Over the past 3 decades, thermal stress events have damaged corals globally. Few studies, however, have tracked the recovery process or assessed whether winners in the short term are also winners in the long term. In the present study, we repeatedly sampled a coral assemblage over a 14 yr period, from 1997 to 2010, through 2 thermal stress events (in 1998 and 2001). Our goal was to examine the consistency of short-term winner and loser outcomes over the recovery period. Although species richness had recovered after 10 yr, the reef composition had changed, and few pocilloporids were to be found. The short-term winners were the thermally tolerant encrusting and massive coral morphologies (Porites and faviids) and Acropora colonies < 5 cm in diameter. Long-term winners were revealed as (1) thermally tolerant, locally persistent colonies, (2) remnant survivors that rapidly regrew, and (3) regionally persistent colonies that recruited. KEY WORDS: Coral bleaching · Climate adaptation · Temperature · Reefs · RecoveryResale or republication not permitted without written consent of the publisher Mar Ecol Prog Ser 434: 67-76, 2011 enced anomalous temperatures only every 50 to 60 yr (Thompson & van Woesik 2009). For example, the southern islands of Japan, the Great Barrier Reef and Micronesia all historically show low-frequency return periods (~50 yr), whereas the Galapagos Islands and Kiribati historically show high-frequency return periods (~5 yr) (Thompson & van Woesik 2009). Not only are contemporary worldwide patterns in thermal anomalies similar to those in the past, but the localities that have shown high-frequency return periods in the past few centuries have also recently experienced the most severe thermal stress (Thompson & van Woesik 2009). If these patterns persist into the near future, then some localities will receive both more intensive and more frequent thermal stress than other localities.Short-term studies suggest that some coral species are destined to become the 'winners', whereas others are destined to become the 'losers' (Loya et al. 2001). However, simply tolerating local thermal stress is not the only viable life-history strategy to persist through time. Ephemeral assemblages that are usually susceptible to local stress also survive regionally by being highly fecund and growing quickly (MacArthur & Wilson 1967, Loya 1976, Gates & Edmunds 1999. Regional persistence also depends on (1) the local survival of small fragments, which have an inherent capacity for regrowth, (2) whether corals on neighboring reefs survived through the thermal stress, (3) whether those neighboring corals have the capacity to supply recruits, (4) the survival of recruits from neighboring reefs, and (5) the return frequency and intensity of the thermal stresses.Few studies have examined the longterm recovery of coral assemblages following thermal stress. Here, we ask whether a winning species in the short term is also a winner in the long term. Previously we reported on the shortterm effects of a severe thermal s...

Ecosystem monitoring is central to effective management, where rapid reporting is essential to provide timely advice. While digital imagery has greatly improved the speed of underwater data collection for monitoring 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 reef monitoring 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 in monitoring with automated image annotation can dramatically improve how we measure and monitor coral reefs worldwide, particularly in terms of allocating limited resources, rapid reporting and data integration within and across management areas.

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 2 ).

Structural complexity strongly influences biodiversity and ecosystem productivity. On coral reefs, structural complexity is typically measured using a single and small-scale metric (‘rugosity’) that represents multiple spatial attributes differentially exploited by species, thus limiting a complete understanding of how fish associate with reef structure. We used a novel approach to compare relationships between fishes and previously unavailable components of reef complexity, and contrasted the results against the traditional rugosity index. This study focused on damselfish to explore relationships between fishes and reef structure. Three territorial species, with contrasting trophic habits and expected use of the reef structure, were examined to infer the potential species-specific mechanisms associated with how complexity influences habitat selection. Three-dimensional reef reconstructions from photogrammetry quantified the following metrics of habitat quality: 1) visual exposure to predators and competitors, 2) density of predation refuges and 3) substrate-related food availability. These metrics explained the species distribution better than the traditional measure of rugosity, and each species responded to different complexity components. Given that a critical effect of reef degradation is loss of structure, adopting three-dimensional technologies potentially offers a new tool to both understand species-habitat association and help forecast how fishes will be affected by the flattening of reefs.

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.

No abstract

in order to collect high quality photographs of the reef at regular intervals along sampling transects that are usually 1.5-2.0 km in length. The system also collected camera altitude from the reef substrate and GPS data to assist in the processing of the photographs and assessment of 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.