Distribution of leaflet traits across different habitats: a phylogenetically controlled test using Neotropical palms

Traits associated with resource acquisition and use are related to current or past environmental conditions. Consequently, similar environments are occupied by closely related lineages or by different lineages that have converged to occupy them. Understanding how functional traits vary in relation to environmental gradients facilitates a more predictive ecology of the Anthropocene. We investigated whether leaflet traits of palm species are phylogenetically conserved and how this influences their distribution in the Neotropics. We measured specific leaflet area (SLA) and leaf thickness in 79 Neotropical palm species. We then assessed the relationship between these variables and their distribution at different habitat type scales using phylogenetically controlled linear mixed models. Phylogenetically close species showed greater similarities in SLA and lower similarities in leaflet thickness than expected from their phylogenetic relationships. Species from open habitats and forest canopy species had thick leaves and low SLA, while the opposite was found for understorey species. Phylogenetic conservatism in SLA combined with phylogenetic divergence in leaflet thickness reveals leaflet trade-off strategies in palms and is related to their distribution in the Neotropics. Anthropogenic impacts may threaten understorey species in particular, which in the long-term may cause homogenisation of palm communities and loss of functional diversity.</p

Functional and historical drivers of leaf shape evolution in Palms (Arecaceae)

This repository contains the supplementary material, data, and scripts associated with the manuscript titled "Plant height predicts leaf shape in palms (Arecaceae)" The figure legends for the supplementary figures are in the word document. The data we used is in the folder data_files except for the CHELSA climate layers which are available online. The notebooks folder contains annotated python notebooks to generate the data matrices from the raw layers, annotate the species traits as well as solving taxonomic incongruences in the GBIF data, run the analyses, and produce the figures. The folder chains contains the figures of the chains from the MCMC GLMMs and a table summary for the estimates. The folder notebooks/Rscripts contain all the R scripts used for running the MCMC GLMMs in a high-performance computing facility. The folder scipts contain two scripts (python and batch) to process the text files resulting from the MCMC GLMM runs.</p

Mass of charcoal in the soil (mg/cm³) for each soil layer from 10 cm to 50 cm in depth.

<p>The plots are in order of increasing distance from the sites to the rivers, expressed by the index of rivers distances. Each point is the average mass of charcoal in the soil from 14–15 samples in each area. The vertical lines represent the standard deviation. The dotted line represents the median value of the mass of charcoal found at each depth in the soil of forests north of Manaus in Central Amazonia, which is the value expected in soils without past human activities <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048559#pone.0048559-Piperno2" target="_blank">[26]</a>.</p

Means and standard deviations of all botanical data and distances measured in the six study sites along the Purus-Madeira interfluve, Amazonas, Brazil.

1<p>The values of species abundance are the number of all useful trees and palms with DBH≥10 cm in 1 ha.</p>2<p>The value of species basal area are the basal area of all useful trees and palms with DBH≥10 cm in 1 ha.</p>3<p>The values of species richness are the sum of all trees and palm species with DBH≥10 and <30 cm in 0.5 ha and the largest in 1 ha plot.</p

Relationships between useful tree parameters and the distance to secondary rivers.

<p>Relationships between the relative abundance of useful species per plot (A), the relative abundance of useful palms per plot (B), the relative basal area of useful species per plot (C) and the relative richness of useful species per plot (D), with the distances from secondary rivers. Points are the plots of sites M2, M3, M4, M5, totaling seven plots. The index of rivers distances is the sum of the inverse distances from each plot to all perennial rivers greater than 50 m wide in a 25 km diameter zone around the sites.</p

Multi-taxa responses to climate change in the Amazon

Data and code from 'Multi-taxa responses to climate change in the Amazon forest'. Authors: Carlos A. S. Rodrigues-Filho; Flávia R. C. Costa; Juliana Schietti; Anselmo Nogueira; Rafael Pereira Leitão; Juliana Menger; Gabriel Borba; Caian Souza Gerolamo; Stefano S. Avilla; Thaise Emilio; Carolina Volkmer de Castilho; Douglas Aviz Bastos; Elisangela Xavier Rocha; Itanna O. Fernandes; Cintia Cornelius; Jansen Zuanon; Jorge L. P. Souza; Ana C. S. Utta1; Fabricio B. Baccaro</p

Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology

In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics.</p