1. Coral restoration has emerged globally as a form of life support for coral reefs, awaiting urgent mitigation of anthropogenic pressures. Yet its efficiency is difficult to assess, as sizeable transplantation programmes handle hundreds of thousands of fragments, with survival rates inherently time intensive to monitor.Owing to limited available data, the influence of most environmental and methodological factors is still unknown. 2. To address this issue, machine learning and computer vision were used to track individual colonies' survival, in a world first. Fragments from several species of Acropora and Pocillopora were transplanted over 12 sites across two Maldivian atolls. These colonies grew on coral frames, placed between 1 and 30 m deep.Analysis of monitoring pictures provided health and growth data on 77,574 individual coral colonies to inform the influence of genus, depth, initial fragment size, and substrate on their survival.3. Among 77,574 fragments, individual survival rate was 31% after 2 years (21% after 4 years), which is much lower than most reported results. Deeper placement was an important success factor for Acropora transplants, but not for Pocillopora.In both genera, smaller initial fragment size was key to increased survival rates.Pocillopora fragments survived better than Acropora fragments at shallow depths (≤7 m), regardless of initial fragment size. Deeper, both genera had similar survival rates, which were influenced by initial fragment size and depth with comparable importance. During the mid-2019 heat wave, previously transplanted Acropora fragments were 38% more likely to die than Pocillopora fragments. 4. Overall, the total volume of live coral steadily increased over time, by more than 3.7 Â 10 6 cm 3 per year, as the volume increase in surviving fragments more than compensated for the volume loss due to mortality. This finding supports the use of targeted coral restoration to accelerate reef recovery after mass bleaching events.
Coral restoration emerged globally as a form of life support for coral reefs, awaiting urgent mitigation of anthropogenic pressure. Yet its efficiency is difficult to assess, as ambitious transplantation programs handle hundreds of thousands of fragments, with survival rates inherently time-intensive to monitor. Due to limited available data, the influence of most environmental and methodological factors is still unknown.We therefore propose a new method which leverages machine learning to track each colony’s individual health and growth on a large sample size. This is the first time artificial intelligence techniques were used to monitor coral at a colony scale, providing an unprecedented amount of data on coral health and growth. Here we show the influence of genus, depth and initial fragment size, alongside providing an outlook on coral restoration’s efficiency.We show that among 77,574 fragments, individual survival rate was 31% after 2 years (21% after 4 years), which is much lower than most reported results. In the absence of significant anthropogenic pressure, we showed that there was a depth limit below which Pocillopora fragments outperformed Acropora fragments, while the opposite was true past this threshold. During the mid-2019 heatwave, our research indicates that Pocillopora fragments were 37% more likely to survive than Acropora fragments.Overall, the total amount of live coral steadily increased over time, by more than 3,700 liters a year, as growth compensated for mortality. This supports the use of targeted coral restoration to accelerate reef recovery after mass bleaching events.
As biodiversity plummets due to anthropogenic disturbances, the conservation of oceanic species is made harder by limited knowledge of their distributions and migrations. Indeed, tracking species distributions in the open ocean is particularly challenging due to scarce observations, and the complex and variable nature of the ocean system. In this study, we propose a new method that leverages deep learning, specifically convolutional neural networks (CNNs), to capture spatial features of environmental variables. This novelty eliminates the need to predefine these features before modelling and creates opportunities to discover unexpected correlations. Our aim is to present the results of the first trial of this method in the open oceans, discuss limitations, and provide feedback for future improvements or adjustments. In this case study, we considered 38 taxa which include pelagic fishes, elasmobranchs, marine mammals, as well as marine turtles and birds. We trained a model to make probability predictions from the environmental conditions at any specific point in space and time, using species occurrence data from the Global Biodiversity Information Facility (GBIF) and environmental data from various sources. These variables included sea surface temperature, chlorophyll concentration, salinity, and fifteen others. During the testing phase, the model was applied to environmental data at locations where species occurrences were recorded. The model accurately predicted the observed taxon as the most likely taxon in 69% of cases and included the observed taxon among the top three most likely predictions in 89% of cases. These findings show the adequacy of deep learning for species distribution modelling in the open ocean and demonstrate the relevance of CNNs for prospective modelling of the impacts of future ocean conditions on oceanic species. Additionally, this black box model was then analysed with explicability tools to understand which variables had an influence on the model's predictions. While variable importance was species-dependent, we identified finite-size Lyapunov exponents (FSLEs), sea surface temperature, pH, bathymetry and salinity as the most influential variables, in that order. These insights can prove valuable for future species-specific movement ecology studies.
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