. For. 75. 32s 329 (19771. 51cuer. R. E. and Schuher. A. T. An interactive multiple-objectice linear programming approach to a problem in forest management. Opc*rtrtiott.s Rex 26, 254769 (19781.
INTERDISCIPLINARY RESEARCH AND EDUCATION FOR ECOSYSTEMS MANAGEMENT* R. RAJAGOPAL The School
of Forestry
and Environmental Studies, NC 27706, U.S.A.
Duke University,
Durham.
Abstract-Contemporary problems in resource and environmental management often cut across disciplinary and other man-made boundaries. Recently several governmental. educational, and research institutions have come up with a variety of organizational plans to address environmental problems that are interdisciplinary in nature. Within the scope of many of these plans. systems analysis has been considered a potential framework in which varied disciplinary efforts can be integrated and unified. This paper discusses the scope and limitation of systems analysis as such an integrative tool. In particular, the roles of objectives, orchestration, synthesis, and implementation in the solution of resource and environmental problems are discussed in detail. Finally, an integrated curriculum for ecosystems management and a set of recommendations to improve the organizational efficiency and the design of interdisciplinary research efforts are also provided.
INTRODUCTION IN THE sixties and the early seventies a score of environmental problems emerged in the national scenes of technologically developed countries of the world. For example, the impact of residuals, both toxic and ndn-toxic, on the environment became a major issue for quite some time-reaching a peak in the late sixties. Recently several governmental institutions have expressed the need to better understand the impact of key transportation, energy, and other major public service facilities on the environment. Traditionally, institutions have dealt with resource and environmental programs along disciplinary lines of land, water, air, timber, wildlife, etc. Often such narrow single resource considerations have led to crisis situations and politically and/or economically expedient solutions have been sought and implemented. Having encountered such crisis situations, many units of government, educational institutions, research organizations, and even a few single or small group of individuals have come up with organizational plans to address multiple resource problems. These plans have included the creation of:
Disciplinary divisions within a department natural resources, environmental protection,
of the etc.
environment,
ecology,
A multi-disciplinary research team brought together by skilful administrative fiat to address various disciplinary components of an inter-disciplinary problem. Often the research results of these efforts are integrated by a common binder of the final report [l]. An interdisciplinary department formed trained in one or more disciplines [2].
by several
faculty
and
researchers
A research team consisting of a lone wolf with an extended multi-disciplinary training or a pack of 2-5 wolves who have perfected the art of organic interdisciplinary research through professional competence, personal friendship, mutual respect, and a high tolerance for differing views. Within the scope of many of these plans, systems analysis (mathematical modeling) has been considered as a framework to integrate and unify varied disciplinary efforts. In recent years multi-disciplinary research endeavors such as the Sea-Grant program, * An earlier version of this paper CA, May, 1977.
was presented
at the TlMSjORSA
43
Joint National
Meeting,
San Francisco,
R. RAJAGOPAL
14
the International Biological Program, and the Research Applied to National Needs (RANN of NSF) program, among others, have been undertaken through various federally-supported grants. Under these programs, many talented scientists from varied disciplines such as ecology, engineering, mathematics, chemistry, economics, statistics, computer science, geography, hydrology and forestry have been brought together in an attempt to understand and plan for the management of large scale human engineered ecosystems. A few teams headed by Mar [3]. Mitchell c’t ~1.[4], and Kramer rt ul. [S] have also been supported to evaluate multi-disciplinary research efforts (RANN. BIOME, IBP) and suggest possible ways to improve their effectiveness. Unfortunately, the diverse background of the participants with their specialized disciplinary training has led to serious obstacles in the development of unified approaches to the description, analysis and documentation of such research efforts. In particular, whenever analytical methodologies have formed the basis of such efforts. attempts to compare and interrelate the research findings of different individuals have met with a variety of problems 161. As O’Neill[7], Wolman [I] and Hare [S] have pointed out, limitations occurred not only due to the differences in analytical and information processing methodologies but also due to such factors as training, peer pressure, rewards and incentives, and jurisdictional and administrative constraints surrounding interdisciplinary research and educational efforts. Also due to several years of specialized training. it is perhaps difficult for disciplinarians to accommodate and understand broad based interdisciplinary goals and objectives.
