Water depth and land-use intensity indirectly determine phytoplankton functional diversity and further regulate resource use efficiency at a multi-lake scale.

The Science of the total environment(2022)

引用 8|浏览5
暂无评分
摘要
Biodiversity-ecosystem functioning relationships under multiple pressures have recently been the subject of broad studies. For the key primary producer in aquatic ecosystems, phytoplankton, several studies have focused on trait-based functional diversity (FD) and the related functioning (e.g., resource use efficiency, RUE), and their linkages. However, investigations of the effects of environmental factors at different levels (e.g., land use, lake morphometry, climate and nutrients) on FD and RUE are sparse. We developed a data-driven-model framework to simultaneously elucidate the effects of multiple drivers on FD (functional diversity based on dendrograms, FDc and functional richness, FRic) and RUE (of nitrogen and phosphorus) of phytoplankton based on data from 68 Yunnan-Guizhou Plateau lakes, Southwest China. We found that the concentration of total phosphorus, which is mainly affected by land-use intensity and influenced by water depth, was the primary (positive) driver of changes in both FDc and FRic, while RUE was mainly explained by phytoplankton FD (i.e., FRic). These results indicate that water depth and land-use intensity influence indirectly phytoplankton FD and further regulate RUE. Moreover, nonlinear correlations of RUE with FRic were found, which may be caused by interspecific competition and niche differentiation of the phytoplankton community related to nutrient levels. Our finding may help managers to set trade-off targets between FD and RUE in lake ecosystems except for extremely polluted ones, in which the thresholds derived from the Bayesian network, of total phosphorus, total nitrogen and land-use intensity were approximately 0.04 mg/L, 0.50 mg/L and 244 (unitless), respectively. The probability of meeting the RUE objectives was lower in shallow lakes than in deep lakes, but for FRic the opposite was observed.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要