Spatial distribution modelling of plant functional diversity in the mountain rangeland, north of Iran

By modeling different indices that describe functional diversity in communities as well as the mean, maximum and minimum of trait values - As a function of abiotic gradients - the impact of environmental changes on the functional components of biodiversity could be predicted. Moreover, functional traits provide the possibility to understand and predict the structure of plant communities as well as ecosystem performance better than species-specific identity-based approaches. Therefore, the main aim of this research was to assess the response of the plant functional diversity to environmental gradients in rangeland areas. As study case, we selected Iran which has 90 million hectares of rangelands that occupy nearly 54.6% of the total area and 65% of natural resources with 8000 plant species showing a richness genetic storage. Specifically, this research was carried out in Lasem, a mountain rangeland of the Mazandaran province. However, this area suffers from unsuitable conditions because of mismanagement as well as the high livestock intensity that cause a decreasing trend in species diversity. To achieve the above-mentioned goals, we applied three functional traits (specific leaf area, leaf dry matter content, and height) for 114 of the most common species. A set of functional indices were then calculated for these traits within 350 experimental plots including Community Weighted Mean (CWM), Functional Regularity (FRo), Functional Richness (FRic), Functional Divergence (FDiv) and Functional Evenness (FEve). Together with the functional indices, we also considered the mean, 5th and 95th quartiles of the trait values. The main results showed that the models of functional traits can provide a powerful tool to assess ecological patterns in the landscape. The most powerful models were the Community Weighted Mean index and Functional Divergence index, while the predictive power of the functional evenness models was consistently the weakest in our study. The results indicated that single-trait diversity indices were better predictor rather than multiple- trait diversity. We conclude that aggregated traits at the community level can give new insights into the ecosystem processes, services, and resilience.