Spatial prediction of plant invasion using a hybrid of machine learning and geostatistical method.

ABSTRACT: Modeling ecological patterns and processes often involve large-scale and complex high-dimensional spatial data. Due to the nonlinearity and multicollinearity of ecological data, traditional geostatistical methods have faced great challenges in model accuracy. As machine learning has increased our ability to construct models on big data, the main focus of the study is to propose the use of statistical models that hybridize machine learning and spatial interpolation methods to cope with increasingly large-scale and complex ecological data. Here, two machine learning algorithms, boosted regression tree (BRT) and least absolute shrinkage and selection operator (LASSO), were combined with ordinary kriging (OK) to model plant invasions across the eastern United States. The accuracies of the hybr

SUBMITTER: Shen L 

PROVIDER: S-EPMC11199124 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature