DOI: 10.5061/dryad.x69p8czzz
title: Endemic Mediterranean seagrasses poised to survive climate change challenges

description: This dataset contains in-situ metabolic measurements from benthic chamber
 incubations conducted on Posidonia oceanica seagrass meadows in the
 Tyrrhenian Sea (Civitavecchia, Italy) from February to October 2023. The
 primary data file (Data.xlsx) includes seasonal measurements of community
 respiration (CR), net community production (NCP), and gross community
 production (GCP), expressed as dissolved oxygen fluxes (mmol O₂ g⁻¹ fresh
 weight m⁻² d⁻¹), along with corresponding water temperatures (11–29°C) and
 sampling dates. The repository also includes nine fully documented R
 scripts that implement: (1) thermal performance curve (TPC) fitting using
 the rTPC package with 999 bootstrap iterations for uncertainty
 quantification; (2) spatial predictions of NCP across the Mediterranean
 basin using Sea Surface Temperature data from Copernicus Marine Service;
 and (3) ecosystem services calculations (oxygen production, CO₂
 absorption, carbon fixation) under present and future climate scenarios
 (RCP 4.5 and 8.5 for 2050). These data enable researchers to: reproduce
 all analyses and figures from the associated publication; develop
 alternative thermal performance models for P. oceanica; validate metabolic
 predictions against independent datasets; and extend spatial projections
 to other climate scenarios or time horizons. The standardized benthic
 chamber methodology and comprehensive metadata facilitate comparisons with
 seagrass metabolism studies from other Mediterranean locations. External
 data sources (SST rasters, Posidonia distribution shapefiles) are
 documented with access instructions but not included due to third-party
 licensing.
description: # Data and R Code for: Endemic Mediterranean seagrasses poised to survive
 climate change challenges ## Publication Information **Journal:**
 Ecological Applications **Authors:** Francesco Paolo Mancuso^1,2,*^ Mar
 Bosch-Belmar*^1,2,3,^ Mario Francesco Tantillo^1^, Martina Russi^1,2^,
 Viviana Piermattei^4^, Marco Marcelli^5^, Gianluca Sarà^1,2^ *Co-first
 authors: these two authors contributed equally to this work
 **Affiliations:** * Laboratory of Ecology, Department of Earth and Marine
 Science (DiSTeM), University of Palermo, Palermo, Italy * NBFC, National
 Biodiversity Future Center, Spoke 1, Palermo, Italy * Consorzio Nazionale
 Interuniversitario per le Scienze del Mare (CoNISMa), Rome, Italy * CMCC
 Foundation - Euro-Mediterranean Center on Climate Change, Lecce, Italy *
 Laboratory of Experimental Oceanology and Marine Ecology, Department of
 Ecological and Biological Sciences (DEB), University of Tuscia, Port of
 Civitavecchia, Rome, Italy **Corresponding authors:** * Mar Bosch-Belmar
 ([mariadelmar.boschbelmar@unipa.it](mailto:mariadelmar.boschbelmar@unipa.it)) - Francesco Paolo Mancuso ([francesco.mancuso@unipa.it](mailto:francesco.mancuso@unipa.it)) --- ## Description of the Data and Analysis This repository contains the data and R code necessary to reproduce the analyses and figures presented in the manuscript. The study investigates the thermal performance of *Posidonia oceanica* meadows in the Mediterranean Sea using *in-situ* benthic chamber measurements. We developed thermal performance curves (TPCs) and generated spatial predictions of ecosystem services (oxygen production, CO₂ absorption, and carbon fixation) under present and future climate scenarios. --- ## Files Description ### Data Files | Filename | Description | | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `Data.xlsx` | Raw data from *in-situ* benthic chamber incubations of *Posidonia oceanica* meadows in the Tyrrhenian Sea. Contains 37 incubations with measurements of community respiration (CR), net community production (NCP), and gross community production (GCP). | #### Data.xlsx