To explore how climatic factors influence tree growth within the context of global climate changes, we used a dendroclimatological analysis to understand the response of Larix gmelinii to climatic variations along a broad latitudinal gradient from 47.27° to 52.66° N in the Greater Khingan Mountains of Northeastern China. The growth-limiting climate factors and a detailed association between radial growth and climate were identified along the gradient using redundancy analysis (RDA) and standard correlation function analysis over the period 1960–2013. The results showed that temperatures during current June to July represented the most important factor affecting tree radial growth in the study area. Across all studied latitudes, Larix gmelinii growth might be decreasing in radial growth due to higher monthly maximum temperature (Tmax) and monthly mean temperatures (Tm) in the current June, especially for the stands at low and middle latitudes. With continued warming, Larix gmelinii radial growth at high latitudes (e.g., Mangui (MG) and Mohe (MH)) might be reduced by warmer temperatures in July. In addition, Larix gmelinii might be decreasing in radial growth from decreasing precipitation. Our results show that there is a decreasing trend in Larix gmelinii radial growth under the observed general increase of temperatures in the Greater Khingan Mountains in recent years.

Chinese fir (Cunninghamia lanceolata) is one of southern China's most important native tree species, which has experienced noticeable climate-induced changes. Published papers (1978–2020) on tree growth of Chinese fir forests in China were collected and critically reviewed. After that, a comprehensive growth data set was developed from 482 sites, which are distributed between 102.19° and 130.07°E in longitude, between 21.87° and 37.24°N in latitude and between 5 and 2260 m in altitude. The dataset consists of 2265 entries, including mean DBH (cm), mean H (m), volume (m³), biomass (dry weight) (kg) (stem (over bark) biomass, branches biomass, leaves biomass, bark biomass, aboveground biomass, roots biomass, total trees biomass) and related information, i.e. geographical location (Country, province, study site, longitude, latitude, altitude, slope, and aspect), climate (mean annual precipitation-MAP and mean annual temperature-MAT), stand description (origin, age, canopy density and stand density), and sample regime (plot size, number and investigation year). Our results showed that (1) the best prediction of height was obtained using nonlinear composite model Height = $$1.3 + 34.23*(1 - {\text{e}}^{{\left( { - 0.01025*{\text{DBH}}^{1.347} } \right)}} )$$1.3 + 34.23 ∗ ( 1 - e - 0.01025 ∗ DBH 1.347 ) , (R² = 0.8715, p < 0.05), (2) the equation Volume = DBH²/(387.8 + 19,190/Height) (R² = 0.9833, p < 0.05) was observed to be the most suitable model for volume estimation. Meanwhile, when the measurements of the variables are difficult to carry out, the volume model Volume = 0.03957 − 0.01215*DBH + 0.00118*DBH² (R² = 0.9573, p < 0.05) is determined from DBH only has a practical advantage, (3) the regression equations of component biomass against DBH explained more significant than 86% variability in almost all biomass data of woody tissues, which were ranked as total trees (97.25%) > aboveground (96.55%) > stems (with bark) (96.17%) > barks (88.95%) > roots (86.71%), and explained greater than 64% variability in branch biomass. The foliage biomass equation was the poorest among biomass components (R² = 0.6122). The estimation equations derived in this study are particularly suitable for the Chinese fir forests in China. This dataset can provide a theoretical basis for predicting and assessing the potential of carbon sequestration and afforestation activities of Chinese fir forests on a national scale.

Climate, topography, and tree structure have different effects on plant diversity that vary with spatial scale. In this study, we assessed the contribution of these drivers and how they affect the vascular plant richness of different functional groups in a temperate forest ecosystem in Northeast China. We investigated about 0.986 million plants from 3160 sites to quantify the impact of annual mean temperature, sunshine duration, annual precipitation, standard deviation of diameter at breast height, and forest type on richness of vascular plants (total species, tree, treelet, shrub, and herb, separately) using the gradient boosting model. The results show that annual mean temperature had the strongest impact on plant richness. The tree richness peaked at intermediate annual mean temperature and sunshine duration and increased with annual precipitation. The Shannon diversity index and Simpson dominance index increased with annual precipitation and standard deviation of diameter at breast height, decreased with sunshine duration, and peaked at intermediate annual mean temperature and forest type. The total richness and understory richness increased with annual mean temperature and standard deviation of diameter at breast height and peaked at intermediate sunshine duration and annual precipitation. A comprehensive mechanism was found to regulate the plant diversity in forest ecosystems. The relationship between tree richness and annual mean temperature with latitudinal effect could be affected by the differences in number and size of tree individuals, indicating that plant diversity varies with the utilization of energy. The force driving plant richness varied with the functional group due to the different environmental resource requirements and the life history strategies of plants layers.

