Click for next page ( 125


The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 124 7 Estimating Risk The concept of risk is central to the implementation of the Endangered Species Act. The committee was asked to review the role of risk in decisions made under the act, review whether different levels of risk apply to different types of decisions made under the act, and identify practical methods for assessing risk. Risk is the probability that something (usually a bad outcome) will occur. Risk assessment aims to estimate the likelihood of a particular (usually bad) outcome occurring. Risk management is an integrating framework that assesses the likelihood of bad outcomes and analyzes ways to minimize the risk of bad outcomes, or at least to respond appropriately if they occur. Many risk assessments follow the framework developed by the National Research Council to apply to human health (NRC, 1983); an example of a specific risk assessment framework is the one developed by EPA (Risk Assessment Forum, 1992), which tracks patterns of exposure to harmful substances and responses of ecological systems to these exposures. The sometimes confusing terminology of risk assessment and some of the issues in applying risk assessment to ecological systems were described by Policansky (1993); further examples were discussed by the National Research Council (NRC, 1993). The main risks involved in the implementation of the Endangered Species Act are the risk of extinction and the risks associated with unnecessary expenditures or curtailment of land use in the face of substantial uncertainties about the accuracy of estimated risks of extinction and about future events. In this chapter, we consider the problem of estimating the risk of use the print version of this publication as the authoritative version for attribution.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 125 extinction and the limitations of our current ability to estimate this risk. Models are an important tool for analyzing the consequences of complex processes, because intuition is often not reliable. In some cases, the predictions of the models discussed are not precise because information is lacking or because the underlying processes are not fully understood. They are valuable as guides to research and as tools for analyzing the comparative effects of various environmental and management scenarios. ESTIMATING THE RISK OF EXTINCTION Since the inception of the ESA, there have been enough developments in conservation biology, population genetics, and ecological theory that substantial scientific input can be used in the listing and recovery-planning processes. The following synthesizes and evaluates the various approaches and conclusions that have emerged from recent attempts to understand the vulnerability of small populations to extinction. The material focuses on random changes in population sizes and in their structure, changes in genetic variability, environmental fluctuations, and habitat fragmentation. Additional theoretical and field research are needed to resolve or reduce uncertainties, but existing analyses give insight into the relative magnitude and possible scaling of various influential factors in the extinction process. More thorough and technical reviews were provided by Dennis et al. (1991), Thompson (1991), and Burgman et al. (1992). SOURCES OF RISK Habitat loss, effects of introduced species, and in some cases overharvesting are almost always the ultimate causes of species extinction. Decline of populations to a low density makes them vulnerable to chance events and sets into play the extinction risks outlined below. When conditions have deteriorated to the point that a wild population cannot maintain a positive growth rate, no sophisticated risk analysis is required to tell us that extinction is inevitable without human intervention. Our attention will be focused instead on cases in which a population with a positive capacity for growth in an average year is still vulnerable to chance events that cause short-term excursions to low densities. Limitations of these approaches are discussed at the end of the section. Random Demographic Changes Demographic features, such as family size, sex, and age at mortality, vary naturally among individuals. In populations containing more than about 100 individuals, individual variation averages out and has little effect use the print version of this publication as the authoritative version for attribution.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 126 on the dynamics of population growth. However, in small populations, random variation in demographic factors can occasionally reach such an extreme state that extinction is certain. This can arise, for example, if all members of one sex die before reaching maturity or if all progeny are of the same sex, as was the case in the dusky seaside sparrow (Ammodramus maritimus nigrescens) after loss of habitat led to its population decline. Substantial effort has been expended to develop general models for predicting the risk to small populations of extinction due to demographic stochasticity. Several assumptions must be made about the ways in which populations grow, in particular, about the way population growth rates respond to density. From the standpoint of an endangered species, the simplest conceivable model assumes that the population has been pushed to its limits—resources (habitat and food availability) have become so scarce that, on average, the expected number of births in an interval is the same as the expected number of deaths. In this case, with individual births and deaths being random, the mean time to extinction for a population starting with N individuals is simply N generations (Leigh, 1981), i.e., the time to extinction increases linearly with the population size. (Box 7-1 contains definitions of terms; Box 7-2 has definitions of symbols used in analyses.) A more common situation is one in which resources are sufficient to support an average positive population growth when the population density is below a threshold. Due to chance, the actual growth rate in any generation will deviate somewhat from its expected value, and in the rare event that the cumulative growth rate realized over several consecutive generations is sufficiently negative, the population size will be reduced to zero (i.e., extinction will occur).1 All the demographic models discussed in this section assume that all members of the population are functionally identical. There is no variation based on age or sex; individuals are assumed to be identical with respect to reproductive and mortality rates. Thus, strictly speaking, the results apply best to short-lived asexual organisms or to hermaphrodites that synchronously reproduce toward the end of their life, as do many annual plants and some invertebrates. Models incorporating age structure, which are appropriate for vertebrates, require information on the mean and variance of age-specific mortality and fecundity schedules (Lande and Orzack, 1988; Tuljapurkar, 1989), information that is limited for even the best-studied species in nature. For species with separate sexes (most vertebrates and many other or use the print version of this publication as the authoritative version for attribution. 1With this type of model, the mean time to extinction increases exponentially with the product of the expected population growth rate at low density, , and the population carrying capacity, K, where K can be viewed as the number of individuals that a 2 reserve can sustain at stable density (see Example 7-1).

