OA observed and simulated values

This file includes a large array (result5) for using in R, that represents the summary statistic employed in the OAA test. This array is the overall sum of the overlapping areas of intersecting polygons between each species pair. The observed overlapping area (i.e., the real overlapping area) is the first value in the vector, and the subsequent ones (i.e. 9999) are the overlapping areas of randomly simulated spatial configuration

Overlapptest tutorial

This file includes an R script tutorial that explains how to conduct an overlapping area analysis, as in Pescador et al. (in press) “The shape is more important than we ever thought: plant to plant interactions in a high mountain community”, using the overlapptest package

Data from: Plant domestication disrupts biodiversity effects across major crop types

Plant diversity fosters productivity in natural ecosystems. Biodiversity effects might increase agricultural yields at no cost in additional inputs. However, the effects of diversity on crop assemblages are inconsistent, probably because crops and wild plants differ in a range of traits relevant to plant-plant interactions. We tested whether domestication has changed the potential of crop mixtures to over-yield by comparing the performance and traits of major crop species and those of their wild progenitors under varying levels of diversity. We found stronger biodiversity effects in mixtures of wild progenitors, due to larger selection effects. Variation in selection effects was partly explained by within-mixture differences in leaf size. Our results indicate that domestication might disrupt the ability of crops to benefit from diverse neighbourhoods via reduced trait variance. These results highlight potential limitations of current crop mixtures to over-yield and the potential of breeding to re-establish variance and increase mixture performance

Data from: The shape is more important than we ever thought: plant to plant interactions in a high mountain community

1. Plant to plant interactions are probably the most important driver of species coexistence at fine spatial scales, but their detection represents a challenge in Ecology. Spatial point pattern analysis (SPPA) is likely the approach most used to identify them however, it suffers from some limitations related to the over-simplification of individuals to points. 2. Here, we propose a new approach called Overlapping Area Analysis (OAA) to test whether the consideration of the shape and orientation of the individuals reveal signs of interactions between species that would remain undetected with SPPA. We used this approach to analyze a fully-mapped cryophilic grassland in Sierra de Guadarrama National Park (Spain), where the crown of each individual plant (i.e. the canopy) was approximated by a polygon. We then computed and compared the total overlapping area between the canopy of a focal species and that of any other species in the community with the expectations of a null model of random rotation of each plant around its centroid. We complemented the results of our new approach by comparing with that of SPPA of plants’ centroids. 3. Results of OAA showed that up to 41% of species pairs had less canopy overlap than expected, suggesting that many interspecific canopy associations in this plant community were significantly negative at the finest spatial scale. Contrarily, SPPA estimated that 12% of species pairs were positively associated at spatial scales up to 20 cm, confirming the facilitative effect displayed by the main engineer in the community (Festuca curvifolia Lag.) and by some other dominant species. 4. Our new approach quantifying canopy associations provides new insights into the processes guiding community assembly. Whereas the results of SPPA suggested the prevalence of traditional “stress gradient hypothesis” (i.e. prevalence of positive interactions under stressful abiotic conditions), OAA revealed that many interspecific canopy associations were significantly negative. Overall, most facilitated species optimized this positive effect by placing their centroids as close to the benefactor species as their foraging behaviour allowed while avoiding crown overlap. The method proposed is available in a dedicated R-package that will facilitate its application by other ecologists

Data from: Estimating belowground plant abundance with DNA metabarcoding

Most work on plant community ecology has been performed aboveground, neglecting the processes that occur in the soil. DNA metabarcoding, where multiple species are computationally identified in bulk samples, can help overcome the logistical limitations involved in sampling plant communities belowground. A major limitation of this methodology is, however, the quantification of species’ abundances based on the percentage of sequences assigned to each taxon. Using root tissues of the five dominant species in a semiarid Mediterranean shrubland (Bupleurum fruticescens, Helianthemum cinereum, Linum suffruticosum, Stipa pennata and Thymus vulgaris), we built pairwise mixtures of relative abundance (20, 50 and 80% biomass), and implemented two methods (linear models fits and correction indices) to improve root biomass estimates. We validated both methods with multispecies mixtures that simulate field-collected samples. For all species, we found a positive and highly significant relationship between the percentage of sequences and biomass in the mixtures (R2 = 0.44-0.66), but the equations for each species (slope and intercept) differed among them, and two species were consistently over- and under-estimated. The correction indices greatly improved the estimates of biomass percentage for all five species in the multispecies mixtures, and reduced the overall error from 17% to 6%. Our results show that, through the use of post-sequencing quantification methods on mock communities, DNA metabarcoding can be effectively used to determine not only species’ presence but also their relative abundance in field samples of root mixtures. Importantly, knowledge on these aspects will allow to study key, yet poorly understood, belowground processes

The role of root community attributes in predicting soil fungal and bacterial community patterns

Roots are assumed to play a major role in structuring soil microbial communities, but most studies exploring the relationships between microbes and plants at the community level have only used aboveground plant distribution as a proxy. However, a decoupling between belowground and aboveground plant components may occur due to differential spreading of plant canopies and root systems. Thus, soil microbe–plant links are not completely understood. Using a combination of DNA metabarcoding and spatially explicit sampling at the plant neighbourhood scale, we assessed the influence of the plant root community on soil bacterial and fungal diversity (species richness, composition and b-diversity) in a dry Mediterranean scrubland. We found that root composition and biomass, but not richness, predict unique fractions of variation in microbial richness and composition. Moreover, bacterial b-diversity was related to root b-diversity, while fungal b-diversity was related to aboveground plant b-diversity, suggesting that plants differently influence both microbial groups. Our study highlights the role of plant distribution both belowground and aboveground, soil properties and other spatially structured factors in explaining the heterogeneity in soil microbial diversity. These results also show that incorporating data on both plant community compartments will further our understanding of the relationships between soil microbial and plant communities