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生物的進(jìn)化范文第1篇

【關(guān)鍵詞】達(dá)爾文；進(jìn)化論；意義；演變；生物

進(jìn)化生物學(xué)是生物學(xué)中最基本的理論之一，它是由大家都熟悉的達(dá)爾文提出的生物進(jìn)化論構(gòu)成的。即指出關(guān)于生物由低級到高級，由簡單到復(fù)雜逐步演變過程的學(xué)說。隨著進(jìn)化論的發(fā)展，產(chǎn)生了現(xiàn)代綜合進(jìn)化論，而當(dāng)今演化學(xué)絕大部分就是以查爾斯·羅伯特·達(dá)爾文的演化論為指導(dǎo)。除此之外，埃爾溫·薛定諤的《生命是什么》為主體方向，進(jìn)化論已為當(dāng)代生物學(xué)的核心思想之一。其進(jìn)化論有三大經(jīng)典證據(jù)：比較解剖學(xué)、古生物學(xué)和胚胎發(fā)育重演律。進(jìn)化論除了作為生物學(xué)的重要分支得到重視和發(fā)展外，其思想和原理在其它學(xué)術(shù)領(lǐng)域也得到廣泛的應(yīng)用，并形成許多新興交叉學(xué)科，如演化金融學(xué)、演化證券學(xué)、演化經(jīng)濟(jì)學(xué)等。

“物競天擇，適者生存”是進(jìn)化論的基本含義，也就是說進(jìn)化論里生命的演進(jìn)是以自然選擇和適應(yīng)自然的能力為標(biāo)準(zhǔn)的。達(dá)爾文的進(jìn)化論也曾遭受過質(zhì)疑，曾有一個學(xué)說叫做：智能設(shè)計假說（又稱“智慧設(shè)計假說”），這一種思想認(rèn)為，“宇宙和生物的某些特性用智能原因可以更好地解釋，而不是來自無方向的自然選擇?！边@一假說的主要支持者包括發(fā)現(xiàn)研究院等基督教智囊團(tuán)體，他們認(rèn)為，智能設(shè)計假說是同等重要的科學(xué)理論，甚至比現(xiàn)有的科學(xué)理論對生命起源問題的解釋更加合理。

但是智能設(shè)計并沒有進(jìn)化論的能力，相反的，智能設(shè)計本來就是悖論。如果智能先于設(shè)計存在，那么智能必定有個存在的“環(huán)境”——請問沒有被智能設(shè)計的環(huán)境產(chǎn)生之前，智能存在于什么樣的環(huán)境中？那么這個環(huán)境又是誰設(shè)計的？如果說根本沒有環(huán)境，那么智能設(shè)計者也可以說根本不存在，因?yàn)闆]有背景環(huán)境，何來本體。

另外，智能設(shè)計是從開始到現(xiàn)在一直在設(shè)計嗎？如果是，它既然是“智能設(shè)計”且超越一切被設(shè)計物，應(yīng)當(dāng)同時設(shè)計出所有的被設(shè)計者，而不必待被設(shè)計者自行設(shè)計后來物；或者說，對它而言，開始到現(xiàn)在一直都是“開始”，為什么我們不是從開始到未來永生的？

如果不是，那么它開始設(shè)計和終止設(shè)計都分別在什么時候？它又是否知道它設(shè)計出的物中一部分有自行設(shè)計的能力？如果承認(rèn)被設(shè)計物有設(shè)計能力，只不過這種自行設(shè)計的能力就是它賦予的，那么智能設(shè)計只能解釋“第一因”或者“第一至第N個因”，卻不能適用于全部物。而如果是這樣，它怎么能算得上“智能全知全能”？

因此到目前為止，達(dá)爾文提出的進(jìn)化論到目前為止即使受到過質(zhì)疑，依然被承認(rèn)和認(rèn)可。生物的進(jìn)化沿著怎樣的規(guī)律，探討這個問題有助于對物種滅絕和各種生物的進(jìn)化演變提供理論依據(jù)