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 127 BOX 7-1 DEFINITION OF TERMS adaptive variation—genetic variation for characters upon which natural selection operates and which may be favored within the range of environments experienced by a species. character—the overt (phenotypic) expression of a gene or group of genes. deme—a local population of interbreeding organisms. density dependence—the influence of population density on a specific phenomenon, e.g., density- dependent growth. effective population size—the number of breeding adults that would give rise to the rate of inbreeding observed in a population if mating were at random and the sexes were equal in number. The effective population size is always less than the actual population size. fitness—relative reproductive success or genetic contribution to future generations. gametic mutation rate—the average total number of new mutations arising de novo in a gamete. gene pool— the total set of genes contained within a population or species. genetic variance- variability in the genomes of individuals within and between populations. geome—the complete set of genetic material carried by an individual. genotype—the specific set of genes—including the specification of their allelic forms—carried by an individual; may refer to a single genetic locus (e.g., blood genotype of an individual) or to the allelic forms of the complex of genes influencing the expression of a multifactorial trait. homozygous—most species inherit parallel sets of genes from their parents. For a gene with a particular function, a homozygous individual is one that inherits identical copies of the gene (i.e., the same allelic form) from both parents. If the allelic forms are different, the individual is heterozygous. mutation—a heritable change in a gene. outbreeding depression—a reduction in fitness in the hybrid progeny, or later descendants, of crosses between members of different populations. population—a group of closely related, interbreeding individuals. population bottleneck—a transient and extreme reduction in population size relative to normal population sizes such that genetic diversity is reduced simply by the reduction in population size. random genetic drift—changes in gene frequencies arising from chance sampling of gametes in small populations. stochasticity—random variation. use the print version of this publication as the authoritative version for attribution.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 128 BOX 7-2 DEFINITIONS OF MATHEMATICAL NOTATION C the rate at which a subpopulation will recolonize an area. E probability of extinction of a subpopulation. K population carrying capacity; number of adults the environment can sup-port. N population size. Ne effective population size. pe the average probability of extinction per generation. Only in the special case that pe is constant in time does .. the intrinsic rate of population growth; i.e., the expected exponential rate of population increase at densities less than K. the long-run average growth rate; equal to s the selection intensity operating against a deleterious mutation in the homozygous state. For example, if the deleterious gene affects viability to maturity such that s = 0.05, then a homozygote for the deleterious allele (all other things being equal) has a 5% reduction in the probability of surviving to maturity. the mean time to extinction, measured in generations. Ve between-generation variance of the population growth rate; i.e., the mean squared deviation of in any generation from the expected value of . µ the genomic deleterious mutation rate. Almost no data exist on this, except for Drosophila, although a fair amount of empirical work is now going on to fill this gap in our knowledge. The general principle of all experiments to estimate µ is the same—start with a genetically uniform stock; create sublines; maintain the sublines in isolation from each other with a minimum possible population size (to minimize the efficiency of natural selection against new mutations); and then over time watch the lines decline and diverge in terms of mean fitness. The details are somewhat complex statistically, but from this information (on the rate of decline in mean fitness and the rate of divergence of subline-specific fitness), it is possible to get a downwardly biased estimate of µ (Mukai, 1979; Houle et al., 1992). use the print version of this publication as the authoritative version for attribution.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 129 EXAMPLE 7-1 Suppose that the expected per capita growth rate is an average so long as the population size is below a carrying capacity K defined by the features of the environment. ( is a logarithmic growth rate, and an equal to 0.7/year implies that, at low density, the population can double its size in a year.) Assuming the population actually has positive growth potential ( ),_the mean extinction time (in generations) is approximately , where e = 2.72 is the base of natural logarithms (Leigh, 1981; . The term (e2rK/2rK) Goodman, 1987 a,b). Notice that this expression can be written as entirely determines the scaling with the population carrying capacity; i.e., the scaling with K depends on the composite parameter rK. The term (1 + r)/r is independent of population size. This shows that if random fluctuations in offspring number and individual survivorship alone were responsible for extinction, the mean extinction time would scale nearly exponentially with the product of the rate of increase and the carrying capacity of the environment. Unless this product is very small, this model predicts extreme longevity of populations-for , the mean extinction time is predicted to be 44, 24,000, and 267 million generations. Ecologists still do not have a broad understanding of mechanisms of density-dependence for most species. However, the logistic model, in which the expected growth rate of the population gradually declines to zero as the population approaches its carrying capacity, perhaps approximates reality more closely than the exponential model just described, where there is no damping of until K is reached (Begon and Mortimer, 1986). With logistic growth, the mean extinction time is predicted to be r where p = 3.14 (Leigh, 1981; Tier and Hanson, 1981; Gabriel and Bürger, 1992). As in the case of exponential growth (previous paragraph), the mean extinction time scales with the product but at only about half the rate. The reason for this is that under logistic growth conditions, populations are less strongly bounded away from zero density, and as a consequence, recover less rapidly from bottlenecks. For populations with low demographic potential, the mean time to extinction can be substantially less under the logistic growth model than under the exponential growth model. For example, with a carrying capacity of K = 25 adults and , the mean extinction times under the logistic and exponential growth models are 140 and 330 generations, respectively. use the print version of this publication as the authoritative version for attribution.