Commentary: IUCN Classifications Under Uncertainty

Transcription

1 NOTICE: this is the author s version of a work that was accepted for publication in Environmental Modelling & Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling & Software 38, (2012); doi: /j.envsoft Citation: Akçakaya, H.R., S. Ferson, M.A. Burgman, D.A. Keith, G.M. Mace and C.R. Todd Commentary: IUCN classifications under uncertainty. Environmental Modelling & Software 38: Commentary: IUCN Classifications Under Uncertainty H. Reşit Akçakaya 1*, Scott Ferson 2, Mark A. Burgman 3, David A. Keith 4, Georgina M. Mace 5, Charles R. Todd 6 1 Department of Ecology and Evolution, Stony Brook University, New York, USA 2 Applied Biomathematics, 100 North Country Road, Setauket, NY, USA 3 School of Botany, University Of Melbourne, Parkville, Australia 4 Australian Wetlands and Rivers Centre, University of New South Wales, Sydney, and New South Wales Office of Environment & Heritage, Hurstville, Australia 5 Centre for Population Biology and Division of Biology, Imperial College, London, UK 6 Arthur Rylah Institute for Environmental Research, Heidelberg, Victoria, Australia * Corresponding author; Resit.Akcakaya@gmail.com; Tel: ; Fax: Abstract We comment on a recent article by Newton (Environ. Model. Softw. (2010), 25, 15-23), which proposed a method, based on a Bayesian belief networks, for classifying the threat status of species under the IUCN Red List Categories and Criteria, and compared this method to an earlier one that we had developed that is based on fuzzy logic. There are three types of differences between the results of the two methods, the most consequential of which is different threat status categories assigned to some species for which the input data were uncertain. We demonstrate that the results obtained using the fuzzy logic approach are consistent with IUCN Red List criteria and guidelines. The application of Bayesian Networks to the IUCN Red List criteria to assist uncertain risk assessments may yet have merit. However, in order to be consistent with IUCN Red List assessments, applications of Bayesian approaches to actual Red List assessments would need an explicit and objective method for assigning likelihoods based on uncertain data. Introduction In a recent article, Newton (2010) proposed a method based on Bayesian Networks for red listing under uncertainty, and compared his method to RAMAS Red List (Akçakaya and 1

2 Ferson 2001). RAMAS implements the fuzzy-logic approach we proposed (Akçakaya et al. 2000), which classifies species based on the IUCN Red List Categories and Criteria (IUCN 2001) and allows uncertain data to be incorporated into the assessments. Based on a comparison of results for 16 species, Newton (2010) stated that the fuzzy logic approach gives "anomalous" results (p.20, 22), suggested that it is not as reliable (p.22) or transparent (p. 15) as the alternative, and implied that its performance has not been evaluated sufficiently (p.22). This paper has led to some concern among people using the criteria for species with uncertain data, who then doubt the validity of the fuzzy logic method. Our goal is to demonstrate that the results obtained are consistent with IUCN criteria. We do not intend to review or correct Newton's approach in this paper; we simply aim to point out that, contrary to what Newton (2010) implied, the results obtained using fuzzy logic are neither anomalous nor incorrect with respect to IUCN Criteria and guidelines (IUCN 2001, 2011; Mace et al. 2008). The comparison by Newton (2010) involved 16 species that were assessed by both methods. There were 3 types of differences between the results of the two methods: (i) in 2 of 16 cases, the methods assigned the species to a different threat category; (ii) in 5 cases, both methods assigned the species to the same threat category, but the range of plausible categories were different; (iii) in a few additional cases, the criteria under which the species were listed differed. We discuss these three types of differences in separate sections below. Difference in threat category The most important difference between the methods involved two species that were assigned to different threat categories. For Inyo California Towhee, the number of mature individuals is given as [194,250,300] (which means a best estimate of 250 and a plausible range of 194 to 300); there is continuing decline; and all individuals are in one subpopulation. RAMAS Red List (RRL) gives a range of categories CR-EN (Critically Endangered to Endangered), under criterion C2a(ii) (see IUCN 2001). This is because it is possible that there are fewer than 250 mature individuals (the range includes values <250), and the relevant subcriteria are met. According to IUCN (2001), the correct listing would be CR with fewer than 250 mature individuals, and EN with more 250 mature individuals. Thus, neither EN nor CR can be ruled out, and our result of CR-EN is consistent with IUCN (2001). According to Newton (2010), the Bayesian Network method (RLBN) results in EN(100%) when applied to the Inyo California Towhee. This result suggests that there is no possibility that the species is CR, even though uncertainty of the number of mature individuals results in a range that extends below the threshold of 250 (actually, more of the range is below than above). As discussed above, according to the IUCN criteria, CR cannot be ruled out in this case. Thus, classifying a species as EN would not be consistent with IUCN criteria, nor would it be precautionary. For Desert Tortoise, past reduction is [60,90] (which means a plausible range of 60% to 90% reduction in the past 3 generations); future reduction is [42,81]; and criterion A1 is not applicable (see IUCN 2001). RAMAS Red List gives a range of categories CR-EN, under A2 and A3 because both past and future reduction ranges include values that are <80% and 80%. 2

