Factors Affecting Spatial Variation of Annual Apparent Q10 of Soil Respiration in Two Warm Temperate Forests

A range of factors has been identified that affect the temperature sensitivity (Q10 values) of the soil-to-atmosphere CO2 flux. However, the factors influencing the spatial distribution of Q10values within warm temperate forests are poorly understood. In this study, we examined the spatial variation of Q10 values and its controlling factors in both a naturally regenerated oak forest (OF) and a pine plantation (PP). Q10 values were determined based on monthly soil respiration (RS) measurements at 35 subplots for each stand from Oct. 2008 to Oct. 2009. Large spatial variation of Q10 values was found in both OF and PP, with their respective ranges from 1.7 to 5.12 and from 2.3 to 6.21. In PP, fine root biomass (FR) (R = 0.50, P = 0.002), non-capillary porosity (NCP) (R = 0.37, P = 0.03), and the coefficients of variation of soil temperature at 5 cm depth (CV of T5) (R = −0.43, P = 0.01) well explained the spatial variance of Q10. In OF, carbon pool lability reflected by light fractionation method (LLFOC) well explained the spatial variance of Q10 (R = −0.35, P = 0.04). Regardless of forest type, LLFOC and FR correlation with the Q10 values were significant and marginally significant, respectively; suggesting a positive relationship between substrate availability and apparent Q10 values. Parameters related to gas diffusion, such as average soil water content (SWC) and NCP, negatively or positively explained the spatial variance of Q10 values. Additionally, we observed significantly higher apparent Q10 values in PP compared to OF, which might be partly attributed to the difference in soil moisture condition and diffusion ability, rather than different substrate availabilities between forests. Our results suggested that both soil chemical and physical characters contributed to the observed large Q10 value variation

A microbial functional group-based CH4 model integrated into a terrestrial ecosystem model : model structure, site-level evaluation and sensitivity analysis

Wetlands are one of the most important terrestrial ecosystems for land-atmosphere CH4 exchange. A new process-based, biophysical model to quantify CH4 emissions from natural wetlands was developed and integrated into a terrestrial ecosystem model (Integrated Biosphere Simulator). The new model represents a multisubstance system (CH4, O-2, CO2, and H-2) and describes CH4 production, oxidation, and three transport processes (diffusion, plant-mediated transport, and ebullition). The new model uses several critical microbial mechanisms to represent the interaction of anaerobic fermenters and homoacetogens, hydrogenotrophic, and acetoclastic methanogens, and methanotrophs in CH4 production and oxidation. We applied the model to 24 different wetlands globally to compare the simulated CH4 emissions to observations and conducted a sensitivity analysis. The results indicated that (1) for most sites, the model was able to capture the magnitude and variation of observed CH4 emissions under varying environmental conditions; (2) the parameters that regulate dissolved organic carbon and acetate production, and acetoclastic methanogenesis had the significant impact on simulated CH4 emissions; (3) the representation of the process components of CH4 cycling showed that CH4 oxidation was about half or more of CH4 production, and plant-mediated transport was the dominant pathway at most sites; and (4) the seasonality of simulated CH4 emissions can be controlled by soil temperature, water table position, or combinations thereof.Peer reviewe

Mechanical alignment tolerance of a cruciate-retaining knee prosthesis under gait loading—A finite element analysis

Component alignment is one of the most crucial factors affecting total knee arthroplasty’s clinical outcome and survival. This study aimed to investigate how coronal, sagittal, and transverse malalignment affects the mechanical behavior of the tibial insert and to determine a suitable alignment tolerance on the coronal, sagittal, and transverse planes. A finite element model of a cruciate-retaining knee prosthesis was assembled with different joint alignments (−10°, −7°, −5°, −3°, 0°, 3°, 5°, 7°, 10°) to assess the effect of malalignment under gait loading. The results showed that varus or valgus, extension, internal rotation, and excessive external rotation malalignments increased the maximum Von Mises stress and contact pressure on the tibial insert. The mechanical alignment tolerance of the studied prosthesis on the coronal, sagittal, and transverse planes was 3° varus to 3° valgus, 0°–10° flexion, and 0°–5° external rotation, respectively. This study suggests that each prosthesis should include a tolerance range for the joint alignment angle on the three planes, which may be used during surgical planning

Comparison of navigation systems for total knee arthroplasty: A systematic review and meta-analysis

