msocc: Fit and analyse computationally efficient multi‐scale occupancy models in R
Methods in Ecology and Evolution
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Abstract
- Environmental DNA (eDNA) sampling is a promising tool for the detection of rare and cryptic taxa, such as aquatic pathogens, parasites and invasive species. Environmental DNA sampling workflows commonly rely on multi‐stage hierarchical sampling designs that induce complicated dependencies within the data. This complex dependence structure can be intuitively modelled with Bayesian multi‐scale occupancy models. However, current software for such models are computationally demanding, impeding their use.
- We present an r package, msocc, that implements a data augmentation strategy to fit fully Bayesian, computationally efficient multi‐scale occupancy models. The msocc package allows users to fit multi‐scale occupancy models, to estimate and visualize posterior summaries of site, sample and replicate‐level occupancy, and to compare different models using Bayesian information criterion. Additionally, we provide a supplemental web application that allows users to investigate study design for multi‐scale occupancy models and acts as a graphical user interface to the msocc package.
- The utility of the msocc package is illustrated on a published dataset and the functions in msocc are compared to the primary Bayesian toolkit for multi‐scale occupancy modelling, eDNAoccupancy, using various computational benchmarks. These benchmarks indicate that msocc is capable of fitting models 50 times faster than eDNAoccupancy.
- We hope that access to software that efficiently fits, analyses and conducts study design investigations for multi‐scale occupancy models facilitates their implementation by the research and wildlife management communities.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | msocc: Fit and analyse computationally efficient multi‐scale occupancy models in R |
| Series title | Methods in Ecology and Evolution |
| DOI | 10.1111/2041-210X.13442 |
| Volume | 11 |
| Issue | 9 |
| Year Published | 2020 |
| Language | English |
| Publisher | Wiley |
| Contributing office(s) | Northern Rocky Mountain Science Center |
| Description | 8 p. |
| First page | 1113 |
| Last page | 1120 |
| Google Analytic Metrics | Metrics page |