Perennial herb diversity contributes more than annual herb diversity to multifunctionality in dryland ecosystems of North-western China

Abstract

BackgroundConsiderable attention has been given to how different aspects of biodiversity sustain ecosystem functions. Herbs are a critical component of the plant community of dryland ecosystems, but the importance of different life form groups of herbs is often overlooked in experiments on biodiversity-ecosystem multifunctionality. Hence, little is known about how the multiple attributes of diversity of different life form groups of herbs affect changes to the multifunctionality of ecosystems.MethodsWe investigated geographic patterns of herb diversity and ecosystem multifunctionality along a precipitation gradient of 2100 km in Northwest China, and assessed the taxonomic, phylogenetic and functional attributes of different life form groups of herbs on the multifunctionality.ResultsWe found that subordinate (richness effect) species of annual herbs and dominant (mass ratio effect) species of perennial herbs were crucial for driving multifunctionality. Most importantly, the multiple attributes (taxonomic, phylogenetic and functional) of herb diversity enhanced the multifunctionality. The functional diversity of herbs provided greater explanatory power than did taxonomic and phylogenetic diversity. In addition, the multiple attribute diversity of perennial herbs contributed more than annual herbs to multifunctionality.ConclusionsOur findings provide insights into previously neglected mechanisms by which the diversity of different life form groups of herbs affect ecosystem multifunctionality. These results provide a comprehensive understanding of the relationship between biodiversity and multifunctionality, and will ultimately contribute to multifunctional conservation and restoration programs in dryland ecosystems.

More effect of ephemeral plant species diversity on aboveground biomass than functional diversity and functional composition

“…To further identify critical attributes affecting AGB, a linear mixed effects model was constructed also using the "lme4" package in combination with environmental variables (site was used as a random effect and REML (restricted estimation maximum likelihood) was used to obtain variance components; Equation 1, Table 2). The model was also subjected to a selection procedure based on ΔAICc <2 using Akaike information criteria (AICc) to determine the best predictor variables for AGB (H. Guo et al, 2023). We excluded community-weighted mean of specific leaf area (CWM SLA ), community-weighted mean of leaf phosphorus concentration (CWM P ), mean annual temperature (MAT), soil water content (SWC), soil organic carbon concentration (SOC), soil total nitrogen concentration (STN), and soil total phosphorus concentration (STP).…”

“…Furthermore, the H reflected with the efficiency of water use by the plant. SLA was chosen mainly because it reflected the efficiency of plant carbon fixation and the ability of leaves to capture light (H. Guo et al, 2023; Q. Zhang et al, 2017). Leaf C, N, and P are the basic elemental components of organisms and essential nutrients for plant growth and metabolism and play a key role in plant functioning (Elser et al, 2000; Sardans et al, 2017).…”

“…We standardized all variables into Z ‐scores and ran a correlation coefficient (Pearson's r ) analysis to examine multiple covariates for diversity, FC, and environment. When a pair of candidate variables had a correlation coefficient >0.6, one of the two variables was excluded to avoid high collinearity (H. Guo et al, 2023). At our study sites, the correlation coefficients as diversity and FC indicators were less than 0.6; hence, these indicators could be directly used in subsequent analyses.…”

“…SR is the number of species in a subplot. SR was chosen because it reflects the sensitivity with which we can predict which species a randomly selected individual in a community belongs to (H. Guo et al, 2023).…”

“…Furthermore, the H reflected with the efficiency of water use by the plant. SLA was chosen mainly because it reflected the efficiency of plant carbon fixation and the ability of leaves to capture light (H. Guo et al, 2023;Q. Zhang et al, 2017).…”