The impacts of climate change (CC) on natural and cultural resources are far‐reaching and complex. A major challenge facing resource managers is not knowing the exact timing and nature of those impacts. To confront this problem, scientists, adaptation specialists, and resource managers have begun to use scenario planning (SP). This structured process identifies a small set of scenarios—descriptions of potential future conditions that encompass the range of critical uncertainties—and uses them to inform planning. We reflect on a series of five recent participatory CC SP projects at four US National Park Service units and derive guidelines for using CC SP to support natural and cultural resource conservation. Specifically, we describe how these engagements affected management, present a generalized CC SP approach grounded in management priorities, and share key insights and innovations that (1) fostered participant confidence and deep engagement in the participatory CC SP process, (2) shared technical information in a way that encouraged informed, effective participation, (3) contextualized CC SP in the broader picture of relevant longstanding or emerging nonclimate stressors, (4) incorporated quantitative approaches to expand analytical capacity and assess qualitative findings, and (5) translated scenarios and all their complexity into strategic action.

Plant communities are composed of complex phenotypes that not only differ among taxonomic groups and habitats but also change over time within a species. Restoration projects (e.g. translocations and reseeding) can introduce new functional variation in plants, which further diversifies phenotypes and complicates our ability to identify locally adaptive phenotypes for future restoration. Near‐infrared spectroscopy (NIRS) offers one approach to detect the chemical phenotypes that differentiate plant species, populations, and phenological states of individual plants over time. We use sagebrush (Artemisia spp.) as a case study to test the accuracy by which NIRS can classify variation within taxonomy and phenology of a plant that is extensively managed and restored. Our results demonstrated that NIRS can accurately classify species of sagebrush within a study site (75–96%), populations of sagebrush within a subspecies (99%), annual phenology within a population (>99%), and seasonal phenology within individual plants (>97%). Low classification accuracy by NIRS in some sites may reflect heterogeneity associated with natural hybridization, translocation of nonlocal seed sources from past restoration, or complex gene‐by‐environment interactions. Advances in our ability to detect and interpret spectral signals from plants may improve both the selection of seed sources for targeted conservation and the capacity to monitor long‐term changes in vegetation.