Rare Plant Distribution Modelling: Exploring Predictor Datasets - A Case Study in Southeast Alberta
Date
2015-03-30
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Abstract
Modelling rare plant distribution has challenges, but that does not preclude its use supporting conservation management. Challenges relate to uncertainties in source datasets, rare species ecology, modelling techniques, and how they interact to influence model predictions. Further challenges are introduced when biased model predictions are applied to rare plant conservation efforts.
Through a case study modelling the distribution of Moquin’s sea-blite (Suaeda moquinii), a rare grassland plant, study design considerations were framed with emphasis on deriving ecologically relevant habitat predictors and addressing bias introduced during model development. Models performed well and provided insight into Moquin’s sea-blite habitat specificity in southeast Alberta. Biases were reduced in model design through data handling methods, validating predictions with field observations, and comparing different predictor data treatments. Uncertainties associated with a variety of biases persisted and the effects of these on model performance were discussed in the context of two conservation management scenarios.
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Ecology
Citation
Hamilton, L. M. (2015). Rare Plant Distribution Modelling: Exploring Predictor Datasets - A Case Study in Southeast Alberta (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/27532