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Chapter 8 is long. For loading and other efficiencies, it has been placed into 3 sections:
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Thwarting Entropy
After high school physics and introductory chemistry, biology, and ecology, almost everyone comprehends entropy. This word is a code for a complex world of second-law-of-thermodynamics phenomena relating to the tendency of everything, everywhere, toward maximum dispersion. It is the loss of energy when things change form (e.g., when a rabbit, probably unwillingly, assumes the form of a fox). Entropy can be sensed in the heat from an object. It is the perpetual flight of all things toward cosmic otherness.
Biological things are negentropic. That they temporarily defy entropy is their uniqueness. There are no feedbacks, no conserving forces, in the rocks, rills, or untempled hills. Such things are energy-in and energy-out systems, but the biological systems have anti-entropy mechanisms. They not only actively collect energy (as by orienting their leaves) but also, more importantly, store and hold it ... apparently for as long as necessary to assure that whatever conservative traits are present are passed on in a continuous, unbroken life current of one individual. The current merges with that of another and, like streams of water of different temperatures, they join but only slightly mix and remain distinguishable. The individual remains. When the environment is "ready" it will select those it deems unsuitable and send them and their energy units to the cosmic vastness. The fit individuals remain ... for a while. Their time will come.
Analyzing and studying forest faunal systems management needs to be rooted in fundamental concepts such as entropy. New theory is needed and that must be consistent with other knowledge.
It is often useful, especially for theoretical work, even for general understanding of anything, especially entropy, to use examples (i.e., models of the larger truth). Here are a few examples:
1. A snake crawls to a tar-covered road at night. Mortality is high. The snake responds to the black-body phenomenon of physics, namely the absorption of solar energy during the day and its re-radiation at night. Re-radiation is entropic.
2. A manager eats a steak. He feels especially warm after the meal. The energy of digestion and metabolism may be comforting in the cold, thus the desire for a"good hot meal." Digestive heat losses are unpleasant in the summer heat (as suggested by: "I don't want a heavy meal!").
3. A polar bear or Arctic fox in white fur and with limited energy (relative to the energy drains of the cold environment) can afford no re-radiation losses during the long nights. A black bear, usually with abundant food supplies and usually in an energy-conserving den over-winter, is well suited to energy loss, "designed" for dissipating that taken in and digested.
4. The hound dog under a porch on a hot summer day spreads its legs to increase conductive heat loss and maximize its surface area and parts, then panting, losing 500 calories of body heat per gram of water evaporated. The hound has a "water cooled engine"; the rabbit, with its large blood-filled ears, is "air cooled."
5. Hunters huddle and shiver in the cold, making their surface area minimum (when a sphere) and gaining warmth for their appendages from using small amounts of stored energy.
6. A snowshoe hare is white in winter (as the polar bear) resulting in reduced energy losses in an energy-short world. There is abundant food for the hare, but it is low in energy and the costs of chewing and digesting it are high. Winter nights are long. Net energy is in short supply. In summer, the energy problem is one of a balance between heating and cooling and heat losses during the nights. Black is best for day, white for night. The optimization - half way - is brown. The zebra, without the mixture and in a constant environment, employs the same energetic strategy to reduce energy loss and achieve a desirable energy balance. We can reject the camouflage coloration hypothesis.
7. The manager's axe rusts. Rusting is oxidation, a slow burn. Axes are entropic. The manager may oil the axe, thereby reducing its rusting but at some slight energy cost. The axe has no feedback mechanism to reduce entropy. The manager has feedbacks, as do animals, personally as well as for the system managed (a part of which is the axe).
Entropy is one of the most fundamental laws of the world. There are no exceptions except in the metaphysical realm "where neither moth nor rust doth corrupt."
