The traditional model of undergraduate research is less effective for engaging students who have little or no previous exposure to research, are unfamiliar with available research opportunities, or face financial or time constraints that prevent them from engaging in co-or extracurricular activities. Given today's changing student demographics, models such as course-embedded research need to be explored so that undergraduate research participation may be broadened across disciplines. This article describes how a community of practitioners was created to infuse research in courses at both two-and four-year campuses, with four examples of courses with embedded research activities. Discussed are strategies for implementing discipline-specific research activities at all levels of the undergraduate curriculum to expose a broader student population to the benefits of mentored research.

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The observed greening of Arctic vegetation and the expansion of shrubs in the last few decades probably have profound implications for the tundra ecosystem, including feedbacks to climate. Uncertainty surrounding this vegetation shift and its implications calls for monitoring of vegetation structural parameters, such as fractional cover of shrubs. In this study, CANAPI, a semi-automated image interpretation algorithm that identifies and traces crowns by locating its crescent-shaped sunlit portion, was evaluated for its ability to derive structural data for tall (> 0.5 m) shrubs in the Arctic. CANAPI estimates of shrub canopy parameters were obtained from high-resolution imagery at 26 sites (250 m x 250 m each) by adjusting the algorithm's parameters and filter settings for each site, such that the number of crowns delineated by CANAPI roughly matched those observed in the high-resolution imagery. The CANAPI estimates were then compared with field measurements to evaluate the algorithm's performance. CANAPI successfully retrieved fractional cover (R2 = 0.83, P < 0.001), mean crown radius (R2 = 0.81, P < 0.001), and total number of shrubs (R2 = 0.54, P < 0.001). CANAPI performed best in sparse vegetation where shrub canopies were distinct, while it tended to underestimate shrub cover where shrubs were clustered. The CANAPI algorithm and the regression equations presented here can be used in Arctic tundra environments to derive vegetation parameters from any sub-meter panchromatic imagery.

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