Phylogenetic, functional, and taxonomic richness have both positive and negative effects on ecosystem multifunctionality

Abstract

Biodiversity encompasses multiple attributes such as the richness and abundance of species (taxonomic diversity), the presence of different evolutionary lineages (phylogenetic diversity), and the variety of growth forms and resource use strategies (functional diversity). These biodiversity attributes do not necessarily relate to each other and may have contrasting effects on ecosystem functioning. However, how they simultaneously influence the provision of multiple ecosystem functions related to carbon, nitrogen, and phosphorus cycling (multifunctionality) remains unknown. We evaluated the effects of the taxonomic, phylogenetic, and functional attributes of dominant (mass ratio effects) and subordinate (richness effect) plant species on the multifunctionality of 123 drylands from six continents. Our results highlight the importance of the phylogenetic and functional attributes of subordinate species as key drivers of multifunctionality. In addition to a higher taxonomic richness, we found that simultaneously increasing the richness of early diverging lineages and the functional redundancy between species increased multifunctionality. In contrast, the richness of most recent evolutionary lineages and the functional and phylogenetic attributes of dominant plant species (mass ratio effects) were weakly correlated with multifunctionality. However, they were important drivers of individual nutrient cycles. By identifying which biodiversity attributes contribute the most to multifunctionality, our results can guide restoration efforts aiming to maximize either multifunctionality or particular nutrient cycles, a critical step to combat dryland desertification worldwide.

Keywords: functional diversity; mass-ratio hypothesis; nutrient cycling; phylogenetic diversity; taxonomic diversity.

Fig. 1.

Relative importance of eight uncorrelated biodiversity metrics, geographical, climatic, and soil predictors across multiple thresholds of (A) multifunctionality and (B–D) sets of functions related to C, N, and P cycling. The relative importance of predictors is expressed as the percentage of variance they explain and is based on the absolute value of their standardized regression coefficients. CWM.H and CWM.SLA, community-weighted mean for height and SLA; PSV, phylogenetic species variability; w.FDIS and FDIS, abundance-weighted and nonweighted functional dispersion; w.MNTD and MNTD, weighted and nonweighted mean nearest taxon distance.

Fig. 2.

Relative importance of mass ratio vs. richness effects across multiple thresholds of (A) multifunctionality and (B–D) sets of functions related to C, N, and P cycles. The importance of predictors is expressed as the percentage of variance they explain and is based on the absolute value of their standardized regression coefficients. Mass ratio effects were calculated as the sum of the variances explained by the abundance-weighted metrics (CWM.H, CWM.SLA, w.FDIS, and w.MNTD). Richness effects were calculated as the explained variances of nonweighted metrics (species richness, FDIS, PSV, and MNTD). Abbreviations are as in Fig. 1.

Fig. 3.

Net effects of biodiversity attributes on (A) multifunctionality and on (B) carbon, (C) nitrogen, and (D) phosphorus cycling indices. The net effect was calculated as the sum of the standardized regression coefficients of all biodiversity metrics selected during the model selection procedures.

Fig. 4.

Net effects of biodiversity on multifunctionality depending on the number of biodiversity attributes and multifunctionality thresholds considered. These effects are presented at (A) 30%, (B) 50%, and (C) 70% thresholds and are based on the number of plant diversity attributes retained in all models after the backward model selection procedure. The net effect was calculated as the sum of the standardized regression coefficients of all biodiversity attributes selected during the model selection procedures. Note that we used a violin function to highlight the density of points and a jitter function to visualize the data distribution.

Fig. 5.

Standardized regression coefficients of model predictors and associated 95% confidence intervals for (A) multifunctionality and (B–D) C, N, and P cycling indices. Standardized regression coefficients result from model averaging procedures and are averaged across the entire spectrum of thresholds (20–80%) evaluated. Confidence intervals that do not cross the zero line indicate that the predictors under consideration are associated with a statistically significant (P < 0.05) change in multifunctionality. See SI Appendix, Table S4, for the variation of standardized regression coefficients of each predictor along the threshold gradient evaluated.