Complex problems need detailed solutions: Harnessing multiple data types to inform genetic management in the wild

Area of Science:

• Conservation genetics
• Population biology
• Wildlife management

Background:

• Tasmanian devil populations have declined significantly due to devil facial tumour disease (DFTD).
• Genetic rescue is a potential strategy for threatened species, but its effectiveness can be uncertain with complicating factors like disease.
• Multifaceted approaches are needed for species management when primary threats cannot be fully mitigated.

Purpose of the Study:

• To assess the genetic diversity and suitability of three Tasmanian devil populations for genetic rescue.
• To model the impact of devil facial tumour disease (DFTD) and population size on genetic diversity retention.
• To inform genetic management strategies for the endangered Tasmanian devil.

Main Methods:

• Analysis of 31 neutral and 11 MHC-linked microsatellite loci in three wild Tasmanian devil populations.
• Computational diversity projections parameterized by field data from DFTD-present and DFTD-absent sites.
• Stochastic simulations to project future genetic diversity under different scenarios.

Main Results:

• Tasmanian devil populations exhibited low genetic diversity and poor connectivity, with likely recent declines.
• Devil facial tumour disease (DFTD) demographic effects did not significantly impact diversity retention; population size was the primary driver.
• Population sizes of at least 500 individuals were projected to be necessary for maintaining diversity without ongoing management, even with DFTD.

Conclusions:

• Genetic diversity in Tasmanian devils is low and declining, necessitating strategic management.
• Population size is the key factor for maintaining genetic diversity, more so than disease-induced demographic changes.
• Combining multiple data and analysis types is essential for effective population genetic management of threatened species.