Systematic review of the uncertainty of coral reef futures under climate change, datasets
Data files
Feb 27, 2024 version files 121.05 KB
Abstract
Climate change impact syntheses, such as those by the Intergovernmental Panel on Climate Change (IPCC), consistently assert that limiting global warming to 1.5°C is unlikely to safeguard most of the world’s coral reefs. This prognosis primarily stems from 'excess heat’ threshold models, which assume that widespread coral bleaching predictably occurs when temperatures accumulate beyond a specific threshold. Our systematic review of research projecting coral reef futures to climate change (n=79) revealed that 'excess heat' models constituted only one third (32%) of all studies but attracted a high proportion (68%) of citations in the field. We observed that most methods employed deterministic cause-and-effect rules rather than probabilistic relationships, impeding the field's ability to estimate uncertainties of coral reef futures. In attempting to assess the consistency of projected impacts, we aimed to identify common coral reef metrics under the same emissions scenarios. However, disparate choices in metrics and emissions scenarios hindered a cohesive synthesis and limited the exploratory analysis to a small fraction of available studies. We found substantial discrepancies in expected impacts to coral reefs, suggesting that some 'excess heat' models may project more extreme impacts than other methods. Drawing on lessons from the field of climate change science, we propose that an IPCC ensemble-like approach to generating probabilistic projections for coral reef futures is feasible. Successful implementation will require improved coordination among modeling efforts to select common output metrics and emission scenarios, addressing existing geographical biases, among other gaps in current modeling efforts.
README: Systematic review of the uncertainty of coral reef futures under climate change, datasets
Published paper resulting from this data can be found at: https://doi.org/10.1038/s41467-024-46255-2
Summary
This study conducted a systematic review of 79 published articles projecting coral reef responses to future climate change. This dataset contains qualitative and quantitative data extracted from the published studies, including model types, geographic focus, and projected impacts on coral reefs.
Description of the data and file structure
Supplementary Data File
Extracted Data: Source data for effect size calculations (n=8 published studies).
- Short.reference used to identify the published study from which the data were extracted. See Full Reference List within this Read.Me file
- Scenario.ID identifies the individual scenario within each published study, numbered sequentially as scenario 1 (S1), scenario 2 (S2)
- N.c is n/number of model runs for control scenario
- N.e is n/number of model runs for future end-of-century (experimental) scenario
- M.c is the Model estimate for baseline scenario
- M.e is the model estimate of end of century projections
- Sd.c is the standard deviation of end of century projection estimates
- Sd.e is the standard deviation of the baseline scenario estimates
Supplementary_Data1
Summary Database: Overview of the dataset including study details, geographic focus, spatial scale, modeling approach, and examined stressors.
- Author(s) describes the authors of the published studies from which the data were extracted. See Full Reference List within this Read.Me file
- Year refers to the year of publication of the published studies
- Ref number identifies the full reference in the Full Reference List within this Read.Me file
- Approach type classified the models into five broad categories of methodologies: (a) ‘excess heat’/threshold models, (b) population dynamic models, (c) species distribution models, (d) ecological-evolutionary models, and (e) projective meta-analyses of published data (see published study for formal definitions). In a few cases where studies could not be categorized, the model type was recorded as ‘other’
- Focal projection(s) units is the unit in which the published studies delivered their projections
- Spatial scale refers to the spatial scale of the projections published, classified as either regional or global
- Geographic focus refers to the region the projections were formulated for (e.g. Great Barrier Reef, Australia)
- Major stressor(s) examined refer to the main drivers that were used to parameterize the models (e.g. warming, ocean acidification)
Supplementary_Data2
Complete Database: Detailed information from all 79 reviewed studies (qualitative characteristics)
- Unique_ID is a random unique ID assigned to each of the published papers within the dataset
- Author_list is a comprehensive list of all authors of the published studies within the dataset
- Article_ttle is the title of the published article
- Source_journal is the scientific journal in which the article was published
- Publication_year refers to the year of publication of the published studies
- Times_cited is the number of citations received by the published studies according to the Thomson Reuters Web of Science database on March 6, 2023.
