Capturing expert knowledge for threatened species assessments: a case study using NatureServe conservation status ranks

Acta Oecologica 26 (2004) 95–107 www.elsevier.com/locate/actoec

Original article

Capturing expert knowledge for threatened species assessments: a case study using NatureServe conservation status ranks Tracey J. Regan a,*, Lawrence L. Master b, Geoffrey A. Hammerson b a b

School of Botany, The University of Melbourne, Parkville, Victoria, 3010, Australia NatureServe, 11 Avenue de Lafayette, 5th Floor, Boston, Massachusetts 02111, USA Received 18 April 2003; accepted 1 March 2004 Available online 15 July 2004

Abstract Assessments for assigning the conservation status of threatened species that are based purely on subjective judgements become problematic because assessments can be influenced by hidden assumptions, personal biases and perceptions of risks, making the assessment process difficult to repeat. This can result in inconsistent assessments and misclassifications, which can lead to a lack of confidence in species assessments. It is almost impossible to understand an expert’s logic or visualise the underlying reasoning behind the many hidden assumptions used throughout the assessment process. In this paper, we formalise the decision making process of experts, by capturing their logical ordering of information, their assumptions and reasoning, and transferring them into a set of decisions rules. We illustrate this through the process used to evaluate the conservation status of species under the NatureServe system (Master, 1991). NatureServe status assessments have been used for over two decades to set conservation priorities for threatened species throughout North America. We develop a conditional point-scoring method, to reflect the current subjective process. In two test comparisons, 77% of species’ assessments using the explicit NatureServe method matched the qualitative assessments done subjectively by NatureServe staff. Of those that differed, no rank varied by more than one rank level under the two methods. In general, the explicit NatureServe method tended to be more precautionary than the subjective assessments. The rank differences that emerged from the comparisons may be due, at least in part, to the flexibility of the qualitative system, which allows different factors to be weighted on a species-by-species basis according to expert judgement. The method outlined in this study is the first documented attempt to explicitly define a transparent process for weighting and combining factors under the NatureServe system. The process of eliciting expert knowledge identifies how information is combined and highlights any inconsistent logic that may not be obvious in subjective decisions. The method provides a repeatable, transparent, and explicit benchmark for feedback, further development, and improvement. © 2004 Elsevier SAS. All rights reserved. Keywords: Subjective judgement; Extinction; Conservation status; NatureServe

1. Introduction Setting conservation priorities is a key process for many conservation agencies throughout the world. A critical step in setting priorities for biodiversity conservation is an assessment of extinction risk. Methods used for assessing the conservation status of species are varied but follow three general styles. The World Conservation Union (IUCN) uses a set of five quantitative rules with explicit thresholds to assign

* Corresponding author. School of Life Sciences, University of Queensland, St. Lucia, Queensland, 4072, Australia. E-mail address: [email protected] (T.J. Regan). © 2004 Elsevier SAS. All rights reserved. doi:10.1016/j.actao.2004.03.013

a risk of extinction (IUCN, 2001). Other methods adopt point-scoring approaches where points are assigned for a number of attributes and summed to indicate conservation priority (Millsap et al., 1990; Lunney et al., 1996; Carter et al., 2000). For these two approaches, the assessment process is transparent, and when discrepancies occur, the causes can be determined. Other methods assess conservation status using qualitative criteria; judgements about a species’ status are determined intuitively based on available information and expert opinion (Master, 1991). The NatureServe system (Master, 1991) developed initially by The Nature Conservancy (TNC) and applied throughout North America, uses trained experts who evaluate quantitative data and make intuitive judgements about species vulnerability.

96

T.J. Regan et al. / Acta Oecologica 26 (2004) 95–107

Subjective decisions made by experts use a combination of logic, common sense, skill, experience and judgement, to generate a final decision that is timely, relevant and meaningful (Rush and Rajkumar, 2001). The knowledge and experience of experts is valuable, particularly in conservation biology where empirical data are often scarce. However, using subjective approaches for assessing the conservation status of species are problematic because assessments are difficult to repeat. Subjective assessments are influenced by personal judgements, perceptions of risk, heuristics and systematic biases (Tversky and Kahneman, 1982; Plous, 1993; Burgman, 2001). It is almost impossible to understand an experts logic or visualise the underlying reasoning behind the many hidden assumptions used throughout the process (Keith, 1998; Rush and Rajkumar, 2001). This can lead to lack of confidence in decisions because assessments cannot be repeated, can be inconsistent, and can lead to misclassifications. As a consequence, an increasing number of individual assessors have been held accountable for their decisions and have been required to defend their assessments (Rohlf, 1991). In order to make tacit or hidden knowledge more explicit and repeatable, the thought processes and reasoning of experts needs to be captured (Rush and Rajkumar, 2001). This involves determining how information is sorted, logically ordered and combined to result in an objective decision. It requires discovering any illogical inconsistencies in the experts reasoning and any hidden assumptions that may not be evident. This practice is commonly used in medicine, economics, engineering, and computer science to build expert systems (Rush and Rajkumar, 2001; Tuthill, 1990). It is the process of extracting the knowledge of the human expert and formalising it in such a way that it can be applied repeatedly (Tuthill, 1990). Formalising expert knowledge in this way helps experts clarify their thinking about a decision and determine any illogical inconsistencies and assumptions. The hidden knowledge is captured for future use and the reasoning behind decisions becomes available for others to critique and learn from (Rush and Rajkumar, 2001). More confidence can be placed in decisions and they become defendable, as the reasoning behind the decision is evident. In this paper, we formalise the decision process of conservation experts by capturing their logical ordering of information, their assumptions and reasoning, and transfer them into a set of decisions rules. We illustrate this through the process used to evaluate the conservation status of species under the NatureServe system (Master, 1991). We begin by describing the NatureServe system, its relevance to the conservation of biodiversity and the factors and information used to make decisions. The process of capturing expert knowledge leads to the development of an explicit method that reflects the subjective thinking of experts applying the NatureServe system. The method known henceforth as the explicit NatureServe method, is a prototype developed to explore the process of formalising expert judgement by mapping subjective processes onto explicit rules.

2. The NatureServe system In 1982, The Nature Conservancy’s Science Division developed a method for evaluating the conservation status of species and ecological communities. The method (now known as the NatureServe system) assesses species and ecological communities according to various biological and external factors that may affect the persistence of a species or ecological community (Master, 1991). Assessments are done by experienced scientists who consider information on factors pertinent to the persistence of the species or ecological community. Each factor has quantitative thresholds but the relative weight given to each factor is based on expert judgement. The assessor’s expertise and overall knowledge of the element is used to weigh each factor in relation to the others to determine an overall threat category. The subjective and hidden nature of this aspect of the assessment process has the potential to lead to inconsistencies in the classification of a species’ conservation status because there is no guarantee that the factors weights are consistent among different assessors and across all species. The factors and thresholds used in NatureServe assessments have been described previously (Master, 1991; Master et al., 2000), but the process of weighting and combining each of the factors to determine an overall NatureServe rank has not previously been documented. The NatureServe system has been used extensively throughout the United States and Canada and elsewhere in the Western Hemisphere over the last two decades to aid in conservation decisions. In July 1999, The Nature Conservancy, along with the Natural Heritage Network, jointly established NatureServe (formerly Association for Biodiversity Information, ABI). As an independent organization, NatureServe was established to provide sound scientific information to conservationists, land managers, and the public. NatureServe has the most comprehensive and current database for ecosystems and at-risk species in the United States and Canada. This includes NatureServe status assessments for over 50,000 plants, animals, and ecological communities including all known North American vascular plants, vertebrates, and invertebrates in selected groups at the global, national, and sub-national (state or provincial) scales (Master et al., 2003). NatureServe ranks are generally independent of federal and state listings and currently have no formal role in the protection of species in the United States or Canada. However, they do form the foundation for management decisions and for setting priorities for conservation actions for hundreds of regional centres that are members of NatureServe and partners of the Natural Heritage Network throughout the United States and Canada (Master et al., 2000). The scientific information and assessments of species and ecosystems provided by NatureServe is used by all sectors of society, including conservation groups, government agencies, corporations, academia, and the public, to make informed decisions about managing natural resources (http://www.natureserve.org).

T.J. Regan et al. / Acta Oecologica 26 (2004) 95–107

The aim of the NatureServe system is to determine the relative susceptibility of a species or ecological community to extinction or extirpation. To achieve this, assessments consider both deterministic and stochastic processes that can lead to extinction (Master et al., 2003). Deterministic factors include such things as habitat destruction or alteration, nonindigenous predators, competitors, or parasites, overharvesting, and environmental shifts such as climate change. Stochastic factors include, environmental and demographic stochasticity, natural catastrophes, and genetic effects (Shaffer, 1981). All NatureServe assessments are performed on a basic unit called an element, defined as a unit of biological diversity, generally species or ecological communities. An element can be any plant or animal species or infraspecific taxon (subspecies or variety), ecological community, or other nontaxonomic biological entity, such as a distinctive population (e.g., evolutionarily significant unit or distinct population segment, as defined by some agencies) or a consistently occurring mixed species aggregation of migratory species (e.g., shorebird migratory concentration area). Defining elements in this way ensures that a broad spectrum of biodiversity and ecological processes are identified and targeted for conservation (Stein et al., 2000). This approach is believed to be an efficient and effective approach to capturing biodiversity in a network of reserves (e.g., Jenkins, 1976; 1985; 1996). The NatureServe assessment results in a numeric code or rank that reflects an element’s relative degree of imperilment or risk of extinction. The ranks are assigned to each element on a 1–5 scale, G1 being critically imperilled on a global scale, G5 being globally secure and abundant (Table 1). A prefix of a G, N, or S indicates whether the ranking is at the global, national, or sub-national level, respectively (Master et al., 2000). For example, if an element has a rank of G1 it is critically imperilled across its entire range whereas an element with a rank of S1 is critically imperilled in a particular state, province, or other sub-national jurisdiction regardless

97

of its status elsewhere. An element may have a rank of G5S1. This translates as an element that is secure on a global scale but critically imperilled in a particular jurisdiction (S1). For instance, Atlantic salmon, Salmo salar, is globally secure (G5) but is critically imperilled (S1) in the states of Massachusetts and Rhode Island. Ranks that represent other categories in the NatureServe system include GX and GH, given to elements that are presumed extinct and possibly extinct, respectively. GNR is used for elements that have not been ranked yet. A rank of G#G# represents elements whose rank (e.g., G1G2) spans more than one category due to insufficient data to assign a more precise status. A rank of GU is used when the range of the conservation status spans more than three categories. NatureServe ranks for infraspecific elements (subspecies and varieties) are represented by a “T” (Table 1). The global rank of a critically imperilled subspecies of an otherwise widespread and common species would be G5T1. Because elements are ranked using the same criteria, regardless of whether the assessment is at the species or infraspecific level, or at the global, national, or sub-national scale, the numeric value of the “T” subrank cannot be more than the “G” rank value for the species. That is, it is illogical to have a G1T2 subrank for a subspecies, and similarly a national or subnational rank cannot imply that the species is more secure in the nation or sub-nation than it is globally (e.g., G3S4 is illogical). 2.1. The factors The factors used in NatureServe assessments are summarised in Table 2 with full definitions appearing in Appendix 1. The factors considered in assessing the conservation status of elements reflect two theories as to why species go extinct. These theories are encapsulated in the small population paradigm and the declining population paradigm (Caughley and Gunn, 1996). The small population paradigm pertains to the risk of extinction for species that are rare

Table 1 Definition of global NatureServe ranks (Master, 1991; Master et al., 2003) Global NatureServe rank GX GH G1 G2 G3 G4 G5 GNR GU G#G# T#

Description Presumed extinct: not located despite intensive searches and virtually no likelihood of rediscovery Possibly extinct: missing; known from only historical occurrences but still some hope of rediscovery Critically imperilled: at very high risk of extinction due to extreme rarity (often five or fewer populations), very steep declines, or other factors Imperilled: at high risk of extinction due to very restricted range, very few populations (often 20 or fewer), steep declines, or other factors Vulnerable: at moderate risk of extinction due to restricted range, relatively few populations (often 80 or fewer), recent and widespread declines, or other factors Apparently secure: uncommon but not rare; some cause for long-term concern due to declines or other factors; or stable over many decades and not threatened but of restricted distribution or population size Secure: common; widespread and abundant Not ranked Unrankable Range rank Taxonomic subdivision

