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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Defining soft bottom habitats and potential indicator species as tools for monitoring coastal systems: A case study in a subtropical bay

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Autor(es):
Checon, Helio H. [1] ; Vieira, Danilo C. [2] ; Corte, Guilherme N. [3, 1] ; Sousa, Ediunetty C. P. M. [3] ; Fonseca, Gustavo [4] ; Amaral, A. Cecilia Z. [1]
Número total de Autores: 6
Afiliação do(s) autor(es):
[1] Univ Estadual Campinas, Inst Biol, Dept Biol Anim, Monteiro Lobato St 255, BR-13083862 Campinas, SP - Brazil
[2] Univ Fed Parana, Ctr Estudos Mar, Programa Posgrad Sistemas Costeiros Ocean, Beira Mar Av, POB 61, BR-83255976 Pontal Do Parana, Parana - Brazil
[3] Univ Sao Paulo, Inst Oceanog, Dept Oceanog Biol, Praca Oceanog 191, BR-05508120 Sao Paulo, SP - Brazil
[4] Univ Fed Sao Paulo, Inst Mar, Campus Baixada Paulista, Silva Jardim St 136, BR-11015020 Santos, SP - Brazil
Número total de Afiliações: 4
Tipo de documento: Artigo Científico
Fonte: OCEAN & COASTAL MANAGEMENT; v. 164, n. SI, p. 68-78, OCT 1 2018.
Citações Web of Science: 1
Resumo

The definition of habitats and indicator species is a prerequisite for monitoring and conservation programs. Nonetheless, defining habitats in marine soft-bottom environments is challenging given their spatiotemporal dynamics and apparent homogeneity. The selection of indicator species is also complicated given the large number of occasional species usually presented in benthic communities. This study aims to elaborate a framework based on well-established analytical methodologies to identify soft-bottom habitats and select indicator species to support monitoring and conservation programs. The proposed framework consists of four steps: 1) perform a Redundancy Analysis (RDA) on the community data to identify the community structure response to environmental gradients; 2) conduct a kernel density analysis on the RDA biplot to determine the habitats; 3) use the indicator values analyses (IndVal) to select indicator species of each habitat; 4) run polynomial quantile regression analysis to find the optimum distribution of each indicator species. Such framework allows the determination of habitats based on the association of environmental and biological datasets, instead of relying solely on abiotic surrogates. As a case study, we used data of macro and meiofauna of a biodiverse coastal ecosystem in Southeast Brazil which is under anthropogenic pressure. Three main habitats were identified in the bay, and macro and meiofaunal assemblages were influenced by similar environmental variables. Nevertheless, macrofauna was more sensitive to changes in sediment composition, whereas meiofauna responded strongly to changes in total organic content and water depth. Macro- and meiofauna indicator taxa showed high specificity and fidelity values to each habitat, supporting their use in monitoring and conservation programs. The spatiotemporal organization of each habitat and the optimum distribution of each indicator species provide baseline knowledge to be used to monitor environmental changes in the study area and help in its conservation. (AU)

Processo FAPESP: 11/50317-5 - Biodiversidade e funcionamento de um ecossistema costeiro subtropical: subsídios para gestão integrada
Beneficiário:Antonia Cecília Zacagnini Amaral
Linha de fomento: Auxílio à Pesquisa - Programa BIOTA - Temático
Processo FAPESP: 16/10810-8 - Ecologia bêntica da Baía do Araçá: análise da fauna associada ao sedimento e da relação ambiente biodiversidade
Beneficiário:Guilherme Nascimento Corte
Linha de fomento: Bolsas no Brasil - Pós-Doutorado