Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

Browse

Author: search.filters.author.Wang ZSubject: search.filters.subject.06 Biological Sciences

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Different effects of grazing and nitrogen addition on ecosystem multifunctionality are driven by changes in plant resource stoichiometry in a typical steppe
    (5/08/2022) Li L; He XZ; Zhang X; Hu J; Wang M; Wang Z; Hou F
    Purpose: Herbivore grazing and nitrogen (N) input may alter the multiple ecosystem functions (i.e., multifunctionality, hereafter) associated with carbon (C), N, and phosphorus (P) cycling. Most studies on variations in plant diversity, soil biotic or abiotic factors, and linkages to ecosystem functions have focused on grazing or N enrichment alone. Few studies have combined these two factors to explore the role of plant resource stoichiometry (C:N:P ratios) in ecosystem multifunctionality (EMF) control. Here, we evaluated the direct and indirect effects of stocking rate (0, 2.7, 5.3, and 8.7 sheep ha− 1) and N addition rate (0, 5, 10, and 20 g N m− 2 yr− 1) on a range of ecosystem functions and EMF via changing plant diversity, soil pH and plant resource stoichiometry in a typical steppe on the Loess Plateau. Results: We found that increasing stocking rate and interaction between grazing and N addition significantly decreased EMF, while increasing N addition rate significantly promoted EMF. Grazing decreased soil NH4+-N, soil NO3−-N, aboveground biomass, and plant C, N, and P pools, but increased soil total N and P at 8.7 and 5.3 sheep ha− 1, respectively. N addition increased soil NH4+-N, NO3−-N, and total P. Plant aboveground biomass, and plant C, N, and P pools increased at the lower N addition rate (≤ 5 g N m− 2 yr− 1) under grazing. The structural equation models indicated that (1) EMF was driven by the direct effects of grazing and the indirect effects of grazing on plant resource stoichiometry and soil pH; (2) EMF increased with increasing N addition rates, but such positive response of EMF to increasing N addition rates was alleviated at high levels of plant resource stoichiometry and diversity; and (3) the indirect effects of plant diversity induced by grazing and N addition had moderate effects on EMF via the variations of plant resource stoichiometry. Conclusions: This study demonstrated grazing and N addition had contrasting effects on ecosystem multifunctionality in a typical steppe, and highlighted the capacity of plant diversity in balancing plant elements that serve as a key mechanism in the maintenance of EMF in response to intensive grazing and N enrichment.
  • Thumbnail Image
    Item
    Obligate mutualism within a host drives the extreme specialization of a fig wasp genome
    (BioMed Central Ltd, 20/12/2013) Xiao J-H; Yue Z; Jia L-Y; Yang X-H; Niu L-H; Wang Z; Zhang P; Sun B-F; He S-M; Li Z; Xiong T-L; Xin W; Gu H-F; Wang B; Werren JH; Murphy RW; Wheeler D; Niu L-M; Ma G-C; Tang T; Bian S-N; Wang N-X; Yang C-Y; Wang N; Fu Y-G; Li W-Z; Yi SV; Yang X-Y; Zhou Q; Lu C-X; Xu C-Y; He L-J; Yu L-L; Chen M; Zheng Y; Wang S-W; Zhao S; Li Y-H; Yu Y-Y; Qian X-J; Cai Y; Bian L-L; Zhang S; Wang J-Y; Yin Y; Xiao H; Wang G-H; Yu H; Wu W-S; Cook JM; Wang J; Huang D-W
    Background: Fig pollinating wasps form obligate symbioses with their fig hosts. This mutualism arose approximately 75 million years ago. Unlike many other intimate symbioses, which involve vertical transmission of symbionts to host offspring, female fig wasps fly great distances to transfer horizontally between hosts. In contrast, male wasps are wingless and cannot disperse. Symbionts that keep intimate contact with their hosts often show genome reduction, but it is not clear if the wide dispersal of female fig wasps will counteract this general tendency. We sequenced the genome of the fig wasp Ceratosolen solmsi to address this question. Results: The genome size of the fig wasp C. solmsi is typical of insects, but has undergone dramatic reductions of gene families involved in environmental sensing and detoxification. The streamlined chemosensory ability reflects the overwhelming importance of females finding trees of their only host species, Ficus hispida, during their fleeting adult lives. Despite long-distance dispersal, little need exists for detoxification or environmental protection because fig wasps spend nearly all of their lives inside a largely benign host. Analyses of transcriptomes in females and males at four key life stages reveal that the extreme anatomical sexual dimorphism of fig wasps may result from a strong bias in sex-differential gene expression. Conclusions: Our comparison of the C. solmsi genome with other insects provides new insights into the evolution of obligate mutualism. The draft genome of the fig wasp, and transcriptomic comparisons between both sexes at four different life stages, provide insights into the molecular basis for the extreme anatomical sexual dimorphism of this species. © 2013 Xiao et al.; licensee BioMed Central Ltd.
  • Thumbnail Image
    Item
    DeepPN: a deep parallel neural network based on convolutional neural network and graph convolutional network for predicting RNA-protein binding sites.
    (29/06/2022) Zhang J; Liu B; Wang Z; Lehnert K; Gahegan M
    BACKGROUND: Addressing the laborious nature of traditional biological experiments by using an efficient computational approach to analyze RNA-binding proteins (RBPs) binding sites has always been a challenging task. RBPs play a vital role in post-transcriptional control. Identification of RBPs binding sites is a key step for the anatomy of the essential mechanism of gene regulation by controlling splicing, stability, localization and translation. Traditional methods for detecting RBPs binding sites are time-consuming and computationally-intensive. Recently, the computational method has been incorporated in researches of RBPs. Nevertheless, lots of them not only rely on the sequence data of RNA but also need additional data, for example the secondary structural data of RNA, to improve the performance of prediction, which needs the pre-work to prepare the learnable representation of structural data. RESULTS: To reduce the dependency of those pre-work, in this paper, we introduce DeepPN, a deep parallel neural network that is constructed with a convolutional neural network (CNN) and graph convolutional network (GCN) for detecting RBPs binding sites. It includes a two-layer CNN and GCN in parallel to extract the hidden features, followed by a fully connected layer to make the prediction. DeepPN discriminates the RBP binding sites on learnable representation of RNA sequences, which only uses the sequence data without using other data, for example the secondary or tertiary structure data of RNA. DeepPN is evaluated on 24 datasets of RBPs binding sites with other state-of-the-art methods. The results show that the performance of DeepPN is comparable to the published methods. CONCLUSION: The experimental results show that DeepPN can effectively capture potential hidden features in RBPs and use these features for effective prediction of binding sites.