Daily minimum temperature and precipitation control on spring phenology in arid‐mountain ecosystems in China

Abstract

Climate anomaly has caused substantial shifts in phenology of mountain ecosystems, but the underlying mechanism of phenological responses to climate change is still not well understood. In essence, the abundance of vegetation communities increases the complexity of phenology‐climate relationships, leading to a certain limitation in predictions of future dynamics among different vegetation types using a unified model. In this study, we focused on the climatic constraints on spring phenology in arid mountains (AMs) of China, and emphasis was laid on accurate representation of mechanisms that control phenology across different vegetation types. We Firstly explored spatio‐temporal variations in satellite‐derived estimates of starting date of vegetation growing season (SOS) over the period 2000–2015 using moderate‐resolution imaging spectro‐radiometer (MODIS) normalized difference vegetation index (NDVI). Phenological models in response to climate variability were then established by using mixed‐effect models based on satellite observations and an extensive dataset of climatic measurements. Our results showed that the climatic regulation on SOS varied greatly over vegetation types. More climatic factors that regulate phenological development were found in grassland than forest and shrubland. At ecosystem level, two critical climate factors, daily minimum temperature (Tmin) and precipitation, explained 74–95% of total variability in predicted SOS. The observed sensitivity to Tmin is expected to be closely linked with the risk of frost damage, while preseason precipitation determines water availability in spring. The varying ecosystem sensitivity revealed the different resilience and adaptability to changing climate among vegetation types, which have been linked to their eco‐physiological characteristics (e.g., water use efficiency) and environmental conditions (e.g., elevation). Overall, our results indicate a strong dependence of spring phenology on Tmin and precipitation, and create an opportunity for a more realistic representation of vegetation phenology and growth of AM plants in China in land surface models.

Effects of aspect on phenology of Larix gmelinii forest in Northeast China

“…From the details of different aspects, the variation of LOS in the shady slpoe was larger than that of the sunny aspect, the average shortening of the south was 0.56 d•a −1 , while the average shortening of the was 1.26 d•a −1 . Especially, in 2001, the south LOS was as long as 166 d, while the west was only 135 d, the difference is 31d, this phenomenon also occurred in the study of RSP in the forest of Qilian Mountain because Long-term records of vegetation phenology suggest that temperature sensitivity can vary in space and time 9,22,35 . Under the aspect control, the overall change trends of the RSP were basically the same in zone I and zone II, and there was no statistical difference in RSP between two adjacent latitudes in the L. gmelinii forest area.…”

“…Vegetation RSP based on NDVI change is one of the main approach to judge regional environmental changes at a large scale, and its high generalization and relative sensitivity to temperature factors make it have a unique interpretation in screening the impact of global climate change on a certain ecosystem [21][22][23][24] . Thus, the distribution area of L. gmelinii in The Greater Khingan Mountains in Northeast Asia has become an ideal forest phenology research area after nearly 40 years of forest conservation.…”

“…According to the effect of LST on SOS of L. gmelinii, LST contributed the most in April and spring, because they corresponded with SOS in time matching. Although the simultaneous (or early) precipitation also has an impact on SOS 22 , this effect is secondary to the impact of LST in the corresponding period of phenology 24 . None of the relationships of EOS to LST on different time scales have reached a statistically significant level.…”

A dataset of daily near-surface air temperature in China from 1979 to 2018

“…In summary, this dataset has high spatial resolution and high accuracy, which compensates for the temperature values (T max , T min , and T avg ) previously missing at high spatial resolution and provides key parameters for the study of climate change, especially high-temperature drought and low-temperature chilling damage. The dataset is publicly available at https://doi.org/10.5281/zenodo.5502275 (Fang et al, 2021a).…”

What Quantitative Anatomy Can Provide for Kinetics of Xylogenesis: Analysis of Cell Radial Diameters

“…Численность клеток в камбии, пожалуй, показатель, имеющий наибольшую относительную ошибку в измерениях, для оценки которого было использовано несколько цитологических методов (Larson, 1994). Более того, он существенно варьирует у разных деревьев с разной энергией роста, что вносит еще большую вариабельность при оценке данного кинетического показателя (Wodziski, 1971;Deslauriers et al, 2003 (Vaganov et al, 2006;Evans et al, 2006;Yang et al, 2017;Du et al, 2020;Tychkov et al, 2019). Тем не менее радиальные размеры клеток, продуцируемых в это время в сезоне 2014 г., оказались в итоге больше, чем в аналогичный период в годичных кольцах 2013 г.…”