Ecological network inference is not consistent across sales or approaches

Identifying the most suitable method of ecological network inference in line with individual research considerations is a non-trivial task, which significantly hinders adoption of network approaches to forest management applications. To advance the study of ecological networks and better guide their use in managing forest ecosystems, we propose a framework that aligns pairwise species-association inference methods with specific research questions, biological interaction types, data availability, and spatial scales of study. We motivate the adoption of this framework through an empirical comparison of multiple inference methods, highlighting substantial inconsistencies that arise across scales and methodologies. Using data on species distributions and attributes at local, regional, and continental scales for temperate conifer forests in North America, we show that network inference varies significantly depending on whether occurrence, abundance, or performance data are used and the degree to which confounding factors are accounted for. Across four widely used and/or cutting-edge inference methods (COOCCUR, NETASSOC, HMSC, NDD-RIM), we find notable disparities in both whole-network metrics and pairwise species associations, particularly at continental scales. These findings underscore that no single method is likely to universally outperforms others across scales, emphasizing the importance of choosing an inference approach that aligns with specific ecological and spatial contexts. Our framework aids in interpreting network topologies and interactions in light of these method- and datatype-driven variances, providing a structured approach to more reliably infer ecological associations and address complex network dynamics in forest management practices.