Published in

Wiley, Marine Ecology, 3(44), 2023

DOI: 10.1111/maec.12746

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Coastal mesozooplankton respond to decadal environmental changes via community restructuring

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Data provided by SHERPA/RoMEO

Abstract

AbstractLong‐term ecological research has revealed the impact of climate on marine ecosystems at multiple time scales. Changes in the pelagic system have been detected at the LTER‐MC site in the Gulf of Naples (Tyrrhenian Sea, western Mediterranean) since 1984. Here we analyzed the time series to determine whether zooplankton had significantly changed over the three decades 1984–2015. In addition to the seasonal cycle as the main mode of temporal variability, we observed long‐term trends in the functional groups and species. Copepods, the most abundant group, declined over the years owing to a decrease in the abundance of Acartia clausi, Centropages typicus, the Paracalanus parvus complex, and Oithona spp. Increasing trends were observed for strict carnivores (chaetognaths) and typical filter feeders (cladocerans, appendicularians, and thaliaceans); the latter may be linked to a higher density of <5 μm phytoplankton. Two main temporal shifts were detected: (i) in 1985–87, similarly to the regime shifts registered in other basins of the Northern Hemisphere, and (ii) after 2011, likely related to local atmospheric forcing. The disappearance or decrease in some neritic copepods and the increased abundance of typical offshore species seem to reflect the enhanced influence of the open Tyrrhenian waters at the sampling site. Despite these changes, no significant trends were detected in the total zooplankton abundance and overall composition, which indicates restructuring within the whole community. These results underline the need to examine the entire zooplankton diversity spectrum and improve our knowledge of their ecological traits to detect and interpret long‐term variability.