10.5061/DRYAD.VQ83BK3S3
Pinho, Bruno
0000-0002-6588-3575
Federal University of Pernambuco
Tabarelli, Marcelo
Federal University of Pernambuco
ter Braak, Cajo
Wageningen University & Research
Wright, S. J.
Smithsonian Tropical Research Institute
Arroyo-Rodriguez, Victor
National Autonomous University of Mexico
Benchimol, Maíra
Universidade Estadual de Santa Cruz
Engelbrecht, Bettina
University of Bayreuth
Pierce, Simon
University of Milan
Hietz, Peter
University of Natural Resources and Life Sciences
Santos, Bráulio
Federal University of Paraíba
Peres, Carlos
University of East Anglia
Müller, Sandra
Federal University of Rio Grande do Sul
Wright, Ian
Macquarie University
Bongers, Frans
Wageningen University & Research
Lohbeck, Madelon
Wageningen University & Research
Niinemets, Ülo
Estonian University of Life Sciences
Slot, Martijn
Smithsonian Tropical Research Institute
Jansen, Steven
University of Ulm
Jamelli, Davi
Federal University of Pernambuco
Ferreira de Lima, Renato Augusto
University of Sao Paulo
Swenson, Nathan
University of Maryland, Baltimore
Condit, Richard
Field Museum of Natural History
Barlow, Jos
Lancaster University
Slik, Ferry
Universiti Brunei Darussalam
Hernández-Ruedas, Manuel
National Autonomous University of Mexico
Mendes, Gabriel
Federal University of Pernambuco
Martinez-Ramos, Miguel
National Autonomous University of Mexico
Pitman, Nigel C. A.
Duke University
Kraft, Nathan
University of California Los Angeles
Garwood, Nancy
Southern Illinois University
Andino, Juan
Universidad de las Américas
Faria, Deborah
Universidade Estadual de Santa Cruz
Chacon, Eduardo
University of Costa Rica
Mariano-Neto, Eduardo
Federal University of Bahia
Junior, Valdecir
Federal University of Paraíba
Kattge, Jens
Max Planck Institute for Biogeochemistry
Melo, Felipe
Federal University of Pernambuco
Functional biogeography of Neotropical moist forests: trait-climate
relationships and assembly patterns of tree communities
Dryad
dataset
2021
climate seasonality
species pool
biogeographic regions
FOS: Biological sciences
National Council for Scientific and Technological Development
https://ror.org/03swz6y49
Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior*
Centro del Cambio Global y la Sustentabilidad en el Sureste*
2021-11-13T00:00:00Z
2021-11-13T00:00:00Z
en
https://doi.org/10.1111/geb.13309
https://doi.org/10.5281/zenodo.5566612
1036459 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Aim: Here we examine the functional profile of regional tree species pools
across the latitudinal distribution of Neotropical moist forests, and test
trait-climate relationships among local communities. We expected
opportunistic strategies (acquisitive traits, small seeds) to be
overrepresented in species pools further from the equator due to long-term
instability, but also in terms of abundance in local communities in
currently wetter, warmer and more seasonal climates. Location: Neotropics.
Time period: Recent. Major taxa studied: Trees. Methods: We obtained
abundance data from 471 plots across nine Neotropical regions, including
~100,000 trees of 3,417 species, in addition to six functional traits. We
compared occurrence-based trait distributions among regional species
pools, and evaluated single trait-climate relationships across local
communities using community abundance-weighted means (CWM). Multivariate
trait-climate relationships were assessed by a double-constrained
correspondence analysis that tests both how CWMs relate to climate and how
species distributions, parameterized by niche centroids in climate space,
relate to their traits. Results: Regional species pools were
undistinguished in functional terms, but opportunistic strategies
dominated local communities further from the equator, particularly in the
northern hemisphere. Climate explained up to 57% of the variation in CWM
traits, with increasing prevalence of lower-statured, light-wooded and
softer-leaved species bearing smaller seeds in more seasonal, wetter and
warmer climates. Species distribution were significantly but weakly
related to functional traits. Main conclusions: Neotropical moist forest
regions share similar sets of functional strategies, from which local
assembly processes, driven by current climatic conditions, select for
species with different functional strategies. We can thus expect
functional responses to climate change driven by changes in relative
abundances of species already present regionally. Particularly, equatorial
forests holding the most conservative traits and large seeds are likely to
experience the most severe changes if climate change triggers the
proliferation of opportunistic tree species.
The data are from 471 forest plots from nine biogeographic regions
distributed across the Neotropics, covering the whole latitudinal
distribution of Neotropical moist forests. All plots were located in
lowland (up to 800 m a.s.l.), old-growth forests within a variable matrix
of land uses. The two folders contain data and codes for two specific
analyses: (a) [MixedModels_Averaging] test of trait-climate relationships
from mixed-effects models followed by model selection and multimodel
averaging (see Table 1 in Pinho et al. 2021) (b) [dcCA_VarPart] variation
partitioning of two sets of site predictors (climate and geo) on all CWM
traits, based on results from double constrained correspondence analysis
(dcCA; see Table 2 in Pinho et al. 2021). More on the dc-CA analysis is
available at https://doi.org/10.6084/m9.figshare.13259534.v2 Ad (a). The
mixed-models are based on the plot-level data, including community
abundance-weighted mean (CWM) traits, geographic location and average
climate variables (based on monthly average data from 1970 to 2000) for
each plot. The climate data were obtained from global maps, such as from
WorldClim version 2.0 Ad (b). The variation partitioning using dc-CA is
based on spatial clusters (N = 59) of nearby plots, including the
abundance per cluster and functional traits of 3,417 tree species. Species
traits are averages based on multiple records from multiple data sources.
Missing values in the species trait dataset were filled by genus-level or
imputed data (see Table S2 in Pinho et al. 2021, for a summary)
Information of species abundance by plot (instead of clusters) are partly
available from the BIEN database, in addition to private data from the
authors that can be requested.