  Estimating the geographical range of a species from sparse observations is a
challenging and important geospatial prediction problem. Given a set of
locations where a species has been observed, the goal is to build a model to
predict whether the species is present or absent at any location. This problem
has a long history in ecology, but traditional methods struggle to take
advantage of emerging large-scale crowdsourced datasets which can include tens
of millions of records for hundreds of thousands of species. In this work, we
use Spatial Implicit Neural Representations (SINRs) to jointly estimate the
geographical range of 47k species simultaneously. We find that our approach
scales gracefully, making increasingly better predictions as we increase the
number of species and the amount of data per species when training. To make
this problem accessible to machine learning researchers, we provide four new
benchmarks that measure different aspects of species range estimation and
spatial representation learning. Using these benchmarks, we demonstrate that
noisy and biased crowdsourced data can be combined with implicit neural
representations to approximate expert-developed range maps for many species.
