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Reef monitoring programmes often focus on limited sites, predominantly on reef slope areas, which do not capture compositional variability across zones. This study assessed spatial and temporal changes in hard coral cover at four hierarchical spatial scales. ~ 55,000, geo-referenced photoquadrats were collected annually from 2002 to 2018 and analysed using artificial intelligence for 31 sites across reef flat and reef slope zones on Heron Reef, Southern Great Barrier Reef, Australia. Trends in hard coral cover were examined at three spatial scales: (1) “reef scale”, all data; (2) “geomorphic zone scale”—north/south reef slope, inner/outer reef flat; and (3) “site scale”—31 sites. Coral cover trajectories were also examined at: (4) “sub-site scale”—sub-division of sites into 567 sub-sites, to estimate variability in coral cover trajectories via spatial statistical modelling. At reef scale coral cover increased over time to 25.6 ± 0.4 SE % in 2018 but did not recover following disturbances caused by disease (2004–2008), cyclonic conditions (2009) or severe storms (2015) to the observed pre-disturbance level (44.0 ± 0.7 SE %) seen in 2004. At geomorphic zone scale, the reef slope had significantly higher coral cover than the reef flat. Trends of decline and increase were visible in the slope zones, and the southern slope recovered to pre-decline levels. Variable coral cover trends were visible at site scale. Furthermore, sub-site spatial modelling captured eight years of coral recovery that occurred at different times and magnitudes across the four geomorphic zones, effectively estimating variability in the trajectory of the reef’s coral community. Derived spatial predictions for the entire reef show patchy coral recovery, particularly on the southern slope, and that recovery hotspots are distributed across the reef. These findings suggest that to fully understand and interpret coral decline or recovery on a reef, more accurate assessment can be achieved by examining sites distributed within different geomorphic zones to capture variation in exposure, depth and consolidation.

Anticipating future changes of an ecosystem's dynamics requires knowledge of how its key communities respond to current environmental regimes. The Great Barrier Reef (GBR) is under threat, with rapid changes of its reef-building hard coral (HC) community structure already evident across broad spatial scales. While several underlying relationships between HC and multiple disturbances have been documented, responses of other benthic communities to disturbances are not well understood. Here we used statistical modelling to explore the effects of broad-scale climate-related disturbances on benthic communities to predict their structure under scenarios of increasing disturbance frequency. We parameterized a multivariate model using the composition of benthic communities estimated by 145,000 observations from the northern GBR between 2012 and 2017. During this time, surveyed reefs were variously impacted by two tropical cyclones and two heat stress events that resulted in extensive HC mortality. This unprecedented sequence of disturbances was used to estimate the effects of discrete versus interacting disturbances on the compositional structure of HC, soft corals (SC) and algae. Discrete disturbances increased the prevalence of algae relative to HC while the interaction between cyclones and heat stress was the main driver of the increase in SC relative to algae and HC. Predictions from disturbance scenarios included relative increases in algae versus SC that varied by the frequency and types of disturbance interactions. However, high uncertainty of compositional changes in the presence of several disturbances shows that responses of algae and SC to the decline in HC needs further research. Better understanding of the effects of multiple disturbances on benthic communities as a whole is essential for predicting the future status of coral reefs and managing them in the light of new environmental regimes. The approach we develop here opens new opportunities for reaching this goal.

This paper describes benthic coral reef community composition point-based field data sets derived from georeferenced photoquadrats using machine learning. Annually over a 17 year period (2002–2018), data were collected using downward-looking photoquadrats that capture an approximately 1 m2 footprint along 100 m–1500 m transect surveys distributed along the reef slope and across the reef flat of Heron Reef (28 km2), Southern Great Barrier Reef, Australia. Benthic community composition for the photoquadrats was automatically interpreted through deep learning, following initial manual calibration of the algorithm. The resulting data sets support understanding of coral reef biology, ecology, mapping and dynamics. Similar methods to derive the benthic data have been published for seagrass habitats, however here we have adapted the methods for application to coral reef habitats, with the integration of automatic photoquadrat analysis. The approach presented is globally applicable for various submerged and benthic community ecological applications, and provides the basis for further studies at this site, regional to global comparative studies, and for the design of similar monitoring programs elsewhere., Measurement(s) marine benthic feature Technology Type(s) photoquadrat transect surveys Factor Type(s) benthic composition Sample Characteristic - Organism benthic communities Sample Characteristic - Environment coral reef • marine coral reef flat zone • marine coral reef crest • marine coral reef back reef • marine coral reef fore reef Sample Characteristic - Location Heron Island Reef, 23–052 Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14034320

