Title

Combining Genetic Structure And Demographic Analyses To Estimate Persistence In Endangered Key Deer (Odocoileus Virginianus Clavium)

Keywords

Deer; Endangered; Fecal; Noninvasive

Abstract

Recent improvements in genetic analyses have paved the way in using molecular data to answer questions regarding evolutionary history, genetic structure, and demography. Key deer are a federally endangered subspecies assumed to be genetically unique, homogeneous, and have a female-biased population of approximately 900 deer. We used 985 bp of the mitochondrial cytochrome b gene and 12 microsatellite loci to test two hypotheses: (1) that Key deer are isolated and have reduced diversity compared to mainland deer and (2) that isolation of the Florida Keys has led to a small population size and a high risk of extinction. Our results indicate that Key deer are indeed genetically isolated from mainland white-tailed deer and that there is a lack of genetic substructure between islands. While Key deer exhibit reduced levels of genetic diversity compared to their mainland counterparts, they contain enough diversity to uniquely identify individual deer. Based on genetic identification, we estimated a census size of around 1000 individuals with a heavily skewed female-biased adult sex ratio. Furthermore, our genetic and contemporary demographic data were used to generate a species persistence model of the Key deer. Sensitivity tests within the population viability analysis brought to light the importance of fetal sex ratio and female survival as the primary factors at risk of driving the subspecies to extinction. This study serves as a prime example of how persistence models can be used to evaluate population viability in natural populations of endangered organisms.

Publication Date

10-1-2017

Publication Title

Conservation Genetics

Volume

18

Issue

5

Number of Pages

1061-1076

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/s10592-017-0958-2

Socpus ID

85017160136 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85017160136

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