Fast and accurate mapping of fine scale abundance of a VME in the deep sea with computer vision

With growing anthropogenic pressure on deep-sea ecosystems, large quantities of data are needed to understand their ecology, monitor changes over time and inform conservation managers. Current methods of image analysis are too slow to meet these requirements. Recently, computer vision has become more accessible to biologists, and could help address this challenge. In this study we demonstrate a method by which non-specialists can train a YOLOV4 Convolutional Neural Network (CNN) able to count and measure a single class of objects. We apply CV to the extraction of quantitative data on the density and population size structure of the xenophyophore Syringammina fragilissima, from more than 58,000 images taken by an AUV 1200 m deep in the North-East Atlantic. The workflow developed used open-source tools, cloud-base hardware, and only required a level of experience with CV commonly found among ecologists. The CNN performed well, achieving a recall of 0.84 and precision of 0.91. Individual counts per image and size measurements resulting from model predictions were highly correlated (0.96 and 0.92, respectively) with manually collected data. The analysis could be completed in less than 10 days thus bringing novel insights into the population size structure and fine scale distribution of this Vulnerable Marine Ecosystem. It showed S. fragilissima distribution is patchy. The average density is 2.5 ind.m−2 but can vary from up to 45 ind.m−2 only a few tens of meter away from areas where it is almost absent. The average size is 5.5 cm and the largest individuals (>15 cm) tend to be in areas of low density. This study demonstrates how researchers could take advantage of CV to quickly and efficiently generate large quantitative datasets data on benthic ecosystems extent and distribution. This, coupled with the large sampling capacity of AUVs could bypass the bottleneck of image analysis and greatly facilitate future deep-ocean exploration and monitoring. It also illustrates the future potential of these new technologies to meet the goals set by the UN Ocean Decade

A risk assessment for the remote ocean: the case of the South East Atlantic

Degradation of the natural world and associated ecosystem services is attributed to a historical failure to include its ‘value’ in decision-making. Uncertainty in the quantification of the relationship between natural capital ‘assets’ that give rise to critical societal benefits and people is one reason for the omission of these values from natural resource management. As this uncertainty increases in marine systems and further still with distance from the coast, the connection between society and natural capital assets is less likely to be included adequately in decision-making. Natural capital assets of Areas Beyond National Jurisdiction (ABNJ), including those of the deep sea, are distant but are known to generate many benefits for society, from the diffuse and broad-scale benefits of climate regulation to the provision of wild fish for food. While our understanding of the precise relationships (the status of asset stocks, ecosystem functions and processes) that control the availability of ecosystem services and the flows of benefits is limited, this does not preclude opening a discourse on how these natural capital assets could best be managed to continue to benefit society. Here we apply a natural capital approach to the South East Atlantic ABNJ, one of the least scientifically understood regions of the planet, and develop a framework for risk assessment. We do this by describing the benefit flows from the natural capital assets of the region, appraising how activities are creating pressures on these flows and whether the controls for these pressures protect them. Our risk register highlights how governance currently favours the protection of direct (extractive) benefit flows from natural capital assets of the region, which are primarily targeted for financial benefit. Without a systems-based framework that can account for the cumulative pressures on natural capital assets their status, associated ecosystem services and benefits are at risk. Such an approach is essential to capture and protect the foundational and often diffuse connections between marine natural capital and global society.</jats:p

Combining Distribution and Dispersal Models to Identify a Particularly Vulnerable Marine Ecosystem

Habitat suitability models are being used worldwide to help map and manage marine areas of conservation importance and scientific interest. With groundtruthing, these models may be found to successfully predict patches of occurrence, but whether all patches are part of a larger interbreeding metapopulation is much harder to assert. Here we use a North Atlantic deep-sea case study to demonstrate how dispersal models may help to complete the picture. Pheronema carpenteri is a deep-sea sponge that, in aggregation, forms a vulnerable marine ecosystem in the Atlantic Ocean. Published predictive distribution models from United Kingdom and Irish waters have now gained some support from targeted groundtruthing, but known aggregations are distantly fragmented with little predicted habitat available in-between. Dispersal models were used to provide spatial predictions of the potential connectivity between these patches. As little is known of P. carpenteri’s reproductive methods, twenty-four model set-ups with different dispersal assumptions were simulated to present a large range of potential dispersal patterns. The results suggest that up to 53.1% of the total predicted habitat may be reachable in one generation of dispersal from known populations. Yet, even in the most dispersive scenario, the known populations in the North (Hatton-Rockall Basin) and the South (Porcupine Sea Bight) are predicted to be unconnected, resulting in the relative isolation of these patches across multiple generations. This has implications for Ireland’s future conservation efforts as they may have to conserve patches from more than one metapopulation. This means that conserving one patch may not demographically support the other, requiring additional attentions to ensure that marine protected areas are ecologically coherent and sustainable. This example serves as a demonstration of a combined modeling approach where the comparison between predicted distribution and dispersal maps can highlight areas with higher conservation needs.publishedVersio

