As species extinction rates increase, genomics provides a powerful tool to support intensive management of threatened species. We use the Tasmanian devil (Sarcophilus harrisii) to demonstrate how conservation genomics can be implemented in threatened species management. We conducted whole genome sequencing (WGS) of 25 individuals from the captive breeding programme and reduced-representation sequencing (RRS) of 98 founders of the same programme. A subset of the WGS samples was also sequenced by RRS, allowing us to directly compare genome-wide heterozygosity with estimates from RRS data. We found good congruence in interindividual variation and gene-ontology classifications between the two data sets, indicating that our RRS data reflect the genome well. We also attempted genome-wide association studies with both data sets (regarding breeding success), but the genomic data suffered from small sample size, while the RRS data suffered from lack of precision, highlighting a key trade-off in the design of conservation genomic research. Nevertheless, we identified a number of candidate genes that may be associated with variation in breeding success. Individual heterozygosity, as measured by WGS or RRS, was not associated with breeding success in captivity but was negatively associated with litter sizes of breeding females in the RRS data set. Our findings enable conservation managers to have confidence in RRS data while understanding its limitations, and provide avenues for further investigation into which processes underlie variation in breeding success in captive Tasmanian devils. We caution, however, that deep functional insights using RRS may be impaired by a lack of precision, especially when marker density is low.

Background Recent advances in genomics have greatly increased research opportunities for non-model species. For wildlife, a growing availability of reference genomes means that population genetics is no longer restricted to a small set of anonymous loci. When used in conjunction with a reference genome, reduced-representation sequencing (RRS) provides a cost-effective method for obtaining reliable diversity information for population genetics. Many software tools have been developed to process RRS data, though few studies of non-model species incorporate genome alignment in calling loci. A commonly-used RRS analysis pipeline, Stacks, has this capacity and so it is timely to compare its utility with existing software originally designed for alignment and analysis of whole genome sequencing data. Here we examine population genetic inferences from two species for which reference-aligned reduced-representation data have been collected. Our two study species are a threatened Australian marsupial (Tasmanian devil Sarcophilus harrisii ; declining population) and an Arctic-circle migrant bird (pink-footed goose Anser brachyrhynchus ; expanding population). Analyses of these data are compared using Stacks versus two widely-used genomics packages, SAMtools and GATK. We also introduce a custom R script to improve the reliability of single nucleotide polymorphism (SNP) calls in all pipelines and conduct population genetic inferences for non-model species with reference genomes. Results Although we identified orders of magnitude fewer SNPs in our devil dataset than for goose, we found remarkable symmetry between the two species in our assessment of software performance. For both datasets, all three methods were able to delineate population structure, even with varying numbers of loci. For both species, population structure inferences were influenced by the percent of missing data. Conclusions For studies of non-model species with a reference genome, we recommend combining Stacks output with further filtering (as included in our R pipeline) for population genetic studies, paying particular attention to potential impact of missing data thresholds. We recognise SAMtools as a viable alternative for researchers more familiar with this software. We caution against the use of GATK in studies with limited computational resources or time. Electronic supplementary material The online version of this article (10.1186/s12864-019-5806-y) contains supplementary material, which is available to authorized users.

Devil facial tumor disease (DFTD) is renowned for its successful evasion of the host immune system. Down regulation of the major histocompatabilty complex class I molecule (MHC-I) on the DFTD cells is a primary mechanism of immune escape. Immunization trials on captive Tasmanian devils have previously demonstrated that an immune response against DFTD can be induced, and that immune-mediated tumor regression can occur. However, these trials were limited by their small sample sizes. Here, we describe the results of two DFTD immunization trials on cohorts of devils prior to their wild release as part of the Tasmanian Government’s Wild Devil Recovery project. 95% of the devils developed anti-DFTD antibody responses. Given the relatively large sample sizes of the trials (N = 19 and N = 33), these responses are likely to reflect those of the general devil population. DFTD cells manipulated to express MHC-I were used as the antigenic basis of the immunizations in both trials. Although the adjuvant composition and number of immunizations differed between trials, similar anti-DFTD antibody levels were obtained. The first trial comprised DFTD cells and the adjuvant combination of ISCOMATRIX™, polyIC, and CpG with up to four immunizations given at monthly intervals. This compared to the second trial whereby two immunizations comprising DFTD cells and the adjuvant combination ISCOMATRIX™, polyICLC (Hiltonol®) and imiquimod were given a month apart, providing a shorter and, therefore, more practical protocol. Both trials incorporated a booster immunization given up to 5 months after the primary course. A key finding was that devils in the second trial responded more quickly and maintained their antibody levels for longer compared to devils in the first trial. The different adjuvant combination incorporating the RNAase resistant polyICLC and imiquimod used in the second trial is likely to be responsible. The seroconversion in the majority of devils in these anti-DFTD immunization trials was remarkable, especially as DFTD is hallmarked by its immune evasion mechanisms. Microsatellite analyzes of MHC revealed that some MHC-I microsatellites correlated to stronger immune responses. These trials signify the first step in the long-term objective of releasing devils with immunity to DFTD into the wild.

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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, parameterized 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.