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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 MAKING ESA DECISIONS IN THE FACE OF UNCERTAINTY 151 BOX 8-2 U.S. FISH AND WILDLIFE SERVICE DEFINITIONS FOR CATEGORIES 1, 2, AND 3, UNDER THE ENDANGERED SPECIES ACT (FED. REG. 58:51145, SEPT. 30, 1993). Category 1.—Taxa for which the Service has on file sufficient information on biological vulnerability and threat(s) to support proposals to list them as endangered or threatened species. Proposed rules have not yet been issued because this action is precluded at present by other listing activity. Development and publication of proposed rules on Category 1 taxa are anticipated, however, and the Service encourages other Federal agencies to give consideration to such taxa in environmental planning. Category 2.—Taxa for which information now in the possession of the Service indicates that proposing to list as endangered or threatened is possibly appropriate, but for which sufficient data on biological vulnerability and threat are not currently available to support proposed rules. The Service emphasizes that these taxa are not being proposed for listing by this notice, and that there are not current plans for such proposals unless additional supporting information becomes available. Further biological research and field study usually will be necessary to ascertain the status of taxa in this category. It is likely that many will be found not to warrant listing, either because they are not threatened or endangered or because they do not qualify as species under the definitions in the Act, while others will be found to be in greater danger of extinction than some taxa in Category 1. Category 3.—Taxa in Category 3 are not current candidates for listing. Such taxa are further divided into three subcategories to indicate the reason(s) for their removal from consideration: Category 3A.—Taxa for which the Service has persuasive evidence of extinction. If rediscovered, such taxa might acquire high priority for listing. At this time, however, the best available information indicates that the taxa in this subcategory, or the habitats from which they were known, have been lost. Category 3B.—Names that, on the basis of current taxonomic understanding (usually as represented in published revisions and monographs), do not represent distinct taxa meeting the Act's definition of "species." Such supposed taxa could be reevaluated in the future on the basis of new information. Category 3C.—Taxa that have proven to be more abundant or widespread than previously believed and/or those that are not subject to any identifiable threat. If further research or changes in habitat indicate a significant decline in any of these taxa, they may be reevaluated for possible inclusion in categories 1 or 2. use the print version of this publication as the authoritative version for attribution.

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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 MAKING ESA DECISIONS IN THE FACE OF UNCERTAINTY 152 PROVIDING OBJECTIVE RISK STANDARDS The qualitative definitions in the act provide a framework for decision-making, that is, they provide a list of administrative and management actions and a general rationale for selecting each action. However, qualitative definitions alone can be interpreted in different ways by different people, and agencies have provided no guidance on the appropriate degrees of extinction risk for making the different decisions required by the act, such as listing a species as either threatened or endangered or declaring a species recovered. To ensure that ESA decisions protect endangered species as they are intended to and do so in a scientifically defensible way requires objective methods for assessing risk of extinction (see Chapter 7) and for assigning species to categories of protection according to their risk of extinction. Standards for assigning species to categories should be quantitative wherever possible, and, when this is not possible, qualitative procedures should at least be systematic and clearly defined. Developing Quantitative Risk Standards Agencies could achieve greater consistency in decisions if they provided quantitative risk standards for such terms as "endangered" and "threatened" to the many agency personnel involved in implementing the act. Risk levels should be defined as the probability of extinction within a specific time. As an example of quantitative guidance, Shaffer (1981) defined a minimum viable population size as that which would have at least a 99% chance of surviving for 1,000 years. The committee is not endorsing Shaffer's definition but noting that by providing quantitative guidance, Shaffer has made it possible to discuss or disagree with his definition objectively and apply the definition in a standard manner. As another example of a quantitative definition, Mace and Lande (1991) suggested that endangered could be defined as a 20% chance of extinction in 20 years. Time Frame for Estimating Risk of Extinction When providing a quantitative standard for assigning risk categories, risk of extinction must be defined with a specific time frame in mind, i.e., x probability of extinction in y years. Critical levels of extinction risk, which will trigger ESA decisions, such as listing or delisting, must be associated with a particular number of years or generations. As the time frame increases, the probability of extinction also increases, approaching 100% for all species if the period is long enough (see Chapter 2). The choice of a time frame for evaluating risk of extinction for purposes of the ESA reflects scientific and societal concerns. From a scientific standpoint, time should use the print version of this publication as the authoritative version for attribution.

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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 MAKING ESA DECISIONS IN THE FACE OF UNCERTAINTY 153 be long enough to guard against making management choices that might have favorable effects over the short term (e.g., the next 5 to 10 years), but consequent adverse effects over the longer term (e.g., the next 100 years or more). An example of such a choice might be management that extends the lifespan of adults currently in a population (enhancing short-term survival), but jeopardizes successful reproduction (leading to population decline as the current adult population ages and dies). Species' life-history characteristics help determine an appropriate time frame, with longer times being more appropriate for longer-lived species. Another scientific consideration is the time scale for natural cycles of disturbance and regeneration in the species' habitat. Evaluating the success of endangered species management over only a portion of the natural habitat cycle runs the risk of confusing natural fluctuations in population size with adverse reactions to management. Societal considerations regarding time frame include the desire to preserve species and their habitats over time scales meaningful to humans and their offspring, political cycles of 2-6 years, economic cycles, and many others. Because of the large variety of societal factors, this committee cannot specify all the appropriate time scales that should be considered in decision making or even the range of time scales for which extinction probabilities should be calculated. However, it is clear that the time scales implicit in the various calculations should be made explicit for informed decision making. In some cases, where there is an immediate and potentially reversible threat to species survival (such as a proposed development), it could make sense to analyze the probability of survival over a short period, perhaps 5 to 10 years or less, when comparing options for species recovery over the short term. Such an analysis should be followed, however, by another assessment of extinction risk over a longer period to ensure that short-term gains do not become long-term losses. Listing Systems Based on Objective Criteria and Rules A system for making listing decisions requires a set of objective criteria for assigning species to risk categories, such as endangered or threatened. The objective criteria most suitable for making listing decisions would be different degrees of extinction risk. However, we rarely have sufficient data to allow good estimates of extinction risk, so we need a system allowing the use of other criteria as well. Such a system could be based on objective criteria, such as some combination of population size and number, believed to represent a specific level of extinction risk. The need to develop a listing system based on objective criteria has been recognized by the principal international organization concerned with the listing and conservation of endangered species, the International Union use the print version of this publication as the authoritative version for attribution.