The jaguar (Panthera onca) is the dominant predator in Central and South America, but is now considered near‐threatened. Estimating jaguar population size is difficult, due to uncertainty in the underlying dynamical processes as well as highly variable and sparse data. We develop a stochastic temporal model of jaguar abundance in the Peruvian Amazon, taking into account prey availability, under various climate change scenarios. The model is calibrated against existing data sets and an elicitation study in Pacaya Samiria. In order to account for uncertainty and variability, we construct a population of models over four key parameters, namely three scaling parameters for aquatic, small land, and large land animals and a hunting index. We then use this population of models to construct probabilistic evaluations of jaguar populations under various climate change scenarios characterized by increasingly severe flood and drought events and discuss the implications on jaguar numbers. Results imply that jaguar populations exhibit some robustness to extreme drought and flood, but that repeated exposure to these events over short periods can result in rapid decline. However, jaguar numbers could return to stability—albeit at lower numbers—if there are periods of benign climate patterns and other relevant factors are conducive., We develop novel statistical methods to address urgent conservation problems characterized by high levels of uncertainty. We demonstrate these methods by forecasting the effects of severe climate events on jaguar populations in the Peruvian Amazon basin.

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., Measurement(s) ecosystem • coral reef • composition Technology Type(s) automated image annotation • machine learning Factor Type(s) year of data collection • geographic location Sample Characteristic - Organism Anthozoa • Algae • Porifera Sample Characteristic - Environment marine coral reef biome • marine coral reef fore reef Sample Characteristic - Location Atlantic Ocean • Eastern Australia • Indian Ocean • Southeast Asia • Pacific Ocean • Great Barrier Reef Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13007516

Preserving coral reef resilience is a major challenge in the Anthropocene, yet recent studies demonstrate failures of reef recovery from disturbance, globally. The wide and vigorous outer-reef system of French Polynesia presents a rare opportunity to assess ecosystem resilience to disturbances at a large-scale equivalent to the size of Europe. In this purpose, we analysed long-term data on coral community dynamics and combine the mixed-effects regression framework with a set of functional response models to evaluate coral recovery trajectories. Analyses of 14 years data across 17 reefs allowed estimating impacts of a cyclone, bleaching event and crown-of-thorns starfish outbreak, which generated divergence and asynchrony in coral community trajectory. We evaluated reef resilience by quantifying levels of exposure, degrees of vulnerability, and descriptors of recovery of coral communities in the face of disturbances. Our results show an outstanding rate of coral recovery, with a systematic return to the pre-disturbance state within only 5 to 10 years. Differences in the impacts of disturbances among reefs and in the levels of vulnerability of coral taxa to these events resulted in diverse recovery patterns. The consistent recovery of coral communities, and convergence toward pre-disturbance community structures, reveals that the processes that regulate ecosystem recovery still prevail in French Polynesia.

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.

Coral reefs are likely to be exposed to more intense cyclones under climate change. Cyclone impacts are spatially highly variable given complex hydrodynamics, and coral-specific sensitivity to wave impacts. Predicting reef vulnerability to cyclones is critical to management but requires high resolution environmental data that are difficult to obtain over broad spatial scales. Using 30m-resolution wave modelling, we tested cyclonic and non-cyclonic wave metrics as predictors of coral damage on 22 reefs after severe cyclone Ita impacted the northern Great Barrier Reef, Australia in 2014. Analyses of coral cover change accounting for the type of coral along a gradient of vulnerability to wave damage (e.g., massive, branching, Acroporids) excluded cyclone-generated surface wave metrics (derived from wave height) as important predictors. Increased bottom stress wave environment (near-bed wave orbital velocity) due to Ita (Ita-Ub) explained spatial patterns of 17% to 46% total coral cover loss only when the initial abundance of Acroporids was accounted for, and only when exceeding 35% cover. Greater coral losses occurred closer to the cyclone path irrespective of coral type. Massive and encrusting corals, however, had losses exacerbated in higher non-cyclonic bottom-wave energy environments (nc-Ub). The effect of community composition on structural vulnerability to wave damage was more important predicting damage that the magnitude of the cyclone-generated waves, especially when reefs are surveyed well beyond where damaging waves are expected to occur. Exposure to Ita-Ub was greater in typically high nc-Ub environments with relatively low cover of the most fragile morphologies explaining why these were the least affected overall. We reveal that the common surface-wave metrics of cyclone intensity may not always be able to predict spatial impacts and conclude that reef vulnerability assessments need to account for chronic wave patterns and differences in community composition in order to provide predictive tools for future conservation and restoration.

