Easy, fast and reproducible Stochastic Cellular Automata with chouca
Stochastic cellular automata (SCA) are models that describe spatial dynamics using a grid of cells that switch between discrete states over time. They are widely used to understand how small-scale processes scale up to affect ecological dynamics at larger spatial scales, and have been applied to a w...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Peer Community In
2024-10-01
|
| Series: | Peer Community Journal |
| Subjects: | |
| Online Access: | https://peercommunityjournal.org/articles/10.24072/pcjournal.466/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1825206410079633408 |
|---|---|
| author | Génin, Alexandre Dupont, Guillaume Valencia, Daniel Zucconi, Mauro Ávila-Thieme, M. Isidora Navarrete, Sergio A. Wieters, Evie A. |
| author_facet | Génin, Alexandre Dupont, Guillaume Valencia, Daniel Zucconi, Mauro Ávila-Thieme, M. Isidora Navarrete, Sergio A. Wieters, Evie A. |
| author_sort | Génin, Alexandre |
| collection | DOAJ |
| description | Stochastic cellular automata (SCA) are models that describe spatial dynamics using a grid of cells that switch between discrete states over time. They are widely used to understand how small-scale processes scale up to affect ecological dynamics at larger spatial scales, and have been applied to a wide diversity of theoretical and applied problems in all systems, such as arid ecosystems, coral reefs, forests, bacteria, or urban growth. Despite their wide applications, SCA implementations are often ad-hoc, lacking performance, guarantees of correctness and poorly reproducible. De novo implementation of SCA for each specific system and application also represents a major barrier for many practitioners. To provide a unifying, well-tested technical basis to this class of models and facilitate their implementation, we built chouca, an R package that translates definitions of SCA models into compiled code, and runs simulations in an efficient way. chouca supports SCA based on rectangular grids where transition probabilities are defined for each cell, with performance typically two to three orders of magnitude above typical implementations in interpreted languages (e.g. R, Python), all while maintaining an intuitive interface in the R environment. Exact and mean-field simulations can be run, and both numerical and graphical results can be easily exported. Besides providing better reproducibility and accessibility, a fast engine for SCA unlocks novel, computationally intensive statistical approaches, such as simulation-based inference of ecological interactions from field data, which represents by itself an important avenue for research. By providing an easy and efficient entry point to SCAs, chouca lowers the bar to the use of this class of models for ecologists, managers and general practitioners, providing a leveled-off reproducible platform while opening novel methodological approaches. |
| format | Article |
| id | doaj-art-84e967fcba7c4bd28bacfd64aae2b5d4 |
| institution | Kabale University |
| issn | 2804-3871 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Peer Community In |
| record_format | Article |
| series | Peer Community Journal |
| spelling | doaj-art-84e967fcba7c4bd28bacfd64aae2b5d42025-02-07T10:17:17ZengPeer Community InPeer Community Journal2804-38712024-10-01410.24072/pcjournal.46610.24072/pcjournal.466Easy, fast and reproducible Stochastic Cellular Automata with chouca Génin, Alexandre0https://orcid.org/0000-0002-3333-1338Dupont, Guillaume1Valencia, Daniel2Zucconi, Mauro3Ávila-Thieme, M. Isidora4Navarrete, Sergio A.5https://orcid.org/0000-0003-4021-3863Wieters, Evie A.6https://orcid.org/0009-0001-1642-6466Copernicus Institute of Sustainable Development, Utrecht University – Utrecht, The Netherlands; Estación Costera de Investigaciones Marinas (ECIM) and Núcleo Milenio para la Ecología y Conservación de los Ecosistemas de Arrecifes Mesofóticos Templados (NUTME), Pontificia Universidad Católica de Chile – Las Cruces, Chile; Experimental and Theoretical Ecology Station, Centre National de la Recherche Scientifique – Moulis, FranceEstación Costera de Investigaciones Marinas (ECIM) and Núcleo Milenio para la Ecología y Conservación de los Ecosistemas de Arrecifes Mesofóticos Templados (NUTME), Pontificia Universidad Católica de Chile – Las Cruces, Chile; International Master of Science in Marine Biological Resources (IMBRSea), Ghent University – Ghent, BelgiumEstación Costera de Investigaciones Marinas (ECIM) and Núcleo Milenio para la Ecología y Conservación de los Ecosistemas de Arrecifes Mesofóticos Templados (NUTME), Pontificia Universidad Católica de Chile – Las Cruces, ChileEstación Costera de Investigaciones Marinas (ECIM) and Núcleo Milenio para la Ecología y Conservación de los Ecosistemas de Arrecifes Mesofóticos Templados (NUTME), Pontificia Universidad Católica de Chile – Las Cruces, Chile; Centro de