Spatial heterogeneity in leaf coloring date and the phenological response to thermal environment variations of Beijing landscape trees
-
摘要:目的
城市化进程影响下，城市内部热环境的空间分布不均导致植物物候的空间异质性突出。秋色盛期作为落叶树种生长季的终点，其空间异质性对于城市植被的年周期固碳量及整个城市生态系统的初级生产力具有重要影响，同时会引起秋季季相景观的空间变化，是监测城市生态及景观动态的一个关键角度，值得深入探究。本研究以此为切入点，旨在揭示北京城市环境中秋色盛期的空间异质性特征及其对下垫面热环境的响应。
方法本研究以北京主城区西北城郊梯度上9处公园绿地中的5种秋色叶树种为研究对象，基于地面物候观测对2017—2019年的秋色盛期数据进行采集，基于MODIS地温反演对样地热环境数据进行采集，对秋色盛期空间差异及其与秋季热环境的相关性进行分析。
结果（1）北京主城区各树种的秋色盛期整体发生于10月中旬至12月上旬、集中于11月上中旬，由早到晚依次为洋白蜡、元宝枫、银杏、水杉、旱柳，且银杏雌株的秋色盛期显著早于雄株。（2）各树种秋色盛期整体上由二环—三环—五环—五环外逐渐提前，城郊物候天数差异达（10.1 ± 0.3） d；样地间物候期整体差异显著，尤其四环外样地的秋色盛期显著早于三环内。（3）各树种秋色盛期与样地秋季平均地温（LSTa）呈显著正相关（P < 0.01），表明北京主城区内秋季地表热量的大量积累会导致秋色盛期延后；各树种秋色盛期对LSTa空间差异的响应敏感度平均为（4.11 ± 0.83） d/℃，以洋白蜡和水杉响应最为敏感。
结论北京主城区的秋色盛期表现出对城市秋季热环境空间差异的显著响应，城市热岛效应是未来气候变化的一个缩影，城市环境中物候期对热环境空间差异的响应可反映未来气候变化对植物物候的潜在影响，即具有“空间代替时间”的研究价值。
Abstract:ObjectiveUnder the impact of urbanization, the spatial unevenness of thermal environment within urbanized area leads to more prominent spatial heterogeneity of urban plant phenology. The spatial unevenness of leaf coloring date (LCD), the end of deciduous trees’ growing season has a far-reaching influence on the annual carbon sequestration of urban vegetation and the primary productivity of urban ecosystem, also causes spatial variation in the autumn seasonal aspect. Therefore, the spatial heterogeneity of leaf senescence is one critical cut-in point for monitoring urban ecology and landscape dynamics, and deserves profound exploration. Regarding this point, this research aimed to explore the spatial heterogeneity characteristics of leaf coloring date (LCD) and the phenological response to thermal environment of underlying surface in the highly urbanized Beijing City.
MethodThis research selected five autumn-color tree species as the research objects that distributed in 9 green spaces along the urban-suburb gradient in Beijing’s northwestern urban area. We applied ground phenological observation to collect the LCD data during 2017−2019, and collected thermal environment data by retrieving MODIS land surface temperature (LST). Then, we analyzed the spatial difference of LCD and its correlation with the autumn thermal environment.
Result(1) the LCD of various tree species in Beijing’s urban area occurred during mid-October to early December and clustered in early and mid-November, with the species order of LCD ranked as follows: Fraxinus pennsylvanica, Acer truncatum, Ginkgo biloba, Metasequoia glyptostroboides, Salix matsudana. The LCD for female ginkgo trees occurred significantly earlier than male ones. (2) The LCD of various species gradually advanced from the central urban area in 2nd Ring to the outskirts beyond 5th Ring, with the spatial range of LCD reaching (10.1 ± 0.3) d in average. A significant phenological difference existed among different sample plots and the LCD outside of 4th Ring occurred significantly earlier than that within 3rd Ring. (3) The LCD of various species showed a significantly positive correlation with the mean LST in autumn-LSTa (P < 0.01), which means the high accumulation of surface heat in autumn can drive leaf senescence to delay in Beijing’s urban area. The response sensitivity of LCD to the spatial variation of LSTa was (4.11 ± 0.83) d/℃ with F. pennsylvanica and M. glyptostroboides as the most sensitive species.
ConclusionThe leaf coloring date shows a significant response to the spatial variation of surface thermal environment in Beijing’s urban area. UHI effect represents the microcosms of long-term climate change, and the phenological response to thermal environment variation in urban area can reflect the potential impact of future climate change on plant phenology, i.e., a space-time substitution.
