Chapter 2
East Asia Xiaoqiu Chen
Abstract Phenological observations and research have a long history in East Asia. Countrywide phenological networks have been established mostly by national meteorological administrations or agencies during 1950s to 1980s. Since 2000, phenological research has made significant progress in China, Japan, and South Korea. The recent network-related research focuses mainly on three aspects: first, the temporal and spatial variation of plant phenology and its responses to climate change at individual and community levels by means of statistical methods; second, the effect of genetic diversity on phenological responses to climate change; and third, identification and extrapolation of the vegetation growing season on the basis of plant community phenology and satellite data.
2.1 2.1.1
Phenological Observation and Research in China Historical Background
Modern phenological observation and research in China started in the 1920s with Dr. Coching Chu (1890–1974). As early as 1921 he observed spring phenophases of several trees and birds in Nanjing. In 1931, he summarized phenological knowledge from the past 3,000 years in China. He also introduced phenological principles (e.g. species selection, criteria of phenological observations and phenological laws) developed in Europe and the United States from the middle of the eighteenth to the early twentieth century (Chu 1931). According to his literature survey, phenological observation can be traced back to the eleventh century B.C. in China. The earliest phenological calendar, Xia Xiao Zheng, stems from this period and recorded (on a monthly basis) phenological events, weather, X. Chen (*) College of Urban and Environmental Sciences, Peking University, Beijing, China e-mail:
[email protected] M.D. Schwartz (ed.), Phenology: An Integrative Environmental Science, DOI 10.1007/978-94-007-6925-0_2, © Springer Science+Business Media B.V. 2013
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astronomical phenomena, and farming activities in the region between the Huai River drainage area and the lower reaches of Yangtze River. In addition, extensive phenological data were recorded in other ancient literatures over the past 3,000 years. These data could to some extent reflect past climate. Using ancient phenological data and other data, he reconstructed a temperature series of the past 5,000 years in China (Chu 1973).
2.1.2
Networks and Data
In 1934, Dr. Coching Chu established the first phenological network in China. Observations covered some 21 species of plants, nine species of animals, some crops, and several hydro-meteorological events, and ceased in 1937 because of the War of Resistance Against Japan (1937–1945). Twenty-five years later the Chinese Academy of Sciences (CAS) established a countrywide phenological network under the guidance of Dr. Chu. The observations began in 1963 and continued until 1996. Observations resumed in 2003, but with a reduced number of stations, species, and phenophases. The observation program of the CAS network included a total of 173 observed species. Of these, 127 species of woody and herbaceous plants had a localized distribution. Table 2.1 lists the 33 species of woody plants, two species of herbaceous plants, and 11 species of animals that were observed across the network (Institute of Geography at the Chinese Academy of Sciences 1965). During 1973–1986, several stations added phenological observation of major crops, such as rice, winter wheat, spring wheat, corn, grain sorghum, millet, cotton, soybean, potato, buckwheat, rape, etc. The observations were carried out mainly by botanical gardens, research institutes, universities and middle schools according to unified observation criteria (Institute of Geography at the Chinese Academy of Sciences 1965; Wan and Liu 1979). The phenophases of woody plants included bud swelling, budburst, first leaf unfolding, 50 % leaf unfolding, flower bud or inflorescence appearance, first flowering, 50 % flowering, the end of flowering, fruit or seed maturing, first fruit or seed shedding, the end of fruit or seed shedding, first leaf coloration, full leaf coloration, first defoliation, and the end of defoliation. Changes to the stations and in observers over the years resulted in data that were spatially and temporally inhomogeneous. The number of active stations varied over time. The largest number of stations operating was 69 in 1964 