SYSTEMS
ANALYSIS
FOR
PROBLEM
SOLVING
A system can be defined as a collection of interacting components with each component described by one or more interacting attributes or properties. One type of a system is an ecosystem. defined by Dale [9] as a special case in which at least one component is classed as living. In general. analysis of ecosystems can be viewed as relating to all of the following areas:
. . production transfer
and movement
of biomass
or flow of energy
bio-geo-chemical . . biogeographic matter in space. a valuation populations. . a combination
between
in nature.
components
cycles or flows in ecological patterns
and
(often economic)
distributions
process
leading
of ecological
systems.
systems.
of genetic
material
to monetary
and
living
flows in human
of some or all of the above.
Systems analytic studies of productivity followed by bio-energetic flows in ecosystems have dominated the attention of ecologists (especially those with biological training) over the last two decades. Ecosystems analysis can be further developed into a broad study and action area to include the socio-economic, political, and legal ramifications of the societal system within which we live. Also, in addition to the traditional reductionist approaches to scientific problem solving, another approach is needed. One needs to consider ecological problems in a macroscopic and holistic framework in order to understand systems as interacting components that form functional wholes [lo]. This paper will address interdisciplinary research in ecosystems analysis with a clear mission, implying the existence of a definite objective or orientation in solving problems [I 11. In particular, the scope and limitations of systems analysis as an integrative framework will be discussed in detail. Explorative and speculative research in an interdisciplinary context, however important, are not considered in this paper. Within the scope of a mission orientation, it is further assumed that ecosystems will be analyzed essentially
Interdisciplinary
research
45
and education
Problem identification, formulation of mcosurablc objectives (quantitative ond qualitative)
-
t Orchestration of o set of measurable environmental voriobles within an onolyttc framework (quantitotlve ond quolitotive)
,
t Implementation within existing or modified institutional arrangements
Fig. 1. Necessary
components
of a resource
and environmental
management
program
for purposes of management, prediction, and/or control, including occasional cases of non-intervention as in forest fires, insect defoliation, etc. In short, only those aspects of ecosystems analysis that provide direct support to natural resource and environmental management in a human context will be considered. Resource and environmental management in a general context can be viewed as management of people, material, and technology within a set of interrelating constraints (ecologic, economic, legal, social, etc). in order to meet certain predefined objectives. In general such a management process can be grouped into three major activities: . . . Problem
definition
. . . Generation models having cepts.
in terms of clearly
defined
and analysis of alternatives measurable input and output
. . * Selection and implementation range of alternatives as limited and legal constraints.
objectives
in terms variables
and constraints.
of a broad range of and synthesizing con-
of solutions developed within the above by socio-political, economic, institutional,
The above three activities are highly interrelated (Fig. 1). In order to define meaningful problems, one needs to know the availability and limitations of models and related measurement concepts as well as the feasibility of implementing the generated solutions.
ECOSYSTEM
COMPREHENSION
Objectives
Contemporary problems in resource and environmental management are perceived in different form and scope by different individuals. They are expressed as problems of production, allocation, technology, ecology, economics, ethics, information, institutions, law, or some combination of these factors. The actual perspective of a particular problem at hand depends heavily on the point of view held by the observer, a group of observers, the affected organization, or the agency managing the resource in question. This perspective eventually leads to the definition of objectives and criteria for the evaluation of management alternatives. For example, an objective within an ecological perspective, such as the preservation of specific flora or fauna, can be defined as a
R. RAJAGOPAI
Single Management less
Fig.
