Modelling the impact of zooplankton grazing on phytoplankton biomass during a dinoflagellate bloom in the Swan River Estuary, Western Australia

Griffin, Sandra L.; Herzfeld, Michael; Hamilton, David P.

doi:10.1016/s0925-8574(00)00122-1

Cited by 52 publications

(18 citation statements)

References 55 publications

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“…result in lower estimates of r d and r m ). However, fast-growing smaller copepods might respond, and other modelling efforts have indicated that metazoan zooplankton grazing can control dinoflagellate blooms (Griffin et al 2001, Gismervik et al 2002, so this may be an area for further exploration. We have also not considered viruses, which may influence phytoplankton dynamics (Brussaard 2004), specifically that of dinoflagellates (Nagasaki et al 2006).…”

Section: Discussionmentioning

confidence: 99%

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Responsibility of microzooplankton and parasite pressure for the demise of toxic dinoflagellate blooms

Montagnes¹,

Chambouvet²,

Guillou³

et al. 2008

Aquat. Microb. Ecol.

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Two mechanisms proposed to control dinoflagellate blooms -parasitism by eukaryotes (e.g. Amoebophrya sp.) and grazing by microzooplankton -have been explored through previous laboratory and field studies but lack quantitative assessment. We modelled the relative effect of these mechanisms. We used literature values to embed an Anderson-May host-microparasite model into a microbial food web model (nano-and microphytoplankton, nano-and microzooplankton, a toxic dinoflagellate, a dinoflagellate parasite). Three scenarios were examined to simulate the introduction of a toxic dinoflagellate and its parasite into an environment: (1) a food web, including autotrophic nanoand microplankton, heterotrophic flagellates, and ciliates; (2) as for Case 1 but with a toxic dinoflagellate; (3) as for Case 2 but with a dinoparasite. This mimics observations in a French estuary, where a toxic dinoflagellate began blooming in 1988; since 1998, blooms appear to have become regulated, and numerous parasitic infections by Amoebophrya sp. occurred from 2004 to 2006. After supporting parasite control of dinoflagellate blooms, we assessed the effects of observed ranges of key variables associated with the parasite and other components of the food web on parasite control of the dinoflagellate population. Population dynamics were examined over 50 d. In Case 1, all taxa had 10 to 20 d blooms. In Case 2, the toxic dinoflagellate population dynamics mimicked that of the microphytoplankton, and this dinoflagellate was reduced in numbers but not extirpated by microzooplankton grazing. In Case 3 population blooms occurred, and the parasite virtually eliminated the dinoflagellate over ~10 d. Sensitivity analysis indicated that our assessment was robust. We propose that the decline in toxic dinoflagellates in the French estuary may have been due to an introduced dinoparasite. In general, we suggest that parasites may have greater impact on toxic dinoflagellate blooms than microzooplankton grazers; the parasites have the potential to eliminate the toxic dinoflagellates. We recommend that such parasites be incorporated into more complex food web models.

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Section: Discussionmentioning

confidence: 99%

“…H-flagellate: heterotrophic flagellate plex pelagic models extending over longer periods (e.g. that of Griffin et al 2001) should include such eukaryotic parasites.…”

Section: Discussionmentioning

confidence: 99%

Responsibility of microzooplankton and parasite pressure for the demise of toxic dinoflagellate blooms

Montagnes¹,

Chambouvet²,

Guillou³

et al. 2008

Aquat. Microb. Ecol.

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“…Notably, buoyancy regulation by cyanobacteria was not invoked in the model structure. [29] For this work, the model was not further calibrated or modified beyond the work of Romero et al [23] and therefore no performance metrics are presented. The lack of extensive calibration to HVR water quality dynamics means the results of the study can be considered to be a general test of the response sensitivity of ELCOM-CAEDYM to climate drivers and not an investigation of the likely effects of climate change on water quality in Happy Valley Reservoir.…”

Section: Model Descriptionmentioning

confidence: 99%

Suitability of a Coupled Hydrodynamic Water Quality Model to Predict Changes in Water Quality from Altered Meteorological Boundary Conditions

Linden

Daly

Burch

2015

Water

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Downscaled climate scenarios can be used to inform management decisions on investment in infrastructure or alternative water sources within water supply systems. Appropriate models of the system components, such as catchments, rivers, lakes and reservoirs, are required. The climatic sensitivity of the coupled hydrodynamic water quality model ELCOM-CAEDYM was investigated, by incrementally altering boundary conditions, to determine its suitability for evaluating climate change impacts. A series of simulations were run with altered boundary condition inputs for the reservoir. Air and inflowing water temperature (TEMP), wind speed (WIND) and reservoir inflow and outflow volumes (FLOW) were altered to investigate the sensitivity of these key drivers over relevant domains. The simulated water quality variables responded in broadly plausible ways to the altered boundary conditions; sensitivity of the simulated cyanobacteria population to increases in temperature was similar to published values. However the negative response of total chlorophyll-a suggested by the model was not supported by an empirical analysis of climatic sensitivity. This study demonstrated that ELCOM-CAEDYM is sensitive to climate drivers and may be suitable for use in climate impact studies. It is recommended that the influence of structural and parameter derived uncertainty on the results be evaluated. Important factors in determining phytoplankton growth were identified and the importance of inflowing water quality was emphasized. OPEN ACCESSWater 2015, 7 349

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“…Once a definitive map was obtained the different land covers were evaluated using a geographical information system and an areal content of labile and refractory OM was assigned to each land use class considering both the vegetation and the soil. For the vegetation, we used information contained in the Forestry Griffin et al (2000). i Krü ger and Eloff (1978).…”

Section: Reservoir Maturation Modelmentioning

confidence: 99%

Tailoring dam structures to water quality predictions in new reservoir projects: Assisting decision-making using numerical modeling

Marcé

Moreno-Ostos

García-Barcina

et al. 2010

Journal of Environmental Management

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Modelling the impact of zooplankton grazing on phytoplankton biomass during a dinoflagellate bloom in the Swan River Estuary, Western Australia

Cited by 52 publications

References 55 publications

Responsibility of microzooplankton and parasite pressure for the demise of toxic dinoflagellate blooms

Responsibility of microzooplankton and parasite pressure for the demise of toxic dinoflagellate blooms

Suitability of a Coupled Hydrodynamic Water Quality Model to Predict Changes in Water Quality from Altered Meteorological Boundary Conditions

Tailoring dam structures to water quality predictions in new reservoir projects: Assisting decision-making using numerical modeling

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