splancs: Spatial and Space-Time Point Pattern Analysis

TL;DR: In this article, a simple method was proposed to exploit information on leaf length, width and shape from species descriptions to robustly estimate leaf areas, thus closing this considerable knowledge gap for this important plant functional trait.

TL;DR: This work introduces several specific algorithms to detect and visualize spatio-temporal clusters, and presents an integrated hotspot visualizer which allows the efficient identification and visualization of clusters in one environment.

TL;DR: In this article, the authors used the Random Forests (RF) machine learning algorithm to predict the 3 overstory responses via univariate regression or classification, or multivariate k-NN imputation.

TL;DR: The proposed semi-quantitative pathology score provided good agreement between experienced raters, showing it to be a powerful and widely applicable tool for defining WNS severity.