The fresh-water fishes of North Borneo [by] Robert F. Inger and Chin Phui Kong.

v.45(1962)

Systematics of the land and fresh-water mollusca of the New Hebrides, [by] Alan Solem.

v.43:no.1(1959)

Molt cycle of the natural population of Palaemonetes argentinus (Crustacea, Palaemonidae) from Los Padres Lagoon (Buenos Aires, Argentina)

The molt cycle of the natural population of Palaemonetes argentinus Nobili, 1901 from Los Padres Lagoon, Buenos Aires, Argentina, was studied in relation to age, sex, and environmental factors. A total of 1645 individuals (740 females, 539 males, and 366 juveniles) were collected and analyzed between December 1995 and December 1996. The results indicate that the sex ratio (males:females) remains around 1:1.4 throughout most of the year. The reproductive period extends from September until February (spring and summer), with maximum sexual activity in October and November. Two cohorts originated in the spring and in the summer were differentiated. Ovigerous females arrest their molt cycle during the intermolt period to restart it after oviposition. The duration of the intermolt period does not differ between adults and juveniles. Since the percentage of premolt individuals represents 60% of the total cycle, it was classified as a diecdysic cycle. Within the studied range of water temperatures, the observed variations in the span of the different stages, indicate that this factor does not alter the molt frequency. Like in the rest of decapods, the intermolt duration of P. argentinus is modified by ovarian maturation.

Check list of North American water-mites / Rodger D. Mitchell.

v.35:no.3(1954)

Marine and fresh-water mollusks of the Solomon Islands / Alan Solem.

v.34:no.22(1953)

Seasonal variations in the intermediate metabolism of the crayfish Parastacus brasiliensis (Crustacea, Decapoda, Parastacidae) in the natural environment and experimental culture

The goal of this study was to evaluate the effects of seasonal variations on energy metabolism in different tissues of the freshwater crayfish Parastacus brasiliensis (von Martens, 1869). Crayfish were collected monthly from January 2001 to January 2003 in São Francisco de Paula, Rio Grande do Sul, Brazil, in a stream and in a culture tank. Haemolymph samples were collected from each crayfish in the field with a syringe, by puncturing the membrane at the base of the chelipeds. Hepatopancreas, gills, and abdominal muscle were removed for determination of free glucose, glycogen, total lipids, and triglycerides. The haemolymph samples were used for determination of glucose, total proteins, total lipids, and triglycerides. Statistical analysis revealed significant differences in biochemical composition in crayfish collected in the stream compared to the experimental tank during the year, principally in glucose and triglycerides in haemolymph, glycogen and total lipids in all tissues study, and triglycerides only in abdominal muscle. The regular food intake partially modified these seasonal variations of the metabolic pattern. Environmental conditions (e.g., food availability and water temperature) and reproductive period appeared to be the main factors influencing the seasonal patterns of variation in energy metabolism.

Land and fresh-water mollusks from Peru.

v.31:no.28(1949)

Fresh-water limestone from the Torola Valley, northeastern El Salvador / Sharat Kumar Roy -- and Robert Kriss Wyant --.

v.10:no.16(1953)

New species of fresh-water catfishes from North Borneo [by] Robert F. Inger and Chin Phui Kong.

v.39:no.27(1959)

Benthic macroinvertebrates as bioindicators of water quality in an Atlantic forest fragment

The objective of this study was to evaluate benthic macroinvertebrate communities as bioindicators of water quality in five streams located in the "Reserva Particular do Patrimônio Natural" (RPPN) Mata Samuel de Paula and its surroundings, in the municipality of Nova Lima near the city of Belo Horizonte, Minas Gerais State, southeastern Brazil. This region has been strongly modified by human activities including mining and urbanization. Samples were collected in the field every three months between August 2004 and November 2005, totaling six samplings in the rainy and dry seasons. This assessment identified one area ecologically altered while the other sampling sites were found to be minimally disturbed systems, with well-preserved ecological conditions. However, according to the Biological Monitoring Work Party (BMWP) and the Average Score Per Taxon (ASPT) indices, all sampling sites had excellent water quality. A total of 14,952 organisms was collected, belonging to 155 taxa (148 Insecta, two Annelida, one Bivalvia, one Decapoda, one Planariidae, one Hydracarina, and one Entognatha). The most abundant benthic groups were Chironomidae (47.9%), Simuliidae (12.3%), Bivalvia (7.5%), Decapoda (6.1%), Oligochaeta (5.2%), Polycentropodidae (3.7%), Hydropsychidae (2.5%), Calamoceratidae (1.8%), Ceratopogonidae (1.7%), and Libellulidae (1.2%). The assessment of the benthic functional feeding groups showed that 34% of the macroinvertebrates were collector-gatherers, 29% predators, 24% collector-filterers, 8% shredders, and 5% scrapers. The RPPN Mata Samuel de Paula comprises diversified freshwater habitats that are of great importance for the conservation of many benthic taxa that are intolerant to organic pollution.

Mass migration of juvenile Biomphalaria glabrata snails bred under semi-natural conditions

For the development of studies on snail interspecific competition special in-door laboratory channels were built. In the all five channels seeded with adult specimens of Biomphalaria glabrata mass migration of juvenile snails outside the water was observed. Most of the migrant snails presented apertural lamellae. Data collected during the period of two years, showed the regression of the migration phenomenon and the disappearance of the lamellate snails.

Comparison of time-lapse GPR data collected under natural and forced infiltration conditions to estimate vadose zone hydraulic parameters

Time-lapse crosshole ground-penetrating radar (GPR) data, collected while infiltration occurs, can provide valuable information regarding the hydraulic properties of the unsaturated zone. In particular, the stochastic inversion of such data provides estimates of parameter uncertainties, which are necessary for hydrological prediction and decision making. Here, we investigate the effect of different infiltration conditions on the stochastic inversion of time-lapse, zero-offset-profile, GPR data. Inversions are performed using a Bayesian Markov-chain-Monte-Carlo methodology. Our results clearly indicate that considering data collected during a forced infiltration test helps to better refine soil hydraulic properties compared to data collected under natural infiltration conditions

Microhabitat preferences of Biomphalaria pfeifferi and Lymnaea natalensis in a natural and a man-made habitat in Southeastern Tanzania

Schistosoma mansoni is an important human parasitic disease which is widespread throughout Africa. As Biomphalaria pfeifferi snails act as intermediate host, knowledge of their population ecology is an essential prerequisite towards understanding disease transmission. We conducted a field study and assessed the density and microhabitat preferences of B.pfeifferi in a natural habitat which was a residual pool of a river. Repeated removal collecting revealed a density of 26.6 [95% confidence interval (CI): 24.9-28.3] snails/m2. B.pfeifferi showed microhabitat preferences for shallow water (depths: 0-4cm). They were found most abundantly close to the shoreline (distances: 0-40cm), and preferred either plant detritus or bedrock as substratum. Lymnaea natalensis, a snail which may act as a host for human Fasciola gigantica, also occurred in this habitat with a density of 34.0 (95% CI: 24.7-43.3) snails/m2, and preferred significantly different microhabitats when compared to B.pfeifferi. Microhabitat selection by these snail species was also investigated in a man-made habitat nearby, which consisted of a flat layer of concrete fixed on the riverbed, covered by algae. Here, B.pfeifferi showed no preference for locations close to the shoreline, probably because the habitat had a uniform depth. We conclude that repeated removal collecting in shallow habitats provides reliable estimates of snail densities and that habitat changes through constructions may create favourable microhabitats and contribute to additional disease transmission.

Free-living amoebae: Biological by-passes in water treatment.

Free-living amoebae constitute reservoirs for many bacteria including not only well-known pathogens but also emerging pathogens responsible for respiratory diseases, and contribute to the protection, survival and dissemination of these bacteria in water systems, despite the application of disinfection or thermal treatments. In this article we review the available information on the presence of free-living amoebae and amoebae-resisting bacteria in drinking water systems, on the factors that contribute to their presence in the water and/or the biofilms, on the possible control measures and their effectiveness, and we identify some gaps in current knowledge needing further research.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.