DOI: 10.5061/dryad.pk0p2ngsd
title: Understanding different dominance patterns in western Amazonian forests

description: Dominance of neotropical tree communities by a few species is widely
 documented, but dominant trees show a variety of distributional patterns
 still poorly understood. Here, we used 503 forest inventory plots (93,719
 individuals ≥ 2.5 cm diameter, 2,609 species) to explore the relationships
 between local abundance, regional frequency, and spatial aggregation of
 dominant species in four main habitat types in western Amazonia. Contrary
 to the widely supported positive abundance-occupancy relationship in
 ecology, we found that among dominant Amazonian tree species, there is a
 strong negative relationship between local abundance and regional
 frequency and/or spatial aggregation across habitat types. Our findings
 suggest an ecological trade-off whereby dominant species can be locally
 abundant (local dominants) or regionally widespread (widespread
 dominants), but rarely both (oligarchs). Given the importance of dominant
 species as drivers of diversity and ecosystem functioning, unraveling
 different dominance patterns is a research priority to direct conservation
 efforts in Amazonian forests.
description: We used data from 503 forest
 inventory plots spread across western Amazonia, from Colombia to Bolivia.
 A total of 363 plots had an area of 0.1 ha, 134 plots were smaller than
 0.1 ha (range from 0.025 to 0.08 ha), and 6 plots were larger (range from
 0.128 to 0.213 ha). Plots are included in the RedGentry
 network.
 Across all plots, we
 measured stems with a diameter at breast height ≥ 2.5 cm within the plot
 limits. Plots covered the main four
 habitat types in western Amazonia: 383 in terra firme (76%), 54 in
 floodplain (11%), 35 in swamp (7%) and 31 in white sand (6%)
 forests. We excluded all individuals not identified to
 species level (mean 14% of individuals per plot), since plot data came
 from different projects and morphospecies were not cross-checked. We also
 excluded individuals from doubtful identifications, e.g. ‘cf.’ and ‘aff.’
 (mean 3% of individuals per plot). To the remaining individuals, we
 checked species names for synonym and spelling mistakes, using the R
 package ‘Taxonstand’.
 Identifications that were difficult to designate to a species were
 considered morphospecies and were also removed. Finally, we cross-checked
 our species names list against the most recent checklists of Amazonian
 species. Species not found
 in these checklists (635 species) were compared with collection records in
 the Tropicos database, and were excluded because: 572 species of them were
 growth forms not consistently included in all datasets (epiphytes, lianas,
 herbs and ferns), 25 were illegitimate Amazonian species with ranges
 outside of our region and 38 species were considered wrong identifications
 because they do not have recorded collection since their descriptions.
 After these filters, 2,609
 species and
 93,719
 individuals remained
 available for our analyses. Since plot size varied among datasets, we
 transformed abundances into relative abundances (i.e., number of
 individuals per species/total individuals per plot). Then, we defined
 dominant species as those species that together accounted for 50% of the
 total relative abundance of all individual trees in each
 habitat. We analyzed
 separately dominant species by habitat type.
 Since our plots are not evenly
 distributed in space, identifying dominant species considering all plots
 in each habitat type could favor the selection of spatially clumped
 species. To explore the effect of this potential bias, we divided our
 study area into equal 100 x 100 km squares, and we extracted 100 random
 subsamples from the complete set of plots in each habitat type drawing one
 plot from each square each time. We identified dominant species in the
 complete dataset and each subsample.
 To test the relationship
 between local abundance and regional frequency of dominant species by
 habitat type, we built beta regression models with a logit link function.
 We used the mean local relative abundance of each dominant species as the
 dependent variable and both the regional relative frequency and the
 habitat type as predictors. We built species-level rank
 abundance distribution graphs within each habitat type to explore if local
 abundance in each plot of each dominant species gave similar information
 that their mean local abundance. We conducted
 these analyses for: i) the complete dataset, including all plots of each
 habitat type; and ii) for the 100 subsamples. We further wanted to explore
 how the tendency changed adding sequentially rarer species. Therefore, we
 conducted the same analyses for species that account for 60%, 70%, 80%,
 85%, 90%, 92.5%, 95%, 97.2% and 100% of the total relative
 abundance. To study
 the relative spatial aggregation of species, we analyzed the co-dominance
 of each species at each spatial extent and habitat. To do so, we
 calculated the F index related to each geographical distance between plots
 to all species and relativized these values to the community-level
 aggregation curve. 
description: # Title: Understanding different dominance patterns in western Amazonian
 forests Dryad doi: https://doi.org/10.5061/dryad.pk0p2ngsd NOTE: * Version
 two published Sep 26, 2023 includes a revised R script file in Zenodo. No
 files on Dryad changed. * Version three published Oct 17, 2023 includes
 additional raster and shape files. * Version four published Oct 18, 2023
 includes a revised R script file in Zenodo and a revised
 Raw\_to\_ecology3.csv file. ## Data frame name: Raw\_to\_Ecology3.csv
 93719 rows = each plant individual 5 columns = 1. Cod\_plot: unique
 identifier of each plot 2. Species: plant genus and species name for all
 dominant species found in the research. Morphospecies for the non-dominant
 species 3. Forest\_type: Habitat type classification (Terra firme,
 Floodplain, Swamp, White sand) 4. Latitude: latitudinal geographic
 coordinates in decimal degrees 5. Longitude: longitudinal geographic
 coordinates in decimal degrees ## Data frame name: Metadata4.csv 503 rows
 = each plot 7 columns = 1. Cod\_plot: unique identifier of each plot 2.
 PI/Main contact: person/people main contact associated with each plot 3.
 Latitude: latitudinal geographic coordinates in decimal degrees 4.
 Longitude: longitudinal geographic coordinates in decimal degrees 5.
 Plot\_size: size of the plot (in hectares) 6. DBH\_min: minimum diameter
 at breast height of individuals collected in that plot (in centimeters) 7.
 Forest\_type: Habitat type classification (Terra firme, Floodplain, Swamp,
 White sand). 8. Country: country where the plot was established ## 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 Raster files to create the elevation map of western Amazonia
 ## 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