Bayesian inference to partition determinants of community dynamics from observational time series

Mutshinda, CM; Finkel, ZV; Widdicombe, CE; Irwin, AJ. 2019 Bayesian inference to partition determinants of community dynamics from observational time series. Community Ecology, 20 (3). 238-251.

[img] Text
ComEc-D-19-00053_R1 (2).docx
Restricted to Repository staff only
Available under License All Rights Reserved.

Download (4MB)
Official URL:


Ecological communities are shaped by a complex interplay between abiotic forcing, biotic regulation and demographic stochasticity. However, community dynamics modelers tend to focus on abiotic forcing overlooking biotic interactions, due to notorious challenges involved in modeling and quantifying inter-specific interactions, particularly for species-rich systems such as planktonic assemblages. Nevertheless, inclusive models with regard to the full range of plausible drivers are essential to characterizing and predicting community response to environmental changes. Here we develop a Bayesian model for identifying, from in-situ time series, the biotic, abiotic and stochastic factors underlying the dynamics of species-rich communities, focusing on the joint biomass dynamics of biologically meaningful groups. We parameterize a multivariate model of population co-variation with an explicit account for demographic stochasticity, density-dependent feedbacks, pairwise interactions, and abiotic stress mediated by changing environmental conditions and resource availability, and work out explicit formulae for partitioning the temporal variance of each group in its biotic, abiotic and stochastic components. We illustrate the methodology by analyzing the joint biomass dynamics of four major phytoplankton functional types namely, diatoms, dinoflagellates, coccolithophores and phytoflagellates at Station L4 in the Western English Channel using weekly biomass records and coincident measurements of environmental covariates describing water conditions and potentially limiting resources. Abiotic and biotic factors explain comparable amounts of temporal variance in log-biomass growth across functional types. Our results demonstrate that effective modelling of resource limitation and inter-specific interactions is critical for quantifying the relative importance of abiotic and biotic factors.

Item Type: Publication - Article
Additional Keywords: Bayesian inference, Biotic interactions, Community dynamics, Environmental forcing, Markov chain Monte Carlo, Spike-and-slab prior
Divisions: Plymouth Marine Laboratory > National Capability categories > Added Value
Plymouth Marine Laboratory > National Capability categories > Western Channel Observatory
Plymouth Marine Laboratory > Science Areas > Marine Ecology and Biodiversity
Depositing User: S Hawkins
Date made live: 11 Mar 2020 10:40
Last Modified: 25 Apr 2020 10:02

Actions (login required)

View Item View Item