A metabolome‐based core hybridisation strategy for the prediction of rice grain weight across environments

Marker‐based prediction holds great promise for improving current plant and animal breeding efficiencies. However, the predictabilities of complex traits are always severely affected by negative factors, including distant relatedness, environmental discrepancies, unknown population structures, and indeterminate numbers of predictive variables. In this study, we utilised two independent F₁ hybrid populations in the years 2012 and 2015 to predict rice thousand grain weight (TGW) using parental untargeted metabolite profiles with a partial least squares regression method. A stable predictive model for TGW was built based on hybrids from the population in 2012 (r = 0.75) but failed to properly predict TGW for hybrids from the population in 2015 (r = 0.27). After integrating hybrids from both populations into the training set, the TGW of hybrids could be predicted but was largely dependent on population structures. Then, core hybrids from each population were determined by principal component analysis and the TGW of hybrids in both environments were successfully predicted (r > 0.60). Moreover, adjusting the population structures and numbers of predictive analytes increased TGW predictability for hybrids in 2015 (r = 0.72). Our study demonstrates that the TGW of F₁ hybrids across environments can be accurately predicted based on parental untargeted metabolite profiles with a core hybridisation strategy in rice. Metabolic biomarkers identified from early developmental stage tissues, which are grown under experimental conditions, may represent a workable approach towards the robust prediction of major agronomic traits for climate‐adaptive varieties.     

Weighted average regression and environmental calibration as a tool for quantifying climate-driven changes in vegetation

Aims Studies of the climatic responses of plant assemblages via vegetation-based environmental reconstructions by weighted averaging (WA) regression and calibration are a recent development in modern vegetation ecology. However, the performance of this technique for plot-based vegetation datasets has not been rigorously tested. We assess the estimation accuracy of the WA approach by comparing results, mainly the root mean square error of prediction (RMSEP) of WA regressions for six different vegetation datasets (total species, high-frequency species and low-frequency species as both abundance and incidence) each from two sites.     

Environmental factors function as constraints on soil nitrous oxide fluxes in bioenergy feedstock cropping systems

Nitrous oxide (N₂O) is a potent greenhouse gas and major component of the net global warming potential of bioenergy feedstock cropping systems. Numerous environmental factors influence soil N₂O production, making direct correlation difficult to any one factor of N₂O fluxes under field conditions. We instead employed quantile regression to evaluate whether soil temperature, water‐filled pore space (WFPS), and concentrations of soil nitrate () and ammonium () determined upper bounds for soil N₂O flux magnitudes. We collected data over 6 years from a range of bioenergy feedstock cropping systems including no‐till grain crops, perennial warm‐season grasses, hybrid poplar, and polycultures of tallgrass prairie species each with and without nitrogen (N) addition grown at two sites. The upper bounds for soil N₂O fluxes had a significant and positive correlation with all four environmental factors, although relatively large fluxes were still possible at minimal values for nearly all factors. The correlation with was generally weaker, suggesting it is less important than in driving large fluxes. Quantile regression slopes were generally lower for unfertilized perennials than for other systems, but this may have resulted from a perpetual state of nitrogen limitation, which prevented other factors from being clear constraints. This framework suggests efforts to reduce concentrations of in the soil may be effective at reducing high‐intensity periods—”hot moments”—of N₂O production.     

Resilience of ecosystem processes: a new approach shows that functional redundancy of biological control services is reduced by landscape simplification

Functional redundancy can increase the resilience of ecosystem processes by providing insurance against species loss and the effects of abundance fluctuations. However, due to the difficulty of assessing individual species’ contributions and the lack of a metric allowing for a quantification of redundancy within communities, few attempts have been made to estimate redundancy for individual ecosystem processes. We present a new method linking interaction metrics with metabolic theory that allows for a quantification of redundancy at the level of ecosystem processes. Using this approach, redundancy in the predation on aphids and other prey by natural enemies across a landscape heterogeneity gradient was estimated. Functional redundancy of predators was high in heterogeneous landscapes, low in homogeneous landscapes and scaled with predator specialisation. Our approach allows quantifying functional redundancy within communities and can be used to assess the role of functional redundancy across a wide variety of ecosystem processes and environmental factors.     

