Landscape genetics

This allows for the analysis of microevolutionary processes affecting the species in light of landscape spatial patterns, providing a more realistic view of how populations interact with their environments.

Because it focuses on sampling individuals, landscape genetics has the advantage of not having to subjectively define discrete populations prior to analysis.

[3] The results of landscape genetics studies have potentially important applications to conservation biology and land management practices.

According to that article, the concept that landscape patterns affect how organisms are distributed dates back to the 18th and 19th centuries in the writings of Augustin Pyramus de Candolle and Alfred Russel Wallace.

Topography, altitude, habitat types, and potential barriers such as rivers or road are examples of landscape variables.

A Bayesian population assignment test found no distinct subpopulations based on the genetic data.

Correlograms were used to elucidate fine-scale social structure, and found that more heavily forested and fragmented townships had more genetic relatedness between individual deer.

Partial Mantel tests found a correlation between genetic distance and geographic barriers, particularly roads and rivers.

The finding of high genetic connectivity among the sampled deer has management implications for the setting of harvest number and population goals.

[11] Seascape genomics is a tool that utilizes genetic markers in tandem with current patterns to better understand dispersal.

In seascape analyses, genetic data allows for greater species understanding and tracking when the full life history is unknown or unable to be studied with ecology.

One example, Salmoni et at[14] used environmental data and genetic analysis to identify a heat tolerant gene in corals.

DiBattista and his team[18] studied how hydrodynamics influences snapper larval disbursement and were able to characterize connectivity between populations.

[4] As a new and fast growing interdisciplinary field with no explicitly identified best practices, it has been subject to a number of flaws in both study design and interpretation.

These include assuming gene flow is always advantageous, over-generalizing results, failing to consider other processes that affect the genetic structure of populations, and mistaking quantitative methods for robust study design.