Os efeitos da alta temperatura e do myleran (busulfan) na espermatogênese de Devario aequipinnatus (teleostei, cyprinidae)

Pós-graduação em Ciência e Tecnologia Animal - FEIS

Diet and ecomorphology of Leporinus reticulatus (Characiformes: Anostomidae) from the upper Rio Juruena, MT, Brazil: ontogenetic shifts related to the feeding ecology

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Ecology of the Skink, Mabuya arajara Reboucas-Spieker, 1981, in the Araripe Plateau, Northeastern Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Agro-ecology as a mediating science between the training of the agronomist and sustainable agriculture

Agro-ecology plays an important role in the implementation of sustainable development, and an agronomist can be a leading agent in the dissemination of that culture. In this perspective, a survey was carried out with the attendants of the course in Agronomy at the Instituto Federal de Educacao, Ciencia e Tecnologia do Mato Grosso, Campus Campo Novo do Parecis, Brazil. The object of the study was to analyze the participation of the academics in agro-ecological activities, seeking to observe the acceptance, perceptions and possible indications of incorporation of these principles in the training. The survey was conducted during the period July-December 2011. The approach was the qualitative method and the instruments used in data collection were participant observation and interviews during the activities. The results revealed the satisfaction of students in engaging in agro-ecological activities: Students developed concepts and incorporated the proposal of agro-ecology throughout the project. The contents of the agronomy course offered at the institution, in the perception of students, is strongly linked to the principles of conventional farming, not fully considering training to meet the challenges, and current and emerging demands of agriculture.

Population ecology of the planktonic shrimp lucifer faxoni borradaile, 1915 (crustacea, sergestoidea, luciferidae) of the southeastern coast of brazil

Luciferid shrimps have short life spans and a rapid turnover of generations, engage in sequential spawning, and protect their eggs during incubation. This study investigates the ecology of Lucifer faxoni Borradaile, 1915 in the littoral zone, Ubatuba region, São Paulo. Sampling was conducted monthly from July 2005 to December 2006 using a Renfro net trawled over a distance of 50 m for a total sampling effort of 50 m² at each station. Nine stations were sampled, ranging from 1 to 15 m deep. Three stations each were grouped into zones 1, 2 and 3 (Z1, Z2 and Z3). Monthly values of salinity, temperature and rainfall were recorded at each station. The pre-buccal somite length (SL) of each specimen was measured. The results showed that in shallower zones (Z1 and Z2), 6306 individuals were captured, whereas in the deeper zone (Z3), 3808 specimens were captured, but no significant differences in SL was detected between the specimens from Z1 and Z2 and those from Z3 (ANOVA, p=0.25). The abundance of shrimps did not differ significantly between seasons (Tukey’s test, p=0.02) except in the spring. The sex ratio differed significantly over the seasons (χ², p<0.05). The results were closely associated with environmental factors with respect to the spatial and seasonal distribution of L. faxoni. Rainfall affected salinity directly, and contributed to the displacement of these shrimps to deeper areas.

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.

Ciclo reprodutivo de fêmeas de Devario aequipinnatus (Cypriniformes, Cyprinidae) submetidas a diferentes temperaturas

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Ciclo testicular e espermatogênese de Devario aequipinnatus (cypriniformes, cyprinidae) submetido a diferentes temperaturas

Pós-graduação em Ciência e Tecnologia Animal - FEIS

Eastern temperate North Pacific offshore killer whales (Orcinus orca): Occurrence, movements, and insights into feeding ecology

Beginning in the late 1980s, large groups of previously unidentified killer whales (Orcinus orca) were sighted off the west coast of Vancouver Island and in the Queen Charlotte Islands, British Columbia. Scientists working in this region produced two killer whale photo-identification catalogues that included both transient (mammal-eating) whales and 65 individual whales that investigators believed represented a distinct killer whale community (Ford et al. 1992, Heise et al. 1993). It was thought that these killer whales maintained a generally offshore distribution and were provisionally termed “offshores”; a term that has since been used as a population identifier for the eastern temperate North Pacific offshore killer whale population. Then in September 1992, 75 unidentified whales entered the Strait of Juan de Fuca just south and east of Victoria, British Columbia (Walters et al. 1992). Although most of these whales had not been seen before, two were matched to killer whales in the Queen Charlotte photo-identification catalogue (Ford et al. 1992, Heise et al. 1993) and were thus listed as “offshore” killer whales. During a similar time period, other large groups of killer whales, previously unidentified, were also being sighted off Alaska and California (Dahlheim et al. 1997; Nancy Black and Alisa Schulman- Janiger, unpublished data, respectively).

Studies on the Helminth Fauna of Alaska. XXV. The Ecology and Public Health Significance of Echinococcus sibiricensis Rausch & Schiller, 1954, on St. Lawrence Island

The occurrence of a species of Echinococcus (Rudolphi, 1801) on St Lawrence Island was noted by the writers in early 1950. Recognition of its unusual host relationships led to an investigation of the ecology of this cestode, E. sibiricensis Rausch & Schiller, 1954. It is the purpose of this paper to report the results of this work, with emphasis on alveolar hydatid disease in man, of which this cestode is the etiologic agent.

Seeking a Second Opinion: Uncertainty in Disease Ecology

Analytical methods accounting for imperfect detection are often used to facilitate reliable inference in population and community ecology. We contend that similar approaches are needed in disease ecology because these complicated systems are inherently difficult to observe without error. For example, wildlife disease studies often designate individuals, populations, or spatial units to states (e.g., susceptible, infected, post-infected), but the uncertainty associated with these state assignments remains largely ignored or unaccounted for. We demonstrate how recent developments incorporating observation error through repeated sampling extend quite naturally to hierarchical spatial models of disease effects, prevalence, and dynamics in natural systems. A highly pathogenic strain of avian influenza virus in migratory waterfowl and a pathogenic fungus recently implicated in the global loss of amphibian biodiversity are used as motivating examples. Both show that relatively simple modifications to study designs can greatly improve our understanding of complex spatio-temporal disease dynamics by rigorously accounting for uncertainty at each level of the hierarchy.