Premium
assignR : An r package for isotope‐based geographic assignment
Author(s) -
Ma Chao,
Vander Zanden Hannah B.,
Wunder Michael B.,
Bowen Gabriel J.
Publication year - 2020
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.13426
Subject(s) - geolocation , computer science , r package , data quality , data mining , bayesian probability , data science , information retrieval , engineering , world wide web , metric (unit) , operations management , computational science , artificial intelligence
Methods for inferring geographic origin from the stable isotope composition of animal tissues are widely used in movement ecology, but few computational tools and standards for data interpretation are available. We introduce the assignR r package, which provides a structured, flexible toolkit for isotope‐based migration data analysis and interpretation using a widely adopted semi‐parametric Bayesian inversion method. assignR bundles data resources and functions that support data interpretation, hypothesis‐testing and quality assessment, allowing end‐to‐end data analysis with only a few lines of code. Tools for post hoc analysis offer robust, standardized methods for aggregating information from multiple individuals, assignment of individuals to a sub‐region of the study area and comparison of potential regions of origin using odds ratios. Assessment tools quantify the quality and power of the isotopic assignments and can be used to test prototype study designs. The assignR package should increase the accessibility of isotopic geolocation methods. assignR supports flexible data sources and analysis decisions, making it suitable for a wide range of applications, but also promotes standardization that will help foster increased consistency and comparability among studies and a more holistic understanding of animal migration. Lastly, assignR can help make isotope‐based geolocation research more efficient by helping researchers plan projects to be optimally aligned with their research questions.