气候条件是影响生物生存的主要环境因子之一, 已经有足够的证据表明气候变化, 尤其是温度升高会对物种产生影响, 主要表现为物种分布、物候、种群大小等的变化, 如一些动植物的分布, 植物种子萌发期、开花期, 动物的迁徙期、产卵期等会发生不同程度的改变(Walther et al., 2002; Parmesan & Yohe, 2003; Root et al., 2003; Parmesan, 2006; Thuiller, 2007)。联合国政府间气候变化专门委员会(IPCC)第四次评估报告显示: 全球温度普遍升高, 最近100年(1906-2005年)的平均温度上升了0.74℃; 地区降雨量也发生变化, 北半球中高纬度区降雨量增加了5-10%, 与之相反, 热带、亚热带地区降雨量减少3% (IPCC, 2007)。近年来, 气候变化对鸟类的影响也受到越来越多的关注, 世界自然基金会(World Wide Fund for Nature, WWF)近期的报告指出, 全球气候变化使多数鸟类的生存受到威胁(Wormworth & Mallon, 2006, 2007)。
鸟类作为生态系统的重要组成成分, 对气候和环境的改变反应相当敏感, 可以作为监测全球气候变化的一项重要依据(Lemoine et al., 2007b)。科学家在鸟类受气候变化的影响方面已开展了大量研究(Crick, 2004; Huntley et al., 2006; Both et al., 2006; Leech & Crick, 2007)。发现气候变暖使得许多鸟类的分布向极地或高海拔区移动, 鸟类的物候包括产卵期和迁徙期都发生明显的改变, 种群数量也受到气候变化的影响。我国鸟类资源丰富, 气候变化对我国鸟类的分布、迁徙等也产生了一定的影响(孙全辉和张正旺, 2000; Shi et al., 2006; 杜寅等, 2009)。本文综合分析了国内外在这一领域的研究 进展。
1 鸟类分布的变化
物种的分布是物种与一个区域内生态因子长期作用达到平衡的结果。当气候变化使得原先的栖息地不再适宜生存的时候, 将迫使许多物种转移而开拓新的领地。对鸟类而言, 气候变暖是改变其分布范围的主要影响因素。Walther等(2002)认为气候变暖使得气候阈值向极地或高纬度区移动变化, 鸟类等物种在扩散能力和资源利用能力有限的范围内, 其分布将会沿着气候阈值移动。已有研究发现很多物种的分布向高纬度或高海拔区移动(Hughes, 2000; Parmesan & Yohe, 2003; Hickling et al., 2006; Parmesan, 2006; Thuiller, 2007), Parmesan和Yohe (2003)综合分析包括鸟类在内的许多物种的分布变化, 发现气候变化导致物种平均每10年向极地移动6.1 km或向高海拔区移动6.1 m。约克大学最新的研究则将这一变化速率提高了2-3倍, 认为鸟类等物种每10年向极地移动16.9 km, 向高海拔区移动11.0 m (Chen et al., 2011)。而且大部分鸟类分布范围在缩小而非扩大(Huntley et al., 2006), 分布范围的减小将增加物种灭绝的可能性(Erasmus et al., 2002; Gage et al., 2004; Parmesan, 2006; Pimm et al., 2006; Sekercioglu et al., 2008), 尤其对于那些分布在高纬度和高海拔区的物种, 几乎没有可供生存的栖息地去开拓(Benning et al., 2002; Williams et al., 2003; Pimm et al., 2006)。
已经观测到很多鸟类的分布发生变化, 并且与气候变暖有关。在欧洲, Thomas和Lennon(1999)研究英国鸟类繁殖地分布变化, 通过分析近20年间的分布数据后发现, 北方区域的种类已经向北平均移动了18.9 km。一些芬兰繁殖地鸟类的分布也在向北移动(Brommer, 2004)。在北美地区, Hitch和Leberg (2007)研究了56种鸟类的分布变化, 发现分布在北部的鸟类没有向南扩展, 而分布在南部的鸟类每年向北移动2.35 km。Žalakevicius和Švažas (2005)发现白额雁(Anser albifrons)等鸟类迁徙的路线向东北方向移动。很多鸟类繁殖地分布范围向北扩展(Parmesan & Yohe, 2003), 而一些物种非繁殖地分布范围也发生改变, 并且向其繁殖地靠近(Austin & Rehfisch, 2005; Visser et al., 2009)。Valiela和Bowen (2003)研究认为北美科德角(Cape Cod)的鸟类越冬地分布的北移与当地冬季最低气温升高有关。气候变化可能使得鸟类迁徙变得容易完成, 假如气候变化使得繁殖地能越冬, 将导致部分迁徙群倾向于 居留。
在垂直分布变化方面的研究比较少, 仅有的一些研究也显示鸟类向高海拔区移动(Parmesan, 2006)。在低纬度的热带地区, 由于缺乏强的纬度气候梯度, 森林毁坏加快和鸟类本身的扩散能力低等因素(Moore et al., 2008), 使得该区域鸟类分布受气候变化影响较重, 分布主要向高海拔区域转移(Pounds et al., 1999; Shoo et al., 2005; Peh, 2007)。Pounds等(1999)在哥斯达黎加蒙特沃德国家公园, 发现低地鸟类的繁殖地在过去20年中已经扩展到山区云雾林。鸟类在移动过程中也难免受到阻碍, 除自然界的山脉或者河流外, 人为因素也很突出, 比如栖息地破碎化, 城市、交通公路等障碍, 这无疑将加重气候变化带来的危害。而对于那些移动能力弱以及岛屿或者高山种类而言, 未来将可能会失去整个生存空间。
2 鸟类物候的变化
2.1 迁徙期的变化
气候变暖导致很多鸟类的春季迁徙期提前。Butler(2003)对北美迁徙鸟类做大规模的研究后发现, 受气候变化影响, 鸟类的春季迁回期发生显著变化, 总体而言, 长距离迁徙的鸟类(提前13天)比短距离迁徙的鸟类(提前4天)变化明显, 大多数鸟类春季迁回期提前, 如田雀鹀(Spizella pusilla)迁徙期平均每10年提前约17天, 也有少数种类推迟, 如棕榈林莺(Dendroica palmarum)迁徙期平均每10年推迟了约3天。类似的变化发生在很多地区(Marra et al., 2005; Mills, 2005; Sparks et al., 2005; Végváriet al., 2010)。鸟类秋季离开期也发生了改变, 但秋季迁徙期的变化比较难以监测(Crick, 2004), 而且变化也比较复杂, 提前或者延后都有发生(Walther et al., 2002)。Jenni和Kéry(2003)研究西欧秋季飞往非洲撒哈拉越冬的鸟类, 发现长距离鸟类的迁徙期提前, 短距离鸟类的迁徙期则推迟。此外, Cotton(2003)研究英国20种迁徙鸟类, 结果显示, 在过去的30年中鸟类迁回和离开繁殖地的时间都提前了8天, 而Gordo等(2005)的研究却发现由于越冬地气候变暖, 从南撒哈拉迁徙到西班牙繁殖地的鸟类时间推迟。
研究认为, 影响鸟类迁徙期变化的因素很多, 除气候变暖外, 北大西洋涛动(NAOI)以及南方涛动(SOI)都能影响鸟类的迁徙(Cotton, 2003; Hüppop & Hüppop,2003; Hubálek,2004; Vahatalo et al., 2004)。其次, 一些生态因素包括繁殖地、迁徙距离的不同也可能导致迁徙期变化不同(Butler, 2003; Tryjanowski et al., 2005; Miller-Rushing et al., 2008; Hubálek & Čapek,2008)。一般短距离的迁徙受温度影响比较大, 而长距离迁徙比较复杂。Miller-Rushing等(2008)研究北美马萨诸塞州32种迁徙鸟类的迁徙期, 发现短距离的迁徙与温度变化有关, 而中距离迁徙与南方涛动相关, 长距离迁徙期则无明显变化。
2.2 产卵期的变化
鸟类产卵期的变化主要与其食物量丰富度达到高峰期变化有关。由于气候变化, 某些植物的开花期和昆虫的繁殖期可能提前, 这种变化容易与鸟类的物候变化不一致(Both & Visser, 2001; Visser & Both, 2005), 改变了原来正常的食物链关系, 导致成鸟或幼鸟与食物供应不能同步(Sanz et al., 2003; Visser et al., 2004), 影响鸟类存活。Visser等(2006)发现气候变化改变大山雀(Parus major)的食物毛虫的丰富度高峰期, 最终导致大山雀的繁殖期发生改变。其次, 短距离迁徙鸟类容易受到温度变化影响, 因此一般短距离迁徙鸟类的产卵期比长距离鸟类变化显著(Leech & Crick, 2007), 尽管长距离迁徙鸟类为适应气候变化带来的影响, 也会改变迁徙期(Jonzénet al., 2006)。
3 鸟类种群数量的变化
4 主要监测和评估方法
通常主要使用两种方法研究气候变化对鸟类的影响, 一种是传统监测方法, 另一种是采用模型预测未来气候情景下鸟类的变化, 且现有模型法的应用主要集中于鸟类的分布预测, 物候和种群相对较少, 本文重点介绍气候模型在预测鸟类分布方面的应用。
4.1 主要监测方法和模型
传统的监测方法, 包括长期观测和样本位点重复调查(Thuiller, 2007), 如在适宜的栖息地进行大规模的重复调查来监测分布的变化, Thomas和Lennon(1999)用两份不同时期的鸟类繁殖地分布地图集进行对比, 分析平均分布位置变化, 得出了鸟类受气候变化影响的事实。这种长期的观测结果可靠, 却需要大量的人力。相较于国内, 大规模的监测在国外更加成熟, 斯幸峰和丁平(2011)介绍了英国繁殖鸟类调查(the Breeding Bird Survey, BBS)、北美繁殖鸟类调查(the North American Breeding Bird Survey, BBS)和圣诞鸟类调查(the Christmas Bird Count, CBC)等重要的陆地鸟类长期监测计划, 可以对我国今后的监测提供一定的帮助。
4.2 气候模型在预测鸟类分布中的应用
