Sentence examples for model dispersal from inspiring English sources

A GIS was used to model dispersal distances between biocontrol agent recovery sites and the nearest conspecific release site.

When included in the 1D population model, dispersal responses to flow 'scaled-up' to yield distributions of drifting macroinvertebrates that showed a strong inverse relationship with flow velocity.

We consider a landscape consisting of a set of habitat patches surrounded by unsuitable matrix, and model dispersal by assuming that the individuals follow a random walk with parameters that may be specific to the habitat type.

In this paper, we present a Bayesian method to model dispersal using spatial configuration and climatic data (distances between emitters and receptors; main wind direction) while accounting for uncertainty, with an application to the prediction of adventitious presence rate of genetically modified maize (GM) in a non-GM field.

Of the alternative scenarios modelled, all variants of the continuous dispersal models were significantly correlated with the craniometric data, regardless of whether the dispersals were modelled as originating in Anatolia or in the Levant, and regardless of which geographic parameter was employed to model dispersal (500, 1000 or 1500 km).

Several dispersal kernel functions have been used to model dispersal [ 65, 66].

Because species of conservation concern generally occur in landscapes with <20% habitat, modeling dispersal as a random process may not be warranted for these species.

Because of their peculiar topology, and the increasing concern about conservation issues in hydrosystems, there has been a recent revival of interest in modelling dispersal in river networks.

This can be addressed by modelling dispersal in all possible directions using a two-dimensional fitting approach in the form of a probability density function (pdf).

In both models, dispersal mode in the sister group (five levels) was the fixed effect and biogeographical distribution type (12 levels) was a random effect because the latter was a sample of an unknown number of potential distribution types [63].

Hence the generality of using a positive scaling of diameter with fecundity deserves greater scrutiny when modeling dispersal within a species if tree size-fecundity relationships tend to flatten beyond a reproductive size-threshold [51].