That living systems have overcome entropy to a degree, slowing its effect for a brief period, is, for some of us, amazing. Not just the coalescing of molecules into complex forms but the intrusion of feedback is the spectacular phenomenon. With understanding of entropy arises a new understanding of life and life processes. More important than death or mortality (death rates) is life and survival (also a rate). While mathematically these concepts are almost indistinguishable (mortality = 1.0 - survival) and in the high tones of pure theory should be treated alike, they just are not alike. Emphasis on death takes the faunal resource manager down an entirely different pathway and probably to a different end-point than emphasis on life, a negentropic phenomenon. The view from the survival pathway is likely to be different, perhaps pleasing and with rich insights. I suspect the destination will be different for most faunal system managers. Theory aside, it is easier and less costly to influence population mortality than birth rate.
Existence
Matson (1965) argued well the existence of god. We may first argue the existence of a species or life group. This is in part the problem of specifying the context of the system or its boundary. The issue is for the biologist who must decide whether the reproductive stage of life (e.g., an egg) is an animal, whether certain aquatic organisms with chloroplasts are plants, etc., etc. Closer to the immediate concerns of the faunal system manager is whether a group of individuals is significantly different from others or not and what difference that difference might make. This is a question that may be handled by modern statistical techniques such as discriminant analysis. When only one factor is to be used and two groups are involved and the question is how to draw the line to separate two species, then this may be depicted as in Fig. 8.1. The probability of correctly discriminating between the two can be expressed.
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| Fig. 8.1. When one dimension of two similar-appearing animal population is measured (e.g., tail length), the results may overlap. Where should the decision line be drawn (near Z) to separate life groups (of one type)? Discriminant analysis is a statistical technique that can assist in deciding on the probabilities of a correct decision. |
The answer to the manager's question about a threatened or rare species may be approached through several epistemological routes leading to a probability statement about existence. The existence questions with their epistemological bases include:
Since non-existence of an animal cannot be proven, then the manager may state existence in terms of probability of from 0 to 1.0. For an observed animal, the existence is probably 0.99 (not 1.0, for the eyes are deceiving. "I know (a probability >0.99) at least one spiked-speckled monster exists in the Black Lagoon" is different than "I guess (>0) that a spiked-speckled monster could be in the Lagoon." People see wolves where they do not exist; things look alike; I once clearly "saw " (!) a long-tailed bobcat on a remote mountain in a car headlight beam. (It was a large feral house cat.) It is very hard to be very sure, truly knowing (at the 1.0 level of confidence) anything (See CAP9072).
Genotype
The average faunal system manager will probably assume all animals in a small area have the same genotype, therefore the question of whether an individual has genotype X is irrelevant. It is relevant, of course, in defining the animal system, the species, the relevant group.
A primary reason for focusing upon the individual is that natural selection occurs at the level of the individual. This is classic Darwinism.
"Group selection," a peculiar phrase, has come of interest (e.g., Wilson 1980). It is not unexpected that over a large area where thousands of individuals exist, the groups can be distinguished within the area. They are simply more homogeneous than the others. (The variance in some characteristic in the group is notably different than the variance in the total population.) These are identified as sub-populations and are called"demes" by some people, suggesting similarities probably of greater interest to geneticists than to others.
My interest here is that the reader concentrate on the group of individuals of interest. If it is some small group (e.g., all chipmunks of species d on the north-facing slope of a watershed), then name it and get on with its management, recognizing that there is variance within it, that by looking at anything in nature, groups can be found, and that by really looking it will be discovered that every individual is unique. The relevant scale of the system (the context) to be managed is a human decision. It will not be discovered; it is not a biological or ecological phenomenon. Once decided, then the manager may work with the system, dealing as necessary with the variance as it is perceived. Later, eventually, as time and funds become less limiting, then and only then will the practical manager probably seek out the major roots of the variance within the system and then perhaps re-specify the managerial systems, allocating resources differently to the different groups so identified. If no allocation differences are likely, then knowledge of the difference is of no value (worthless) to the manager. Here we may quibble because some will argue that any knowledge may be useful. True, but we must continue to emphasize the role of the manager of the faunal system, all populations within it, the clear objectives, the often-immediate need, and the cost component of those objectives. The difference may be summarized as the need-to-know vs. nice-to-know controversy and for the manager ... need should always win. [How to tell the difference remains a challenge.]
There is controversy among biologists who must decide upon what to study given a probability of Z years until their death or a probability that a published finding will never be rediscovered and used (as if some Mendelian pea study). There is similar controversy among research administrators, i.e., what proposal to fund? Need-to vs. nice-to is an on-going war and it is not surprising that the side that is ahead changes with technology, new knowledge, forest age, and other factors.
The war analogy fits the natural population subsystem well. Within natural populations there are the two aligned forces of (1) energy conservation and (2) reproductive fitness (Geist 1978). Opposing them are polycidal selective forces. It is not surprising to me that these forces change (e.g., a severe cold spell) and most of one group of animals is killed (say those on a high north-facing ridge), the others survive. There just have to be differences in the residual population, the sum of the individuals left in the entire area. The assaults of the selective forces are patchy (a hailstorm, a lightning strike, a forest fire, a beetle kill). A thousand such changes and their permutations over 10,000 years, a brief period, has to result in groups of animals with relatively homogeneous characteristics. They have one major thing in common: they survived.
There are learned differences within groups of animals. A bear learns to open cans. The knowledge is passed on by observation to learning cubs. Eventually the first opening event, probably by chance, is passed on and an entire population of bears can open tin cans. Separating mass behavior, the phenotype from genotype, is difficult. Whether such behavior can be "inherited" remains questionable. This observation is not neo-Lamarkian, it only suggests the difficulties of specifying the roots of behavior. (I am told that a simple organism, planaria, after consuming kindred that have been "trained" to respond to light, acquire the light-response.) Others claim no inheritance of behavior (such as animal response to hunters or wildness), saying it is learned de novo or learned from copying parental behavior.
Rarely emphasized in genetics classes (or else quickly forgotten) is that traits which may arise (always at the individual level) may be "selected against" (translate: the animal dies) or not. A trait that emerges and is observed does not have to have survival value. One that exists is one that has not yet been selected against. Thus it is unexplained; more uncertainty!
There are innumerable questions that can be answered from studies of the genotype. Such studies would be done by microscopic work, by electrophoresis, or by breeding experiments, but all are expensive and time consuming. Whether organisms that are morphologically similar are genetically similar is a question asked by fisheries managers, those concerned about insecticide resistance or response of any animal to toxicants, and to law enforcement agents who want to know if a captured or dead animal is from an area where it is illegal to take it.
Small populations in restricted areas may inbreed. Inbreeding depression is not essential but, by definition, is the appearance of undesirable recessives. Of course inbreeding results in accentuation of desirable traits. About 1 percent inbreeding is all that is allowed within domestic stocks. Because of the inbreeding problem potential, some have suggested a population of 50 animals is the absolute minimum. Smaller than this, inbreeding depression is thought to be excessive. Others (see Shonewald-Cox et al., 1983) suggest 500, dependent upon breeding characteristics. Whatever the actual number, it is very low. See CAP10A.
The frustrating consequences of facing that every individual is of a unique genotype are:
Alas, we cannot know. Such awareness is enough to make some people leave the work of managing faunal system. For others, it is the inspirational mountain that exists to be climbed.
Life Group
For management purposes, I believe that life groups should be used. This is a step more refined than the species-specific approach which is many steps more refined than featured-species (Giles 1962), the key species, the guild (Cody 1974, Short and Burnham 1982, Short 1983, Verner 1984, Bayer and Porter 1989, Terrell et al. 1982 (Table 8.1), Droege and Sauer 1990 (Table 8.2)), or the life-form approach (Thomas 1979). Life groups are, I believe, the best resource unit for field work (contra "prime pelts" or "quality lean meat").
