The goal of BlueCarbon is to facilitate the estimation of organic carbon stocks and sequestration rates from soil/sediment cores from blue carbon ecosystems.
It contains seven main functions to deal with core compaction (estimate and correct for the compaction of cores when sampling), transform laboratory data (estimate sample thickness and estimate organic carbon content from organic matter content) and estimate organic carbon stocks and sequestration rates (estimate organic carbon stocks and sequestration rates and visualize the error of stock extrapolation).
Sampling soil cores by manual percussion usually leads to the compaction of the material retrieved. This function will estimate the percentage of compaction from measurements taken in the field after inserting the corer tube and before extracting it: length of the corer tube (sampler_length), distance between the surface of the soil and the top of the tube in the outside (external_distance) and distance between the surface of the soil and the top of the tube in the inside of the tube (internal_distance).
Core compaction derived from field extraction can be mathematically corrected to estimate the original depth of the samples. This function will apply a linear correction (all the core material is assumed to have been compacted equally) to correct sample depth and, if provided, dry bulk density.
Typically the relationship between organic carbon and organic matter
content can be modelled with a linear regression model. This
relationship can change between ecosystems and sampling sites due to
changes in organic matter composition among other factors. This function
(estimate_oc()) fits a linear model between organic matter
and organic carbon content in your samples to estimate the content of
organic carbon in those samples lacking those values. The estimation of
organic carbon is done by means of linear regressions on log(organic
carbon) ~ log(organic matter). If there is a value for organic carbon
for that sample it returns the same value; otherwise, it estimates
organic carbon from a model fitted to that site, or a model fitted to
that species, or else a model fitted to that ecosystem. If there are too
few samples to build a reliable model or the model fit is too
poor,estimate_oc() uses the equations in Fourqurean et al. (2012) for
seagrasses, Maxwell
et al. (2023) for salt marshes and Piñeiro-Juncal (in prep.) for
mangroves to estimate the organic carbon. It is unlikely, but possible,
that a model predicts higher organic carbon than organic matter content.
As this is not possible in nature, the function will give a warning and
it is recommended to discard that model.
For those cores where only selected samples were measured it is
necessary to assign a carbon density to the empty spaces to estimate the
total stock. This function (estimate_h()) checks for gaps
between samples and, if any, divides this space between the previous and
next sample to return sample thickness without gaps in the core. The
middle point between one sample and the other is estimated from the
bottom of the previous sample to the top of the next sample, and not
from the middle point of one sample to the middle part of the next, as
this would unequally distribute the gaps between samples if the samples
have different thickness. The stock and sequestration rate estimation
functions (estimate_oc_stock() and
estimate_seq_rate()) have this function incorporated and it
is not necessary to run it beforehand.
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Estimates carbon stocks from soil core data down to a specified depth, 100 cm by default. If the core does not reach the desired depth, it extrapolates the stock from a linear model between accumulated mass of organic carbon and depth.
This function subset those cores that reach the desired depth, estimates the stock (observed stock), estimates the stock from the linear relation of organic carbon accumulated mass and depth using the 90, 75, 50 and 25% top length of the indicated desired depth, and compares the observed stock with the estimated stocks by extrapolation. This function requires that some of your cores do reach the desired depth.
Estimate the average organic carbon sequestration rate in the soil in a given time frame (by default last 100 years) from the organic carbon concentration and the age of the samples.
BlueCarbon can be installed directly from GitHub:
# install.packages("remotes")
remotes::install_github("EcologyR/BlueCarbon")or from R-universe:
install.packages("BlueCarbon", repos = c("https://pakillo.r-universe.dev", "https://cloud.r-project.org"))If using this package, please cite it:
citation("BlueCarbon")
To cite package 'BlueCarbon' in publications use:
Piñeiro-Juncal N, Astigarraga J, Costa V, Martins M,
Rodriguez-Sanchez F (2025). _BlueCarbon: Estimation of Organic Carbon
Stocks and Sequestration Rates From Soil Core Data_. R package
version 0.1.0, https://EcologyR.github.io/BlueCarbon/,
<https://github.com/EcologyR/BlueCarbon>.
A BibTeX entry for LaTeX users is
@Manual{,
title = {BlueCarbon: Estimation of Organic Carbon Stocks and Sequestration Rates From Soil Core Data},
author = {Nerea Piñeiro-Juncal and Julen Astigarraga and Valentina Costa and Marcio Martins and Francisco Rodriguez-Sanchez},
year = {2025},
url = {https://github.com/EcologyR/BlueCarbon},
note = {R package version 0.1.0, https://EcologyR.github.io/BlueCarbon/},
}Please note that the BlueCarbon project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
The development of this software has been funded by Fondo Europeo de Desarrollo Regional (FEDER) and Consejería de Transformación Económica, Industria, Conocimiento y Universidades of Junta de Andalucía (proyecto US-1381388 led by Francisco Rodríguez Sánchez, Universidad de Sevilla). NPJ was supported by a Juan de la Cierva fellowship (JDC2022-048342-I). JA acknowledges funding from the CLIMB-FOREST Horizon Europe Project (No 101059888) that was funded by the European Union.