Column Descriptions: | Column | Units | Description | | ------------------ | ----------------------- | -------------------------------------------------------------------------------------------------- | | `season` | - | Season of measurement (Winter, Spring, Summer, Autumn) following Kotsias et al. (2021) definitions | | `date` | dd/mm/yyyy | Date of incubation | | `temp` | °C | Water temperature during incubation | | `RCdo2mmoldaywwm2` | mmol O₂ g⁻¹\_FW m⁻² d⁻¹ | Daily Community Respiration rate (absolute value) | | `NCPo2mmoldaywwm2` | mmol O₂ g⁻¹\_FW m⁻² d⁻¹ | Daily Net Community Production rate | | `GCPo2mmoldaywwm2` | mmol O₂ g⁻¹\_FW m⁻² d⁻¹ | Daily Gross Community Production rate | ### R Scripts Scripts should be run in the following order for full reproducibility: | Script | Description | Generates | | ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------- | | `TPC_ANALYSIS_FOR_POSIDONIA_OCEANICA.R` | Fits thermal performance curves (TPCs) to NCP data using the rTPC package. Selects best-fit model (Flinn) and calculates bootstrap confidence intervals (999 iterations). | Figure 2 (TPC), model parameters | | `Figure1.R` | Generates seasonal bar plots of community dissolved oxygen fluxes (GCP, NCP, CR). | Figure 1 | | `creating_maps.R` | Creates spatial predictions of NCP for present-day conditions. Masks SST rasters by *Posidonia* distribution and calculates ecosystem services by Mediterranean sector. | Figure 3 (present), ES calculations | | `creating_future_maps_rcp2050_4.5.R` | Generates future NCP predictions under RCP 4.5 (2050) climate scenario. | Figure 3 (RCP 4.5 2050) | | `creating_future_maps_rcp2050_8.5.R` | Generates future NCP predictions under RCP 8.5 (2050) climate scenario. | Supplementary Figure | | `Figure3_spatial_predictions.R` | Combines present and future predictions into composite maps with difference plots. | Figure 3 (complete) | | `Figure4_circular_plots_three_scenarios.R` | Creates circular (radial) bar plots comparing ecosystem services across scenarios with bootstrap-derived uncertainty. | Figure 4 | | `FigureS3_regional_ES.R` | Generates regional ecosystem services plots for West, Center, and East Mediterranean sectors. | Figure S3 | --- ## External Data Sources The following external datasets are required but not included in this repository due to size or licensing restrictions: ### Sea Surface Temperature (SST) data **Present-day SST:** * **Source:** Copernicus Marine Service Information * **Product:** Mediterranean Sea Ultra High Resolution SST Analysis (0.01° × 0.01°) * **Reference:** Buongiorno Nardelli et al. (2013) Remote Sensing of Environment * **URL:** [https://resources.marine.copernicus.eu](https://resources.marine.copernicus.eu) * **Variables:** Seasonal averages (Winter, Spring, Summer, Autumn) for 2021–2023 * **Season definitions:** Following Kotsias et al. (2021): Winter (3/12–22/3), Spring (23/3–14/6), Summer (15/6–5/9), Autumn (6/9–2/12) **Future SST projections:** * **Scenario:** RCP 4.5 and RCP 8.5 for year 2050 * **Reference:** Thomson et al. (2011) Climatic Change ### *Posidonia oceanica* Distribution * **Source:** EMODnet Seabed Habitats (2023) * **URL:** [https://emodnet.ec.europa.eu/en/seabed-habitats](https://emodnet.ec.europa.eu/en/seabed-habitats) * **Format:** Shapefiles for Mediterranean sectors (West, Center, East) ### Mediterranean bathymetry * **Source:** EMODnet Bathymetry * **Description:** 0–50 m depth limit (potential *Posidonia* habitat) --- ## Software Requirements ### R Version R version 4.3.0 or higher recommended ### Required R Packages Core packages: ```r install.packages(c( "ggplot2", # Data visualization (&gt;= 3.5.2 required for geomtextpath) "dplyr", # Data manipulation "tidyr", # Data tidying "tidyverse", # Tidyverse collection "openxlsx" # Excel file handling )) ``` Spatial analysis: ```r install.packages(c( "terra", # Modern raster