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Abstract There is a contemporary trend in many major research institutions to de‐emphasize the importance of natural history education in favor of theoretical, laboratory, or simulation‐based research programs. This may take the form of removing biodiversity and field courses from the curriculum and the sometimes subtle maligning of natural history research as a “lesser” branch of science. Additional threats include massive funding cuts to natural history museums and the maintenance of their collections, the extirpation of taxonomists across disciplines, and a critical under‐appreciation of the role that natural history data (and other forms of observational data, including Indigenous knowledge) play in the scientific process. In this paper, we demonstrate that natural history knowledge is integral to any competitive science program through a comprehensive review of the ways in which they continue to shape modern theory and the public perception of science. We do so by reviewing how natural history research has guided the disciplines of ecology, evolution, and conservation and how natural history data are crucial for effective education programs and public policy. We underscore these insights with contemporary case studies, including: how understanding the dynamics of evolutionary radiation relies on natural history data; methods for extracting novel data from museum specimens; insights provided by multi‐decade natural history programs; and how natural history is the most logical venue for creating an informed and scientifically literate society. We conclude with recommendations aimed at students, university faculty, and administrators for integrating and supporting natural history in their mandates. Fundamentally, we are all interested in understanding the natural world, but we can often fall into the habit of abstracting our research away from its natural contexts and complexities. Doing so risks losing sight of entire vistas of new questions and insights in favor of an over‐emphasis on simulated or overly controlled studies.

Researchers in the environmental studies and sciences play a critical role in influencing real-world decision-making and policies. However, interference during research and sharing of results has been documented in Canada and around the world. Further, research has shown that workers from marginalized social identitie(s) experience discrimination in the workplace. Whether interference in research is related to social identity has never been examined. Using a mixed-methods design, we surveyed 741 environmental researchers in Canada to understand the relationship between social identity (gender, disability status, 2SLGBTQI+ status, race, and perception of racial identity) and reported experiences of interference. Results found that researchers with marginalized identities experienced worse outcomes across 11 of the 25 quantitative measures. For example, most marginalized groups experienced significantly greater fear of misrepresentation by media and (or) fear of negative career consequences due to public commentary, and racialized and disabled persons reported greater external interference in their work (e.g., from management and workplace policy). Given these findings, we express concern that the experience of interference in research can (1) threaten the personal well-being of marginalized researchers, (2) limit the representativeness of information disseminated, thererby impacting environmental decision-making and policy, and (3) contribute to inequities in representativeness of marginalized researchers in environmental sciences in Canada.

Abstract Ecologists often rely on randomized control trials (RCTs) to quantify causal relationships in nature. Many of our foundational insights of ecological phenomena can be traced back to well‐designed experiments, and RCTs continue to provide valuable insights today. Although RCTs are often regarded as the “gold standard” for causal inference, it is important to recognize that they too rely on a set of causal assumptions that must be justified and met by the researcher to draw valid causal conclusions. We use key ecological examples to show how biases such as confounding, overcontrol, and collider bias can occur in experimental setups. In tandem, we highlight how such biases can be removed through the application of the structural causal model (SCM) framework. The SCM framework visualizes the causal structure of a system or process under study using directed acyclic graphs (DAGs) and subsequently applies a set of graphical rules to remove bias from both observational and experimental data. We show how DAGs can be applied across ecological experimental studies to ensure proper study design and statistical analysis, leading to more accurate causal estimates drawn from experimental data. Although causal conclusions drawn from RCTs are often taken at face value, ecologists are increasingly becoming aware that experimental approaches must be carefully designed and analyzed to avoid potential biases. By applying DAGs as a visual and conceptual tool, experimental ecologists can increasingly meet the causal assumptions required for valid causal inference.

Undergraduate students from underrepresented backgrounds (e.g., Black, Indigenous, and people of color [BIPOC], members of the Deaf community, people with disabilities, members of the 2SLGBTQIA+ community, from low-income backgrounds, or underrepresented genders) continue to face exclusion and marginalization in higher education. In this piece, authored and edited by a diverse group of Science, Technology, Engineering, and Mathematics (STEM) scholars, we present 10 simple rules for succeeding as an underrepresented STEM undergraduate student, illuminating the "hidden curriculum" of STEM specifically as it relates to the underrepresented undergraduate experience. Our rules begin by encouraging students to embrace their own distinct identities and scientific voices and explain how students can overcome challenges unique to underrepresented students throughout their undergraduate degrees. These rules are derived from a combination of our own experiences navigating our undergraduate STEM degrees and the growing body of literature on improving success for underrepresented students.

Abstract Recent developments in computer science have substantially advanced the use of observational causal inference under Pearl's structural causal model (SCM) framework. A key tool in the application of SCM is the use of casual diagrams, used to visualize the causal structure of a system or process under study. Here, we show how causal diagrams can be extended to ensure proper study design under quasi‐experimental settings, including propensity score analysis, before‐after‐control‐impact studies, regression discontinuity design, and instrumental variables. Causal diagrams represent a unified approach to variable selection across methodologies and should be routinely applied in ecology research with causal implications.

Abstract Climate‐induced coral bleaching events are a leading threat to coral reef ecosystems and can result in coral–macroalgal regime shifts that are difficult to reverse. It is unclear how different factors causally influence regime shift or recovery trajectories after a bleaching event. Here, we use structural causal modeling (SCM) and its application of directed acyclic graphs (DAGs) to determine how key factors affect regime shift versus recovery potential across coral reefs in Seychelles, which were severely impacted by bleaching events in 1998 and 2016. Our causal models reveal additional causal drivers of regime shifts, including initial macroalgal cover, wave exposure, and branching coral cover. We also find that reduced depth and structural complexity and increased nutrients increase the likelihood of regime shifting. Further, we use a DAG‐informed predictive model to show how recovering reefs are expected to change after a recent 2016 bleaching event, suggesting that three out of 12 recovering reefs are expected to regime shift given their predisturbance conditions. Collectively, our results provide the first causally grounded analysis of how different factors influence postbleaching regime shift versus recovery potential on coral reefs. More broadly, SCM stands apart from previous observational analysis and provides a strong framework for causal inference across other observational ecological studies.