Factors Controlling Changes in Epilithic Algal Biomass in the Mountain Streams of Subtropical Taiwan

Kuo, Yi‐Ming; Yu, Hwa‐Lung; Kuan, Wen-Hui; Kuo, Mei‐Hwa; Lin, Hsing‐Juh

doi:10.1371/journal.pone.0166604

Cited by 7 publications

(7 citation statements)

References 57 publications

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“…GAMs are regression models that generalize the family of generalized linear models (GLMs), by replacing the linear functional form with a sum of smooth functions [69,70]. GAMs have been strongly accepted in several domains as a flexible modeling technique, suited for capturing nonlinear, unspecified relationships between predictor variables and the response variable [71,72]. GAM has been used to explore the AGB [73], but not to map its spatial variability.…”

Section: Statistical Models and Analysismentioning

confidence: 99%

Determinants of Above-Ground Biomass and Its Spatial Variability in a Temperate Forest Managed for Timber Production

Soriano‐Luna

Ángeles‐Pérez

Guevara

et al. 2018

Forests

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The proper estimation of above-ground biomass (AGB) stocks of managed forests is a prerequisite to quantifying their role in climate change mitigation. The aim of this study was to analyze the spatial variability of AGB and its uncertainty between actively managed pine and unmanaged pine-oak reference forests in central Mexico. To investigate the determinants of AGB, we analyzed variables related to forest management, stand structure, topography, and climate. We developed linear (LM), generalized additive (GAM), and Random Forest (RF) empirical models to derive spatially explicit estimates and their uncertainty, and compared them. AGB was strongly influenced by forest management, as LiDAR-derived stand structure and stand age explained 80.9% to 89.8% of its spatial variability. The spatial heterogeneity of AGB varied positively with stand structural complexity and age in the managed forests. The type of predictive model had an impact on estimates of total AGB in our study site, which varied by as much as 19%. AGB densities varied from 0 to 492 ± 17 Mg ha−1 and the highest values were predicted by GAM. Uncertainty was not spatially homogeneously distributed and was higher with higher AGB values. Spatially explicit AGB estimates and their association with management and other variables in the study site can assist forest managers in planning thinning and harvesting schedules that would maximize carbon stocks on the landscape while continuing to provide timber and other ecosystem services. Our study represents an advancement toward the development of efficient strategies to spatially estimate AGB stocks and their uncertainty, as the GAM approach was used for the first time with improved results for such a purpose.

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Section: Statistical Models and Analysismentioning

confidence: 99%

Determinants of Above-Ground Biomass and Its Spatial Variability in a Temperate Forest Managed for Timber Production

Soriano‐Luna

Ángeles‐Pérez

Guevara

et al. 2018

Forests

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“…Nonpoint source pollution will produce a large amount of TP, TN, NH 3 ‐N and COD, which are important factors affecting the health of aquatic ecosystems (Kuo et al, 2016; Song et al, 2012; Zhou et al, 2019), and have a serious impact on the aquatic ecosystem after entering the river. For this purpose, the sampling points (Figure 1) are fuzzy clustered based on the data of TN, TP, COD and NH 3 ‐N, and the results are shown in Figure 2.…”

Section: Resultsmentioning

confidence: 99%

“…Nonpoint source pollution will produce a large amount of TP, TN, NH 3 -N and COD, which are important factors affecting the health of aquatic ecosystems (Kuo et al, 2016;Song et al, 2012;Zhou et al, 2019), and have a serious impact on the aquatic ecosystem after entering the river. For this purpose, the sampling points ( Figure 1 PLSR analysis is performed to determine the impact of water quality factors on structural indicators of food webs at typical points J1, J24, J36, J32, J16 and J23.…”