BackgroundComponent alignment is a crucial factor affecting the clinical outcome of total knee arthroplasty (TKA). Accelerometer-based navigation (ABN) systems were developed to improve the accuracy of alignment during surgery. This study aimed to compare differences in component alignment, clinical outcomes, and surgical duration when using conventional instrumentation (CONI), ABN, and computer navigation (CN) systems.MethodsA comprehensive literature search was carried out using the Web of Science, Embase, PubMed, and Cochrane databases. Articles that met the eligibility criteria were included in the study. Meta-analyses were performed using the Cochrane Collaboration Review Manager based on Cochrane Review Method. The variables used for the analyses were postoperative clinical outcome (PCO), surgical duration, and component alignment, including the hip-knee-ankle (HKA) angle, coronal femoral angle (CFA), coronal tibial angle (CTA), sagittal femoral angle (SFA), sagittal tibial angle (STA), and the outliers for the mentioned angles. The mean difference (MD) was calculated to determine the difference between the surgical techniques for continuous variables and the odds ratio (OR) was used for the dichotomous outcomes.ResultsThe meta-analysis of the CONI and ABN system included 18 studies involving 2,070 TKA procedures, while the comparison of the ABN and CN systems included 5 studies involving 478 TKA procedures. The results showed that the ABN system provided more accurate component alignment for HKA, CFA, CTA, and SFA and produced fewer outliers for HKA, CFA, CTA, and STA. However, while the ABN system also required a significantly longer surgical time than the CONI approach, there was no statistical difference in PCO for the two systems. For the ABN and CN systems, there was no statistical difference in all variables except for the ABN system having a significantly shorter surgical duration.ConclusionThere was no significant difference in the accuracy of component alignment between the ABN and CN systems, but the ABN approach had a shorter surgical duration and at lower cost. The ABN system also significantly improved the accuracy of component alignment when compared to the CONI approach, although the surgery was longer. However, there was no significant difference in PCO between the CONI, ABN, and CN systems

Gross photosynthesis explains the ‘artificial bias’ of methane fluxes by static chamber (opaque versus transparent) at the hummocks in a boreal peatland

The closed chamber technique has been widely employed to detect methane emissions, despite little being known about whether the absence or presence of light will impact the flux estimation. Here, we employed a laser greenhouse gas analyzer with an opaque—transparent chamber pair to measure the methane emission rate in a boreal peatland complex. Microtopography (i.e., hummocks and hollows) in natural and drained peatlands, and plant communities (i.e., grasses and shrubs) in a pasture converted from natural peatlands, were considered to cover the local heterogeneity. Our results indicated that opaque chambers (0.58–0.78 g CH4 m−2 during the growing season) measured a significantly higher (∼2–3 times) methane emission at the hummocks than transparent chambers (∼0.24 g CH4 m−2); however, a similar phenomenon was not found at the hollows or at other measurement plots. Gross photosynthesis explained 44%–47% of the temporal variation of the ‘artificial bias’ (the difference in methane flux obtained by the opaque versus transparent chambers) at the hummocks. Additionally, both water table depth and surface soil moisture significantly explained spatial variations of methane emissions. Our study suggests that microtopography has a significant influence on the artificial bias in methane emission estimation and the artificial properties of a chamber (transparency/opacity) method can be vitally important in some cases (i.e., hummocks), and negligible in others (i.e., hollows). The observed connection between the photosynthesis process and the ‘artificial bias’ of closed chambers (opaque versus transparent) can be used to improve methane flux modeling. Separate parameterization schemes are needed for methane transportation under the presence or absence of light

Factors affecting spatial variation of annual apparent Q₁₀ of soil respiration in two warm temperate forests.

A range of factors has been identified that affect the temperature sensitivity (Q₁₀ values) of the soil-to-atmosphere CO₂ flux. However, the factors influencing the spatial distribution of Q₁₀ values within warm temperate forests are poorly understood. In this study, we examined the spatial variation of Q₁₀ values and its controlling factors in both a naturally regenerated oak forest (OF) and a pine plantation (PP). Q₁₀ values were determined based on monthly soil respiration (R(S)) measurements at 35 subplots for each stand from Oct. 2008 to Oct. 2009. Large spatial variation of Q₁₀ values was found in both OF and PP, with their respective ranges from 1.7 to 5.12 and from 2.3 to 6.21. In PP, fine root biomass (FR) (R = 0.50, P = 0.002), non-capillary porosity (NCP) (R = 0.37, P = 0.03), and the coefficients of variation of soil temperature at 5 cm depth (CV of T₅) (R = -0.43, P = 0.01) well explained the spatial variance of Q₁₀. In OF, carbon pool lability reflected by light fractionation method (LLFOC ) well explained the spatial variance of Q₁₀ (R = -0.35, P = 0.04). Regardless of forest type, LLFOC and FR correlation with the Q₁₀ values were significant and marginally significant, respectively; suggesting a positive relationship between substrate availability and apparent Q₁₀ values. Parameters related to gas diffusion, such as average soil water content (SWC) and NCP, negatively or positively explained the spatial variance of Q₁₀ values. Additionally, we observed significantly higher apparent Q₁₀ values in PP compared to OF, which might be partly attributed to the difference in soil moisture condition and diffusion ability, rather than different substrate availabilities between forests. Our results suggested that both soil chemical and physical characters contributed to the observed large Q₁₀ value variation