2. Components
transient,
outhortty ~__ of
a
small
adustrtol
are anal~red for the express purpose oh~ectlves.
scale system
of meeting
the overall
management
combination of several factors such as stability. diversity. complexity, productivity, net energy How. etc. Objectives defined in various forms of equity, such as income re-distribution and access to facilities. are common in the socio-political. economic and legal arenas. Finally. the objectives defined in terms of economic efficiency, such as the capital investments of major industrial and business organizations, frequently involve aspects of net monetary returns, benefit-cost analyses. and cost-effectiveness procedures. . Closely associated with the above definition of objectives is our ability to measure, synthesize, and evaluate alternatives in order to choose and recommend implementable solutions. The fields of space exploration, initially and industrial production, subsequently, have pioneered new applications of management techniques for mission-oriented projects. Such undertakings are characterized by a clear set of management objective(s) in terms of space. time and money. Project components are disaggregated from the whole and analyzed for the express purpose of meeting the overall management goals and objectives (Fig. 3). Moreover, precise computational and quantitative tools are utilized for planning, scheduling, staging, developing, implementing, and evaluating the proposed actions. Many of these tools did not exist a generation ago. and few have been effectively incorporated into resource management planning. In the management of resources and the environment. especially those that are under the jurisdictions of multiplc public authorities, definitions of objectives are often conflicting and fuzzy. Components may be studied in sufficient detail within their own perspective (Fig. 3). but solutions that are generated for implementation often require subsequent clarification or adjustment for existing legal and institutional arrangements. We are also painfully made aware that modification of such arrangements through litigation, reorganization, public hearing, education. etc. is tedious, time consuming and slow. Therefore, in order to understand multi-authorities resource management problems, it is necessary that we define the problems in meaningful terms of measurable (quantitative and qualitative) objectives. Obviously, objectives can be better defined on the basis of our knowledge of synthesizing concepts and the nature of institutions that help facilitate implementation. Orchrstrutim
Given our predicament (or fortune) that symbolic (language. numbers and pictures) information dominates the decision making process, it is necessary that we understand various measurement concepts and associated synthetic and analytic techniques. Definition of measures, including the units of measurement, and the method of synthesis of such measurements are basic to alternative analysis in management. Because of precision, interpretation, and repeatability, certain quantifiable terms such as dry weight (kg). energy (kcal), BOD (ppm), DO (ppm), particulates (ppm), volume (m3), income
Interdisciplinary
research and education
Multiple
highly
Monagement transient,
47
authorities of a large scale aquatic resource system
interactions ,\
I ‘x,
Fig. 3. Components
_/’
are studied in their own perspective.
($), visitor days (No. of people-days) and the like are given more weight in the analysis than social, aesthetic and other intangibles. As Saint Exupery [ 121 reminds us: I have told you these details of asteroid B612 and I have given you its number because of grownups. Grownups love numbers. When you speak to them of a new friend, they never inquire about essentials. They never say to you: “What is the sound of his voice? What are his favorite games? Does he collect butterflies?” They ask you: “How old is he? How many brothers does he have? How much does he weigh? How much does his father make?” Only then do they think they know him. In the process of extracting information from an ecosystem and coping with the limitations of time, effort and money, several attributes are transformed into alphabetical, numerical or pictorial codes. However, such a coding scheme both creates and destroys information. In order to completely describe an ecosystem, infinite classifications are necessary, but all our coding schemes are finite. Thus, coded data loses most of the information not contained in the category into which it is placed. The emergence of the modern electronic computer has widened the frame of reference in which ecosystems can be analyzed with the following two approaches being the extremes : . . . Simple models of systems along with experience, judgment,
and intuition
are sufficient to obtain insight into ecosystem functions. . . . Since powerful information
storage and retrieval systems exist, as many attributes as possible should be coded. The larger the number of codes, the greater is the amount of information and hence better is the insight.
In addition to inherent problems of information coding, analytical approaches are also susceptible to various kinds of information processing errors. Errors due to interpretation, sampling, instrumentation, and truncation and roundoff in computers are especially common. There is an urgent need tb develop cost-effective measurement and experimental concepts for ecosystem analysis as a function of the number and location of sampling sites, frequency of sampling, precision and type of instrumentation, number of parameters, and modeling methodology. Such ideas have also been expressed under the topics of fidelity, complexity and error propagation in ecosystem models [9, 13, 141.
