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.

Q is a methodology to explore the distinct subjective perspectives that exist within a group. It is used increasingly across disciplines. The methodology is semi-qualitative and the data are analysed using data reduction methods to discern the existing patterns of thought. This package is the first to perform Q analysis in R, and it provides many advantages to the existing software: namely, it is fully cross-platform, the algorithms can be transparently examined, it provides results in a clearly structured and tabulated form ready for further exploration and modelling, it produces a graphical summary of the results, and it generates a more concise report of the distinguishing and consensus statements. This paper introduces the methodology and explains how to use the package, its advantages as well as its limitations. I illustrate the main functions with a dataset on value patterns about democracy.

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.

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Abstract1. Decision-making is a complex process that typically includes a series of stages: identifying the issue, considering possible options, making judgements and then making a decision by combining information and values. The current status quo relies heavily on the informational aspect of decision-making with little or no emphasis on the value positions that affect decisions.2. There is increasing realization of the importance of adopting rigorous methods for each stage such that the information, views and judgements of stakeholders and experts are used in a systematic and repeatable manner. Though there are several methodological textbooks which discuss a plethora of social science techniques, it is hard to judge the suitability of any given technique for a given decision problem.3. In decision-making, the three critical aspects are "what" decision is to be made, "who" makes the decisions and "how" the decisions are made. The methods covered in this paper focus on "how" decisions can be made. We compare six tech- 4. Based on structured reviews of 423 papers covering all six methods, we compare the conceptual and logistical characteristics of the methods, and map their suitability for the different stages of the decision-making process. While interviews and FGD are well-known, techniques such the Nominal Group technique and Q methodology are relatively under-used. In situations where conflict is high, we recommend using the Q methodology and Delphi technique to elicit judgements. Where

Article impact statement: Questions regarding freshwater ecosystem conservation, role of social structure in human-environment interactions, and impacts of conservation need more attention. This article is protected by copyright. All rights reserved.[3] AbstractIn 2008, a group of conservation scientists compiled a list of 100 priority questions for the conservation of the world's biodiversity [Sutherland et al. (2009) Conservation Biology, 23, 557-567]. 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. Here we take a first step toward re-examining the 100 questions and identify key knowledge gaps that still remain. Through a combination of a questionnaire and a literature review, we evaluated each of the 100 questions on the basis of two criteria: relevance and effort. We defined highly-relevant questions as those which -if answered -would have the greatest impact on global biodiversity conservation, while effort was quantified 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 ten years. These questions covered a broad range of topics but predominantly tackled three major themes: the conservation and management of freshwater ecosystems, the role of societal structures in shaping interactions between people and the environment, and the impacts of conservation interventions. We see these questions as important knowledge gaps that have so far received insufficient attention and may need to be prioritised in future research. This article is protected by copyright. All rights reserved.[4]

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olo igD eF nd g st¡ n frotoD F nd l D eF @PHHVA 9sntegr ting multiple perspe tives in so i l multi riteri ev lu tion of )oodEmitig tion ltern tives X the se of w l orghettoE l run F9D invironment nd pl nning g X government nd poli yFD PT @TAF ppF IIRQEIITIF Further information on publisher's website:httpXGGdxFdoiForgGIHFIHTVG HUTSs Publisher's copyright statement:Additional information: olo igD eF nd g st¡ n frotoD F nd l D eF @PHHVAF he de(nitive peerEreviewed nd edited version of this rti le is pu lished in invironment nd pl nning g X government nd poli yD PT @TAD IIRQEIITID doiX IHFIHTVG HUTSs Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. KeywordsFlood mitigation, decision-making processes, social multi-criteria evaluation, qualitative social research methods, social actors" value orientations and valuation languages AbstractThere is an increasing demand for a new paradigm to improve flood mitigation decision processes that calls for risk reduction strategies at several levels. This demand may gain ground only if dialogue is encouraged among different perspectives, disciplines and knowledge types. The aim of this paper is to explore new routes to improve flood mitigation decision processes. A growing body of evidence suggests that the involvement of all the social actors is a key aspect in successful decision making. Following this premise, this paper analyzes a recent case of controversy in flood mitigation in Malborghetto-Valbruna (Northern Italy), using Social MultiCriteria Evaluation (SMCE) and Social Actors" Narratives Analysis. Six scenarios are defined and the different positions adopted by the local actors are described. The different narratives of the actors are also analysed to allow the identification of improvement routes for a more accurate SMCE of flood mitigation scenarios. Thus, this case study suggests that the analysis of narratives is a useful tool to complement SMCE.