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

For bottlenecked populations of threatened species, supplementation often
leads to improved population metrics (genetic rescue), provided that
guidelines can be followed to avoid negative outcomes. In cases where no
“ideal” source populations exist, or there are other complicating factors
such as prevailing disease, the benefit of supplementation becomes
uncertain. Bringing multiple data and analysis types together to plan
genetic management activities can help. Here, we consider three
populations of Tasmanian devil Sarcophilus harrisii as candidates for
genetic rescue. Since 1996, devil populations have been severely impacted
by devil facial tumour disease (DFTD), causing significant population
decline and fragmentation. Like many threatened species, the key
threatening process for devils cannot currently be fully mitigated, so
species management requires a multifaceted approach. We examined diversity
of 31 putatively neutral and 11 MHC-linked microsatellite loci of three
remnant wild devil populations (one sampled at two time-points), alongside
computational diversity projections, parameterised by field data from
DFTD-present and DFTD-absent sites. Results showed that populations had
low diversity, connectivity was poor, and diversity has likely decreased
over the last decade. Stochastic simulations projected further diversity
losses. For a given population size, the effects of DFTD on population
demography (including earlier age at death and increased female
productivity) did not impact diversity retention, which was largely driven
by final population size. Population sizes ≥ 500 (depending on the number
of founders) were necessary for maintaining diversity in otherwise
unmanaged populations, even if DFTD is present. Models indicated that
smaller populations could maintain diversity with ongoing immigration.
Taken together, our results illustrate how multiple analysis types can be
combined to address complex population genetic challenges.