DOI: 10.5061/dryad.2280gb60q
title: Species functional traits affect regional and local dominance across western Amazonian forests

description: Several studies have documented dominance by few species in Amazonian
 forests. Dominance patterns in contrasting forest habitats in lowland
 Amazonia have revealed that dominant species tend to be either locally
 abundant (local dominants) or regionally frequent (widespread dominants)
 but rarely both (oligarchs). However, the mechanisms underlying dominance
 remain unclear. Here, assuming that species traits reflect ecological
 processes that can lead to dominance, we asked across different habitat
 types whether: (i) dominance is defined by specific functional profiles
 and (ii) dominance patterns (local dominants vs. widespread dominants) are
 associated with different functional traits. We combined census data from
 503 forest inventory plots across four lowland forest habitats in western
 Amazonia with trait information for 2600 tree species, encompassing data
 collected in the focal plots and data from published sources. We
 considered traits that relate to leaf, wood, seed and whole-plant
 strategies: specific leaf area (SLA), leaf area (LA), N content per unit
 leaf mass (LN), wood density (WD), seed mass (SM), and maximum diameter at
 breast height (DBHmax~).  Our results reveal that dominant
 species display different trait combinations depending on the habitat type
 where they dominate. Moreover, taller dominant species exhibit higher
 regional frequency, associated with higher dispersal ability, and lower
 local abundance, likely due to negative density dependence. Greater SM
 contributes to higher regional frequency of dominant species via greater
 dispersal ability and seedling survival. Finally, traits related to
 resource conservation strategies, such as lower SLA, LA, LN and greater
 WD, favor higher local densities across most habitats, while the opposite
 pattern was linked to higher regional frequency. Synthesis: Our
 findings reveal that (i) dominance is associated with different functional
 traits depending on the habitat type, and (ii) different functional trait
 values define distinct dominance patterns. Our study exemplifies the
 potential of trait-based approaches to illuminate the ecological
 mechanisms that may underlie dominance in tropical forests. Finally,
 accounting for both local abundance and regional frequency when studying
 dominance is likely to improve our understanding and forecasting of how
 different species will respond to global change drivers in western
 Amazonia.
description: We used an extensive dataset consisting of 503 forest inventory
 plots, ranging from 0.025 to 0.213 ha, across different western Amazonian
 forests, from Colombia to Bolivia. Plots covered the four main habitat
 types found in western Amazonia proportionally to their extension in the
 region, with 76% in <em>terra firme</em> forests, 11% in
 floodplains, 7% in swamps and 6% in white sands. All trees ≥ 2.5 cm DBH
 were recorded and identified to species, representing a total of 93,719
 individuals, and 2609 species.  We defined dominant
 species as those that together account for 50% of the total relative
 abundance of each habitat type, following ter Steege et al. (2013) and
 Matas-Granados <em>et al.</em> (2024). We identified dominant
 species separately by habitat type. We defined two main aspects of
 dominance: (1) local abundance of dominant species, calculated as the mean
 local relative abundance (individuals of the species in a plot divided by
 total individuals in the plot), averaged across the plots where each
 dominant species occurred, and (2) regional frequency of dominant species,
 calculated as the number of plots where a species occurred divided by
 total plots in the habitat type. We considered six
 plant functional traits as they related to resource acquisition,
 dispersal, defense, and competitive ability and can capture species
 differences in their ecological strategies: specific leaf area; leaf area;
 N content per unit leaf mass; maximum diameter at breast height as a proxy
 of maximum plant height; wood density; and seed mass We
 used the plot inventories to determine the maximum diameter at breast
 height as the 95<sup>th</sup> percentile of each species.
