Thesis (Ph. D.) -- Lincoln University, 2009. / Also available via the World Wide Web.

Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-05-09T19:48:14Z No. of bitstreams: 1 MayanaLuana.pdf: 2132103 bytes, checksum: ec28c6abf33e625325d7947c9311143a (MD5) / Made available in DSpace on 2017-05-09T19:48:14Z (GMT). No. of bitstreams: 1 MayanaLuana.pdf: 2132103 bytes, checksum: ec28c6abf33e625325d7947c9311143a (MD5) Previous issue date: 2017-02-23 / CAPES / The Portuguese man-of-war Physalia physalis (Linnaeus, 1758) is recognized for causing poisoning in bathers, mainly in the north and northeast regions of Brazil. In Maranhão, poisonings by man-of-war are common on the beaches of the capital São Luís. But action to prevent and promote health are still deficient. This study aimed to analyze the distribution of the occurrence of Portuguese man-of-war and envenomations on the beaches of São Marcos and Calhau in São Luís, Maranhão between 2015 and 2016. The data was provided by the active search of the animals and geographical marking with GPS (Global Positioning System), geoprocessed by Qgis software version 16.1. The kernel density estimator was used to identify the agglomerations of the events. A specific questionnaire was applied with 66 people poisoned by P. physalis and registered the location of the accident, month, year and emergency care measure used. The animals are distributed throughout the beaches of São Marcos and Calhau, with greater agglomeration in Calhau. The highest density location of P. physalis coincided with the location with the highest occurrence of poisonings. Most of the poisonings occurred in people living in the city of São Luís, in the months of the dry period, with greater occurrence in the beach of São Marcos. The most common first-aid used was the application of vinegar (acetic acid). We concluded that the poisonings occur in the parts of the urban beaches of São Luís that presented great density of P. physalis. In order to improve poison control actions, we suggest signaling with signs warning the risk areas of poisoning, and include the geographical location of the accident in the notification form. / A caravela portuguesa Physalia physalis (Linnaeus, 1758) é reconhecida por provocar envenenamento em banhistas, principalmente nas regiões norte e nordeste do Brasil. No Maranhão, são comuns envenenamentos por caravelas nas praias da capital São Luís, porém as ações de prevenção e promoção â saúde ainda são deficientes. O objetivo desse estudo foi analisar a distribuição da ocorrência de caravelas e dos envenenamentos humanos nas praias urbanas de São Marcos e do Calhau nos anos de 2015 e 2016. Os dados foram obtidos por meio da busca ativa dos animais e marcação da coordenada geográfica com um aparelho de GPS (Sistema de Posicionamento Global); e geoprocessados pelo software Qgis versão 16.1. O estimador da densidade de Kernel foi utilizado para identificar as aglomerações dos eventos. Um questionário específico foi aplicado com 66 pessoas envenenadas pela P. physalis sendo registrados o local do acidente, mês, ano e medida de pronto-atendimento utilizada. As caravelas estão distribuídas em toda a extensão das praias de São Marcos e Calhau, com maior aglomeração no Calhau. Os locais de maior densidade de caravelas coincidiram com os locais de maior ocorrência de envenenamentos. A maioria dos envenenamentos ocorreu em pessoas residentes na cidade de São Luís, nos meses do período de seca, com maior ocorrência na praia do São Marcos. A medida de pronto-atendimento mais utilizada foi a aplicação de vinagre (ácido acético). Concluímos que os envenenamentos ocorrem nos trechos das praias urbanas de São Luís que apresentaram grande densidade de colônias de caravelas. Para melhoria das ações de controle dos envenenamentos sugerimos a sinalização com placas alertando as áreas de risco de envenenamento, e inclusão da localização geográfica do acidente na ficha de notificação.

Individuals of the cnidarian genus Physalia are a common sight at New Zealand beaches and are the primary cause of jellyfish stings to beachgoers each year. The identity of the species and the environmental factors that determine its presence are unknown. Lack of knowledge of many marine species is not unusual, as pelagic invertebrates often lack detailed taxonomic descriptions as well as information about their dispersal mechanisms such that meaningful patterns of distribution and dispersal are almost impossible to determine. Molecular systematics has proven to be a powerful tool for species identification and for determining geographical distributions. However, other techniques are needed to indicate the causal mechanisms that may result in a particular species distribution. The aim of this study was to apply molecular techniques to the cnidarian genus Physalia to establish which species occur in coastal New Zealand, and to apply models to attempt to forecast its occurrence and infer some mechanisms of dispersal. Physalia specimens were collected from New Zealand, Australia and Hawaii and sequenced for Cytochrome c oxidase I (COI) and the Internal transcribed spacer 1 (ITS1). Three clans were found: a Pacific-wide clan, an Australasian clan and New Zealand endemic clan with a distribution confined to the Bay of Plenty and the East Coast of the North Island. Forecasting Physalia occurrence directly from presence data using artificial neural networks (ANN) proved unsuccessful and it was necessary to pre-process the presence data using a variable sliding window to reduce noise and improve accuracy. This modelling approach outperformed the time lagged based networks giving improved forecasts in both regions that were assessed. The ANN models were able to indicated significant trends in the data but would require more data at higher resolution to give more accurate forecasts of Physalia occurrence suitable for decision making on New Zealand beaches. To determine possible causal mechanisms of recorded occurrences and to identify possible origins of Physalia the presence and absence of Physalia on swimming beaches throughout the summer season was modelled using ANN and Naϊve Bayesian Classifier (NBC). Both models were trained on the same data consisting of oceanographic variables. The modelling carried out in this study detected two dynamic systems, which matched the distribution of the molecular clans. One system was centralised in the Bay of Plenty matching the New Zealand endemic clan. The other involved a dynamic system that encompassed four other regions on both coasts of the country that matched the distribution of the other clans. By combining the results it was possible to propose a framework for Physalia distribution including a mechanism that has driven clan divergence. Moreover, potential blooming areas that are notoriously hard to establish for jellyfish were hypothesised for further study and/or validation.