Q-methodology on human-nature relationships and complementary data

Socio-environmental changes are threatening Indigenous peoples and local communities (IP&LC), hampering conservation efforts, jeopardizing biocultural diversity, and escalating environmental injustice. This situation fostered both collaborative research projects showing the potential of engaging with IP&LC for conserving biocultural diversity, and ideas in sustainability science as the relevance of accounting for plural human-nature relationships and nature values in decision making. The success of these initiatives, however, depends on unveiling socio-environmental conflicts as a first step to dialogue and cooperation.

We developed a tool to describe viewpoints on human-nature relationships that underpin socio-environmental conflicts based on a multicultural conceptual framework and Q-methodology. To investigate its usefulness to unveil conflicts within community-based projects, we applied it to community members and researchers working collaboratively in an artisanal fishing community in Brazil.

By purposefully choosing 23 participants who ordered 44 statements producing distributions later analyzed for correlations, we described three viewpoints on human-nature relationships and, from them, the main socio-environmental conflicts within the community.

Two viewpoints associated with community members revealed contrasting standpoints about nature resulting from complex transformations - from resource-dependent to tourism-dependent - in the community. While some people whose occupations depend on outsiders wish to exploit and earn money with nature, others who depend exclusively on fishing fear that nature is under threat. These viewpoints allowed characterizing divergences that can limit collective organization within the community as well as shared desires for the future. One viewpoint was associated with researchers, highlighting differences between theirs and community members’ conceptions.

Although framed to a specific situation, given its replicable development and amplitude of its conceptual basis, the tool can be adapted to different contexts. By underscoring the diversity within local communities, our tool helps leaving behind idealized notions about them. It also facilitates reflexivity among researchers on how their viewpoints can impact their relationship with communities. As a way of collectively reflecting about local perspectives on human-nature relationships, the tool facilitates collaboration and finding ways forward.

Methods
Prior to data collection, the main investigator (BDA) spent two and a half months living in the community (Supporting Information 2). Pilot interviews with 12 residents were conducted aiming at (1) identifying local understandings of "nature" and perceived local socio-environmental issues and values of nature (to be used in the questionnaire), and (2) testing the adequacy of the language in questions, Q-set statements, and instructions for sorting the statements.  

In-person interviews were conducted in Portuguese, with each participant individually, by the same investigator (BDA), lasting ~ 1.5 hours (Fig. 3). All the interview procedures were approved by the Research Ethics Committee from Biosciences Institute, University of São Paulo (CAAE 53059421.7.0000.5464). We first obtained from participants a written voluntary and informed consent to participate in the study. We then applied ashort survey (addressing age, occupation, religion, etc) followed by the Q-Methodology activity. Participants were asked to sort the Q-set into a 11-point ranking scale according to how they saw, perceived and felt nature in Siribinha, using a customized board (Fig. 2). The researcher read each statement aloud – provided in random order in numbered cards – and the participant first indicated whether to place it in provisional categories of agreement, disagreement or neutral. Then, they fine-sorted statements into a fixed distribution. Explanations of their choices were recorded in audio to support subsequent interpretations.

Following the typical Q-methodology procedures for both the quantitative and qualitative analyses (Watts and Stenner, 2012), we started by looking for groups of participants with highly correlated Q-sorts to identify shared viewpoints. We did so by performing a principal component analysis on participant-by-participant correlation matrix of Q-sorts. Then, based on criteria described in Watts and Stenner (2012), we extracted and rotated three factors (Supporting Information 4), each representing a combination of participants who produced similar Q-sorts. We then created an ideal-typical Q-sort for each factor by calculating weighted normalized sum of Q-sorts highly correlated to that factor only (Supporting Information 4).

We qualitatively interpreted these ideal-typical Q-sorts based on information from four sources (Watts and Stenner, 2012). We identified agreement statements that had similar, and disagreement statements that had divergent, rankings across typical Q-sorts. We estimated how strongly statements were ranked (i.e., salience), and the standard deviation of their ranking, across ideal-typical Q-sorts (Table 1). Agreement and disagreement statements are the most (above average) salient statements presenting the lowest (below average) or the highest (above average) standard deviation across typical Q-sorts. We also identified statements ranked higher or lower in each typical Q-sort compared to others. Finally, we examined recorded explanations and looked for patterns in responses to the questionnaire of participants associated with each factor. From these sources, we created a narrative for each factor and chose a symbolic name expressing their main feature.