Williams et al. Size of Coral Reef VME testing in different environments. Importantly, these results should give confidence for stakeholder uptake and form the basis for better predictive VME models at larger spatial scales and beyond single taxa.
The octocoral genus Chrysogorgia (Duchassaing and Michelotti, 1864) contains 81 nominal species that are ecologically important components of benthic communities. Taxonomic examination of a large set of samples revealed many provisional new species, exhibiting a wide range of morphological variation. We established nine, distinct morphological groups of Chrysogorgia s.l. that were hypothesized to represent distinct genera. Here, we applied a recently developed universal target enrichment bait method for octocoral exons and ultraconserved elements (UCEs) on 96 specimens varying in morphology, collection ages and DNA quality and quantity to determine whether there was genetic support for these morphologically defined groups. Following Illumina sequencing and SPAdes assembly we recovered 1,682 of 1,700 targeted exon loci and 1,333 of 1,340 targeted UCE loci. Locus recovery per sample was highly variable and significantly correlated with time since specimen collection (2–60 years) and DNA quantity and quality. Phylogenetically informative sites in UCE and exon loci were ∼35% for 50% and 75% taxon-occupancy matrices. Maximum likelihood analyses recovered highly resolved trees with topologies supporting the recognition of 11 candidate genera, corresponding with morphological groups assigned a priori, nine of which are novel. Our results also demonstrate that this target-enrichment approach can be successfully applied to degraded museum specimens of up to 60 years old. This study shows that an integrative approach consisting of molecular and morphological methods will be essential to a proper revision of Chrysogorgia taxonomy and to understand regional diversity of these ecologically important corals.
A significant proportion of Southern Ocean seafloor biodiversity is thought to be associated with fragile, slow growing, long-lived, and habitat-forming taxa. Minimizing adverse impact to these so-called vulnerable marine ecosystems (VMEs) is a conservation priority that is often managed by relying on fisheries bycatch data, combined with threshold-based conservation rules in which all “indicator” taxa are considered equal. However, VME indicator taxa have different vulnerabilities to fishing disturbance and more consideration needs to be given to how these taxa may combine to form components of ecosystems with high conservation value. Here, we propose a multi-criteria approach to VME identification that explicitly considers multiple taxa identified from imagery as VME indicator morpho-taxa. Each VME indicator morpho-taxon is weighted differently, based on its vulnerability to fishing. Using the “Antarctic Seafloor Annotated Imagery Database”, where 53 VME indicator morpho-taxa were manually annotated generating >40000 annotations, we computed an index of cumulative abundance and overall richness and assigned it to spatial grid cells. Our analysis quantifies the assemblage-level vulnerability to fishing, and allows assemblages to be characterized, e.g. as highly diverse or highly abundant. The implementation of this quantitative method is intended to enhance VME identification and contextualize the bycatch events.
Deep-sea corals are important benthic inhabitants that support the biodiversity and function of the wider faunal community; however, their taxonomy is underdeveloped and their accurate identification is often difficult. In our study, we investigated the utility of a superextended (>3000 bp) barcode and explored the effectiveness of various molecular species delimitation techniques with an aim to put upper and lower bounds on the estimated number of calcaxonian species in Irish waters. We collected 112 calcaxonians (70 Keratoisididae, 22 Primnoidae, 20 Chrysogorgiidae) and one chelidonisid from the Irish continental slope and sequenced a 3390 bp DNA barcode comprising four mitochondrial regions (mtMutS, COI + igr1, 16S rRNA-ND2, and igr4), recovering 38 haplotypes. Individuals that shared a haplotype were often morphologically distinct, and we thus undertook detailed morphological work, including SEM of sclerites, on one representative of each morphotype within each haplotype. GMYC, bGMYC, and mPTP returned incongruent estimates of species numbers. In total, there are between 25 and 40 species, although no definitive number could be assigned, primarily due to poorly defined keratoisidid species boundaries. As expected, the superextended barcode provided greater discrimination power than single markers; bGMYC appeared to be the most effective delimiter. Among the identified species were Chelidonisis aurantiaca, collected deeper than previously known at 1507 m, and Calyptrophora clinata, recorded for the second time from the Northeast Atlantic. A full understanding of the diversity and distribution of calcaxonians requires substantial taxonomic work, but we highlight the Irish continental slope as harbouring significant diversity.
