Comparison of Pearson’s and Spearman’s correlation coefficients for selected traits of Pinus sylvestris L.

Ahmadi K., Kalantar B., Saeidi V., Harandi E.K.G., Janizadeh S., Ueda N. (2020): Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data. Remote Sens 12:3019. https://doi.org/10.3390/rs12183019Search in Google Scholar

Alaimo L.S., Arcagni A., Fattore M., Maggino F., Quondamstefano V. (2022): Measuring Equitable and Sustainable Well-Being in Italian Regions: The Non-aggregative Approach. Soc Indic Res 161: 711–733. https://doi.org/10.1007/s11205-020-02388-7Search in Google Scholar

Ali A. (2019): Forest stand structure and functioning: Current knowledge and future challenges. Ecol Indic 98: 665–677. https://doi.org/10.1016/j.ecolind.2018.11.017Search in Google Scholar

Artusi R., Verderio P., Marubini E. (2002): Bravais-Pearson and Spearman Correlation Coefficients: Meaning, Test of Hypothesis and Confidence Interval. Int J Biol Markers 17: 148–151. DOI:10.1177/172460080201700213Search in Google Scholar

Bonett D.G., Wright T.A. (2000): Sample size requirements for estimating Pearson, Kendall and Spearman correlations. Psychometrika 65: 23–28. https://doi.org/10.1007/BF02294183Search in Google Scholar

Bravais A. (1846): Analyse mathématique sur les probabilités des erreurs de situation d’un point. Mémoires présentés par divers savants à l’Académie Royale des Sciences de l’Institut de France 9: 255–332.Search in Google Scholar

Broadhurst D.I., Kell D.B. (2006): Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2: 171–196. https://doi.org/10.1007/s11306-006-0037-zSearch in Google Scholar

Carter B.E., Wiles J.R. (2014): Scientific consensus and social controversy: exploring relationships between students’ conceptions of the nature of science, biological evolution, and global climate change. Evo Edu Outreach 7: 6. https://doi.org/10.1186/s12052-014-0006-3Search in Google Scholar

Clutton-Brock T., Sheldon B.C. (2010): Individuals and populations: the role of long-term, individual-based studies of animals in ecology and evolutionary biology. Trends Ecol Evol 25: 562–573. https://doi.org/10.1016/j.tree.2010.08.002 Search in Google Scholar

Dormann C.F., Elith J., Bacher S., Buchmann C., Carl G., Carré G., Marquéz J.R.G., Gruber B., Lafourcade B., Leitão P.J., Münkemüller T., McClean C., Osborne P.E., Reineking B., Schröder B., Skidmore A.K., Zurell D., Lautenbach S. (2013): Collinearity: a review of methods to deal with it and a simulation study evaluating their performance Ecography 36: 27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.xSearch in Google Scholar

Dwyer R.G., Krueck N.C., Udyawer V., Heupel M.R., Chapman D., Pratt H.L., Garla R., Simpfendorfer C.A. (2020): Individual and Population Benefits of Marine Reserves for Reef Sharks. Curr Biol 30: 480–489. https://doi.org/10.1016/j.cub.2019.12.005Search in Google Scholar

Eisinga R., Grotenhuis M.T., Pelzer B. (2013): The reliability of a two-item scale: Pearson, Cronbach, or Spearman-Brown? Int J Public Health 58: 637–642. https://doi.org/10.1007/s00038-012-0416-3Search in Google Scholar

Fontana M.D., de Araújo Moreira F., Di Giulio G.M., Malheiros T.F. (2020): The water-energy-food nexus research in the Brazilian context: What are we missing? Environ Sci Policy 112: 172–180. https://doi.org/10.1016/j.envsci.2020.06.021Search in Google Scholar

Hauke J., Kossowski T. (2011): Comparison of values of Pearson’s and Spearman’s correlation coefficient on the same sets of data. Quaestiones Geographicae 30: 87–93. DOI:10.2478/v10117-011-0021-1Search in Google Scholar

Hohenlohe P.A., Funk W.C., Rajora O.P. (2021): Population genomics for wildlife conservation and management. Mol Ecol 30: 62–82. https://doi.org/10.1111/mec.15720Search in Google Scholar

Horváth I.G., Németh Á., Lenkey Z., Alessandri N., Tufano F., Kis P., Gaszner B., Cziráki A. (2010): Invasive validation of a new oscillometric device (Arteriograph) for measuring augmentation index, central blood pressure and aortic pulse wave velocity. J Hypertens 28: 2068–2075. DOI:10.1097/HJH.0b013e32833c8a1aSearch in Google Scholar

Hyyppä J., Hyyppä H., Inkinen M., Engdahl M., Linko S., Zhu Y.H. (2000): Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. For Ecol Manage 128: 109–120. https://doi.org/10.1016/S0378-1127(99)00278-9Search in Google Scholar

Iqbal W., Tang Y.M., Chau K.Y., Irfan M., Mohsin M. (2021): Nexus between air pollution and NCOV-2019 in China: Application of negative binomial regression analysis. Process Saf Environ Prot 150: 557–565. https://doi.org/10.1016/j.psep.2021.04.039Search in Google Scholar

Jankowski A., Wyka T.P., Oleksyn J. (2021): Axial variability of anatomical structure and the scaling relationships in Scots pine (Pinus sylvestris L.) needles of contrasting origins. Flora 274: 151747. https://doi.org/10.1016/j.flora.2020.151747Search in Google Scholar

