Ecological Informatics 

TL;DR: A novel numerical stochastic optimization algorithm inspired from colonizing weeds to mimic robustness, adaptation and randomness of Colonizing weeds in a simple but effective optimizing algorithm designated as Invasive Weed Optimization (IWO).

TL;DR: Comparison studies in environmental sciences are used to assess the performance and to quantify the impacts of data properties on the performance of spatial interpolation methods, finding data variation is a dominant impact factor and has significant effects on theperformance of the methods.

TL;DR: A subsampling routine is used as an exemplar taxon to provide evidence that range model quality is decreasing due to the spatial clustering of distributional records in GBIF and shows that data with less spatial bias produce better predictive models even though they are based on less input data.

TL;DR: It is suggested that remotely sensed spectral heterogeneity information provides a crucial baseline for rapid estimation or prediction of biodiversity attributes and hotspots in space and time.

TL;DR: In this study, EfficientNet deep learning architecture was proposed in plant leaf disease classification and the performance of this model was compared with other state-of-the-art deep learning models.