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New methods developed by the Southwest Biological Science Center utilize species’ unique patterns of genetic diversity to generate maps that may be used to inform management goals.

Graham's beardtongue (Penstemon grahamii)
Graham's beardtongue (Penstemon grahamii) in the Book Cliffs south of the Uinta Basin, Utah. This species is confined to the Green River Formation, which contains oil shale that is actively mined across the species' range. Photo by Daniel Winkler, SBSC, USGS.

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.

For species of conservation concern, planning for recovery requires an in-depth understanding of a species’ biology, genetic diversity, and the factors influencing its rarity. Likewise, knowledge of adaptation to local environments and patterns of genetic diversity support successful restoration outcomes when common plant species are used for ecosystem restoration.

Genetic diversity is a key component of management planning because it relates to how species disperse, the factors that have influenced them over hundreds to thousands of years, and how they interact with biotic and abiotic pressures where they persist. Such knowledge may help increase management success given future challenges like changing climates and drastic ecosystem changes due to large-scale disturbances.




California Bearpoppy (Arctomecon californica) in gypsum-based soil
California bearpoppy (Arctomecon californica) in gypsum-based soil. The declining abundance of this species is being actively tracked in multiple states. Photo by Morgan Andrews, SBSC, USGS.

To support management planning that incorporates species’ naturally occurring genetic patterns, scientists at SBSC developed analytical methods that use genetic data from field-collected leaf samples 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.

The software (POPMAPS, or Population Management using Ancestry Probability Surfaces) is available as an R package that is freely available from GitLab. It is described in a recent Special Feature publication highlighting the UN Decade on Ecosystem Restoration, "Spatially explicit management of genetic diversity using ancestry probability surfaces."  



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