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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 MAKING ESA DECISIONS IN THE FACE OF UNCERTAINTY 154 for the Conservation of Nature (IUCN). In June 1992, the CITES (Convention on the International Trade in Endangered Species of Flora and Fauna) Standing Committee requested that IUCN help to develop new criteria for listing species in the appendices to the CITES treaty, which regulates trade in wildlife and wildlife products. The resulting proposed IUCN system for assessing degree of threat (Mace et al., 1992) was produced after an international effort by groups of scientists in a series of workshops. The proposed system is now being evaluated by members of the IUCN/SSC taxon specialist groups, various CITES committees, and interested scientists (Gnam, 1993) and was considered at the CITES 1994 meeting. At that meeting, a proposal was made to adopt the IUCN criteria; a counterproposal was made by the United States and a working group developed a compromise, which was recently accepted by CITES (Rosemarie Gnam, FWS, pers. commun., March 1, 1995). Much of the substance of the IUCN criteria remains in Annex 5 of the report as definitions, rather than the criteria for listing. In the proposed IUCN system, a species could be listed as endangered based on any of several criteria, each of which was intended to represent approximately the same risk of extinction. Decisions to list a species could be based on any of the following criteria: probability of extinction, trends in abundance, population size, number of populations, and geographical extent (Mace et al., 1992). The IUCN system was based on a combination of two previous approaches for assessing degree of threat: population-based criteria developed by those working with higher vertebrates and area-habitat criteria developed by those working with plants and invertebrates. Population- based criteria, known as the ''Mace-Lande criteria," were proposed in 1991 (Mace and Lande, 1991). They suggested three categories: Critical: 50% probability of extinction within 5 years or two generations, whichever is longer. Endangered: 20% probability of extinction within 20 years or 10 generations, whichever is longer. Vulnerable: 10% probability of extinction within 100 years. Unfortunately, due to the limited data on most taxa of conservation concern, a formal population viability analysis to estimate the probability of extinction in a particular case is often impossible. Moreover, population viability models for plants are poorly developed (Menges, 1990). To address this problem, Mace and Lande offered several surrogate criteria based on other types of information. For example, they considered that a population would qualify as endangered if the current population estimate was fewer than 250 mature individuals, even if insufficient demographic data were available to calculate an estimated probability of extinction. The current IUCN criteria rely heavily on population viability analysis, popula use the print version of this publication as the authoritative version for attribution.

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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 MAKING ESA DECISIONS IN THE FACE OF UNCERTAINTY 155 tion size, and range area. Concern has been expressed that these criteria omit historical data on a species' abundance and distribution, omit data on reproductive fitness, and ignore life cycles (Gnam, 1993). Furthermore, the IUCN system is still incomplete, because it lacks a set of rules to allow decisions to be made when there is uncertainty regarding the criteria used to categorize species according to relative risk of extinction. Nevertheless, those criteria represent the most important scientific effort to date to reach consensus on standard criteria for assigning taxa to threat categories in a uniform, objective manner. The adoption of a similar system in the U.S. would make listing decisions more consistent. Unfortunately, there is as yet no evidence that the criteria used in the IUCN system do represent comparable degrees of risk. Any system of criteria developed for use with the ESA should be thoroughly tested against a variety of population and metapopulation models before implementation. Designing and testing an appropriate system for listing species is a formidable scientific task best accomplished by an independent scientific committee. For example, for a much simpler problem, management of the commercial harvest of whales, the Scientific Committee, of the International Whaling Commission took 6 years to evaluate five alternative proposed management procedures (Donovan, 1989; R. Brownell, pers. commun., NMFS, 1994). Limitations on Estimates of Risk Our ability to estimate risk of extinction for use in assigning species to protection categories is limited by our understanding of the factors influencing extinction. Two areas where we are acutely aware of limitations are in models used to estimate risk of extinction and in our understanding of the role of critical thresholds of risk and of cumulative effects of risk factors. Limits of Models As described in Chapter 7, models for estimating risk of extinction are limited in their ability to incorporate the full complexity of species population dynamics. Estimates of risk derived from these models may reflect only a subset of the factors actually influencing a species' risk of extinction. As discussed in Chapter 7, it seems likely that the simplifications and omissions of current models can underestimate risk of extinction. Poor Understanding of Cumulative Effects and Thresholds Decisions under the ESA regarding take or jeopardy require making decisions regarding incremental risks of extinction. Assessing the added use the print version of this publication as the authoritative version for attribution.

