Genomic Data Describe Population Structure but Struggle to Estimate Directed Connectivity Networks

Area of Science:

• Population genetics
• Conservation biology
• Genomics

Background:

• Population connectivity is vital for species persistence and distribution.
• Genomic data increasingly used to infer connectivity, but method reliability is untested.

Purpose of the Study:

• To systematically evaluate the accuracy of different genomic methods for estimating population connectivity.
• To assess the influence of key parameters (population size, migration, sampling effort) on method performance.

Main Methods:

• Simulated 3240 SNP datasets based on a known connectivity network.
• Applied diverse analytical tools (gene flow, clustering, parentage) to infer connectivity.
• Compared method accuracy under varying population sizes, migration rates, and sampling efforts (individuals and SNPs).

Main Results:

• Reliable connectivity estimates require unrealistic conditions: 100% or 30% individual sampling with low migration (<10%).
• Low individual sampling (10%) resulted in inconsistent recovery of true connectivity networks across all methods.
• Increasing SNP numbers did not compensate for low individual sampling proportions.
• BA3-SNPs showed the most overall reliability, but other methods may suit specific scenarios.

Conclusions:

• Current genomic methods for population connectivity are unreliable under typical low individual sampling.
• High SNP density cannot substitute for adequate individual sampling.
• Findings necessitate a re-evaluation of population genomic study designs for connectivity inference to avoid misleading conclusions and flawed management decisions.