Whole-of-system models in the Chilean Patagonia

The projection of territorial futures through the use of computational models constitutes a powerful tool for the integration of observational data (spatial, time series, etc.), the analysis of scenarios and the definition of safe spaces for the operation of human activities.

​Computer models developed by CSIRO and its marine ecosystem modeling group have provided a variety of national and international agencies (including the National Fisheries Service, Australian government agencies, NOAA and FAO) with information to support decision-making. decisions and for regional marine planning, managing the impacts of fisheries, supporting sustainable aquaculture, alternative livelihoods, as well as understanding and managing climate change.​

System modeling combines independent agent-based models, cellular automata, empirical models based on observations, and models based on stochastic differential equations. Together, they form the system model that simulates the temporal and spatial dynamics of the marine and terrestrial ecosystem and the socio-economic activities carried out in the territory.

An example of the application of these models and tools is the project “Regional development strategies and public digital transformation: development of support tools for the evaluation of future climate change scenarios in Chilean Patagonia” and which was awarded in 2019 through the Research and Development Idea contest (FONDEF of CONICYT). Its main beneficiary institution corresponds to CSIRO Chile, in collaboration with the University of Magallanes and CIEP (Aysén Regional Center for Cooperative Research and Development).

Results of this project (under execution) are being collated to www.futurosterritoriales.cl.

In this study, we used a stochastic hydrodynamic connectivity-based disease spread model to determine the role of hydrodynamic connectivity and the effect of seawater temperature and salinity on the dynamics of piscirickettsiosis in the Los Lagos region of Chile. Results demonstrate that environmental dynamics play a major role in disease prevalence.