DOI: 10.5061/dryad.v15dv424v
title: Data from: Provenance variation in functional traits of European forest trees: Meta-analysis reveals effects of taxa and age despite critical research gaps

description: Climate change is driving profound transformations in European forests.
 Understanding the adaptive potential of tree species is a key challenge
 for conservation and adaptation measures. A critical component of this
 adaptive potential lies in the intraspecific variation of functional
 traits. The long tradition in ecological genetics resulted in a plethora
 of studies across species, regions age classes and traits. Prior syntheses
 have rarely quantified trait-specific patterns and their variation across
 taxa and tree age. We conducted a systematic literature search to examine
 intraspecific variation in natural European tree populations. We
 identified four approaches to study intraspecific variation (i.e.
 provenance-effects, provenance environment interaction-effects,
 clinal-effects and transfer-effects). For each approach, we compared their
 prevalence to show an effect while also accounting for species, species
 group, and age. Our results found that intraspecific variation is common
 in European tree species, with tested traits showing significant
 provenance effects (73%), provenance environment-interaction effects
 (45%), linear clinal-effects (30%) and linear transfer-effects (38%).
 While growth traits were predominantly studied, several other traits
 showed higher frequencies of significant results. Specifically,
 reproduction, survival, phenology, plant morphology, plasticity, drought
 and frost tolerance are highly relevant but still understudied in
 comparison to growth. Conifer species demonstrated a higher prevalence of
 intraspecific variation compared to broadleaves. Despite the research
 clearly focusing on young trials, older trials tended to show higher
 frequencies of effects in phenology, growth, plant morphology and
 survival, suggesting accumulating environmental selection with growing
 tree age. Europe lacks essential information on intraspecific variation of
 tree species for the diversification, conservation and adaptation of its
 forests, especially in southern and south-eastern parts, where many
 species harbor high genetic diversity and are most vulnerable. The
 significant influence of age urges for a reanalysis, reestablishment, and
 maintenance of long-term trials. These trials should consider species and
 environmental conditions relevant for future scenarios.
description: We conducted a systematic literature search of scientific
 articles on the 88 European tree species that are present in the European Forest
 Genetic Resource Program using the websites Web of Science and Scopus, We used the scientific and the botanical English names of European tree species in combination with the search terms: “local adaptation” OR “genetic heterogeneity” OR “intraspecific variation” OR “population genomics” OR “genecolog*” OR “(provenance AND clima* AND respon*))”. The query included searches in title, abstract, keywords (i.e., author keywords and keywords plus® in WOK). It was conducted on February 2<sup>nd</sup>, 2022, with no date range applied (Appendix 1). Based on title and abstract, we selected 269 articles that were further examined for extraction by two reviewers (see Figure A1 for a flow diagram and Appendix 1 for a selection criteria list). If an article examined several species, we treated each species as a distinct case (see also Matensaz & Ramírez-Valiente (2019)). Additionally, articles were split into distinct cases if multiple methods (e.g., common garden and growth chamber experiment), or statistical analyses (e.g. ANOVA and Correlation) were used. For instance, if an article focused on a single species and a single trait, such as height, within one experimental design, like a common garden, it was considered one case. However, if the study analyzed height and provided separate, appropriate outputs for correlation and regression against climate at origin, we considered these two distinct cases. Similarly, if the article focused on a single trait but investigated multiple species, each species was treated as a separate case. Articles that proved to be not relevant or turned out to be duplicates initially not identified were subsequently excluded from the list and the reason for exclusion noted. We extracted 13 variables detailing study metadata (Table A1). In addition, we extracted the trait that was studied, the effect that was evaluated (i.e. PE, P×E, CE variables or TE variables) and whether this effect was significant or not. A total of 915 distinct cases were extracted from the 198 investigated articles. Most articles evaluated intraspecific variation using categorical tests (like ANOVA and t-test) leading to 361 cases for PE of which 160 cases included interactions with environmental site conditions (Table 1) and therefore tested for P×E. We did not differentiate between the specific testing conditions since the overall goal was to examine the general tendency for P×E. Accounting for the various testing environments while also keeping track of species and age classes would have resulted in a very imbalanced dataset. Likewise, although certainly of interest, we did not consider clinal variation against site conditions (so called response functions) as this would have clearly exceeded the scope of this review. Some of the tests did not examine PE explicitly but tested for range