那么生物進(jìn)化論在現(xiàn)實(shí)生活中有哪些意義呢，這就主要從物種方面開始討論：

（1）物種形成是生物對不同生存環(huán)境適應(yīng)的結(jié)果生物生存的環(huán)境具有變化性和異質(zhì)性。環(huán)境隨時間的變化導(dǎo)致生物的適應(yīng)進(jìn)化，環(huán)境在空間上的異質(zhì)性導(dǎo)致生物的分異（性狀分歧），分歧的結(jié)果是不同類型的生物，即物種的形成。不同的物種適應(yīng)不同的局部環(huán)境，不能設(shè)想有能夠適應(yīng)各種不同環(huán)境的一種生物。各種生物在進(jìn)化過程中不斷分化，歧異產(chǎn)生更多的物種意味著也能夠占領(lǐng)更多的生存環(huán)境，生物的不連續(xù)性是生物對環(huán)境的不連續(xù)性（異質(zhì)性）的適應(yīng)對策。

（2）物種間的生殖隔離保證了生物類型的穩(wěn)定性物種在種間生殖隔離的存在下能保持物種相對穩(wěn)定的基因庫，沒有種間的生殖隔離就會使已獲得的適應(yīng)因雜交而溶化丟失。所以物種的存在使得生物及保持遺傳的相對穩(wěn)定，又使進(jìn)化不致停滯，保持進(jìn)化的不可逆性，成為進(jìn)化的基本途徑。

（3）物種是生物進(jìn)化的基本單位。

物種具有可變化性以適應(yīng)環(huán)境的變化，但這只是相對的，一個物種不能永遠(yuǎn)適應(yīng)變化著的環(huán)境。當(dāng)環(huán)境變化的速度范圍超出原有物種的適應(yīng)能力，滅絕就會發(fā)生，這時新的環(huán)境也有待新的物種去占領(lǐng)。生態(tài)系統(tǒng)也要適應(yīng)環(huán)境的變化，物種的更替（種形成和滅絕）和種間生態(tài)關(guān)系的改變可以使生態(tài)系統(tǒng)適應(yīng)變化的環(huán)境，生物與環(huán)境之間從不平衡又達(dá)到新的平衡，從而推動整個生物界的進(jìn)化。在這種宏觀大進(jìn)化的過程中每一步都是由物種進(jìn)化所推動，物種是小進(jìn)化的終點(diǎn)，同時又是大進(jìn)化的起點(diǎn)，所以說物種是生物進(jìn)化的基本單位。

（4）物種是生態(tài)系統(tǒng)中的功能單位不同的物種因其不同的適應(yīng)特征而在生態(tài)系統(tǒng)中占有不同的生態(tài)位。因此，物種是生態(tài)系統(tǒng)中物質(zhì)與能量轉(zhuǎn)移和轉(zhuǎn)換的環(huán)節(jié)，是維持生態(tài)系統(tǒng)能流、物流和信息流的關(guān)鍵。 [科]

【參考文獻(xiàn)】

[1]張昀.生物進(jìn)化，北京：北京大學(xué)出版社，1988.

[2]李難.進(jìn)化生物學(xué)基礎(chǔ)，北京：高等教育出版社，2005.

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

[4]趙曉明，宋秀英.生物遺傳進(jìn)化學(xué)，北京：中國林業(yè)出版社，2003.

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

生物的進(jìn)化范文第2篇

現(xiàn)代生物進(jìn)化理論的主要內(nèi)容有：

1、種群是生物進(jìn)化的基本單位，生物進(jìn)化的實(shí)質(zhì)在于種群基因頻率的改變。

2、突變和基因重組、自然選擇、隔離是物種形成的三個基本環(huán)節(jié),通過它們的綜合作用,種群產(chǎn)生分化,并最終導(dǎo)致新物種的形成。

3、突變和基因重組產(chǎn)生生物進(jìn)化的原材料。

4、自然選擇使種群的基因頻率定向改變并決定生物進(jìn)化的方向。

生物的進(jìn)化范文第3篇

關(guān)鍵詞：進(jìn)化

自然誘導(dǎo)――生物自組織　遺傳　變異

中圖分類號：Q3

文獻(xiàn)標(biāo)識碼：A

文章編號：1007．3973(2011)010．073．02

物種是不斷進(jìn)化的，對此我們都不會再有任何懷疑，已成定論。然而，物種具體是如何進(jìn)化的呢?對此，還一直處于爭論之中，最有影響的是達(dá)爾文的自然選擇學(xué)說，影響了人們一百多年，并被當(dāng)作正式理論寫進(jìn)了教科書。但是，隨著人們對生命現(xiàn)象了解的深入，越來越多的進(jìn)化現(xiàn)象無法用自然選擇進(jìn)行解釋。