| Table 8.1. Criteria for forming guilds among fish species (adapted from Terrell et al. 1982). |
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| Table 8.2. One set of bird guild names (Droege and Sauer 1990). |
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| Table 8.3. Potential guilds or guild cells based on Short and Burnham 1982. Foraging spaces or energy sources (the list of categories at the top) are the same categories as listed at the left with only one exception | ||||||||||||
| Foraging Spaces | ||||||||||||
| Breeding Spaces |
A | B | C | D | E | F | G | H | I | J | Air | |
| A | Breeds Elsewhere | |||||||||||
| B | Tree Canopy | |||||||||||
| C | Tree Bole | |||||||||||
| D | Shrub Strata | |||||||||||
| E | Terrestrial Surface |
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| F | Terrestrial Subsurface |
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| G | Water Surface | |||||||||||
| H | Water Column | |||||||||||
| I | Bottom Water Column |
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| J | Temporary Water |
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Guilds may have to suffice in some areas for objectives that can be expressed as changes or functions. A faunal guild is a functional category used for combining species that overlap significantly in their use of a particular resource, i.e., along one dimension of their hyperspace. (See Verner 1984.) An insectivore-forest layer guild is an example (French 1978). A guild is a group of taxa (a fuzzy set) or life stages that exploits the same class of ecological resources in a similar way (e.g., all adult birds and lizards that feed on bark-dwelling insects) (see Burns 1989). Short and Burnham (1982) developed an analytical procedure for classifying guilds of vertebrates based exclusively on where they obtained food and bred. Table 8.3 shows the matrix. Each cell of the matrix is called a guild. Animals known in an area are placed in each cell. A conspicuous in the table may be the proportion of species (the size of a guild) that would be harmed if that habitat was to be destroyed. Table 8.3 presents a convenient way to organize "ecological resources" and to simplify the typically long list of species into a mere 110 guilds.
This structure differs slightly from Burnham and Short's (1982) pattern, partially because of the insights provided by Burns (1989) and partially due to the need for dealing with life groups (not simply species). The same species may fall within several cells of the matrix. The matrix needs to be expanded into the third dimension in order to accommodate the third major action category: feeding, breeding, and resting.
Short (1983:180) indicated that a ponderosa pine habitat provided breeding-feeding habitat for 154 vertebrate species. In commenting on effects of management such as removal of large tree boles and snags, he reported a deleterious impact on 50 guilds.
The relatively limited classes (Table 8.3) produced with the two fundamental axes, feeding and breeding, represent a useful way to see the forest and its fauna. There is a managerial limitation because it is impossible (at least difficult) to retain a canopy while removing a tree bole. The guild blocks are not managerially independent. This is not a serious flaw. In this work, the manager needs to know the species and their requirements in order to position them properly. Perhaps use of breeding-feeding variables with each species may be sufficient for most categorical analyses and predictions. The age of each guild cell in the matrix might be added. Unfortunately, using "life group" appears to be in opposition to the current trend (as interpreted from publications) away from species-oriented management. This trend is particularly surprising given the large number of well-informed graduates from university wildlife programs, new computer power, new laws requiring planning and statement of consequences of proposed changes, new knowledge through research about species differences, new wildlife data bases, and algorithms for multi-species management. By careful study of objectives and by careful analyses of multiple effects on many life groups from a single managerial action, great effectiveness can be gained.
Life groups are identifiable, definable (i.e., bounded) subsystems of a species. They are, for example, turkey poults (requiring management much different than that for adults), antlered buck deer, and the aquatic stage of salamanders. They are insect instars, as genetically similar but as different in appearance and function as caterpillars are from butterflies. For many species we have not identified life groups. Thus the species itself is the relevant life group.
By what authority can such re-definition of the categories of population analyses be done? By the same authority of the taxonomist.
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| Fig. 8.2a. It is likely that most forest fauna life groups exhibit a linear survivorship curve. In short-lived species (45 years) separated as life groups, there is little evidence of major physiological or anatomical change that would cause survival differences among ages. Learned behaviors probably equal or surpass physical limitations associated with advanced age providing equal (uniform) survival in age classes. |
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| Fig 8.2b. The stages shown are used in CAP9088. |
Deevey (1947) said "animals differ characteristically in their order of dying." Hickey (1952) said life tables were tabulations of populations according to age groups and went on to add emphasis to how natural selection may cull out mutations and ecologists may gauge environmental (negative) forces. Even though the phrase"survival curve" or"survivorship lines" are used, there usually is an emphasis on mortality. When an entire population is seen (100%), then there are two categories, those that live and those that die. It may be a half-empty, half-full argument, but there has been an emphasis on mortality factors and thus there may be a residual opportunity to look at survival factors and thereby gain alternative insights and means of population analyses.
