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Observation and Modeling","monthly_downloads":267,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"---\noutput: github_document\neditor_options: \n  markdown: \n    wrap: sentence\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\"\n)\n```\n\n# BlueCarbon \u003ca href=\"https://ecologyr.github.io/BlueCarbon/\"\u003e\u003cimg src=\"man/figures/logo.png\" alt=\"BlueCarbon website\" align=\"right\" height=\"126\"/\u003e\u003c/a\u003e\n\n\u003c!-- badges: start --\u003e\n\n`r badger::badge_cran_release()` ![](https://img.shields.io/github/r-package/v/EcologyR/BlueCarbon) [![r-universe version](https://ecologyr.r-universe.dev/BlueCarbon/badges/version)](https://ecologyr.r-universe.dev/BlueCarbon) [![r-universe status](https://ecologyr.r-universe.dev/BlueCarbon/badges/checks)](https://ecologyr.r-universe.dev/BlueCarbon) [![R-CMD-check](https://github.com/EcologyR/BlueCarbon/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/EcologyR/BlueCarbon/actions/workflows/R-CMD-check.yaml) [![codecov](https://codecov.io/gh/EcologyR/BlueCarbon/graph/badge.svg)](https://app.codecov.io/gh/EcologyR/BlueCarbon) `r badger::badge_lifecycle(\"stable\")` `r badger::badge_repostatus(\"Active\")` [![DOI](https://joss.theoj.org/papers/10.21105/joss.08333/status.svg)](https://doi.org/10.21105/joss.08333) [![](https://cranlogs.r-pkg.org/badges/grand-total/BlueCarbon)](https://cran.r-project.org/package=BlueCarbon)\n\n\u003c!-- badges: end --\u003e\n\nThe goal of BlueCarbon is to facilitate the estimation of organic carbon stocks and sequestration rates from soil/sediment cores from blue carbon ecosystems.\nFollowing the protocols published by the Blue Carbon initiative [Howard et al. (2014)](https://www.cifor-icraf.org/knowledge/publication/5095/).\n\nIt includes seven main [functions](https://ecologyr.github.io/BlueCarbon/reference/index.html) to (1) estimate core compaction, (2) correct core compaction, (3) estimate sample thickness, (4) estimate organic carbon content from organic matter content, (5) estimate organic carbon stocks and (6) sequestration rates, and (7) visualize the error in stock extrapolation.\n\n![Blue Carbon package workflow](man/figures/BC_workflow.png){width=\"527\"}\n\n#### ***estimate_compaction*** **- Estimate Core Compaction**\n\nSampling soil cores by manual percussion often results in the compaction of the material retrieved.\nThis function (`estimate_compaction()`) estimates the percentage of compaction using measurements taken before and after inserting the corer tube (Fig. 2): the 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).\n\n![Soil compaction from field sampling](man/figures/compaction.png){width=\"429\"}\n\n#### ***decompact*** **- Calculate sediment properties after decompaction**\n\nCore compaction derived from field extraction can be mathematically corrected to estimate the original depth of the samples.\nThis function (`decompact()`) applies a linear correction (assuming uniform compaction of the core material) to adjust the sample depth accurately.\nIf dry bulk density was estimated before compaction correction, it can be provided as a variable and the function will correct it accordingly.\n\n#### ***estimate_oc*** **- Organic carbon % estimation from organic matter %**\n\nThere is a linear correlation between organic carbon and organic matter content.\nThis correlation can vary across ecosystems and sampling sites.\nThis function (`estimate_oc()`) fits a linear regression model between organic matter and organic carbon content of the samples and predicts organic carbon values for samples where the latter information is missing.\nEstimation of organic carbon is performed using a linear regression between the logarithm of the organic carbon content and the logarithm of the organic matter content (log(organic carbon) \\\\\\~ log(organic matter)), providing an organic carbon value for each organic matter value.\nIt fits a model for each sampling station, dominant species, and ecosystem.\nIf an organic carbon value is already available for a sample, the function returns it.\nOtherwise, it applies the model for the corresponding sampling station.\nIf a model cannot be fitted for that station (e.g. because of limited sample size) or if the model fit is poor, the function instead applies the model for the dominant species.\nIf no suitable species-level model exists, it then applies the ecosystem-level model.\nIf no models are available at any of these levels, the function defaults to published models: [Fourqurean et al. (2012)](https://doi.org/10.1038/ngeo1477) for seagrasses, [Maxwell et al. (2023)](https://doi.org/10.1038/s41597-023-02633-x) for salt marshes, and [Piñeiro-Juncal et al. (2025)](https://doi.org/10.1038/s43247-025-02390-2) for mangroves.\nIt is unlikely, but possible, that the model predicts higher organic carbon than organic matter content.\nIf this occurs, the function issues a warning, and it is recommended to discard that model.\n\n#### ***estimate_h*** **- Sample thickness estimation**\n\nFor cores where only selected samples were measured, it is necessary to assign a carbon density to the unmeasured sections before estimating the total stock.\nThis function (`estimate_h()`) identifies gaps between samples and, if any are present, divides the space between the previous and next sample, ensuring continuous samples without gaps in the core (Fig. 3).