EPU.bib

@article{Host1996,
abstract = {Ecological land classification systems have recently been developed at continental, regional, state, and landscape scales. In most cases, the map units of these systems result from subjectively drawn boundaries, often derived by consensus and with unclear choice and weighting of input data. Such classifications are of variable accuracy and are not reliably repeatable. We combined geographic information systems (GIS) with multivariate statistical analyses to integrate climatic, physiographic, and edaphic databases and produce a classification of regional landscape ecosystems on a 29 340-km2 quadrangle of northwestern Wisconsin. Climatic regions were identified from a high-resolution climatic database consisting of 30-yr mean monthly temperature and precipitation values interpolated over a 1-km2 grid across the study area. Principal component analysis (PCA) coupled with an isodata clustering algorithm was used to identify regions of similar seasonal climatic trends. Maps of Pleistocene geology and major soil morphosequences were used to identify the major physiographic and soil regions within the landscape. Climatic and physiographic coverages were integrated to identify regional landscape ecosystems, which potentially differ in characteristic forest composition, successional dynamics, potential productivity, and other ecosystem-level processes. Validation analysis indicated strong correspondence between forest cover classes from an independently derived Landsat Thematic Mapper classification and ecological region. The development of more standardized data sets and analytical methods for ecoregional classification provides a basis for sound interpretations of forest management at multiple spatial scales.},
author = {Host, G E and Polzer, P L and Mladenoff, D J and White, M A and Crow, T R},
doi = {10.2307/2269395},
isbn = {1051-0761},
issn = {1051-0761},
journal = {Ecological Applications},
keywords = {Climate,GIS,Geographic information systems,Landscape ecosystem,Physiography,USA, Wisconsin,Wisconsin,ecological land classification,land classification,regional classification},
mendeley-groups = {EPU},
number = {2},
pages = {608--618},
title = {{A quantitative approach to developing regional ecosystem classifications}},
url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0030422545{\&}partnerID=40{\&}md5=677abc5555c1396430e40ec38a5cbb7b},
volume = {6},
year = {1996}
}

@book{Legendre1998,
abstract = {The book describes and discusses the numerical methods which are successfully being used for analysing ecological data, using a clear and comprehensive approach. These methods are derived from the fields of mathematical physics, parametric and nonparametric statistics, information theory, numerical taxonomy, archaeology, psychometry, sociometry, econometry and others. Compared to the first edition of Numerical Ecology, this second edition includes three new chapters, dealing with the analysis of semiquantitative data, canonical analysis and spatial analysis. New sections have been added to almost all other chapters. There are sections listing available computer programs and packages at the end of several chapters. As in the previous English and French editions, there are numerous examples from the ecological literature, and the choice of methods is facilitated by several synoptic tables.},
archivePrefix = {arXiv},
arxivId = {0-444-89250-8},
author = {Legendre, Pierre and Legendre, Louis},
booktitle = {Numerical Ecology Second English Edition},
doi = {10.1002/1521-3773(20010316)40:6<9823::AID-ANIE9823>3.3.CO;2-C},
eprint = {0-444-89250-8},
isbn = {0444892508},
issn = {14337851},
mendeley-groups = {EPU},
number = {6},
pages = {9823},
pmid = {12012888},
title = {{Numerical Ecology}},
url = {http://books.google.com/books?hl=en{\&}lr={\&}id=6ZBOA-iDviQC{\&}oi=fnd{\&}pg=PP2{\&}dq=Numerical+ecology{\&}ots=uwaj1-VaWk{\&}sig=NDdt44YeBnqA4m3ZT18JYWNAfG4{\%}5Cnhttp://books.google.com/books?hl=en{\&}lr={\&}id=KBoHuoNRO5MC{\&}oi=fnd{\&}pg=PP1{\&}dq=Numerical+Ecology{\&}o},
volume = {40},
year = {1998}
}