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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 MAKING ESA DECISIONS IN THE FACE OF UNCERTAINTY 156 risk from specific human actions is usually an even more difficult task than estimating the overall extinction risk to a species. Individual human actions, such as developing a few acres of habitat, pose small incremental risks of extinction. At some point, however, the small incremental risks from numerous human actions, if not stemmed, will accumulate so as to produce a major effect. Not enough is known about cumulative effects and threshold points to make accurate risk predictions possible (e.g., Beanlands et al., 1986), although there has been some theoretical work on critical thresholds of habitat patch size or fragmentation (e.g., Lande, 1988 for spotted owl habitat fragmentation). When considering the probable effects of incremental human activities, it is reasonable to assume that additional activity means additional risk, but we rarely know whether the relationship between additional activity and additional risk is linear or whether there might be critical levels of activity above which the risk of extinction increases dramatically. Should Different Risk Standards Apply to Different ESA Decisions? Endangered, Threatened, Recovered In judging whether different levels of risk should apply to different types of decisions under the act, the committee considered carefully the terminology of the act shown in Box 8-1. The definition of an endangered species as one that is already in danger of extinction and a threatened species as one that is likely to become an endangered species implies that a species listed as endangered is at greater risk of extinction than a threatened one. The determination that a species should be removed from the list implies that its risk of extinction has decreased to the point where it is no longer considered threatened. Thus, it is clear that determinations of a single species as successively endangered, threatened, and recovered should represent a series of decreasing levels of risk of extinction faced by the species. Different Taxa Although cross-species comparisons are complicated by many factors specific to the biology of individual species, it is appropriate to set the same degree of risk as a standard for listing any species, whether plant or animal, as endangered and another, somewhat lower degree of risk, for listing any species as threatened. Thus it is reasonable to expect that species determined to be endangered should, on the average, face a greater risk of extinction than those determined to be threatened. This seemingly obvious use the print version of this publication as the authoritative version for attribution.

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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 MAKING ESA DECISIONS IN THE FACE OF UNCERTAINTY 157 ordering of risk has not always been followed in practice (Wilcove et al., 1993). Public Versus Private Land Controversy has arisen over whether the inclusion of habitat destruction or modification as a form of taking under Section 9 sets a different standard of responsibility for protection of endangered species by private versus public entities. (We note again that this interpretation of taking is under court review at this writing.) In particular, it has appeared to some that the standard of responsibility might be interpreted to be more stringent for private than for public entities. This seems, if anything, the reverse of what was intended by the ESA. From a scientific perspective, actions resulting in a given degree of risk of extinction are equally hazardous to species whether they are carried out by public or private entities on public or on private land. The committee sees no scientific reason for setting different standards for categorizing risk of extinction under different sections of the act. However, because public and private entities behave differently, achieving the same degree of biological protection on public versus private lands does not necessarily imply identical regulatory requirements on behalf of species experiencing the same risk of extinction on public and on private lands. USING STRUCTURED APPROACHES TO DECISION MAKING Why use structured approaches to ESA decision making? Because the issues are complex and relevant scientific data are often fragmentary, it can be difficult to make decisions regarding endangered or threatened species. Decisions regarding endangered species are often characterized by insufficient data, probabilistic predictions regarding future events, considerable uncertainty regarding the accuracy of these predictions, conflicting management objectives, disagreement over the best course of action, and the need to justify whatever decision is made. Given these problems, it is important that we try to make our decision-making process as explicit as possible, especially because research on the psychology of human decision making (Hogarth, 1980; Kahneman et al., 1982; von Winterfeldt and Edwards, 1986) has shown that intuitive decisions exhibit many inconsistencies and biases, particularly when probabilistic information and multiple objectives are involved. The use of an explicit framework to guide decisions can help us make choices that are consistent with our goals, data, and beliefs and facilitate compromise among those with differing views. Several techniques developed in the fields of operations research and management science—the academic disciplines dealing with scientific approaches to decision making—provide helpful frameworks use the print version of this publication as the authoritative version for attribution.

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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 MAKING ESA DECISIONS IN THE FACE OF UNCERTAINTY 158 for making these difficult decisions. These techniques help decision makers think about the decision in a systematic way; break down difficult decisions into a series of smaller, easier decisions; and document the process used to reach the decision, which makes the decision easier to justify and defend. Some applications of these structured approaches, particularly the qualitative ones, might appear to be simple pleas for clear thinking, and they are. But clear thinking does not come easily or naturally in the face of scientific complexity and uncertainty, competing objectives within recovery programs and beyond, and political pressures from multiple constituencies. We propose decision analysis as an example of a structured problem- solving method, although other methods (such as the Analytic Hierarchy Process (Saaty, 1990)) or approaches based on goal programming and multiattribute ranking (Ralls and Starfield, 1995) can be useful as well. We stress the merits of using these tools as conceptual frameworks, not just as number-crunching devices. And we emphasize that using these approaches is not necessarily a call for more information, but rather for more coherent use of existing information. Using Subjective Probability and Expert Opinion In some cases, there is very little "hard" information that seems relevant to estimating the risks affecting endangered species, but some experts have accumulated experience that allows them to make informed judgments about these risks. Such expert judgment is so often available for endangered species decisions that it is of great benefit to have orderly methods of eliciting and using it for decision making. One of the strengths of decision analysis is its ability to estimate "subjective probabilities" and then use them for analysis in the same way that long-term frequency estimates would be used (Behn and Vaupel, 1982; von Winterfeldt and Edwards, 1986; Maguire, 1987). The methods allow a concrete expression of expert opinion, facilitating scrutiny by the public and comparison with other views. Another place where expert opinion can be essential is in cases when some background information is available, but unique aspects of a situation differentiate it from similar situations that have occurred. An example might be assessing the likelihood of successful reproduction in a captive population where experience has been dismal but new reproductive technologies have been developed that might prove helpful, as for example with the endangered Hawaiian crow (NRC, 1992a). In this case, it is relevant to amend or update the historical information in light of the new techniques. There are formal ways to combine old data with new opinion (and new data with old opinion) using Bayesian estimation (Raiffa, 1968; Clemen, 1991). use the print version of this publication as the authoritative version for attribution.