98

T.J. Regan et al. / Acta Oecologica 26 (2004) 95–107

Table 2 Definitions of the factors used in assessing NatureServe conservation status (adapted from (Master et al., 2003)). Full definitions are available in Appendix 1 Factor Number of occurrences Viability of occurrences (species) or ecological integrity (communities) Population size Area of occupancy Range extent Trends Threats Protected occurrences Intrinsic vulnerability Environmental specificity

Definition Number of distinct populations Relative condition based on their size, condition, and landscape context Number of mature individuals (species only) Total area of occupied habitat across the range Extent of overall geographic range Short (10 years or three generations, whichever is longer) and long-term increase or decrease in population size, area or extent of occupancy, or number or condition of occurrences Known or suspected current threats, or likely near-term future threats Number of adequately protected and managed populations Inherent susceptibility to threats due to intrinsic biological factors The vulnerability or resilience of the element due to habitat preferences or restrictions or other environmental specificity

(Rabinowitz, 1981). For example, the rare shrub, Epacris stuartii, is known from only one location in south-eastern Tasmania, has a restricted range of about 300 m2 and a population size of approximately 1000 individuals (Keith and Ilowski, 1999). Such rare species are particularly vulnerable to anthropogenic disturbances, predators, catastrophes, and natural fluctuations. Rarity is addressed by considering factors such as the number of element occurrences (i.e., number of populations) and their condition (likelihood of persistence), the total population size, the geographic range of the species, and the occupied habitat within the range. Vulnerability to extinction is not isolated to rare species. Abundant species that are declining are also cause for concern. For example, the passenger pigeon (Ectopistes migratorius) was once North America’s most abundant bird species until it was driven to extinction through hunting and deforestation (Schorger, 1995). The NatureServe system addresses the declining population paradigm (Caughley, 1994) by considering additional factors such as trends in population size and the scope, severity, and immediacy of any perceived threats. The adequacy of management initiatives for protecting the element are also considered, as well as the degree to which inherent factors such as intrinsic vulnerabilities (such as life history or behavioural characteristics of species) or environmental specificity (habitat preferences or restrictions) make an element vulnerable to natural or anthropogenic disturbances (Master et al., 2003). 2.2. The thresholds Biological information for an element is amassed and then a value is assigned to each factor according to the thresholds in Table 3. Existing thresholds include many of those used by the World Conservation Union in its Red List assessments (IUCN, 2001), allowing documentation to be used for either system (Master et al., 2003). In cases where available data are not sufficient to assign one of these values, a range may be designated (e.g., area of occupancy = CD = 4–100 km2). An overall threat value is calculated by combining the scores for scope, severity, and immediacy (see Appendix 2).

2.3. Weighting and combining factors 2.3.1. Current method for weighting and combining factors The current process for weighting and combining rank factors includes weighting some (primary) factors more than other (secondary) factors. In particular, the number of occurrences (i.e., the number of subpopulations), population size, area of occupancy, trends in these factors, and threats are emphasised above other factors. Lacking knowledge about number of occurrences and area of occupancy, environmental specificity becomes more important. When information is lacking about threats, the number of adequately protected occurrences and the species’ intrinsic vulnerability are given increased consideration. The degree to which different factors are emphasised by a particular assessor is a subjective process. Hence, it is difficult to establish whether an individual is being internally consistent across all element assessments and whether there is any consistency between the assessments of different individuals. Furthermore, because the process is not explicit, assessments can be clouded by heuristics, subjective judgements, and inherent biases (Tversky and Kahneman, 1982). 2.3.2. Explicit method for weighting and combining factors Developing a set of rules for this subjective process involved several iterations, each one more closely approximating the way in which an experienced NatureServe assessor currently weights and combines factors to determine an overall rank. Factor weightings were assigned, evaluated in combination, and adjusted subjectively to closely approximate the methods scientists actually employ when assessing a wide range of taxa. Each iteration of weighting and combining the factors was done for 40 species from different taxonomic groups by one of us (LLM), an experienced NatureServe assessor, and then compared to his subjective assessments. Early iterations considered all factors in the assessment. First we used number of occurrences to produce a preliminary score for each taxon. Depending on the state of each of

T.J. Regan et al. / Acta Oecologica 26 (2004) 95–107

99

Table 3 Thresholds for each rank factor (Master et al., 2003) Rank factor Number of occurrences

Thresholds Z = 0 (zero) A = 1–5 B = 6–20 C = 21–80

Condition of occurrences

A = No occurrences with good condition B = Very few (1–3) C = Few (4–12) D = Some (13–40)

E = Many (41–125) F = Very many (>125) U = Number of occurrences with good condition unknown

Population size (number of mature individuals)

Z = Zero A = 1–50 B = 50–250 C = 250–1000 D = 1000–2500

E = 2500–10,000 F = 10,000–100,000 G = 100,000–1,000,000 H = >1,000,000 U = Unknown

Area of occupancy

Area Z = Zero (no occurrences believed extant) A = <0.4 km2 B = 0.4–4 km2 C = 4–20 km2 D = 20–100 km2 E = 100–500 km2 F = 500–2000 km2 G = 2000–20,000 km2 H = >20,000 km2 U = Unknown

Length LZ = Zero (no occurrences believed extant) LA = <4 km LB = 4–40 km LC = 40–200 km LD = 200–1000 km LE = 1000–5000 km LF = 5000–20,000 km LG = 20,000–200,000 km LH = >200,000 km LU = Unknown

Geographic range

Z = Zero (no occurrences believed extant) A = <100 km2 B = 100–250 km2 C = 250–1000 km2 D = 1000–5000 km2

E = 5000–20,000 km2 F = 20,000–200,000 km2 G = 200,000–2,500,000 km2 H = > 2,500,000 km2 U = Unknown

Trends

Threats

D = 81–300 E = >300 U = Unknown

Long-term

Short-term

A = Very large decline (>90%) B = Large decline (75–90%) C = Substantial decline (50–75%) D = Moderate decline (25–50%) E = Relatively stable (±25% change) F = Increase (>25%) U = Long-term trend unknown

