Classification of Natural and Semi‐natural Vegetation

Vegetation classifi cation has been an active fi eld of scientifi c research since well before the origin of the word ecology and has remained so through to the present day. As with any fi eld active for such a long period, the conceptual underpinnings as well as the methods employed, the products generated and the applications expected have evolved considerably. Our goal in this chapter is to provide an introductory guide to participation in the modern vegetation classifi cation enterprise, as well as suggestions on how to use and interpret modern vegetation classifi cations. Some notes on the historical development of classifi cation and the associated evolution of community concepts are provided by van der Maarel & Franklin in Chapter 1 , and Austin describes numerical methods for community analysis in Chapter 3 . While we present some historical and conceptual context, our goal in this chapter is to help the reader learn how to create, interpret and use modern vegetation classifi cations, particularly those based on large scale surveys.

[1]  Ladislav Mucina,et al.  European vegetation survey: current state of some national programmes. , 1993 .

[2]  János Podani.  Comparison of Fuzzy Classifications , 1991 .

[3]  E. Maarel,et al.  The Braun-Blanquet Approach , 1978 .

[4]  J. Schaminée,et al.  TURBOVEG, a comprehensive data base management system for vegetation data , 2001 .

[5]  Stephan Hennekens,et al.  Assignment of relevés to pre-defined classes by supervised clustering of plant communities using a new composite index , 2008 .

[6]  D. W. Goodall,et al.  Sample Similarity and Species Correlation , 1978 .

[7]  M. Chytrý,et al.  Statistical determination of diagnostic species for site groups of unequal size , 2006 .

[8]  D. Faith,et al.  Compositional dissimilarity as a robust measure of ecological distance , 1987, Vegetatio.

[9]  János Podani,et al.  Detecting indicator species: Some extensions of the IndVal measure , 2010 .

[10]  Z. Hubálek.  COEFFICIENTS OF ASSOCIATION AND SIMILARITY, BASED ON BINARY (PRESENCE‐ABSENCE) DATA: AN EVALUATION , 1982 .

[11]  W. T. Williams,et al.  Multivariate Methods in Plant Ecology: V. Similarity Analyses and Information-Analysis , 1966 .

[12]  M. O. Hill,et al.  TWINSPAN: a FORTRAN program of arranging multivariate data in an ordered two way table by classification of individual and attributes , 1979 .

[13]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[14]  László Orlóci,et al.  An Agglomerative Method for Classification of Plant Communities , 1967 .

[15]  Martin Kent,et al.  Vegetation Description and Data Analysis: A Practical Approach , 2011 .

[16]  J. Wilson.  Species presence/absence sometimes represents a plant community as well as species abundances do, or better , 2012 .

[17]  William H. McWilliams,et al.  Forest Inventory and Analysis Database of the United States of America (FIA) , 2012 .

[18]  Robert H. Whittaker,et al.  Classification of natural communities , 2008, The Botanical Review.

[19]  Jaroslav Moravec,et al.  International Code of Phytosociological Nomenclature. 3rd edition , 2000 .

[20]  E. W. Beals,et al.  Bray-curtis ordination: an effective strategy for analysis of multivariate ecological data , 1984 .

[21]  R. Sokal,et al.  Principles of numerical taxonomy , 1965 .

[22]  K. R. W. Brewer,et al.  The use of gradient directed transects or gradsects in natural resource surveys , 1985 .

[23]  Milan Chytrý,et al.  Plot sizes used for phytosociological sampling of European vegetation , 2003 .

[24]  J. Lambert,et al.  Investigations into the Properties of Quantitative Vegetational Data: II. Further Data Type Comparisons , 1976 .

[25]  Nick Spencer,et al.  SPECIAL FEATURE: ECOINFORMATICS Veg-X - an exchange standard for plot-based vegetation data , 2011 .

[26]  Panayotis Dimopoulos,et al.  A new algorithm for the determination of differential taxa , 2009 .

[27]  Bruce McCune,et al.  Improving community analysis with the Beals smoothing function , 1994 .

[28]  Daniel P. Faith,et al.  Compositional dissimilarity as a robust measure of ecological distance , 1987, Vegetatio.

[29]  L. Hubert,et al.  A general statistical framework for assessing categorical clustering in free recall. , 1976 .

[30]  László Orlóci,et al.  Analysis of concentration and detection of underlying factors in structured tables , 1979, Vegetatio.

[31]  E. van der Maarel,et al.  Transformation of cover-abundance values in phytosociology and its effects on community similarity , 1979, Vegetatio.

[32]  Leo A. Goodman,et al.  Corrigenda: Measures of Association for Cross Classifications , 1957 .

[33]  Jason D. Fridley,et al.  CONNECTING FINE‐ AND BROAD‐SCALE SPECIES–AREA RELATIONSHIPS OF SOUTHEASTERN U.S. FLORA , 2005 .

