Modeling the dynamics of three functional groups of macroalgae in tropical seagrass habitats

Biber, Patrick D.; Harwell, Mark A.; Cropper, Wendell P.

doi:10.1016/j.ecolmodel.2003.10.003

Cited by 42 publications

(36 citation statements)

References 114 publications

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“…A combined growth model for drift-algae, ephiphytes and ryzome-algae by Biber et al (2004) made it clear that currently data on recruitment and mortality rates in seagrasses are lacking, especially for tropical waters. However, through model calibration and parameterisation they were able to approximate them.…”

Section: Modelling Seagrass Productivitymentioning

confidence: 99%

Satellite-based monitoring of tropical seagrass vegetation: current techniques and future developments

Ferwerda¹,

Leeuw

Atzberger³

et al. 2007

Hydrobiologia

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Decline of seagrasses has been documented in many parts of the world. Reduction in water clarity, through increased turbidity and increased nutrient concentrations, is considered to be the primary cause of seagrass loss. Recent studies have indicated the need for new methods that will enable early detection of decline in seagrass extent and productivity, over large areas. In this review of current literature on coastal remote sensing, we examine the ability of remote sensing to serve as an information provider for seagrass monitoring. Remote sensing offers the potential to map the extent of seagrass cover and monitor changes in these with high accuracy for shallow waters. The accuracy of mapping seagrasses in deeper waters is unclear. Recent advances in sensor technology and radiometric transfer modelling have resulted in the ability to map suspended sediment, sea surface temperature and below-surface irradiance. It is therefore potentially possible to monitor the factors that cause the decline in seagrass status. When the latest products in remote sensing are linked to seagrass production models, it may serve as an early-warning system for seagrass decline and ultimately allow a better management of these susceptible ecosystems.

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Section: Modelling Seagrass Productivitymentioning

confidence: 99%

Satellite-based monitoring of tropical seagrass vegetation: current techniques and future developments

Ferwerda¹,

Leeuw

Atzberger³

et al. 2007

Hydrobiologia

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“…Hypnea also has a competitive advantage over Gracilaria in relation to environmental light availability -due to its lower half-saturation photosynthetic coeffi cient (Ki) (Biber et al 2004). This at least partially explains the greater abundance of Hypnea in the reef pool as compared to other more exposed regions of the intertidal zone.…”

Section: Discussionmentioning

confidence: 99%

“…Models of tropical benthic communities still require greater refi nement, however, especially in terms of parameters such as recruitment and interspecifi c competition (Biber et al 2004) between algae and invertebrates (Vinueza et al 2006, Guimaraens et al 2011a. The model described here considers physiological processes that mimic competitive interactions (Fong et al 1994, 2005 and their effects on temporal variations of Gracilaria and Hypnea assemblages through the use of different nutrient absorption curves (Fong et al 1994, Biber et al 2004) and different photosynthesis rates for these genera. The present study therefore sought to evaluate the seasonal variations of both environmental factors and Gracilaria and Hypnea assemblages on the reef platforms of Piedade Beach (Pernambuco State) and use ecological modeling to test the hypothesis that model parameters (representing abiotic factors) affect the temporal abundances of these algae to different degrees.…”

Section: Introductionmentioning

confidence: 99%

“…Mansilla & Pereira (2001) observed signifi cant reductions in red algae biomasses and lower algal diversity during the rainy season on a reef off the southern coast of Pernambuco state that were found to be related to rainfall, temperature, and salinity. Abiotic factors (such as temperature, nutrient concentrations and salinity) together with biological interactions have been shown to control the spatial-temporal distributions of benthic algae (Sauer-Machado et al 1992, which in turn affects macroalgal abundance and hydrodynamic transport (Biber et al 2004). The Piedade reefs have been impacted by human use, organic pollution, and industrial wastes (Pereira et al 2002), so that studies on algal populations in this environment could aid in establishing policies designed to preserve and manage reef ecosystems.…”

Section: Introductionmentioning

confidence: 99%

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Modeling temporal variations of Gracilaria Greville and Hypnea J.V. Lamouroux (Rhodophyta) assemblages on a midlittoral reef platform at Piedade Beach, Pernambuco State, Brazil

Guimaraens¹,

Macêdo²,

Oliveira-Carvalho³

et al. 2012

Braz. J. Bot.

