Data for: Spatial prediction of plant invasion using a hybrid of machine learning and geostatistical method

Modelling ecological patterns and processes often involves 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 the
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 accuracy of the hybrid models and conventional models was evaluated by
10-fold cross-validation. Based on an invasive plants dataset of 15
ecoregions across the eastern United States, the results showed that the
hybrid algorithms were significantly better at predicting plant invasion
when compared to commonly used algorithms in terms of RMSE and
paired-samples t-test (with the p-value < 0.0001). Besides, the
additional aspect of the combined algorithms is to have the ability to
select influencial variables associated with the establishment of invasive
cover, which can not be achieved by conventional geostatistics. Higher
accuracy in the prediction of large-scale biological invasions improves
our understanding of the ecological conditions that lead to the
establishment and spread of plants into novel habitats across spatial
scales. The results demonstrate the effectiveness and robustness of the
hybrid BRTOK and LASOK that can be used to analyze large-scale and
high-dimensional spatial datasets, and it has offered an optional source
of models for spatial interpolation of ecology properties. It will also
provide a better basis for management decisions in early-detection
modelling of invasive species.