Enhanced spatiotemporal heterogeneity and the climatic and biotic controls of autumn phenology in northern grasslands
Graphical abstract
Introduction
Grassland ecosystems cover approximately 40.5% of the terrestrial area and are the main source of feed for the world's animal husbandry (Murray et al., 2000). These ecosystems are however at a high risk of getting effected by climate changes owing to their sensitivity towards thermal and moisture conditions. Plant phenology, studying the timing of annually recurring plant growth phenomena (such as leaf-out in spring and leaf coloring in autumn) and its intrinsic and extrinsic drivers, is often considered as a key parameter to monitor the response mechanisms of biological processes against global climate change (Penuelas et al., 2009; Piao et al., 2008; Richardson et al., 2013). In the field of plant phenology research, spring phenology has gained immense attention in the past years. This is mainly attributed to its simple observation methods, relatively clear mechanisms, and abundance of field records. Many studies over the past decades have therefore revealed trends in spring phenology of grassland vegetation, and the climatic conditions effecting it (Bloor et al., 2010; Lesica and Kittelson, 2010; Wu and Liu, 2013; Xia and Wan, 2013). In comparison, autumn phenology has been often neglected because of its complexity and diversity, fewer observation records, as well as the lack of recognizing its importance (Fu et al., 2018b; Gallinat et al., 2015). However, the importance of autumn phenology to determining growing season length and net primary productivity has been suggested to be in fact greater than that of spring phenology (Bao et al., 2019; Garonna et al., 2015). A stronger regulation effect on the community structure and function was also found for autumn phenology rather than spring phenology (Gallinat et al., 2015). Thus, understanding autumn phenology processes is crucial to developing an in-depth comprehension of plant physiological and ecological processes.
Preseason temperature plays an important role in regulating autumn phenological activities of woody plants (Gill et al., 2015; Jeong et al., 2011; Piao et al., 2006; Yang et al., 2015). On the other hand, autumn phenology of grassland vegetation could be also significantly influenced by preseason precipitation, as shallow-rooted herbaceous plants are very susceptive to autumn rainfall (Zelikova et al., 2015). However, the dominant controlling factors of autumn phenology in herbaceous plants remain controversial. It was reported by Liu et al. (2016) that preseason precipitation is the dominant factor effecting the growing season end date for grasslands across the Northern Hemisphere, while Yang et al. (2015) demonstrated a stronger influence of preseason temperature on the growing season end date for temperate and alpine grasslands in China. In addition, the roles of previous temperature and precipitation in controlling autumn phenology of grassland vegetation may also differ with species and grassland types at various spatial scales (Ren et al., 2016; Ren et al., 2017). At the site scale, Ren et al. (2016) reported that autumn phenology of grassland vegetation was primarily determined by preseason precipitation in the Inner Mongolian Grassland, while their further work at the regional scale revealed an overall dominant control of air temperature on the autumn phenology of grassland vegetation (Ren et al., 2017). This study can be supported by similar results from previous studies on frozen ground regions of Mongolia (Sun et al., 2014) and temperate grassland in China (Yang et al., 2015). These earlier studies indicate the complexity of the responses of grassland autumn phenology to climate change and highlight the limited knowledge we have on the process of grassland autumn phenology (Li et al., 2018).
Apart from climatic variables, interactions among various phenology events have also attracted great attention recently in phenology research. For instance, positive effects of spring phenology events on autumnal senescence have been frequently reported for deciduous woody species (Fu et al., 2014; Keenan and Richardson, 2015). However, a similar causal relationship in case of grassland vegetation is still controversial and unclear (Fu et al., 2018a; Liu et al., 2016). Another important factor to consider is the significant increase in the net primary production of the vegetation in the past several decades due to global warming (Liu et al., 2019). A most recent study by means of in-situ observation, experiments and modelling has found that the increased growing season production can lead to earlier leaf senescence of woody species. This is attributed to the elevated carbon capture which can prohibit the capacity of photosynthesis and act as a self-regulatory mechanism to constrain the productive season length in sink-limited plants (Zani et al., 2020). However, it is still unknown on whether plant net primary production regulates the variation in autumn phenology for grassland vegetation at regional and global scales.
In this study, the start of growing season (SOS) and end of growing season (EOS) of the mid-latitude (30°N ~ 55°N) grasslands of the Northern Hemisphere were retrieved from the remote sensing data (1981–2014). In order to examine their accuracy, phenological metrics extracted from ground digital images time series over North America were used to compare with them in landscape scale. By means of the gridded climate data and remote sensing-based net primary production (NPP) data, the objectives of the current study are to: (1) investigate the long-term spatiotemporal shifts in SOS and EOS; (2) explore the climatic controls on EOS; (3) analyze the biotic effects of SOS and NPP on EOS.
Section snippets
Study region
Our study focused on the mid-latitude grasslands of the Northern Hemisphere between 30°N and 55°N (Fig. 1), which was defined according to the International Geosphere-Biosphere Program global land cover classification system (Loveland and Belward, 1997). Spatially, most grasslands are located in North America and Asia. For analyzing the spatial heterogeneity of phenological variations, the Asian grasslands were separated into East Asian grasslands (mainly located in Mongolia and Inner Mongolia
Comparison between remotely-sensed SOS/EOS and PhenoCam-based phenology metrics
Remotely-sensed SOS is significantly correlated with PhenoCam-based SOS_T10 (r = 0.72, P < 0.01), with an average bias of 18.8 days (Fig. 2a). Remotely-sensed EOS is also correlated well with the Phenocam-based EOS_T10 (r = 0.5, P < 0.01), but with a higher average bias of 23.6 days (Fig. 2b). SOS/EOS based on remote sensing data is generally earlier/later than that based on PhenoCam data. Although a relatively better correlation was found between remotely-sensed SOS/EOS and Phenocam-based
Discussion
PhenoCam images installed over the world are thought to provide a good tool to bridge ground observations and remote sensing metrics (Browning et al., 2017). Our study revealed good correlations between remotely-sensed EOS and PhenoCam-based EOS. This indicated that the remotely-sensed EOS could reflect the spatiotemporal variation of ground vegetation autumn phenology. Satellite-based SOS and Phenocam-based SOS were also correlated well but largely determined by several isolated site-camera
Conclusion
In this study, the responses of autumn phenology to preseason temperature, rainfall, spring phenology, and net primary productivity were comprehensively analyzed for the mid-latitude (30°N ~ 55°N) grasslands of the Northern Hemisphere by means of remote sensing data. The results demonstrated an extended duration to complete spring and autumn phenology for the entire study region, which indicates an increased spatial heterogeneity of grassland phenology in northern grasslands. Preseason rainfall
CRediT authorship contribution statement
SLR designed the research and wrote the paper. MP contributed some valuable ideas and helped to improve the manuscript.
Declaration of competing interest
The authors declare no conflict of interest.
Acknowledgments
This work was supported by Shandong Provincial Natural Science Foundation (No. ZR2020QD001) and Shandong University Future Plan for Young Scholars.
Data availability statement
All data used in this study were acquired from open data source. The remote sensing data were obtained from https://vip.arizona.edu/viplab_data_explorer.php and https://search.earthdata.nasa.gov. The gridded meteorological data were downloaded from http://www.eu-watch.org.
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