Hurricanes, seed-predators and elasticities: Global patterns with local consequences
Global change is expected to alter the frequency and intensity of hurricanes. What is unknown is how such changes to environmental drivers are likely to affect plants and insects that are adapted to historical climate patterns. In subtropical forests, hurricanes affect canopy dynamics, which in turn affect growth, survival and reproduction and thus average fitness of understory species. We investigated how increasing hurricane frequency would alter the effect of a host-specific insect seed-predator on the fitness of an understory shrub in a southern Florida hurricane-prone forest. We defined average fitness as the stochastic population growth rate, λs. Using field data from four study plots that were differentially impacted by a strong hurricane, we quantified stage-specific demographic rates and seed predation rates for a native understory shrub in each of seven canopy-openness environments. We then created a model of post-hurricane seed predator colonization, including a parameter that represented the speed of re-colonization. We also defined a Markov chain of environmental dynamics for changes in forest canopy cover, where the parameters were functions of hurricane frequency. Finally, for each hurricane frequency × seed predator re-colonization speed, we generated a long sample path of environments, to analyze the fitness effects.
We found that at either extremely high or low hurricane frequency the relative impact of the insect was lower than at historical frequency, with a peak effect at somewhat more frequent than historical. The key to understanding this issue is understanding how hurricane frequency alters environment-specific sensitivity of the stochastic growth rate to perturbations of seed production. Envrionment-specific elasticity is presented as a tool for addressing how changes in particular environmental states result in changes in overall population dynamics and fitness for organisms that inhabit inherently variable environments. It is a parameter that depends upon how a given state of the environment is embedded within a likely sequence of other environments that may be worse, better or complementary to it. This parameter provides new insights and it integrates over the expected future. In general, it is of interest to understand how sensitive population level properties (like average fitness) are to alteration of environmental driver processes.