DOI: 10.5061/dryad.m37pvmdc1
title: Data from: Plant traits associated with nesting resources and flower availability determine bee’s functional trait diversity in a highly diverse tropical Amazon Forest

description: Functional traits help understand biological diversity and the mechanism
 by which ecological communities are structured and how they respond to the
 environment. For example, the high tree species diversity within tropical
 forests can be grouped into a few functional attributes like wood density,
 size, and dependence on animal pollination or seed dispersal. However,
 little is known about how these traits influence animal taxonomic and
 functional diversity. We carried out a vegetation census on six plots (20
 x 100 m) within the National Forest of Carajás (Amazon biome) to identify
 forest canopy species and their functional traits. Within the same plot,
 we also applied three bee sampling methods (entomological nets, honey
 traps, and scent traps). By characterizing the functional traits of trees
 and bees, we were able to predict bee functional diversity better than
 with taxonomic diversity alone via combinations of tree traits like size,
 wood density, dependence on pollinators, and extinction risk. We found
 that the basal area of trees with low wood density was negatively
 associated with small, eusocial tree cavity-nesting bees. The richness of
 medium-sized solitary bees was positively associated with the richness and
 abundance of trees with extinction risk. The community dominance (average
 diameter at the basal area) of pollinator—dependent trees was negatively
 associated with the richness of aboveground and cavity-nesting bees. Our
 findings suggest that tree community composition limits the availability
 of nesting resources for specific bee groups. Moreover, the presence of
 trees with high conservation value was associated with a greater variety
 of bee traits and was the only metric associated with overall bee
 richness. As expected, functional traits shed light on the
 mechanism that might drive high diversity within tropical forests.
 Moreover, there seems to be complementarity in terms of conservation value
 and carbon stock potential, as areas harboring tree species with
 extinction risk and higher wood density are also those with overall
 greater bee and functional diversity. Finally, our study can contribute to
 the restoration of plant—pollinator community by providing an
 understanding of the vegetation community that contributes to biodiversity
 maintenance.   
description: <em>1. Vegetation sampling</em>
 The Carajas National Forest protected area comprises a set of
 mountain ranges covered with pristine Amazon Forest. Within the National
 Forest, we implemented vegetation census on 6 plots. Each plot consisted
 of a 20 m x 100 m area in which all the plants whose diameter at breast
 height (DBH) was greater than 10 cm were identified to the species level,
 and their DBH was measured. The minimum distance between plots was more
 than 2.5 km. For all trees, palms, and vines, we measured height, and DBH
 and assessed wood density and dependency on bee pollination from the
 literature (Table S3). For species with no data available (20%) we
 estimate wood density using the average wood density among the genera of
 that same individual for which wood density was missing. As we focused on
 trees, palms and vines, one vegetation census was performed in each plot
 over two years (2021 and 2022) of sampling, and the vegetation census
 occurred independently of bee sampling (2022).  
 Additionally, we classified species according to their
 vulnerability to extinction status, based on the International Union for
 Conservation of Nature (IUCN) Red List. Species were considered threatened
 if they were classified as endangered, vulnerable, or near threatened. The
 species classification is based upon one or multiple criteria, including
 population size decline, geographic range, and very small populations.
 Species classification is based upon one or multiple criteria, including
 population size decline, geographic range, and very small populations.
 Although threatened to extinction is not a function associated with a
 process, it can be considered a function in terms of a species’ shared
 susceptibility to stressors (all traits are functional traits).
 We considered five functional traits: i) Plant size (DBH), ii)
 wood density, iii) height, iv) melittophily and v) extinction risk
 (threatened or not). To characterize the community considering categorical
 traits, we considered the abundance of each level (e.g., the abundance of
 melittophilous plants) and the aggregated plant size (sum of the DBH of
 individuals with a specific trait level) to characterize the dominance of
 functional traits in the community.  For wood density, for example, we
 first categorized trees using a wood density threshold of 0.68 g/cc
 (median), grouping species as either having low wood density or high wood
 density. Then, for each category, we calculated the average, maximum value
 and accumulated total plant size (sum) of low- and high—wood—density
 trees. The same was done for all bee pollination dependent species and for
 all tree species with some level of extinction threat.    
