Data from: Integrated species distribution models: combining presence-background data and site-occupany data with imperfect detection

Two main sources of data for species distribution models (SDMs) are
site-occupancy (SO) data from planned surveys, and presence-background
(PB) data from opportunistic surveys and other sources. SO surveys give
high quality data about presences and absences of the species in a
particular area. However, due to their high cost, they often cover a
smaller area relative to PB data, and are usually not representative of
the geographic range of a species. In contrast, PB data is plentiful,
covers a larger area, but is less reliable due to the lack of information
on species absences, and is usually characterised by biased sampling. Here
we present a new approach for species distribution modelling that
integrates these two data types. We have used an inhomogeneous Poisson
point process as the basis for constructing an integrated SDM that fits
both PB and SO data simultaneously. It is the first implementation of an
Integrated SO–PB Model which uses repeated survey occupancy data and also
incorporates detection probability. The Integrated Model's
performance was evaluated, using simulated data and compared to approaches
using PB or SO data alone. It was found to be superior, improving the
predictions of species spatial distributions, even when SO data is sparse
and collected in a limited area. The Integrated Model was also found
effective when environmental covariates were significantly correlated. Our
method was demonstrated with real SO and PB data for the Yellow-bellied
glider (Petaurus australis) in south-eastern Australia, with the
predictive performance of the Integrated Model again found to be superior.
PB models are known to produce biased estimates of species occupancy or
abundance. The small sample size of SO datasets often results in poor
out-of-sample predictions. Integrated models combine data from these two
sources, providing superior predictions of species abundance compared to
using either data source alone. Unlike conventional SDMs which have
restrictive scale-dependence in their predictions, our Integrated Model is
based on a point process model and has no such scale-dependency. It may be
used for predictions of abundance at any spatial-scale while still
maintaining the underlying relationship between abundance and area.