Transients drive the demographic dynamics of plant populations in variable environments

J Ecol. 2016 Mar;104(2):306-314. doi: 10.1111/1365-2745.12528. Epub 2016 Feb 22.

Abstract

The dynamics of structured plant populations in variable environments can be decomposed into the 'asymptotic' growth contributed by vital rates, and 'transient' growth caused by deviation from stable stage structure.We apply this framework to a large, global data base of longitudinal studies of projection matrix models for plant populations. We ask, what is the relative contribution of transient boom and bust to the dynamic trajectories of plant populations in stochastic environments? Is this contribution patterned by phylogeny, growth form or the number of life stages per population and per species?We show that transients contribute nearly 50% or more to the resulting trajectories, depending on whether transient and stable contributions are partitioned according to their absolute or net contribution to population dynamics.Both transient contributions and asymptotic contributions are influenced heavily by the number of life stages modelled. We discuss whether the drivers of transients should be considered real ecological phenomena, or artefacts of study design and modelling strategy. We find no evidence for phylogenetic signal in the contribution of transients to stochastic growth, nor clear patterns related to growth form. We find a surprising tendency for plant populations to boom rather than bust in response to temporal changes in vital rates and that stochastic growth rates increase with increasing tendency to boom. Synthesis. Transient dynamics contribute significantly to stochastic population dynamics but are often overlooked in ecological and evolutionary studies that employ stochastic analyses. Better understanding of transient responses to fluctuating population structure will yield better management strategies for plant populations, and better grasp of evolutionary dynamics in the real world.

Keywords: asymptotic dynamics; demography; environmental stochasticity; matrix population models; plant population dynamics; stochastic; transient dynamics.