Assessing surf-zone fish assemblage variability in southern Brazil
Fábio L. Rodrigues A C , Henrique N. Cabral B and João P. Vieira A
+ Author Affiliations
- Author Affiliations
A Laboratório de Ictiologia, Instituto de Oceanografia, Universidade Federal do Rio Grande (FURG), Avenida Itália km 8, 96203-900 Rio Grande, Rio Grande do Sul, Brasil.
B Centro de Oceanografia, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal.
C Corresponding author. Email: oceanolameiro@yahoo.com.br
Marine and Freshwater Research 66(2) 106-119 https://doi.org/10.1071/MF13210
Submitted: 6 August 2013 Accepted: 28 April 2014 Published: 29 October 2014
Abstract
Surf-zone fish assemblage was investigated to assess its patterns of variability for two sandy beaches in southern Brazil. Fish were collected monthly (July 2009–June 2011), and the hauls were conducted with two different beach seine nets. Generalized linear models were used in the following two different approaches: (1) a Poisson model was used to assess and compare fish species richness; and (2) a logistic and gamma models were used to explain the occurrence and variability in abundance of Mugil liza and Trachinotus marginatus (dominant species). A set of nine explanatory variables was used to explain the patterns of ecological variability. At a large scale, seven variables explain 24.8% of the variance in species richness; and season (16.2%) and beach seine (3.0%) were the most important. The presence of M. liza was associated with beach seine (20.4%), whereas the abundance was associated with beach seine (12.1%) and five other explanatory variables. The presence and abundance of T. marginatus was associated with season (18.6% and 22.3%, respectively). Species richness seems to be strongly related to season, although the choice of net used can play an important role. Warmer months are richer in number of species, and the larger net is able to catch more species than the smaller one.
Additional keywords: beaches, beach seines, nursery grounds, shallow water, statistical analysis.
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