School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand.
PLoS One. 2010 Feb 17;5(2):e9264. doi: 10.1371/journal.pone.0009264.
Diseases often display complex and distinct associations with their environment due to differences in etiology, modes of transmission between hosts, and the shifting balance between pathogen virulence and host resistance. Statistical modeling has been underutilized in coral disease research to explore the spatial patterns that result from this triad of interactions. We tested the hypotheses that: 1) coral diseases show distinct associations with multiple environmental factors, 2) incorporating interactions (synergistic collinearities) among environmental variables is important when predicting coral disease spatial patterns, and 3) modeling overall coral disease prevalence (the prevalence of multiple diseases as a single proportion value) will increase predictive error relative to modeling the same diseases independently. Four coral diseases: Porites growth anomalies (PorGA), Porites tissue loss (PorTL), Porites trematodiasis (PorTrem), and Montipora white syndrome (MWS), and their interactions with 17 predictor variables were modeled using boosted regression trees (BRT) within a reef system in Hawaii. Each disease showed distinct associations with the predictors. Environmental predictors showing the strongest overall associations with the coral diseases were both biotic and abiotic. PorGA was optimally predicted by a negative association with turbidity, PorTL and MWS by declines in butterflyfish and juvenile parrotfish abundance respectively, and PorTrem by a modal relationship with Porites host cover. Incorporating interactions among predictor variables contributed to the predictive power of our models, particularly for PorTrem. Combining diseases (using overall disease prevalence as the model response), led to an average six-fold increase in cross-validation predictive deviance over modeling the diseases individually. We therefore recommend coral diseases to be modeled separately, unless known to have etiologies that respond in a similar manner to particular environmental conditions. Predictive statistical modeling can help to increase our understanding of coral disease ecology worldwide.
疾病通常由于病因、宿主间传播方式以及病原体毒力和宿主抗性之间平衡的变化而与环境呈现出复杂而独特的关联。在珊瑚疾病研究中，统计建模尚未得到充分利用，以探索由这三种相互作用产生的空间模式。我们检验了以下假设：1）珊瑚疾病与多种环境因素存在明显关联；2）在预测珊瑚疾病空间模式时，纳入环境变量之间的相互作用（协同共线性）非常重要；3）对整体珊瑚疾病流行率（多种疾病作为单一比例值的流行率）进行建模将增加预测误差，而独立建模相同疾病则不会。在夏威夷的一个珊瑚礁系统中，我们使用增强回归树（BRT）对四种珊瑚疾病（Porites 生长异常（PorGA）、Porites 组织损失（PorTL）、Porites 纤毛虫病（PorTrem）和 Montipora 白色综合征（MWS））及其与 17 个预测变量的相互作用进行了建模。每种疾病与预测因子都有明显的关联。与珊瑚疾病总体关联最强的环境预测因子既有生物因素也有非生物因素。PorGA 与浊度呈负相关，PorTL 和 MWS 分别与蝴蝶鱼和幼年鹦嘴鱼数量的减少呈负相关，PorTrem 与 Porites 宿主覆盖率呈模态关系。纳入预测变量之间的相互作用有助于提高我们模型的预测能力，特别是对于 PorTrem。合并疾病（使用整体疾病流行率作为模型响应）会导致交叉验证预测偏差平均增加六倍，而不是单独建模疾病。因此，我们建议单独对珊瑚疾病进行建模，除非已知病因以类似方式对特定环境条件作出反应。预测性统计建模可以帮助我们更好地了解全球范围内的珊瑚疾病生态学。
