OPUS: CRS Synthesizing long-term data to forecast native understory plant community structure & dynamics with invasion--species interactions, abiotic change & physiology

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Forests in many parts of the world are negatively impacted by both high populations of native ungulates herbivores (deer, moose, elk) and non-native invasive plants. This project will explore overabundant herbivores’ and invaders’ impacts at the individual, population, and community levels for common native perennial wildflowers of forests. The project has three goals: 1. Determine the relative effect and importance of deer herbivory, invasion, and surrounding plant neighborhood on individual wildflower population’s persistence using a community coexistence model. 2. Evaluate how the anthropogenic stressors (deer, invasion) interact and forecast future forest wildflower composition under the different scenarios created in the long-term experiment. 3. Share the wildflower databases and collaborate with artists/designers on students’ training in design. Undergraduates will create products such as virtual reality experiences, apps, or board games. Since US forests often experience both high deer populations and invasion by non-native plants, the results of the community models will be applicable at wide regional scales for basic scientists and land managers; the student products will foster a broader public understanding of ongoing changes to our forests. The project will use individual wildflower databases and abiotic data collected with NSF support to build a cohesive community model and to project community coexistence using four biotic and abiotic interaction landscapes imposed by a long-term field experiment. The planned analyses will use growth, survival, flowering and seed production databases for each wildflower species collected in the long-term field experiment, in which the presence of both white-tailed deer and a noxious plant invader are manipulated for nearly two decades. This approach is novel, in that rather than being phenomenological or applying simulations, it will build a mechanistic, process-based model to assess understory community coexistence leveraging data from long-term experimental treatments. This modeling will add value to prior individual or pair-wise interaction studies by providing a unified framework to assess critical links and interactions between biotic stressors, environmental characteristics, and species’ population dynamics to forecast future community dynamics and composition. Undergraduate students will be trained in data visualization using the same long-term databases used in the community modeling of the forest understory. The design process, “Act Reflect Change”, requires students to define and explore connections between the specific content they attempt to visualize and refine their product for their target audiences.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.