Ecological and environmental monitoring has become increasingly important, with increasing threats from human disturbances. Monitoring usually involves sampling from several sites of a similar habitat at regular (or irregular) intervals through time. The purpose of monitoring is to determine where and when an impact may have occurred or, once detected, may still be occurring. Sequential statistical methods, including control charts, as developed for industrial applications, offer some promise in this regard. These provide a way of identifying when a system (e.g., in a factory) is going “out of control,” so as to trigger an alarm to stop the system and employ appropriate remedial measures. Such techniques clearly would be useful in the context of environmental monitoring. Traditional control charts, however, cannot be used for many ecological applications because they do not handle multivariate data, and individual counts of species abundances do not generally fulfill the necessary statistical assumptions. A distance‐based multivariate control chart method is described here, with some examples of its use in monitoring coral reef fish assemblages of the Great Barrier Reef, Australia. The method is flexible, as it can be based on any dissimilarity measure of choice, and useful, as it does not require any specific assumptions regarding distributions of variables. Bootstrapping techniques are used to provide control‐chart limits for an appropriate multivariate distance‐based criterion through time. The method is designed to identify impacts at individual sites as quickly as possible, thus triggering an “alarm bell” in the context of ecological monitoring. It can also be applied at several spatial scales in hierarchical designs.

In the face of increasing cumulative effects from human and natural disturbances, sustaining coral reefs will require a deeper understanding of the drivers of coral resilience in space and time. Here we develop a high‐resolution, spatially explicit model of coral dynamics on Australia's Great Barrier Reef (GBR). Our model accounts for biological, ecological and environmental processes, as well as spatial variation in water quality and the cumulative effects of coral diseases, bleaching, outbreaks of crown‐of‐thorns starfish (Acanthaster cf. solaris), and tropical cyclones. Our projections reconstruct coral cover trajectories between 1996 and 2017 over a total reef area of 14,780 km2, predicting a mean annual coral loss of −0.67%/year mostly due to the impact of cyclones, followed by starfish outbreaks and coral bleaching. Coral growth rate was the highest for outer shelf coral communities characterized by digitate and tabulate Acropora spp. and exposed to low seasonal variations in salinity and sea surface temperature, and the lowest for inner‐shelf communities exposed to reduced water quality. We show that coral resilience (defined as the net effect of resistance and recovery following disturbance) was negatively related to the frequency of river plume conditions, and to reef accessibility to a lesser extent. Surprisingly, reef resilience was substantially lower within no‐take marine protected areas, however this difference was mostly driven by the effect of water quality. Our model provides a new validated, spatially explicit platform for identifying the reefs that face the greatest risk of biodiversity loss, and those that have the highest chances to persist under increasing disturbance regimes.

Climate change threatens coral reefs across the world. Intense bleaching has caused dramatic coral mortality in many tropical regions in recent decades, but less obvious chronic effects of temperature and other stressors can be equally threatening to the long-term persistence of diverse coral-dominated reef systems. Coral reefs persist if coral recovery rates equal or exceed average rates of mortality. While mortality from acute destructive events is often obvious and easy to measure, estimating recovery rates and investigating the factors that influence them requires long-term commitment. Coastal development is increasing in many regions, and sea surface temperatures are also rising. The resulting chronic stresses have predictable, adverse effects on coral recovery, but the lack of consistent long-term data sets has prevented measurement of how much coral recovery rates are actually changing. Using long-term monitoring data from 47 reefs spread over 10 degrees of latitude on Australia's Great Barrier Reef (GBR), we used a modified Gompertz equation to estimate coral recovery rates following disturbance. We compared coral recovery rates in two periods: 7 years before and 7 years after an acute and widespread heat stress event on the GBR in 2002. From 2003 to 2009, there were few acute disturbances in the region, allowing us to attribute the observed shortfall in coral recovery rates to residual effects of acute heat stress plus other chronic stressors. Compared with the period before 2002, the recovery of fast-growing Acroporidae and of "Other" slower growing hard corals slowed after 2002, doubling the time taken for modest levels of recovery. If this persists, recovery times will be increasing at a time when acute disturbances are predicted to become more frequent and intense. Our study supports the need for management actions to protect reefs from locally generated stresses, as well as urgent global action to mitigate climate change.

