Multiple stressor effects on coral reefs
Ban, Stephen Shigeyoshi (2014) Multiple stressor effects on coral reefs. PhD thesis, James cook University.
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Abstract
Coral reef ecosystems around the world face a number of threats, including ocean acidification, increased ocean temperatures due to anthropogenic global warming (AGW), increased disease outbreaks, crown-of-thorns starfish outbreaks, terrestrial sedimentation, eutrophication, pollution, and fishing pressure. At the same time, coral reef ecosystems provide valuable direct and indirect economic and social benefits to millions of people worldwide. However, the intensity and spatial distribution of threats are likely to change with increasing human population and economic development, and thus understanding how multiple stressors may interact and affect coral reefs – particularly in the face of incomplete knowledge about these stressors – is an issue of pressing importance.
This thesis aims to explore and advance the understanding of interactions between multiple stressors and their effects as they pertain to coral reefs generally and the Great Barrier Reef specifically. I review several of the components that are integral to this issue, including: stressors and stress ecology, research to date on the issue of multiple stressors and coral reefs, the multiple threats from climate change to coral reefs, and approaches to modelling and managing multiple stressors.
The overall aim of this thesis is to quantitatively evaluate the importance of multiple stressor interactions to coral reef ecosystems and to assess alternative management approaches to mitigating the effects of potentially increased prevalence and severity of these stressors. I do this through both assessing the state of existing knowledge as well as by using new approaches to model stressors and stressor effects within the context of the GBR. In addition, I seek to provide an example of how these effects can be conceptualized and managed more effectively in the face of uncertain knowledge and incomplete data.
The specific research objectives of my thesis are as follows:
1. To synthesize the available knowledge of multiple stressors on coral reefs;
2. To use the occurrence of bleaching and disease in the GBR as a case study to determine the spatial and temporal overlap of these stressors;
3. To use expert knowledge to identify key uncertainties and knowledge gap(s) regarding multiple stressor interactions on coral reef systems;
4. To apply expert-elicited knowledge about stressors and stressor interactions on the GBR to map potential threats to reefs under a variety of different climate change and management scenarios.
Chapter 2 addresses research objective 1 by using a formal literature search to provide the foundation for a qualitative and selected quantitative meta-analysis of multiple-stressors as they pertains to coral reef ecosystems, and by examining the evidence for the prevalence of synergistic, antagonistic, and additive interactions between stressors. Here I investigate stressor interactions in two ways: first by examining stressor interactions with other stressors, and secondly by looking at potentially synergistic effects between two or more stressors on a response variable (where stressors interact to produce an effect that is greater than purely additive). For stressor-stressor interactions, I found 176 studies that examined interactions between at least two stressors. Applying network analysis to analyse relationships between stressors, I found that pathogens were exacerbated by more co-stressors than any other stressor, while 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. In terms of responses to multiple stressors, I 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 than any other combination of stressors, with many studies reporting a synergistic effect on coral symbiont photosynthetic performance. Second, I performed a quantitative meta-analysis of existing literature on the interaction between temperature and irradiance. Although the sample size was small, I found that the mean effect size of combined treatments was statistically indistinguishable from a purely additive interaction. This chapter provides evidence that considerable gaps remain in our knowledge regarding numerous stressor interactions and effects, and that the available evidence is inconclusive on whether synergistic effects are widespread in coral reef systems.
Chapter 3 addresses research objective 2 by using data from the AIMS Long-term Monitoring Program (LTMP) to examine the spatial and temporal overlap of bleaching and disease in the GBR. 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. I examine whether an explicit relationship between white syndrome and bleaching exists using assemblage-level monitoring data from up to 112 sites on the reef slopes spread throughout the GBR over 11 years of monitoring. None of the temperature metrics commonly used to predict mass bleaching performed strongly when applied to these data, and the inclusion of bleaching as a predictor did not improve model in predicting white syndrome outbreaks. Conversely, 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 dataset 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.
