Dataset Information

Secondary predation constrains DNA-based diet reconstruction in two threatened shark species.

ABSTRACT: Increasing fishing effort, including bycatch and discard practices, are impacting marine biodiversity, particularly among slow-to-reproduce taxa such as elasmobranchs, and specifically sharks. While some fisheries involving sharks are sustainably managed, collateral mortalities continue, contributing towards > 35% of species being threatened with extinction. To effectively manage shark stocks, life-history information, including resource use and feeding ecologies is pivotal, especially among those species with wide-ranging distributions. Two cosmopolitan sharks bycaught off eastern Australia are the common blacktip shark (Carcharhinus limbatus; globally classified as Near Threatened) and great hammerhead (Sphyrna mokarran; Critically Endangered). We opportunistically sampled the digestive tracts of these two species (and also any whole prey; termed the 'Russian-doll' approach), caught in bather-protection gillnets off northern New South Wales, to investigate the capacity for DNA metabarcoding to simultaneously determine predator and prey regional feeding ecologies. While sample sizes were small, S. mokkaran fed predominantly on stingrays and skates (Myliobatiformes and Rajiformes), but also teleosts, while C. limbatus mostly consumed teleosts. Metabarcoding assays showed extensive intermixing of taxa from the digestive tracts of predators and their whole prey, likely via the predator's stomach chyme, negating the opportunity to distinguish between primary and secondary predation. This Russian-doll effect requires further investigation in DNA metabarcoding studies focussing on dietary preferences and implies that any outcomes will need to be interpreted concomitant with traditional visual approaches.

SUBMITTER: de Bruyn M

PROVIDER: S-EPMC8443726 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Identifying the species that are at risk of local extinction in highly diverse ecosystems is a big challenge for conservation science. Assessments of species status are costly and difficult to implement in developing countries with diverse ecosystems due to a lack of species-specific surveys, species-specific data, and other resources. Numerous techniques are devised to determine the threat status of species based on the availability of data and budgetary limits. On this basis, we developed a framework that compared occurrence data of historically exploited reef species in Kenya from existing disparate data sources. Occurrence data from archaeological remains (750-1500CE) was compared with occurrence data of these species catch assessments, and underwater surveys (1991-2014CE). This comparison indicated that only 67 species were exploited over a 750 year period, 750-1500CE, whereas 185 species were landed between 1995 and 2014CE. The first step of our framework identified 23 reef species as threatened with local extinction. The second step of the framework further evaluated the possibility of local extinction with Bayesian extinction analyses using occurrence data from naturalists' species list with the existing occurrence data sources. The Bayesian extinction analysis reduced the number of reef species threatened with local extinction from 23 to 15. We compared our findings with three methods used for assessing extinction risk. Commonly used extinction risk methods varied in their ability to identify reef species that we identified as threatened with local extinction by our comparative and Bayesian method. For example, 12 of the 15 threatened species that we identified using our framework were listed as either least concern, unevaluated, or data deficient in the International Union for the Conservation of Nature red list. Piscivores and macro-invertivores were the only functional groups found to be locally extinct. Comparing occurrence data from disparate sources revealed a large number of historically exploited reef species that are possibly locally extinct. Our framework addressed biases such as uncertainty in priors, sightings and survey effort, when estimating the probability of local extinction. Our inexpensive method showed the value and potential for disparate data to fill knowledge gaps that exist in species extinction assessments.

Project description:Translocations are globally a popular tool used with the intention of improving threatened species conservation and re-establishing ecosystem function. While practitioners strive for successful outcomes the failure rate of translocations continues to be high. We demonstrate how predictive modelling can contribute to more informed decision making and hence potentially improve the success rate of translocation programs. Two species, the Djoongari (Shark Bay mouse) Pseudomys fieldi and the golden bandicoot Isoodon auratus barrowensis, were introduced independently to Doole Island in the Exmouth Gulf of Western Australia. We used population viability analysis to critique the outcomes of these translocations and provide an example of how this tool can be incorporated with expert knowledge to predict likely outcomes of translocations. Djoongari did not establish on the island after seven translocations over nine years, while golden bandicoots established a population after just one release event. Retrospective population viability analysis (of data that was unavailable prior to the translocations) predicted and clarified the reasons behind the outcomes of both translocations. Golden bandicoots have considerably higher demographic plasticity than Djoongari, which were never likely to establish on the island. We conclude that the failure of the Djoongari translocation was due to interactions between sparse habitat, native predators and cyclonic storm surges, whereas golden bandicoots have demonstrated habitat flexibility and an ability to recover from multiple natural disasters. As a result we (1) remind conservation planners of the importance of quantifying likely refuges and habitat availability at release sites, (2) suggest practitioners consider how different threats (including natural disasters) may interact at potential release sites and (3) advocate for the incorporation of predictive modelling during the planning stages of translocations, particularly for conservation introductions where no precedent exists for the species' survival at a particular location.

Dataset Information

Secondary predation constrains DNA-based diet reconstruction in two threatened shark species.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets