Validation of a spatial simulation model of a spreading alien plant population

By Steven Higgins, D. Richardson, Richard M. Cowling. 2001

122 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/6a4d1e207c5941c50fdb7434b459d52e8a715380

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In Economics and Econometrics

In Global and Planetary Change

In Nature and Landscape Conservation

Citations in the corpus

Pinus: a model group for unlocking the secrets of alien plant invasions? (2006, methodology): “In a series of studies by Steven Higgins and collaborators, this model framework was parameterized with data from real invasions in South African fynbos to provide a spatially-explicit model which could be tested and validated through its application in other situations ( Higgins et al. 1996, 2000 )”
The state of plant population modelling in light of environmental change (2008, background): “This issue is in particular important for understanding and managing the spread of invasive species (e.g. Higgins et al., 2001; Wang et al., 2003; Clark et al., 2003; Volin et al., 2004; Rinella and Sheley, 2005) or the spread of genes of genetically modified organisms (e.g. Thompson et al., 2003;…”
Management of plant invasions mediated by frugivore interactions (2006, unknown): “…species, sites and times implies that this modelling approach is best used for a posteriori analysis of invasions (Higgins & Richardson 1999; Higgins, Richardson & Cowling 2001), which can also be achieved by models that correlate the observed patterns of spatial spread of invasive species…”
Measuring plant dispersal: an introduction to field methods and experimental design (2006, background): “Knowledge of dispersal is essential for predicting the ability of species to track climate change (Watkinson and Gill 2002), range expansion of non-native species (Higgins et al. 2001), or re-colonisation by endangered species (Coulson et al. 2001).”
Reproductive biology of Australian acacias: important mediator of invasiveness? (2011, background): “…in Australian acacias clearly predispose these species to spread rapidly in a new environment (see discussion of this for A. cyclops in South African fynbos by Higgins et al., 2001) because of the importance of long-distance dispersal events in driving invasions (Trakhtenbrot et al., 2005).”
Forecasting plant migration rates: managing uncertainty for risk assessment (2003, methodology): “Using data on the distribution of parent plants and seedlings collected by Higgins et al . (2001) we use an inverse method (Ribbens et al . 1994) to estimate the parameters of a log-normal kernel and R 0 . Using these parameter estimates and assuming a generation time of 12 years, which correspond”
Evaluating the Feasibility of Eradicating an Invasion (2006, background): “…the factors influencing whether particular weeds are likely to become established at particular locations (Grevstad 1999; Snyder 2003), and factors that influence the subsequent rate of spread of established species (Lonsdale 1993; Higgins and Richardson 1996; Higgins et al. 1996, 2001).”
Commercially Important Trees as Invasive Aliens – Towards Spatially Explicit Risk Assessment at a National Scale (2002, background): “Such information is useful for management at the scale of landscapes (e.g. for tree invasion in fynbos, van Wilgen et al. 1992; Higgins et al. 2000, 2001), but is not particularly helpful for planning long-term strategies at regional and national scales.”
Long‐term spatial dynamics of Succisa pratensis in a changing rural landscape: linking dynamical modelling with historical maps (2006, unknown): “Dispersal parameters are generally the driving force for this and similar kinds of model (Higgins et al .”
Predicting rapid invasion of the Florida Everglades by Old World Climbing Fern (Lygodium microphyllum) (2004, methodology): “…fit between 1999 survey data and model predictions of new infestations for the validation period from 1993 through 1999 was evaluated for 1000 independent model runs using a spatial permutation test that is not biased by spatial autocorrelation of the data (Costanza, 1989; Higgins et al. , 2001).”