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"Without a destination, any road will get you there" is a quaint saying, one with logical difficulties, but one with great potential meaning. Without the objectives for a forest faunal system, a manager is helplessly free. Any action is as good as any other action...or none. I remember well the boring Sunday auto rides with my parents. We were just "riding", not going anywhere. I have seen such riding around in wildlife management for over 40 years. Perhaps objectives have not been needed. As long as the wanderer is healthy and happy, there is nothing particularly wrong with wandering, but it is not appropriate for me personally, for many stockholders or taxpayers, or for many corporations, foundations, or agencies. See also "Cross Currents - Meanders vs. Objectives".
Perhaps in the past the objectives (or whatever passed for them) were sufficient. Now things are different: needs are greater, knowledge of how to formulate them has increased, resources for achieving them have become more scarce, procedures for operating on them have been developed, and the awareness that design begins with them is now present.
Animals, plants, or areas do not have objectives, only humans do. (If they did, what would they be? Some claim the cryptic, fundamental rule of biology: start, get energy, store energy, and reproduce.) They have them personally and for their families and communities. In most cases, neither people nor corporations have well-identified objectives. They seem to have them, but they readily change and can rarely be put to any of the simplest tests of evidence, such as "show me." There is no reason why objectives need to be shown; some one decision-maker, under pressure to somehow be convincing that objectives exist, would probably say "follow me and watch." A skilled analyst could probably discern objectives from observing that person's behavior, and if they were consistent over time, could probably write them down for communicating them to others. Observers are few, and costly. It makes sense (at least it is cost-effective) for a person to write down his or her objectives as they exist at some time. These may then be examined and used, for example, to compare them with performance, or with the perceptions of others. Objectives have to be written sooner or later; without the words there is no evidence, nothing concrete.
Sources of objectives may be:
All of these means are limited by incomplete observations, difficulties of analyses, and inconsistencies observed such as between statements made and observed behavior. Nevertheless, estimates can be made and studied. Because systems repeatedly demonstrate equifinality or different pathways to the same condition, the estimates, even poor ones, may be sufficient. We may not need to spend any more time attempting to improve them. On the other hand, some estimates may need to be made very precisely. We need to identify these and to work specifically on them.
The needs for clearly-stated objectives have been voiced for years throughout the managerial and resource economics literature (USFS 1981, Duerr et al. 1982, Giles and Lee 1982, Giles and Hamill 1977), health (U.S. Dept. HEW 1977), and education (Maxwell and Tovatt 1970). The same concern also appears in the literature of wildlife management (Giles 1971, 1978), fisheries (Hampton and Lackey 1975, Fisheries Division 1984), wildlife law enforcement (Beattie et al. 1977, Ritter 1975) and wildlife education (Giles 1978, 1981). Wetterberg (1974) observed that the lack of specific objectives is the fundamental problem in South American national park management. Hamed (1988) has extensively analyzed objectives and their use in landscape planning.
The need for statements of objectives still exists. There are few examples of objectives which are in active use. If objectives are not used, it is reasonable for observers to conclude that objectives-setting is a wasteful activity. It may be more logical to conclude that they were so poorly formulated that they could not be used. Some managers prefer to have no objectives or only the most vague ones because that condition allows them great flexibility. Without them there are few standards by which they may be judged inadequate. Of course, agencies and individuals that are not objectives-oriented or that are unwilling to consider operating with stated objectives will find no set of objectives useful.
I suggest that the needs for objectives are real but that past expressions may have been flawed. It is difficult to judge, precisely, arrival at an ambiguous destination. A list of criteria by which the expression of objectives may be judged is given in Table 4.1 (revised from Giles 1981:126). (See CAP14.) The problem of expression has roots in taxonomic clarity. Careful analysis may reveal where past efforts at setting objectives have failed, thus
| Table 4.1. A guide for evaluating the wording and structure of objectives |
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Rather than starting to design with: "what is the problem?" as suggested in so much of the planning literature, the key questionis: "what are the objectives?" Knowledge of problems is the perception of gaps between where an individual or agency presently is and where it desires to be. The greater distance between the situation and the objective, the greater the problem. If objectives are not known or somehow seen, then gaps cannot be perceived well or precisely. In some cases, stating objectives may make apparent "problems" go away. In all cases, objectives must be stated sooner than later, even if a problem-analysis-first approach is used. It is difficult work.
The difficulty in formulating objectives is inversely related to the firmness of the manager's or agency's concept of the public's or corporate leadership's sense of who they are and what will be expected of them in the future. Nodding agreement that objectives should be defined is far easier to obtain than the expression of the objectives themselves. Difficulty for faunal managers will continue as long as they unduly involve themselves with questions of how to manage the animal resource without equal or greater involvement with why they should manage. Managers' interests easily and naturally turn from the importance of problems to the possibilities of their solution. Temporarily resisting this turn, concentrating on desired results and conditions can allow a set of functional objectives to be developed.
When faunal system management is practiced by a public agency in a society in which the interests of all citizens are to be considered and benefits optimized, or by an expert serving a private client, benefits as well as value-systems must be objectively articulated. It is the responsibility of the manager to use existing techniques (e.g., mail questionnaires, computer-terminals, voting machines for interpreting desired benefits and conditions of systems). It is also the managers' responsibility to assist the public in articulating its wishes and to help the agency be responsive to these wishes, even though they may not parallel those of the manager. (Of course laws, a manifestation of objectives, continue to be obeyed. Preventing species extinction is a typical law.) Potential wildlife resource benefits and value systems become increasingly clearer in the process of conceiving, discussing, and eventually writing objectives.
Seeking objectives of clients, the public, or agencies can open new opportunities, involve the creative talents of the staff, and release the inventive power usually abundant in faunal system managers. It can ensure that things are done for the right reasons and that success can be measured because criteria (i.e., the objectives) are available and stated openly, quantitatively, and early. Objectives and their encompassing system can be viewed as the rationale for action.
Needs for Objectives
For some people, the need to state and write objectives is self-evident. Other people are very reluctant to develop and use them. After decades, some people, corporations, even counties and states, do not have or cannot show evidence for having objectives. A pragmatic attitude is a good reason for this. This attitude is that if something works (implying the organization, agency, or project without written objectives), then it is sufficient. How do you know it works? What is "it"? Works well as compared to what? Works the best possible? Over the long run? Has no side effects? These are questions that others wish the pragmatists would answer. In their attempts to do so, they usually revert at least to criteria such as "no-one got hurt" (implicit is a safety objective).
There are other reasons why objectives have not been written. These include:
Any one of the above is a good reason why written objectives now rarely exist. All 10 reasons suggest that there are almost insurmountable odds against preparing them. The situation as it exists in faunal agencies or in corporations and communities throughout the land is understandable. A proper assessment of the odds against setting objectives is fundamental to success in this as well as other activities. The need, hopefully to be met in this chapter, is to be convincing that, even though difficult, it makes sense to develop written objectives. To do so requires (a) stating likely uses and limits on use, (b) providing aids, (c) providing computational power, and (d) demonstrating potential use.
For the skeptic, giving only one need for stating objectives is likely to be insufficient. A set may be convincing - at least enough to "give it a try." Few reasons are as convincing as the tale told me by a natural resource agency field man. He confessed that he had hated writing objectives as he was required to do by his agency. He did the work, nevertheless. A year later he went to a meeting anticipating a three-day debate on a very tough problem. When there, he got out and used his list of objectives; the meeting was over by noon!
The reasons why objectives should be stated, the needs for them, include:
Objectives are at the heart of strong public participation. Public participation at its best is not on particular crisis issues, but in (1) articulating community or organization objectives, (2) expressing weights and importance, (3) suggesting alternative strategies for achieving objectives, and (4) suggesting risks and consequences of various proposed acts. The faunal resource manager's job, at least the job of those working public agencies, is to capture the above and to provide advice, aids, computations, and designs to aid the decision-maker in selecting among alternatives to achieve citizens' objectives. Often their job is to explain trade-offs that were required and why some objectives were not, or could not be achieved. Computers can also be used to assist in these functions. (See CAP24.)
At all levels of agricultural and land use planning and natural resource management, there is ample evidence that equally competent decision makers disagree on the correctness of significant decisions. These conflicts are becoming more numerous and crucial to agencies and corporations because of the money and risks involved, the people affected, and the length of time that land and resources are affected. There are many reasons for the differences and disagreements, namely:
Perhaps the disagreement is also due to the differences among the objectives of the decision makers. It cannot be assumed that the above differences would be insignificant among equally competent decision makers. Beyond these five reasons, objectives make the difference. By understanding objectives better, the conflicts can be understood, then improved statements of them may reduce conflicts. Appropriate actions to achieve the clarified objectives may then be planned and taken.
