DOI: 10.5061/dryad.n5tb2rc41
title: Data from: Climate interacts with the functional trait structure of tree communities to influence forest productivity

description: Tree functional diversity can increase forest productivity by enhancing
 species interactions and providing greater growth stability. However, very
 few studies have examined the influence of tree community trait structure
 on survivor growth, recruitment, and mortality simultaneously, which are
 the main drivers of forest population dynamics. Here we explore
 the interactions among functional diversity, productivity, and climate to
 investigate the role of the trait structure of communities on forest
 productivity and to determine under what circumstances functional
 diversity should be promoted to ensure forest adaptive capacity under
 future climate. Using random-forest modeling and a network of
 permanent sample plots covering a broad gradient of climatic conditions,
 we isolated the effects of functional diversity—described as the
 distribution of trait values in a community—and climate variables on net
 forest productivity (NFP), survivor growth, recruitment, and
 mortality. Based on our findings, community-level trait structure
 affects forest productivity in different ways. NFP was influenced by three
 traits from three different plant strategy dimensions, whereas survivor
 growth and recruitment were strongly correlated with leaf and resource
 acquisition traits, and tree mortality with a mix of traits reflecting
 various plant strategies. We also observed climate interactions
 with the functional trait structure of tree communities. For instance, we
 observed an interaction between drought tolerance and mean annual
 temperature: at low temperatures, NFP biomass accumulation increased with
 the value of the drought tolerance trait; however, at higher temperatures,
 the opposite pattern was observed. However, we found contrasting patterns
 of population response to climate variability, depending on their
 functional diversity. Greater functional diversity does not necessarily
 increase biomass accumulation under different climatic conditions.
 Synthesis. As all components of forest productivity contribute to NFP,
 studies on forest productivity should not only consider survivor growth
 but also recruitment and mortality. Each component responds differently in
 terms of biomass changes to climatic variation, according to the trait
 structure of tree communities. This study provides a framework to identify
 the trait structure that should be targeted under different climate
 scenarios to anticipate change and help strengthen forest response
 capacity to climate change.
description: No local data collection was conducted in this study, as it
 relied on a meta-analysis of secondary data. The team of authors and
 collaborators, however, is representative of the main regions covered by
 the meta-analysis, ensuring appropriate interpretation of the data and
 results from the studied regions. Data were derived
 from the following resources available in the public domain: Québec
 permanent sample plot forest inventory (https://www.donneesquebec.ca/recherche/dataset/placettes-echantillons-permanentes-1970-a-aujourd-hui/resource/ccf8d0d7-85fe-49b0-a965-e914b2395fb7), Newfoundland and Labrador Department of Fisheries, Farming and Natural Resources (https://www.gov.nl.ca/ffa/programs-and-funding/forestry-programs-and-funding/managing/inv-plan/). Functional diversity indices and traits data were derived from the TOPIC database with the permission from TOPIC (Aubin et al., 2020). Traits data from Paquette and Messier (2011) were also used.
description: # Data from: Climate interacts with the functional trait structure of tree
 communities to influence forest productivity
 [https://doi.org/10.5061/dryad.n5tb2rc41](https://doi.org/10.5061/dryad.n5tb2rc41) No local data collection was conducted in this study, as it relied on a meta-analysis of secondary data. The team of authors and collaborators, however, is representative of the main regions covered by the meta-analysis, ensuring appropriate interpretation of the data and results from the studied regions. Data were derived from the following resources available in the public domain: Québec permanent sample plot forest inventory ([https://www.donneesquebec.ca/recherche/dataset/placettes-echantillons-permanentes-1970-a-aujourd-hui/resource/ccf8d0d7-85fe-49b0-a965-e914b2395fb7](https://www.donneesquebec.ca/recherche/dataset/placettes-echantillons-permanentes-1970-a-aujourd-hui/resource/ccf8d0d7-85fe-49b0-a965-e914b2395fb7), Newfoundland and Labrador Department of Fisheries, Farming and Natural Resources ([https://www.gov.nl.ca/ffa/programs-and-funding/forestry-programs-and-funding/managing/inv-plan/](https://www.gov.nl.ca/ffa/programs-and-funding/forestry-programs-and-funding/managing/inv-plan/). Functional diversity indices and traits data were derived