R package for multicollinearity management in data frames with numeric and categorical variables.
Deep explanation of what Variance Inflation Factors (VIF) are, how they work, what they really mean, and how they are used to manage multicollinearity in linear models.
Fairy circles (FCs) are intriguing regular vegetation patterns that have only been described in Namibia and Australia so far. We conducted a global and systematic assessment of FC-like vegetation patterns and discovered hundreds of FC-like locations on three continents. We also characterized the range of environmental conditions that determine their presence, which is restricted to narrow and specific soil and climatic conditions. Areas showing FC-like vegetation patterns also had more stable productivity over time than surrounding areas having non-FC patterns. Our study provides insights into the ecology and biogeography of these fascinating vegetation patterns and the first atlas of their global distribution.
R package for spatial regression with Random Forest
R package to assess ecological memory in multivariate time-series.
Here we draw attention to an emerging subdiscipline of artificial intelligence, explainable AI (xAI), as a toolbox for better interpreting SDMs. xAI aims at deciphering the behavior of complex statistical or machine learning models (e.g. neural networks, random forests, boosted regression trees), and can produce more transparent and understandable SDM predictions.
Herein we investigate the distribution and conservation problems of a relict interaction in the Sierra Nevada mountains (southern Europe) between the butterfly *Agriades zullichi* —a rare and threatened butterfly— and its larval foodplant *Androsace vitaliana* subsp. *nevadensis*. We designed an intensive field survey to obtain a comprehensive presence dataset. This was used to calibrate species distribution models with absences taken at local and regional extents, analyze the potential distribution, evaluate the influence of environmental factors in different geographical contexts, and evaluate conservation threats for both organisms.
A time series of 14-year distribution data of Zostera marina in the Ems estuary (The Netherlands) was used to build different data subsets: (1) total presence area; (2) a conservative estimate of the total presence area, defined as the area which had been occupied during at least 4 years; (3) core area, defined as the area which had been occupied during at least 2/3 of the total period; and (4–6) three random selections of monitoring years. On average, colonized and disappeared areas of the species in the Ems estuary showed remarkably similar transition probabilities of 12.7% and 12.9%, respectively. SDMs based upon machine-learning methods (Boosted Regression Trees and Random Forest) outperformed regression-based methods. Current velocity and wave exposure were the most important variables predicting the species presence for widely distributed data. Depth and sea floor slope were relevant to predict conservative presence area and core area.