and the lowest number occurred between 1969 and 1972 with only 4–6 stations active. The phenological data from 1963 to 1988 were published in form of Yearbooks of Chinese Animal and Plant Phenological Observation (Volume 1–11). Since then, the data have not been published. In 1980 the China Meteorological Administration (CMA) established another countrywide phenological network. The CMA phenological network is affiliated with the national-level agrometeorological monitoring network and came into operation in 1981. The phenological observation criteria for woody and herbaceous plants, and animals were adopted from the CAS network. There are 28 common species of woody plants, one common species of herbaceous plant and 11 common species of animals. The main phenophases are the same as those of the CAS network. In addition to the natural phenological observations, the network also carries out professional
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Latin names Woody plants Ginkgo biloba Metasequoia glyptostroboides Platycladus orientalis Sabina chinensis Populus simonii Populus canadensis Salix babylonica Juglans regia Castanea mollissima Quercus variabilis Ulmus pumila Morus alba Broussonetia papyrifera Paeonia suffruticosa Magnolia denudata Firmiana simplex Malus pumila Prunus armeniaca Prunus persica Prunus davidiana Albizia julibrissin Cercis chinensis Sophora japonica Robinia pseudoacacia Wisteria sinensis Melia azedarach Koelreuteria paniculata Zizyphus jujuba Hibiscus syriacus Lagerstroemia indica Osmanthus fragrans Syringa oblata Fraxinus chinensis Herbaceous plants Paeonia lactiflora Dendranthema indicum Animals Apis mellifera Apus apus pekinensis Hirundo rustica gutturalis Hirundo daurica japonica Cuculus canorus canorus Cuculus micropterus micropterus Cryptotympana atrata Gryllulus chinensis Anser fabalis serrirostris Oriolus chinensis diffusus Rana nigromaculata
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80°E
90°E
100°E
110°E
120°E
130°E
140°E
50°N 50°N
40°N 40°N
30°N 30°N
Phenological Station
20°N
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Cold Temperate Zone Middle Temperate Zone Warm Temperate Zone Northern Subtropical Zone Middle Subtropical Zone Southern Subtropical Zone Northern Tropical Zone Middle Tropical Zone Southern Tropical Zone Plateau Climate Region
10°N
0
500
10°N
1,000 km
0°
0° 90°E
100°E
110°E
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Fig. 2.1 Locations of phenological stations in the CMA network
phenological observation of crops on the basis of a specific observation criterion (National Meteorological Administration 1993). The main crop varieties include rice, wheat, corn, grain sorghum, millet, sweet potato, potato, cotton, soybean, rape, peanut, sesame, sunflower, sugarcane, sugar beet, and tobacco. In grassland areas, phenophases of dominant grass species are also observed. The CMA network is the largest phenological observation system in China. There were 587 agrometeorological measurement stations in 1990. At present, 446 stations are undertaking phenological observations. These phenological stations are distributed in different climate zones and regions throughout the country, especially in eastern China (Fig. 2.1). The CMA-archive keeps the original
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phenological observation records from 1981 to the present and provides the data freely to research institutes and universities. As the phenological and meteorological observations are mostly parallel at the same location in this network, the data are especially valuable for understanding phenology-climate relationships. Moreover, these data can also be used to provide an agrometeorological service and prediction on crop yield, soil moisture and irrigation amounts, plant diseases and insect pests, and forest fire danger (Cheng et al. 1993). Recently, Xiaoqiu Chen and his students have digitized the phenological data set with permission from the China Meteorological Administration. In addition, there were also some regional phenological networks. One example was the network established by Guodong Yang and Xiaoqiu Chen during the period 1979–1990. The network consisted of approximately 30 stations in the Beijing area (about 1,6410.54 km2). Based on the observed data of this network, they worked out and published 16 phenological calendars, about one phenological calendar per 1,026 km2 (Yang and Chen 1995).