Chapter 4 addresses research objective 3 by exploring the use of Bayesian Belief Networks (hereafter BBNs) in conjunction with expert elicitation to determine the degree of expert consensus about the greatest threats to the GBR, and assessing the degree of confidence that experts have about the effects of various stressors both alone and in combination. BBNs are finding increasing application in adaptive ecosystem management where data are limited and uncertainty is high. I used a formal expert-elicitation process to obtain estimates of outcomes associated with a variety of scenarios in the GBR that combined stressors both within and outside the control of local managers. Among consulted experts, 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 health. In general, models generated from the mean responses from experts predicted that climate change effects could potentially overshadow the mitigating effects of management actions to reduce local stressors.
Chapter 5 addresses research objective 4 by implementing the model developed in Chapter 4 in a spatial way through the use of several scenarios. 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. In this chapter, I use a combination of an expert-elicited BBN and empirical, spatial environmental data to examine how hypothetical scenarios of climate change and local management would result in different outcomes for coral reefs on the GBR. I also assess whether reefs within the existing protected area network differ in their predicted probability of decline from reefs outside the protected area network. Parameterizing the BBN using the mean responses from my expert pool resulted in predictions of limited efficacy of local management in combating the effects of climate change; however, there was considerable variability in expert responses; thus, I also examine the effect that using optimistic versus pessimistic expert responses has on the model predictions of coral cover decline on the GBR. Many reefs within the central GBR appear to be at risk of further decline, but further parameterization of the model as data and knowledge become available will improve predictive power. This approach serves as a proof of concept for subsequent work that can fine-tune parameters and explore uncertainties in predictions of responses to management.
My thesis thus addresses two critical elements that are often missing from studies examining the conservation implications of multiple stressors (especially on coral reefs): interactions between stressor/stressor effects and assessing the effect of different management options on these interactions.
| Item ID: | 40712 |
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| Item Type: | Thesis (PhD) |
| Keywords: | acidification; Bayesian belief network; climate change; climatic factors; conservation planning; coral bleaching; coral declines; coral disease; coral reef ecology, coral reef management; coral reef; expert elicitation; global change biology; global warming; irradiance; marine management; meta-analysis; multiple stressors, ocean warming; overfishing; resilience; risk assessment; stress effect; stressors; synergy |
| Related URLs: | |
| Additional Information: | Publications arising from this thesis are available from the Related URLs field. The publications are: Chapter 2: Ban, Stephen S., Graham, Nicholas A.J., and Connolly, Sean R. (2014) Evidence for multiple stressor interactions and effects on coral reefs. Global Change Biology, 20 (3). pp. 681-697. Chapter 3: Ban, S.S., Graham, N.A.J., and Connolly, S.R. (2013) Relationships between temperature, bleaching and white syndrome on the Great Barrier Reef. Coral Reefs, 32 (1). pp. 1-12. Chapter 4: Ban, Stephen S., Pressey, Robert L., and Graham, Nicholas A.J. (2014) Assessing interactions of multiple stressors when data are limited: a Bayesian belief network applied to coral reefs. Global Environmental Change, 27. pp. 64-72. |
| Date Deposited: | 14 Oct 2015 00:12 |
| FoR Codes: | 05 ENVIRONMENTAL SCIENCES > 0502 Environmental Science and Management > 050202 Conservation and Biodiversity @ 33% 06 BIOLOGICAL SCIENCES > 0602 Ecology > 060205 Marine and Estuarine Ecology (incl Marine Ichthyology) @ 33% 06 BIOLOGICAL SCIENCES > 0699 Other Biological Sciences > 069902 Global Change Biology @ 34% |
| SEO Codes: | 96 ENVIRONMENT > 9607 Environmental Policy, Legislation and Standards > 960701 Coastal and Marine Management Policy @ 34% 96 ENVIRONMENT > 9605 Ecosystem Assessment and Management > 960501 Ecosystem Assessment and Management at Regional or Larger Scales @ 33% 96 ENVIRONMENT > 9603 Climate and Climate Change > 960302 Climate Change Mitigation Strategies @ 33% |
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