I am a scientist: Overcoming biased assumptions around diversity in science through explicit representation of scientists in lectures.

The lack of diversity in Science, Technology, Engineering, and Mathematics (STEM) is a significant issue for the sector. Many organisations and educators have identified lack of representation of historically marginalised groups within teaching materials as a potential barrier to students feeling that a Science, Technology, Engineering, and Mathematics (STEM) career is something that they can aspire to. A key barrier to addressing the issue is providing accessible and effective evidence-based approaches for educators to implement. In this study, we explore the potential for adapting presentation slides within lectures to 'humanise' the scientists involved, presenting their full names and photographs alongside a Harvard style reference. The intervention stems from an initial assumption that many formal scientific referencing systems are demographic-neutral and exacerbate prevailing perceptions that STEM is not diverse. We adopt a questionnaire based methodology surveying 161 bioscience undergraduates and postgraduates at a UK civic university. We first establish that students project assumptions about the gender, location, and ethnicity of the author of a hypothetical reference, with over 50% of students assuming they are male and Western. We then explore what students think of the humanised slide design, concluding that many students see it as good pedagogical practice with some students positively changing their perceptions about diversity in science. We were unable to compare responses by participant ethnic group, but find preliminary evidence that female and non-binary students are more likely to see this as good pedagogical practice, perhaps reflecting white male fragility in being exposed to initiatives designed to highlight diversity. We conclude that humanised powerpoint slides are a potentially effective tool to highlight diversity of scientists within existing research-led teaching, but highlight that this is only a small intervention that needs to sit alongside more substantive work to address the lack of diversity in STEM.

Crop pests and predators exhibit inconsistent responses to surrounding landscape composition.

The idea that noncrop habitat enhances pest control and represents a win-win opportunity to conserve biodiversity and bolster yields has emerged as an agroecological paradigm. However, while noncrop habitat in landscapes surrounding farms sometimes benefits pest predators, natural enemy responses remain heterogeneous across studies and effects on pests are inconclusive. The observed heterogeneity in species responses to noncrop habitat may be biological in origin or could result from variation in how habitat and biocontrol are measured. Here, we use a pest-control database encompassing 132 studies and 6,759 sites worldwide to model natural enemy and pest abundances, predation rates, and crop damage as a function of landscape composition. Our results showed that although landscape composition explained significant variation within studies, pest and enemy abundances, predation rates, crop damage, and yields each exhibited different responses across studies, sometimes increasing and sometimes decreasing in landscapes with more noncrop habitat but overall showing no consistent trend. Thus, models that used landscape-composition variables to predict pest-control dynamics demonstrated little potential to explain variation across studies, though prediction did improve when comparing studies with similar crop and landscape features. Overall, our work shows that surrounding noncrop habitat does not consistently improve pest management, meaning habitat conservation may bolster production in some systems and depress yields in others. Future efforts to develop tools that inform farmers when habitat conservation truly represents a win-win would benefit from increased understanding of how landscape effects are modulated by local farm management and the biology of pests and their enemies.