Ecological time-series analysis through structural modelling with latent constructs: concepts, methods and applications.

Abstract

Time-series analyses in ecology usually involve the use of autoregressive modelling through direct and/or delayed difference equations, which severely restricts the ability of the modeler to structure complex causal relationships within a multivariate frame. This is especially problematic in the field of population regulation, where the proximate and ultimate causes of fluctuations in population size have been hotly debated for decades. Here it is shown that this debate can benefit from the implementation of structural modelling with latent constructs (SEM) to time-series analysis in ecology. A nonparametric bootstrap scheme illustrates how this modelling approach can circumvent some problems posed by the climate-ecology interface. Stochastic Monte Carlo simulation is further used to assess the effects of increasing time-series length and different parameter estimation methods on the performance of several model fit indexes. Throughout, the advantages and limitations of the SEM method are highlighted.