4.7 Article

Higher incidence of high-severity fire in and near industrially managed forests

期刊

FRONTIERS IN ECOLOGY AND THE ENVIRONMENT
卷 20, 期 7, 页码 397-404

出版社

WILEY
DOI: 10.1002/fee.2499

关键词

-

资金

  1. National Science Foundation Graduate Research Fellowship [DGE-2039656]

向作者/读者索取更多资源

The increasing severity of wildfires in California forests is linked to past forest management practices, but it is uncertain how land ownership affects these trends. A study found that high-severity fires were more likely to occur on private industrial lands compared to public lands and other lands. The incidence of high-severity fires was also higher in areas adjacent to private industrial land. These findings highlight the need for cross-boundary cooperation to protect ecological and social systems.
The increasing prevalence of high-severity wildfire in forests in the US state of California is connected to past forest management, but uncertainty remains regarding the differential effects of land ownership on these trends. To determine whether differing forest management regimes, inferred from land ownership, influence high-severity fire incidence, we assembled and analyzed a large dataset of 154 wildfires that burned a combined area of more than 971,000 ha in California. We found that where fires occurred, the odds of high-severity fire on private industrial lands were 1.8 times greater than on public lands and 1.9 times greater than on other lands (that is, remaining lands classified as neither private industrial nor public). Moreover, high-severity fire incidence was greater in areas adjacent to private industrial land, indicating this trend extends across ownership boundaries. Overall, these results indicate that prevailing forest management practices on private industrial timberland may increase high-severity fire occurrence, underscoring the need for cross-boundary cooperation to protect ecological and social systems.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据