Spatial Heterogeneity of Climate Change Effects on Dominant Height of Larch Plantations in Northern and Northeastern China

Zang, Hao; Lei, Xiangdong; Wörns, MA; Zeng, Weisheng

doi:10.3390/f7070151

Cited by 6 publications

(4 citation statements)

References 46 publications

(62 reference statements)

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“…Our study found that climate was the main factor affecting the productivity of larch in the study area. The results were highly consistent with other studies ( Zang et al., 2016 ; Duan et al., 2022 ). The climatic factors affecting the site index of larch were mainly the temperature of the warmest quarter (BIO10), the mean diurnal range (BIO2), and the annual precipitation (BIO12) ( Figure 3 ).…”

Section: Discussionsupporting

confidence: 93%

“…Area proportions of the distribution regions with different larch SI values for current and future periods (2030s, 2050s, and 2090s) under SSP1-2.6, SSP2-4.5, and SSP5-8 climate scenarios. consistent with other studies (Zang et al, 2016;Duan et al, 2022). The climatic factors affecting the site index of larch were mainly the temperature of the warmest quarter (BIO10), the mean diurnal range (BIO2), and the annual precipitation (BIO12) (Figure 3).…”

Section: Figuresupporting

confidence: 91%

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Predicting the growth suitability of Larix principis-rupprechtii Mayr based on site index under different climatic scenarios

et al. 2023

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Larix principis-rupprechtii Mayr (larch) is one of the main afforestation and timber production species used in North China. Climate change has led to a change in its suitable distribution and growth. However, the impact of climate change on its growth suitability is not clear. In this study, using forest resource inventory data and spatially continuous environmental factor data (temperature, precipitation, topography, and soil) in Hebei and Shanxi Provinces, China, the random forest model (RF) was used to simulate the larch site index (SI) and growth suitability under three shared socioeconomic pathways (SSPs: SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the current and future (2021–2040, 2041–2060 and 2080–2100). The results revealed that (1) RF had excellent performance in predicting the regional SI (R2 = 0.73, MAE = 0.93 m, RMSE = 1.35 m); (2) the main factors affecting the productivity of larch were the mean temperature of the warmest quarter (BIO10), elevation (ELEV), mean diurnal range (BIO2), and annual precipitation (BIO12); and (3) larch currently had a higher SI in the Bashang areas and in the high-altitude mountains. The areas characterized as unsuitable, poorly suitable, moderately suitable, and highly suitable accounted for 15.45%, 42.12%, 31.94%, and 10.49% of the total area, respectively. (4) Future climate warming had an obvious inhibitory effect on the SI, and the effect strengthened with increasing radiation intensity and year. (5) The moderately suitable and highly suitable areas of larch growth showed a downward trend under future climate scenarios. By the end of this century, the suitable growth areas would decrease by 14.14% under SSP1-2.6, 15.17% under SSP2-4.5, and 19.35% under SSP5-8.5. The results revealed the impact of climate change on larch growth suitability, which can provide a scientific basis for larch forest management.

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Section: Discussionsupporting

confidence: 93%

Section: Figuresupporting

confidence: 91%

Predicting the growth suitability of Larix principis-rupprechtii Mayr based on site index under different climatic scenarios

et al. 2023

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“…Besides these aforementioned factors, climate factors such as temperature, light and precipitation are not negligible in determining tree growth [10]. Climate-sensitive tree growth models have also been developed recently [11][12][13]. The climate, site condition and competition interactively influence individual tree growth.…”

Section: Introductionmentioning

confidence: 99%

Individual Tree Diameter Growth Models of Larch–Spruce–Fir Mixed Forests Based on Machine Learning Algorithms

Lei

Shen

2019

Forests

Self Cite

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Individual tree growth models are flexible and commonly used to represent growth dynamics for heterogeneous and structurally complex uneven-aged stands. Besides traditional statistical models, the rapid development of nonparametric and nonlinear machine learning methods, such as random forest (RF), boosted regression tree (BRT), cubist (Cubist) and multivariate adaptive regression splines (MARS), provides a new way for predicting individual tree growth. However, the application of these approaches to individual tree growth modelling is still limited and short of a comparison of their performance. The objectives of this study were to compare and evaluate the performance of the RF, BRT, Cubist and MARS models for modelling the individual tree diameter growth based on tree size, competition, site condition and climate factors for larch–spruce–fir mixed forests in northeast China. Totally, 16,619 observations from long-term sample plots were used. Based on tenfold cross-validation, we found that the RF, BRT and Cubist models had a distinct advantage over the MARS model in predicting individual tree diameter growth. The Cubist model ranked the highest in terms of model performance (RMSEcv [0.1351 cm], MAEcv [0.0972 cm] and R2cv [0.5734]), followed by BRT and RF models, whereas the MARS ranked the lowest (RMSEcv [0.1462 cm], MAEcv [0.1086 cm] and R2cv [0.4993]). Relative importance of predictors determined from the RF and BRT models demonstrated that the competition and tree size were the main drivers to diameter growth, and climate had limited capacity in explaining the variation in tree diameter growth at local scale. In general, the RF, BRT and Cubist models are effective and powerful modelling methods for predicting the individual tree diameter growth.

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“…Dominant tree height, for example, has been used to calculate the site index for even-aged and pure forests [ 6 ]. The dominant height growth model with climate factors is used to help predict the site index under future climate change and to analyze the adaptation strategies of trees [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Impact of climate factors on height growth of Pinus sylvestris var. mongolica

Zhou

Ze-yong

Zhou³

et al. 2019

PLoS ONE

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Tree height growth is sensitive to climate change; therefore, incorporating climate factors into tree height prediction models can improve our understanding of this relationship and provide a scientific basis for plantation management under climate change conditions. Mongolian pine (Pinus sylvestris var. mongolica) is one of the most important afforestation species in Three-North Regions in China. Yet our knowledge on the relationship between height growth and climate for Mongolian pine is limited. Based on survey data for the dominant height of Mongolian pine and climate data from meteorological station, a mixed-effects Chapman-Richards model (including climate factors and random parameters) was used to study the effects of climate factors on the height growth of Mongolian pine in Zhanggutai sandy land, Northeast China. The results showed that precipitation had a delayed effect on the tree height growth. Generally, tree heights increased with increasing mean temperature in May and precipitation from October to April and decreased with increasing precipitation in the previous growing season. The model could effectively predict the dominant height growth of Mongolian pine under varying climate, which could help in further understanding the relationship between climate and height growth of Mongolian pine in semiarid areas of China.

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Spatial Heterogeneity of Climate Change Effects on Dominant Height of Larch Plantations in Northern and Northeastern China

Cited by 6 publications

References 46 publications

Predicting the growth suitability of Larix principis-rupprechtii Mayr based on site index under different climatic scenarios

Predicting the growth suitability of Larix principis-rupprechtii Mayr based on site index under different climatic scenarios

Individual Tree Diameter Growth Models of Larch–Spruce–Fir Mixed Forests Based on Machine Learning Algorithms

Impact of climate factors on height growth of Pinus sylvestris var. mongolica

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