DOI: 10.5061/dryad.vq83bk3s3
title: Functional biogeography of Neotropical moist forests: trait-climate relationships and assembly patterns of tree communities

description: 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.
description: The data are from 471 forest plots from nine biogeographic
 regions distributed across the Neotropics, covering the whole latitudinal
 distribution of Neotropical moist forests.<br> 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: <br> (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)<br> (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<br> 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.