Document Type
Article
Publication Date
11-1-2021
Publication Title
Ecology and Evolution
Publication Title
Ecology and Evolution
Volume
11
Issue
22
Abstract
Neighborhood competition models are powerful tools to measure the effect of interspecific competition. Statistical methods to ease the application of these models are currently lacking. We present the forestecology package providing methods to (a) specify neighborhood competition models, (b) evaluate the effect of competitor species identity using permutation tests, and (cs) measure model performance using spatial cross-validation. Following Allen and Kim (PLoS One, 15, 2020, e0229930), we implement a Bayesian linear regression neighborhood competition model. We demonstrate the package's functionality using data from the Smithsonian Conservation Biology Institute's large forest dynamics plot, part of the ForestGEO global network of research sites. Given ForestGEO’s data collection protocols and data formatting standards, the package was designed with cross-site compatibility in mind. We highlight the importance of spatial cross-validation when interpreting model results. The package features (a) tidyverse-like structure whereby verb-named functions can be modularly “piped” in sequence, (b) functions with standardized inputs/outputs of simple features sf package class, and (c) an S3 object-oriented implementation of the Bayesian linear regression model. These three facts allow for clear articulation of all the steps in the sequence of analysis and easy wrangling and visualization of the geospatial data. Furthermore, while the package only has Bayesian linear regression implemented, the package was designed with extensibility to other methods in mind.
First Page
15556
Last Page
15572
Recommended Citation
Kim, Albert Y.; Allen, David N.; and Couch, Simon P., "The Forestecology R Package for Fitting and Assessing Neighborhood Models of the Effect of Interspecific Competition on the Growth of Trees" (2021). Statistical and Data Sciences: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/sds_facpubs/41
Digital Object Identifier (DOI)
10.1002/ece3.8129
Rights
© 2021 The Authors.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Included in
Data Science Commons, Other Computer Sciences Commons, Statistics and Probability Commons
Comments
Archived as published.