Predicting population trends of birds worldwide with big data and machine learning

Birds are crucial for the functioning of Earth's ecosystems but bird population declines have been documented worldwide in recent decades. A global assessment of potential causes of population declines is needed. Our goal here was to combine the power of big data and machine learning to identify predictors correlated with bird population declines and to predict population declines for species with unknown population trends on the IUCN Red List. From existing online databases, we gathered detailed species-level data for 10 964 extant bird species around the world, featuring life history, ecology, distribution, taxonomy and categorical population trend information (i.e. decreasing or not decreasing). For the 10 163 species with known population trends, we split the data into a 75% training set to tune and train a machine-learning model (Light Gradient Boosting Machine - 'LightGBM') and a 25% test set to evaluate the trained model. Our model predicted (i) bird population declines with an ROC AUC score of 0.828, F1 score of 0.748 and average accuracy of 0.747, and (ii) that 47% (n = 801) of bird species with currently unknown population trends are declining. Correlation analyses suggested that, globally, the top predictor associated with bird population declines was a severely fragmented population, with non-migratory birds in South American and Southeast Asian tropical and subtropical forests being particularly vulnerable. Despite the lack of long-term quantitative population trend data for all species worldwide, our study presents big data and machine learning as a useful tool for informing conservation priorities, lending insight, albeit imperfect, into bird population declines on the global scale for the first time.