10.5061/DRYAD.9S4MW6MG0
Baraloto, Christopher
0000-0001-7322-8581
Florida International University
Vleminckx, Jason
Florida International University
Engel, Julien
Botany and Modelling of Plant Architecture and Vegetation
Petronelli, Pascal
Centre de Coopération Internationale en Recherche Agronomique pour le
Développement
Dávila, Nállarett
Instituto de Investigaciones de la Amazonía Peruana
Ríos, Marcos
Instituto de Investigaciones de la Amazonía Peruana
Valderrama Sandoval, Elvis Harry
Universidad Nacional de la Amazonía Peruana
Mesones, Italo
University of California, Berkeley
Guevara Andino, Juan Ernesto
Universidad de las Américas
Fortunel, Claire
Botany and Modelling of Plant Architecture and Vegetation
Allie, Elodie
National Research Institute for Agriculture, Food and Environment
Paine, C. E. Timothy
University of New England
Dourdain, Aurélie
Centre de Coopération Internationale en Recherche Agronomique pour le
Développement
Goret, Jean-Yves
National Research Institute for Agriculture, Food and Environment
Valverde-Barrantes, Oscar J.
Florida International University
Draper, Freddie
Florida International University
V. A. Fine, Paul
University of California, Berkeley
Data from: Biogeographic history and habitat specialisation shape
floristic and phylogenetic composition across Amazonian forests
Dryad
dataset
2021
Biogeography, plant community ecology
National Science Foundation
https://ror.org/021nxhr62
DEB-0743103/0743800
Agence Nationale de la Recherche
https://ror.org/00rbzpz17
ANR- 13-BSV7-009
National Science Foundation
https://ror.org/021nxhr62
DEB 1254214
Agence Nationale de la Recherche
https://ror.org/00rbzpz17
ANR-10-LABX-25-01
2021-04-28T00:00:00Z
2021-04-28T00:00:00Z
en
478030 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
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).