生物多样性
温带雨林
生态学
环境科学
温带气候
生产力
气候变化
生态系统
温带森林
全球变化
森林生态学
沉积(地质)
全球变暖
系统发育多样性
航程(航空)
陆地生态系统
农林复合经营
地理
氮气
全球变暖的影响
热带气候
热带
β多样性
物种多样性
初级生产
植物生态学
氮气循环
环境变化
排序
α多样性
作者
Jie Li,Senxuan Lin,Minhui Hao,Chunyu Fan,Juan Wang,Klaus von Gadow,Chunyu Zhang,Xiuhai Zhao
摘要
ABSTRACT Aim Nitrogen deposition is a widespread global change driver that significantly affects terrestrial ecosystems. However, evidence on the specific mechanisms of productivity response to nitrogen deposition in temperate forests across ecological and climatic gradients is scarce. This study evaluates the effects of nitrogen deposition, multiple attributes of biodiversity and climate on productivity in a large temperate forest region. Location North‐eastern China. Time Period 2017. Major Taxa Studied Woody plants. Methods We assessed taxonomic, functional, and phylogenetic attributes of tree diversity and functional identity of forest communities across a wide range of ecological and climatic conditions. Using partial linear mixed models (LMMs), multivariate LMMs, and structural equation modelling, we investigated the effects and pathways of nitrogen deposition, biodiversity, and climate conditions on productivity. We also explored the relationship between nitrogen deposition and productivity across biotic and climatic gradients. Results Our results show that nitrogen deposition, phylogenetic diversity (PD), and the community‐weighted mean of maximum tree height (CWMHM) are significant positive drivers of productivity. Climate conditions primarily affected productivity indirectly through PD. In species‐poor high‐latitude forests characterised by cold and dry climates, nitrogen deposition had strong positive effects on productivity. That effect weakened towards warmer climatic conditions. Main Conclusions The findings of this study suggest that the response of productivity to nitrogen deposition depends on local ecological and climatic conditions. Our results underscore the importance of considering interactions among multiple global change drivers to improve the accuracy of ecosystem response predictions.
科研通智能强力驱动
Strongly Powered by AbleSci AI