A = Severely declining. >70% B = Very rapidly declining. 50–70% C = Rapidly declining. 30–50% D = Declining. 10–30% E = Stable. ±10% fluctuation F = Increasing >10% U = Short-term trend unknown

Severity

Scope

Immediacy

High: loss of species population (all individuals) or destruction of habitat; irreversible effects or requiring long-term recovery (>100 years) Moderate: major reduction in population or habitat requiring 50–100 years recovery Low: non-trivial reduction of species population or reversible degradation or reduction of habitat in area affected, with recovery expected in 10– 50 years Insignificant: essentially no reduction of population or degradation of habitat due to threats, or populations or habitats able to recover quickly (within 10 years) from minor temporary loss

High: >60% affected

High: threat is operational

Moderate: 20–60% affected

Moderate: threat is likely to be operational within 2–5 years Low: threat is likely to be operational within 5–20 years

Number of protected and managed occurrences

A = None B = Few (1–3) C = Several (4–12)

D = Many (13–40) E = Very many (>40) U = unknown

Intrinsic vulnerability

A = Highly vulnerable B = Moderately vulnerable

C= Not intrinsically vulnerable U = Unknown

Environmental specificity

A = Very narrow. Specialist with key requirements scarce B = Narrow. Specialist with key requirements common C = Moderate. Generalist with some key requirements scarce D = Broad. Generalist with all key requirements common U = Unknown

Low: 5–20% affected

Insignificant: <5% affected

Insignificant: threat not likely to be operational within 20 years

100

T.J. Regan et al. / Acta Oecologica 26 (2004) 95–107

Table 4 Point allocation for factor thresholds as developed in the explicit rule-based approach Rank factor Occurrences Condition of occurrences Population size Area of occupancy Range Trends

Threats Intrinsic vulnerability Protected occurrences Environmental specificity

Point allocation and weightings A = 1, B = 2, C = 3, D = 4, E = 5, U = 3.5 A = –0.5, B = –0.25, C = 0, D = 0, E = 0, F = +0.25, U = 0 (Note: B = 0 if number of occurrences = 1) A = –1, B = –0.75, C = –0.5, D–E = –0.25, F = 0, G = +0.25, H = +0.5, U = 0 A = –1, B = –0.75, C = –0.5, D = –0.25, E = 0, F = 0, G = 0, H = +0.25, U = 0 A–B = –0.5, C–D = –0.25; E–H = 0, U = 0 Short-term A = –1, B = –0.75, C = –0.5, D = –0.25, E = 0, F = +0.25, U = 0 Long-term A = –0.5, B = –0.25, C–E = 0, F = +0.25, U = 0 A = –1, B = –0.75, C = –0.5, D = –0.25, E = 0, F = +0.5 G = +0.75, H = +1.0, U = 0 A = –0.5, B = –0.25, C & U = 0 A = –0.75, B = –0.5, C = –0.25, D = 0, E = +0.5, U = 0 A = –0.5, B & C = 0, D = +0.5, U = 0

the other factors, the preliminary score was either increased or decreased by discrete amounts to produce a final score, which was then used to rank the taxon. It became evident after several iterations that when all factors contributed to the rank in some way, the overall global rank tended to be over precautionary. In the subjective approach, even though there may have been information available for all nine factors, not necessarily all the factors were used in the assessment. This implied that subjective assessments considered correlations and other dependencies between factors. Subsequent iterations of the score-based method involved detailed discussions with NatureServe scientists about how they combine the various factors, how missing data are treated, and how the different factors contribute to a final rank. After numerous iterations, a conditional point-scoring process was developed whereby factors are unequally weighted and surrogates are used when data are missing. The use of surrogates in the absence of data explicitly addressed correlations and dependencies between factors used in the subjective assessments. The conditional point-scoring process (i.e., the explicit NatureServe method) we developed works as follows. Number of occurrences is considered initially. If this is unknown, then an intermediate initial rank is assumed with an initial point allocation of 3.5. Each factor has a point allocation (Table 4) corresponding to the thresholds outlined in Table 3. The number of occurrences, population size, occupied area,

short-term trends, and threats have the highest weights. Factors such as condition of occurrences (a subjective or quantitative estimate of the condition of the occurrence, see Appendix 1 for definitions), extent of entire range, long-term trends, environmental specificity, intrinsic vulnerability, and protected occurrences have lesser weights. The conditions or rules for including factors are outlined in Table 5. Number and condition of occurrences, population size, short-term trends, and threats are always considered if information regarding them is available. Information on long-term trends is only utilised if there is no information on short-term trends. If data on these factors are unavailable other factors act as surrogates. For example, geographic distribution is considered only if either number of occurrences or population size are unknown and the greater value from extent of occurrence and area occupied is used. Similarly, environmental specificity is only considered if both the number of occurrences and the area of occupancy are unknown. If there is no information about threats then consideration is given to protected occurrences, and/or intrinsic vulnerability. The scoring system results in a final global rank for each taxon (Table 6). Thresholds would be identical for assessments at the national (N-rank), sub-national (S-rank), or subspecies levels (T-rank), but we did not test assessments at all levels for this exercise.

Table 5 Hierarchical combination of factors and conditions of use as developed in the explicit rule-based approach Factor Number of occurrences Condition Population size Geographic distribution Environmental specificity Trends Threats Protected occurrences Intrinsic vulnerability

Condition Always consider if available. If not available assume an initial G3 ranking Always consider if available Always consider if available Only consider if either number of occurrences or population size are unknown. Use the most significant value from full extent or area of occupancy Only consider if number of occurrences and area of occupancy (AOO) are unknown Always consider if available. Only use long-term trends if short-term trends are unknown Always consider if available Only consider if information on threats are unknown Only consider if information on threats are unknown

T.J. Regan et al. / Acta Oecologica 26 (2004) 95–107 Table 6 Final point translations into global NatureServe ranks Points (P) P ≤ 1.5 1.5 < P ≤ 2.5 2.5 < P ≤ 3.5 3.5 < P ≤ 4.5 P > 4.5

