世界生物分布的宏观特征—生物地理区划研究之III
The Macroscopic Characteristics of Distribution of Global Terrestrial Biota—Biogeographical Regionalization Research III

作者: 申效诚 :河南省农业科学院植物保护研究所,河南 郑州;郑州大学生命科学学院,河南 郑州; 任应党 , 刘新涛 , 王光华 , 杨琳琳 , 冯超红 , 马晓静 :河南省农业科学院植物保护研究所,河南 郑州; 申 琪 :河南中医药大学第一临床医学院,河南 郑州; 游志兴 :中国科学院数学及系统科学研究院,北京; 张书杰 :郑州大学生命科学学院,河南 郑州;

关键词: 生物地理学地理区划聚类分析分布特征Biogeography Regionalization Cluster Analysis Distributional Characteristics

摘要:
为了解析陆生生物的分布规律,应用我们新提出的多元相似性聚类分析方法,对全球的动物、植物、菌物的分布格局进行分析,逐渐明晰并证实世界生物分布的几项宏观特征:生物物种的分布可能是间断的,但地区间区系组成的变化是连续的,距离愈远,变化愈大;一个地理区域只与相邻地区相似性最高,它的聚类对象只能是相邻地区,这种地域约束性为地理区划带来便利;生物分布的不均衡性使多样性丰富地区在聚类分析中发挥核心作用,它的凝聚力及独立性是生物分布区形成的内在动力;一个地理区域的物种多样性随着调查的深入不断增加,但它的区系构成是相对稳定的,这种稳定性保证了这个地区生物区系性质和地理区划的稳定性与可测性,是生物地理学科产生的基础;动物、植物和菌物虽然进化时期不同,生命形态及新陈代谢方式也不同,但它们在全球的分布格局是相同的,由生态因素对生物分布的这种同质性影响将会促进对生物分布规律的揭示和生物地理学科的发展。

Abstract: In order to analyze the distributional law of terrestrial organisms, we analyzed the global distri-bution pattern of animals, plants and microorganisms using multivariate similarity clustering analysis method, and gradually clarified and confirmed several macroscopic features of the biota distribution. The distribution of biological species is likely to be disconnected, but the change of the floristic or faunal composition between regions is continuous; the farther the distance, the greater the difference. The similarity of a geographical area is only highest with adjacent areas, and its clustering object can only be adjacent areas. This restrictive condition brings convenience for geographic regionalization; biological distribution is not balanced. The richer biodiversity areas can play a key role in the clustering analysis. Their cohesion and independence are the inner motive power of biological regional formation. Although a geographic area’s species diversity increases with the deepening of the research, its floristic or faunal composition is relatively stable, to ensure the stability of distributional pattern and measurability of biogeographical regionalization. Though different evolution periods between animals, plants and fungus, life form and manner of metabolism were different, their global distribution patterns were the same. This homogeneity will promote the reveal of biological distributional law and the development of biological geography.

1. 引言

世界1.49亿平方千米陆地上,生活着200多万种生物。它们以不同的生命形态遍布世界各个角落。陆块的漂移、山地的隆起、气候的变化、海洋的阻隔等影响着生物的繁衍与扩散,生物也以自身的进化与适应能力构建自己的分布格局。没有任何两种生物的分布区域完全相同,也没有任何两个地理区域的生物种类完全相同。对生物分布规律及形成机制的分析与总结,并进而进行地理分布区的划分是生物地理学的研究范畴。它既是人们保护生物多样性的重要基础学科,又是达到合理有效、永续利用自然资源的有效工具 [1] [2] [3]。

生物地理学由法国博物学家布丰1761年创立以来 [4]。19世纪,英国鸟类学家斯克莱特(P. Sclater)根据鸟类特别是雀形目Passeriformes的分布,英国动物学家华莱士(A. R. Wallace)主要根据哺乳动物的分布,共同奠定了动物地理学的基础,提出6界24亚界的动物地理区划方案 [5] [6]。德国植物学家洪堡(A. von Humboldt)于1805年奠基植物地理学后 [7] ,瑞士学者德康得勒(A. de Candolle)和德国学者恩格勒(A. Engler)根据有花植物的分布构建了植物地理学大厦 [8] [9]。后经人们修改 [10] [11] [12] [13] ,也形成了与动物不同的6界区划系统。他们这些19世纪的阐释为人们普遍接受并持续使用着 [14]。

人们的普遍接受并长期使用,自然说明它的合理内核。但也无须讳言,由定性方法得到的这些结论不可避免地在划分标准及分界线的确定上存在失衡之处。整个20世纪探索生物地理的脚步并未停止。人们一方面讨论早期学者们的历史功绩及存在问题 [15] [16] [17] [18] [19] ,另一方面积极尝试用定量分析的方法装备生物地理学 [20] - [29]。人们逐渐形成共识,数学的介入与支撑应是生物地理学发展的不可回避或逾越的重要途径。否则它是不可能真正成熟的 [30]。

进入21世纪,人们对生物地理区划的关注迅速高涨起来 [31] - [36]。用不同的方法对不同的生物类群分别提出7-14界的形形色色的、各不相同的地理区划方案 [37] - [47]。C.B. Cox提出建议,撤掉植物地理新设的好望角界及南极界,同时将古热带界分成非洲界及印度–太平洋界 [41]。吴征镒提出增设古地中海植物界和东亚植物界 [42]。S. Proches对蝙蝠的分布进行聚类分析,将世界分作10个地理区,并认为适用于动物地理和植物地理 [43]。H. Kreft用Simpson公式及UPGMA法将世界聚为7个界,除新设马达加斯加界以外,其它界的分界线也有所变动 [44]。B.G. Holt等同样用Simpson公式及UPGMA聚类方法对陆生哺乳动物、两栖动物、非海洋鸟类共20,000多个物种进行分析,把全世界分成11个界 [45]。而M. Rueda 同样对这些动物进行分析,认为不必修改华莱士方案 [46]。E.J. Defriez和D.C. Reuman共同论证了世界陆地植被变化的同步性(synchrony) [47]。

与热烈讨论的高等生物地理区划相比,低等生物非常冷清。虽然昆虫学家已有数百篇涉及昆虫地理学文献 [48] [49] [50]。但直至近20年,才陆续对昆虫的个别类群,如毛翅目、蚤目、广腰亚目、隐翅虫科、粉虱科、金龟科、蚁科昆虫进行分析,提出各自的地理区划意见 [51] - [58]。而微生物至今还没有人进行初步的尝试 [59] - [68]。

综观生物地理学两个半世纪的发展历程,① 还基本处于定性分析阶段。尚未与数学有效融合。原因是缺少令生物地理学者满意的数学工具,不能够真正进入定量分析阶段;② 分析对象只注重高等生物,很少顾及低等生物;③ 对分布规律的探讨还处在对个别类群、个别地域的描述、统计阶段,缺少高阶元、广区域的比较、分析,更没有宏观规律的揭示;④ 对分布格局形成机制的认知还比较模糊、零碎,缺乏系统化、整体性的整理。因此,目前生物地理区划研究呈现分散、孤立、机遇的状态,不能够进行有组织的、系统化的协作攻关。以致成为困扰生物地理学发展的幽灵 [69]。

我们在对多种定量分析方法进行尝试与比较后,提出一个新的相似性通用公式(Similarity general formula, SGF) [70] 和与之配套的多元相似性聚类分析法(Multivariate similarity clustering analysis method, MSCA) [71]。经过对不同地理区域、不同生物类别、不同分类阶元、不同生态类群的分析验证,均比传统方法能够得到快捷、准确、合理的分析结果 [72] - [79]。鉴于MSCA法的简便快捷,我们在对全球昆虫进行分析的基础上 [80] ,组织团队力量对世界范围内的陆生生物的分布格局进行分析。本文先行报告在大型分析中所逐渐感悟到的生物分布的几个宏观特征。