 <em>2. Bee sampling</em> We
 applied three complementary protocols for sampling bees in each vegetation
 sampling plot: 1) We actively collected bees using entomological nets by
 sampling across flowering plants in the understory. We collected bees from
 flowers by walking in the sampling area (plot) for two non-consecutive
 hours, one-hour sampling, at 30-minute intervals, followed by a second
 hour of sampling. Sampling was performed by one researcher in each area
 between 7 and 10 am. Three independent researchers collected the samples
 at the same time; thus, all areas were sampled on two consecutive days. 2)
 We used a honey trap, which consisted of a mixture of honey and water
 (1:2, i.e. 250 ml of honey and 500 ml of water) that was sprayed on a
 square meter on a plant with broad leaves chosen at random at the same
 sampling point. We performed two 10-minute bee samples (using
 entomological nets) from the honey traps (first between 8 and 9 am, and
 second between 10 and 11 am), with one-hour intervals between each sample.
 Finally, 3) we used a scent trap (four different scents were used), which
 was most effective for attracting bees from the Euglossini group (orchid
 bees). We used two sets of traps (with four scents: eucalyptol, eugenol,
 methyl salicylate and vanillin), and each set was placed at 2- and 20 m in
 height and remained in place for 48 h. Each of the sampling protocols was
 applied three times in each sampling area. Honey and scent traps were
 placed in the center of the plot, and flower visitors were actively
 collected through the plot. In total, we applied three sampling protocols
 (flower visitors, honey and scent traps) three times (March, May and
 August of 2022) in each area. We identified all bees to the species level,
 and we deposited them in Museu Paraense Emilio Goeldi (Belém, Pará,
 Brazil). Using literature information, specimens’
 measurements, and the species taxonomy, we were able to classify the bees
 accordingly to their nesting traits (type and location), body size and
 sociality. Nesting types were either below- or aboveground. Nesting
 location refers to whether a bee nested in tree cavities or not (other
 types were not considered due to the low number of species,
 cleptoparasitic and exposed). Body size was classified into three size
 classes according to the intertegular distance: small (0.9–2.1 mm), medium
 (2.2–3.9 mm) and large (&gt;3.9 mm), following the classification from
 Borges et al., 2020. Moreover, we grouped them accordingly to their social
 behavior (eusocial, solitary and semisocial).
 <em>3. Functional trait richness</em>
 For each functional trait categorized either for plants (DBH,
 wood density, height, melittophily, or extinction risk) or bees (nesting
 type, nesting location, body size, and sociality) we also calculated the
 taxonomic richness within each categorical functional trait presented. For
 instance, the richness of bee-pollinated plants in each plot or the
 richness of plants with extinction risk <em>per </em>plot.
 Additionally, we calculated the Shannon index for each plot based on plant
 species abundance. For continuous variables, we also calculate average and
 total plot values within each category. The total plot value of plant size
 is aggregated per functional trait; for example, the size (DBH) of plants
 pollinated by bees is considered a proxy of how much of the plot’s canopy
 is dominated by plants that depend on bees for pollination. In the case of
 bee traits, a similar approach was implemented: we calculated the richness
 of small bees, for example, or the richness of bee that nest aboveground,
 or that are classified as solitary. Additionally, we calculated functional
 diversity metrics using the categorical and continuous abovementioned
 functional traits, with the functional diversity function of the FD
 <em>package</em> in r (R core Team, 2023).
description: # Plant traits associated with nesting resources and flower availability
 determine bee’s functional trait diversity in a highly diverse tropical
 Amazon Forest.
