Plant Recommendation using Environment and Biotic Associations

Automatically predicting species make-up in geographic locations is of great importance in the context of the current conversation about biodiversity. Inspired by the ecological concepts of Grinnellian and Eltonian niches, we investigate two neural network architectures that aim to that aim to exploit the respective features of these two types of niches in order to tackle the plant recommendation task. The first proposal uses environmental rasters and leverages advanced feature extraction techniques based on distributed representations and convolutional neural networks. The second proposal relies on neighboring co-occurrences of plants and organisms from an expert-curated list of taxa. We find that the former solution outperforms the latter in prediction accuracy, yet the second solution provides interesting and more interpretable indicators. Both approaches yield promising results on the GeoLifeCLEF 2019 challenge.