利用模型方法预测未来气候情景下的鸟类分布变化, 结果显示很多鸟类的分布同样向北或高海拔区移动, 并且多数分布范围缩小。预测方法主要有两类, 一类是相关性方法, 应用最多的是气候包络模型(climate envelope models, CEM)或称作生物气候包络模型(bioclimatic envelope models, BEM) (Pearson & Dawson, 2003)。气候包络模型代表某一物种当前的分布与当前气候变量之间的联系, 将这种联系投影到未来气候情景中来评估物种未来的分布。Peterson(2003)用规则集遗传算法模型(genetic algorithm for rule-set prediction, GARP)结合大气环流模型(general circulation models of climate change), 预测了北美中西部落基山的26种鸟类和大平原的19种鸟类到2055年的分布变化, 结果显示气候变化导致平原地带鸟类分布缩减和移动变化明显。Virkkala等(2008)用广义加法模型(generalized additive model, GAM)预测了北寒带27种陆地鸟类在21世纪的分布变化, 在未来A2和B1两种气候情景下(A2和B1在此表示到2100年时全球平均温度分别上升了3.8℃和2.0℃), 发现鸟类分布范围到2080年缩小情况严重(A2, 减少83.6%; B1, 减少73.6%)。模型用于预测鸟类繁殖地分布变化的情形要多于非繁殖地, 并且发现繁殖地和非繁殖地的分布变化结果不同。Doswald等(2009)用气候响应面模型(climatic response surface model, CRSM)结合GAM模型, 预测了欧洲林莺属鸟类的繁殖地和非繁殖地的分布范围变化, 结果显示繁殖地的分布范围向北扩展, 而非繁殖地的分布没有发生定向的转移, Doswald还发现气候变化对迁徙型的种类比居留和部分迁徙群或者短距离迁徙的种类影响严重。Hu等(2010)用最大熵模型(maximum entropy model, MAXENT)预测了黑面琵鹭(Platalea minor)的越冬地分布变化, 发现在未来气候变化影响下分布地向北扩展。在预测垂直分布变化方面, Maggini等(2011)用GAM模型预测了瑞士鸟类的分布变化, 发现35%的种类分布向高海拔区移动。Buermann等(2011)用最大熵模型评估了5种安第斯山蜂鸟沿海拔梯度上升变化, 在两种气候变化情景下, 预测结果表明除了栖息地丧失和破碎化因素外, 鸟类分布沿海拔梯度上升300-700 m幅度的变化都是由于气候变化所导 致的。
另一类是机理性方法, 以物种生理知识为基础, 试图寻找更多气候变量和物种生理方面的联系, 称作机理模型(mechanistic models)。机理模型被认为能更好地预测物种对气候变化的响应, 可以从本质上去解释物种变化及原因(Helmuth, 2009; Kearney & Porter, 2009), 已经有人用机理模型来检验一般生物气候模型的预测能力(Hijmans & Graham, 2006), 或者用一些机理模型模拟物种在气候情景下的分布(Morin et al., 2008)。多数模型预测结果显示, 鸟类受气候变化的影响正在扩大, 未来一些物种甚至可能灭绝(Thomas et al., 2004)。
气候包络模型虽然应用广泛, 但也有很多不确定性(Pearson & Dawson, 2003; Hampe, 2004)。近来有很多研究用零模型(null models)结合其他模型来提高预测准确度(Beale et al., 2008; Araújoet al., 2009; Jiménez-Valverdeet al., 2010)。此外, 还有人用集成模型法来预测未来的变化, 通过利用组合技术集合一系列的模型和气候情景, 可以得到更强的预测效果(Thuiller, 2004; Araújo & New,2006)。Araújo等(2005)利用集成模型方法, 通过模拟已经观测到的过去两个时期英国繁殖鸟的分布, 检验了各种模型的准确性, 认为选用集成模型得出一个相对一致的预测结果可以减少单一模型的不确定性。Coetzee等(2009)采用集成模型预测了南非50种鸟类的分布, 在4个未来气候情景下, 利用BIOMOD下的8个模型进行模拟, 结果显示有近62%的种类失去适生空间, 至少有5个种失去85%的气候适生区。
4.3 生物因素的影响
气候变化对鸟类等物种的影响突出, 然而一些生物因素可能会干扰或减弱这种影响, 如物种间相互作用、物种扩散能力以及进化等(Pearson & Dawson, 2003)。这些生物因素将不可避免地影响模型等方法对未来预测的准确性。物种间相互作用和扩散能力可以决定物种应对气候变化的能力和速率(Brooker et al., 2007; Araújo & Luoto,2007), 其中种间相互作用对那些相互依赖大的物种而言更为重要(Preston et al., 2008)。此外, 进化和表型可塑被认为可以减弱气候变化对鸟类的不利影响。Charmantier等(2008)在研究气候变化等环境因子对英国大山雀繁殖期的影响时发现, 个体为应对环境变化而调整行为, 可以促使种群更紧密地跟随环境快速变化的步伐, 由此认为表型可塑对鸟类适应气候变化起到核心作用, 未来也需要深入研究。Karell等(2011)最近的研究显示, 气候变化影响了灰林鸮(Strix aluco)的微进化(microevolution), 由于近30年冬天越来越温暖, 当地同种群不同颜色的灰林鸮在生存竞争中的优劣地位随之发生变化, 导致它们的比例发生改变, 气候变化影响了物种的微进化, 从而也证明了物种可以通过微进化来适应气候变化。
5 总结和展望
综合上述国外目前对气候变化影响鸟类研究的最新成果, 以及从我国当前的研究实际状况出发, 我们认为:
(1)气候变化对鸟类的影响越来越显著。各种观测和预测的研究结果证实气候变化对鸟类的分布、物候以及种群动态产生了重要影响。鸟类的分布向高纬度或高海拔区移动, 大部分鸟类的分布范围将进一步缩小; 物候期产生复杂变化, 多数鸟类迁徙期和产卵期提前, 也有部分推迟; 气候变化导致鸟类种群下降, 甚至可能灭绝。当然一些进化因素也可能减弱这种发展趋势。
(2)进一步发展预测方法和模型。目前的研究方法主要有两个途径, 一是长期监测, 二是采用生物气候模型在未来气候情景下预测鸟类的变化。长期监测的数据准确, 研究结果可靠性强。生物气候模型已经得到广泛应用并且取得良好的效果, 然而模型的准确性一直以来备受质疑, 除了所用样本大小以及地理分辨率, 数据的可靠性等因素外, 模型主要考虑气候因素, 经常忽略掉一些重要的生物或非生物因素, 如土地利用、种间相互作用、物种扩散能力和进化, 这无疑将影响模型的预测能力。为获得更加准确的预测效果, 预测模型应考虑生理因素, 开发机理模型研究气候变化对鸟类的影响将成为一种新的趋势。
(3)加强气候变化对我国鸟类的影响和保护对策研究。我国鸟类资源丰富, 但研究甚少。从地理区系看, 我国地跨古北、东洋两个动物地理界, 鸟类区系丰富, 很多迁徙鸟类在我国既有繁殖地又有越冬地, 容易受到全球气候变化带来的不利影响。尽管已经有研究发现气候变化对我国部分鸟类产生影响, 但总的来看我国在这方面的研究相对薄弱, 与国外差距明显。究其原因, 一方面是全球气候变化及其对生物带来的影响并未引起人们的过早重视。另一方面, 我国缺乏长期完整的鸟类监测数据, 这也是主要的研究限制因素。
面对当前越来越严峻的气候变化所引起的生态危害, 人们理当加以重视, 一方面努力减少人为因素加剧气候变化; 另一方面提出更严格的保护鸟类的举措, 首先, 急需建立完整的鸟类数据监测系统, 这方面可以参考国外, 培养志愿者, 集数据的采集、上传、共享和研究于一体, 对某一对象进行长期的监测和研究, 开展多次大尺度的陆地鸟类监测工作。其次, 学习和研究新的预测评估方法, 能够准确地预测未来气候情景下我国鸟类的变化趋势。最后, 在综合各种现代研究技术的基础上, 完善管理方式, 加强宣传教育, 以便更好地应对气候变化带来的危害, 保护我国鸟类的多样性。
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Timing of reproduction has major fitness consequences, which can only be understood when the phenology of the food for the offspring is quantified. For insectivorous birds, like great tits (Parus major), synchronisation of their offspring needs and abundance of caterpillars is the main selection pressure. We measured caterpillar biomass over a 20-year period and showed that the annual peak date is correlated with temperatures from 8 March to 17 May. Laying dates also correlate with temperatures, but over an earlier period (16 March-20 April). However, as we would predict from a reliable cue used by birds to time their reproduction, also the food peak correlates with these temperatures. Moreover, the slopes of the phenology of the birds and caterpillar biomass, when regressed against the temperatures in this earlier period, do not differ. The major difference is that due to climate change, the relationship between the timing of the food peak and the temperatures over the 16 March-20 April period is changing, while this is not so for great tit laying dates. As a consequence, the synchrony between offspring needs and the caterpillar biomass has been disrupted in the recent warm decades. This may have severe consequences as we show that both the number of fledglings as well as their fledging weight is affected by this synchrony. We use the descriptive models for both the caterpillar biomass peak as for the great tit laying dates to predict shifts in caterpillar and bird phenology 2005-2100, using an IPCC climate scenario. The birds will start breeding earlier and this advancement is predicted to be at the same rate as the advancement of the food peak, and hence they will not reduce the amount of the current mistiming of about 10 days.
