Year of Award


Document Type


Degree Type

Doctor of Philosophy (PhD)

Degree Name

Fish and Wildlife Biology

Department or School/College

W.A. Franke College of Forestry and Conservation

Committee Chair

Joshua J. Millspaugh

Commitee Members

Jedediah Brodie, Angela Luis, Paul Lukacs, Suguru Ishizaki, Kevin McKelvey


Canada lynx, conservation design, design thinking, goal efficient monitoring, rare species, threshold


University of Montana


Conservation of rare species is widely valued and important for ecosystems. Unfortunately, many of the approaches to conserve rare species have been developed with common species (e.g., harvested species) which have larger populations and targeted objectives. Conservation of rare species is difficult in part because of problems created by scarcity and low information. With low information, learning leads to new questions and the utility of information in decisions can quickly become obsolete. Therefore, monitoring strategies that can adapt as well as provide information tailored to relevant decisions are needed. To address rare species monitoring, I developed a long-term monitoring approach for rare species called goal efficient monitoring (GEM). GEM allows monitoring questions to evolve as we obtain information. GEM includes sampling rules connected to a Bayesian integrated population model (IPM), which allows for changing questions and data collection while maintaining long-term inference. For example, GEM sampling rules work when populations are small (less than 10 individuals) and provide guidance to adjust monitoring observations if the population gets large (over 100 individuals), all while maintaining the same long-term inference because of the IPM structure. I outline the GEM approach using Canada lynx (Lynx canadensis), which is Threatened under the Endangered Species Act. To test GEM, I simulated 100 small populations with constant demographic rates for 11 years, applied GEM sampling rules to simulate observations, and predicted population values with the GEM model. On average, the predicted range of values from the GEM model contained the true values 97.1% of the time. These and other results contained within demonstrate how a GEM approach can provide long-term inference for rare species while addressing changing information needs. To address the problem of rare species information that is tailored to decisions made with rare species information, I propose the use of processes from the professional field of Design to reframe the user needs of the rare species information. I provide an overview of how some Design methods are already in use in conservation and how adopting Design processes more formally through the creation of the field of conservation design may aid in rare species conservation.


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