Predator – Prey Dynamics and the Red Queen Hypothesis
explicitly allows for a trade-off between the amount of energy expended by individuals versus their success rate , so that they can improve , but only at a cost . Conversely , the predators can also reduce their effectiveness in order to conserve resources . As this simulation shows , when the trade-off is high this is exactly what they will do .
Throughout this paper we have assumed that improving or reducing predation effectiveness has been an evolutionary mechanism ; i . e ., through the mutation and spread of changes in the genotype . This is taken from the original formulation of the Red Queen hypothesis . However , these models are not limited to that interpretation . The changes are abstracted into a very simple measure of effectiveness , and this could be interpreted to mean that the predators simply learn to become more ( or less ) effective hunters . Thus , the applicability of this model is much broader than just changes on the evolutionary scale .
This work also has substantial policy implications . The issue of predation efficacy versus energy / resources investments has clear and direct relevance to the questions of innovation and creativity , company investment strategies in a competitive space , arms race , political and military conflicts , sustainability and resilience , ecology , or marketing , to name a few . The models developed in this research effort can be easily adapted to any of the above , or other , application domains .
There is much more to be done in future work . The most obvious is to put similar energy constraints on the prey population as well as the predators . If they can also adapt their ability to escape predation , given similar resource tradeoffs , how will they react , both individually and collectively ? Another consideration is to instantiate a more complicated food web . Due to the constraints of the competitive exclusion principle , there is not yet a simple way to add ( and preserve ) a diversity of species at a particular trophic level . However , we can easily add and experiment with a “ top predator ,” in order to extend the food chain to a four-trophic-level system . As seen in previous results , this addition will affect all population levels in both size and average age . While we expect that it is the size of the changes , rather than the direction , that would be affected , it remains to be seen if the results presented here are robust enough to accommodate such an addition .
V - Conclusions
Even in a simple model of population
dynamics , with very basic assumptions , we find many outcomes that are non-intuitive in nature . The power of this ABM model , however , is that by understanding the fundamental properties of the simplest model we can perhaps better understand how additional and more complicated factors affect dynamical food webs in the real world . Furthermore , an ABM allows us to monitor many aspects of these simulated populations that are difficult or impossible to monitor for their realworld counterparts . For example , average consumption by a predator population would be time- and resource intensive to record in the field , while in a simulated environment it is elementary . Even more exciting , however , is the possibility that a properly calibrated ABM — one that is grounded in a specific , real-world food web — might also provide other simulated measures that can be collected in the field , as well as infer those that cannot be . For example , the average age of each population is a variable that has a particular ( and sometimes non-intuitive ) consequence that it at least indicates what ( for example ) average consumption rates might be . Average age is much easier to
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