2. 材料和方法

2.1. 生物类群

世界生物共7界96门352纲1466目,约280万种 [81]。排除海洋种类、化石种类、藻类、病毒及原生生物,本研究涉及的生物类群包括动物、植物、真菌、细菌共4界33门99纲530目4922科169,153属2,139,974种(表1)。种类分布信息来源于生物分类学家整理的分类学专著或名录 [82] - [187] ,生物专业性网站整理的数据库资料 [188] - [250] ,也随时补充一些新发表的新种、新分布资料 [251] - [301]。本着提高分布资料的利用率及分析结果的清晰度,本研究以属级阶元作为分析的基础生物单元(basic biological units, BBU)。

2.2. 基础地理单元的划分

按照地形、气候等生态条件和生物分布资料的详略程度,本研究把全球陆地(除南极洲)划分为67个基础地理单元(basic geographical unit, BGU) (图1)。作为聚类分析与地理区划的基础。其中以平原为主的BGU有21个,以丘陵为主的BGU有11个,以山地为主的BGU有12个,以高原为主的BGU有11个,以荒漠为主的BGU有5个,岛屿型的BGU有7个。有27个BGU处在热带,有34个BGU地处温带,有6个BGU的地域跨入寒带。

01 北欧Northern Europe,02 西欧Western Europe,03 中欧Central Europe,04 南欧Southern Europe,05 东欧Eastern Europe,06 俄罗斯欧洲地区European Russia,11 中东Middle East,12 沙特阿拉伯Saudi Arabia,13 也门与阿曼Yemen and Oman,14 伊朗高原Plateau of Iran,15 中亚Central Asia,16 西西伯利亚Western Siberia,17 东西伯利亚Eastern Siberia,18 乌苏里地区Ussuri region,19 蒙古Mongolia,20 帕米尔高原Plateau of Pamir,21 中国东北Northeastern region of China,22 中国西北Northwestern region of China,23 中国青藏地区Qinghai-Xizang region of China,24 中国西南地区Southwestern region of China,25 华南地区Southern region of China,26 中国中东部Centre-eastern China,27 中国台湾Taiwan region of China,28 朝鲜半岛Korea Peninsula,29 日本Japan,31 喜马拉雅地区Himalayan region,32 印度与斯里兰卡Indian and Sri Lanka,33 缅甸Myanmar,34 中南半岛Indochina Peninsula,35 菲律宾Philippines,36 印度尼西亚Indonesia,37 新几内亚New Guinea,38 太平洋岛屿Islands of Pacific Ocean,41 北非Northern Africa,42 西非Western Africa,43 中非Central Africa,44 刚果河流域Reaches of Congo river,45 埃塞阿比亚地区Ethiopia region,46 坦桑尼亚地区Tanzania region,47 安哥拉地区Angola region,48 南非South Africa,49 马达加斯加Madagascar,51 西澳大利亚Western Australia,52 澳大利亚北部地区Northern Territory,53 南澳大利亚South Australia,54 昆士兰Queensland,55 新南威尔士New South Wales,56 维多利亚Victoria,57 塔斯马尼亚Tasmania,58 新西兰New Zealand,61 加拿大东部Eastern Canada,62 加拿大西部Western Canada,63 美国东部山地Mts. Eastern US,64 美国中部平原Plain Central US,65 美国中部丘陵Hills Central US,66 美国西部山地Mts. Weatern US,67 墨西哥Mexico,68 中美地区Central America region,69 加勒比海岛屿Caribbean Islands,71 委内瑞拉Venezuela,72 圭亚那高原Plateau Guyana,73 安第斯山脉北段Northern Mt. Andes,74 亚马孙平原Amazon Plain,75 巴西高原Plateau Brazil,76 玻利维亚Bolivia,77 阿根廷Argentina,78 安第斯山脉南段Southern Mt. Andes。

