When and how to use Q methodology to understand perspectives in conservation research.

Understanding human perspectives is critical in a range of conservation contexts, for example, in overcoming conflicts or developing projects that are acceptable to relevant stakeholders. The Q methodology is a unique semiquantitative technique used to explore human perspectives. It has been applied for decades in other disciplines and recently gained traction in conservation. This paper helps researchers assess when Q is useful for a given conservation question and what its use involves. To do so, we explained the steps necessary to conduct a Q study, from the research design to the interpretation of results. We provided recommendations to minimize biases in conducting a Q study, which can affect mostly when designing the study and collecting the data. We conducted a structured literature review of 52 studies to examine in what empirical conservation contexts Q has been used. Most studies were subnational or national cases, but some also address multinational or global questions. We found that Q has been applied to 4 broad types of conservation goals: addressing conflict, devising management alternatives, understanding policy acceptability, and critically reflecting on the values that implicitly influence research and practice. Through these applications, researchers found hidden views, understood opinions in depth and discovered points of consensus that facilitated unlocking difficult disagreements. The Q methodology has a clear procedure but is also flexible, allowing researchers explore long-term views, or views about items other than statements, such as landscape images. We also found some inconsistencies in applying and, mainly, in reporting Q studies, whereby it was not possible to fully understand how the research was conducted or why some atypical research decisions had been taken in some studies. Accordingly, we suggest a reporting checklist.

Ten-year assessment of the 100 priority questions for global biodiversity conservation.

In 2008, a group of conservation scientists compiled a list of 100 priority questions for the conservation of the world's biodiversity. However, now almost a decade later, no one has yet published a study gauging how much progress has been made in addressing these 100 high-priority questions in the peer-reviewed literature. We took a first step toward reexamining the 100 questions to identify key knowledge gaps that remain. Through a combination of a questionnaire and a literature review, we evaluated each question on the basis of 2 criteria: relevance and effort. We defined highly relevant questions as those that - if answered - would have the greatest impact on global biodiversity conservation and quantified effort based on the number of review publications addressing a particular question, which we used as a proxy for research effort. Using this approach, we identified a set of questions that, despite being perceived as highly relevant, have been the focus of relatively few review publications over the past 10 years. These questions covered a broad range of topics but predominantly tackled 3 major themes: conservation and management of freshwater ecosystems, role of societal structures in shaping interactions between people and the environment, and impacts of conservation interventions. We believe these questions represent important knowledge gaps that have received insufficient attention and may need to be prioritized in future research.

Bootstrapping Q Methodology to Improve the Understanding of Human Perspectives.

Q is a semi-qualitative methodology to identify typologies of perspectives. It is appropriate to address questions concerning diverse viewpoints, plurality of discourses, or participation processes across disciplines. Perspectives are interpreted based on rankings of a set of statements. These rankings are analysed using multivariate data reduction techniques in order to find similarities between respondents. Discussing the analytical process and looking for progress in Q methodology is becoming increasingly relevant. While its use is growing in social, health and environmental studies, the analytical process has received little attention in the last decades and it has not benefited from recent statistical and computational advances. Specifically, the standard procedure provides overall and arguably simplistic variability measures for perspectives and none of these measures are associated to individual statements, on which the interpretation is based. This paper presents an innovative approach of bootstrapping Q to obtain additional and more detailed measures of variability, which helps researchers understand better their data and the perspectives therein. This approach provides measures of variability that are specific to each statement and perspective, and additional measures that indicate the degree of certainty with which each respondent relates to each perspective. This supplementary information may add or subtract strength to particular arguments used to describe the perspectives. We illustrate and show the usefulness of this approach with an empirical example. The paper provides full details for other researchers to implement the bootstrap in Q studies with any data collection design.