陕西不同区域苹果林土壤水分动态和水分生产力模拟

基于WinEPIC和偏最小二乘回归模型对1981—2016年陕西不同区域成龄苹果林的水分生产力影响因子和土壤水分动态进行比较.结果表明: 研究期间,陕北丘陵沟壑区、渭北残塬区和关中平原区成龄苹果林年均产量分别为16.94、22.62和25.70 t·hm^-2,年均蒸散量分别为511.2、614.9和889.88 mm,水分生产力分别为3.81、3.82和3.24 kg·m^-3.在陕北区和渭北区,林地水分胁迫最严重,年均胁迫天数分别为54.89、28.38 d,关中区的N素胁迫较为剧烈,年均胁迫天数为25.87 d.陕北区和渭北区影响苹果林产量的最大因子是降水量,其标准化回归系数分别为0.274和0.235,但施N量对产量也有较大影响,回归系数分别达0.224和0.232;关中区的最大影响因子为施N量,回归系数为0.335,其次是供水量和施P量,回归系数分别为0.154和0.147.陕北区和渭北区影响苹果林水分生产力的最大因子是降水量,其标准化回归系数分别0.238和0.194;关中区最主要的影响因子为施N量和供水量,回归系数分别为0.182和0.178.在模拟期间,陕北区、渭北区和关中区苹果林地的过耗水总量分别为1152.17、1342.95和1372.42 mm,2~15 m土层土壤有效含水量下降速率分别为63.44、57.08、51.41 mm·a^-1,深层土壤干层出现时间分别为8、13和17年后,干层稳定至11 m深的时间分别为18、21和26年,干燥化严重.不同区域苹果林的管理重心应参考水分生产力的主导因子确定.     

Habitat use of the ocelot (Leopardus pardalis) in Brazilian Amazon

Amazonia forest plays a major role in providing ecosystem services for human and sanctuaries for wildlife. However, ongoing deforestation and habitat fragmentation in the Brazilian Amazon has threatened both. The ocelot is an ecologically important mesopredator and a potential conservation ambassador species, yet there are no previous studies on its habitat preference and spatial patterns in this biome. From 2010 to 2017, twelve sites were surveyed, totaling 899 camera trap stations, the largest known dataset for this species. Using occupancy modeling incorporating spatial autocorrelation, we assessed habitat use for ocelot populations across the Brazilian Amazon. Our results revealed a positive sigmoidal correlation between remote‐sensing derived metrics of forest cover, disjunct core area density, elevation, distance to roads, distance to settlements and habitat use, and that habitat use by ocelots was negatively associated with slope and distance to river/lake. These findings shed light on the regional scale habitat use of ocelots and indicate important species–habitat relationships, thus providing valuable information for conservation management and land‐use planning.     

Dynamic analysis of GS-NS0 cells producing a recombinant monoclonal antibody during fed-batch culture

In this study we have analyzed the dynamic covariation of the mammalian cell proteome with respect to functional phenotype during fed-batch culture of NS0 murine myeloma cells producing a recombinant IgG(4) monoclonal antibody. GS-NS0 cells were cultured in duplicate 10 L bioreactors (36.5 degrees C, 15% DOT, pH 7.0) for 335 h and supplemented with a continuous feed stream after 120 h. Cell-specific growth rate declined continuously after 72 h of culture. Cell-specific recombinant monoclonal antibody production rate (qP) varied sixfold through culture. Whilst qP correlated with relative recombinant heavy chain mRNA abundance up to 216 h, qP subsequently declined, independent of recombinant heavy chain or light chain mRNA abundance. GS-NS0 cultures were sampled at 48 h intervals between 24 and 264 h of culture for proteomic analyses. Total protein abundance and nascent polypeptide synthesis was determined by 2D PAGE of unlabeled proteins visualized by SYPRO Ruby and autoradiography of (35)S-labeled polypeptides, respectively. Covariation of nascent polypeptide synthesis and abundance with biomass-specific cell growth, glucose and glutamate consumption, lactate and Mab production rates were then examined using two partial least squares regression models. Most changes in polypeptide synthesis or abundance for proteins previously identified by mass spectrometry were positively correlated with biomass-specific growth rate. We conclude that the substantial transitions in cell physiology and qP that occur during culture utilize a relatively constant complement of the most abundant host cell machines that vary primarily with respect to induced changes in cell growth rate.