- Model_category classified the models into five broad categories of methodologies: (a) ‘excess heat’/threshold models, (b) population dynamic models, (c) species distribution models, (d) ecological-evolutionary models, and (e) meta-analyses of published data (see published study for formal definitions). In a few cases where studies could not be categorized, the model type was recorded as ‘other’
- Model_technique refers to the method used to model heat stress (thermal threshold technique versus continuous variable technique). For studies to be classified as threshold techniques, the use of these metrics had to form the primary framework of the models that delivered projections. The second technique represents approaches that abandon the central threshold concept to focus on empirical relationships between continuous variables.
- if_TM_Threshold type records the type of thermal threshold used. N/a is used when the study did not use a thermal threshold, or it was not clearly reported.
- Focal_projection_unit records the units in which the published studies delivered their projections.
- Spatial_scale refers to the spatial scale of the projections published, classified as either regional or global.
- Reported_geographic_focus refers to the region the projections were formulated for (e.g. Great Barrier Reef, Australia)
- Drivers_used_summary records a summary of drivers used to parameterise the models.
- Underlying model structure/ description is a summary of the model structure and its purpose
- Key_assumptions is a description of the main assumptions made by the model
- Future_scenarios_examined refers to the exact future emissions pathways used
- Model_geographic_resolution records the spatial resolution of the model output
- Downscaled_yes_no records yes for when downscaling techniques were used to improve spatial resolution and no when downscaling techniques were not used
- Downscaled_method records which type of downscaling technique was used (either statistical or dynamic). N/A is used when the study did not use a downscaling technique
- Study_purpose is a summary of the published study's aims and its findings
- Study advantages is a synthesis of the published study's key advantages
- Study_gaps is a synthesis of the published study's key limitations
Supplementary_Data 3
Exploratory Meta-analysis Database: Scenario descriptions for data included in the effect size analysis.
- Author(s) describes the authors of the published studies from which the data were extracted. See Full Reference List within this Read.Me file
- Year refers to the year of publication of the published studies
- Ref is a number that identifies the full reference in the Full Reference List within this Read.Me file
- Scenario identifies the individual scenario within each published study, numbered sequentially as scenario 1 (S1), scenario 2 (S2)
- Scenario description is a summary of the future scneario modelled
- Reported warming refers to the future emissions pathway used to model future warming
- Classified warming categorizes these warming levels into different scenarios of 1.5 - 2ºC, 2 - 4ºC, and >4ºC represent projections at the end-of-century (years 2090-2100)
- Reported projection unit is the unit in which the published studies delivered their projections
- Classified projection unit represents the categories in which the projection units were analysed (e.g. % reef cells at risk)
Klein_et_al.,_2024
- R script: Script used for exploratory meta-analysis
Reference List
We use numbers that reference the sources we used to collect our data. Below is a list of the sources and their corresponding numbers.
Supplementary References
1 Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372, n71, doi:10.1136/bmj.n71 (2021).
2 Khalil, I., Muslim, A. M., Hossain, M. S. & Atkinson, P. M. Modelling and forecasting the effects of increasing sea surface temperature on coral bleaching in the Indo-Pacific region. International Journal of Remote Sensing 44, 194-216 (2023).
3 Abe, H., Kumagai, N. H. & Yamano, H. Priority coral conservation areas under global warming in the Amami Islands, Southern Japan. Coral Reefs 41, 1637-1650 (2022).
4 Sully, S., Hodgson, G. & van Woesik, R. Present and future bright and dark spots for coral reefs through climate change. Global Change Biology 28, 4509-4522, doi:https://doi.org/10.1111/gcb.16083 (2022).
5 DeFilippo, L. B. et al. Assessing the potential for demographic restoration and assisted evolution to build climate resilience in coral reefs. Ecological applications 32, e2650 (2022).
6 Holstein, D. M., Smith, T. B., van Hooidonk, R. & Paris, C. B. Predicting coral metapopulation decline in a changing thermal environment. Coral Reefs 41, 961-972, doi:10.1007/s00338-022-02252-9 (2022).
