DOI: 10.5061/dryad.d51c5b03r
title: Diminishing potential for tropical reefs to function as coral diversity strongholds under climate change conditions

description: Aim: Forecasting the influence of climate change on coral biodiversity and
 reef functioning is important for informing policy decisions. Dominance
 shifts, tropicalisation and local extinctions are common responses of
 climate change, but uncertainty surrounds the reliability of predicted
 coral community transformations. Here, we use species distribution models
 (SDMs) to assess changes in suitable coral habitat and associated patterns
 in biodiversity across Western Australia (WA) under present-day and future
 climate scenarios (RCP 2.6 and RCP 8.5). Location: Coral reef systems in
 WA. Methods: We developed SDMs with model prediction uncertainty analyses,
 using specimen-based occurrence records of 188 hermatypic scleractinian
 coral species and seven variables to estimate present-day and future
 changes to coral species distribution and biodiversity patterns in WA
 under climate change conditions. Results: We found that suitable habitat
 is predicted to increase across all regions in WA under RCP2.6 2050,
 RCP8.5 2050 and RCP2.6 2100 scenarios with all tropical and subtropical
 regions remaining coral biodiversity strongholds. Under the extreme RCP8.5
 2100 scenario however, a clear tropicalisation trend could be observed
 with coral species expanding their range to mid-higher latitude regions,
 while a substantial drop in coral species richness was predicted at low
 latitude tropical coral reefs, such as the inshore Kimberley and offshore
 NW reefs. Despite the predicted expansion south, we identified a net
 decline in biodiversity across the WA coastline. Main Conclusions: Results
 from the models predicted higher net biodiversity loss at low latitude
 tropical regions compared to net gains at mid-high latitude regions under
 RCP8.5 2100. These results are likely to be representative of latitudinal
 trends across the southern hemisphere and highlight that increases in
 habitat suitability at higher latitudes may not lead to equivalent
 biodiversity benefits. Urgent action is needed to limit climate change to
 prevent spatial erosion of tropical coral communities, extinction events
 and loss of tropical ecosystem services.
description: <b>Model fitting,
 variable selection and parsimonious model evaluation</b>
 In order to estimate the
 present-day and future suitable coral habitat along the WA coastline, we
 used the Maximum Entropy (MaxEnt) algorithm in the ‘dismo’ R package
 (Hijmans et al., 2017). MaxEnt is one of the most common modelling
 approaches for SDMs and was chosen due to its ability to handle
 presence-only data (Elith et al., 2006; Elith et al., 2011; Phillips
 & Dudík, 2008). To avoid overprediction, only species with
 &gt; 10 spatially unique records were included (Wisz et al.,
 2008). Prior to
 constructing the present-day and future coral suitable habitat, models
 were fitted and evaluated for these 205 species using background spatial
 point customization (Merow et al., 2013), data splitting into train/test
 datasets (Araújo et al., 2005) and variable selection to construct the
 most parsimonious model (Austin & Van Niel, 2011). For each
 species modelled, 1,000 background spatial points were randomly selected
 across the study area, bounded by the Australian EEZ depth of £ 40 m.
 Before the background points were generated, a 9 km radius mask around
 known locations was applied to exclude background points close to the
 occurrence locations and avoid variable replication. A radius of 9 km was
 used based on the coarsest resolution of raw environmental and
 geomorphological variable data. Model performance for each
 species was evaluated with an independent test dataset, where the
 occurrence and background data were partitioned 75/25 into
 training/testing datasets (Araújo et al., 2005; Hastie et al., 2009; Merow
 et al., 2014; Vignali et al., 2020). Five replicate models were
 constructed on the training dataset with default model features and
 regularization settings, configured to select the highest contributing
 variables for the most parsimonious model (Elith et al., 2011). Average
 variable permutation importance across the five model replicates was used
 to exclude variables with a permutation importance &gt;= 1% in the
 most parsimonious model (Li et al., 2020; Sobek-Swant et al., 2012;
 Williams et al., 2012). The threshold independent and dependent metrics,
 area under the curve (AUC) (Beaumont et al., 2019) and sensitivity, were
 used to evaluate model performance on the withheld test data.
 <b>Model
 predictions</b> For every species, continuous
 habitat suitability predictions were converted into binary values
 (occurrence [1] and absence [0]) using the maximum sensitivity and
 specificity threshold (Liu et al., 2013). Total area of occurrences was
 converted to km<sup>2</sup> to determine the present-day and
 future suitable habitat. Shifts in suitable habitat were estimated by
 overlaying the future binary habitat suitability predictions with
 present-day binary predictions using package ‘biomod2’ (Thuiller et al.,
 2009). The R
 script details the model calibration, validation and prediction
 steps.
description: Sources of raw environmental and geomorphological data used to
 integrate into the models can be found in Table 1 in the main manuscript.
 Coral species occurrence museum records can be found in the Atlas of
 Living Australia (https://www.ala.org.au/) and upon request at the Western
 Australian Museum. The R script details the model
 calibration, validation and prediction steps as described in the method
 section of the manuscript.