Download or read book Integrating Environmental, Social, and Institutional Factors to Predict Conservation Opportunity written by Matthew Alan Williamson and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Much of contemporary conservation science is devoted to developing algorithms and processes capable of identifying locations on the landscape where preservation, conservation, or restoration is necessary to retain functioning ecosystems and preserve biodversity. Despite a number of significant advances on this front, translating those priorities into actions remains a challenge. In this dissertation, I suggest that designing conservation strategies that address global change requires quantification of the role of humans, their institutions, and their environment in producing conservation action. Further, I argue that integrating these factors into spatially explicit, empirical estimates of the likelihood of conservation action is critical for identifying those locations where conservation action is both biologically necessary and socio-politically feasible. Chapter One presents an empirical framework for estimating the likelihood of conservation occurrence and illustrate its utility in a case study of conservation easements along the west coast of the United States. Results for that case study indcate that models that incorporate ecological, social, and institutional variables outperform models based solely on one class of variables. Chapter Two explores the role of ecological, social, and institutional context in differentiating between congressionally protected and presidentially protected areas in the United States. The anlaysis presented there indicates little evidence that different designation modes result target different environmental, institutional, or social contexts. Finally, Chapter Three explores the potential biases that arise due to incomplete or voluntary reporting of conservation action and develop an analytical method to facilitate broad-extent, high-resolution estimates of the probability of conservation easement occurrence. Results indicate that models that explicitly incorporate variation in reporting probability are substantially less biased than those that do not and that those biases can lead to substantial differences in inference based on a case study from Idaho and Montana.