生物的进化

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生物的进化范文第1篇

【关键词】达尔文；进化论；意义；演变；生物

进化生物学是生物学中最基本的理论之一，它是由大家都熟悉的达尔文提出的生物进化论构成的。即指出关于生物由低级到高级，由简单到复杂逐步演变过程的学说。随着进化论的发展，产生了现代综合进化论，而当今演化学绝大部分就是以查尔斯·罗伯特·达尔文的演化论为指导。除此之外，埃尔温·薛定谔的《生命是什么》为主体方向，进化论已为当代生物学的核心思想之一。其进化论有三大经典证据：比较解剖学、古生物学和胚胎发育重演律。进化论除了作为生物学的重要分支得到重视和发展外，其思想和原理在其它学术领域也得到广泛的应用，并形成许多新兴交叉学科，如演化金融学、演化证券学、演化经济学等。

“物竞天择，适者生存”是进化论的基本含义，也就是说进化论里生命的演进是以自然选择和适应自然的能力为标准的。达尔文的进化论也曾遭受过质疑，曾有一个学说叫做：智能设计假说（又称“智慧设计假说”），这一种思想认为，“宇宙和生物的某些特性用智能原因可以更好地解释，而不是来自无方向的自然选择。”这一假说的主要支持者包括发现研究院等基督教智囊团体，他们认为，智能设计假说是同等重要的科学理论，甚至比现有的科学理论对生命起源问题的解释更加合理。

但是智能设计并没有进化论的能力，相反的，智能设计本来就是悖论。如果智能先于设计存在，那么智能必定有个存在的“环境”——请问没有被智能设计的环境产生之前，智能存在于什么样的环境中？那么这个环境又是谁设计的？如果说根本没有环境，那么智能设计者也可以说根本不存在，因为没有背景环境，何来本体。

另外，智能设计是从开始到现在一直在设计吗？如果是，它既然是“智能设计”且超越一切被设计物，应当同时设计出所有的被设计者，而不必待被设计者自行设计后来物；或者说，对它而言，开始到现在一直都是“开始”，为什么我们不是从开始到未来永生的？

如果不是，那么它开始设计和终止设计都分别在什么时候？它又是否知道它设计出的物中一部分有自行设计的能力？如果承认被设计物有设计能力，只不过这种自行设计的能力就是它赋予的，那么智能设计只能解释“第一因”或者“第一至第N个因”，却不能适用于全部物。而如果是这样，它怎么能算得上“智能全知全能”？

因此到目前为止，达尔文提出的进化论到目前为止即使受到过质疑，依然被承认和认可。生物的进化沿着怎样的规律，探讨这个问题有助于对物种灭绝和各种生物的进化演变提供理论依据

那么生物进化论在现实生活中有哪些意义呢，这就主要从物种方面开始讨论：

（1）物种形成是生物对不同生存环境适应的结果生物生存的环境具有变化性和异质性。环境随时间的变化导致生物的适应进化，环境在空间上的异质性导致生物的分异（性状分歧），分歧的结果是不同类型的生物，即物种的形成。不同的物种适应不同的局部环境，不能设想有能够适应各种不同环境的一种生物。各种生物在进化过程中不断分化，歧异产生更多的物种意味着也能够占领更多的生存环境，生物的不连续性是生物对环境的不连续性（异质性）的适应对策。

（2）物种间的生殖隔离保证了生物类型的稳定性物种在种间生殖隔离的存在下能保持物种相对稳定的基因库，没有种间的生殖隔离就会使已获得的适应因杂交而溶化丢失。所以物种的存在使得生物及保持遗传的相对稳定，又使进化不致停滞，保持进化的不可逆性，成为进化的基本途径。

（3）物种是生物进化的基本单位。

物种具有可变化性以适应环境的变化，但这只是相对的，一个物种不能永远适应变化着的环境。当环境变化的速度范围超出原有物种的适应能力，灭绝就会发生，这时新的环境也有待新的物种去占领。生态系统也要适应环境的变化，物种的更替（种形成和灭绝）和种间生态关系的改变可以使生态系统适应变化的环境，生物与环境之间从不平衡又达到新的平衡，从而推动整个生物界的进化。在这种宏观大进化的过程中每一步都是由物种进化所推动，物种是小进化的终点，同时又是大进化的起点，所以说物种是生物进化的基本单位。

（4）物种是生态系统中的功能单位不同的物种因其不同的适应特征而在生态系统中占有不同的生态位。因此，物种是生态系统中物质与能量转移和转换的环节，是维持生态系统能流、物流和信息流的关键。 [科]

【参考文献】

[1]张昀.生物进化，北京：北京大学出版社，1988.

[2]李难.进化生物学基础，北京：高等教育出版社，2005.

[3]Merrell D J，Ecological Genetics London：Longman，1981.

[4]赵晓明，宋秀英.生物遗传进化学，北京：中国林业出版社，2003.

[5]Klug W S，Cummings M R.Concepts of Genetics.New York：Macmillan，1993.

生物的进化范文第2篇

现代生物进化理论的主要内容有：

1、种群是生物进化的基本单位，生物进化的实质在于种群基因频率的改变。

2、突变和基因重组、自然选择、隔离是物种形成的三个基本环节,通过它们的综合作用,种群产生分化,并最终导致新物种的形成。

3、突变和基因重组产生生物进化的原材料。

4、自然选择使种群的基因频率定向改变并决定生物进化的方向。

生物的进化范文第3篇

关键词：进化

自然诱导――生物自组织　遗传　变异

中图分类号：Q3

文献标识码：A

文章编号：1007．3973(2011)010．073．02

物种是不断进化的，对此我们都不会再有任何怀疑，已成定论。然而，物种具体是如何进化的呢?对此，还一直处于争论之中，最有影响的是达尔文的自然选择学说，影响了人们一百多年，并被当作正式理论写进了教科书。但是，随着人们对生命现象了解的深入，越来越多的进化现象无法用自然选择进行解释。

下面，我们将自然环境在生物进化中的作用重新定位，由对生物间接性的自然选择作用改为直接性的自然诱导作用，并将生物自身在进化中的作用命名为生物自组织，建立起以自然诱导一生物自组织为核心的进化机制。自然诱导即自然环境的诱发、向导，显示了自然环境在生物进化过程中的直接作用。生物自组织是指生物本身的一种自我组织、自我构建，与自然环境的诱导作用紧密相连，并不是我们常说的不受环境影响的孤立的自组织。

从信息的角度分析，可以将自然诱导看作是自然环境变化信息的一种传递，生物自组织就是生物对自然环境变化信息的一种处理。自然诱导一生物自组织就是自然环境的变化信息传递给生物，生物对传递给它的变化信息进行处理。当自然环境发生变化的时候，变化的环境信息会传递给生物，生物对这种变化信息会进行处理，从而形成生物的进化。