4x
R. RAJAGOPAI
The potential of computer models, display techniques and gaming simulations in environmental planning and resource management far exceeds those actually being used [S]. Incredibly sophisticated information storage, retrieval and display techniques already exist. But comparable efficiency is not achieved in resynthesizing, or adapting to real situations, the output of this stored information. A selected study of several RANN modeling efforts by Mar [3] indicates that multidisciplinary teams have difficulty in defining, orchestrating, validating, and documenting environmental modeling efforts. In particular, each new effort also fails to benefit from previous research experiences. Kramer et al. [S], in an effort to evaluate the U.S. IBP. report that one of the troublesome aspects of the program was the storage and retrieval of voluminous data for future users. Due to the diversity of background of the people involved, there is no specific framework for the development, description, and documentation of models. Also. the scope and nature of some of the models vary in the following important areas: Methodology: Variations in methodology tical to simple and descriptive.
range from rigorous
‘Time-Scale: Long-range planning or management short-range or time independent type. Resolution and Complexity: Models of parameters to be measured.
with
varying
models
and mathema-
versus
confidences
Linkages: Models which are closely linked in terms of inputs of other models and those that are studied independently.
and
those
of
number
or outputs
Due to the synergetic nature of ecosystems and the enormity of information content, it is almost impossible to know all the mechanisms operating at any one interval of time. Also, due to our continuous resource needs, frequent decisions have to be made affecting these systems. Therefore. it is necessary to study systems considering available information rather than to wait for more information which would be available at a future date. Also. every component modeling effort could have considerable merit in its own right: however. it is in their total interrelationship with one another that effective resource management principles can meet interdisciplinary research ob.jectives. Input-output analysis, mathematical programming, algebraic and probabilistic methods of systems analysis, econometric modeling, land-use analysis, delphi exercises, and system dynamics are some of the major synthesizing techniques that have been successfully applied to resolve resource and environmental management problems. In particular, Patten [ 151, Watt [ 161, Linstone and Turoff [17], Russell [IS]. and Ott [ 191 have brought together several applications of such synthesizing techniques in ecosystems analysis, environmental modeling. and governmental planning.
In order to stage successful interdisciplinary research efforts there is an urgent need to develop flexible institutional arrangements to facilitate and promote healthy interactions between participants on a research team. In academic institutions, perhaps it might also be beneficial to reward faculty for implementation of models and concepts in the real world in addition to the usual reward of recognition by the publication of scholarly writings in disciplinary journals. Some common reasons given by administrators. managers, and policy makers for non-implementation of research modeling efforts are: the models are too gross or general to adequately represent ena modeled and detect variations in alternative policies [20].
the phenom-
Interdisciplinary
. the models are too detailed alternative policies [20]. . . the models of the problem
do not include at hand.
research
and education
and complicated
socio-economic,
. . . the models do not include the technical, tations of the implementing agency.
to comprehend
political,
financial,
and evaluate
and
legal
and administrative
realities
limi-
In dealing with the development and mxnagement of natural resources and land, and the shaping of regulation and policy associated with their use, a variety of forces will come into play. Eventually it is the community itself, through existing or modified institutional framework, that will have to come to grips with the issues in resource and environmental management. Therefore understanding the functioning of a community in terms of its socio-political, legal, administrative, and planning institutions will be an asset in defining problems meaningfully for appropriate analysis. Hence. it is strongly advocated that managers, decision-makers, and planners from appropriate agencies be sought from the very beginning and made active participants in large scale interdisciplinary research efforts. Such a process will increase the chances of implementation of research findings. In certain specific cases, where there has been successful man-computer problem solving, Sackman [21] reports the existence of a compatible team with the division of labor between a mission-oriented leader who is confident cf this importance and a well trained computer specialist who prefers to work alone. Also, he relates problem success to senior project leaders who tend to obtain major problem insight alone, instead of as a member of a team.
