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Due to the stochastic natures of the juvenile sex ratio in each population and the dispersal function (below), the sex ratio of dispersers among cells was therefore also stochastic.
We do not think this variation introduces much bias into the model as the parameter values we chose for the dispersal function specify exceptionally low probabilities of dispersing beyond 20 50 cells.
Using a spatial individual-based simulation model for simulating different movement behaviours, we derive fitting functions for the functional relationship between the parameters of the dispersal function and several details of movement behaviour.
Attention to both scales of disturbance is thus needed to recover the seed dispersal function.
We present a way of integrating movement behavioural parameters into a particular dispersal function in a simple way.
Additionally, we provide measures which characterise the shape of the dispersal function and are interpretable in terms of landscape connectivity.
Similar(32)
Dispersal functions were calibrated applying inverse modeling.
It models both components of dispersal functions, i.e., the amount of seeds produced and the dispersal distance.
Dispersal functions are an important tool for integrating dispersal into complex models of population and metapopulation dynamics.
In our case, maximum likelihood based dispersal functions determined the probability to observe a certain number of seeds, given a particular set of parameters.
These include a mix of riparian and upslope management approaches to address the breeding, foraging, overwintering, and dispersal functions of these animals.
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