DOI: 10.5061/dryad.3xsj3txn1
title: Western Indian Ocean coral diversity observations from 1998–2022

description: Coral reefs are threatened by climate change, thus effective policies and
 management require spatial prioritization for conservation investments.
 Our aim was to develop a spatially explicit ecological model to predict
 current (2020) and future (2050) numbers of coral taxa at moderate scales
 (i.e., ~6 km2). Our machine learning predictive models of coral community
 attributes in 7039 mapped reef cells in the western Indian Ocean were
 based on 35 spatially complete influential environmental proxies trained
 with ~1000 field surveys. Four models explored: influences of climate
 change, water quality, direct human-resource extraction, and variable
 selection processes on numbers of coral taxa. Two predictive models
 examined the predictions of all variables and compared them to a
 variable-restricted climate change (8 commonly used variables) and human
 influence model (9 variables). The most frequently selected temperature
 variables in all models were the median, skewness, excess heat, rate of
 temperature rise, and kurtosis. However, non-temperature variables of
 observer, depth, wave energy, dissolved oxygen, salinity, chlorophyll-a,
 calcite concentrations, sunlight, and net primary productivity were
 frequently as important or stronger. Human influences of national
 jurisdiction, distance to people, sediments, and nutrients were selected
 but less influential when compared to the climate or the full variable
 models. Comparing models indicated the importance of variable
 pre-selection processes and variable interactions in predicting climate
 change and human influences on coral diversity. Comparing climate
 scenarios in the moderate RCP2.6 and extreme RCP8.5 emission scenarios
 indicated fewer losses in coral taxa (RCP2.6 = 5.2%, RCP8.5 = 8.1%
 respectively) relative to cover (RCP2.6 = 14%, RCP8.5 = 34%) over the 30
 years. Excess heat and rate of temperature rise variables used in the
 Intergovernmental Panel on Climate Change (IPCC) forecasts predict more
 negative effects on corals than our four models but shown here to have low
 to modest effects.
description: Corals were sampled haphazardly while snorkeling or scuba diving
 in either visually estimated or measured quadrats of ~2
 m<sup>2</sup> where all corals &gt;5-cm were identified
 and counted in ~15-20 replicates (McClanahan et al. 2007). We also
 recorded the depth and habitats of the sites as reef edge, reef crest,
 reef flat, or reef lagoon. Thus, the values used here were the total
 number of taxa in ~40 m<sup>2</sup>. Taxa identification was
 to the genus level, but <em>Porites</em> colonies were
 distinguished further as massive, branching, or <em>Porites
 rus</em> and <em>Galaxea</em> as either <em>G.
 astreata </em>or <em>G. fascicularis</em>. A total of 67
 taxa in 1001 sites were sampled in 6 of the ecoregions. Sixteen observers
 contributed to the database, but most contributed few sites and three
 observers combined sampled 939 of the 1001 sites and had non-significant
 differences between them. We used one of the latest iterations of coral
 reef maps to establish reef distribution patterns
 (https://data.unep-wcmc.org/datasets/1). Specifically, we used the map of
 the Western Indian Ocean O composed of ~7039 6-km<sup>2</sup>
 cells in 9 ecoregions, namely the Northern Monsoon Current Coast, East
 African Coral Coast, Seychelles, Cargados Carajos/Tromelin Island,
 Mascarene Islands, Southeast Madagascar, Western and Northern Madagascar,
 Bight of Sofala/Swamp Coast, and Delagoa. Empirical data were pooled to
 the 6-km<sup>2</sup> reef cells over the entire sampling
 period (1998 – 2022). Environmental data compilations accessed several
 sources from satellite and shipboard measurements. Environmental layers
 included those expected to influence marine organisms including
 oceanographic layers such as wave energy, photosynthetic active radiation
 (PAR), light diffusion attenuation, pH, calcite, dissolved oxygen,
 salinity, net primary productivity, chlorophyll-a, and phytoplankton
 carbon. Additionally, several water temperature or thermal stress metrics
 known to influence chronic and acute stress on marine organisms were
 calculated including SST mean, median, range, standard deviation,
 skewness, kurtosis, rate of rise, and cumulative degree-heating weeks. Two
 multivariate integrated metrics of thermal stress and water quality were
 included. Finally, we used several layers that measure connectivity,
 including average net flow, indegree, outdegree, and retention metrics for
 each reef cell. Human influence variables included several
 geographic variables including nation, wilderness (&gt;4 hours travel
 time from human population), travel distance to people, shore and ports,
 and market gravity or the number of people living on the shore or cities
 as divided by the square of the distance or travel time. The ecoregion was
 also included. Reefs were assigned 4 fisheries management categories
 including unrestricted fishing, restricted fishing, low compliance
 closures, and high compliance closures.  These classifications were based
 on information in published literature, the experience of the data
 providers, and discussions with knowledgeable observers.