Figure 1. BGUs of the world

图1. 世界基础地理单元的划分

Table 1. Biodiversity of global terrestrial biota

表1. 全球陆生生物多样性

注:各生物类群数据为综合汇总本文所有参考资料种类信息而成。

2.3. 构建生物分布数据库

用微软Access构建数据库,将各个BGU作为各列,将各个BBU作为各行。将一个属内每种生物分布的行政区域记录转化为BGU记录并汇总为该属分布,录入数据库中,有分布记“1”,无分布不记。本文涉及的几类生物在各BGU的属数如表2

2.4. 聚类分析方法

前人提出的数十个相似性系数计算公式 [302] ,但都是只能计算两个地区间的相似性系数,我们提出的相似性通用公式突破了二元比较的束缚。它的定义是:多个地区间的相似性系数是参加分析的各个地区的共有种类的平均数占总种类的比例 [69]。相伴提出的还有相异性公式、相似性贡献率公式、相异性贡献率公式:

相似性通用公式(similarity genera formula, SGF):

S I n = H i / n S n = ( S i T i ) / n S n (1)

相异性通用公式(difference general formula, DGF):

D I n = 1 S I n = [ n T + ( H H i ) ] / n S (2)

相似性贡献率公式(contribution of the similarity, CSI):

C S I i = H i / H i (3)

相异性贡献率公式(contribution of the difference, DSI):

C D I i = ( n T i + H H i ) / ( n S H i ) (4)

式中,SIn是n个地理单元的相似性系数,Si,Hi和Ti分别是i地理单元的种类数、共有种类(common species)数、独有种类(unique species)数,且满足Hi = Si − Ti,Sn是n个地理单元的总种类数。计算时所需各个数值都可以很方便地从数据库的查询页面上获得。无论手工计算或计算机软件分析都非常方便快捷。

与SGF配套使用的多元相似性聚类分析法(MSCA)是任何组群的相似性系数都由参与分析的BGU原始数据直接计算,不受聚类顺序的限制。甚至可以先行计算67个BGU的总相似性系数。最后按相似性系数大小排列聚类图 [70]。总相似性系数、总相异性系数、各个BGU的相似性贡献率、相异性贡献率都是传统分析方法所没有的概念和无法计算的指标。

Table 2. The distribution of several groups biota in BGUs

表2. 几类生物在各BGU分布的属数

注:BGU基础地理单元:basic geographical unit;BGR基础分布记录:basic distributional record;BBU基础生物单元:basic biological unit。各BGU的属数为综合汇总所有参考文献的分布信息而成。

3. 结果与分析

3.1. 区系变化的连续性

生物种或属的分布有连续的,更有间断的。但不同地区间生物区系组成的变化是连续的,不是间断的。地理距离愈远,变化愈大。而不同方向上变化的速率是不同的。也即一个地区的生物区系,只与邻近地区关系密切,距离愈远,关系愈疏远。这种不均衡的连续性变化是开展生物地理研究,划分地理分布区的基础。中国如此,世界也如此。以中国各省区昆虫为例,陕西省昆虫有7934种,各省区与陕西的共有种类如图2

3.2. 地理条件的约束性

由于一个地理区域的生物区系,只与相邻地区关系最密切,地理距离愈远,关系愈疏远。所以一个特定地区只与邻近地区产生聚类关系(图3),而不能够与甚远的地区相聚,形成互不相连的“飞地”。这个特征使生物地理的聚类分析成为一种带约束的聚类(restrictive clustering)或称条件系统聚类(conditional hierarchical clustering),不是人们想象或期望中的“自由聚类”。这种约束性为软件分析带来很大的挑战,但也为手工计算带来便利,不必广泛地寻找、测试聚类伙伴,可以节省80%以上的无效劳动时间。其它实用聚类分析项目也会遇到约束性问题。这些形形色色的限制条件是聚类分析由理论走向实用的将常遇到又必须解决的关键问题之一。

