Ecological Network Inference is not Consistent Across Scales or Approaches

Abstract

Several methods of ecological network inference have been proposed, but their consistency and applicability for use across ecologically relevant scales require further investigation. Here, we infer ecological networks using two data sets (YFDP, FIA) describing distributional and attribute information at local, regional, and continental scales for woody species across North America. We accomplish this inference using four different methodologies (COOCCUR, NETASSOC, HMSC, NDD-RIM), incorporating biological data along an occurrence-performance spectrum while accounting (or not) for various confounding parameters. We contrast 1-1 associations at each evaluated scale to quantify consistency amongst inference approaches. We also assess consistency across scales within each inference approach. Ultimately, we find that inferred networks are inconsistent across scales and methodologies, particularly at continental scales. We highlight how such inconsistencies between network inference methods may be linked to using occurrence or performance information and incorporating or not confounding factors. Finally, we argue that identifying the “best” inference method is non-trivial. Thus, to facilitate the choice of inference methods for a given purpose, we suggest aligning specific research questions and the scale applicability of the method when interpreting the inferred links, network topology, and ecological processes governing network assembly.