The 2022 Kunming-Montreal Global Biodiversity Framework (GBF) of the Convention on Biological Diversity recognized the protection of genetic diversity (GD) as a major objective for biodiversity conservation. To monitor GD for the GBF, genetic indicators have been proposed, including an effective population size above 500 (Ne&gt;500) as an indicator for a healthy evolutionary status and adaptive potential.Numerous DNA datasets have recently been published due to increasingly cost-effective DNA sequencing technologies and enhanced collaborations between researchers and conservation practitioners. These datasets allow estimating GD within and across species. However, Ne estimates from DNA data are sensitive to species features including population spatial genetic structure and life history traits such as reproductive system or life span, sampling designs and estimation methods. The general aim of my PhD is to use both simulations and existing empirical datasets to perform sensitivity analyses for GD estimation. I will select DNA reference datasets representative of contrasted evolutionary histories, biological and data features to explore how their properties impact Ne estimates and will conduct simulations mimicking some of these features. This approach will eventually provide standardized workflows for robust sampling, molecular and statistical procedures, and best practices for reliable estimation of Ne to support conservation managers.Read less <