Katherine L. Gerst | Search USGS

USGS2023

Lessons learned in knowledge co-production for climate-smart decision-making

Knowledge co-production, a process that involves both creators and users of information in knowledge generation, is growing in popularity in the conservation and ecology fields. While examples of successful co-production are becoming more common, many barriers and challenges remain in this work. Here, we reflect on our experiences in knowledge co-production from three recent case studies, using a prominent framework to understand and improve our efforts at each phase of the co-production process. Our reflections yield insights that may help other scientists seeking to support decision-making. We found that paying particular attention to the composition of the team and connecting with agency representatives early and often are key to success. Long-term commitment to the project and the people involved are also key. We conclude with suggestions for refining the framework to incorporate our primary lessons learned and include the valuation of a plurality of knowledge systems and empowerment as an ultimate impact of knowledge co-production.

Environmental Science and Policy↗

USGS2017

USA National Phenology Network’s volunteer-contributed observations yield predictive models of phenological transitions

In support of science and society, the USA National Phenology Network (USA-NPN) maintains a rapidly growing, continental-scale, species-rich dataset of plant and animal phenology observations that with over 10 million records is the largest such database in the United States. Contributed voluntarily by professional and citizen scientists, these opportunistically collected observations are characterized by spatial clustering, inconsistent spatial and temporal sampling, and short temporal depth. We explore the potential for developing models of phenophase transitions suitable for use at the continental scale, which could be applied to a wide range of resource management contexts. We constructed predictive models of the onset of breaking leaf buds, leaves, open flowers, and ripe fruits – phenophases that are the most abundant in the database and also relevant to management applications – for all species with available data, regardless of plant growth habit, location, geographic extent, or temporal depth of the observations. We implemented a very basic model formulation - thermal time models with a fixed start date. Sufficient data were available to construct 107 individual species × phenophase models. Of these, fifteen models (14%) met our criteria for model fit and error and were suitable for use across the majority of the species’ geographic ranges. These findings indicate that the USA-NPN dataset holds promise for further and more refined modeling efforts. Further, the candidate models that emerged could be used to produce real-time and short-term forecast maps of the timing of such transitions to directly support natural resource management.

PLoS ONE↗