@book{Longhurst2007,
abstract = {This book presents an in-depth discussion of the biological and ecological geography of the oceans. It synthesizes locally restricted studies of the ocean to generate a global geography of the vast marine world. Based on patterns of algal ecology, the book divides the ocean into four primary compartments, which are then subdivided into secondary compartments. *Includes color insert of the latest in satellite imagery showing the world's oceans, their similarities and differences *Revised and updated to reflect the latest in oceanographic research *Ideal for anyone interested in understanding ocean ecology -- accessible and informative. {\textcopyright} 2007 Elsevier Inc. All rights reserved.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Longhurst, Alan R.},
booktitle = {Ecological Geography of the Sea},
doi = {10.1016/B978-0-12-455521-1.X5000-1},
eprint = {arXiv:1011.1669v3},
isbn = {9780124555211},
issn = {0096-3941},
mendeley-groups = {EPU},
pmid = {25246403},
title = {{Ecological Geography of the Sea}},
year = {2007}
}

@article{Milligan1985,
abstract = {A Monte Carlo evaluation of 30 procedures for determining the number of clusters was conducted on artificial data sets which contained either 2, 3, 4, or 5 distinct nonoverlapping clusters. To provide a variety of clustering solutions, the data sets were analyzed by four hierarchical clustering methods. External criterion measures indicated excellent recovery of the true cluster structure by the methods at the correct hierarchy level. Thus, the clustering present in the data was quite strong. The simulation results for the stopping rules revealed a wide range in their ability to determine the correct number of clusters in the data. Several procedures worked fairly well, whereas others performed rather poorly. Thus, the latter group of rules would appear to have little validity, particularly for data sets containing distinct clusters. Applied researchers are urged to select one or more of the better criteria. However, users are cautioned that the performance of some of the criteria may be data dependent.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Milligan, Glenn W. and Cooper, Martha C.},
doi = {10.1007/BF02294245},
eprint = {arXiv:1011.1669v3},
isbn = {0033-3123},
issn = {00333123},
journal = {Psychometrika},
keywords = {classification,numerical taxonomy,stopping rules},
mendeley-groups = {EPU},
number = {2},
pages = {159--179},
pmid = {25246403},
title = {{An examination of procedures for determining the number of clusters in a data set}},
volume = {50},
year = {1985}
}

@book{Pielou1984,
author = {Pielou, Evelyn Chris},
mendeley-groups = {EPU},
publisher = {John Wiley {\&} Sons},
title = {{The Interpretation of Ecological Data: A Primer on Classification and Ordination}},
year = {1984}
}


@article{Roff2000,
abstract = {1. Development of environmental protected areas has been driven more by opportunity than design, scenery rather than science (Hackman A. 1993. Preface. A protected areas gap analysis methodology: planning for the conservation of biodiversity. World Wildlife Fund Canada Discussion Paper; i-ii). If marine environments are to be protected from the adverse effects of human activities, then identification of types of marine habitats and delineation of their boundaries in a consistent classification is required. Without such a classification system, the extent and significance of representative or distinctive habitats cannot be recognized. Such recognition is a fundamental prerequisite to the determination of location and size of marine areas to be protected. 2. A hierarchical classification has been developed based on enduring/recurrent geophysical (oceanographic and physiographic) features of the marine environment, which identifies habitat types that reflect changes in biological composition. Important oceanographic features include temperature, stratification and exposure; physiographic features include bottom relief and substrate type. 3. Classifications based only on biological data are generally prohibited at larger scales, due to lack of information. Therefore, we are generally obliged to classify habitat types as surrogates for community types. The data necessary for this classification are available from mapped sources and from remote sensing. It is believed they can be used to identify representative and distinctive marine habitats supporting different communities, and will provide an ecological framework for marine conservation planning at the national level.},
author = {Roff, John C. and Taylor, Mark E.},
doi = {10.1002/1099-0755(200005/06)10:3<209::AID-AQC408>3.0.CO;2-J},
isbn = {1052-7613},
issn = {10527613},
journal = {Aquatic Conservation: Marine and Freshwater Ecosystems},
keywords = {Classification,Conservation,Marine protected areas,Marine representative habitats},
mendeley-groups = {EPU},
number = {3},
pages = {209--223},
title = {{National frameworks for marine conservation - A hierarchical geophysical approach}},
volume = {10},
year = {2000}
}