Species distributions models (SDMs) rely on accurate climatic data to predict the potential ranges of species, however, climatic datasets are often based on interpolations between weather stations. In regions where stations are scarce, such as the tropics, modelling can therefore be problematic.
When modelling species distributions based on occurrences, absence is often derived from lack of presence (pseudo-absence), but absence could be due to poor surveying or species behaviour. This paper presents a novel methodology to predict absence information from presence-only information.
Bumblebees are important global providers of ecological services. This study used more than 360,000 GBIF-mediated occurrence records from Europe and North America to evaluate the impacts of climate change on the future of the bumblebee.
Combining GBIF-mediated occurrences with data from a novel database, the Global Naturalized Alien Flora (GloNAF), this study seeks to identify and quantify the spread and distribution of naturalized plant species worldwide.