ICML 2025

Feedforward Few-shot Species Range Estimation
Christian Lange 1 Max Hamilton 2 Elijah Cole 3 Alexander Shepard 4 Samuel Heinrich 5 Angela Zhu 2
Subhransu Maji 2 Grant Van Horn 2 Oisin Mac Aodha 1
Abstract
Knowing where a particular species can or can-
not be found on Earth is crucial for ecological
research and conservation efforts. By mapping
the spatial ranges of all species, we would ob-
tain deeper insights into how global biodiversity
is affected by climate change and habitat loss.
However, accurate range estimates are only avail-
able for a relatively small proportion of all known
species. For the majority of the remaining species,
we typically only have a small number of records
denoting the spatial locations where they have
previously been observed. We outline a new ap-
proach for few-shot species range estimation to
address the challenge of accurately estimating the
range of a species from limited data. During infer-
ence, our model takes a set of spatial locations as
input, along with optional metadata such as text or
an image, and outputs a species encoding that can
be used to predict the range of a previously un-
seen species in a feedforward manner. We evalu-
ate our approach on two challenging benchmarks,
where we obtain state-of-the-art range estimation
performance, in a fraction of the compute time,
compared to recent alternative approaches.
1. Introduction
Understanding the spatial distribution of plant and ani-
mal species is essential to mitigate the ongoing decline
in global biodiversity (Jetz et al., 2019). Monitoring these
distributions over time allows us to quantify the impacts
of climate change, habitat loss, and conservation interven-
tions (Mantyka-pringle et al., 2012). Estimating a species’
spatial distribution typically starts with collecting a set of
observations that denote the locations where the species has
been confirmed to be present or absent. Traditionally, this
1University of Edinburgh
2UMass Amherst
3GenBio AI
4iNaturalist
5Co...