 Other trait data was compiled from various sources following standardized
 protocols, including (i) previous work by our research groups, (ii)
 publicly available trait databases such as TRY, funAndes, the Seed
 Information Database, and the Global Wood Density Database, and (iii)
 additional sources for seed mass. When more than one measure per species
 was available, we calculated the species mean trait value. The compiled
 trait dataset represented between 29% (for SM) and 100% (for
 DBH<sub>max</sub>) of our 2609 species, and all species had
 information for at least one of the six traits. We log transformed all
 trait values (except WD) for subsequent analyses. As protocols can vary
 between studies, we compiled data for SLA and LA including and excluding
 petiole, and data for WD taken from the branch or from the sapwood and
 heartwood. Species coverages were greater when using SLA and LA measured
 without the petiole and WD measured from the branch, and they were highly
 correlated with SLA and LA measured with petiole and WD measured from the
 trunk, respectively. To evaluate trait differences
 between species, considering their regional abundance across multiple and
 single dimensions, we conducted both multivariate and univariate
 analyses. First, we performed principal component
 analyses (PCAs) with scaled functional trait values to characterize the
 main dimensions of functional variation among all species within each
 habitat type separately and to illustrate trait relationships. We
 extracted species scores for axes 1 and 2, as these together accounted for
 more than 55% of functional variation across all habitat types. To test
 the relationship between each main axis of functional variation and
 species regional abundance, we built two linear models (LMs) for each
 habitat type separately: one with axis 1 as the dependent variable and the
 other with axis 2. In both models, species regional abundance was the
 explanatory variable. Given that few species had values for all six traits
 (16% of all species), we repeated the analyses without SM (34% species had
 values for the remaining five traits) and presented these results in the
 main text. Second, for each trait separately, we conducted LMs to test the
 relationships between functional traits and species regional
 abundance. To account for potential bias due to
 disproportionate sampling of species functional traits clustered in
 specific lineages (i.e., some lineages could be more represented than
 others), we subsampled one species per genus from the species list of each
 habitat type 100 times and performed all the multivariate and univariate
 analyses each time to compare the subsampled results to our observed
 results. To explore the role of traits in the two
 variables of dominance measured to each dominant species (local abundance
 and regional frequency) in each of the four habitat types, we built
 Bayesian models. We built a unique model for each combination of dependent
 variable (local abundance and regional frequency) and single trait,
 resulting in 12 models. All traits were rescaled to facilitate model
 comparisons. We fitted all models with weakly informative priors. Model
 convergence was tested visually with trace and density plots and
 numerically estimating if <em>Rhat </em>was equal to one.
 Models usually converged after 4000 iterations. Model fit was evaluated
 using Bayesian <em>R<sup>2</sup></em>. We focus on
 the interaction between trait and habitat (i.e., the slopes of the
 relationships). All analyses were conducted in R v4.1.3.
description: # Exploring different dominance patterns in western Amazonian forests from
 a functional trait perspective
 [https://doi.org/10.5061/dryad.2280gb60q](https://doi.org/10.5061/dryad.2280gb60q) ### Excel file name = Traits_species_data.xlsx 14 sheets "Traits_values2" sheet = compiled species mean trait values without separating by habitat type. "Legend_Traits_values2" sheet = Legend for the Traits_values2 sheet. "Traits_values_FT" sheet = compiled species mean trait values separated by habitat type. "Legend_Traits_values_FT" sheet = Legend for the Traits_values_FT sheet. "Table_compilation" sheet = Table where rows are the species of our study and columns are the functional traits that we studied. To each species and trait, we included the references where the functional data was extracted from. "Legend" sheet = Legend for the Table_compilation sheet. "Workflow" sheet = Steps followed to generate the compiled trait database. "References" sheet = References from the functional data were extracted. "DISPLAMAZ_data" sheet = functional data from the project led by Manuel J. Macía and Luis Cayuela (CGL2016-75414-P). "Legend_DISPLAMAZ" sheet = Legend for the DISPLAMAZ_data sheet. "DBH_max" sheet = Maximum diameter at breast height calculated for each species from the forest inventory plot data. "Legend_DBH_max" sheet = Legend for the DBH_max sheet. "Forest_structure" = Stem density and basal area for the four habitat types studied at the plot level. "Legend_Forest_structure" sheet = Legend for the Forest_structure sheet. ### File names: BOL_alt.grd, BOL_alt.gri, BOL_alt.vrt, BRA_alt.grd, BRA_alt.gri, BRA_alt.vrt, COL_alt.grd, COL_alt.gri, COL_alt.vrt, ECU_alt.grd, ECU_alt.gri, ECU_alt.vrt, PER_alt.grd, PER_alt.gri, PER_alt.vrt, VEN_alt.grd, VEN_alt.gri, VEN_alt.vrt Raster files to create the elevation map of western Amazonia .grd file = header file describing the raster .gri file = binary file that includes the raster value of the elevation .vrt file = file that describes how the raster is organized "BOL" = Bolivia "BRA" = Brazil "COL" = Colombia "ECU" = Ecuador "PER" = Peru "VEN" = Venezuela ### File names: TM_WORLD_BORDERS-0.3.dbf, TM_WORLD_BORDERS-0.3.prj, TM_WORLD_BORDERS-0.3.shp, TM_WORLD_BORDERS-0.3.shx Shapefile to create the map of South America .dbf file = file that stores the attribute data table of the shapefile .prj file = file that stores the projection information (coordinate system) of the shapefile .shp = primary geometry file (polygons) of shapefile .shx = shape index file in the ESRI shapefile format ### File names: Script_JofE_R3.R, Script_JofE2_R3.R Scripts to generate tables and graphs.