Mismatch between IUCN range maps and species interactions data illustrated using the Serengeti food web

Author:

Higino Gracielle T.1,Banville Francis234,Dansereau Gabriel34,Forero Muñoz Norma Rocio34,Windsor Fredric5,Poisot Timothée34

Affiliation:

1. Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada

2. University of Sherbrooke, Sherbrooke, Québec, Canada

3. University of Montreal, Montréal, Québec, Canada

4. Quebec Centre for Biodiversity Science, Montréal, Québec, Canada

5. School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom

Abstract

Background Range maps are a useful tool to describe the spatial distribution of species. However, they need to be used with caution, as they essentially represent a rough approximation of a species’ suitable habitats. When stacked together, the resulting communities in each grid cell may not always be realistic, especially when species interactions are taken into account. Here we show the extent of the mismatch between range maps, provided by the International Union for Conservation of Nature (IUCN), and species interactions data. More precisely, we show that local networks built from those stacked range maps often yield unrealistic communities, where species of higher trophic levels are completely disconnected from primary producers. Methodology We used the well-described Serengeti food web of mammals and plants as our case study, and identify areas of data mismatch within predators’ range maps by taking into account food web structure. We then used occurrence data from the Global Biodiversity Information Facility (GBIF) to investigate where data is most lacking. Results We found that most predator ranges comprised large areas without any overlapping distribution of their prey. However, many of these areas contained GBIF occurrences of the predator. Conclusions Our results suggest that the mismatch between both data sources could be due either to the lack of information about ecological interactions or the geographical occurrence of prey. We finally discuss general guidelines to help identify defective data among distributions and interactions data, and we recommend this method as a valuable way to assess whether the occurrence data that are being used, even if incomplete, are ecologically accurate.

Funder

NSERC Computational Biodiversity Science and Services (BIOS2) CREATE program

Institute for Data Valorization

The Courtois Foundation

Natural Sciences and Engineering Research Council

The Fond de Recherche du Québec - Nature et Techonologie (FRQNT) doctoral scholarships

The Canadian Institute of Ecology & Evolution

The Royal Society

Publisher

PeerJ

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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