Browsing by Subject "hierarchical model"

Now showing 1 - 3 of 3
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    Data, R Code, and Output Supporting: A “How-to” Guide for Estimating Animal Diel Activity Using Hierarchical Models
    (2024-10-22) Iannarilli, Fabiola; Gerber, Brian D; Erb, John; Fieberg, John R; jfieberg@umn.edu; Fieberg, John; Fieberg Lab
    This repository contains data, code, and associated output supporting work presented in "Iannarilli F., Gerber B. D., Erb J., and Fieberg J. R. (2024). A “How-to” Guide for Estimating Animal Diel Activity Using Hierarchical Models. Journal of Animal Ecology". We provide a series of .Rmd files that can be compiled to form a step-by-step tutorial demonstrating how to quantify animal activity patterns from time-stamped data using trigonometric and cyclic cubic spline hierarchical models. These models can accommodate site-to-site variability in the frequency of site use and timing of activity, while accounting for sampling effort. The text is accompanied by a series of examples in which we address common ecological questions related to the study of animal diel activity, such as the shape of the underlying activity pattern (unimodal, bimodal, or cathemeral) and the effect of covariates or the co-occurrence of other species on activity patterns. An HTML version of this tutorial is available at https://hms-activity.netlify.app/.
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    Occupancy Survey Data and analysis code for shorebird and waterfowl habitat use in NW North Dakota, 2014-2015
    (2018-11-19) Specht, Hannah; spech030@umn.edu; Specht, Hannah; University of Minnesota Fisheries, Wildlife and Conservation Biology
    Data and R code examine quarter-section site occupancy rates of upland nesting waterbirds relative to oil well density and traffic activity to accompany H. Specht PhD Thesis 2018, University of Minnesota.
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    R code and output supporting: Time series sightability modeling of animal populations
    (2017-03-29) ArchMiller, Althea A; Fieberg, John R; Dorazio, Robert M; St. Clair, Katherine; althea.archmiller@gmail.com; ArchMiller, Althea A
    The goal of our study was to expand a previously developed model-based approach to include random effects and a temporal spline for time series modeling of multiple years of operational survey data. We developed a Bayesian hierarchical model as our framework to build and compare fixed-effects and temporal model-based sightability models applied to 12 years of MN moose operational survey data. Here, we share the Program R code and data necessary to replicate the manuscript results that demonstrate how our time series sightability modeling approach can increase the precision of population estimators and predict population dynamics with smoother (and thus more realistic trends) through time.