Ecologists use various data mining techniques to make predictions and estimations, to identify patterns in datasets and relationships between qualitative and quantitative variables, or to classify variables. The aim of this study was to investigate if the application of data mining could be used to study geographical variation in the morphometry, craniometry, and diet of a mammalian species (Martes foina), and to determine whether data mining can complement genetic analysis to recognize subspecies. Morphometric, craniometric, and dietary data were collected from three different geographical regions in Greece (mainland, Aegean islands, and the island of Crete), and data mining techniques were applied. Our results showed that there is no geographical differentiation between morphometry, craniometry, and diet of the species; therefore, the species cannot be separated into subspecies. Our results support preliminary results from a genetic study that annuls previous classification into three subspecies. Data mining techniques could be used to examine the geographical variation of a species to support separation not subspecies.
Biogeography, craniometric data, data mining, dietary data, geographical variation, morphometric data
PAPAKOSTA, MALAMATI; KITIKIDOU, KYRIAKI; BAKALOUDIS, DIMITRIOS; VLACHOS, CHRISTOS; CHATZINIKOS, EVANGELOS; ALEXANDROU, OLGA; and SAKOULIS, ANASTASIOS
"Geographical variation in morphometry, craniometry, and diet of amammalian species (Stone marten, Martes foina) using data mining,"
Turkish Journal of Zoology: Vol. 42:
1, Article 11.
Available at: https://journals.tubitak.gov.tr/zoology/vol42/iss1/11