水毛茛有两种不同的叶片，在水面上呈片状，而在水下则丝裂成带状，不同的环境诱导产生了不同形态的叶子。水毛茛水上叶和水下叶的不同，为我们认识水生植物向陆生植物的进化提供了参考，水下叶相当于水生植物的叶子，水上叶相当于陆生植物的叶子。在植物的进化中，由于地壳的运动，一些海洋地区变成了沼泽、陆地，生存于这些地区的水生植物会在陆生环境的诱导下自组织产生适应于陆地环境的性状特征，并不断发展进化，形成陆生植物。水生植物向陆生植物的进化，并不是一开始就完全脱离水环境的，而是生活在半水生半陆地的沼泽中，逐渐脱离水环境甚至适应干旱环境。

许多旱生植物的叶子很小甚至缩小成针刺状，而根系发达。仙人掌的叶子在干旱的沙漠中变为刺状，依靠肉质茎进行光合作用。生活在沙漠中的豆科植物骆驼刺，地上部分只有几厘米，而地下部分可以深达十几米，根系覆盖的面积达六七百平方米，发达的根系是旱生植物增加水分吸收的重要途径。这些性状不可能通过基因突变产生，它们是在干旱环境的诱导下植物自身的生长发育发生变化而产生的。它们对环境的适应无需自然选择，变异本身就是一种适应性变异。

会飞的昆虫由于某种原因定居到海岛上之后，新的环境会改变它们的生长发育。大风、恶劣的气候、潮湿的环境、新的食物、光照的变化都可能会导致昆虫翅膀的生长发育发生障碍，形成残翅或无翅的昆虫，这种影响持续存在最终使昆虫的翅膀趋于退化消失。非洲马德拉岛上的甲虫翅膀发育不全或退化消失，应该就是这样形成的。靠基因突变完成甲虫翅膀的退化消失，恐怕所有的甲虫早就掉到海里灭绝了，还没有突变形成一个翅膀残缺的甲虫呢。

当人类穿上衣服、使用火之后，人类抵御寒冷的能力就在一代一代的退化，抗寒性逐渐降低，表现为与此功能相关的一些性状特征发生变异，如体毛、皮肤腠理、皮肤脂肪厚度、皮肤毛孔等。而且，这些组织器官的变化还会进一步诱导其它组织器官生长发育的变化，如肺脏、血管、体液调节系统、神经调节系统等。在人类的进化中，一系列的进化特征很明显都不是自然选择的结果，如人的大脑、寿命、身高、牙齿和体毛等，这些特征的进化是在自然环境的诱导刺激下再经过人类自身生长发育的重新调整而完成的。人类的进化以铁的事实否定了基因突变一自然选择理论，自然诱导一生物自组织才是人类进化所遵循的机制。

动物眼睛的前体是一些原始的感光细胞，用来感知光线，这种感光细胞的分化是在光的作用下形成的。随着环境的复杂化，感光细胞在光的不断诱导下逐渐自组织出了功能更加完善的复杂结构，其中包括感光点周围色素的沉积，视网膜的感光神经组织及其他一些附属结构的形成。光在地球上所有动物眼睛的形成中都起着决定性的作用，是诱导眼睛进化的一个重要环境因子。因为眼睛结构的复杂性，达尔文无法解释眼睛这种完美地器官是怎么逐渐进化的，其实任何一种生物的结构都是完美复杂的。就连结构看上去十分简单的细菌鞭毛，如果分析其成分时，也会惊叹它的复杂性，是基因突变所无法完成的。

在自然界中，动物的眼睛千奇百怪，它们眼中的世界也是不同的，这些都与它们所处的环境特征有关，特别是光的特征。一些鸟类习惯在夜晚活动，如猫头鹰，它的视网膜中没有锥状细胞，无法辨认色彩，但可以在微弱的光线中捕捉猎物。本来眼睛良好的动物，由于生存环境改变，由光线充足的环境生存到黑暗的环境中，动物的眼睛就会退化而成为摆设。一些穴居动物，具有不发达的眼睛，外为表皮所覆盖。但是，把它的早期胚胎放在有光线的地方培养，它的眼睛各方面发育良好。深海的鱼类，因为看不到光。眼睛退化。深海鱼类之所以眼睛瞎，是因为在深海中光线微弱甚至不存在，使鱼的眼睛不能正常的发育，最终导致它们眼睛的退化，而不是有眼睛的鱼类不适用环境被淘汰了。从猿猴向人进化的过程中，眼睛对光线的分辨、眼睛的视力等也是不断进化的，这些都与其所处环境中的光特征、生活习性有关。生活在原始森林里的猿猴对光线的分辨不强，只看到较近的东西，不像人看得那么远。

有些生物变异可以遗传，有些生物变异不可以遗传，生物的变异性状是否具有遗传性根本该变异性状是否与自然环境相协调。杂交育种是我们培育品种的一个重要方法，所得的品种具有很多明显的优势性状，表现为器官发达、产量增加、抗性增强等，我们称之为杂种优势。杂种优势一个重要的特点就是具有不稳定性，很多杂交品种在杂种二代就没有任何优势性状可言。杂种优势之所以不能稳定遗传就在于它的优势性状与自然环境是不协调的，自然环境会诱导它自身的遗传物质系统重新组织，导致优势性状消失。

生物的变异性状是否具有遗传性不在于是否发生在DNA分子水平，即使是生物的变异性状发生在DNA分子水平，相应的性状只要不与环境相协调，也会由于自然环境的诱导作用而使相应的基因发生沉默，从而使变异性状不能遗传给下一代。某一物种迁移到某一寒冷的环境中后，该物种的抗寒机制会得到加强，同时表现出相应的抗寒特征，该物种长期生存在寒冷的环境中，相应的抗寒特征就可以遗传，也可以说物种的抗寒特征与环境的寒冷是相协调、相统一的。非DNA分子水平的生物变异只要与环境相协调，也是会遗传给下一代的。

生物的进化范文第4篇

关键词：主动适应被动适应自然选择社会选择可持续发展

从1859年达尔文的《物种起源》出版至今，由于对进化论的理解不深，因而出现了2种极端现象：一是生物进化中的自然主义倾向，即忽视社会选择的巨大作用，仅仅将生物进化归结为自然选择作用的结果；二是绝对的人类中心主义倾向，过分夸大社会选择的作用，而低估了自然选择在生物进化中的作用。

目前，全球生物多样性的减少和生态环境的不断恶化，使我们必须把大尺度上的生物进化和小尺度上的人类可持续发展结合起来，才能把生物多样性保护落实到人类的生产生活实践活动中，保证人类的各种行为不偏离可持续发展的轨道，使人类走上真正的可持续发展之路。因此，笔者从一个全新的角度来探索生物的进化和人类可持续发展的问题，旨在为生物进化大背景下人类的可持续发展研究奠定基础。

1生物进化与生物的适应

达尔文在《物种起源》中阐明了生命是进化的产物，现代的生物是在长期进化过程中发展起来的，给神创论以巨大打击，使生物学摆脱了神学的羁绊…。达尔文认为由于随机变异的产生和自然选择的作用，适应的变异被保留了下来，而不适应的变异则被淘汰。因此，自然选择的过程，就是生存斗争及适者生存的过程，适应是生物进化的最终结果。

进化论及进化生物学的研究发现多细胞生物起源于单细胞生物，结构复杂的生命体总是源于结构简单的生命体。据此，部分学者认为进化就是指事物由低级到高级的变化发展过程。生物的进化就是生物体由低级到高级、从简单到复杂的前进发展过程，其中存在着一个从低级到高级的方向性，这和达尔文对生物进化这一基本问题的理解是相背的，这是人类中心说的判定标准在生物进化论中的体现。即使现代的进化观也并未认为“进化就是革命性的进步”，而把“进化”定义为“进化是生物适应性的改变和生物群体多样性的变化”，和达尔文的进化理论一致，在进化理论中坚持了彻底的唯物主义，是达尔文整个进化理论体系和现代进化观的奠基石。

适应是生物进化的最终结果。生物的进化是生物物种的趋异化过程，是生物的随机变异和自然选择的过程。自然选择是对随机的多种变异的选择，大自然为选择者，而随机的各种变异成为被选择的对象，被大自然最终所选择的那种变异就得以保存下来，而同一物种中的其他变异就被淘汰，得以保存的变异就是适应大自然的。可见，生物物种产生的各种变异，无论是变异的程度上、方向上，还是变异范围的大小、数目的多少上，都是随机的、不定向的，但又是客观存在的。而大自然的选择相对于物种的变异来看，却是有一定方向的。自然选择的方向性和物种变异的随机性，客观上就决定了生物对自然的适应是一种被动的过程，生物体在结构、功能上对自然的适应都是自然选择的结果。生物对自然的适应性总是滞后于自然对生物物种的选择性，也就是说，生物物种对环境的适应是相对的、暂时的、有条件的，而不适应才是绝对的、永恒的。这就从根本上澄清了达尔文自然选择理论和现代进化论所基于的客观事实，在进化论中坚持了彻底的唯物主义，划清了进化论和神创论的界限。

2自然选择与社会选择

生存斗争及适者生存的过程就是自然选择的过程。除此之外，还有另外1种选择——社会选择也与生物的进化密切相关。伴随着人类社会工业文明的开始，现代工业和现代农业的日新月异，市场经济和资源环境私有制的全球化浪潮的冲击，加上当代生物工程技术的飞速发展，人类对生物界的改造力度越来越大，表现在一些物种逐渐消失；一些物种数量急剧减少，成为濒危物种；一些物种地理分布区域大幅度缩小；一些物种生活习性及部分性状发生改变；不时有新品种出现等现象，表明人类的社会实践活动对生物物种的演化具有不可低估的选择作用，这种选择称为社会选择。社会选择是人类主动适应自然环境的表现和手段，是人类为了求得自身的生存和发展，更好地适应自然的一种必然。从本质上说，人类的农业生产、工业生产和科学实践活动等都是人类自主选择的结果，无论是农业生产还是工业生产以及科学实践活动等人类行为的发生发展和演化等各个方面都属于社会选择的范畴。

事实证明，现在人类社会选择的力量的确是越来越强大，无论是对自然的改造力还是对自然的破坏力都超过了人类发展历史上的任何一个时期。但是，人类、人类社会本身以及社会选择等都是自然选择的结果，都是在自然选择的基础上发挥效能的。在一定程度上，社会选择是人类社会对自然选择作用的一种应答和反映，可以看作是生物与环境相互联系、相互作用的一个典型。但社会选择一经发生后，便有其独立作用的一面，可以和自然选择作用一道共同作用于生物的进化过程。

自然选择和社会选择的辩证关系表现在：一方面，当二者一致时，社会选择对自然选择起到了促进和加速的正向作用，使自然选择的力度、范围、时效得以加强，而自然选择使社会选择的目标得以快速实现，二者互相促进，共同加速生物物种的演化。另一方面，当二者不一致时，有3种情况：

①当自然选择的力量大于社会选择时，生物物种的演化由自然选择所控制，社会选择在一定程度上被抑制，自然选择成为了社会选择的阻力。这种现象在人类的动植物新品种的选育过程中表现得最为明显。

②当二者力量近于相等时，自然选择和社会选择都在自己一定的范围内作用，社会选择的目标停留在研究成果阶段，无法有效推广和应用，而自然选择也以其自身的作用规律对生物进行着选择。

③当自然选择的力量小于社会选择时，社会选择的结果在自然界中得以快速体现，自然所固有的一些平衡体系被打破，自然选择的方向被改变，社会选择在一定时空范围内控制着生物的演化。

2种选择的相互作用是一个动态的过程。从人类社会诞生起，2种选择过程都直接或间接地贯穿在每一个具体的物种的演化过程当中。但是，社会选择的对象、原始材料和最终归宿都统一在自然界当中，社会选择无论多么强大。都必须以自然选择为基础。因此，正确的做法是在尊重自然和自然规律的前提下，充分发挥社会选择对生物和环境的再创造作用，同时利用社会选择来抑制或从根本上扭转对人类或自然界(如物种多样性及生态环境等)都不利的自然选择，或减缓各种对物种多样性、生态系统的平衡具有毁灭性打击的自然灾害等，降低灾害对自然环境的破坏力，保护生物的多样性。

3社会选择与可持续发展的关系

可持续发展本质上是人类的一种社会性选择，是一种非常理智的自主性选择，同时也是人类主动适应不断变化的自然环境的一种机制，是一种实现长期自我演化的策略和手段。可持续发展战略的实施使人类的现代化工业和现代化农业以及现代科学实践活动等各个方面的发展都有了正确的方向，把人类的社会选择和人类对自然环境的主动适应都有机地统一在可持续发展这个大框架下，使人类的社会选择和主动适应终于走上了“有法可依”和“有法必依”的道路，从而实现了人类在自己的演化历史上第一次按自己所设计的演化模式去谋求自身的生存和发展。

人类的可持续发展问题本质上转化为人类的社会选择和大自然的自然选择二者间的关系问题，但这种相互关系无论是从时间、空间维度还是二者间力量强弱的对比情况来看，都是不对称的。从生物进化的时空尺度上来看，人类必须充分发挥自己所特有的主动适应力来确保社会选择在最大时空尺度上与大自然的自然选择相适应，人类才可能实现自身的可持续发展以实现长期的自主演化。

从纯生物学的观点来看，自然和自然选择都不会支持人类在社会经济文化等领域内发展水平的全方位提高，因为这意味着人类作为一个生物学种群，将占有越来越多的物质和能量，因而会剥夺其他物种生存和演化的机会，这与生物界的演化趋势相背离。因此，在生物进化的大背景下，人类要实现自身的可持续发展还需要全人类长期的艰苦努力，还必须同时处理好进化、适应和选择等重大问题，只有这样人类的可持续发展才能落到实处。

