StevenHoekman/Multi-Observer-Methods: Version 1.0.0

• Main Author: Hoekman, Steven T
• Format: Article in Journal/Newspaper
• Language: English
• Published: Zenodo 2021
• Subjects: Glacier Bay; glacier; Alaska
• Online Access: https://dx.doi.org/10.5281/zenodo.4738002; https://zenodo.org/record/4738002

Multi-Observer Methods for Estimating Uncertain Species Identification Initial release version 1.0.0 (the latest version is recommended for most users) Summary This repository archives data and novel R computer code from the companion article: Hoekman, Steven T. 2021. Multi-observer methods for estimating uncertain species identification. Ecosphere. 12(9):e03648. 10.1002/ecs2.3648 The article presents statistical models for multi-observer methods for estimating uncertain species identification (species misidentification and partial identification, which is identification to the lowest certain taxon) during population ecology surveys that enumerate individuals. Each Data Supplement (below) provides necessary R computer code, supporting files, and documentation. Analyses were conducted in the R Statistical Computing Environment version 3.6. Data supplements Subdirectories contain files for four data supplements. Documentation in a metadata file for each lists dependencies and provides instructions for use. DataS1: Example R Code DataS2: Input Files for Simulations DataS3: R Code for Simulations DataS4: Analyses of Murrelet Surveys DataS1 provides simple examples of multi-observer method models, with R code, example input, and example output. DataS2 and DataS3 provide input files and R code to replicate simulation study described in the companion article or to conduct user-specified simulations. DataS4 provides data and R code used to conduct statistical analyses of multi-observer method models applied to line transect surveys for Kittlitz’s ( Brachyramphus brevirostris ) and marbled ( B. marmoratus ) murrelets in Glacier Bay, Alaska, USA.