We develop a quantitative genetic model for conditional strategies that incorporates the ecological realism of previous strategic models. Similar to strategic models, the results show that environmental heterogeneity, cue reliability, and environment-dependent fitness trade-offs for the alternative tactics of the conditional strategy interact to determine when conditional strategies will be favored and that conditional strategies should be a common form of adaptive variation in nature. The results also show that conditional and unconditional development can be maintained in one of two ways: by frequency-dependent selection or by the maintenance of genetic variation that exceeds the threshold for induction. We then modified the model to take into account variance in exposures to the environmental cue as well as variance in response to the cue, which allows a derivation of a dose-response curve. Here the results showed that increasing the genetic variance for response both flattens and shifts the dose-response curve. Finally, we modify the model to derive the dose-response curve for a population polymorphic for a gene that blocks expression of the conditional strategy. We illustrate the utility of the model by application to predator-induced defense in an intertidal barnacle and compare the results with phenotypic models of selection.

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An agedependent ovulatory strategy explains the evolution of dizygotic twinning in humans, Nature Ecology and Evolution 2020. DOI to be finalized. This function estimates lifetime reproduction in a cohort of women by simulating ovulatory cycles that can result in successful live singleton or twin births depending on inputs controlling prenatal and postnatal mortality, probabilities of single or double ovulation, maternal mortality both during child birth and independent of childbirth. It allows for up to 20 parities, makes an object that is a list of seven objects that can be further analysed and saves that object in the working directory as an .Rda file for future use. A set of input variables needed by the function, based on data from Gambia is provided by the function "prepGambiaBasic".

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