3.3. 核心区域的凝聚力与独立性

受历史条件、自然条件、适应能力、以及人为因素的影响,生物分布不是均匀的。存在着若干个多样性丰富的核心区域。核心区域以其大量的共有类群(common group)凝聚邻近区域形成自己的分布区,又以自己的特有类群(endemic group)或独有类群(unique group)标志与其它分布区的独立性。聚集力量相等的地方是分布区的分界线。这种生物分布的集中性是分布区形成的内在原因。生物地理区划实际上是用聚类分析的定量方法对这些核心区域的凝聚力与独立性进行评估,准确地界定各个分布区域的地理范围。如世界共有昆虫104,344属,有具体分布记录的58,357属在0.300的相似性水平上聚为a~t共20个小单元群(small unit crowd, SUC),在0.200时聚为A~G共7个大单元群(large unit crowd, LUC)。表3是各个BGU的相似性贡献率和相异性贡献率,显然04、26、36、48、54、68、73号BGU是各大单元群的核心区域,对分布区的形成具有关键作用。

注:两个地区间的共有种类数表明关系的亲密程度。新疆、青海、西藏、海南没有四川、甘肃、河南、湖北与陕西关系密切。

Figure 2. The common insect species of every province with Shaanxi

图2. 中国各省区与陕西省(★)共有昆虫种类数

Figure 3. The similarity coefficient of animal genera of some BGUs with southern Europe

图3. 一些BGUs与南欧地理单元(★)的动物属级相似性系数

Table 3. The contribution of similarity and difference of every BGU

表3. 各个BGU的昆虫属级相似性贡献率与相异性贡献率

Note: LUC: large unit crowd; SUC: small unit crowd; BGU: basic geographical unit; CSI: contribution of the similarity; CDI: contribution of the difference.

3.4. 区系构成的稳定性

一个相对独立的地理区域的生物区系是各种环境因素长期作用的结果,其种类是随着调查研究的深入在不断增加的,但区系构成又是相对稳定的。这种稳定性保证了这个地区生物区系性质和地理区划的稳定性与可测性,是生物地理学科产生的基础。不同时期的构成是稳定的,不同生物类群的构成也是稳定的。如中国河南省目前有昆虫8422种,其中中国种类最多,其次是古北种类,再次是东洋种类,广布种类最少。而该省在1993年以前有昆虫3128种,也是中国种类最多,依次是古北种类、东洋种类及广布种类。中国目前的93,661种昆虫与上世纪40年代的20,069种也是如此。目前世界哺乳动物5412种与1876年前华莱士时代已知哺乳动物2034种的分布格局也是相同的,分别在0.120和0.200时聚成7个单元群,各群的组成也相同。只有15、69号BGU在相邻群间移动,不违背地理学原则(表4图4图5)。

3.5. 环境对生物分布影响的同质性、积累性

在生物漫长的进化过程中,地球板块分分合合,气候不断地冷热干湿变化,生物类群的诞生与灭绝,逐渐形成现在的分布格局。虽然它们的出现时间不同,经历的历史变迁不同,但现生生物的属级阶元大都出现在新生代,它们都经受了迄今6700万年的新生代的环境影响,决定了它们的总体分布格局应该是相同的,也就表明环境对各类生物影响的同质性。这种同质性是我们创建世界生物地理区划体系的理论基础,可以使研究不同生物类群的学者不再单打独斗,可以联合起来共同谱写生物地理的新篇章。图6~9是脊索动物门、节肢动物门、双子叶植物门、子囊菌门四类主要生物的聚类结果。而更高或更低的分类阶元的分析结果也表现出同样明显的同质性特征(表5)。

但由于各类生物出现的历史时期不同,早分化的类群比晚分化的类群要多留下些生存信息于后代,主要体现在总相似性系数(general similarity coefficient, GSC)和平均分布域(average distributional territory, ADT)的差异,微生物出现最早,分布最广,GSC最高,为0.125,ADT最广,为4.80;植物次之,GSC为0.112,ADT为4.63;动物最晚,GSC为0.061,ADT为2.50。这是历史环境影响的积累性(表5)。

