Hager Radi

Predicting Species Occurrence Patterns from Partial Observations

Hager Radi

Mélisande Teng

To address the interlinked biodiversity and climate crises, we need an understanding of where species occur and how these patterns are chang… (voir plus)ing. However, observational data on most species remains very limited, and the amount of data available varies greatly between taxonomic groups. We introduce the problem of predicting species occurrence patterns given (a) satellite imagery, and (b) known information on the occurrence of other species. To evaluate algorithms on this task, we introduce SatButterfly, a dataset of satellite images, environmental data and observational data for butterflies, which is designed to pair with the existing SatBird dataset of bird observational data. To address this task, we propose a general model, R-Tran, for predicting species occurrence patterns that enables the use of partial observational data wherever found. We find that R-Tran outperforms other methods in predicting species encounter rates with partial information both within a taxon (birds) and across taxa (birds and butterflies). Our approach opens new perspectives to leveraging insights from species with abundant data to other species with scarce data, by modelling the ecosystems in which they co-occur.

2024-03-26

ArXiv (prépublication)

doi.org

arxiv.org

SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data

Mélisande Teng

Amna Elmustafa

Benjamin Akera

Yoshua Bengio

Hager Radi

Hugo Larochelle

David Rolnick

Biodiversity is declining at an unprecedented rate, impacting ecosystem services necessary to ensure food, water, and human health and well-… (voir plus)being. Understanding the distribution of species and their habitats is crucial for conservation policy planning. However, traditional methods in ecology for species distribution models (SDMs) generally focus either on narrow sets of species or narrow geographical areas and there remain significant knowledge gaps about the distribution of species. A major reason for this is the limited availability of data traditionally used, due to the prohibitive amount of effort and expertise required for traditional field monitoring. The wide availability of remote sensing data and the growing adoption of citizen science tools to collect species observations data at low cost offer an opportunity for improving biodiversity monitoring and enabling the modelling of complex ecosystems. We introduce a novel task for mapping bird species to their habitats by predicting species encounter rates from satellite images, and present SatBird, a satellite dataset of locations in the USA with labels derived from presence-absence observation data from the citizen science database eBird, considering summer (breeding) and winter seasons. We also provide a dataset in Kenya representing low-data regimes. We additionally provide environmental data and species range maps for each location. We benchmark a set of baselines on our dataset, including SOTA models for remote sensing tasks. SatBird opens up possibilities for scalably modelling properties of ecosystems worldwide.

2023-11-02

ArXiv (prépublication)

doi.org

arxiv.org

SatBird: a Dataset for Bird Species Distribution Modeling using Remote Sensing and Citizen Science Data

Mélisande Teng

Amna Elmustafa

Benjamin Akera

Yoshua Bengio

Hager Radi

Hugo Larochelle

David Rolnick