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In the past decade, numerous studies have successfully mapped thousands of former charcoal production sites (also called relict charcoal hearths) manually using digital elevation model (DEM) data from various forested areas in Europe and the north-eastern USA. The presence of these sites causes significant changes in the soil physical and chemical properties, referred to as legacy effects, due to high amounts of charcoal that remain in the soils. The overwhelming amount of charcoal hearths found in landscapes necessitates the use of automated methods to map and analyse these landforms. We present a novel approach based on open source data and software, to automatically detect relict charcoal hearths in large-scale LiDAR datasets (visualized with Simple Local Relief Model). In addition, the approach simultaneously provides both general as well as domain-specific information, which can be used to further study legacy effects. Different versions of the methodology were fine-tuned on data from north-western Connecticut and subsequently tested on two different areas in Connecticut. The results show that these perform adequate, with F1-scores ranging between 0.21 and 0.76, although additional post-processing was needed to deal with variations in LiDAR quality. After testing, the best performing version of the prediction model (with an average F1-score of 0.56) was applied on the entire state of Connecticut. The results show a clear overlap with the known distribution of charcoal hearths in the state, while new concentrations were found as well. This shows the usability of the approach on large-scale datasets, even when the terrain and LiDAR quality varies. 

Research processes are often messy and include tensions that are unnamed in the final products. In our attempt to update and generalize a framework used to examine teachers’ support for collective argumentation in mathematics education classrooms to examining teachers’ work in interdisciplinary STEM contexts, we have experienced significant linguistic tensions because of the context-dependent nature of language. We aim to acknowledge the difficulty of generalizing research beyond the mathematics education community, describe our attempts to resolve the problem we face, and discuss potential conclusions pertaining to the feasibility of generalizing frameworks beyond mathematics education. 

Understanding factors that allow highly virulent parasites to reach high infection prevalence in host populations is important for managing infection risks to human and wildlife health. Multiple transmission routes have been proposed as one mechanism by which virulent pathogens can achieve high prevalence, underscoring the need to investigate this hypothesis through an integrated modelling-empirical framework. Here, we examine a harmful specialist protozoan infecting monarch butterflies that commonly reaches high prevalence (50–100%) in resident populations. We integrate field and modelling work to show that a combination of three empirically-supported transmission routes (vertical, adult transfer and environmental transmission) can produce and sustain high infection prevalence in this system. Although horizontal transmission is necessary for parasite invasion, most new infections post-establishment arise from vertical transmission. Our study predicts that multiple transmission routes, coupled with high parasite virulence, can reduce resident host abundance by up to 50%, suggesting that the protozoan could contribute to declines of North American monarchs.