Research sites get closer to field camps over time: Informing environmental management through a geospatial analysis of science in the McMurdo Dry Valleys, Antarctica.

As in many parts of the world, the management of environmental science research in Antarctica relies on cost-benefit analysis of negative environmental impact versus positive scientific gain. Several studies have examined the environmental impact of Antarctic field camps, but very little work looks at how the placement of these camps influences scientific research. In this study, we integrate bibliometrics, geospatial analysis, and historical research to understand the relationship between field camp placement and scientific production in the McMurdo Dry Valleys of East Antarctica. Our analysis of the scientific corpus from 1907-2016 shows that, on average, research sites have become less dispersed and closer to field camps over time. Scientific output does not necessarily correspond to the number of field camps, and constructing a field camp does not always lead to a subsequent increase in research in the local area. Our results underscore the need to consider the complex historical and spatial relationships between field camps and research sites in environmental management decision-making in Antarctica and other protected areas.

The demise of caterpillar fungus in the Himalayan region due to climate change and overharvesting.

Demand for traditional medicine ingredients is causing species declines globally. Due to this trade, Himalayan caterpillar fungus (Ophiocordyceps sinensis) has become one of the world's most valuable biological commodities, providing a crucial source of income for hundreds of thousands of collectors. However, the resulting harvesting boom has generated widespread concern over the sustainability of its collection. We investigate whether caterpillar fungus production is decreasing-and if so, why-across its entire range. To overcome the limitations of sparse quantitative data, we use a multiple evidence base approach that makes use of complementarities between local knowledge and ecological modeling. We find that, according to collectors across four countries, caterpillar fungus production has decreased due to habitat degradation, climate change, and especially overexploitation. Our statistical models corroborate that climate change is contributing to this decline. They indicate that caterpillar fungus is more productive under colder conditions, growing in close proximity to areas likely to have permafrost. With significant warming already underway throughout much of its range, we conclude that caterpillar fungus populations have been negatively affected by a combination of overexploitation and climate change. Our results underscore that harvesting is not the sole threat to economically valuable species, and that a collapse of the caterpillar fungus system under ongoing warming and high collection pressure would have serious implications throughout the Himalayan region.

A survival guide to Landsat preprocessing.

Landsat data are increasingly used for ecological monitoring and research. These data often require preprocessing prior to analysis to account for sensor, solar, atmospheric, and topographic effects. However, ecologists using these data are faced with a literature containing inconsistent terminology, outdated methods, and a vast number of approaches with contradictory recommendations. These issues can, at best, make determining the correct preprocessing workflow a difficult and time-consuming task and, at worst, lead to erroneous results. We address these problems by providing a concise overview of the Landsat missions and sensors and by clarifying frequently conflated terms and methods. Preprocessing steps commonly applied to Landsat data are differentiated and explained, including georeferencing and co-registration, conversion to radiance, solar correction, atmospheric correction, topographic correction, and relative correction. We then synthesize this information by presenting workflows and a decision tree for determining the appropriate level of imagery preprocessing given an ecological research question, while emphasizing the need to tailor each workflow to the study site and question at hand. We recommend a parsimonious approach to Landsat preprocessing that avoids unnecessary steps and recommend approaches and data products that are well tested, easily available, and sufficiently documented. Our focus is specific to ecological applications of Landsat data, yet many of the concepts and recommendations discussed are also appropriate for other disciplines and remote sensing platforms.