7 Raharinirina, N. A., Acevedo-Trejos, E. & Merico, A. Modelling the acclimation capacity of coral reefs to a warming ocean. PLOS Computational Biology 18, e1010099 (2022).
8 Chollett, I. et al. Planning for resilience: Incorporating scenario and model uncertainty and trade‐offs when prioritizing management of climate refugia. Global Change Biology 28, 4054-4068 (2022).
9 Setter, R. O., Franklin, E. C. & Mora, C. Co-occurring anthropogenic stressors reduce the timeframe of environmental viability for the world’s coral reefs. PLOS Biology 20, e3001821, doi:10.1371/journal.pbio.3001821 (2022).
10 McWhorter, J. K., Halloran, P. R., Roff, G., Skirving, W. J. & Mumby, P. J. Climate refugia on the Great Barrier Reef fail when global warming exceeds 3° C. Global Change Biology 28, 5768-5780 (2022).
11 Kalmus, P., Ekanayaka, A., Kang, E., Baird, M. & Gierach, M. Past the precipice? Projected coral habitability under global heating. Earth's Future 10, e2021EF002608 (2022).
12 McWhorter, J. K. et al. The importance of 1.5°C warming for the Great Barrier Reef. Global Change Biology 28, 1332-1341, doi:https://doi.org/10.1111/gcb.15994 (2022).
13 Klein, S. G. et al. Projecting coral responses to intensifying marine heatwaves under ocean acidification. Global Change Biology n/a, doi:https://doi.org/10.1111/gcb.15818 (2021).
14 Adam, A. A. et al. Diminishing potential for tropical reefs to function as coral diversity strongholds under climate change conditions. Diversity and Distributions 27, 2245-2261 (2021).
15 Cant, J. et al. The projected degradation of subtropical coral assemblages by recurrent thermal stress. Journal of Animal Ecology 90, 233-247 (2021).
16 Principe, S. C., Acosta, A. L., Andrade, J. E. & Lotufo, T. M. Predicted shifts in the distributions of Atlantic reef-building corals in the face of climate change. Frontiers in Marine Science 8, 673086 (2021).
17 Strona, G. et al. Global tropical reef fish richness could decline by around half if corals are lost. Proceedings of the Royal Society B 288, 20210274 (2021).
18 Bleuel, J., Pennino, M. G. & Longo, G. O. Coral distribution and bleaching vulnerability areas in Southwestern Atlantic under ocean warming. Scientific Reports 11, 1-12 (2021).
19 Cornwall, C. E. et al. Global declines in coral reef calcium carbonate production under ocean acidification and warming. Proceedings of the National Academy of Sciences 118, e2015265118, doi:doi:10.1073/pnas.2015265118 (2021).
20 McManus, L. C. et al. Evolution and connectivity influence the persistence and recovery of coral reefs under climate change in the Caribbean, Southwest Pacific, and Coral Triangle. Global change biology 27, 4307-4321 (2021).
21 McClanahan, T. R. & Azali, M. K. Environmental Variability and Threshold Model’s Predictions for Coral Reefs. Frontiers in Marine Science 8, doi:10.3389/fmars.2021.778121 (2021).
22 Zuo, X. et al. Spatially Modeling the Synergistic Impacts of Global Warming and Sea-Level Rise on Coral Reefs in the South China Sea. Remote Sensing 13, 2626 (2021).
23 McManus, L. C. et al. Extreme temperature events will drive coral decline in the Coral Triangle. Global Change Biology 26, 2120-2133 (2020).
24 Rodriguez, L., García, J. J., Tuya, F. & Martínez, B. Environmental factors driving the distribution of the tropical coral Pavona varians: predictions under a climate change scenario. Marine Ecology 41, 1-12 (2020).
25 Cacciapaglia, C. W. & van Woesik, R. Reduced carbon emissions and fishing pressure are both necessary for equatorial coral reefs to keep up with rising seas. Ecography 43, 789-800, doi:https://doi.org/10.1111/ecog.04949 (2020).
