Identifying Conservation Priorities using a Return on Investment Analysis 400 Biodiversity benefit 350 300 250 200 Standardized two-action independent Standardized three-action independent 150 Action dependent Action dependent with spatial targetting 100 10 Years Figure Comparison of the relative performance of ROI analysis recognizing the dependency between actions and their returns Reproduced from Evans MC, Possingham HP, and Wilson KA (2011) What to in the face of multiple threats? Incorporating dependencies within a return on investment framework for conservation Diversity and Distributions 17: 437–450, with permission from Wiley community-based protected-area initiatives, where the investment cost is largely born by the community that is sacrificing some of its land or sea and numerous cultural, social, economic, and geographic factors influence its willingness to convert part of the customary land or sea to conservation Game et al (2011) describe a prioritization process used by The Nature Conservancy and partners in Choiseul Province, Solomon Islands, to help establish a representative network of community-based protected areas When a community expresses an interest in establishing a protected area, it is presented with a map of the conservation return across its lands and seas The community is then asked to choose the piece of land or sea with the highest conservation return where it is willing to establish a protected area After each decision, the map of conservation return for the whole province is updated to consider complementarity with the current set of protected areas In this way, each decision obtains the highest ROI possible for that decision without having a priori knowledge of the investment side of the equation This is fundamentally equivalent to an ROI approach to conservation prioritization Opposition and Challenges Despite their compelling intention (more conservation for our dollars) and wide array of potential prioritization applications, ROI methods face a series of cultural and philosophical challenges in conservation General philosophical opposition exists to all prioritization schemes that weigh benefit, cost and probability of success, based on the belief that everything has value and prioritizing means sanctioning the loss of some pieces (e.g., Jachowski and Kesler, 2009) There is also a belief that prioritization schemes 197 are unable to be responsive to opportunities that are the real driver of conservation actions and therefore unreasonably limit flexibility or are not useful Neither argument is logically sound (for discussion see Bottrill et al., 2009; Game et al., 2011) There is also a deeper philosophical distaste for the application of financially driven, business-like approaches to biodiversity conservation (a field that undeniably draws great strength from the beliefs and value systems of those involved and has a far more complicated and existential value system than is present in most businesses) This is a legitimate argument, especially given the clear failure of conventional economic systems during the 2008 global financial crisis This fundamental debate remains challenging, productive, and relatively unexplored philosophical ground ROI methods for conservation also face resistance for a number of more pragmatic reasons In transferring these approaches from theory to practice, it has been difficult to define return functions that convincingly reflect what people care about People’s belief in the conservation value of a place or strategy are composed of many variables that are not necessarily consistently applied, which makes replicating this in a model extremely difficult Similarly, the available cost data are often either not robust enough or not considered relevant enough to the decisions at hand (even when the consideration of cost is recognized as critical) A study looking at fine-scale conservation-management costs found them to be poorly correlated with commonly used proxies such as the agricultural value of land (Armsworth et al., 2010) Essentially, it is challenging to capture the nuances of conservation value and opportunity in single-return and investment functions This challenge has frequently resulted in prioritizations that are biased and opaque but that validate existing beliefs about conservation priority Not unique to ROI prioritizations, there is also a technical barrier to its successful implementation Even the relatively modest mathematical familiarity required for the analyses described here are beyond the expertise of many conservation planners Consequently, the problem-formulation and optimization methods, although rigorous, are often less trusted (the black-box syndrome) than straightforward approaches that suffer serious methodological deficits (such as ranking and additively combining different variables) However, these same challenges were faced by other systematic decision-support methods such as minimum-set reserve design algorithms (e.g., Marxan) that are now considered standard practice and used around the world by conservation practitioners with widely divergent training Despite these challenges, current trends in both conservation culture and research suggest that ROI analysis will play an increasing role in future determinations of conservation priority Appendix List of Courses Conservation Biology Applied Ecology Environmental Economics