[34]  R. Whittaker,et al.  Structure, pattern, and diversity of a mallee community in New South Wales , 1979, Vegetatio.

[35]  D. Moss,et al.  The Diversity of European Vegetation: an overview of phytosociological alliances and their relationships to EUNIS habitats , 2002 .

[36]  Milan Chytrý,et al.  Determination of diagnostic species with statistical fidelity measures , 2002 .

[37]  Xavier Font,et al.  The management of vegetation classifications with fuzzy clustering , 2010 .

[38]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[39]  T. M. Smith,et al.  A new model for the continuum concept , 1989 .

[40]  R. W. Becking.  The zürich-montpellier school of phytosociology , 1957, The Botanical Review.

[41]  J. T. Curtis,et al.  An Ordination of the Upland Forest Communities of Southern Wisconsin , 1957 .

[42]  M. Austin,et al.  A new model for the continuum concept , 1989, Vegetatio.

[43]  D. H. Knight,et al.  Aims and Methods of Vegetation Ecology , 1974 .

[44]  William A. Bechtold,et al.  The enhanced forest inventory and analysis program - national sampling design and estimation procedures , 2005 .

[45]  Transformation of cover-abundance values for appropriate numerical treatment – Alternatives to the proposals by Podani , 2007 .

[46]  F. Oliva,et al.  Assessing species diagnostic value in large data sets: A comparison between phi-coefficient and Ochiai index , 2008 .

[47]  J. Ludwig,et al.  Statistical ecology: a primer on methods & computing , 1988 .

[48]  Jörg Ewald,et al.  A critique for phytosociology , 2003 .

[49]  Frank van der Meulen,et al.  Optimum-transformation of plant species cover-abundance values , 1989, Vegetatio.

[50]  Jean-Claude Gégout,et al.  The right relevé in the right vegetation unit: a new typicality index to reproduce expert judgement with an automatic classification programme , 2012 .

[51]  H. E. Brogden.  A new coefficient: Application to biserial correlation and to estimation of selective efficiency , 1949, Psychometrika.

[52]  鈴木 時夫.  ○植生研究の問題と方法(Aufgaben und Methoden der Vegetationskunde) ELLENBERG, H. 1956,Heinrich Walter. Einfuhrung in die phytologie IV. 1 Teil. ss. 136. Eugen Ulmer, Stuttgart, \1030 , 1958 .

[53]  B. McCune,et al.  Analysis of Ecological Communities , 2002 .

[54]  Wim A. Ozinga,et al.  Use of the ecological information system SynBioSys for the analysis of large datasets , 2007 .

[55]  R. Bailey.  Ecoregions of the United States , 2009 .

[56]  D. Goodall.  Objective methods for the classification of vegetation. II. Fidelity and indicator value , 1953 .

[57]  Helge Bruelheide,et al.  Towards unification of national vegetation classifications: A comparison of two methods for analysis of large data sets , 2000 .

[58]  Albert E. Radford,et al.  Manual of the Vascular Flora of the Carolinas , 1970 .

[59]  Robert D. Pfister,et al.  Classifying Forest Habitat Types Based on Potential Climax Vegetation , 1980 .

[60]  Claire Waterton.  From Field to Fantasy , 2002 .

[61]  Shawn Bowers,et al.  The New Bioinformatics: Integrating Ecological Data from the Gene to the Biosphere , 2006 .

[62]  M. Chytrý,et al.  Bias in vegetation databases? A comparison of stratified-random and preferential sampling. , 2011 .

[63]  G. N. Lance,et al.  A General Theory of Classificatory Sorting Strategies: 1. Hierarchical Systems , 1967, Comput. J..

[64]  Attila Lengyel,et al.  Heterogeneity‐constrained random resampling of phytosociological databases , 2011 .

[65]  F. James Rohlf,et al.  Biometry: The Principles and Practice of Statistics in Biological Research , 1969 .

[66]  János Podani.  Simulation of Random Dendrograms and Comparison Tests: Some Comments , 2000, J. Classif..

[67]  Maurice K. Wong,et al.  Algorithm AS136: A k-means clustering algorithm. , 1979 .

[68]  Don Faber-Langendoen,et al.  VegBank – a permanent, open-access archive for vegetation-plot data , 2012 .

[69]  H. Wehrden,et al.  Surveying Southern Mongolia: application of multivariate classification methods in drylands with low diversity and long floristic gradients , 2011 .

[70]  P. Legendre,et al.  SPECIES ASSEMBLAGES AND INDICATOR SPECIES:THE NEED FOR A FLEXIBLE ASYMMETRICAL APPROACH , 1997 .

[71]  M. B. Dale,et al.  The Use of Statistics in Phytosociology , 1964 .

