Sampling networks of ecological interactions

By Pedro Jordano. bioRxiv, 2015

337 citations recorded by the source corpus.

Sampling ecological interactions presents similar challenges, problems, potential biases, and constraints as sampling individuals and species in biodiversity inventories. Interactions are just pairwise relationships among individuals of two different species, such as those among plants and their seed dispersers in frugivory interactions or those among plants and their pollinators. Sampling interactions is a fundamental step to build robustly estimated interaction networks, yet few analyses have attempted a formal approach to their sampling protocols. Robust estimates of the actual number of interactions (links) within diversified ecological networks require adequate sampling effort that needs to be explicitly gauged. Yet we still lack a sampling theory explicitly focusing on ecological interactions. While the complete inventory of interactions is likely impossible, a robust characterization of its main patterns and metrics is probably realistic. We must acknowledge that a sizable fraction of the maximum number of interactions Imax among, say, A animal species and P plant species (i.e., Imax = AP) is impossible to record due to forbidden links, i.e., life-history restrictions. Thus, the number of observed interactions I in robustly sampled networks is typically I ≪ Imax, resulting in extremely sparse interaction matrices with low connectance. Reasons for forbidden links are multiple but mainly stem from spatial and temporal uncoupling, size mismatches, and intrinsically low probabilities of interspecific encounter for most potential interactions of partner species. Ad-equately assessing the completeness of a network of ecological interactions thus needs knowledge of the natural history details embedded, so that for-bidden links can be “discounted” when addressing sampling effort. Here I provide a review and outline a conceptual framework for interaction sampling by building an explicit analogue to individuals and species sampling, thus extending diversity-monitoring approaches to the characterization of complex networks of ecological interactions. This is crucial to assess the fast-paced and devastating effects of defaunation-driven loss of key ecological interactions and the services they provide and the analogous losses related to interaction gains due to invasive species and biotic homogenization.

Subfields: Ecology, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/6ebbb68cf8b4e78a6dea38c729c73278773bb408

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In Ecology

In Ecology, Evolution, Behavior and Systematics

In Nature and Landscape Conservation

Citations in the corpus

Describe, understand and predict: why do we need networks in ecology? (2016, background): “…can infer from network data can only be as good as our understanding of the network itself; that is, our understanding of (i) which species are present (Jordano 2016b), (ii) the ways they interact (Nielsen & Bascompte 2007) and (iii) their local characteristics when observed (Poisot et al. 2015).”
Resolving Food-Web Structure (2020, methodology): “Critical evaluations of recent data have highlighted inadequacies and asymmetries of sampling effort, which bias inferences by missing rare species and/or interactions (Blüthgen 2010, Fründ et al. 2016, Jordano 2016).”
Identifying Causes of Patterns in Ecological Networks: Opportunities and Limitations (2017, background): “As with any sampling, interaction networks also inevitably suffer from incomplete sampling or even sampling bias (reviewed in Jordano 2016).”
Morphological trait matching shapes plant–frugivore networks across the Andes (2018, background): “Since data on species interactions are notoriously difficult to record (Jordano 2016), it would be desirable to approximate the degree of functional specialization of plant and animal species directly from their own morphological traits.”