Larval competition in Chrysomya megacephala (F.) (Diptera: Calliphoridae): effects of different levels of larval aggregation on estimates of weight, fecundity and reproductive investmentIn insects that utilize patchy and ephemeral resources for feeding and egg laying, the outcome of larval competition for food resources depends on the amount of resources and the spatial distribution of immatures among patches of food. In the present study, the results of larval competition for food in Chrysomya megacephala, in traits such as female weight, fecundity and reproductive investment, were different in situations where the level of larval aggregation (proportion of competitors per amount of food) was the same, but with densities of competitors and amounts of food proportionally different. These results are indicative that the larval competition may depend both on the larval density and the amount of food, in different situations with the same proportion of larvae per gram of food.Key words: Chrysomya megacephala, fecundity, female weight, larval aggregation, reproductive investment. RESUMOEm insetos que se utilizam de substratos discretos e efêmeros para a alimentação e postura de ovos, o resultado da competição larval por alimento depende da quantidade de recursos e da distribuição espacial dos imaturos nos substratos alimentares. No presente estudo, os resultados da competição larval por alimento em Chrysomya megacephala, em caracteres como peso de fêmeas, fecundidade e investimento reprodutivo, foram diferentes em situações em que o nível de agregação larval (proporção de competidores por quantidade de alimento) é o mesmo, mas com densidades de competidores e quantidades de alimento proporcionalmente diferentes. Esses resultados indicam que a competição larval pode depender tanto da densidade larval como da quantidade de alimento, em situações diferentes com a mesma proporção de larvas por grama de alimento.Palavras-chave: Chrysomya megacephala, fecundidade, peso de fêmeas, agregação larval, investimento reprodutivo.

Theory recently developed to construct confidence regions based on the parametric bootstrap is applied to add inferential information to graphical displays of sample centroids in canonical variate analysis. Problems of morphometric differentiation among subspecies and species are addressed using numerical resampling procedures.

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We present an analogue of the usual Cramer-Rao development, in which median-unbiasedness replaces unbiasedness, the first absolute moment of the sample score replaces the second, "local kurtosis" replaces variance, and the maximum likelihood estimate enjoys optimality in a certain "exponential" family analogous to the exponential family of Koopman-Darmois form.