Every group with whom I have worked has had a majority of members who desire to discuss projects, programs, or actions - not objectives. They have insisted on discussing "needs" or "goals." (A project to build 2 miles of road is called a goal in some groups.) The contentions made in this chapter are that only after objectives are specified is it appropriate to discuss roads. How can it be known that a road (in a wildland system) is needed in the first place? Is it, over all other things, most needed? How did it get to top priority? To concentrating on roads and "things" is to suppress potential creativity about transportation. Maybe a road is not needed, only transportation of supplies to a point once a year. If so, then maybe a heliport will be cost effective, or a foot trail for occasional visits, or air drops of supplies. Perhaps a road would not be cost effective if it was to be built to achieve only one objective, but roads can meet other objectives of fire control, recreation, health care delivery, and communication as well as those of transportation. Only when the objectives are listed can all of these payoffs be seen, the full trade-offs made, the creative capacities of the group exploited, and cost effectiveness assured and achieved.
Definitions
Readers may have real differences in their understanding of and definitions of goals and objectives. There is a set of words used synonymously in different fields, sometimes within the same field. These include: mission, aims, role, target, purposes, goals, objectives, standards, yardsticks, needs, wants, priorities, policy, criteria, guidelines, constraints, and probably some others. The set can be very confusing. One agency used "priority" synonymously with a high-order objective. Priority means the sequential position of an item in a ranked set. I have analyzed these concepts and found them to be readily classified into types of objectives (CAP93) (Table 4.2). This will be described in a later section of this chapter. The analysis is believed to be relatively new and clarifies a maze of concepts. Importantly, it can reduce the hours spent in wildlife resource committees debating the meaning of goals and objectives.
| Table 4.2. Seven types of objectives may be readily remembered by using four methods. | ||||
| Types of Objectives | 1- Nominal | 2- Functional | 3- Architect Analogy | 4- Travel Analogy |
| 1 | General | What the system is | Decision to build a house | Decision to take a trip |
| 2 | Fundamental | The fundamental benefit categories | Spatial and quality needs of the client | Stating the purposes, e.g., recreation and healt (fresh air) |
| 3 | Success Criteria | Fundamental algorithm for decision; the success criterion | Maximum energy efficiency over 20 years for the lowest cost; benefit/cost ratio | Checking; most safe, shortest, and lowest cost trip? Arrival? |
| 4 | Constraints | Policies, limitations on how things will be done | Below a budget of X; handicap access; no green paint | Within speed limits; frequent stops; only after a tune-up |
| 5 | Primary | The key descriptions of what a system should be or do | Specific needs of the client; arrangement of rooms and utilities | To go to Q, to maximize new land seen, to return by Rt. 202; to picnic |
| 6 | Futuristic | Design criteria for the future | Ease of making future additions; flexibility; ease of maintenance | Responsiveness to new roads, mass transit, and communication advances |
| 7 | Actions | Means to achieve higher-type objectives | Paint room B an ivory color | Take Route 3 and 6 from A to B, then... |
A wildlife agency or resource manager will probably find it useful to develop all types of objectives, at least an understanding of the differences. The development itself is part of the operation of an objectives-formation subsystem.
Though disagreements based on prior use may still persist, the reader is encouraged to accept the terminology used here, mentally editing wherever needed. Committees often spend more time discussing differences between "a goal" and "an objective" than they do about substantive issues of what these should be, whatever they are called. The definition is not a trivial issue and needs to be addressed, clarified, and some conventions accepted such as: "For the duration of this project we shall call nothing a goal and speak only of types of objectives."
Working with many people, the task of deciding on the meaning of goals or objectives is difficult and thus definitions are needed. These definitions need to be discussed, but some point needs to be reached at which a word creates a particular image or idea, no matter how blurred around the edges. Goals are objectives; they may be used synonymously in different fields. There are no satisfactory roots for the words; dictionaries provide mixed messages; usage is by agreed definition and varies widely among and within professional groups and fields. The following words and definitions provide the general form, structure, and function of some ideas related to objectives. By precisely and consistently using these words, the discussions can proceed, as they must, rapidly and creatively.
An objective is a stated desired output, outcome, end or end state, or continuing condition of a system. It is that to which a person or group directs its efforts or that to which it aims or reaches. Most humans have many objectives, thus a set.
Criteria for Writing Objectives
In Table 4.1 are the criteria by which objectives should be judged. How shall we know when an objective is well stated and fully developed? Not a play on words, in Table 4.1 are the objectives for objectives. There are few examples of well-stated objectives published, and fewer in use. The use is directly related to the precision with which they are written. The use is also related to the agenda of the user and the context within which he or she operates. Perfectly written objectives, however, will not guarantee success because they can be ignored, poorly used, or selectively used in negotiated situations.
The Source of Objectives
The typical forest faunal system manager, responsible for a public area in the U.S., is usually following a democratic policy, symbolized in phases like "one-person, one-vote" and "maximum public participation." It is likely that only those people who reach the formal operations stage of cognitive development, perhaps no more than 30 percent (Elkind 1974), can deal with such concepts. These statements are, roughly, concepts of "the possible" in contrast to "the here-and-now." Hard to believe, it is impossible for some people to deal actively with concepts of the future, with images of desired and possible states. This is a limitation for those in the concrete operations stage of cognitive development. The wildlife manager may play a significant role in assisting the public (or client, if not "the general public") in articulating objectives and then quantifying them. This may be his or her most important function because after this, the entire system, if working properly with feedback fully operational, will have been shaped and specified.
One of the more controversial aspects of objective setting is the answer to: who does it? At least who should quantify them is at issue. "The wildlife manager knows what is good for wildlife" is one extreme. "The public knows what it wants" is another. Most faunal system managers enter the profession with a desire to work with wildlife, care for it, and protect it. It is not unnatural that a hostile reaction is elicited by a call for "let the public decide." The issue needs further analysis for it is not as strained as it may seem at first. First, we assume that under the law (and general agreement) that no native species is to be allowed to become extinct or even extirpated from an area. [I hope never to hear this disputed.] Then we assume we agree on a democratic policy...at least a representative form of such a system. [Without this we need only argue that the leader or those in power decide. There is no discussion, for perhaps the leader will not allow that either.] For reasons of technical details, editorial quality, and precision of statements the wildlifer may assist the public in stating its objectives. After free dialog, the public ultimately agrees that each written statement properly expresses (is a close match with) their objective. The wildlife resource manager participates like others, one person, one expression. The manager, in addition to an editorial role, assists in gaining public or representative quantification of each objective. These include expressions of amounts desired and the importance of each objective, but the wildlifer, as other citizens, gets one and only one input to the system that he or she has devised. There may be bias in the system (a frequent criticism of questionnaires and referenda) and this is regrettable; it may be unavoidable; it may be purged by using feedback over time.
Then comes into play the conventional and well-agreed-upon functions of the faunal system manager. How are the objectives achieved? The wildlife manager selects and acts to achieve the objectives! No one tells the good plumber what wrench to use, the good carpenter what tool, the good doctor what pill to prescribe. They do work to achieve objectives: stop the leak!, frame the door!, make me well! One paper seems to refute this argument (Moss et al. 1986). The authors implied that people should be allowed to select the techniques in managing fauna other than game. Where there are no risks, no cost differentials, and no responsibility, then anyone can make any decision they want. Society is just "riding around!" The argument tightens when objectives are voiced about the techniques. Some techniques are objectionable it seems. Rephrased: They fail to achieve objectives! Perhaps the public has not articulated fully its objectives. Perhaps the manager has not elicited from them all of the constraints (they may feel confident in stating what they do not want...until they understand the costs). "Hidden objectives" are much discussed in the literature of objective setting. Perhaps the wildlifer has not been skillful in uncovering and expressing hidden objectives. Examples of activities opposed by the public where hidden objectives have been discovered are:
Hidden objectives may result from lacking perception, failing to articulate difficult topics, and sensing the infeasibility of certain expressions due to fear of likely political consequences. Articulated, they may be secretly used to assess their influence and to help understand apparent irrationalities and "political realities" of some wildlife agency work (Giles and Lee 1982).
The manager must seek objectives...all of them, well stated. Then he or she must have the opportunity (as well as the responsibility) to decide among many alternatives for achieving them. Unless decisions can be made, then faunal system management cannot be done ... and certainly not optimally.
This stressful issue of the proper role of the manager needs to be put aside, but it has one more part needing attention because of its persistence, not its importance. The part is the general objective of the neophyte wildlife manager who wants to "do good for wildlife." May the sentiment never pass! The functional irrationality of the statement must be recognized quickly and the phrase put away. The chief reason is that good for wildlife can never be measured or evaluated. Feeding the ducks feels good to the feeder until there are too many ducks, until some ducks cannot get enough of limited food, until a pond is polluted by ducks and the fish die. What is very good for some ducks is very bad for others, deadly for fish, another form of wildlife. Wildlife cannot tell the manager when good is being done. Wildlife must be viewed as a resource if it is to be managed, if any actions are to be taken. Thus, good is in the eyes of the beholder, of a person. Good is an expression of net tolerable benefits from a resource. Unifying this concept of "the good" for many people is a classic problem. Goodness is a human concept without origin in animals or their environments.