Global rank G1 G2 G3 G4 G5

2.3.3. Case study: Snake River spring/summer Chinook salmon ESU We conducted a risk ranking for the Snake River spring/summer Chinook salmon ESU, Oncorhynchus tshawytscha to demonstrate the utility of the proposed explicit NatureServe method. This Evolutionarily Significant Unit (ESU) consists of 39 local spawning subpopulations spread over a large geographic area in various tributaries of the river system (Lichatowich et al., 1993). Various impacts have caused the population to decline over the last century, including harvesting, irrigation diversions, logging, mining, grazing, obstacles to migration, and hydropower development. An experienced NatureServe status assessor (LLM) was supplied with extensive information regarding the Snake River spring/summer Chinook salmon ESU. The information enabled him to make parameter estimates for all the factors involved in a NatureServe assessment at the subspecies level (T-rank). The estimates were then used to determine a T-rank by applying the thresholds, weightings, and rules outlined in Tables 3–5, respectively. Parameter estimates for each of the factors appear in Table 7. To calculate a rank, the number of element occurrences is initially considered. The initial score is three since there are 39 occurrences or subpopulations. The score is then reduced by 0.5 because none of the occurrences are deemed to be of high quality. The estimate for population size reduces the score by a further 0.25. Additional points are subtracted due to the state of the short-term trends (0.75 points) and high threats (1.0). Although information is available for the other factors (i.e., distribution, intrinsic vulnerability, protected occurrences, and environmental specificity) these are not used in the explicit NatureServe approach as they act as

101

surrogates when information is lacking for the factors used. The final score for the Snake River spring/summer Chinook salmon is 0.5, which corresponds to a T1 NatureServe ranking.

3. Comparing the methods We compared risk-rankings between the current subjective system and the proposed explicit NatureServe method to highlight inconsistencies and determine how the method results compared to assessments done by other NatureServe scientists. Subjective assessments have internal inconsistencies and value judgements that can affect the overall assessment, so comparisons can be used only as a general guide to how results from the two systems vary. The comparison was done in two phases. First, one of us (LLM), interpreted detailed information for 40 species from various taxonomic groups and varying levels of threat. For each species, parameter estimates were made for each of the NatureServe factors. These estimates were used in the calculation of a conservation status rank using the explicit NatureServe method. The parameter estimates were also used to determine a rank using the current subjective technique. Second, another experienced NatureServe assessor (GAH) ranked 30 amphibian species. GAH made parameter estimates for each of the factors based on knowledge of each species. Parameter estimates were used to determine a NatureServe rank using (1) the explicit NatureServe method and (2) subjective evaluations of the parameter estimates using the current method. The results of the two comparisons appear in Tables 8 and 9. In the 40-species assessments done by LLM, 70% of species had the same NatureServe rank using both approaches, nine species were ranked higher and the remaining two species had lower ranks using the explicit method. None of the paired ranks differed by more than one rank level. The explicit NatureServe method tended to produce more conservative assessments than the subjective method. The 30species amphibian comparison done by GAH, indicates that

Table 7 Estimates for factors on the Snake River spring/summer Chinook salmon ESU Factor Occurrences Condition of occurrences Population size Area of occupancy Geographic range Trends Threats Intrinsic vulnerability Protected occurrences Environmental specificity

Parameter estimate 39 0 2500–3500 Length: 50,000 km 58,000 km2 Short-term: 70% Long-term: 99% Scope: High; severity: high; immediacy: high Not intrinsically vulnerable 0 Moderate. Generalist with some key requirements scarce

Category C A E LG F ST: B LT: A H C 0 C Score T-rank

Point allocation 3 –0.5 –0.25 Not used Not used –0.75 Not used –1.0 Not used Not used Not used 0.5 T1

102

T.J. Regan et al. / Acta Oecologica 26 (2004) 95–107

Table 8 Conservation status ranks for 40 species, based on qualitative and quantitative assessments Quantitative assessment G1 G2 G3 G4 G5

Qualitative assessment G1 G2 15 4 1 5 1

G3

G4

2 7

2

G5

2 1

Table 9 Conservation status ranks for 30 amphibians based on qualitative and quantitative assessments Quantitative assessment G1 G2 G3 G4 G5

Qualitative assessment G1 G2 1 9 1

26 (87%) of the species had the same rank. The other four species differed by one rank level (Table 9).

4. Discussion The explicit NatureServe method outlined in this paper provides a transparent alternative to the current method of assigning NatureServe ranks. The method captures the reasoning of NatureServe experts when combining and weighting factors to determine a NatureServe rank. The method incorporates the factors that are believed to cause extinction of species under both the small population paradigm and the declining population paradigm. The method will potentially minimise inconsistencies inherent in subjective assessments, as the rules are explicit and the method repeatable. In two test comparisons, 16 (23%) of the species’ assessments did not match qualitative assessments done by NatureServe staff. The rank differences that emerged from our comparison of the two ranking methods may be due, at least in part, to the flexibility of the qualitative system that allows different factors to be weighted on a species-by-species basis according to expert judgement. In addition, more recent tests of the proposed explicit method at a sub-national level (Ramsay, personal communication) have indicated that the results from the explicit method may correspond more closely to the current subjective approach if information on area of occupancy is always included when it is available. The weightings and combination of factors reflected in this paper do not necessarily account for the actual correlations between factors in the environment nor do they necessarily reflect their actual contribution to extinction. Deciding on the appropriate weightings for each factor is a difficult task. It requires knowledge of how each factor contributes to extinction relative to the others. It also requires knowledge of how the different factors interact and the magnitude of the

