A model for the estimation of annual production rates of macrophyte algae

Ferreira, J.G.; Ramos, Leonardo Rocha Vidal

doi:10.1016/0304-3770(89)90020-x

Cited by 30 publications

(22 citation statements)

References 25 publications

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“…Similarly, many studies have found that Ulva sp. biomass peaked in spring and summer (Ferreira and Ramos, 1989;Solidoro et al, 1997;Plus et al, 2005). Zhuang and Chen (2003) found similar summer peaks in Moon Bay, near Fisherman's Wharf, on the Yantai coast.…”

Section: Discussionmentioning

confidence: 88%

Temporal and spatial variations in the distribution of macroalgal communities along the Yantai coast, China

Han

Liu

2014

Chin. J. Ocean. Limnol.

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To explore the impact of environmental variables on macroalgal communities, their temporal and spatial distributions were examined along the Yantai coast, China between April 2010 and March 2011. Macroalgae sampling was conducted monthly at four sites along the coast: Jiahe River estuary, Zhifu Island, Fisherman's Wharf, and Yangma Island. The species composition and abundance, and their relationship with environmental variables were assessed. Along the Yantai coast, 35 macroalgae species were identifi ed, including 24 Rhodophyta spp., 7 Chlorophyta, and 4 Phaeophyta spp. Highest species numbers were recorded in the summer at all sampling sites, except in the Jiahe River estuary. Macroalgae biomass was the greatest in the summer. Year-round, the highest species number and dry biomass recorded at Fisherman's Wharf and Yangma Island was attributed to the substrate type. In summer, Ulva pertusa Kjellman was the dominant species identifi ed along the Yantai coast, which indicates a risk of macroalgae blooms. Our results show that seawater temperature and nutrients appear to signifi cantly affect the temporal and spatial patterns of macroalgal abundance along the Yantai coast. The effects of environmental variables on the macroalgae on the Yantai coast need further study.

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Section: Discussionmentioning

confidence: 88%

Temporal and spatial variations in the distribution of macroalgal communities along the Yantai coast, China

Han

Liu

2014

Chin. J. Ocean. Limnol.

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“…5. The model provides a reasonable representation of biomass (Brinkhuis, 1977;Ferreira & Ramos, 1989). It can be seen from Fig.…”

Section: Model Simulationsmentioning

confidence: 93%

“…(i) Sub-model for tidal height simulation; Calculates the depth of the simulated algal population at every time step as described by Ferreira & Ramos (1989).…”

Section: Model Structurementioning

confidence: 99%

A methodology for parameter estimation in seaweed productivity modelling

Duarte

Ferreira

1993

Hydrobiologia

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This paper presents a combined approach for parameter estimation in models of primary production. The focus is on gross primary production and nutrient assimilation by seaweeds.A database of productivity determinations, biomass and mortality measurements and nutrient uptake rates obtained over one year for Gelidium sesquipedale in the Atlantic Ocean off Portugal has been used. Annual productivity was estimated by harvesting methods, and empirical relationships using mortality/ wave energy and respiration rates have been derived to correct for losses and to convert the estimates to gross production.In situ determinations of productivity have been combined with data on the light climate (radiation periods, intensity, mean turbidity) to give daily and annual productivity estimates. The theoretical nutrient uptake calculated using a 'Redfield ratio' approach and determinations of in situ N and P consumption by the algae during incubation periods have also been compared.The results of the biomass difference and incubation approaches are discussed in order to assess the utility of coefficients determined in situ for parameter estimation in seaweed production models.

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“…The water is very turbid, with annual values of suspended matter ranging from 45 to 120 mg l −1 . Salinity ranges from 0 to 32 and is strongly influenced by the semi-diurnal and fortnightly tidal cycle (Ferreira and Ramos, 1989). Table 2 presents the main characteristics of these marine ecosystems.…”

Section: Study Areasmentioning

confidence: 99%

Phytoplankton production modelling in three marine ecosystems—static versus dynamic approach

Macedo

Duarte

2006

Ecological Modelling

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Phytoplankton productivity is usually determined from water samples incubated at a number of irradiance levels during several hours. The resultant productivity-irradiance (P-E) curves are then used to estimate local and/or global phytoplankton production. However, there is growing evidence that these curves, referred as static, underestimate phytoplankton photosynthesis to a great deal, by assuming a stable response to light over the incubation period. One of the drawbacks of static P-E curves is the overestimation of photoinhibition.In this work, three one-dimensional vertically resolved models were developed as simply as possible, to investigate differences between static and dynamic phytoplankton productivity in three marine ecosystems: a turbid estuary, a coastal area and an open ocean ecosystem. The results show that, when photoinhibition development time is considered (dynamic model), the primary production estimates are always higher than when calculated with the static model. The quantitative importance of these differences varies with the type of ecosystem and it appears to be more important in coastal areas and estuaries (from 21 to 72%) than in oceanic waters (10%). Thus, these results suggest that primary production estimates, obtained under the assumption of a static behaviour response to light, may underestimate the real values of global phytoplankton primary production. Calculations suggest that the quantitative importance of this underestimation may be larger than the global missing carbon sink.

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A model for the estimation of annual production rates of macrophyte algae

Cited by 30 publications

References 25 publications

Temporal and spatial variations in the distribution of macroalgal communities along the Yantai coast, China

Temporal and spatial variations in the distribution of macroalgal communities along the Yantai coast, China

A methodology for parameter estimation in seaweed productivity modelling

Phytoplankton production modelling in three marine ecosystems—static versus dynamic approach

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