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Klein SG, Roch C, Duarte CM. Systematic review of the uncertainty of coral reef futures under climate change. Nat Commun 2024; 15:2224. [PMID: 38472196 DOI: 10.1038/s41467-024-46255-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 02/19/2024] [Indexed: 03/14/2024] Open
Abstract
Climate change impact syntheses, such as those by the Intergovernmental Panel on Climate Change, consistently assert that limiting global warming to 1.5 °C is unlikely to safeguard most of the world's coral reefs. This prognosis is primarily based on a small subset of available models that apply similar 'excess heat' threshold methodologies. Our systematic review of 79 articles projecting coral reef responses to climate change revealed five main methods. 'Excess heat' models constituted one third (32%) of all studies but attracted a disproportionate share (68%) of citations in the field. Most methods relied on deterministic cause-and-effect rules rather than probabilistic relationships, impeding the field's ability to estimate uncertainty. To synthesize the available projections, we aimed to identify models with comparable outputs. However, divergent choices in model outputs and scenarios limited the analysis to a fraction of available studies. We found substantial discrepancies in the projected impacts, indicating that the subset of articles serving as a basis for climate change syntheses may project more severe consequences than other studies and methodologies. Drawing on insights from other fields, we propose methods to incorporate uncertainty into deterministic modeling approaches and propose a multi-model ensemble approach to generating probabilistic projections for coral reef futures.
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Affiliation(s)
- Shannon G Klein
- Marine Science Program, Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.
- Red Sea Research Center (RSRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.
| | - Cassandra Roch
- Marine Science Program, Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
- Red Sea Research Center (RSRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Carlos M Duarte
- Marine Science Program, Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.
- Red Sea Research Center (RSRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.
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Bland LM, Regan TJ, Dinh MN, Ferrari R, Keith DA, Lester R, Mouillot D, Murray NJ, Nguyen HA, Nicholson E. Using multiple lines of evidence to assess the risk of ecosystem collapse. Proc Biol Sci 2018; 284:rspb.2017.0660. [PMID: 28931744 PMCID: PMC5627190 DOI: 10.1098/rspb.2017.0660] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Accepted: 08/10/2017] [Indexed: 11/23/2022] Open
Abstract
Effective ecosystem risk assessment relies on a conceptual understanding of ecosystem dynamics and the synthesis of multiple lines of evidence. Risk assessment protocols and ecosystem models integrate limited observational data with threat scenarios, making them valuable tools for monitoring ecosystem status and diagnosing key mechanisms of decline to be addressed by management. We applied the IUCN Red List of Ecosystems criteria to quantify the risk of collapse of the Meso-American Reef, a unique ecosystem containing the second longest barrier reef in the world. We collated a wide array of empirical data (field and remotely sensed), and used a stochastic ecosystem model to backcast past ecosystem dynamics, as well as forecast future ecosystem dynamics under 11 scenarios of threat. The ecosystem is at high risk from mass bleaching in the coming decades, with compounding effects of ocean acidification, hurricanes, pollution and fishing. The overall status of the ecosystem is Critically Endangered (plausibly Vulnerable to Critically Endangered), with notable differences among Red List criteria and data types in detecting the most severe symptoms of risk. Our case study provides a template for assessing risks to coral reefs and for further application of ecosystem models in risk assessment.
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Affiliation(s)
- Lucie M Bland
- Deakin University, Australia, School of Life and Environmental Sciences, Centre for Integrative Ecology, Burwood, 3121, Victoria, Australia .,School of BioSciences, The University of Melbourne, Parkville, 3010, Victoria, Australia
| | - Tracey J Regan
- The Arthur Rylah Institute for Environmental Research, the Department of Environment, Land, Water and Planning, 123 Brown Street, Heidelberg, 3084, Victoria, Australia
| | - Minh Ngoc Dinh
- Research Computing Centre, University of Queensland, St Lucia, 4072, Queensland, Australia
| | - Renata Ferrari
- Coastal and Marine Ecosystems Group, School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia
| | - David A Keith
- Centre for Ecosystem Science, School of Biological, Earth and Environmental Science, University of New South Wales, Kensington, 2052, New South Wales, Australia.,New South Wales Office of Environment and Heritage, Hurstville, 2220, New South Wales, Australia.,Long Term Ecological Research Network, Terrestrial Ecosystem Research Network, Australian National University, Canberra, 0200, Australian Capital Territory, Australia
| | - Rebecca Lester
- Deakin University, Australia, Centre for Regional and Rural Futures, Geelong, 3220, Victoria, Australia
| | - David Mouillot
- UMR 5119-Écologie des Systèmes marins côtiers, Université Montpellier 2, Montpellier Cedex 5, France.,ARC Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, 4881, Queensland, Australia
| | - Nicholas J Murray
- Centre for Ecosystem Science, School of Biological, Earth and Environmental Science, University of New South Wales, Kensington, 2052, New South Wales, Australia
| | - Hoang Anh Nguyen
- Research Computing Centre, University of Queensland, St Lucia, 4072, Queensland, Australia
| | - Emily Nicholson
- Deakin University, Australia, School of Life and Environmental Sciences, Centre for Integrative Ecology, Burwood, 3121, Victoria, Australia
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Ligmann-Zielinska A, Kramer DB, Spence Cheruvelil K, Soranno PA. Using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance. PLoS One 2014; 9:e109779. [PMID: 25340764 PMCID: PMC4207681 DOI: 10.1371/journal.pone.0109779] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2014] [Accepted: 09/06/2014] [Indexed: 11/19/2022] Open
Abstract
Agent-based models (ABMs) have been widely used to study socioecological systems. They are useful for studying such systems because of their ability to incorporate micro-level behaviors among interacting agents, and to understand emergent phenomena due to these interactions. However, ABMs are inherently stochastic and require proper handling of uncertainty. We propose a simulation framework based on quantitative uncertainty and sensitivity analyses to build parsimonious ABMs that serve two purposes: exploration of the outcome space to simulate low-probability but high-consequence events that may have significant policy implications, and explanation of model behavior to describe the system with higher accuracy. The proposed framework is applied to the problem of modeling farmland conservation resulting in land use change. We employ output variance decomposition based on quasi-random sampling of the input space and perform three computational experiments. First, we perform uncertainty analysis to improve model legitimacy, where the distribution of results informs us about the expected value that can be validated against independent data, and provides information on the variance around this mean as well as the extreme results. In our last two computational experiments, we employ sensitivity analysis to produce two simpler versions of the ABM. First, input space is reduced only to inputs that produced the variance of the initial ABM, resulting in a model with output distribution similar to the initial model. Second, we refine the value of the most influential input, producing a model that maintains the mean of the output of initial ABM but with less spread. These simplifications can be used to 1) efficiently explore model outcomes, including outliers that may be important considerations in the design of robust policies, and 2) conduct explanatory analysis that exposes the smallest number of inputs influencing the steady state of the modeled system.
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Affiliation(s)
- Arika Ligmann-Zielinska
- Department of Geography, Michigan State University, East Lansing, Michigan, United States of America
| | - Daniel B. Kramer
- James Madison College, Michigan State University, East Lansing, Michigan, United States of America
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan, United States of America
| | - Kendra Spence Cheruvelil
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan, United States of America
- Lyman Briggs College, Michigan State University, East Lansing, Michigan, United States of America
| | - Patricia A. Soranno
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan, United States of America
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