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\begin{document}

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



\author[1]{Albert Kim}%
\author[2]{David Allen}%
\author[3]{Simon Couch}%
\affil[1]{Smith College}%
\affil[2]{Middlebury College}%
\affil[3]{Reed College}%


\vspace{-1em}



  
  \date{June 16, 2024}


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\begin{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.%
\end{abstract}%



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