Department of Forest Resources Management, Faculty of Forestry, The University of British Columbia, 2424 Main Mall, Vancouver, British Columbia, V6T 1Z4, Canada.
USDA Forest Service, Pacific Northwest Research Station, 3200 SW Jefferson Way, Corvallis, Oregon, 97331, USA.
Ecol Appl. 2021 Apr;31(3):e02283. doi: 10.1002/eap.2283. Epub 2021 Feb 9.
Forest wildfires consume and redistribute carbon within forest carbon pools. Because the incidence of wildfires is unpredictable, quantifying wildfire effects is challenging due to the lack of prefire data or controls from experiments over a large landscape. We explored a quasi-experimental method, propensity score matching, to estimate wildfire effects on aboveground forest woody carbon mass in Washington and Oregon, United States. Observational data, including national forest inventory plot measurements and satellite imagery metrics, were utilized to obtain a control set of unburned plots that are comparable to burned plots in terms of environmental conditions as well as spatial locations. Three matching methods were implemented: propensity score matching (PSM), spatial matching (SM), and distance-adjusted propensity score matching (DAPSM). We investigated if propensity score matching with and without spatial adjustment led to different outcomes in terms of (1) balance in covariate distributions between burned and control plots, (2) mean carbon mass obtained from the selected control plots compared to burned and all unburned plots, and (3) estimates of wildfire effects by burn severity. We found that PSM and SM, which use only the environmental covariate set or the spatial distance for estimating propensity scores, respectively, did not appear to produce a comparable set of control plots in terms of the estimated propensity scores and the outcomes of mean carbon mass. DAPSM was the preferred method both in balancing the observed covariates and in dealing with unobservable confounding variables through spatial adjustment. The average wildfire effects estimated by DAPSM showed clear evidence of redistribution of carbon among aboveground woody pools, from live to dead trees, but the consumption of total woody carbon by wildfire was not substantial. Only moderate burn severity led to significant reduction of total woody carbon mass across Washington and Oregon forests (64% of control plots remained on average). This study provides an applied example of a quasi-experimental approach to quantify the effects of a natural disturbance for which experimental settings are unavailable. The study results suggest that incorporating spatial information in addition to environmental covariates would yield a comparable set of control plots for wildfire effects quantification.
森林野火在森林碳库中消耗和再分配碳。由于野火的发生不可预测，因此由于缺乏大火前的数据或大景观实验的对照，量化野火的影响具有挑战性。我们探索了一种准实验方法，即倾向评分匹配，以估计美国华盛顿州和俄勒冈州野火对地上森林木质碳质量的影响。利用观测数据，包括国家森林清查图测量值和卫星图像指标，获得了一套可比的未燃烧图的对照集，这些对照图在环境条件以及空间位置方面与燃烧图相似。实施了三种匹配方法：倾向得分匹配（PSM），空间匹配（SM）和距离调整倾向得分匹配（DAPSM）。我们研究了在以下方面是否倾向得分匹配（包括空间调整）会导致不同的结果：（1）燃烧和对照图之间协变量分布的平衡，（2）从选定的对照图中获得的平均碳质量与燃烧和所有未燃烧图相比，以及（3）根据燃烧严重程度估计的野火效应。我们发现，PSM 和 SM 分别仅使用环境协变量集或空间距离来估算倾向得分，在估算倾向得分和平均碳质量的结果方面，似乎并未产生可比的对照集。 DAPSM 是一种在平衡观测到的协变量和通过空间调整处理不可观测的混杂变量方面都更优的方法。通过 DAPSM 估算的平均野火效应清楚地表明了地上木质池之间碳再分配的证据，从活树到枯树，但野火对总木质碳的消耗并不多。仅中度燃烧严重程度就会导致华盛顿州和俄勒冈州森林中总木质碳质量明显减少（平均有 64％的对照图仍然存在）。这项研究提供了一种准实验方法的应用实例，可用于量化无法进行实验设置的自然干扰的影响。研究结果表明，除环境协变量外，纳入空间信息将产生可比的野火影响量化对照集。
