Soil organic carbon and total nitrogen pools in permafrost zones of the Qinghai-Tibetan Plateau.

There are several publications related to the soil organic carbon (SOC) on the Qinghai-Tibetan Plateau (QTP). However, most of these reports were from different parts of the plateau with various sampling depth. Here, we present the results from a systematic sampling and analysis of 200 soil pits. Most of the pits were deeper than 2 m from an east-west transect across the plateau. The SOC and total nitrogen (TN) pools of the 148 × 104 km2, the area of the permafrost zone, for the upper 2 m soils calculated from the vegetation map were estimated to be 17.07 Pg (interquartile range: 11.34-25.33 Pg) and 1.72 Pg (interquartile range: 1.08-2.06 Pg), respectively. We also predicted the distribution of land cover types in 2050 and 2070 using decision tree rules and climate scenarios, and then predicted SOC and TN pools of this region. The results suggested that the SOC and TN pools will decrease in the future. The results not only contribute to the carbon and nitrogen storage and stocks in the permafrost regions as a whole but most importantly, to our knowledge of the possible changes of C and N storage on the QTP in the future.

Soil moisture and texture primarily control the soil nutrient stoichiometry across the Tibetan grassland.

Soil nutrient stoichiometry and its environmental controllers play vital roles in understanding soil-plant interaction and nutrient cycling under a changing environment, while they remain poorly understood in alpine grassland due to lack of systematic field investigations. We examined the patterns and controls of soil nutrients stoichiometry for the top 10cm soils across the Tibetan ecosystems. Soil nutrient stoichiometry varied substantially among vegetation types. Alpine swamp meadow had larger topsoil C:N, C:P, N:P, and C:K ratios compared to the alpine meadow, alpine steppe, and alpine desert. In addition, the presence or absence of permafrost did not significantly impact soil nutrient stoichiometry in Tibetan grassland. Moreover, clay and silt contents explained approximately 32.5% of the total variation in soil C:N ratio. Climate, topography, soil properties, and vegetation combined to explain 10.3-13.2% for the stoichiometry of soil C:P, N:P, and C:K. Furthermore, soil C and N were weakly related to P and K in alpine grassland. These results indicated that the nutrient limitation in alpine ecosystem might shifts from N-limited to P-limited or K-limited due to the increase of N deposition and decrease of soil P and K contents under the changing climate conditions and weathering stages. Finally, we suggested that soil moisture and mud content could be good predictors of topsoil nutrient stoichiometry in Tibetan grassland.

Vertical patterns and controls of soil nutrients in alpine grassland: Implications for nutrient uptake.

Vertical patterns and determinants of soil nutrients are critical to understand nutrient cycling in high-altitude ecosystems; however, they remain poorly understood in the alpine grassland due to lack of systematic field observations. In this study, we examined vertical distributions of soil nutrients and their influencing factors within the upper 1m of soil, using data of 68 soil profiles surveyed in the alpine grassland of the eastern Qinghai-Tibet Plateau. Soil organic carbon (SOC) and total nitrogen (TN) stocks decreased with depth in both alpine meadow (AM) and alpine steppe (AS), but remain constant along the soil profile in alpine swamp meadow (ASM). Total phosphorus, Ca2+, and Mg2+ stocks slightly increased with depth in ASM. K+ stock decreased with depth, while Na+ stock increased slightly with depth among different vegetation types; however, SO42- and Cl- stocks remained relatively uniform throughout different depth intervals in the alpine grassland. Except for SOC and TN, soil nutrient stocks in the top 20cm soils were significantly lower in ASM compared to those in AM and AS. Correlation analyses showed that SOC and TN stocks in the alpine grassland positively correlated with vegetation coverage, soil moisture, clay content, and silt content, while they negatively related to sand content and soil pH. However, base cation stocks revealed contrary relationships with those environmental variables compared to SOC and TN stocks. These correlations varied between vegetation types. In addition, no significant relationship was detected between topographic factors and soil nutrients. Our findings suggest that plant cycling and soil moisture primarily control vertical distributions of soil nutrients (e.g. K) in the alpine grassland and highlight that vegetation types in high-altitude permafrost regions significantly affect soil nutrients.

An efficient non-dominated sorting method for evolutionary algorithms.

We present a new non-dominated sorting algorithm to generate the non-dominated fronts in multi-objective optimization with evolutionary algorithms, particularly the NSGA-II. The non-dominated sorting algorithm used by NSGA-II has a time complexity of O(MN(2)) in generating non-dominated fronts in one generation (iteration) for a population size N and M objective functions. Since generating non-dominated fronts takes the majority of total computational time (excluding the cost of fitness evaluations) of NSGA-II, making this algorithm faster will significantly improve the overall efficiency of NSGA-II and other genetic algorithms using non-dominated sorting. The new non-dominated sorting algorithm proposed in this study reduces the number of redundant comparisons existing in the algorithm of NSGA-II by recording the dominance information among solutions from their first comparisons. By utilizing a new data structure called the dominance tree and the divide-and-conquer mechanism, the new algorithm is faster than NSGA-II for different numbers of objective functions. Although the number of solution comparisons by the proposed algorithm is close to that of NSGA-II when the number of objectives becomes large, the total computational time shows that the proposed algorithm still has better efficiency because of the adoption of the dominance tree structure and the divide-and-conquer mechanism.