Influence of habitat characteristics on small mammals in a Mexican high-altitude grassland

Host species influence on flea (Siphonaptera) infection parameters of terrestrial micromammals in a temperate forest of Mexico

Studies of abundance and distribution of organisms are fundamental to ecology. The identity of host species is known to be one of the major factors influencing ectoparasitic flea abundance, but explanations are still needed regarding how host taxa influence abundance parameters of different flea species. This study was carried out at La Malinche National Park (LMNP), Tlaxcala, Mexico, where previously 11 flea species had been recorded on 8 host species. Our aims were to list micromammal flea species, to determine flea infection parameters [flea prevalence (FP) and flea mean abundance (FMA)] and to analyse the influence of host species on these parameters. A total of 16 species of fleas were identified from 1178 fleas collected from 14 species of 1274 micromammals captured with Sherman® traps from March 2014 to December 2015 in 18 sites at LMNP. Some host species influence FP and FMA, in particular, Microtus mexicanus and Peromyscus melanotis showed particularly higher infection values than other host species. Plusaetis aztecus and Plusaetis sibynus were identified as the most abundant flea species.

A new study method of microhabitat structure of small mammals

The variables in habitat studies are usually transformed in such a way that they become abstractions and intuition is lost. We tested a new method for the analysis of habitat using data collected in a grid laid at the "restinga de Maricá", in Rio de Janeiro State, Brazil. Thirty-seven microhabitat variables were measured at each trap station. We assume that these variables characterize the microhabitat structure at the moment when measurements were taken. The data are transformed in 3 ways: (1) the averages of the measured variables were calculated, as usual in habitat studies; (2) the data are transformed in densities per area in square meters without reduction in the number of variables; and (3) we calculated the natural logarithms of the data thus transformed. The 3 sets were analised through discriminant analysis. The logarithms of densities were the most efficient kind of transformation. This transformation resulted in a significance of p = 0.0001 with 39.72% of variance in the 1st discriminant function and of p = 0.0426 with 31.45% of variance in the 2nd function, and 84.62% of groups correctly classified. We obtained better classification results than previous similar works and we kept the intuition through the data analysis.