from the TOPIC database with the permission from TOPIC (Aubin et al., 2020). Traits data from Paquette and Messier (2011) were also used. The data derived from other sources can be published under CC0. ## Description of the data RF modeling was performed using the recursive feature elimination (rfe) algorithm from the *caret* package (Kuhn, 2020) in R. The RF was calculated to find the best subset of predictors—from a model having a single explanatory variable to a model using all explanatory variables—that generates the lowest root mean square error (RMSE) with a tolerance of 3% (3% more error than the model with the lowest RMSE) and with 50 repetitions of 10-fold cross-validation. We used this set of 37 explanatory variables describing forest structure, environment, and trait structure to predict NFP and the aboveground biomass increment attributed to survivor growth, recruitment, and mortality. The optimal model was recalibrated using the *randomForest* package (Liaw and Wiener, 2002) in R, with the number of features sampled at each split set to the default value for regression (i.e., *p*/3 where *p* is number of variables) and the number of random trees set to 20,000. | **Explanatory variables** | **Description** | | :------------------------ | :----------------------------------------------------------------------------------------------------------------------------------- | | *ID\_PE\_MES* | Plot id and no measurement | | *Biomass\_NetP* | Net forest productivity biomass increment (Mg ha−1 yr−1) | | *Biomass\_Grow* | Survivor growth biomass increment (Mg ha−1 yr−1) | | *Biomass\_Mort* | Tree recruitment biomass increment (Mg ha−1 yr−1) | | *Biomass\_Recr* | Tree mortality biomass loss (Mg ha−1 yr−1) | | *Biomass\_cov* | Plot total biomass (Mg ha−1) | | *BAmerch* | Basal area (m2 ha−1) | | *DomHeight* | Dominant height (m) | | *Compo\_DomSp* | Dominant species | | *Compo\_prop* | Basal area of the dominant species (m2·ha−1) | | *TPI\_20m\_5c* | Topographic position index | | *TWI\_20m* | topographic wetness index | | *Pmean\_int* | Mean total annual precipitation (mm) | | *Tmean\_int* | Mean annual temperatures (°C) | | *Tmax\_int* | Maximum annual temperatures (°C) | | *Pmin\_int* | Minimum total annual precipitation (mm) | | *nbsp* | Species richness (N) | | *shannon\_true* | True Shannon-Wiener diversity index | | *simpson* | Gini–Simpson index calculated with all functional traits | | *FDis* | Functional dispersion index calculated with all functional traits | | *FGR* | Functional group richness calculated with all functional traits | | *CMW\_SeFreq* | Community-level weighted means of the SeFreq trait | | *CMW\_TolD* | Community-level weighted means of the TolD trait | | *CMW\_LMA* | Community-level weighted means of the LMA trait | | *FDis\_Pb* | Functional dispersion of the Pb trait | | *FDis\_Nmass* | Functional dispersion of the Nmass trait | | *FDis\_WDR* | Functional dispersion of the WDR trait | | *simps\_tree* | Gini–Simpson index based on the mix of four traits from the tree stature group (maxH, GR, WD, WDR) | | *FDis\_leaf* | Functional dispersion index of four traits from the leaf group (LL, LMA, Nmass, LS) | | *raoQ\_Eco506* | Rao’s quadratic entropy index based on the mix of three traits from the plant strategy dimensions (EM, FrostFMin, SeM) | | *raoQ\_Eco242* | Rao’s quadratic entropy index based on the mix of three traits from the plant strategy dimensions (Nmass, RainMin, SeM) | | *simps\_Eco1* | Gini–Simpson index based on the mix of three traits from the plant strategy dimensions (LS, maxH, Veg) | | *redon\_Eco5* | Functional redundancy index based on the mix of three traits from the plant strategy dimensions (LS, maxH, SeFreq) | | *FDis\_Eco296* | Functional dispersion index of three traits from the plant strategy dimensions (GR, TolW, AsexM) | | *raoQ\_Eco277* | Rao’s quadratic entropy index based on the mix of three traits from the plant strategy dimensions (GR, TolS, SeFreq) | | *FDis\_Eco457* | Functional dispersion index of three traits from the plant strategy dimensions (EM, WDR, Veg) | | *FDis\_Logi310* | Functional dispersion index of five traits from the functional group dimensions (maxH, SeFreq, TolD, RootD, LL) | | *simps\_Logi1* | Gini–Simpson index based on the mix of five traits from the functional group dimensions (maxH, Veg, TolS, AM, LS) | | *redon\_Logi1252* | Functional redundancy index based on the mix of five traits from the functional group dimensions (WD, SeM, TolW, AM, Nmass) | | *redon\_Logi394* | Functional redundancy index based on the mix of five traits from the functional group dimensions (maxH, SeOptP, TolW, RootD, LL) | | *raoQ\_Logi354* | Rao’s quadratic entropy index based on the mix of five traits from the functional group dimensions (maxH, SeFreq, FrostFMin, EM, LL) | | *FDis\_Logi2106* | Functional dispersion of five traits from the functional group dimensions (WDR, SeOptP, TolD, EM, LL) |