Stochastic models for population viability analysis and adaptive management in restoration biology

Kurzfassung

An Italian university has developed a methodology for constructing stochastic models of biological systems. The research is aimed to apply the methodology to Population Viability Analysis (PVA) and adaptive management. The advantage is the use of automatic verification tools for assessing properties of the modelled system to predict behaviours and find conditions for new behaviours. Cooperation is sought for testing the methodology on real systems, adapting existing tools & developing new ones.

Details

A group of researchers of an Italian university has developed a methodology for constructing stochastic models for the description of biological systems.
The model consists of a set of replacement rules, the application whereof depends on a probability. Each rule describes a possible evolution step of the system, that is how a new situation replaces a previous one in the system (as an example, a rule describing a successful mating changes the system by adding the offspring). The models allow capturing the natural stochasticity of biological systems. Moreover, as the models are based on replacement rules, they can be easily modified by adding, deleting and changing rules. Population Viability Analysis (PVA) is a systematic examination of the interacting factors that place a population or species at risk and may influence its viability. In recent years there has been great emphasis on projecting population viability over short time frames under alternative management strategies. PVA has therefore become integrated with the process of adaptive management, where models are developed, populations are monitored under prescribed management strategies, and then model and management strategies are updated based on data collected. The features of these models can be expressed in the proposed formalism. In particular model updating can be easily obtained by adding, deleting and changing rules.

Innovative Aspects:
The proposed methodology allows predicting the evolution of an ecological system under certain conditions (which may be varied). This offers a clear advantage with respect to experimenting “in vivo