Abstract

Macro- and micro-environmental factors modulate parasite loads in fish, determining parasitic abundances, diversity, and interaction dynamics. In this study, seasonal variations in larval ectoparasites on fish larvae in the northern Humboldt Current System (HCS) were evaluated using a delta-gamma generalized linear model to predict their occurrence frequencies. Fish larvae were collected from two nearshore areas during austral spring–summer and autumn–winter. Only five (of 38) larval fish species were parasitized by copepods: Gobiesox marmoratus, Ophiogobius jenynsi, Helcogrammoides cunninghami, Myxodes sp., and Auchenionchus crinitus. A binomial model showed that the presence/absence of parasitized fish larvae varied among the fish species and their larval abundances, while a positive delta-gamma model showed that ectoparasite frequency varied among the seasons and fish species. Seasonal variations in parasitized fish larvae frequency could be associated with host and parasite reproductive processes, which are related to oceanographic features responsible for larval retention and subsequent higher infestation probabilities. Host length was positively correlated with ectoparasite length, suggesting early infection and combined growth until the detachment of the ectoparasite. Our results suggest that infestation patterns in larval fish species can be identified using delta-gamma models and that they respond to local (retention) and high-scale (HCS) processes.

Highlights

  • Larval fish species are an important component of coastal e­ cosystems1,2, and they are highly vulnerable to environmental variation, predation, and parasitic i­nfection3

  • Micro-environmental variations are recognized as the characteristics of the host, such as body size, host density, reproductive periods, food availability, and mortality r­ ates17, while macro-environmental variations are recognized as the habitats of the host, which are associated with the natural changes in climate, environmental conditions, and interspecific relationships that occur in every ­ecosystem17,18

  • The Humboldt Current System is a highly productive marine ecosystem that is largely influenced by constant coastal upwelling that maintains a high level of biological ­productivity19 and supports a high abundance of larval fish species in the nearshore areas, especially in northern C­ hile20, which is characterized by a high retention of planktonic organisms and high food ­availability21

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Summary

Introduction

Larval fish species are an important component of coastal e­ cosystems, and they are highly vulnerable to environmental variation, predation, and parasitic i­nfection. Micro-environmental variations are recognized as the characteristics of the host, such as body size, host density, reproductive periods, food availability, and mortality r­ ates, while macro-environmental variations are recognized as the habitats of the host, which are associated with the natural changes in climate, environmental conditions (e.g., temperature, oxygen), and interspecific relationships that occur in every ­ecosystem17,18 Variations in these components may result in large differences in the abundance and diversity of parasites as well as different host-parasite dynamic interactions. The Humboldt Current System is a highly productive marine ecosystem that is largely influenced by constant coastal upwelling that maintains a high level of biological ­productivity and supports a high abundance of larval fish species in the nearshore areas, especially in northern C­ hile, which is characterized by a high retention of planktonic organisms and high food ­availability21 These local oceanographic conditions could favour the acquisition and retention of parasites in the water column and increase the probability of parasites encountering potential hosts. The objectives of this study were (1) to determine the magnitude of parasitism on larval fish species and (2) to predict the seasonal variations in ectoparasites affecting larval fish species from two nearshore areas of northern Chile using a delta-gamma generalized linear model (GLM) approach

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