Conservation by Consensus: Reducing Uncertainty from Methodological Choices in Conservation-based Models

• Main Author: Poos, Mark S.
• Other Authors: Jackson, Donald Andrew
• Language: en_ca
• Published: 2010
• Subjects: statistical methods; conservation biology; endangered species; ensemble models; 0329
• Online Access: http://hdl.handle.net/1807/24858

Modeling species of conservation concern, such as those that are rare, declining, or have a conservation designation (e.g. endangered or threatened), remains an activity filled with uncertainty. Species that are of conservation concern often are found infrequently, in small sample sizes and spatially fragmented distributions, thereby making accurate enumeration difficult and traditional statistical approaches often invalid. For example, there are numerous debates in the ecological literature regarding methodological choices in conservation-based models, such as how to measure functional traits to account for ecosystem function, the impact of including rare species in biological assessments and whether species-specific dispersal can be measured using distance based functions. This thesis attempts to address issues in methodological choices in conservation-based models in two ways. In the first section of the thesis, the impacts of methodological choices on conservation-based models are examined across a broad selection of available approaches, from: measuring functional diversity; to conducting bio-assessments in community ecology; to assessing dispersal in metapopulation analyses. It is the goal of this section to establish the potential for methodological choices to impact conservation-based models, regardless of the scale, study-system or species involved. In the second section of this thesis, the use of consensus methods is developed as a potential tool for reducing uncertainty with methodological choices in conservation-based models. Two separate applications of consensus methods are highlighted, including how consensus methods can reduce uncertainty from choosing a modeling type or to identify when methodological choices may be a problem.