-
随着全球工业化和城市化的迅速发展，颗粒物污染已经严重影响到了生态环境。颗粒物污染尤以京津冀为代表的城市群最为严重。在目前尚不能完全依赖污染源治理以解决环境问题情况下，借助自然界的清除机制是缓解城市大气污染压力的有效途径[1]。
有研究表明, 植物能够作为生物漏斗在植被表面沉积一些颗粒物质，减少空气中悬浮颗粒物的浓度[2-3]，吸附在叶片上的颗粒物一部分被固定于植物蜡质层中，最终随着叶片凋落回到土壤；另一部分则存在于叶片表面，一段时间后叶表面会达到最大饱和滞尘状态，此后叶表面吸附颗粒物量不会再增加，叶表面这部分颗粒物可以通过再悬浮回到大气中或者在降雨、降雪的淋洗作用下进入土壤[4]。有研究通过构建降雨过程，阐释说明了降雨影响下叶表面滞尘过程[5]。Räsänen等[6]认为降水能够有效清洗掉悬铃木(Platanus hispanica)叶面上附着的颗粒物。Tiwary[7]也认为降雨可以使滞留在叶表面的颗粒物迅速洗脱，而通过植物粗糙表皮、角质层、纤毛等附着的颗粒物则需要较大雨量才能洗脱。同样，王蕾等[8]对北京市部分针叶树种叶面滞尘量进行观测，发现附着牢固的颗粒物不易被中等强度(14.5 mm)降水洗脱。王会霞等[9]对典型天气下植物叶面滞尘动态变化的研究表明, 12 mm以下的降水并不能有效去除叶面上滞留的颗粒物。Pullman等[10]的研究表明雨量足够大的时候，叶片颗粒物的洗脱率均在80%以上；然而Beckett[11]也认为，降雨并不能冲洗掉叶面上滞留的颗粒物。王赞红等[1]通过扫描电镜观察，叶面颗粒物被清洗的程度与模拟降水的强度和降水量有关。目前多数研究都提出降雨能够有效淋洗植物叶表面颗粒物[12-14]，洗脱量可能受降雨强度、降雨历时的影响[9]。但由于天然降雨条件的可控性较差，降雨强度不均匀，难以量化植物叶面颗粒物的洗脱量是否与降雨强度和降雨历时有关，而模拟降雨广泛应用于土壤侵蚀研究的实验，是一个合适的工具降雨特性控制[15]，可以弥补天然降雨研究实验的空缺部分。
因此，量化降雨在不同降雨强度和降雨历时下对植物叶表面颗粒物的洗脱量，对阐释自然界中通过降雨过程缓解颗粒物污染有指导作用。本文通过模拟降雨实验，研究植物在不同降雨强度和降雨历时下叶表面颗粒物的洗脱变化及对不同粒径颗粒物的洗脱特征，丰富降雨对植物叶表面颗粒物洗脱影响的研究数据，为进一步探讨降雨清除颗粒物机制提供实验依据。
1. 材料与方法
1.1 实验设计
模拟降雨实验在北京林业大学降雨大厅进行，位于北京市鹫峰实验林场。模拟降雨装置为QYJY-503C。降雨过程采用旋转的下喷式和叠加式喷头模拟自然降雨，模拟降雨装置的雨水为自来水。每个降雨区的大小为8 m×8 m，降雨高度为12 m，实验使用一个独立降雨区(图 1)。在模拟降雨开始之前，在平地上对降雨机降雨均匀性进行标定，标定用集雨瓶6个，计算得到均匀系数均大于85%，符合降雨实验要求，降雨强度分别为15、30和45 mm/h(选取的降雨强度远大于天然降雨常见降雨强度，而由于设备的限制，较小强度的模拟降雨均匀度达不到实验要求)，降雨历时为5、10、15 min。
1.2 实验材料
本实验所选北京市常见植物共4种，分别为卫矛(Euonymus alatus)、迎春(Jasminum nudiflorum)、银杏(Ginkgo biloba)、杜仲(Eucommia ulmoides)，采样时间为2017年6月。有研究表明植物的叶面滞尘能力约在15~24 d以上达到饱和[1, 16]。本实验降雨前采集的为雨后滞留18 d的叶片样品，采集高度为银杏和杜仲在2~3 m，卫矛和迎春在灌顶部，在东、西、南、北4个方向采样。每种样品采集18组，分别用于3个不同降雨频率下的3个不同降雨历时，设置3个重复。
1.3 分析方法
植物叶表面颗粒物的洗脱量通过测算雨水对单位叶面积的洗脱量表达，采用洗脱称质量法[17]。首先将收集的雨水倒入烧杯中，利用真空泵过滤装置对雨水进行抽滤，先通过孔径为100 μm的不锈钢筛，以去除直径超过100 μm的大颗粒物质。在抽滤装置相应位置放置孔径为10 μm的滤膜，过滤雨水。过滤完成后，更换为孔径为2.5 μm的滤膜，进行第2次过滤，再将滤膜更换为孔径为0.45 μm的滤膜，进行第3次过滤，从而得到10~100 μm、2.5~10 μm和0.45~2.5 μm的颗粒物。各孔径滤膜在过滤前、后均先在烘箱中60℃下烘干30 min，恒温恒湿箱中平衡24 h后再用十万分之一的天平称质量。通过3次过滤，10、2.5、0.45 μm 3种滤膜过滤前后差值为3种粒径段颗粒物的质量。叶片经过扫描仪扫描后使用Image J计算图像，扫描3次取平均得到叶片表面积。本实验中单位洗脱面积的洗脱量计算方程如下：
W=(M2−M1)/S (1) 式中：W为单位叶面积洗脱量(μg/cm2)；M2、M1为过滤前后滤膜质量(μg)；S为叶面积(cm2)。
1.4 数据处理
