Published October 4, 2022 | Version v1
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Data from: Are genetic variation and demographic performance linked?

  • 1. University of Minnesota
  • 2. Duke University
  • 3. University of Montana

Description

Quantifying the empirical relationships between genetic variation and population viability is important from both basic biological and applied conservation perspectives, yet few populations have been monitored with both long-term demographic and population genetics approaches. Here, we present eight years of historical demographic data from five populations of Boechera fecunda (Brassicaceae), a rare, self-compatible perennial plant endemic to Montana, USA, and use integral projection models to estimate the stochastic population growth rate (λS) and extinction risk of each population. We combine these demographic estimates with previously published metrics of genetic variation in the same populations to test whether genetic diversity within populations is linked to demographic performance. Our results show that in this predominantly inbred species, genetics and demography are not strongly correlated, suggesting that more inbred populations are not necessarily less viable or at higher extinction risk than more genetically diverse populations. Interestingly, however, a contemporary re-census revealed that, among these populations, genetic rather than demographic parameters were better predictors of current population density, with populations harboring greater genetic diversity maintaining denser populations at present. In the absence of evidence for inbreeding depression decreasing population viability in this species, we recommend conservation of distinct, potentially locally adapted populations of B. fecunda rather than alternatives such as translocations or reintroductions.

Notes

Usage notes are provided in the enclosed README.txt file.

Funding provided by: National Science Foundation
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000001
Award Number: Graduate Research Fellowship

Funding provided by: National Science Foundation
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000001
Award Number: 9100397

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