Inferring Species Interactions From Co-occurrence Networks With Environmental DNA Metabarcoding Data in a Coastal Marine Food Web

IF 4.5 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Elizabeth Boyse, Kevin P. Robinson, Ian M. Carr, Elena Valsecchi, Maria Beger, Simon J. Goodman
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Abstract

A good understanding of biotic interactions is necessary to accurately predict the vulnerability of ecosystems to climate change. Recently, co-occurrence networks built from environmental DNA (eDNA) metabarcoding data have arisen as a tool to explore interspecific interactions in ecological communities exposed to different human and environmental pressures. Such networks can identify environmentally driven relationships in microbial and eukaryotic communities, but whether inferred co-occurrences robustly represent biotic interactions remains unclear. Here, we tackle this challenge and compare spatio-temporal variability in the structure and complexity of inferred co-occurrence networks and food webs, using 60 eDNA samples covering vertebrates and other eukaryotes in a North Sea coastal ecosystem. We compare topological characteristics and identify highly connected species across spatial and temporal subsets to evaluate variance in community composition and structure. We find consistent trends in topological characteristics across eDNA-derived co-occurrence networks and food webs that support some ability for the co-occurrence networks to detect real ecological processes, despite trophic interactions forming a minority of significant co-occurrences. The lack of significant trophic interactions detected in co-occurrence networks may result from ecological complexities, such as generalist predators having flexible interactions or behavioural partitioning, the inability to distinguish age class with eDNA or co-occurrences being driven by non-trophic or abiotic interactions. We find support for using eDNA-derived co-occurrence networks to infer ecological interactions, but further work is needed to assess their power to reliably detect and differentiate different interaction types and overcome methodological limitations, such as species detection uncertainties, which could influence inferred ecosystem complexity.

Abstract Image

利用沿海海洋食物网中的环境 DNA 元条码数据,从共现网络中推断物种间的相互作用
要准确预测生态系统在气候变化面前的脆弱性,就必须充分了解生物之间的相互作用。最近,根据环境 DNA(eDNA)元条码数据建立的共生网络开始成为一种工具,用于探索面临不同人类和环境压力的生态群落中的种间相互作用。这种网络可以识别微生物和真核生物群落中受环境驱动的关系,但推断出的共生关系是否能稳健地代表生物间的相互作用仍不清楚。在这里,我们利用北海沿岸生态系统中涵盖脊椎动物和其他真核生物的 60 个 eDNA 样本,应对这一挑战,并比较推断出的共生网络和食物网的结构和复杂性的时空变异性。我们比较了拓扑特征,并确定了不同时空子集之间高度关联的物种,以评估群落组成和结构的差异。我们发现 eDNA 衍生的共生网络和食物网的拓扑特征有一致的趋势,支持共生网络有一定的能力检测真实的生态过程,尽管营养互作在重要的共生现象中只占少数。在共现网络中没有发现明显的营养相互作用可能是由于生态复杂性造成的,如通性捕食者具有灵活的相互作用或行为分区,无法用eDNA区分年龄等级,或共现是由非营养或非生物相互作用驱动的。我们发现使用源自 eDNA 的共现网络来推断生态相互作用是可行的,但还需要进一步的工作来评估其可靠检测和区分不同相互作用类型的能力,并克服方法学上的局限性,如物种检测的不确定性,这些局限性可能会影响推断的生态系统复杂性。
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来源期刊
Molecular Ecology
Molecular Ecology 生物-进化生物学
CiteScore
8.40
自引率
10.20%
发文量
472
审稿时长
1 months
期刊介绍: Molecular Ecology publishes papers that utilize molecular genetic techniques to address consequential questions in ecology, evolution, behaviour and conservation. Studies may employ neutral markers for inference about ecological and evolutionary processes or examine ecologically important genes and their products directly. We discourage papers that are primarily descriptive and are relevant only to the taxon being studied. Papers reporting on molecular marker development, molecular diagnostics, barcoding, or DNA taxonomy, or technical methods should be re-directed to our sister journal, Molecular Ecology Resources. Likewise, papers with a strongly applied focus should be submitted to Evolutionary Applications. Research areas of interest to Molecular Ecology include: * population structure and phylogeography * reproductive strategies * relatedness and kin selection * sex allocation * population genetic theory * analytical methods development * conservation genetics * speciation genetics * microbial biodiversity * evolutionary dynamics of QTLs * ecological interactions * molecular adaptation and environmental genomics * impact of genetically modified organisms
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