Setting Reliability Bounds on Habitat Suitability Indices

Burgman, Mark A.; Breininger, David R.; Duncan, Brean W.; Ferson, Scott

doi:10.1890/1051-0761(2001)011[0070:srbohs]2.0.co;2

Cited by 119 publications

(62 citation statements)

References 14 publications

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“…Some methods are available to deal with uncertainty explicitly, but different methods reflect very different aspects. For instance, subjective and linguistic uncertainties may be represented in habitat suitability maps with fuzzy numbers (Burgman et al 2001). In contrast, parameter uncertainty in statistical models may be represented with confidence intervals.…”

Section: Habitat Modelsmentioning

confidence: 99%

Managing Landscapes for Conservation Under Uncertainty

Burgman

Lindenmayer

Elith

2005

Ecology

164

131

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Abstract. In ecology, populations may be linked conceptually with landscapes through habitat and spatial population models. Usually, these models deal with single species and treat a range of uncertainties implicitly and explicitly. They assist managers in testing different management scenarios and making strategic decisions. Landscape pattern analysis was the first attempt to deal with multiple species, and it led to a range of landscape management strategies. Advances in landscape ecology, driven largely by the pragmatic needs of conservation, are building approaches to multispecies management that have stronger ecological foundations. However, their treatment of uncertainty is in its infancy. In this paper, we provide examples to illustrate some of these issues. We conclude that one of the most important sources of uncertainty is the choice of the modeling frame. We recommend that landscape planners use different kinds of models, identify important sources of uncertainty that may affect planning decisions, and seek options that are likely to result in tolerable outcomes, despite uncertainty.

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Section: Habitat Modelsmentioning

confidence: 99%

Managing Landscapes for Conservation Under Uncertainty

Burgman

Lindenmayer

Elith

2005

Ecology

164

131

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“…This layer was then overlaid on the digitized orthophoto image, and each grid cell occurring in potential Florida scrub-jay habitat (Breininger et al 1991) was then assigned attributes for each of eight landscape variables (Table 1), based on photo-interpretation. Three additional attributes were derived from a combination of six of these variables using a modification of a Florida scrub-jay Habitat Suitability Index (Breininger 1992, Breininger et al 1998, Burgman et al 2001, Duncan et al1995. HSI Scale 1 was calculated by modifying the habitat suitability index formula to fit the habitat variables determined by photo-interpretation within each cell (Figure 1).…”

Section: Gis/mappingmentioning

confidence: 99%

“…We relied on a combination of expert opinion and a HSI (Breininger 1992, Breininger et al 1998, Burgman et al 2001, Duncan et al1995 to generate hypotheses (models) that relate habitat and landscape variables to scrub-jay habitat occupancy. The use of a HSI in the model development stage is not necessary but given the significance of HSI in management decisions (Brooks 1997) and their utility in generating hypotheses (Van Home 2002) we believe that more emphasis should be placed on using existing HSIs to investigate the mechanisms of species-habitat relationships.…”

Section: Variable Selection and Model Developmentmentioning

confidence: 99%

“…The grid cell classification scheme was derived from a set of apriori hypotheses (models) developed using existing knowledge. This knowledge was based in part on a modified habitat suitability index, HSI, (Breininger 1992, Breininger et al 1998, Burgman et al 2001, Duncan et al1995. Although HSI are not explicitly designed to predict occurrence they can be useful to generate a priori hypotheses for model selection procedures (Van Horne 2002) such as the information-theoretic approach based on Akaikes Information…”

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confidence: 99%

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A rapid approach to modeling species–habitat relationships

Carter

Stolen

Breininger

2006

Biological Conservation

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A growing number of species require conservation or management efforts. Success of these activities requires knowledge of the species' occurrence pattern. Species-habitat models developed from GIS data sources are commonly used to predict species occurrence but commonly used data sources are often developed for purposes other than predicting species occurrence and are of inappropriate scale and the techniques used to extract predictor variables are often time consuming and cannot be repeated easily and thus cannot efficiently reflect changing conditions. We used digital orthophotographs and a grid cell classification https://ntrs.nasa.gov/search.jsp?R=20110024058 2018-05-13T02:08:21+00:00Z scheme to develop an efficient technique to extract predictor variables. We combined our classification scheme with a priori hypothesis development using expert knowledge and a previously published habitat suitability index and used an objective model selection procedure to choose candidate models. We were able to classify a large area (57,000 ha) in a fraction of the time that would be required to map vegetation and were able to test models at varying scales using a windowing process. Interpretation of the selected models confirmed existing knowledge of factors important to Florida scrub-jay habitat occupancy. The potential uses and advantages of using a grid cell classification scheme in conjunction with expert knowledge or an HSI and an objective model selection procedure are discussed.

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“…Burgman et al (2001) point out several possible sources of uncertainty in the construction of a typical HSI. They emphasize the reliance, due to limited data, on expert opinion.…”

Section: Fuzziness Of Habitat Suitability Index (Hsi)mentioning

confidence: 99%

Issues and Challenges of Incorporating Fuzzy Sets in Ecological Modeling

Robinson¹

Geographic Uncertainty in Environmental Security

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Abstract. An information-based framework is presented for spatially explicit GIS-based ecological modeling. Within this framework some of the important issues and challenges of incorporating fuzzy sets in spatially explicit population models (SEPM) are discussed. Examples of current work are used to illustrate the main issues and challenges facing the incorporation of fuzzy sets in ecological modeling. Among the challenges to be discussed are fuzzy-based techniques for data acquisition, model control/ evaluation, heterogeneous representations of spatial data, parameterization of models, and hypothesis testing. There is special attention given to the issue of habitat modeling and presence/absence problem. Many scientific issues facing the incorporation of ecological models will be raised such as hypothesis testing, and relationship between statistical analysis and fuzzy

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Setting Reliability Bounds on Habitat Suitability Indices

Cited by 119 publications

References 14 publications

Managing Landscapes for Conservation Under Uncertainty

Managing Landscapes for Conservation Under Uncertainty

A rapid approach to modeling species–habitat relationships

Issues and Challenges of Incorporating Fuzzy Sets in Ecological Modeling

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