Integrating machine learning, remote sensing and citizen science to create an early warning system for biodiversity

• Application of machine learning approaches is aiding biodiversity conservation and research at a time of rapid global change. Two emerging topics and their data requirements are presented. First, to identify areas of priority protection for preventing biodiversity loss, reinforcement learning is used by training models that take into account human disturbance and climate change under recurrent monitoring schemes. Second, neural networks are used to approximate classification of species into Red List categories of the International Union for Conservation of Nature, offering the possibility of real-time re-classification after events such as widespread fires and deforestation. We discuss how the identification of areas and species most at risk could be integrated into an ‘early warning system’ based on climatic monitoring, remotely sensed land-use changes and near-real time biological and threat data from citizen science initiatives. Such system would help guide actions to prevent biodiversity loss at the speed required for effective conservation.

Subject headings

NATURVETENSKAP  -- Geovetenskap och miljövetenskap (hsv//swe)

NATURAL SCIENCES  -- Earth and Related Environmental Sciences (hsv//eng)

Keyword

artificial intelligence

citizen science

climate change

conservation

biology

machine learning

modelling

neural network

warning system

climate-change impacts

patterns

plants

Biodiversity & Conservation

Plant Sciences

Ecology

Publication and Content Type

ref (subject category)

art (subject category)