| Literature DB >> 32547722 |
Arantzazu L Luzuriaga1, Pablo Ferrandis2, Joel Flores3, Adrián Escudero1.
Abstract
Previous studies found that plant communities on infertile soils are relatively resistant to climatic variation due to stress tolerance adaptations. However, the species assemblies in gypsum soil habitats require further investigation. Thus, we considered the following questions. (1) Do harsher arid conditions determine the characteristics of the species that form plant assemblages? (2) Is the selection of the species that assemble in arid conditions mediated by their ability to grow on gypsum soils? (3) Is the selection of species that assemble in harsher conditions related to phylogenetically conserved functional traits? Perennial plant communities were analysed in 89 gypsum-soil sites along a 400 km climate gradient from the central to southeastern Iberian Peninsula. Each local assemblage was analysed in 30 × 30 m plots and described based on taxonomic, functional (soil plant affinity) and phylogenetic parameters. The mean maximum temperatures in the hottest month, mean annual precipitation and their interaction terms were used as surrogates for the aridity conditions in generalized linear models. In the hottest locations, the gypsophily range narrowed and the mean gypsophily increased at the community level, thereby suggesting the filtering of species and the dominance of soil specialists in the actual plant assemblies. Drier sites had higher taxonomic diversity. The species that formed the perennial communities were close in evolutionary terms at the two ends of the aridity gradient. The mean maximum temperatures in the hottest month had the main abiotic filtering effect on perennial plant communities, which was mediated by the ability of species to grow on gypsum soils, and thus gypsum specialists dominated the species assemblies in the hottest locations. In contrast, the perennial communities on gypsum soils were relatively resistant to changes in precipitation. Our findings suggest that the warmer environmental conditions predicted by global change models will favour gypsum specialists over generalists.Entities:
Keywords: Aridity gradient; Mediterranean; assembly rules; community weighted mean (CWM); edaphic endemism; functional diversity; gypsum soil; phylogenetic diversity; semiarid; soil affinity
Year: 2020 PMID: 32547722 PMCID: PMC7288742 DOI: 10.1093/aobpla/plaa020
Source DB: PubMed Journal: AoB Plants Impact factor: 3.276
Chi-square values obtained by the generalized linear models for taxonomic, functional and phylogenetic diversity indices. Each model included the total plant cover in the plot as a covariable in order to statistically control for the differences in the vegetation productivity along the aridity gradient. T: Mean maximum temperatures in the hottest month; P: mean annual precipitation; GR: gypsophily range; CMG: community mean gypsophily index; GD: gypsophily diversity; PSR: phylogenetic species richness; PSV: phylogenetic species variability; NRI: net relatedness index. The error distributions (Family) and link functions assumed in the models are indicated. Id: identity link function; Log: logarithmic link function. The signs of the coefficients are shown in parentheses. *0.01 < P < 0.05; **0.001 < P < 0.01; ***P < 0.001.
| Family (link) |
|
|
| Cover | |
|---|---|---|---|---|---|
| Taxonomic indices | |||||
| Richness |
| 1.06 | 2.6 | 0.9 |
|
| Diversity |
|
|
| 2.3 | 0.97 |
| Functional indices | |||||
| GR |
|
| 0.9 | 1.1 | 0.12 |
| CMG |
|
| 0.2 | 0.1 | 0.2 |
| GD |
| 2.1 | 0.1 | 3.2 | |
| Phylogenetic indices | |||||
| PSR |
| 2.2 | 0.4 | 2.3 |
|
| PSV |
| 1.9 | 0.7 |
| 0.09 |
| NRI |
| 2.4 | 0.2 |
| 0.7 |
Figure 1.Graphic representation of the interaction between the mean maximum temperature in the hottest month and mean annual precipitation at the sampling locations based on: (A) the phylogenetic species variability (PSV) and (B) the Net Relatedness Index (NRI) in each plot. Circles represent the observed values. Lines join points with the same predicted PSV (A) or NRI (B) values based on our generalized linear models. Darker areas represent higher predicted values and lighter areas lower predicted values for PSV and NRI indices.