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 130 ganisms), another source of demographic stochasticity can lead to extinction. When the population is small, there is some probability that all of the offspring produced in a generation will be of the same sex. For a population at size N, the probability of this event is 2(0.5N), and the reciprocal of this quantity, 2N-1, gives the mean extinction time if sex-ratio fluctuations are the only source of extinction.2 Unless the population is very small, sex-ratio fluctuations alone are unlikely to cause extinction. However, if the population birth rate is a function of the number of females, as is usually the case, sex-ratio fluctuations will generate fluctuations in the population birth rate. This type of synergism can reduce the mean survival time of a population by orders of magnitude relative to expectations from models that ignore sex (Gabriel and Bürger, 1992). For example, if the number of adults the environment can support (K) is less than 25 individuals or so, the mean time to extinction can be as low as 100 generations, even when the maximum rate of population growth is quite high. The preceding results apply to populations for which the initial density is at the carrying capacity. When a species is recognized as endangered, however, it usually has declined dramatically, at which point the recovery goal is to increase the population density to some higher sustainable level. Richter-Dyn and Goel (1972) developed a general solution for the mean extinction time starting from an arbitrary density, again assuming that random fluctuations in birth and death rates are the only source of extinction risk. Their model is quite flexible in that it allows for any pattern of density-dependence in the birth and death rates. Random Environmental Changes Demographic stochasticity becomes less important as the density of a population increases and individual differences average out; however, this is not the case when temporal variation in an exogenous factor, such as the weather, influences the reproductive or survival rates of all individuals in a population simultaneously. Environmental fluctuations influence different individuals to different degrees, but to this point, the theory has only been developed for the situation in which all individuals respond in an identical manner to environmental change. The discussion below expands on the use the print version of this publication as the authoritative version for attribution. 2The derivation of this relationship is as follows: the probability that an individual is male is 0.5, and the probability of all individuals being male in a sample of N individuals is 0.5N Similarly, the probability that the entire sample consists of females is 0.5N. Thus, the probability that the sample consists of either all males or all females is 2(0.5 N), in which case the population goes extinct due to its inability to reproduce.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 131 preceding section by incorporating environmental as well as demographic stochasticity. Most models consider the population to be growing with an average growth rate of per capita per year, and variance in this rate among generations, Ve, is due to environmental fluctuations. Typically, it is assumed that the variance is independent of population size and that there is no correlation between the state of the environment in one generation and the next. Such assumptions are probably rarely fulfilled in natural populations, and violations of them would most likely enhance the risk of extinction, as when generations of poor growth conditions tend to be clustered. These caveats aside, a general prediction of models that incorporate environmental stochasticity is that the mean extinction time is determined by the ratio —the higher the average growth rate and the lower the variance, the longer the population is likely to survive. Moreover, the rate of increase of population longevity with increasing K is much slower when environmental stochasticity is present than when demographic stochasticity operates alone (Example 7-2). Depending on the magnitude of Ve relative to , even populations with several hundreds or thousands of individuals can be vulnerable to environmental stochasticity. The theory just discussed treats environmental variation as a factor that drives variation in the intrinsic rate of population growth, . Although this is certainly likely to be true in many cases, environmental factors can also define the carrying capacity of a population. Thus, an alternative approach to the treatment of environmental stochasticity is to let K, as well as , vary. Variation in K alone cannot cause extinction, unless the carrying capacity actually declines below zero. However, K puts a ceiling on the attainable population size, and bottlenecks in K can magnify the effects of demographic stochasticity by enhancing the variation in the population growth rate due to the smaller sample of reproductive adults. Only limited work has been done on these issues (see Roughgarden, 1975; Slatkin, 1978). Catastrophes Catastrophes are extreme forms of environmental variation that suddenly and unpredictably reduce the population size. To the extent that these events are determined by the weather, lightning fires, epidemics, etc., human intervention can do little to influence their frequency. However, because catastrophes affect most members of a population to more or less the same extent, it is clear that, on the basis of chance alone, larger populations will have an increased likelihood of some individuals surviving this kind of event. Hanson and Tuckwell (1981) and Lande (1993) have considered the time to extinction for populations exposed to randomly occurring events, use the print version of this publication as the authoritative version for attribution.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 132 each reducing the population size to a constant fraction of its current size, the former using a logistic, and the latter an exponential growth model. In these models, there is no demographic or environmental stochasticity of the kinds noted above. Rather, extinction only occurs when, by chance, a cluster of catastrophes occurs. Provided the long-run growth rate is posi EXAMPLE 7-2 In a temporally varying environment, the long-run growth rate, , governs the vulnerability of a population to extinction (Lewontin and Cohen, 1969; Lande, 1993). Long-run growth rate is the average geometric rate of population expansion. In a constant environment, if a population could expand indefinitely after t time units, its expected size would be times its initial size. But in a variable environment, the expected size would be times the initial size. The fact that can be negative even when the average growth rate is positive underscores the importance of variation in population growth rates—if ( , the population will decline towards extinction deterministically. For the case in which the population grows with an expected positive rate as long as it is below the carrying capacity K, the mean time to extinction is proportional to (Leigh, 1981). This shows that the scaling of the time to extinction with population size depends on the ratio of the mean to the variance of the rate of population growth, If this ratio is 1/2, which implies a long-run growth rate of zero, the extinction time is expected to increase linearly with K (as in the case of demographic stochasticity, with ). When the variance in the rate of growth is greater than 2 , the extinction time increases less rapidly than linearly with K. Unless they incorporate all major sources of variability, these models cannot provide reliable estimates of extinction time. To gain some appreciation for the synergistic effects that can arise between demographic and environmental stochasticity, consider the situation in which K = 100, = 0.1, and Ve = 0.1. In this case, the long-run growth rate is slightly positive, . In the absence of environmental stochasticity (using results given for demographic stochasticity), the mean extinction time is predicted to be about 27 million generations. On the other hand, a formula derived by Lande (1993), which ignores demographic sources of stochasticity but includes environmental stochasticity, yields a predicted extinction time of 1900 generations. Leigh's formula, which incorporates both sources of variation, predicts a mean extinction time of only 145 generations. Models that further allow for density-dependent (logistic) population growth (Ludwig, 1976; Leigh, 1981; Tier and Hanson, 1981) yield still shorter times to extinction. use the print version of this publication as the authoritative version for attribution.