Estado de ameaça e distribuição potencial da Icuria dunensis no Centro de Endemismo de Rovuma, Moçambique

Abstract

Environmental changes resulting from anthropogenic activities are promoting losses in global biodiversity at an unprecedented rate. In Mozambique, extinction rates of plant species have been increasing as botanical research efforts cover areas that have been little explored until now. The main objective of the study is to assess the degree of extinction threat and apply species distribution models for a plant species endemic to Mozambique: “Icuria dunensis”, in light of new data on the species’ occurrence, applying the geographic range criterion established by the International Union for Conservation of Nature (IUCN) and products generated by species distribution models (MaxEnt Software). The Minimum Convex Polygon method and the standard 2 km x 2 km grid method were used to calculate the extent of occurrence (EOO) to determine the area of ​​occupancy (AOO). Previous assessments of Icuria dunensis classify it as Endangered (EN) under criterion B2ab (i, ii, iii, iv, v). With the new occurrence data, its classification does not change even with an increase in AOO and EOO (135,172 km2 and 11,085,313 km2, respectively). These results are supported by the map of suitable habitat predicted for the species produced by the MaxEnt modeling method. The generated models were able to accurately predict the current area of ​​suitable habitat for the species, the value of the area under the curve (AUC) was above 0.90. The estimated potential niche was 1282.37 km2 for the model generated with data from GBIF and 698.07 km2 for the model generated with the new occurrence records acquired in the field. The study confirmed that the use of accurate and more comprehensive occurrence data can constitute an effective approach to increase the accuracy of the assessment of the species' extinction risk under the criteria of the IUCN Red List and that the models developed constitute an essential tool for understanding the species' biogeographic patterns and can assist forest conservation actions, since the areas mapped by the models as potential for the species' occurrence are at risk of disappearance due to several factors: mining, unsustainable land use (slash-and-burn agriculture), urban expansion, and logging.(TRADUÇÃO NOSSA)