Identification of Bamboo Species Based on Extreme Gradient Boosting (XGBoost) Using Zhuhai-1 Orbita Hyperspectral Remote Sensing Imagery

Mapping the distribution of bamboo species is vital for the sustainable management of bamboo and for assessing its ecological and socioeconomic value. However, the spectral similarity between bamboo species makes this work extremely challenging through remote sensing technology. Existing related studies rarely integrate multiple feature variables and consider how to quantify the main factors affecting classification. Therefore, feature variables, such as spectra, topography, texture, and vegetation indices, were used to construct the XGBoost model to identify bamboo species using the Zhuhai-1 Orbita hyperspectral (OHS) imagery in the Southern Sichuan Bamboo Sea and its surrounding areas in Sichuan Province, China. The random forest and Spearman’s rank correlation analysis were used to sort the main variables that affect classification accuracy and minimize the effects of multicollinearity among variables. The main findings were: (1) The XGBoost model achieved accurate and reliable classification results. The XGBoost model had a higher overall accuracy (80.6%), kappa coefficient (0.708), and mean F1-score (0.805) than the spectral angle mapper (SAM) method; (2) The optimal feature variables that were important and uncorrelated for classification accuracy included the blue band (B1, 464–468 nm), near-infrared band (B27, 861–871 nm), green band (B5, 534–539 nm), elevation, texture feature mean, green band (B4, 517–523 nm), and red edge band (B17, 711–720 nm); and (3) the XGBoost model based on the optimal feature variable selection showed good adaptability to land classification and had better classification performance. Moreover, the mean F1-score indicated that the model could well balance the user’s and producer’s accuracy. Additionally, our study demonstrated that OHS imagery has great potential for land cover classification and that combining multiple features to enhance classification is an approach worth exploring. Our study provides a methodological reference for the application of OHS images for plant species identification

Assessment of Sustainable Livelihood and Geographic Detection of Settlement Sites in Ethnically Contiguous Poverty-Stricken Areas in the Aba Prefecture, China

The Chinese government aims to deal with poverty by 2020 for people living in ethnic and rural regions, including mountainous ethnic regions with the highest concentration of poverty and chronic poverty. Based on a sustainable livelihood Framework, five capitals and 33 evaluation indices of livelihood were built, and 13 counties’ resources of the Aba Tibetan and Qiang Autonomous Prefecture were compared in order to calculate the degree of poverty. Topographic factors index of settlement sites (TFIS) were constructed by eight topographic factors, and diagnoses of the dominant factors of differentiation of 2699 settlements were calculated by using the geographical detector model to establish the poverty alleviation policies and models for different regions. The results showed that the livelihood capital evaluation indices were different (0.56–1.88), and natural capitals (mean value 1.56) had obvious advantages, but physical (mean value 0.56), financial (mean value 0.78), and human capital were lower (mean value 0.93), limiting the rate of transforming the ecological resources advantage into the economy. In the TFIS, the settlement points indicate topographic factors of natural breakpoint classification superposition, including elevation, slope, relief amplitude, surface incision, variance coefficient in elevation, surface roughness, distance to roads, and distance to rivers. These are within the 8–34 range, and their power determinant value to TFIS are 0.02, 0.70, 0.77, 0.76, 0.51, 0.66, 0.06, and 0.09. Livelihood capital evaluation indices and TFIS classification one (8–14) are positively correlated, and negative correlation (22–26 and 27–34) is at the 0.05 level. The county's poverty alleviation measures and development under different livelihood indices and TFIS indicate that the ecotourism industry has become the inevitable choice for promoting rapid and coordinated development of economy, society, and the environment in ethnic regions