AN
INTEGRATED
CURRICULUM
FOR
ECOSYSTEMS
MANAGEMENT
In order to partially overcome some of the above-mentioned problems, at least a modest effort needs to be launched wherein quantitative concepts (including modeling) arc couched within ecological or resource management contexts so as to be palatable, comprehendable, and retainable by students, scientists. and practitioners of resource and environmental management. Undergraduate students in the natural sciences take introductory courses in calculus and computer science during their freshman and/or sophomore years in college. In their junior or senior year, or in some cases the first year of graduate work, fields of major study become somewhat more crystallized. For example, resource (forestry, fisheries, hydrology, wildlife, land, etc.) ecology, management, planning, policy, and environmental studies are some of the areas in which natural science (as well as a few social science) students are increasingly declaring majors. However, the majority of such students do not retain the contents of their freshman year calculus and computer-related coursework. This lack of retention can be partially attributed to the general framework in which natural science (and social science) courses are taught. In particular, concepts in binary logic and drill exercises in differentiation and integration are always introduced and almost never tied to real-life scenarios. By the senior year or in the first year of graduate work, many of these students often must meet certain program requirements in biometry, calculus, computer science, quantitative ecology, resource management, etc. by taking a series of independent “methods” courses. But because of the inherent conceptual nature of analytical methodologies, along with the prevailing bias of students in the natural and social sciences that he or she cannot do mathematics, these quantitative program supplements often fail to fully relate to the applications of resource management. This problem is frequently heightened by the presence of life science faculty themselves, many of whom have not been exposed or have an aversion to analytical methodologies. In addition, the synthetic quantitative courses are offered primarily for mathematics, physics, computer science and engineering majors. Consequently, life I’S 4 I~ I>
50
R. RAJAGOPAI
science majors aspiring to become resource managers are faced with an almost insurmountable problem of integrating subject matter which has been packaged for other users. Currently computer based algorithms and procedures are extensively used in the analysis of several resource and environmental management problems. Such procedures are readily available to users through the libraries of computer centers and software vendors. In order to fully appreciate the existence of such computational capabilities and to realize their benefits, the resource manager. students and researchers must understand the assumptions and the methods behind the development and application of algorithms and procedures. Eficient time saving algorithms about in the field of operations research and systems analysis, especially in the areas of linear programming, network analysis, and simulation. Knowledge of these algorithms, coupled with the understanding of the information storage and retrieval capability of the computer, releases the student, the scientist. or the manager from the constraints of problem size and thus enables him to devote more time to problem formulation and consideration of underlying assumptions. It is not advocated here that all students, researchers, and managers learn and practice the mechanics of problem solving through the use of algorithms and procedures but they should spend at least sufficient time to understand the concepts behind such procedures. A common erroneous assumption is that a student or a manager does not have to understand algorithms or procedures but only to learn how to use computer outputs. This fallacy is clearly exposed by Ackoff [22] who states that the manager should actively participate in the design of the management information system that is supposed to serve him so that he can evaluate its performance against projections. Those managers who do not invest enough time in this activity will probably not use the system well and may even become a victim of its abuse. It is clear that within a university setting, teaching computational ideas, statistical concepts, design of experiments, calculus, quantitative ecology. mathematical modeling. etc. as discrete courseware has been of marginal value to most students. It is thercforc necessary to synthesize and synchronize the contents of such courses within the context of specific resource management and decision making problems. This can be done by selecting a number of current management problems in forestry-, energy-. water-, air-. and land-related