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Citation

Harris C, Stanford HL, Edwards C, Travis JM & Park K (2011) Integrating demographic data and a mechanistic dispersal model to predict invasion spread of Rhododendron ponticum in different habitats. Ecological Informatics, 6 (41002), pp. 187-195. https://doi.org/10.1016/j.ecoinf.2011.03.004

Abstract
Understanding the invasion potential of a species in different habitat types within the non-native range is crucial in prioritising management and control efforts, and in the protection of vulnerable habitats through monitoring. Here, using the invasive shrub Rhododendron ponticum as a case study, we integrate information on both the demographics and spatial dynamics within an individual-based, spatially-explicit model to investigate invasion potential in different habitats. Firstly, empirical demographic data were used to establish relationships between demographic traits, such as height and fecundity, and habitat variables. The outputs from models fitted using a Generalised Linear Model approach were then incorporated into an individual based simulation model of plant spread to investigate the invasion potential in different habitats using a factorial design of treatments. Plant height, and thus seed release height, was the main driver of invasion speed through an increase in dispersal potential, which resulted in the highest invasion speeds predicted for evergreen woodlands, and relatively low speeds for open habitats. Conversely, invasion density was driven by plant fecundity and seedling survival and not dispersal potential; the highest invasion densities were predicted for open habitats, with relatively low densities for evergreen habitats. Deciduous woodland had features resulting in intermediate invasion potential, both in terms of speed and density and may, therefore be the habitat that is most vulnerable to relatively rapid and dense invasion

Keywords
Generalised Linear Model; Individual-based modelling; Invasion speed; Wind dispersal; Woodland management; Rhododendrons; Forest management; Linear models (Statistics) Data processing

Journal
Ecological Informatics: Volume 6, Issue 41002