Section: Fuzzy Clustering To Determine Typical Points Of Nonpoint Smentioning

confidence: 99%

“…The harm of nonpoint source pollution to the water body is the change of river water quality caused by pollutants entering as the result of rainfall erosion. The main water quality factors include NH 3 ‐N, TN, TP, chemical oxygen demand (COD), biochemical oxygen demand (BOD) and total dissolved solids, of which TP, TN, NH 3 ‐N and COD are important factors affecting the health of aquatic ecosystems (Kuo et al, 2016; Song et al, 2012; Zhou et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…In summary, in the study of classification of sampling point data, it is feasible to use fuzzy clustering to perform cluster classification and then use the centre point selection principle to determine typical points.5.2 | Analysis of important factors of nonpoint source pollution in food web structureIn this paper, NH 3 -N and COD are the important factors of nonpoint source pollution that affect the food web structure. In the study of the effects of NH 3 -N on the structure of aquatic ecosystems, Kuo et al's study in the Dajia River watershed at China found that NH 3 -N is the main factor that affects the seasonal changes of the epilithic algal biomass in streams, which in turn affects the structural composition of the upper aquatic animal community and eventually leading to changes in the food web structure(Kuo et al, 2016) Shrestha and Dorevitch's (2019). research in Lake Michigan beaches at the United States found that the increase of NH 3 -N increased the density and quantity of Escherichia coli.…”

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confidence: 99%

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Effects and prediction of nonpoint source pollution on the structure of aquatic food webs

et al. 2020

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Nonpoint source pollution entering rivers will pollute water quality, degrading the health of aquatic ecosystems. However, owing to the lack of quantitative research on the effects of nonpoint source pollution on the structure of aquatic food webs, there is a lack of quantitative basis for river management. Nonpoint source pollution is not only difficult to control effectively, but also the success rate of water ecological restoration projects is low. With the increasing proportion of nonpoint source pollution in water environmental problems, it is urgent to quantitatively assess and predict the impact of nonpoint source pollution on the structure of food webs. Therefore, this thesis presents a method for quantitatively assessing and predicting the impact of nonpoint source pollution on the structure of food webs through using fuzzy clustering to screen the typical points of the impact of nonpoint source pollution, then using canonical correspondence analysis (CCA) and partial least squares regression analysis to comprehensively filtrate the driving factors affect food web that results in nonpoint source pollution, and then determining the impact of each driving factor on the structure of food webs. Finally, the change trend of food web structure is predicted. The results show that (1) the driving factors that the nonpoint source pollution that affects the food web structure is NH3‐N and chemical oxygen demand (COD). The increase in NH3‐N and COD promotes the growth of phytoplankton, causing the change of the primary productivity of the ecosystem, and ultimately changes the entire food web structure; (2) NH3‐N and COD affect the stability, maturity, connectivity and complexity of the aquatic food web structure. The increase of NH3‐N increases the connectivity and maturity of the food web structure but reduces complexity and stability; the increase of COD increases the connection of the food web structure, while reducing the other three indicators; (3) in some areas with good water quality, aquatic species diversity is high, the relationship of interspecies dietary is complex, food web structure level index is high and the structure of food web is stable. The food web structure in the rainy season will be better than that in the dry season. In some areas with severe water pollution and poor food web structure, the ability of the food web to resist external interference is weak. The food web structure in the rainy season will be worse than that in the dry season owing to rainfall into the river. The methods and conclusions in this treatise can provide a reliable and quantitative scientific basis for river ecosystem management and ecosystem restoration and can improve the success rate of ecological restoration projects.

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Using generalized additive models to investigate factors influencing cyanobacterial abundance through phycocyanin fluorescence in East Lake, China

Kuo

Yang

Liu

et al. 2018

Environ Monit Assess

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Factors Controlling Changes in Epilithic Algal Biomass in the Mountain Streams of Subtropical Taiwan

Cited by 7 publications

References 57 publications

Determinants of Above-Ground Biomass and Its Spatial Variability in a Temperate Forest Managed for Timber Production

Determinants of Above-Ground Biomass and Its Spatial Variability in a Temperate Forest Managed for Timber Production

Effects and prediction of nonpoint source pollution on the structure of aquatic food webs

Using generalized additive models to investigate factors influencing cyanobacterial abundance through phycocyanin fluorescence in East Lake, China

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