综上所述，生物的进化、适应和大自然的选择以及人类的可持续发展，都统一在生命的演化过程中。进化是生物适应自然的结果，适应是选择的结果，而选择是自然界所固有的属性。换句话说，进化、适应和选择都是自然界所固有的运动规律在生物物种演化过程中的体现，是物种演化过程中3个最重要的环节。人类的社会选择和可持续发展必须以此为前提，才能正确地发挥作用，为人类造福。

生物的进化范文第5篇

（江西省林业科技实验中心，江西 信丰 341600）

【摘要】随着《中国生物多样性保护战略和行动指南（2010-2030）》的贯彻实施，生物多样性监测与评价工作将在全国范围陆续开展。进化生态学作为阐述生物多样性演化规律和机理的基础性学科，其数量研究方法在20世纪70年代后得到了迅速的发展。本文从三个层面系统性总结、筛选了进化生态学在植物生态领域的主流研究方法，其中在生态系统层面，群落演替的主成分分析和聚类分析方法、群落的可恢复性、可持续性、变异性、抗干扰性、边缘效应等主题被筛选为主要分析方法；在种群层面，种间关联指数、相关系数、分离指数、生态位宽度指数、生态位重叠指数等概念可以全面阐释植物种群的演替规律；在遗传层面，哈迪-温伯格平衡度的检测、等位基因频率、多态位点百分数、平均位点的等位基因数、平均位点的预期杂合度、Nei氏遗传分化系数、Nei氏遗传一致度、遗传距离、聚类分析、遗传贡献率等方法在分子进化分析中的应用相对广泛。

关键词 生物多样性评价；生物多样性监测；进化生态学

Review of Evolutionary Ecology Study and Its Application on Biodiversity Monitoring and Assessment

LIU Huan　OUYANG Tianlin　TIAN Cheng-qing

（Jiangxi Provincial Forest science and Technology Experiment Cente， Xinfeng Jiangxi 341600，China）

【Abstract】After Chinese Biodiversity Conservation Strategy and Action Planning (2010-2030) is implemented in China, biodiversity monitoring and assessment projects are increasing steadily in national wide. The statistical methods of evolutionary ecology study have been developed quickly since 1970s, which provides the theory underlying the interpretation of biodiversity evolution in ecosystem. This article summarizes the evolutionary ecology methods which have been relatively broadly applied on botanical species from three layers: for ecosystem diversity, the principle component index (PCI) and cluster analysis for community succession analysis, ecosystem resilience, sustainability, variance, resistance capacity and edge effects are identified as the main analysis methods; for species diversity, the conceptions of inter-specific association, rank correlation coefficient, segregation index, coefficient of niche breadth and coefficient of niche overlap can fully interpret the succession of plant populations in ecosystem; for genetic diversity, the methods including Hardy-Weinberg equilibrium, allele frequency, percentage of polymorphic loci, mean number of alleles per locus, mean expected heterozygosity per locus, Nei’ coefficient of gene differentiation, Nei’ genetic identity, genetic distance and cluster analysis, genetic contribution rate have been identified as main methods for analysis of molecular evolution.

【Key words】Biodiversity assessment；Biodiversity monitoring；Evolutionary ecology

0　Introduction

According to the Chinese Biodiversity Conservation Strategy and Action Planning (2010-2030), there are three thorny issues threatening biodiversity conservation in national wide: degradation of ecosystem function in some area; deterioration of endangered species; continuous loss of genetic resources. The methods of evolutionary ecology study from three layers (ecosystem, species, genetics) provides substantial theory explaining these threats so that conservation strategies can be worked out properly.

After Environmental Standard for the Assessment of Regional Biodiversity (HJ623-2011) is implemented in China, multivariate methods of evolutionary ecology study become essential to classify the basic units for biodiversity assessment at both ecosystem layer (classification of communities) and genetic layer (classification of sub-populations).

After Environmental Standard on Classifying the Categories of Genetic Resources (HJ 626-2011) comes into force in China, the methods of evolutionary ecology provide the theoretical basis not only for understanding the evolutionary process of endangered species, but also becomes compulsory for ranking genetic resources (or endangered species) between CategoryⅠand categoryⅡ.

This review article systematically summarizes the main themes of evolutionary ecology study of plant species from three layers, with discussion of selecting suitable methods for biodiversity monitoring and assessment work.

1　Ecosystem Diversity

1.1　Cluster Analysis and Principal Component Analysis (PCA)

According to the Technical Guideline for Ecological Assessment, the significance of dominant plant species is calculated by a combination of density, frequency and dominance, which becomes the basis of cluster analysis or PCA for community classification[1], which becomes the essential units for biodiversity assessment at ecosystem layer. Bu et al.,(2005) adopted both fuzzy cluster analysis and principal component analysis (PCA) methods to classify 13 sampling plots into 5 communities, which included 15 botanical species located in loess hilly region. Both methods led to similar conclusions in terms of community classification. According to the restoration duration required by each community, the temporal succession of 5 plant communities was identified as: Artemisia scoparia community-Leymus scalinus community-Stipa bungeana community-Artemisia gmelinii community-Hippophae rhamnoides community [2].

Anwar et al.,(2009) selected multivariate methods of cluster analysis and principal component analysis to understand corticolous lichen species composition and community structure characteristics in the forest ecosystem of Southern Mounffiins of Urumqi, China. There were thirty nine corticolous lichen species found, which were classified into 5 orders, 13 families and 26 genera. According to the multivariate analysis, three types of communities were classified, including community Lecanora hageni(Ach.)Ach. + Physcia stellaris(L.)Nyl. + L.saligna(Schrad.)Zahlbr; community Physcia aipolia(Humb.)Furm. + Ph.dimidiata(Arn.)Nyl + Cladonia pyxidata(L.)Hoffm; and community Xanthoria fallax (Hepp) Arnold + X.elegans(Link.)Th.Fr, whose structures were significantly influenced by altitude and tree type [3].

The composition and community structure of dominant species were analyzed by Cai et al., (2007) on the basis of multivariate methods of both principal component analysis and cluster analysis with the survey data of phytoplankton in spring, summer, autumn and winter from 1998 to 1999 in the West Guangdong Waters. According to the cluster analysis, phytoplankton species were classified into 2 communities in each season of spring, summer and autumn, with one inshore group and one offshore group, whereas the differentiation of species community was not significant in winter time. The seasonal succession of dominant species was Skeletonema costatum, Navicula subminuscula, Thalassionema nitzschioides, and Thalassiosira subtilis in spring, summer, autumn and winter respectively. However, the freshwater species, Oscillatoria sp. became the dominant species in summer as well [4].

Wang & Peng adopted both species similarity analysis (including coefficient of community, percentage of similarity and coefficient of similarity) and cluster analysis methods to classify plant communities and examine the environmental gradient effects on community succession in Dinghu Mountain, which indicated that Cryptocarya chinensis communities varied with different altitude gradient. Ten plant communities were compared and contrasted, revealing the mutual effects and evolutionary patterns among these communities [5].

1.2　Ecosystem Resilience

Ecological resilience is the capacity of disturbed ecosystem restored into its primitive conditions[6]. Zhang et al., (2013) assessed the ecosystem resilience quantitatively by using social-ecological system (SES) model in Northern Highlands of Yuzhong County, and resulted in the conclusion that the resilience of ecosystem was determined by both drought stress and ecosystem sensitivity to drought condition [7].

To order to assess community resilience and restoration success, Renaud et al., (2013) developed two indices including Community Structure Integrity Index measuring the proportion of species diversity for the reference community in comparison to the restored or degraded community, as well as the Higher Abundance Index assessing the proportion of the species abundance which was higher than the reference community. Three examples were illustrated for the application of two indices, including fictitious communities; A recently restored (2 years) Mediterranean temporary wetland (Camargue in France) for the assessment of restoration efficiency; and a recently disturbed pseudo-steppe plant community (La Crau area in France) assessing the natural community resilience, which demonstrated that these two indices were not only able to assess the static value of ecosystem function, but also to analyze the temporal and spatial dynamics of ecosystem evolution [8]. Nevertheless, compared with Zhang et al., (2013) model, social disturbance was not integrated into Renaud et al., (2013) model.

Additionally, 5 succession phases of the restoration of degraded ecosystem in Jinyun Mountain were investigated by Li et al.,(2007), including Shrubby grass land, Masson Pine early stage, Masson Pine late stage, Coniferous broad-leaved mixed forest and Evergreen broad --- leaved forest stage. Under the same climate conditions, criteria of species diversity, light absorption, community temperature, cumulate cover of arbor and community pole temperature became the main indicators for the succession of ecosystem restoration. However, among these indicators, both cumulate cover of arbor and community pole temperature were identified to be the best two indicators, and the other indicators were advised as the minor ones for consideration [9].

1.3　Ecosystem Sustainability

Ecosystem sustainability is the potential or manifested ability for ecosystem to perpetually sustain its interior composition, structure and function so that ecosystem is able to develop and evolve healthily [6]. Hu Dan (1997) presented methodology for assessment of ecosystem sustainability on the basis of identifying and evaluating ecosystem components, structure and function, which was consisted of 12 items and more than 30 variables, indicating the dynamics of sustainable ecosystem[10]. However, social factors were not considered in this methodology. In comparison, Yu et al., (2007) developed a quantitative index system for the assessment of eco-tourism sustainability in TianMuShan Natural Reserve, which included 25 criteria selected from three aspects: Environment, Society-Culture and Economics. On the basis of this method, a case study in Tianmushan Nature Reserve was introduced to demonstrate sustainability assessment in ecosystem [11].

1.4　Ecosystem Resistance

Ecosystem resistance is the ability of ecosystem to boycott the external disturbance and sustain its primitive conditions[6]. Hou et al., (2012) pointed out that the criteria of assessing eco-resistance were consisted of decomposition rate of ground combustibles, increase of ground combustibles, spontaneous combustion caused by lightning, indigenous pest, invasive pests and occurrence of pest[12]. However, quantitative method (such as the weight of each criterion) was not presented in this research. In comparison, Guo et al., (2012) presented the criteria for the assessment of eco-resistance which were consisted of the degree of pest invasion (or disease infection) and the fire incidence, with a weight of 0.6891 and 0.3109 respectively [13].

1.5　Ecosystem Variance

Ecosystem variance is divided into spatial heterogeneity and functional heterogeneity, which reflects the complex or variance of species distribution pattern and community structure influenced by available resources and environmental conditions [6]. Liu et al., (2010) adopted β Sorenson index to investigate the variability of plant communities of grass land in Ordos, Inner Mongolia of China, which was restored from grazing land. The relations between restoration duration and variability of plant communities was deduced in this research: compared with stabilized sand (25～30 a), higher variability existed in semi- mobile sand (restoration duration:5～10 a) and semi- stabilized sand (restoration duration:15～20 a). β Sorenson index for plant communities with dominant species Artemisia ordosica or Hedysarum laeve (restoration duration:5～20 a) was approximately 1.2, while the variability index of Artemisia ordosica (restoration duration: 30a) sand was twice than that of Hedysarum laeve (restoration duration: 30a), and faster growth rate was reported in Artemisia ordosica (restoration duration: 30a) sand [14].

Zhang et al.,(1988) analyzed the succession of pioneer meadow communities in abandoned farmland located in the high land of Gansu Province South. Heterogeneity index of H1 was deduced in this study, with value ranging from 0 to 1. Two meadow communities were investigated, with H1 heterogeneity indices of 0.11 and 0.15 respectively, which revealed relatively low heterogeneity between them[15].

1.6　Edge Effect

Edge effect typically exists in the ecotone between different plant communities, which is caused by the mutual interactions between different plant species from various communities, leading to characteristics in terms of species composition, configuration and function differed from the original communities [6]. Wang & Peng (1986) quantified the edge effects of plant communities in DingHuShan Nature Reserve by a model, with discussion of both positive and negative effects of community edges [16].

Eugenie et al., (2001) quantified the edge effects on plant communities caused by 6 recent clearcut edges adjacent to Pinus banksiana and Pinus resinosa plantations in the Great Lakes region. 10 sampling plots were randomly placed at 19 distances along a 240 transect which spanned from clearcut, across the edge, into the forest interior, with an estimation of percentage cover of each understory plant species. Species richness was significantly higher in Pinus banksiana lines than Pinus resinosa lines, with 18 and 2 unique species respectively. Species with clear preference for the clearcut, edge habitats or interior were respectively reflected by depth-of-edge influence, with composition gradient examined by the Detrended correspondence analysis (DCA) of distance sampled on the basis of species richness. Finally a synthesis model was designed to calculate the plant species distributions across forest/clearcut edges [17].

2　Species Diversity

2.1　Inter-specific Association, Rank Correlation Coefficient, Segregation Index

Inter-specific association is the mutual association between different species in terms of spatial distribution patterns in various habitats, which is divided into the competition relationship defined by segregation index (negative correlation), as well as interdependence relation calculated by rank correlation coefficient (positive correlation) [6].

On the basis of 25 sampling plots, 375 quadrats and 150 transect lines, Zhang et al., (2013) adopted eight indices of Diffusion Coefficient (C), Negative Binomial Parameters (K), Average Crowed Degree (m＊), Index of Clumping (I), Index of Patchiness (PI), Green index (GI), Cassie index (CA), Moristia index (Iδ ) and Variance of Percentages (VP) to analyze the spatial distribution patterns and overall correlation between dominant plant species in Gansu Donghuang xihu Desert Wetland ecosystems. The results revealed that significant positive correlation existed between dominant species populations in shrub layer and tree layer, whereas significant negative correlation was reported between dominant species in tree-shrub-grass layer and grass layer. Further more, the 2×2Contigency Table of Chi-square statistics, Association Coefficient (AC), Percentage of Co-occurence (PC) and other methods were conducted additionally to analyze the correlation significance and intensity between dominant species, leading to the results that correlation between dominant species was not significant in most cases and logarithm with significantly negative correlation was more than positive one, which indicated various requirements of habitat and resources for different species [18].

Yan et al., (2009) adopted Contingency Table and Spearman Rank coefficient to analyze the inter-specific association and inter-specific covariance between Artemisia annua and its associated plant species in the natural fostering base from 2006 to 2007. The results showed that flooding disturbance led to insignificant effects on inter-specific association, but significant effects on inter-specific covariance. However, flooding effect on inter-specific covariance varied between different species pairs, indicating that inter-specific covariance of paired species was depended on both environmental conditions and ecological characters, which became more sensitive to environmental disturbance than inter-specific association [19].

Wang et al., (2014) applied statistical methods of 2×2 contingency table V ratio, X2 (Yate’ s correction), Ochiai Index (OI), Dice Index (DI), Point Correlation Coefficient (PCC), Jaccard index (JI), Association Coefficient (AC) and Spearman correlation coefficient to analyze the inter-specific association between epiphytic plant species in ancient cultivated tea plantation. For the 127 tea trees measured at individual scale, significant inter-specific association was reported, whereas insignificant association was found among 31 plots measured at plot scale. Indices of both Association Coefficient (AC) and Spearman correlation coefficient well indicated the inter-specific association between epiphytic species in consistence with X2 test, which revealed positive association between Bulbophyllum sp. and Drynaria propinqua, Davallia cylindrica and Liparis elliptica, Dendrobium capillipes and Lysionotus petelotii，as well as negative association between Bulbophyllum ambrosia and Dendrobium capillipes, Bulbophyllum ambrosia and Lysionotus petelotii, Bulbophyllum nigrescens and Dendrobium chrysanthum, Ascocentrum ampullaceum and Peperomia tetraphylla [20].

2.2　Coefficient of Niche Breadth and Coefficient of Niche Overlap

Niche breadth is the total available resources which can be utilized by a species (or other biological unit), and niche overlap is the competition phenomenon that two or more species with similar niche breadth compete for the limited resources in the common space for survival [6].

Field study were conducted by Chen et al.,(2014) to analyze the niche breadth and overlap of 12 plant species on 70 forest plots in Bawangling National Nature Reserve, presenting the descending order of niche breadth for 12 species: Aquilaria sinensis, Nephelium topengii, Camellia sinensis var. assamica, Alseodaphne hainanensis, Keteleeria hainanensis, Podocarpus imbricatus, Firmiana hainanensis, Parakmeria lotungensis, Cephalotaxus mannii, Michelia hedyosperma, Ixonanthes reticulata, Dacrydium pierrei. The results revealed that the niche breadth of a species was determined by its range of spatial distribution; in most cases, higher niche overlap value was usually found between species with broader niche breadth, except Michelia hedyosperma and Firmiana hainanensis species of narrow niche breadth; the low niche breadth of Michelia hedyosperma and Ixonanthes reticulate species partially led to smaller populations, which was advised to give the priority for conservation [21].

Both niche breadth and niche overlap of 10 shrub species and 11 herb species were examined by Gao et al., (2014) under a mixed forest consisted of Picea crassifolia and Betula platyphylla in high hill regions in Datong County, Qinghai Province. The results indicated broader niche breadth for species Potentilla fruticosa and Salix cupularis in shrub layer, as well as species Polygonum viviparum and Fragaria orientalis in herb layer. Higher niche overlap was found usually between populations with broader niche breadth. Nevertheless, some populations with narrow niche breadth also showed high niche overlap. The niche overlap between different species of a genera tended to be smaller, which would be attributed to their evolution and succession [22].

Statistical methods of Variance ratio, χ2-test based on a 2×2 contingency table and the test of association indices (Jaccard, Dice and Ochiai) were selected by Yu et al.,(2012) to examine the inter-specific association of 22 Pyrola decorata communities in Taibai Mountain. Results reported that only 5 paired species showed significant positive association (P<0.05), with 2 paired species showing highly significant positive association (P<0.01), whereas insignificant association was reported between the rest species pairs. For Jaccard index analysis, 84.42% of total species pairs were under 0.25 value of Jaccard index, and 12.31 % of total species pairs ranged from 0.25 to 0.50, while only 3.26% of total species pairs were over 0.50. These results revealed weak inter-specific association between investigated communities which tended to be independent [23].

3　Genetic Diversity

3.1　Hardy-Weinberg Equilibrium

Hardy-Weinberg equilibrium is the principle for the parental generation and their offspring to assess the degree of equilibrium between observed genotypic frequencies and allele frequencies in sexual reproduction process[6]. Both Hardy-Weinberg equilibrium and population structure of 283 Hevea brasiliensis Wickham germplasm were examined and analyzed by Fang et al., (2013), with 25 EST-SSRs loci detected. According to the results, 13 of total 25 EST-SSRs loci deviated Hardy-Weinberg equilibrium. The 283 Hevea brasiliensis Wickham germplasm were divided into 4 groups, and the amount of each group was 155, 110, 61 and 22 respectively. 20 locus combinations (6.67%) were significant linkage disequilibrium (P<0.05), and 5 of them were significant linkage disequilibrium at P<0.01 level [24].

3.2　Genetic Diversity

There are a number of conceptions to quantify genetic diversity, mainly including allele frequency, percentage of polymorphic loci, mean number of alleles per locus, mean expected heterozygosity per locus, Nei’ coefficient of gene differentiation, Nei’ genetic identity.

90 accessions were chosen by Xu et al., (1999) from total 22637 accessions in the National Genebank of soybean species, with selection criteria of nine agronomic traits, including disease resistance to SCN race No.3 and SCN race No.4, rust, SMV, and tolerance to cold, drought, salt, 100 seed weight and protein content. Five maximum and five minimum accessions in the Genebank were selected for comparison for each trait. The genetic diversity of 90 (G. max) soybean and one wild soybean (G. soja) accession were assessed by both agronomic trait analysis and microsatellite DNA or SSR markers. In total twelve pairs of SSR primers were applied and 83 alleles were detected with an average of 6.9 alleles per locus. Simple matching similarity coefficients between each pairs of genotypes were analyzed and clustered by Unweighted Paired Group Method Using Arithmetic Averages (UPGMA), revealing that soybean germplasms could be identified by SSR technique. However, the cluster analysis based on agronomic traits was not identical to SSR markers [25].

