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Jornal de Biodiversidade e Espécies Ameaçadas

Assessing Extinction Risk for Mexican Dry and Cloud Forest Rodents: A Case Study

Abstract

Rafael S Ramírez and Esther Quintero

Red Lists have been traditionally used as instruments to guide conservation strategies to avoid extinctions. However, there is little consensus in the best way to perform assessments and thus, different countries have developed different methods according to their specific needs. In this study we used a set of ten rodents species, half of them from the cloud forest and halve from the dry forest listed as endangered by the IUCN Red List, but not included in the Mexican red list NOM-059-SEMARNAT-2010. We assessed these 10 species using Mexico´s national Risk Assessment Method (MER) guidelines and then compared the outcomes of these assessments with those from the IUCN Red List evaluations. In addition, in order to support our comparison and to verify if both methods deliver equivalent results, we compared 67 endangered mammals which inhabit in Mexico and have been assessed by both methods. We found that both assessment methods yield equivalent results for the five species of cloud forest rodents. However, it was different for those from the dry forest species, where three had different results. Moreover, we found several discrepancies in the assessment results of the 67 endangered mammals assessed by both methods, suggesting that the assessment methods may be not entirely equivalent. We conclude that the MER in its current form might not be entirely objective, and the assessments could be artificially biased. The method could be an even better assessment instrument if the something is done to account for the lack of objectivity and the bias that the lack of information that we face with many endangered species is taken into account. In this way, the MER could clearly define the conservation status of a given species in a simple and transparent, relevant in terms of impact on conservation actions.

Isenção de responsabilidade: Este resumo foi traduzido usando ferramentas de inteligência artificial e ainda não foi revisado ou verificado

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