Cumulative impacts assessments on marine ecosystems have been hindered by the difficulty of collecting environmental data and identifying drivers of community dynamics beyond local scales. On coral reefs, an additional challenge is to disentangle the relative influence of multiple drivers that operate at different stages of coral ontogeny. We integrated coral life history, population dynamics, and spatially explicit environmental drivers to assess the relative and cumulative impacts of multiple stressors across 2,300 km of the world's largest coral reef ecosystem, Australia's Great Barrier Reef (GBR). Using literature data, we characterized relationships between coral life history processes (reproduction, larval dispersal, recruitment, growth, and mortality) and environmental variables. We then simulated coral demographics and stressor impacts at the organism (coral colony) level on >3,800 individual reefs linked by larval connectivity and exposed to temporally and spatially realistic regimes of acute (crownof-thorns starfish outbreaks, cyclones, and mass coral bleaching) and chronic (water-quality) stressors. Model simulations produced a credible reconstruction of recent (2008-2020) coral trajectories consistent with monitoring observations, while estimating the impacts of each stressor at reef and regional scales. Overall, simulated coral populations declined by one-third across the GBR, from an average of ~29% to ~19% hard coral cover. By 2020, <20% of the GBR had coral cover higher than 30%, a status of reef health corroborated by scarce and sparsely distributed monitoring data. Reef-wide annual rates of coral mortality were driven by bleaching (48%) ahead of cyclones (41%) and starfish predation (11%). Beyond the reconstructed status and trends, the model enabled the emergence of complex interactions that compound the effects of multiple stressors while promoting a mechanistic understanding of coral cover dynamics. Drivers of coral cover growth were identified; notably, water quality (suspended sediments) was estimated to delay recovery for at least 25% of inshore reefs. Standardized rates of coral loss and recovery allowed the integration of all cumulative impacts to determine the equilibrium cover for each reef. This metric, combined with maps of impacts, recovery potential, water-quality thresholds, and reef state metrics, facilitates strategic spatial planning and resilience-based management across the GBR.

Many sampling strategies have been proposed as appropriate for describing spatial patterns in marine organisms. There remain, however, many problems with the description, analysis and interpretation of temporal variation in abundances of organisms. In particular, there is a need to understand temporal error in the estimation of abundance of mobile organisms. In this paper we report estimates of temporal variation in abundances of tropical reef fishes attributable to sampling error at diurnal, daily and 'monthly' scales and compare these to inter-annual variation that might arise from processes such as mortality and recruitment. Uncertainty in estimates taken from the same sites over consecutive days was large for several species and accounted for the majority of error in estimates of abundance within years. Sources of error in estimates of abundance are discussed with consideration of the implications for long-term sampling and monitoring of fish assemblages. Shortterm temporal variation must be considered along with spatial variation in the design and interpretation of temporal studies of mobile species. KEY WORDS: Reef fish···Visual census···Temporal errorResale or republication not permitted without written consent of the publisher

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We investigated spatial patterns of synchrony among coral reef fish populations and environmental variables over an eight-year period on the Great Barrier Reef, Australia. Our aims were to determine the spatial scale of intra- and interspecific synchrony of fluctuations in abundance of nine damselfish species (genus Pomacentrus) and assess whether environmental factors could have influenced population synchrony. All species showed intraspecific synchrony among populations on reefs separated by < or =100 km, and interspecific synchrony was also common at this scale. At greater spatial scales, only four species showed intraspecific synchrony, over distances ranging from 100-300 km to 500-800 km, and no cases of interspecific synchrony were recorded. The two mechanisms most likely to cause population synchrony are dispersal and environmental forcing through regionally correlated climate (the Moran effect). Dispersal may have influenced population synchrony over distances up to 100 km as this is the expected spatial range for ecologically significant reef fish dispersal. Environmental factors are also likely to have synchronized population fluctuations via the Moran effect for three reasons: (1) dispersal could not have caused interspecific synchrony that was common over distances < or =100 km because dispersal cannot link populations of different species, (2) variations in both sea surface temperature and wind speed were synchronized over greater spatial scales (>800 km) than fluctuations in damselfish abundance (< or =800 km) and were correlated with an index of global climate variability, the El Niño-Southern Oscillation (ENSO), and (3) synchronous population fluctuations of most damselfish species were correlated with ENSO; large population increases often followed ENSO events. We recorded regional variations in the strength of population synchrony that we suspect are due to spatial differences in geophysical, oceanographic, and population characteristics, which act to dilute or enhance the effects of synchronizing mechanisms. We conclude that synchrony is common among Pomacentrus populations separated by tens of kilometers but less prevalent at greater spatial scales, and that environmental variation linked to global climate is likely to be a driving force behind damselfish population synchrony at all spatial scales on the Great Barrier Reef.

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