The genetic diversity of 38 Paulownia fortunei provenances, with 15 individuals per provenance, was deduced by Li et al., (2011) with technique of inter-simple sequence repeats (ISSR). In total 95 amplified DNA fragments were detected by 9 primers leading to clear and unique polymorphic bands, which were screened from 100 ISSR primers. There were 88 polymorphic loci among 95 amplified DNA fragments, resulting in the percentage of polymorphic loci (PPL) of 92.63%. The PPL at species level ranged from 32.63% (Fuzhou, Jiangxi) to 56.84% (WuZhou Guangxi and Jiu Jiang, Jiangxi) with the mean percentage of 47.16%. The mean values of effective number of alleles (Ne), Nei&acute;s gene diversity index (H) and Shannon&acute;s Information index (I) between different provenances were calculated as 1.3910, 0.2424 and 0.3765 respectively, indicating abundant genetic diversity between them. The Coefficient of Gene Differentiation (GGst) of provenances was 0.3539, and the genetic variation between provenances accounted for 35.39% of total genetic variation, revealing that genetic variation between different individuals of each provenance was higher. Genetic Identity of provenances varied from 0.39 to 0.82, showing the relatively broad genetic basis and abundant genetic variation among provenances. According to Genetic Identity, the provenances of Kaili, Guizhou, and Liuzhou, Guangxi showed closest relationship with Genetic Identity of 0.82, whereas longer genetic distance was reported between Hengyang (Hunan) and Zhuji (Zhejiang) populations, and between Hengyang (Hunan) and Zhenning County (Guizhou) populations, with Genetic Identity of 0.39. In total 38 provenances were classified into 3 groups by UPGMA cluster analysis, with little correlation between genetic distance and geographic distance among those provenances [26].

Genetic diversity of wild soybean population in the region of Beijing China was evaluated by Yan et al., (2008) with 40 primer pairs. In total ten populations were sampled with 28-30 individuals per population. 526 alleles were detected with a mean value of 13.15 per locus. The average value of Expected Heterozygosity per locus (He) and Observed Heterozygosity per locus (Ho) were 0.369 and 1.29% respectively for the wild soybean populations, and the mean Shannon index (I) was 0.658. The mean value of between-population genetic diversity (Hs) and within-population genetic diversity (DST) were 0.446 and 0.362 respectively. The average Coefficient of Gene Differentiation for loci (GGst) between populations was estimated as 0.544. Center-Western ecotype showed more abundant genetic diversity than the Northern and Eastern ecotypes, geographic heterozygosity was found in the genetic divergence patterns of natural populations between the Taihang and the Yanshan mountains. The genetic diversity of drought-tolerant population was poor, indicating the potential value of tolerance gene (s) for breeding [27].

Genetic diversity of totally 13 Cannabis populations from different origins was deduced by Hu et al., (2012) using POPGENE 3.2 Software. AFLP results indicated that the most abundant genetic diversity was found in Yunnan population, with Percentage of Polymorphic Loci (PPL) of 88.82%, Nei&acute;s total genetic diversity (He) of 0.3011, and Shannon Index (I) of 0.4571; and followed by the Heilongjiang population with Percentage of Polymorphic Loci (PPL) of 75.66%, Nei&acute;s total genetic diversity (He) of 0.2572, and Shannon Index (I) of 0.3897. The PPL, Ht and Hs of 13 Cannabis populations was 92.11%, 0.3837 and 0.1640 respectively. Coefficient of genetic differentiation between populations (GGst) was 0.5725, revealing that genetic variation between populations accounted for 57.25% of the total genetic variation, and the other 42.75% of total genetic variation was attributed to the genetic variation between individuals within population. Both genetic distance and genetic identity of Cannabis were calculated on the basis of Nei&acute;s (1978) method, for further analysis of genetic differentiation among populations. Genetic identity among populations ranged from 0.6556 to 0.9258, with the highest value of 0.9258 between Guangxi population and Sichuan population. The genetic identity between Yunnan population and Guizhou population, Yunnan population and Sichuan population were 0.9196 and 0.9173 respectively, while the lowest genetic identity was found between Gansu and Shanxi populations. These findings became the scientific evidence for identification of Cannabis seed and provided the indicators for breeding and evolutionary analysis [28].

The genetic diversity of 120 individuals from six natural populations of Abies chensiensis was analyzed by Li et al., (2012) on the basis of 10 simple sequence repeat markers. The genetic diversity, genetic structure and changes in gene flow between different populations were analyzed, revealing 149 alleles in 10 microsatellite loci with a value of 14.9 as the average number of alleles per locus (A). The effective number of alleles per locus (Ne), the mean expected heterozygosity (He), the mean observed heterozygosities per locus (Ho), the Shannon diversity index (I), the proportion of genetic differentiation among populations (FST), and gene flow between the populations were 7.7, 0.841, 0.243, 2.13, 6.7% and 3.45, respectively. Insignificant correlation was found between genetic distance and geographic distance (r=0.4906, P>0.05). The relatively low genetic diversity was reported in the 6 natural populations of A.chensiensis, and inner-population genetic variation accounted for the majority of total genetic variation [29].

However, it is worthwhile mentioning that the analysis of genetic diversity is significantly influenced by sampling size. For example, the genetic integrity of Sorghum bicolor L. Moench. was studied by Xu et al.,(2012) adopting SSRs technique, as one of the most commonly used markers for the assessment of genetic diversity, population structure studies and marker-assisted selection. In total ten groups of sorghum with different sample sizes (including 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 individuals per group) were selected randomly and 25 polymorphic microsatellite primers were conducted for the assessment of genetic diversity indices (the average number of alleles, effective number of alleles, Shannon Index, Observed Heterozygosity, Expected Heterozygosity, Percentage of Polymorphic loci and the Frequency of Rare Alleles). According to the correlation between genetic diversity indices and sample sizes, the number of alleles, effective number of alleles, Shannon index increased correspondingly to the increase of sample sizes, with the peak increase rate at a sample size of 40 individuals. Consequently, the sample size of 40 individuals accounted for 98.5% of total numbers of alleles, 99.1 % of total effective numbers of alleles and 98.5% of total Shannon indexes among 100 individuals, indicating the 40 individuals as optimal sample size for the SSRs technique in gorghum integrity assessment [30].

3.3　Genetic Variation

Genetic variation assessment mainly adopts the conception of genetic distance and evolutionarily significant unit (ESU), usually deduced by cluster analysis, PCA, or evolutionary tree analysis. However, both cytological and DNA molecular markers are able to achieve this.

The karyotype of characteristics and evolutionary relationships among the traditional Chinese medicine Sophora flavescens from four different origins was investigated by Duan et al., (2014). The karyotypes and chromosome numbers of Sophora flavescens were calculated by using root-tip squashing method and clustered by the karyotype resemblance-near coefficient, which linked all the genetic materials.

The chromosome numbers of Sophora flavescens from Chifeng Inner Mongolia， Changzhi Shaanxi, Meixian Shaanxi and Chengdu Sichuan all were 18 and belonged to 1 A type, with karyotype formulas of 2n = 2x = 18 = 18m(2SAT), 2n = 2x = 18 = 14m(1SAT) + 4sm(1SAT), 2n = 2x = 18 = 16m(2SAT) + 2sm and 2n = 2x = 18 = 18m(2SAT) respectively. The karyotype asymmetry index of Sophora flavescens from Chifeng Inner Mongolia, Changzhi Shaanxi, Meixian Shaanxi and Chengdu Sichuan were 56.32%, 57.88%, 59.41 % and 54. 32%, respectively. According to Karyotype clustering analysis, the closest genetic relationship was reported between S. flavescens from Chengdu and Chifeng, with the highest karyotype resemblance-near coefficient of 0.9929, and their evolution distance was 0.0072. In comparison, the farthest genetic relationship was found between S. flavescens from Chengdu and Meixian, with the lowest karyotype resemblance-near coefficient of 0.9533, and their evolution distance was 0.0478. Karyotype of Sophora flavescents from Chengdu was the most primitive among them, followed by those from Chifeng, Changzhi and Meixian. The conclusion of this study provided cytological information for germplasms identification, and became the basis of genetic variation and genetic relationship analysis of Sophora flavescens[31].

