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Factors influencing the occurrence of stinging jellyfish (Physalia spp.) at New Zealand beaches

Research Archive

Factors influencing the occurrence of stinging jellyfish (Physalia spp.) at New Zealand beaches

Pontin, David R.
Type: Thesis
Permalink: https://hdl.handle.net/10182/1580
Originally published: 2009 by Lincoln University and available at http://hdl.handle.net/10182/1580
Keywords: Physalia; Cnidaria; DNA barcoding; artificial neural networks

Abstract:

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.

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