Host-associated microbial communities are shaped by extrinsic and intrinsic factors to the holobiont organism. Environmental factors and microbe-microbe interactions act simultaneously on the microbial community structure, making the microbiome dynamics challenging to predict. The coral microbiome is essential to the health of coral reefs and sensitive to environmental changes. Here, we develop a dynamic model to determine the microbial community structure associated with the surface mucus layer (SML) of corals using temperature as an extrinsic factor and microbial network as an intrinsic factor. The model was validated by comparing the predicted relative abundances of microbial taxa to the relative abundances of microbial taxa from the sample data. The SML microbiome from Pseudodiploria strigosa was collected across reef zones in Bermuda, where inner and outer reefs are exposed to distinct thermal profiles. A shotgun metagenomics approach was used to describe the taxonomic composition and the microbial network of the coral SML microbiome. By simulating the annual temperature fluctuations at each reef zone, the model output is statistically identical to the observed data. The model was further applied to six scenarios that combined different profiles of temperature and microbial network to investigate the influence of each of these two factors on the model accuracy. The SML microbiome was best predicted by model scenarios with the temperature profile that was closest to the local thermal environment, regardless of the microbial network profile. Our model shows that the SML microbiome of P. strigosa in Bermuda is primarily structured by seasonal fluctuations in temperature at a reef scale, while the microbial network is a secondary driver.IMPORTANCE Coral microbiome dysbiosis (i.e., shifts in the microbial community structure or complete loss of microbial symbionts) caused by environmental changes is a key player in the decline of coral health worldwide. Multiple factors in the water column and the surrounding biological community influence the dynamics of the coral microbiome. However, by including only temperature as an external factor, our model proved to be successful in describing the microbial community associated with the surface mucus layer (SML) of the coral P. strigosa The dynamic model developed and validated in this study is a potential tool to predict the coral microbiome under different temperature conditions.

中文翻译：

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珊瑚微生物组的建模：温度和微生物网络的影响。

与宿主相关的微生物群落是由全生物体的外在和内在因素塑造的。环境因素和微生物之间的相互作用同时作用于微生物群落结构，使得微生物组动态难以预测。珊瑚微生物组对于珊瑚礁的健康至关重要，并且对环境变化敏感。在这里，我们开发了一个动态模型来确定与珊瑚表面粘液层（SML）相关的微生物群落结构，使用温度作为外在因素，微生物网络作为内在因素。通过将预测的微生物类群相对丰度与样本数据中微生物类群的相对丰度进行比较来验证模型。来自 Pseudodiploria strigosa 的 SML 微生物组是在百慕大的珊瑚礁区域收集的，其中内礁和外礁暴露于不同的热剖面。使用鸟枪法宏基因组学方法来描述珊瑚 SML 微生物组的分类组成和微生物网络。通过模拟每个珊瑚礁区域的年度温度波动，模型输出在统计上与观测数据相同。该模型进一步应用于结合不同温度和微生物网络的六种场景，以研究这两个因素对模型精度的影响。SML 微生物组最好通过具有最接近当地热环境的温度分布的模型场景来预测，无论微生物网络分布如何。我们的模型表明，百慕大 P. strigosa 的 SML 微生物组主要由珊瑚礁尺度温度的季节性波动构成，而微生物网络是次要驱动因素。 重要性 珊瑚微生物组失调（即微生物群落结构或完全改变）环境变化引起的微生物共生体的丧失是全球珊瑚健康下降的一个关键因素。水体和周围生物群落中的多种因素影响珊瑚微生物组的动态。然而，通过仅将温度作为外部因素，我们的模型被证明能够成功地描述与 P. strigosa 珊瑚表面粘液层 (SML) 相关的微生物群落。本研究中开发和验证的动态模型是一个潜在的工具预测不同温度条件下的珊瑚微生物组。