Data from: Making better use of tracking data can reveal the spatiotemporal and intraspecific variability of species distributions

Understanding geographic ranges and species distributions is crucial for
effective conservation, especially in the light of climate and land use
change. However, the spatial, temporal and intraspecific resolution of
digital accessible information on species distributions is often limited.
Here, we suggest to make better use of high-resolution tracking data to
address existing limitations of occurrence records such as spatial biases
(e.g. lack of observations in parts of the geographic range), temporal
biases (e.g. lack of observations during a certain period of the year),
and insufficient information on intraspecific variability (e.g. lack of
population- or individual-level variation). Addressing these gaps can
improve our knowledge on geographic ranges, intra-annual changes in
species distributions, and population-level differences in habitat and
space use. We demonstrate this with tracking data and species distribution
models (SDMs) of the Barnacle Goose, a migratory bird species wintering in
western Europe and breeding in the Arctic. Our analyses show that tracking
data can (1) supplement occurrence records from the Global Biodiversity
Information Facility (GBIF) in remote areas such as the European and
Russian Arctic, (2) improve information on the temporal use of wintering,
staging and breeding areas of migratory species, and (3) provide insights
into the differences of population-level responses to environmental
variables. We recommend a broader use of tracking data to address the
Wallacean shortfall (i.e. the incomplete knowledge on the geographic
distribution of species) and to improve forecasts of biodiversity
responses to climate and land use change (e.g. species vulnerability
assessments). To avoid common pitfalls, we provide six recommendations for
consideration during the research cycle when using tracking data in
species distribution modelling, including steps to assess biases and
integrate information on intraspecific variability in modelling
approaches.