Geological control of Podocnemis expansa and Podocnemis unifilis nesting areas in Rio Javaés, Bananal Island, Brazil

The distribution of the nests of Podocnemis expansa (Amazon turtle) and Podocnemis unifilis (yellow-spotted side neck turtle) along the point bars of the Javaés River in Bananal Island, demonstrates a clear preference of these chelonians for differentiated geological environments, in respect to the morphology, grain size or height of the nests in relation to the level of the river. The topographical distribution and the differences in the grain size of the sediments that compose the point bars of the river, originated from the multiple sedimentary processes, and make possible the creation and separation of different nesting environments. Each turtle species takes advantage of the place that presents physiographic characteristics appropriate to the hatching success of their eggs. The superposition of the P. expansa and P. unifilis nest placement areas is rare. The P. expansa nests are concentrated on the central portion of the beaches where successive depositional sedimentary events produced sandy banks more than 3.3 m above the river water level. The P. unifilis nests are distributed preferentially in the upstream and downstream portions along the point bars where the sandy deposits rarely surpass 1.5 m at the moment of laying. P. expansa nests located on the beaches of fine to medium sized sand hatch in a mean of 68 days, while those incubated on beaches of medium to coarse sand size take a mean of 54 days to hatch.

Geological characteristics of the nesting areas of the giant Amazon river turtle (Podocnemis expansa) in the Crixás-Açu river in Goiás State, Brazil

A large population of the giant Amazon river turtle (Podocnemis expansa) nests along the beaches of the Crixás-Açu River in the central western region of Brazil. In spite of the existence of several point bars in the area, only a selected group of beaches is used for nesting by P. expansa. Geological aspects, such as river width and depth, beach height above 200 cm with sandy sediments, were indispensable for the choice of these nesting sites. The relatively reduced dimensions of the point bars and the great number of turtles, which nest in the same local, contributed to the existence of a high nest concentration. The rapid rise of the Crixás-Açu River caused the flooding of the beaches and the drowning of hatchlings and embryos. It is estimated that nearly all the nests were lost. The height of the nesting place and the time of flooding related to the incubation period are decisive in embryo survivorship. The Retiro, Júnior, Assombrado and Limoeiro beaches, which are situated at heights of 183 to 310 cm, were inundated on 8 November 2000. The Barreira Branca beach, with a height of up to 380 cm was completely inundated on 13 December 2000. All of these beaches were flooded before the hatchlings emerged.

LAGO AMAZONAS: FACT OR FANCY?

It has been suggested that a huge lake, Lago Amazonas, covered a large part of the Amazon basin until as recently as two thousand years ago. According to this hypothesis, the topmost sediments in western Amazonia are almost universally young deposite of lacustrine and deltaic origin. The hypothesis has gained some attention among biologists because of its implications for biological phenomena in Amazonia, especially biogeography and biodiversity. According to the available geological data, however, Amazonia is geologically far more complex than assumed by the lake hypothesis. In the following discussion we will point out the weaknesses of the Lago Amazonas hypothesis, and indicate alternative explanations of the surface geology that are based on tectonically controlled fluvial deposition.

PALEOENVIRONMENTAL MODEL FOR THE LATE CENOZOIC OF SOUTHWESTERN AMAZONIA: PALEONTOLOGY AND GEOLOGY

Our study provides paleontological and geological data substantiating a paleoenvironmental model for the upper Miocene-Pliocene of Southwestern Amazonia. The extensive Late Tertiary sediments of The Solimões Formation, outcropping in Southwestern Amazonia, were deposited by a complex megafan system, originating in the high Peruvian Andes. The megafan system was the sedimentological response to the Andean Quechua tectonic phase of Tertiary age, producing sediments that fdled the foreland basin of Southwestern Amazonia. Occurrences of varied vertebrate fossil assemblages of the Huayquerian-Montehermosan Mammal age collected in these sediments support this interpretation. The fauna includes several genera and species of fishes, reptiles, birds, mammals and appears to be one that could have lived in or near a riverine habitat. In the Late Pliocene, the megafan system became inactive as a result of the influence of the Diaguita Tectonical Phase.

