Large-scale disturbances such as wildfire can have profound impacts on the composition, structure, and functioning of ecosystems. Bees are critical pollinators in natural settings and often respond positively to wildfires, particularly in forests where wildfire leads to more open conditions and increased floral resources. The use of Light Detection and Ranging (LiDAR) provides opportunities for quantifying habitat features across large spatial scales and is increasingly available to scientists and land managers for post-fire habitat assessment. We evaluated the extent to which LiDAR-derived forest structure measurements can predict forest bee communities after a large, mixed-severity fire. We hypothesized that LiDAR measurements linked to post-fire forest structure would improve our ability to predict bee abundance and species richness when compared to satellite-based maps of burn severity. To test this hypothesis, we sampled wild bee communities within the Douglas Fire Complex in southwestern Oregon, USA. We then used LiDAR and Landsat data to quantify forest structure and burn severity, respectively, across bee sampling locations. We found that the LiDAR forest structure model was the best predictor of abundance, whereas the Landsat burn severity model had better predictive ability for species richness. Furthermore, the Landsat burn severity model was better at predicting the presence and species richness of bumble bees (Bombus spp.), an ecologically distinct and economically important group within the Pacific Northwest. We posit that the divergent responses of the two modeling approaches are due to distinct responses by bee taxa to variation in forest structure as mediated by wildfire, with bumble bees in particular depending on closed-canopy forest for some portions of their life cycle. Our study demonstrates that LiDAR data can provide information regarding the drivers of bee abundance in post-wildfire conifer forest, and that both remote sensing approaches are useful for predicting components of wild bee diversity after large-scale wildfire.

中文翻译：

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陆地卫星和激光雷达衍生的测量结果都可以预测森林蜜蜂对大规模野火的反应

野火等大规模干扰可能对生态系统的组成、结构和功能产生深远影响。蜜蜂是自然环境中重要的传粉者，通常对野火做出积极反应，特别是在野火导致更开放的条件和增加花卉资源的森林中。光探测和测距 (LiDAR) 的使用为量化大空间尺度的栖息地特征提供了机会，并且越来越多地被科学家和土地管理者用于火灾后栖息地评估。我们评估了激光雷达衍生的森林结构测量可以在多大程度上预测大规模、混合严重程度的火灾后森林蜜蜂群落。我们假设，与基于卫星的烧伤严重程度地图相比，与火灾后森林结构相关的激光雷达测量将提高我们预测蜜蜂丰度和物种丰富度的能力。为了检验这一假设，我们对美国俄勒冈州西南部道格拉斯火灾综合体内的野生蜜蜂群落进行了采样。然后，我们使用激光雷达和陆地卫星数据分别量化蜜蜂采样地点的森林结构和燃烧严重程度。我们发现 LiDAR 森林结构模型是丰富度的最佳预测因子，而 Landsat 燃烧严重程度模型对物种丰富度具有更好的预测能力。此外，Landsat 烧伤严重程度模型能够更好地预测熊蜂的存在和物种丰富度（我们对美国俄勒冈州西南部道格拉斯消防综合体内的野生蜜蜂群落进行了采样。然后，我们使用激光雷达和陆地卫星数据分别量化蜜蜂采样地点的森林结构和燃烧严重程度。我们发现 LiDAR 森林结构模型是丰富度的最佳预测因子，而 Landsat 燃烧严重程度模型对物种丰富度具有更好的预测能力。此外，Landsat 烧伤严重程度模型能够更好地预测熊蜂的存在和物种丰富度（我们对美国俄勒冈州西南部道格拉斯消防综合体内的野生蜜蜂群落进行了采样。然后，我们使用激光雷达和陆地卫星数据分别量化蜜蜂采样地点的森林结构和燃烧严重程度。我们发现 LiDAR 森林结构模型是丰富度的最佳预测因子，而 Landsat 燃烧严重程度模型对物种丰富度具有更好的预测能力。此外，Landsat 烧伤严重程度模型能够更好地预测熊蜂的存在和物种丰富度（而 Landsat 燃烧严重程度模型对物种丰富度具有更好的预测能力。此外，Landsat 烧伤严重程度模型能够更好地预测熊蜂的存在和物种丰富度（而 Landsat 燃烧严重程度模型对物种丰富度具有更好的预测能力。此外，Landsat 烧伤严重程度模型能够更好地预测熊蜂的存在和物种丰富度（熊蜂属 ），太平洋西北地区生态独特且经济重要的群体。我们假设两种建模方法的不同反应是由于蜜蜂类群对野火介导的森林结构变化的不同反应所致，特别是熊蜂在其生命周期的某些部分依赖于封闭的树冠森林。我们的研究表明，激光雷达数据可以提供有关野火后针叶林蜜蜂丰度驱动因素的信息，并且两种遥感方法都可用于预测大规模野火后野蜂多样性的组成部分。