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

Adam Smith, Benjamin Hofner, Juliet S. Lamb, Jason Osenkowski, Taber Allison, Giancarlo Sadoti, Scott McWilliams & Peter Paton
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...
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54 downloads reported since publication in 2019.

These counts follow the COUNTER Code of Practice, meaning that Internet robots and repeats within a certain time frame are excluded.
What does this mean?