Winter snow is an important driver of tree growth in regions where growing-season precipitation is limited. However, observational evidence of this influence at larger spatial scales and across diverse bioclimatic regions is lacking. Here, we investigated the interannual effects of winter (here defined as previous October to current February) snow depth on tree growth across temperate China over the period of 1961-2015, using a regional network of tree ring records, in situ daily snow depth observations, and gridded climate data. We report uneven effects of winter snow depth on subsequent growing-season tree growth across temperate China. There shows little effect on tree growth in drier regions that we attribute mainly to limited snow accumulation during winter. By contrast, winter snow exerts important positive influence on tree growth in stands with high winter snow accumulation (e.g., in parts of cold arid regions). The magnitude of this effect depends on the proportion of winter snow to pre-growing-season (previous October to current April) precipitation. We further observed that tree growth in drier regions tends to be increasingly limited by warmer growing-season temperature and early growing-season water availability. No compensatory effect of winter snow on the intensifying drought limitation of tree growth was observed across temperate China. Our findings point toward an increase in drought vulnerability of temperate forests in a warming climate.
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Vet Med Sci
January 2025
Andırın Vocational School, Department of Computer Technologies, Kahramanmaraş Sütçü İmam University, Kahramanmaraş, Türkiye.
Prediction of body weight (BW) using biometric measurements is an important tool especially for animal welfare and automatic phenotyping tools that needs mathematical models. In this study, it was aimed to predict the BW using body length (BL), chest girth (CG) and width of the waist (WW) for rabbits of the maternal form of Hyla NG. The standard rabbit-raising practices were applied for the animals.
View Article and Find Full Text PDFMicrobiol Spectr
January 2025
Laboratory of Microbiology and Immunology, School of Basic Medical Science, Inner Mongolia Medical University, Hohhot, China.
Colorectal cancer (CRC) is one of the malignant tumors globally, with high morbidity and mortality rates. The mainstay treatment of CRC includes surgery, radiotherapy, and chemotherapy. However, these treatments are associated with a high recurrence rate, poor prognosis, and highly toxic side effects.
View Article and Find Full Text PDFNanomaterials (Basel)
December 2024
College of Materials Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
The design and preparation of advanced hybrid nanofibers with controllable microstructures will be interesting because of their potential high-efficiency applications in the environmental and energy domains. In this paper, a simple and efficient strategy was developed for preparing hybrid nanofibers of zinc oxide-molybdenum disulfide (ZnO-MoS) grown on polyimide (PI) nanofibers by combining electrospinning, a high-pressure hydrothermal process, and in situ growth. Unlike simple composite nanoparticles, the structure is shown in PI-ZnO to be like the skeleton of a tree for the growth of MoS "leaves" as macro-materials with controlled microstructures.
View Article and Find Full Text PDFGenome Biol Evol
January 2025
Earlham Institute, Norwich, UK.
Somion occarium is a wood-decaying bracket fungus belonging to an order known to be rich in useful chemical compounds. Despite its widespread distribution, S. occarium has been assessed as endangered on at least one national Red List, presumably due to loss of old-growth forest habitat.
View Article and Find Full Text PDFFront Plant Sci
December 2024
College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China.
Foliage plants have strict requirements for their growing environment, and timely and accurate soil temperature forecasts are crucial for their growth and health. Soil temperature exhibits by its non-linear variations, time lags, and coupling with multiple variables, making precise short-term multi-step forecasts challenging. To address this issue, this study proposes a multivariate forecasting method suitable for soil temperature forecasting.
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