Why ecologists need to know about AI and ML?
The complex interactions that take place in nature makes it difficult for ecologists to determine causality. Nowadays, gathering large amounts of environmental data over multiple sites and at different points in time is easier than ever. This data, if sampled and analysed with the right tools, has the potential to enable ecologists to gain a holistic understanding of natural ecosystems.
ML algorithms are well-suited to analyse both large and small data sets. ML can outperform the predictive power and inference of common statistical tests used in ecology (e.g. Chi-square test, t-test, or ANOVA models). However, the use of ML in ecology is low compared to other scientific fields.
There are seeds and seedlings of change though. The number of conservation projects using AI increases as more ecologists understand the benefits of ML and NGOs, governments and private companies support its use for conservation. Some examples include ML algorithms applied to model species distribution, explore human-nature interactions, and recognise species from photos, video, or audio data.
The use of AI tools in ecology is far from reaching its peak. Further collaborations between ecologists, statisticians and computer scientists will elucidate the true potential of AI for wildlife conservation, ecology, and management of natural resources.