Types of Objectives
There are seven types of objectives (CAP93):
Awareness of the types can reduce confusion in the lexicon of objectives, goals, etc. and can hasten effective use of objectives. Granger (1964) and Rahmatian (1985) discussed a slightly related hierarchy of objectives. Modern agencies can and should use the types suggested. If objectives are actively and properly created, the activity can avoid most of the ad hoc and piecemeal goals and policies that form in conflicts over difficult faunal management decisions. In parallel health-agency work on objectives, the observation was made (U.S. Dept. HEW 1977:2):
Of course, not all will agree with and support the achievement of any statement of goals and standards. This is inevitable and desirable, in a pluralistic society. Rather, such guidelines will be valuable if they focus attention and interest on certain critical issues and influence individuals and institutions throughout the country as well as in the National government to consider how best to deal with them in light of their own circumstances and views. Thereby, the guidelines can help enrich health deliberations, decisions, and developments in many ways.
The types of objectives presented here are global but of course the individual units or phrases under each are unique to each decision-making group. It is necessary, regrettably, based on my experience in objective setting, to specify that there is no connotation in the word "objective" of a position along any objectivity-subjectivity continuum. The observation seems trivial but it is important to emphasize that the source of objectives is the human mind. Objectives are not discovered but are decided. More scientific work is needed on the physiological, psychological, and sociological states and other factors that influence such statements, the ways they are stated, and how they are interrelated. By one concept, objectives are very subjective. The written statement, its source, structure, related variables, and dynamics is a topic worthy of much objective study.
The seven types of the objectives, again, are:
These will be discussed, examples given, and a means given by which they may be used.
Type 1 - General
These are the broad, brief, general statements about the identity of an agency or program. They use statements of ultimate being, and are largely grand in scope, brief, motivational, and usually more poetic than practical. They many contain phrases such as in a preamble to a constitution or from law that establishes an agency. For example: "To provide opportunities for personal fulfillment in an amenable environment." Their greatest use is for political purposes and in general policy sessions. They are of limited operational value. An objective "to preserve and manage the wildlife of the region" would surprise no one. It could be argued that as long as two creatures are left of any species the species has been preserved. To "manage" is directionless (increase?, decrease?) for it specifies no limits, time, basis for judging the quality, or for whom.
Often called a mission, role, or goal, those statements are posturing and often serve to stake out organizational turf. An example is: "to preserve and wisely use all of the game resources of the Snake Creek Forest." This Type 1 objective captures the essence (or seeks to do so) of the lower type objectives. Type 1 objectives usually meet few of the criteria in Table 4.1. The types of objectives refine other types and become very functional at Type 5.
Type 2 - Fundamental
Textbooks for years have asked and answered "Why manage wildlife?" None has said: "To achieve objectives!" There are 11 fundamental objectives. (See Giles (1978), CAP94, and the list below.)
The fundamental objectives are rarely articulated directly but can be identified within the General objectives (Type 1). Most people have one dominant fundamental Type 2 objective but some may have many. These fundamental objectives may be considered as "dimensions" and each person may be located, probably uniquely, in space. (A space with more than three dimensions is called an hypervolume. There are 11 dimensions of the Fundamental or Type 2 objectives which are as follows:
1. Metaphysical: These are the private, non-discussable, and often-called spiritual or religious feelings, insights, or benefits which people may receive from the resource. A strategy for achieving this objective for people is very difficult to devise because it may require the manager to have the same metaphysical state or awareness as the people for whom he or she works. One possible strategy is to provide diverse settings and opportunities for interactions with wildlife at important (metaphysically significant) times and places for such people. Heightened awareness (e.g., education) may also enhance those interactions when they do occur.
2. Character: A forest is not the same forest without a bobcat or a wolf. Wildlife provide part of the wild character, a vitality, to forests.
3. Esthetic: An evening grosbeak on a snow-covered hemlock bough, a pileated woodpecker on a fire-scarred tree stump -- these are the beautiful things. Wildlife may create a "scene" or may be like Kierkegaard's touch of crimson that enhances or makes splendid an otherwise good view or picture. Not only visual stimuli but also odor and sound are wildlife's enhancements to the esthetics of the forest. The representation of wildlife in the arts, from cave paintings to television, attests to the importance that humans have attributed to its esthetic benefits.
4. Preservation: Knowledge that animals are being cared for and preserved is beneficial to many people. Quantity is unimportant; presence is the key. Richness (the number of species) must stay the same or, where species have been extirpated, increase since reintroduction may be possible.
5. Existence: Knowledge that a species exists, even though it may never be seen, is held by some as a great value. Not necessarily tied to the human action of preserving a species (as in 4 above), this objective can be achieved by information.
6. Recreation: Hunting and fishing are conspicuous wildlife-related recreational activities. Others are tracking, taking bird walks, making bird censuses, watching wildlife at feeders, reading about wildlife, and watching televised wildlife programs.
Further characterizing recreational benefits remains to be done. There are pre-hunt preparations such as conditioning and practicing with dogs and weapons, stalking, spending time with friends, escaping from the mundane every-day experiences of the world, making the kill, boasting, and later, the pleasures of recalling the hunting events.
Hunting and angling trips may have positive, neutral, and negative effects on the resource user. Crippling an animal or violating a law may result in net social or psychological loss. Net effects eventually need to be accounted by faunal system managers, perhaps in some expression of quality-weighted events or time-spent.
7. Physical Utility: Wildlife can provide meat, fur, hides, hair, bones and other products for direct use. It provides subsistence ("bushmeat" and medicines) for some native people.
8. Monetary: Monetary values have been assigned to wildlife. Monetary values (part of the study of finance) are only one aspect of "wildlife economics", the broad study of allocating limited resources among conflicting objectives and opportunities. There are many ways of assigning monetary values, each useful for some situations, some more generally useful that others. See Table 4.3.
| Table 4.3. Possible means by which monetary or related values may be assigned to fauna. Each has relevance only in a situation. |
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While the tendency to express the value of wildlife in general, qualitative, and non-monetary ways can (and should) be strongly criticized, so should insistence upon monetary expressions. Fair play! Money is an expression of a low value, not full value (e.g., an item must be worth at least this much but probably more in order for me, as a rational person, to buy it.) Money is no longer a free market medium because of subsidies, embargoes, etc., world wide. It changes in value over time. It differs in value depending on the quantity possessed (a dollar to a pauper is not a dollar to a prince), and its value is a function of time and place. (Try to buy a place on a life raft!) Wildlifers have too often been hooked by the demand: "express wildlife in monetary terms." They can, and need to do it with more sophistication. However it is not the only game in town. Wildlifers might request others to express their resource values in terms that the wildlife manager may require so that allocation decisions may be compared "in several coin."
Pearse (1986), commenting on the Canadian Wildlife Service's estimate that direct benefits enjoyed by Canadians from wildlife- related recreational activities in 1981 amounted to $0.8 billion, said:
Such statistics are sometimes intended to impress people with their size and hence importance. But $0.8 billion strikes me as remarkably small for the direct value attributable to resources which are so prevalent and widely appreciated in Canada... I want to suggest to you that the values we derive from our wildlife resources are too low, and they fall short of their potential value to us. ...We have imposed on ourselves a framework of wildlife policies that prevent the potential benefits from being realized.
9. Ecosystem: Wildlife is a part of natural systems. These systems rarely work naturally, i.e., perform according to previous patterns, when animals are removed or become very abundant. After so much has been written about the "balance of nature" and natural ecosystem functions, it remains very difficult to find examples of the disastrous predicted effects in such systems from animal losses. The losses of all forms of wildlife in northern Nigeria, for example, has had yet-to-be described effects. Nevertheless, "dire results" is not a prophesy to be openly challenged! On the other hand, the rational manager realizes the robustness of most ecosystems, the omnivory that is present, the competitors and equalizers ready to fill any vacant niche.
I once found a significant reduction in a forest chipmunk population (Tamias straitus) associated with a pesticide application (Giles 1970). I still ask "So what?", embarrassed by my ignorance of that forest and the role, over time, of the chipmunk population within it.
10. Genetic: Wildlife can provide the resources for genetic engineering, hybridization, and regaining lost traits in domestic animals. They may allow knowledge about genetic factors and forces to be gained not otherwise possible in laboratory studies.
11. Environmental Monitor: Racoons (Procyon lotor) may be studied to learn of pollutants along streams, woodland mice and shrews to learn of heavy metal buildups along roads. By observing wildlife behavior, physiology, or body characteristics, the quality of the environment may be assessed (Kirkpatrick 1980). Comparisons among the characteristics of animal and human populations may be useful (e.g., the effects of crowding).
Monitoring is not feedback. Only when a problem is seen and corrective action taken can the real benefits from the wildlife that has been changed be legitimately claimed.
Type 3 - Success Criteria
How does a person know when objectives are achieved? "Well, you just know!" is not a satisfactory answer. There must be some agreed criterion for success, some pattern for comparison and conclusion such as density, rate of increase, risk, benefit-to-cost ratio, estimated loss, or present-discounted net value.
