Evaluation of caridean ecological distribution in the Ubatuba region, southeastern Brazilian coast using unsupervised machine learning technique
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1111/maec.12673 http://hdl.handle.net/11449/233346 |
Resumo: | We used the unsupervised machine learning technique to evaluate the environmental factors responsible for modulating the spatial and seasonal distribution of caridean shrimps from a southeastern region of the Brazilian coast. Samplings were collected from seven transects with an artisanal shrimp fishery boat with two double-rig nets. Samplings occurred every month from October 2008 to September 2009. The most frequently captured species were Exhippolysmata oplophoroides, Leander paulensis, and Nematopalaemon schmitti. The highest abundance of shrimps occurred in autumn at the II, III, and V transects, which present a higher amount of coarse sediment and biodetritic fragments on the bottom. During autumn, the temperatures were the highest and salinity values were the lowest. Data evaluation indicated efficiency in the visualization of interactions of different shrimp species and environmental data. This kind of sediment may be allowing shrimps to burrow in shelters that prevent predation. The seasons with high temperatures and low salinities can offer better conditions for the establishment of the studied species, despite the fact that there is no hypothesis to prove it. Additionally, the higher abundance of such shrimps coincides with vegetal debris deposition, which could serve as food and provide protection for these shrimps. In this region, the vegetation matter deposited at the bottom of the bay comes from the Atlantic Forest. Overall, the preservation of the coastal forest strongly influences the abundance of this taxon, as it provides protection and food for these shrimps. |
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Evaluation of caridean ecological distribution in the Ubatuba region, southeastern Brazilian coast using unsupervised machine learning techniquebiodetritusbycatchclusteringcompositionenvironmental protection areashelterWe used the unsupervised machine learning technique to evaluate the environmental factors responsible for modulating the spatial and seasonal distribution of caridean shrimps from a southeastern region of the Brazilian coast. Samplings were collected from seven transects with an artisanal shrimp fishery boat with two double-rig nets. Samplings occurred every month from October 2008 to September 2009. The most frequently captured species were Exhippolysmata oplophoroides, Leander paulensis, and Nematopalaemon schmitti. The highest abundance of shrimps occurred in autumn at the II, III, and V transects, which present a higher amount of coarse sediment and biodetritic fragments on the bottom. During autumn, the temperatures were the highest and salinity values were the lowest. Data evaluation indicated efficiency in the visualization of interactions of different shrimp species and environmental data. This kind of sediment may be allowing shrimps to burrow in shelters that prevent predation. The seasons with high temperatures and low salinities can offer better conditions for the establishment of the studied species, despite the fact that there is no hypothesis to prove it. Additionally, the higher abundance of such shrimps coincides with vegetal debris deposition, which could serve as food and provide protection for these shrimps. In this region, the vegetation matter deposited at the bottom of the bay comes from the Atlantic Forest. Overall, the preservation of the coastal forest strongly influences the abundance of this taxon, as it provides protection and food for these shrimps.Group of Studies on Crustacean Biology Ecology and Culture (NEBECC) Institute of Biosciences Doctoral Program of Zoology University of the State of São Paulo (UNESP)Laboratory of Ecology and Evolution of Crustaceans (LABEEC) Federal University of Rio Grande do Norte (UFRN)Group of Studies on Crustacean Biology Ecology and Culture (NEBECC) Institute of Biosciences Doctoral Program of Zoology University of the State of São Paulo (UNESP)Universidade Estadual Paulista (UNESP)Federal University of Rio Grande do Norte (UFRN)Marques, Alexandre Oliveira [UNESP]de Sousa, Aline Nonato [UNESP]Bernardes, Veronica Pereira [UNESP]Bernardo, Camila Hipolito [UNESP]Reis, Danielle Monique [UNESP]Godoy, Amanda Thaís [UNESP]Fransozo, Adilson [UNESP]2022-05-01T07:58:49Z2022-05-01T07:58:49Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1111/maec.12673Marine Ecology.1439-04850173-9565http://hdl.handle.net/11449/23334610.1111/maec.126732-s2.0-85111745362Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengMarine Ecologyinfo:eu-repo/semantics/openAccess2022-05-01T07:58:49Zoai:repositorio.unesp.br:11449/233346Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:16:14.841108Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Evaluation of caridean ecological distribution in the Ubatuba region, southeastern Brazilian coast using unsupervised machine learning technique |
