Relative effects of climate, stand environment and tree characteristics on annual tree growth in subtropical Cunninghamia lanceolata forests

Liang, Ruiting; Sun, Yujun; Qiu, Siyu; Wang, Baoying; Xie, Yunhong

doi:10.1016/j.agrformet.2023.109711

Cited by 4 publications

References 62 publications

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Demographic and net primary productivity dynamics of primary and secondary tropical forests in Southwest China under a changing climate

Oo,

Panthi,

Fan

et al. 2024

Integrative Conservation

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Tropical forests are major carbon sinks on the Earth's land surface. However, our understanding of how the demographic rate and carbon sink capacities of tropical forests respond to climate change remains limited. In this study, we investigated the impacts of environmental drivers on forest growth, mortality, recruitment, and stem net primary productivity (NPPstem) over 16 years at five tropical forest plots in Xishuangbanna, Southwest China. These plots are along a successional gradient spanning three tropical secondary forests (tropical secondary forest‐1 [TSF‐1], tropical secondary forest‐2 [TSF‐2], and tropical secondary forest‐3 [TSF‐3]) and two primary forests (tropical rainforest [TRF] and tropical karst forest [TKF]). Our results showed that early successional secondary forests (TSF‐2 and TSF‐3) had higher diameter growth rates and relative mortality rates. An extreme drought event during 2009–2010 reduced the growth rate, relative recruitment rate, and NPPstem for most plots while increasing mortality in early successional forest plots. We observed significant negative effects of maximum temperature (Tmax) on NPPstem and diameter growth rate across all plots. Additionally, we found that precipitation had significant positive effects on diameter growth rate across all plots. Furthermore, tree mortality increased with rising Tmax, whereas precipitation significantly enhanced tree recruitment. Our findings highlight the vulnerability of tree growth, mortality, recruitment, and productivity in tropical forests to extreme drought events in Southwest China. Continued climate warming and more frequent droughts will induce higher mortality rates and impede growth, thus reducing the carbon sink capacity of tropical forests, especially in early successional stage tropical secondary forests.

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Demographic and net primary productivity dynamics of primary and secondary tropical forests in Southwest China under a changing climate

Oo,

Panthi,

Fan

et al. 2024

Integrative Conservation

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Optimizing variables selection of random forest to predict radial growth of Larix gmelinii var. principis-rupprechtii in temperate regions

Zhang,

Liao,

et al. 2024

Forest Ecology and Management

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Monthly climate prediction using deep convolutional neural network and long short-term memory

Guo,

He,

Wang

2024

Sci Rep

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Climate change affects plant growth, food production, ecosystems, sustainable socio-economic development, and human health. The different artificial intelligence models are proposed to simulate climate parameters of Jinan city in China, include artificial neural network (ANN), recurrent NN (RNN), long short-term memory neural network (LSTM), deep convolutional NN (CNN), and CNN-LSTM. These models are used to forecast six climatic factors on a monthly ahead. The climate data for 72 years (1 January 1951–31 December 2022) used in this study include monthly average atmospheric temperature, extreme minimum atmospheric temperature, extreme maximum atmospheric temperature, precipitation, average relative humidity, and sunlight hours. The time series of 12 month delayed data are used as input signals to the models. The efficiency of the proposed models are examined utilizing diverse evaluation criteria namely mean absolute error, root mean square error (RMSE), and correlation coefficient (R). The modeling result inherits that the proposed hybrid CNN-LSTM model achieves a greater accuracy than other compared models. The hybrid CNN-LSTM model significantly reduces the forecasting error compared to the models for the one month time step ahead. For instance, the RMSE values of the ANN, RNN, LSTM, CNN, and CNN-LSTM models for monthly average atmospheric temperature in the forecasting stage are 2.0669, 1.4416, 1.3482, 0.8015 and 0.6292 °C, respectively. The findings of climate simulations shows the potential of CNN-LSTM models to improve climate forecasting. Climate prediction will contribute to meteorological disaster prevention and reduction, as well as flood control and drought resistance.

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Relative effects of climate, stand environment and tree characteristics on annual tree growth in subtropical Cunninghamia lanceolata forests

Cited by 4 publications

References 62 publications

Demographic and net primary productivity dynamics of primary and secondary tropical forests in Southwest China under a changing climate

Demographic and net primary productivity dynamics of primary and secondary tropical forests in Southwest China under a changing climate

Optimizing variables selection of random forest to predict radial growth of Larix gmelinii var. principis-rupprechtii in temperate regions

Monthly climate prediction using deep convolutional neural network and long short-term memory

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