Type 1 and 2 are usually too broadly stated for the success criteria to be identifiable. It is fascinating to observe that each of the Type 2 objectives may have a unique success criterion. Old arguments about how to mix forest and wildlife resource benefits, usually denied in the phrase "you can't mix apples and oranges", are herewith raised to a new level of debate. For some objectives for faunal systems, managers seek to reduce risks, for others to minimize costs, for others to maximize present net value. All may exist within the same system. How shall we determine the optimum mix, the proper trade-offs among these (Type 3) questions? We have begun to understand how to make the mix with other types. This question is not answered in this chapter. It is a field for useful work, perhaps with an expert system (CAP54).
In optimization or game theory (Chapter 17) a major question is one of how shall the objective function be formulated or how shall we tell, precisely, when we have won? The Type 3 objective is an expression of the way a win is formulated, not the details of winning. It is a decision about the fundamental procedure for measuring the output of the faunal system.
| Table 4.4 Representative Type-3 objectives. Usually only one or a very few are selected. |
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The literature is rich with examples of how the same problem solved with different criteria for winning can produce quite different results (e.g., some poker players play to win, others play to stay in the game but rarely if ever are big winners). Systems for managing a deer herd for (1) a fixed level of annual harvests or (2) a maximum number of user sightings per 1000 dollars invested, can be very different because of the selection of one of these Type 3 objective.
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| Figure 4.1. Three general words provide a complete range of options in faunal system work with objectives. Rates or variability in each may be adjusted (or attempted) by investments or managerial action. A denotes an objective of minimizing to a level , then stabilizing the system at that level. B suggests stability; C shows minimizing to a limit or constraint, then stabilizing. |
EV30,2 =< EV100,2
A high benefit(B)-to-cost(C) ratio (Q) is expressed as an objective in many wildlife management systems. It has been criticized (O'Riordan and Turner 1983:87-104) but when the major criticisms are addressed, namely:
When expected values (i.e., benefits adjusted based on their probability of being realized or 1.0 minus the risk level for failure) are included with estimating benefits and costs, an even better criterion is achieved.
Achieving a high rate of change (usually an increase) is often an objective. It is found in forest economics texts (Duerr et al., 1982). Wild animal population performance is often judged by this criterion. If not carefully related to density or other criteria, a rate change can lead to difficulties (like the irrelevancy of an auto driver slowing before a fatal crash).
Minimizing risk, the probability of significant failure or departure from a minimum standard, is a Type 3 objective. Unable to express costs, unable to quantify benefits (e.g., "...inestimably great"), some people elect to operate systems so as to minimize risks.
A specific density of animals, (N/A), estimated number (N) per estimated area (A), is often used as a success criterion. The objective may be a specified number or a condition greater or less than the number. Such a number is called, variously, the need, target, or standard.
Harvest or sightings, either total animals (N*) or number per unit area (N*/A) by hunting, fishing, trapping, or other techniques is also a success criterion.
Maximum sustained yield, much debated, is a Type 3 objective used with production, recreation, and other Type 2 objectives. In the U.S., sustained yield was somewhat clarified by the Multiple Use-Sustained Yield Act of 1960 (74 Stat.215; 16 U.S.C. 528-531; Public Law 86-517). Like "multiple-use objectives" it had stronger political grounds than scientific ones. It has been criticized in many ways (Smith 1968) including the meaning of "sustained." It has been defined as the maximum yield that can be taken on a sustained, presumably perpetual, basis. It arose in 1897 and meant then simply "a continuous supply of timber" (Schallau 1990). The problems lie in questions such as: As long as a species is not extinct, then is the species sustained? How much fluctuation is tolerable over what range of years to be satisfactory? Is stability at the maximum required? Perhaps trying to maximize an annual average would be useful? "Maximum" is also problematic. (The highest ever achieved? Over what period? The maximum average? The maximum annual median or modal value?) "Yield" is less troublesome because the criticism can be more sharp. (What are the units? Animals born, animals surviving, animals harvested, useable meat harvested, hunter or angler hours?) More difficult to address is the omission of costs or externalities from the concept. Is yield to be maintained regardless of cost? What is the proper investment of labor, capital, and technology to achieve any yield? What level? No criteria are provided for catastrophic losses (fire, insects, floods) and what are appropriate actions after such events to achieve the yields. Perhaps "yield" was intended to mean net yield, monetary returns minus all costs. At least for game, monetary returns are difficult to assign, even though costs of management and harvests can be approximated. It is not likely that monetary return was the early intent.
Smith (1968) observed that in forestry, and presumably elsewhere, sustained yield is "just a vague idea that confuses decision makers...Continuous production has advantages, and confused objectives often provide wide political and administrative flexibility..." He argued for its replacement with "maximizing the present worth of net benefits from forest lands" to which Overton and Hunt (1974) would add adjustments. (See Chapter 2 and CAP5016.) In forestry, it means "the management of a forest area in such a way that an equal, or near equal, volume of merchantable wood can be harvested annually, or periodically, in perpetuity" (Haley 1966). In the U.S. all forest benefits are now included (The Multiple Use Sustained-Yield Act of 1960) as well as the limitation "without impairment of the productivity of the land." Modern critics suggest sustained profit or a sustained production index (not wood, but rarely expressed alternatives) be the measure.
The concept of sustained yield for fauna can be laid aside, both as a concept and an objective (and a replacement sought), because:
Savidge and Ziesenis (1980) hardly acknowledged any difficulty in the concept. They defined sustained yield as "the numbers or biomass of animals that can be removed from a population over a long period of time while assuring persistence of the resource." They asserted that for small game, providing for maximal numbers to be harvested on a sustained yield basis "...will also maximize the recreational aspects of the harvest." it seems unlikely and the approach will not provide maximum trophy animals harvested.
Talbot (1976) criticized maximum sustained yield on the grounds that it does not take into account:
Optimum sustained yield is a phase replacing "maximum sustained yield" as an objective. It usually implies that yield (e.g., of harvested fish, but probably of profit from fish) be accounted at the economic margin. Sociological, economic, and ecological dimensions are needed in objectives. A formulation is needed to overcome the current limitations.
What is it that the faunal system manager needs to say, precisely, to help the public articulate this objective. It needs to be precisely stated so that progress (or failure) can be measured. Perhaps it should be the optimum sustained population of a species, the number which will achieve the maximum expected productivity of a population subject to retaining the role of the animal in the ecosystem, maintaining the animals in a healthy state, and not diminishing the habitat. Problems remain because benefits related to each population are not addressed.
"Optimization" means maximizing, minimizing, or stabilizing something subject to a set of constraints. The meaning is often harsh. It says "Make your best selection, but only if it is within the limits set." These might include:
An optimum sustained population may not, and is not likely to, be the condition that produces an optimum sustained yield. The shift away from "yield" to the productive population is noteworthy. Somehow a complex set of population criteria needs to be articulated. The problem is one of language, of conflicting or differentially weighted objectives, and of techniques of measurement. For example, population estimates are typically minimum expressions. Maximum productivity (and potential yield) does not occur when populations are at their maximum. Costs to maximize populations are very great. Desired harvests (yield) of forest game often cannot be achieved. At very high populations, disease is often very great. The criteria (rephrase "objectives") must be stated, weighted, and trade-offs made. Eberhardt (1977) observed that human influence
"...now pervades virtually all natural systems. The imbalances thus created [by hunting] may well be ecologically less dangerous under managed harvests, than if left to the inevitable levelling processes of nature."The criteria for natural forest faunal populations might include:
Eberhardt (1977:168) said, "At the moment, it does not seem that 'optimal' abundance of a species can be determined on an ecosystem basis alone." He needed more information about ecosystems, but I contend the optimum cannot be determined even with perfect knowledge of the ecosystem alone. Optimal is a concept of benefits, of values, demands, and tolerable risks, and substitutability. It is unfortunately a very large, interwoven, and complicated idea. It must not be surrendered to as if to some philosophical master, but it must be attacked with great determination and patience...but with some haste, for entire populations depend upon its proper definition and resolution in law and practice. There are alternatives to the expression as will be described later, but the phrase is now in the law. Sometimes progress, as in adopting a concept such as "sustained production," can consume all of the managerial energy available and cause a halt.
Type 4 - Constraints
Within Type 4 objectives are found the statements of constraints or limits beyond which inputs cannot or should not be gained. Type 4 objectives include the concept of "policy" interpreted as an expression of rule, law, or limitation.
Policy is a formal response to problems and needs as they arise. Thus current policy in any natural resource area is an attic of cumulative responses to problems that achieved enough notoriety to attract the attention of governments. They are usefully seen as constraints and are often formulated as: "we shall do X ... subject to A, B, and C."
Suppose a resource area manager wants (loosely speaking for now) to maximize game harvest, minimize soil erosion, maximize profits from wood sales, stabilize employment, and stabilize a visual quality index. A way is needed to formulate this so it can be solved. One algorithm or procedure for solving a problem accepts that only one resource can ever be maximized at one time. This may be phrased, for example maximize wildlife harvests ... and then the action starts...with the additional part of the phrase: subject to a set of constraints of money, time, enforcement staff, erosion limits, etc. The method usually adopts a readily quantifiable, primary objective (Type 5) that can, based on the experience and knowledge of the systems analyst or modeler, be readily measured, then constrained. The other "objectives" listed, such as for no soil erosion, can be translated into constraints. They sound like objectives, but so do all 7 types! That is why the taxonomy and analysis herein is needed. Here the unity, wholeness, and interactive nature of the systems approach is evident. It is not just a systematic, orderly, or sequential approach to problems. Here the process component of the systems approach itself as well as details of the inputs are seen to impinge upon the objectives component before it is finalized. The primary objectives, Type 5, are discussed later.