26 Matz, M. V., Treml, E. A. & Haller, B. C. Estimating the potential for coral adaptation to global warming across the Indo‐West Pacific. Global Change Biology (2020).
27 Kubicek, A., Breckling, B., Hoegh-Guldberg, O. & Reuter, H. Climate change drives trait-shifts in coral reef communities. Scientific Reports 9, 3721, doi:10.1038/s41598-019-38962-4 (2019).
28 Rodriguez, L., Martínez, B. & Tuya, F. Atlantic corals under climate change: modelling distribution shifts to predict richness, phylogenetic structure and trait-diversity changes. Biodiversity and Conservation 28, 3873-3890, doi:10.1007/s10531-019-01855-z (2019).
29 Jones, L. A. et al. Coupling of palaeontological and neontological reef coral data improves forecasts of biodiversity responses under global climatic change. Royal Society Open Science 6, 182111 (2019).
30 Yan, H. et al. Regional coral growth responses to seawater warming in the South China Sea. Science of the total environment 670, 595-605 (2019).
31 Woesik, R. v., Köksal, S., Ünal, A., Cacciapaglia, C. W. & Randall, C. J. Predicting coral dynamics through climate change. Scientific reports 8, 17997 (2018).
32 Wolff, N. H., Mumby, P. J., Devlin, M. & Anthony, K. R. N. Vulnerability of the Great Barrier Reef to climate change and local pressures. Global Change Biology 24, 1978-1991, doi:10.1111/gcb.14043 (2018).
33 Cacciapaglia, C. & van Woesik, R. Marine species distribution modelling and the effects of genetic isolation under climate change. Journal of Biogeography 45, 154-163 (2018).
34 Kornder, N. A., Riegl, B. M. & Figueiredo, J. Thresholds and drivers of coral calcification responses to climate change. Global Change Biology 24, 5084-5095, doi:https://doi.org/10.1111/gcb.14431 (2018).
35 Langlais, C. et al. Coral bleaching pathways under the control of regional temperature variability. Nature Climate Change 7, 839-844 (2017).
36 Kendall, M. S., Poti, M. & Karnauskas, K. B. Climate change and larval transport in the ocean: fractional effects from physical and physiological factors. Global Change Biology 22, 1532-1547, doi:https://doi.org/10.1111/gcb.13159 (2016).
37 Yara, Y. et al. Potential future coral habitats around Japan depend strongly on anthropogenic CO 2 emissions. Aquatic biodiversity conservation and ecosystem services, 41-56 (2016).
38 Van Hooidonk, R. et al. Local-scale projections of coral reef futures and implications of the Paris Agreement. Scientific reports 6, 39666 (2016).
39 Schleussner, C.-F. et al. Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 C and 2 C. Earth system dynamics 7, 327-351 (2016).
40 Ainsworth, T. D. et al. Climate change disables coral bleaching protection on the Great Barrier Reef. Science 352, 338-342, doi:doi:10.1126/science.aac7125 (2016).
41 Cooper, J. K., Spencer, M. & Bruno, J. F. Stochastic dynamics of a warmer Great Barrier Reef. Ecology 96, 1802-1811 (2015).
42 Bozec, Y.-M. & Mumby, P. J. Synergistic impacts of global warming on the resilience of coral reefs. Philosophical Transactions of the Royal Society B: Biological Sciences 370, 20130267 (2015).
43 Bozec, Y. M., Alvarez‐Filip, L. & Mumby, P. J. The dynamics of architectural complexity on coral reefs under climate change. Global change biology 21, 223-235 (2015).
44 van Hooidonk, R., Maynard, J. A., Liu, Y. & Lee, S. K. Downscaled projections of Caribbean coral bleaching that can inform conservation planning. Global change biology 21, 3389-3401 (2015).
45 Kwiatkowski, L., Cox, P., Halloran, P. R., Mumby, P. J. & Wiltshire, A. J. Coral bleaching under unconventional scenarios of climate warming and ocean acidification. Nature Climate Change 5, 777-781 (2015).
