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Background: Mexico is the fourth richest country in amphibians and the second country with the highest quantity of threatened amphibian species, and this number could be higher as many species are too poorly known to be accurately assigned to a risk category. The absence of a risk status or an unknown population trend can slow or halt conservation action, so it is vital to develop tools that in the absence of specific demographic data can assess a species’ risk of extinction, population trend, and to better understand which variables increase their vulnerability. Recent studies have demonstrated that the risk of species decline depends on extrinsic and intrinsic trait, thus including both of them for assessing extinction might render more accurate assessment of threat. Methods: In this study harvested data from the Encyclopedia of Life (EOL) and the published literature for Mexican amphibians and used these data to assess the population trend of some of the Mexican species that have been assigned to the Data Deficient category of the IUCN using Random Forests, a Machine Learning method that gives a prediction of complex processes and identifies the most important variables that account for the predictions. Results: Our results show that most data deficient Mexican amphibians have decreasing population trends. We found that Random Forests is a solid and accurate way to identify species with decreasing population trends when no demographic data is available. Moreover, we point the most important variables that make species more vulnerable for extinction. This exercise is a very valuable first step in assigning conservation priorities for poorly known species.
This preprint is part of the PeerJ collection "Using Digital Resources for Biodiversity and Evolution research". This version contains the results of new analyses that amend our previous version.
Supplemental Table 1: Mexican amphibian species and intrinsic and extrinsic traits used to assess the risk status for each species
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