Abstract
The concerns related to climate changes have been gaining attention in the last few years due to the negative impacts on the environment, economy, and society. To better understand and anticipate the effects of climate changes in the distribution of species, several techniques have been adopted comprising models of different complexity. In general, these models apply algorithms and statistical methods capable of predicting in a particular study area, the locations considered suitable for a species to survive and reproduce, given a set of eco-geographical variables that influence species behavior. Logistic regression algorithm and Probability density function are two common methods that can be used to model the species suitability. The former is a representative of a class of models that requires the availability (or imputation) of presence-absence data whereas the latter represents the models that only require presence data. Both approaches are compared regarding the capability to accurately predict the environmental suitability for species. On a different way, the behaviour of the species in the projected environments are analysed by simulating its potential distribution in the projected environment. A case study reporting results from two types of species with economical interest is presented: the strawberry tree (Arbutus unedo) in mainland Portugal, and the Apis mellifera (African Lineage) in the Iberian Peninsula.
Keywords
- Agent-based modelling and simulation
- Species distribution models
- Environmental modelling
- Logistic regression
- Density probability function
- Pseudo-absence data
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Acknowledgments
This work was supported by operation Centro-01-0145-FEDER-000019 - C4 - Centro de Competências em Cloud Computing, cofinanced by the European Regional Development Fund (ERDF) through the Programa Operacional Regional do Centro (Centro 2020), in the scope of the Sistema de Apoio à Investigação Científica e Tecnológica - Programas Integrados de IC&DT. This work was also funded by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/50008/2020.
We thank all the authors of the paper [13] for providing the occurrence data used in the first case study.
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Bioco, J., Prata, P., Canovas, F., Fazendeiro, P. (2022). On the Modelling of Species Distribution: Logistic Regression Versus Density Probability Function. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 507. Springer, Cham. https://doi.org/10.1007/978-3-031-10464-0_25
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DOI: https://doi.org/10.1007/978-3-031-10464-0_25
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