Viable Populations
A population is a decided upon group of animals in an area for a specified period. A population has the tetrahedronal characteristics of matter (or energy; the animals per se), time, space, and variety (sex, age, and behavioral differences within the group), Fig. 8.3.
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| Fig. 8.3. The fundamental natural resource is a concept of four related dimensions. A resource must meet conditions imposed by the four dimensions. Only when inside the described tetrahedron does a resource exist. The dimensions are major ones used to describe the context of any resource system. |
A population is dynamic due to behavior, age and physiology; response to a changing environment; and migration into and out of the named area. The population and its area is fuzzy.
"Over-populated" is a value-laden concept meaning that the population exceeds some number beyond which a range of objectives cannot be met. Below that number, either total animals or a density objective may be achieved. The phrase expresses human value and objectives more than population size.
Richness, already discussed under objectives, is the number of species or taxa present. Since total taxa are so difficult to determine, it is reasonable to discuss the common or conspicuous forms. Some writers discuss the populations that are present. Abundance is the total count of animals; density is the abundance divided by area, the number per unit area.
Rare, threatened, or endangered also express value, for they symbolize population size relative to criteria of goodness or sufficient abundance. Is there some way to arrive at a decision about sufficient abundance? What is the epistemological base for knowledge about rareness? A large number can be selected and rules drawn, but are there grounds of knowledge that allow a decision about when is a population likely to cease to exist? At what point is further managerial investment in low populations wasteful and irrational? By analogy, when does society "pull the plug" from the life-support systems for people that are terminally ill? The question for species as well as for human patients is difficult and the answers lie within both ethics and economics.
Is a population on the brink of disappearing, a population? A linguist would say yes but a biologist would probably say no since there is intrinsic to"population" the connotation of life and living, of continuance, of vitality. Thus has emerged the concept of the viable population, a redundancy. A viable population"maintains its vigor and its potential for evolutionary adaptation" (Soule 1987). To comprehend the full complexity and challenge of this concept, it is useful to assume the role of the manager in charge of the species over the eons, to"play at being a god." What are the objectives? The biological fundamentals: I. Budget energy well in your time and place; II. Survive short-term extreme energy-shortages over the long-term; III. Reproduce; IV. Survive short-term extreme losses in animals reproduced. (cf Geist 1978). The laws are brief, profound, and pervasive in human society and business as well as in biology. They require trade-offs, for they are in conflict.
In some role of a god-manager, the task is to design a population that obeys these laws. The task is to create a master of game theory (Chapter 17), a winner in the game of Life against the Forces of Nature. The task (from this point of view) can be a life-long challenge for understanding. Here, only parts of the task are addressed. The animal must be an efficient energy budgeter for the place. This means it must be of the size, shape, and surface area that can be assumed to minimize energy losses when the environment is cold (large, spherical, nose-under-tail) or hot (small, air-cooled ears, horns or antlers), shade-seeking, or water-cooled (panting or submerged), and expanded (as outstretched on the ground)). An animal species in a place has met the maximum and minimum of energy gains and losses that have ever occurred in that place and at least a few have survived. Cooling is more of a problem for some animals than others, thus many herbivores are nocturnal, shade seeking, and even subterranean. Overcoming food shortages (read: energy shortages) can be achieved by reducing activity, moving to a warm place, reducing convective losses (fur), and other means. When cold stressed or energy short, an animal must have stored energy reserves (fat), an alternative food source, or an escape. Omnivores are notably successful animals. Animals that behaviorally have only one food, or physiologically can process only one food type, are in jeopardy. They may have poor"design" for the current environment; probably optimum for the original conditions.
