The Radial Growth of Picea wilsonii Was More Restricted by Precipitation Due to Climate Warming on Mt. Guandi, China

Huang, Xiaoxia; Sun, Xiaoneng; Jiang, Yuan; Xue, Feng; Cui, Minghao; Zhao, Shoudong; Kang, Muyi

doi:10.3390/f12111602

Cited by 3 publications

(1 citation statement)

References 79 publications

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“…Radial tree growth is related to temperature rise and altitude differences in the Guandi Mountains. Due to climate change, Picea wilsonii has been more susceptible to high temperatures (Huang et al, 2021). Tree growth has also been shown to be sensitive to climate change in the Northwestern Yunnan Plateau.…”

Section: Introductionmentioning

confidence: 99%

Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models

Guo,

He,

Wang

2023

Front. For. Glob. Change

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IntroductionAtmospheric temperature affects the growth and development of plants and has an important impact on the sustainable development of forest ecological systems. Predicting atmospheric temperature is crucial for forest management planning.MethodsArtificial neural network (ANN) and deep learning models such as gate recurrent unit (GRU), long short-term memory (LSTM), convolutional neural network (CNN), CNN-GRU, and CNN-LSTM, were utilized to predict the change of monthly average and extreme atmospheric temperatures in Zhengzhou City. Average and extreme atmospheric temperature data from 1951 to 2022 were divided into training data sets (1951–2000) and prediction data sets (2001–2022), and 22 months of data were used as the model input to predict the average and extreme temperatures in the next month.Results and DiscussionThe number of neurons in the hidden layer was 14. Six different learning algorithms, along with 13 various learning functions, were trained and compared. The ANN model and deep learning models were evaluated in terms of correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), and good results were obtained. Bayesian regularization (trainbr) in the ANN model was the best performing algorithm in predicting average, minimum and maximum atmospheric temperatures compared to other algorithms in terms of R (0.9952, 0.9899, and 0.9721), and showed the lowest error values for RMSE (0.9432, 1.4034, and 2.0505), and MAE (0.7204, 1.0787, and 1.6224). The CNN-LSTM model showed the best performance. This CNN-LSTM method had good generalization ability and could be used to forecast average and extreme atmospheric temperature in other areas. Future climate changes were projected using the CNN-LSTM model. The average atmospheric temperature, minimum atmospheric temperature, and maximum atmospheric temperature in 2030 were predicted to be 17.23 °C, −5.06 °C, and 42.44 °C, whereas those in 2040 were predicted to be 17.36 °C, −3.74 °C, and 42.68 °C, respectively. These results suggest that the climate is projected to continue warming in the future.

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Section: Introductionmentioning

confidence: 99%

Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models

Guo,

He,

Wang

2023

Front. For. Glob. Change

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Soil C, N, and P Contents and Their Stoichiometry as Impacted by Main Forest Vegetation Types in Xinglongshan, China

Huang,

Zhao,

Zhang

et al. 2023

J Soil Sci Plant Nutr

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Altitudinal Variation in Species Diversity, Distribution, and Regeneration Status of a Secondary Picea Forest in Guandi Mountain, Northern China

Zhang,

Liu,

Yang

et al. 2024

Forests

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Altitude plays a crucial role in shaping the diversity and distribution of species in forest landscapes in mountainous regions. However, existing studies often lack comprehensive analysis of the intricate relationships among environmental factors, disturbances, and species diversity. This study aims to address this gap by thoroughly analyzing major species’ diversity, distribution, and regeneration status, along with their influencing factors, across different altitudinal gradients (1850 to 2350 m). Field surveys were conducted to examine the major species in different vegetation layers of the secondary Picea forest in Guandi Mountain. The key findings from the study include the following: (1) the study area recorded a total of nine species of adult trees, belonging to eight genera in five families, fourteen species of shrubs from eight genera in five families, and fifty-two herb species representing forty-eight genera in twenty-six families. Dominant species varied across layers, with Picea asperata Mast. and Larix principis-rupprechtii Mayr. dominating the arborous layer, Lonicera ferdinandii Franch. and Cotoneaster acutifolius Turcz. in the shrub layer, and Poa pratensis L. and Duchesnea indica (Andrews.) Focke in the herb layer. (2) Altitude significantly influenced species diversity, with the most pronounced effects observed in the shrub and herb layers. (3) Tree species regeneration varied with altitude, with Picea asperata exhibiting the highest regeneration performance, particularly in the high-altitude zone (2250–2350 m). Regeneration parameters were significantly correlated with species diversity indices in both tree and shrub layers. (4) Redundancy analysis revealed that, apart from altitude, cutting (p = 0.015) influenced the species diversity of the tree layer, while annual precipitation (p = 0.006) and temperature (p = 0.01) determined the diversity of the shrub layer. Grazing (p = 0.042) was identified as a determining factor for species diversity in the herb layer. Overall, these findings provide valuable insights into the sustainable management of forest ecosystems in the study area and offer practical guidance for forest management in similar mountainous landscapes worldwide.

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The Radial Growth of Picea wilsonii Was More Restricted by Precipitation Due to Climate Warming on Mt. Guandi, China

Cited by 3 publications

References 79 publications

Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models

Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models

Soil C, N, and P Contents and Their Stoichiometry as Impacted by Main Forest Vegetation Types in Xinglongshan, China

Altitudinal Variation in Species Diversity, Distribution, and Regeneration Status of a Secondary Picea Forest in Guandi Mountain, Northern China

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