Spatially explicit predictions of food web structure from regional-level data

Abstract

Knowledge about how ecological networks vary across global scales iscurrently limited given the complexity of acquiring repeated spatial datafor species interactions. Yet, recent developments in metawebs highlightefficient ways to first document possible interactions within regionalspecies pools. Downscaling metawebs towards local network predictionsis a promising approach to using the current data to investigate thevariation of networks across space. However, issues remain in how torepresent the spatial variability and uncertainty of species interactions,especially for large-scale food webs. Here, we present a probabilisticframework to downscale a metaweb based on the Canadian mammalmetaweb and species occurrences from global databases. We investigatedhow our approach can be used to represent the variability of networksand communities between ecoregions in Canada. Species richness andinteractions followed a similar latitudinal gradient across ecoregions butsimultaneously identified contrasting diversity hotspots. Network motifsrevealed additional areas of variation in network structure compared withspecies richness and number of links. Our method offers the potentialto bring global predictions down to a more actionable local scale, andincreases the diversity of ecological networks that can be projected in space.This article is part of the theme issue 'Connected interactions: enrichingfood web research by spatial and social interactions.