Ecosystem Models Based on Artificial Intelligence
Paper in proceeding, 2022
Ecosystem models can be used for understanding general phenomena of evolution, ecology, and ethology. They can also be used for analyzing and predicting the ecological consequences of human activities on specific ecosystems, e.g., the effects of agriculture, forestry, construction, hunting, and fishing. We argue that powerful ecosystem models need to include reasonable models of the physical environment and of animal behavior. We also argue that several well-known ecosystem models are unsatisfactory in this regard. Then we present the open-source ecosystem simulator Ecotwin, which is built on top of the game engine Unity. To model a specific ecosystem in Ecotwin, we first generate a 3D Unity model of the physical environment, based on topographic or bathymetric data. Then we insert digital 3D models of the organisms of interest into the environment model. Each organism is equipped with a genome and capable of sexual or asexual reproduction. An organism dies if it runs out of some vital resource or reaches its maximum age. The animal models are equipped with behavioral models that include sensors, actions, reward signals, and mechanisms of learning and decision-making. Finally, we illustrate how Ecotwin works by building and running one terrestrial and one marine ecosystem model.
Author
Claes Strannegård
University of Gothenburg
Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI
Karolinska Institutet
Niklas Engsner
Karolinska Institutet
Chalmers, Computer Science and Engineering (Chalmers), Data Science
Jesper Eisfeldt
Karolinska Institutet
John Endler
Deakin University
Amanda Hansson
University of Gothenburg
Rasmus Lindgren
Student at Chalmers
Petter Mostad
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
University of Gothenburg
Simon Olsson
Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI
Irene Perini
Linköping University
Center for Medical Image Science and Visualization
Heather Reese
University of Gothenburg
Fulya Taylan
Karolinska Institutet
Simon Ulfsbäcker
University of Gothenburg
Ann Nordgren
Karolinska Institutet
University of Gothenburg
34th Workshop of the Swedish Artificial Intelligence Society, SAIS 2022
9781665471268 (ISBN)
34th Workshop of the Swedish Artificial Intelligence Society, SAIS 2022
Stockholm, Sweden,
Stockholm, Sweden,
Subject Categories
Bioinformatics (Computational Biology)
Information Science
Computer Science
DOI
10.1109/SAIS55783.2022.9833026