Species functional traits affect regional and local dominance across western Amazonian forests

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.

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 terra firme 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 et al. (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 95th 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
DBHmax) 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 Rhat was equal to one.
Models usually converged after 4000 iterations. Model fit was evaluated
using Bayesian R2. 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.

# 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.