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-(Modeling temporal variations of Gracilaria Greville and Hypnea J.V. Lamouroux (Rhodophyta) assemblages on a midlittoral reef platform at Piedade Beach, Pernambuco State, Brazil). The diversity of algal banks composed of species out the genera Gracilaria Greville and Hypnea J.V. Lamouroux have been impacted by commercial exploitation and coastal eutrophication. The present study sought to construct dynamic models based on algal physiology to simulate seasonal variations in the biomasses of Gracilaria and Hypnea an intertidal reef at Piedade Beach in Jaboatão dos Guararapes, Pernambuco State, Brazil. Five 20 × 20 cm plots in a reef pool on a midlittoral reef platform were randomly sampled during April, June, August, October, and December/2009 and in January and March/2010. Water temperature, pH, irradiance, oxygen and salinity levels as well as the concentrations of ammonia, nitrate and phosphate were measured at the sampling site. Forcing functions were employed in the model to represent abiotic factors, and algal decay was simulated with a dispersal function. Algal growth was modeled using a logistic function and was found to be sensitive to temperature and salinity. Maximum absorption rates of ammonia and phosphate were higher in Hypnea than in Gracilaria, indicating that the former takes up nutrients more effi ciently at higher concentrations. Gracilaria biomass peaked at approximately 120 g (dry weight m -2 ) in March/2010 and was signifi cantly lower in August/2009; Hypnea biomasses, on the other hand, did not show any signifi cant variations among the different months, indicating that resource competition may infl uence the productivity of these algae.

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“…Através dos experimentos de campo e modelagem, a importância relativa dos efeitos bottom-up (recrutamento e eutroficação) e top-down (predação) sobre as populações de invertebrados e algas tem sido avaliada (EHRHARDT et al, 2001;TANAKA & MAGALHÃES, 2002;BIBER et al, 2004;FREIDENBURG et al, 2007). O efeito bottom-up dos nutrientes sobre o crescimento algal e a conseqüente competição com os invertebrados será avaliada de forma implícita nos modelos aqui propostos.…”

Section: Introductionunclassified

Simulações da dinâmica, regulação populacional e recrutamento de cirrípedes e bivalves marinhos bentônicos

Guimaraens¹,

Costa²,

Costa³

et al. 2007

TropOcean

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RESUMOOs modelos de simulação do recrutamento usados na análise de sensibilidade foram baseados no modelo logístico, ao qual adicionaram-se funções de mortalidade e extração. A função de crescimento incluiu valores diários de recrutamento como input para o modelo e a taxa de mortalidade diária. A série de dados que representa os valores diários de recrutamento, a taxa de mortalidade, a capacidade de suporte (K) e a condição inicial foram modificadas para calibrar o modelo e avaliar a capacidade de recuperação da população. Os parâmetros também foram mudados um de cada vez + e -10% para avaliar a estabilidade dos diferentes cenários usando o modelo que assume o recrutamento diário e mortalidade, além das simulações com a densidade de diferentes classes do ciclo de vida, como variáveis de estado. Os resultados foram comparados com a simulação-padrão usando a seguinte equação: 100/(n*Smed) *SOMA([S1-S2]), onde n é o número de dias da simulação; Smed é a média dos valores da simulação-padrão; S1 são os valores da simulação-padrão e S2 os valores de cada experimento de sensibilidade. Simulações básicas experimentais resultaram de populações de cracas que oscilaram entre 900 e 400 indivíduos em 100 cm 2 . Simulações posteriores resultaram em valores abaixo de 500 indivíduos e decréscimo populacional seguido de recuperação. Experimentos preliminares com mudanças de + e -10% nos valores dos parâmetros mostraram variações de menos de 10 %, indicando uma estabilidade do sistema. No entanto, a diminuição em 10% do valor da taxa de mortalidade pode causar a desestabilização do sistema, evidenciada por uma variabilidade maior que 90% entre os valores das simulações-padrão e os experimentos de sensibilidade.

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Modeling the dynamics of three functional groups of macroalgae in tropical seagrass habitats

Cited by 42 publications

References 114 publications

Satellite-based monitoring of tropical seagrass vegetation: current techniques and future developments

Satellite-based monitoring of tropical seagrass vegetation: current techniques and future developments

Modeling temporal variations of Gracilaria Greville and Hypnea J.V. Lamouroux (Rhodophyta) assemblages on a midlittoral reef platform at Piedade Beach, Pernambuco State, Brazil

Simulações da dinâmica, regulação populacional e recrutamento de cirrípedes e bivalves marinhos bentônicos

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