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  • Habitat suitability models ...
    Zlateva, Ivelina; Raykov, Violin; Slabakova, Violeta; Stefanova, Elitsa; Stefanova, Kremena

    Oceanologia, 10/2022, Letnik: 64, Številka: 4
    Journal Article

    •Habitat suitability maps of 5 keynote species in the Bulgarian region of the Black Sea.•SDMs utilize species presence only (PO) localities data.•SDMs are fitted considering biotic interactions and abiotic environment.•The estimated probabilities of species occurrences are validated with species abundance.•Resultant SDMs can be further implemented for conservation and stock management. Over the past few years, predicting species spatial distributions has been recognized as a powerful tool for studying biological invasions in conservation biology and planning, ecology, and evolutionary biology. Species spatial distribution models (SDMs) are used extensively for assessing the effects of changes in habitat suitability, the impacts of climate change, and the realignment of the existing conservation priorities. SDMs relate known patterns of species occurrences to a specific set of environmental conditions. Accordingly, we have used MaxEnt SDM tool in order to provide habitat suitability models of 5 keynote fish species: European sprat (Sprattus sprattus L.), red mullet (Mullus barbatus, L.), horse mackerel (Trachurus mediterraneus, L.), bluefish (Pomatomus saltatrix, L.) and whiting (Merlangius merlangus, L.), inhabiting the Bulgarian region of the Black Sea. Presence-only (PO) data collected by pelagic surveys performed between 2017 and 2019 was further utilized to link known species occurrence localities with selected abiotic factors, such as surface sea temperature and salinity, dissolved oxygen, and speed of currents. Biotic interactions were also considered for fitting the patterns of habitat suitability models. The SDMs, obtained from the present research study, prove to have satisfactory predictive accuracy to be further implemented for conservation measures and planning, stock management policy-making, or ecological forecasting.