Habitat structure: a fundamental concept and framework for urban soil ecology

Habitat structure is defined as the composition and arrangement of physical matter at a location. Although habitat structure is the physical template underlying ecological patterns and processes, the concept is relatively unappreciated and underdeveloped in ecology. However, it provides a fundamental concept for urban ecology because human activities in urban ecosystems are often targeted toward management of habitat structure. In addition, the concept emphasizes the fine-scale, on-the-ground perspective needed in the study of urban soil ecology. To illustrate this, urban soil ecology research is summarized from the perspective of habitat structure effects. Among the key conclusions emerging from the literature review are: (1) habitat structure provides a unifying theme for multivariate research about urban soil ecology; (2) heterogeneous urban habitat structures influence soil ecological variables in different ways; (3) more research is needed to understand relationships among sociological variables, habitat structure patterns and urban soil ecology. To stimulate urban soil ecology research, a conceptual framework is presented to show the direct and indirect relationships among habitat structure and ecological variables. Because habitat structure serves as a physical link between sociocultural and ecological systems, it can be used as a focus for interdisciplinary and applied research (e.g., pest management) about the multiple, interactive effects of urbanization on the ecology of soils

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

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 x 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 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