of origin (e.g. marginal or central population), climatic group or country of origin. These were considered as provenances by extension and included. Results for families or clones were not included in any analysis. CE were predominantly tested via linear regression followed by correlations and quadratic models (134, 120 and 22 cases respectively). In contrast, TE included predominantly quadratic models followed by linear regressions and correlations against climate at origin (42, 36 and 20 cases respectively). Finally, 27 cases studied CE or TE but did not use the analyses mentioned above. To enable a trait-centered meta-analysis, we harmonized and grouped the extracted data into defined functional trait categories. Specifically, we identified and grouped traits into 15 functional categories based on trait function, organ specificity, and their role in plant performance under environmental variation. While some of these categories (e.g., leaf anatomy, photosynthetic rate) represent classic functional traits sensu (Violle et al., 2007), others—such as survival, reproduction, and growth—are performance metrics or fitness-related outcomes. We nonetheless included them in our framework because they were frequently reported in trait-based studies, and they represent meaningful axes of plant response to environmental variation. This integrative approach allowed us to capture a broad spectrum of trait expression across studies. The full list of individual traits and their respective group assignments is available in Appendix 2. This categorization allowed for consistent comparison across studies and supports the identification of trait-specific patterns of intraspecific variation across multiple dimensions of environmental response and adaptation. Our grouping decisions balanced biological relevance with the practical need for comparability across studies reporting heterogeneous trait data and reflect both ecological function and methodological consistency. Please refer to the article's material and methods part for information on how the dataset was further analyzed!
description: # Provenance variation in functional traits of European forest trees:
 meta-analysis reveals effects of taxa and age despite critical research
 gaps
 [https://doi.org/10.5061/dryad.v15dv424v](https://doi.org/10.5061/dryad.v15dv424v) Review of provenance trials and experiments in Europe. The focus lay in analyzing the prevalence of trait groups to show provenance effects, provenance environment interaction effects, clinal effects, and transfer effects. There are empty cells in some columns, and infilling these cells will interfere with the code. ## Description of the data and file structure S1_Data_extractions.xlsx is a simple Excel sheet with single observations (i.e., tested trait-relationships) in rows. It consists of the following columns: **Title:** Title of the article. **Year:** Year of publication **Paper.ID:** Identifier of research article **Case.ID:** Identifier of case within the article. Observations within an article have been split for each species, analysis, age, etc. Hence, an individual identifier. **Species:** The species name in Latin. Genus is abbreviated, e.g., Fagus sylvatica = F.sylvatica **Spec.group:** Broadleaves or Conifers **No. Species:** Number of species that were studied in the article (i.e, per Paper ID) **Age:** Age at assessment **Age.class:** Age at assessment grouped (seedling ≤ 5, 5 &lt; sapling ≤ 20, 20 &lt; mature tree) **Provenances/Populations:** Number of provenances tested in case (i.e., per Case ID) **Trials. Treatments.locations:** Number of trials tested per case **Study.type:** Type of study (e.g., Common garden experiment, Growing chamber, Greenhouse, etc.) **Function:** Type of analysis conducted (e.g., linear Regression, correlation, ANOVA, etc.) **Analysis:** simplified type of analysis, either Provenance-effect (continuous), Provenance-effect(categorical,) or Provenance X Environment-effect **Trait:** The trait that was studied **Predictor:** The environmental predictor (usually climate at origin), but provenance or provenance environment interaction was also coded in case of categorical tests **TD:** is continuous predictor transfer distance (1) or climate of origin (0) **value:** The significance of the test. 1 for significant, 0 not significant. Significant tests against climate with negative relationships were coded as -1 **Group.x:** The predictor group. (e.g., Temperature, Precipitation, Provenance, PXE, etc.) **Group.y:** The trait group. (e.g., Growth, Phenology, etc.) **P.countries:** ISO2 country abbreviations where the provenances originated **T.countries:** ISO2 country abbreviations where trials were conducted **P.range.part:** range of natural distribution of species, the provenances were collected (central, southern marginal, northern marginal, etc.) **T.range.part:** range of natural distribution of species, the trials were conducted (central, southern marginal, northern marginal, etc.) Please refer to the methods section of the research article for further information on data collection, extraction, organization, and analysis. ## Sharing/Access information The data and analysis code are freely accessible and may be shared. In case of use and publication, citing the original research article is appreciated. ## Code/Software Data was analyzed using the R script S2_Analysis_code. The script is available via ([https://doi.org/10.5281/zenodo.11506163](https://doi.org/10.5281/zenodo.11506163))