The realm of wildland fire science encompasses both wild and prescribed fires. Most of the research in the broader field has focused on wildfires, however, despite the prevalence of prescribed fires and demonstrated need for science to guide its application. We argue that prescribed fire science requires a fundamentally different approach to connecting related disciplines of physical, natural, and social sciences. We also posit that research aimed at questions relevant to prescribed fire will improve overall wildland fire science and stimulate the development of useful knowledge about managed wildfires. Because prescribed fires are increasingly promoted and applied for wildfire management and are intentionally ignited to meet policy and land manager objectives, a broader research agenda incorporating the unique features of prescribed fire is needed. We highlight the primary differences between prescribed fire science and wildfire science in the study of fuels, fire behavior, fire weather, fire effects, and fire social science. Wildfires managed for resource benefits ("managed wildfires") offer a bridge for linking these science frameworks. A recognition of the unique science needs related to prescribed fire will be key to addressing the global challenge of managing wildland fire for long-term sustainability of natural resources.Keywords: fire behavior, fire effects, fire weather, fireline interactions, fuels characterization, post-fire tree mortality, prescribed burning, wildland fire research Resumen El ámbito de la ciencia del fuego comprende tanto a los incendios de vegetación no controlados como a las quemas prescriptas. La mayoría de las investigaciones en este amplio campo se han enfocado en los incendios de vegetación, a pesar de la prevalencia de las quemas prescriptas y la probada necesidad de que la ciencia guíe su aplicación. Argüimos que la ciencia de las quemas prescriptas requiere de un enfoque fundamentalmente diferente para conectarse con las disciplinas relacionadas de la ciencias físicas, sociales y naturales. También postulamos que la investigación enfocada a preguntas relevantes para las quemas prescriptas va a mejorar la ciencia de fuegos de vegetación en general y estimular el desarrollo del conocimiento útil sobre el manejo de fuegos de vegetación. Dado que las quemas prescriptas son propuestas y aplicadas de manera incremental para para el manejo de fuegos (Continued on next page) de vegetación, y que son intencionalmente iniciadas para lograr metas y objetivos de manejo de tierras, una agenda más amplia de investigación, incorporando aspectos únicos de las quemas prescriptas, se hace necesaria. Ilustramos las diferencias primarias entre la ciencia de las quemas prescriptas y la de la ciencia de fuegos naturales de vegetación en lo que hace al estudio de los combustibles, el comportamiento del fuego, la meteorología, los efectos del fuego, y las ciencias sociales relacionadas con el fuego. Los incendios manejados para beneficio de los recursos ("fuegos manejados") ofrecen un puente para li...
Species distribution modeling often involves high‐dimensional environmental data. Large amounts of data and multicollinearity among covariates impose challenges to statistical models in variable selection for reliable inferences of the effects of environmental factors on the spatial distribution of species. Few studies have evaluated and compared the performance of multiple machine learning (ML) models in handling multicollinearity. Here, we assessed the effectiveness of removal of correlated covariates and regularization to cope with multicollinearity in ML models for habitat suitability. Three machine learning algorithms maximum entropy (MaxEnt), random forests (RFs), and support vector machines (SVMs) were applied to the original data (OD) of 27 landscape variables, reduced data (RD) with 14 highly correlated covariates being removed, and 15 principal components (PC) of the OD accounting for 90% of the original variability. The performance of the three ML models was measured with the area under the curve and continuous Boyce index. We collected 663 nonduplicated presence locations of Eastern wild turkeys (Meleagris gallopavo silvestris) across the state of Mississippi, United States. Of the total locations, 453 locations separated by a distance of ≥2 km were used to train the three ML algorithms on the OD, RD, and PC data, respectively. The remaining 210 locations were used to validate the trained ML models to measure ML performance. Three ML models had excellent performance on the RD and PC data. MaxEnt and SVMs had good performance on the OD data, indicating the adequacy of regularization of the default setting for multicollinearity. Weak learning of RFs through bagging appeared to alleviate multicollinearity and resulted in excellent performance on the OD data. Regularization of ML algorithms may help exploratory studies of the effects of environmental factors on the spatial distribution and habitat suitability of wildlife.
The residue left on glass surfaces by human hands was found to be attractive to femaleAedes aegypti (L.) andAnopheles quadrimaculatus Say mosquitoes. The material lost half of its activity in 1 hr. A solvent wash technique was developed to recover and concentrate the residuum from handled glass beads. The residuum could be recovered effectively with absolute ethanol and less effectively with several other solvents. More mosquitoes were attracted to heated than to unheated residuum, an indication of its volatility. Also, attraction of the residuum decreased with decreasing concentration or dose. Concentrated residuum collections, stored under refrigeration and tested for longevity, showed no appreciable loss of attractiveness up to 60 days of storage.
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