4. 结论与讨论

1) 本研究应用MSCA法成功地分析了世界的动物、植物和微生物,不仅得到层次清晰、结构合理、符合学科要求的聚类结果,还能获得传统方法无法计算的多项指标,如总相似性系数、总相异性系数、相似性贡献率、相异性贡献率等。再次证明MSCA的数据挖掘能力明显优于传统聚类方法。

Figure 4. Clustering tree of species of mammal

图4. 哺乳动物聚类图

Figure 5. Clustering tree of mammal before 1876

图5. 1876年前哺乳动物聚类图

Figure 6. Dendrogram of chordata

图6. 脊索动物聚类图

Figure 7. Dendrogram of arthropoda

图7. 节肢动物聚类图

Figure 8. Dendrogram of magnoliophyta

图8. 双子叶植物聚类图

Figure 9. Dendrogram of ascomycota

图9. 子囊菌聚类图

Table 4. The distribution of mammal in every BGU

表4. 不同历史时期哺乳动物在各个BGU的分布

Table 5. Clustering results of every biota groups

表5. 各生物类群的聚类分析结果

Note: BBU: basic biological unit; GSC: general similarity coefficient: ADT: average distributional territory; LUC: large unit crowd; SUC: small unit crowd.

2) 本研究所提出的几项生物分布特征从宏观角度解析了生物分布区形成的基础、原因以及进行地理区划应遵循的原则,为进一步总结生物分布规律、阐释形成机制提供了探讨途径和事实依据,丰富了生物地理学的理论内容。

3) 虽然人们对动、植物分布格局的一致性早有预期,但一直无从证实。本研究首次从多维度证明生态条件对生物分布格局影响的同质性将促进生物地理学科的发展,使生物地理区划这一游走在象牙塔里的幽灵变为人们得心应手的实用工具。

致谢

我们感谢世界各地学者,如英国伦敦国王学院C. Barry Cox教授,德国格丁根大学Holger Kreft教授, 美国克莱姆森大学John C. Morse教授,美国犹他大学Daniel R. Gustafsson教授,斯洛伐克科学院地理研究所Peter Vrsansky教授,法国医学院Jean-Claude Beaucournu教授,英国牛津大学Robert J. Whittaker教授,捷克兽医及制药大学Tomas Najer教授,法国巴黎大学Maram Caesar 教授,巴西圣保罗大学Michel P. Valim教授,美国加利福尼亚州州立大学Miklos D.F. Udvardy教授,德国格赖夫斯瓦尔德大学Nikki H.A. Dagamac教授,爱沙尼亚塔尔图大学Leho Tedersoo教授,美国新墨西哥大学Jennifer A. Rudgers 教授,德国约翰古登堡大学Janine Fröhlich-Nowoisky教授,美国加利福尼亚州立大学欧文分校Kathleen K. Treseder教授,瑞士洛桑大学Antoine Guisan教授,澳大利亚维多利亚博物馆Kevin C. Rowe教授,老挝国立大学Daosavanh Sanamxay教授,泰国宋卡王子大学Pipat Soisook教授,巴西戈亚斯联邦大学M.V. Cianciaruso教授,匈牙利自然历史博物馆Gabor Csorba教授,巴西帕拉伊巴联邦大学Anderson Feijo教授,墨西哥国立大学Tania Escalante博士,巴西国立癌症研究所Cibele R. Bonvicino教授,智利康塞普西翁大学Daniel González-Acuñad教授等,或赠送文献,或修饰文稿,或深入讨论,或提出建议。

基金项目

河南省重点实验室建设专项(112300413221);河南省基础与前沿研究计划项目(082300430370)。

NOTES

*通讯作者。

文章引用: 申效诚 , 任应党 , 申 琪 , 游志兴 , 刘新涛 , 张书杰 , 王光华 , 杨琳琳 , 冯超红 , 马晓静 (2018) 世界生物分布的宏观特征—生物地理区划研究之III。 世界生态学, 7, 98-128. doi: 10.12677/IJE.2018.72014

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