Temperature sensitivity thresholds to warming and cooling in phenophases of alpine plants

Meng, Fanyu; Zhou, Yang; Wang, Shiping; Duan, Jianan; Zhang, Zhenhua; Niu, Haishan; Jiang, Lili; Cui, Shujuan; Li, Xin’e; Luo, Caiyun; Zhang, Lrirong; Wang, Qi; Bao, Xiaoyu; Dorji, Tsechoe; Li, Yingnian; Du, Mingyuan; Zhao, Xinquan; Zhao, Liang; Wang, Guojie; Inouye, David W.

doi:10.1007/s10584-016-1802-2

Cited by 8 publications

(4 citation statements)

References 40 publications

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“…In addition, the responses of spring phenological events to temperature increase depend on the net effects of winter daytime and night-time temperatures due to their opposite effects 136 . These findings suggest that the responses of phenological events to temperature could be nonlinear , with threshold ranging from 0.53 to 1.5 o C, depending on the phenological event 137 .…”

Section: Temperature Manipulationsmentioning

confidence: 91%

Plant phenology changes and drivers on the Qinghai–Tibetan Plateau

Shen

Wang

Jiang

et al. 2022

Nat Rev Earth Environ

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173

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Start of growing season advanced by 9.4 ± 2.2 and 8.3± 2.0 days during 1982-1999 and 2000-2020 respectively, whereas its end delayed only by 8.2 ± 1.9 days during 2000-2020.Current models project an advance in season start by 8.8 days and a delay in season end by 14.0 days in 2086-2100 relative to 2000-2014 under Shared-Socioeconomic-Pathway 5-8.5. Warming and increasing precipitation are the main climatic drivers of advancing spring phenology (start of vegetative growing season and first flowering) and delaying end of the growing season. The direction and magnitude of responses of phenophases to temperature depend on soil water availability, with greater temperature sensitivity of the start and end of the season under wetter conditions.First flowering date is more sensitive to temperature on the Qinghai-Tibetan Plateau than in Arctic grasslands.The temperature sensitivities of the start and end of the growing season are greater than those of Arctic grasslands, but smaller than those of mid-latitude alpine and subalpine grasslands.

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Section: Temperature Manipulationsmentioning

confidence: 91%

Plant phenology changes and drivers on the Qinghai–Tibetan Plateau

Shen

Wang

Jiang

et al. 2022

Nat Rev Earth Environ

Self Cite

173

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“…One reason was due to the uncertainty of satellite-based vegetation phenology estimations and the data quality of EC-based observations [64]. Another possible reason was that the empiric prethreshold settings were not fixed on the space-time scale [68]. Actually, our results showed that there was considerable interannual variation in carbon phenology thresholds in two cold temperate grasslands in China (Figure 8).…”

Section: Vegetation Carbon Phenology and Thresholdsmentioning

confidence: 81%

Dynamic Threshold of Carbon Phenology in Two Cold Temperate Grasslands in China

Niu

Zhang

et al. 2021

Remote Sensing

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Plant phenology, especially the timing of the start and the end of the vegetation growing season (SOS and EOS), plays a major role in grassland ecosystem carbon cycles. As the second-largest grassland country in the world, China’s grasslands are mainly distributed in the northern cold temperate climate zone. The accuracies and relations of plant phenology estimations from multialgorithms and data resources are poorly understood. Here, we investigated vegetation phenology in two typical cold temperate grasslands, Haibei (HB) and Inner Mongolia (NM) grasslands, in China from 2001 to 2017. Compared to ground vegetation phenology observations, we analyzed the performance of the moderate resolution imaging spectroradiometer MODIS phenology products (MCD12Q2) and two remote sensing-based vegetation phenology algorithms from the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) time series (five satellite-based phenology algorithms). The optimal algorithm was used to compare with eddy covariance (EC)-based carbon phenology, and to calculate the thresholds of carbon phenology periods (SOSt and EOSt) in each site. Results showed that satellite-based phenology estimations (all five algorithms in this study) were strongly coupled with the temporal variation of the observed phenological period but significantly overestimated the SOS, predicting it to be over 21 days later than the field data. The carbon phenology thresholds of HB grassland (HB_SOSt and HB_EOSt) had a significant upward trend, with the multiyear average values being 0.14 and 0.29, respectively. In contrast, the thresholds of NM grasslands (NM_SOSt and NM_EOSt) also showed a certain upward trend, but it was not significant (p > 0.05), with the multiyear average values being 0.17 and 0.2, respectively. Our study suggested the thresholds of carbon phenology periods (SOSt and EOSt, %) could be simply and effectively estimated based on their significant relationship with the EC-based maximum of gross primary productivity observations (GPPmax) at a specific site and time. Therefore, this study suggested the thresholds of carbon phenology were not fixed even in a specific ecosystem, which also provided simple bridges between satellite-based vegetation phenology and EC-based carbon phenology in similar grasslands.

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“…Quantification of crop yield potential under changing climate condition typically rely on crop models, which are lacking in most cases due to a lack of field data [ 33 ]. Temperature sensitivity model may be key in accurately predicting the phenological responses to climate change [ 40 ]. At the same time, the impact of extreme temperature duration on yield needs to be factored in the model [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

Responses of Grain Yield and Yield Related Parameters to Post-Heading Low-Temperature Stress in Japonica Rice

Ali

Tang

Dai

et al. 2021

Plants

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There is unprecedented increase in low-temperature stress (LTS) during post-heading stages in rice as a consequence of the recent climate changes. Quantifying the effect of LTS on yields is key to unraveling the impact of climatic changes on crop production, and therefore developing corresponding mitigation strategies. The present research was conducted to analyze and quantify the effect of post-heading LTS on rice yields as well as yield and grain filling related parameters. A two-year experiment was conducted during rice growing season of 2018 and 2019 using two Japonica cultivars (Huaidao 5 and Nanjing 46) with different low-temperature sensitivities, at four daily minimum/maximum temperature regimes of 21/27 °C (T1), 17/23 °C (T2), 13/19 °C (T3) and 9/15 °C (T4). These temperature treatments were performed for 3 (D1), 6 (D2) or 9 days (D3), at both flowering and grain filling stages. We found LTS for 3 days had no significant effect on grain yield, even when the daily mean temperature was as low as 12 °C. However, LTS of between 6 and 9 days at flowering but not at filling stage significantly reduced grain yield of both cultivars. Comparatively, Huaidao 5 was more cold tolerant than Nanjing 46. LTS at flowering and grain filling stages significantly reduced both maximum and mean grain filling rates. Moreover, LTS prolonged the grain filling duration of both cultivars. Additionally, there was a strong correlation between yield loss and spikelet fertility, spikelet weight at maturity, grain filling duration as well as mean and maximum grain filling rates under post-heading LTS (p < 0.001). Moreover, the effect of post-heading LTS on rice yield can be well quantified by integrating the canopy temperature (CT) based accumulated cold degree days (ACDDCT) with the response surface model. The findings of this research are useful in modeling rice productivity under LTS and for predicting rice productivity under future climates.

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Temperature sensitivity thresholds to warming and cooling in phenophases of alpine plants

Cited by 8 publications

References 40 publications

Plant phenology changes and drivers on the Qinghai–Tibetan Plateau

Plant phenology changes and drivers on the Qinghai–Tibetan Plateau

Dynamic Threshold of Carbon Phenology in Two Cold Temperate Grasslands in China

Responses of Grain Yield and Yield Related Parameters to Post-Heading Low-Temperature Stress in Japonica Rice

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