To explore the genetic distance in evolutionary process among 6 Bupleurum medical plants, including B.longeradiatum, B.smityii, B. longicaule var. amplexicaule, B. scorzonerifolium, B. chinense, B. falcatum, karyotype parameters identification was adopted by Song et al.,(2012), which used the cluster analysis of karyotype resemblance-near coefficient and evolutionary distance, based on the calculation of the relative length, arm ratio, centromere index. The highest karyotype resemblance-near coefficient (0.9920) and smallest evolutionary distance (De = 0.0080) existed between B. scorzonerifolium and B. chinense, revealing the closest relationship between them. In comparison, the minimum karyotype resemblance-near coefficient (0.4794) and the maximum evolutionary distance (De = 0.7352) was reported between B. smityii and B. falcatum [32].

In Luo et al., (2006) study, 200 two-line combinations were matched by mating 5 photo/ thermal-sensitive genic male-sterile lines and 40 varieties. The genetic distance (GD) between 5 sterile lines and 40 varieties was examined by SSR markers, with the discussion between genetic distance and heterosis. The correlation of genetic distance varied with yield per F1 plant, heterobeltiosis of F1 yield, effective panicles, panicle length spikelets per panicle, density of spikelet setting, seed setting rate, and 1000 grain weight, due to various gene materials or different range of genetic distance. When the genetic distance between Tianfeng S and its paternal varieties ranged from 0.6286 to 2.5257, the correlation of genetic distance with yield per F1 plant or its heterobeltiosis appeared to be significant at P<0.05 level; As the genetic distance between Peiai 64S and paternal varieties ranged from 0.8247 to 1.5315, their correlation between genetic distance and yield per F1 plant was significant at P<0.05 level; furthermore, for all parents of two-line combinations with genetic distance ranged from 0. 5333 to 1.5, the correlation between heterobeltiosis of yield per F1 plant and genetic distance appeared to be significant at P < 0. 05 level; the correlation of yield per F1 plant with genetic distance was significant at P < 0. 05 level, as the genetic distance ranged from 0.5333 to 1.0; the significance of correlation between yield per F1 plant and genetic distance was at P < 0. 01 level, when genetic distance ranged at three layers: between 1.0 and 1.5; 0.5333 and 1.5; 0.5333 and 2.5257. This genetic distance analysis indicated the appropriate range for mating combinations of hybrid rice [33].

An endemic species of Sinomanglietia glauca, which is unique in Yichun in Jiangxi Province and Yongshun of Hunan Province in Central China, has been listed in Category I of the National Key Protected Wild Plants in 1999 (as asynonym of Manglietia decidua). Xiong et al.,(2014) study covered all of four populations of S. glauca, which had been identified so far, and the genetic diversity and genetic variation was investigated by nuclear microsatellite markers. According to the results, S. glauca showed relatively low genetic diversity with the average number of alleles (A) of 2.604 and the mean expected heterozygosity (HE) of 0.423, but presented significant genetic variation with high genetic differentiation FST of 0.425. Cluster analysis by STRUCTURE and Principal Coordinated Analysis indicated that Jiangxi and Hunan populations were classified into two independent groups. Only one natural breeding population was identified in Jiangxi, while two were found in Hunan, with significant genetic variation. The heterozygosity was found to be excessive significantly, which might be caused by allelic frequencies differed between male and female parents occasionally in a small population. The results indicated that S. glauca would experience bottleneck(s) in recent evolution history, which led to reduction of population size, loss of genetic diversity and strong population differentiation. The genetic diversity study resulted in the advices that S.glauca should be classified as three conservation units according to their evolutionary units: Jiangxi unit and Hunan unit, and the Hunan populations could be further divided into two sub-management units (YPC and LJC) [34].

3.4　Genetic Contribution Rate

Genetic contribution rate was firstly proposed by Petit et al., (1998). For the standardization of the allelic richness results across populations, the technique of rarefaction is established to facilitate assessment of the expected number of different alleles among equal-sized samples derived from different populations, which is divided into two components: the first is relevant to the degree of population diversity and the second is related to its divergence from the other populations [35].

4　Conclusion

As discussed above, the multivariate methods of evolutionary ecology study become essential to classify the communities and sub-populations at ecosystem layer and at genetic layer respectively for biodiversity assessment work. Due to three thorny issues threatening biodiversity defined by Chinese Biodiversity Conservation Strategy and Action Planning (2010-2030), biodiversity monitoring projects need to be implemented at three layers, and application of 3S technology on biodiversity monitoring with high-resolution remote sensing imagines is advised by Liu et al., (2014) [36], e.g. investigation of the distribution change of dominant plant species over ten years in a national park by using object-oriented classification of Quickbird remote sensing imagines, and then the temporal and spatial dynamics of biodiversity evolution at both ecosystem layers and species layers should be discussed on the basis of evolutionary ecology study. Additionally, biodiversity monitoring projects should be conducted according to the Technical Guidelines for Biodiversity Monitoring --- Terrestrial Vascular Plant (HJ 710.1-2014).

For genetic layer, a combination of cytological markers and DNA molecular markers is advised by Liu et al., (2014) for classification of sub-populations [37], mainly due to the consideration of saving the cost and reliability of differentiation methods. Nevertheless, it is worthwhile mentioning that the conclusion drawn by multivariate cluster analysis between cytological markers and DNA molecular markers would not be consistent, possibly due to gene recombination and gene mutation. Consequently, the multivariate cluster analysis for sub-population classification would be more reliable on the basis of DNA molecular markers. The software of computing both polygenetic (gene by gene analysis) and phylogenomic (the whole genome comparison) methods is suggested by Ahmed (2009) [38].

According to the Environmental Standard on Classifying the Categories of Genetic Resources (HJ 626-2011) in China, there are three kinds of DNA molecular methods pointed out for ranking genetic resources (or endangered species) between categoryⅠand categoryⅡ, including assessment of genetic diversity, evolutionarily significant unit (ESU), or genetic contribution rate, which have been substantially discussed above. However, it is worthwhile noting that any one of these three methods is acceptable for environmental engineers to conduct this environmental standard, although there is debate between these methods in terms of selection priority, such as Chen et al., (2002) [39].

According to the Chinese Biodiversity Conservation Strategy and Action Planning (2010-2030), prediction of climate change effects on biodiversity conservation is significant, and the application of CTMs model on prediction of climate change effects on biodiversity is advised by Liu Huan (2014) [40]. However, the knowledge of evolutionary ecology study derived from the biodiversity monitoring projects in the past may be required for this prediction work.

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