Emergent Properties of Patch Shapes Affect Edge Permeability to Animals

Animal travel between habitat patches affects populations, communities and ecosystems. There are three levels of organization of edge properties, and each of these can affect animals. At the lowest level are the different habitats on each side of an edge, then there is the edge itself, and finally, at the highest level of organization, is the geometry or structure of the edge. This study used computer simulations to (1) find out whether effects of edge shapes on animal behavior can arise as emergent properties solely due to reactions to edges in general, without the animals reacting to the shapes of the edges, and to (2) generate predictions to allow field and experimental studies to test mechanisms of edge shape response. Individual animals were modeled traveling inside a habitat patch that had different kinds of edge shapes (convex, concave and straight). When animals responded edges of patches, this created an emergent property of responding to the shape of the edge. The response was mostly to absolute width of the shapes, and not the narrowness of them. When animals were attracted to edges, then they tended to collect in convexities and disperse from concavities, and the opposite happened when animals avoided edges. Most of the responses occurred within a distance of 40% of the perceptual range from the tip of the shapes. Predictions were produced for directionality at various locations and combinations of treatments, to be used for testing edge behavior mechanisms. These results suggest that edge shapes tend to either concentrate or disperse animals, simply because the animals are either attracted to or avoid edges, with an effect as great as 3 times the normal density. Thus edge shape could affect processes like pollination, seed predation and dispersal and predator abundance

Effects of structural and functional habitat gaps on breeding woodland birds: working harder for less

The effects of habitat gaps on breeding success and parental daily energy expenditure (DEE) were investigated in great tits (Parus major) and blue tits (Cyanistes caeruleus) in urban parkland (Cardiff, UK) compared with birds in deciduous woodland (eastern England, UK). Tree canopy height, the percentage of gap in the canopy and the percentage of oak (in the wood only) within a 30 m radius of nest boxes were obtained from airborne remote-sensed data. Breeding success was monitored and parental DEE (great tits: both habitats; blue tits: park only) was measured using doubly labelled water in birds feeding young. In the park, mean (± SD) tree height (7.5 ± 4.7 m) was less than in the wood (10.6 ± 4.5 m), but the incidence of gaps (32.7 ± 22.6%) was greater (9.2 ± 14.7%). Great tits and blue tits both reared fewer young in the park and chick body mass was also reduced in park-reared great tits. Park great tits had a higher DEE (86.3 ± 12.3 kJ day-1) than those in the wood (78.0 ± 11.7 kJ day-1) and, because of smaller brood sizes, worked about 64% harder for each chick reared. Tits in the park with more than about 35% gap around their boxes had higher DEEs than the average for the habitat. In the wood, great tits with less oak around their boxes worked harder than average. Thus structural gaps, and functional gaps generated by variation in the quality of foraging habitat, increased the costs of rearing young

Telebus Berlin: Vehicle Scheduling in a Dial-a-Ride System

Telebus is Berlin's dial-a-ride system for handicapped people who cannot use the public transportation system. The service is provided by a fleet of about 100 mini-buses and includes assistance in getting in and out of the vehicle. Telebus has between 1,000 and 1,500 transportation requests per day. The problem is to schedule these requests onto the vehicles such that punctual service is provided while operation costs are minimized. Addi tional constraints include pre-rented vehicles, fixed bus driver shift lengths, obligatory breaks, and different vehicle capacities. We use a set partitioning approach for the solution of the bus scheduling problem that consists of two steps. The first clustering step identifies segments of possible bus tours ("orders") such that more than one person is transported at a time; the aim in this step is to reduce the size of the problem and to make use of larger vehicle capacities. The problem of selecting a set of orders such that the traveling distance of the vehicles within the orders is minimal is a set partitioning problem that can be solved to optimality. I