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

Area of Science:

• Ecology
• Network Theory
• Biodiversity Research

Background:

• Understanding global-scale ecological network variation is limited by data acquisition challenges.
• Metawebs offer a method to document regional species interactions, but downscaling to local networks remains complex.
• Representing spatial variability and uncertainty in species interactions for large food webs is a key challenge.

Purpose of the Study:

• To present a probabilistic framework for downscaling metawebs to local network predictions.
• To investigate the spatial variability of ecological networks and communities across Canadian ecoregions.
• To enhance the projection of ecological network diversity across space.

Main Methods:

• Developed a probabilistic framework to downscale a metaweb using the Canadian mammal metaweb.
• Utilized species occurrence data from global databases.
• Analyzed network variability and community structure across different ecoregions.

Main Results:

• Species richness and interactions exhibited a latitudinal gradient across ecoregions, alongside distinct diversity hotspots.
• Network motifs identified variations in network structure beyond species richness and link counts.
• The framework successfully represented network and community variability between ecoregions.

Conclusions:

• The probabilistic framework enables downscaling global metaweb data to local ecological network predictions.
• This approach improves the representation of spatial variability and uncertainty in species interactions.
• The method facilitates actionable local-scale ecological predictions and expands the spatial projection of food web diversity.