The Hub Conservation Area - Montanans 4 Safe Wildlife Passage

Bayesian ratingThe number of ratings per linkage (1≤ n ≤ 34)and the derived linkage scores (8 ≤ MLS ≤ 128)varied considerably, a result which, becauseof the inherent uncertainty in expert opinionbasedstudies, could be a result of expert orsampling bias. For example: 1) not every expertis familiar with the entire study area and canthus identify and rate only a certain percentageof the set of potential linkages; 2) expertsself-select the linkages they wish to rate, thus“popular” or well-known linkages may receivea disproportionately high number of ratings; 3)the value of ratings is not necessarily consistentamong experts (i.e. some experts may give alllinkages the highest possible rating whereasother experts may never give the highestrating); 4) experts assign ratings based on thequality of the linkage from their professionalperspective (i.e. a grizzly bear expert mayperceive a linkage as very high quality while anelk expert may perceive the same linkage areaas only moderate quality).To account for this inherent uncertainty, aBayesian averaging approach was used to adjustthe linkage scores considering the number oftimes linkages were identified and the relativescore of linkages compared to other linkages inthe sampling set:Eq. 3where C is a constant value proportional to thetypical data set size (in this case, the averagenumber of scores), ‾m ‾ is the prior mean (i.e. theaverage MLS), and x is the MLS. This algorithmconsiders the linkage score as well as thenumber of times it was rated based on the ideathat a greater number of ratings will increasethe accuracy of the data. Thus, the algorithmcalculates a new ranking, the Bayesian average(‾x), which adjusts for expert and sampling bias,including reducing the impact of aberrantly highor low values.Linkages were next ranked by ‾x values. Becausethe Bayesian average is an estimate based onthe best available information about the samplepopulation, linkages were presented in groupsof quality, need and potential instead of in anumbered list, thus avoiding the implication ofsignificance in minute differences of valueswhen no verifiable significance exists. Groupswere divided into five hierarchical classes (veryhigh, high, moderate, low, very low) using thenatural breaks (Jenks) algorithm which groupssimilar values and maximizes the differencesbetween groups. It is important to notethat, because experts were asked to identify“important wildlife linkages,” even linkagesthat fell in the “low” and “very low” groupsare important areas to consider for maintainingwildlife connectivity. The grouping is insteadintended to help prioritize the linkages whereimmediate conservation action may be mosteffective or needed if limited resources areavailable and such prioritization is necessary.ResultsData AnalysisAfter the interviews in the Hub conservationarea we had 370 identified linkages, manyof which overlapped. Based on the experts’descriptions of the connectivity, the confluenceof linkage boundaries, and our own knowledge,we grouped the linkages into 56 major linkages.Ratings of linkages comprising the majorlinkages were averaged so that all majorlinkages ended up with one value each forEcological Quality, Conservation Threat, andConservation Opportunity. Instead of ranking theadjusted overall linkage scores, we prioritizedeach linkage into one of five hierarchicalgroups of threat and opportunities (very high,high, intermediate, low, very low) based onnatural breaks in the score distribution. Thismethod groups similar values and maximizesthe difference between groups, henceavoiding the data misrepresentation incurredby distancing very similar scores the same asvery unlike scores. Whereas all of the majorlinkages identified in this analysis are importantfor connectivity in the Cabinet Purcell, the28