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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 MAKING ESA DECISIONS IN THE FACE OF UNCERTAINTY 168 Statistical Errors In the usual procedures for formulating scientific tests of hypotheses, it is customary to phrase the null hypothesis as the "no effect" case (e.g., a proposed action will not affect the survival of a listed species) and to use confidence levels that limit the probability of falsely rejecting that null hypothesis to a known level (often, 0.05) while permitting much larger probabilities of falsely accepting the null hypothesis. We are concerned that when such statistical procedures are followed in ESA decision-making, they will too often place the burden of proof (for demonstrating a significant effect) on those who want to institute some protective action (usually the FWS or petitioners for listing of a species), without taking into account the practical consequences of falsely concluding that no effect is occurring. This could lead to a systematic bias against species that are candidates for listing or for listed species in need of protective actions. Cumulative Effects/Thresholds One situation where this type of problem could arise is when those concerned with species protection suspect that there might be a critical threshold of effect (above which the risk of extinction might increase dramatically) or where cumulative effects might push a species past such a threshold. We have indicated above that our technical ability to predict such thresholds is very limited. If the burden of proof is on those who must show that such a threshold exists (and where it is and just what increase in risk of extinction will occur), there will be few instances in which such a threshold can be demonstrated. As a solution to this problem, we are not advocating that such thresholds simply be assumed unless proven otherwise (which would reverse the burden of proof), but rather that the consequences of each type of error (failing to identify a threshold when one actually exists versus assuming a threshold when one does not exist) be examined to design a decision-making procedure that properly controls the risk of errors, from the point of view of species protection and from the point of view of avoiding unneeded constraints on other interests. In other words, it is advantageous to make the assumptions and their predicted consequences explicit. Listing Decisions Another area where we are concerned about asymmetric risk functions for endangered species is in decisions to list them. Lack of information can work against species at risk at the listing stage. Under current conditions, FWS resources for evaluating information on candidate species and for gathering additional information to make a decision are severely limited. use the print version of this publication as the authoritative version for attribution.

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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 MAKING ESA DECISIONS IN THE FACE OF UNCERTAINTY 169 The solution is not simply to reverse the burden of proof and confer protection on all species proposed for listing but to consider explicitly the consequences of both types of error: of listing species that are not really endangered and of failing to list those that really are. Listing decisions are not one of the points in ESA decision making where socioeconomic consequences are to be weighed against species protection, so these need not be part of the equation for determining where the burden of proof should lie for a particular case. However, we acknowledge that with limited time and money for reviewing the eligibility of species for listing, only those species whose situations are known to be the most desperate will receive priority. Reducing Asymmetry of Risk for Listed Species In addition to concerns about risks for species at the point of listing, we are also concerned that similar asymmetry of risk functions can occur during decisions regarding protection of already listed species. The usual way of deciding whether there is likely to be a harmful effect is to pose the null hypothesis of no harm and set a low (usually 0.05) rate of error for falsely concluding that there will be harm. This way of framing the question, in combination with limited information on the effects of habitat alteration on listed species, is more likely to deny needed protection than to afford unneeded protection. If the burden of proof were to show that an action would not harm a species rather than to show that it would harm a species, increased protection would result. The importance of shifting the burden of proof this way has been widely recognized, especially in the context of marine conservation issues, and is known as "the precautionary principle" (Cameron and Werksman, 1991; Porter and Brown, 1991; Earll, 1992; Norse, 1993). This principle has already been endorsed in several international legal documents (Porter and Brown, 1991). Recently, the National Marine Fisheries Service (NMFS) explicitly took this kind of asymmetry and the potential for irreversibility into account in deciding to list the anadromous Snake River sockeye salmon (Oncorhynchus nerka) as endangered in the face of uncertainty (Waples, in press). The uncertainty concerned whether the anadromous form, which spawns in Redfish Lake, Idaho, was genetically identical to the landlocked form of O. nerka, the kokanee, which is common in Redfish Lake. The decision to list1 was based in part on "the recognition that the consequences of taking the alternate course (i.e., assuming that recent anadromous [fish] in Redfish Lake were derived from use the print version of this publication as the authoritative version for attribution. 1Fed. Reg. 56:58619, Nov. 20, 1991. Information obtained after the decision indicated that the anadromous form was indeed genetically different from the landlocked form (Waples, in press).

