Nonparametric approaches for estimating regional lake nutrient thresholds

Huo, Shouliang; Ma, Chunzi; Xi, Beidou; He, Zhuoshi; Su, Jing; Wu, Fengchang

doi:10.1016/j.ecolind.2015.05.065

Cited by 19 publications

(4 citation statements)

References 35 publications

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“…Classification and regression tree (CART) analysis is a nonparametric and binary recursive partitioning method that is useful for environmental and ecological studies because of its inherent ability to allow the predictors of a model to interact and due to the hierarchical structure of the models. 29 CART was used to identify change points in community α diversity values over time and in response to variation in aquatic TP (DI-TP: diatominferred total phosphorus concentrations).…”

Section: Dating and Paleoenvironmentalmentioning

confidence: 99%

Climate and Nutrient-Driven Regime Shifts of Cyanobacterial Communities in Low-Latitude Plateau Lakes

Zhang

Huo

Xiao

et al. 2021

Environ. Sci. Technol.

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Cyanobacterial blooms that form in response to climate warming and nutrient enrichment in freshwater lakes have become a global environmental challenge. Historical legacy effects and the mechanisms underlying cyanobacterial community succession are not well understood, especially for plateau lakes that are important global freshwater resources. This study investigated the temporal dynamics of cyanobacterial communities over centuries in response to nutrient enrichment and climate warming in low-latitude plateau lakes using high-throughput DNA sequencing of sedimentary DNA combined with traditional paleolimnological analyses. Our results confirmed that nutrients and climate warming drive shifts in cyanobacterial communities over time. Cyanobacterial community turnover was pronounced with regime shifts toward new ecological states, occurring after exceeding a tipping point of aquatic total phosphorus (TP). The inferred species interactions, niche differentiation, and identity of keystone taxa significantly changed after crossing the aquatic TP ecological threshold, as demonstrated by network analysis of cyanobacterial taxa. Further, the contribution of aquatic TP to cyanobacterial community dynamics was greater than that of air temperature when lakes were in an oligotrophic state. In contrast, as the aquatic TP threshold was exceeded, the contribution to community dynamics by air temperature increased and potentially surpassed that of aquatic TP. Overall, these results provide new evidence for how past nutrient levels in lacustrine ecosystems influence contemporary cyanobacterial community responses to global warming in low-latitude plateau lakes.

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Section: Dating and Paleoenvironmentalmentioning

confidence: 99%

Climate and Nutrient-Driven Regime Shifts of Cyanobacterial Communities in Low-Latitude Plateau Lakes

Zhang

Huo

Xiao

et al. 2021

Environ. Sci. Technol.

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“…The RF method is an improved and robust version of the classified and regression tree (CART) method. It introduces the bootstrapping aggregation approach into CART [37][38][39] and calculates the predicted values by averaging the results of CART trees on bootstrap samples [40]. The variables used in the RF method should have an independent identical distribution [40].…”

Section: Statistical Analysesmentioning

confidence: 99%

Recognizing Crucial Aquatic Factors Influencing Greenhouse Gas Emissions in the Eutrophication Zone of Taihu Lake, China

Wang

Cheng

et al. 2019

Sustainability

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Greenhouse gas (GHG) emissions, which are closely related to climate change and serious ecological instability, have attracted global attention. The estimation of crucial aquatic factors for the flux of GHGs in lakes is a key step in controlling and reducing GHG emissions. The importance of 14 aquatic factors for GHG emissions was estimated in Meiliang Bay, which is an eutrophication shallow bay in Taihu Lake in eastern China. The random forest (RF) method, which is an improved version of the classified and regression tree (CART) model, was employed. No distribution assumption on variables was required in this method and it could include nonlinear actions and interactions among factors. The results show significant positive correlations among the fluxes of CO2, CH4, and N2O. The most crucial factor influencing CO2 emissions is the water temperature (WT) followed by sulfate (SO42−), alkalinity (Alk), dissolved oxygen (DO), and nitrate (NO3−–N). The important factors for CH4 emissions are WT, SO42−, DO, Alk, and NO2−–N. The outcome for N2O, in which the key factor is NO2−–N, was slightly different from those of CO2 and CH4. A comprehensive ranking index (CRI) for the fluxes of all three GHGs was also calculated and showed that WT, NO2−–N, SO42−, DO, and Alk are the most crucial aquatic factors. These results indicate that increasing DO might be the most effective means of controlling GHG emissions in eutrophication lake bays. The role of SO42− in GHG emissions, which has previously been ignored, is also worth paying attention to. This study provides a useful basis for controlling GHG emissions in eutrophication shallow lake bays.

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“…Coastal eutrophication has caused serious damage to the ecosystem and disturbed the balance of original ecosystem, resulting in outbreak of harmful algal blooms and the formation of hypoxic or anoxic zones (DePinto and Verhoff, 1977;Boynton et al, 1982;Burkholder et al, 1992;Arai, 2001;Wu, 2007;Lin et al, 2018;Xu, 2020;Ke et al, 2022;Cai et al, 2011). Therefore, developing and categorizing the numeric nutrients criteria played an important role in assessing the attainment of designated uses and measuring progress toward achieving water quality goals (US EPA, 1998;US EPA, 2001;Huo et al, 2015;Yang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Categorizing numeric nutrients criteria and implications for water quality assessment in the Pearl River Estuary, China

Zhang

et al. 2022

Front. Mar. Sci.

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Coastal eutrophication, the over-enrichment of water with nutrients, has become a global ecological problem. As coastal waters are subjected to great pressure due to anthropogenic influences and climate change, establishing numeric nutrient criteria for coastal waters has been exceedingly complex at present. To control and improve the water quality of the Pearl River Estuary (PRE), based on the data from 2015 to 2020, the nutrient criteria of the PRE and adjacent waters were established using frequency statistical analysis. Based on the spatiotemporal salinity patterns, the coastal waters of the PRE were divided in three subareas namely freshwater (Zone I), mixed (Zone II), and seawater (Zone III) using cluster analysis. The recommended criteria values of dissolved inorganic nitrogen (DIN) were 0.573, 0.312, and 0.134 mg·L-1 in Zones I, II, and III, respectively. The total nitrogen (TN) criterion for Zone III (0.222 mg·L-1) was much lower than those for Zone I (0.902 mg·L-1) and Zone II (0.885 mg·L-1).The dissolved inorganic phosphorus (DIP) criteria were different for the three Zones, ranging from 0.004 to 0.009 mg·L-1, and the total phosphorus (TP) recommended criteria in Zones I, II, and III were 0.039, 0.028, and 0.020 mg·L-1, respectively. In the water quality assessment, the categorizing numeric nutrients criteria can be referred and applied into fresh, mixed, and seawater zones of PRE. The results of this study provide a new nutrient reference condition in the PRE, which could be helpful in establishing integrated land-ocean unified nutrient criteria and water quality assessment, and implementing effective coastal eutrophication control in the future.

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Nonparametric approaches for estimating regional lake nutrient thresholds

Cited by 19 publications

References 35 publications

Climate and Nutrient-Driven Regime Shifts of Cyanobacterial Communities in Low-Latitude Plateau Lakes

Climate and Nutrient-Driven Regime Shifts of Cyanobacterial Communities in Low-Latitude Plateau Lakes

Recognizing Crucial Aquatic Factors Influencing Greenhouse Gas Emissions in the Eutrophication Zone of Taihu Lake, China

Categorizing numeric nutrients criteria and implications for water quality assessment in the Pearl River Estuary, China

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