2.1.3
Recent Network-Related Phenological Research
2.1.3.1
Measuring Plant Community Seasonality in Eastern China
Determining phenological seasons of plant communities is important for identifying the vegetation growing season combining surface phenology and satellite data at regional scales (Chen et al. 2000, 2001, 2005). Other than the empirical method for identifying phenological seasons (Chen 2003), a simulating method of phenological cumulative frequency has been developed to measure plant community seasonality using phenological data of the CAS network. The basic idea of the method was to establish a mixed data set composed of the occurrence dates of all phenophases of observed deciduous trees and shrubs (Chen 2003) at each station and for each year. The phenological data were acquired from seven stations in the temperate zone (Chen and Han 2008) and four stations in the northern subtropical zone (Chen et al. 2011) of eastern China. The study period was from 1982 to 1996. Based on the data set, the cumulative frequency of the occurrence dates of phenophases in every 5-day period (pentad) throughout each year and at each station was calculated. In order to simulate the phenological cumulative frequency using a logistic function, the empirical phenological cumulative frequency curve was divided into two parts, namely, the spring cumulative frequency curve and the autumn cumulative frequency curve (Chen 2003). Further, corresponding dates with the maximum changing rate of the curvature were computed on the spring and autumn simulating curves as onset dates of phenological stages (Fig. 2.2). Because four turning points with the maximum changing rate of the curvature are detectable on the spring and autumn simulating curves, four phenological stages were identified in each year and at each station, namely, green-up, active photosynthesis, senescence, and dormancy stages.
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Fig. 2.2 Simulating cumulative frequencies of spring or autumn phenophases (a) and determining turning points of phenological stages (b) A: onset date of green-up or senescence stage, B: onset date of active photosynthesis or dormancy stage Table 2.2 Comparison in phenological stage onset dates (month, day) and growing season durations (days) between the temperate zone and the northern subtropical zone
Climate zone Temperate zone Northern subtropical zone
Green-up onset date 3.19 3.1
Active photosynthesis onset date 5.23 5.8
Senescence onset date 9.9 9.7
Dormancy onset date 11.9 11.24
Growing season duration 235 268
The comparison in multiyear mean onset dates of phenological stages between temperate stations and northern subtropical stations indicates that the onset dates of green-up and active photosynthesis stages are obviously earlier in the northern subtropical zone than in the temperate zone. In contrast, the onset dates of dormancy stage are obviously later in the northern subtropical zone than in the temperate zone. The growing season duration (from the onset date of the green-up stage to the end date of the senescence stage) in the northern subtropical zone is 33 days longer than that in the temperate zone (Table 2.2). From each northern subtropical station (located between 29 500 N and 33 210 N) to the northernmost temperate station (located at 45 450 N), the multiyear mean onset dates of green-up and active photosynthesis stages show a significant delay at a rate of 2.7–4.0 days and 1.8–2.8 days per latitudinal degree northwards, respectively, whereas the multiyear mean onset dates of senescence and dormancy stages indicate a non-significant advancement and a significant advancement at a rate of 2.9–3.3 days per latitudinal degree northwards, respectively.