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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 MAKING ESA DECISIONS IN THE FACE OF UNCERTAINTY 170 kokanee) and being wrong were irreversible, since the original sockeye salmon gene pool could easily become extinct before the mistake was realized." Making Tradeoffs Among Competing Objectives There are always too few resources for the size of the job, and the government has been criticized for allocating funds for species protection without regard for its own stated priorities (Mann and Plummer, 1992). Most people recognize that not everything can be done at once, but these limitations probably would be more acceptable if there were a clearer connection between objectives being pursued and actions taken. Although the language of the ESA suggests that the standards for making decisions about listing, jeopardy, etc., are to be purely scientific, analyses of ESA implementation (e.g., Yaffee, 1982) show clearly that tradeoffs among conflicting objectives must be made in almost every instance. In a few cases, these conflicting objectives and the necessity for balancing them are made explicit in the act and its implementing regulations. For example, in treating critical habitat, the act (Section 4(b)(2)) recognizes that designating critical habitat might have socioeconomic costs and directs those implementing the act to weigh benefits to the listed species against these costs. Similarly, the exemption process specifically directs the exemption committee to ask whether there is an overriding benefit to society from the proposed project that would justify its approval, despite its threat to listed species. In most cases, however, tradeoffs among competing objectives arise in the course of implementing the act with too few resources, whether financial, human, or natural. For example, there are almost 4,000 candidates for listing (categories 1 and 2), not all of which can be acted on at once. Those responsible for listing decisions must decide which to consider first and which to delay, based on their best judgments about immediacy of threat, distinctiveness of the taxonomic group, etc. Again, structured decision-making techniques can be helpful when deciding on the best use of limited resources. Thibodeau's (1983) application of decision analysis to choose sites for a recovery program is an early application of a structured problem-solving technique to allocate scarce financial resources for conservation. Maguire and Lacy (1990) used decision analysis to help allocate limited zoo space among tiger subspecies in need of captive conservation. To make clear choices among competing objectives and to justify those choices to interested publics, it would be helpful to follow a more explicit framework for evaluating tradeoffs, such as that included in the repertoire of multiattribute decision analysis (Keeney and Raiffa, 1976; Keeney, 1992), which has been applied to several endangered-species problems (Maguire, 1986; Maguire and Servheen, 1992; Ralls and Starfield, 1995). use the print version of this publication as the authoritative version for attribution.

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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 MAKING ESA DECISIONS IN THE FACE OF UNCERTAINTY 171 Among the criteria appropriate for setting priorities among species (or other units of protection), all of which qualify for listing or recovery, are those reflecting how protection of a particular taxon might contribute to maintenance of biological diversity (see Chapter 3). Distinctiveness of various sorts is one measure of this contribution: Genetic. Does the taxon contain genetic material not represented elsewhere that could provide raw material valuable for adaptation and evolution in future environments? Phylogenetic. Does the taxon represent a branch of the evolutionary tree of life that has few or no other living members? Ecological. Does the taxon exhibit an unusual adaptation to its environment, particularly to a rare habitat type (such as vernal pools or hot springs), or does it participate in an unusually close interdependence with other rare (i.e., threatened, endangered, or candidate) species (such as obligate mutualists or parasites), or does it have critical functional roles (i.e., is it a "keystone" species)? In addition to measures of distinctiveness, other considerations in setting priorities among units of protection include the degree to which conserving that taxon would enhance protection of overall diversity. Higher priority would thus be given to taxa with unusually high levels of genetic diversity, to ecosystems with high levels of endemism, and to taxa whose demise would be likely to precipitate further extinctions of taxa dependent on them. Finally, there are species often referred to as "umbrella species," i.e., they are species whose protection entails the protection of habitats and ecosystems that would confer protection on other (endangered) species. Clearly, if priorities have to be set, an umbrella species should receive a high priority. FWS (FWS, 1983) and NMFS (NOAA, 1990) have developed hierarchical systems for determining listing, delisting and reclassification, and species-recovery priorities; the NMFS systems are simplified versions of the FWS systems. The FWS system for listing priorities considers the magnitude of the threat to the species, the immediacy of the threat to the species, and the distinctiveness of the species based on its taxonomy, e.g., a monotypic genus is given a higher priority than a species, which in turn is given a higher priority than a subspecies. The system for setting recovery priorities also uses taxonomic level as an indicator of distinctiveness. However, as explained in Chapter 3, taxonomic ranking does not necessarily reflect the same degree of phylogenetic distinctiveness among all groups of organisms. use the print version of this publication as the authoritative version for attribution.