不同降雨量、降雨强度与降雨历时和降雨对植物叶表面颗粒物洗脱量的相关性采用Pearson相关性分析，分析软件为SPSS 22.0，使用Sigmaplot12.5软件进行图表绘制。
2. 结果与分析
2.1 不同降雨量下的颗粒物洗脱量
在降雨过程中，降雨强度并不是恒定的，因此将在不同降雨历时下的3种强度合并，建立不同降雨量下的植物叶表面颗粒物洗脱关系。Pearson相关性分析显示1.25、2.50、3.75 mm这3种降雨量的植物叶片表面洗脱量无显著相关关系(P>0.05)，降雨量为3.75、5.00、7.50、11.25 mm这4种降雨量的植物叶片表面洗脱量呈显著正相关(r=0.98、0.96、0.99，P < 0.05)，即随着降雨量的增大，植物叶表面的洗脱量也显著增大。根据图 2可以看出卫矛、银杏、迎春3种植物在降雨量>5 mm时显著升高，而杜仲叶表面洗脱量增大的幅度相对较小。
![]()
图 2 不同降雨量下叶表面颗粒物的洗脱量Figure 2. Elution volume of leaf surface particulate matter (PM) under different precipitation2.2 不同降雨强度下颗粒物洗脱量
4种植物在不同降雨强度下，降雨对植物叶表面的洗脱量随降雨强度的变化如图 3所示。卫矛、迎春、银杏叶表面的洗脱量均与降雨强度呈显著正相关(P < 0.05)，而杜仲叶表面的洗脱量与降雨强度无显著相关关系(P>0.05)。在不同降雨强度下，降雨对叶片表面颗粒物的洗脱量在树种之间的显著相关(P < 0.05)，叶表面平均洗脱量迎春>银杏>卫矛>杜仲，平均洗脱量由大到小依次为(54.70±33.08)μg/cm2、(49.30 ± 26.80)μg/cm2、(32.99±17.62)μg/cm2、(25.90±6.4)μg/cm2。
![]()
图 3 不同降雨强度下叶表面颗粒物的洗脱量Figure 3. Elution volume of leaf surface PM under different rainfall intensities2.3 不同降雨历时下颗粒物洗脱量
4种植物在不同降雨历时下，降雨对植物叶表面的洗脱量随降雨历时的变化如图 4所示。4种植物叶表面的洗脱量随降雨历时的增大而增大，在5 min时，4种植物的洗脱量基本一致；在10和15 min时，卫矛、迎春、银杏的叶表面颗粒物洗脱量显著增大(P < 0.05)，而杜仲在降雨历时10和15 min时的洗脱量无显著相关性(P>0.05)。
![]()
图 4 不同降雨历时下叶表面颗粒物的洗脱量Figure 4. Elution volume of leaf surface PM under different rainfall durations2.4 不同降雨强度下不同粒径的颗粒物洗脱特征
根据图 5，可以明显看出4种植物在不同降雨历时和不同降雨条件下，3种粒径的洗脱量在颗粒物总洗脱量中占的百分比不同，呈现出0.45~2.5 μm < 2.5~10 μm < 10~100 μm，洗脱量百分比分别为18.21%±4.37%，31.63%±5.63%, 50.16%±6.6%。Pearson相关性分析结果显示10~100 μm的洗脱量显著高于0.45~2.5 μm和2.5~10 μm(P < 0.05)，而2.5~10 μm和0.45~2.5 μm的洗脱量无显著相关性(P>0.05)，降雨强度与各粒径洗脱百分比也没有显著相关性(P>0.05)。
![]()
图 5 不同降雨强度下不同粒径叶表面颗粒物的洗脱量百分比Figure 5. Elution volume percentage of leaf surface PM for different particle sizes under varied rainfall intensities3. 讨论
3.1 不同降雨量下的颗粒物洗脱量
实验结果显示，降雨量1.25、2.50、3.75 mm这3种降雨量的植物叶片表面洗脱量无显著相关关系，植物叶片表面颗粒物洗脱量无显著相关关系，降雨量在3.75、5.0、11.25 mm时植物叶片表面颗粒物的洗脱量随降雨量的增大而增大。这与在典型天气下植物叶面滞尘动态变化中女贞(Ligustrum lucidum)和珊瑚树(Viburnum odoratissimum)在2.3、14.8 mm的降雨后，滞尘量分别降低了30%和50%结果一致[9]。卫矛、银杏、迎春3种植物在降雨量>5 mm时显著升高，而杜仲叶表面洗脱量增大的幅度相对较小。这可能是因为杜仲叶中有胶丝网络结构，这种胶丝具有黏性，有文献证明具有黏性表面更易于颗粒物的滞留，不易洗脱[8, 18]。
3.2 不同降雨强度和历时下对叶表面颗粒物的洗脱影响
