Trait-based ecology suggests that abiotic filtering is the main mechanism structuring the regional species pool in different subsets of habitat-specific species. At more local spatial scales, other ecological processes may add on giving rise to complex patterns of functional diversity (FD). Understanding how assembly processes operating on the habitat-specific species pools produce the locally observed plant assemblages is an ongoing challenge. Here, we evaluated the importance of different processes to community assembly in an alpine fellfield, assessing its effects on local plant trait FD. Using classical randomization tests and linear mixed models, we compared the observed FD with expectations from three null models that hierarchically incorporate additional assembly constraints: stochastic null models (random assembly), independence null models (each species responding individual and independently to abiotic environment), and co-occurrence null models (species responding to environmental variation and to the presence of other species). We sampled species composition in 115 quadrats across 24 locations in the central Pyrenees (Spain) that differed in soil conditions, solar radiation and elevation. Overall, the classical randomization tests were unable to find differences between the observed and expected functional patterns, suggesting that the strong abiotic filters that sort out the flora of extreme regional environments blur any signal of other local processes. However, our approach based on linear mixed models revealed the signature of different ecological processes. In the case of seed mass and leaf thickness, observed FD significantly deviated from the expectations of the stochastic model, suggesting that fine-scale abiotic filtering and facilitation can be behind these patterns. Our study highlights how the hierarchical incorporation of ecological additional constraints may shed light on the dim signal left by local assembly processes in alpine environments.

The folder contains: a) NullAssemblagesSimulation - R source code to: 1) estimate the occurrence probability of each specie based on hierarchically increasingly complex null models 2) simulate the null assemblages  3) compare the observed and expected functional diversity (RAO). Generation of Standardized Effect Sizes (SES). 4) test for significance based on i) randomization tests and ii) generalized linear mixed models b) DataSets.Rdata - R file containing the datasets of vegetation cover, functional traits and predictor variables. c) DataSets.xlsx - Excel file containing sheets with the metadata and the datasets of vegetation cover, functional traits and predictor variables.