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 133 tive, the mean extinction time increases exponentially with the carrying capacity under this model, with the rate of scaling increasing with the frequency of occurrence and magnitude of catastrophes. Assuming catastrophes act locally, spatial subdivision of a species provides a simple means of protection against extinction caused by devastating events. Accumulation of Deleterious Genetic Factors The reduction of a population to a low density has several negative genetic consequences that can magnify vulnerability to extinction. Most species harbor far more than enough deleterious recessive genes to kill individuals if they were to become completely homozygous (Simmons and Crow, 1977; Charlesworth and Charlesworth, 1987; Ralls et al., 1988; Hedrick and Miller, 1992). This large genetic load is essentially unavoidable because it is maintained by a deleterious mutation rate of approximately one per individual per generation (Mukai, 1979; Houle et al., 1992). In large populations, deleterious genes, particularly lethal genes, have only minor consequences—the frequencies of most deleterious genes are kept low by natural selection, and their expression is minimal because they are usually masked in the heterozygous state. This situation can change dramatically in small populations. During bottlenecks in population size, mildly deleterious genes, previously kept at low frequency by natural selection, can rise to high frequency by chance. When these genes become completely fixed (reach a frequency of 100%), a permanent reduction in population fitness results.3 Although some deleterious genes may be purged from a population early in a population bottleneck (Templeton and Read, 1984), the continued maintenance of a population at small size can only magnify the long- term accumulation of mildly deleterious genes. As noted above, deleterious mutations arise at a rate of about one per individual per generation. Provided the individual selective effects of these genes are small (on the order of 1/4Ne or less), they will accumulate at the genomic mutation rate (µ ), causing use the print version of this publication as the authoritative version for attribution. 3Roughly speaking, if N is the effective number of breeding adults and s is the selection intensity opposing a deleterious e gene in the homozygous state, then selection is ineffective if 4Nes < 1. Typically, because of high variance in family size, the effective population size is a third to a tenth of the actual number of breeding adults (Heywood, 1986; Briscoe et al., 1992). Thus, as a first approximation, if the number of breeding adults is less than 2/s, natural selection will be essentially incapable of eliminating a deleterious gene—its future frequency will be governed by chance, with the probability of fixation being equal to the initial frequency. The current wisdom is that s for an average mutation is approximately 0.025 (Simmons and Crow, 1977; Houle et al., 1992). Noting that 2/0.025 = 80, this implies that a substantial number of the rare deleterious genes in a population can drift to high frequency if the number of breeding adults is reduced to 100 or fewer individuals for a prolonged period.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 134 a decline in mean fitness of approximately µs per generation (Lynch, 1994). Thus, if µ = 1 and s = 0.025 (as described in footnote 3), a small population would be expected to experience a roughly 2.5% decline in fitness per generation due to deleterious mutations alone, and the rate of mutation accumulation declines with increasing population size. If the effective population size (Ne) is greater than 1,000, mutation accumulation is essentially halted for time scales relevant to endangered species management. However, if the accumulation of deleterious genes reaches the point at which the net reproductive rate of individuals is less than 1, the population is incapable of replacing itself. At this point, the population size begins to decline, and random drift progressively overwhelms natural selection; consequently, decline in fitness accelerates due to the accumulation of deleterious mutations. This synergism, whereby the rate of decline in fitness increases with the accumulation of deleterious genes, has been referred to as a ''mutational meltdown" (Lynch and Gabriel, 1990; Lynch et al., 1993) and, once initiated, can lead to rapid extinction. Loss of Adaptive Variation Within Populations Most populations, even those undisturbed by human activity, are exposed regularly to temporal and spatial variation in physical and biotic features of the environment. In principle, some species can cope with such selective challenges by simply migrating to suitable habitat (Pease et al., 1989). However, endangered species often live in highly fragmented habitats with inhospitable barriers; migration might not be an option. This leaves adaptive evolutionary change, which requires heritable genetic variation, as the primary means of responding to selective challenges (habitat degradation, global climatic change, species introductions, etc.) that threaten species with extinction. Consider a population that is faced with a gradual change in a critical environmental factor, such as temperature, humidity, or prey size. If the rate of change is sufficiently slow and the amount of genetic variance for the relevant characters in the population sufficiently high, then the population will be able to evolve slowly in response to the environmental change, without a major reduction in population size. If the rate of environmental change is too high, the selective load (reduced viability and fecundity) on the population will exceed the population's' capacity to maintain a positive rate of growth, and although the population might respond evolutionarily, it will become extinct in the process. Thus, for any population, there must be a critical rate of environmental change that allows the population to evolve just fast enough to maintain a stable size. Lynch and Lande (1993) showed that this critical rate is directly proportional to the genetic variance for the character upon which selection is acting. use the print version of this publication as the authoritative version for attribution.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 137 of the species, demonstrate that locally abundant species can sometimes be very close to extinction if the proportion of suitable habitat is near the extinction threshold. This again emphasizes that population size alone is not always a good indicator of vulnerability to extinction. Lande's (1987) models are idealized in that they envision a world consisting of two kinds of habitat patches —hospitable and inhospitable, all of equal size. The real world, of course, is more complex. Patches differ in size and shape, patch quality is usually a continuous variable, and some patches are connected by corridors, others not at all (see Chapter 5). More generalized approaches are discussed by Akçakaya and Ginzburg (1991). A significant feature of their approach is the inclusion of a correlation between the extinction probabilities of adjacent patches. This correlation, if positive, causes a reduction in the expected time to extinction. In other words, if all patches in an area became inhospitable at the same time, there would be no refuges available. For many species, the adverse consequences of habitat fragmentation are not caused so much by a loss of total area as by changes in the quality of habitat due to the development of edge effects on the margins of reserves (Lovejoy et al., 1986). Edge effects range from microclimatic changes resulting from structural changes in the environment to major alterations in the vegetational community to invasions by exotic species from agricultural and urban settings. The complete impact of edge effects may require several years to develop and may ultimately extend for several kilometers beyond the edge of the reserve. Some attempts have been made to capture the key features of edge effects in mathematical models (Cantrell and Cosner, 1991; 1993). The issues are very complex because they involve interspecific interactions, such as competition between reserve and invading species. Ultimately, the practical application of any of these models requires a deep understanding of the ecology of the species under consideration. Supplementation An increasingly common strategy for maintaining wild populations of endangered species is augmentation with stock from breeding facilities, as in the case of hatcheries for Pacific salmonids. An implicit assumption of such procedures is that recipient populations, when they still exist, actually derive some benefit from an artificial boost in population size. There are, however, several reasons why long-term deleterious consequences of supplementation may outweigh the short-term advantage of increased population size. First, over evolutionary time, successful populations are expected to become morphologically, physiologically, and behaviorally adapted to their local environments. Thus, the introduction of nonnative stock has the po use the print version of this publication as the authoritative version for attribution.