Biology subjects

Curnick, D.

Publications and source records attributed to Curnick, D..

3 recordsLinked to original sources

bioRxiv2025

Mapping Risk and Resilience Across Indo-Pacific Reefs with Shark Genomescapes

Overfishing has severely depleted marine populations worldwide, including within protected areas. Illegal and unreported fishing are major contributors to this decline. Large-bodied apex predators such as sharks are among the most affected, with overfishing causing dramatic species declines and ecosystem destabilization due to trophic downgrading. Key barriers to effective marine conservation and management include: Data deficiencies that hinder population benchmarks and impact assessments, limited surveillance, allowing illegal fisheries to disproportionately affect apex predators, and insufficient capacity in vulnerable nations to monitor and protect species within their waters. Our study addresses these challenges through a novel genomic framework that enables assessment of shark population diversity and health, while also improving fisheries traceability by detecting instances of illegal fishing across the Indian and Pacific Oceans. We present the Reefshark Genomescape, the first genome-wide reference database for Indo-Pacific reef sharks, an assessment of genetic diversity, structure, and connectivity of two key species across their Indo-Pacific range and geographic assignment of fished individuals using population-specific genetic signatures. We show that grey reef shark (Carcharhinus amblyrhynchos) populations exhibit high genetic diversity, strong population structure, and elevated Fst values, with previously unknown connectivity between the central and western Indian Ocean and clear isolation of populations in the Andaman Sea. In contrast, silvertip sharks (Carcharhinus albimarginatus) display high connectivity, but show genomic signals of declining population health, supporting a reassessment of their IUCN status. Using supervised machine learning with Monte Carlo cross-validation, we assigned geographic origins to fished grey reef sharks with 96% accuracy. These findings provide critical insights into population structure, connectivity, and health of two ecologically important reef shark species, while establishing a robust method for assigning geographic origin. We anticipate this framework will support regional conservation assessments and targeted management. Moreover, by enabling the identification of fishing hotspots and detection of IUU fishing, it lays the groundwork for a broader traceability system in marine ecosystems. Much like the landmark elephant ivory tracing study, our approach has the potential to transform marine conservation globally. Graphical AbstractWe developed the Reefshark Genomescape, a genomic framework for assessing shark population health and fisheries traceability across the Indo-Pacific. Genome-wide data from grey reef and silvertip sharks revealed contrasting patterns, unexpected connectivity, and genomic signals of decline. Geographic assignment of fished individuals reached 96% accuracy, enabling detection of illegal fishing and identification of hotspots. This framework strengthens regional management, supports IUCN reassessments, and lays the foundation for global marine traceability systems. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=175 SRC="FIGDIR/small/676358v2_ufig1.gif" ALT="Figure 1"> View larger version (84K): org.highwire.dtl.DTLVardef@1eafd3aorg.highwire.dtl.DTLVardef@96ebd1org.highwire.dtl.DTLVardef@541d02org.highwire.dtl.DTLVardef@3c9ca2_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2024

Prioritising Functionally Distinct and Globally Endangered (FuDGE) sharks for conservation action

Robust species-level methods for quantifying ecological differences have yet to be incorporated into conservation strategies. Here, we present a conservation prioritisation approach that integrates species trait data and extinction risk to quantify the contribution of individual species to overall functional diversity. The Functionally Irreplaceable with Risk of Extinction (FIRE) metric directs conservation action to species whose extinction is expected to result in significant losses of functional diversity. We applied our framework to sets of species at the global scale. First we assessed the worlds birds, highlighting congruent and divergent priorities identified by trait-based and phylogenetic approaches. Second, we applied FIRE to the worlds sharks, exploring the impact of imputed traits on prioritisation robustness. For birds and sharks, we show that prioritising by functional irreplaceability is an effective strategy to conserve exploited species. The FIRE metric provides a robust tool to facilitate the incorporation of functional diversity into conservation policy and practice, revealing species that may be overlooked by existing approaches.

ecology↗

bioRxiv2024

Unlocking the soundscape of coral reefs with artificial intelligence

Passive acoustic monitoring can offer insights into the state of coral reef ecosystems at low-costs and over extended temporal periods. Comparison of whole soundscape properties can rapidly deliver broad insights from acoustic data, in contrast to the more detailed but time-consuming analysis of individual bioacoustic signals. However, a lack of effective automated analysis for whole soundscape data has impeded progress in this field. Here, we show that machine learning (ML) can be used to unlock greater insights from reef soundscapes. We showcase this on a diverse set of tasks using three biogeographically independent datasets, each containing fish community, coral cover or depth zone classes. We show supervised learning can be used to train models that can identify ecological classes and individual sites from whole soundscapes. However, we report unsupervised clustering achieves this whilst providing a more detailed understanding of ecological and site groupings within soundscape data. We also compare three different approaches for extracting feature embeddings from soundscape recordings for input into ML algorithms: acoustic indices commonly used by soundscape ecologists, a pretrained convolutional neural network (P-CNN) trained on 5.2m hrs of YouTube audio and a CNN trained on individual datasets (T-CNN). Although the T-CNN performs marginally better across the datasets, we reveal that the P-CNN is a powerful tool for identifying marine soundscape ecologists due to its strong performance, low computational cost and significantly improved performance over acoustic indices. Our findings have implications for soundscape ecology in any habitat. Author SummaryArtificial intelligence has the potential to revolutionise bioacoustic monitoring of coral reefs. So far, a limited set of work has used machine learning to train detectors for specific sounds such as individual fish species. However, building detectors is a time-consuming process that involves manually annotating large amounts of audio followed by complicated model training, this must then be repeated all over again for any new dataset. Instead, we explore machine learning techniques for whole soundscape analysis, which compares the acoustic properties of raw recordings from the entire habitat. We identify multiple machine learning methods for whole soundscape analysis and rigorously test these using datasets from Indonesia, Australia and French Polynesia. Our key findings show use of a neural network pretrained on 5.2m hours of unrelated YouTube audio offers a powerful tool to produce compressed representations of reef audio data, conserving the datas key properties whilst being executable on a standard personal laptop. These representations can then be used to explore patterns in reef soundscapes using "unsupervised machine learning", which is effective at grouping similar recordings periods together and dissimilar periods apart. We show these groupings hold relationships with ground truth ecological data, including coral coverage, the fish community and depth.

ecology↗