SETTING RELIABILITY BOUNDS ON HABITAT SUITABILITY INDICES

By Mark A. Burgman, D. Breininger, Brean W. Duncan, Scott Ferson. 2001

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WHAT CAN HABITAT PREFERENCE MODELS TELL US? TESTS USING A VIRTUAL TROUT POPULATION (2003, background): “1580 Key words: complexity; habitat modeling; habitat preference; habitat quality; habitat selection; individual-based model; trout; virtual ecology.”
Error and uncertainty in habitat models (2006, background): “© 2006 Bureau of Rural Sciences, Journal of Applied Ecology, 43, 413–423 about uncertainty can be represented as intervals or fuzzy numbers, resulting in bounds (Burgman et al. 2001).”
Forest and woodland stand structural complexity: Its definition and measurement (2005, background): “Additive indices assume attributes are compensatory and independent, so that a reduction or absence of one attribute may be balanced by an increase in another (Burgman et al., 2001).”
Conservation planning in a changing world. (2007, background): “Responses to uncertainty include identifying plausible bounds to predictions [ 69 ], precautionary decisions that avoid underestimating threats, regular revisions of predictions [7], alertness for novel threats and scenario modelling [70,71].”
A TAXONOMY AND TREATMENT OF UNCERTAINTY FOR ECOLOGY AND CONSERVATION BIOLOGY (2002, background): “Instead, we must construct multidimensional measures involving measures of population size, growth/decline rates, extent of habitat decline, and so on (IUCN 1994, see also Burgman et al. 2001 for a treatment of the vague predicate ‘‘habitat suitability’’).”