Optimization procedures for establishing reserve networks for biodiversity conservation taking into account population genetic structure

TLDR: A theoretical framework in which target persistence and genetic representation of targets defined using multiple genetic criteria can be explicitly incorporated into the broad-scale reserve network models used to optimize biodiversity conservation based on multiple species data is discussed.

Abstract: Conservation genetics has been focused on the ecological and evolutionary persistence of targets (species or other intraspecific units), especially when dealing with narrow-ranged species, and no generalized solution regarding the problem of where to concentrate conservation efforts for multiple genetic targets has yet been achieved. Broadly distributed and abundant species allow the identification of evolutionary significant units, management units, phylogeographical units or other spatial patterns in genetic variability, including those generated by effects of habitat fragmentation caused by human activities. However, these genetic units are rarely considered as priority conservation targets in regional conservation planning procedures. In this paper, we discuss a theoretical framework in which target persistence and genetic representation of targets defined using multiple genetic criteria can be explicitly incorporated into the broad-scale reserve network models used to optimize biodiversity conservation based on multiple species data. When genetic variation can be considered discrete in geographical space, the solution is straightforward, and each spatial unit must be considered as a distinct target. But methods for dealing with continuous genetic variation in space are not trivial and optimization procedures must still be developed. We present a simple heuristic and sequential algorithm to deal with this problem by combining multiple networks of local populations of multiple species in which minimum separation distance between conserved populations is a function of spatial autocorrelation patterns of genetic variability within each species.