Detecting interspecific macroparasite interactions from ecological data: patterns and process
Detecting interspecific macroparasite interactions from ecological data: patterns and process
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DOI：
10.1111/j.1461-0248.2010.01458.x
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发表时间：
2010-05-01
期刊：
影响因子：
8.8
通讯作者：
Lello, Jo
中科院分区：
文献类型：
--
作者：
Fenton, Andy;Viney, Mark E.;Lello, Jo
人们对共感染寄生虫种间相互作用的发生和后果非常感兴趣。然而，相互作用发生的程度是未知的，因为没有有效的方法来检测它们。我们开发了一个模型，生成了两种相互作用的大型寄生虫（如蠕虫）物种的丰度数据，并用各种方法对数据进行了挑战，以确定它们是否可以检测到潜在的相互作用。目前的方法表现不佳——要么表明没有互动，而实际上，有很强的互动发生，要么在没有互动的情况下推断出互动的存在。我们建议采用基于广义线性混合模型（GLMM）的新方法，即使两种寄生虫的感染率相关（例如，通过共享传播途径），我们也证明该方法比当前方法更可靠。我们认为，在自然系统中存在或不存在相互作用的缺乏明确性可能主要归因于现有检测它们的方法的不可靠性。然而，GLMM方法的应用可以从生态数据中为这些潜在重要的种间相互作用提供更可靠的检测方法。
P>There is great interest in the occurrence and consequences of interspecific interactions among co-infecting parasites. However, the extent to which interactions occur is unknown, because there are no validated methods for their detection. We developed a model that generated abundance data for two interacting macroparasite (e.g., helminth) species, and challenged the data with various approaches to determine whether they could detect the underlying interactions. Current approaches performed poorly - either suggesting there was no interaction when, in reality, there was a strong interaction occurring, or inferring the presence of an interaction when there was none. We suggest the novel application of a generalized linear mixed modelling (GLMM)-based approach, which we show to be more reliable than current approaches, even when infection rates of both parasites are correlated (e.g., via a shared transmission route). We suggest that the lack of clarity regarding the presence or absence of interactions in natural systems may be largely attributed to the unreliable nature of existing methods for detecting them. However, application of the GLMM approach may provide a more robust method of detection for these potentially important interspecific interactions from ecological data.