Variation of community weighted means values (CWM) along altitudinal gradient for each functional trait (a-i).

<p>Response of two CWM values along altitudinal gradient: using mean trait value measured for each species across all sites (CWM_f—open triangles) and mean trait value at each site (CWM_s—black dots). Trait labels are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118876#pone.0118876.g001" target="_blank">Fig. 1</a>.</p

Variation of functional diversity (FD) along altitudinal gradient for each functional trait (a-i).

<p>Partition of FD into inter- and intra-specific variability (difference between Total—inter-specific FD) expressed as yellow and blue vertical bars respectively along altitudinal gradient. Trait labels are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118876#pone.0118876.g001" target="_blank">Fig. 1</a>.</p

Principal component analysis (PCAs) using the nine traits measured in eleven species for a total of 856 individuals.

<p>The variability of individuals of each species and their distributions along trait axes is represented in the main PCA by lines that arise from species mean value and by an ellipse of dispersion. The minor PCA shows the correlation between the nine traits using all data. Species names are included by using acronyms and colour-coded by their growth form: (i) Hemicryptophyte (dark grey): PV (<i>Pilosella vahlii</i>), SP (<i>Senecio carpetanus</i>), JH (<i>Jurinea humilis</i>); (ii) cushion chamaephyte (white): AC (<i>Armeria caespitosa</i>), JC (<i>Jasione crispa</i>), MR (<i>Minuartia recurva</i>), SC (<i>Silene ciliata</i>); (iii) caespitosous hemicryptophyte (light grey) FC (<i>Festuca curvifolia</i>), DF (<i>Deschampsia flexuosa</i>), AD (<i>Agrostis delicatula</i>); (iv) shrub (black) JN (<i>Juniperus communis</i> subsp. <i>alpina</i>). Acronyms for traits: plant size (IS), plant height (H), leaf thickness (LT), specific leaf area (SLA), leaf dry matter content (LDMC), leaf carbon content (LCC), leaf nitrogen content (LNC), carbon and nitrogen isotopes ratios (δ¹³C and δ¹⁵N, respectively).</p

Partition of variance between and within sites for each species and trait (a-i).

<p>Decomposition of variance into two levels, within and between sites (grey and white bars respectively), obtained from an analysis of variance model for each of the eleven species and nine traits. Species and trait labels are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118876#pone.0118876.g001" target="_blank">Fig. 1</a>.</p

Databases, R script and accessory functions used for Los Santos plot analysis

R script (Methods for Pescador et al 2020 JVS.R), databases (Covariates. RData and PPP_plants.RData) and additional functions (Accessory functions.R) used in the paper "Tales from the underground: soil heterogeneity and not only aboveground plant interactions explain fine-scale species patterns in a Mediterranean dwarf-shrubland" by David S. Pescador, Marcelino de la Cruz, Julia Chacón, Javier Pavón-García and Adrián Escudero.Databases include two R lists, one (Covariates.RData) with the covariates used in the models fixed in the paper and the other with the planar point pattern of two species (Linum suffruticosum and Helianthemum hirtum) present in the community sapled. </div

Determinants of high mountain plant diversity in the Chilean Andes: From regional to local spatial scales

<div><p>Mountains are considered excellent natural laboratories for studying the determinants of plant diversity at contrasting spatial scales. To gain insights into how plant diversity is structured at different spatial scales, we surveyed high mountain plant communities in the Chilean Andes where man-driven perturbations are rare. This was done along elevational gradients located at different latitudes taking into account factors that act at fine scales, including abiotic (potential solar radiation and soil quality) and biotic (species interactions) factors, and considering multiple spatial scales. Species richness, inverse of Simpson’s concentration (D_equiv), beta-diversity and plant cover were estimated using the percentage of cover per species recorded in 34 sites in the different regions with contrasted climates. Overall, plant species richness, D_equiv and plant cover were lower in sites located at higher latitudes. We found a unimodal relationship between species richness and elevation and this pattern was constant independently of the regional climatic conditions. Soil quality decreased the beta-diversity among the plots in each massif and increased the richness, the D_equiv and cover. Segregated patterns of species co-occurrence were related to increases in richness, D_equiv and plant cover at finer scales. Our results showed that elevation patterns of alpine plant diversity remained constant along the regions although the mechanisms underlying these diversity patterns may differ among climatic regions. They also suggested that the patterns of plant diversity in alpine ecosystems respond to a series of factors (abiotic and biotic) that act jointly at different spatial scale determining the assemblages of local communities, but their importance can only be assessed using a multi-scale spatial approach.</p></div

Coefficients of the models (GLMs and GLMMs) examining the effects of environmental factors on the diversity indices at plot (20 x 20 m), at quadrat (2.4 x 2.4 m) and cell (30 x 30 cm) scales.

<p>Coefficients of the models (GLMs and GLMMs) examining the effects of environmental factors on the diversity indices at plot (20 x 20 m), at quadrat (2.4 x 2.4 m) and cell (30 x 30 cm) scales.</p

Conceptual diagram showing the relationship between species richness and the standardized elevation across contrasted latitudes.

<p>Species richness variation along an elevational gradient in a sub-Antarctic mountain (blue lines) and in two Mediterranean-climate type mountains with different length in dry season: long dry season (red lines) and short dry season (purple lines). The solid lines represent the richness patterns when the main environmental stressor is coldness. The dotted lines represent the richness patterns when summer drought (red dotted line), facilitation (blue dotted line), or both mechanisms (purple dotted lines) act modulating the original monotonic pattern. Decreasing and increasing species richness are represented by the red and blue shaded area, respectively.</p

Experimental design.

<p>(a) Locations of the three study areas (black quadrats) along the Chilean Andes. Colours (purple = Mediterranean-type climate region with a severe drought summer, green = Mediterranean-type climate region with a milder drought summer and red = sub-Antarctic region) represent three different climatic zones according to Sarricolea [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0200216#pone.0200216.ref045" target="_blank">45</a>]; (b) plot distribution along the three areas; and (c) typical structure of the vegetation in each area.</p

Data from "Every little helps: the functional role of individuals in assembling any plant community, from the richest to monospecific ones"

Spectral and functional data used for massive phenotyping using Vis-NIR spectrometry. Visible and Near Infrared absorbance spectra were collected in July 2020 using the LabSpec 4 Standard-Res Lab Analyzer (Malvern Panalytical) from 1-4 sets of ten Pinus sylvestris needles for 170 trees in a forest stand located at the tree-line of the Guadarrama National Park (1900 m asl; Lat. 40.81, Long. -3.95). Average absorbance spectrum of each needle set is included as a row in the Spectral Data sheet. For this sample set, we used a constant spectral resolution value of 1 nm, which produced 2151 spectral points between 350 and 2500 nm. Needle sets were functionally characterized through three functional traits (LT – Leaf Thickness, SLA – Specific Leaf Area and LDMC – Leaf Dry Matter Content) which were measured according to standardized protocols (Cornelissen et al., 2003). Specifically, for each set, we weighed ten fresh well-developed needles using a microbalance (Mettler Toledo MX5, Columbus, OH; weight uncertainty ±1 μg). Projected surface area of ten needles was estimated with a digital scanner (Epson Perfection 4870) and WinFolia software (Régent Instruments, QC, Canada). The needles were then oven-dried at 60°C for 72 hours, and weighed to obtain dry mass. We also estimated leaf thickness using a dial thickness gauge (Mitutoyo Co., Aurora, IL, USA).</div