Search

Here we provide the 'Global Spectrum of Plant Form and Function Dataset', containing species mean values for six vascular plant traits. Together, these traits –plant height, stem specific density, leaf area, leaf mass per area, leaf nitrogen content per dry mass, and diaspore (seed or spore) mass – define the primary axes of variation in plant form and function. The dataset is based on ca. 1 million trait records received via the TRY database (representing ca. 2,500 original publications) and additional unpublished data. It provides 92,159 species mean values for the six traits, covering 46,047 species. The data are complemented by higher-level taxonomic classification and six categorical traits (woodiness, growth form, succulence, adaptation to terrestrial or aquatic habitats, nutrition type and leaf type). Data quality management is based on a probabilistic approach combined with comprehensive validation against expert knowledge and external information. Intense data acquisition and thorough quality control produced the largest and, to our knowledge, most accurate compilation of empirically observed vascular plant species mean traits to date. Measurement(s) plant trait Technology Type(s) various Factor Type(s) none Sample Characteristic - Organism Tracheophyta Sample Characteristic - Environment natural environment Sample Characteristic - Location global [ABSTRACT FROM AUTHOR]

Several papers have recently raised the occurrence of some problems with between-group Principal Component Analysis (bgPCA). This method inflates the differences between the groups, and can even display completely artificial differences when none exist, for example when applied to random numbers tables with many variables (columns) and few individuals (rows). Lately, cross-validation has been proposed as a way to circumvent this problem. Here we present some tools and several functions of the ade4 package for the R statistical software to compute a bgPCA, test the presence of statistically significant groups, perform a cross-validation of this analysis and compute associated statistics. We also describe how to use these functions to avoid running into the spurious groups problem. Several examples, including a real data set and random numbers tables, are used to validate this approach in various experimental and numerical conditions. The integrated framework of the duality diagram, as implemented in ade4, allows to extend this approach to other multivariate analysis methods beyond principal component analysis, which could prove useful in the case of other types of variables. The R code and the real data table used to make the computations and graphs of this paper are available as supplementary material. [ABSTRACT FROM AUTHOR]

In large mammal communities, little is known about modification of interspecific interactions through habitat structure changes. We assessed the effects of African elephants (Loxodonta africana) on features of woody habitat structure that can affect predator–prey interactions. We then explored how this can influence where African lions (Panthera leo) kill their prey. Indeed, lions are stalk-and-ambush predators and habitat structure and concealment opportunities are assumed to influence their hunting success. During 2 years, in Hwange National Park, Zimbabwe, kill sites (n = 167) of GPS-collared lions were characterized (visibility distance for large mammals, distance to a potential ambush site and presence of elephant impacts). We compared characteristics of lion kill sites with characteristics of random sites (1) at a large scale (i.e. in areas intensively used by lions, n = 418) and (2) at the microhabitat scale (i.e. in the direct surrounding available habitat, < 150 m, n = 167). Elephant-impacted sites had a slightly higher visibility and a longer distance to a potential ambush site than non-impacted sites, but these relationships were characterized by a high variability. At large scale, kill sites were characterized by higher levels of elephant impacts compared to random sites. At microhabitat scale, compared to the direct nearby available habitat, kill sites were characterized by a reduced distance to a potential ambush site. We suggest a conceptual framework whereby the relative importance of habitat features and prey abundance could change upon the scale considered. [ABSTRACT FROM AUTHOR]

Correspondence analysis with linear external constraints on both the rows and the columns has been mentioned in the ecological literature, but lacks full mathematical treatment and easily available algorithms and software. This paper fills this gap by defining the method as maximizing the fourth-corner correlation between linear combinations, by providing novel algorithms, which demonstrate relationships with related methods, and by making a detailed study of possible biplots and associated approximations. The method is illustrated using ecological data on the abundances of species in sites and where the species are characterized by traits and sites by environmental variables. The trait data and environment data form the external constraints and the question is which traits and environmental variables are associated, how these associations drive species abundances and how they can be displayed in biplots. With microbiome data becoming widely available, these and related multivariate methods deserve more study as they might be routinely used in the future. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) is a standard technique to summarize the main structures of a data table containing the measurements of several quantitative variables for a number of individuals. Here, we study the case where some of the data values are missing and propose a review of methods which accommodate PCA to missing data. In plant ecology, this statistical challenge relates to the current effort to compile global plant functional trait databases producing matrices with a large amount of missing values. We present several techniques to consider or estimate (impute) missing values in PCA and compare them using theoretical considerations. We carried out a simulation study to evaluate the relative merits of the different approaches in various situations (correlation structure, number of variables and individuals, and percentage of missing values) and also applied them on a real data set. Lastly, we discuss the advantages and drawbacks of these approaches, the potential pitfalls and future challenges that need to be addressed in the future. [ABSTRACT FROM AUTHOR]

Spatial relationships between tree species and hydrological soilconstraints are analysed within a 10-ha rainforest plot at PistedeSt Elie in French Guiana. We used canonical correlation analysis to crossdirectly the occurrence-by-species table of 4 992 individuals (d.b.h. ≥10 cm) belonging to 120 species with qualitative soil variablesandquantitative spatial data. Firstly, the list of species occurrences wasconfronted to nine soildescriptors characterising a weathering sequence from the initial well-drainedferralitic cover to transformed hydromorphic soil conditions. This analysisrevealed that, apart from some specialised species restricted to the swampsthatexperience prolonged water saturation, the most abundant species can be orderedalong two intermingled gradients of tolerance limiting their niche amplitude: amain gradient of tolerance to prolonged water saturation that appears downslopeduring the weathering sequence; a second gradient of less importance,displayingthe species intolerant of prolonged water saturation according to theirtolerance to temporary confinement of the uphill transformed soil systems duetothe late appearance of a perched water-table. The results support thehypothesisthat at Piste de St Elie, the constraining soil conditions imposed by surfacewater saturation are more important determinants for tree zonation of many treespecies than water shortage. Secondly, the list of species occurrences wasconfronted to a spatialdata table built from a trend surface regression of the tree coordinates. Thisanalysis indicated that soil drainage is the main structuring factor of thelocal multispecies spatial pattern. After partialling out the soil effect, themultispecies pattern revealed a broader scale of heterogeneity that we supposedto be linked to endogenous factors resulting from population dynamics.Implications of the results are then discussed in the perspective offuture research on tree zonation, local diversity pattern and communitystructuring in tropical rainforests. [ABSTRACT FROM AUTHOR]