[PCA] ARTICLE: Incorporating Genetic Diversity into Restoration and Conservation Decision-making

[Massatti, Robert T]

Spatially explicit management of genetic diversity using ancestry probability surfaces

Rob Massatti and Daniel Winkler, Methods in Ecology and Evolution

Summary: An increasing number of native species in the United States are being scrutinized as natural resource managers and practitioners seek to protect those that are declining in abundance or utilize those that provide important ecosystem services to restore degraded landscapes. To support management planning that incorporates species’ naturally occurring genetic patterns, scientists at the U.S. Geological Survey Southwest Biological Science Center developed analytical methods that use field-collected genetic data to estimate species-specific maps of genetic similarity. These maps can help managers understand the distribution of genetic units across the landscape and inform decisions influencing, for example, how species are moved among locations to support management goals.

Read USGS Southwest Biological Science Center news article: https://www.usgs.gov/centers/southwest-biological-science-center/news/incorporating-genetic-diversity-restoration-and
Read full article: https://doi.org/10.1111/2041-210X.13902
Access software: https://code.usgs.gov/GWRC/popmaps/


Abstract:
1. Ecological restoration and conservation efforts are increasing worldwide and the management of intraspecific genetic variation in plants and animals, an important component of biodiversity, is increasingly valued. As a result, tailorable, spatially explicit approaches to map genetic variation are needed to support decision-making and management frameworks related to the recovery of threatened and endangered species and the maintenance of genetic resources in species utilized by humans, such as for restoration or agricultural purposes.


2. Here, we describe and demonstrate a workflow to spatially interpolate patterns of genetic differentiation using novel functions in the R package POPMAPS (Population Management using Ancestry Probability Surfaces). Our approach uses empirical genetic data to estimate ancestry coefficients across a user-defined landscape correlated with patterns of differentiation in the focal species. The resulting surface, which we term the ancestry probability surface, includes two components: hard population boundaries and estimations of uncertainty that represent confidence in population assignments (i.e., ancestry probabilities).


3. An ancestry probability surface developed for Hilaria jamesii, an important graminoid utilized in restoration across the western United States, demonstrates the functionality of POPMAPS. Genetic distances among empirical sites correlated better with least-cost distances across suitable habitat than with geographic distances, informing the surface over which the interpolation was conducted (i.e., a model indicating habitat suitability). A jackknifing procedure identified parameter values resulting in robust population assignments across the species’ range, which were utilized in downstream analyses to estimate ancestry coefficients from empirical data. Ancestry coefficients were translated into ancestry probabilities, which tended to be low for cells that were intermediate in distance between empirical sampling locations representing different populations or when influenced by empirical sampling locations with mixed genetic ancestry.


4. POPMAPS allows users to tailor parameter values and analytical approaches and thereby incorporate species-specific biological characteristics and desired levels of uncertainty into maps illustrating patterns of genetic differentiation. Ancestry probability surfaces may be used to guide management or investigate further ecological or evolutionary hypotheses. We discuss how maps produced by POPMAPS can inform multiple management challenges including species recovery planning and the utilization of commonly used species in restoration.

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