Investigación Oceanográfica en el Pacifico Sur-Oriental (COPAS COASTAL), Universidad de Concepción – Concepción, ChileEscuela de Negocios, Facultad de Ciencias Sociales y Artes, Universidad Mayor – Temuco, Chile; Center for Resilience, Adaptation and Mitigation (CReAM), Universidad Mayor – Temuco, Chile; Instituto Milenio en Socio-Ecología Costera (SECOS), Pontificia Universidad Católica de Chile – Santiago, Chile; Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile – Santiago, ChileEstación Costera de Investigaciones Marinas (ECIM) and Núcleo Milenio para la Ecología y Conservación de los Ecosistemas de Arrecifes Mesofóticos Templados (NUTME), Pontificia Universidad Católica de Chile – Las Cruces, Chile; Centro de Investigación Oceanográfica en el Pacifico Sur-Oriental (COPAS COASTAL), Universidad de Concepción – Concepción, Chile; Center for Applied Ecology and Sustainability (CAPES), Pontificia Universidad Católica de Chile – Santiago, Chile; Instituto Milenio en Socio-Ecología Costera (SECOS), Pontificia Universidad Católica de Chile – Santiago, ChileEstación Costera de Investigaciones Marinas (ECIM) and Núcleo Milenio para la Ecología y Conservación de los Ecosistemas de Arrecifes Mesofóticos Templados (NUTME), Pontificia Universidad Católica de Chile – Las Cruces, ChileStochastic cellular automata (SCA) are models that describe spatial dynamics using a grid of cells that switch between discrete states over time. They are widely used to understand how small-scale processes scale up to affect ecological dynamics at larger spatial scales, and have been applied to a wide diversity of theoretical and applied problems in all systems, such as arid ecosystems, coral reefs, forests, bacteria, or urban growth. Despite their wide applications, SCA implementations are often ad-hoc, lacking performance, guarantees of correctness and poorly reproducible. De novo implementation of SCA for each specific system and application also represents a major barrier for many practitioners. To provide a unifying, well-tested technical basis to this class of models and facilitate their implementation, we built chouca, an R package that translates definitions of SCA models into compiled code, and runs simulations in an efficient way. chouca supports SCA based on rectangular grids where transition probabilities are defined for each cell, with performance typically two to three orders of magnitude above typical implementations in interpreted languages (e.g. R, Python), all while maintaining an intuitive interface in the R environment. Exact and mean-field simulations can be run, and both numerical and graphical results can be easily exported. Besides providing better reproducibility and accessibility, a fast engine for SCA unlocks novel, computationally intensive statistical approaches, such as simulation-based inference of ecological interactions from field data, which represents by itself an important avenue for research. By providing an easy and efficient entry point to SCAs, chouca lowers the bar to the use of this class of models for ecologists, managers and general practitioners, providing a leveled-off reproducible platform while opening novel methodological approaches.https://peercommunityjournal.org/articles/10.24072/pcjournal.466/spatial ecologyspatial modellingstochastic cellular automatonR packagechouca |
| spellingShingle | Génin, Alexandre Dupont, Guillaume Valencia, Daniel Zucconi, Mauro Ávila-Thieme, M. Isidora Navarrete, Sergio A. Wieters, Evie A. Easy, fast and reproducible Stochastic Cellular Automata with chouca Peer Community Journal spatial ecology spatial modelling stochastic cellular automaton R package chouca |
| title | Easy, fast and reproducible Stochastic Cellular Automata with chouca
|
| title_full | Easy, fast and reproducible Stochastic Cellular Automata with chouca
|
| title_fullStr | Easy, fast and reproducible Stochastic Cellular Automata with chouca
|
| title_full_unstemmed | Easy, fast and reproducible Stochastic Cellular Automata with chouca
|
| title_short | Easy, fast and reproducible Stochastic Cellular Automata with chouca
|
| title_sort | easy fast and reproducible stochastic cellular automata with chouca |
| topic | spatial ecology spatial modelling stochastic cellular automaton R package chouca |
| url | https://peercommunityjournal.org/articles/10.24072/pcjournal.466/ |
| work_keys_str_mv | AT geninalexandre easyfastandreproduciblestochasticcellularautomatawithchouca AT dupontguillaume easyfastandreproduciblestochasticcellularautomatawithchouca AT valenciadaniel easyfastandreproduciblestochasticcellularautomatawithchouca AT zucconimauro easyfastandreproduciblestochasticcellularautomatawithchouca AT avilathiememisidora easyfastandreproduciblestochasticcellularautomatawithchouca AT navarretesergioa easyfastandreproduciblestochasticcellularautomatawithchouca AT wieterseviea easyfastandreproduciblestochasticcellularautomatawithchouca |