Objectives of Type 1 and 2 are subject to the achievement of Type 4 objectives. They are conditional upon them. These objectives include staff, information, finance, and space. There may be very long lists of such objectives. See Table 4.5.
| Table 4.5. Representative Type 4 constraint objectives |
Staff |
Each may contribute to several higher objectives. The list may change, for example, during annual planning. Failure to achieve one Type 4 objective (for whatever reason) does not change the Type 1 and 2 objectives and since several ways are usually used to gain an objective, exceeding one Type 4 objective (e.g., slightly over-spending a budget) usually does not represent complete failure to achieve a lower-type objective. The objectives are only partially hierarchical. "Staff", in my view, are stored information, experience, and idea sources. They are addressed in Type 4 objectives because all aspects of the forest faunal system are conditional upon their limitations or freeing energies.
Various writers use words like diversity (Dasmann 1972), sustainability (Prakash et al. 1986), equitable, economical, resilient, adaptive, flexible, just, evolutionary, and "responsive to future contingencies" as either objectives or things that objectives should achieve. These are worth noting and, to the limits of the manager's ability to understand their meaning and implications, should be incorporated into a set of objectives. These words imply constraints. They express that realistic Type 5 objectives should be achieved subject to or also meeting these standards or criteria. They are poorly worded Type 4 objectives.
Other examples of faunal agency and enterprise policies are:
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| Fig. 4.2. A system of linear constraints can define a region of available resources, feasibility, legality, practicality, and other similar ideas. The green area is a zone of "ok-ness." Anywhere within the constraints, within the green, is good. Selecting the very best point from within this shaded area (the maximum or minimum point becomes the task of the optimizer. Shown here are two dimensions; an n-dimensional volume, a hypervolume, with many planes or surfaces is more realistic for faunal resource situations. |
It is easy to confuse policies with other types of objectives. "Our objective is to be safe" or "Our objective is to be fiscally sound" hardly seem debatable. They are a part of the means to the end. They are the bounds within which other more specific objectives are achieved. Because different words such as "policies," "constraints," and "objectives" are used for the same concepts, I suggest for purposes of analysis, clarification, and "getting on with the business" that these be called Type 4 objectives.
Within the North American Wildlife Policy (Allen 1973), it was said that a policy, as used in that document, "...is a course of action recommended as a preferred means of serving the continuing public interest." This definition suggests Type 7 objectives (to be discussed) along with a Type 1 objective of the policy statement which was "...to preserve and improve the wildlife resource." The committee supported "...the traditional maxim of conservationists, that wildlife should contribute to the greatest good of the most people over the longest time."
Pearse (1986), discussing "Guidelines for Wildlife Policy in Canada," said they begin with three broad goals of (1) to maintain ecosystems; (2) to preserve the diversity of species; and (3) to ensure that the uses of wildlife are sustainable. "This," he said,
"suggests a holding operation. There is no parallel objective to enhance and improve wildlife resources, or to develop and increase the values we derive from them; only to maintain, preserve and sustain what we have. It certainly implies more modest ambitions than we have for fisheries and forest resources. And I want to suggest to you that it will not be enough."
An analyst can assist in unscrambling the mess of words common in committees assigned to formulate objectives. A one-hour training session on concepts and common language can save hours of debate later on the meanings of these words. Skillful faunal system managers can expedite these often torturously slow and exasperating meetings by providing preliminary sets of representative objectives.
Diversity or biodiversity is a popular objective. It is a constraint, Type 4, but it is analyzed in detail in the next section (and elsewhere under "Variety") where its practical use (and misuses) can be emphasized and made clear in relation to primary objectives, those called Type 5.
Type 5 - Primary
This type of objective expresses what a system should do. All of the others are general, conditional, structural, or methodological. Type 5, however, is at the center of the objectives formulation subsystem. When it comes to the hard decisions, the forest faunal system manager will be working with primary objectives.
Examples of these are shown in Tables 4.6 and 4.7.
| Table 4.6. Examples of Type 5 objectives. There is no one set of objectives for wildlife managers. Objectives need to be developed for each area and each population of resource users. The more specific, the better. The first list, A, shows increasing precision. Set B is representative. |
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| Table 4.7. A set of Type 5 potential deer herd or deer resource management objectives. It is likely that each will result in very different decisions made about actions and expenditures to achieve them. Rarely will more than 6 be selected. |
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All resource systems have multiple objectives. The human mind is too creative and human needs too diverse to allow only one or two objectives. Thus, Type 5 objectives almost always appear as a set. They may be in conflict with each other and many usually are. Setting them out in print and as a set can assist in making the conflicts clear, agreements evident, and may allow progress to be made in reducing adverse effects. Stating Type 5 objectives (writing them) does not require solutions, or resolutions, and only limited agreement (see Table 4.1). These are the lists of the criteria for the "perfect world" of the forest faunal resource manager and the client(s) whom he or she serves. There is no specific best number of Type 5 objectives. I suspect it will be greater than 10 for any thoughtful group; it will probably be close to 40. The preferred expressions are "to maximize...," "to minimize...," or "to stabilize (or retain)...". An equation or graph can be created to express how some parameter (e.g., erosion) may change over time or with certain investments (Fig. 4.3). The objective will be "to minimize erosion" which means to lower the position on the curve. Another objective may be "to maximize hunter access to the grouse population." This may be depicted as in the three figures below.
| Fig. 4.3. Hypothetical change in erosion as a result of various levels of investment and management action. In the lower figure (C) the optimum expenditure (along the x axis) is suggested at Z where erosion (-) and access (+) are about of equal importance. Clear objectives allow an optimum allocation of limited resources in conflicting situations to be made. |  |
|---|---|
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Starting from the present length of roads and the proportion of the area influenced (see CAP22 for clarification of the road zone of influence) the objective may be achieved by spending funds. The objectives may be in conflict because road construction and runoff thereafter is a major contributor to stream sediments. Roads may cut down on costs of erosion control. Thus the interaction and the need for solution as a set ... for these two typical objectives (suggested in Fig. 4.3c above ) ... and three dozen more.
Hendee (1974) described a "multiple satisfactions" approach to game management. In the terms of this chapter, these would be personal Type 5 objectives such as to experience companionship, to improve muscle tone and bodily fitness (exercise), to make new and familiar observations of nature (nature appreciation). With others, he later described "experience opportunity zones," accessible areas with different characteristics, that were to be analyzed in terms of how well each might achieve the objectives and how consistent hunters were in selecting areas that match with their before- or after-hunt objectives. The system suggested is that each expert can judge the probability that each cell of a map can achieve a set of importance-weighted objectives such as mentioned above. Similarly and in concert, each map unit has a distance from a local center, and objectives can be set to minimize road roughness and hazards, to maximize remoteness, to minimize contacts in the other hunter-groups, to maximize the probability of seeing game (prior harvest reports + preseason scouting), to maximize hiking or horse trail quality, and others. There are sufficient criteria to produce a very detailed map of an area, cell-by-cell, that provides a means for (a) evaluating areas for acquisition or tax purposes, (b) directing hunters based on their expressed objectives, (c) evaluating success (the match between areas used and expressed objectives, and reasons for any disparity), and (d) studying the dynamics of areas in their changing ability to meet the changing objectives of the hunter (and other user) population.
The Type 5 objectives are similar to contractual clauses. The wildlife resource system manager is not often in court defending his or her action, but the courtroom analogy may be a good one. The public may one day feel prepared to (or may at least already feel the need) to take the public faunal resource system manager to court. The grounds? Mismanagement or malfeasance. By what criteria? Failure to achieve the public contract rooted in the set of Type 5 objectives. Perhaps the analogy is carried too far. At least it has relevance in terms of professional performance, peer review, and personnel evaluation.
Objectives may be grouped in different ways. Some may say that they see them from a different perspective (e.g., populations, habitats, and people; micro and macro; long- and short-term; region specific; species-specific; game and non-game; preservation-protection-education-management; and others). The perspectives are needed and useful and can prevent oversights.
Hopefully the reader will not argue that objectives have to be too carefully worded! This would bring us full circle: How can we design an optimum system to achieve an unspecified or poorly specified system? How shall feedback operate if we have no criteria for judging effective performance or quality of decision-making?
A past problem with formulating objectives has been that once they were listed, little further was done with them. No wonder some people have been dissatisfied! The problems of the past have been in lack of clarity about the types, insufficient knowledge about the Type 5 objectives, and lack of computational aids for selecting actions to achieve objectives once they were developed.
