HABITAT: A Procedure for Modelling a Disjoint Environmental Envelope for a Plant or Animal Species

Walker, Paul; Cocks, K. D.

doi:10.2307/2997706

Cited by 161 publications

(97 citation statements)

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“…36,37 Several approaches have been used to approximate species' ecologic niches; [38][39][40][41][42][43] of these, one that has seen considerable testing is the genetic algorithm for rule-set prediction (GARP), which includes several inferential approaches in an iterative, evolutionary-computing environment. 23 All modeling in this study was carried out on a desktop implementation of GARP now available publicly for download.…”

Section: Methodsmentioning

confidence: 99%

Geographic Potential for Outbreaks of Marburg Hemorrhagic Fever

Peterson¹,

Lash²,

Carroll³

et al. 2006

The American Journal of Tropical Medicine and Hygiene

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Abstract. Marburg virus represents one of the least well-known of the hemorrhagic fever-causing viruses worldwide; in particular, its geographic potential in Africa remains quite mysterious. Ecologic niche modeling was used to explore the geographic and ecologic potential of Marburg virus in Africa. Model results permitted a reinterpretation of the geographic point of infection in the initiation of the 1975 cases in Zimbabwe, and also anticipated the potential for cases in Angola, where a large outbreak recently (2004)(2005) occurred. The geographic potential for additional outbreaks is outlined, including in several countries in which the virus is not known. Overall, results demonstrate that ecologic niche modeling can be a powerful tool in understanding geographic distributions of species and other biologic phenomena such as zoonotic disease transmission from natural reservoir populations.

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Section: Methodsmentioning

confidence: 99%

Geographic Potential for Outbreaks of Marburg Hemorrhagic Fever

Peterson¹,

Lash²,

Carroll³

et al. 2006

The American Journal of Tropical Medicine and Hygiene

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“…Some techniques were specifically designed for modeling presence-only data (Busby 1991, Walker and Cocks 1991, Carpenter et al 1993, Hirzel et al 2002. Alternatively, pseudo-absences can supplement presence-only records and then presence-absence techniques can be used (Manly et al 1993, Stockwell and Peterstechniques to predict species' occurrence with six data sets from study areas around the world (Elith et al 2006).…”

Section: Introductionmentioning

confidence: 99%

What Matters for Predicting the Occurrences of Trees: Techniques, Data, or Species' Characteristics?

Guisan¹,

Zimmermann²,

Elith³

et al. 2007

Ecological Monographs

333

252

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Abstract. Data characteristics and species traits are expected to influence the accuracy with which species' distributions can be modeled and predicted. We compare 10 modeling techniques in terms of predictive power and sensitivity to location error, change in map resolution, and sample size, and assess whether some species traits can explain variation in model performance. We focused on 30 native tree species in Switzerland and used presenceonly data to model current distribution, which we evaluated against independent presenceabsence data. While there are important differences between the predictive performance of modeling methods, the variance in model performance is greater among species than among techniques. Within the range of data perturbations in this study, some extrinsic parameters of data affect model performance more than others: location error and sample size reduced performance of many techniques, whereas grain had little effect on most techniques. No technique can rescue species that are difficult to predict. The predictive power of speciesdistribution models can partly be predicted from a series of species characteristics and traits based on growth rate, elevational distribution range, and maximum elevation. Slow-growing species or species with narrow and specialized niches tend to be better modeled. The Swiss presence-only tree data produce models that are reliable enough to be useful in planning and management applications.

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“…Although widely used, BIOCLIM overpredicts when species distribution is influenced by a combination of environmental predictors rather than by each one individually (Carpenter and others 1993). Walker and Cocks (1991) produced HABITAT, which attempts to form a convex envelope more tightly containing all species occurrences than the simple rectilinear envelope of BIOCLIM. Carpenter and others (1993) found that BIOCLIM overpredicted and HABITAT underpredicted habitat, and they proposed a new method, DOMAIN, based on similarity with all occupied points using Gower's metric.…”

Section: Quantitative Ecoregions For Single Speciesmentioning

confidence: 99%