在本研究结果中，除了杜仲与降雨强度无显著相关关系外，其他3种植物随着降雨强度的增大，洗脱量也随之增大。有研究认为强降雨可以有效洗脱植物叶表面的颗粒物，将植物毛孔附近或毛孔内侧，或者是绒毛沟壑之间夹的比较深的滞留颗粒物冲洗下来，而弱降雨只有部分颗粒物被洗刷下来[19]。模拟降雨对常绿植物叶表面滞尘的研究结果表明，油松(Pinus tabuliformis)叶表面颗粒物的滞留率因降雨强度的增大而显著减少[18]，再次说明降雨对植物叶表面颗粒物洗脱量有影响，且为降雨强度越大，植物叶表面颗粒物的洗脱量越大。降雨对叶片表面颗粒物的洗脱量在树种之间的显著相关，洗脱量从大到小依次为迎春>银杏>卫矛>杜仲，这种不同植物间洗脱量的差异可能是由植物叶片的叶表面特性，树种及物理属性，植物群落特点，叶面积等决定的[20]。
本研究结果显示，4种植物叶表面的洗脱量随降雨历时的延长而增大，在5 min时，4种植物的洗脱量基本一致，在10和15 min时，卫矛、迎春、银杏的叶表面颗粒物洗脱量显著增大(P < 0.05)，而杜仲在10和15 min的叶表面颗粒物没有显著相关关系。这说明在降雨历时为5 min时，4种植物表面颗粒物的洗脱量一致，而在10和15 min时产生了差异，进一步验证了在降雨事件中，叶片表面的颗粒物洗脱容易被凹陷、沟槽、表皮毛等结构限制，需要水流长时间，反复冲洗的研究结果[21]；也因此说明充分洗脱植物叶片表面的颗粒物，需要更长的降雨历时。
3.3 不同降雨强度下不同粒径的颗粒物洗脱量
各个粒径段的颗粒物因其生成的来源不同，物理、化学性质存在着很大的差别[22]，这可能导致降雨对于各粒径段的洗脱量存在差别。在不同降雨强度下的不同粒径洗脱量的对比研究中发现，降雨强度与各粒径洗脱百分比没有显著相关性，10~100 μm粒径颗粒物的洗脱量显著高于0.45~2.5 μm和2.5~10 μm粒径颗粒物，而2.5~10 μm和0.45~ 2.5 μm粒径颗粒物的洗脱量无显著差异。说明降雨对10~100 μm粒径颗粒物的洗脱作用明显高于对2.5~10 μm粒径颗粒物和0.45~2.5 μm粒径颗粒物的洗脱作用，在对大叶黄杨(Buxus megistophylla)的清洗作用研究中发现，降雨对大叶黄杨0.2~10 μm颗粒物的洗脱作用不如10~100 μm颗粒物。在Przybysz等[2]的研究中发现，10~100 μm、2.5~10 μm、0.2~10 μm的洗脱量分别占总颗粒物的33%~42%、25%~36%、21%~30%，并且结果与本研究结果相同粗颗粒物和细颗粒物差异不显著。
4. 结论
(1) 随着降雨量的增大，植物叶表面的洗脱量也显著增大。卫矛、银杏、迎春3种植物在降雨量>5 mm时显著升高，而杜仲叶表面洗脱量增大的幅度相对较小。
(2) 在不同降雨强度下，降雨对叶片表面颗粒物的洗脱量在树种之间的显著相关(P < 0.05)，叶表面平均洗脱量从大到小依次为迎春>银杏>卫矛>杜仲。
(3) 4种植物叶表面的洗脱量随降雨历时的增大而增大，在5 min时，4种植物的洗脱量基本一致，在10和15 min时，卫矛、迎春、银杏的叶表面颗粒物洗脱量显著增大(P < 0.05)，而杜仲在降雨历时10和15 min时的洗脱量无显著相关性(P>0.05)。
(4) 3种粒径的洗脱量在颗粒物总洗脱量中占的百分比不同，呈现出0.45~2.5 μm < 2.5~10 μm < 10~100 μm，10~100 μm的洗脱量显著高于0.45~2.5 μm和2.5~10 μm(P < 0.05)，而2.5~10 μm和0.45~2.5 μm的洗脱量无显著相关性(P>0.05)，降雨强度与各粒径颗粒物洗脱百分比也没有显著相关性(P>0.05)。
-
![]()
图 2 各树种秋色盛期的植株形态
Figure 2. Plant morphology of various tree species during the leaf coloring date
![]()
图 3 MOD11A1产品日间和夜间地温（LST）波段遥感数据（2019年10月20日影像）
Figure 3. Satellite data of day and night land surface temperature(LST) in MOD11A1 product (image of October 20th, 2019)
![]()
图 4 银杏雌株与雄株的秋色盛期对比
Figure 4. Comparison in leaf coloring date between female andmale ginkgo trees
![]()
图 6 2017—2019年北京主城区各样地秋季平均地温
Figure 6. Mean LST of autumn (LSTa) in various sample plots of Beijing’s urban area during 2017−2019
![]()
图 7 2017—2019年各树种秋色盛期与样地秋季平均地温的相关性
**指在0.01水平上显著相关。** refers to significant correlation at 0.01 level.
Figure 7. Correlations between leaf coloring date of various tree species and average autumn ground temperature (LST) of the sample plots
表 1 2017—2019年各树种秋色盛期数据（日序）
Table 1 Data in leaf coloring date (DOY) of various species during 2017−2019
树种
Tree species年份
Year样地 Sample plot 北京植物园
Beijing Botanical
Garden颐和园
Summer
Palace奥森南园
South Olympic
Forest Park地坛公园
Ditan
Park玉渊潭公园
Yuyuantan
Park北海公园
Beihai
Park景山公园
Jingshan
Park陶然亭公园
Taoranting
Park龙潭公园
Longtan
Park洋白蜡
Fraxinus
pennsylvanica2017 287.5 289.5 290.0 297.2 297.5 297.5 296.0 295.1 298.0 2018 295.0 297.2 298.9 305.0 303.7 305.5 303.0 306.3 303.3 2019 299.3 300.5 302.1 308.0 304.7 307.0 306.5 307.0 306.5 3年平均
Mean of three years293.9 295.7 297.0 303.4 301.9 303.3 301.8 302.8 302.6 元宝枫
Acer truncatum2017 301.5 302.0 302.5 309.0 308.5 311.0 305.0 309.0 311.0 2018 299.3 301.5 302.3 305.1 308.2 311.0 310.0 310.7 311.0 2019 308.1 308.0 309.0 313.5 311.3 316.0 312.5 314.2 314.3 3年平均
Mean of three years303.0 303.8 304.6 309.2 309.3 312.7 309.2 311.3 312.1 水杉
Metasequoia
glyptostroboides2017 312.5 312.3 313.8 318.0 319.0 319.4 317.0 318.0 321.0 2018 311.0 308.0 308.8 318.0 317.8 322.0 321.0 319.5 321.5 2019 313.2 311.5 312.5 319.5 320.0 319.0 318.0 321.0 319.6 3年平均
Mean of three years312.2 310.6 311.7 318.5 318.9 320.1 318.7 319.5 320.7 旱柳
Salix matsudana2017 322.2 324.1 325.4 330.0 331.8 329.4 330.0 331.3 333.0 2018 319.7 323.9 323.3 328.5 327.5 327.6 329.0 330.8 329.6 2019 318.0 319.8 319.0 325.0 325.4 327.6 326.0 327.3 326.5 3年平均
Mean of three years320.0 322.6 322.5 327.8 328.2 328.2 328.3 329.8 329.7 银杏
Ginkgo biloba2017 302.6 305.5 306.8 308.3 311.9 312.8 311.2 309.4 312.4 2018 304.7 308.0 307.0 311.0 311.1 313.7 311.5 314.7 312.8 2019 306.0 305.1 307.5 311.0 314.6 314.4 312.8 313.2 313.1 3年平均
Mean of three years304.4 306.2 307.1 310.1 312.5 313.6 311.8 312.5 312.7 银杏雌株♀
Female
G. biloba2017 297.4 304.3 303.0 305.8 309.1 307.0 308.2 309.3 308.0 2018 299.7 302.5 300.0 304.8 308.4 308.5 307.4 309.3 309.0 2019 301.2 303.0 302.7 305.9 309.0 309.0 310.7 307.8 308.5 3年平均
Mean of three years299.4 303.3 301.9 305.5 308.8 308.2 308.8 308.8 308.5 银杏雄株♂Male
G. biloba2017 307.4 313.7 310.6 317.0 315.6 315.3 314.2 317.8 316.0 2018 310.5 313.5 313.0 315.7 318.0 317.5 315.4 318.2 317.6 2019 309.6 313.7 310.0 315.2 319.0 318.4 317.0 317.6 317.5 3年平均
Mean of three years309.2 313.6 311.2 315.9 317.5 317.1 315.5 317.9 317.0 注：表中各样地的物候值取自样地内多样株的物候期均值。Note: phenology value for each sample plot takes the average phenology of multiple sampling trees in the sample plot. 
下载: 导出CSV
-
[1] Wang S, Gong D. Enhancement of the warming trend in China[J]. Geophysical Research Letters, 2000, 27(16): 2581−2584. doi: 10.1029/1999GL010825
[2] Neil K, Wu J. Effects of urbanization on plant flowering phenology: A review[J]. Urban Ecosystems, 2006, 9: 243−257. doi: 10.1007/s11252-006-9354-2
[3] Visser M E, Both C. Shifts in phenology due to global climate change: the need for a yardstick[J]. Proceedings of the Royal Society B, 2005, 272(1581): 2561−2569. doi: 10.1098/rspb.2005.3356
[4] Miller-Rushing A J, Høye T T, Inouye D W, et al. The effects of phenological mismatches on demography[J]. Philosophical Transactions of the Royal Society B: Biological Sciences, 2010, 365: 3177−3186. doi: 10.1098/rstb.2010.0148
[5] Richardson A D, Black T A, Ciais P, et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity[J]. Philosophical Transactions of the Royal Society B: Biological Sciences, 2010, 365: 3227−3246. doi: 10.1098/rstb.2010.0102
[6] Tang J, Körner C, Muraoka H, et al. Emerging opportunities and challenges in phenology: a review[J]. Ecosphere, 2016, 7(8): e01436. doi: 10.1002/ecs2.1436
[7] 叶清, 焦庚英, 许晓利, 等. 物候学在植物季相景观规划中的应用[C]// 中国气象学会农业气象与生态学委员会, 江西省气象学会. 全国农业气象与生态环境学术年会论文集. 北京: 中国气象学会农业气象与生态学委员会, 2006: 409−411. Ye Q, Jiao G Y, Xu X L, et al. Application of phenology in the landscape planning of plant seasonal aspects [C]// Agricultural Meteorology and Ecology Committee of the Chinese Meteorological Society, Jiangxi Meteorological Society. Annual Conference Proceeding of National Agrometeorology and Ecology Committee. Beijing: Agrometeorology and Ecology Committee of Chinese Meteorological Society, 2006: 409−411.
