Understanding the way natural communities change over time remains one of the great challenges to the field of ecology. Knowledge of the mechanisms by which communities respond to perturbations is a fundamental aspect of understanding community dynamics. Life history characteristics (e.g. lifespan and dispersal potential) of disturbed taxa, size of initial disturbance, rates of input to the population (recruitment) and species interactions throughout the course of succession may all have important effects on rates of community recovery, yet the relative contributions of these factors is not well understood. Here I combine field data of post-disturbance community development with Markov analyses to broaden our understanding of the mechanisms driving observed differences in successional rates and trajectories. I  have calculated community recovery rates following an experimental disturbance across a major biogeographic break on the California coast in four intertidal assemblages (zones) that are each dominated by a taxon with a unique combination of life history traits: The California mussel Mytilus (long-lived and long-dispersing), the acorn barnacle Chthamalus (short-lived and long-dispersing), the rockweed Silvetia (long-lived and short-dispersing) and the red turf seaweed Endocladia (short-lived and short-dispersing).

Results/Conclusions

Results show that lifespan significantly influences recovery rates such that the fastest recovery rates occur in the zones dominated by Chthamalus and Endocladia (both short-lived) and that the strength of recruitment-driven recovery rates varies significantly by biogeographic region for all assemblages. Markov analysis examining species interactions shows that the proportion of positive, negative and neutral interactions in recovering communities is fundamentally different from those in intact communities such that there are more facilitative interactions in disturbed communities across all three biogeographic regions for the seaweed Endocladia, and the site with the slowest recovery rate also relies most heavily on facilitative interactions. Since human presence or use of an area nearly always leads to removal of biota, the process by which a disturbed area recovers is a key part of understanding the long-term consequences of human impacts to ecosystems. As more and more ecosystems are threatened by human impacts, quantitative knowledge of the factors that contribute to variation in recovery rates is becoming increasingly critical to obtain. My research combines empirical data on succession with the analytical power of Markov models. This multi-faceted approach to understanding successional dynamics will provide useful information to coastal resource managers regarding recovery potential for intertidal organisms while at the same time deepening and quantitatively refining our understanding of the process of succession.