3 According to IUCN (2001), a reduction of 80% or more results in a listing of CR, and a reduction between 50% and 80% results in a listing of EN. Thus, neither CR nor EN can be ruled out, and our result of CR-EN is consistent with IUCN (2001). Newton gives a result of EN(100%), with the following explanation: "In order for CR to be the most probable category using the RLBN, a likelihood of >50% would need to be entered for 80% state of either the A2 or the A3 variable, which does not seem reasonable given the information provided in the taxon file." This explanation seems to suggest that the likelihood for the state of 80% decline is less than 50%. We are not clear about how these likelihoods are assigned, but even if the likelihood of 80% decline is less than 50%, it is still not zero, so CR cannot be ruled out, as discussed above. Thus, listing this species as only EN, without recognizing the possibility of CR, would be inconsistent with IUCN criteria and guidelines. Newton s interpretation of this result embodies an implicit attitude to risk such that classification in a particular risk category requires >50% likelihood of meeting the criteria for that category. This balance of evidence attitude may be reasonable but, in contrast, RAMAS Red List requires users to set their attitudes to risk explicitly, allowing them to justify either precautionary or evidentiary attitudes. IUCN guidelines (2011) recommend a precautionary but realistic attitude to risk, suggesting that a threshold probability below 50% may be appropriate in most circumstances. Difference in range of categories In 5 cases, the two methods gave different ranges of categories, although the threat category selected was the same. The issues for these species are similar to the issues discussed above, so we discuss these cases briefly. For Southwestern Willow Flycatcher, the total population is given as 140 mature individuals, and the size of largest subpopulation is given as [14, 140] mature individuals. RAMAS Red List assigns a range of CR-EN, whereas Newton (2010) gives CR(100%). The range for the size of the largest population includes values both above and below the CR threshold of 50. Dividing this range by the total number of 140, the proportion of mature individuals in the largest subpopulation includes values both above and below the CR threshold of 90%. Thus, both CR and EN are plausible categories, and our result is consistent with IUCN criteria and guidelines (because EN cannot be ruled out). For Swainson's Hawk, RAMAS Red List assigns a range of EN-VU, whereas Newton (2010) gives EN(100%). For this species, the size of largest subpopulation (given as [140, 450] mature individuals) includes values both below and above the EN threshold of 250 mature individuals. In addition, both past and future reduction (given as [0, 75]%) include values that are below and above the EN threshold of 50%. Thus, the plausible range is EN-VU, as given by RAMAS Red List is consistent with IUCN criteria and guidelines (because VU cannot be ruled out). 3

4 Similarly, for Desert Bighorn Sheep, RAMAS Red List assigns a range of EN-VU, whereas Newton (2010) gives EN(100%). For this species, past reduction ([37.8, 53.3]%), the size of the largest subpopulation ([20, 280] mature individuals), and the proportion of the population in the largest subpopulation ([0.07, 1.00]) all include values both above and below the respective EN thresholds of 50% reduction, 250 mature individuals and 95%. Thus, the plausible range EN-VU, as given by RAMAS Red List, is consistent with IUCN criteria and guidelines (because VU cannot be ruled out). Helmeted Honeyeater is classified as CR (90%), EN (10%) by Newton (2010) and as CR by RAMAS Red List. Epacris grandis is classified as EN (95%), CR (5%) by Newton (2010) and as EN by RAMAS Red List. For both species, the crucial parameters are specified as triangular fuzzy numbers, which makes RAMAS results more sensitive to attitude settings. A substantial shift towards a more evidentiary attitude for Helmeted Honeyeater, and a moderate shift toward a more precautionary attitude for Epacris grandis change the RAMAS results to give the same range of categories as Newton (2010). The advantage of using RAMAS Red List is that it obliges the user to be explicit about this reasoning. Differences in listing criteria The two methods differ in the criteria used for listing some species, even though they listed the species under the same threat category. For example, both methods list California Gnatcatcher as EN, but RAMAS lists under criteria B1abc. In contrast, Newton (2010) lists under B1bc, commenting: "However, the input variables for criterion B1a (severely fragmented and the number of locations) are unknown, and therefore it is difficult to understand why RRL lists this subcriterion. Similarly, in the case of the Loggerhead shrike, RRL lists criterion C1 (estimated continuing decline in population size), despite the fact that the input data for this subcriterion is given as unknown in the taxon file." The understanding that Newton (2010) is searching for resides in the interpretation of "unknown". As we discussed in our original paper (Akçakaya et al. 2000, page 1010), there are two approaches to dealing with unknown parameters: the analyst may ignore the corresponding criterion (i.e., assuming that the parameter value does not meet the criterion), or assume that the data are truly unknown and can take any possible value. In the California Gnatcatcher example, the first approach (ignoring the criterion) means assuming that the population is not severely fragmented and the number of locations is large enough to be above the threshold for EN listing. The second approach (assuming the data are truly unknown) means that the analyst should entertain the possibility that fragmentation is severe and/or there may be few locations. In other words, the possibility that B1a is met cannot be ruled out. Newton (2010) seems to favor the first approach as he writes "A more conservative approach, which should arguably be preferred in situations where information is completely lacking (i.e. the input data are unknown), would involve selecting a node state associated with a nonthreatened category (LC/DD), effectively ignoring this criterion during the classification." It is not clear why Newton thinks this approach is "conservative" but it is certainly nonprecautionary. In our paper, we gave several reasons why the second approach is more reliable and less prone to misinterpretation (Akçakaya et al. 2000). However, RAMAS Red List gives 4

