Drosophila melanogaster larvae make nutritional choices that minimize developmental time

Organisms from slime moulds to humans carefully regulate their macronutrient intake to optimize a wide range of life history characters including survival, stress resistance, and reproductive success. However, life history characters often differ in their response to nutrition, forcing organisms to make foraging decisions while balancing the trade-offs between these effects. To date, we have a limited understanding of how the nutritional environment shapes the relationship between life history characters and foraging decisions. To gain insight into the problem, we used a geometric framework for nutrition to assess how the protein and carbohydrate content of the larval diet affected key life history traits in the fruit fly, Drosophila melanogaster. In no-choice assays, survival from egg to pupae, female and male body size, and ovariole number - a proxy for female fecundity - were maximized at the highest protein to carbohydrate (P:C) ratio (1.5:1). In contrast, development time was minimized at intermediate P:C ratios, around 1:2. Next, we subjected larvae to two-choice tests to determine how they regulated their protein and carbohydrate intake in relation to these life history traits. Our results show that larvae targeted their consumption to P:C ratios that minimized development time. Finally, we examined whether adult females also chose to lay their eggs in the P:C ratios that minimized developmental time. Using a three-choice assay, we found that adult females preferentially laid their eggs in food P:C ratios that were suboptimal for all larval life history traits. Our results demonstrate that D. melanogaster larvae make foraging decisions that trade-off developmental time with body size, ovariole number, and survival. In addition, adult females make oviposition decisions that do not appear to benefit the larvae. We propose that these decisions may reflect the living nature of the larval nutritional environment in rotting fruit. These studies illustrate the interaction between the nutritional environment, life history traits, and foraging choices in D. melanogaster, and lend insight into the ecology of their foraging decisions.

Modelled spatial and seasonal distribution of Blue Whiting (Micromesistius poutassou) larvae in the North-East Atlantic (1951 to 2005)

Blue whiting (Micromesistius poutassou, http://www.marinespecies.org/aphia.php?p=taxdetails&id=126439) is a small mesopelagic planktivorous gadoid found throughout the North-East Atlantic. This data contains the results of a model-based analysis of larvae captured by the Continuous Plankton Recorder (CPR) during the period 1951-2005. The observations are analysed using Generalised Additive Models (GAMs) of the the spatial, seasonal and interannual variation in the occurrence of larvae. The best fitting model is chosen using the Aikaike Information Criteria (AIC). The probability of occurrence in the continous plankton recorder is then normalised and converted to a probability distribution function in space (UTM projection Zone 28) and season (day of year). The best fitting model splits the distribution into two separate spawning grounds north and south of a dividing line at 53 N. The probability distribution is therefore normalised in these two regions (ie the space-time integral over each of the two regions is 1). The modelled outputs are on a UTM Zone 28 grid: however, for convenience, the latitude ("lat") and longitude ("lon") of each of these grid points are also included as a variable in the NetCDF file. The assignment of each grid point to either the Northern or Southern component (defined here as north/south of 53 N), is also included as a further variable ("component"). Finally, the day of year ("doy") is stored as the number of days elapsed from and included January 1 (ie doy=1 on January 1) - the year is thereafter divided into 180 grid points.

State-of-the-art procedures and equipment for internal inspection of underground storage tanks /

Mode of access: Internet.

Automobile fuel tanks.

Mode of access: Internet.