Aim Increasingly frequent and intense disturbances of many kinds are reducing the populations of habitat-forming species. For example, disturbances, including coral bleaching, storms and cyclones, coral disease and Crown-of-Thorns starfish (CoTS) outbreaks, have been important contributors to the global decline of coral populations. Understanding these the effects of such disturbances is challenging but necessary for their effective management. In this paper, we provide a novel spatially and temporally explicit perspective on the role of multiple and interacting disturbances on population dynamics. Location Great Barrier Reef (GBR), Australia. Time period 1995–2011. Major taxa studied Acropora species. Methods A semi-parametric Bayesian hierarchical model was used to estimate the form and strength of the cumulative effects of multiple disturbances that have affected coral populations at different locations, frequencies and intensities, and in different orders over 16 years. Boosted regression trees and random forest models were used to rank the relative importance of four types of disturbances in reducing the ability of coral populations to recover following disturbance. Results We show that while all the disturbance agents examined had clear negative impacts, coral predation by CoTS was the strongest contributor to the decline of coral cover and resilience of coral populations. The effects of multiple disturbances on individual reef sites were overwhelmingly additive. Main conclusions Reduction of coral predation through control of CoTS could substantially reduce the severity of disturbances along the GBR and promote greater resilience, and thereby recovery, of coral populations.

Global environmental change, such as ocean warming and increased cyclone activity, is driving widespread and rapid declines in the abundance of key ecosystem engineers, reef-building corals, on the Great Barrier Reef. Our ability to understand how coral associated species, such as reef fishes, respond to coral loss can be impeded by uncertainty surrounding natural spatio-temporal variability of coral populations. To address this issue, we developed a semi-parametric hierarchical Bayesian model to estimate long-term trajectories of habitat-forming coral cover as a function of three spatial scales (sub-region, habitat and site) and environmental disturbances. The relationships between coral cover trajectories and fish community structure were examined using posterior predictive distributions of estimated coral cover from the statistical model. In the absence of direct observations of fish community structure, we used the probability of coral cover being above some ecological threshold values as a proxy for potential disruptions of fish community structure. Threshold values were derived from published field studies that estimated changes in the structure of coral-reef fish communities and coral cover after major disturbances. In these studies, fish community structure did not change where post-disturbance coral cover was > 20%. Disruptions in the structure of these communities were observed when coral cover dropped to between 10–20% and declines in fish diversity were typical where coral cover ranged from between 5 and 10%. Based on these thresholds values, posterior probabilities of coral cover being above 20% and between 10 and 20% and between 5 and 10% were calculated across spatial scales on the Great Barrier Reef (GBR) from 1995 to 2011. At the GBR scale, probabilities of coral cover being above these thresholds remained relatively stable through time. Across years, probabilities of coral cover being at least > 20% remained null for the sub-regions of Cairns, Townsville, Whitsundays and Swain but highly variable between reef sites within these sub-regions, with the exception of Townsville. In the Townsville area, probabilities of coral cover being between 10–20% and 5–10% declined from 0.75 to 0 during the study period. This finding highlights potential sub-regional fish community structure disruptions which have not yet been observed at this spatial scale. As frequency and intensity of disturbance events continue to rise, and consequently, as coral cover declines further, the probabilistic Bayesian approach presented in this chapter could be used to help provide early warnings of major ecological shifts at management relevant scales in the absence of direct observations.

