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Mapping Uncertainty: Sensitivity of Wildlife Habitat Ratings to Expert Opinion

In the paper “Mapping Uncertainty: Sensitivity of Wildlife Habitat Ratings to Expert Opinion”, the goal of authors is to map the uncertainty in the expert opinion regarding habitat scores. Morta Carlo simulations are utilized to recognize the subtle factors involved in the scoring model of wildlife habitat, the rate of precision for the various ecosystems units, and diversification in the habitats resulting from the uncertainty in the expert opinion. Simulations are carried out by using uniform distribution and a standard deviation determined from a variety of possible design attribute ratings.

Results showed elevated uncertainty in the habitat scores ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"Ex7KHm4H","properties":{"formattedCitation":"(Johnson and Gillingham)","plainCitation":"(Johnson and Gillingham)","noteIndex":0},"citationItems":[{"id":371,"uris":["http://zotero.org/users/local/CKNkWnK9/items/I76QJS44"],"uri":["http://zotero.org/users/local/CKNkWnK9/items/I76QJS44"],"itemData":{"id":371,"type":"article-journal","title":"Mapping uncertainty: sensitivity of wildlife habitat ratings to expert opinion","container-title":"Journal of Applied Ecology","page":"1032-1041","volume":"41","issue":"6","source":"besjournals.onlinelibrary.wiley.com (Atypon)","abstract":"Summary 1 Expert opinion is frequently called upon by natural resource and conservation professionals to aid decision making. Where species are difficult or expensive to monitor, expert knowledge often serves as the foundation for habitat suitability models and resulting maps. Despite the long history and widespread use of expert-based models, there has been little recognition or assessment of uncertainty in predictions. 2 Across British Columbia, Canada, expert-based habitat suitability models help guide resource planning and development. We used Monte Carlo simulations to identify the most sensitive parameters in a wildlife habitat ratings model, the precision of ratings for a number of ecosystem units, and variation in the total area of high-quality habitats due to uncertainty in expert opinion. 3 The greatest uncertainty in habitat ratings resulted from simulations conducted using a uniform distribution and a standard deviation calculated from the range of possible scores for the model attributes. For most ecological units, the mean score, following 1000 simulations, varied considerably from the reported value. When applied across the study area, assumed variation in expert opinion resulted in dramatic decreases in the geographical area of high- (?85%) and moderately high-quality habitats (?68%). The majority of habitat polygons could vary by up to one class (85%) with smaller percentages varying by up to two classes (9%) or retaining their original rank (7%). Our model was based on only four parameters, but no variable consistently accounted for the majority of uncertainty across the study area. 4 Synthesis and applications. We illustrated the power of uncertainty and sensitivity analyses to improve or assess the reliability of predictive species distribution models. Results from our case study suggest that even simple expert-based predictive models can be sensitive to variation in opinion. The magnitude of uncertainty that is tolerable to decision making, however, will vary depending on the application of the model. When presented as error bounds for individual predictions or maps of uncertainty across landscapes, estimates of uncertainty allow managers and conservation professionals to determine if the model and input data reliably support their particular decision-making process.","DOI":"10.1111/j.0021-8901.2004.00975.x","ISSN":"0021-8901","title-short":"Mapping uncertainty","journalAbbreviation":"Journal of Applied Ecology","author":[{"family":"Johnson","given":"Chris J."},{"family":"Gillingham","given":"Michael P."}],"issued":{"date-parts":[["2004",12,1]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Johnson and Gillingham). The average score, after a thousand simulations, fluctuated significantly from the recorded value for the most ecological divisions. If adhered throughout the study area, the presumed fluctuation in expert opinion, culminated in drastic declines in the elevated (−85 percent) or even reasonably high-quality (−68 percent) geographical area. A plurality of polygons in habitat, may differ by one rank (85 percent) and minor numbers, varying by two classes (9 percent) or maintaining the initial level (7 percent).

Authors conclude that even basic expert-based predictive designs may be susceptible to differences in viewpoints. But, the extent of uncertainty that can be tolerated for decision-making may vary based on the model's implementation.

I approve of this paper, because I believe that expert opinion is frequently required to help managers and conservation specialists in the practice of decision making. Expert opinion results in the provision of habitat suitability maps and models. So, the estimation of uncertainty, via this article, serves as a tool of determination for the managers and the conservation specialists to assess if the model correlates with their specific decision-making practice.

Works Cited

ADDIN ZOTERO_BIBL {"uncited":[],"omitted":[],"custom":[]} CSL_BIBLIOGRAPHY Johnson, Chris J., and Michael P. Gillingham. “Mapping Uncertainty: Sensitivity of Wildlife Habitat Ratings to Expert Opinion.” Journal of Applied Ecology, vol. 41, no. 6, Dec. 2004, pp. 1032–41. besjournals.onlinelibrary.wiley.com (Atypon), doi:10.1111/j.0021-8901.2004.00975.x.