4.7 Article

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

期刊

ECOGRAPHY
卷 2022, 期 10, 页码 -

出版社

WILEY
DOI: 10.1111/ecog.06223

关键词

forest stand; mortality; neighborhood model; tree density; tree growth; visualization

资金

  1. Dept of Biology at the Univ. of Washington
  2. Alfred P. Sloan Foundation
  3. Gordon and Betty Moore Foundation
  4. National Science Foundation [LTER8 DEB-2025755, LTER7 DEB-1440409]

向作者/读者索取更多资源

Stem-mapped forest stands provide important opportunities for investigating fine-scale spatial processes in forest ecosystems, but the lack of standardized and efficient tools hampers their widespread use. The forestexplorR package offers flexible and user-friendly functions for calculating neighborhood metrics and implementing tree growth and mortality models, making stem-mapped forest stand data more accessible.
Stem-mapped forest stands offer important opportunities for investigating the fine-scale spatial processes occurring in forest ecosystems. These stands are areas of the forest where the precise locations and repeated size measurements of each tree are recorded, thereby enabling the calculation of spatially-explicit metrics of individual growth rates and of the entire tree community. The most common use of these datasets is to investigate the drivers of variation in forest processes by modeling tree growth rate or mortality as a function of these neighborhood metrics. However, neighborhood metrics could also serve as important covariates of many other spatially variable forest processes, including seedling recruitment, herbivory and soil microbial community composition. Widespread use of stem-mapped forest stand datasets is currently hampered by the lack of standardized, efficient and easy-to-use tools to calculate tree dynamics (e.g. growth, mortality) and the neighborhood metrics that impact them. We present the forestexplorR package that facilitates the munging, exploration, visualization and analysis of stem-mapped forest stands. By providing flexible, user-friendly functions that calculate neighborhood metrics and implement a recently-developed rapid-fitting tree growth and mortality model, forestexplorR broadens the accessibility of stem-mapped forest stand data. We demonstrate the functionality of forestexplorR by using it to investigate how the species identity of neighboring trees influences the growth rates of three common tree species in Mt Rainier National Park, WA, USA. forestexplorR is designed to facilitate researchers to incorporate spatially-explicit descriptions of tree communities in their studies and we expect this increased diversity of contributors to develop exciting new ways of using stem-mapped forest stand data.

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