Subtropical reefs are unique ecosystems that require effective management – informed by regular ecological monitoring – to foster resilience to environmental changes. Resources to conduct monitoring are limited, and citizen science can complement data from local management agencies. Here, citizen science efforts document the ecological status of a subtropical reef, Flinders Reef, Moreton Bay Marine Park, Australia. Ecological surveys, following Reef Check Australia and CoralWatch protocols, were conducted by 44 trained volunteers. Ten sites at Flinders Reef were surveyed at 5-10 m depth in autumn and spring. Additionally, underwater photos and depth surveys were integrated with satellite imagery to create a detailed habitat map. Coral cover across sites ranged between 14% and 67%. Branching corals dominated the site with 67% cover and showed 89% dissimilarity in coral community composition compared to other sites. Coral community composition was mostly explained by spatial variation, of which 16% was influenced by wave exposure. Observed reef impacts including physical damage, unknown scars and coral disease were three times lower than studies on more accessible reefs in Moreton Bay Marine Park. Invertebrate abundance was relatively low (6.65 individuals per 100 m2), with the most abundant groups observed being sea urchins (Diademaspp.), gastropods (Drupellaspp.) and anemones. Butterflyfish were recorded at every site and were the most abundant fish group surveyed. Findings highlight the healthy condition of Flinders Reef, likely influenced by its offshore location and protection status as a ‘no-take’ zone. This study demonstrates that increasing the current 500m radius protection zone by a further 500m could double the protected area of coral, offering potential further conservation benefits. The findings resulting from the ecological data analysis and created benthic habitat map, provide an example of how citizen science based projects can assist marine park authorities and the public in ongoing conservation efforts.

Subtropical reefs are important habitats for many marine species and for tourism and recreation. Yet, subtropical reefs are understudied, and detailed habitat maps are seldom available. Citizen science can help fill this gap, while fostering community engagement and education. In this study, 44 trained volunteers conducted an ecological assessment of subtropical Flinders Reef using established Reef Check and CoralWatch protocols. In 2017, 10 sites were monitored to provide comprehensive information on reef communities and to estimate potential local drivers of coral community structure. A detailed habitat map was produced by integrating underwater photos, depth measurements, wave-exposure modelling and satellite imagery. Surveys showed that coral cover ranged from 14% to 67%. Site location and wave exposure explained 47% and 16% respectively, of the variability in coral community composition. Butterflyfishes were the most abundant fish group, with few invertebrates being observed during the surveys. Reef impacts were three times lower than on other nearby subtropical reefs. These findings can be used to provide local information to spatial management and Marine Park planning. To increase the conservation benefits and to maintain the health of Flinders Reef, we recommend expanding the current protection zone from 500- to a 1000-m radius.

Karimunjawa National Park is one of Indonesia&rsquo, 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&rsquo, 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 &plusmn, 0.12% (Standard Deviation, SD) to 26.3 &plusmn, 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.

The global erosion of biodiversity presents unique challenges for identifying major changes in population dynamics, establishing their causes, and managing and conserving affected ecosystems at broad spatial scales. Adaptive learning approaches connecting different spatial scales through the transfer of hierarchical information are powerful tools to address such challenges. Here, we use a Semi-Parametric Bayesian Hierarchical (SPa-BaH) model to estimate coral cover trajectories using 16 years of a broad-scale survey on Australia’s Great Barrier Reef (GBR). The spatiotemporal variability of coral populations has been considered by separating three-tiered spatial scales and allowing for alternating phases of increasing and decreasing in the estimation of their trajectories. Model estimates revealed coral cover trajectories that were highly variable according to location but that fairly consistently declined at a regional spatial scale. Notwithstanding this general trend, individual reefs within subregions in the central part of the GBR often displayed different trajectory types between sites separated by only a few hundred meters. These coral dynamics were also associated with reduced recovery rates in the Cairns and Swain subregions. Our study highlights the importance of accounting for local variation in coral cover when estimating the spatiotemporal trends in coral cover trajectories, in this case, at the GBR scale. By retaining information at different hierarchical spatial scales, our SPa-BaH model supports better estimation of large-scale coral cover trajectories. The quantitative approaches developed here can also be applied to other species with complex dynamics thereby enhancing estimations of their trajectories at local- and larger-scales and options for their management.

