Vulnerability of Black Sea Mesozooplankton to Anthropogenic and Climate Forcing

Mesozooplankton are pivotal for Black Sea food webs, yet they are highly vulnerable to hydrographic variability, eutrophication, and human pressures. This study analysed mesozooplankton dynamics along the Romanian coast (2013–2020) across three sectors (north, central, and south) and two distinct periods (cold and warm seasons), integrating Abundance–Biomass Comparison (ABC) curves with Fuzzy Cognitive Mapping (FCM). Results revealed a clear disturbance gradient: the Danube-influenced north supported high abundances of small-bodied taxa; the central sector maintained the most resilient and functionally diverse assemblages; and the southern sector showed chronic degradation with Noctiluca scintillans dominance. ABC curves quantified disturbance, with curve convergence in the north and near overlap in the south during summer, while FCM highlighted network simplification and reduced functional redundancy. Climate scenario simulations projected further declines in cladocerans and meroplankton under warming and freshening, whereas copepods showed relative resilience. Collectively, the findings demonstrate progressive simplification of mesozooplankton and declining energy transfer efficiency, underscoring the need to integrate zooplankton-based indicators into Black Sea monitoring and management frameworks.

Authors

Institutions

Publication Details

Journal
Journal of Marine Science and Engineering
Published
2025-11-13
DOI
https://doi.org/10.3390/jmse13112151
Citations
1
Primary Topic
Marine and coastal ecosystems
Type
article
Field-Weighted Citation Impact
1.79

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Vulnerability of Black Sea Mesozooplankton to Anthropogenic and Climate Forcing

Luminița Lazăr, Elena Bișinicu
1 citations
Journal of Marine Science and Engineering
Marine and coastal ecosystems
1.79
article

Vulnerability of Black Sea Mesozooplankton to Anthropogenic and Climate Forcing

Luminița Lazăr, Elena Bișinicu
article en
1 citations

Abstract

Mesozooplankton are pivotal for Black Sea food webs, yet they are highly vulnerable to hydrographic variability, eutrophication, and human pressures. This study analysed mesozooplankton dynamics along the Romanian coast (2013–2020) across three sectors (north, central, and south) and two distinct periods (cold and warm seasons), integrating Abundance–Biomass Comparison (ABC) curves with Fuzzy Cognitive Mapping (FCM). Results revealed a clear disturbance gradient: the Danube-influenced north supported high abundances of small-bodied taxa; the central sector maintained the most resilient and functionally diverse assemblages; and the southern sector showed chronic degradation with Noctiluca scintillans dominance. ABC curves quantified disturbance, with curve convergence in the north and near overlap in the south during summer, while FCM highlighted network simplification and reduced functional redundancy. Climate scenario simulations projected further declines in cladocerans and meroplankton under warming and freshening, whereas copepods showed relative resilience. Collectively, the findings demonstrate progressive simplification of mesozooplankton and declining energy transfer efficiency, underscoring the need to integrate zooplankton-based indicators into Black Sea monitoring and management frameworks.

Journal of Marine Science and EngineeringVol. 13(11)
Institutul Naţional de Cercetare Dezvoltare Marină Grigore Antipa (RO)
Ministerul Cercetării, Inovării şi Digitalizării
Openalex Percentile: Top 12%
Marine and coastal ecosystems
1.79
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.