A Suggestion
There are many ways that the objectives can be used, ranging from checking a list, to making assignments of staff to a task force, to obtaining solutions from complex integrated economic optimization models. Maximization has been suggested as one decision strategy under Type 3 objectives. A major forest faunal system objective is to maximize animal population benefits to people or B. B is a systems performance measure (Churchman 1968). It unifies Type 3, 4, and 5 and becomes the basis for a functional objective for almost all faunal systems.
Deciding on how to measure or express B is a problem. Decision makers consider many factors and integrate them in many ways, but I believe that in the final moments before a decision is made, there is usually one such measure used. There is some "go, do not go" or "act, do not act" decision based on a singular, highly-synthetic variable. There is a need to develop a pattern and aids to assist faunal system managers to think through the very large number of factors and compute correctly such a measure for faunal system decisions. Fully aware of many algorithms for optimization and aware of their many limitations such as non-linear functions, unequal risk, non-market measures of value, expressed but not actual demand, and a host of others, I believe a modified benefit-to-cost (Type 3) algorithm is useful in forest faunal systems work. The simple idea is maximize B for the fewest dollars possible. Where Q is benefits over costs ((B + 1)/(C + 1)), then we work to make Q very large. (A unit of 1 is added to both items of the equation to allow it to perform mathematically even if C has zero value.) There is empirical evidence that the relationship is used now by expert decision makers, that it can be communicated at an introductory level, that it has intuitive appeal, that it is now computable (not depending upon a future theoretical breakthrough), and that it can be improved. It appears to have fewer limitations than other approaches to optimum allocation within this extremely complex field of diverse values and mixed public and private budget allocations, volunteer and subsidized efforts, long as well as short planning periods, and market as well as non-market end-products and benefits.
The forest system manager seeks a desirable performance for the system of responsibility. That desired performance, the top score or grade, will be designated Q*. As the manager brings the system under control and holds it, the difference, Dt, between the actual state of the system at time t, Qt, and the desired state will be zero.
Dt = Qt - Q*
(The manager should consider "the system" at this point, as that subsystem, a life group or species group for which he or she is responsible, e.g., an elk herd.) It is reasonable that the manager will want to gain control over Dt and will want a "small difference." This difference may be viewed as (1) the absolute value, (2) squared value , or (3) the absolute cubed value as follows:
(1) Dt = | Qt - Q* |
or
(2) Dt = (Qt - Q*)2
or
(3) Dt = | (Qt - Q*) 3 |
Qt may exceed the "best" score (e.g., an excessive harvest or excessive production of forage). The absolute or squared values result in extreme penalties for excesses as well as for under production or achievement. Failure to achieve the objective or reduce the difference may be mismanagement. The faunal resource manager will typically try to minimize Dt and minimize cost in a relationship such as
Dt = a - b (Costt + 1)c
As we study this relationship, we can see the need for a relationship between the actual and the desired condition. If we substitute Dt for a and (k x Dt) for b, the equation becomes
Dt+1 = Dt - (k Dt)(Costt + 1)c
This is an equation expressing a gross non-linear relationship between the managerial money spent and the proportion of control (k) gained over the objectives of the system. The coefficient k expressed the proportion of change that can be made in the difference. Cost (or each dollar to be spent) is more or less effective, depending on the amount spent, timing, and other factors. This is expressed within the coefficient, c. Where c = 1.0, a dollar is a dollar. Where c > 1.0 expenditures become especially effective as typical results of team work, achieving optimum size, equipment, and leadership. The coefficient c (less than 1.0) implies the fallacy of the more you spend the more control you get. When less than 1.0, desired levels of operation may never be reached. There will be budgetary and other limits on money spent. Cost relations in faunal resource management are almost impossible to develop but gross estimates can be made, captured in expert systems, and used in models (at least with high, median, and low estimates).
A proposed scoring mechanism for the resource performance (or any subsystem such as a population) is:
Kt = [ 1.0 - (|Bt - B*| / B* ) ] x 100
where Bt is a gross expression of the benefits (e.g., as related to a particular population). Since it will be rare that Dt is zero, then Kt represents a score ranging from 0 to 100. Also note that an estimate of cost or managerial investment can be used in this equation to replace (as shown in the equation above) the numerator |Bt -B*| with the difference, Dt+1. Similarly and more simply, costs can be plotted with Bt to show how investments are narrowing the gap between the actual and the desired state of the system, i.e., progress to the perfect score of Kt=100.
Where there is a natural tendency of any system to proceed along some "natural" or set pathway (to remain in its present condition), then, where k is the control exercised by the manager (and often directly related to "cost" as shown in the above equation) the naturalness or dominant trend can be expressed as (S - k). The manager can study the expected systems performance using the system equation for negative feedback:
Bt+1 = B* - (S - k) (Bt - B*)
or
Bt+1 = B* - (S - k) (Dt)
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| Fig. 4.4. With substantial managerial control, k, the performance of the system, Bt , can be expected to be brought under control to achieve an objective, B*, over sufficient time. |
Having managerial control is the concept of using system feedback and includes the use of budgets, ideas, labor, equipment, or natural processes. The manager may attempt to adjust progressively the difference changing the value of k until the desired state is achieved. A prediction of the time needed to arrive at the desired end state can be obtained. Similarly, the level of control needed or feasible in some selected planning period can be determined.
Such equations are very useful for understanding systems. From it we can see that when S is greater than 1, the system is out of control, dispersing, and will eventually fail when some threshold is passed. The manager is always looking at future time (t+1). What will the next state of affairs be if I do project X today? When S is 1.0 and k is 1.0 the difference is zero; there is complete mastery of the system and in the next period, t+1, the objective will be achieved! It is more reasonable to assume, however, that managerial control in any one year is likely to be less than 0.1. Q* can be expressed and Qt or Qt+1 can be estimated as will be shown.
S, the tendency of the deviation to remain constant, can be best understood as
S = (1.0 - s ).
The value s is the rate of change in the unmanaged system (e.g., as seen in our interpretation as due to ecological succession). S or s can be approximated, estimated, or hypothesized. In natural systems, a production/respiration ratio can be used. The climax or old-growth forest condition typically has a value of 1.0 (s is zero). Long term stability of an agency or program can be expressed using the same equation while assuming S equals or is less than 1.0. Evidence of a species being in a threatened or endangered state suggests an S value of greater than 1.0. The equation can inspire questions such as: "what if S were 0.8, and managerial control (k) could be brought to 0.05, how long will it take to get a population of size 50 (Qt) to a population of size 600?" The answers are both in the quantitative results and in the thought process itself. (See CAP6144.)
The Kt scoring procedure suggested above works for employees with their eye on objectives. The desired system performance measure is stated. The work performed produces or allows Kt. Performance may be averaged over n years. If the difference is zero, the employee gets a perfect score of 100 and should get the maximum salary or other rewards allowable. It is just as bad to overproduce as to underproduce. (Overproduction implies misallocation of time, resources, equipment, space, etc.). The intense inspector or manager, will use the average absolute cubed value, giving extreme penalty for deviations. See CAP15.
A refinement in all of the above is to weight the performance in each year so that more recent years are given more importance or influence in determining the value of D. This reduces the impact of a new manager inheriting a "bad" area, the results of prior mismanagement, or having to work on a new area with a system performing far from the established objectives. Of course, it rewards learning and "experience."
Comparisons between managers or cost per point estimate can then be made with a simple benefit-to-cost ratio such as:
Qt = (Bt + 1) / (Ct-1 + 1 )
where C is dollars expended in the previous year. Emphasizing annual change per dollar in the direction of the objective, Q*, this equation suggests between-year dependencies since Qt+1 is often a function of Qt. Estimated expenditure or investment of any type (C) is estimated at the end of year t-1 or at the beginning of t+1 to appraise its effects in the period from t to (t+1). The objective is to reach 100 with Qt, to maximize Bt, minimize Ct and Dt. Once a constraint on C is imposed (e.g., spend at least the allocated funds!) then minimizing the difference between Q and Q* becomes the task. The good inspector praises and rewards those that maximize Qt.
There are further dimensions of this very important system-synthetic equation estimating Qt+1 (or Bt as in Fig. 4.4). Realistically, resources are rarely available to do a comprehensive annual analysis to determine each parameter of this equation. It is used to estimate a future condition. The value of each component of the equation is poorly known, variable, and subject to catastrophe. Thus, it is important to realize that each component may vary or be only a certain percent correct and that the "clean" picture in Fig. 4.4 may appear more like that in Fig. 4.5 when even moderate random variability (the coefficients) is included in a 100-year simulation (CAP6144).
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| Fig. 4.5. When variation is included in all dimensions of the system, a typical curve appears as shown here. Objectives, Q*, may change over time. There may be periods (suggested at Z) when Q* is unknown or very uncertain. The deviations of a system under the control of a superior manager are less than suggested here, but they do occur. |
Qt+1 = (1.0 - a)Q* - ( ( (1.0 - b)S - (1.0- c )k ) ((1.0 - d)Qt - (1.0 - a)Q* ).
