Dryad
Published February 15, 2022 | Version v1
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Data from: Long-term and year-to-year stability and its drivers in a Mediterranean grassland

Authors/Creators

  • 1. Universidad de Navarra*
  • 2. Instituto Pirenaico de Ecología (CSIC)*
  • 3. University of South Bohemia in České Budějovice; Institute of Botany of the Czech Academy of Sciences*

Description

Understanding the mechanisms underlying community stability has become an urgent need in order to protect ecosystems from global change and resulting biodiversity loss. While community stability can be influenced by richness, synchrony in annual fluctuations of species, species stability and functional traits, the relative contributions of these drivers to stability are still unclear. In semi-natural grasslands, land-use changes such as fertilization might affect stability by decreasing richness and influencing year-to-year fluctuations. In addition, they can promote long-term directional trends, shifting community composition and influencing grassland maintenance. Thus, it is important to consider how species and community stability vary year-to-year but also in the long term.

Using a 14-year vegetation time series of a species-rich semi-natural Mediterranean grassland, we studied the relative importance of richness, synchrony, species stability and functional traits on community stability. To assess land-use change effects on stability, we applied a fertilization treatment. To distinguish stability patterns produced by year-to-year fluctuations from those caused by long-term trends, we compared the results obtained using a detrending approach from those without detrending.

Stability is influenced by richness, synchrony and functional traits. Fertilization decreases species and community stability by promoting long-term trends in species composition, favouring competitive species and decreasing richness. Studying stability at the community and species level, and accounting for the effect of trends is essential to understand stability and its drivers more comprehensively.

Notes

datasheet.xlsx

Data used in the analyses, consists on two sheets:    
- boxplotindices: data with stability, synchrony and richness values for each plot or community, the approach used to calculate each index (long-term or year-to-year), and the treatment applied in each plot (control or fertilized).
    
- matrixalldatacom: data used to carry out the analyses at the community level. For each community it is given: the number of the plot or community, stability, synchrony, richness, functional composition and diversity of the five traits studied, and treatment (control or fertilized). For each of these variables there are two columns, one with the variable calculated using the long-term approach (named "longterm_" or "cum_") and the other one for the variable calculated using the year-to-year approach (named "yeartoyear_" or "mean_").

- matrixalldataspp: data used to carry out the analyses at the species level. For each species it is given: species name, long-term and year-to-year stability, species values for the five traits studied, and treatment (control or fertilized). Missing values are indicated by NAs.

 

indicescom.R: calculation of richness, synchrony and stability indices at the community level.

functionalindicescom.R: calculation of functional composition (Community Weighted Mean) and diversity (Rao) for the five traits studied (plant height, Leaf Dry Matter Content, Specific Leaf Area, Leaf Area, Seed Mass), at the community level.

spstabilityindices.R: calculation of stability indices at the species level. 

models_boxplots.R: R script for multiple linear regression models analysing the relationship between richness, synchrony or stability values and the approach used (long-term or year-to-year), treatment (control or fertilized) and the interaction between approach and treatment.
    
models_stability_communitylevel.R: R script for simple and multiple linear regression models analysing the drivers of long-term and year-to-year community stability.
    
SEMs.R: R script for Piecewise Structural Equation Models analysing the relationships between long-term and year-to-year community stability and their drivers.
    
models_stability_splevel.R: R script for simple and multiple linear regression models analysing the relationship between long-term and year-to-year species stability and functional traits. 
    
spstability.R: R script for Pearson's correlations and paired t-tests analysing the effects of treatment and long-term trends on species stability.

README.txt: text file describing the data.

Funding provided by: Fundación Caja Navarra
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100006671
Award Number: Ref. 10833 (Programa "Tú Eliges, Tú Decides")

Funding provided by: Universidad de Navarra
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100004435
Award Number: Project "Biodiversity Data Analytics and Environmental Quality"

Funding provided by: Universidad de Navarra
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100004435
Award Number: Project "Red de Observatorios de la Biodiversidad de Navarra (ROBIN)"

Funding provided by: Departamento de Educación, Gobierno de Navarra
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100003425
Award Number: Ayudas predoctorales para la realización de programas de doctorado de interés para Navarra; Plan de Formación y de I+D 2018

Funding provided by: Ministerio de Ciencia, Innovación y Universidades
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100014440
Award Number: Ref. RTI2018-096884-B-C31 (Project FORMAL)

Funding provided by: Czech Academy of Sciences
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100004240
Award Number: No. RVO 67985939

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Additional details

Related works

Is source of
10.5061/dryad.8pk0p2npt (DOI)
Is supplemented by
10.15470/pxktos (DOI)
https://lotvs.csic.es/ (URL)