Morphological Diversity of Springtails in Land Use Systems
Springtails (Collembola) are soil organisms with wide morphological diversity and are sensitive to alterations in the soil, regardless of whether they are human-caused or not. The aim of this study was to evaluate the influence of land use on the morphological diversity of springtails and verify their relationships with soil physical, chemical, and microbiological properties. Samples were collected in the eastern region of Santa Catarina, in three municipalities: Joinville, Blumenau, and Timbó. They included the following land use systems (LUSs): native forest (NF), Eucalyptus plantation (EP), pasture (PA), integrated crop-livestock (ICL), and no tillage (NT). Samples were collected to determine soil properties, and pitfall traps were set in the winter and summer at the same points. The captured springtails were counted and morphotyped, observing features such as presence or absence of ocelli and setae, pigmentation, antenna length, and furcula length. The data were analyzed based on abundance, the Shannon-Wiener (H’) and Margalef diversity indices, Pielou’s evenness index (J), morphotype richness, modified Soil Biological Quality index (QBS), and Principal Component Analysis (PCA). Springtails abundance was higher in ICL and PA, whereas morphotype richness was higher in NF and ICL in the winter. The Shannon-Wiener and Margalef indices were higher in the winter in NF. In the summer, only H’ differed significantly among the LUSs and was higher in NF. The QBS values did not precisely follow the human intervention gradient in either of the two periods. The PCA showed difference among the periods and LUSs. In the winter, the occurrence of morphotypes was related to soil microbiological and chemical properties, whereas in the summer, the distribution of morphotypes was explained by soil physical and chemical properties. Morphological diversity analysis is a good alternative to study springtail distribution and soil biological quality, especially when associated with multivariate techniques.