DOI: 10.5061/dryad.7wm37pw1j
title: Data and code from: Tree species identity effect on herb-layer species community distribution

description: 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.
description: # 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: 1.
 Dataset_IGN.RData --&gt; main R environment that contains all dataset used
 in this study with differents dataframes contains: a) IGN &lt;- complete
 dataset which are then decomposed on multiple subdatasets described below:
 b) XData &lt;- complete dependant abiotic variables used [columns 1:15
 --&gt; 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 --&gt; described in section 2.2 with coniferous,
 deciduous and multiple functional groups of trees). c) xy &lt;- only
 longitude and latitude coordinates of surveys points d) Y_abiotique &lt;-
 subset of XData with only abiotic variables, used to run Mabiotic. e)
 Y_conditional and Y_conditional_lonlat &lt;- the combinaison of XData
 [columns 1:39] + all data cover from trees species [columns 40:143 --&gt;
 described in Table S2]. f) Y_conditional_allspecies --&gt; 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
 --&gt; subset of XData with only one particular functional group of trees
 to run correspondant model (Mtrophic; Mhydric...). h) Y --&gt; All biotic
 variables with independant variables corresponding to herb-layer species
 [columns 1:85 --&gt; described in section 2.1 and Table S1] and trees
 cover data [columns 86: 189 --&gt; described in section 2.2 and Table S2].
 2. Table_SSI_plants_Mobaied_2015.csv --&gt; 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. 3. TableS1.csv --&gt; 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 --&gt; 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 --&gt; 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).