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 138 tential to disrupt adaptations that are specific to the local habitat. This type of problem takes on added significance when the population employed in stocking has been maintained in captivity. Captive environments are often radically different than those in the wild, and over a period of several generations, "domestication selection" can potentially lead to the evolution of rather different behavioral or morphological phenotypes (Doyle and Hunte, 1981; Frankham and Loebel, 1992; NRC, 1995)—genotypes that perform well in the captive environment are expected to gradually displace those that do not. Furthermore, an overly protective captive breeding program may simply result in a relaxation of natural selection and the gradual accumulation of deleterious genes. For hatchery salmonids, egg-to-smolt survivorship is typically 50% or greater, as compared with 10% or less in natural populations (Waples, 1991; NRC, 1995). Second, local gene pools can be coadapted intrinsically (Templeton, 1986). Just as the external environment molds the evolution of local adaptations by natural selection, the internal genetic environment of individuals is expected to lead to the evolution of local complexes of genes that interact in a mutually favorable manner. The particular gene combinations that evolve in any local population will be largely fortuitous, depending in the long run on the chance variants that mutation provides for natural selection. The break-up of coadapted gene complexes by hybridization can lead to the production of individuals that have lower fitness than either parental type (outbreeding depression) and takes its extreme form in crosses between true biological species that cannot produce viable progeny. However, outbreeding depression can even occur between populations that appear to be adapted to identical extrinsic environments. The most dramatic evidence comes from reduced fitness in crosses of inbred lines of flies (Templeton et al., 1976) and plants (Parker, 1992), but crosses between outbreeding plants separated by several tens of meters can exhibit reduced fitness (Waser and Price, 1989), as can crosses between fish derived from different sites in the same drainage basin (Leberg, 1993). Outbreeding depression in response to stock transfer is a major concern in the management of Pacific salmon, which are subdivided into demes that are home to specific breeding grounds (Waples, 1991; Hard et al., 1992; NRC, 1995). Third, augmentation of wild populations with stock from captive breeding programs can have negative ecological or behavioral consequences. Unlike genetic effects, which can take several generations to emerge fully, ecological and behavioral effects can be immediate. For example, high-density hatchery populations of fish are prone to epidemics involving diseases that are uncommon in the natural environment. Such events provide strong selection for disease-resistant varieties of hatchery-reared fish, which subsequently can act as vectors to the wild population. The Norwegian Atlantic salmon is now threatened with extinction resulting from a parasite brought use the print version of this publication as the authoritative version for attribution.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 139 to Atlantic drainages by resistant stock from the Baltic (Johnsen and Jensen, 1986). Fourth, if a wild population is small because of habitat loss or alteration, the increased population density that results from augmentation can increase competition for food, space, or whatever else the habitat provides. That competition can further reduce the size of the wild population. Harvest of augmented wild populations (particularly if harvest levels are based on total population) can reduce the wild segment of the population unless the harvest effort is directed away from the wild population. A captive breeding and reintroduction program is appropriate only when there is no alternative means of ensuring short-term population viability or when there is strong evidence of historical gene flow. Habitat loss and degradation are the main reasons species become threatened or endangered; therefore, the protection of habitat plays a greater role in preserving these species than captive breeding and reintroduction. For example, as of 1991, the species specialist groups of the International Union for the Conservation of Nature (IUCN), which are international groups of scientists with expertise on specific kinds of animals, had completed conservation plans for 1,370 mammals. Of the recommendations in these plans, 517 concern protecting or managing habitat, while only 19 concern captive breeding and reintroduction (Stuart, 1991). Captive breeding and reintroduction are appropriate when suitable unoccupied habitat exists and the factors leading to extirpation of the species from this habitat have been identified and reduced or eliminated. Under these circumstances, captive breeding and reintroduction of threatened and endangered species can be part of a comprehensive strategy that also addresses the problems affecting species in the wild (Foose, 1989; Povilitis, 1990; Ballou, 1992; NRC, 1992a). For example, captive breeding and reintroduction enabled the peregrine falcon (Falco peregrinus) to repopulate much of North America after the use of DDT was eliminated (Cade, 1990). Similarly, Arabian oryx (Oryx leucoryx) were successfully reintroduced in several areas of their original range where hunting was prohibited (Stanley-Price, 1989). Captive breeding and reintroduction programs should be avoided when possible; however, once the need for a captive breeding program has been identified, it is advisable to initiate it as soon as possible. Starting the program before the wild population has been reduced to a mere handful of individuals increases a program's chances of success. Starting sooner provides time to solve husbandry problems, increases the likelihood that enough wild individuals can be captured to give the new captive population a secure genetic and demographic foundation, and minimizes adverse effects of removing individuals from the wild population. Captive breeding and reintroduction programs are the most expensive use the print version of this publication as the authoritative version for attribution.