The ultimate biological reality is that energy going into energy budgeters should persist. It should be budgeted indefinitely. Thus reproduction is a strategy, but it also has high energy costs, entropy, both direct costs as well as those to stored energy supplies. Reproduction includes passing on energy-budgeting patterns, the knowledge or innate behaviors that assure at least some obeyance of the four laws. These are passed in the genetic code, very concentrated energy, or by parental teaching. Certain insects "know" all they ever will when they hatch. They do not see another insect of like kind. All knowledge for survival is coded. Bears (Ursus horridus) for example, have increased survival rates related to the length of an undisturbed learning period that cubs have with the female parent, an offspring of a previously well-bound family group. The energy strategies are many and varied and, all, I believe, are found in the continuous tetrahedronal volume formed of the four laws. Each individual surrounded by others in the population exists at one unique point in this volume.
Endangerment, the tendency of a species to violate or have violated one or more of the four laws (because they can and usually are interactive), remains our topic. In order to allocate money and effort well, it is necessary to (a) know when to stop allocating money, and (b) to know how much money or effort is needed to bring a species out of an endangered class or how much to spend to keep one from becoming endangered.
Populations have viability, a continuous concept. Past some point of abundance, it is no longer viable although it may still exist. See CAP08 and CAP12.
Sex
There are bisexual animals found (e.g., deer with testicles and ovaries). The probability of their occurrence is small (unless observations are not proportional to the number in the population). I suspect these unusual animals are discussed more than others, suggesting greater probability of occurrence than exists. One thing known is that the unusual is not unusual. For the experienced wildlifer, another "freak story" is as boring as another one about "the fish that got away." Better attention needs to be paid to recording these unusual events. This will allow a probability of occurrence to be expressed.
The probability of any animal being a female is determined from observations. For example, 130 males are seen and 240 females so 240 out of 370 total animals is 0.65. This is the proportion but it also can readily be interpreted as the probability of an animal seen being a female (CAP09, CAP408, and CAP148). (What is said to be the first such treatment of frequency as probability was by Von Neumann and Morgenstern 1944.)
If I see 2 males and 1 female, I am not ready to say that the proportion of females is 0.333. There are too few observations; I could have missed seeing other males or females, or both. The numbers could be 1/4, 2/4, or 2/5, from 0.25 to 0.5. This is best done by using statistical procedures to compare proportions (1) with a standard (e.g., is the proportion significantly different than 0.50?); (2) with a proportion from another area based on a different number of observations; or (3) to determine the number needed to be observed to be able to draw a suitable conclusion (CAP408 and CAP5020).
A sex ratio of 100:100 (males per 100 females is the convention) or 0.50 females is usually expected in unhunted populations. The manager needs to be skeptical in the light of small samples making this exact ratio rare. It is also rare due to natural phenomena such as for wood rats (Neotoma) (McClure 1981). These animals subjected to food shortages favor females during lactation. Other behavioral phenomena such as distances traveled, home range size, and energy efficiency of body size in periods of food shortage all tend to cause unbalanced sex ratios.
The system manager typically works with the proportion of females in a population. This proportion times the density estimate is an expression of the producers of young. The proportion suggests the size of a life group because the females in most species at some time of the year are very different creatures, more different from males than subspecies. Their water, calcium, and energy needs during pregnancy and lactation are very different than those in males. Knowledge of the proportion allows median total weights to be assigned; change in proportion is one means to estimate population abundance (discussed later); in young, it may provide an index to habitat conditions (a positive correlation to habitat quality), and allows for accounting of differential mortality as well as birth rates. See CAP40, CAP41, and CAP408.
Age
The same logic used in analyzing sex is used in determining the probability that an animal seen or known to be presentpresent being of a particular age. Having observed a sample, there being in age 1, 10 animals; age 2, 30 animals; and in age 3,70 animals, then the total is 110 and the probability of various animals in the population for which the sample is representative is then:
| Age 1 | 10/100 | 0.091 |
| Age 2 | 30/110 | 0.273 |
| Age 3 | 70/110 | 0.636 |
| 1.000 |
In Table 8.4 below it can be seen that the probability of an animal being a female (e.g., 0.333 based on a sex ratio analysis) of age 2 (e.g., 0.273) is 0.333 x 0.273 or 0.09.
The entire matrix for males and females is shown below. In a few cases, data may be available for each sex and age class.
