Proof-of-Principle That Cellular Automata Can Be Used to Predict Infestation Risk by Reticulitermes grassei (Blattodea: Isoptera)

• Autor(a) principal: Sequeira, Joao GN
• Data de Publicação: 2022
• Outros Autores: Nobre, Tânia, Duarte, Sonia, Jones, Dennis, Esteves, Bruno, Nunes, Lina
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10174/31255; https://doi.org/Sequeira, J.G.N.; Nobre, T.; Duarte, S.; Jones, D.; Esteves, B.; Nunes, L. Proof-of-Principle That Cellular Automata Can Be Used to Predict Infestation Risk by Reticulitermes grassei (Blattodea: Isoptera). Forests 2022, 13, 237. https://doi.org/10.3390/f13020237; https://doi.org/10.3390/f13020237
• topic: Subterranean termites; model; infestation risk; cellullar automata

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.