Short communicationCommentary: IUCN classifications under uncertainty
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 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, IUCN, 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.
Section snippets
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 194–300); 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
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
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
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
References (6)
Use of a Bayesian network for red listing under uncertainty
Environmental Modelling & Software
(2010)- et al.
RAMAS Red List: Threatened Species Classification Under Uncertainty
(2001) - et al.
Making consistent IUCN classifications under uncertainty
Conservation Biology
(2000)