| Authors |
Duysebaeva Tat'yana Nikolaevna, Candidate of biological sciences, leading researcher, Association of Biodiversity Conservation of Kazakhstan (67 Khodzhanova street, Almaty, Republic of Kazakhstan), E-mail: dujsebayeva@mail.ru
Doronin Igor' Vladimirovich, Candidate of biological sciences, senior staff scientist, Zoological Institute of Russian Academy of Sciences (1 Universitetskaya embankment, Saint-Petersburg, Russia), E-mail: ivdoronin@mail.ru
Malakhov Dmitriy Viktorovich, Leading researcher, National Center of Space Researchers and Technology (15A Shevchenko street, Almaty, Republic of Kazakhstan), E-mail: d_malakhov_73@mail.ru
Kukushkin Oleg Vital'evich, Researcher, Karadag Scientific Station named after T. I. Vyazemsky – Natural Reserve of Russian Academy of Sciences (24 Nauki street, Kurortnoye village, Feodosiya, Republic of Crimea, Russia), E-mail: mtasketi2018@gmail.com
Bakiev Andrey Gennad'evich, Candidate of biological sciences, senior researcher, Institute of Ecology of the Volga River Basin of Russian Academy of Sciences (10 Komzina street, Togliatty, Russia), E-mail: herpetology@list.ru
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| Abstract |
Background. In spite of widespread use of a GIS modeling in the zoological studies, a question of whether a model matches an actual or potential range of an animal remains relevant. A goal of our work was the GIS-modeling of the range and habitat suitability of the European Marsh Turtle, Emys orbicularis (Linnaeus, 1758) using different software, different sets of environment variables and different amount of the ground data.
Materials and methods. The ground data on distribution of E. o. orbicularis (genetic lineage I) were collected during numerous field surveys, revision of literature and museum collections. The models were developed in MaxEnt and ESRI ArcGIS software using BIOCLIM dataset and the ground data from the whole taxon range (for both programs); WORLDCLIM, DEM, Global-PET and CLIMOND datasets and the ground data from the whole range and only its eastern part (for ArcGIS).
Results. The models in MaxEnt and ArcGIS, which covered all the ground data, were similar with respect to the core range and its western borders. However, they gave the different patterns of climatic well-being for the northern, eastern and southern limits of E. o. orbicularis distribution as well as different number of the key bioclimatic variables. The large number of the key variables shown by ArcGIS may indicate a certain ecological differentiation of the taxon, which has an intrazonal distribution and occupies the territory with high diversity of the landscape and climatic conditions. A lack of input data seriously affects the spatial pattern of the model and a type of the key variables that limit the taxon distribution.
Conclusions. For successful GIS modeling, the following is required: maximum coverage of the taxon range with the ground data; a selection of the initial variables taking into account the basic characteristics of natural zones and landscapes occupied by taxon, and its ecological peculiarities; the modeling using different software with subsequent comparative analysis of the results.
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