resources and developing Management Information Systems that can be used as tools in the decision making process. Development of such information systems can be progressively accomplished with the help of concepts that are already being taught in introductory and intermediate biometry, calculus. life science and computational courses. There may be some loss in rigor from current discipIinary course offerings, but on the other hand, we are reasonably sure to gain substantially in the areas of relevance, retention, timeliness, and exposure of our students to interdisciplinary real world contexts. In fact it might also be feasible and advantageous [23] to train a disciplinarian in another discipline, for example, an ecologist in engineering and vice versa. It is neither suggested that one should duplicate excellent text materials that already exist in biometry, calculus, or computer science for life science or resource management majors, nor compile a set of Ph.D. dissertations or research reports that are beyond the grasp of many seniors, beginning graduate students and professionals. We should clearly synthesize and synchronize the contents of introductory and intermediate quantitative science concepts within a set of contemporary resource management problems. In recent years, universities and public and private agencies have all voiced a need for continuing education for their employees. The need is particularly critical for those concerned with natural resources. Foresters, wildlife managers, hydrologists, land LISL’ planners and a host of other professionals are finding it increasingly difficult to cope with the day-to-day management problems with which they are faced. Updating the skills once learned and the acquisition of new skills in an integrated holistic context are cited widely [24] as some of the needs of these practicing professionals. If continuing
Interdisciplinary
research
and education
51
education of the practicing professionals is one of the great challenges of higher education today, as is commonly believed, then it is imperative to develop,innovative experimental courseware and to explore alternative delivery patterns to reach as many of these professionals as possible. OBSERVATIONS
AND
RECOMMENDATIONS
Ecosystems analysis in an interdisciplinary framework is a new and an emerging field of study. Several researchers have reported substantial gain and understanding of the structure and function of ecosystems through various multi-disciplinary research efforts. The following observations and recommendations are provided with a hope of increasing the benefits from such understandings in the planning and management of natural and environmental resources: . . In addition to studying the dynamics of biomass, energy, bio-geo-chemicals, genetic matter, and populations, ecosystem studies should expand to include the flow of money (quality of life, welfare, etc.) in human populations with reference to socio-political, legal, and cultural constraints. . . Success of systems analytic studies in ecology for the current state of the art depends on our ability to define measurable and comparable objectives. It is urgent that we develop measurement or valuation concepts and qualitative modes of analysis to include intangibles in the evaluation of management alternatives. A potential area for further research is the study of problems with multiple objectives measured under different frames of references (goal programming, [25-81). systems often depends on the . . . Success or failure of human engineered efficiency of pre-planning activities. Fund granting agencies should encourage or even support pre-planning activities in the realm of proposal writing. A well thought out and pre-planned proposal would have more chances of completion and implementation (use) than one hastily put together to meet deadline requirements. (number of . . Fidelity in ecosystem research design in terms of complexity parameters to measure), error propagation, and information content with reference to a clearly defined objective is one of the areas of research that need major consideration. Lack of understanding in this area often results in consultation with a statistician at the end of a data collection endeavor. . . Currently it is a common practice to develop component models first and eventually give consideration to system synchronization and synthesis. Such a practice often leads to different chapters in a text book or a research report without continuity or linkage. System and linkage considerations should play a dominant role throughout mission oriented research efforts. . . There is need to understand and perceive processes that go on within the minds of individual disciplinarians with reference to multi-disciplinary research goals and objectives. Further research is necessary to verify the hypothesis that problem insight is an individual activity whereas successful implementation of programs is a group activity. . Motivational structure of individual scientists, organizational structure for team activity, and institutional arrangements for incentives, rewards, and recognition need substantial consideration for successful implementation of multi-disciplinary research efforts. . . Planners and managers from appropriate resource and environmental agencies should be made a part of large scale multi-disciplinary research efforts for successful implementation.
