Comparison of aquatic ecosystem functioning between eutrophic and hypereutrophic cold-region river-lake systems

Akomeah, Eric; Chapra, Steven C.

doi:10.1016/j.ecolmodel.2018.12.004

Cited by 7 publications

(3 citation statements)

References 42 publications

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“…The upper Qu'Appelle River drainage system in southern Saskatchewan encompasses an area of intensive agriculture in the northern Great Plains of North America. The river and associated lakes have been characterized as eutrophic to hypereutrophic (Akomeah et al., 2019) and include a number of lakes that have cyanobacterial blooms every summer and early fall (McGowan et al., 2005). In addition to the annual cyanobacterial blooms, there is an annual summer spike of the cyanobacterial toxin microcystin (Hayes et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Grazing preferences of three species of amoebae on cyanobacteria and green algae

Weger,

Polasek,

Wright

et al. 2024

J Eukaryotic Microbiology

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Twenty species/isolates of cyanobacteria and green algae were isolated from cyanobacterial bloom samples in lakes associated with the upper Qu'Appelle River drainage system in southern Saskatchewan, Canada. Three amoebae species (Cochliopodium sp., Vannella sp. and Vermamoeba vermiformis) were also isolated from one of these samples, and were subjected to grazing assays to determine which species of cyanobacteria or algae could potentially serve as a food source. Amoeba grazing rates were quantified based on the diameter of the plaque after 12 days on agar plate assays, and by estimation of the amoeba population growth rate from the rate of increase of plaque area. The common cyanobacterial bloom‐formers Dolichospermum sp. and Aphanizomenon flos‐aquae supported high growth rates for all three amoebae, while green algae, with the exception of one green alga/amoeba combination, did not support growth of the tested amoebae. Many of the cyanobacterial and algal isolates that did not support amoebae growth were ingested, suggesting that ingestion did not determine grazing success. Overall, while the cyanobacteria Dolichospermum sp. and Aphanizomenon flos‐aquae were suitable food sources for the amoebae, the other cyanobacteria were grazed in an unpredictable manner, with some species/strains grazed by some amoebae and some species not grazed at all.

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Section: Introductionmentioning

confidence: 99%

Grazing preferences of three species of amoebae on cyanobacteria and green algae

Weger,

Polasek,

Wright

et al. 2024

J Eukaryotic Microbiology

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“…Compared with large water areas, they explained the particularity of CyanoHABs and eutrophication in rivers from the aspects of evolution trend, driving factors, model construction, and application (Nguyen et al, 2018). In describing the variation of chl‐a concentration and its influencing factors, most of the studies were concerned about the average value of whole river or different river reaches, and most models have been one‐dimensional (Akomeah et al, 2019). However, at present, few studies considered the transverse chl‐a distribution of river from a two‐dimensional perspective because of its shape and fluidity (Gao et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Transverse distribution of cyanobacteria in a regulated urban river

Yan

Wang

et al. 2021

Ecohydrology

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River eutrophication and CyanoHABs are severe problems that are often ignored because of high current speed and strong self‐purification. In this paper, Liangxi River, Taihu Basin, was selected as the research area. Combined with field investigation, a 2D water environment mathematical model was developed to simulate the chl‐a distribution in Liangxi River. An indicator (Transverse Distribution Center, TDC) and its normalized form (NTDC) to quantitatively represent material transverse distribution in rivers is proposed and coupled in the model. The calculation showed that TDC and NTDC had the property of random fluctuation, seasonal consistency, and water transfer dependence. The multiple regression equation with normalized data indicated that the maximum offset, average variation rate, and average reverting rate of Liangxi River chl‐a NTDC were most affected by the chl‐a dry matter flux ratio between tributaries and the mainstream, followed by flow and chl‐a concentration ratio. From the perspective of river morphology, for different river width change modes and river bending directions, when water flows into and out of these river sections, the chl‐a transverse distribution is subject to different specific effects. In addition, the position of nutrients and dissolved oxygen significantly affected the position of chl‐a growth when the N/P ratio was not very high. Conversely, a high N/P ratio may contribute to decrease of the chl‐a concentration. Proliferation with abundant nutrients may cause the settlement of chl‐a resulting in a decrease of chl‐a in the water column.

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“…Here, we contribute to the given‐data paradigm by developing a highly efficient and robust data‐driven GSA, based on the theory of variogram analysis of response surfaces (VARS) (Razavi & Gupta, 2016a, 2016b). Despite the increasing popularity of VARS (e.g., Akomeah et al, 2019; Becker, 2020; Korgaonkar et al, 2020; Krogh et al, 2017; Leroux & Pomeroy, 2019; Lilhare et al, 2020; Schürz et al, 2019; Sheikholeslami et al, 2017; Yassin et al, 2017), its current version cannot be applied in the given‐data setting. Conversely, our new version of VARS works on any given data, by approximating the anisotropic variogram structure of the underlying (but unknown) response surface, when only a (small) sample of the input‐output space is available.…”

Section: Introductionmentioning

confidence: 99%

A Fresh Look at Variography: Measuring Dependence and Possible Sensitivities Across Geophysical Systems From Any Given Data

Sheikholeslami

Razavi

2020

Geophysical Research Letters

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Sensitivity analysis in Earth and environmental systems modeling typically demands an onerous computational cost. This issue coexists with the reliance of these algorithms on ad hoc designs of experiments, which hampers making the most out of the existing data sets. We tackle this problem by introducing a method for sensitivity analysis, based on the theory of variogram analysis of response surfaces (VARS), that works on any sample of input-output data or pre-computed model evaluations. Called data-driven VARS (D-VARS), this method characterizes the relationship strength between inputs and outputs by investigating their covariograms. We also propose a method to assess "robustness" of the results against sampling variability and numerical methods' imperfectness. Using two hydrologic modeling case studies, we show that D-VARS is highly efficient and statistically robust, even when the sample size is small. Therefore, D-VARS can provide unique opportunities to investigate geophysical systems whose models are computationally expensive or available data is scarce. Plain Language Summary Sensitivity analysis (SA) is about assessing how the properties of a system are influenced by different factors. It can also help us better understand the behavior of a mathematical model and the underlying real-world system that it mimics. Almost always, classic SA estimates the sensitivities by sampling the entire problem space in a specific manner. They are incapable of using a pre-existing set of input-output data or pre-computed model evaluations. Hence, classic SA becomes useless in cases where a sample of input-output data, obtained from physical experiments or computationally expensive simulations, is already available. We propose a purely data-driven method that can effectively be used in such situations. Based on the principles of variography, our method measures dependence and possible sensitivities across a system from any given data. Here, we illustrate our method in the context of hydrologic modeling, but it can potentially be applied to study other geophysical systems models.

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Comparison of aquatic ecosystem functioning between eutrophic and hypereutrophic cold-region river-lake systems

Cited by 7 publications

References 42 publications

Grazing preferences of three species of amoebae on cyanobacteria and green algae

Grazing preferences of three species of amoebae on cyanobacteria and green algae

Transverse distribution of cyanobacteria in a regulated urban river

A Fresh Look at Variography: Measuring Dependence and Possible Sensitivities Across Geophysical Systems From Any Given Data

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