Workshop: Estimating Uncertainty and Trend Detection in Ecological Data Sets Collected by the National Ecological Observatory Network
Organizers: Jeffery Taylor, Steve Berukoff, Andrew Fox, Katherine Thibault
Description: Ecology is entering a particularly exciting phase, in which the number and availability of long-term data-sets are increasing and the construction of the nation’s first ecological observatory (NEON) has begun. This unprecedented opportunity requires a departure from traditional approaches to ecological data analyses, which have not emphasized uncertainty characterization. New approaches will demand increased proficiency and rigor in both the design of NEON sampling and the analysis of existing and forthcoming long-term ecological data. For example, NEON will collect continental-scale observations of over 500 different ecological variables at 70 sites across the country. Developing a unified approach to estimating uncertainty and detecting trends for all of these data products is therefore a significant challenge faced in the design process. Further development, understanding, and dissemination of the latest statistical techniques for deriving these quantities will both inform
sampling design and equip up-and-coming ecologists with critical skills. This half-day workshop will highlight the underlying mathematical approaches to uncertainty quantification and trend detection and relate these methods to the nationally/internationally accepted standards. Emphasis will be placed on unified approaches to spatio-temporal scaling and the
detection/attribution of long-term trending. The format will consist of 2-3 overview talks, followed by several breakout sessions, and end with a unified synthesis session. Attendees are encouraged to provide feedback and suggest alternative approaches based on their own experience. Lunch will not be provided, but afternoon snacks and refreshments (alcoholic/non-
alcoholic) will be available. NEON will sponsor all costs and the workshop will be free of charge for participants.