[8] Nagai S, Saitoh T, Yoshitake S. Cultural ecosystem services provided by flowering of cherry trees under climate change: a case study of the relationship between the periods of flowering and festivals[J]. International Journal of Biometeorology, 2019, 63(4): 1051−1058.
[9] World Health Organization. Phenology and Human Health: Allergic Disorders: report of WHO Regional Office for Europe (No. 108750) [R]. Rome: WHO, 2003.
[10] Grimm N B, Faeth S H, Golubiewski N E, et al. Global change and the ecology of cities[J]. Science, 2008, 319: 756−760. doi: 10.1126/science.1150195
[11] Zhao S, Liu S, Zhou D. Prevalent vegetation growth enhancement in urban environment[J]. Proceedings of the National Academy of Sciences, 2016, 113: 6313−6318. doi: 10.1073/pnas.1602312113
[12] Farrell C, Szota C, Arndt S K. Urban plantings: ‘Living Laboratories’ for climate change response[J]. Trends in Plant Science, 2015, 20: 597−599. doi: 10.1016/j.tplants.2015.08.006
[13] Jia W, Zhao S, Zhang X, et al. Urbanization imprint on land surface phenology: the urban-rural gradient analysis for Chinese cities[J]. Global Change Biology, 2021, 27(12): 2895−2904. doi: 10.1111/gcb.15602
[14] 王静. 北京市土地利用空间格局对城市热岛强度的影响研究[D]. 呼和浩特: 内蒙古师范大学, 2014. Wang J. Impacts of spatial pattern of land use on urban heat island intensity in Beijing [D]. Huhhot: Inner Mongolia Normal University, 2014.
[15] 陈朱. 城市化对春季开花植物物候的影响[D]. 上海: 华东师范大学, 2011. Chen Z. Effects of urbanization on phenology of spring-flowering plants[D]. Shanghai: East China Normal University, 2011.
[16] Qiu T, Song C H, Li J X. Impacts of urbanization on vegetation phenology over the past three decades in Shanghai, China[J]. Remote Sensing, 2017, 9(9): 970−985. doi: 10.3390/rs9090970
[17] Zhou D, Zhao S, Zhang L, et al. Remotely sensed assessment of urbanization effects on vegetation phenology in China’s 32 major cities[J]. Remote Sensing of Environment, 2016, 176: 272−281. doi: 10.1016/j.rse.2016.02.010
[18] Yang J, Luo X, Jin C, et al. Spatiotemporal patterns of vegetation phenology along the urban-rural gradient in coastal Dalian, China [J/OL]. Urban Forestry & Urban Greening, 2020, 54: 126784[2021−09−20]. https://doi.org/10.1016/j.ufug.2020.126784Get.
[19] Melaas E K, Wang J A, Miller D L, et al. Interactions between urban vegetation and surface urban heat islands: a case study in the Boston metropolitan region[J/OL]. Environmental Research Letters, 2016, 11(5): 054020[2021−09−20]. https://doi.org/10.1088/1748-9326/11/5/054020.
[20] Mimet A, Pellissier V, Quénol H, et al. Urbaniszation induces early flowering: evidence from Platanus acerifolia and Prunus cerasus[J]. International Journal of Biometeorology, 2009, 53(3): 287−298. doi: 10.1007/s00484-009-0214-7
[21] Parece T, Campbell J. Intra-urban microclimate effects on phenology[J]. Urban Science, 2018, 2(26): 1−22.
[22] 曹广真, 侯鹏, 毛显强. 北京市城市化对地表温度时空特征的影响[J]. 气象, 2010, 36(3): 19−26. doi: 10.7519/j.issn.1000-0526.2010.03.003 Cao G Z, Hou P, Mao X Q. Impacts f urbanization on temporal and spatial characteristics of land surface temperature in Beijing[J]. Meteorological Monthly, 2010, 36(3): 19−26. doi: 10.7519/j.issn.1000-0526.2010.03.003
[23] Neil K, Wu J G, Bang C, et al. Urbanization affects plant flowering phenology and pollinator community: effects of water availability and land cover[J]. Ecological Processes, 2014, 3(17): 1−12.