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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 MAKING ESA DECISIONS IN THE FACE OF UNCERTAINTY 172 Resolving Conflicts Among Interest Groups Parties with a stake in the outcome of ESA decisions include conservationists; developers; other private and public industries; private individuals; academics; local, state, and federal agencies; tribes; and others. Public participation in ESA decisions is a part of the legislation and implementing regulations in the form of opportunities to petition for listing of species and to offer comments on proposed actions. Negotiated agreements among FWS and interested parties are a common part of ESA implementation through Section 7 provisions for consultation among federal agencies and Section 10 provisions for development of habitat conservation plans by private developers. In Section 7 consultations, FWS is charged with issuing an opinion on whether a proposed action by a federal agency is likely to jeopardize the continued existence of a listed species. In practice, the proposed actions are almost always negotiated informally, with comments by FWS and changes in plans by the developing agency, such that by the time the formal consultation occurs, jeopardy opinions are extremely rare (Houck, 1993). Although the results might be criticized, the process of negotiating agreement between federal agencies is entirely consistent with directives for constructive cooperation among federal agencies (Cleland, 1991; Wondolleck et al., 1994). The process of developing habitat conservation plans under Section 10 (Chapter 4; Bean et al., 1991) is also a collaborative, negotiated process between FWS and individuals seeking the incidental take permit. Again, the results of this process often have been criticized. A combination of methods from decision analysis and dispute resolution can offer disputing parties a way out of the dilemma of how to combine the best scientific analysis with attention to the conflicting values that often are involved in making controversial decisions regarding endangered species. Dispute resolution brings multiple parties together to develop a plan that meets the essential needs of all (Fisher et al., 1991). Decision analysis can help facilitate this process by providing a structure for representing the values and the scientific beliefs that inform each party's positions (Maguire and Boiney, 1994). Decision analysis and dispute resolution direct attention to the objectives and underlying interests that a decision is supposed to reflect. The parts of decision analysis that focus on objectives, priorities, tradeoffs, and criteria can help with this values-structuring part of the analysis (Keeney, 1992). One of the tasks of a collaborative problem-solving process is disentangling disagreements about facts from differences in values. This process is difficult at best, and even harder when there is substantial scientific uncertainty, as is so often the case in ESA decisions. Decision analysis separates each party's view of the facts of the matter from that party's value structure, which is helpful in identifying what each party use the print version of this publication as the authoritative version for attribution.

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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 MAKING ESA DECISIONS IN THE FACE OF UNCERTAINTY 173 believes in terms of facts and values. Some of the benefits of decision analysis and dispute resolution can be realized with a qualitative analysis. In other cases, quantitative analysis, such as sensitivity analysis or value of information analysis, can help promote agreement by showing where additional information might help develop a consensus plan and how much additional information might be worth acquiring (Maguire and Boiney, 1994). Such procedures can help ensure that negotiations under Section 7 and Section 10 do a good job of incorporating both science and values. Implementing Structured Approaches in the Agencies A call for a more structured approach to ESA decisions using tools such as decision analysis is not necessarily a call for more extensive analysis or research, neither of which could be supported with current resources. Rather, it is a way of making better use of available information to address the problems at hand. Many of the benefits of structured analysis can be realized with a relatively quick and qualitative application of decision and risk concepts (Behn and Vaupel, 1982). Those concepts include explicitly identifying objectives, setting priorities among objectives, and establishing criteria for measuring progress toward those objectives; clearly weighing tradeoffs among conflicting objectives, whether these tradeoffs are forced by the language of the ESA or by limited resources for implementing it; and explicitly considering probabilistic information, making better use of expert opinion, and providing more coherent ways of combining data and opinion to estimate probabilities. All of these can help provide a better connection between the values being pursued under the ESA and the scientific information available to support decision making. In the long run, the decisions will improve, and they will be better justified with reference to both values and science. It would be possible to provide many of the managers responsible for administering the ESA with the tools to conduct qualitative decision analyses and viability analyses themselves, augmenting the informal methods of analysis they use now. Several short courses and seminars for the Fish and Wildlife Service, U.S. Forest Service, Bureau of Land Management, and state wildlife agencies, all of which have ESA responsibilities, have included decision analysis and population viability analysis as tools for endangered species management. In some cases, more thorough quantitative analyses will be needed, with consultation from a decision analyst or population biologist. That input could be handled in the same way as subject matter input from species experts when preparing listing documents and recovery plans. Quantitative analysis, such as sensitivity analysis or analysis of the value of information (Maguire, 1986; 1987; Maguire and Servheen, 1992; Ralls and Starfield, use the print version of this publication as the authoritative version for attribution.

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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 MAKING ESA DECISIONS IN THE FACE OF UNCERTAINTY 174 1995), can help build confidence in the robustness of a particular course of action or direct further research to critical parts of a problem. CONCLUSIONS AND RECOMMENDATIONS • Major advances in both theory and methods of estimating risk of extinction allow us to base listing and recovery decisions on scientific principles. • Many previous ESA decisions did not meet the guidelines suggested by current scientific thinking, listing species as endangered only after populations had dropped to the point where the risk of extinction was very high and proposing recovery goals that left the species still at high risk of extinction. • Where natural history and demographic data are available, analytical and simulation models can be used to provide quantitative estimates of risks of extinction. • General results from these extinction models have been used to develop rating systems based on objective criteria (such as population size, number of populations, and other demographic and environmental characteristics) to categorize species according to relative risk of extinction. Rating systems for use in situations where detailed data are not available should be developed and tested with simulation and observational methods. • Because current extinction models do not consider the interactions of all factors promoting extinction, estimates of extinction risk might underestimate the true risk. • Setting levels of risk to trigger listing and recovery decisions entails scientific and public policy considerations. • We can find no scientific basis for setting different levels of risk for different taxonomic groups, such as plants and animals, or for public versus private actions that might affect listed species. However, it is critical to understand that achieving the same biological risks for listed species might well entail different management policies on public and private lands, because public and private entities behave differently from each other. No implementation of the Endangered Species Act can be fully successful without recognition of these differences. • To the degree that they can be quantified, the levels of risk associated with endangered status should be higher than those for threatened status. Once a species no longer qualifies as threatened, it should be considered recovered and delisted. • Levels of risk to trigger ESA decisions must be framed as a probabil use the print version of this publication as the authoritative version for attribution.