G3

G4

7 1

2 9

G5

correlations between them. This is an important aspect of the process but a difficult one to measure. The magnitude of the correlations and the extent of the dependencies are taxonspecific. Determining one weighting value appropriate for all taxa and ecological communities may be an impossible task. The magnitude of the correlations between factors and their contribution to extinction have not been quantified, either for the NatureServe system or for IUCN’s Red List system. To demonstrate the effect of correlations on extinction risk estimates, we would need to develop a detailed population viability model for each species. We could then determine how much each factor contributes to extinction predictions and determine how sensitive the model is to change in factor values. This may illuminate some general rules of thumb on the extent of the dependencies between factors and how they contribute to the risk of extinction. The proposed explicit NatureServe method accounts for missing data with the use of surrogates. This allows assessments to be done even in the face of incomplete knowledge. However, this method does not provide any guidelines with respect to the minimum set of information required to conduct an assessment. This aspect of the NatureServe assessments requires further attention. 4.1. Dealing with uncertainty The NatureServe system acknowledges that data for threatened species is often insufficient for determination of a precise rank. The system deals with this uncertainty by allowing ranks that span more than one category (Master et al., 2000). For example, Spermophilus mohavensis, the Mohave ground squirrel, is classified as a G2G3. This translates to the threatened element’s classification being either imperilled (G2) or vulnerable to extirpation (G3). Similarly, Pristis pectinata, the small-tooth sawfish, has a NatureServe rank of G1G3. The IUCN Red List categories can incorporate uncertainty by using intervals for uncertain data and fuzzy sets to com-

T.J. Regan et al. / Acta Oecologica 26 (2004) 95–107

bine and propagate the uncertainty through to the final classification. The resultant classification can span more than one category of threat depending on the quality of the data (Colyvan et al., 1999; Akçakaya et al., 2000; Regan et al., 2000). The explicit method for the NatureServe system described in this paper has the potential to incorporate uncertain data in a clear and unambiguous manner by using best estimates and upper and lower bounds for each of the factors when they are uncertain. Methods for incorporating uncertainty such as interval arithmetic can be used to combine the uncertainty in the factors to determine a final range rank. The uncertainty in the input parameters is propagated through to the final value so that the resulting bounds reflect the full extent of the uncertainty in the input parameters. 5. Conclusions This paper illustrates a simple approach of formalising expert knowledge to aid in the development of an explicit and repeatable method that is largely based on expert judgement. The explicit method developed in this study is the first documented attempt to define a transparent and repeatable process for weighting and combining factors under the NatureServe system by capturing expert process and reasoning for decision making. The explicit method goes a long way to formulating the foundation for a purely quantitative and objective approach for NatureServe assessments based on sound biological principles to assess the risk of extinction of species. Only once the process is formulated into explicit rules can the method be critically evaluated, refined and reapplied. This process of formalising expert knowledge has wider applications in conservation biology, which relies heavily on the opinions and judgements of experts and requires them to make decisions that are consistent, logical and objective. This process of eliciting expert knowledge identifies how information is combined and highlights any inconsistent logic or anomalies that may not be obvious in subjective decisions. NatureServe ranks play a significant role in the distribution of limited resources and guide conservation actions throughout North America. Therefore, it is essential that the assessment is objective and repeatable in order to display confidence in the decision process. The method developed in this study requires input and feedback from the many agencies that use the system but provides a repeatable and explicit benchmark for further development and improvement. Detailed discussions and ongoing development and refinement of the method amongst NatureServe scientists and managers are required to determine whether the explicit NatureServe method best reflects theories on species endangerment. Acknowledgements This work was done as part of the Extinction Risk working group at the National Center for Ecological Analysis and

103

Synthesis in Santa Barbara. The project was jointly funded by NCEAS and the Australian Research Council.

Appendix 1 Definitions for factors from used in NatureServe assessments (Master et al., 2003). Element occurrence An element occurrence is an area of land and/or water in which a species or ecological community is, or was, present. For species, the occurrence often corresponds with the local population, but when appropriate may be a portion of a population (e.g., long distance dispersers) or a group of nearby populations (e.g., metapopulation). For many taxa, occurrences are similar to “subpopulations” as defined by IUCN (2001). “Subpopulations are defined as geographically or otherwise distinct groups in the population between which there is little demographic or genetic exchange (typically one successful migrant individual or gamete per year or less).” An occurrence should have practical conservation value for the species or ecological community as evidenced by historical or potential continued presence and/or regular recurrence at a given location. For further discussion of the element occurrence concept, see “Element Occurrence Data Standard” (The Nature Conservancy and Association for Biodiversity Information 1999). Condition of occurrences The number of occurrences believed extant in the area of interest that have excellent or good condition (e.g., for species, at least a 95% probability of persistence for 20 years or five generations, whichever is longer—up to 100 years) in the area of interest (globe, nation, or sub-nation); for communities, a 95% probability of persistence over the next 20– 100 years, depending on the inherent dynamics of the element, with only minor to moderate alterations to composition, structure and/or ecological processes. Condition can be assessed as a quantitative (probability of persistence) or qualitative measure. When element occurrence (EO) ranks are available for individual occurrences, occurrence ranks of “A” or “B” indicate good (to excellent) condition. These ranks provide an assessment of estimated condition, or probability of persistence (based on condition, size, and landscape context) of occurrences of a given element. In other words, EO ranks provide an assessment of the likelihood that if current conditions prevail an occurrence will persist for a defined period of time, typically 20–100 years. See NatureServe’s Element Occurrence Data Standard (The Nature Conservancy and Association for Biodiversity Information 1999) for additional explanation of EO ranking.

104

T.J. Regan et al. / Acta Oecologica 26 (2004) 95–107

Population size The estimated, naturally occurring, current wild population size of the species within the area of interest (globe, nation, or sub-nation). Estimate the number of individuals of reproductive age or stage (at an appropriate time of the year), including mature but currently non-reproducing individuals. As a guide, use the IUCN (2001) definition when estimating population numbers: mature individuals that will never produce new recruits should not be counted (e.g., densities are too low for fertilization) [but see note below regarding long-persisting non-reproductive clones]. In the case of populations with biased adult or breeding sex ratios it is appropriate to use lower estimates for the number of mature individuals, which take this into account (e.g., the estimated effective population size). Where the population size fluctuates use a lower estimate. In most cases this will be much less than the mean. Reproducing units within a clone should be counted as individuals, except where such units are unable to survive alone (e.g., corals). In the case of taxa that naturally lose all or a subset of mature individuals at some point in their life cycle, the estimate should be made at the appropriate time, when mature individuals are available for breeding. Re-introduced individuals must have produced viable offspring before they are counted as mature individuals.

Range extent is described by IUCN (2001) for taxa Extent of occurrence is defined as the area contained within the shortest continuous imaginary boundary that can be drawn to encompass all the known, inferred or projected sites of present occurrence of a taxon, excluding cases of vagrancy. This measure may exclude discontinuities or disjunctions within the overall distribution of a taxon (e.g., large areas of obviously unsuitable habitat) (but see ‘area of occupancy’).

