Sensitivity analysis of Markov models for communities of competing sessile organisms

1. Communities of competing sessile organisms are often modelled using Markov chains. Sensitivity analysis of the stationary distribution of these models tells us how we expect the abundance of each organism to respond to changes in interactions between species. This is important for conservation and management. 2. Markov models for such communities have usually been formulated in discrete time. Each column of the discrete-time transition matrix must sum to 1 (column stochasticity). Sensitivity analysis therefore involves defining a pattern of compensation that maintains column stochasticity as a single transition probability changes. There is little biological theory about the appropriate compensation pattern, but the usual choices involve changing only the elements of a single column of the transition matrix. 3. I argue that if the underlying dynamics occur in continuous time, each transition probability is the net outcome of direct and many indirect interactions. 4. Determining the consequences of changing a single direct interaction will often be of interest. I show how this can be achieved using a continuous-time model. The resulting discrete-time compensation pattern is quite different from those that have been considered elsewhere, with changes occurring in many columns. 5. I also show how to determine which direct interactions are being changed under any discrete-time compensation pattern.