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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 MAKING ESA DECISIONS IN THE FACE OF UNCERTAINTY 175 ity of extinction during a specified period (e.g., x% probability of extinction over the next y years). Although some crises might call for short time horizons (on the order of tens of years), ordinarily it will be necessary to view extinction over longer periods (on the order of hundreds of years), so that short- term solutions do not create long-term problems. • The selection of particular degrees of risk associated with particular periods to trigger ESA decisions reflects scientific knowledge and societal values. • When implementing the ESA with limited resources, it will probably be necessary to allocate effort among species, all of which qualify for protection according to the risk level that has been adopted. Scientific considerations, such as whether a species or its habitat possesses unusually distinctive attributes or whether protection of a taxon would confer protection on other candidate taxa or their habitats, should help set priorities for action. • There will always be uncertainty in the estimates of risk used to trigger decisions under the ESA, requiring policies and processes for making decisions with incomplete and uncertain data. Making decisions under uncertainty poses the possibility of errors of various types, such as delisting when a species has not actually recovered or listing when a species is not really endangered or threatened. For a variety of statistical reasons, including those pertaining to availability of data, protection would be more likely if the burden of proof were to show that a proposed action would not harm a listed species rather than to show that it would. • Because ESA decisions are often difficult and controversial, the procedures used to make them should be explicit and well documented. Structured methods can improve the substance of these decisions and the justification for them. Structured methods can be particularly appropriate to ESA decisions when • scientific risk assessments and societal values must be integrated; • tradeoffs among conflicting objectives must be made or negotiations among disputing parties must be conducted; • the costs and benefits of delaying decisions while gathering additional information to reduce uncertainty must be weighed; and • empirical data are lacking and information derived from expert opinion should be used. use the print version of this publication as the authoritative version for attribution.

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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 MAKING ESA DECISIONS IN THE FACE OF UNCERTAINTY 176 REFERENCES Barlow, J., J. Sisson, and S.B. Reilly. 1993. Status of California Cetacean Stocks: A Summary of the Workshop held March 31 to April 12. Administrative Report LJ-93-20. National Marine Fisheries Service, Southwest Fisheries Science Center, La Jolla, Calif. Bart, J., and D. Robson. In Press. Design of a monitoring program for northern spotted owls. In Proceedings of the Symposium on Monitoring Bird Population Trends by Point Counts, C.J. Ralph, J.R. Sauer, and S. Droege, technical coordinators. USDA Gen. Tech. Rep. PSW-000. U.S. Department of Agriculture Forest Service, Pacific Southwest Research Station, Albany, Calif. Bean, M.J., S.G. Fitzgerald, and M.A. O'Connell. 1991. Reconciling Conflicts Under the Endangered Species Act: The Habitat Conservation Planning Experience. World Wildlife Fund, Washington, D.C. Beanlands, G.E., W.J. Erckmann, G.H. Orians, J. O'Riordan, D. Policansky, M.H. Sadar, and B. Sadler. 1986. Cumulative Environmental Effects: A Binational Perspective. Canadian Environmental Assessment Council, Ottawa, Ontario, and the National Research Council, Washington, D.C. Behn, R.D. and J.W. Vaupel. 1982. Quick Analysis for Busy Decision Makers. New York: Basic Books. Brownell, R.L., Jr., K. Rails, and W.F. Perrin. 1989. The plight of the "forgotten" whales. Oceanus 32(1):5-11. Cameron, J., and J.D. Werksman. 1991. The Precautionary Principle: A Policy for Action in the Face of Uncertainty. Centre for International Environmental Law, London. Cleland, J.C. 1991. Application of Alternative Dispute Resolution to Endangered Species Act Interagency Consultations. Master of Environmental Management Project, School of Forestry and Environmental Studies, Duke University, Durham, N.C. Clemen, R.T. 1991. Making Hard Decisions. An Introduction to Decision Analysis. Boston, Mass.: PWS-Kent. Donovan, G.P., ed. 1989. The Comprehensive Assessment of Whale Stocks: The Early Years. International Whaling Commission, Cambridge, U.K. Earll, R.C. 1992. Common sense and the precautionary Principle—An environmentalist's perspective. Mar. Pollut. Bull. 24:182-186. Fisher, R., W. Ury, and B. Patton. 1991. Getting to Yes. 2nd. ed. New York: Penguin Books. FWS (U.S. Fish and Wildlife Service). 1983. Endangered and threatened species listing and recovery priority guidelines. Fed. Reg. 48 (184):43098-43105. Gnam, R. 1993. Comments invited on species' risk. BioScience 43:430. Goodman, D. 1993. Scientific standards for endangered species management. Appendix 1 in Research on Methods of Biodiversity Management. Annual Report No. 1. Cooperative Agreement No. CR-8200-8601. U.S. Environmental Protection Agency, Office of Research and Development, Washington, D.C. Hogarth, R. 1980. Judgment and Choice: The Psychology of Decisions. Chichester, U.K.: John Wiley & Sons. Houck, O.A. 1993. The Endangered Species Act and its implementation by the U.S. Departments of Interior and Commerce. Univ. Colo. Law Rev. 64(2):277-370. Kahneman, D., P. Slovic, and A. Tversky. 1982. Judgement Under Uncertainty: Heuristics and Biases. Cambridge, U.K.: Cambridge University Press. Keeney, R.L. 1992. Value-Focused Thinking. Cambridge, Mass.: Harvard University Press. Keeney, R.L., and H. Raiffa. 1976. Decisions with Multiple Objectives: Preferences and Value Tradeoffs. New York: John Wiley & Sons. use the print version of this publication as the authoritative version for attribution.

