Determining minimum sample sizes for IUCN Red List endangered oak species through simulation [Poster]
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posters
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Article
2021
A scientific poster presented by Kaylee Rosenberger, which correlates to the article, "Proportional sampling strategy often captures more genetic diversity when population sizes vary" (https://doi.org/10.1016/j.biocon.2021.109261)
Collecting and conserving genetic diversity from plant populations for ex situ collections is a major conservation objective of seed banks and botanic gardens. However, current guidelines for collecting germplasm to preserve plant species ex situ might not adequately capture the genetic diversity of wild plant populations. In particular, sampling guidelines tailored to rare species are lacking, but can be produced by simulation. Here, we aim to recommend minimum sample sizes for several IUCN Red List oak species to aid in practical seed sampling guidelines. Oaks have high ecological importance, as they are keystone species in many environments. In addition, oaks are exceptional species, meaning they cannot be seed banked using traditional methods. Thus, it becomes increasingly important to develop sampling strategies that efficiently and effectively capture the genetic diversity of these rare species. In this project, we used genetic simulations to represent several rare oak species and ‘sample’ from these populations using R. Specifically, we aim to quantify performance of sampling across the range of possible sizes--from 1 to 500 individuals. From this, we determined the minimum sample size required to capture 95% of the species’ genetic diversity, which is a common threshold for sufficient genetic diversity. With this data, we aim to recommend a minimum sample size to capture sufficient genetic diversity for each of these oak species, which would be directly useful to botanic gardens and arboreta. We can also answer the question, “Does one sampling strategy fit all rare oak species?”
Calendar Year 2021 Staff Publications
The Morton Arboretum Staff Publications and Dataset Collection
Publications
The Morton Arboretum Staff Publications and Dataset Collection
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English
