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 (Artemisia spp.) 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.

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.

A rapidly changing climate during this century poses a high risk for impacts to ecosystems, biodiversity and traditional livelihoods. A better understanding of how climate change might alter temperature, precipitation, heat stress, water availability and other extreme weather metrics in the coming century would be useful to natural resource managers at the U.S. Fish & Wildlife Service in the North Central region. Particularly, when they prepare to conduct Species Status Assessments to better evaluate risk to ecosystems, biodiversity and traditional livelihoods resulting from a changing climate. Scientists have traditionally gone through the time intensive process of extracting and analyzing different climate datasets (e.g., temperature and precipitation) to produce a comprehensive quantitative summary for different climate scenarios. However, these methods have not been efficient in meeting the growing demand and is challenging the capacity of the human resources. This project aims to develop a web-based interactive tool to deliver such information in a much more timely and user-friendly manner. This research project will develop an interactive tool using the existing computational and data-intensive platform provided by the Climate Toolbox, a highly recognized data delivery and climate analytic tool. Using this existing structure to develop this much needed tool will make the process more efficient, cost effective, and assure its long-term maintenance. The US Fish & Wildlife Service, the National Park Service and regional Tribe cooperators will inform the development of this tool, including developing new datasets and functionalities for the tool, and assessing its usability. The resulting open-source tool will be accessible and applicable to a wide variety of CASC-stakeholders across the contiguous United States.