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Mechanistic reasoning is an epistemic practice central within science, technology, engineering, and mathematics disciplines. Although there has been some work on mechanistic reasoning in the research literature and standards documents, much of this work targets domaingeneral characterizations of mechanistic reasoning; this study provides domain-specific illustrations of mechanistic reasoning. The data in this study comes from the Assessment of Mechanistic Reasoning Project (AMRP) (Weinberg, 2012), designed using item response theory modeling to diagnose individuals' mechanistic reasoning about systems of levers. Such a characterization of mechanistic reasoning illuminates what is easy and difficult about this form of reasoning, within the subdomain of simple machines. Moreover, this work indicates how domain-general principles may be limited. The study participants included elementary, middle, and high school students as well as college undergraduates and adults without higher education. Although the majority of participants responded to the AMRP by diagnosing at least one mechanistic element (elements inherent to the working of systems of levers) as they predicted its motion, such reasoning was not trivial. In fact, the diverse reasoning by participants shows how systems of levers support elements of mechanistic reasoning. Moreover, this study provides evidence that the development of mechanistic reasoning is dependent on domainspecific experience.

Large carnivores are currently disappearing from many world regions because of habitat loss, prey depletion, and persecution. Ensuring large carnivore persistence requires safeguarding and sometimes facilitating the expansion of their populations. Understanding which conservation strategies, such as reducing persecution or restoring prey, are most effective to help carnivores to reclaim their former ranges is therefore important. Here, we systematically explored such alternative strategies for the endangered Persian leopard (Panthera pardus saxicolor) in the Caucasus. We combined a rule‐based habitat suitability map and a spatially explicit leopard population model to identify potential leopard subpopulations (i.e., breeding patches), and to test the effect of different levels of persecution reduction and prey restoration on leopard population viability across the entire Caucasus ecoregion and northern Iran (about 737,000 km2). We identified substantial areas of potentially suitable leopard habitat (~120,000 km2), most of which is currently unoccupied. Our model revealed that leopards could potentially recolonize these patches and increase to a population of >1,000 individuals in 100 yr, but only in scenarios of medium to high persecution reduction and prey restoration. Overall, reducing persecution had a more pronounced effect on leopard metapopulation viability than prey restoration: Without conservation strategies to reduce persecution, leopards went extinct from the Caucasus in all scenarios tested. Our study highlights the importance of persecution reduction in small populations, which should hence be prioritized when resources for conservation are limited. We show how individual‐based, spatially explicit metapopulation models can help in quantifying the recolonization potential of large carnivores in unoccupied habitat, designing adequate conservation strategies to foster such recolonizations, and anticipating the long‐term prospects of carnivore populations under alternative scenarios. Our study also outlines how data scarcity, which is typical for threatened range‐expanding species, can be overcome with a rule‐based habitat map. For Persian leopards, our projections clearly suggest that there is a large potential for a viable metapopulation in the Caucasus, but only if major conservation actions are taken towards reducing persecution and restoring prey.

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Reasoning about mechanism is central to disciplined inquiry in science and engineering and should thus be one of the foundations of a science, technology, engineering, and mathematics education. In addition, mechanistic reasoning is one of the core competencies listed in the Next Generation Science Standards (NGSS) Engineering Concepts and Practices (NGSS Lead States, 2013). Mechanistic explanations focus on the processes that underlie cause-effect relationships and consider how the activities of system components affect one another. While some assessment work has been accomplished in engineering education, to date mechanistic reasoning is an area where limited assessment development has been accomplished for pre-college populations. The data in this study come from the calibration of the Assessment of Mechanistic Reasoning Project (AMRP) (Weinberg, 2012), designed to diagnose individuals' mechanistic reasoning about systems of levers. This assessment presents a domain-specific characterization of mechanistic reasoning and illuminates what is easy and difficult about this form of reasoning. The study participants included elementary, middle, and high school students as well as college undergraduates and adults without higher education. Within this calibration study, item analyses, reliability, and validity measures were conducted using item response theory modeling; the AMRP assessment was found to have high reliability and validity. In addition, this study shows that machine characteristics such as number of levers, lever type, and arrangement of levers can affect the difficulty of mechanistic reasoning.