Western Indian Ocean coral diversity observations from 1998–2022

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.

珊瑚礁正面临气候变化的严重威胁，因此有效的保护政策与管理手段，亟需针对保护投入开展空间优先级划分。本研究旨在构建空间显式生态模型，以在约6平方千米的中等尺度下，预测2020年当前及2050年的珊瑚类群数量。研究针对西印度洋7039个已测绘的珊瑚礁网格单元，构建了珊瑚群落属性的机器学习预测模型；该模型以35套空间完整且具有影响力的环境代理变量为基础，依托约1000份野外调查数据完成训练。本次共构建四类模型，分别探究气候变化、水质、直接人类资源开发以及变量选择流程对珊瑚类群数量的影响。另有两组预测模型，分别对全变量的预测结果进行检验，并将其分别与仅包含8个常用变量的限制性气候变化模型、以及仅包含9个变量的人类影响模型进行对比。在所有模型中，被频繁选中的温度变量包括温度中位数、偏度、超额热、升温速率以及峰度。但诸如观测者因子、水深、波浪能、溶解氧、盐度、叶绿素a、方解石浓度、日照以及净初级生产力等非温度变量，其重要性同样突出，甚至更为显著。国家管辖范围、距人类聚居区距离、沉积物与营养盐等人类影响因子虽被纳入模型，但相较于气候变化模型与全变量模型，其影响力相对较弱。模型对比结果表明，在预测气候变化与人类活动对珊瑚多样性的影响时，变量预筛选流程与变量交互作用均具有关键意义。对比中等典型浓度路径（Representative Concentration Pathway, RCP）2.6与极端典型浓度路径RCP8.5下的气候情景可知，在未来30年间，珊瑚类群数量的损失幅度（RCP2.6为5.2%，RCP8.5为8.1%）低于珊瑚覆盖度的损失幅度（RCP2.6为14%，RCP8.5为34%）。政府间气候变化专门委员会（Intergovernmental Panel on Climate Change, IPCC）的预测中所采用的超额热与升温速率变量，其预测得出的珊瑚负面影响程度高于本研究的四类模型，但本研究结果显示，上述变量的影响仅处于低至中等水平。