Seaview Survey Photo-quadrat and Image Classification Dataset

The primary scientific dataset arising from the XL Catlin Seaview Survey project is the “Seaview Survey Photo-quadrat and Image Classification Dataset”, consisting of: (1) over one million standardised, downward-facing “photo-quadrat” images covering approximately 1m2 of the sea floor; (2) human-classified annotations that can be used to train and validate image classifiers;\ua0(3) benthic cover data arising from the application of machine learning classifiers to the photo-quadrats; and\ua0(4)\ua0the triplets of raw images (covering 360o) from which the photo-quadrats were derived.Photo-quadrats were collected between 2012 and 2018 at 860 transect locations around the world, including: the Caribbean and Bermuda, the Indian Ocean (Maldives, Chagos Archipelago), the Coral Triangle (Indonesia, Philippines, Timor-Leste, Solomon Islands), the Great Barrier Reef, Taiwan and Hawaii.For additional information regarding methodology, data structure, organization and size, please see attached document “Dataset documentation”

Impacts of 1.5°C Global Warming on Natural and Human Systems

An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate povert

Nitrogen fixation rates in algal turf communities of a degraded versus less degraded coral reef

Algal turf communities are ubiquitous on coral reefs in the Caribbean and are often dominated by N-fixing cyanobacteria. However, it is largely unknown (1) how much N is actually fixed by turf communities and (2) which factors affect their N fixation rates. Therefore, we compared N fixation activity by turf communities at different depths and during day and night-time on a degraded versus a less degraded coral reef site on the island of Curaçao. N fixation rates measured with the acetylene reduction assay were slightly higher in shallow (5–10-m depth) than in deep turf communities (30-m depth), and N fixation rates during the daytime significantly exceeded those during the night. N fixation rates by the turf communities did not differ between the degraded and less degraded reef. Both our study and a literature survey of earlier studies indicated that turf communities tend to have lower N fixation rates than cyanobacterial mats. However, at least in our study area, turf communities were more abundant than cyanobacterial mats. Our results therefore suggest that turf communities play an important role in the nitrogen cycle of coral reefs. N fixation by turfs may contribute to an undesirable positive feedback that promotes the proliferation of algal turf communities while accelerating coral reef degradation

Scaling up ecological measurements of coral reefs using semi-automated field image collection and analysis

Ecological measurements in marine settings are often constrained in space and time, with spatial heterogeneity obscuring broader generalisations. While advances in remote sensing, integrative modelling and meta-analysis enable generalisations from field observations, there is an underlying need for high-resolution, standardised and geo-referenced field data. Here, we evaluate a new approach aimed at optimising data collection and analysis to assess broad-scale patterns of coral reef community composition using automatically annotated underwater imagery, captured along 2 km transects. We validate this approach by investigating its ability to detect spatial (e.g., across regions) and temporal (e.g., over years) change, and by comparing automated annotation errors to those of multiple human annotators. Our results indicate that change of coral reef benthos can be captured at high resolution both spatially and temporally, with an average error below 5%, among key benthic groups. Cover estimation errors using automated annotation varied between 2% and 12%, slightly larger than human errors (which varied between 1% and 7%), but small enough to detect significant changes among dominant groups. Overall, this approach allows a rapid collection of in-situ observations at larger spatial scales (km) than previously possible, and provides a pathway to link, calibrate, and validate broader analyses across even larger spatial scales (10-10,000 km)

『伊勢物語抒海』の位置 : 段の大意を中心として

Algal turf communities are ubiquitous on coral reefs in the Caribbean and are often dominated by N-fixing cyanobacteria. However, it is largely unknown (1) how much N is actually fixed by turf communities and (2) which factors affect their N fixation rates. Therefore, we compared N fixation activity by turf communities at different depths and during day and night-time on a degraded versus a less degraded coral reef site on the island of Curaçao. N fixation rates measured with the acetylene reduction assay were slightly higher in shallow (5–10-m depth) than in deep turf communities (30-m depth), and N fixation rates during the daytime significantly exceeded those during the night. N fixation rates by the turf communities did not differ between the degraded and less degraded reef. Both our study and a literature survey of earlier studies indicated that turf communities tend to have lower N fixation rates than cyanobacterial mats. However, at least in our study area, turf communities were more abundant than cyanobacterial mats. Our results therefore suggest that turf communities play an important role in the nitrogen cycle of coral reefs. N fixation by turfs may contribute to an undesirable positive feedback that promotes the proliferation of algal turf communities while accelerating coral reef degradation

