Proof-of-Principle That Cellular Automata Can Be Used to Predict Infestation Risk by Reticulitermes grassei (Blattodea: Isoptera)
Over the past few decades, species distribution modelling has been increasingly used to monitor invasive species. Studies herein propose to use Cellular Automata (CA), not only to model the distribution of a potentially invasive species but also to infer the potential of the method in risk predictio...
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Luleå tekniska universitet, Träteknik
2022
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Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-89928 https://doi.org/10.3390/f13020237 |
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ftluleatu:oai:DiVA.org:ltu-89928 2023-05-15T17:09:13+02:00 Proof-of-Principle That Cellular Automata Can Be Used to Predict Infestation Risk by Reticulitermes grassei (Blattodea: Isoptera) Sequeira, João G. N. Nobre, Tânia Duarte, Sónia Jones, Dennis Esteves, Bruno Nunes, Lina 2022 application/pdf http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-89928 https://doi.org/10.3390/f13020237 eng eng Luleå tekniska universitet, Träteknik BioISI-Biosystems & Integrative Sciences Institute, Faculty of Sciences, University of Lisbon, Campo Grande, C8 bdg, 1749-016 Lisboa, Portugal MED-Mediterranean Institute for Agriculture, Environment and Development, Instituto de Investigação e Formação Avançada, Universidade de Évora, Pólo da Mitra, Ap. 94, 7006-554 Evora, Portugal Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portugal; LEAF-Linking Landscape, Environment, Agriculture and Food, Tapada da Ajuda, 1349-017 Lisboa, Portugal Department of Wood Processing and Biomaterials, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamýcká 1176, Praha 6-Suchdol, 16521 Prague, Czech Republic Department of Wood Engineering, Polytechnic Institute of Viseu, Av. Cor. José Maria Vale de Andrade, 3504-510 Viseu, Portugal; Centre for Natural Resources, Environment and Society-CERNAS-IPV Research Centre, Av. Cor. José Maria Vale de Andrade, 3504-510 Viseu, Portugal Structures Department, National Laboratory for Civil Engineering, Av. do Brasil, 101, 1700-066 Lisbon, Portugal; cE3c, Centre for Ecology, Evolution and Environmental Changes, Azorean Biodiversity Group, University of Azores, 9700-042 Angra do Heroísmo, Portugal Forests, 1999-4907, 2022, 13:2, orcid:0000-0003-3246-5964 orcid:0000-0002-1855-7451 orcid:0000-0001-7309-5440 orcid:0000-0002-5565-6651 orcid:0000-0001-6660-3128 orcid:0000-0001-6849-3241 http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-89928 doi:10.3390/f13020237 ISI:000850353600001 Scopus 2-s2.0-85124218530 info:eu-repo/semantics/openAccess subterranean termites infestation risk cellullar automata model Ecology Ekologi Article in journal info:eu-repo/semantics/article text 2022 ftluleatu https://doi.org/10.3390/f13020237 2022-10-25T20:58:58Z Over the past few decades, species distribution modelling has been increasingly used to monitor invasive species. Studies herein propose to use Cellular Automata (CA), not only to model the distribution of a potentially invasive species but also to infer the potential of the method in risk prediction of Reticulitermes grassei infestation. The test area was mainland Portugal, for which an available presence-only dataset was used. This is a typical dataset type, resulting from either distribution studies or infestation reports. Subterranean termite urban distributions in Portugal from 1970 to 2001 were simulated, and the results were compared with known records from both 2001 (the publication date of the distribution models for R. grassei in Portugal) and 2020. The reported model was able to predict the widespread presence of R. grassei, showing its potential as a viable prediction tool for R. grassei infestation risk in wooden structures, providing the collection of appropriate variables. Such a robust simulation tool can prove to be highly valuable in the decision-making process concerning pest management. Validerad;2022;Nivå 2;2022-03-28 (joosat); Funder: Fundação para a Ciência e Tecnologia, FCT–Portugal, (PTDC/ASP-PLA/30650/2017); Advanced research supporting the forestry and wood-processing sector’s adaptation to global change and the 4th industrial revolution, OP RDE (CZ.02.1.01/0.0/0.0/16_019/0000803); CT WOOD, Luleå University of Technology Article in Journal/Newspaper Luleå Luleå Luleå Luleå University of Technology Publications (DiVA) Forests 13 2 237 |
