Large-scale biodiveristy patterns, such as the species-area relationship, the species-abundance distribution and the species-body size distribution, are a "signature" of the underlying processes that created these patterns. Understanding these processes is of the utmost importance in battling the current biodiversity crisis. I am looking for potential explanations for these patterns, and I am developing (Bayesian) methods with which these explanations can be tested using the data on these patterns.
Ecological communities are, at least partly, defined by the way they are sampled (e.g. fixing the area sampled, the number of individuals, or the number of species, Etienne & Olff 2005). Theory that predicts the properties of ecological communities should therefore be based on sampling theory that interacts with the underlying dynamical processes structuring communities. I have developed such a sampling framework (Etienne & Alonso 2005). Although inspired by new theory for dispersal-limited communities, the framework equally applies to communities structured by niche differentiation. It allows the prediction of sampling distributions which can be used in likelihood methods when estimating model parameters from data or comparing alternative mechanistic models of community structure (see Chave, Alonso & Etienne 2006 and Etienne & Olff 2005). One key mathematical result is a generalization of the famous Ewens sampling formula of population genetics (Etienne & Olff 2004, Etienne 2005. This generalized formula, based on a genealogical approach, is now starting to be widely applied by field ecologists wishing to compare alternative models of community organization to their data. I have discovered that the new sampling formula contains multiple likelihood peaks (Etienne et al. 2006) making inferences more difficult. Within this new sampling framework I have studied the impact of different speciation modes on community structure (Etienne et al. 2007) which will be further studied in the proposed project. To put my work in context, I have contributed two review papers on neutral theory, showing merits and limitations (Alonso, Etienne & McKane 2006; Etienne & Alonso 2007).
Furthermore, I have explored the foundations of metabolic theory. I have analyzed how the network model of allometric scaling depends on its assumptions (Etienne, Apol & Olff 2006), and I am currently scrutinizing these assumptions. In one of the first attempts to reconcile dispersal and niche assembly I have connected neutral theory to this metabolic theory of ecology. I assumed that metabolic allometric scaling covers any niche differentiation with all residual variation being neutral. With this approach I have been able to explain variation in shapes of the body size – diversity relationship (Etienne & Olff 2004).
My PhD. research involved a different topic. I developed mathematical models and methods to understand the factors determining metapopulation persistence and to provide metapopulation management tools. A metapopulation is a network of populations living in distinct habitat patches; these populations occasionally go extinct, but the metapopulation can nevertheless persist, because extinction is balanced by colonization of empty patches by individuals from other populations. I explored simple analytical, stochastic and deterministic, models to gain insight in basic metapopulation properties and the impact of additional ecological effects (e.g. preference for empty or occupied patches), and the consequences for metapopulation conservation. I studied a stochastic, spatially explicit model in more detail to derive rules of thumb concerning optimal conservation strategies with respect to colonization and extinction probabilities, and with respect to patch area and interpatch distance. For example: the largest patch in a network should be enlarged to prolong metapopulation persistence most, if relative enlargements are considered. Furthermore, I applied uncertainty analysis of model predictions in a human impact assessment on two amphibians, and I developed a Bayesian method to estimate model parameters from occupancy data, applying it to a tree frog metapopulation.
