Data from: Studying long-term, large-scale grassland restoration outcomes to improve seeding methods and reveal knowledge gaps

1 .Studies are increasingly investigating effects of large-scale management activities on grassland restoration outcomes. These studies are providing useful comparisons among currently used management strategies, but not the novel strategies needed to rapidly improve restoration efforts. Here we illustrate how managing restoration projects adaptively can allow promising management innovations to be identified and tested. 2. We studied 327 Great Plains fields seeded after coal mining. We modelled plant responses to management strategies to identify the most effective previously used strategies for constraining weeds and establishing desired plants. Then we used the model to predict responses to new strategies our analysis identified as potentially more effective. 3. Where established, the weed Agropyron cristatum L. increased through time, indicating a need to manage establishment of this grass. Seeding particular grasses reduced annual weed cover, and because these grasses appeared to become similarly abundant whether sown at low or high rates, low rates could likely be safely used to reduce seeding costs. More importantly, lower than average grass seed rates increased cover of shrubs, the plants most difficult to restore to many grassland ecosystems. After identifying grass seed rates as a driver, we formulated model predictions for rates below the range managers typically use. These predictions require testing but indicated atypically low grass seed rates would further increase shrubs without hindering long-term grass stand development. 4. Synthesis and applications. Designing management around empirically based predictions is a logical next step toward improving ecological restoration efforts. Our predictions are that reducing grass seed rates to atypically low levels will boost shrubs without compromising grasses. Because these predictions derive from the fitted model, they represent quantitative hypotheses based on current understanding of the system. Generating data needed to test and update these hypotheses will require monitoring responses to shifts in management, specifically shifts to lower grass seed rates. A paucity of data for confronting hypotheses has been a major sticking point hindering adaptive management of most natural resources, but this need not be the case with degraded grasslands, because ongoing restoration efforts around the globe are providing continuous opportunities to monitor and manage processes regulating grassland restoration outcomes.

1. 当前，越来越多的研究聚焦于大规模管理活动对草地恢复（grassland restoration）成效的影响。此类研究虽为现有主流管理策略提供了有效的对比参考，却未能针对快速提升恢复工作质量所需的新型管理策略展开探索。本文阐释了修复项目的适应性管理（adaptive management）模式，该模式可助力识别并验证具有应用前景的管理创新方案。
2. 本研究以北美大平原（Great Plains）地区327块经煤矿开采后复垦的播种农田为研究对象。通过构建植物对管理策略的响应模型，我们筛选出在抑制杂草滋生、促进目标植物定植方面效果最优的现有常规管理策略。随后，利用该模型对本研究识别出的潜在更高效新型策略的响应效果进行预测。
3. 当冰草（Agropyron cristatum L.）成功定植后，其种群覆盖度随时间推移呈上升趋势，这表明需针对性管控该禾草的定植过程。播种特定禾草品种可降低一年生杂草的盖度，且由于无论播种量高低，这些禾草最终的种群丰度均无显著差异，因此可采用低播种量以降低播种成本。更为关键的是，低于平均水平的禾草播种量可提升灌木（shrubs）的覆盖度——而灌木是多数草地生态系统恢复中最难定植的植物类群。在明确禾草播种量为关键影响因子后，我们针对管理者常规使用范围以下的播种量构建了模型预测结果。尽管这些预测仍需实验验证，但结果显示，异常低的禾草播种量可进一步提升灌木覆盖度，且不会阻碍禾草群落的长期构建过程。
4. 结论与应用展望：基于实证预测制定管理方案，是提升生态恢复（ecological restoration）工作水平的合理进阶方向。我们的预测结果显示，将禾草播种量降至异常低的水平，可在不损害禾草群落发展的前提下提升灌木覆盖度。由于该预测基于拟合后的模型得出，其代表了基于当前系统认知的量化假说。为获取检验并更新这些假说所需的数据，需开展管理策略调整后的响应效果监测，具体而言，即调整至更低的禾草播种量。长期以来，缺乏用于验证假说的有效数据，是阻碍多数自然资源适应性管理的核心瓶颈，但退化草地恢复领域并非如此——全球范围内持续推进的草地恢复工作，为监测和调控草地恢复进程提供了持续的实践契机。