Lest an important point be missed or forgotten, the reason for looking at the population is to understand the structure, dynamics, and relations of the individuals, (recall the definition of faunal resource system management) then to return to the population to which human benefits will be related. Modeling hundreds of individuals will help understand populations; the results, namely statistics, will be the inputs needed for making decisions.
That age ratios tell little by themselves (Caughley 1974) is no reason to avoid collecting age data. Typically, stable bird and terrestrial mammal populations will have about 3 young per adult. Typically, a broad-based age pyramid, young at the base, is desired for populations that are growing or that are stable. The integrated pest damage specialist will probably begin touting success if the age base is less than the mid-year age classes. Age ratios, like other ratios, are useful only relative to a specific objective. They do express (1) reproductive potential (often
| Table 8.4. The probabilities of an animal being of a particular sex and age in a hypothetical population discussed in the text. | ||
| Female Proportion in Age Class | Male Proportion in Age Class | |
|---|---|---|
| Age 1 | 0.33 x 0.091 = 0.030 | 0.67 x 0.091 = 0.061 |
| Age 2 | 0.33 x 0.273 = 0.090 | 0.67 x 0.273 = 0.183 |
| Age 3 | 0.33 x 0.636 = 0.209 | 0.67 x 0.636 = 0.426 |
| Sums | 0.329 | 0.670 |
| Total = 0.999 | ||
limited or non-existent in young animals) and (2) body weight (related to forage needs, energy losses, home range, reproductive activity, competitive advantages, and, of course, often with potential trophy value). Age can determine when an animal should be moved from one life group to another for analysis. Ratios or relative numbers are used by managers for cursory, preliminary, and low risk decisions. When risks are high, the age data flow to models in which various scenarios can be explored and consequences evaluated.
Survival
Survival (a major topic of dynamics in Section 3) is discussed briefly to help make the significance of life groups more clear. Young wild animals and plants have notoriously low survival. This is a short-term phenomenon, usually accounted within 1 year because of the grossness of most sampling. The young are a life group with population characteristics radically different from older animals. Trying for parsimony in expressing three life groups in one equation does not seem to be in the manager's best interest.
Most wild vertebrate adult animals of the forests have a relatively constant survival rate in each age class. At least the small numbers available prevent such an hypothesis being rejected very quickly. The numbers reported group nicely about a straight line which relates proportions of total population to proportion of maximum ecological longevity (Fig. 8.2). There are variations about this line but I suspect they are less than 5%. I suggest that if a fawn deer was described as a new deer or added to the population when it first ate forage and that a healthy adult was defined as one with metabolic efficiency equal to or greater than a 3-year animal of the same sex, then these would each comprise only one life group and each line would appear quite straight. The alleged concavity of most survival curves for forest fauna is thus often a function of the definition of a new animal and how many of these poorly-defined animals then cascade down through the years to their destined entropic state, short-term senescence (Eberhardt 1985). There are some that linger (a few that do not reproduce, eat differently than others, and in almost no way are similar to others of the species). The life group is the management topic, and old animals, for example, are of a different life group, very different than the young life group, even more different than some genera. Continuing to focus on some anatomical discriminator when the managerial task is so great and the few creatures of old life groups so insignificant probably constitutes misallocation of resources. Let it be noted then, that the manager should define the animal of importance (e.g., a 4-month old fawn deer, count or estimate those animals, and call them 100% of a life group. Then the manager may define the condition of a group such as "old healthy animals." These too may be counted or estimated. They may not represent an equal reduction at each end of the survivor curve. The results for different species show some tilts and variety in the curves. It is not surprising that among few studies with small, variable samples that the curvilinear survival curve hypothesis has been developed. For a few very long-lived forest creatures, a continuous curvilinear expression may be warranted ... but denying life group differences.
