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Journals / Turkish Journal of Zoology / 2018 / Cilt: 42 - Sayı: 1

Geographical variation in morphometry, craniometry, and diet of amammalian species (Stone marten, Martes foina) using data mining

Kyriaki KITIKIDOU
Malamati A. PAPAKOSTA
Dimitrios E. BAKALOUDIS
Christos G. VLACHOS
Evangelos CHATZINIKOS
Olga ALEXANDROU
Anastasios SAKOULIS
Pages
99–106
Publication Date
2022-11-05
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data: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.
💡
Conclusion & Impact:Data mining techniques could be used to examine the geographical variation of a species to support separation not subspecies.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

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.

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Cite This Article

KITIKIDOU, K., PAPAKOSTA, M. A., BAKALOUDIS, D. E., VLACHOS, C. G., CHATZINIKOS, E., ALEXANDROU, O., SAKOULIS, A. (2022). Geographical variation in morphometry, craniometry, and diet of amammalian species (Stone marten, Martes foina) using data mining. Turkish Journal of Zoology, Cilt: 42 - Sayı: 1, 99–106. https://ajindex.com/articles/616115

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