Large, solitary, marine predators such as sharks have been observed to aggregate at specific areas. Such aggregations are almost certainly driven by foraging and behavioural strategies making space for diverse spatial organizations. Reef-associated shark species often show strong patterns of site fidelity that could be viewed as a prerequisite for sociality. However, there is limited empirical evidence that such aggregations are driven by intrinsic social factors. Association data for blacktip reef sharks, Carcharhinus melanopterus, were obtained from photoidentification surveys conducted in Moorea coral reefs (French Polynesia). We adapted a social network approach to demonstrate evidence of four main communities and two subcommunities within the population. We confronted the resulting structure with candidate explanatory variables. Sharks formed spatial groups characterized by nonrandom and long-term associations, despite opportunities for social relationships to develop between communities. Sex and length of sharks tended to influence assortment at the population and community levels. Individual space use also explained community structure, although spatial assortment was globally weaker than random expectations, suggesting that observed associations were not an artefact of the sampling design or spatial distribution of individuals. We conclude that the observed grouping patterns not only resulted from passive aggregations for specific resources, but rather the communities developed from an active choice of individuals as a sign of sociability. Individual preferences and adaptation to local conditions, as well as demographic, ecological and anthropogenic factors, may explain the social variability between communities. This suggests that a stable grouping strategy may confer substantial benefits in this marine predator.

International audience; Recently, attempts to improve decision making in species management have focussed on uncertainties associated with modelling temporal fluctuations in populations. Reducing model uncertainty is challenging; while larger samples improve estimation of species trajectories and reduce statistical errors, they typically amplify variability in observed trajectories. In particular, traditional modelling approaches aimed at estimating population trajectories usually do not account well for nonlinearities and uncertainties associated with multi-scale observations characteristic of large spatio-temporal surveys. We present a Bayesian semi-parametric hierarchical model for simultaneously quantifying uncertainties associated with model structure and parameters, and scale-specific variability over time. We estimate uncertainty across a four-tiered spatial hierarchy of coral cover from the Great Barrier Reef. Coral variability is well described; however, our results show that, in the absence of additional model specifications, conclusions regarding coral trajectories become highly uncertain when considering multiple reefs, suggesting that management should focus more at the scale of individual reefs. The approach presented facilitates the description and estimation of population trajectories and associated uncertainties when variability cannot be attributed to specific causes and origins. We argue that our model can unlock value contained in large-scale datasets, provide guidance for understanding sources of uncertainty, and support better informed decision making.

International audience; Outbreaks of the coral-killing seastar Acanthaster planci are intense disturbances that can decimate coral reefs. These events consist of the emergence of large swarms of the predatory seastar that feed on reef-building corals, often leading to widespread devastation of coral populations. While cyclic occurrences of such outbreaks are reported from many tropical reefs throughout the Indo-Pacific, their causes are hotly debated, and the spatio-temporal dynamics of the outbreaks and impacts to reef communities remain unclear. Based on observations of a recent event around the island of Moorea, French Polynesia, we show that Acanthaster outbreaks are methodic, slow-paced, and diffusive biological disturbances. Acanthaster outbreaks on insular reef systems like Moorea's appear to originate from restricted areas confined to the ocean-exposed base of reefs. Elevated Acanthaster densities then progressively spread to adjacent and shallower locations by migrations of seastars in aggregative waves that eventually affect the entire reef system. The directional migration across reefs appears to be a search for prey as reef portions affected by dense seastar aggregations are rapidly depleted of living corals and subsequently left behind. Coral decline on impacted reefs occurs by the sequential consumption of species in the order of Acanthaster feeding preferences. Acanthaster outbreaks thus result in predictable alteration of the coral community structure. The outbreak we report here is among the most intense and devastating ever reported. Using a hierarchical, multi-scale approach, we also show how sessile benthic communities and resident coral-feeding fish assemblages were subsequently affected by the decline of corals. By elucidating the processes involved in an Acanthaster outbreak, our study contributes to comprehending this widespread disturbance and should thus benefit targeted management actions for coral reef ecosystems.