We investigated pollen dispersal using fluorescent dye as pollen analogue in Lychnis flos-cuculi, an insect-pollinated wetland herb, along the Woluwe River, in Brussels urban zone (Belgium).
Most theoretical work has been focused on either competition for light or nutrients or, on the other hand, seed dispersal using in most cases probabilistic approaches.
Because these personality traits predict dispersal, we tested the hypothesis that in the barn swallow, Hirundo rustica, melanic coloration predicts natal dispersal, using a solid matched case control sampling design and a large sample.
The goal of most of her current work is to forecast volcanic ash dispersal using NAME modeling, satellite imaging of plumes, and data obtained with lidar, an optical remote sensing technology in which a ground-based laser is reflected from aerosols to detect volcanic ash in the atmosphere.
To this aim, we hindcasted propagule (egg and larva) dispersal using Lagrangian simulations, fed with species-specific information on early life history traits (ELTs), with two approaches: 1) back-tracking and 2) comparing settler distribution obtained from simulations to the analogous distribution resulting from otolith chemical analysis.
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.
For both local and global population dispersal, using local habitat dynamics always increases population density relative to the case with global habitat dynamics, due to the resulting segregation of habitat turnover, decrease in effective habitat turnover rate, and presence of stable habitat corridors.
Although tracing the movement of marine larvae directly is generally difficult, many studies have estimated the extent of marine larval dispersal using genetic markers, e.g., crown-of-thorns starfish [13], French grunt [14], and blue mussel [15].
The seed dispersal kernel was modeled as pollen dispersal using the exponential kernel described by eqn 4.
(2012) made the connection between phylogenetic networks and species dispersal, using the network approach of Boc et al.
