Systematic conservation at the regional scale: the role of species distribution models in priority setting
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2007
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Abstract
Present day rates of species loss are of world-wide concern, with species distribution modelling (SDM) being an important means to identify areas of high conservation value and direct conservation efforts more efficiently. The key purpose of this thesis was to implement a full cycle of species distribution modelling to evaluate the advantages and limitations of different modelling approaches for identifying candidate areas for conservation. Key objectives included: (1) assessment of broad- scale spatial pattern and prevalence of spatial autocorrelation for a set of breeding birds, observed during the North American Breeding Bird Survey; (2) examination of the potential for improving predictive accuracy through the incorporation of autocorrelation and non-stationarity; (3) evaluation of model predictive accuracy, bias, and generalisability; (4) analysis of the sensitivity of automated reserve selection to modelling method, reserve selection algorithm, and information quality. Assessment of broad-scale pattern indicated that patchy abundance distributions were common, and nearly universally autocorrelated (24 of 27 species, or 89%). Modelling results demonstrated that predictive accuracy was generally related to model complexity. However, the response in goodness-of-fit was more complicated and depended upon the species in question. The impact of spatial autocorrelation depended on the species, with the American Crow benefiting the most from the application of a spatial autologistic approach. Accuracy assessment , based on random test points, confirmed that autologistic models provided substantially higher predictive power, as did the non-stationary models produced by geographically weighted regression (GWR). From the perspective of generalisability, the simplest models were the least vulnerable to predictive bias and were also the most consistently accurate across species. The GWR method was the most sensitive to the geographic locations used to train the model. The reserve selection analysis identified that the the combination of a greedy selection algorithm with high quality information was effective for maximising the habitat value of the reserve network. The value of SDMs for conservation planning will be maximised if they are as biologically plausible as possible. Autologistic regression and GWR, on a case-by-case basis, can lead to more accurate selection of high value habitat, thereby improving our ability to support long-term species persistence.
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Bibliography: p. 169-187
Some pages are in colour.
Some pages are in colour.
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Citation
Lieske, D. J. (2007). Systematic conservation at the regional scale: the role of species distribution models in priority setting (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/1333