institution |
Open Polar |
collection |
Luleå University of Technology Publications (DiVA) |
op_collection_id |
ftluleatu |
language |
English |
topic |
subterranean termites infestation risk cellullar automata model Ecology Ekologi |
spellingShingle |
subterranean termites infestation risk cellullar automata model Ecology Ekologi Sequeira, João G. N. Nobre, Tânia Duarte, Sónia Jones, Dennis Esteves, Bruno Nunes, Lina Proof-of-Principle That Cellular Automata Can Be Used to Predict Infestation Risk by Reticulitermes grassei (Blattodea: Isoptera) |
topic_facet |
subterranean termites infestation risk cellullar automata model Ecology Ekologi |
description |
Over the past few decades, species distribution modelling has been increasingly used to monitor invasive species. Studies herein propose to use Cellular Automata (CA), not only to model the distribution of a potentially invasive species but also to infer the potential of the method in risk prediction of Reticulitermes grassei infestation. The test area was mainland Portugal, for which an available presence-only dataset was used. This is a typical dataset type, resulting from either distribution studies or infestation reports. Subterranean termite urban distributions in Portugal from 1970 to 2001 were simulated, and the results were compared with known records from both 2001 (the publication date of the distribution models for R. grassei in Portugal) and 2020. The reported model was able to predict the widespread presence of R. grassei, showing its potential as a viable prediction tool for R. grassei infestation risk in wooden structures, providing the collection of appropriate variables. Such a robust simulation tool can prove to be highly valuable in the decision-making process concerning pest management. Validerad;2022;Nivå 2;2022-03-28 (joosat); Funder: Fundação para a Ciência e Tecnologia, FCT–Portugal, (PTDC/ASP-PLA/30650/2017); Advanced research supporting the forestry and wood-processing sector’s adaptation to global change and the 4th industrial revolution, OP RDE (CZ.02.1.01/0.0/0.0/16_019/0000803); CT WOOD, Luleå University of Technology |
format |
Article in Journal/Newspaper |
author |
Sequeira, João G. N. Nobre, Tânia Duarte, Sónia Jones, Dennis Esteves, Bruno Nunes, Lina |
author_facet |
Sequeira, João G. N. Nobre, Tânia Duarte, Sónia Jones, Dennis Esteves, Bruno Nunes, Lina |
author_sort |
Sequeira, João G. N. |
title |
Proof-of-Principle That Cellular Automata Can Be Used to Predict Infestation Risk by Reticulitermes grassei (Blattodea: Isoptera) |
title_short |
Proof-of-Principle That Cellular Automata Can Be Used to Predict Infestation Risk by Reticulitermes grassei (Blattodea: Isoptera) |
title_full |
Proof-of-Principle That Cellular Automata Can Be Used to Predict Infestation Risk by Reticulitermes grassei (Blattodea: Isoptera) |
title_fullStr |
Proof-of-Principle That Cellular Automata Can Be Used to Predict Infestation Risk by Reticulitermes grassei (Blattodea: Isoptera) |
title_full_unstemmed |
Proof-of-Principle That Cellular Automata Can Be Used to Predict Infestation Risk by Reticulitermes grassei (Blattodea: Isoptera) |
title_sort |
proof-of-principle that cellular automata can be used to predict infestation risk by reticulitermes grassei (blattodea: isoptera) |
publisher |
Luleå tekniska universitet, Träteknik |
publishDate |
2022 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-89928 https://doi.org/10.3390/f13020237 |
genre |
Luleå Luleå Luleå |
genre_facet |
Luleå Luleå Luleå |
op_relation |
Forests, 1999-4907, 2022, 13:2, orcid:0000-0003-3246-5964 orcid:0000-0002-1855-7451 orcid:0000-0001-7309-5440 orcid:0000-0002-5565-6651 orcid:0000-0001-6660-3128 orcid:0000-0001-6849-3241 http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-89928 doi:10.3390/f13020237 ISI:000850353600001 Scopus 2-s2.0-85124218530 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.3390/f13020237 |
container_title |
Forests |
container_volume |
13 |
container_issue |
2 |
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237 |
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1766065201087512576 |