Distinguishing the distributions of two cryptic frogs (Anura: Discoglossidae) using molecular data and environmental modeling

Currently, the identification of two cryptic Iberian amphibians, Discoglossus galganoi Capula, Nascetti, Lanza, Bullini and Crespo, 1985 and Discoglossus jeanneae Busack, 1986, relies on molecular characterization. To provide a means to discern the distributions of these species, we used 385-base-pair sequences of the cytochrome b gene to identify 54 Spanish populations of Discoglossus. These data and a series of environmental variables were used to build up a logistic regression model capable of probabilistically designating a specimen of Discoglossus found in any Universal Transverse Mercator (UTM) grid cell of 10 km × 10 km to one of the two species. Western longitudes, wide river basins, and semipermeable (mainly siliceous) and sandstone substrates favored the presence of D. galganoi, while eastern longitudes, mountainous areas, severe floodings, and impermeable (mainly clay) or basic (limestone and gypsum) substrates favored D. jeanneae. Fifteen percent of the UTM cells were predicted to be shared by both species, whereas 51% were clearly in favor of D. galganoi and 34% were in favor of D. jeanneae, considering odds of 4:1. These results suggest that these two species have parapatric distributions and allow for preliminary identification of potential secondary contact areas. The method applied here can be generalized and used for other geographic problems posed by cryptic species.

Evidence formulti-cycle sedimentation and provenance constraints from

Laser ablation ICP-MS U–Pb analyses were conducted on detrital zircons of Triassic sandstone and conglomerate from the Lusitanian basin in order to: i) document the age spectra of detrital zircon; ii) compare U–Pb detrital zircon ages with previous published data obtained from Upper Carboniferous, Ordovician, Cambrian and Ediacaran sedimentary rocks of the pre-Mesozoic basement of western Iberia; iii) discuss potential sources; and iv) test the hypothesis of sedimentary recycling. U–Pb dating of zircons established a maximum depositional age for this deposit as Permian (ca. 296Ma),which is about sixty million years older compared to the fossil content recognized in previous studies (Upper Triassic). The distribution of detrital zircon ages obtained points to common source areas: the Ossa–Morena and Central Iberian zones that outcrop in and close to the Porto–Tomar fault zone. The high degree of immaturity and evidence of little transport of the Triassic sediment suggests that granite may constitute primary crystalline sources. The Carboniferous age of ca. 330 Ma for the best estimate of crystallization for a granite pebble in a Triassic conglomerate and the Permian–Carboniferous ages (ca. 315Ma) found in detrital zircons provide evidence of the denudation of Variscan and Cimmerian granites during the infilling of continental rift basins in western Iberia. The zircon age spectra found in Triassic strata are also the result of recycling from the Upper Carboniferous Buçaco basin,which probably acted as an intermediate sediment repository.U–Pb data in this study suggest that the detritus from the Triassic sandstone and conglomerate of the Lusitanian basin is derived fromlocal source areas with features typical of Gondwana,with no sediment from external sources from Laurussia or southwestern Iberia.

Digital soil mapping using reference area and artificial neural networks.

Digital soil mapping is an alternative for the recognition of soil classes in areas where pedological surveys are not available. The main aim of this study was to obtain a digital soil map using artificial neural networks (ANN) and environmental variables that express soillandscape relationships. This study was carried out in an area of 11,072 ha located in the Barra Bonita municipality, state of São Paulo, Brazil. A soil survey was obtained from a reference area of approximately 500 ha located in the center of the area studied. With the mapping units identified together with the environmental variables elevation, slope, slope plan, slope profile, convergence index, geology and geomorphic surfaces, a supervised classification by ANN was implemented. The neural network simulator used was the Java NNS with the learning algorithm "back propagation." Reference points were collected for evaluating the performance of the digital map produced. The occurrence of soils in the landscape obtained in the reference area was observed in the following digital classification: medium-textured soils at the highest positions of the landscape, originating from sandstone, and clayey loam soils in the end thirds of the hillsides due to the greater presence of basalt. The variables elevation and slope were the most important factors for discriminating soil class through the ANN. An accuracy level of 82% between the reference points and the digital classification was observed. The methodology proposed allowed for a preliminary soil classification of an area not previously mapped using mapping units obtained in a reference area