 [https://doi.org/10.5061/dryad.m37pvmdc1](https://doi.org/10.5061/dryad.m37pvmdc1) ## Description of the data and file structure Six vegetation census were done to sample all trees with DBH greater than 10 cm. Aditionally, in the middle of each plot we sampled bees with three protocols, actively net samples, honey traps and scent traps.  ### Files and variables #### File: fuctional\_trait\_data\_Dryad.xlsx **Description:**  ##### Variables * rich_flora: species richness of plants (count data) * abund_flora: species abundance (count data) * Shannon_Index: Shannon index of plant in the plot using plant abundance and richness * rich_of_bees: bee richness (count data) * bee_active_rich: bee richness  sampled with nets (count data) * bee_honey_rich: bee richness sampled with honey traps (count data) * bee_scent_rich: bee richness sampled with scent traps (count data)  * samplingLocationID: each sampling site/plot ID * Richness_tree: richness of trees (count data) * tree_abund: abundance of trees (count data) * Richness_vine: richness of vines (count data) * vine_abund: abundances of vines (count data) * Richness_palm: richness of palms (count data) * palm_abund: abudance of palms  (count data) * Fbeepol:  Richness of plant species that benefit from bee pollination * Fbeepol_abund: Abudance of plant species that benefit from bee pollination * plantDBH.mean: Average of all plants diameter at breast height (cm) * plantDBH.sum: Sum of all plants diameter at breast height (cm) * plantDBH.max: Maximum of all plants diameter at breast height (cm) * plantDBH_pol.mean: Average diameter at breast height (cm) of plants that benefit from bee pollination * plantDBH_pol.sum: Sum of diameter at breast height (cm) of plants that benefit from bee pollination * plantDBH_pol.max: Maximum diameter at breast height (cm) of plants that benefit from bee pollination * WoodDensity: Average wood density of all plants (g/cm3) * WoodDensity_tree: Average wood density of tree species only (g/cm3) * plantWoodDensity_pol: Average wood density of tree species that benefit from bee pollination (g/cm3) * plantHeight_max: Maximum height of plant individual in the plots (m) * plantDBH_highWD.mean: Average diameter at breast height (cm) of plants classified as having high wood density * plantDBH_highWD.sum: Sum of diameter at breast height (cm) of plants classified as having high wood density * plantDBH_lowWD.mean: Average diameter at breast height (cm) of plants classified as having low wood density * plantDBH_lowWD.sum: Sum of diameter at breast height (cm) of plants classified as having low wood density * Fthreat: Richness of plants threatened with extiction (count data)  * Fthreat_abund: Abundance of plants threatened with extiction (count data)  * plantDBH_threat.mean: Average diameter at breast height (cm) of plants threatened with extiction (count data)  * plantDBH_threat.sum: Sum of diameter at breast height (cm) of plants threatened with extiction (count data)  * plantDBH_threat.max: Maximum diameter at breast height (cm) of plants threatened with extiction (count data)  * FRic: Functional richness index based on categorical traits * ITD: Intertegular distance of all bees (cm)  * ITD_act: Intertegular distance of bees actively sampled with entomological nets (cm) * ITD_hon: Intertegular distance of bees sampled with honey traps (cm) * ITD_scent:  Intertegular distance of bees sampled with scent traps (cm) * ITDsmall: Richness of bee classified as small (count data)  * smallB_A: Richness of bee classified as small and sampled with entomological nets (count data)  * smallB_H: Richness of bee classified as small and sampled with honey traps (count data)  * smallB_S: Richness of bee classified as small and sampled with scent traps (count data)  * ITDmedium: Richness of bee classified as medium (count data)  * mediumB_A: Richness of bee classified as medium and sampled with entomological nets (count data)  * mediumB_H: Richness of bee classified as medium and sampled with honey traps (count data)  * mediumB_S: Richness of bee classified as medium and sampled with scent traps (count data)  * ITDlarge: Richness of bee classified as large (count data)  * largeB_A: Richness of bee classified as large and sampled with entomological nets (count data)  * largeB_H: Richness of bee classified as large and sampled with honey traps (count data)  * largeB_S:  Richness of bee classified as large and sampled with scent traps (count data)  * cavityB: Richness of bees that nest in cavities (count data)  * cavityB_A: Richness of bees that nest in cavities and sampled with entomological nets (count data)  * cavityB_H: Richness of bees that nest in cavities and sampled with honey traps (count data)  * cavityB_S: Richness of bees that nest in cavities and sampled with scent traps (count data)  * aboveG_B: Richness of bees that nest aboveground (count data)  * aboveG_B_A: Richness of bees that nest aboveground and sampled with entomological nets (count data)  * aboveG_B_H: Richness of bees that nest abovegroundand sampled with honey traps (count data)  * aboveG_B_S: Richness of bees that nest aboveground and sampled with scent traps (count data)  * ITD_cavity: Intertegular distance of bees that nest in tree cavities (cm) * ITD_AG: Intertegular distance of bees that nest aboveground (cm) * sociality: Richness of bee classified as eusocial bees (count data)  * solitarity: Richness of bees clasified as solitary (count data)  * sociality_A: Richness of social bees sampled with entomological nets (count data)  * sociality_H: Richness of social bees sampled with honey traps (count data)  * sociality_S: Richness of social bees sampled with scent traps (count data)  * solitarity_A: Richness of solitary bees sampled with entomological nets (count data)  * solitarity_H: Richness of solitary bees sampled with honey traps (count data)  * solitarity_S: Richness of solitary bees sampled with scent traps (count data)  * ITD_social: Intertegular distance of bees classified as social (cm) * ITD_solitary: Intertegular distance of bees classified as solitary (cm) ## Code/software Data file can be viewed with excel