Data from: Modeling spatiotemporal abundance of mobile wildlife in highly variable environments using boosted GAMLSS hurdle models

1. Modeling organism distributions from survey data involves numerous
statistical challenges, including zero-inflation, overdispersion, and
selection and incorporation of environmental covariates. In environments
with high spatial and temporal variability, addressing these challenges
often requires numerous assumptions regarding organism distributions and
their relationships to biophysical features. These assumptions may limit
the resolution or accuracy of predictions resulting from survey-based
distribution models. 2. We propose an iterative modeling approach that
incorporates a negative binomial hurdle, followed by modeling the
relationship of organism distribution and abundance to environmental
covariates using generalized additive models (GAM) and generalized
additive models for location, scale, and shape (GAMLSS). Our approach
accounts for key features of survey data by separating binary
(presence-absence) from count (abundance) data, separately modeling the
mean and dispersion of count data, and incorporating selection of
appropriate covariates and response functions from a suite of potential
covariates while avoiding overfitting. 3. We apply our modeling approach
to surveys of sea duck abundance and distribution in Nantucket Sound
(Massachusetts, USA), which has been proposed as a location for offshore
wind energy development. Our model results highlight the importance of
spatiotemporal variation in this system, as well as identifying key
habitat features including distance to shore, sediment grain size, and
seafloor topographic variation. 4. Our work provides a powerful, flexible,
and highly repeatable modeling framework with minimal assumptions that can
be broadly applied to the modeling of survey data with high spatiotemporal
variability. Applying GAMLSS models to the count portion of survey data
allows us to incorporate potential overdispersion, which can dramatically
affect model results in highly dynamic systems. Our approach is
particularly relevant to systems in which little a priori knowledge is
available regarding relationships between organism distributions and
biophysical features, since it incorporates simultaneous selection of
covariates and their functional relationships with organism responses.