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Bayesian belief networks are finding increasing application in adaptive ecosystem management where data are limited and uncertainty is high. The combined effect of multiple stressors is one area where considerable uncertainty exists. Our study area, the Great Barrier Reef is simultaneously data-rich – concerning the physical and biological environment – and data-poor – concerning the effects of interacting stressors. We used a formal expert-elicitation process to obtain estimates of outcomes associated with a variety of scenarios that combined stressors both within and outside the control of local managers. There was much stronger consensus about certain stressor effects – such as between temperature anomalies and bleaching – than others, such as the relationship between water quality and coral cover. In general, the expert outlook for the Great Barrier Reef is pessimistic, with the potential for climate change effects potentially to overshadow the effects of local management actions.

Concern is growing about the potential effects of interacting multiple stressors, especially as the global climate changes. We provide a comprehensive review of multiple stressor interactions in coral reef ecosystems, which are widely considered to be one of the most sensitive ecosystems to global change. First, we synthesized coral reef studies that examined interactions of two or more stressors, highlighting stressor interactions (where one stressor directly influences another) and potentially synergistic effects on response variables (where two stressors interact to produce an effect that is greater than purely additive). For stressor-stressor interactions, we found 176 studies that examined at least 2 of the 13 stressors of interest. Applying network analysis to analyze relationships between stressors, we found that pathogens were exacerbated by more costressors than any other stressor, with ca. 78% of studies reporting an enhancing effect by another stressor. Sedimentation, storms, and water temperature directly affected the largest number of other stressors. Pathogens, nutrients, and crown-of-thorns starfish were the most-influenced stressors. We found 187 studies that examined the effects of two or more stressors on a third dependent variable. The interaction of irradiance and temperature on corals has been the subject of more research (62 studies, 33% of the total) than any other combination of stressors, with many studies reporting a synergistic effect on coral symbiont photosynthetic performance (n = 19). Second, we performed a quantitative meta-analysis of existing literature on this most-studied interaction (irradiance and temperature). We found that the mean effect size of combined treatments was statistically indistinguishable from a purely additive interaction, although it should be noted that the sample size was relatively small (n = 26). Overall, although in aggregate a large body of literature examines stressor effects on coral reefs and coral organisms, considerable gaps remain for numerous stressor interactions and effects, and insufficient quantitative evidence exists to suggest that the prevailing type of stressor interaction is synergistic.

Coral bleaching and disease have often been hypothesized to be mutually reinforcing or co-occurring, but much of the research supporting this has only drawn an implicit connection through common environmental predictors. In this study, we examine whether an explicit relationship between white syndrome and bleaching exists using assemblage-level monitoring data from up to 112 sites on reef slopes spread throughout the Great Barrier Reef over 11 years of monitoring. None of the temperature metrics commonly used to predict mass bleaching performed strongly when applied to these data. Furthermore, the inclusion of bleaching as a predictor did not improve model skill over baseline models for predicting white syndrome. Similarly, the inclusion of white syndrome as a predictor did not improve models of bleaching. Evidence for spatial co-occurrence of bleaching and white syndrome at the assemblage level in this data set was also very weak. These results suggest the hypothesized relationship between bleaching and disease events may be weaker than previously thought, and more likely to be driven by common responses to environmental stressors, rather than directly facilitating one another.

Coral reefs are in dire need of effective governance, yet the science and planning of coral reef protected areas largely stem from wealthy, developed nations, with very different social, economic, and cultural characteristics than the nations in which most coral reefs occur. Much has been written about coral reefs and the use of marine protected areas (MPAs) as a management tool, but emerging trends and recommendations have not been adequately synthesized for the context of developing nations. We found that 60% of studies on MPA design and planning are from North America, Australia, Europe and the Mediterranean. As a result, many recommendations about how best to design, implement and manage coral reef protected areas may need to be adapted to address the needs of other nations. Based on the literature and our experiences, we review three emerging trends in MPA design and management, and relate these to the context of coral reef developing nations. First, MPA design is evolving to merge community (usually bottom-up) and regional (usually top-down) planning approaches. Second, the increasing recognition that social and ecological systems are tightly coupled is leading to planning and management of MPAs that better incorporate the human dimensions of reef systems and their linkages with reef ecology. Finally, there has been a trend toward adaptive management of MPAs and the emergence of related ideas about adaptive planning. These three trends provide crucial and much needed opportunities for improving MPAs and their effectiveness in coral reef nations.

Steller sea lions (Eumetopias jubatus) are known to have occupied the same terrestrial haul-out and rookery sites across the North Pacific Rim for centuries, but it is not known why they choose and stay at these locations, or what defines their preferred habitat. Classifying and comparing the shoreline type of haul-outs and rookeries against sites not used by Steller sea lions showed that they preferentially locate their haul-outs and rookeries on exposed rocky shorelines and wave-cut platforms. However, no preference was found for selecting rookeries on sheltered shore types. Shoreline types used less frequently by sea lions included fine-to-medium-grained sand beaches, mixed sand and gravel beaches, gravel beaches, and sheltered rocky shores. Quantifying the shoreline types used by sea lions confirms anecdotal reports of habitat preferences and may prove useful in identifying and protecting sea lion terrestrial habitat, or in forecasting how climate change might affect the distribution of sea lions.

Multiple stressors are an increasing concern in the management and conservation of ecosystems, and have been identified as a key gap in research. Coral reefs are one example of an ecosystem where management of local stressors may be a way of mitigating or delaying the effects of climate change. Predicting how multiple stressors interact, particularly in a spatially explicit fashion, is a difficult challenge. Here we use a combination of an expert-elicited Bayesian network (BN) and spatial environmental data to examine how hypothetical scenarios of climate change and local management would result in different outcomes for coral reefs on the Great Barrier Reef (GBR), Australia. Parameterizing our BN using the mean responses from our experts resulted in predictions of limited efficacy of local management in combating the effects of climate change. However, there was considerable variability in expert responses and uncertainty was high. Many reefs within the central GBR appear to be at risk of further decline based on the pessimistic opinions of our expert pool. Further parameterization of the model as more data and knowledge become available could improve predictive power. Our approach serves as a starting point for subsequent work that can fine-tune parameters and explore uncertainties in predictions of responses to management.