The impact of impoundment on the rotifer communities in two tropical floodplain environments: interannual pulse variations

Bonecker, Cláudia Cósta; Aoyagui, Anderson Setsuo Miyashiro; Santos, Rubens Manoel dos

doi:10.1590/s1519-69842009000300008

Cited by 42 publications

(32 citation statements)

References 20 publications

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“…: Brachionus falcatus falcatus Zacharias, 1898Ahlstrom 1938;Hauer 1953;Krau 1962; Gillard 1967 (RB);Schaden 1976;Hardy 1980;Reid and Turner 1988;Paranaguá and Neumann-Leitão 1982;Koste and Robertson 1983; NeumannLeitão and de Souza 1987;Matsumura-Tundisi et al 1990;Lansac-Tôha et al 1993;Segers et al 1993b;Bozelli 1992;Dabés 1995;Campos et al 1996;Espíndola et al 1996;Nunes et al 1996;de Souza et al 1998;Garcia et al 1998;Oliveira- Landa et al 2002;Keppeler 2003;Neves et al 2003;Bonecker et al 2005; Branco et al 2005 (RT) ;Cabianca 2005;Almeida et al 2006;de Melo et al 2006;Leitão et al 2006;Vitorio 2006;Batista 2007;Branco et al 2007;Eskinazi-Sant´Anna et al 2007;Nascimento 2008;Palazzo et al 2008;Bonecker et al 2009;Ferraz et al 2009; Hardy 1980;Dabés 1995;Reid and Turner 1988;Sendacz and Costa 1991;Bozelli 1992;Espíndola et al 1996;…”

Section: Genus Brachionus Pallas 1766mentioning

confidence: 99%

“…Dicranophorus epicharis Myers, 1928 Koste 1972a,b;2000;Koste and Robertson 1983;Neves et al 2003;. Dicranophorus forcipatus (Müller, 17862000;Dabés 1995 (Zacharias, 1981) Ahlstrom 1938Hauer 1953;Krau 1967;Hardy 1980;Koste and Robertson 1983;Sendacz and Costa 1991;Bozelli 1992;Campos et al 1996;Espíndola et al 1996;de Souza et al 1998;Garcia et al 1998;OliveiraNeto and Moreno 1998;Landa et al 2002;Neves et al 2003;Keppeler 2003;Ulloa 2004;Bonecker et al 2005;Cabianca 2005;Sendacz et al 2006;Vitorio 2006;Abra 2008;Batista 2007;Eskinazi-Sant´Anna et al 2007;Neves and SerafimJúnior 2007;Nascimento 2008;Bonecker et al 2009;Santos 2010;. Filinia passa (Müller, 1786 Turner and da Silva 1992; de Paggi and Koste 1995.…”

Section: Species Literature Brachionus Quadridentatus Minormentioning

confidence: 99%

“…1998 (RB); Garcia et al 1998; Crispim and Watanabe 2000 (ZE); Landa and Mourgés-Schurter 2000a,b; Moretto 2001;Neto 2001;Landa et al 2002;Neves et al 2003;Bonecker et al 2005;Cabianca 2005; Crispim and Freitas 2005 (ZE); Leitão et al 2006 (ZE) ; Vitorio 2006 (RE); Eskinazi-Sant´Anna et al 2007 (ZE); Nascimento 2008; Almeida et al 2009 (ZE); Ferraz et al 2009; Sendacz and Costa 1991;Nunes et al 1996; NeumannLeitão 1990;Vitorio 2006;. Brachionus budapestinensis Daday, 1885Ahlstrom 1938Hauer 1953;Krau 1967; Schaeden, 1976;Reid and Turner 1988;Campos et al 1996;Nunes et al 1996;Garcia et al 1998;Landa et al 2002;Bonecker et al 2005;Cabianca 2005;Leitão et al 2006;Palazzo et al 2008; Rousselet, 1901Ahlstrom 1938Hauer 1953;Krau 1962; Oliveira et al, 1967 (RE);Hardy 1980; NeumannLeitão 1990;Matsumura-Tundisi et al 1990; LansacTôha et al 1993 (ZE);Campos et al 1996;Nunes et al 1996;de Souza et al 1998;Garcia et al 1998; Landa and Mourgés-Schurter 2000a,b; Moretto 2001;Neto 2001;Branco et al 2002; Leitão et al 2006; EskinaziSant´Anna et al 2007;Batista 2007; Cavalcanti et al 2008 (ZE); Bonecker...…”

