Co‐occurrence is not evidence of ecological interactions

Blanchet, F. Guillaume; Cazelles, Kévin; Gravel, Dominique

doi:10.1111/ele.13525

Cited by 567 publications

(628 citation statements)

References 100 publications

(151 reference statements)

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“…We find that this component can be biased, especially when species have broad environmental niches ( Figure 2). It is widely recognized that there are particular challenges in quantifying and interpreting species co-distributions (reviewed by Blanchet et al 2020 interactions from these confounding factors should thus be a major focus for future work.…”

Section: Discussionmentioning

confidence: 99%

The Internal Structure of Metacommunities

Leibold

Rudolph

Blanchet

et al. 2020

Preprint

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ABSTRACTMetacommunity ecology has become an important subdiscipline of ecology, but it is increasingly evident that its foundational theoretical and analytical frameworks do not adequately incorporate a realistic continuum of environmental and biotic process at play. We propose an approach that develops stronger links between theoretical and statistical frameworks to shift the focus towards the study of the ‘internal structure’ of metacommunities by dissecting how different species and different sites contribute to overall metacommunity structure. To illustrate this, we simulate data from a model that includes environmental variation, dispersal, biotic interactions, and stochasticity as the basic ecological processes that influence species’ (co)-distributions. We analyze the simulated data with hierarchical community models and propose a new method to visualize and analyze the simultaneous role of species co-distribution, environment, space, and stochasticity in this emerging statistical approach. We focus in particular on quantifying how species affect the overall structure of the metacommunity via differences in their dispersal and niche traits, and how environmental filtering, dispersal and species interactions varies from site to site in relation to environmental conditions and connectivity. Although there are still challenges ahead, this framework provides a roadmap for a more comprehensive approach by jointly developing more mechanistic theory based on community assembly processes and the analytical tools needed to map these concepts onto data in diverse landscapes and systems.

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Section: Discussionmentioning

confidence: 99%

The Internal Structure of Metacommunities

Leibold

Rudolph

Blanchet

et al. 2020

Preprint

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“…Another approach to unravel potential host-parasite relationships when the hosts are unknown, is to use co-occurrence network analyses. Although network analyses based on co-occurrences do not confirm biotic interactions (Blanchet et al 2020), cooccurrence network can highlight potentially interesting taxonomic groups as potential hosts.…”

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confidence: 90%

“…Co-occurrences can be inferred because of similar environment preferences, and actual biotic interactions may not have been inferred because the signal was too weak in the data (Blanchet et al, 2020). Additionally, some of the co-occurrences here could have been inferred just because the cercozoans, fungi, and apicomplexans were extremely OTU-rich in the dataset.…”

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confidence: 95%

Identifying potential hosts of short-branch Microsporidia

Doliwa¹,

Dunthorn²,

Rassoshanska³

et al. 2020

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Microsporidia are obligate parasites that are closely related to Fungi. While the widely-known long-branch Microsporidia infect mostly animals, the hosts of short-branch Microsporidia are only partially characterized or not known at all. Here, we used network analyses from Neotropical rainforest soil metabarcoding data, to infer co-occurrences between environmental lineages of short-branch microsporidians and their potential hosts. We found significant co-occurrences with several taxa, especially with Apicomplexa, Cercozoa, Fungi, as well as some Metazoa. Our results are the first step to identify potential hosts of the environmental lineages of short-branch microsporidians, which can be targeted in future molecular and microscopic studies.

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“…The effort that gained traction in the 1980's and 1990's (Cody andDiamond 1979, Connor and, and has recently been revived with the promise of joint species distribution models as a tool to disentangle interactions from shared environmental requirements among species (Warton et al 2015, Ovaskainen et al 2017, Zurell et al 2018. The various approaches of revealing interactions from presence-absence co-occurrence data, as well as a suite of cautionary arguments against the endeavor, has been summarized recently by Blanchet et al (2020).…”

Section: Isa Species-area Relationships and Species-accumulation Curvesmentioning

confidence: 99%

“…For example, ISA may result from shared habitat requirements among species, dispersal limitation, and/or interspecific interactions (Table 1), and there is a long tradition of inferring these mechanisms from spatial patterns of ISA (Forbes 1907, Cody and Diamond 1979, Calatayud et al 2020. However, it is not always possible, nor useful, to infer causal mechanisms from emergent static patterns (Peters 1991, McGill andNekola 2010), and this is particularly problematic in the case of ISA for presence-absence data (Blanchet et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Measurement and analysis of interspecific spatial associations as a facet of biodiversity

Keil¹,

Wiegand²,

Tóth³

et al. 2019

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Interspecific spatial associations (ISA), which include co-occurrences, segregations, or attractions among two or more species, can provide important insights into the spatial structuring of communities. However, ISA has primarily been examined in the context of understanding interspecific interactions, while other aspects of ISA, including its relations to other biodiversity facets and how it changes in the face of anthropogenic pressures, have been largely neglected. This is likely because it is unclear what makes ISA useful in a biodiversity context, little is known about the theoretical connections between ISA and other biodiversity facets, and there is a confusing variety of approaches to measuring ISA. Here, we first review the metrics of ISA. These include both spatially implicit and explicit indices of association for both binary and abundance data. We test and compare these approaches on empirical and simulated data, and we provide specific recommendations for how to use and interpret them in biodiversity science. We argue that measurements of ISA are more informative when they are spatially explicit (i.e. distance dependent). We then review links of ISA to other classical biodiversity facets, such as alpha, beta, and gamma diversity, and show that they mostly fail to reflect changes/variation in ISA, with the exception of average pair-wise beta diversity. This underscores the need for a specific focus on ISA in large-scale biodiversity assessments. Finally, we argue that there are important, and underappreciated, reasons to study ISA that are unrelated to its link to biotic interactions. Specifically, ISA can provide strong tests of biodiversity theories that require multiple patterns to benchmark against, and it can be explored for potentially predictive macroecological patterns.

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Co‐occurrence is not evidence of ecological interactions

Cited by 567 publications

References 100 publications

The Internal Structure of Metacommunities

The Internal Structure of Metacommunities

Identifying potential hosts of short-branch Microsporidia

Measurement and analysis of interspecific spatial associations as a facet of biodiversity

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