A contemporary baseline record of the world’s coral reefs

Addressing the global decline of coral reefs requires effective actions from managers, policymakers and society as a whole. Coral reef scientists are therefore challenged with the task of providing prompt and relevant inputs for science-based decision-making. Here, we provide a baseline dataset, covering 1300 km of tropical coral reef habitats globally, and comprised of over one million geo-referenced, high-resolution photo-quadrats analysed using artificial intelligence to automatically estimate the proportional cover of benthic components. The dataset contains information on five major reef regions, and spans 2012-2018, including surveys before and after the 2016 global bleaching event. The taxonomic resolution attained by image analysis, as well as the spatially explicit nature of the images, allow for multi-scale spatial analyses, temporal assessments (decline and recovery), and serve for supporting image recognition developments. This standardised dataset across broad geographies offers a significant contribution towards a sound baseline for advancing our understanding of coral reef ecology and thereby taking collective and informed actions to mitigate catastrophic losses in coral reefs worldwide

Coral Reef Community Changes in Karimunjawa National Park, Indonesia: Assessing the Efficacy of Management in the Face of Local and Global Stressors

Karimunjawa National Park is one of Indonesia's oldest established marine parks. Coral reefs across the park are being impacted by fishing, tourism and declining water quality (local stressors), as well as climate change (global pressures). In this study, we apply a multivariate statistical model to detailed benthic ecological datasets collected across Karimunjawa's coral reefs, to explore drivers of community change at the park level. Eighteen sites were surveyed in 2014 and 2018, before and after the 2016 global mass coral bleaching event. Analyses revealed that average coral cover declined slightly from 29.2 +/- 0.12% (Standard Deviation, SD) to 26.3 +/- 0.10% SD, with bleaching driving declines in most corals. Management zone was unrelated to coral decline, but shifts from massive morphologies toward more complex foliose and branching corals were apparent across all zones, reflecting a park-wide reduction in damaging fishing practises. A doubling of sponges and associated declines in massive corals could not be related to bleaching, suggesting another driver, likely declining water quality associated with tourism and mariculture. Further investigation of this potentially emerging threat is needed. Monitoring and management of water quality across Karimunjawa may be critical to improving resilience of reef communities to future coral bleaching

Monitoring of coral reefs using artificial intelligence: A feasible and cost-effective approach

Ecosystemmonitoring is central to effectivemanagement, where rapid reporting is essential to provide timely advice. While digital imagery has greatly improved the speed of underwater data collection formonitoring benthic communities, image analysis remains a bottleneck in reporting observations. In recent years, a rapid evolution of artificial intelligence in image recognition has been evident in its broad applications in modern society, offering new opportunities for increasing the capabilities of coral reef monitoring. Here, we evaluated the performance of Deep Learning Convolutional Neural Networks for automated image analysis, using a global coral reefmonitoring dataset. The study demonstrates the advantages of automated image analysis for coral reef monitoring in terms of error and repeatability of benthic abundance estimations, as well as cost and benefit. We found unbiased and high agreement between expert and automated observations (97%). Repeated surveys and comparisons against existing monitoring programs also show that automated estimation of benthic composition is equally robust in detecting change and ensuring the continuity of existing monitoring data. Using this automated approach, data analysis and reporting can be accelerated by at least 200x and at a fraction of the cost (1%). Combining commonly used underwater imagery inmonitoring with automated image annotation can dramatically improve how wemeasure andmonitor coral reefs worldwide, particularly in terms of allocating limited resources, rapid reporting and data integration within and acrossmanagement areas.</p