Genetic data improve the assessment of the conservation status based only on herbarium records of a Neotropical tree

Abstract

Although there is a consensus among conservation biologists about the importance of genetic information, the assessment of extinction risk and conservation decision-making generally do not explicitly consider this type of data. Genetic data can be even more important in species where little other information is available. In this study, we investigated a poorly known legume tree, Dimorphandra exaltata , from the Brazilian Atlantic Forest, a hotspot for conservation. We coupled species distribution models and geospatial assessment based on herbarium records with population genetic analyses to evaluate its genetic status and extinction risk, and to suggest conservation measures. Dimorphandra exaltata shows low genetic diversity, inbreeding, and genetic evidence of decrease in population size, indicating that the species is genetically depleted. Geospatial assessment classified the species as Endangered. Species distribution models projected a decrease in range size in the near future (2050). The genetic status of the species suggests low adaptive potential, which compromises its chances of survival in the face of ongoing climatic change. Altogether, our coupled analyses show that the species is even more threatened than indicated by geospatial analyses alone. Thus, conservation measures that take into account genetic data and the impacts of climate change in the species should be implemented.

Ancient DNA From Museum Specimens and Next Generation Sequencing Help Resolve the Controversial Evolutionary History of the Critically Endangered Puebla Deer Mouse

“…This new evidence could offer a robust and reliable resolution of the evolutionary history and taxonomic status of species, resulting in the re-evaluation of previous conservation strategies or the establishment of new conservation programs in order to better protect and manage biodiversity. Information used to decide if a species is at risk of extinction and its threat category are usually based on ecological and demographic data such as the number of known individuals, current or projected declines in population size and the extent of occurrence or distribution (IUCN, 2014;Carneiro Muniz et al, 2019). However, for rare species where little additional information is available, genetic data is extremely important (Carneiro Muniz et al, 2019).…”

“…Information used to decide if a species is at risk of extinction and its threat category are usually based on ecological and demographic data such as the number of known individuals, current or projected declines in population size and the extent of occurrence or distribution (IUCN, 2014;Carneiro Muniz et al, 2019). However, for rare species where little additional information is available, genetic data is extremely important (Carneiro Muniz et al, 2019).…”

A Semi-Automated SNP-Based Approach for Contaminant Identification in Biparental Polyploid Populations of Tropical Forage Grasses

“…In this work, we propose to use GA measures for performing clustering analyses (CAs) and automatically identifying contaminants in forage grass biparental populations, grouping individuals based on GA similarity measures instead of their raw SNP data. Although CA of large SNP datasets has been extensively used to discover patterns in population relatedness and structure (Gori et al, 2016;Muniz et al, 2019;Yousefi-Mashouf et al, 2021), its use for parentage assignment is not common because of the nonspecificity and constancy of the clusters, but has already been combined with previously described techniques for parentage and sibship inference in diploids (Ellis et al, 2018).…”

An Automated SNP-Based Approach for Contaminant Identification in Biparental Polyploid Populations of Tropical Forage Grasses

“…In this work, we propose to use GA measures for performing clustering analyses (CAs) and automatically identifying contaminants in forage grass biparental populations, grouping individuals based on GA similarity measures instead of their raw SNP data. Although CA of large SNP datasets has been extensively used to discover patterns in population relatedness and structure (Gori et al, 2016;Muniz et al, 2019;Yousefi-Mashouf et al, 2021), its use for parentage assignment is not common because of the clusters' nonspecificity and constancy, but has already been combined with previously described techniques for parentage and sibship inference in diploids (Ellis et al, 2018).…”