Abstract
Modeling organism distributions from survey data involves numerous statistical challenges, including accounting for 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. We propose an iterative modeling approach that incorporates a negative binomial hurdle, followed by modeling of 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. 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. 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.
Highlights
Understanding how the spatial distribution and abundance of an organism responds to biophysical features is fundamental to many aspects of ecology and conservation (Schröder & Seppelt, 2006)
We propose an iterative mode‐ ling approach that incorporates a negative binomial hurdle, followed by modeling of the relationship of organism distribution and abundance to environmental co‐ variates using generalized additive models (GAM) and generalized additive models for location, scale, and shape (GAMLSS)
Since continuous sampling of the entire range or population of a species is usually impossible, distribution mapping typically in‐ volves a series of steps that include surveying a representative subset of the area or population of interest at various time pe‐ riods, fitting models to represent the relationships of observed data to environmental covariates, using these models to predict utilization of un‐sampled areas or time periods based on biophys‐ ical habitat features, and validating predictions with on‐ the‐ground observations (Borchers, Buckland, & Zucchini, 2002; Certain & Bretagnolle, 2008; Kinlan, Menza, & Huettmann, 2012; Nur et al, 2011)
Summary
Understanding how the spatial distribution and abundance of an organism responds to biophysical features is fundamental to many aspects of ecology and conservation (Schröder & Seppelt, 2006). Temporal varia‐ tion in both the distribution and habitat preferences of a species can introduce further uncertainty, because organisms’ responses to changes in habitat conditions may not be instantaneous and may vary across the annual cycle (Selonen, Varjonen, & Korpimäki, 2015; Yamanaka, Akasaka, Yamaura, Kaneko, & Nakamura, 2015) Both occupancy and abundance may respond to biophysical habitat features, and to the distribution of other organisms such as conspecifics, competitors, predators, or prey (Blackburn, Cassey, & Gaston, 2006; Guisan & Thuiller, 2005). We demonstrate an application of our modeling framework to informing conservation planning in the face of both high variability and ecological uncertainty by de‐ veloping models from systematic aerial survey data of sea ducks in this system
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