How costly is clutch formation in the Audouin's Gull Larus audouinii?

During the Audouin's Gull's breeding season at the Ebro Delta in 1993, 24 fresh eggs from eight three-egg clutches (modal clutch-size) were collected at the peak of the laying period. Eggs were processed to obtain formalin-fixed yolks, which were halved and stained using the potassium dichromate method. Digitized images of the yolks were examined to assess the daily rates of yolk deposition. We used these data in combination with egg compositional analysis to build a model of energy demands during the formation of an average clutch in Audouin's Gull. To show how the different parameters of clutch formation affect the daily energy investment peak, we performed a simulation analysis in which the rapid yolk development (RYD) period, the follicle triggering interval (FTI), the laying interval (LI) and the albumen synthesis period (ASP) were allowed to vary simultaneously. In our sample, the mean RYD period was seven days with a range from six to eight days. There were no significant differences in yolk volume among eggs in a clutch, but albumen volume was significantly smaller in third eggs. According to our model the albumen synthesis of the a-egg coincides with the energy demand peak for clutch formation. This peak represents an increase by ca. 42% in female energy requirements. Values obtained from the simulation analysis showed that only the ASP of the a-egg and the RYD durations of the second and third follicles produced noticeable reductions in peak energy investment. We predict that in gulls, whose laying intervals seem to be kept constant, significant increases of the durations of the RYD periods of second and third eggs, or even significant reductions of yolk size of these eggs, may operate simultaneously to match the energy demands during clutch formation to the prevailing food conditions.     

Optimization by simulating molecular evolution

Based on the analogy between mathematical optimization and molecular evolution and on Eigen's quasi-species model of molecular evolution, an evolutionary algorithm for combinatorial optimization has been developed. This algorithm consists of a versatile variation scheme and an innovative decision rule, the essence of which lies in a radical revision of the conventional philosophy of optimization: A number of configurations of variables with better values, instead of only a single best configuration, are selected as starting points for the next iteration. As a result the search proceeds in parallel along a number of routes and is unlikely to get trapped in local optima. An important innovation of the algorithm is introduction of a constraint to let the starting points always keep a certain distance from each other so that the search is able to cover a larger region of space effectively. The main advantage of the algorithm is that it has more chances to find the global optimum and as many local optima as possible in a single run. This has been demonstrated in preliminary computational experiments.