Area of occupancy is described by IUCN (2001) for taxa as Area of occupancy is defined as the area within its ‘extent of occurrence’ (see definition), which is occupied by a taxon, excluding cases of vagrancy. The measure reflects the fact that a taxon will not usually occur throughout the area of its extent of occurrence, which may contain unsuitable or unoccupied habitats. In some cases (e.g., colonial nesting sites, feeding sites for migratory taxa) the area of occupancy is the smallest area essential at any stage to the survival of existing populations of a taxon. The size of the area of occupancy will be a function of the scale at which it is measured, and should be at a scale appropriate to relevant biological aspects of the taxon, the nature of threats and the available data.

Short-term trend The observed, estimated, inferred, suspected, or projected short-term trend in population size, extent of occurrence, area of occupancy, number of occurrences, and/or condition of occurrences, whichever most significantly affects the rank in the area of interest (globe, nation, or sub-nation). Consider short-term historical trend within 10 years or three generations (for long-lived taxa), whichever is the longer (up to a maximum of 100 years), or, for communities, 10–100 years depending on characteristics of the type. The trend may be recent, current, or projected (based on recent past), and the trend may or may not be known to be continuing. Trends may be smooth, irregular or sporadic. Fluctuations will not normally count as trends, but an observed change should not be considered as merely a fluctuation rather than a trend unless there is evidence for this. In considering trends, do not consider newly discovered but presumably long existing occurrences, nor newly discovered individuals in previously little-known occurrences. Also, do not consider increases in the number of occurrences due to fragmentation of previously larger occurrences into more but smaller occurrences, but instead consider fragmentation of occurrences as indicative of decreasing an area of occupancy. Specify what is known about various pertinent trends in the comment field, including trend information for particular factors, more precise information, regional trends, etc. Also comment, if known, on whether the causes of decline, if any, are understood, reversible, and/or ceased. If the trend is known not to be continuing, specify that in comments.

Long-term trend The observed, estimated, inferred, or suspected degree of change in population size, extent of occurrence, area of occupancy, and/or number or condition of occurrences over the long-term (ca. 200 years) in the area of interest (globe, nation, or sub-nation). Specify in the comment field the time period for the change noted, as well as a longer-term view (e.g., back to European or Polynesian exploration) if information is available. If there are data on more than one aspect, specify which aspect is most influential.

Threats (severity, scope, and immediacy) Indicate the degree to which the species or ecological community is observed, inferred, or suspected to be directly or indirectly threatened in the area of interest (globe, nation, or sub-nation). Evaluate the impact of extrinsic threats, which typically are anthropogenic but may be natural. The impact of human activity may be direct (e.g., destruction of habitat) or indirect (e.g., invasive species introduction). Effects of natural phenomena (e.g., fire, hurricane, flooding)

T.J. Regan et al. / Acta Oecologica 26 (2004) 95–107

may be especially important when the species or ecological community is concentrated in one location or has few occurrences, which may be a result of human activity. Characteristics of the species or ecological community that make it inherently susceptible to threats should be considered under the rank factor Intrinsic Vulnerability. Threats considerations apply to the present and the future. Effects of past threats (whether or not continuing) should be addressed instead under the short-term trend and/or longterm trend factors. For species or ecological communities known only historically in the area of interest, but with significant likelihood of rediscovery in identifiable areas, current or foreseeable threats in those areas may be addressed here where appropriate if they would affect any extant (but unrecorded) occurrences of the species or ecological community. Threats may be observed, inferred, or projected to occur in the near term. They should be characterised in terms of severity (how badly and irreversibly the species population or the area of occupancy of the ecological community is affected), scope (what proportion of it is affected), and degree of imminence (how likely the threat is and how soon is it expected). ″Magnitude″ is sometimes used to refer to scope and severity collectively. Consider threats collectively, and for the foreseeable threat with the greatest magnitude (severity and scope combined), rate the severity, scope, and immediacy each as High, Moderate, Low, or Unknown. Identify in the comment field the threat to which severity, scope, and immediacy pertains, and discuss additional threats identified, or interactions among threats, including any high-magnitude threats considered insignificant in immediacy. Number of Protected and Managed Occurrences The observed, estimated, inferred, or suspected number of occurrences that are appropriately protected and managed for the long-term persistence of the element in the area of interest (globe, nation, or sub-nation). Both criteria (protection and management) must be met to assign a given code. Assign the code that represents the most restrictive criteria. Intrinsic vulnerability The observed, inferred, or suspected degree to which intrinsic or inherent factors of the Element (such as life

105

history or behaviour characteristics of species, or likelihood of regeneration or recolonisation for ecological communities) make it vulnerable or resilient to natural or anthropogenic stresses or catastrophes. Examples of such factors include reproductive rates and requirements, time to maturity, dormancy requirements, and dispersal patterns. Since geographically or ecologically disjunct or peripheral occurrences may show additional vulnerabilities not generally characteristic of the element, these factors are to be assessed for the species or ecological community throughout the area of interest, or at least for its better occurrences. Do not consider here such topics as population size, number of occurrences, area of occupancy, extent of occurrence, or environmental specificity; these are addressed as other ranking factors. Note that the intrinsic vulnerability factors exist independent of human influence, but may make the species or ecological community more susceptible to disturbance by human activities. The extent and effects of current or projected extrinsic influences themselves should be addressed in the Appendix 1H.

Environmental specificity The observed, inferred, or suspected vulnerability or resilience of the Element due to habitat preferences or restrictions or other environmental specificity or generality. Appendix 2

Threats The NatureServe system calculates an overall threat value according to the table below. If two of the three parameters are known, the overall threat value will be calculated by treating the unknown (or not assessed [null]) parameter as “Low.” If only one of the threat factors is rated (as high, moderate, or low), the resulting overall threat value will be “U” (unknown).

