Dealing With the Complexity of Effective Population Size in Conservation Practice.

Effective population size () is one of the most important parameters in evolutionary biology, as it is linked to the long-term survival capability of species. Therefore, greatly interests conservation geneticists, but it is also very relevant to policymakers, managers, and conservation practitioners. Molecular methods to estimate rely on various assumptions, including no immigration, panmixia, random sampling, absence of spatial genetic structure, and/or mutation-drift equilibrium.

Population Size in Evolutionary Biology Is More Than the Effective Size.

In population genetics idealized Wright-Fisher (WF) populations are generally considered equivalent to real populations with regard to the major evolutionary processes that influence genotype and allele frequencies. As a result we often model the response of populations by focusing on the effective size . The Diversity Partitioning Theorem (DPT) shows that you cannot model the behavior of a system solely on the basis of a diversity (accounting for unevenness among items) without taking richness into account.

Comparing Genetic Reconstructions Over Time With Long-Time Wolf Monitoring Data in Two Populations.

Many methods are now available to calculate , but their performance varies depending on assumptions. Although simulated data are useful to discover certain types of bias, real empirical data supported by detailed known population histories allow us to discern how well methods perform with actual messy and complex data. Here, we focus on two genomic data sets of grey wolf populations for which population size changes of the past 40-120 years are well documented.

Dealing With Assumptions and Sampling Bias in the Estimation of Effective Population Size: A Case Study in an Amphibian Population.

Accurately estimating effective population size ( ) is essential for understanding evolutionary processes and guiding conservation efforts. This study investigates estimation methods in spatially structured populations using a population of moor frog () as a case study. We assessed the behaviour of estimates derived from the linkage disequilibrium (LD) method as we changed the spatial configuration of samples.