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 140 forms of wildlife management (Conway, 1986; Kleiman, 1989) and involve research and management actions. Although genetic and demographic management techniques for captive populations are fairly well developed and can be applied to most species (Ballou, 1992; Ralls and Ballou, 1992), husbandry and reintroduction techniques tend to be species specific. Zoos do not know how to breed many species, such as cheetahs (Actinomyxjubatus), reliably in captivity. In such cases, expensive and time-consuming research on genetics, behavior, nutrition, disease, or reproduction might be necessary to find the reasons for lack of breeding success. The reintroduction of captive-bred individuals also poses substantial technical challenges. Considerable research, in captivity and in the field, often is necessary during the early stages of the reintroduction process to develop successful techniques (Kleiman, 1989; Stanley-Price, 1991). FOCUSING CONSERVATION EFFORTS Life-history models can also help to identify the stages of an organism's life history most likely to be sensitive to conservation efforts. For example, the National Research Council (NRC, 1992b) concluded from life- history data and models that protecting juvenile and sub-adult sea turtles would have a greater effect on increasing population growth than reducing human-caused deaths of eggs and hatchlings. Similarly, by performing an analysis of the sensitivity of the population growth rate of the northern spotted owl to various demographic parameters, Lande (1988), based on the data available then, concluded that the most important contributors to the owl's survival were the adults' annual survival rate, followed by the survival rate of juveniles during their dispersal phase, and annual fecundity. DISTRIBUTION OF EXTINCTION TIMES The preceding discussion summarizes the state of our knowledge of how various factors contribute to the risk of population extinction. For practical reasons, the existing theory focuses almost entirely on the expected time to extinction. However, in the listing and management of endangered species, the primary focus is usually on the likelihood of extinction within a given time frame (Shaffer, 1981, 1987; Mace and Lande, 1991). Risk analysis requires information on the dispersion of the probability distribution of extinction times about the mean. For the models previously cited and many others (Burgman et al., 1992), the distribution of extinction times typically is strongly skewed to the right, with the most likely extinction time (the mode) being substantially less than the mean. In general, it is probably more useful to estimate extinction probabilities as a function of time for different population sizes than to identify some specific MVP. use the print version of this publication as the authoritative version for attribution.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 141 One conceptually simple way of relating risk to the mean extinction time is to assume that if the current ecological conditions remain stable, the probability of extinction per generation also remains stable.5 That cannot be strictly true, even in a constant environment, because demographic and genetic sources of stochasticity will ensure that the probability of extinction is not constant in time. For example, if by chance the population size dwindles, the risk of extinction will be elevated above the average risk until the population has recovered to its average size. LIMITATIONS OF OUR ABILITY TO ESTIMATE RISK We close this section by again emphasizing that the practical utility of any extinction model depends on the validity of its underlying assumptions. Virtually all work on the vulnerability to extinction has taken a single- factor approach, under the assumption that this will at least yield an understanding of how the expected extinction time scales with population size when a single factor is operating. Other than analytical and computational simplicity, there seems to be little justification for this approach to population viability analysis. Chapter 5 gives some examples of population viability analyses that have been useful and points out the need to recognize the uncertainties discussed here. In nature, populations are exposed to multiple sources of risk simultaneously. Synergism between different risk factors is not reflected in many models, and therefore the risk of extinction can be underestimated, as shown in Example 7-2 (see also Gabriel and Bürger, 1992). A field example of such synergism was described by Woolfenden and Fitzpatrick (1991); epizootic infections of the Florida scrub jay, which reduced local populations by 50%, also lowered reproductive success in the following seasons even after the death rates had returned to normal. Although analytical results are valuable as guides to research and as methods of comparing the effects of various environmental and management scenarios, they are probabilistic in nature, so they often ignore the use the print version of this publication as the authoritative version for attribution. 5 In this case, the conditional probability of extinction in any generation (given that the population has survived to that point) is simply the reciprocal of the mean extinction time, i.e., where is the mean time to extinction measured in generations. Because the probability that extinction does not occur in (x - 1) consecutive generations is (1-pe)x-1, and the probability that those (x - 1) generations are immediately followed by extinction is pe, the probability of extinction in generation x is pe(1 - pe)x-1. With this approach, the cumulative probability that the population will be extinct by generation t can be computed by solving the preceding expression for x = 1 to x = t, and summing these probabilities. Results in Gabriel and Bürger (1992) and Tier and Hanson (1981) suggest that this approach might provide a good first-order approximation to the distribution of extinction times due to demographic and environmental stochasticity under a broad range of conditions.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 142 underlying mechanisms. Perhaps their greatest potential is in combination with empirical evidence on extinction times, both in the laboratory and in the field (see for example Pimm et al., 1993). It remains to be seen how relevant such results are to natural populations. Most of the work on vulnerability of species has also focused on nonfragmented populations and, except in the case of asexual populations (Lynch et al., 1993), few formal attempts have been made to incorporate genetics into extinction models. There is a clear need for models that predict distributions of extinction times as a function of population density, demographic rates, mating system, environmental variation, etc. These models, which can only be evaluated by computer simulation (Shaffer and Samson, 1985; Caswell, 1989; Menges, 1992), can be expected to advance substantially in the next few years because computational power is now widely available. CONCLUSIONS AND RECOMMENDATIONS • Since the implementation of the Endangered Species Act, numerous models have been developed for estimating the risk of extinction for small populations. Although most of these models have shortcomings, they do provide valuable insights into the potential impacts of various management (or other) activities and of recovery plans. With only a few exceptions, biologically explicit, quantitative models for risk assessment have played only a minor role in decisions associated with the ESA. They should play a more central role, especially as guides to research and as tools for comparing the probable effects of various environmental and management scenarios. • Despite the major advances that have been made in models for predicting mean extinction times, the existing treatments still have substantial limitations. Most of the models are unifactorial in nature and fail to incorporate the negative synergistic effects that multiple risk factors have on the time to extinction. Efforts to jointly integrate genetic, demographic, and environmental stochasticity into spatially explicit frameworks are badly needed. • Most extinction models primarily address the mean extinction time. Because decisions associated with endangered species usually are couched in fairly short time frames—less than 100 years—models that predict the cumulative probability of extinction through various time horizons would have greater practical utility. • Results from population-genetic theory provide the basis for one fairly rigorous conclusion. Small population sizes usually lead to the loss of genetic variation, especially if the populations remain small for long periods. If the members of the population do not mate with each other at random (the case for most natural populations), then the effect of small size on loss of genetic variation is made more severe; the population is said to use the print version of this publication as the authoritative version for attribution.