Environmental Variability and Threshold Model’s Predictions for Coral Reefs

Tim Rice Mcclanahan,Maxwell Kodia Azali

doi:10.3389/fmars.2021.778121

Tim Rice Mcclanahan, Maxwell Kodia Azali

Open Access

https://doi.org/10.3389/fmars.2021.778121

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Abstract

Current models of the future of coral reefs rely on threshold (TM) and multivariate environmental variability models (VM) that vary in how they account for spatial and temporal environmental heterogeneity. Here, a VM based on General Additive Model (GAM) methods evaluated the empirical relationships between coral cover (n= 905 sites pooled to 318 reef cells of the Western and Central Indian Ocean Provinces) and 15 potentially influential variables. Six environmental and one fisheries management variables were selected as significant including SST shape distributions, dissolved oxygen, calcite, and fisheries management. Common predictive variables, including cumulative degree-heating weeks (DHW), pH, maximum light, SST bimodality and rate of rise, and two multivariate metrics were either weak or not significant predictors of coral cover. A spatially-resolved 2020 baseline for future predictions of coral cover within 11,678 reef ∼6.25 km2cells within 13 ecoregions and 4 fisheries management categories using the 7 top VM variables was established for comparing VM and TM coral cover prediction for the year 2050. We compared the two model’s predictions for high and low Relative Concentration Pathway (CMIP5; RCP8.5 and 2.6) scenarios using the four available future-cast SST variables. The excess heat (DHW)-coral mortality relationship of the TM predicted considerably lower coral cover in 2050 than the VM. For example, for the RCP8.5 and RCP2.6 scenarios, the decline in coral for the TM predicted was 81 and 58% compared to a 29 and 20% for the VM among reef cells with &gt;25% coral cover in 2020, if a proposed optimal fisheries management was achieved. Despite differences, coral cover predictions for the VM and TM overlapped in two environmental regions located in the southern equatorial current region of the Indian Ocean. Historical and future patterns of acute and chronic stresses are expected to be more influential than cumulative heat stress in predicting coral cover, which is better accounted for by the VM than the TM.

Highlights

The ecosystem services provided by coral reefs are under threat from a number of forces of which climate warming, eutrophication, and fishing are among the most wide-spread concerns (Cornwall et al, 2021; Donovan et al, 2021)
Are there missing but potentially important variables that are not being considered by the threshold model (TM) that might greatly modify the outcomes of climate change? What affects do the five TM simplifications above have on the ability to predict coral cover? In order to begin this process, we explored seldom considered variables that are currently spatially resolved and available to produce a variability models (VM) option
Sea-surface temperature distribution patterns in the western Indian Ocean (WIO) were highly variable, differentiated by most sea-surface temperature (SST) metrics, and indicated that sites clustered into six SST groups (Table 1 and Figure 1A)

Summary

Introduction

The ecosystem services provided by coral reefs are under threat from a number of forces of which climate warming, eutrophication, and fishing are among the most wide-spread concerns (Cornwall et al, 2021; Donovan et al, 2021). Predictions are frequently based on climate model’s predicted temperature deviations from historical warmest seasonal months from satellite temperature histories and rate of rise in seasurface temperatures and the production of future “excess heat” (Cornwall et al, 2021) This accumulated excess heat above +1◦C of warm-season temperatures is the current threshold model (TM) best-estimate of both the historical and future exposure of corals to thermal stress. Common modifications include between-year thermal thresholds, coral acclimation and genetic adaptation rates, light attenuation, and future climate model scenarios (Donner et al, 2005; McManus et al, 2020; Gonzalez-Espinosa and Donner, 2021) Most of this class of models can generally be described as a TM because their core predictions rely on a threshold to provoke the coral stress responses that often but not always coincident with +1◦C above summer mean temperatures (Donner, 2011)

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Conclusion