Enhanced inference of ecological networks by parameterizing ensembles of population dynamics models constrained with prior knowledge.

ABSTRACT: Background: Accurate network models of species interaction could be used to predict population dynamics and be applied to manage real world ecosystems. Most relevant models are nonlinear, however, and data available from real world ecosystems are too noisy and sparsely sampled for common inference approaches. Here we improved the inference of generalized Lotka-Volterra (gLV) ecological networks by using a new optimization algorithm to constrain parameter signs with prior knowledge and a perturbation-based ensemble method.

Results: We applied the new inference to long-term species abundance data from the freshwater fish community in the Illinois River, United States. We constructed an ensemble of 668 gLV models that explained 79% of the data on average. The models indic

SUBMITTER: Liao C 

PROVIDER: S-EPMC6950893 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature