Constrained shuffled complex evolution algorithm and its application in the automatic calibration of Xinanjiang model

The Shuffled Complex Evolution—University of Arizona (SCE-UA) is a classical algorithm in the field of hydrology and water resources, but it cannot solve constrained optimization problems directly. Using penalty functions has been the preferred method to handle constraints, but the appropriate selection of penalty parameters and penalty functions can be challenging. To enhance the universality of the SCE-UA, we propose the Constrained Shuffled Complex Evolution Algorithm (CSCE) to conveniently and effectively solve inequality-constrained optimization problems. Its performance is compared with the SCE-UA using the adaptive penalty function (SCEA) on 14 test problems with inequality constraints. It is further compared with seven other algorithms on two test problems with low success rates. To demonstrate its effect in hydrologic model calibration, the CSCE is applied to the parameter optimization of the Xinanjiang (XAJ) model under synthetic data and observed data. The results indicate that the CSCE is more advantageous than the SCEA in terms of the success rate, stability, feasible rate, and convergence speed. It can guarantee the feasibility of the solution and avoid the problem of deep soil tension water capacity (WDM)<0 in the optimization process of the XAJ model. In the case of synthetic data, the CSCE can accurately find the theoretical optimal parameters of the XAJ model under the given constraints. In the case of observed data, the XAJ model optimized by the CSCE can effectively simulate the hourly rainfall-runoff events of the Hexi Basin and achieves mean Nash efficiency coefficients greater than 0.75 in the calibration period and the validation period

De novo assembly and Characterisation of the Transcriptome during seed development, and generation of genic-SSR markers in Peanut (Arachis hypogaea L.)

<p>Abstract</p> <p>Background</p> <p>The peanut (<it>Arachis hypogaea </it>L.) is an important oilseed crop in tropical and subtropical regions of the world. However, little about the molecular biology of the peanut is currently known. Recently, next-generation sequencing technology, termed RNA-seq, has provided a powerful approach for analysing the transcriptome, and for shedding light on the molecular biology of peanut.</p> <p>Results</p> <p>In this study, we employed RNA-seq to analyse the transcriptomes of the immature seeds of three different peanut varieties with different oil contents. A total of 26.1-27.2 million paired-end reads with lengths of 100 bp were generated from the three varieties and 59,077 unigenes were assembled with N50 of 823 bp. Based on sequence similarity search with known proteins, a total of 40,100 genes were identified. Among these unigenes, only 8,252 unigenes were annotated with 42 gene ontology (GO) functional categories. And 18,028 unigenes mapped to 125 pathways by searching against the Kyoto Encyclopedia of Genes and Genomes pathway database (KEGG). In addition, 3,919 microsatellite markers were developed in the unigene library, and 160 PCR primers of SSR loci were used for validation of the amplification and the polymorphism.</p> <p>Conclusion</p> <p>We completed a successful global analysis of the peanut transcriptome using RNA-seq, a large number of unigenes were assembled, and almost four thousand SSR primers were developed. These data will facilitate gene discovery and functional genomic studies of the peanut plant. In addition, this study provides insight into the complex transcriptome of the peanut and established a biotechnological platform for future research.</p

3,5-Bis(4-hy­droxy­phen­yl)-4H-1,2,4-triazol-4-amine monohydrate

The triazole ring in the title compound, C14H12N4O2·H2O, makes dihedral angles of 36.9 (1) and 37.3 (1)° with the two benzene rings. Each hy­droxy group is a hydrogen-bond donor to a two-coordinate N atom of an adjacent mol­ecule; these O—H⋯N hydrogen bonds generate a layer parallel to the ab plane. Adjacent layers are linked by N—-H⋯O and Owater—H⋯O hydrogen bonds into a three-dimensional network

Regenerated woody plants influence soil microbial communities in a subtropical forest

