By Xiaoqiu Chen
This e-book offers with phenology, the examine of ordinary organic lifestyles cycle levels, and particularly their timing and relationships with biotic and abiotic forces. Given the theoretical and methodological suggestions concerned, the chapters on defining spatiotemporal styles of plant phenology and developing day-by-day temperature-based temporal/spatial versions and process-based local unified versions could be of specific curiosity. aiding readers realize and discover plant phenology’s views when it comes to spatiotemporal styles, methods and mechanisms, the booklet also will equip younger scientists and graduate scholars to appreciate the motives of spatiotemporal version in plants seasonality.
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Additional info for Spatiotemporal Processes of Plant Phenology: Simulation and Prediction
Spatial extrapolation tests of the mean spring and autumn spatial phenology models (Fig. 2) were carried out using multi-year mean daily temperatures during the optimum spring and autumn LPs and observed multi-year mean BGS and EGS dates at the 16 external stations. 8 days, respectively (Fig. 3). Comparing external validation accuracy (Fig. 3) with internal simulation accuracy (Fig. 3 Model Applications 39 Fig. 2 Spatial regression analyses, a between multi-year mean daily temperature during the optimum spring LP and multi-year mean BGS date (day of year, DOY) of Ulmus pumila and b between multi-year mean daily temperature during the optimum autumn LP and multi-year mean EGS date (DOY) of Ulmus pumila over the 46 stations [Reprinted from Chen and Xu (2012a), with permission from Elsevier] Fig.
The dependence of yearly spatial phenology model regression slope on yearly regional mean temperature can be decomposed into two parts, namely, the dependences of yearly BGS spatial variation (dependent variable of yearly spatial phenology model) and yearly optimum spring LP temperature spatial variation (independent variable of yearly spatial phenology model) on yearly regional mean temperature. 01). That is, the higher the regional February-April mean temperature in a year, the larger the spatial standard deviation of BGS in the year (Fig.
2007; Shinoda et al. 2007; Jentsch et al. 2009; Crimmins et al. 2010, 2011; Lesica and Kittelson 2010). Examining the combined effects of air temperature and precipitation during late winter and early spring on green-up date of the dominant grass species by means of process-based models is crucial not only for revealing ecological mechanisms of grassland phenology, but also for predicting appropriate grazing and harvesting times, as well as estimating net primary productivity and carbon sequestration in grassland ecosystems (Chen et al.