46 Maynard, J. et al. Projections of climate conditions that increase coral disease susceptibility and pathogen abundance and virulence. Nature Climate Change 5, 688-694 (2015).
47 Descombes, P. et al. Forecasted coral reef decline in marine biodiversity hotspots under climate change. Global Change Biology 21, 2479-2487 (2015).
48 Freeman, L. A. Robust performance of marginal Pacific coral reef habitats in future climate scenarios. PLoS One 10, e0128875 (2015).
49 Cacciapaglia, C. & van Woesik, R. Reef‐coral refugia in a rapidly changing ocean. Global Change Biology 21, 2272-2282 (2015).
50 Mumby, P. J., Wolff, N. H., Bozec, Y.-M., Chollett, I. & Halloran, P. Operationalizing the Resilience of Coral Reefs in an Era of Climate Change. Conservation Letters 7, 176-187, doi:https://doi.org/10.1111/conl.12047 (2014).
51 Yara, Y., Fujii, M., Yamano, H. & Yamanaka, Y. Projected coral bleaching in response to future sea surface temperature rises and the uncertainties among climate models. Hydrobiologia 733, 19-29 (2014).
52 Logan, C. A., Dunne, J. P., Eakin, C. M. & Donner, S. D. Incorporating adaptive responses into future projections of coral bleaching. Global Change Biology 20, 125-139 (2014).
53 van Hooidonk, R., Maynard, J. A., Manzello, D. & Planes, S. Opposite latitudinal gradients in projected ocean acidification and bleaching impacts on coral reefs. Global Change Biology 20, 103-112, doi:https://doi.org/10.1111/gcb.12394 (2014).
54 Ortiz, J. C., González-Rivero, M. & Mumby, P. J. An ecosystem-level perspective on the host and symbiont traits needed to mitigate climate change impacts on Caribbean coral reefs. Ecosystems 17, 1-13 (2014).
55 Lane, D. R. et al. Quantifying and valuing potential climate change impacts on coral reefs in the United States: Comparison of two scenarios. PloS one 8, e82579 (2013).
56 Kennedy, E. V. et al. Avoiding coral reef functional collapse requires local and global action. Current Biology 23, 912-918 (2013).
57 van Hooidonk, R., Maynard, J. A. & Planes, S. Temporary refugia for coral reefs in a warming world. Nature Climate Change 3, 508-511, doi:10.1038/nclimate1829 (2013).
58 Frieler, K. et al. Limiting global warming to 2 C is unlikely to save most coral reefs. Nature Climate Change 3, 165 (2013).
59 Ortiz, J. C., González‐Rivero, M. & Mumby, P. J. Can a thermally tolerant symbiont improve the future of Caribbean coral reefs? Global change biology 19, 273-281 (2013).
60 Freeman, L. A., Kleypas, J. A. & Miller, A. J. Coral reef habitat response to climate change scenarios. PloS one 8, e82404 (2013).
61 Couce, E., Ridgwell, A. & Hendy, E. J. Future habitat suitability for coral reef ecosystems under global warming and ocean acidification. Global Change Biology 19, 3592-3606, doi:https://doi.org/10.1111/gcb.12335 (2013).
62 Couce, E., Irvine, P. J., Gregoire, L., Ridgwell, A. & Hendy, E. Tropical coral reef habitat in a geoengineered, high‐CO2 world. Geophysical Research Letters 40, 1799-1805 (2013).
63 Wooldridge, S. A. et al. Safeguarding coastal coral communities on the central Great Barrier Reef (Australia) against climate change: realizable local and global actions. Climatic Change 112, 945-961 (2012).
64 Meissner, K., Lippmann, T. & Sen Gupta, A. Large-scale stress factors affecting coral reefs: open ocean sea surface temperature and surface seawater aragonite saturation over the next 400 years. Coral Reefs 31, 309-319 (2012).
65 van Hooidonk, R. & Huber, M. Effects of modeled tropical sea surface temperature variability on coral reef bleaching predictions. Coral Reefs 31, 121-131, doi:10.1007/s00338-011-0825-4 (2012).