Lateef M., Keikhosrokiani P. (2023): Predicting Critical Success Factors of Business Intelligence Implementation for Improving SMEs’ Performances: a Case Study of Lagos State., Nigeria. J Knowl Econ 14: 2081–2106. https://doi.org/10.1007/s13132-022-00961-8Search in Google Scholar

Lefsky M.A., Hudak A.T., Cohen W.B., Acker S.A. (2005): Patterns of covariance between forest stand and canopy structure in the Pacific Northwest. Remote Sens Environ 95: 517–531. https://doi.org/10.1016/j.rse.2005.01.004Search in Google Scholar

Lin W.B. (2007): Factors affecting the correlation between interactive mechanism of strategic alliance and technological knowledge transfer performance. J High Technol Manage Res 17: 139–155. https://doi.org/10.1016/j.hitech.2006.11.003 Search in Google Scholar

Lindinger-Sternart S., Kaur V., Widyaningsih Y., Patel A.K. (2021): COVID-19 phobia across the world: Impact of resilience on COVID-19 phobia in different nations. Couns Psychother Res 21: 290–302. https://doi.org/10.1002/capr.12387Search in Google Scholar

Min Q., Lu Y., Liu Z., Su C., Wang B. (2019): Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry. Int J Inf Manage 49: 502–519. https://doi.org/10.1016/j.ijinfomgt.2019.05.020Search in Google Scholar

Moews B., Herrmann J.M., Ibikunle G. (2019): Lagged correlation-based deep learning for directional trend change prediction in financial time series. Expert Syst Appl 120: 197–206. https://doi.org/10.1016/j.eswa.2018.11.027Search in Google Scholar

Moradi F., Darvishsefat A.A., Pourrahmati M.R., Deljouei A., Borz S.A. (2022): Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data. Forests 13: 104. https://doi.org/10.3390/f13010104Search in Google Scholar

Nakagawa S., Cuthill I.C. (2007): Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol Rev 82: 591–605. https://doi.org/10.1111/j.1469-185X.2007.00027.xSearch in Google Scholar

Neumann M., Starlinger F. (2001): The significance of different indices for stand structure and diversity in forests. For Ecol Manage 145: 91–106. https://doi.org/10.1016/S0378-1127(00)00577-6Search in Google Scholar

Noor M.B.T., Zenia N.Z., Kaiser M.S., Al Mamun S., Mahmud M. (2020): Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia. Brain Inf 7: 11. https://doi.org/10.1186/s40708-020-00112-2Search in Google Scholar

Nowosad K., Bocianowski J., Kianersi F., Pour-Aboughadareh A. (2023): Analysis of Linkage on Interaction of Main Aspects (Genotype by Environment Interaction, Stability and Genetic Parameters) of 1000 Kernels in Maize (Zea mays L.). Agriculture 13: 2005. https://doi.org/10.3390/agriculture13102005Search in Google Scholar

Orsini L., Vanoverbeke J., Swillen I., Mergeay J., De Meester L. (2013): Drivers of population genetic differentiation in the wild: isolation by dispersal limitation, isolation by adaptation and isolation by colonization. Mol Ecol 22: 5983–5999. https://doi.org/10.1111/mec.12561Search in Google Scholar

Pearson K. (1896): Mathematical contributions to the theory of evolution. III. Regression, heredity, and panmixia. Phil Trans R Soc A 187: 253–318.Search in Google Scholar

Pearson K. (1908): On a mathematical theory of determinantal inheritance, from suggestions and notes of the late W. F. R. Weldon. Biometrika 6: 80–93. https://doi.org/10.1093/biomet/6.1.80Search in Google Scholar

Pearson K. (1920): Notes on the history of correlation. Biometrika 13: 25–45.Search in Google Scholar

Piovani J.I. (2008): The historical construction of correlation as a conceptual and operative instrument for empirical research. Qual Quant 42: 757–777.Search in Google Scholar

Puliti S., Breidenbach J., Astrup R. (2020): Estimation of Forest Growing Stock Volume with UAV Laser Scanning Data: Can It Be Done without Field Data? Remote Sens 12: 1245. https://doi.org/10.3390/rs12081245Search in Google Scholar

Rosato A., Tenori L., Cascante M., De Atauri Carulla P.R., dos Santos V.A.P.M., Saccenti E. (2018): From correlation to causation: analysis of metabolomics data using systems biology approaches. Metabolomics 14: 37. https://doi.org/10.1007/s11306-018-1335-ySearch in Google Scholar

Rutledge J., Oh H., Wyss-Coray T. (2022): Measuring biological age using omics data. Nat Rev Genet 23: 715–727. https://doi.org/10.1038/s41576-022-00511-7Search in Google Scholar

Saalidong B.M., Aram S.A., Out S., Lartey P.O. (2022): Examining the dynamics of the relationship between water pH and other water quality parameters in ground and surface water systems. PLoS ONE 17: e0262117. https://doi.org/10.1371/journal.pone.0262117Search in Google Scholar

Samal K., Mahapatra S., Ali H. (2022): Pharmaceutical wastewater as Emerging Contaminants (EC): Treatment technologies, impact on environment and human health. Energy Nexus 6: 100076. https://doi.org/10.1016/j.nexus.2022.100076 Search in Google Scholar

Schober P., Boer C., Schwarte L.A. (2018): Correlation Coefficients: Appropriate Use and Interpretation. Anesthesia & Analgesia 126: 1763–1768. https://doi.org/10.1213/ANE.0000000000002864Search in Google Scholar

Shimizu I., Kikukawa M., