转载自： http://jiangeu.bokee.com/5762716.html 生态学的诺贝尔奖－克拉夫奖（The Crafoord Prize） 生态学是一门多学科(地学、生物学、环境科学、信息科学等)相交叉、宏观与微观相结合、实验性质（包括室内与野外实验）很强的学科。它是进展神速的年轻学科，分化得非常厉害，有产业生态学、恢复生态学、进化生态学、行为生态学、分子生态学、城市生态学、空间生态学几十个分支。 生态学考虑了人与自然相互作用的方方面面，正反馈、负反馈、反馈的锁链及循环等，其复杂程度难以想象。要想正确理解生态学，一个人首先要杜绝孤立地简化地看待自然界的任何现象，事无巨细，都是如此。这也是生态学作为科学其研究手段和角度日益增多、结果日渐复杂的主要原因。这是一个如此生动的学科，数理化天地生中，没有哪个能像它这样如此形象地说明了人是自然的产物，它必定要经受自然的约束；也没有哪个学科能像它这样如此清晰地凸现了人类智慧的力量，它在自然强大的约束下熠熠发光。 生态学对现代社会的影响有着其他学科无法与之相比的重要性，联想到该学科目前受公众的漠视，强烈的对比之下，令人不禁常常叹息。 The Crafoord Prize: 由克拉夫夫妇捐资瑞典皇家科学院于1980年设立；1982年开始授奖，授奖范围是不为诺贝尔奖所涵盖的各个学科，包括数学与天文学、地球科学、和生物科学（着重于生态学）；逐年轮流授奖，每隔3年生态学有一次获奖；每项获奖不超过3人，获奖者必须是仍然健在的科学家，由瑞典国王每年颁奖。我们可以看到，以上这些规定都是和诺贝尔奖一致的。2003年奖金额为50万美元。 有时，人们以为泰勒奖是生态学的最高奖，但实际上这是环境科学的最高奖。我国的刘东生院士最近就曾荣获该奖，可喜可贺，国内终于有诺贝尔奖级别的荣誉了，可是什么时候国内生态学家能获得克拉夫奖呢。 生态学克拉夫奖历年获得者名单如下： 1984 DANIEL H. JANZEN (宾夕法尼亚大学, USA). For his imaginative and stimulating studies on co-evolution which have inspired many researchers to further work in this field. 1987 EUGENE P. ODUM (佐治亚大学, USA) and HOWARD T. ODUM (佛罗里达大学, USA). For pioneering contributions within the field of ecosystem ecology. 1990 EDWARD O. WILSON (哈佛大学, USA). For the theory of island biography and other research on species diversity and community dynamics on islands and in other habitats with differing degrees of isolation. PAUL R. EHRLICH (斯坦福大学, USA). For his research on the dynamics and genetics of fragmented populations and the importance of the distribution pattern for their survival probabilities. 1993 WILLIAM D. HAMILTON (Great Britain). For his theories concerning kin selection and genetic relationship as a prerequisite for the evolution of altruistic behavior. 1996 ROBERT MAY (Great Britain). For his pioneering ecological research concerning theoretical analysis of the dynamics of populations, communities and ecosystems. 1999 JOHN MAYNARD SMITH (Great britain), ERNST MAYR (哈佛大学, USA), and GEORGE C. WILLIAMS(纽约州立大学石溪分校, USA). For fundamental contributions to the conceptual development of evolutionary biology. 2003 CARL R. WOESE (伊利诺大学, USA). For his discovery of a third domain of life. 欲知更多详情，可查阅： http://www.kva.se/KVA_Root/eng/awards/international/crafoord/index.asp?br=iever=4up 或者参见Science (Vol. 286, Number 5444 Issue of 19 Nov 1999, p 1490)的有关报导。 Jiangeu转自： http://www.rainbowplan.org/webjb/edu/messages/15238.shtml 【entomology】补充：生态学克拉夫奖历年获得者名单： 2006 Wallace S. Broecker, Lamont-Doherty Earth Observatory, Columbia University, USA, for his innovative and pioneering research on the operation of the global carbon cycle within the oceanatmosphere-biosphere system, and its interaction with climate. 【这个虽然是Geosciences（地学）的，也可以算是Global Ecology（全球生态学）的】 2007 Robert L. Trivers, Rutgers University, USA, for his fundamental analysis of social evolution, conflict and cooperation. 补充历年获奖方向： 1987 协同进化 1990 岛屿生物地理学、片段化居群生态学 1993 亲属选择与利他行为 1996 种群、群落和生态系统动态 1999 进化生物学 2003 古细菌界（生命第三界） 2006 全球生态学（全球碳循环与气候），勉强算，呵呵 2007 社会生态学（社会进化、冲突和合作）
http://ckrd.cnki.net/grid20/GetInfoByDOI.aspx?DOI=CNKI:SUN:XTBZ.0.2008-02-010 复合生态系统自组织特征分析 推荐 下载阅读CAJ格式全文 下载阅读PDF格式全文 【英文篇名】 Analysison on Characteristics of Compound Ecosystem of Self-organization 【作者中文名】 秦书生 ; 【作者英文名】 QIN Shusheng（Research Center of Science ; Technology and Society ; Northeastern University ; Shenyang 110004 ; China） ; 【作者单位】 东北大学科技与社会研究中心 辽宁沈阳 ; 【文献出处】 系统科学学报 , Chinese Journal of Systems Science , 编辑部邮箱 2008年 02期 期刊荣誉：ASPT来源刊CJFD收录刊 【关键词】 复合生态系统 ; 自组织 ; 可持续发展 ; 【英文关键词】 compound ecosystem ; self-organization ; sustainable development ; 【摘要】 自组织的生态自然观,是根据自然科学提供的事实材料进行概括、提升到哲学层次的 一种生态哲学观点。复合生态系统自组织特征包括:动态演化特征、非线性特征、自反馈特征、循环再生特征、协同共生特征。动态演化是复合生态系统自组织有序 之源。非线性是复合生态系统自组织的内在动因。自反馈是复合生态系统自组织运行的基本方法。循环再生是复合生态系统自组织运行的可靠保证。协同共生是复合 生态系统自组织运行的必然要求。 【英文摘要】 The natural view of the self-organization and ecology offer guidance view of global and method for sustainable development.To carry out sustainable development strategy is beneficial to promote operation of self-organization of compound ecosystem.Characteristics of Self-organization of compound ecosystem include: mechanism of the evolution of flow,characteristic of nonlinear,mechanism of self-fleedback,characteristic of circulation and renaissance,characteristic of synergy and symbiosis. 【DOI】 CNKI:SUN:XTBZ.0.2008-02-010
http://bb.jxedu.gov.cn/webapps/login/?action=guest_loginnew_loc=/bin/common/course.pl?course_id=_902_1 1 中英对照，从实际出发 Chinese-English bilingual crossreferencing from acutal conditions 从学生英语水平、考研要求和课程实际出发，考虑到本校学生英语水平普通，考研一般采用中文参考书，生态学有些概念中文理解已经不易，因此采用重点概念和知识点中英文对照，非重点、难点或者争论点才用中文，英文图例中较难的单词采用中文注释等办法。符合专业知识为主，英文提高为辅的目标。 2 图文并茂，激发求知好兴趣 Arousing students' thirst for knowledge with plenty of vivid pictures 生态学是讲述生物与环境、生物与生物之间关系的科学，生物在生存斗争和进化过程中有许多有趣的例子。通过图片展示这些例子，可以激发学生的学习兴趣，并从图例中了解隐藏的生态学知识。 3 师生互动，教学相长 Benefit both teachers and students by interactive teaching methods 通过讨论版，并公布教师的电子邮件、QQ号和手机号，有助于师生之间的交流互动，学生咨询教学、考研和就业等问题，教师也可以开展某知识点的讨论。通过问答和讨论，不但学生获得了知识，教师也开阔了视野，并可进行针对性的教学，达到教学相长的目的。
R Code for CRW simulation #copy and paste the following code in R #to simulate Correlated Random Walk in an open space #Original code by Xiaohua Dai #required libraries require(circular) require(CircStats) ##CRW initial parameters #length ~ gamma distribution (sh, sc) #For a gamma distribution: gamma(shape, scale) # mean = shape*scale # variance = shape*scale*scale #Then, scale = variance/mean, shape = mean/scale #shape parameter: sh = 0.285 #scale parameter: sc = 362 #turning angle ~ wrapped cauchy distribution (m, rh, s) #mean turning angle in radians: m = 0.145 #mean resultant length rho: rh = 0.356 #square displacements R = matrix(0,1000,25) #x,y coordinates x = matrix(0,1000,25) y = matrix(0,1000,25) #turning angles the = matrix(0,1000,25) #lower 2.5% CI of R r25 = matrix(0,25) #mean of R rm = matrix(0,25) #upper 2.5% CI of R r975 = matrix(0,25) #Start simulation; sim = times of simulation for(sim in 1:1000){ for(step in 2:25){ l - rgamma(1,shape=sh,scale=sc) ta - rwrappedcauchy(1,mu=m,rho=rh) the = the +ta x = x +l*cos(the ) y = y +l*sin(the ) R = x ^2+y ^2 } } for(step in 1:25){ r25 = sort(R ) rm = mean(R ) r975 = sort(R ) } #output write.table(data.frame(r25,rm,r975),CRWoutput.txt) write.csv(data.frame(r25,rm,r975),CRWoutput.csv) Wednesday July 5, 2006 - 11:15am (EEST) Permanent Link | 0 Comments
R code for grid-based movement simulation Grid size: 1km 1km square Initial Agent: Individual animal Local movements: Habitat selection index H i (according to the percentage levels of utilization distribution, UD i , incell i ): ## H could be also determined according to the habitat quality, prey density, etc. Time step: 0.5hr At time step t : agent atcell m (center coordinate = ( x t , y t )) When t +1 the agent move to (or stay at) one of the nine cells ( n = m -4, , m +4) as follows ( x t -1, y t -1) ( x t , y t -1) ( x t +1, y t -1) ( x t -1, y t ) ( x t , y t ) ( x t +1, y t ) ( x t -1, y t +1) ( x t , y t +1) ( x t +1, y t +1) Possibility ( p ) of moving to/staying atcell n is P n = H n / SUM ( H i ), i from m- 4 to m +4. #####Here's the R script to simulate animal movement###### #Original code by Xiaohua Dai # Required R packages require(adehabitat) require(car) require(spdep) ## Initial parameters # Location time series (x,y) # time = number of time steps time - 15000 x - array(0,time) y - array(0,time) # Number of animal occurences at location x,y: location # Grid map of Kruger # (NOTE: zero-value grids buffer around its border: # 1. to make the grid contains NRow * NCol cells # 2. to ensure each cell in Kruger has 8 neighbouring cells) location - image.asc(Kruger) # The values of habitat selection index H decrease with the increasing of utilization level # H = 0 when the cells are not in home range therefore elephants wont move to the cells H - location UD - image.asc(KrugerUD) H - round(100/UD) BB - array(H) neigh - cell2nb(NRow,NCol,torus=FALSE,type=queen) # Generate 8 neigHours for each cell image(as.asc(H)) # Display the grid space of habitats # Location coordinates (lx, ly) # Use lxy to combine lx and ly together as a data frame lx - rep(1:NRow, NCol) # e.g. 123412341234 ly - rep(1:NCol, each=NRow) # e.g. 111122223333 lxy - data.frame(lx,ly) # Initial location of animal loc - round(runif(1,min=1,max=length(lx))) ##Movement simulation for(t in 1:time){ # Record location time series x - lxy$lx y - lxy$ly # Draw location point points(lxy$lx ,lxy$ly , col = round(runif(1, max=10)), pch = 19) # 9-cell neigHourhood matrix of habitat selection # Repeat the number of k according to its selection level BB ] # Previous cell also included since animal have a certain probability to stay in it. cxy - rep(loc,BB ) for(i in 1:8) { k - neigh ] #8 neigHouring cells cxy - c(cxy, rep(k,BB )) } # Sample one value in the selection array cxy # The larger BB ] is, the higer probability for the animal to move to cell k # Move to the new location and add 1 to the number of animal occurence at loc loc - some(cxy,1) location - location +1 }# Simulate the next move Wednesday July 5, 2006 - 11:22am (EEST) Permanent Link | 0 Comments
R code to simulate animal movement in a torus # Original code by Xiaohua Dai # Required R packages require(adehabitat) require(car) require(spdep) ## Initial parameters # Location time series (x,y) # time = number of time steps time - 15000 x - array(0,time) y - array(0,time) # Number of animal occurences at location x,y: location # location - round(runif(length(HB),min=1,max=3)) BB - array(HB) neigh - cell2nb(CellN,CellN,torus=TRUE,type=queen) # Generate 8 neighbours for each cell image(as.asc(HB)) # Display the grid space of habitats # Location coordinates (lx, ly) # Use lxy to combine lx and ly together as a data frame lx - rep(1:CellN, CellN) ly - rep(1:CellN, each=CellN) lxy - data.frame(lx,ly) # Initial location of animal loc - round(runif(1,min=1,max=length(lx))) ##Movement simulation for(t in 1:time){ # Record location time series x - lxy$lx y - lxy$ly # Draw location point points(lxy$lx ,lxy$ly , col = round(runif(1, max=10)), pch = 19) # 9-cell neighbourhood matrix of habitat selection cxy - loc for(i in 1:8) { k - neigh ] #8 neighbouring cells in a torus # Repeat the number of k according to its preference degree BB ] # Previous cell also included since animal have a certain probability to stay in it. cxy - c(cxy, rep(k,BB )) } # Sample one value in the selection array cxy # The larger BB ] is, the higer probability for the animal to move to cell k # Move to the new location and add 1 to the number of animal occurence at loc loc - some(cxy,1) location - location +1 }# Simulate the next move ## Estimation of Kernel Home-Range with 25%, 50% and 95% percentage # for home range contour estimation xy - data.frame(x,y) ud - kernelUD(xy) ver - getverticeshr(ud, 95) plot(ver, add=TRUE) ver - getverticeshr(ud, 50) plot(ver, add=TRUE) ver - getverticeshr(ud, 25) plot(ver, add=TRUE) Wednesday July 5, 2006 - 11:23am (EEST) Permanent Link | 0 Comments
R code to generate convex hulls around point clusters #Original code by Roger Bivand #Modified by Xiaohua Dai require(maptools) require(sp) require(amap) require(shapefiles) #reading point shape foodloc - readShapePoints(foodtree.shp) # yourloc - readShapePoints(yourshape.shp) xy - coordinates(foodloc) xy_clusts - hcluster(xy, method=euclidean, link=complete) # hcluster use twice less memory, as it doesn't store distance matrix # complete linkage hierarchical clustering plot(xy_clusts) # shows the clustering tree cl - cutree(xy_clusts, 200) # 200 is the number of clusters which_cl - tapply(1:nrow(xy), cl, function(i) xy ) chulls_cl - lapply(which_cl, function(x) x ) plot(xy) res - lapply(chulls_cl, polygon) n - length(chulls_cl) polygons - lapply(1:n, function(i) { chulls_cl ] - rbind(chulls_cl ], chulls_cl ] ) # the convex hulls do not join first and last points, so we copy here Polygons(list(Polygon(coords=chulls_cl ])), ID=i) }) out - SpatialPolygonsDataFrame(SpatialPolygons(polygons), data=data.frame(ID=1:n)) plot(out) # note standard-violating intersecting polygons! tempfile - tempfile() writePolyShape(out, tempfile) in_again - readShapePoly(tempfile) plot(in_again, border=blue, add=TRUE) #output test - read.shapefile(tempfile) write.shapefile(test,ptcluster) #Refer to: #http://www.google.com/search?hl=zh-CNq=%22outline+polygons+of+point+clumps%22+r-projectbtnG=Google+%E6%90%9C%E7%B4%A2lr= Wednesday July 5, 2006 - 12:34pm (EEST) Permanent Link | 0 Comments
LetsR来用R entomology 发表于 2005-6-16 17:27:00 Lets R 来用 R In bilingual English-Chinese What is R? R 是什么？ *R is not only a programming language; R is also a graphic statistical environment withplenty of easily-loaded packages. (I like it, same as theeasy-to-useextensions for ArcView) R 是程序语言， R 是具有大量易装载功能包的图形统计环境。我喜欢这点，如同 ArcView 中使用方便的扩展部件。 How to R? 怎么用 R *You can write your own scripts, you can also call a large number of powerful functions. 你可以自己写脚本，也可以调用大量有用函数。 Why to R? 为什么R * You can run R on UNIX, Windows and Mac OS R 可以运行于 UNIX, Windows 和 Mac 操作系统 * R is free: free of charge and free to use 你可以免费和自由的使用 R * R is a combination of functional programming and object-oriented programming R 是函数型程序设计与面向对象程序设计的综合体 * You need not to be a programmer; you can quickly be a programmer 你不必是程序员；你能够很快地成为程序员 * Many R users and big name statisticians around the world will answer your questions in maillists 你可以通过邮件列表向为数众多的 R 使用者和统计牛人咨询问题 * Where is R? R 在哪里 * Home page: http://www.R-project.org/ and many mirrors 主页与镜像 * Useful m ini-course for beginners: http://life.bio.sunysb.edu/~dstoebel/R/ 初学者快速入门教程 * R introduction in Chinese: http://www.biosino.org/pages/newhtm/r/schtml/ 中文 R 导论 * R resources for ecologists: http:// c r an. r -p r oject.o r g/web/ views /Envi r onmet r ics.html 生态学家的 R 资源 * Last update 2000.06.16 Xiaohua Dai @ ecoinformatics.blog.edu.cn 搜索引擎关键词： 统计软件R， R中文， 中文R， R语言
R常见工具和网站 entomology发表于-2008-7-26 20:02:00 0 推荐 这是我学R几年来觉得最有用的工具和网站，先写一部分，以后想起来慢慢补充。 1 R Task Views --to install packages for a special task. 用于特定专业研究的包组合: http://cran.r-project.org/web/views/ 如生态学的 http://cran.r-project.org/web/views/Environmetrics.html 2 R Reference Card--as a printed guideat hand, just several pages, but many useful hints.R参考手册，只有几页，最简单的只有一页，可以打印出来随时参考： （1）一页版英文： http://cran.r-project.org/doc/contrib/Short-refcard.pdf （2）多页版英文： http://cran.r-project.org/doc/contrib/refcard.pdf （3）多页版中文： http://cran.r-project.org/doc/contrib/Liu-R-refcard.pdf 3 Tinn-R--to make the use of R easier in a graphic interface. 图形界面的R编辑器： http://sourceforge.net/projects/tinn-r 4 Rcmdr--R GUI inteface.R的GUI界面套件： http:// cran.r-project.org/web/packages/ Rcmdr /index.html http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/ 5 升级包的时候可以选择韩国的服务器，速度快，而且更新要比国内快得多。
标签: 生态学