title |
Evaluation of caridean ecological distribution in the Ubatuba region, southeastern Brazilian coast using unsupervised machine learning technique |
spellingShingle |
Evaluation of caridean ecological distribution in the Ubatuba region, southeastern Brazilian coast using unsupervised machine learning technique Marques, Alexandre Oliveira [UNESP] biodetritus bycatch clustering composition environmental protection area shelter |
title_short |
Evaluation of caridean ecological distribution in the Ubatuba region, southeastern Brazilian coast using unsupervised machine learning technique |
title_full |
Evaluation of caridean ecological distribution in the Ubatuba region, southeastern Brazilian coast using unsupervised machine learning technique |
title_fullStr |
Evaluation of caridean ecological distribution in the Ubatuba region, southeastern Brazilian coast using unsupervised machine learning technique |
title_full_unstemmed |
Evaluation of caridean ecological distribution in the Ubatuba region, southeastern Brazilian coast using unsupervised machine learning technique |
title_sort |
Evaluation of caridean ecological distribution in the Ubatuba region, southeastern Brazilian coast using unsupervised machine learning technique |
author |
Marques, Alexandre Oliveira [UNESP] |
author_facet |
Marques, Alexandre Oliveira [UNESP] de Sousa, Aline Nonato [UNESP] Bernardes, Veronica Pereira [UNESP] Bernardo, Camila Hipolito [UNESP] Reis, Danielle Monique [UNESP] Godoy, Amanda Thaís [UNESP] Fransozo, Adilson [UNESP] |
author_role |
author |
author2 |
de Sousa, Aline Nonato [UNESP] Bernardes, Veronica Pereira [UNESP] Bernardo, Camila Hipolito [UNESP] Reis, Danielle Monique [UNESP] Godoy, Amanda Thaís [UNESP] Fransozo, Adilson [UNESP] |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Federal University of Rio Grande do Norte (UFRN) |
dc.contributor.author.fl_str_mv |
Marques, Alexandre Oliveira [UNESP] de Sousa, Aline Nonato [UNESP] Bernardes, Veronica Pereira [UNESP] Bernardo, Camila Hipolito [UNESP] Reis, Danielle Monique [UNESP] Godoy, Amanda Thaís [UNESP] Fransozo, Adilson [UNESP] |
dc.subject.por.fl_str_mv |
biodetritus bycatch clustering composition environmental protection area shelter |
topic |
biodetritus bycatch clustering composition environmental protection area shelter |
description |
We used the unsupervised machine learning technique to evaluate the environmental factors responsible for modulating the spatial and seasonal distribution of caridean shrimps from a southeastern region of the Brazilian coast. Samplings were collected from seven transects with an artisanal shrimp fishery boat with two double-rig nets. Samplings occurred every month from October 2008 to September 2009. The most frequently captured species were Exhippolysmata oplophoroides, Leander paulensis, and Nematopalaemon schmitti. The highest abundance of shrimps occurred in autumn at the II, III, and V transects, which present a higher amount of coarse sediment and biodetritic fragments on the bottom. During autumn, the temperatures were the highest and salinity values were the lowest. Data evaluation indicated efficiency in the visualization of interactions of different shrimp species and environmental data. This kind of sediment may be allowing shrimps to burrow in shelters that prevent predation. The seasons with high temperatures and low salinities can offer better conditions for the establishment of the studied species, despite the fact that there is no hypothesis to prove it. Additionally, the higher abundance of such shrimps coincides with vegetal debris deposition, which could serve as food and provide protection for these shrimps. In this region, the vegetation matter deposited at the bottom of the bay comes from the Atlantic Forest. Overall, the preservation of the coastal forest strongly influences the abundance of this taxon, as it provides protection and food for these shrimps. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 2022-05-01T07:58:49Z 2022-05-01T07:58:49Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1111/maec.12673 Marine Ecology. 1439-0485 0173-9565 http://hdl.handle.net/11449/233346 10.1111/maec.12673 2-s2.0-85111745362 |
url |
http://dx.doi.org/10.1111/maec.12673 http://hdl.handle.net/11449/233346 |
identifier_str_mv |
Marine Ecology. 1439-0485 0173-9565 10.1111/maec.12673 2-s2.0-85111745362 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Marine Ecology |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128338345066496 |