Old data, new tools: Using random forest modelling to reveal multi-species habitat associations from spoor data

In their new study, Searle, Kaszta, and co-authors from Botswana, Zimbabwe, Germany, the UK, and the US discuss how machine learning can be used to disentangle multi-species habitat relationships and inform conservation planning over large areas. The importance of policy and governance in preserving wildlife areas has historically meant that conservation has been restricted to efforts within country borders. This approach is at odds with … Continue reading Old data, new tools: Using random forest modelling to reveal multi-species habitat associations from spoor data

A deep learning model for pollinator plant surveys

Buff-tailed bumblebee (Bombus terrestris) feeding on the nectar of Creeping thistle (Cirsium arvense) flowers © Damien Hicks Authors Damien Hicks and Christoph Kratz introduce their team’s latest research demonstrating the use of machine learning for quadrat surveys to improve accessibility and resource efficiency of current methods for floral vegetation monitoring. The nectar sugar contained in flowers is a key driver of pollinator abundance and diversity. … Continue reading A deep learning model for pollinator plant surveys