[24] Katz D S, Dzul A, Kendel A, et al. Effect of intra-urban temperature variation on tree flowering phenology, airborne pollen, and measurement error in epidemiological studies of allergenic pollen[J]. Science of the Total Environment, 2019, 653: 1213−1222. doi: 10.1016/j.scitotenv.2018.11.020
[25] 王静, 常青, 柳冬良. 早春草本植物开花物候期对城市化进程的响应−以北京市为例[J]. 生态学报, 2014, 34(22): 6701−6710. Wang J, Chang Q, Liu D L. The flowering phenophase response of early spring herb to the urbanization process in Beijing[J]. Acta Ecologica Sinica, 2014, 34(22): 6701−6710.
[26] Woo H R, Kim H J, Lim P O, et al. Leaf senescence: systems and dynamics aspects[J]. Annual Review of Plant Biology, 2019, 70: 347−376. doi: 10.1146/annurev-arplant-050718-095859
[27] Wu C, Chen J M, Black T A, et al. Interannual variability of net ecosystem productivity in forests is explained by carbon flux phenology in autumn[J]. Global Ecology and Biogeography, 2013, 22(8): 994−1006. doi: 10.1111/geb.12044
[28] 董丽, 邢小艺. 气候变化对城市植被的影响研究综述[J]. 风景园林, 2021, 28(11): 61−67. Dong L, Xing X Y. Review of researches on impacts of climate change on urban vegetation[J]. Landscape Architecture, 2021, 28(11): 61−67.
[29] 宛敏渭, 刘秀珍. 中国物候观测方法[M]. 北京: 科学出版社, 1979. Wan M W, Liu X Z. Chinese phenological observation method [M]. Beijing: Science Press, 1979.
[30] 田丽媛. 温湿度对北京5种秋色叶树种秋叶变色的影响[D]. 北京: 北京林业大学, 2012. Tian L Y. The impact of temperature and humidity on five kinds of fall-color trees’ color changing in autumn of Beijing [D]. Beijing: Beijing Forestry University, 2012.
[31] Shi D, Wei X, Chen G, et al. Changes in photosynthetic characteristics and antioxidative protection in male and female ginkgo during natural senescence[J]. Journal of the American Society for Horticultural Science, 2012, 137(5): 349−360. doi: 10.21273/JASHS.137.5.349
[32] Curtis A E, Smith T A, Ziganshin B A, et al. The mystery of the Z-score[J]. Aorta, 2016, 4(4): 124−130.
[33] Alqasemi A S, Hereher M E, Al-Quraishi A M F, et al. Retrieval of monthly maximum and minimum air temperature using MODIS aqua land surface temperature data over the United Arab Emirates[J]. Geocarto International, 2020, 37(10): 2996−3013.
[34] Prevéy J, Vellend M, Rüger N, et al. Greater temperature sensitivity of plant phenology at colder sites: implications for convergence across northern latitudes[J]. Global Change Biology, 2017, 23(7): 2660−2671. doi: 10.1111/gcb.13619
[35] 杨敏, 杨贵军, 王艳杰, 等. 北京城市热岛效应时空变化遥感分析[J]. 国土资源遥感, 2018, 30(3): 213−223. Yang M, Yang G J, Wang Y J, et al. Remote sensing analysis of temporal-spatial variations of urban heat island effect over Beijing[J]. Remote Sensing for Land and Resources, 2018, 30(3): 213−223.
[36] Jeong S J, Park H, Ho C H, et al. Impact of urbanization on spring and autumn phenology of deciduous trees in the Seoul capital area, South Korea[J]. International Journal of Biometeorology, 2019, 63(5): 627−637. doi: 10.1007/s00484-018-1610-7
[37] Estiarte M, Peñuelas J. Alteration of the phenology of leaf senescence and fall in winter deciduous species by climate change: effects on nutrient proficiency[J]. Global Change Biology, 2015, 21(3): 1005−1017. doi: 10.1111/gcb.12804
[38] Yan H, Fan S, Guo C, et al. Assessing the effects of landscape design parameters on intra-urban air temperature variability: the case of Beijing, China[J]. Building and Environment, 2014, 76: 44−53. doi: 10.1016/j.buildenv.2014.03.007
[39] 陈睿智. 城市公园景观要素的微气候相关性分析[J]. 风景园林, 2020, 27(7): 94−99. Chen R Z. Microclimate correlation analysis of landscape elements in city parks[J]. Landscape Architecture, 2020, 27(7): 94−99.
[40] 游晓婕, 李琼, 孟庆林. 城市热岛空间格局及形态差异化调控策略研究: 以广州市中心城区为例[J]. 风景园林, 2021, 28(5): 74−79. You X J, Li Q, Meng Q L. Research on spatial patterns and morphological differentiation control strategy of urban heat islands: a case study of downtown area of Guangzhou City[J]. Landscape Architecture, 2021, 28(5): 74−79.
[41] 李膨利, Muhammad Amir Siddique, 樊柏青, 等. 下垫面覆盖类型变化对城市热岛的影响: 以北京市朝阳区为例[J]. 北京林业大学学报, 2020, 42(3): 99−109. Li P L, Siddique M A, Fan B Q, et al. Effects of land surface type changes on urban heat island: a case study of Chaoyang District, Beijing[J]. Journal of Beijing Forestry University, 2020, 42(3): 99−109.
