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

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. LocationThe 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 spatiallystructured 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 conclusionsThe 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.

Bacterial community succession during in situ uranium bioremediation: spatial similarities along controlled flow paths

“…We first compared bacterial community composition shifts between all wells and found that although the majority of the variation could be explained by spatial and environmental variables, a portion of the variance remained unexplained. Other ecological studies that used variance partitioning have also observed high levels of unexplained variation (Titeux et al, 2004). When we examined the bacterial community composition between the bioreduction wells only, the unexplained variation decreased.…”

“…When we examined the bacterial community composition between the bioreduction wells only, the unexplained variation decreased. Titeux et al (2004) suggested that the high unexplained species assemblages could be because of unaccounted factors such as the fluctuations of communities along temporal and spatial scales, unmeasured environmental variables and limitations in separating geographical factors that could be because of spatially structured processes. Our results suggested that other geochemical variables could be important indicators, and while it is difficult to measure everything, future efforts should attempt to include a range of variables to represent an even more holistic approach.…”

Relationships between structure of the tree component and environmental variables in a subtropical seasonal forest in the upper Uruguay River valley, Brazil

“…Notably, spatially explicit phenomena must be considered in terms of scale and experimental design, so as not to overestimate the influence of horizontal space in a community. Similarly, the proportion of the variation in the species matrix that could not be explained by the available environmental variables does not affect the significance of the species-environment relationships (Brack & Smilauer 1998), and such "noise" is common in vegetation data, due in part to the fact that not all favorable sites are occupied by their respective species (Titeux et al 2004).…”

How to increase the value of urban areas for butterfly conservation? A lesson from Prague nature reserves and parks

“…We ran two separate RDA analyses for each variable, one without and one with control for spatial position of the sites (Legendre, 1993). For the latter, we first defined a minimum adequate model describing the geographic position of the reserves by defining a model with latitude (lat), longitude (long), their second-degree polynomials, and all possible combinations (e.g., lat + long, lat · lat + long 2 …) (Titeux et al, 2004), and then simplified this model using the CANOCO forward selection procedure. The selected covariate model, y ~ lat + long + lat · long, was used in all spatially controlled analyses.…”