An Autologistic Model for the Spatial Distribution of Wildlife

Augustin, Nicole; Mugglestone, M. A.; Buckland, Stephen T.

doi:10.2307/2404755

Cited by 576 publications

(570 citation statements)

References 9 publications

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“…This is an example where spatial autocorrelation should be an intrinsic part of the model. Whenever a statistical model is established with spatial autocorrelation or autologistic components, one of two assumptions is being made (Wu and Huffer, 1997;Augustin et al, 1996;Gumpertz et al, 1997;Weir and Pettit, 2000). The model may have been mis-specified and an important environmental predictor showing spatial autocorrelation left out of the model.…”

Section: Ecological Processesmentioning

confidence: 99%

Spatial prediction of species distribution: an interface between ecological theory and statistical modelling

Austin¹

2002

Ecological Modelling

1,491

1,275

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Neglect of ecological knowledge is a limiting factor in the use of statistical modelling to predict species distribution. Three components are needed for statistical modelling, an ecological model concerning the ecological theory to be used or tested, a data model concerning the collection and measurement of the data, and a statistical model concerning the statistical theory and methods used. This component framework is reviewed with emphasis on ecological theory. The expected shape of a species response curve to an environmental gradient is a central assumption on which agreement has yet to be reached. The nature of the environmental predictors whether indirect variables, e.g. latitude that have no physiological impact on plants, or direct variables, e.g. temperature also influence the type of response expected. Straight-line relationships between organisms and environment are often used uncritically. Many users of canonical correlation analysis use linear (straight-line) functions to relate ordination axes to variables such as slope and aspect though this is not a necessary part of the method. Some statisticians have used straight lines for species/environment relationships without testing, when evaluating new statistical procedures. Assumptions used in one component often conflict with those in another component. Statistical models can be used to explore ecological theory. Skewed species response curves predominate contrary to the symmetric unimodal curves assumed by some statistical methods. Improvements in statistical modelling can be achieved based on ecological concepts. Examples include incorporating interspecific competition from dominant species; more proximal predictors based on water balance models and spatial autocorrelation procedures to accommodate non-equilibrium vegetation. #

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Section: Ecological Processesmentioning

confidence: 99%

Spatial prediction of species distribution: an interface between ecological theory and statistical modelling

Austin¹

2002

Ecological Modelling

1,491

1,275

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“…Examples include the use of regression analyses to predict the distribution of tree and shrub species (Austin et al, 1983(Austin et al, , 1990Lenihan, 1993;Franklin, 1998;Guisan et al, 1999), of herbaceous species (Guisan et al, 1998;Guisan and Theurillat, 2000), of aquatic plant species (Lehmann, 1998), of terrestrial animal species (Pereira and Itami, 1991;Augustin et al, 1996;Manel et al, 1999;Guisan and Hofer, 2001;Jaberg and Guisan, 2001;Zimmermann and Breitenmoser, 2002), of birds (Manel et al, 1999(Manel et al, , 2000, of aquatic animal species (invertebrates; Manel et al, 2000), of plant communities (Zimmermann and Kienast, 1999), or of structural vegetation types (Brown, 1994;Frescino et al, 2001). At a higher level of complexity, these approaches have also been used to investigate the distribution of plant (Currie and Paquin, 1987;Margules et al, 1987;Pausas, 1994;Heikkinen, 1996;Wohlgemuth, 1998) and animal diversity (Owen, 1989;Currie, 1991;Fraser, 1998).…”

Section: A Framework For Use Of Statistical Models In Ecological Studiesmentioning

confidence: 99%

Generalized linear and generalized additive models in studies of species distributions: setting the scene

Guisan

Edwards

Hastie

2002

Ecological Modelling

1,851

1,188

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“…The analysis involved extrapolating the original logistic model for the relationship between environment and known established I. scapularis populations in the United States. As for the national scale model, spatial autocorrelation was accounted for in the model by applying an autologistic approach (Augustin, Mugglestone et al 1996). This methodology incorporates a smoothing filter, called the autologistic term, as an additional covariate in the logistic model (Augustin, Mugglestone et al 1996;Osborne, Alonso et al 2001).…”

Section: Climate Model Overviewmentioning

confidence: 99%

“…As for the national scale model, spatial autocorrelation was accounted for in the model by applying an autologistic approach (Augustin, Mugglestone et al 1996). This methodology incorporates a smoothing filter, called the autologistic term, as an additional covariate in the logistic model (Augustin, Mugglestone et al 1996;Osborne, Alonso et al 2001). Because vector population status in many locations in the United States is unknown, and because we did not include any surveillance data from Canada or Mexico, the model incorporated the modified Gibbs sampler to estimate the distribution in unknown areas (Augustin, Mugglestone et al 1996;Augustin, Mugglestone et al 1998).…”

Section: Climate Model Overviewmentioning

confidence: 99%

“…This methodology incorporates a smoothing filter, called the autologistic term, as an additional covariate in the logistic model (Augustin, Mugglestone et al 1996;Osborne, Alonso et al 2001). Because vector population status in many locations in the United States is unknown, and because we did not include any surveillance data from Canada or Mexico, the model incorporated the modified Gibbs sampler to estimate the distribution in unknown areas (Augustin, Mugglestone et al 1996;Augustin, Mugglestone et al 1998). This Monte Carlo-type method involves iterating the procedure of fitting the autologistic model, deriving the probability surface for all locations, and then recalculating the autologistic term until stability is achieved.…”

Section: Climate Model Overviewmentioning

confidence: 99%

See 1 more Smart Citation

Effect of Climate Change on Lyme Disease Risk in North America

2005

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An understanding of the influence of climate change on Ixodes scapularis, the main vector of Lyme disease in North America, is a fundamental component in assessing changes in the spatial distribution of human risk for the disease. We used a climate suitability model of I. scapularis to examine the potential effects of global climate change on future Lyme disease risk in North America. A climatebased logistic model was first used to explain the current distribution of I. scapularis in North America. Climate change scenarios were then applied to extrapolate the model in time and produce forecasts of vector establishment. The spatially modeled relationship between I. scapularis presence and large-scale environmental data generated the current pattern of I. scapularis across North America with an accuracy of 89% (p<0.0001). Extrapolation of the model revealed a significant expansion of I. scapularis north into Canada with an increase in suitable habitat of 213% by the 2080's. Climate change will also result in a retraction of the vector from southern United States, and movement into the central United States. This report predicts the effect of climate change on Lyme disease risk and specifically forecasts the emergence of a tick-borne infectious disease in Canada. Our modeling approach could thus be used to outline where future control strategies and prevention efforts need to be applied.

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An Autologistic Model for the Spatial Distribution of Wildlife

Cited by 576 publications

References 9 publications

Spatial prediction of species distribution: an interface between ecological theory and statistical modelling

Spatial prediction of species distribution: an interface between ecological theory and statistical modelling

Generalized linear and generalized additive models in studies of species distributions: setting the scene

Effect of Climate Change on Lyme Disease Risk in North America

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