Modelling for Field Biologists and Other Interesting PeopleStudents of evolutionary and behavioural ecology are often unfamiliar with mathematical techniques, though much of biology relies on mathematics. Evolutionary ideas are often complex, meaning that the logic of hypotheses proposed should not only be tested empirically but also mathematically. There are numerous different modelling tools used by ecologists, ranging from population genetic 'bookkeeping', to game theory and individual-based computer simulations. Due to the many different modelling options available, it is often difficult to know where to start. Hanna Kokko has designed this 2007 book to help with these decisions. Each method described is illustrated with one or two biologically interesting examples that have been chosen to help overcome fears of many biologists when faced with mathematical work, whilst also providing the programming code (Matlab) for each problem. Aimed primarily at students of evolutionary and behavioural ecology, this book will be of interest to any biologist interested in mathematical modelling. |
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Page 18
... probability 1Àx. What sort of matings are possible in the population? It could be that the mother is a, and the ... probability'. Hang on! In the above, we were assuming haploid genetics, which means that females and males have either ...
... probability 1Àx. What sort of matings are possible in the population? It could be that the mother is a, and the ... probability'. Hang on! In the above, we were assuming haploid genetics, which means that females and males have either ...
Page 19
... probabilities behave. If Joanna has a blue bag with 3 oranges and 7 apples, and Judith has a red bag that also contains 3 oranges and 7 apples, and each of the girls chooses one fruit blindly from each bag, then the probability that ...
... probabilities behave. If Joanna has a blue bag with 3 oranges and 7 apples, and Judith has a red bag that also contains 3 oranges and 7 apples, and each of the girls chooses one fruit blindly from each bag, then the probability that ...
Page 20
... probability x; hence x2. But the whole point of our model is that mating success of males is not random with respect ... probability that a mating female is A still equals x. But when m>1, the probability of a mating male being A exceeds ...
... probability x; hence x2. But the whole point of our model is that mating success of males is not random with respect ... probability that a mating female is A still equals x. But when m>1, the probability of a mating male being A exceeds ...
Page 21
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Page 22
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allele answer approach argument assume assumptions average become behaviour better birds breeding calculate called choice choose condition consider costs course create denote depends derivative detail develop distribution dynamic effect equals equation equilibrium evolution evolutionary exactly example expected expression fact females find first fitness foraging frequency function future gain gene genetic give given grow growth happens height higher important increase individuals interested leads less look male mathematical mating Matlab means migration nature Note offspring once optimal options parameter particular patch perhaps phenotypic plant plot population positive possible predators predict present probability problem produce proportion question reason reproductive requires resident response result rules selection simple simulations solutions strategy success territories things trait values zero
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