下面，我們將自然環(huán)境在生物進(jìn)化中的作用重新定位，由對生物間接性的自然選擇作用改為直接性的自然誘導(dǎo)作用，并將生物自身在進(jìn)化中的作用命名為生物自組織，建立起以自然誘導(dǎo)一生物自組織為核心的進(jìn)化機(jī)制。自然誘導(dǎo)即自然環(huán)境的誘發(fā)、向?qū)В@示了自然環(huán)境在生物進(jìn)化過程中的直接作用。生物自組織是指生物本身的一種自我組織、自我構(gòu)建，與自然環(huán)境的誘導(dǎo)作用緊密相連，并不是我們常說的不受環(huán)境影響的孤立的自組織。

從信息的角度分析，可以將自然誘導(dǎo)看作是自然環(huán)境變化信息的一種傳遞，生物自組織就是生物對自然環(huán)境變化信息的一種處理。自然誘導(dǎo)一生物自組織就是自然環(huán)境的變化信息傳遞給生物，生物對傳遞給它的變化信息進(jìn)行處理。當(dāng)自然環(huán)境發(fā)生變化的時候，變化的環(huán)境信息會傳遞給生物，生物對這種變化信息會進(jìn)行處理，從而形成生物的進(jìn)化。

水毛茛有兩種不同的葉片，在水面上呈片狀，而在水下則絲裂成帶狀，不同的環(huán)境誘導(dǎo)產(chǎn)生了不同形態(tài)的葉子。水毛茛水上葉和水下葉的不同，為我們認(rèn)識水生植物向陸生植物的進(jìn)化提供了參考，水下葉相當(dāng)于水生植物的葉子，水上葉相當(dāng)于陸生植物的葉子。在植物的進(jìn)化中，由于地殼的運(yùn)動，一些海洋地區(qū)變成了沼澤、陸地，生存于這些地區(qū)的水生植物會在陸生環(huán)境的誘導(dǎo)下自組織產(chǎn)生適應(yīng)于陸地環(huán)境的性狀特征，并不斷發(fā)展進(jìn)化，形成陸生植物。水生植物向陸生植物的進(jìn)化，并不是一開始就完全脫離水環(huán)境的，而是生活在半水生半陸地的沼澤中，逐漸脫離水環(huán)境甚至適應(yīng)干旱環(huán)境。

許多旱生植物的葉子很小甚至縮小成針刺狀，而根系發(fā)達(dá)。仙人掌的葉子在干旱的沙漠中變?yōu)榇虪?，依靠肉質(zhì)莖進(jìn)行光合作用。生活在沙漠中的豆科植物駱駝刺，地上部分只有幾厘米，而地下部分可以深達(dá)十幾米，根系覆蓋的面積達(dá)六七百平方米，發(fā)達(dá)的根系是旱生植物增加水分吸收的重要途徑。這些性狀不可能通過基因突變產(chǎn)生，它們是在干旱環(huán)境的誘導(dǎo)下植物自身的生長發(fā)育發(fā)生變化而產(chǎn)生的。它們對環(huán)境的適應(yīng)無需自然選擇，變異本身就是一種適應(yīng)性變異。

會飛的昆蟲由于某種原因定居到海島上之后，新的環(huán)境會改變它們的生長發(fā)育。大風(fēng)、惡劣的氣候、潮濕的環(huán)境、新的食物、光照的變化都可能會導(dǎo)致昆蟲翅膀的生長發(fā)育發(fā)生障礙，形成殘翅或無翅的昆蟲，這種影響持續(xù)存在最終使昆蟲的翅膀趨于退化消失。非洲馬德拉島上的甲蟲翅膀發(fā)育不全或退化消失，應(yīng)該就是這樣形成的?？炕蛲蛔兺瓿杉紫x翅膀的退化消失，恐怕所有的甲蟲早就掉到海里滅絕了，還沒有突變形成一個翅膀殘缺的甲蟲呢。

當(dāng)人類穿上衣服、使用火之后，人類抵御寒冷的能力就在一代一代的退化，抗寒性逐漸降低，表現(xiàn)為與此功能相關(guān)的一些性狀特征發(fā)生變異，如體毛、皮膚腠理、皮膚脂肪厚度、皮膚毛孔等。而且，這些組織器官的變化還會進(jìn)一步誘導(dǎo)其它組織器官生長發(fā)育的變化，如肺臟、血管、體液調(diào)節(jié)系統(tǒng)、神經(jīng)調(diào)節(jié)系統(tǒng)等。在人類的進(jìn)化中，一系列的進(jìn)化特征很明顯都不是自然選擇的結(jié)果，如人的大腦、壽命、身高、牙齒和體毛等，這些特征的進(jìn)化是在自然環(huán)境的誘導(dǎo)刺激下再經(jīng)過人類自身生長發(fā)育的重新調(diào)整而完成的。人類的進(jìn)化以鐵的事實(shí)否定了基因突變一自然選擇理論，自然誘導(dǎo)一生物自組織才是人類進(jìn)化所遵循的機(jī)制。

動物眼睛的前體是一些原始的感光細(xì)胞，用來感知光線，這種感光細(xì)胞的分化是在光的作用下形成的。隨著環(huán)境的復(fù)雜化，感光細(xì)胞在光的不斷誘導(dǎo)下逐漸自組織出了功能更加完善的復(fù)雜結(jié)構(gòu)，其中包括感光點(diǎn)周圍色素的沉積，視網(wǎng)膜的感光神經(jīng)組織及其他一些附屬結(jié)構(gòu)的形成。光在地球上所有動物眼睛的形成中都起著決定性的作用，是誘導(dǎo)眼睛進(jìn)化的一個重要環(huán)境因子。因?yàn)檠劬Y(jié)構(gòu)的復(fù)雜性，達(dá)爾文無法解釋眼睛這種完美地器官是怎么逐漸進(jìn)化的，其實(shí)任何一種生物的結(jié)構(gòu)都是完美復(fù)雜的。就連結(jié)構(gòu)看上去十分簡單的細(xì)菌鞭毛，如果分析其成分時，也會驚嘆它的復(fù)雜性，是基因突變所無法完成的。

在自然界中，動物的眼睛千奇百怪，它們眼中的世界也是不同的，這些都與它們所處的環(huán)境特征有關(guān)，特別是光的特征。一些鳥類習(xí)慣在夜晚活動，如貓頭鷹，它的視網(wǎng)膜中沒有錐狀細(xì)胞，無法辨認(rèn)色彩，但可以在微弱的光線中捕捉獵物。本來眼睛良好的動物，由于生存環(huán)境改變，由光線充足的環(huán)境生存到黑暗的環(huán)境中，動物的眼睛就會退化而成為擺設(shè)。一些穴居動物，具有不發(fā)達(dá)的眼睛，外為表皮所覆蓋。但是，把它的早期胚胎放在有光線的地方培養(yǎng)，它的眼睛各方面發(fā)育良好。深海的魚類，因?yàn)榭床坏焦?。眼睛退化。深海魚類之所以眼睛瞎，是因?yàn)樵谏詈Ｖ泄饩€微弱甚至不存在，使魚的眼睛不能正常的發(fā)育，最終導(dǎo)致它們眼睛的退化，而不是有眼睛的魚類不適用環(huán)境被淘汰了。從猿猴向人進(jìn)化的過程中，眼睛對光線的分辨、眼睛的視力等也是不斷進(jìn)化的，這些都與其所處環(huán)境中的光特征、生活習(xí)性有關(guān)。生活在原始森林里的猿猴對光線的分辨不強(qiáng)，只看到較近的東西，不像人看得那么遠(yuǎn)。

有些生物變異可以遺傳，有些生物變異不可以遺傳，生物的變異性狀是否具有遺傳性根本該變異性狀是否與自然環(huán)境相協(xié)調(diào)。雜交育種是我們培育品種的一個重要方法，所得的品種具有很多明顯的優(yōu)勢性狀，表現(xiàn)為器官發(fā)達(dá)、產(chǎn)量增加、抗性增強(qiáng)等，我們稱之為雜種優(yōu)勢。雜種優(yōu)勢一個重要的特點(diǎn)就是具有不穩(wěn)定性，很多雜交品種在雜種二代就沒有任何優(yōu)勢性狀可言。雜種優(yōu)勢之所以不能穩(wěn)定遺傳就在于它的優(yōu)勢性狀與自然環(huán)境是不協(xié)調(diào)的，自然環(huán)境會誘導(dǎo)它自身的遺傳物質(zhì)系統(tǒng)重新組織，導(dǎo)致優(yōu)勢性狀消失。

生物的變異性狀是否具有遺傳性不在于是否發(fā)生在DNA分子水平，即使是生物的變異性狀發(fā)生在DNA分子水平，相應(yīng)的性狀只要不與環(huán)境相協(xié)調(diào)，也會由于自然環(huán)境的誘導(dǎo)作用而使相應(yīng)的基因發(fā)生沉默，從而使變異性狀不能遺傳給下一代。某一物種遷移到某一寒冷的環(huán)境中后，該物種的抗寒機(jī)制會得到加強(qiáng)，同時表現(xiàn)出相應(yīng)的抗寒特征，該物種長期生存在寒冷的環(huán)境中，相應(yīng)的抗寒特征就可以遺傳，也可以說物種的抗寒特征與環(huán)境的寒冷是相協(xié)調(diào)、相統(tǒng)一的。非DNA分子水平的生物變異只要與環(huán)境相協(xié)調(diào)，也是會遺傳給下一代的。

生物的進(jìn)化范文第4篇

關(guān)鍵詞：主動適應(yīng)被動適應(yīng)自然選擇社會選擇可持續(xù)發(fā)展

從1859年達(dá)爾文的《物種起源》出版至今，由于對進(jìn)化論的理解不深，因而出現(xiàn)了2種極端現(xiàn)象：一是生物進(jìn)化中的自然主義傾向，即忽視社會選擇的巨大作用，僅僅將生物進(jìn)化歸結(jié)為自然選擇作用的結(jié)果；二是絕對的人類中心主義傾向，過分夸大社會選擇的作用，而低估了自然選擇在生物進(jìn)化中的作用。

目前，全球生物多樣性的減少和生態(tài)環(huán)境的不斷惡化，使我們必須把大尺度上的生物進(jìn)化和小尺度上的人類可持續(xù)發(fā)展結(jié)合起來，才能把生物多樣性保護(hù)落實(shí)到人類的生產(chǎn)生活實(shí)踐活動中，保證人類的各種行為不偏離可持續(xù)發(fā)展的軌道，使人類走上真正的可持續(xù)發(fā)展之路。因此，筆者從一個全新的角度來探索生物的進(jìn)化和人類可持續(xù)發(fā)展的問題，旨在為生物進(jìn)化大背景下人類的可持續(xù)發(fā)展研究奠定基礎(chǔ)。

1生物進(jìn)化與生物的適應(yīng)

達(dá)爾文在《物種起源》中闡明了生命是進(jìn)化的產(chǎn)物，現(xiàn)代的生物是在長期進(jìn)化過程中發(fā)展起來的，給神創(chuàng)論以巨大打擊，使生物學(xué)擺脫了神學(xué)的羈絆…。達(dá)爾文認(rèn)為由于隨機(jī)變異的產(chǎn)生和自然選擇的作用，適應(yīng)的變異被保留了下來，而不適應(yīng)的變異則被淘汰。因此，自然選擇的過程，就是生存斗爭及適者生存的過程，適應(yīng)是生物進(jìn)化的最終結(jié)果。

進(jìn)化論及進(jìn)化生物學(xué)的研究發(fā)現(xiàn)多細(xì)胞生物起源于單細(xì)胞生物，結(jié)構(gòu)復(fù)雜的生命體總是源于結(jié)構(gòu)簡單的生命體。據(jù)此，部分學(xué)者認(rèn)為進(jìn)化就是指事物由低級到高級的變化發(fā)展過程。生物的進(jìn)化就是生物體由低級到高級、從簡單到復(fù)雜的前進(jìn)發(fā)展過程，其中存在著一個從低級到高級的方向性，這和達(dá)爾文對生物進(jìn)化這一基本問題的理解是相背的，這是人類中心說的判定標(biāo)準(zhǔn)在生物進(jìn)化論中的體現(xiàn)。即使現(xiàn)代的進(jìn)化觀也并未認(rèn)為“進(jìn)化就是革命性的進(jìn)步”，而把“進(jìn)化”定義為“進(jìn)化是生物適應(yīng)性的改變和生物群體多樣性的變化”，和達(dá)爾文的進(jìn)化理論一致，在進(jìn)化理論中堅(jiān)持了徹底的唯物主義，是達(dá)爾文整個進(jìn)化理論體系和現(xiàn)代進(jìn)化觀的奠基石。

適應(yīng)是生物進(jìn)化的最終結(jié)果。生物的進(jìn)化是生物物種的趨異化過程，是生物的隨機(jī)變異和自然選擇的過程。自然選擇是對隨機(jī)的多種變異的選擇，大自然為選擇者，而隨機(jī)的各種變異成為被選擇的對象，被大自然最終所選擇的那種變異就得以保存下來，而同一物種中的其他變異就被淘汰，得以保存的變異就是適應(yīng)大自然的?？梢?，生物物種產(chǎn)生的各種變異，無論是變異的程度上、方向上，還是變異范圍的大小、數(shù)目的多少上，都是隨機(jī)的、不定向的，但又是客觀存在的。而大自然的選擇相對于物種的變異來看，卻是有一定方向的。自然選擇的方向性和物種變異的隨機(jī)性，客觀上就決定了生物對自然的適應(yīng)是一種被動的過程，生物體在結(jié)構(gòu)、功能上對自然的適應(yīng)都是自然選擇的結(jié)果。生物對自然的適應(yīng)性總是滯后于自然對生物物種的選擇性，也就是說，生物物種對環(huán)境的適應(yīng)是相對的、暫時的、有條件的，而不適應(yīng)才是絕對的、永恒的。這就從根本上澄清了達(dá)爾文自然選擇理論和現(xiàn)代進(jìn)化論所基于的客觀事實(shí)，在進(jìn)化論中堅(jiān)持了徹底的唯物主義，劃清了進(jìn)化論和神創(chuàng)論的界限。

2自然選擇與社會選擇

生存斗爭及適者生存的過程就是自然選擇的過程。除此之外，還有另外1種選擇——社會選擇也與生物的進(jìn)化密切相關(guān)。伴隨著人類社會工業(yè)文明的開始，現(xiàn)代工業(yè)和現(xiàn)代農(nóng)業(yè)的日新月異，市場經(jīng)濟(jì)和資源環(huán)境私有制的全球化浪潮的沖擊，加上當(dāng)代生物工程技術(shù)的飛速發(fā)展，人類對生物界的改造力度越來越大，表現(xiàn)在一些物種逐漸消失；一些物種數(shù)量急劇減少，成為瀕危物種；一些物種地理分布區(qū)域大幅度縮?。灰恍┪锓N生活習(xí)性及部分性狀發(fā)生改變；不時有新品種出現(xiàn)等現(xiàn)象，表明人類的社會實(shí)踐活動對生物物種的演化具有不可低估的選擇作用，這種選擇稱為社會選擇。社會選擇是人類主動適應(yīng)自然環(huán)境的表現(xiàn)和手段，是人類為了求得自身的生存和發(fā)展，更好地適應(yīng)自然的一種必然。從本質(zhì)上說，人類的農(nóng)業(yè)生產(chǎn)、工業(yè)生產(chǎn)和科學(xué)實(shí)踐活動等都是人類自主選擇的結(jié)果，無論是農(nóng)業(yè)生產(chǎn)還是工業(yè)生產(chǎn)以及科學(xué)實(shí)踐活動等人類行為的發(fā)生發(fā)展和演化等各個方面都屬于社會選擇的范疇。

