Ensuring the availability of adequate seed supplies of species and sources appropriate for restoration projects and programs necessitates extensive science‐based planning. The selection of target species requires a review of disturbance conditions and reference areas, development of a reference model, and consideration of specific objectives, timeframes, available resources, and budgets as well as the performance of prospective species in past restoration efforts. Identification of seed sources adapted to site conditions is critical to provide for short‐term establishment and long‐term sustainability. Seed zones and plant movement guidelines provide tools for sourcing plant materials with reduced risk of maladaptation. A seed zone framework also facilitates seed use planning and contributes to stability and predictability of the commercial market, thereby reducing costs and improving the availability of adapted seed supplies. Calculating the amount of seed required for each species is based on seed quality (viability, purity), seed weight, expected seedling establishment, and desired composition of the seeding. If adequate collections from wildland stands are not feasible, then seed increase in seed fields or use of nursery stock may be warranted. Adherence to seed collection and seed production protocols for conserving genetic diversity is critical to protect genetic resources and buffer new seedings and plantings against environmental stressors. Maintenance of genetic diversity becomes even more critical considering current or expected climate change impacts. Collaboration and partnerships can benefit seed selection and procurement programs through sharing of information, coordination in project planning, and increasing the availability of native seed.

Aspen (Populus tremuloides Michx.) comprises only a small fraction (1 %) of the Sierra Nevada landscape, yet contributes significant biological diversity to this range. In an effort to rejuvenate declining aspen stands, the Bureau of Land Management conducted conifer removal in three sites (2004 to 2006) and prescribed fire in two sites (2007). The goal of this study was to evaluate the efficacy of these treatments. In each site, aspen densities in three regeneration size classes were measured in treated and untreated transects before and up to five years post-treatment. Five years after treatment, two of the three conifer removal sites showed significant improvement over controls in the density of total stems and two of three regeneration size classes. The third site did not show significant gains over controls in any size class and experienced significant aspen overstory mortality three years after treatment, which was attributed to sunscald and advanced age at the time of treatment. Three years after treatment, the two prescribed fire sites showed significant increases in total stem density and two regeneration size classes, but also exhibited significant stem mortality, which was likely due to a combination of herbivory and drought. Overall, both treatments can be effective, but future treatments should incorporate methods to reduce post-treatment mortality of residual aspen and new sprouts.

UAV-based hyperspectral remote sensing capabilities developed by the Idaho National Lab and Idaho State University, Boise Center Aerospace Lab, were recently tested via demonstration flights that explored the influence of altitude on geometric error, image mosaicking, and dryland vegetation classification.The test flights successfully acquired usable flightline data capable of supporting classifiable composite images. Unsupervised classification results support vegetation management objectives that rely on mapping shrub cover and distribution patterns. Overall, supervised classifications performed poorly despite spectral separability in the image-derived endmember pixels.In many cases, the supervised classifications accentuated noise or features in the mosaic that were artifacts of color balancing and "feathering" areas of flightline overlap. Future mapping efforts that leverage ground reference data, ultra-high spatial resolution photos and time series analysis should be able to effectively distinguish native grasses such as Sandberg bluegrass (Poa secunda), from invasives such as burr buttercup (Ranunculus testiculatus).

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