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      Species groups can be transferred across different scales

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          Abstract

          Aim  To test whether species groups (i.e. assemblages of species co‐occurring in nature) that are statistically derived at one scale (broad, medium, or fine scale) can be transferred to another scale, and to identify the driving forces that determine species groups at the various scales.

          Location  Northern Bohemia (Czech Republic, central Europe) in the Ještědský hřbet mountain range and its neighbourhood.

          Methods  Three data sets were sampled: a floristic data set at the broad scale, another floristic data set at the intermediate scale, and a vegetation data set at the habitat scale. First, in each data set, species groups were produced by the COCKTAIL algorithm, which ensures maximized joint occurrence in the data set using a fidelity coefficient. Corresponding species groups were produced in the individual data sets by employing the same species for starting the algorithm. Second, the species groups formed in one data set, i.e. at a particular scale, were applied crosswise to the other data sets, i.e. to the other scales. Correspondence of a species group formed at a particular scale with a species group at another scale was determined. Third, to highlight the driving factors for the distribution of the plant species groups at each scale, canonical correspondence analysis was carried out.

          Results  Twelve species groups were used to analyse the transferability of the groups across the three scales, but only six of them were found to be common to all scales. Correspondence of species groups derived from the finest scale with those derived at the broadest scale was, on average, higher than in the opposite direction. Forest (tree layer) cover, altitude and bedrock type explained most of the variability in canonical correspondence analysis across all scales.

          Main conclusions  Transferability of species groups distinguished at a fine scale to broader scales is better than it is in the opposite direction. Therefore, a possible application of the results is to use species groups to predict the potential occurrence of missing species in broad‐scale floristic surveys from fine‐scale vegetation‐plot data.

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          Most cited references35

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          TURBOVEG, a comprehensive data base management system for vegetation data

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            Spatial patterns in species distributions reveal biodiversity change.

            Interpretation of global biodiversity change is hampered by a lack of information on the historical status of most species in most parts of the world. Here we show that declines and increases can be deduced from current species distributions alone, using spatial patterns of occupancy combined with distribution size. Declining species show sparse, fragmented distributions for their distribution size, reflecting the extinction process; expanding species show denser, more aggregated distributions, reflecting colonization. Past distribution size changes for British butterflies were deduced successfully from current distributions, and former distributions had some power to predict future change. What is more, the relationship between distribution pattern and change in British butterflies independently predicted distribution change for butterfly species in Flanders, Belgium, and distribution change in British rare plant species is similarly related to spatial distribution pattern. This link between current distribution patterns and processes of distribution change could be used to assess relative levels of threat facing different species, even for regions and taxa lacking detailed historical and ecological information.
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              Extrapolating Species Abundance Across Spatial Scales

              W E Kunin (1998)
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                Author and article information

                Journal
                J Biogeogr
                J. Biogeogr
                10.1111/(ISSN)1365-2699
                JBI
                Journal of Biogeography
                Blackwell Publishing Ltd (Oxford, UK )
                0305-0270
                1365-2699
                12 June 2006
                September 2006
                : 33
                : 9 ( doiID: 10.1111/jbi.2006.33.issue-9 )
                : 1628-1642
                Affiliations
                [ 1 ]Institute of Botany, Academy of Sciences of the Czech Republic, Průhonice, Czech Republic
                [ 2 ]Institute of Geobotany and Botanical Garden, Martin Luther University Halle‐Wittenberg, Halle, Germany
                Author notes
                [*] [* ] *Petr Petřík, Institute of Botany, Academy of Sciences of the Czech Republic, 252 43 Průhonice, Czech Republic.
E‐mail: petrik@ 123456ibot.cas.cz
                Article
                JBI1514
                10.1111/j.1365-2699.2006.01514.x
                7159123
                ddc2a029-854e-47bb-8268-35b82f425e50

                This article is being made freely available through PubMed Central as part of the COVID-19 public health emergency response. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency.

                History
                Page count
                Figures: 2, Tables: 5, Pages: 15
                Categories
                Vegetation Pattern and Process
                Custom metadata
                2.0
                September 2006
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.0 mode:remove_FC converted:15.04.2020

                Geography
                cocktail method,czech republic,distribution,ellenberg indicator values,grid mapping,multivariate analysis,sampling bias,scaling,vascular plants,vegetation

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