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 143 have a smaller effective size than its true size. Populations with long-term mean sizes greater than approximately 1,000 breeding adults can be viewed as genetically secure; any further increase in size would be unlikely to increase the amount of adaptive variation in a population. If the effective population size is substantially smaller than actual population size, this conclusion can translate into a goal for many species for survival of maintaining populations with more than a thousand mature individuals per generation, perhaps several thousand in some cases. An appropriate specific estimate of the number of individuals needed for long-term survival of any particular population must be based on knowledge of the biology of the organisms involved, such as sex ratios, breeding behavior, and so on. If information on the breeding structure of that species is lacking, information about a related species might be useful. REFERENCES Akçakaya, H.R., and L.R. Ginzburg. 1991. Ecological risk analysis for single and multiple populations. Pp. 78-87 in Species Conservation: A Population Biological Approach, A. Seitz and V. Loeschcke, eds. Basel, Switzerland: Birkhauser. Ballou, J.D. 1992. Genetic and demographic considerations in endangered species captive breeding and reintroduction programs. Pp. 262-275 in Wildlife 2001: Populations. D. McCullough and R. Barrett, eds. Barking, U.K.: Elsevier. Barton, N.H., and M. Turelli. 1989. Evolutionary quantitative genetics: How little do we know? Annu. Rev. Genet. 23:337-370. Begon, M., and M. Mortimer. 1986. Population Ecology. Sunderland, Mass.: Sinauer Associates. Briscoe, D.A., J.M. Malpica, A. Robertson, G.J. Smith, R. Frankham, R.G. Banks, and J.S.F. Barker. 1992. Rapid loss of genetic variation in large captive populations of Drosophila flies: Implications for the genetic management of captive populations. Conserv. Biol. 6:416-425. Bürger, R., G.P. Wagner, and F. Stettinger. 1989. How much heritable variation can be maintained in finite populations by a mutation- selection balance? Evolution 43:1748-1766. Burgman, M.A., S. Ferson, and H.R. Akçakaya. 1992. Risk Assessment in Conservation Biology. New York: Chapman and Hall. Cade, T. 1990. Peregrine falcon recovery. Endangered Species Update 8:40-45. Cantrell, R.S., and C. Cosner. 1991. The effects of spatial heterogeneity in population dynamics . J. Math. Biol. 29:315-338. Cantrell, R.S., and C. Cosner. 1993. Should a park be an island? SIAM J. Appl. Math. 53:219-252. Caswell, H. 1989. Matrix Population Models. Sunderland, Mass.: Sinauer Associates. Charlesworth, D., and B. Charlesworth. 1987. Inbreeding depression and its evolutionary consequences. Annu. Rev. Ecol. Syst. 18:237-268. Conway, W. 1986. The practical difficulties and financial implications of endangered species breeding programs. Int. Zoo Yearbook 24/25:210-219. Dennis, B., P.L. Munholland, and J.M. Scott. 1991. Estimation of growth and extinction parameters for endangered species. Ecol. Monogr. 61:115-143. use the print version of this publication as the authoritative version for attribution.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 144 Doyle, R.W., and W. Hunte. 1981. Demography of an estuarine amphipod (Gammarus lawrencianus) experimentally selected for high ''r": A model of the genetic effects of environmental change. Can. J. Fish. Aquat. Sci. 38:1120-1127. Foley, P. 1992. Small population genetic variability at loci under stabilizing selection. Evolution 46:763-774. Foose, T.J. 1989. Species survival plans: the role of captive propagation in conservation strategies. Pp. 210-222 in Conservation Biology and the Black-footed Ferret, U.S. Seal, E.T. Thorne, M.A. Bogan, and S.H. Anderson, eds. New Haven, Conn.: Yale University Press. Frankham, R., and D.A. Loebel. 1992. Modeling problems in conservation genetics using captive Drosophila populations: Rapid genetic adaptation to captivity. Zoo Biol. 11:333-342. Franklin, I.R. 1980. Evolutionary changes in small populations. Pp. 135-149 in Conservation Biology: an Evolutionary-Ecological Perspective, M. E. Soulé and B. A. Wilcox, eds. Sunderland, Mass.: Sinauer Associates. Gabriel, W., and R. Bürger. 1992. Survival of small populations under demographic stochasticity. Theor. Pop. Biol. 41:44-71. Gilpin, M.E. 1988. A comment on Quinn and Hastings: Extinction in subdivided habitats. Conserv. Biol. 2:290-292. Gilpin, M.E., and I. Hanski, eds. 1991. Metapopulation Dynamics: Empirical and Theoretical Investigations. New York: Academic. Goodman, D. 1987a. The demography of chance extinction. Pp. 11-43 in Viable Populations for Conservation, M. E. Soulé, ed. New York: Cambridge University Press. Goodman, D. 1987b. Consideration of stochastic demography in the design and management of biological reserves. Nat. Res. Model. 1:205-234. Hanson, F.B., and H.C. Tuckwell. 1981. Logistic growth with random density independent disasters. Theor. Pop. Biol. 19:1-18. Hard, J.J., R.P. Jones, Jr., M.R. Delman, and R.S. Waples. 1992. Pacific Salmon and Artificial Propagation under the Endangered Species Act. NOAA Tech. Memo. NMFS-NWFSC2. U.S. Department of Commerce, Washington, D.C. Hedrick, P.W., and P.S. Miller. 1992. Conservation genetics: Techniques and Fundamentals. Ecol. Appl. 2:30-46. Heywood, J. 1986. The effect of plant size variation on genetic drift in populations of annuals. Am. Naturalist 127:851-861. Houle, D., D.K. Hoffmaster, S. Assimacopoulos, and B. Charlesworth. 1992. The genomic mutation rate for fitness in Drosophila. Nature 359:58-60. Johnsen, B.O., and A.J. Jensen. 1986. Infestations of Atlantic salmon, Salmo salar, by Gyrodactylus salaris in Norwegian rivers. J. Fish. Biol. 29:233-241. Keightley, P.D., and W.G. Hill. 1989. Quantitative genetic variability maintained by mutation-stabilizing selection balance: sampling variation and response to subsequent directional selection. Genet. Res. 54:45-57. Kleiman, D.G. 1989. Reintroduction of captive animals for conservation. BioScience 39:152-161. Lande, R. 1987. Extinction thresholds in demographic models of territorial populations. Am. Naturalist 130:624-635. Lande, R. 1988. Demographic models of the northern spotted owl (Strix occidentalis caurina). Oecologia 75:601-607. Lande, R. 1993. Risks of population extinction from demographic and environmental stochasticity, and random catastrophes. Am. Naturalist 142:911-927. Lande, R., and G. F. Barrowclough. 1987. Effective population size, genetic variation, and use the print version of this publication as the authoritative version for attribution.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 145 their use in population management. Pp. 87-123 in Viable Populations for Conservation, M.E. Soulé, ed. New York: Cambridge University Press. Lande, R., and S.H. Orzack. 1988. Extinction dynamics of age-structured populations in a fluctuating environment. Proc. Natl. Acad. Sci. USA 85:7418-7421. Leberg, P.L. 1993. Strategies for population reintroduction: Effects of genetic variability on population growth and size. Conserv. Biol. 7:194-199. Leigh, E.G., Jr. 1981. The average lifetime of a population in a varying environment. J. Theor. Biol. 90:213-239. Levins, R. 1970. Extinction. Pp. 77-107 in Some Mathematical Questions in Biology, M. Gerstenhaber, ed. Providence, R.I.: American Mathematical Society. Lewontin, R.C., and D. Cohen. 