Data from: High precipitation and seeded species competition reduce seeded shrub establishment during dryland restoration

Drylands comprise 40% of Earth's land mass and are critical to food security, carbon sequestration and threatened and endangered wildlife. Exotic weed invasions, overgrazing, energy extraction, and other factors have degraded many drylands, and this has placed an increased emphasis on dryland restoration. The increased restoration focus has generated a wealth of experience, innovations and empirical data, yet the goal of restoring diverse, native dryland plant assemblages comprised of grasses, forbs and shrubs has generally proven beyond reach. Of particular concern are shrubs which often fail to establish or establish at trivially low densities. We used data from two Great Plains, U.S. coal mines to explore factors regulating shrub establishment. Our predictor data related to weather and restoration (e.g. seed rates, rock cover) variables, and our response data described shrub abundances on fields of the mines. We found that seeded non-shrubs, especially grasses, formed an important competitive barrier to shrub establishment: With every one standard deviation increase in non-shrub seed rate, the probability shrubs were present decreased ~0.1 and shrub cover decreased ~35%. Since new fields were seeded almost every year for >20 years, the data also provided a unique opportunity to explore effects of stochastic drivers (i.e. precipitation, year effects). With every one standard deviation increase in precipitation the first growing season following seeding, the probability shrubs were present decreased ~0.07 and shrub cover decreased ~47%. High precipitation appeared to harm shrubs by increasing grass growth/competition. Also, weak evidence suggested shrub establishment was better in rockier fields where grass abundance/competition was lower. Multiple lines of evidence suggest reducing grass seed rates below levels typically used in Great Plains restoration would benefit shrubs without substantially impacting grass stand development over the long term. We used Bayesian statistics to estimate effects of seed rates and other restoration predictors probabilistically to allow knowledge of the predictors' effects to be refined through time in an adaptive management framework. We believe this framework could improve restoration planning in a variety of systems where restoration outcomes remain highly uncertain and ongoing restoration efforts are continually providing new data of value for reducing the uncertainty.旱地占地球陆地总面积的40%，对粮食安全、碳封存以及受威胁和濒危野生动植物的存续至关重要。外来杂草入侵、过度放牧、能源开采及其他诸多因素已导致大量旱地退化，这使得旱地修复工作的重要性日益凸显。随着修复工作关注度的提升，相关领域积累了丰富的经验、创新成果与实证数据，但构建由禾草、杂类草与灌木组成的多样本土旱地植物群落的目标，总体上仍难以实现。尤为令人担忧的是灌木的定植表现：它们往往难以成功定植，或以极低的密度存活。本研究借助美国大平原两处煤矿的相关数据，探究调控灌木定植的关键因素。研究的预测变量数据涵盖天气与修复相关变量（如播种量、岩石覆盖度），响应变量则为煤矿矿区样地内的灌木丰度。研究发现，人工播种的非灌木类植物（尤其是禾草）会对灌木定植形成显著的竞争阻碍：非灌木播种量每增加1个标准差，灌木的定植概率下降约0.1，灌木盖度降低约35%。由于该矿区连续20余年几乎每年都会对新样地进行播种，本数据集也为探究随机驱动因子（即降水、年度效应）的影响提供了独特契机。播种后首个生长季的降水每增加1个标准差，灌木定植概率下降约0.07，灌木盖度降低约47%。高降水会通过促进禾草生长、加剧种间竞争对灌木存活产生不利影响。此外，仅有较弱的证据表明，在岩石覆盖度更高、禾草丰度与竞争压力更低的样地中，灌木的定植效果更佳。多项证据表明，将禾草播种量降至大平原地区常规修复所用的标准以下，可在长期内促进灌木生长，且不会对禾草群落的发育造成显著负面影响。本研究采用贝叶斯统计（Bayesian statistics）方法，对播种量及其他修复相关预测变量的效应进行概率化估计，以便在自适应管理框架下，随着时间推移逐步完善对这些预测变量效应的认知。我们认为，该框架可在多种修复结果仍存在高度不确定性、且持续开展的修复工作不断提供有助于降低不确定性的有效新数据的生态系统中，优化修复规划工作。