@article{
 title = {Finding essential scales of spatial variation in ecological data: a multivariate approach},
 type = {article},
 year = {2009},
 pages = {161-168},
 volume = {32},
 id = {8dcdf02f-de13-3972-b3f9-d260853b7e46},
 created = {2010-11-03T21:13:25.000Z},
 file_attached = {true},
 profile_id = {976aa121-3316-304c-8340-7ca54d70abe6},
 last_modified = {2019-03-01T08:37:11.428Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Jombart2009},
 source_type = {article},
 private_publication = {false},
 abstract = {The identification of spatial structures is a key step in understanding
the ecological processes structuring the distribution of organisms.
Spatial patterns in species distributions result from a combination
of several processes occuring at different scales: identifying these
scales is thus a crucial issue. Recent studies have proposed a new
family of spatial predictors (PCNM: principal coordinates of neighbours
matrices; MEMs: Moran's eigenvectors maps) that allow for modelling
of spatial variation on different scales. To assess the multi-scale
spatial patterns in multivariate data, these variables are often
used as predictors in constrained ordination methods. However, the
selection of the appropriate spatial predictors is still troublesome,
and the identification of the main scales of spatial variation remains
an open question. This paper presents a new statistical tool to tackle
this issue: the multi-scale pattern analysis (MSPA). This ordination
method uses MEMs to decompose ecological variability into several
spatial scales and then summarizes this decomposition using graphical
representations. A canonical form of MSPA can also be used to assess
the spatial scales of the species-environment relationships. MSPA
is compared to constrained ordination using simulated data, and illustrated
using the famous oribatid mites dataset. The method is implemented
in the free software R.},
 bibtype = {article},
 author = {Jombart, T and Dray, Stéphane and Dufour, Anne-Béatrice},
 journal = {Ecography}
}

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Recent studies have proposed a new\nfamily of spatial predictors (PCNM: principal coordinates of neighbours\nmatrices; MEMs: Moran's eigenvectors maps) that allow for modelling\nof spatial variation on different scales. To assess the multi-scale\nspatial patterns in multivariate data, these variables are often\nused as predictors in constrained ordination methods. However, the\nselection of the appropriate spatial predictors is still troublesome,\nand the identification of the main scales of spatial variation remains\nan open question. This paper presents a new statistical tool to tackle\nthis issue: the multi-scale pattern analysis (MSPA). This ordination\nmethod uses MEMs to decompose ecological variability into several\nspatial scales and then summarizes this decomposition using graphical\nrepresentations. A canonical form of MSPA can also be used to assess\nthe spatial scales of the species-environment relationships. MSPA\nis compared to constrained ordination using simulated data, and illustrated\nusing the famous oribatid mites dataset. The method is implemented\nin the free software R.","bibtype":"article","author":"Jombart, T and Dray, Stéphane and Dufour, Anne-Béatrice","journal":"Ecography","bibtex":"@article{\n title = {Finding essential scales of spatial variation in ecological data: a multivariate approach},\n type = {article},\n year = {2009},\n pages = {161-168},\n volume = {32},\n id = {8dcdf02f-de13-3972-b3f9-d260853b7e46},\n created = {2010-11-03T21:13:25.000Z},\n file_attached = {true},\n profile_id = {976aa121-3316-304c-8340-7ca54d70abe6},\n last_modified = {2019-03-01T08:37:11.428Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jombart2009},\n source_type = {article},\n private_publication = {false},\n abstract = {The identification of spatial structures is a key step in understanding\nthe ecological processes structuring the distribution of organisms.\nSpatial patterns in species distributions result from a combination\nof several processes occuring at different scales: identifying these\nscales is thus a crucial issue. Recent studies have proposed a new\nfamily of spatial predictors (PCNM: principal coordinates of neighbours\nmatrices; MEMs: Moran's eigenvectors maps) that allow for modelling\nof spatial variation on different scales. To assess the multi-scale\nspatial patterns in multivariate data, these variables are often\nused as predictors in constrained ordination methods. However, the\nselection of the appropriate spatial predictors is still troublesome,\nand the identification of the main scales of spatial variation remains\nan open question. This paper presents a new statistical tool to tackle\nthis issue: the multi-scale pattern analysis (MSPA). This ordination\nmethod uses MEMs to decompose ecological variability into several\nspatial scales and then summarizes this decomposition using graphical\nrepresentations. A canonical form of MSPA can also be used to assess\nthe spatial scales of the species-environment relationships. MSPA\nis compared to constrained ordination using simulated data, and illustrated\nusing the famous oribatid mites dataset. The method is implemented\nin the free software R.},\n bibtype = {article},\n author = {Jombart, T and Dray, Stéphane and Dufour, Anne-Béatrice},\n journal = {Ecography}\n}","author_short":["Jombart, T.","Dray, S.","Dufour, A."],"biburl":"https://bibbase.org/service/mendeley/2845861","bibbaseid":"jombart-dray-dufour-findingessentialscalesofspatialvariationinecologicaldataamultivariateapproach-2009","role":"author","urls":{},"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"article","biburl":"https://bibbase.org/service/mendeley/2845861","dataSources":["2252seNhipfTmjEBQ"],"keywords":[],"search_terms":["finding","essential","scales","spatial","variation","ecological","data","multivariate","approach","jombart","dray","dufour"],"title":"Finding essential scales of spatial variation in ecological data: a multivariate approach","year":2009}