R. RAJAGOPAL
. For a variety of reasons, largely related to sequencing, disciplinary content. and relevance of college courseware, the environmental and resource management students, scientists, and practitioners under-utilize analytical decision making tools. It is necessary to design, develop, test, and evaluate courseware that will synthesize and synchronize a wide range of mensurational, computational. and analytical tools in a resource management framework. It is then necessary to disseminate such material to advanced undergraduate and first year graduate students, college teachers, scientists, and practicing professionals in resource and environmental management. Such interdisciplinary courseware development and dissemination will magnify the understanding of analytical tools and promote their acceptance and subsequent use in resource and environmental problem solving. REFERENCES research in the Univcrsitj setting. E~7r.i~ I. Mar. B. W.. Newell. W. T. and Saxberg. B. 0. interdisciplinary SC,;. Terilno/. IO, 650-653 (1976). Erlucrr~ion: A Conriming E.upwinwm. Scirwc. 198, XOG-804 (I Y77). 2. Wolman. M. Ci. Intrrdisciplinury resources and environmental rlmulation model\ 3. Mar. B. W. Problems cncounterrd in multidiscipltnary development. J. Erwir. .hlqmt. 2, 83. 100 (1974). 4 Mitchell. Rodger tat ui. An evaluation of three hiome programs. Scirrwc: 192. 859-865 (1976). 5. Kramer, Paul YI rri. An evaluation of the int~rnation~ll biological program. Committee to evaluate the ISP. National Academv of Sciences. National Technical Information Service. PB 253 15X (1976). for non-point pollution. Prw. of’ C’IPS- -1C21 6. Rajagopal. R. Informaiion processing and communication Pm$c Reyioikd S!‘atp. pp. 45-57 (1974). Mode/iu/. of large-scale environmental modeling projects. In Erdoyicrrl 1. O’Neill. R. V. Management Clifford S. Russell (Ed.) Resources for the Future. Washington, DC (1975). b. Hare. F. K. How should we treat environment? Scic,,~cc. 167, 352- 355 (19701. 9. Dale, M. B. Systems analysis and ecology. Ecoolog,r. 51, 2 -16 (19701. I (L Odum. E. P. The emergence of ecology as a new integrative dis~ipI~ne. Scicncr. 195. 178% I X3 (19771. wirh u Mirsim. American Society of Agronomy. Special Puhiication I I. Nelson. W. L. Preface to Resrarth Number 14. (1969). to Gcwrul .S~sfotr.s Thirkirly. Wiley-Interscicnce. New York (I Y7.5). I? Weinberg. G. M. A/t Inrroducrio/l 13. O’Neill. R. V. Error analysis of ecological models. In /?oc. o/ rllr 7Iird iVuriow/ Synrp. on Rur’iowo/o~/~~. D. .I. Nelson (Ed.), pp. 89%907 (1971). 14. Slnhodkin. I_. B. Comments from a Biologist to a Mathematiczan. In Ecnsrstc,nt A~rrr/~.*iz trnci I’~~~~~icri~~rr. S A. Lcvin [Ed.). SIAM Institute for Mathematics and Society (lY75). Press. N Y. ctrtti Sil?iz~~~j~i~)}l irr Ecr&q~.. GoIs I. 1. 3 and 4. Academic 15. Pattrn. H. C. S~.~te+ns .4n~i1+ 11971. ‘77. ‘75. ‘76). I h. Watt. K. E. F. S~.str/ns A,~ulJxis i,t E~j/aqy. Academic Press. New York (I 966). I 7. Linstone. H. A. and Turoff, M. T/w Delphi M~~thotl. Addtson-Wesley. MA (1975). Resources for the Future, Washington. DC ( 1975). IX Russell, C S. (Ed.). Ecoloqic~ul Motldiny. hftddiry md Sirnuhtiort. EPA 600;9-76-01 h I’). Ott. W. R. (Ed.), Pro<,. offhe EPA C~orfcvwzw m Etwrrtmnunr~rl ( 1976). 20. Grecnherger, M., Crenson. M. A. and Crissey. 3. L. M&r/r irt ihr Pdict. Procc~ss. Russell Sage Foundation. New York. (1976). Sackman. H. Stages of real-world problem solving with and without computers. Pvrst,. I$ CIPS-rfCZ,f Ptrc~if~c Raqiwtrl Swp. pp. 345-358 (19741. I&off, R. L. Management misinformation systems. Mqmt Sci. (Application Series), 14, B147 -156 (1967). Werner. P. .4. and Goodman, E. II. New academic training for ecologists and engineers. rlIB.S Erluccrrrrrr7 K~,r.int-. 3, Dec. (1974). Lynn. W. R. Engineering and society programs in engineering cducatmn. Scicnct: 195, 1% IS5 (19771. Bell. E. F. Mathematical programming in forestry. .I. For. 75, 317 319 (1977). Schuler. A. T.. Webster. ii. H. and Meadows, J, C. C&xi/ P~~~~~~~~~I??z~~~~~ irl F<>re,>i ‘~~~~z~r~~~,??~~ff~. .1. For. 75, 33% 324 (1977). Dane. C. W.. Meador. N. C. and White. J. B. C;rx!/ Prrqpwnmim/ in I,mr/ L’se Pltrminy, .I. For. 75. 32s 329 (19771. 51cuer. R. E. and Schuher. A. T. An interactive multiple-objectice linear programming approach to a problem in forest management. Opc*rtrtiott.s Rex 26, 254769 (19781.