This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1111/jvs.13046. This is version 2 of this Preprint.
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Abstract
The Terrestrial Ecosystem Research Network (TERN), Australia’s national land ecosystem monitoring program, measures critical environmental attributes from local to continental scale and generates quality data for research and land management. Since 2011, TERN has performed standardised field surveys and sampling across a national plot network. At each plot, TERN records vegetation structure, composition and diversity, soil characteristics, and collects plant and soil samples for analysis. At the time of submission, TERN has established over 750 plots and performed over 1000 plot surveys across Australia. Here we present ausplotsR, an R package for the R statistical computing environment that provides a user-friendly interface to rapidly import, visualise, and analyse TERN plot survey data. Easy-to-use functions extract the data and compile data tables that can be incorporated into a variety of statistical analysis, most notably multivariate applications requiring plant community data with standardised relative abundances. ausplotsR includes functions to calculate useful vegetation metrics, such as species presence/absence, cover, and basal area. The package also provides information on TERN’s extensive soil and plant sample collection. We expect ausplotsR will help facilitate and advance ecological research and management throughout Australia and provide useful data for vegetation modellers globally.
DOI
https://doi.org/10.32942/osf.io/25phx
Subjects
Biostatistics, Botany, Life Sciences, Other Plant Sciences, Physical Sciences and Mathematics, Plant Biology, Plant Sciences, Statistics and Probability
Keywords
Australia, big data, biodiversity, ecosystem monitoring, environmental modelling, plot survey, R statistical environment, TERN, Terrestrial Ecosystem Research Network
Dates
Published: 2020-12-15 07:00
Last Updated: 2021-03-23 02:19
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