The survey was designed and distributed using the software package Qualtrics (Qualtrics, Provo, UT). The survey was administered in English and started with demographic questions on gender, age and employment status (see supporting information for survey questions). Participants were also asked for country of residence and whether they considered themselves to work in biodiversity conservation. Participants who did consider themselves to work in biodiversity conservation were asked how long they had been working in conservation, their employer (academic, government, non-governmental organization or other), the broad geographic regions where they worked (participants could select more than one option), whether they considered themselves a conservation biologist, and their motivation for working in conservation (a free text box). Participants who did not consider themselves to work in biodiversity conservation skipped this section and were not asked these questions.
The next survey section focused on situational optimism. Participants were asked about their optimism about the future of conservation, and their optimism about three conservation issues: whether we can prevent the cheetah going extinct, whether cosmetic and cleaning products in the UK will be free from microbeads by 2020, and whether declines in UK bumblebees can be reduced. Bees and microbeads were chosen as they were topical issues receiving news coverage at the time of the survey, and we assumed participants were more likely to feel optimistic or pessimistic about conservation issues they had previously been exposed to. This rational was also used to select the cheetah as an example of a well-known charismatic mega-fauna of conservation concern (Durant et al. 2017), we assumed many conservation professionals would be familiar with the species. Participants answered these questions using a five point Likert scale (from very optimistic to very pessimistic). Finally, all participants were asked to complete the LOT-R (Scheier et al. 1994). The test consists of ten statements, and participants are asked to state their agreement on a five point Likert scale (from strongly disagree to strongly agree). Three statements assess optimism and are positively scored, and three statements assess pessimism and are negatively scored. Four statements are filler items. There remains debate on whether optimism and pessimism represent opposing ends of a single scale or are two distinct constructs (Hinz et al. 2017; Segerstrom et al. 2017), but here we consider optimism and pessimism as two extremes in a unidimensional construct (following Segerstrom et al. 2017). To calculate the LOT-R score, which can range from 0 to 24, the optimism and inverted pessimism scores are summed. Questions asked in the survey that are not included in this analysis are not reported here, but are included in the supporting information. These include more specific questions (for example, asking participants which species or ecosystems they worked with) that were designed to test for potential biases in participants (e.g. if all participants worked in the same area) but were voluntary and answered by only a small subsection of participants.
Distribution and ethical information
A sample of conservation professionals was targeted using seven online sampling drives. One drive consisted of posting a link on social media platforms (Twitter and Facebook), which generated 54.5% of participants. Unique links were also distributed by email to two conservation NGOs (generating 11.1% and 6.8% of participants), online in an article for Marine Ecosystems and Management (generating 15.7% of participants), and during three presentations by SP (generating 2.2%, 4.0%, and 5.8% of participants). Distribution occurred between April and October 2017. Participation in the survey was not limited to conservation professionals, but the question “do you consider yourself to work in biodiversity conservation?” was used to identify a self-classified group of conservation professionals, in the sense that they identified as being employed within the sector. This ensured an inclusive range from on-the-ground field staff to NGO executives. Individuals who answered negatively to this question were classified as non-conservation professionals, and were used as the comparator group to determine whether conservation professionals are more or less optimistic than the overall sampling population for the study. A total of 325 participants completed the primary survey targeted at conservation professionals. Of these 325 participants, 264 self-classified themselves as working in biodiversity conservation and were considered ‘conservation professionals’. Due to the location of the original postings, the sample had a strong bias toward participants from the UK, with 171 UK conservation professionals participating. This represents around 0.86% of the 20,000 conservation professionals working in the UK (Office for National Statistics UK 2018). As previous research on LOT-R has found significant differences in dispositional optimism between countries (Schou-Bredal et al. 2017), the comparison between conservation professionals and the comparator group was therefore restricted to individuals based in the UK. Of the participants, only 44 individuals from the UK did not consider themselves conservation professionals (suggesting the distribution methods successfully targeted the intended audience of conservation professionals). To gain a larger comparator group to assess whether dispositional optimism of conservation professionals differs from non-conservation professionals, the LOT-R scores of these 44 participants were supplemented with data for an additional 219 UK participants from a separate unpublished study on optimism and gardening by SP and RT. This combined sample is referred to as the ‘enlarged comparator group’ below. This survey on optimism and gardening was distributed using social media (Twitter, Facebook and an online newsletter) for six weeks in September and October 2017 and was targeted at people in the UK with gardens. This sample may have included conservation professionals, but no specific question was asked during this survey to identify conservation professionals. Seven participants with missing data were excluded, leaving a comparator group sample size of 256. All participants in both surveys were 18 or above in age, and no incentives were provided to complete the survey. At the end of the survey, participants were given feedback on their LOT-R score and typical LOT-R scores for someone of their age and gender. Both surveys were approved by the Royal Holloway ethical approval process. The datasets generated by this study are available from the corresponding author on reasonable request.
Analyses were conducted in RStudio 1.0.153 (R Studio Team 2016). There is debate about how to analyze Likert scale data (Carifio and Perla 2008), but to test for differences in dispositional optimism between conservation professionals (including both those based in the UK and elsewhere), the LOT-R score was expressed as binomial count data in a generalized linear model, so that predictions from the model were bounded at zero and 24. Employment sector (government, non-governmental organization, academia or other), whether the participant considered themselves a conservation biologist (binomial variable, yes or no), number of years working in conservation, and whether the participant worked in Europe, Asia, Africa, North America or South America (binomial variable, yes or no) were used as explanatory variables. Gender, age, and whether the participant was from the UK (binomial variable, yes or no) were also included as confounding factors. For analyses, categorical ranges of years working in conservation and age were assigned a numerical value at the mid-point of the category. McFadden’s R2 was calculated using the function pR2 in the pscl package (Jackman 2017). Wald’s tests were conducted using the function regTermTest in the survey package (Lumley 2004). To compare the dispositional optimism of conservation professionals to the enlarged comparator group, an identical model and methodology was used, with gender, age and employment status as potential confounding factors. Due to small sample size, unemployed and retired participants were classed together for this analysis.
Motivations for working in conservation were classified by SP and STT over two iterations, with discrepancies and classification definitions discussed between each iteration. RLT checked the clarity of classification definitions, and the assignment of statements of motivation into these categories. To determine whether there was a relationship between LOT-R score and motivations for working in conservation, we used individual logistic regressions (participants either did, or did not express a particular motivation) to test for relationships between LOT-R score and motivations expressed by > 15% of participants, using Bonferroni corrections for multiple tests to reduce the critical alpha value to 0.01. Finally, the relationship between situational optimism and dispositional optimism was assessed using ordinal logistic regression with the function polr in the MASS package (Venables and Ripley 2002). For the questions on microbeads and bees in the UK, we limited the sample to conservation professionals based in the UK.