1969. On population growth in a randomly varying environment. Proc. Natl. Acad. Sci. USA 62:1056-1060. Lovejoy, T.E., R.O. Bierregaard, Jr., A.B. Rylands, J.R. Malcolm, C.E. Quintela, L.H. Harper, K.S. Brown, Jr., A.H. Powell, G.V.N. Powell, H.O.R. Schubart, and M.B. Hays. 1986. Edge and other effects of isolation on Amazon forest fragments. Pp. 257-285 in Conservation Biology: The Science of Scarcity and Diversity, M.E. Soulé, ed. Sunderland, Mass.: Sinauer Associates. Ludwig, D. 1976. A singular perturbation problem in the theory of population extinction. Soc. Ind. Appl. Math.-Am. Math. Soc. Proc. 10:87-104. Lynch, M. 1994. Neutral models of phenotypic evolution. Pp. 86-108 in Ecological Genetics, L. Real, ed. Princeton, N.J.: Princeton University Press. Lynch, M., R. Bürger, D. Butcher, and W. Gabriel. 1993. The mutational meltdown in asexual populations. J. Hered. 84:339-344. Lynch, M., and W. Gabriel. 1990. Mutation load and the survival of small populations. Evolution 44:1725-1737. Lynch, M., and R. Lande. 1993. Evolution and extinction in response to environmental change. Pp. 234-250 in Biotic Interactions and Global Change, P. M. Kareiva, J. G. Kingsolver, and R. B. Huey, eds. Sunderland, Mass.: Sinauer Associates. Mace, G.M., and R. Lande. 1991. Assessing extinction threats: toward a reevaluation of IUCN threatened species categories. Conserv. Biol. 5:148-157. Menges, E.S. 1992. Stochastic modeling of extinction in plant populations. Pp. 253-276 in Conservation Biology, P.L. Fiedler and S.K. Jain, eds. New York: Chapman and Hall. Mukai, T. 1979. Polygenic mutations. Pp. 177-196 in Quantitative Genetic Variation, J.N. Thompson, Jr., and J.M. Thoday, eds. New York: Academic. NRC (National Research Council). 1983. Risk Assessment in the Federal Government: Managing the Process. Washington, D.C.: National Academy Press. NRC (National Research Council). 1992a. The Scientific Bases for the Preservation of the Hawaiian Crow. Washington, D.C.: National Academy Press. NRC (National Research Council). 1992b. Decline of the Sea Turtles: Causes and Prevention. Washington, D.C.: National Academy Press. NRC (National Research Council). 1993. Issues in Risk Assessment. Washington, D.C.: National Academy Press. NRC (National Research Council). 1995. Upstream: Salmon and Society in the Pacific Northwest. Washington, D.C.: National Academy Press. Parker, M.A. 1992. Outbreeding depression in a selfing annual. Evolution 46:837-841. Pease, C.M., R. Lande, and J.J. Bull. 1989. A model of population growth, dispersal and evolution in a changing environment. Ecology 70:1657-1664. Pimm, S.L., J. Diamond, T.M. Reed, G.J. Russell, and J. Verner. 1993. Times to extinction for small populations of large birds. Proc. Natl. Acad. Sci. USA 90:10871-10875. Policansky, D. 1993. Application of ecological knowledge to environmental problems: Eco use the print version of this publication as the authoritative version for attribution.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 146 logical risk assessment. Pp. 37-51 in Comparative Risk Assessment, C.R. Cothern, ed. Boca Raton, Fla.: Lewis Publishers. Povilitis, T. 1990. Is captive breeding an appropriate strategy for endangered species conservation? Endangered Species Update 8:20-23. Quinn, J.F., and A. Hastings. 1987. Extinction in subdivided habitats. Conserv. Biol. 1:198-208. Quinn, J.F., and A. Hastings. 1988. Extinction in subdivided habitats: Reply to Gilpin. Conserv. Biol. 2:293-296. Ralls, K., and J.D. Ballou. 1992. Managing genetic diversity in captive breeding and reintroduction programs. Trans. North Am. Wild). Nat. Resour. Conf. 57:263-282. Ralls, K., J.D. Ballou, and A. Templeton. 1988. Estimates of lethal equivalents and the cost of inbreeding in mammals. Conserv. Biol. 2:185-193. Richter-Dyn, N., and N.S. Goel. 1972. On the extinction of a colonizing species. Theor. Pop. Biol. 3:406-433. Risk Assessment Forum. 1992. Framework for Ecological Risk Assessment. EPA/630/R-92/001. U.S. Environmental Protection Agency, Washington, DC. Roughgarden, J. 1975. A simple model for population dynamics in stochastic environments. Am. Naturalist 109:713-736. Shaffer, M.L. 1981. Minimum population sizes for species conservation. BioScience 31:131-134. Shaffer, M.L. 1987. Minimum viable populations: Coping with uncertainty. Pp. 69-86 in Viable Populations for Conservation, M.E. Soulé, ed. New York: Cambridge University Press. Shaffer, M.L., and F.B. Samson. 1985. Population size and extinction: A note on determining critical population sizes. Am. Naturalist 125:144-152. Simmons, M.J., and J.F. Crow. 1977. Mutations affecting fitness in Drosophila populations. Annu. Rev. Genet. 11:49-78. Slatkin, M. 1978. The dynamics of a population in a Markovian environment. Ecology 59:249-256. Soulé, M.E., M. Gilpin, N. Conway, and T. Foose. 1986. The millennium ark: How long a voyage, how many staterooms, how many passengers? Zoo Biol. 5:101-114. Stanley-Price, M.R. 1989. Animal Reintroductions: The Arabian Oryx in Oman. Cambridge, U.K.: Cambridge University Press. 291 pages. Stanley-Price, M.R. 1991. A review of mammal re-introductions, and the role of the reintroduction specialist group of IUCN/SSC. Pp. 9-25 in Beyond Captive Breeding: Reintroducing Endangered Mammals to the Wild, J.H.W. Gipps, ed. Zoological Society of London Symposia 62. Oxford, U.K.: Clarendon Press. Stuart, S.N. 1991. Re-introductions: To what extent are they needed? Pp. 27-37 in Beyond Captive Breeding: Reintroducing Endangered Mammals to the Wild, J.H.W. Gipps ed. Zoological Society of London Symposia 62. Oxford, U.K.: Clarendon Press. Templeton, A.R. 1986. Coadaptation and outbreeding depression. Pp. 105-116 in Conservation Biology: The Science of Scarcity and Diversity, M.E. Soulé, ed. Sunderland, Mass.: Sinauer Associates. Templeton, A.R., and B. Read. 1984. Factors eliminating inbreeding depression in a captive herd of Speke's gazelle. Zoo Biol. 3:177-199. Templeton, A.R., C.F. Sing, and B. Brokaw. 1976. The unit of selection in Drosophila mercatorum. I. The interaction of selection and meiosis in parthenogenetic strains. Genetics 82:349-376. Thompson, G.G. 1991. Determining Minimum Viable Populations under the Endangered Species Act. NOAA Tech. Memo. NMFS F/ NWC-198. U.S. Department of Commerce, Washington, D.C. use the print version of this publication as the authoritative version for attribution.

OCR for page 124
About this PDF file: This new digital representation of the original work has been recomposed from XML files created from the original paper book, not from the original typesetting files. Page breaks are true to the original; line lengths, word breaks, heading styles, and other typesetting-specific formatting, however, cannot be retained, and some typographic errors may have been accidentally inserted. Please ESTIMATING RISK 147 Tier, C., and F.B. Hanson. 1981. Persistence in density dependent stochastic populations. Math. Biosci. 53:89-117. Tuljapurkar, S. 1989. An uncertain life: Demography in random environments. Theor. Pop. Biol. 35:227-294. Waples, R.S. 1991. Genetic interactions between hatchery and wild salmonids: Lessons from the Pacific Northwest. Can. J. Fish. Aquat. Sci. 48:124-133. Waser, N.M., and M.V. Price. 1989. Optimal outcrossing in Ipomopsis aggregata: seed set and offspring fitness. Evolution 43:1097-1109. Woolfenden, G.E., and J. Fitzpatrick. 1991. Florida Scrub Jay Ecology and Conservation. Pp. 542-565 in Bird Population Studies: Relevance to Conservation and Management, C.M. Perrins, J.D. Leberton, and G.J.M. Hirons, eds. New York: Oxford University Press. Wright, S. 1931. Evolution in mendelian populations. Genetics 16:97-159. Zeng, Z.-B., and C.C. Cockerham. 1991. Variance of neutral genetic variances within and between populations for a quantitative character. Genetics 129:535-553. use the print version of this publication as the authoritative version for attribution.