66 Teneva, L. et al. Predicting coral bleaching hotspots: the role of regional variability in thermal stress and potential adaptation rates. Coral Reefs 31, 1-12 (2012).
67 Yara, Y. et al. Ocean acidification limits temperature-induced poleward expansion of coral habitats around Japan. Biogeosciences 9, 4955-4968 (2012).
68 Edwards, H. J. et al. How much time can herbivore protection buy for coral reefs under realistic regimes of hurricanes and coral bleaching? Global Change Biology 17, 2033-2048 (2011).
69 Anthony, K. R. N. et al. Ocean acidification and warming will lower coral reef resilience. Global Change Biology 17, 1798-1808, doi:https://doi.org/10.1111/j.1365-2486.2010.02364.x (2011).
70 Hoegh-Guldberg, O. Coral reef ecosystems and anthropogenic climate change. Regional Environmental Change 11, 215-227 (2011).
71 Hoeke, R. K., Jokiel, P. L., Buddemeier, R. W. & Brainard, R. E. Projected changes to growth and mortality of Hawaiian corals over the next 100 years. PloS one 6, e18038 (2011).
72 McLeod, E. et al. Warming seas in the Coral Triangle: coral reef vulnerability and management implications. Coastal Management 38, 518-539 (2010).
73 Baskett, M. L., Gaines, S. D. & Nisbet, R. M. Symbiont diversity may help coral reefs survive moderate climate change. Ecological Applications 19, 3-17 (2009).
74 Vivekanandan, E., Ali, M. H., Jasper, B. & Rajagopalan, M. Vulnerability of corals to warming of the Indian seas: a projection for the 21st century. Current Science, 1654-1658 (2009).
75 Donner, S. D. Coping with commitment: projected thermal stress on coral reefs under different future scenarios. PLoS One 4, e5712 (2009).
76 Buddemeier, R. W. et al. A modeling tool to evaluate regional coral reef responses to changes in climate and ocean chemistry. Limnology and Oceanography: Methods 6, 395-411 (2008).
77 Donner, S. D., Skirving, W. J., Little, C. M., Oppenheimer, M. & Hoegh‐Guldberg, O. Global assessment of coral bleaching and required rates of adaptation under climate change. Global Change Biology 11, 2251-2265 (2005).
78 McNeil, B. I., Matear, R. J. & Barnes, D. J. Coral reef calcification and climate change: The effect of ocean warming. Geophysical Research Letters 31 (2004).
79 Guinotte, J., Buddemeier, R. & Kleypas, J. Future coral reef habitat marginality: temporal and spatial effects of climate change in the Pacific basin. Coral reefs 22, 551-558 (2003).
80 Hoegh-Guldberg, O. Climate change, coral bleaching and the future of the world's coral reefs. Marine and freshwater research 50, 839-866 (1999).
Methods
We conducted a comprehensive literature search using the Thomson Reuters Web of Science database to identify studies that projecting the impacts of climate change on shallow tropical and sub-tropical coral reefs. This search, adhering to PRISMA guidelines, yielded 2705 peer-reviewed articles, which we refined to 79 relevant articles published between 1999 and 2023 based on a specific selection criteria (Dataset 1). These studies were categorized into five major methodology types and further classified based on their approaches to simulating heat stress. Key characteristics such as the model output variables, spatial scale, and geographic area of each study were extracted, along with their methodological approaches, assumptions, and the techniques used.
Our study aimed to assess and compare the projected impacts and uncertainties of various model types using a meta-analysis approach. The database of 79 studies was considered for inclusion in the exploratory meta-analysis based on specific criteria (view published article and supplementary methods for detailed list and Supplementary Figure 1). Briefly, to enable a meaningful analysis, we identified the three most frequently used model outputs in our database. Among those, only studies that provided: 1) sufficient data for projection estimates and uncertainty measures to be reliably extracted or calculated, 2) reported end-of-century projections, and 3) used a baseline period between 2000 and 2015, were selected for the exploratory meta-analysis. In cases where projection and uncertainty estimates were presented in figures, values were extracted using PlotDigitizer, where possible.