Singh Minerva, Zhu Xiaoxiang
Imperial College London, LONDON, United Kingdom.
Technical University of Munich, Munich, Germany.
PeerJ. 2021 Oct 7;9:e12029. doi: 10.7717/peerj.12029. eCollection 2021.
In the past two decades, Amazon rainforest countries (Brazil, Bolivia, Colombia, Ecuador, Guyana, Peru and Venezuela) have experienced a substantial increase in fire frequency due to the changes in the patterns of different anthropogenic and climatic drivers. This study examines how both fire dynamics and bioclimatic factors varied based on the season (wet season and dry season) El Niño years across the different countries and ecosystems within the Amazon rainforest. Data from publicly available databases on forest fires (Global Fire Atlas) and bioclimatic, topographic and anthropogenic variables were employed in the analysis. Linear mixed-effect models discovered that year type (El Niño . non-El Niño), seasonality (dry . wet), land cover and forest strata (in terms of canopy cover and intactness) and their interactions varied across the Amazonian countries (and the different ecosystems) under consideration. A machine learning model, Multivariate Adaptive Regression Spline (MARS), was utilized to determine the relative importance of climatic, topographic, forest structure and human modification variables on fire dynamics across wet and dry seasons, both in El Niño and non-El Niño years. The findings of this study make clear that declining precipitation and increased temperatures have strong impact on fire dynamics (size, duration, expansion and speed) for El Niño years. El Niño years also saw greater fire sizes and speeds as compared to non-El Niño years. Dense and relatively undisturbed forests were found to have the lowest fire activity and increased human impact on a landscape was associated with exacerbated fire dynamics, especially in the El Niño years. Additionally, the presence of grass-dominated ecosystems such as grasslands also acted as a driver of fire in both El Niño and non-El Niño years. Hence, from a conservation perspective, increased interventions during the El Niño periods should be considered.
在过去二十年中，亚马逊雨林国家（巴西、玻利维亚、哥伦比亚、厄瓜多尔、圭亚那、秘鲁和委内瑞拉）由于不同人为和气候驱动因素模式的变化，火灾发生频率大幅上升。本研究考察了在厄尔尼诺年期间，整个亚马逊雨林不同国家和生态系统中，火灾动态和生物气候因素如何随季节（湿季和干季）而变化。分析采用了来自森林火灾（全球火灾地图集）以及生物气候、地形和人为变量的公开数据库中的数据。线性混合效应模型发现，年份类型（厄尔尼诺年与非厄尔尼诺年）、季节性（干季与湿季）、土地覆盖和森林层次（就树冠覆盖和完整性而言）及其相互作用在亚马逊国家（以及不同生态系统）中存在差异。利用机器学习模型多元自适应回归样条（MARS）来确定气候、地形、森林结构和人类改造变量在厄尔尼诺年和非厄尔尼诺年的湿季和干季对火灾动态的相对重要性。本研究结果表明，降水减少和气温升高对厄尔尼诺年的火灾动态（面积、持续时间、蔓延和速度）有强烈影响。与非厄尔尼诺年相比，厄尔尼诺年的火灾面积和速度也更大。发现茂密且相对未受干扰的森林火灾活动最低，而人类对景观影响的增加与火灾动态加剧有关，尤其是在厄尔尼诺年。此外，在厄尔尼诺年和非厄尔尼诺年，以草地为主的生态系统（如草原）的存在也是火灾的一个驱动因素。因此，从保护角度来看，应考虑在厄尔尼诺期间加强干预措施。
