Eco-social metrics of marsh restoration

Understanding why habitat restoration is viewed as successful or not is key to evaluating past projects, planning future projects, and building support. Additionally, connecting public perceptions to the restoration process and habitats can improve project outcomes and generate greater public support. Yet, restoration science lacks fundamental information about the extent to which restoration actions align with measured ecological outcomes and social perceptions. Focusing on tidal marshes, we gathered qualitative and quantitative data on public perceptions of these habitats and restoration through focus groups in three estuaries in Oregon, USA. We also gathered environmental data from nine restoration projects spanning these estuaries to understand ecological responses to restoration focusing on responses of hydrology and vegetation. We mined project reports for mentions of priority project goals from an ecological perspective to compare with community priorities. Lastly, we interviewed..., We assessed nine restoration projects spanning three Oregon estuaries: Kunz, Frederickson, Dalton, and Cox marshes in Coos Bay; Y27, Y3, and Poole Slough in Yaquina Bay; and Lint Slough and Drift Creek in Alsea Bay. We reviewed project reports and available data for all projects to determine project characteristics and environmental metrics measured. We limited our ecological assessment to metric categories that were measured at most of the projects: vegetation, hydrology, mammal use, and fish use. Other categories of ecological function were also considered including water quality and marsh elevation, but not included due to a lack of these data across estuaries and years at a given site, precluding comparison. We then collected present-day data for vegetation and channel sinuosity (as a proxy for hydrologic function) so we could compare pre-restoration and present-day data. Mammal and fish use were recorded as post-restoration presence/absence data due to the logistical inability to s..., R, R Studio, Excel