  Presence-only records may provide data on the distributions of rare species,
but commonly suffer from large, unknown biases due to their typically haphazard
collection schemes. Presence-absence or count data collected in systematic,
planned surveys are more reliable but typically less abundant.
  We proposed a probabilistic model to allow for joint analysis of
presence-only and survey data to exploit their complementary strengths. Our
method pools presence-only and presence-absence data for many species and
maximizes a joint likelihood, simultaneously estimating and adjusting for the
sampling bias affecting the presence-only data. By assuming that the sampling
bias is the same for all species, we can borrow strength across species to
efficiently estimate the bias and improve our inference from presence-only
data.
  We evaluate our model's performance on data for 36 eucalypt species in
southeastern Australia. We find that presence-only records exhibit a strong
sampling bias toward the coast and toward Sydney, the largest city. Our
data-pooling technique substantially improves the out-of-sample predictive
performance of our model when the amount of available presence-absence data for
a given species is scarce.
  If we have only presence-only data and no presence-absence data for a given
species, but both types of data for several other species that suffer from the
same spatial sampling bias, then our method can obtain an unbiased estimate of
the first species' geographic range.