2.1.3.2
Measuring Plant Community Growing Season in Eastern China
Since phenological stations and conventional phenological data of the CAS network are comparatively scarce in eastern China, the only option for detecting growing
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season trends at regional scales is to estimate the growing season of land vegetation using the limited station phenology data and satellite data (Chen and Pan 2002). Because metrics and thresholds of vegetation indices may not directly correspond to conventional, ground-based phenological events, but rather provide indicators of vegetation dynamics, a detailed comparison of these satellite measures with ground-based phenological events is needed. In recent years, some studies have been carried out to compare satellite sensor-derived onset and offset of greenness with surface phenological stages of individual plant species, mono-specific forests, and mixed forests for selected biomes (White et al. 1997; Duchemin et al. 1999; Schwartz et al. 2002; Badeck et al. 2004). Other than the above top-down method, namely determining the satellite-sensor-derived growing season at a regional scale first, and then validating it using conventional phenological data at local scales, Chen et al. (2000, 2001) developed a bottom-up method, namely determining the phenological growing season at sample stations first and then finding out the corresponding threshold values of normalized difference vegetation index (NDVI) at pixels overlaying the sample stations in order to extrapolate the phenological growing season at a regional scale. Using phenological and NDVI data from 1982 to 1993 at seven sample stations in temperate eastern China, Chen et al. (2005) calculated the cumulative frequency of leaf unfolding and leaf coloration dates for deciduous species every 5 days throughout the study period. Then, they determined the growing season beginning and end dates by computing times when 50 % of the species had undergone leaf unfolding and leaf coloration for each station in every year. Next, they used these beginning and end dates of the growing season as time markers to determine corresponding threshold NDVI values on NDVI curves for the pixels overlaying phenological stations. Based on a cluster analysis of the annual NDVI curves, they determined extrapolation areas for each phenological station in every year, and then, implemented the spatial extrapolation of growing season parameters from the seven sample stations to 87 meteorological stations in the study area. Results show that spatial patterns of growing season beginning and end dates correlate significantly with spatial patterns of mean air temperatures in spring and autumn, respectively. Contrasting with results from similar studies in Europe and North America, this study suggests that there is a significant delay in leaf coloration dates, along with a less pronounced advance of leaf unfolding dates in different latitudinal zones and the whole area from 1982 to 1993. The growing season has been extended by 1.4–3.6 days per year in the northern zones and by 1.4 days per year across the entire study area on average (Table 2.3). The apparent delay in growing season end dates is associated with regional cooling from late spring to summer, while the insignificant advancement in beginning dates corresponds to inconsistent temperature trend changes from late winter to spring. On an interannual basis, growing season beginning and end dates correlate negatively with mean air temperatures from February to April and from May to June, respectively (Chen et al. 2005).
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Table 2.3 Linear trends (days per year) of growing season beginning (BGS) and end dates (EGS) and lengths (LGS) in different latitudinal zones and the whole area during 1982 to 1993
2.1.3.3
Region Zone 1 (32 N–34.99 N) Zone 2 (35 N–37.99 N) Zone 3 (38 N–40.99 N) Zone 4 (41 N–43.99 N) Zone 5 (44 N) Whole area *P < 0.1, **P < 0.05, ***P
BGS EGS LGS 0.7 0.0 0.7 0.7 0.9** 1.6** 0.7 1.8*** 2.5** 1.7* 1.9**** 3.6*** 0.7 0.7 1.4** 0.4 1.0*** 1.4*** < 0.01, ****P < 0.001
Assessing the Phenology of Individual Plant Species
So far, almost all conventional phenological studies in China have been based on discontinuous time series from a few stations in a data set of the CAS network. Since the only continuous phenological records exist in Beijing, several studies focused on plant phenological responses to urban climate change. The trends detected are more or less similar to those observed for a variety of tree species in Europe and North America. Lu et al. (2006) analyzed spring flowering dates of four species during 1950–2004 in Beijing, and found that flowering date of early-blossom species advanced much quicker than other late-blossom species, which tend to stretch the flowering interval among species. With regard to phenological response to temperature, the flowering sensitivity of four tree species to daily maximum, minimum and average temperature is ‘species-specific’. On the basis of spring and autumn phenological data of three species during 1962–2004 in Beijing, Luo et al. (2007) assessed differences in phenological responses of plant to urban climate change for the period 1962–1977 and 1978–2004, and found that the urban heat island effect from 1978 onwards is the dominant cause of the observed phenological changes. With respect to phenological variations across a broad area, Zheng et al. (2006) used the discontinuous time series of 32 spring phenophases during the period 1963–1996 at 16 stations of eastern China to analyze statistical relationships between plant phenology and temperature. Only seven phenophases at five stations show a significant advancing trend (P < 0.1), and six phenophases at five stations indicate a significant delay trend (P < 0.1). In general, the advancing trends in both early and late spring phenophases occurred mainly in the northern regions. This is in line with the observed warming temperature trend. Opposite changes, that is a delay, were detected in the southwestern regions consistent with the cooling temperature trend. In addition, the individual phenophases were significantly correlated either with the mean temperature of that month in which the mean phenophase occurred or preceding months, and sometimes with both. Similar results were also obtained by another study using first flowering data of 23 plant species at 22 stations of eastern China during 1963–2006 (Ge et al. 2011).
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2.2
2.2.1
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Phenological Observation and Research in Japan and South Korea Networks and Data
In 1953, the Japan Meteorological Agency (JMA) established a national phenological observation network consisting of 102 stations. The aims were to monitor local climate via phenological phenomena of some specific plants and animals. The Observation Division at the Headquarters of the Japan Meteorological Agency in Tokyo is responsible for phenological observations in Japan. The observation program consists of 12 species of plants and 11 species of animals, and related phenophases (Table 2.4). The observation criteria are defined in “Guidelines for the Observation of Phenology” (Japan Meteorological Agency 1985). The phenological data were published monthly in the “Geophysical Review” under categories of “Agrometeorological Summary” or “Applied Meteorology”, and are available from
Table 2.4 Phenological observation program in Japan Plants Prunus yedoensis Prunus mume Camellia japonica Taraxacum (T. platycarpum, T. albidum, and T. japonicum) Rhododendron kaempferi Wisteria floribunda Lespedeza bicolor var. japonica Hydrangea macrophylla var. otaksa Lagerstroemia indica Miscanthus sinensis Ginkgo biloba Acer palmatum
Budding
First flowering X X X X
Full flowering X
Color change
Leaf fall
X X
X X
X X X X X X X
Animals Alauda arvensis Cettia diphone Lanius bucephalus Graptopsaltria nigrofuscata Tanna japonensis Hirundo rustica Pieris rapae crucivora Papilio machaon hippocrates Orthetrum albistylum speciosum Lampyridae (Luciola cruciata and L. lateralis) Rana nigromaculata
First heard X X X X X
First seen
Last seen
X X X X X X
X
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Japan Meteorological Agency (http://www.jmbsc.or.jp/english/index-e.html). The phenological network in Japan was encountering difficulties in continuing reliable observations because of the effects of urbanization, and the function of phenological observation in monitoring local climate has been weakened with the modernization of surface weather observation network (from personal correspondence with Dr. Mitsuhiko Hatori, Director of Observations Division, Observations Department, JMA). There is less information on the phenological observation network in South Korea. As reported by Primack et al. (2009a), the Weather Service of South Korea has been gathering data on 20 phenological events at 74 weather stations with some observations dating from 1921. The data are available from Korea Meteorological Administration: (http://web.kma.go.kr/edu/unv/agricultural/seasonob/1173374_1389.html).
2.2.2
Recent Network-Related Phenological Research
In 2001, Ministry of the Environment of Japan published a report addressing the effects of global warming on Japan, in which influences of climate change on plant and animal phenology were summarized (Ministry of the Environment 2001). Since then, phenological research in Japan has made great progress. The recent research undertaken on phenology in Japan and South Korea includes a wide range of issues, such as phenological data-based temperature reconstruction of past centuries (Aono and Kazui 2008; Aono and Saito 2010), plant phenology and its relation to the local environment based on specified field observations (Yoshie 2010; Ohashi et al. 2011), pollen fertility and flowering phenology (Ishida and Hiura 1998), application of digital camera for phenological observation (Ide and Oguma 2010) and phenological responses to climate change (Matsumoto et al. 2003; Ho et al. 2006; Doi and Katano 2008; Doi and Takahashi 2008; Chung et al. 2009; Primack et al. 2009b; Doi et al. 2010; Fujisawa and Kobayashi 2010; Matsumoto 2010; Doi 2011) etc. It is worth noting that the network-related phenological research focuses mainly on phenological responses to climate change. Matsumoto et al. (2003) systematically studied the extension of the growing season between 1953 and 2000 in Ginkgo biloba in relation to climate change using a data set of the annual budding and leaf fall dates from 67 stations in Japan. In contrast to the traditional method for detecting phenological responses to temperature by computing monthly mean temperature (Chen 1994; Sparks et al. 2000; Chmielewski and Ro¨tzer 2001; Menzel 2003; Gordo and Sanz 2010), they used a daily mean temperature-based method proposed by Shinohara (1951). In this method, the length of the period (LP) during which a particular daily mean temperature might influence a phenological event is defined as: LP ¼ EP BP