operations "sf", # Spatial features "raster", # Raster data (for compatibility) "ncdf4", # NetCDF handling "rnaturalearth" # World map data )) ``` Thermal performance curve fitting: ```r install.packages(c( "rTPC", # TPC model fitting "nls.multstart", # Multi-start NLS "broom", # Tidy model outputs "minpack.lm", # Levenberg-Marquardt algorithm "boot", # Bootstrap analysis "nlstools" # NLS diagnostics )) ``` Visualization: ```r install.packages(c( "cowplot", # Plot arrangement "patchwork", # Plot composition "gridExtra", # Grid arrangement "geomtextpath", # Text on paths "ggpubr", # Publication-ready plots "ggrepel" # Non-overlapping labels )) ``` Statistical analysis: ```r install.packages(c( "car", # Companion to Applied Regression "plotrix", # Standard error calculation "GAD" # General ANOVA designs )) ``` --- ## Thermal Performance Curve parameters The Flinn (1991) model was selected as the best-fit TPC based on AICc criterion: **Model equation:** ``` NCP(T) = 1 / (1 + a + b*T + c*T²) ``` **Fitted parameters:** * a = -0.8639539 * b = -0.0116270 * c = 0.0002518 **Key thermal thresholds:** | Parameter | Value | Description | | ----------- | ------------------------------ | --------------------------------------- | | T\_opt | 23.08 °C | Optimal temperature for NCP | | CT\_min | 11.07 °C | Critical thermal minimum | | CT\_max | 35.10 °C | Critical thermal maximum | | T\_br (69%) | 3.82 °C | Thermal performance breadth at 69% µmax | | T\_br (80%) | 9.93 °C | Thermal performance breadth at 80% µmax | | µmax | 540.46 mmol O₂ g⁻¹\_FW m⁻² d⁻¹ | Maximum NCP rate | --- ## Reproducibility Notes 1. **File paths:** Scripts use relative paths. Ensure all files are in the same working directory, or modify paths as needed. 2. **External data:** Download SST and *Posidonia* distribution data from the sources listed above before running spatial prediction scripts. 3. **Execution order:** Run scripts in the order listed above. Some scripts depend on objects created by previous scripts. 4. **Random seed:** Bootstrap analyses use 999 iterations. Results may vary slightly due to random sampling. 5. **Memory requirements:** Spatial analyses with high-resolution rasters may require &gt;8 GB RAM. --- ## Citation If you use this data or code, please cite: Mancuso, F.P., Bosch-Belmar, M., Tantillo, M.F., Russi, M., Piermattei, V., Marcelli, M., & Sarà, G. (2025). Endemic Mediterranean seagrasses poised to survive climate change challenges. *Ecological Applications*. [DOI] 
 
 --- ## References * Bosch-Belmar, M. et al. (2025). Metabolic traits and
 thresholds to inform marine ecological conservation and restoration.
 *Journal of Applied Ecology*.
 [https://doi.org/10.1111/1365-2664.70049](https://doi.org/10.1111/1365-2664.70049) * Buongiorno Nardelli, B. et al. (2013). High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project. *Remote Sensing of Environment*, 129, 1–16. * Kotsias, G. et al. (2021). An objective definition of seasons for the Mediterranean region. *International Journal of Climatology*, 41(S1), E1889–E1905. * Padfield, D. et al. (2021). rTPC and nls.multstart: A new pipeline to fit thermal performance curves in R. *Methods in Ecology and Evolution*, 12, 1138–1143. * Thomson, A.M. et al. (2011). RCP4.5: A pathway for stabilization of radiative forcing by 2100. *Climatic Change*, 109, 77–94. --- ## License This dataset is released to the public domain under a Creative Commons Zero (CC0 1.0) waiver. You may copy, modify, and distribute the data without permission or attribution. However, citation of this dataset is appreciated and expected in academic contexts. --- ## Contact For questions about the data or code, please contact: * Francesco Paolo Mancuso: [francesco.mancuso@unipa.it](mailto:francesco.mancuso@unipa.it) * Mar Bosch-Belmar: [mariadelmar.boschbelmar@unipa.it](mailto:mariadelmar.boschbelmar@unipa.it) --- *Last updated: February 2025*