biological networks & biogeography

Understanding where species occur and how they interact are two of the oldest questions in ecology — and increasingly, they are being asked together. The Swain Lab works at this intersection, using network theory and large biodiversity databases to study both the structure of ecological interactions and the geographic organization of biological diversity. A recurring methodological concern across both threads is how reliably ecological and biogeographic structure can be inferred from incomplete or biased data — a question that has practical consequences for how we interpret both fossil and modern biodiversity records.

Published work on ecological networks has reconstructed plant–herbivore interaction networks from the fossil record, examined the taphonomic and ecological signals embedded in ancient food webs, and shown that sampling bias can substantially distort ecological metrics derived from fossil interaction data — findings with broad implications for paleontological and macroecological inference. Work on microbial communities has demonstrated that higher-order species interactions, continuous trait variation, and spatial dynamics together shape community structure in ways that simple pairwise models miss, connecting network theory to community ecology and evolutionary dynamics. On the biogeography side, the lab has used soundscape ecology to detect fine-grained signatures of Amazon forest degradation from animal acoustic communities — showing that biological networks encode information about ecosystem state that traditional biodiversity metrics miss. Ongoing work is extending biogeographic approaches to global scales, partitioning bee and avian diversity into functional biotic regions using occurrence and trait data, and asking how the scaling of microbial diversity relates to broader macroecological laws.

Across these projects, a unifying theme is that species do not exist or evolve in isolation — their interactions, distributions, and dynamics are structured by ecological and evolutionary processes that operate across multiple scales simultaneously, and understanding those structures requires both empirical breadth and methodological rigor.