The Chapman-Robson methods described in Eberhardt (1971:474-476) and in CAP9081 and CAPW04 can be used for estimating survival rates among age classes for conventional analyses and making sense for deer and other animals where numbers in the older age classes are small (estimates of variance very high), and age discrimination usually questionable. Where age class 1 (young or fawns) contains 274 animals; age class 2 (yearling) contains 149 animals; and age class 3, 89 (all other animals lumped together); then, dropping the 274 in the young life group from considerations, and when the total animals is 512, then
T = 1 N1 + 2 N2
T = 1(149) + 2 (89) = 327
and the survival rate is
S = T/(n - m + T)
S = 327/(512 - 89 + 327) = 327/750
S = 0.44
This survival rate is likely to be very close to that obtained from detailed ages obtained at very high costs and a procedure that still requires doubtful assumptions about the influential factors being constant. Assuming the numbers represent a population that is performing under normal conditions ( or to which you wish to compare an abnormal situation), then a geometric series can be used to give these proportions in each age class (Table 8.5 and CAP9081). Eberhardt (1990) described other ways to approximate a survival rate.
| Table 8.5. Using an estimated survival rate(s) based on observed ages of animals and grouped older-age classes, the proportion in each age class can be estimated using a geometric series. Here S is 0.44 as computed in the text. | ||
| Age Class | Computation | Likely Proportion in Each Age Class |
| 1 | 1 - D | 0.56 |
| 2 | (1 - S)S | 0.25 |
| 3 | (1 - S)S2 | 0.11 |
| 4 | (1 - S)S3 | 0.05 |
| 5 | (1 - S)S4 | 0.02 |
| 6 | Others | 0.01 |
| 1.00 | ||
Survival for age classes is calculated as the probability of an animal surviving to the start of the next age.
Blower et al. (1981:72) showed the mean expectation of life of an individual (starting at time zero) being
eo = 1.0 / (1.0-S)
S, being the average survival rate. Faunal space quality (including that of humans) will surely be reflected by eo . When S is 0.8, then eo = 5 years. Modified for an assumed exponential decline in numbers, the life expectancy may then be
eo = -1.0 /logeS
See CAP9082.
The animal resource, like other natural resources, is a function of its substance (energy and/or matter, interchangeable), time, space and variety (Watt 1973). Any resource can be depicted as Fig. 8.3. The utility of the figure is that it emphasizes and gives wholeness to the idea that at any particular time, a resource (think of it as a point) can exist anywhere within the volume. When the point falls outside of the tetrahedron, it is no longer a resource. An animal that may be legally taken but which is in an area that cannot be reached before the season runs out is not a resource. Hunter access to a helicopter might make it become one. Nonmigratory animals on secret military areas are not a resource. A hunter with a stomach and freezer full of elk may not find that another elk is a resource. Variety is desired.
The faunal resource manager may manipulate any or all (preferably many because of likely cost-effectiveness) components of the resource tetrahedron. In this chapter the emphasis is on the physical animal, the energy-matter corner of the volume, but no one idea can be isolated for long.
Forest animal populations can be analyzed or designed (Fig. 1.7). The analytical categories are structure, dynamics, and relations. The structural elements are the species or life group comprised of the related abundance (or density), proportion of females (or sex ratio), and age (Fig. 8.4A);
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| Fig. 8.4a. The forest fauna all have the same fundamental structure. |
the dynamics are shown in Fig. 8.4b;
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| Fig. 8.4b. The major dynamic phenomena for population design and analysis are related. |
the fundamental relations are shown in Fig. 8.4c.
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| Fig. 8.4c. The fundamental relations within animal populations are those of energy budgeting and species perpetuation. |
What has been summarized above can bring order out of a vast literature on populations. The relevant subsystem is a population. Additional analytical work reveals that relatively parallel, nominal groups of ideas can emerge. These are the conceptual subsystems, and they are highly isomorphic with those of habitats or resource user groups. A null hypothesis is continually tested by the systems person. The hypothesis is that there is no isomorphism. The frequency with which this hypothesis is rejected is great; there is often pleasure found in the experience of such rejection. Once learned and when a clear structure becomes realized, then that taxonomy may be quickly ignored, just as after identifying a plant. Once known, named with confidence, sure of no surprises or no further need to reorganize or totally rethink an entire field of knowledge, then the names are laid aside for work in the cold, dark, and pestiferous heat.
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Last revision May 25, 2001.
