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Multivariate analysis of a fine-scale breeding bird atlas using a geographical information system and partial canonical correspondence analysis: environmental and spatial effects

Authors


*Pierre Defourny, Department of Environmental Sciences and Land Use Planning, Université catholique de Louvain, Croix du Sud 2/16, B-1348 Louvain-la-Neuve, Belgium. E-mail: defourny@enge.ucl.ac.be

Abstract

Aim  To assess the relative roles of environment and space in driving bird species distribution and to identify relevant drivers of bird assemblage composition, in the case of a fine-scale bird atlas data set.

Location  The study was carried out in southern Belgium using grid cells of 1 × 1 km, based on the distribution maps of the Oiseaux nicheurs de Famenne: Atlas de Lesse et Lomme which contains abundance for 103 bird species.

Methods  Species found in < 10% or > 90% of the atlas cells were omitted from the bird data set for the analysis. Each cell was characterized by 59 landscape metrics, quantifying its composition and spatial patterns, using a Geographical Information System. Partial canonical correspondence analysis was used to partition the variance of bird species matrix into independent components: (a) ‘pure’ environmental variation, (b) spatially-structured environmental variation, (c) ‘pure’ spatial variation and (d) unexplained, non-spatial variation.

Results  The variance partitioning method shows that the selected landscape metrics explain 27.5% of the variation, whilst ‘pure’ spatial and spatially-structured environmental variables explain only a weak percentage of the variation in the bird species matrix (2.5% and 4%, respectively). Avian community composition is primarily related to the degree of urbanization and the amount and composition of forested and open areas. These variables explain more than half of the variation for three species and over one-third of the variation for 12 species.

Main conclusions  The results seem to indicate that the majority of explained variation in species assemblages is attributable to local environmental factors. At such a fine spatial resolution, however, the method does not seem to be appropriated for detecting and extracting the spatial variation of assemblages. Consequently, the large amount of unexplained variation is probably because of missing spatial structures and ‘noise’ in species abundance data. Furthermore, it is possible that other relevant environmental factors, that were not taken into account in this study and which may operate at different spatial scales, can drive bird assemblage structure. As a large proportion of ecological variation can be shared by environment and space, the applied partitioning method was found to be useful when analysing multispecific atlas data, but it needs improvement to factor out all-scale spatial components of this variation (the source of ‘false correlation’) and to bring out the ‘pure’ environmental variation for ecological interpretation.

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