In this study, ring-width chronology of Picea jezoensis var. microsperma from the Changbai Mountain (CBM) area, Northeast China, was constructed. Growth/climate responses suggested that mean maximum temperature (T_max) was the limiting factor affecting radial growth of PJ trees in the study region. According to the correlation analysis between the ring-width index and meteorological data, a June–July mean maximum temperature (T_max6–7) series between 1772 and 2004 was reconstructed by using the standard chronology. For the calibration period (1959–2004), the explained variance of the reconstruction was 41.6%. During the last 233 years, there were 36 warm years and 34 cold years, accounting for 15.5% and 14.7% of the total reconstruction years, respectively. Cold periods occurred in 1899–1913, 1955–1970, and 1975–1989, while warm periods occurred in 1881–1888. The reconstructed temperature series corresponded to the historical disaster records of extreme climatic events (e.g., drought and flood disasters) in this area. Comparisons with other temperature reconstructions from surrounding areas and spatial correlation analysis between the gridded temperature data and reconstruction series indicated that the regional climatic variations were well captured by the reconstruction. In addition, multi-taper method spectral analysis indicated the existence of significant periodicities in the reconstructed series. The significant spatial correlations between the reconstructed temperature series and the El Niño–Southern Oscillation (ENSO), solar activity, and Pacific Decadal Oscillation (PDO) suggested that the temperature in the CBM area indicated both local-regional climate signals and global-scale climate changes.

Tree growth in mountain ecosystems is affected by complex environments, and its relationship with climatic and environmental factors varying with elevation. In order to examine the spatial variation and temporal stability of the growth/climate relationship of Betula platyphylla (BP), the dendrochronological method was used to analyze the radial growth/climate relationship between 1946 and 2016 of the BP trees along an altitudinal gradient in the Changbai Mountain of northeast China. Our results showed that the mean sensitivity of BP was higher than that of other species studied in Changbai Mountain. The growth/climate relationship of BP trees varies with altitude, and this conclusion has reached a consensus from the study of tree growth response to climate change. More specifically, at low altitudes (550–995 m a.s.l.), the radial growth of BP is mainly affected by spring precipitation and temperature in May and October of the current year. However, at high-altitude areas (1210–1425 m a.s.l.), it is mainly affected by the temperature in September of the previous year and May of the current year. Furthermore, the growth/climate relationship of BP trees showed temporal instability. After 1970, the rise in temperature inhibited the growth of BP at low altitudes and promoted the growth of BP trees at high altitudes. In the context of continued warming in the future, the white birch stands in Changbai Mountain will move to higher altitudes.

In this study, we constructed a ring-width chronology derived from Betula ermanii (BE) near the transitional zone between forests and tundra within the Changbai Mountain (CBM) region. This chronology was established utilizing 55 cores obtained from 30 trees. Our analysis of growth/climate responses underscores the pivotal role of the mean maximum winter temperature in influencing radial growth. Drawing upon these growth/climate associations, we reconstructed the mean maximum temperature series for December of the preceding year through January of the current year for the years 1787 and 2005 CE, employing a standardized chronology. During the calibration period (1960–2005), the reconstructed series exhibited an explained variance of 36%. This reconstruction provides crucial insights into historical temperature fluctuations within the study area. Our findings indicate that year-to-year temperature variations did not manifest synchronously along the altitude gradient of Changbai Mountain. Notably, the response to recent winter warming exhibited disparities with the altitude on Changbai Mountain. Specifically, the higher altitude range (1950–2000 m a.s.l.) displayed a response to warming around 1960, the mid-altitude range (765–1188 m a.s.l.) responded around 1975, and the lowest altitude (650 m a.s.l.) responded by 1977. Consequently, the paleotemperature research outcomes from Changbai Mountain alone may not adequately characterize climate change in this region. We recommend future high-resolution temperature records be obtained through sampling at various altitudes to enhance the comprehensiveness of our understanding.