Data from: A hierarchical distance sampling model to estimate abundance and covariate associations of species and communities

Distance sampling is a common survey method in wildlife studies, because
it allows accounting for imperfect detection. The framework has been
extended to hierarchical distance sampling (HDS), which accommodates the
modelling of abundance as a function of covariates, but rare and elusive
species may not yield enough observations to fit such a model. We
integrate HDS into a community modelling framework that accommodates
multi-species spatially replicated distance sampling data. The model
allows species-specific parameters, but these come from a common
underlying distribution. This form of information sharing enables
estimation of parameters for species with sparse data sets that would
otherwise be discarded from analysis. We evaluate the performance of the
model under varying community sizes with different species-specific
abundances through a simulation study. We further fit the model to a
seabird data set obtained from shipboard distance sampling surveys off the
East Coast of the USA. Comparing communities comprised of 5, 15 or 30
species, bias of all community-level parameters and some species-level
parameters decreased with increasing community size, while precision
increased. Most species-level parameters were less biased for more
abundant species. For larger communities, the community model increased
precision in abundance estimates of rarely observed species when compared
to single-species models. For the seabird application, we found a strong
negative association of community and species abundance with distance to
shore. Water temperature and prey density had weak effects on seabird
abundance. Patterns in overall abundance were consistent with known
seabird ecology. The community distance sampling model can be expanded to
account for imperfect availability, imperfect species identification or
other missing individual covariates. The model allowed us to make
inference about ecology of species communities, including rarely observed
species, which is particularly important in conservation and management.
The approach holds great potential to improve inference on species
communities that can be surveyed with distance sampling.