事實(shí)證明，現(xiàn)在人類社會選擇的力量的確是越來越強(qiáng)大，無論是對自然的改造力還是對自然的破壞力都超過了人類發(fā)展歷史上的任何一個時期。但是，人類、人類社會本身以及社會選擇等都是自然選擇的結(jié)果，都是在自然選擇的基礎(chǔ)上發(fā)揮效能的。在一定程度上，社會選擇是人類社會對自然選擇作用的一種應(yīng)答和反映，可以看作是生物與環(huán)境相互聯(lián)系、相互作用的一個典型。但社會選擇一經(jīng)發(fā)生后，便有其獨(dú)立作用的一面，可以和自然選擇作用一道共同作用于生物的進(jìn)化過程。

自然選擇和社會選擇的辯證關(guān)系表現(xiàn)在：一方面，當(dāng)二者一致時，社會選擇對自然選擇起到了促進(jìn)和加速的正向作用，使自然選擇的力度、范圍、時效得以加強(qiáng)，而自然選擇使社會選擇的目標(biāo)得以快速實(shí)現(xiàn)，二者互相促進(jìn)，共同加速生物物種的演化。另一方面，當(dāng)二者不一致時，有3種情況：

①當(dāng)自然選擇的力量大于社會選擇時，生物物種的演化由自然選擇所控制，社會選擇在一定程度上被抑制，自然選擇成為了社會選擇的阻力。這種現(xiàn)象在人類的動植物新品種的選育過程中表現(xiàn)得最為明顯。

②當(dāng)二者力量近于相等時，自然選擇和社會選擇都在自己一定的范圍內(nèi)作用，社會選擇的目標(biāo)停留在研究成果階段，無法有效推廣和應(yīng)用，而自然選擇也以其自身的作用規(guī)律對生物進(jìn)行著選擇。

③當(dāng)自然選擇的力量小于社會選擇時，社會選擇的結(jié)果在自然界中得以快速體現(xiàn)，自然所固有的一些平衡體系被打破，自然選擇的方向被改變，社會選擇在一定時空范圍內(nèi)控制著生物的演化。

2種選擇的相互作用是一個動態(tài)的過程。從人類社會誕生起，2種選擇過程都直接或間接地貫穿在每一個具體的物種的演化過程當(dāng)中。但是，社會選擇的對象、原始材料和最終歸宿都統(tǒng)一在自然界當(dāng)中，社會選擇無論多么強(qiáng)大。都必須以自然選擇為基礎(chǔ)。因此，正確的做法是在尊重自然和自然規(guī)律的前提下，充分發(fā)揮社會選擇對生物和環(huán)境的再創(chuàng)造作用，同時利用社會選擇來抑制或從根本上扭轉(zhuǎn)對人類或自然界(如物種多樣性及生態(tài)環(huán)境等)都不利的自然選擇，或減緩各種對物種多樣性、生態(tài)系統(tǒng)的平衡具有毀滅性打擊的自然災(zāi)害等，降低災(zāi)害對自然環(huán)境的破壞力，保護(hù)生物的多樣性。

3社會選擇與可持續(xù)發(fā)展的關(guān)系

可持續(xù)發(fā)展本質(zhì)上是人類的一種社會性選擇，是一種非常理智的自主性選擇，同時也是人類主動適應(yīng)不斷變化的自然環(huán)境的一種機(jī)制，是一種實(shí)現(xiàn)長期自我演化的策略和手段?？沙掷m(xù)發(fā)展戰(zhàn)略的實(shí)施使人類的現(xiàn)代化工業(yè)和現(xiàn)代化農(nóng)業(yè)以及現(xiàn)代科學(xué)實(shí)踐活動等各個方面的發(fā)展都有了正確的方向，把人類的社會選擇和人類對自然環(huán)境的主動適應(yīng)都有機(jī)地統(tǒng)一在可持續(xù)發(fā)展這個大框架下，使人類的社會選擇和主動適應(yīng)終于走上了“有法可依”和“有法必依”的道路，從而實(shí)現(xiàn)了人類在自己的演化歷史上第一次按自己所設(shè)計的演化模式去謀求自身的生存和發(fā)展。

