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The relationship between species richness (SR) and aboveground net primary productivity (ANPP) is still a central and debated issue in community ecology. Previous studies have often emphasized the relationship of alpha diversity (number of species identity) to the mean ANPP with respect to the SR-ANPP relationship while neglecting the contribution of beta diversity (dissimilarity in species composition) to the mean ANPP and to the stability of ANPP (coefficient of ANPP: CV of ANPP). In this study, we used alpha and beta diversity, mean ANPP and the CV of ANPP collected from 159 sites and belonging to three vegetation types in the Xinjiang temperate grassland to first examine their trends along climatic factors and among different vegetation types and then test the relationship among alpha (beta) diversity and mean ANPP and the CV of ANPP. Our results showed that in the Xinjiang temperate grasslands, alpha diversity was positively and linearly correlated with MAP but unimodally correlated with MAT. Meanwhile, beta diversity was unimodally correlated with MAP but linearly correlated with MAT. Relative to desert steppe, meadow steppe and typical steppe had the highest alpha and beta diversity, respectively. Except for ANPP exhibiting a quadratic relationship with MAP, no significant relationship was found among ANPP, the CV of ANPP and climatic factors. ANPP and the CV of ANPP also exhibited no apparent patterns in variation among different vegetation types. Our results further showed that mean ANPP was closely associated with alpha diversity. Both linear and unimodal relationships were detected between alpha diversity and mean ANPP, but their particular form was texture-dependent. Meanwhile, the CV of ANPP was positively correlated with beta diversity. Our results indicated that in addition to incorporating alpha diversity and mean ANPP, incorporating beta diversity and the CV of ANPP could expand our understanding of the SR-ANPP relationship. [ABSTRACT FROM AUTHOR]

Background: Climate change scenarios that include precipitation shifts and nitrogen (N) deposition are impacting carbon (C) budgets in arid ecosystems. Roots constitute an important part of the C cycle, but it is still unclear which factors control root mass loss and nutrient release in arid lands. Methodology/Principal Findings: Litterbags were used to investigate the decomposition rate and nutrient dynamics in root litter with water and N-addition treatments in the Gurbantunggut Desert in China. Water and N addition had no significant effect on root mass loss and the N and phosphorus content of litter residue. The loss of root litter and nutrient releases were strongly controlled by the initial lignin content and the lignin:N ratio, as evidenced by the negative correlations between decomposition rate and litter lignin content and the lignin:N ratio. Fine roots of Seriphidium santolinum (with higher initial lignin content) had a slower decomposition rate in comparison to coarse roots. Conclusion/Significance: Results from this study indicate that small and temporary changes in rainfall and N deposition do not affect root decomposition patterns in the Gurbantunggut Desert. Root decomposition rates were significantly different between species, and also between fine and coarse roots, and were determined by carbon components, especially lignin content, suggesting that root litter quality may be the primary driver of belowground carbon turnover. [ABSTRACT FROM AUTHOR]

• Low-altitude UAV remote sensing is used to obtain high-precision positions of Haloxylon ammodendron in deserts. • The RGRI (Red-Green Ratio Index) and NGBDI (Normalized Green-Blue Difference Index) resulted in high accuracy of positioning extraction. • Spatial patterns of H. ammodendron are analyzed by applying Ripley's L(r) functions to UAV images. • A random distribution within the scale of 0–15 m is found in H. ammodendron population. • The integrated UAV and spatial pattern analysis approach is applicable to other vegetations for environmental and ecological studies. Spatial patterns are not only the foundation for the understanding of plant interactions, but also reflect the spatial processes among plant populations. The primary requirement of spatial pattern analysis is the collections of location information of individual plants. In this study, we used low-altitude Unmanned Aerial Vehicle (UAV) remote sensing technology to obtain regional high-precision remote sensing images for Haloxylon ammodendron (H. ammodendron) forest in the southwestern Gurbantunggut Desert, and extracted spatial position information to analyze the spatial patterns using Ripley's L(r) function. Applying and comparing seven spatial position extraction methods, this study showed that the index RGRI (Red-Green Ratio Index) made 83.46% accuracy in the spatial position extraction, and an accuracy of 79.48% was obtained using NGBDI (Normalized Green-Blue Difference Index), while other five location information extraction methods resulted relatively lower accuracy. Results from spatial pattern analysis indicated that the extraction by UAV remote sensing were consistent with those obtained by field measurements. The H. ammodendron population showed a random distribution within the scale of 0–15 m, which suggested that the dependence of mutual asylum between individuals was low and not important. This distribution may be caused by the intense competition of individual vegetation for soil moisture, nutrients and other resources in desert areas. This study with low-altitude UAV imagery index analysis provided an efficient approach to rapid monitoring of plant population distribution characteristics in desert areas. [ABSTRACT FROM AUTHOR]