Section: Genus Brachionus Pallas 1766mentioning

confidence: 99%

“…Brachionus dolabratus Harring, 1915 Syn. : Brachionus dolabratus dolabratus (Harring, 1915) Ahlstrom 1938Hauer 1953;Krau 1967;Hardy 1980;Koste and Robertson 1983;Sendacz and Costa 1991;Bozelli 1992;Segers et al 1993b (RT); Dabés 1995;Campos et al 1996;Espíndola et al 1996;Nunes et al 1996;Garcia et al 1998;Espíndola et al 2000; Landa and Mourgés-Schurter 2000a,b; Sampaio and Lopez 2000;Keppeler 2003;Neves et al 2003;Bonecker et al 2005;Cabianca 2005; de Melo et al 2006 (ZE); Abra 2007;Batista 2007;Branco et al 2007;Eskinazi-Sant´Anna et al 2007;Bonecker et al 2009;Ferraz et al 2009;Santos 2010;. Brachionus donneri Brehm, 1951 Turner and da Silva 1992.…”

Section: Genus Brachionus Pallas 1766mentioning

confidence: 99%

“…: Keratella lenzi caudata Syn. : Keratella lenzi lenzi Hauer, 1953Hauer 1953Krau 1967;Schaden 1978;Koste and Robertson 1983;Reid and Turner 1988;Matsumura-Tundisi et al 1990;Sendacz and Costa 1991;Bozelli 1992;Dabés 1995;Campos et al 1996;Espíndola et al 1996;Nunes et al 1996;Garcia et al 1998;Branco et al 2000;Esteves et al 2000; Landa and Mourgés-Schurter 2000a,b;Espíndola et al 2000; Sampaio and Lopez 2000; Moretto 2001;Landa et al 2002;Neves et al 2003;Ulloa 2004;Bonecker et al 2005;Cabianca 2005;Leitão et al 2006;Serafim-Júnior et al 2006; Abra 2008;Batista 2007; EskinaziSant´Anna et al 2007;Nascimento 2008;Palazzo et al 2008;Bonecker et al 2009;Ferraz et al 2009;. Keratella paludosa (Lucks, 19122000.…”

Section: Genus Brachionus Pallas 1766mentioning

confidence: 99%

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Synthesis of Brazilian Rotifera: an updated list of species

Garraffoni¹,

Lourenço²

2012

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A checklist of species of Rotifera recorded for Brazilian inland waters is provided. A total of 625 species, with representatives from 84 genera are listed. The list comprises reports of rotifers documented in 132 studies, including regional ecological (58%) and taxonomic/biodiversity (42%) studies carried out until early of the year 2012. More than 75% of the species were recorded from the states of Pará, São Paulo, Mato Grosso do Sul, Paraná, Rio de Janeiro, Pernambuco, and Minas Gerais. We hope that this survey will serve as a baseline for future studies.

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Section: Genus Brachionus Pallas 1766mentioning

confidence: 99%

Section: Species Literature Brachionus Quadridentatus Minormentioning

confidence: 99%

Section: Genus Brachionus Pallas 1766mentioning

confidence: 99%

Section: Genus Brachionus Pallas 1766mentioning

confidence: 99%

Section: Genus Brachionus Pallas 1766mentioning

confidence: 99%

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Synthesis of Brazilian Rotifera: an updated list of species

Garraffoni¹,

Lourenço²

2012

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Changes and drivers of zooplankton diversity patterns in the middle reach of Yangtze River floodplain lakes, China

