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Understanding how private landholders make deforestation decisions is of paramount importance for conservation. Behavioural frameworks from the social sciences have a lot to offer researchers and practitioners, yet these insights remain underutilised in describing what drives landholders' deforestation intentions under important political, social, and management contexts. Using survey data of private landholders in Queensland, Australia, we compare the ability of two popular behavioural models to predict future deforestation intentions, and propose a more integrated behavioural model of deforestation intentions. We found that the integrated model outperformed other models, revealing the importance of threat perceptions, attitudes, and social norms for predicting landholders' deforestation intentions. Social capital, policy uncertainty, and years of experience are important contextual moderators of these psychological factors. We conclude with recommendations for promoting behaviour change in this deforestation hotspot and highlight how others can adopt similar approaches to illuminate more proximate drivers of environmental behaviours in other contexts. SUPPLEMENTARY INFORMATION: The online version of this article (10.1007/s13280-020-01491-w) contains supplementary material, which is available to authorized users.

This study focuses on the Wildlife Management Units in Mexico (UMAs), a market-based policy instrument with coupled objectives of biodiversity conservation and rural development through the sustainable use of wildlife. UMAs have been a success story in terms of number of registrations at the national level, but adoption is unevenly distributed across the rural community in Mexico. We use diffusion of innovation theory - the study of how, why and at what rate ideas and practices are adopted by individuals, groups, organizations, or countries [1]- as the theoretical framework to understand drivers of adoption of UMAs by the rural community in Mexico. We ask: what are the characteristics of UMAs that facilitate or hinder adoption of UMAs?; and do those characteristics vary depending on the target population? We triangulate information from three complementary sources: quantitative information from existing government statistics, qualitative information from a review of relevant literature, and qualitative information from semi-structured phone interviews with key informants. We use general elimination methodology (GEM) [2], a theory-based qualitative evaluation method that seeks to understand the social processes behind the observed outcomes. Theory-based qualitative evaluation methods have been developed in other fields to address attribution of cause and effect when sample sizes are small or there is limited baseline data [3]. Following GEM, we first identify as many as possible alternative explanations or factors influencing adoption of UMAs by reviewing available literature. We then interview key informants from expert groups involved on adoption and implementation of UMAs (e.g. government, NGOs). Finally, we systematically assess whether there is evidence to either validate or rule out each of the possible alternative explanations gathered from the interviews. The results of this research help diagnose the issue of uneven distribution of UMAs across the Mexican rural community, guiding further research on UMAs adoption. This type of research enables practitioners to tailor UMAs to the target population and scale-up conservation through the sustainable use of wildlife where is most needed, and empowers conservation decision makers to achieve their policy objectives. We also hope to spark new directions in conservation research, highlighting the social processes that drive adoption of UMAs by using both an alternative theoretical angle and an innovative methodology within the conservation field. 1. Rogers, E.M., Diffusion of innovations. 2010: New York: Free Press. 2. Scriven, M., A Summative Evaluation of RCT Methodology: & An Alternative Approach to Causal Research. Journal of MultiDisciplinary Evaluation, 2008. 5(9): p. 11-24. 3. White, H. and D. Phillips, Addressing attribution of cause and effect in small n impact evaluations: towards an integrated framework. New Delhi: International Initiative for Impact Evaluation, 2012. ; peerReviewed