\nThe midpoint between two consecutive samples is estimated from the bottom of the previous sample to the top of the next sample, preventing the uneven distribution of gaps between samples with different thickness.\nThe stock and sequestration rate estimation functions (`estimate_oc_stock()` and `estimate_seq_rate()`) already incorporate this function, so there is no need to run it separately.\n\n![Gap distribution between samples to estimate accumulated organic carbon mass](man/figures/estimate_h.png){width=\"486\"}\n\n#### ***estimate_oc_stock*** **- Organic carbon stock estimation**\n\nEstimates carbon stocks from soil core data down to a specified depth, with 100 as the default.\nIf the core does not reach the desired depth, the function extrapolates the stock using a linear model based on the relationship between accumulated organic carbon mass and depth.\nIn this model, accumulated organic carbon mass (stock) is the target variable and depth the explanatory variable (lm(accumulated organic carbon mass \\~ depth)).\nTherefore, this function will always provide either a estimated stock (if the core reaches the desired depth) or a predicted stock (if not).\nHowever, if the max depth of the core and the desired depth differ greatly, this predicted depth could differ greatly from the real stock.\nWe recommend that, if possible, the users use the function `test_extrapolation()` to assess the error of this extrapolation, and that they clearly indicate the maximum depth of the cores in the methods section of the resulting research outputs.\n\n![Organic carbon stock estimation diagram](man/figures/estimate_stock.png){width=\"408\"}\n\n#### ***test_extrapolation*** **- Visualize the error of stock extrapolation**\n\nThis function subset the cores that reach the desired depth, estimates the observed stock, and estimates the stock using the linear model on the relationship between accumulated organic carbon mass and depth.\nExtrapolations are performed using the top 90, 75, 50 and 25% length of the specified depth.\nThe function then compares the observed stock with the extrapolated stock estimates.\nNote that this function requires that at least some cores reach the desired depth.\n\n#### ***estimate_seq_rate*** **- Organic carbon sequestration rates estimation**\n\nEstimates the average organic carbon sequestration rate in the soil over a specified time frame (by default 100).\nThe average sequestration rate is calculated by dividing the stock at the depth corresponding to the target time frame by the length of the time frame itself.\n\n## Installation\n\nBlueCarbon can be installed directly from [CRAN](https://cran.r-project.org/package=BlueCarbon):\n\n``` r\ninstall.packages(\"BlueCarbon\")\n```\n\nor from [R-universe](https://ecologyr.r-universe.dev/BlueCarbon):\n\n``` r\ninstall.packages(\"BlueCarbon\", repos = c(\"https://ecologyr.r-universe.dev\", \"https://cloud.r-project.org\"))\n```\n\nOr from [GitHub](https://github.com/EcologyR/BlueCarbon):\n\n``` r\n# install.packages(\"remotes\")\nremotes::install_github(\"EcologyR/BlueCarbon\")\n```\n\n## Citation\n\nIf using this package, please cite it:\n\nPiñeiro-Juncal et al., (2025). BlueCarbon R package: Estimation of Organic Carbon Stocks and Sequestration Rates From Soil Core Data. Journal of Open Source Software, 10(115), 8333, \u003chttps://doi.org/10.21105/joss.08333\u003e\n\nA BibTeX entry for LaTeX users is\n\n```{verbatim}\n@article{Piñeiro-Juncal2025,\n  doi = {10.21105/joss.08333},\n  url = {https://doi.org/10.21105/joss.08333},\n  year = {2025},\n  publisher = {The Open Journal},\n  volume = {10},\n  number = {115},\n  pages = {8333},\n  author = {Piñeiro-Juncal, Nerea and Astigarraga, Julen and Costa, Valentina and Martins, Márcio and Rodríguez-Sánchez, Francisco},\n  title = {BlueCarbon R package: Estimation of Organic Carbon Stocks and Sequestration Rates From Soil Core Data},\n  journal = {Journal of Open Source Software}\n}\n```\n\n\n\n## Code of Conduct\n\nPlease note that the BlueCarbon project is released with a [Contributor Code of Conduct](https://ecologyr.github.io/BlueCarbon/CODE_OF_CONDUCT.html).\nBy contributing to this project, you agree to abide by its terms.\n\n## Funding\n\nThe 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 (project US-1381388 led by Francisco Rodríguez Sánchez, Universidad de Sevilla).\nNPJ was supported by a Juan de la Cierva fellowship (JDC2022-048342-I, MCIN/AEI/10.13039/501100011033, European Union “NextGenerationEU”/PRTR”).\nJA acknowledges funding from the CLIMB-FOREST Horizon Europe Project (No 101059888) funded by the European Union.\nFRS was supported by VI PPIT-US from Universidad de Sevilla.\nMM was supported by a FCT PhD grant (\u003chttps://doi.org/10.54499/2020.06996.BD\u003e).\n\n![](https://ecologyr.github.io/workshop/man/figures/logos.png)\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.21105/joss.08333","https://doi.org/10.1038/ngeo1477","https://doi.org/10.1038/s41597-023-02633-x","https://doi.org/10.1038/s43247-025-02390-2","https://doi.org/10.54499/2020.06996.BD"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["marine-data","seagrass"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/298263","html_url":"https://ost.ecosyste.ms/projects/298263"}