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There is now ample evidence of the ecological impacts of recent climate change, from polar terrestrial to tropical marine environments. The responses of both flora and fauna span an array of ecosystems and organizational hierarchies, from the species to the community levels. Despite continued uncertainty as to community and ecosystem trajectories under global change, our review exposes a coherent pattern of ecological change across systems. Although we are only at an early stage in the projected trends of global warming, ecological responses to recent climate change are already clearly visible.
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[本文引用: 1]
It is now widely accepted that global climate change is affecting many ecosystems around the globe and that its impact is increasing rapidly. Many studies predict that impacts will consist largely of shifts in latitudinal and altitudinal distributions. However, we demonstrate that the impacts of global climate change in the tropical rainforests of northeastern Australia have the potential to result in many extinctions. We develop bioclimatic models of spatial distribution for the regionally endemic rainforest vertebrates and use these models to predict the effects of climate warming on species distributions. Increasing temperature is predicted to result in significant reduction or complete loss of the core environment of all regionally endemic vertebrates. Extinction rates caused by the complete loss of core environments are likely to be severe, nonlinear, with losses increasing rapidly beyond an increase of 2 degrees C, and compounded by other climate-related impacts. Mountain ecosystems around the world, such as the Australian Wet Tropics bioregion, are very diverse, often with high levels of restricted endemism, and are therefore important areas of biodiversity. The results presented here suggest that these systems are severely threatened by climate change.
Biodiversity and climate change
Global climate change and its impact on wetlands and waterbird populations