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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 MAKING ESA DECISIONS IN THE FACE OF UNCERTAINTY 177 Lande, R. 1988. Demographic models of the northern spotted owl (Strix occidentalis caurina). Oecologia 75:601-607. Mace, G., N. Collar, J. Cooke, K. Gaston, J. Ginsberg, N. Leader Williams, M. Maunder, and E.J. Milner-Gulland. 1992. The development of new criteria for listing species on the IUCN Red List. Species 19:16-22. Mace, G.M., and R. Lande. 1991. Assessing extinction threats: Toward a reevaluation of IUCN threatened species categories. Conserv. Biol. 5:148-157. Maguire, L.A. 1986. Using decision analysis to manage endangered species populations. J. Environ. Manage. 22:345-360. Maguire, L.A. 1987. Decision analysis: A tool for tiger conservation and management. Pp. 75-486 in Tigers of the World, R.L. Tilson and U.S. Seal, eds. Park Ridge, N.J.: Noyes. Maguire, L.A. 1989. Managing black-footed ferret populations under uncertainty: An analysis of capture and release decisions. Pp. 268-292 in Conservation Biology and the Black-footed Ferret. U.S. Seal, M. Bogan, T. Thorne, and S.F. Anderson, eds. New Haven, Conn.: Yale University Press. Maguire, L.A. and L.G. Boiney. 1994. Resolving environmental disputes: A framework incorporating decision analysis and dispute resolution techniques. J. Environ. Manage. 42:31-48. Maguire, L.A. and R.C. Lacy. 1990. Allocating scarce resources for conservation of endangered species: Partitioning zoo space for tigers. Conserv. Biol. 4:157-166. Maguire, L.A., and C. Servheen. 1992. Integrating biological and sociological concerns in endangered species management: Augmentation of grizzly bear populations. Conserv. Biol. 6:426-434. Mann, C.C. and M.L. Plummer. 1992. The butterfly problem. Atlantic Mon. 269(1):47-70. Menges, E.S. 1990. Population viability analysis for an endangered plant. Conserv. Biol. 4:52-62. NOAA (U.S. National Oceanic and Atmospheric Administration). 1990. Endangered and threatened species; listing and recovery priority guidelines. Fed. Reg. 55(116):24296-24298. Norse, E.A., ed. 1993. Global Marine Biological Diversity: A Strategy for Building Conservation into Decision Making. Washington, D.C.: Island Press. 383 pp. Noss, R.F. 1992. Biodiversity: Many scales and many concerns. Pp. 17-22 in Proceedings of the Symposium on Biodiversity of Northwestern California, H.F. Kerner, ed., Oct. 28-30, Santa Rosa, Calif. 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. Porter, G., and J.W. Brown. 1991. Global Environmental Politics. Boulder, Colo.: Westview. Raiffa, H. 1968. Decision Analysis: Introductory Lectures on Choices under Uncertainty. Reading, Mass.: Addison-Wesley. Ralls, K., and J.D. Ballou. 1992. Managing genetic diversity in captive breeding and reintroduction programs. Trans. North Am. Wildl. Nat. Resour. Conf. 57:263-282. Ralls, K., and A.M. Starfield. 1995. Two decision analysis methods for conservation problems. Conserv. Biol. 9:175-181. Reilly, S. 1992. Population biology and status of eastern Pacific gray whales: Recent developments. Pp. 1062-1074 in Wildlife 2001: Populations, D.R. McCullough and R.H. Barrett, eds. London: Elsevier Applied Science. Saaty, T.L. 1990. How to make a decision: The analytic hierarchy process. Eur. J. Oper. Res. 48:9-26. use the print version of this publication as the authoritative version for attribution.

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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 MAKING ESA DECISIONS IN THE FACE OF UNCERTAINTY 178 Shaffer, M.L. 1981. Minimum viable population sizes for species conservation. BioScience 31:131-134. Taylor, B.L. and T. Gerrodette. 1993. The uses of statistical power in conservation biology: The vaquita and northern spotted owl. Conserv. Biol. 7:489-500. Tear, T.H., J.M. Scott, P.H. Hayward, and B. Griffith. 1993. Status and prospects for success of the Endangered Species Act: A look at recovery plans. Science 262:976-977. Thibodeau, F.R. 1983. Endangered species: Deciding which species to save. Environ. Manage. 7:101-107. von Winterfeldt, D., and W. Edwards. 1986. Decision Analysis and Behavioral Research. Cambridge, U.K.: Cambridge University Press Waples, R.S. In press. Evolutionarily Significant Units and the Conservation of Biological Diversity Under the Endangered Species Act. In Evolution and the Aquatic Ecosystem, J.L. Nielsen and D.A. Powers, eds. American Fisheries Society, Bethesda, Md. Wilcove, D.S., M. McMillan, and K.C. Winston. 1993. What exactly is an endangered species? An analysis of the endangered species list, 1985-1991. Conserv. Biol. 7:87-93. Wondolleck, J.M. , S.L. Yaffee, and J.E. Crowfoot. 1994. Applying the principles of alternative dispute resolution to endangered species conservation. In Improving Endangered Species Conservation: Reviewing the Experience and Learning the Lessons, T. Clark and A. Clarke, eds. Covelo, Calif.: Island Press. Yaffee, S.L. 1982. Prohibitive Policy: Implementing the Federal Endangered Species Act. Cambridge, Mass.: MIT Press. use the print version of this publication as the authoritative version for attribution.