Defining Q*
Q* is a Type 3 objective. Q* is expressed as follows, a formulation readily developed within a computer program. (This equation, in its complexity, in part, explains past difficulties in formulating objectives, but it points to future opportunities possible only with computer assistance.) The benefit-to-cost ratio to be described is unconventional. Its strength is in its comprehensiveness, its computability, and in its intuitive appeal as a measurable criterion with almost unlimited potential for managerial leverage. Q* may be re-formulated as a B/C ratio. Herein it is not a monetary ratio which typically (and under some laws) must be significantly greater than 1.0 for legal or wise public development. Herein, the simple ratio, Q*, is to be maximized or achieved at a set level by the manager. The benefits are expressed in many varied units - consistent only in a particular application. Costs are measured or estimated in discounted dollars. (See CAP99.) Thus the manager may increase Q* by increasing B or decreasing C or, preferably, working with both simultaneously.
The equation is:
| P | T | J | |||||||
| B* | = |  |
|
|
Dptj | Vptj | Eptj | [Sptj ] | H |
| Easy memory... key parts... D V E S H |
B>Publics
The ptj components, the publics, the planning period units, and the numbered objectives...P is the total of the subgroups of the human population, perhaps based on location, age, socioeconomic class, residence, and experience. p is one significantly different group of people, a public. (It may be an individual.) See Fig. 4.6.
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| Fig. 4.6. Each pathway though the "tree" is a separate public. A single public is shown as a dark dotted line. If there were only 2 divisions in each of the classes shown, there would be 64 publics. Publics or the subpopulations that virtually define the resource need to be recognized and their needs met individually wherever feasible. This can be done by managing areas and uses differently and by separating user groups from each other in time and space. |
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| Fig. 4.7. When alternatives are presented, there may be differences in culture, education, prior experience, age, and many other factors that cause "publics" or significantly different human groups to exist. Alternative preference may range from total preservation to very intensive use. Two are shown here. The "average citizen" does not exist. |
Population preferences change over time. In one study in Michigan (Leenhouts 1976), it was demonstrated that the attitudes and values of the deer hunter public changed with their average age (as the population became older; not unlike ecological succession). Trapper activity and interest also changes predictably with age (Clark, Maine). Public satisfaction from the resource may not result from the manager working with the resource (e.g., to increase deer populations) but may occur naturally as the age distribution within society or the hunter population changes.
Time
T is the total period for decision making (recommended as 50 or more but with 3-5-year re-analyses). Prakash et al. (1986) observed that "... it is only if accountability is based not on immediate financial flows but rather on the larger social and economic benefits and costs that the design profession can have adequate tools and criteria for measuring the full impact of their work.
t is the designated year among T years. Faunal system managers are well aware of changes in public appreciation of trophies, in the value of furs, and in the importance of hunting. They are well aware of the changing effectiveness of a road scraper to make repairs on a hunter trail, and of an employee to do more, often better, work with experience. These changes have rarely been formalized but the concept has been present, "maximum benefits over the long run" (not unfamiliar).
The period of "the long run" probably differs in each managerial situation. A decision about what annual game bird food mix to select has a 3-year period at best. Most managers would find the extra work in analysis of year 2 and 3 not rewarding (but increasingly rewarding as the precise optimum selection becomes more important with increasing costs, decreasing habitat, and lessening available skilled-worker hours).
Conlin (1973) explored the question of the optimum wildlife agency planning period (Why the "5-year plan"? Why not 4 or 8.5?). He concluded it should be the longest possible, the primary reason being that managerial investments in long-term producers (e.g., a road, an orchard) must logically be discounted over a long period and returns typically increased as costs declined for each year that the period was extended.
The Set of Objectives
J is the total number of Type 5 objectives. j is one objective in a list.
Change in objectives and weights can be included in the procedure being described. It is possible to estimate the changes in value, efficiency, and other characteristics of objectives which will be discussed. Such estimates are made daily in simple personal purchase decisions and I have observed them being a part of group discussions about wildlife management for over 30 years.
The accuracy of such estimates is always in question. The future cannot be known. Trend analyses and other procedures (especially these formalized for continual improvement) can reduce the uncertainty and increase the accuracy. Once the procedure for analysis is computerized, simulations of the effects that changes in the numbers might make in the actions selected can then be conducted.
Alternative Demand
D is an unconventional expression of demand. It is the proportion of the maximum number of faunal benefit units that are expressed as desired by a public that can be made available, in a desired period, in an area. It recognizes reasonable production capability. For example, if 1000 units (d*) can be produced or made available under reasonably wild conditions of management. There are now available 800 units (d). But 2000 units (d**) are the expressed demand, then
D = (d*-d)/d* or (1000-800)/1000 or 0.2
since
d* < d** and d* > d.
D is a value for demand that is scaled to ecological reality, to an estimate of the maximum units possible under wild or semi-domestic conditions. It is an expression of a perceived reasonable deficit, one likely that can be reduced by managerial investments.
Demand, as a component of objectives, must have some unit of expression like one animal of a species, tons of erosion or biomass, hours of recreation, costs or returns in dollars, sightings of animals or rare events, or hours spent in recreation or in experiencing esthetically-pleasing events. Demand and value must be expressed for something specific (e.g., 1000 board feet of pine lumber or 100 pounds of deer meat). Some of the comparisons between units will be difficult; of that there is not doubt. The method described here is best used in the most difficult of such situations, for it helps to encourage making precise expressions of specific comparisons. It then provides a means for communicating and comparing objectives between people and even sub populations of people.
One animal may be used in the proposed accounting system to meet more than one objective. This is not "double counting." One animal may meet (satisfy) more than one demand unit (e.g., kg of meat, hides, and quality-weighted hunter-hours spent in harvest) (Weithman and Katti 1979). Poorly studied, various estimates of demand can be used. The U.S. Forest Service suggested that species diversity laws reflect a demand for entire faunal and floral systems. Bag limits relate to demand; hunter expressions of desired frequency of achieving a limit or taking a particular animal are also related. Dropout rates of hunters, anglers, or recreationists may suggest when demand has not been realized.
The demand expression is constrained (d) so that no fewer than those animals essential for population continuance are maintained (perhaps "minimum viable population" size). Demand is believed to vary over time (as the age structure of a human population changes) and with location.
"I intensely want that resource, but not much of it." This might be the expression of someone given a list of wildlife species as objectives. They would be expressing three concepts: weight, risk, and demand. Managerial demand is a simplistic notion of the expressed amount of good or service desired by a group of people (the population or clientele for whom a faunal system manager works). It has upper and lower limits, over a short range is assumed to be a linear function, and is a deficit proportion of an amount that, if provided, results in expression of "satisfied" or the existence of a generally satisfactory condition. That is, it is the relative difference between the present condition and a desired condition. Because there are assumed to be multiple objectives in all situations, and also limited resources, then resources (time, skill, money, etc.) are allocated by the rational manager to achieve expressed demand, and not one unit more. Expressed demand is used as an index to actual demand. It is probably not identical to actual demand but is assumed to be well correlated with it over the range of most faunal system management work. It is also assumed to be highly substitutable, discussed later. This formulation of "demand" expresses deficits in a system in common units of proportions, thereby expediting their manipulation.
Value
V is the value assigned to each jth objective by the pth group for the tth year. The value can (and probably does) change over time as a population of people, economic conditions, and needs change. Human values are described and defined in many ways. Behavioral scientists are said (Dunlap et al. 1975) to define values as standards that guide or determine attitudes and behavior or "desired end-states of existence." These end-states translate in this chapter as fundamental human objectives such as freedom, equality, or desirable conduct such as being independent. The relative importance of each objective in this Type 5 performance measure is a coefficient for each objective. This coefficient, not the objective itself, is the value.
Weighting of objectives means assigning expressions of perceived relative importance to items in a list of objectives. Making such assignments is widely practiced. These weights have been viewed as surrogate values or shadow prices and simply as expressions of relative worth or significance. I first saw the technique in Churchman et al. (1957) but it appears in similar form in many other places. Lobdell (1972) used it in designing the Federal Aid Division system for assisting states in allocating wildlife management funds. Cowles (1981) used it in a modified form in assessing impacts of offshore oil development in Alaska. Smart (1976) and Giles et al. (1976) used it in power line impact analyses; Koeln (1980) in analyzing airport impacts; Cason (1980) in analyzing interbasin water transfer impacts; and LeFranc (1977) and Kroll (1982) in selecting plants for surface mine reclamation. Weighting objectives in wildlife management is described in various places within Giles (1978:cf 217-219) and Giles (1981). Software for it and several related procedures exists (e.g., Criterium). Herein, the procedures are made more specific, limitations overcome, and extensions made to achieve an expression of faunal resource benefits that may result from selecting one action or program from a set of discrete alternatives. The concept can be expanded into an algorithm for deciding on optimum actions in any faunal management system.
In each agency and for each group of people, each objective has a different social value. The output or results of achieving each will be weighted differently among communities or publics. The problem is related to joint production in economics. Break points for interactive conflicting objectives, when weighted and relative scales used, can be determined (Fig. 4.8 below). Allocations of resources may then be made to them over time.
