Deciphering Probabilistic Species Interaction Networks
Francis Banville, Tanya Strydom, Penelope S. A. Blyth, Chris Brimacombe, Michael D. Catchen, Gabriel Dansereau, Gracielle Higino, Thomas Malpas, Hana Mayall, Kari Norman, Dominique Gravel, Timothée Poisot

TL;DR
This paper explores how to better understand and represent uncertainties in species interactions using probabilistic networks at different spatial scales.
Contribution
The paper introduces a framework to distinguish between local interaction realizations and metaweb feasibility, highlighting biases in inferring interactions.
Findings
Local networks and metawebs differ in their spatial and temporal scaling of interactions.
Systematic biases arise when inferring local networks directly from metawebs.
Rigorous descriptions of probabilistic interactions should include conditional variables and uncertainty sources.
Abstract
Representing species interactions probabilistically as opposed to deterministically conveys uncertainties in our knowledge of interactions. The sources of uncertainty captured by interaction probabilities depend on the method used to evaluate them: uncertainty of predictive models, subjective assessment of experts, or empirical measurement of interaction spatiotemporal variability. However, guidelines for the estimation and documentation of probabilistic interaction data are lacking. This is concerning because our understanding of interaction probabilities depend on their sometimes elusive definition and uncertainty sources. We review how probabilistic interactions are defined at different spatial scales. These definitions are based on the distinction between the realisation of an interaction at a specific time and space (local networks) and its biological or ecological feasibility…
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Taxonomy
TopicsPlant and animal studies · Species Distribution and Climate Change · Plant Parasitism and Resistance
