Effects of future climate conditions on the zooplankton of a Mediterranean coastal lagoon

Simantiris, Nikolaos; Avlonitis, Markos

doi:10.1016/j.ecss.2023.108231

Cited by 6 publications

(2 citation statements)

References 36 publications

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“…The lagoon is of high economic and ecologic importance being the habitat of many marine mammals, fish, birds, reptiles, etc. Local fishermen exploit the lagoon as a natural aquaculture for fish for at least the past three decades [84,85].…”

Section: Study Sitementioning

confidence: 99%

Computing Invasive Species Population Based on a Generalized Random Walk Process: Application to Blue Crab (Callinectes sapidus)

Simantiris

Violaris

Avlonitis

2023

JMSE

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Animal population abundance is a significant parameter for studies on invasive species that can threaten the ecosystem. Researchers have been developing population estimation methods since the 18th century, in order to evaluate species’ evolution and environmental effects. However, studies on the population density of the invasive species Callinectes sapidus are very limited. The present work, using a simulation model combined with field measurements, examines an innovative methodology for estimating the current population of the invasive species Callinectes sapidus in a shallow Mediterranean coastal lagoon. The methodology presented here builds the first stage of modeling and predicting the evolution of this species’ population in marine environments. The simulation model’s results are validated with an estimation of the total population based on juvenile abundance, and a curvature of the species population estimation based on cage catch is implemented. The simulation experiments presented here show the possibility of a robust prediction for blue crab population estimation.

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Section: Study Sitementioning

confidence: 99%

Computing Invasive Species Population Based on a Generalized Random Walk Process: Application to Blue Crab (Callinectes sapidus)

Simantiris

Violaris

Avlonitis

2023

JMSE

Self Cite

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“…However, recent monitoring has indicated their decline, habitat loss, and environmental vulnerability [17][18][19]. These declines have been attributed to a variety of indirect and direct causes, including climate change [20] and anthropogenic activities with their associated pollution [21]. High levels of stress result in environmental threats to these treasured ecosystems [22].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

Lemenkova

2024

JMSE

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This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in the Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds and rare species. The vulnerable ecosystems of the Chilika Lagoon are subject to climate effects (monsoon effects) and anthropogenic activities (overexploitation through fishing and pollution by microplastics). Such environmental pressure results in the eutrophication of the lake, coastal erosion, fluctuations in size, and changes in land cover types in the surrounding landscapes. The habitat monitoring of the coastal lagoons is complex and difficult to implement with conventional Geographic Information System (GIS) methods. In particular, landscape variability, patch fragmentation, and landscape dynamics play a crucial role in environmental dynamics along the eastern coasts of the Bay of Bengal, which is strongly affected by the Indian monsoon system, which controls the precipitation pattern and ecosystem structure. To improve methods of environmental monitoring of coastal areas, this study employs the methods of ML and Artificial Neural Networks (ANNs), which present a powerful tool for computer vision, image classification, and analysis of Earth Observation (EO) data. Multispectral satellite data were processed by several ML image classification methods, including Random Forest (RF), Support Vector Machine (SVM), and the ANN-based MultiLayer Perceptron (MLP) Classifier. The results are compared and discussed. The ANN-based approach outperformed the other methods in terms of accuracy and precision of mapping. Ten land cover classes around the Chilika coastal lagoon were identified via spatio-temporal variations in land cover types from 2019 until 2024. This study provides ML-based maps implemented using Geographic Resources Analysis Support System (GRASS) GIS image analysis software and aims to support ML-based mapping approach of environmental processes over the Chilika Lake coastal lagoon, India.

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Microplastics in the Mediterranean and elsewhere in coastal seas

Simantiris¹,

Vardaki²,

Kourkoumelis³

et al. 2024

Treatise on Estuarine and Coastal Science (Second Edition)

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Effects of future climate conditions on the zooplankton of a Mediterranean coastal lagoon

Cited by 6 publications

References 36 publications

Computing Invasive Species Population Based on a Generalized Random Walk Process: Application to Blue Crab (Callinectes sapidus)

Computing Invasive Species Population Based on a Generalized Random Walk Process: Application to Blue Crab (Callinectes sapidus)

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

Microplastics in the Mediterranean and elsewhere in coastal seas

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