There is increasing interest in utilizing the high carbon sequestration capacity of wetlands as a rationale for their restoration, but this requires careful assessment of their greenhouse gas (GHG) fluxes. We used a spatially extensive sampling approach across salinity gradients and management regimes (disturbed, restored, and reference) in 22 wetland sites across two Oregon, USA estuaries, Tillamook and Coos Bays, to measure fluxes of methane (CH₄), ecosystem respiration as carbon dioxide (CO₂), and nitrous oxide (N₂O) over 1 year and related them to environmental forcing variables. Boosted regression tree (BRT) models explained drivers of GHG fluxes reasonably well despite highly nonlinear and interactive relationships and many flux measurements below detection. We used the BRT models to predict annual GHG fluxes in a subset of restored and reference sites where continuous environmental data were recorded and compared them to previously published soil carbon sequestration rates. Most sites had net removal of CO₂-equivalents from the atmosphere over both 20 and 100 year timeframes. Our results show that a spatially extensive GHG flux sampling scheme and machine-learning statistical techniques can be used to estimate GHG fluxes in other current and former wetlands within the region if environmental data are collected at a spatial resolution that reflects site variability and at sufficient duration to reflect seasonality (i.e., at least one full year). Such an approach can save time and money in determining the feasibility of wetland restoration as a climate mitigation strategy. We use our results to suggest wetland restoration strategies that optimize climate benefits.

人们越来越有兴趣利用湿地的高碳固存能力作为其恢复的理由，但这需要仔细评估其温室气体（GHG）通量。我们在美国俄勒冈州两个河口蒂拉穆克和库斯湾的 22 个湿地地点使用了跨盐度梯度和管理制度（干扰、恢复和参考）的空间广泛采样方法，以测量甲烷 (CH 4 ) 通量、生态_系统呼吸二氧化碳 (CO ₂ ) 和一氧化二氮 (N ₂O) 超过 1 年并将它们与环境强迫变量相关联。尽管高度非线性和交互关系以及许多通量测量值低于检测值，增强回归树 (BRT) 模型仍相当好地解释了温室气体通量的驱动因素。我们使用 BRT 模型来预测记录了连续环境数据的恢复和参考地点子集的年度温室气体通量，并将其与之前发布的土壤碳固存率进行比较。大多数场地的 CO _{2净去除量}- 20 年和 100 年时间范围内大气的当量。我们的结果表明，如果以反映地点变化的空间分辨率和足够的持续时间收集环境数据，则可以使用空间广泛的温室气体通量采样方案和机器学习统计技术来估计该区域内其他当前和前湿地的温室气体通量以反映季节性（即至少一整年）。这种方法可以节省时间和金钱来确定湿地恢复作为气候缓解策略的可行性。我们利用我们的研究结果提出优化气候效益的湿地恢复策略。