[42] 范舒欣, 李坤, 张梦园, 等. 城市居住区绿地小微尺度下垫面构成对环境微气候的影响: 以北京地区为例[J]. 北京林业大学学报, 2021, 43(10): 100−109. Fan S X, Li K, Zhang M Y, et al. Effects of micro scale underlying surface type and pattern of urban residential area on microclimate: taking Beijing as a case study[J]. Journal of Beijing Forestry University, 2021, 43(10): 100−109.
[43] 王爱霞, 任光淳, 秦亚楠. 半干旱区城市广场树木形态对微气候的影响研究[J]. 风景园林, 2020, 27(7): 100−107. Wang A X, Ren G C, Qin Y N. Research on influence of tree shape of city squares on microclimate in semi-arid areas[J]. Landscape Architecture, 2020, 27(7): 100−107.
[44] 乔治, 黄宁钰, 徐新良, 等. 2003-2017 年北京市地表热力景观时空分异特征及演变规律[J]. 地理学报, 2019, 74(3): 475−489. Qiao Z, Huang N Y, Xu X L, et al. Spatio-temporal pattern and evolution of the urban thermal landscape in metropolitan Beijing between 2003 and 2017[J]. Acta Geographica Sinica, 2019, 74(3): 475−489.
[45] Fracheboud Y, Luquez V, Björkén L, et al. The control of autumn senescence in European aspen[J]. Plant Physiology, 2009, 149(4): 1982−1991. doi: 10.1104/pp.108.133249
[46] Way D A, Montgomery R A. Photoperiod constraints on tree phenology, performance and migration in a warming world[J]. Plant Cell & Environment, 2015, 38(9): 1725−1736.
[47] Estrella N, Menzel A. Responses of leaf coloring in four deciduous tree species to climate and weather in Germany[J]. Climate Research, 2006, 32(3): 253−267.
[48] Dai J, Wang H, Ge Q. Multiple phenological responses to climate change among 42 plant species in Xi’an, China[J]. International Journal of Biometeorology, 2012, 57(5): 749−758.
[49] Dai J, Wang H, Ge Q. The spatial pattern of leaf phenology and its response to climate change in China[J]. International Journal of Biometeorology, 2013, 58(4): 521−528.
[50] Liu Q, Piao S, Campioli M, et al. Modeling leaf senescence of deciduous tree species in Europe[J]. Global Change Biology, 2020, 26(7): 4104−4118. doi: 10.1111/gcb.15132
[51] Archetti M, Richardson A D, O’Keefe J, et al. Predicting climate change impacts on the amount and duration of autumn colors in a New England forest[J/OL]. PLoS One, 2013, 8(3): e57373[2022−02−22]. https://doi.org/10.1371/journal.pone.0057373.
[52] Lang W, Chen X, Qian S, et al. A new process-based model for predicting autumn phenology: How is leaf senescence controlled by photoperiod and temperature coupling?[J]. Agricultural and Forest Meteorology, 2019, 268: 124−135. doi: 10.1016/j.agrformet.2019.01.006
[53] Wu C, Wang J, Ciais P, et al. Widespread decline in winds delayed autumn foliar senescence over high latitudes[J/OL]. Proceedings of the National Academy of Sciences, 2021, 118(16): e2015821118[2022−02−22]. https://doi.org/10.1073/pnas.2015821118.
[54] Cook B I, Wolkovich E M, Davies T J, et al. Sensitivity of spring phenology to warming across temporal and spatial climate gradients in two independent databases[J]. Ecosystems, 2012, 15(8): 1283−1294. doi: 10.1007/s10021-012-9584-5
[55] Wilson K B, Baldocci D D. Seasonal and interannual variability of energy fluxes over a broadleaved temperate deciduous forest in North America[J]. Agricultural and Forest Meteorology, 2000, 100(1): 1−18. doi: 10.1016/S0168-1923(99)00088-X
[56] Xing X Y, Dong L, Konijnendijk C, et al. The Impact of microclimate on the reproductive phenology of female Populus tomentosa in a micro-scale urban green space in Beijing[J/OL]. Sustainability, 2021, 13(6): 3518[2022−03−23]. https://doi.org/10.3390/su13063518.
[57] Rosique-Esplugas C, Cottrell J E, Cavers S, et al. Clinal genetic variation and phenotypic plasticity in leaf phenology, growth and stem form in common ash ( Fraxinus excelsior L.)[J]. Forestry, 2021, 95(1): 83−94.
[58] Keskitalo J, Bergquist G, Gardestrom P, et al. A cellular timetable of autumn senescence[J]. Plant Physiology, 2005, 139(4): 1635−1648. doi: 10.1104/pp.105.066845
[59] Alberto F J, Aitken S N, Alía R, et al. Potential for evolutionary responses to climate change: evidence from tree populations[J]. Global Change Biology, 2013, 19: 1645−1661. doi: 10.1111/gcb.12181
计量
- 文章访问数: 391
- HTML全文浏览量: 68
- PDF下载量: 49
