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Ecological Indicative Stressors of Native vs. Non-Native Fish in an Ultra-Oligotrophic Region of the Mediterranean Sea

Author

Listed:
  • Erhan Mutlu

    (Department of Basic Sciences, Fisheries Faculty, Akdeniz University, Main Campus, 07058 Antalya, Turkey)

  • Ilaria de Meo

    (Independent Researcher, 50060 Londa, FI, Italy)

  • Claudia Miglietta

    (Independent Researcher, 72020 Cellino San Marco, BR, Italy)

  • Mehmet Cengiz Deval

    (Department of Basic Sciences, Fisheries Faculty, Akdeniz University, Main Campus, 07058 Antalya, Turkey)

Abstract

In the present study, we investigated the different ecological characteristics of native and non-native demersal fish collected in 2014–2015 on the shelf of the Antalya Gulf in the Eastern Mediterranean Sea. Lessepsian migrants originating from the Indo-Pacific Ocean were classified as non-indigenous species (NIS) and the other species, which were mostly Atlanto-Mediterranean, were classified as indigenous species (IS). The results showed that the faunistic characteristics of IS and NIS differed significantly in space but only partly over time. The density and species diversity of the IS increased with the seafloor depth, while the opposite pattern was observed for the NIS, which were found mostly in shallow waters. Proximity to rivers and Posidonia oceanica meadows and the presence of a marine protected area (MPA) were also important factors determining the differences in the ecological characteristics of IS and NIS. The ecological ordination of the fish assemblages in the canonical correspondence analysis (CCA) space was V-shaped for the IS and =-shaped for the NIS, and it was mainly determined by bottom depth. Altogether, the ordination took the shape of a double strikethrough (V) due to the NIS filling an available niche. Hierarchically, the NIS (“occupiers”) and IS (“resisters”) shared the shallowest waters, while the middle-shelf waters were occupied by NIS (“gapers”) and IS (“escapers”) separately. The upper shelf was occupied only by IS (“homekeepers”) and “minorities” of NIS. Finally, we identified eight factors as ecological indicators of NIS and IS: bottom depth, bottom vegetation status, fish hierarchy, key species, water productivity, fish trophic level, life strategy and morphometry.

Suggested Citation

  • Erhan Mutlu & Ilaria de Meo & Claudia Miglietta & Mehmet Cengiz Deval, 2023. "Ecological Indicative Stressors of Native vs. Non-Native Fish in an Ultra-Oligotrophic Region of the Mediterranean Sea," Sustainability, MDPI, vol. 15(3), pages 1-28, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2726-:d:1056195
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    References listed on IDEAS

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    1. Robert J. Wilson & Chris D. Thomas & Richard Fox & David B. Roy & William E. Kunin, 2004. "Spatial patterns in species distributions reveal biodiversity change," Nature, Nature, vol. 432(7015), pages 393-396, November.
    2. Marsaglia, George & Tsang, Wai Wan & Wang, Jingbo, 2003. "Evaluating Kolmogorov's Distribution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 8(i18).
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