caa a22 4500 605509107 CHVBK 20210128100639.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s10452-015-9504-9 doi (NATIONALLICENCE)springer-10.1007/s10452-015-9504-9 Medium- and long-term temporal trends in the fish assemblage inhabiting a surf zone, analyzed by Bayesian generalized additive models [Elektronische Daten] [Ana Martins, Paul Kinas, Juliano Marangoni, Leonardo Moraes, João Vieira] The present study characterizes the fish assemblage in the surf zone of Cassino Beach, Rio Grande, Brazil, and analyzes temporal fluctuations in richness and abundance of these species in medium (months) and long terms (years), associating them also with abiotic covariates. Data were collected monthly between 1996 and 2012 at two locations. Bayesian generalized additive models (GAMs) were used as statistical tool, placing this study among few that have used Bayesian GAMs in Ecology. Our results show a decrease in both species richness and abundance of the most representative species, over the last 16years, but no significant distinction between locations. Water temperature and salinity along with seasonality were the statistically most influential explanatory covariates to describe fluctuations in richness and abundance. Higher discharge rates of the three main rivers that flow into Patos Lagoon (Jacuí, Taquari, Camaquã) were associated with increased richness and abundance of some species in the assemblage. Hence, our findings show that medium- and long-term fluctuations in richness and species abundance are controlled by abiotic factors related to seasonal cycles (temperature) and productivity of the ecosystem. Long-term changes seem to be also related to man-induced factors and climate change; but further research is needed. Springer Science+Business Media Dordrecht, 2015 Species richness nationallicence Abundance nationallicence Covariates nationallicence Bayesian GAM nationallicence Martins Ana Grupo de Pesquisa em Estatística Ambiental, Universidade Federal do Rio Grande (FURG), Av. Itália Km. 8, 96201-900, Rio Grande, Brazil aut Kinas Paul Grupo de Pesquisa em Estatística Ambiental, Universidade Federal do Rio Grande (FURG), Av. Itália Km. 8, 96201-900, Rio Grande, Brazil aut Marangoni Juliano Grupo de Pesquisa em Estatística Ambiental, Universidade Federal do Rio Grande (FURG), Av. Itália Km. 8, 96201-900, Rio Grande, Brazil aut Moraes Leonardo Laboratório de Ictiologia, IO, FURG, Av. Itália Km. 8, 96201-900, Rio Grande, RS, Brazil aut Vieira João Instituto de Oceanografia (IO), FURG, Av. Itália Km. 8, 96201-900, Rio Grande, RS, Brazil aut Aquatic Ecology Springer Netherlands 49/1(2015-03-01), 57-69 1386-2588 49:1<57 2015 49 10452 https://doi.org/10.1007/s10452-015-9504-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10452-015-9504-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Martins Ana Grupo de Pesquisa em Estatística Ambiental, Universidade Federal do Rio Grande (FURG), Av. Itália Km. 8, 96201-900, Rio Grande, Brazil aut NATIONALLICENCE 700 1- Kinas Paul Grupo de Pesquisa em Estatística Ambiental, Universidade Federal do Rio Grande (FURG), Av. Itália Km. 8, 96201-900, Rio Grande, Brazil aut NATIONALLICENCE 700 1- Marangoni Juliano Grupo de Pesquisa em Estatística Ambiental, Universidade Federal do Rio Grande (FURG), Av. Itália Km. 8, 96201-900, Rio Grande, Brazil aut NATIONALLICENCE 700 1- Moraes Leonardo Laboratório de Ictiologia, IO, FURG, Av. Itália Km. 8, 96201-900, Rio Grande, RS, Brazil aut NATIONALLICENCE 700 1- Vieira João Instituto de Oceanografia (IO), FURG, Av. Itália Km. 8, 96201-900, Rio Grande, RS, Brazil aut NATIONALLICENCE 773 0- Aquatic Ecology Springer Netherlands 49/1(2015-03-01), 57-69 1386-2588 49:1<57 2015 49 10452