Life histories and population dynamics in variable environments

Thesis

Environmental variability has broad impacts on the population dynamics of species across the tree of life. Importantly, global climate change is projected to increase environmental variability in regions hosting the highest biodiversity. Therefore, understanding the life history strategies by which populations can evolve to accommodate the often negative effects of environmental variability is critical to understand how global climate change may impact populations across taxa. In this dissertation, I explore one strategy by which populations can accommodate the impacts of environmental variability – i.e., demographic buffering. Specifically, I: (1) identify criteria for determining a buffering mechanism in ecological modelling, (2) demonstrate the utility of “new” perturbation approaches (i.e., the summation of stochastic elasticities of variance and self-second derivatives) when identifying demographic buffering, (3) analyse how environmental autocorrelation and variance impact demographic buffering (as measured by the summation of stochastic elasticities of variance) and the demographic mechanisms that underly these effects, (4) test for efficacy across four measures of demographic buffering (i.e., one correlational method, two methods using terms from Tuljapurkar’s approximation and the summation of stochastic elasticities of variance) and (5) broadly review modern perspectives and suggest new directions regarding where future life history research may lead. Overall, I suggest that the summation of stochastic elasticities of variance is an effective measure of demographic buffering, and that environmental autocorrelation and variance influence this measure through population structure and demographic rate variance, respectively. Furthermore, I outline multiple avenues for future research to better understand how life histories evolve in variable environments.

Authors: Gascoigne, SJL

• DOI: 10.5287/ora-xqogzp69j
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: elasticities; integral projection model; matrix population model; comparative demography; stochastic demography; demographic buffering
• Subjects: Evolution; Ecology