Research Article
Türler Arası Etkileşimin Belirleyicilerinden Mesafenin Ölçülmesinde İstatistiksel Açıdan Alternatif Bir Yaklaşım Çalışması
Abstract
Ekolojik çalışmalarda bitki tür ve çeşitleri arasında gözlenen mesafelerin sağlıklı bir şekilde ölçülmesi yapılacak tanımlama çalışmalarında başarı şansını artırabilmektedir. Ancak uzun zamandır konu ile ilgili çalışan araştırmacılar klasik yöntemleri kullanmakta ve mesafeyi yaklaşık olarak ifade etmeyi tercih etmektedirler. Yaklaşık olarak tanımlamak verilerin bulunduğu alanın tam olarak tanımlanmasını yapmakta yetersiz kalmaktadır. Elbette bu lokasyonlara göre değişebildiği gibi türlerin yayılımlarına göre de değişiklik göstermektedir. Bu çalışmada uzaklık ölçümlerinden Öklid, Chebyshev, Manhattan, Minkowski ve Lorentzian uzaklıkları kullanılarak çalışmada yapılmıştır. Kırşehir koşullarında 10 farklı tür ile yapılan çalışmada türler arasındaki mesafeleri en iyi tanımlayan uzaklık belirlenmeye çalışılmıştır. Yapılan çalışmada Öklid uzaklığının tanımlama başarısının diğerlerine göre istatistiksel olarak önemli ve belirgin şekilde ayrıldığı tespit edilmiştir. Yapılacak çalışmalarda hata varyansının azaltılabilmesi ve tekrar edilmemesi için ölçümün dikkatli yapılması ve Öklid uzaklığının kullanılması önerilmiştir.
Keywords
References
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Year 2022,
Volume: 9 Issue: 1, 9 - 18, 22.01.2022
Abstract
References
- Akpınar, H. 2014. DATA: Veri Madenciliği Veri Analizi, Papatya Bilim Yayınevi, 448.
- Anderson, M.J., Gorley, R.N. & Clarke, K.R. (2008) PERMANOVA+ forPRIMER: Guide to Software and Statistical Methods. PRIMER-E, Plym-outh, UK
- Braak, C.J.F. & Prentice, I.C. (1988) A theory of gradient analysis. Advancesin Ecological Research, 18, 271–317.
- Chen M.F., Tzeng G.H., Combining Grey Relation and TOPSIS Concepts for Selecting an Expatriate Host Country, Math. Comput. Modell., 40, 1473-1490, 2004.
- David I. Warton1*, Stephen T. Wright1and Yi Wang, (2012). Distance-based multivariate analyses confoundlocation and dispersion effects. Methods in Ecology and Evolution 2012, 3, 89–101
- Gotelli NJ, Ellison AM (2004) A primer of ecological statistics. Sinauer, Massachusetts, USA.
- Henrey, A.J.; Loughin, T.M. 2017. Joint identification of location and dispertion effects in unreplicated two level factorials. Technometrics. 59(1):23-35. in Ecological Research, 18, 271–317.
- Kabak, D , Akçura, M . (2017). Bingöl İlinden Toplanan Yerel Çavdarlarda Tane Verimi ve Bazı Özellikler Arasındaki İlişkilerin Biplot Analizi İle İncelenmesi . Türk Tarım ve Doğa Bilimleri Dergisi , 4 (2) , 227-235.
- Legendre, P. & Legendre, L. (1998) Numerical Ecology. Elsevier Science,Amsterdam. Lu B, Charlton M, Brunsdon C, Harris P. 2016. The Minkowski approach for choosing the distance metric in geographically weighted regression. Inter J Geo Inf Sci, 30(2): 351-368. location and dispersion effects
- McArdle, B.H. & Anderson, M.J. (2004) Variance heterogeneity, transforma-tions, and models of species abundance: a cautionary tale. Canadian Journalof Fisheries and Aquatic Sciences, 61, 1294–1302
- McArdle, B.H. & Anderson, M.J. (2004) Variance heterogeneity, transforma-
- McArdle, B.H., Gaston, K.J. & Lawton, K.J. (1990) Variation in the size of ani-mal populations: patterns, problems and artefacts. The Journal of AnimalEcology, 59, 429–454.
- Merigo JM, Casanovas M. 2011. A new Minkowski distance based on induced aggregation operators. Int J Comput Intel Sys, 4(2): 123-133.
- Myatt GJ, Johnson WP. 2009.Making sense of data II: A practical guide to data visualization, advanced data mining methods, and applications: Wiley Online Library. of Fisheries and Aquatic Sciences, 61, 1294–1302
- Özdağoğlu A., Farklı Normalizasyon Yöntemlerinin TOPSIS’te Karar Verme Sürecine Etkisi, Ege Akademik Bakış, 13 (2), 245-257, 2013.
- Pan, G.1999. The impact of unidentified kocation effectson dispersion effecets idenfication from unreplicated factorial design. Technometrics. 41(4):313-326.
- Routledge, R.D. & Swartz, T.B. (1991) Taylor’s Power Law re-examined. Nor-dic Society Oikos, 60, 107–112
- Sozen, O., Karadavut, U. 2019. Statistical Analysis Of Some Characters Affecting Yield In Chickpea Varieties Which Can Be Breeded In Arid Climate Conditions. The Journal of Global Innovations in Agricultural and Social Sciences, 7 (4): 145-149.
- Tabucanon M.T. Multiple criteria decision making in industry, Elsevier, New York, USA, 1988. ter Braak, C.J.F. & Prentice, I.C. (1988) A theory of gradient analysis. Advances tions, and models of species abundance: a cautionary tale. Canadian Journal
- Wark K, Warner CF (1981) Air Pollution, Its Origin And Control. Harper and Row Publishers, New York Wilson WE, Chow JC, Claiborn C, Fusheng W, Engelbrecht J, Watson JG (2002) Monitoring of particulate matter outdoors. Chemosphere 49, 1009-1043.
- Ye J., Multicriteria Group Decision-Making Method Using Vector Similarity Measures For Trapezoidal Intuitionistic Fuzzy Numbers, Group Decision and Negotiation, 21 (4), 519-530, 2012.
- Yoon K., Hwang C.L., Multiple Attribute Yoon K., Hwang Decision Making: An Introduction, Sage, Thousand Oaks, Kanada, 1995.
There are 22 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Journal Section | Research Articles |
| Authors | |
| Publication Date | January 22, 2022 |
| Submission Date | August 20, 2021 |
| Published in Issue | Year 2022 Volume: 9 Issue: 1 |
