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On the Modelling of Species Distribution: Logistic Regression Versus Density Probability Function
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Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Agent-based Modelling and Simulation Species Distribution Models Environmental Modelling Logistic Regression Density Probability Function Pseudo-absence Data
Contexto Educativo
Citação
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.
Editora
Springer, Cham