Liu

Qiu

et al. 2021

Ecology and Evolution

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Anthropogenic habitat alteration interferes the natural aquatic habitats and the system's hydrodynamics in the Yangtze River floodplain lakes, resulting in a serious decline in freshwater biodiversity. Zooplankton communities possess major position in freshwater ecosystems, which play essential parts in maintaining biological balance of freshwater habitats. Knowledge of processes and mechanisms for affecting variations in abundance, biomass, and diversity of zooplankton is important for maintaining biological balance of freshwater ecosystems. Here, we analyzed that the temporal and spatial changes in the structure of zooplankton community and their temporal and spatial variations respond to changes in environmental factors in the middle reach of Yangtze River floodplain lakes. The results showed that zooplankton samples were classified into 128 species, and Rotifera was the most common taxa. Significant seasonal differences were found among the abundance and diversity of zooplankton. Similarly, we also found significant seasonal differences among the biomass of zooplankton functional groups. The spatial turnover component was the main contributor to the β diversity pattern, which indicated that study areas should establish habitat restoration areas to restore regional biodiversity. The NMDS plot showed that the structure of zooplankton community exhibited significant seasonal changes, where the community structure was correlated with pH, water temperature, water depth, salinity, total nitrogen, chlorophyll‐a, and total phosphorus based on RDA. This study highlights that it is very important to ensure the floodplain ecosystem's original state of functionality for maintaining the regional diversity of the ecosystem as a whole.

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Zooplankton Density Prediction in a Flood Lake (Pantanal – Brazil) Using Artificial Neural Networks

Fantin-Cruz

Pedrollo

Bonecker

et al. 2010

International Review of Hydrobiology

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Ecologic relationships are usually non-linear and highly complex. For this reason, artificial neural networks (ANN) were selected to model zooplankton density groups in the Coqueiro lake in the northern Pantanal of Brazil. The input layer used 11 limnological variables with 13 neurons in the hidden layer; the output layer consisted of three zooplankton groups. Samples were collected monthly between April 2002 and May 2003, at three different points of the lake, two of which were used for training the ANNs and the other for validation. The ANN model performed well at predicting the density of zooplankton groups (coefficients of determination r 2 were 0.88, 0.50 and 0.82 for rotifers, cladocerans and copepods, respectively). The comparison between models, and the ANN techniques used, demonstrated that zooplankton densities, observed one month previously, did not influence current densities, which were determined by limnological conditions in the lake. It was also shown that the processes that relate zooplankton to their environment remained stable during the study, while a model sensitivity analysis showed that the density dynamics of zooplankton groups in the Coqueiro lake were strongly influenced by availability of food (phytoplankton and detritus) and by variations in water-level. It can be concluded from the study that ANNs are a powerful tool both for predicting zooplankton densities and for understanding their relationships with the environment. IntroductionLakes subject to regular flooding exhibit high functional complexity because of large seasonal changes that result from droughts and floods. This variability causes changes in habitat spatial structure, requiring the biota to adapt to changes in the physical and chemical environments (JUNK et al., 1989).Because of the spatial and temporal complexity of ecological processes occurring in such lakes, ecosystem analysis and prediction by means of linear empirical models is of limited value. Such models commonly reduce the data and simplify relationships between variables in a way that frequently leads to the loss of valuable information and to distortions of ecological truth, by failing to take into account the true complexity of non-linear relationships. From the biological viewpoint, patterns of species existence and abundance of species usually show non-linear complexities in their relations with the habitat spatial heterogeneity and interactions with other species.For these reasons, artificial neural networks (ANNs) can be an attractive alternative as a tool for analyzing and modeling ecological data, since they can take account of specific factors such as non-linearity, adaptation and generalization (SCHLEITER et al., 1999). Most applications of ANNs in ecology have been concerned with formulating predictive models, and with pattern recognition.In aquatic ecology, ANNs have been used extensively for predicting algal growth (RECK- NAGEL et al., 1997;OLDEN, 2000;JEONG et al., 2001) and for studies of the relations between environmental variables and...

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The impact of impoundment on the rotifer communities in two tropical floodplain environments: interannual pulse variations

Cited by 42 publications

References 20 publications

Synthesis of Brazilian Rotifera: an updated list of species

Synthesis of Brazilian Rotifera: an updated list of species

Changes and drivers of zooplankton diversity patterns in the middle reach of Yangtze River floodplain lakes, China

Zooplankton Density Prediction in a Flood Lake (Pantanal – Brazil) Using Artificial Neural Networks

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