The exception to the rule : Retreating ice front makes Bewick's swans Cygnus columbianus bewickii migrate slower in spring than in autumn

In the vast majority of migratory bird species studied so far, spring migration has been found to proceed faster than autumn migration. In spring, selection pressures for rapid migration are purportedly higher, and migratory conditions such as food supply, daylength, and/or wind support may be better than in autumn. In swans, however, spring migration appears to be slower than autumn migration. Based on a comparison of tundra swan Cygnus columbianus tracking data with long-term temperature data from wheather stations, it has previously been suggested that this was due to a capital breeding strategy (gathering resources for breeding during spring migration) and/or to ice cover constraining spring but not autumn migration. Here we directly test the hypothesis that Bewick's swans Cygnus columbianus bewickii follow the ice front in spring, but not in autumn, by comparing three years of GPS tracking data from individual swans with concurrent ice cover data at five important migratory stop-over sites. In general, ice constrained the swans in the middle part of spring migration, but not in the first (no ice cover was present in the first part) nor in the last part. In autumn, the swans migrated far ahead of ice formation, possibly in order to prevent being trapped by an early onset of winter. We conclude that spring migration in swans is slower than autumn migration because spring migration speed is constrained by ice cover. This restriction to spring migration speed may be more common in northerly migrating birds that rely on freshwater resources. © 2013 The Authors.

A multi-objective evolutionary algorithm-based decision support system: a case study on job-shop scheduling in manufacturing

In this paper, an evolutionary algorithm is used for developing a decision support tool to undertake multi-objective job-shop scheduling problems. A modified micro genetic algorithm (MmGA) is adopted to provide optimal solutions according to the Pareto optimality principle in solving multi-objective optimisation problems. MmGA operates with a very small population size to explore a wide search space of function evaluations and to improve the convergence score towards the true Pareto optimal front. To evaluate the effectiveness of the MmGA-based decision support tool, a multi-objective job-shop scheduling problem with actual information from a manufacturing company is deployed. The statistical bootstrap method is used to evaluate the experimental results, and compared with those from the enumeration method. The outcome indicates that the decision support tool is able to achieve those optimal solutions as generated by the enumeration method. In addition, the proposed decision support tool has advantage of achieving the results within a fraction of the time.