The ECOPOTENTIAL Virtual Lab – II:

INSTAR: An Agent-based Model for Forecasting and Managing Forest Pests

An interview with María Suárez-Muñoz and Curro (Francisco) Bonet

In this short interview the ECOPOTENTIAL scientists Francisco Bonet (University of Cordoba, formerly University of Grenada) and María Suárez-Muñoz (University of Grenada) are presenting the ecological model INSTAR, aimed to simulate the biological cycle of a Mediterranean forest pest called “pine processionary moth”. It is possible to run INSTAR through the ECOPOTENTIAL Virtual Laboratory.

Q: María and Francisco, can you please shortly explain us what is INSTAR?

A: INSTAR is a digital tool (= a model) able to simulate the biological cycle of a Mediterranean forest pest called “processionary moth” (Thaumetopoea pityocampa). I am sure that you have seen its processions walking through a pine plantation. As you know the larvae of this species are itchy. They feed on pine needles and, since they can reach huge numbers, they can pose a health risk for people sometimes.

Q: What is INSTAR meant for?

A: This model aims to help us to understand the relationships between the activity of the target forest pest and environmental factors such as climate, topography, structure of the forest, etc. We also aim to forecast the outbreaks of the pest, what would help us to anticipate its negative impacts on both forest and human health. INSTAR has been designed to work on a very detailed spatial scale (around hectares). The shortest temporal scale is one day, but the longer the time series, the most interesting outputs are obtained. 

Q: Can you tell us more about this model? How was it developed?

A: INSTAR was firstly developed in the context of a PhD work done by Lucía Torres Muros (currently working in Ecuador) in 2014. Her field work allowed us to conceptualize the first version of the model. Ramón Pérez-Pérez wisely translated these concepts into a Netlogo Agent-Based Model in 2015. Thanks to ECOPOTENTIAL, María took over in 2016, upgraded the model and documented it using a standard procedure for Agent Based Models. The whole team is currently working to publish the preliminary results in an open access journal. 

Q: What are the outputs of INSTAR?

INSTAR can provide daily maps of pest infestation in the area where the model is executed. It can also provide aggregated information showing the impact of processionary moth in a specific area during the execution time. Since it is an Agent-Based Model, it is possible to obtain outputs showing the infestation of single trees. 

Q: What input data are necessary? Which ones are obtained via Remote Sensing?


Our model requires two types of input data. It needs information on the structure of the target ecosystem: distribution of trees, tree size and tree species, topography. For the current work, the distribution of the trees has been kindly provided by Mihai Tanase, also partner in ECOPOTENTIAL. 

He used LIDAR dataset to obtain distribution maps of trees in our target area (Sierra Nevada and Sierra de Baza. Southeastern Spain). This information does not change during the model execution. INSTAR also requires time series showing the spatial distribution of temperature on a daily basis. This information has been obtained via WiMMed, which is a spatially explicit hydrological model created by our colleagues at the University of Granada and the University of Cordoba.

Q: Who are the potential users of INSTAR?

A: Our final aim is that environmental managers can use INSTAR as a tool to aid decisions regarding the management of processionary moth outbreaks. However, the currently existing version is mainly devoted to scientists interested in simulating ecological processes. 

Q: What should I do to use the model?

The model code is fully available in GitHub ( a test can be remotely run at ECOPOTENTAL Vlab ( A detailed description of the model is under review at the moment. We hope it can be accessed very soon.

For further inquiries, feel free to contact us (, we will be very happy to get in touch with interested scientists and potential users. 

About the Authors

I am María Suárez-Muñoz and I studied environmental sciences and ecology in Spain and the Netherlands. I am in my first year as PhD candidate at University of Granada, where I plan to develop tools and knowledge to enhance decision making in pine plantations. I am aware of the great environmental challenges that society will be facing in the coming years and I would like to do my bit in this task which concerns us all.

My name is Curro (Francisco) Bonet and I currently work as assistant professor at the University of Cordoba (Spain) where I teach ecology and ecoinformatics. I studied biology and environmental sciences many years ago (I am 44 years old)… My overarching professional aim is to create the tools and the information needed to aid environmental decision making. In the last decade I have been involved in the construction of long term monitoring observatories.