【中国风采】营养状态和湖泊水深决定了营养盐-叶绿素a关系：来自全球数千个湖泊的证据[Water Research,2023]http://www.jlakes.org/ch/reader/view_news.aspx?id=20230703142956001

Trophic status and lake depth play important roles in determining the  nutrient-chlorophyll a relationship: Evidence from thousands of  lakes globally

Lei Zhao a,b,* , Rao Zhu a , Qichao Zhou c , Erik Jeppesen c,d,e,f,g , Kun Yang a,b,*

Abstract: A fundamental problem in lake eutrophication management is that the nutrient-chlorophyll a (Chl a) relationship  shows high variability due to diverse influences of for example lake depth, lake trophic status, and latitude. To  accommodate the variability induced by spatial heterogeneity, a reliable and general insight into the nutrient-Chl  a relationship may be achieved by applying probabilistic methods to analyze data compiled across a broad spatial  scale. Here, the roles of two critical factors determining the nutrient-Chl a relationship, lake depth and trophic  status, were explored by applying Bayesian networks (BNs) and a Bayesian hierarchical linear regression model  (BHM) to a compiled global dataset from 2849 lakes and 25083 observations. We categorized the lakes into three  groups (shallow, transitional, and deep) according to mean and maximum depth relative to mixing depth. We  found that despite a stronger effect of total phosphorus (TP) and total nitrogen (TN) on Chl a when combined, TP  played a dominant role in determining Chl a, regardless of lake depth. However, when the lake was hypereutrophic and/or TP was >40 μg/L, TN had a greater impact on Chl a, especially in shallow lakes. The response  curve of Chl a to TP and TN varied with lake depth, with deep lakes having the lowest yield Chl a per unit of  nutrient, followed by transitional lakes, while shallow lakes had the highest ratio. Moreover, we found a decrease  of TN/TP with increasing Chl a concentrations and lake depth (represented as mixing depth/mean depth). Our  established BHM may help estimating lake type and/or lake-specific acceptable TN and TP concentrations that  comply with target Chl a concentrations with higher certainty than can be obtained when bulking all lake types.

Keywords:  Eutrophication，Nutrients，Chlorophyll a ，Lake depth，Bayesian networks，Bayesian hierarchical linear regression model