Population genetic data underpin many studies of behavioral, ecological, and evolutionary processes in wild populations and contribute to effective conservation management. However, collecting genetic samples can be challenging when working with endangered, invasive, or cryptic species. Environmental DNA (eDNA) offers a way to sample genetic material non-invasively without requiring visual observation. While eDNA has been trialed extensively as a biodiversity and biosecurity monitoring tool with a strong taxonomic focus, it has yet to be fully explored as a means for obtaining population genetic information. Here, we review current research that employs eDNA approaches for the study of populations. We outline challenges facing eDNA-based population genetic methodologies, and suggest avenues of research for future developments. We advocate that with further optimizations, this emergent field holds great potential as part of the population genetics toolkit.

DNA extraction from environmental samples (environmental DNA; eDNA) for metabarcoding‐based biodiversity studies is gaining popularity as a noninvasive, time‐efficient, and cost‐effective monitoring tool. The potential benefits are promising for marine conservation, as the marine biome is frequently under‐surveyed due to its inaccessibility and the consequent high costs involved. With increasing numbers of eDNA‐related publications have come a wide array of capture and extraction methods. Without visual species confirmation, inconsistent use of laboratory protocols hinders comparability between studies because the efficiency of target DNA isolation may vary. We determined an optimal protocol (capture and extraction) for marine eDNA research based on total DNA yield measurements by comparing commonly employed methods of seawater filtering and DNA isolation. We compared metabarcoding results of both targeted (small taxonomic group with species‐level assignment) and universal (broad taxonomic group with genus/family‐level assignment) approaches obtained from replicates treated with the optimal and a low‐performance capture and extraction protocol to determine the impact of protocol choice and DNA yield on biodiversity detection. Filtration through cellulose‐nitrate membranes and extraction with Qiagen's DNeasy Blood & Tissue Kit outperformed other combinations of capture and extraction methods, showing a ninefold improvement in DNA yield over the poorest performing methods. Use of optimized protocols resulted in a significant increase in OTU and species richness for targeted metabarcoding assays. However, changing protocols made little difference to the OTU and taxon richness obtained using universal metabarcoding assays. Our results demonstrate an increased risk of false‐negative species detection for targeted eDNA approaches when protocols with poor DNA isolation efficacy are employed. Appropriate optimization is therefore essential for eDNA monitoring to remain a powerful, efficient, and relatively cheap method for biodiversity assessments. For seawater, we advocate filtration through cellulose‐nitrate membranes and extraction with Qiagen's DNeasy Blood & Tissue Kit or phenol‐chloroform‐isoamyl for successful implementation of eDNA multi‐marker metabarcoding surveys.

Genetic marker‐based estimators remain a popular tool for measuring relatedness (r_xy) and inbreeding (F) coefficients at both the population and individual level. The performance of these estimators fluctuates with the number and variability of markers available, and the relatedness composition and demographic history of a population. Several methods are available to evaluate the reliability of the estimates of r_xy and F, some of which are implemented in the program COANCESTRY. I used the simulation module in COANCESTRY since assess the performance of marker‐based estimators of r_xy and F in a species with very low genetic diversity, New Zealand's little spotted kiwi (Apteryx owenii). I also conducted a review of published papers that have used COANCESTRY as its release to assess whether and how the reliability of the estimates of r_xy and F produced by genetic markers are being measured and reported in published studies. My simulation results show that even when the correlation between true (simulated) and estimated r_xy or F is relatively high (Pearson's r = 0.66–0.72 and 0.81–0.85, respectively) the imprecision of the estimates renders them highly unreliable on an individual basis. The literature review demonstrates that the majority of studies do not report the reliability of marker‐based estimates of r_xy and F. There is currently no standard practice for selecting the best estimator for a given data set or reporting an estimator's performance. This could lead to experimental results being interpreted out of context and render the robustness of conclusions based on measures of r_xy and F debatable.