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Data from: Biogeographic history and habitat specialisation shape floristic and phylogenetic composition across Amazonian forests

Citation

Baraloto, Christopher et al. (2021), Data from: Biogeographic history and habitat specialisation shape floristic and phylogenetic composition across Amazonian forests, Dryad, Dataset, https://doi.org/10.5061/dryad.9s4mw6mg0

Abstract

A major challenge remains to understand the relative contributions of history, dispersal and environmental filtering to the assembly of hyperdiverse communities across spatial scales. Here, we examine the extent to which biogeographical history and habitat specialization have generated turnover among and within lineages of Amazonian trees across broad geographic and environmental gradients. We replicated standardised tree inventories in 102 0.1-ha plots located in two distant regions - the western Amazon and the eastern Guiana shield. Within each region, we used a nested design to replicate plots on contrasted habitats: white-sand, terra firme, and seasonally-flooded forests. Our plot network encompassed 26386 trees that together represented 2745 distinct taxa, which we standardized across all plots and regions. We combined taxonomic and phylogenetic data with detailed soil measurements and climatic data to: (i) test whether patterns of taxonomic and phylogenetic composition are consistent with recent or historical processes, (ii) disentangle the relative effects of habitat, environment and geographic distance on taxonomic and phylogenetic turnover among plots, and (iii) contrast the proportion of habitat specialists among species from each region. We found substantial species turnover between Peru and French Guiana, with only 8.8% of species shared across regions; genus composition remained differentiated across habitats and regions, whereas turnover at higher taxonomic levels (family, order) was much lower. Species turnover across plots was explained primarily by regions but also substantially by habitat differences and to a lesser extent by spatial distance within regions. Conversely, the composition of higher taxonomic levels was better explained by habitats (especially comparing white-sand forests to other habitats) than spatial distance. White-sand forests harboured most of the habitat specialists in both regions, with stronger habitat specialization in Peru than in French Guiana. Our results suggest that recent diversification events have resulted in extremely high turnover in species and genus composition with relatively little change in the composition of higher lineages. Our results also emphasise the contributions of rare habitats, such as white-sand forests, to the extraordinary diversity of the Amazon and underline their importance as conservation priorities.

Methods

Study areas

We established a nested experimental design with replicated plots in habitats displaying contrasting soil conditions characteristic of lowland Amazonian forests – white-sand (WS), Terra Firme (TF) and seasonally flooded forests (SF) (Baraloto et al. 2011, Fortunel et al. 2014) – at both regional (c.100 km) and basin-wide (2500 km) distances. A total of 102 0.1-ha plots were inventoried between 2008 and 2018 in ten subregions in French Guiana (hereafter FG; 64 plots) and between 2008 and 2011 in three subregions in Peru (38 plots) (Fig. 1). Each plot was inventoried once, with subregions visited during different field missions within the mentioned period. We tried to maintain at least 50 km between subregions, and at least 500 m between plots.

French Guianan forests stand on a Precambrian tableland, with old, highly weathered and nutrient-depleted soils (Gourlet-Fleury et al. 2004). Mean annual rainfall across inventory subregions ranges between 2160 and 3130 mm (http://www.worldclim.com/) and is distributed seasonally throughout the year (Table 1). The wet season stretches from December to July, and it is usually interrupted in February or March by a short dry period; whereas the dry season occurs from August to November with monthly rainfall never exceeding 100 mm. Mean daily temperatures oscillate between 23.0 and 26.6°C with low seasonal variation (Gourlet-Fleury et al. 2004). Elevation among subregions ranged from 42 to 529 m.

Western Amazonian forests in Peru occur on a more heterogeneous series of substrates due to the Andean uplift and the concomitant erosion of volcanic sediments and marine incursions (Hoorn et al. 2010). Climate conditions are less variable during the year. Mean annual rainfall across inventory subregions ranges between 2405-2750 mm (http://www.worldclim.com/) and is less seasonal than in French Guiana (Table 1). Mean temperature is more stable between 26.3 and 26.7°C with low seasonal variation. Elevation among subregions was also much less variable, from 95-173 m. Further details on the climate and geology of the regions and subregions are provided in Appendix S1.

Tree species inventories

Trees were inventoried following a modified version of the Gentry plots proposed by Phillips et al. (2003) and described in Baraloto et al. (2013). Each plot consisted of ten parallel 50 m-long transects departing perpendicularly from a main 190 m-long central line, successively in alternate directions every 20 m along the line (a schematic illustration of a plot is provided in Appendix S2). All stems with a circumference ≥ 8 cm at 1.3 m above the ground (c. 2.5 cm DBH) were inventoried over a two-meter width along each transect. At least one individual of every putatively distinct taxon encountered was collected in the field to create plot-level herbarium vouchers. In rare cases (0.2% of all stems sampled), no identification was made, nor could vouchers be collected, due to lack of leaves or obstructed canopies. Further sorting resulted in standardized project type collections for all distinct taxa, which were identified at regional herbaria for the Peru (AMAZ) and FG (CAY) collections. We then further standardized and resolved vouchers from both these collections during a two-month period at the herbarium of the Missouri Botanical Garden (MOBOT), such that any unnamed, putative novel species could be compared to other congeners from the other region. At the end, we provide a full detail of all project vouchers describing our standardized inventories (see Appendix S3 for full detail on project vouchers; vouchers and/or photos are available for loan upon request; and a full digital library of vouchers is available at environment.fiu.edu).

Species diversity was characterised in each study subregion using species richness, as well as the effective number of species expected from a random sample of 2 individuals, to weight for species abundance (Dauby & Hardy 2011; Table 1).

Environmental data

Soil conditions were characterized in each plot using nine physicochemical properties: texture (percentages of sand, silt and clay), bioavailable cations content (Ca, Mg and K), available phosphorus content (AP), organic matter (OM) and carbon (OC) contents, total N content (TN) and C:N ratio. Variables were measured from bulked soil cores collected at 0–15 cm depth within each plot. Cores were mixed into a 500 g sample that was dried to constant mass (at 25°C), sieved (2 mm mesh). Samples were shipped to the University of California, Davis DANR laboratory for physical and chemical analyses (see Baraloto et al. 2011 for full details).

We calculated environmental data including a Dry Season Index (DSI), which was calculated for each plot, as the sum (over 12 months) of the ratios between the mean monthly temperature and the mean monthly rainfall. This provided an estimate of the potential hydric stress accumulated during the dry seasons. Rainfall and temperature data were extracted from worldclim data (http://www.worldclim.com/)  via the raster package (Hijmans 2018) in R statistical environmental (R Development Core Team 2020). The larger number of soil variables (nine) compared to the unique climate variable (DSI) was taken into account by analysing the relative effect of each variable (see the data analysis section below).

Funding

National Science Foundation, Award: DEB-0743103/0743800

Agence Nationale de la Recherche, Award: ANR- 13-BSV7-009

National Science Foundation, Award: DEB 1254214

Agence Nationale de la Recherche, Award: ANR-10-LABX-25-01