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Credit or debit? Resource input changes population dynamics of city-slicker birds
Authors:Eyal Shochat
Institution:Center for Environmental Studies, Arizona State Univ., Tempe, AZ 85287-3211, USA. Present address: Sutton Avian Research Center, P.O. Box 2007 Bartlesville, OK 74005-2007, USA ().
Abstract:The underlying evolutionary mechanisms of urban bird populations have hardly been studied. High food density and low predation risk serve to explain the global pattern of extremely high urban bird population densities. Both these bottom-up and top-down effects are paradoxical since the per capita amount of food is small due to competition, and domestic predator density is high in cities. The bottom-up paradox can be resolved by taking into account the high food resource-predictability in cities. Concerning the top-down effect, recent studies suggest that at least when it comes to nest predation the effect of cats is minor. I suggest that the combination of high food predictability and low predation risk in cities alter bird foraging behaviour, which in turn affects population dynamics. In terms of density, the result is that bird populations exceed the carrying capacity of the urban environment, costing heavily on body condition and/or life span. Under such conditions the population should consist of a few winners and many losers. Only the winners have sufficient access to food resources and the opportunity to reproduce. The highly predictable continuous input of food in the urban environment allows them to "live on their credit". They may trade off between offspring body condition and clutch size. In the lack of predation, the losers among the fledglings may survive for a relatively long period, getting just enough energy to survive. Though they may never become healthy enough to reproduce, they will have a major contribution to the observed population density. Results of several case studies seem to support the credit card hypothesis and suggest that it can serve as a general rule for the evolution of animal populations and communities in highly predictable human managed environments.
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