The value of V is relative, i.e., one unit of the most important objective is assigned a value (e.g., 100) and all others are compared with numbers in relation to this selected objective. One-half-as-important is assigned by resource users a value of 50. This evaluation produces an internal scale...and is believed to be useful in seeking out trade-offs; allocating limited time, funds, and labor; in expressing production of utility or valued biological units; and in evaluating demand.
The numbers are readily criticized as being "non-market," "shadow prices," subject to technical difficulties (is 1 unit of difference between 90 and 95 the same as 1 unit between 5 and 10?), uncertainty about whether the expressed value for a species is consistent with behavior toward that species, and whether the value of a turkey changes for a person after he or she has just harvested one? These are the same difficulties as found in any kind of valuation, even the dollar, and while criticisms are easily leveled at them, they are not easily solved (here or in the market). The criticisms are not solved by using the dollar either, yet that unit is still used in decision-making, and pressures are brought on wildlife resource managers, needlessly, to obtain financial values for wildlife.
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| Fig. 4.8. Achievement in some measures for only 2 objectives, A and B, are shown here. Their interactions or trade-offs are shown in C. The slope of each objective is related to weighted importance expressed for each. See also Fig. 4.3. |
Value varies with amounts of the resource. A few units may be abnormally important but excessive amounts, beyond those in demand, are of zero or negative value. Thus, V is constrained so that as a concept in the computer B* it is related only to the units and the amount in demand.
It is difficult for some objectives to be assigned weights (i.e., value) for several reasons which may include: (1) they are novel; (2) they require considerable abstraction; (3) the relevant human population is rarely specified for the manager (c.f., which shall I use, my personal weights or those for people in my wildlife management district?); (4) the relevant period (cf., recent past, near future, or long-term future?); (5) the set of weights is (or might be) revealing of a person's psychological state or strategic ploys; (6) the weights may be poorly or improperly used; (7) the weights may be unchangeable (Leenhouts 1976) even though human value systems are likely to change over the years; and (8) long lists may require very thoughtful, tiring, and time-consuming discrimination between closely-related objectives, much more so than is normally done (but should have been.)
To overcome some of the difficulties of weighting, a technique in subjective probability theory may be used (Levin and Kirkpatrick 1978). The method, now widely used (O'Neal and Clayton 1965; Martel 1976; Levin and Kirkpatrick 1978:492), is to ask experts for three weights: highest (a) and lowest (b) estimates of relative value ever likely to be encountered, and then the likely value (m). These are used to compute, v, the expected value, using
v = (a + 4m + 6)/6
and the variance estimate
(v)2 = ((b - a)/6)2.
Confidence is inverse to the variance (CAP2030). A person requested to assign a value to an objective may assign a specific number, say 73, but if in doubt about an objective, may say "It's value could not be higher than 80 or lower than 50, but it probably is about 75." Placed in the equation,
v = (80 +4(75) +50)/6,
and the results, v, is 71.7. This is a more useful number to employ in decision making than 73, under the stated conditions of uncertainty. These are called beta estimates. Best "guesses," the judgements of experts, are accepted in courts, make money for some corporations, and when built into a dynamic system with corrective feedback, need not be denigrated by those employed to work with faunal systems. They generally are better than data from a random table; they must suffice in a clinical environment in which every land unit and situation is unique and funds for research using computed necessary sample sizes are unlikely ever to be available.
Expected Value
E is the probability of success in achieving the jth objective for a subpopulation of people at or before time t. Where the probability is low, then investments will usually be increased to "make certain" the achievement. The results of using E is to make B* approximate an expected value and thus to allow more reasonable comparisons in system outcomes.
No expression of the magnitude of intensity of the consequences of failure is included here. In general, more money will be allocated the higher the likely probability of failure. This is an expression of the likelihood of achieving the valued demand. It too is likely to change over time as habitats change, environmental quality deteriorates, or hunter and other resource users' behaviors shift.
Risk needs to be separated in managerial decisions from intrinsic valuation or assignment of weights. Lobdell (1972) wisely included it within his Federal-aid management activity selection system. He had trouble describing it. My students have consistently been perplexed by the concept. The question is fundamental within managerial science (Levin and Kirkpatrick, 1978:64-66, 126, 142-143). It is briefly: what is the probability (p') of dire consequence if a particular objective is not achieved to some reasonable or sufficient level? How bad the consequence (w) ranges from life-threatening (1.0) to no noticeable or known effect (0). Risk, r, is thus the product of p' and w. The results, r, may be subtracted from 1.0 to give E (E = 1.0 - r), a probability of a positive outcome (of course, assuming the action is truly taken, i.e., with sufficient resources). The risk component of the analysis allows a wildlife manager to compare actions for something that is highly desirable (valued) (e.g., like a harvestable cottontail rabbit (Sylvilagus floridanus) population) but with a low risk of dire consequences (e.g., loss of a population; loss of employment if a population is over-harvested), with actions for something not very important but with extremely bad consequences if not managerially "tended" (e.g., Norway rats, (Rattus norwegicus) near a hunter recreation area).
Substitutability
S is the substitutability of resource units. Driver (1985) mentioned substitutes. (See also Goeller and Weinberg 1976.) Only pairs are considered in a square matrix to evaluate the amount that one resource will substitute in meeting the demand for another. It is the experience of most everyone that demand changes and that "game" or "new birds" may be resource categories, not species. Achieving each objective may possibly substitute for achievement of another. A hunt on which a person takes a bear may easily substitute for 3 years of unsuccessful deer hunting. "To kill a bear" and "To kill a deer" may be weighted objectives but achievement of one may substitute for others, at least in part. Thus, S is a regular matrix of proportional substitutabilities. There may be overlaps in this concept with importance (V) and demand (D) but I believe the concept is separate and is rooted in culture, local experiences, education, television viewing influence, and clearly in past achievements and success. It currently appears too complex to be handled by most wildlife agencies, given the complexity of the other aspects of Q*. It may be very important for a person to be able to see a particular song bird Y on every outdoor experience, but seeing species X might be an equal substitute if Y were not seen on a particular day. Taking one wild turkey might substitute for a daily bag limit of grouse, etc. I suspect that a quest for multi-dimensional substitutability (does one squirrel, 1 turkey, and 5 grouse equal one deer?), while possible, is not likely to be a practical or fruitful line of investigation.
Substitutability is critical because some actions like "constructing a km of hunter trail" has multiple effects on several different species over time. The extent of the substitutability of highly valued, low risk species can significantly influence the projects (and funds) allocated to achieve specified demand.
The concept of substitutability potentially greatly complicates the analysis of a decision for it may influence the group for which the decision is being reached, the perceived risk, demand, and the planning period. Given the limitations and uncertainties of the other components, it seems best for now to concentrate on formulating the overall maximization strategy, then to elaborate the substitution component of the decision procedure as situations may require or allow it.
Based on Holling's (1978) juxtaposition of the concepts of Leopold et al. (1971) and Kane et al. (1973), it is possible to develop a substitution matrix. All objectives are listed in rows then repeated as columns to form a square matrix. The question is asked for each item in a row, "If this objective were completely achieved (1.0), what proportion (0 to 1.0) of the other objective in the column is likely to be achieved or substituted?" In some analyses, there will be no substitutions (a 1.0 diagonal matrix or a very sparsely filled matrix).
Diversity or Variety
H in the equation being described to estimate B* is variety, and it is a constraint. It prevents all available resources from being allocated to one highly-weighted objective, maximizing along one dimension. It expresses the need for meeting some minimum proportion of all objectives before resources are devoted to high payoff activities. It embodies diversity or "biodiversity," a Type 3 or 4 objective. I discuss it at length here because it already is a problem for the manager and is likely to become more serious. I suggest use of variety in place of diversity (Fig. 4.9). It is a word-umbrella opened over a mixture of ideas and models for diversity, richness, dissimilarity, evenness, instability, variance, and others (CAP67). Herein, I make only a small effort to clarify concepts, provide some useful processes, and provide a platform for some new perspectives.
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| Fig. 4.9. Diversity or variety may be a function of several factors including the taxa or classes used; the estimated biomass or other unit of measure such as number of animals in each taxon, the carbon-to-nitrogen ratio, etc.; time; and the location or total space sampled. Where the system exists within such a space is strongly related to observers' decisions as well as to the physical realities of the system. |
For people using the forest bird resource, richness is probably the most sought-after statistic. Most people working for a maximum life list (a checklist of birds positively identified by one person in their lifetime to date) also work for maximum numbers of species on a day or during a trip. Only occasionally is there extra benefit derived from seeing an unusually large flock, or getting an exceptional daily total. (Bird richness found annually in relatively stable communities seems constant and correlated with the total avian faunamass (Jessee Overcash, 2000). Faunal species richness (H1) is a system performance measure, a way of expressing an objective or a current state. The number is useful for individuals preparing life lists, for management areas, and for different periods. Loss of richness over time, as within a region undergoing tropical forest removal, is of genuine concern. Loss or gain needs to be expressed for a single area, for example when 100 species are in a forest before it is cut but 100 are also in the new vegetation stage, only 40 of which are new. The