Abstract
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.
Highlights
The lower resulted from the probability for termite infestation to occurcombined in the threewith north-east countiesnumber resulted from prevalent landscape conditions the reduced of neighboring cells’ a the prevalent landscape combined with the number model
The model predicted the widespread risk of presence of R. grassei, with the exception of a single cell
This cell corresponded to the lowest population density
Summary
Given the importance of monitoring potential pest species and understanding their responses to different environmental conditions, species distribution modelling has assumed an increasingly significant role over the last few decades [1,2]. Cellular Automata (CA) [6] was selected, because of its inherent simplicity and versatility, as it can simulate a variety of real-world systems, including biological and ecological ones. Despite having a relatively simple conceptual basis, CA can handle very complex ecological systems and, as a result, is popular for studying spatially extended dynamics [7]
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