Background
The Incidence Function Model describes presence/absence of a species in the patches of a highly fragmented landscape at discrete time intervals (years) as the result of colonization and extinction processes. The IFM ignores local dynamics since they are faster than metapopulation dynamics in producing changes in the size of local populations (Hanski, 1994). In the IFM, the process of occupancy of patch is described by a first-order Markov chain with two states, {O, i} (empty and occupied, respectively). The extinction probability of a population in a patch is constant in time and is assumed to decrease with increasing patch area, and the colonization probability is assumed to be a sigmoidal function increasing with connectivity. The IFM is the best known spatially explicit metapopulation model in literature. This model has been applied to conservation problems and to area-wide pest-management.
Sound knowledge of Probability and Statistics and basic elements of any programming language is recommended.
Learning Objectives
It is expected that, with this tutorial, participants will be better positioned to:
1. Present the basic elements of discrete, finite-state Markov chains and the mathematics of the IFM;
2. Illustrate model parameters and performances in terms of metapopulation dynamics;
3. Examine the framework of IFM model applicability, how it works and its limitations;
4. Discuss variations to the basic model (rescue effect, time-dependent colonization probabilities);
5. Assess the use of the free software R for data representation, parameter estimation and model simulation.
Contents and Structure
Training Blocks:
1. The ecological problem and the Incidence Function Model;
2. Discrete time, finite state and Markov Chains;
3. The Incidence Function Model;
4. Model Simulation;
5. Model Parameters;
6. Parameter Estimation;
7. Application to Ecological Issues.
Target Audience
The training is intended for researchers in the field of ecological applications aiming at improving their quantitative background and interested in applying a stochastic model to analyse metapopulation dynamics and eventually obtain quantitative information for management.
Production
LifeWatch Italy