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Abstract Accurate predictions of ecological restoration outcomes are needed across the increasingly large landscapes requiring treatment following disturbances. However, observational studies often fail to account for nonrandom treatment application, which can result in invalid inference. Examining a spatiotemporally extensive management treatment involving post-fire seeding of declining sagebrush shrubs across semiarid areas of the western USA over two decades, we quantify drivers and consequences of selection biases in restoration using remotely sensed data. From following more than 1,500 wildfires, we find treatments were disproportionately applied in more stressful, degraded ecological conditions. Failure to incorporate unmeasured drivers of treatment allocation led to the conclusion that costly, widespread seedings were unsuccessful; however, after considering sources of bias, restoration positively affected sagebrush recovery. Treatment effects varied with climate, indicating prioritization criteria for interventions. Our findings revise the perspective that post-fire sagebrush seedings have been broadly unsuccessful and demonstrate how selection biases can pose substantive inferential hazards in observational studies of restoration efficacy and the development of restoration theory. 

Abstract Plants respond to their environment with both short‐term, within‐generation trait plasticity, and long‐term, between‐generation evolutionary changes. However, the relative magnitude of plant responses to short‐ and long‐term changes in the environment remains poorly understood. Shifts in phenological traits can serve as harbingers for responses to environmental change, and both a plant's current and source (i.e., genotype origin) environment can affect plant phenology via plasticity and local adaptation, respectively. To assess the role of current and source environments in explaining variation in flowering phenology ofBromus tectorum, an invasive annual grass, we conducted a replicated common garden experiment using 92 genotypes collected across western North America. Replicates of each genotype were planted in two densities (low = 100 seeds/1 m², high = 100 seeds/0.04 m²) under two different temperature treatments (low = white gravel; high = black gravel; 2.1°C average difference) in a factorial design, replicated across four common garden locations in Idaho and Wyoming, USA. We tested for the effect of current environment (i.e., density treatment, temperature treatment, and common garden location), source environment (i.e., genotype source climate), and their interaction on each plant's flowering phenology. Flowering timing was strongly influenced by a plant's current environment, with plants that experienced warmer current climates and higher densities flowering earlier than those that experienced cooler current climates and lower densities. Genotypes from hot and dry source climates flowered consistently earlier than those from cool and wet source climates, even after accounting for genotype relatedness, suggesting that this genetically based climate cline is a product of natural selection. We found minimal evidence of interactions between current and source environments or genotype‐by‐environment interactions. Phenology was more sensitive to variation in the current climate than to variation in source climate. These results indicate that cheatgrass phenology reflects high levels of plasticity as well as rapid local adaptation. Both processes likely contribute to its current success as a biological invader and its capacity to respond to future environmental change. 

Abstract Interannual variation, especially weather, is an often‐cited reason for restoration “failures”; yet its importance is difficult to experimentally isolate across broad spatiotemporal extents, due to correlations between weather and site characteristics. We examined post‐fire treatments within sagebrush‐steppe ecosystems to ask: (1) Is weather following seeding efforts a primary reason why restoration outcomes depart from predictions? and (2) Does the management‐relevance of weather differ across space and with time since treatment? Our analysis quantified range‐wide patterns of sagebrush (Artemisiaspp.) recovery, by integrating long‐term records of restoration and annual vegetation cover estimates from satellite imagery following thousands of post‐fire seeding treatments from 1984 to 2005. Across the Great Basin, sagebrush growth increased in wetter, cooler springs; however, the importance of spring weather varied with sites' long‐term climates, suggesting differing ecophysiological limitations across sagebrush's range. Incorporation of spring weather, including from the “planting year,” improved predictions of sagebrush recovery, but these advances were small compared to contributions of time‐invariant site characteristics. Given extreme weather conditions threatening this ecosystem, explicit consideration of weather could improve the allocation of management resources, such as by identifying areas requiring repeated treatments; but improved forecasts of shifting mean conditions with climate change may more significantly aid the prediction of sagebrush recovery. 

Large‐scale disturbances, such as megafires, motivate restoration at equally large extents. Measuring the survival and growth of individual plants plays a key role in current efforts to monitor restoration success. However, the scale of modern restoration (e.g., >10,000 ha) challenges measurements of demographic rates with field data. In this study, we demonstrate how unoccupied aerial system (UAS) flights can provide an efficient solution to the tradeoff of precision and spatial extent in detecting demographic rates from the air. We flew two, sequential UAS flights at two sagebrush (Artemisia tridentata) common gardens to measure the survival and growth of individual plants. The accuracy of Bayesian‐optimized segmentation of individual shrub canopies was high (73–95%, depending on the year and site), and remotely sensed survival estimates were within 10% of ground‐truthed survival censuses. Stand age structure affected remotely sensed estimates of growth; growth was overestimated relative to field‐based estimates by 57% in the first garden with older stands, but agreement was high in the second garden with younger stands. Further, younger stands (similar to those just after disturbance) with shorter, smaller plants were sometimes confused with other shrub species and bunchgrasses, demonstrating a need for integrating spectral classification approaches that are increasingly available on affordable UAS platforms. The older stand had several merged canopies, which led to an underestimation of abundance but did not bias remotely sensed survival estimates. Advances in segmentation and UAS structure from motion photogrammetry will enable demographic rate measurements at management‐relevant extents.