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USGS2011

Development of pan-Arctic database for river chemistry

More than 10% of all continental runoff flows into the Arctic Ocean. This runoff is a dominant feature of the Arctic Ocean with respect to water column structure and circulation. Yet understanding of the chemical characteristics of runoff from the pan-Arctic watershed is surprisingly limited. The Pan- Arctic River Transport of Nutrients, Organic Matter, and Suspended Sediments ( PARTNERS) project was initiated in 2002 to help remedy this deficit, and an extraordinary data set has emerged over the past few years as a result of the effort. This data set is publicly available through the Cooperative Arctic Data and Information Service (CADIS) of the Arctic Observing Network (AON). Details about data access are provided below.

Eos, Transactions, American Geophysical Union↗

USGS2007

Random forests for classification in ecology

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature. ?? 2007 by the Ecological Society of America.

Ecology↗