  To address the interlinked biodiversity and climate crises, we need an
understanding of where species occur and how these patterns are changing.
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.