Modelling tidally induced larval dispersal over Anton Dohrn Seamount

Massachusetts Institute of Technology general circulation model is used for the analysis of larval dispersal over Anton Dohrn Seamount (ADS), North Atlantic. The model output validated against the in situ data collected during the 136th cruise of the RRS ‘James Cook’ in May–June 2016 allowed reconstruction of the details of the baroclinic tidal dynamics over ADS. The obtained velocities were used as input data for a Lagrangian-type passive particle tracking model to reproduce the larval dispersal of generic deep-sea water invertebrate species. It was found that the residual tidal flow over ADS has a form of a pair of dipoles and cyclonic and anti-cyclonic eddies located at the seamount periphery. In the vertical direction, tides form upward motions above the seamount summit. These currents control local larval dispersal and their escape from ADS. The model experiment with a large number of particles (7500) evenly seeded on the ADS surface has shown that the trajectory of every individual particle is sensitive to the initial position and the tidal phase where and when it is released. The vast majority of the particles released above 1000 m depth remain seated in the same depth band where they were initially released. Only 8% of passive larvae were able to remain in suspension until competent to settle (maximise dispersal capability) and settle (make contact with the bottom) within the specified limits for this model. It was found that every tenth larval particle could leave the seamount and had a chance to be advected to any other remotely located seamount

Towards ‘ecological coherence’: Assessing larval dispersal within a network of existing Marine Protected Areas

The Convention on Biological Diversity mandates the establishment of Marine Protected Area (MPA) networks worldwide, with recommendations stating the importance of ‘ecological coherence’ (a responsibility to support and perpetuate the existing ecosystem) implying the need to sustain population connectivity. While recommendations exist for integrating connectivity data into MPA planning, little advice exists on how to assess the connectivity of existing networks. This study makes use of recently observed larval characteristics and freely available models to demonstrate how such an assessment could be undertaken. The cold water coral (CWC) Lophelia pertusa (Linnaeus, 1758) is used as a model species, as much of the NE Atlantic MPA network has been designated for CWC reef protection, but the ecological coherence of the network has yet to be assessed. Simulations are run for different behavioural null models allowing a comparison of ‘passive’ (current driven) and ‘active’ (currents + vertical migration) dispersal, while an average prediction is used for MPA assessment. This model suggests that the network may support widespread larval exchange and has good local retention rates but still has room for improvement. The best performing MPAs were large and central to the network facilitating transport across local dispersal barriers. On average, passive and active dispersal simulations gave statistically similar results, providing encouragement to future local dispersal assessments where active characteristics are unknown

Comparing Deep-Sea Larval Dispersal Models: A Cautionary Tale for Ecology and Conservation

Larval dispersal data are increasingly sought after in ecology and marine conservation, the latter often requiring information under time limited circumstances. Basic estimates of dispersal [based on average current speeds and planktonic larval duration (PLD)] are often used in these situations, usually acknowledging their oversimplified nature, but rarely with an understanding of how oversimplified those assumptions are. Larval dispersal models (LDMs) are becoming more accessible and may produce “better” dispersal predictions than estimates, but the uncertainty introduced by choosing one underlying hydrodynamic model over another is rarely discussed. This case study uses theoretical and simplified deep-sea LDMs to compare the passive predictions of dispersal as driven by two different hydrodynamic models (HYCOM and POLCOMS) and a range of informed basic estimates (based on average current speeds of 0.05, 0.1, and 0.2 m/s). The aim is to provide generalizable insight into the predictive variability introduced by (a) choosing a model over an estimate, and (b) one hydrodynamic over another. LDMs were found to be up to an order of magnitude more conservative in dispersal distance predictions than even the slowest tested estimate (0.05 m/s). The difference increased with PLD which may result in a bigger disparity for deep-sea species predictions. Although the LDMs were more spatially targeted than the estimates, the two LDM predictions were also significantly different from each other. This means that choosing one hydrodynamic model over another could result in contrasting ecological interpretations or advice for marine conservation. These results show a greater potential for hydrodynamic model variability than previously appreciated by larval dispersal ecologists and strongly advocates groundtruthing predictions before use in management. Advice is offered for improved model selection and interpretation of predictions.publishedVersio

UK deep‐sea conservation: Progress, lessons learned, and actions for the future

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