DOI: 10.5061/dryad.4f4qrfjkp
title: Systematic review of the uncertainty of coral reef futures under climate change, datasets

description: 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.
description: 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.<br><br>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.
description: # Systematic review of the uncertainty of coral reef futures under climate
 change, datasets Published paper resulting from this data can be found at:
 ## 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 &gt;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
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