Acta Adriatica, Vol. 63 No. 2, 2022.
Original scientific paper
https://doi.org/10.32582/aa.63.2.6
Modeling the spatial distribution of the ctenophore Mnemiopsis leidyi A. Agassiz, 1865 in the Black Sea using a fuzzy rule-based system
Hadi Poorbagher
; Department of Fisheries, Faculty of Natural Resources, University of Tehran, Karaj PO Box 4314, Iran
Zekiye Birinci-Ozdemir
; Department of Marine Biology, Sinop University, Sinop, Turkey
Soheil Eagderi
; Department of Fisheries, Faculty of Natural Resources, University of Tehran, Karaj PO Box 4314, Iran
Erdogan Çicek
; Department of Biology, Faculty of Art and Science, Nevsehir Hacı Bektas Veli University, Nevsehir, Turkey
Abstract
Species distribution models can predict species occurrences in areas where no data is available
by finding relationships between occurrences and environmental parameters. In this study, we
applied a fuzzy rule-based system to model the spatial distribution of Mnemiopsis leidyi in the
Black Sea and predict the probability of its presence throughout the sea. Six variables were used as
predictors, including water turbidity, organic and inorganic particulate carbon, photosynthetically
active radiation, light absorption by phytoplankton, sea surface temperature, and chlorophyll-a
concentration. The results revealed a 0.807 accuracy of the model based on the confusion matrix.
The results also showed that photosynthetically active radiation and sea surface temperature were
the most important predictors shaping the distribution of this species.
The findings also showed that the northern Black Sea was with the highest probability of
presence, especially in Ukraine and Russia’s coastal areas. In the coastal areas of Turkey, the
highest presence probability was found near Rize, Trabzon, Ordu, and from Sinop to Zonguldak.
Therefore, continuous monitoring of the Turkish coastal area is crucial to better understanding the
effects of climate change and anthropogenic influences on the further distribution patterns of this
invasive ctenophore in the southeastern Black Sea.
Keywords
Non-indigenous invasive species; conservation; management; remote sensing; model
Hrčak ID:
289614
URI
Publication date:
30.12.2022.
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