[72]  János Podani,et al.  Multivariate exploratory analysis of ordinal data in ecology: Pitfalls, problems and solutions , 2005 .

[73]  Ladislav Mucina,et al.  Classification of vegetation: past, present and future , 1997 .

[74]  Ben Collen,et al.  Establishing IUCN Red List Criteria for Threatened Ecosystems , 2010, Conservation biology : the journal of the Society for Conservation Biology.

[75]  Nico M. Franz,et al.  5 On the Use of Taxonomic Concepts in Support of Biodiversity Research and Taxonomy , 2006 .

[76]  Ladislav Mucina,et al.  Common data standards for recording relevés in field survey for vegetation classification. , 2000 .

[77]  Miguel Equihua,et al.  Fuzzy clustering of ecological data. , 1990 .

[78]  Helge Bruelheide,et al.  Arranging phytosociological tables by species‐relevé groups , 1994 .

[79]  Milan Chytrý,et al.  Stratified resampling of phytosociological databases: some strategies for obtaining more representative data sets for classification studies , 2005 .

[80]  Ken Aho,et al.  Using geometric and non-geometric internal evaluators to compare eight vegetation classification methods , 2008 .

[81]  Jan Lepš,et al.  Multivariate Analysis of Ecological Data using CANOCO , 2003 .

[82]  Q. Wheeler.  The New Taxonomy , 2008 .

[83]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[84]  R. Whittaker.  Vegetation of the Siskiyou Mountains, Oregon and California , 1960 .

[85]  W. Willner,et al.  Effects of different fidelity measures and contexts on the determination of diagnostic species , 2009 .

[86]  Xavier Font,et al.  Numerical reproduction of traditional classifications and automatic vegetation identification. , 2009 .

[87]  P. White,et al.  A Flexible, Multipurpose Method for Recording Vegetation Composition and Structure , 1998 .

[88]  Milan Chytrý,et al.  Sampling design in large-scale vegetation studies: Do not sacrifice ecological thinking to statistical purism! , 2007, Folia Geobotanica.

[89]  Milan Chytrý,et al.  Supervised classification of plant communities with artificial neural networks , 2005 .

[90]  J. Dengler,et al.  A flexible multi-scale approach for standardised recording of plant species richness patterns , 2009 .

[91]  J. T. Curtis,et al.  The Vegetation of Wisconsin , 1962 .

[92]  Don Faber-Langendoen,et al.  Standards for associations and alliances of the U.S. National Vegetation Classification , 2009 .

[93]  Milan Chytrý,et al.  Modified TWINSPAN classification in which the hierarchy respects cluster heterogeneity , 2009 .

[94]  P. White,et al.  The distance decay of similarity in biogeography and ecology , 1999 .

[95]  H. Gleason,et al.  The individualistic concept of the plant association , 1939 .

[96]  Mike P. Austin,et al.  Vegetation survey design for conservation: Gradsect sampling of forests in North-eastern New South Wales , 1989 .

[97]  J. Oldeland,et al.  The Global Index of Vegetation-Plot Databases (GIVD): a new resource for vegetation science , 2011 .

[98]  C. Woodcock,et al.  Theory and methods for accuracy assessment of thematic maps using fuzzy sets , 1994 .

[99]  Florian Jansen,et al.  Plant names in vegetation databases – a neglected source of bias , 2010 .

[100]  Miquel De Cáceres,et al.  Improving indicator species analysis by combining groups of sites , 2010 .

[101]  Miquel De Cáceres,et al.  Towards consistency in vegetation classification , 2012 .

[102]  J. Podani,et al.  A general strategy for the simultaneous classification of variables and objects in ecological data tables , 1991 .

[103]  Ladislav Mucina,et al.  European Vegetation Survey: update on progress , 1995 .

[104]  Bernard L. Kovalchik,et al.  Forest habitat types of Montana. , 1977 .

[105]  Zoltán Botta-Dukát,et al.  OptimClass: Using species‐to‐cluster fidelity to determine the optimal partition in classification of ecological communities , 2010 .

[106]  T. Stohlgren,et al.  A Modified-Whittaker nested vegetation sampling method , 1995, Vegetatio.

[107]  J. S. Rodwell,et al.  Vegetation-plot data and databases in Europe: an overview , 2008 .

[108]  O. Wildi.  Data Analysis in Vegetation Ecology , 2010 .

[109]  Zoltán Botta-Dukát,et al.  Determination of diagnostic species with statistical fidelity measures , 2002 .

[110]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[111]  Helge Bruelheide,et al.  A new measure of fidelity and its application to defining species groups , 2000 .

[112]  J. Dengler,et al.  Species constancy depends on plot size – a problem for vegetation classification and how it can be solved , 2009 .