Multiple drivers of functional diversity in temperate forest understories: Climate, soil, and forest structure effects.

In macroecology, shifting from coarse- to local-scale explanatory factors is crucial for understanding how global change impacts functional diversity (FD). Plants possess diverse traits allowing them to differentially respond across a spectrum of environmental conditions. We aim to assess how macro- to microclimate, stand-scale measured soil properties, forest structure, and management type, influence forest understorey FD at the macroecological scale. Our study covers Italian forests, using thirteen predictors categorized into climate, soil, forest structure, and management. We analyzed five traits (i.e., specific leaf area, plant size, seed mass, belowground bud bank size, and clonal lateral spread) capturing independent functional dimensions to calculate the standardized effect size of functional diversity (SES-FD) for all traits (multi-trait) and for single traits. Multiple regression models were applied to assess the effect of predictors on SES-FD. We revealed that climate, soil, and forest structure significantly drive SES-FD of specific leaf area, plant size, seed mass, and bud bank. Forest management had a limited effect. However, differences emerged between herbaceous and woody growth forms of the understorey layer, with herbaceous species mainly responding to climate and soil features, while woody species were mainly affected by forest structure. Future warmer and more seasonal climate could reduce the diversity of resource economics, plant size, and persistence strategies of the forest understorey. Soil eutrophication and acidification may impact the diversity of regeneration strategies; canopy closure affects the diversity of above- and belowground traits, with a larger effect on woody species. Multifunctional approaches are vital to disentangle the effect of global changes on functional diversity since independent functional specialization axes are modulated by different drivers.

Plant clonality in a soil-impoverished open ecosystem: insights from southwest Australian shrublands.

BACKGROUND AND AIMS: Clonality is a key life-history strategy promoting on-spot persistence, space occupancy, resprouting after disturbance, and resource storage, sharing and foraging. These functions provided by clonality can be advantageous under different environmental conditions, including resource-paucity and fire-proneness, which define most mediterranean-type open ecosystems, such as southwest Australian shrublands. Studying clonality-environment links in underexplored mediterranean shrublands could therefore deepen our understanding of the role played by this essential strategy in open ecosystems globally. METHODS: We created a new dataset including 463 species, six traits related to clonal growth organs (CGOs; lignotubers, herbaceous and woody rhizomes, stolons, tubers, stem fragments), and edaphic predictors of soil water availability, nitrogen (N) and phosphorus (P) from 138 plots. Within two shrubland communities, we explored multivariate clonal patterns and how the diversity of CGOs, and abundance-weighted and unweighted proportions .of clonality in plots changed along with the edaphic gradients. KEY RESULTS: We found clonality in 65 % of species; the most frequent were those with lignotubers (28 %) and herbaceous rhizomes (26 %). In multivariate space, plots clustered into two groups, one distinguished by sandy plots and plants with CGOs, the other by clayey plots and non-clonal species. CGO diversity did not vary along the edaphic gradients (only marginally with water availability). The abundance-weighted proportion of clonal species increased with N and decreased with P and water availability, yet these results were CGO-specific. We revealed almost no relationships for unweighted clonality. CONCLUSIONS: Clonality is more widespread in shrublands than previously thought, and distinct plant communities are distinguished by specific suites (or lack) of CGOs. We show that weighting belowground traits by aboveground abundance affects the results, with implications for trait-based ecologists using abundance-weighting. We suggest unweighted approaches for belowground organs in open ecosystems until belowground abundance is quantifiable.

Impact of ecological redundancy on the performance of machine learning classifiers in vegetation mapping.

Vegetation maps are models of the real vegetation patterns and are considered important tools in conservation and management planning. Maps created through traditional methods can be expensive and time-consuming, thus, new more efficient approaches are needed. The prediction of vegetation patterns using machine learning shows promise, but many factors may impact on its performance. One important factor is the nature of the vegetation-environment relationship assessed and ecological redundancy. We used two datasets with known ecological redundancy levels (strength of the vegetation-environment relationship) to evaluate the performance of four machine learning (ML) classifiers (classification trees, random forests, support vector machines, and nearest neighbor). These models used climatic and soil variables as environmental predictors with pretreatment of the datasets (principal component analysis and feature selection) and involved three spatial scales. We show that the ML classifiers produced more reliable results in regions where the vegetation-environment relationship is stronger as opposed to regions characterized by redundant vegetation patterns. The pretreatment of datasets and reduction in prediction scale had a substantial influence on the predictive performance of the classifiers. The use of ML classifiers to create potential vegetation maps shows promise as a more efficient way of vegetation modeling. The difference in performance between areas with poorly versus well-structured vegetation-environment relationships shows that some level of understanding of the ecology of the target region is required prior to their application. Even in areas with poorly structured vegetation-environment relationships, it is possible to improve classifier performance by either pretreating the dataset or reducing the spatial scale of the predictions.

Quantifying the effects of ecological constraints on trait expression using novel trait-gradient analysis parameters.

Complex processes related to biotic and abiotic forces can impose limitations to assembly and composition of plant communities. Quantifying the effects of these constraints on plant functional traits across environmental gradients, and among communities, remains challenging. We define ecological constraint (Ci ) as the combined, limiting effect of biotic interactions and environmental filtering on trait expression (i.e., the mean value and range of functional traits). Here, we propose a set of novel parameters to quantify this constraint by extending the trait-gradient analysis (TGA) methodology. The key parameter is ecological constraint, which is dimensionless and can be measured at various scales, for example, on population and community levels. It facilitates comparing the effects of ecological constraints on trait expressions across environmental gradients, as well as within and among communities. We illustrate the implementation of the proposed parameters using the bark thickness of 14 woody species along an aridity gradient on granite outcrops in southwestern Australia. We found a positive correlation between increasing environmental stress and strength of ecological constraint on bark thickness expression. Also, plants from more stressful habitats (shrublands on shallow soils and in sun-exposed locations) displayed higher ecological constraint for bark thickness than plants in more benign habitats (woodlands on deep soils and in sheltered locations). The relative ease of calculation and dimensionless nature of Ci allow it to be readily implemented at various scales and make it widely applicable. It therefore has the potential to advance the mechanistic understanding of the ecological processes shaping trait expression. Some future applications of the new parameters could be investigating the patterns of ecological constraints (1) among communities from different regions, (2) on different traits across similar environmental gradients, and (3) for the same trait across different gradient types.