人類的可持續(xù)發(fā)展問題本質(zhì)上轉(zhuǎn)化為人類的社會選擇和大自然的自然選擇二者間的關(guān)系問題，但這種相互關(guān)系無論是從時間、空間維度還是二者間力量強(qiáng)弱的對比情況來看，都是不對稱的。從生物進(jìn)化的時空尺度上來看，人類必須充分發(fā)揮自己所特有的主動適應(yīng)力來確保社會選擇在最大時空尺度上與大自然的自然選擇相適應(yīng)，人類才可能實(shí)現(xiàn)自身的可持續(xù)發(fā)展以實(shí)現(xiàn)長期的自主演化。

從純生物學(xué)的觀點(diǎn)來看，自然和自然選擇都不會支持人類在社會經(jīng)濟(jì)文化等領(lǐng)域內(nèi)發(fā)展水平的全方位提高，因?yàn)檫@意味著人類作為一個生物學(xué)種群，將占有越來越多的物質(zhì)和能量，因而會剝奪其他物種生存和演化的機(jī)會，這與生物界的演化趨勢相背離。因此，在生物進(jìn)化的大背景下，人類要實(shí)現(xiàn)自身的可持續(xù)發(fā)展還需要全人類長期的艱苦努力，還必須同時處理好進(jìn)化、適應(yīng)和選擇等重大問題，只有這樣人類的可持續(xù)發(fā)展才能落到實(shí)處。

綜上所述，生物的進(jìn)化、適應(yīng)和大自然的選擇以及人類的可持續(xù)發(fā)展，都統(tǒng)一在生命的演化過程中。進(jìn)化是生物適應(yīng)自然的結(jié)果，適應(yīng)是選擇的結(jié)果，而選擇是自然界所固有的屬性。換句話說，進(jìn)化、適應(yīng)和選擇都是自然界所固有的運(yùn)動規(guī)律在生物物種演化過程中的體現(xiàn)，是物種演化過程中3個最重要的環(huán)節(jié)。人類的社會選擇和可持續(xù)發(fā)展必須以此為前提，才能正確地發(fā)揮作用，為人類造福。

生物的進(jìn)化范文第5篇

（江西省林業(yè)科技實(shí)驗(yàn)中心，江西 信豐 341600）

【摘要】隨著《中國生物多樣性保護(hù)戰(zhàn)略和行動指南（2010-2030）》的貫徹實(shí)施，生物多樣性監(jiān)測與評價工作將在全國范圍陸續(xù)開展。進(jìn)化生態(tài)學(xué)作為闡述生物多樣性演化規(guī)律和機(jī)理的基礎(chǔ)性學(xué)科，其數(shù)量研究方法在20世紀(jì)70年代后得到了迅速的發(fā)展。本文從三個層面系統(tǒng)性總結(jié)、篩選了進(jìn)化生態(tài)學(xué)在植物生態(tài)領(lǐng)域的主流研究方法，其中在生態(tài)系統(tǒng)層面，群落演替的主成分分析和聚類分析方法、群落的可恢復(fù)性、可持續(xù)性、變異性、抗干擾性、邊緣效應(yīng)等主題被篩選為主要分析方法；在種群層面，種間關(guān)聯(lián)指數(shù)、相關(guān)系數(shù)、分離指數(shù)、生態(tài)位寬度指數(shù)、生態(tài)位重疊指數(shù)等概念可以全面闡釋植物種群的演替規(guī)律；在遺傳層面，哈迪-溫伯格平衡度的檢測、等位基因頻率、多態(tài)位點(diǎn)百分?jǐn)?shù)、平均位點(diǎn)的等位基因數(shù)、平均位點(diǎn)的預(yù)期雜合度、Nei氏遺傳分化系數(shù)、Nei氏遺傳一致度、遺傳距離、聚類分析、遺傳貢獻(xiàn)率等方法在分子進(jìn)化分析中的應(yīng)用相對廣泛。

關(guān)鍵詞 生物多樣性評價；生物多樣性監(jiān)測；進(jìn)化生態(tài)學(xué)

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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