5 the users an option to take either of these approaches (by setting such parameters to "ignored" for the first approach, and "unknown" for the second approach). When ignored is selected, then the results from RRL are identical to those obtained by Newton (2010) and this explains why RAMAS lists Inyo California Towhee under C1, which Newton (2010; page 20) mistakenly interpreted as "anomalous." Conclusion In a review of an earlier version of this paper, A. Newton stated that the results in Table 4 of his paper were not intended to indicate the "right" answer, but rather the outcome of using his approach if particular likelihood values were entered. Although we do not doubt this explanation, we also believe that most readers would interpret the results presented in his Table 4 to suggest it provides the correct listing, given the data provided. And, if accepted in conjunction with other statements in his paper as discussed above, most readers would conclude that results obtained using the fuzzy logic approach are wrong or anomalous. Given the importance of the IUCN Red List in species conservation, we believe it is necessary to correct such potential misunderstandings by the readers. In all cases in which the two methods differed, our results are consistent with IUCN criteria and guidelines, and, contrary to Newton's (2010) claims, there are no anomalous results. The differences between the methods likely originate from differences in interpreting uncertainty, and in handling unknown values. Newton's method is based on likelihoods assigned by the assessors. Although we are not clear on how these would be assigned in an actual Red List assessment, and although our goal is not to review or correct Newton's approach, we suspect that the assignment of these likelihoods reflect in part the assessor's attitude towards risk and uncertainty. In our approach, these attitudes also play an important role. The difference is that they are explicitly and quantitatively defined, and are specified by the assessors (Akçakaya et al. 2000; Akçakaya and Ferson 2001). The application of Bayesian Networks (BN) to the IUCN Red List criteria to assist uncertain risk assessments may yet have merit. However, many of the results given in Newton (2010; Table 4) are not consistent with IUCN criteria and guidelines. As mentioned above, this may be because of attitudes to risk and uncertainty implied in the likelihood values assigned in these cases. For results of any BN approach, including Newton's (2010), to be consistent with IUCN criteria and guidelines, the likelihoods assigned require an explicit link to risk attitudes. This link should be based on simple characterizations of data uncertainty (such as plausible ranges and/or best estimates), together with an appropriate method for handling unknown values. References Akçakaya, H.R., Ferson, S., RAMAS Red List: threatened species classification under uncertainty. Version 2.0. Applied Biomathematics, Setauket, New York, USA. Akçakaya, H.R., Ferson, S., Burgman, M.A., Keith, D.A., Mace, G.M., Todd, C.R., Making consistent IUCN classifications under uncertainty. Conservation Biology 14,

6 IUCN (International Union for Conservation of Nature), IUCN Red List Categories and Criteria. Version 3.1. IUCN Species Survival Commission, IUCN, Gland, Switzerland and Cambridge, United Kingdom Available from: (accessed October 2011) IUCN Standards and Petitions Subcommittee, Guidelines for Using the IUCN Red List Categories and Criteria. Version 9.0. Prepared by the Standards and Petitions Subcommittee of the IUCN Species Survival Commission. Downloadable from: (accessed October 2011). Mace, G.M., N. Collar, K.J. Gaston, C. Hilton-Taylor, H.R. Akçakaya, N. Leader-Williams, E.J. Milner-Gulland and S.N. Stuart Quantification of extinction risk: IUCN s system for classifying threatened species. Conservation Biology 22: Newton, A.C., Use of a Bayesian network for red listing under uncertainty. Environmental Modelling & Software 25,