Disentangling different pathways to dominance in western Amazonian forests

Dominance of neotropical tree communities by a few species is widely documented, yet the different pathways that Amazonian plants follow to achieve dominance remain 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 across habitats in western Amazonia. Contrary to the well-supported abundance-occupancy relationship, we found that among dominant Amazonian tree species, there is a strong negative relationship between local abundance and regional frequency/spatial aggregation across habitat types. Our findings suggest an ecological trade-off whereby dominant species can allocate resources to being 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 modes of dominance is a research priority to direct conservation efforts in Amazonian forests.Funding provided by: Ministerio de Economía y CompetitividadCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100003329Award Number: CGL2016–75414–PFunding provided by: Ministerio de Ciencia e InnovaciónCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100004837Award Number: PID2019-105064GB-I00Funding provided by: Ministerio de Economía y CompetitividadCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100003329Award Number: CGL2015-72431-EXPWe 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). 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 (15%), since plot data came from different projects and morphospecies were not cross-checked. We also removed 635 species that were not present in the most current checklists of Amazonian species: comprising growth forms not included in all datasets (epiphytes, lianas, herbs, and ferns) (90%), illegitimate Amazonian species (4%), and doubtful identifications (6%). After these filters, 2,849 species and 108,444 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. 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. 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. Lastly, we aimed to test the relationship of both local abundance and regional frequency of dominant species with their maximum spatial aggregation (i.e., the geographical distance where we found the maximum spatial aggregation of each dominant species). We built different beta regression models separated into two groups: one with mean local abundance as a dependent variable and the other with regional frequency. All possible combinations between the maximum spatial aggregation of dominant species and habitat type were used as predictors