106

T.J. Regan et al. / Acta Oecologica 26 (2004) 95–107

Table A1 Calculation of threat factor values from values for severity, scope, and immediacy sub-factors (Master et al., 2003) Severity High High Moderate Moderate

Scope High High High High

Immediacy High Moderate High Moderate

Value =A

Description Moderate to severe, imminent threat for most (>60%) of population, occurrences, or area

High High Moderate Moderate

Moderate Moderate Moderate Moderate

High Moderate High Moderate

=B

Moderate to severe, imminent threat for a significant proportion (20–60%) of population, occurrences, or area

High Moderate

High High

Low Low

=C

Moderate to severe, non-imminent threat for most of population, occurrences, or area

High Moderate

Moderate Moderate

Low Low

=D

Moderate to severe, non-imminent threat for a significant proportion of population, occurrences, or area

High High High Moderate Moderate Moderate

Low Low Low Low Low Low

High Moderate Low High Moderate Low

=E

Moderate to severe threat for small proportion of population, occurrences, or area

Low Low Low Low Low Low

High High High Moderate Moderate Moderate

High Moderate Low High Moderate Low

=F

Low severity threat for most or significant proportion of population, occurrences, or area

Low Low Low

Low Low Low

High Moderate Low

=G

Low severity threat for a small proportion of population, occurrences, or area

References Akçakaya, H.R., Ferson, S., Burgman, M.A., Keith, D.A., Mace, G.M., Todd, C.R., 2000. Making consistent IUCN classifications under uncertainty. Conservation Biology 14, 1001–1013. Burgman, M.A., 2001. Flaws in Subjective Assessments of ecological risks and means for correcting them. Australian Journal of Environmental Management 8, 219–226. Carter, M.F., Hunter, W.C., Pashley, D.N., Rosenberg, K.V., 2000. Setting conservation priorities for land birds in the United States: the Partners in Flight approach. The Auk 117, 541–548. Caughley, G., 1994. Directions in conservation biology. Journal of Animal Ecology 63, 215–244.

Jenkins, R.E., 1985. The identification, acquisition and preservation of land as a species conservation strategy. In: Hoage, R.J. (Ed.), Animal Extinctions. Smithsonian Institution Press, Washington. Jenkins, R.E., 1996. Natural Heritage Data Center Network: Managing Information for Managing Biodiversity. In: Szaro, R.C., Johnston, D.W. (Eds.), Biodiversity in Managed Landscapes: Theory and Practice. Oxford University Press, New York, pp. 176–192. Keith, D., Ilowski, M., 1999. Epacris stuartii recovery plan 1996–2005. Department of Primary Industries, Water and Environment, Tasmania, Hobart. Keith, D.A., 1998. An evaluation and modification of World Conservation Union Red List criteria for classification of extinction risk in vascular plants. Conservation Biology 12, 1076–1090.

Caughley, G., Gunn, A., 1996. Conservation Biology in Theory and Practice. Blackwell Science, Cambridge, MA.

Lichatowich, J.A., Gilbertson, L.G., Mobrand, L.E., 1993. A concise summary of the Snake River Chinook production. Prepared for the Snake River Recovery Team. Mobrand Biometrics, Inc., Vashon Island, WA.

Colyvan, M., Burgman, M.A., Todd, C.R., Akçakaya, H.R., Boek, C., 1999. The treatment of uncertainty and the structure of the IUCN threatened species categories. Biological Conservation 89, 245–249.

Lunney, D., Curtin, A., Ayers, D., Cogger, H.G., Dickman, C.R., 1996. An ecological approach to identifying the endangered fauna of New South Wales. Pacific Conservation Biology 2, 212–231.

IUCN, 2001. International Union for the Conservation of Nature. Red List Categories. IUCN, Gland, Switzerland version 3.1.

Master, L.L., 1991. Assessing threats and setting priorities for conservation. Conservation Biology 5, 559–563.

Jenkins, R.E., 1976. Maintenance of natural diversity: approach and recommendations. Forty-first North American Wildlife and Natural Resources Conference. Washington, DC, pp. 441–451.

Master, L.L., Morse, L.E., Weakley, A.S., Hammerson, G.A., Faber-Langendoen, D., 2003. NatureServe Conservation Status Criteria. NatureServe Arlington, VA, USA.

T.J. Regan et al. / Acta Oecologica 26 (2004) 95–107 Master, L.L., Stein, B.A., Kutner, L.S., Hammerson, G., 2000. Vanishing Assets: Conservation Status of US Species. In: Bruce, A., Stein, Kutner, L.S., Adams, J.S. (Eds.), Precious Heritage: Status of Biodiversity in the United States. Oxford University Press, pp. 93–118. Millsap, B.A., Gore, J.A., Runde, D.E., Cerulean, S.I., 1990. Setting Priorities for the Conservation of Fish and Wildlife Species in Florida. Wildlife Monographs 111, 1–57. Plous, S., 1993. The psychology of judgment and decision making. McGraw-Hill, Inc, New York. Rabinowitz, D., 1981. Seven forms of rarity. In: Synge, H. (Ed.), The Biological Aspects of Rare Plant Conservation. Wiley, Chichester, England, pp. 205–217. Regan, H.M., Colyvan, M., Burgman, M.A., 2000. A proposal for fuzzy International Union for the Conservation of Nature (IUCN) categories and criteria. Biological Conservation 92, 101–108. Rohlf, D.J., 1991. Six reasons why the Endangered Species Act doesn’t work- and what to do about it. Conservation Biology 5.

107

Rush, C., Rajkumar, R., 2001. Expert judgement in cost estimating: modelling the reasoning approach. Concurrent Engineering: Research and Applications. CERA Journal 9. Schorger, A.W., 1995. The Passenger Pigeon. University of Wisconsin Press, Madison, WI. Shaffer, M.L., 1981. Minimum population sizes for species conservation. Bioscience 31, 131–134. Stein, B.A., Kutner, L.S., Adams, J.S., 2000. Precious Heritage. The Status of Biodiversity in the United States. Oxford University Press, New York, USA. Tuthill, S.G., 1990. Knowledge engineering: concepts and practices for knowledge-based systems. Tab Books Inc, Blue Ridge Summit, PA. Tversky, A., Kahneman, D., 1982. Judgment under uncertainty: heuristics and biases. In: Kahneman, D., Slovic, P., Tversky, A. (Eds.), Judgment Under Uncertainty. Cambridge University Press, Cambridge.