Data and code from: Tree species identity effect on herb-layer species community distribution
Data files
Jan 07, 2025 version files 4.01 MB
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IGN_Dryad.zip
4.01 MB
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README.md
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Abstract
The impact of canopy tree species identity on the distribution of herbaceous layer species is known to be influenced by various factors, such as light availability, soil structure and composition, and seedling competition. However, these relationships have been rarely demonstrated empirically across a wide range of species and large spatial areas.
We examined the response of 85 herb-layer species distribution to the cover of 108 tree species across 7255 sites in mainland France. By accounting for site characteristics and canopy diversity, we ensured that the associations between herbaceous and tree species were not a reflection of these factors. We then predicted the distribution of herbaceous species by considering these environmental factors and comparing them with the addition of individual tree species cover.
Our models revealed that tree species identity (using cover data) relationships are important for all herbaceous layer species. While all species benefited with information from tree species, we found that non-forest and open vegetation species were more sensitive to tree species identity. Moreover, generalist non-forest species were the most negatively impacted by them. Models also highlighted that tree species identity cannot be fully replaced by unidimensional functional tree groups based on trophic, dynamic, hydric, or Raunkiaer classification.
Our study emphasizes that incorporating tree species identity composition can enhance the prediction of herbaceous layer distribution at new sites. Consequently, we provide an empirical demonstration of successful improved prediction leveraging on species associations, which synthesizes various factors that are challenging to measure in situ.
Synthesis
Our study is an empirical evidence of the importance of tree species identity in shaping the distribution of the herbaceous layer, surpassing the effects of tree diversity alone. By considering the significance of tree species identity, we can improve forest management or the prediction of the spatial distribution of this herbaceous layer. Overall, this represents a significant advancement in understanding the herbaceous layer relationship with the tree layer.
README: Data and code from: Tree species identity effect on herb-layer species community distribution
DATA-SPECIFIC INFORMATION:
In the folder "Data", there is multiple files described below:
- Dataset_IGN.RData --> main R environment that contains all dataset used in this study with differents dataframes contains: a) IGN <- complete dataset which are then decomposed on multiple subdatasets described below: b) XData <- complete dependant abiotic variables used [columns 1:15 --> described in section 2.3 and Table S3] to explain the spatial distribution of indiviual herb-layer + sum of total area per groups of trees [columns 16: 39 --> described in section 2.2 with coniferous, deciduous and multiple functional groups of trees). c) xy <- only longitude and latitude coordinates of surveys points d) Y_abiotique <- subset of XData with only abiotic variables, used to run Mabiotic. e) Y_conditional and Y_conditional_lonlat <- the combinaison of XData [columns 1:39] + all data cover from trees species [columns 40:143 --> described in Table S2]. f) Y_conditional_allspecies --> subset of Y_conditional without columns in functional groups of trees to run Midentity. g) Y_conditional_confeui; Y_conditional_dynamique; Y_conditional_hydrique; Y_conditional_Raunkier; Y_conditional_trophique --> subset of XData with only one particular functional group of trees to run correspondant model (Mtrophic; Mhydric...). h) Y --> All biotic variables with independant variables corresponding to herb-layer species [columns 1:85 --> described in section 2.1 and Table S1] and trees cover data [columns 86: 189 --> described in section 2.2 and Table S2].
- Table_SSI_plants_Mobaied_2015.csv --> Specialization species index (SSI; herb-layer habitat preferences) described in section 2.2 and based on multiplicative partitioning (Zelený, 2009) and computed for french flora by Mobaied et al. 2015 (from Mobaied et al., 2015). This dataframe contains two columns: Sp_name (herb-layer species names) and ind_Theta_WB (SSI) that are be used in script 2.
- TableS1.csv --> electronical version of Table S1 from the article used in script 2.
CODE MAIN INFORMATION
There is two script to reproduce main analysis presented in the article and with the data shared above (using R version 4.1.1).
1 - Main Script GLM models --> R script used to fit the GLMs models corresponding to the main analysis described in section 2.4.
2 - Correlation herb-traits and AIC improvment models --> R script used to analyze the AIC improvment from Mabiotic to Midentity described in section 2.4 (and in more details in appendix B) and reproduce Figure B2.
OUTPUT FOLDER INFORMATION
There is an folder "OUTPUT" (with two subfolders: Table_metrics and Individual_Models) to save all the results reproduced by the users thanks to the code (script 1 and 2) and data provided below.
Note: We also included the main table metrics dataframes that can be reproduced with script 1 to allows interested users to run script 2 without this previous step (Table_AIC_Occurence_all.RData).