Protecting deep‐sea coral‐based vulnerable marine ecosystems (VMEs) from human impacts, particularly bottom trawling, is a major conservation challenge in world oceans. Management processes for these ecosystems are weakened by key uncertainties that could be substantially addressed by having much greater volumes of quantitative image‐derived data that detail the distribution and abundance of coral reefs and the nature of impacts upon them. Considerably greater volumes of data could be available if the resource costs of image annotation are reduced. In this paper we propose a solution: a deep learning system capable of automatically identifying reef‐building stony corals amongst other seabed substrata in much larger volumes of seabed imagery than was previously possible. Using a previously annotated dataset, we trained a convolutional neural network on approximately 70,000 classified images (‘snips’) comprising six benthic substrate classes, including reef‐building stony coral—‘coral matrix’. Model performance improvements, chiefly by dataset cleaning, transfer learning and hyperparameter optimisation, resulted in the final trained model achieving validation accuracy of 98.19%. The classification was robust: benthic substrate types were accurately differentiated, and in some cases more consistently than was achieved by human annotators. Synthesis and applications. The availability of much larger volumes of automatically annotated image‐derived data will improve spatial management of impacts on coral‐based VMEs in the deep sea by (1) improved cross‐validation and performance of spatial models required to predict coral distribution and abundance over the large scales of managed areas, and (2) establishing empirical relationships between coral abundance on the seabed and coral bycatch landed during fishing operations.
An increased reliance on imagery as the source of biodiversity data from the deep sea has stimulated many recent advances in image annotation and data management. The form of image-derived data is determined by the way faunal units are classified and should align with the needs of the ecological study to which it is applied. Some applications may require only low-resolution biodiversity data, which is easier and cheaper to generate, whereas others will require well-resolved biodiversity measures, which require a larger investment in annotation methods. We assessed these trade-offs using a dataset of 5 939 images and physical collections of black and octocorals taken during surveys from a seamount area in the southwest Pacific Ocean. Coral diversity was greatly underestimated in images: only 55 black and octocoral ‘phototaxa’ (best-possible identifications) were consistently distinguishable out of a known 210 species in the region (26%). Patterns of assemblage composition were compared between the phototaxa and a standardized Australian classification scheme (“CATAMI”) that uses morphotypes to classify taxa. Results were similar in many respects, but the identities of dominant, and detection of rare but locally abundant, coral entities were achieved only when annotation was at phototaxon resolution, and when faunal densities were recorded. A case study of data from 4 seamounts compared three additional classification schemes. Only the two with highest resolution – phototaxon and a combined phototaxon-morphological scheme – were able to distinguish black and octocoral communities on unimpacted vs. impacted seamounts. We conclude that image annotation schemes need to be fit-for-purpose. Morphological schemes such as CATAMI may perform well and are most easily standardized for cross-study data sharing, but high resolution (and more costly) annotation schemes are likely necessary for some ecological and management-based applications including biodiversity inventory, change detection (monitoring) – and to develop automated annotation using machine learning.
The diversity and abundance of fish inhabiting complex reef habitats poses some challenges to surveys based on optical techniques, especially for schooling fish which are difficult to enumerate with such methods. Acoustic surveys are often used effectively to estimate the abundance and distribution of schooling fish but suffer from boundary effects and limited species discrimination. To reconcile these drawbacks, we present an integrated acoustic–optical survey method, to estimate the abundance of fishes in a subtropical reef habitat in Shark Bay, Western Australia, exploiting the unique benefits of each method. Acoustic backscatter attributed to multi‐species groups was partitioned to species with the help of concurrent unbaited remote underwater video. This allowed estimation of the abundance of the important fishery sparid, Chrysophrys auratus, as well as 17 other members of the diverse fish community. The study addresses some of the challenges of assessing abundance of fish species that may be aggregated, but sparsely distributed, associated with a structured habitat, and mixed within a diverse assemblage of other aggregating or solitary fishes in an area where direct capture fisheries survey gears cannot be used. Synthesis and applications. The acoustic–optical survey method provides data that are vital for the assessment of fish species in ecosystems which are difficult, or impossible for certain species, to survey with existing methods. These assessments are, in turn, essential for either ecosystem‐based fishery management or multiple single‐species quota management, which allow for the sustainable management of the associated fisheries.
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