The Arctic is entering a new ecological state, with alarming consequences for humanity. Animal-borne sensors offer a window into these changes. Although substantial animal tracking data from the Arctic and subarctic exist, most are difficult to discover and access. Here, we present the new Arctic Animal Movement Archive (AAMA), a growing collection of more than 200 standardized terrestrial and marine animal tracking studies from 1991 to the present. The AAMA supports public data discovery, preserves fundamental baseline data for the future, and facilitates efficient, collaborative data analysis. With AAMA-based case studies, we document climatic influences on the migration phenology of eagles, geographic differences in the adaptive response of caribou reproductive phenology to climate change, and species-specific changes in terrestrial mammal movement rates in response to increasing temperature.

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The use of machine-learning algorithms capable of rapidly completing intensive computations may be an answer to processing the sheer volumes of highly complex data available to researchers in the field of ecology. In spite of this, the continued use of less effective, simple linear, and highly labor intensive techniques such as stepwise multiple regression continue to be widespread in the ecological community. Herein we describe the use of data-mining algorithms such as TreeNet and Random Forests (Salford Systems), which can rapidly and accurately identify meaningful patterns and relationships in subsets of data that carry various degrees of outliers and uncertainty. We use satellite data from a wintering Golden Eagle as an example application; judged by the consistency of the results, the resultant models are robust, in spite of 30 % faulty presence data. The authors believe that the implications of these findings are potentially far-reaching and that linking computational software with wildlife ecology and conservation management in an interdisciplinary framework cannot only be a powerful tool, but is crucial toward obtaining sustainability.

Concentrations of thirty elements were measured in strong-acid extracts of soil, sagebrush (Artemisia tridentata spp.) leaves and perennial grass from the Idaho National Engineering Laboratory (INEL) and two reference sites in southern Idaho. A bicarbonate-chelating extract of soil was used to estimate plant-available concentrations. The results provide baseline data prior to start-up of a coal-fired steam generation facility on the INEL and other developments in the region. In addition, existing impact from effluents from thirty years of a nuclear fuel reprocessing facility on the INEL was evaluated. Based on the spatial distribution of element concentrations, as well as comparison with references sites, we conclude that concentrations of Zn, and perhaps Ni, Cd, and V, are currently elevated around the fuel reprocessing facility. The spatial distribution of these elements is similar to that of (137)Cs in soil, a radionuclide which is emitted by the facility. Sagebrush and soil appear more responsive than perennial grass for long-term monitoring of element concentrations in this semi-arid environment.