CAREER: Bias, design, and access of ecological monitoring programs

University Of New Hampshire, Durham NH

Investigators

Abstract

Long-term monitoring programs are a cornerstone of ecological research and management. Ideally, these monitoring programs would be designed to ensure they provide sufficient evidence to inform management decisions. However, monitoring programs are often constrained by technology, funding cycles, academic calendars, and personnel availability, which can severely limit their effectiveness. In addition, current methods for teaching researchers the big data skills needed for this work are disconnected from the ecological context where they might be most useful. Thus, the objective of the proposed work is to better understand how designs of monitoring programs influence knowledge generated, while providing opportunities for trainees to develop the necessary skills to work with big data. This project will also develop software infrastructure for other scientists and managers which will allow them to both assess and design environmental monitoring programs. University students and members of the research community will inform the project through surveys to aid the design of new training for field-based data science focused on data relevancy. The course materials will be freely available for all to use and remix for courses at their field stations. This project will support the next generation of researchers working with big data in ecological contexts by training two postdoctoral fellows, two graduate students, and six undergraduate researchers, in addition to approximately 70 undergraduate and graduate students in the data skills course and workshops. The project will develop new simulation approaches and leverage existing long-term datasets, including the National Ecological Observatory Network, to better design ecological monitoring programs. Specifically, the project will seek to understand the effects of monitoring design program decisions, such as temporal and geographic sampling scales, on population trends. The project will focus on how spatial and seasonal population dynamics affect inference on long-term trends. Both resampling techniques of existing data and mechanistic models of population dynamics will be used. The project will also build open-source code and interactive web applications that will allow users to design a cost-effective monitoring program by entering a set of input choices (e.g., site locations, sampling effort). New curricula and assessment toolsets generated will both teach and evaluate how biologists learn quantitative skills in a field-based data science course. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.