10 páginas.- 4 figuras.- 3 tablas.- referencias.- upplementary data to this article can be found online at https://doi. org/10.1016/j.apsoil.2023.104890Forests are critical for supporting multiple ecosystem services such as climate change mitigation. Microbial diversity in soil provides important functions to maintain and regenerate forest ecosystems, and yet a critical knowledge gap remains in identifying the linkage between attributes of regenerated woody plant (RWP) communities and the diversity patterns of soil microbial communities in subtropical plantations. Here, we investigated the changes in soil microbial communities and plant traits in a nine hectare Chinese fir (Cunninghamia lanceolata; CF) plantation to assess how non-planted RWP communities regulate soil bacterial and fungal diversity, and further explore the potential mechanisms that structure their interaction. Our study revealed that soil bacterial richness was positively associated with RWP richness, whereas soil fungal richness was negatively associated with RWP basal area. Meanwhile, RWP richness was positively correlated with ectomycorrhizal (ECM) fungal richness but negatively correlated with the richness of both pathogenic and saprotrophic fungi, suggesting that the RWP-fungal richness relationship was trophic guild-specific. Soil microbial community beta diversity (i.e., dissimilarity in community composition) was strongly coupled with both RWP beta diversity and the heterogeneity of RWP basal area. Our study highlights the importance of community-level RWP plant attributes for the regulation of microbial biodiversity in plantation systems, which should be considered in forest management programs in the future.This work was funded by the National Key Research and Development Program of China (2021YFD2201301 and 2022YFF1303003), the National Natural Science Foundation of China (U22A20612), and the Key Project of Jiangxi Province Natural Science Foundation of China (20224ACB205003).Peer reviewe

Tree diversity depending on environmental gradients promotes biomass stability via species asynchrony in China's forest ecosystems

There is mounting evidence that biodiversity promotes ecological stability in changing environments. However, understanding diversity–stability relationships and their underlying mechanisms across large-scale tree diversity and natural environmental gradients are still controversial and largely lacking. We used thirty-nine 0.12 ha long-term permanent forest plots spanning China's various forest types to test the effects of multiple abiotic (climate, soil, age and topography) and biotic factors (taxonomic and structural diversity, functional diversity and community-mean traits, and species asynchrony) on biomass stability and its components (mean biomass and biomass variability) over time. We used multiple analytical methods to identify the best explanatory variables and complicated causal relationships for community biomass stability. Our results showed that species richness increased biomass stability by promoting species asynchrony. Structural and functional diversity had a weaker effect on biomass stability. Forest age and structural diversity increased mean biomass and biomass variability significantly and simultaneously. Communities dominated by tree species with high wood density had high biomass stability. Soil nitrogen enhanced biomass stability directly and indirectly through its effects on mean biomass. Soil nitrogen to phosphorus ratio increased biomass stability via increasing species asynchrony. Precipitation indirectly increased biomass stability by affecting tree diversity. Moreover, the direct and indirect effects of soil nutrients on biomass stability were greater than that of climate variables. Our results suggest that species asynchrony is the main mechanism proposed to explain the stabilizing effect of diversity on community biomass, supporting two mechanisms, namely, the biodiversity insurance hypothesis and complementary dynamics. Soil and climate factors also play an important role in shaping diversity–stability relationships. Our results provide a new insight into how tree diversity affects ecosystem stability across diverse community types and large-scale environmental gradients

Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: a perspective from long‐term data assimilation

It is critical to accurately estimate carbon (C) turnover time as it dominates the uncertainty in ecosystem C sinks and their response to future climate change. In the absence of direct observations of ecosystem C losses, C turnover times are commonly estimated under the steady state assumption (SSA), which has been applied across a large range of temporal and spatial scales including many at which the validity of the assumption is likely to be violated. However, the errors associated with improperly applying SSA to estimate C turnover time and its covariance with climate as well as ecosystem C sequestrations have yet to be fully quantified. Here, we developed a novel model-data fusion framework and systematically analyzed the SSA-induced biases using time-series data collected from 10 permanent forest plots in the eastern China monsoon region. The results showed that (a) the SSA significantly underestimated mean turnover times (MTTs) by 29%, thereby leading to a 4.83-fold underestimation of the net ecosystem productivity (NEP) in these forest ecosystems, a major C sink globally; (b) the SSA-induced bias in MTT and NEP correlates negatively with forest age, which provides a significant caveat for applying the SSA to young-aged ecosystems; and (c) the sensitivity of MTT to temperature and precipitation was 22% and 42% lower, respectively, under the SSA. Thus, under the expected climate change, spatiotemporal changes in MTT are likely to be underestimated, thereby resulting in large errors in the variability of predicted global NEP. With the development of observation technology and the accumulation of spatiotemporal data, we suggest estimating MTTs at the disequilibrium state via long-term data assimilation, thereby effectively reducing the uncertainty in ecosystem C sequestration estimations and providing a better understanding of regional or global C cycle dynamics and C-climate feedback