Long‐term collar deployment leads to bias in soil respiration measurements

Abstract Accurate measurements of soil respiration (Rs) are critical for understanding how soil carbon will respond to environmental changes. However, a commonly used method for Rs measurements, the collar deployment method, may introduce artefacts that cause bias in Rs measurements. Our objective was to quantify the effect of long‐term collar deployment on Rs and to unravel potential causes due to changes in the soil environment. A field experiment (2017–2019) including short‐term (2–3 days before the measurement) and long‐term collar deployment (lasting three consecutive growing seasons) was conducted to assess the methodological effect on Rs in an alpine grassland of the northeastern Tibetan Plateau. Soil incubation was used to further explore the mechanisms underlying the effects of collar deployment. The effect of long‐term collar deployment on Rs varied over time. In the first one and a half growing seasons, no significant difference in Rs was noted under short‐ and long‐term collar deployment. This may be attributed to the negative effects of lower root biomass inside long‐term collars and the positive effects of higher temperature and pulse input of dead roots following collar deployment. Under the long‐term collar, Rs decreased rapidly in the middle of the second growing season and remained low until the end of the experiment, resulting in an 18.2% decrease relative to short‐term collar deployment in the third growing season. Higher soil bulk density and lower root and microbial biomass inside long‐term collars may explain the decrease in Rs and temperature sensitivity (Q10). Soil incubation experiments revealed that the soil organic carbon (SOC) decomposition rate and Q10 were significantly reduced after long‐term collar deployment. Long‐term collars led to substantial underestimates of Rs after more than 2 years. Our findings suggest that such potential artefacts should be considered when interpreting Rs data based on long‐term collar deployment. Long‐term collars should be relocated every 1–2 years to avoid artefacts if feasible. Alternatively, periodic measurements using short‐term collars are recommended to quantify the magnitude of collar artefacts

Shrub characteristics & herb and soil properties.csv

Using space for time substitution, we established a paired livestock exclusion-free grazing (control) system to evaluate the effects of livestock exclusion on shrubs at Haibei Research Station. Exclosure manipulation experiments were conducted in 1997, 2003, and 2011. Small mammals cannot be eliminated using these fences. There were two 9-year exclosures, six 17-year exclosures, and one 23-year exclosure, all 30 m×30 m in size. The exclosures were adjacent to an open pasture. Control plots were delineated temporarily near the exclosures by outlining the boundary with plastic wires and wood posts in the open pasture. There were four, four, and one control plots for the 9-, 17- and 23-year exclosures, respectively. Notably, the topography, vegetation, and soil types were similar between the exclosures and corresponding plots before fencing. From August to September 2020, we conducted the sampling and investigation.  In each plot, we chose five P. fruticosa shrub patches according to the canopy area frequency distribution of shrub patches (from the investigation of shrub) and randomly chose five herb patches as far away from the shrub patches as possible in the grassy matrix. One quadrat of 50 cm×50 cm was set in each shrub and herb patch. We harvested all aboveground biomass (AGB) at ground level from the quadrats. Live aboveground biomass was divided into four functional groups (grass, sedge, legume, and non-legume forb), and dead aboveground biomass was separated into standing and soil surface litter. The classified biomass was dried in an oven to a constant weight at 65 °C and weighed.  Soil was sampled at the above-mentioned chosen five shrub patches and five herb patches in each plot. For each patch, we used soil bulk density drilling (with a cutting ring volume of 100 cm3) to extract 100 cm3 soil at 0–10, 10–20, 20–30, 30–50, 50–70, and 70–100 cm. The soil samples were rapidly brought back to the laboratory and weighed. After oven-drying at 105 °C to a constant weight, soil samples were weighed again. Soil water content was calculated as the percentage of water weight in the fresh soil. At each shrub and herb patch, we also randomly selected 3–7 sampling points and collected samples at 0–5, 5–10, 10–20, 20–30, 30–50, 50–70, and 70–100 cm using a soil auger of 5 cm diameter and completely mixed soils from the same depth. Soil samples were air-dried indoors immediately after returning to the laboratory and sieved to pass a 2 mm screen with debris eliminated. After sieving, the roots were carefully removed using tweezers. Soil-water mixtures at a 1:5 ratio (w:v) were prepared to determine pH using a pH meter. Representative sub-samples were passed through a 0.25 mm sieve for soil element measurements. Soil total carbon (STC) and total nitrogen (TN) contents were measured using an elemental analyzer (Vario EL cube; Elementar, Langenselbold, Germany) at the College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China. The soil inorganic carbon (SIC) and total phosphorus (TP) contents were determined using a SKALAR carbon element analyzer (2SN100903#; Skalar Analytical B.V., Breda, The Netherlands) and an automated discrete analyzer (Smarchem450; AMS, Italy), respectively, at the State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University. Soil organic carbon (SOC) content was calculated as the difference between the STC and SIC content.  The shrub investigation included two parts: shrub patch investigation and shrub investigation of herb patches. For P. fruticosa shrub patches, it is difficult to count individuals, as P. fruticosa mainly carries out vegetative propagation by creeping stems belowground. We measured each P. fruticosa patch’s (1) canopy longest axis 2a and perpendicular 2b, which we used for the ellipse area formula (πab) as the patch canopy area (shrub patch is regarded as an ellipse); (2) largest height; (3) number of branches from previous years; and (4) number of twigs, which are young hairy red brown stems coming out in the current year. For reproductive output, we counted the number of ovaries surrounded by the calyx for each patch, selected 20–30 of the ovaries in each plot to measure the internal achene (containing one seed) number, and acquired the patch’s seed number by multiplying its ovary number by the corresponding average number of achenes in the ovary. We also estimated the number of seedlings under the patch-projected area. For the larger patches that had especially high numbers of branches, twigs, seeds, or seedlings, we selected a representative part to measure and multiplied by an estimated multiple. Potentilla fruticosa patches close to each other in the 23-year exclosure were separated by the height and color of the patches. For P. fruticosa shrubs in the herb patch, we investigated in the 50 cm×50 cm quadrats set in section 2.2.1. The shrub properties were measured in a similar manner to the shrub patch.  The aboveground biomass of P. fruticosa was nondestructively estimated by the following estimation formula (Liang et al. 2013): AB=113.02P2H+29.77 where AB, P, and H are the aboveground biomass (g), canopy perimeter (m), and height (m), respectively, of the shrub patch. The perimeter of an ellipse cannot be estimated accurately; however, there are approximation formulas, of which one of the Ramanujan formulas is famous for its briefness: P≈π[3(a+b)- √ [(3a+b)(a+3b)]] We obtained climatic data from the meteorological station installed in 1980 at the Haibei Research Station.  </p