Similar understorey structure in spite of edaphic and floristic dissimilarity in Amazonian forests

Forest structure determines light availability for understorey plants. The structure of lowland Amazonian forests is known to vary over long edaphic gradients, but whether more subtle edaphic variation also affects forest structure has not beenresolved. In western Amazonia, the majority of non-flooded forests grow on soils derived either from relatively fertile sediments of the Pebas Formation or from poorer sediments of the Nauta Formation. The objective of this study was to compare structure and light availability in the understorey of forests growing on these two geological formations. We measured canopy openness and tree stem densities in three size classes in northeastern Peru in a total of 275 study points in old-growth terra firme forests representing the two geological formations. We also documented variation in floristic composition (ferns, lycophytes and the palm Iriartea deltoidea) and used Landsat TM satellite image information to model the forest structural and floristic features over a larger area. The floristic compositions of forests on the two formations were clearly different, and this could also be modelled with the satellite imagery. In contrast, the field observations of forest structure gave only a weak indication that forests on the Nauta Formation might be denser than those on the Pebas Formation. The modelling of forest structural features with satellite imagery did not support this result. Our results indicate that the structure of forest understorey varies much less than floristic composition does over the studied edaphic difference.

Temporal Geobiotic Mapping: a conceptual mapping technique toward visualising geobiotic areas in cross-section

Geobiota are defined by taxic assemblages (i.e., biota) and their defining abiotic breaks, which are mapped in cross-section to reveal past and future biotic boundaries. We term this conceptual approach Temporal Geobiotic Mapping (TGM) and offer it as a conceptual approach for biogeography. TGM is based on geological cross-sectioning, which creates maps based on the distribution of biota and known abiotic factors that drive their distribution, such as climate, topography, soil chemistry and underlying geology. However, the availability of abiotic data is limited for many areas. Unlike other approaches, TGM can be used when there is minimal data available. In order to demonstrate TGM, we use the well-known area in the Blue Mountains, New South Wales (NSW), south-eastern Australia and show how surface processes such as weathering and erosion affect the future distribution of a Moist Basalt Forest taxic assemblage. Biotic areas are best represented visually as maps, which can show transgressions and regressions of biota and abiota over time. Using such maps, a biogeographer can directly compare animal and plant distributions with features in the abiotic environment and may identify significant geographical barriers or pathways that explain biotic distributions.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Building predictive models of soil particle-size distribution

Is it possible to build predictive models (PMs) of soil particle-size distribution (psd) in a region with complex geology and a young and unstable land-surface? The main objective of this study was to answer this question. A set of 339 soil samples from a small slope catchment in Southern Brazil was used to build PMs of psd in the surface soil layer. Multiple linear regression models were constructed using terrain attributes (elevation, slope, catchment area, convergence index, and topographic wetness index). The PMs explained more than half of the data variance. This performance is similar to (or even better than) that of the conventional soil mapping approach. For some size fractions, the PM performance can reach 70 %. Largest uncertainties were observed in geologically more complex areas. Therefore, significant improvements in the predictions can only be achieved if accurate geological data is made available. Meanwhile, PMs built on terrain attributes are efficient in predicting the particle-size distribution (psd) of soils in regions of complex geology.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Use of legal reserve areas as geochemical background in hydrosedimentology studies¹

In hydrosedimentology studies the determination of the trace element concentrations at the study site is imperative, since this background can be used to assess the enrichment of sediments with these elements. This enrichment can be the result of the natural process of geological formation or of anthropogenic activities. In the latter case, guidelines are used to indicate the concentrations at which trace elements cause ecotoxicity effects on the environment. Thus, this study used legal reserve areas in the municipality of Toledo, PR, where natural forests are maintained, with no or minimal human interference to establish background levels. The results of atomic emission spectrometry with inductively coupled argon plasma showed that the legal reserves have lower levels of trace elements than other theoretical references, but equivalent concentrations to the safety levels recommended by international guidelines. It was concluded that determining values is fundamental to recommend this background as scientific database for research in the area of hydrosedimentology of this site and also as a way of environmental management of the watershed of this municipality.