Spatial and temporal distribution of litter arthropods in different vegetation covers of Porto Santo Island (Madeira Archipelago, Portugal)

Abstract

t r a c tThe main objective of this study is to assess the soil diversity and temporal and spatial distribution of litter macro-arthropods, captured with pitfall traps, on different vegetation covers from Porto Santo Island (Portugal) with different soil physical and chemical characteristics. The PCA clearly separated sampling areas geographically more exposed to winds and solar radiation, from the others that were not. In this analysis, seasons seemed to have no influence on this distribution. Non-exposed areas were characterized by high soil temperatures, water and organic matter contents. Among these areas, those that were covered by Pinus halepensis and Eucalyptus ficifolia showed the highest C/N ratios in winter, which may be explained by likely inhibitory effects of phenolic compounds of the leaves of both species on the decomposition process. The highest number of organisms was recorded on the exposed areas. Variance partitioning using CCA showed that the different vegetation covers and environmental variables explained 19% and 30%, respectively, of the variance in macro-arthropods distribution. Four environmental variables (exposure, temperature, C/N ratio and exchangeable K) were positively correlated with the taxa abundance matrix. The results showed that non-exposed areas with E. ficifolia and Cupressus macrocarpa seemed to favour the establishment of a high diversity of taxa, especially in the summer, when soil conditions tend to be extreme.

The influence ofCaragana korshinskiishrub on soil and hydrological properties in a revegetation-stabilized desert ecosystem

“…In agreement with some former research, the concentrations of nutrients in soils beneath the plants are significantly higher compared with the soils between shrubs (Titus et al 2002, Schade and Hobbie 2005, Zhang et al 2006, Zhang and Chen 2007, Antunes et al 2008Cao et al 2011). The root architecture, number and biomass of shrubs play a key role in affecting soil nutrients (Wezel et al 2000, Dong et al 2009).…”

“…Concentrations of ions in the upper 10 cm of soil were generally higher under shrubs than in soils between shrubs (Whitford et al 1997). The improvement of soil nutrients by vegetation occurs mainly through the decomposition of organic residues and the absorption of nutrients that have been leached from the surface (Antunes et al 2008).…”

Seasonal and altitudinal variations of soil arthropods in Abies nordmanniana subsp. bornmulleriana forests

“…As mentioned above, differences in soil properties and litter characteristics due to altitudinal and seasonal changes were the possible causes of such variations. In this context, Antunes et al (2008) stated that distribution of arthropods was not random, and strongly depended on chemical, physical and ecological conditions. Furthermore, they demonstrated that factors such as climatic, geomorphological, physical and chemical conditions in addition to the processes related to the distribution, growth and development of flora and fauna as well as the relationships between competition and predation were the main effects on the distribution of soil arthropods.…”

Principal Component Analysis on Spatial Data: An Overview

“…Examples can be found in many of the geosciences: in environmental sciences for environmental indices (Tran et al 2002;Parinet, Lhote, and Legube 2004); for atmospheric, soil, and water pollution (Hernández, Adarve Alcazar, and Pastor 1998;Felipe-Sotelo et al 2006;Zhang 2006); in environmental geochemistry (Zhang and Selinus 1998;Reid and Spencer 2009); in environmental management (Bastianoni et al 2008); and in biogeography for wildlife and vegetation distribution studies (Antunes et al 2008). There are also numerous instances of such simple PCA applications in the social sciences; for example, as a data preprocessor prior to statistical modeling-see the regression modeling of fire and rescue incidents (Corcoran et al 2007) and the nonstationary regression modeling of hedonic house price data (Bitter, Mulligan, and Dall'erba 2007).…”