Warming and altered precipitation independently and interactively suppress alpine soil microbial growth in a decadal-long experiment

Warming and precipitation anomalies affect terrestrial carbon balance partly through altering microbial eco-physiological processes (e.g., growth and death) in soil. However, little is known about how such processes responds to simultaneous regime shifts in temperature and precipitation. We used the 18O-water quantitative stable isotope probing approach to estimate bacterial growth in alpine meadow soils of the Tibetan Plateau after a decade of warming and altered precipitation manipulation. Our results showed that the growth of major taxa was suppressed by the single and combined effects of temperature and precipitation, eliciting 40–90% of growth reduction of whole community. The antagonistic interactions of warming and altered precipitation on population growth were common (~70% taxa), represented by the weak antagonistic interactions of warming and drought, and the neutralizing effects of warming and wet. The members in Solirubrobacter and Pseudonocardia genera had high growth rates under changed climate regimes. These results are important to understand and predict the soil microbial dynamics in alpine meadow ecosystems suffering from multiple climate change factors

Support-Free Hollowing

Offsetting-based hollowing is a solid modeling operation widely used in 3D printing, which can change the model's physical properties and reduce the weight by generating voids inside a model. However, a hollowing operation can lead to additional supporting structures for fabrication in interior voids, which cannot be removed. As a consequence, the result of a hollowing operation is affected by these additional supporting structures when applying the operation to optimize physical properties of different models. This paper proposes a support-free hollowing framework to overcome the difficulty of fabricating voids inside a solid. The challenge of computing a support-free hollowing is decomposed into a sequence of shape optimization steps, which are repeatedly applied to interior mesh surfaces. The optimization of physical properties in different applications can be easily integrated into our framework. Comparing to prior approaches that can generate support-free inner structures, our hollowing operation can reduce more volume of material and thus provide a larger solution space for physical optimization. Experimental tests are taken on a number of 3D models to demonstrate the effectiveness of this framework.Accepted author manuscriptMaterials and Manufacturin