(2.1)
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where LP is the period length (number of days), EP is the end date of the period (day of year, DOY), and BP is the beginning date of the period (DOY). The EP is defined as average budding or leaf fall dates between 1953 and 2000. Using this equation, they calculated the correlation coefficient between the date of budding or leaf fall and the average daily temperature during the period EP – BP. The highest correlation appears within the optimum LP during which air temperature affected phenological events most markedly. Using the average optimum LP at all station, they analyzed the phenology-temperature relationship. The results show that the advancement rate (0.9 days per decade) in the beginning date of the Ginkgo biloba growing season was smaller than the delay rate (1.6 days per decade) of the end date. With regard to the phenological response to temperature, variation in the growing season beginning date (BGS) is closely related to air temperatures in the 45-day period before the average budding date, whereas variation in the growing season end date (EGS) is affected mostly by air temperatures in the 85-day period before the average leaf fall date. On average, an increase in the average air temperature of 1 C in spring may advance BGS by about 3 days. If the average autumn air temperature increases by 1 C, EGS may be delayed by about 4 days. Furthermore, LGS may be extended by about 10 days when the mean annual air temperature increases by 1 C. Further, Matsumoto (2010) examined spatial patterns in long-term phenological trends (1961–2000) and their causal factors using a data set of the annual budding and leaf fall dates of Ginkgo biloba from 60 stations in Japan. The results show that there was no significant relationship between phenological trends and geographical variables: latitude, longitude, and altitude, with the exception of a negative relationship between the trend of leaf fall date and latitude. Namely, the linear trends of leaf fall date at lower latitudes were larger than those at higher latitudes. With respect to relationship between the air temperature trend and the phenological trend, a negative relationship was found with the budding trend, but there was no obvious relationship with the leaf fall trend. By contrast, the spatial variability of the phenological sensitivity to temperature displayed a significant linear relationship with trends in budding and leaf fall. That is, where trees had higher sensitivity to temperature, they showed earlier budding and delayed leaf fall. Therefore, the spatial variations in phenological trends were dependent more on phenological sensitivity to air temperature than temperature trends. Other than the above work, Doi and Takahashi (2008) examined the latitudinal pattern of phenological response to temperature in Japan using a data set of two species during 1953–2005. Negative relationships were found between the phenological response of leaves to temperature and latitude based on leaf coloring and leaf fall data of Ginkgo biloba and Acer palmatum at 63 and 64 stations, respectively. Single regression slopes of the phenological responses at lower latitudes were larger than those at higher latitudes. Similar results were also obtained by another study using leaf budburst and leaf fall data of Morus bombycis during 1953–2005 at 25 stations in Japan (Doi 2011). During the last several decades, many studies have estimated the phenological response of plants to temperature. However, the effect of genetic diversity on
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phenological responses to climate change has less been considered. Doi et al. (2010) tested whether variations in phenological responses to temperature depend on genetic diversity based on flowering dates of ten species and leaf budburst dates of one species across Japan from 1953 to 2005. The results show that the withinspecies variations of phenological response to temperature as well as regional variations were less in the plant populations with lower genetic diversity. Thus, genetic diversity influences the variation in phenological responses of plant populations. Under increased temperatures, low variation in phenological responses may allow drastic changes in the phenology of plant populations with synchronized phenological timings. Maintaining genetic diversity may alleviate the drastic changes in phenology due to future climate change.
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