Comparison Between Lotka-Volterra and Multivariate Autoregressive Models of Ecological Interaction Systems

Lotka-Volterra (LV) and Multivariate Autoregressive (MAR) models are computational frameworks with different mathematical structures that have both been proposed for the same purpose of extracting governing features of dynamic interactions among coexisting populations of different species from observed time series data. We systematically compare the feasibility of the two modeling approaches, using four synthetically generated datasets and seven ecological datasets from the literature. The overarching result is that LV models outperform MAR models in most cases and are generally superior for representing cases where the dependent variables deviate greatly from their steady states. A large dynamic range is particularly prevalent when the populations are highly abundant, change considerably over time, and exhibit a large signal-to-noise ratio. By contrast, MAR models are better suited for analyses of populations with low abundances and for investigations where the quantification of noise is important. We conclude that the choice of either one or the other modeling framework should be guided by the specific goals of the analysis and the dynamic features of the data. Availability of algorithms used https://github.com/LBSA-VoitLab/Comparison-Between-LV-and-MAR-Models-of-Ecological-Interaction-Systems