Prediction of species geographical ranges

By R. Sutherst. 2003

193 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a9bbebfe3462514a66146beadba83d296c530e43

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In Ecology

In Insect Science

Citations in the corpus

Allee Effects, Propagule Pressure and the Probability of Establishment: Risk Analysis for Biological Invasions (2006, background): “Clearly, the availability of suitable habitat is a prerequisite for population establishment, reflected in much recent work in ‘environmentmatching’ (e.g., Peterson and Vieglas 2001; Reichard 2001; Kolar and Lodge 2002; Sutherst 2003; Drake and Bossenbroek 2004).”
Modelling non-equilibrium distributions of invasive species: a tale of two modelling paradigms (2009, methodology): “This demand is increased by the ongoing translocation of biological control agents (Sutherst 2003).”
The potential distribution of an invasive mealybug Phenacoccus solenopsis and its threat to cotton in Asia (2010, methodology): “Although biological data from the laboratory can be used to parameterize the CLIMEX model, it is considered preferable to use geographical distribution because laboratory data do not reflect the more complex outdoor environment (Vera et al., 2002; Kriticos et al., 2003a, b; Sutherst, 2003; Ulrichs &”
Modelling horses for novel climate courses: insights from projecting potential distributions of native and alien Australian acacias with correlative and mechanistic models (2011, background): “In CLIMEX, species range boundaries are mostly defined by climatic stresses (Sutherst, 2003).”
Using district-level occurrences in MaxEnt for predicting the invasion potential of an exotic insect pest in India (2014, background): “1b) and design future sampling strategies for early detection and rapid response.”
Including species interactions in risk assessments for global change (2007, background): “We believe that numerous examples have demonstrated this capacity of the CLIMEXt model (Sutherst & Maywald, 1985; Sutherst, 2003; Sutherst et al., 2004).”
Predicting distributions of invasive species (2013, background): “…(2000) reviewed applications of climate-based mapping that mapped climate without reference to species responses, giving examples both in environmental space (e.g. the early climographs of Cook, 1925) and geographic space (e.g. the Match Climates option in CLIMEX; see Box 5.2 and Sutherst, 2003).”
A physiological analogy of the niche for projecting the potential distribution of plants (2012, methodology): “Alternative physiologically based models (e.g. Chuine & Beaubien, 2001; Sutherst, 2003; Kearney et al., 2010; Buckley et al., 2010; Kooijman, 2010; Dormann et al., 2012; Hartig et al., 2012) could be used and fitted using a combination of distribution data and prior information on limiting processes”
Predicting the potential geographical distribution of the harlequin ladybird, Harmonia axyridis, using the CLIMEX model (2008, methodology): “2005), to assess the establishment potential in the area of release, modelling various stress parameters such as done by using the CLIMEX model may provide a reliable estimate of establishment potential for candidate non-native biological control agents outside its area of origin (Sutherst 2003; Sut”
Climate change: can we predict the impacts on plant pathology and pest management? (2004, background): “Each of these modeling approaches has its supporters and detractors (Harrington 2002b; Pearson and Dawson 2003; Samways et al. 1999; Sutherst 2003; Sutherst et al. 2000; Teng et al. 1996; Thuiller 2003), but it can be argued that the availability of relevant input parameters and the intelligent appl”
Explaining and predicting the success of invading species at different stages of invasion (2006, background): “The use of climate matching has also been controversial in biological invasions (Samways et al. 1999; Samways 2003; Sutherst 2003); here I give three arguments as to why the climatic description of a range may not provide a satisfactory prediction.”
Species' geographic ranges and distributional limits: pattern analysis and statistical issues (2005, unknown): “Indeed, knowledge of species habitat and climate requirements can be used to obtain a probabilistic map of species occurrence based on logistic regression and predictive models (Davis et al. 1998, Pearce and Ferrier 2000, Guisan and Hofer 2003, Sutherst 2003, Loiselle et al. 2003).”