Dynamics of a Seasonally Forced Phytoplankton-Zooplankton Model with Impulsive Biological Control

Zhao, Jiang‐Lin; Yan, Ye

doi:10.1155/2016/2560195

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…The predatorprey type of relationship between the algae and the zooplankton has been investigated via various mathematical models [7,23,26]. We employ the Truscott-Brindley model [23], also used by Freund et al [7] and Zhao and Yan [26], which assumes a logistic growth for the algae and Hollinggs Type III grazing function for the predation of algae by the zooplankton, and an exponential death for the zooplankton, as is common in predator-prey equations. The parameter γ describes the ratio of algae biomass consumed per increase in the biomass of zooplankton.…”

Section: Modelmentioning

confidence: 99%

Mathematical modeling of algal blooms due to swine CAFOs in Eastern North Carolina

Henderson¹,

Kose²,

Lewis³

et al. 2022

DCDS-S

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<p style='text-indent:20px;'>Dramatic strides have been made in treating human waste to remove pathogens and excess nutrients before discharge into the environment, to the benefit of ground and surface water quality. Yet these advances have been undermined by the dramatic growth of Confined Animal Feeding Operations (CAFOs) which produce voluminous quantities of untreated waste. Industrial swine routinely produce waste streams similar to that of a municipality, yet these wastes are held in open-pit "lagoons" which are at risk of rupture or overflow. Eastern North Carolina is a coastal plain with productive estuaries which are imperiled by more than 2000 permitted swine facilities housing over 9 million hogs; the associated 3,500 permitted manure lagoons pose a risk to sensitive estuarine ecosystems, as breaches or overflows send large plumes of nutrient and pathogen-rich waste into surface waters. Understanding the relationship between nutrient pulses and surface water quality in coastal environments is essential to effective CAFO policy formation. In this work, we develop a system of ODEs to model algae growth in a coastal estuary due to a manure lagoon breach and investigate nutrient thresholds above which algal blooms are unresolvable.</p>

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Section: Modelmentioning

confidence: 99%

Mathematical modeling of algal blooms due to swine CAFOs in Eastern North Carolina

Henderson¹,

Kose²,

Lewis³

et al. 2022

DCDS-S

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“…In effect, this brought about changes in the behaviour of surface water. The distribution of phytoplankton is correlated to the variability of the physicochemical influences [6]. Migration of phytoplankton is environment-driven characterized by the dynamics of the sea water.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Patterns of Marine Phytoplankton in Bahrain Sea

Lumeran¹

2019

International Advanced Research Journal in Science, Engineering

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Marine phytoplankton has a wide range of distribution forming patterns. This study generally aims to determine the multi-directional distribution and dynamic patterns of phytoplankton 15 km off coast from the docking area of Bahrain Yacht Club in Sitra, Kingdom of Bahrain. Specifically to identify individual species' population in the sampling stations; identify multi-directional distribution and pattern of marine plankton; find out significant differences in the station-to-station distribution of marine plankton; and relate the effect physicochemical parameter on the population of phytoplankton taxa. Results revealed 21 species of phytoplankton consisting of two phyla, Chrysophyta or diatoms with 13 species and Dinoflagellata or dinoflagellates with 8 species. Plankton population based on microscopy count varies from station-to-station. Thalasionema sp. of the diatom taxon dominates Station 1 (northeast), Station 3 (west) and Station 4 (northwest); Pseudo-Nitzschia australis in Station 2 (southwest) and Asteromphalus sp. in Station 5 (north) and C. furca of the dinoflagellates in Station1 (northeast) and 2 (southwest). Statistical analysis using t-test at p˂.05 depicted significant differences between Station 1 and 5; and Station 1 and 3 among the diatoms taxa while Station 1, 3 and 4 for dinoflagellates. Recorded physicochemical readings are within the acceptable level, therefore exerts insignificant effect on the population of phytoplankton where r =-0.38867. Multi-directional and dynamic patterns are illustrated using radar graph based on species population. The greater number of diatom species indicates good water quality and the physicochemical values which are within the standard level.

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“…To well understand their dynamical behaviors, more realistic models are constructed [3][4][5]. Leslie and Gower [6][7] initiated a predator-prey model where the carrying capacity of predator is proportional to the number of prey.…”

Section: Introductionmentioning

confidence: 99%