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Abstract Populations declining due to climate change may need to evolve to persist. While evolutionary rescue has been demonstrated in theory and the lab, its relevance to natural populations facing climate change remains unknown. Here we link rapid evolution and population dynamics in scarlet monkeyflower,Mimulus cardinalis, during an exceptional drought. We leverage whole-genome sequencing across 55 populations to identify climate-associated loci. Simultaneously we track demography and allele frequency change throughout the drought. We establish range-wide population decline during the drought, geographically variable rapid evolution, and variable population recovery that is predictable by both standing genetic variation and rapid evolution at climate-associated loci. These findings demonstrate evolutionary rescue in the wild, showing that genomic variability at adaptive, but not neutral loci, predicts population recovery. 

IntroductionDisagreements between people on different sides of popular issues in STEM are often rooted in differences in “mental models,” which include both rational and emotional cognitive associations about the issue; especially given these issues are systemic in nature. MethodsIn the research described here, we employ the fuzzy cognitive mapping software MentalModeler (developed by one of the authors)¹as a tool for articulating implicit and explicit assumptions about one’s knowledge of both the environmental and social science and values underpinning complex system related issues. More specifically, we test the assumption that this pedagogical approach will foster certain aspects of perspective taking that can be traced with cognitive development and systems thinking as students not only articulate their own understanding of an issue, but also articulate the view of others. Results and discussionResults are discussed with respect to systems thinking that is developed through this type of modeling. 

Under the recently adopted Kunming‐Montreal Global Biodiversity Framework, 196 Parties committed to reporting the status of genetic diversity for all species. To facilitate reporting, three genetic diversity indicators were developed, two of which focus on processes contributing to genetic diversity conservation: maintaining genetically distinct populations and ensuring populations are large enough to maintain genetic diversity. The major advantage of these indicators is that they can be estimated with or without DNA‐based data. However, demonstrating their feasibility requires addressing the methodological challenges of using data gathered from diverse sources, across diverse taxonomic groups, and for countries of varying socio‐economic status and biodiversity levels. Here, we assess the genetic indicators for 919 taxa, representing 5271 populations across nine countries, including megadiverse countries and developing economies. Eighty‐three percent of the taxa assessed had data available to calculate at least one indicator. Our results show that although the majority of species maintain most populations, 58% of species have populations too small to maintain genetic diversity. Moreover, genetic indicator values suggest that IUCN Red List status and other initiatives fail to assess genetic status, highlighting the critical importance of genetic indicators. 

Systems thinking (ST) skills are often the foundation of sustainability science curricula. Though ST skill sets are used as a basic approach to reasoning about complex environmental problems, there are gaps in our understanding regarding the best ways to promote and assess ST learning in classrooms. Since ST learning provides Science, Technology, Engineering, and Mathematics (STEM) students’ important skills and awareness to participate in environmental problem-solving, addressing these gaps is an important STEM learning contribution. We have created guidelines for teaching and measuring ST skills derived from a hybrid of a literature review and through case study data collection. Our approach is based on semi-quantitative cognitive mapping techniques meant to support deep reasoning about the complexities of social–ecological issues. We begin by arguing that ST should be evaluated on a continuum of understanding rather than a binary of correct/incorrect or present/absent. We then suggest four fundamental dimensions of teaching and evaluating ST which include: (1) system structure, (2) system function, (3) identification of leverage points for change, and (4) trade-off analysis. Finally, we use a case study to show how these ideas can be assessed through cognitive maps to help students develop deep system understanding and the capacity to propose innovative solutions to sustainability problems.