Restoring faith in conservation action: Maintaining wild genetic diversity through the Tasmanian devil insurance program

Farquharson, Katherine A.; McLennan, Elspeth A.; Cheng, Yuanyuan; Alexander, Lauren; Fox, Samantha; Lee, Andrew V.; Belov, Katherine; Hogg, Carolyn J.

doi:10.1016/j.isci.2022.104474

Cited by 16 publications

(10 citation statements)

References 110 publications

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“…However, there is consensus that the species remains under threat, particularly given that its potential for persistence at much reduced density is unknown. It is thus important that adaptive monitoring, research, and management continue to be prioritized to ensure the long-term conservation and resilience of the Tasmanian devil (47,(52)(53)(54).…”

Section: Discussionmentioning

confidence: 99%

The evolution of two transmissible cancers in Tasmanian devils

Stammnitz

Gori

Kwon

et al. 2023

Science

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Tasmanian devils have spawned two transmissible cancer lineages, named devil facial tumor 1 (DFT1) and devil facial tumor 2 (DFT2). We investigated the genetic diversity and evolution of these clones by analyzing 78 DFT1 and 41 DFT2 genomes relative to a newly assembled, chromosome-level reference. Time-resolved phylogenetic trees reveal that DFT1 first emerged in 1986 (1982 to 1989) and DFT2 in 2011 (2009 to 2012). Subclone analysis documents transmission of heterogeneous cell populations. DFT2 has faster mutation rates than DFT1 across all variant classes, including substitutions, indels, rearrangements, transposable element insertions, and copy number alterations, and we identify a hypermutated DFT1 lineage with defective DNA mismatch repair. Several loci show plausible evidence of positive selection in DFT1 or DFT2, including loss of chromosome Y and inactivation of MGA , but none are common to both cancers. This study reveals the parallel long-term evolution of two transmissible cancers inhabiting a common niche in Tasmanian devils.

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Section: Discussionmentioning

confidence: 99%

The evolution of two transmissible cancers in Tasmanian devils

Stammnitz

Gori

Kwon

et al. 2023

Science

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“…However, contrary to predictions, local extinction of devils has not occurred (Farquharson et al 2022), and a natural immune response has been observed in some individuals (Woods et al 2020). However, it is yet to be determined if this resistance will be sufficient to enable the wild population to recover to its pre-DFTD levels (Farquharson et al 2022). As part of the conservation program, wild monitoring activities are undertaken, and an isolated insurance population has been established to keep healthy devils separate from those with DFTD and protect these populations from disease transmission (Rout et al 2018).…”

Section: Introductionmentioning

confidence: 71%

“…This decline has led to changes in the species' ecosystem as feral cat populations have increased and quoll populations have decreased (Drawert et al 2022). However, contrary to predictions, local extinction of devils has not occurred (Farquharson et al 2022), and a natural immune response has been observed in some individuals (Woods et al 2020). However, it is yet to be determined if this resistance will be sufficient to enable the wild population to recover to its pre-DFTD levels (Farquharson et al 2022).…”

Section: Introductionmentioning

confidence: 98%

Automated Tasmanian devil segmentation and devil facial tumour disease classification

Nurçin,

Şentürk,

Imanov

et al. 2023

Wildlife Res.

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Context Artificial intelligence algorithms are beneficial for automating the monitoring of threatened species. Devil facial tumour disease (DFTD) is an endemic disease threatening Australia’s Tasmanian devil. The disease is a cancer that can be transmitted from one devil to another during social interactions. Cameras and trapping techniques have been employed to monitor the spread of the disease in the wild. The use of cameras allows for more frequent monitoring of devils than does trapping, but differentiating wounds from tumours in images is challenging, and this requires time and expertise. Aim The purpose of this work is to develop a computer vision system to assist in the monitoring of DFTD spread. Method We propose a system that involves image segmentation, feature extraction, and classification steps. U-net architecture, global average pooling layer of pre-trained Resnet-18, and support vector machine (SVM) classifiers were employed for these purposes, respectively. In total, 1250 images of 961 healthy and 289 diseased (DFTD) devils were separated into training, validation, and testing sets. Results The proposed algorithm achieved 92.4% classification accuracy for the differentiation of healthy devils from those with DFTD. Conclusion The high classification accuracy means that our method can help field workers with monitoring devils. Implications The proposed approach will allow for more frequent analysis of devils while reducing the workload of field staff. Ultimately, this automation could be expanded to other species for simultaneous monitoring at shorter intervals to facilitate broadened ecological assessments.

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“…Similarly, we could reduce disease prevalence in populations by boosting immunogenetic diversity in vulnerable populations. We do not advocate management actions to promote specific variants within a population but rather promote increased genetic variation at genomic regions with low diversity, particularly regions associated with known threatening processes, such as disease [ 21 ].…”

Section: Genomics and Conservation Planningmentioning

confidence: 99%

“…Genomics provides the capacity to identify genetically distinct populations where the exchange of migrants occurs so infrequently that these populations become ecologically isolated [ 20 ]. Broad-scale high-throughput sequencing also permits the assessment of functional diversity and differentiation within and between populations [ 21 ]. However, genomic data can only be a valuable resource for species conservation if samples for sequencing exist across populations of interest and the resource is made publicly available for the research and management communities.…”

Section: Introductionmentioning

confidence: 99%

Koala Genome Survey: An Open Data Resource to Improve Conservation Planning

Hogg

Silver

McLennan

et al. 2023

Genes

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Genome sequencing is a powerful tool that can inform the management of threatened species. Koalas (Phascolarctos cinereus) are a globally recognized species that captured the hearts and minds of the world during the 2019/2020 Australian megafires. In 2022, koalas were listed as ‘Endangered’ in Queensland, New South Wales, and the Australian Capital Territory. Populations have declined because of various threats such as land clearing, habitat fragmentation, and disease, all of which are exacerbated by climate change. Here, we present the Koala Genome Survey, an open data resource that was developed after the Australian megafires. A systematic review conducted in 2020 demonstrated that our understanding of genomic diversity within koala populations was scant, with only a handful of SNP studies conducted. Interrogating data showed that only 6 of 49 New South Wales areas of regional koala significance had meaningful genome-wide data, with only 7 locations in Queensland with SNP data and 4 locations in Victoria. In 2021, we launched the Koala Genome Survey to generate resequenced genomes across the Australian east coast. We have publicly released 430 koala genomes (average coverage: 32.25X, range: 11.3–66.8X) on the Amazon Web Services Open Data platform to accelerate research that can inform current and future conservation planning.

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Restoring faith in conservation action: Maintaining wild genetic diversity through the Tasmanian devil insurance program

Cited by 16 publications

References 110 publications

The evolution of two transmissible cancers in Tasmanian devils

The evolution of two transmissible cancers in Tasmanian devils

Automated Tasmanian devil segmentation and devil facial tumour disease classification

Koala Genome Survey: An Open Data Resource to Improve Conservation Planning

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