The forestecology R package for fitting and assessing neighborhood models of the effect of interspecific competition on the growth of trees

Abstract

1. 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. 2. We present the forestecology package providing methods to i) specify neighborhood competition models, ii) evaluate the effect of competitor species identity using permutation tests, and iii) measure model performance using spatial cross-validation. Following Allen (2020), we implement a Bayesian linear regression neighborhood competition model. 3. 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. 4. The package features i) tidyverse-like structure whereby verb-named functions can be modularly “piped” in sequence, ii) functions with standardized inputs/outputs of simple features ‘sf‘ package class, and iii) 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.

forestexplorR: an R package for the exploration and analysis of stem‐mapped forest stand data

“…The scope of the fgeo package is understandably limited in order to encourage researchers to explore all aspects of the ForestGEO network in particular, but we believe there is a great deal of insight to be gained from tools that can be applied to the data types common to all stem‐mapped forest stands: the size, species identity and precise locations of trees. For example, the forestecology R package (Kim et al 2021) implements a highly efficient neighborhood growth model that allows users to estimate species interaction coefficients, but it can only handle data from a single large stem‐mapped forest stand (e.g. a ForestGEO stand) and does not allow the user to include additional covariates such as climatic data.…”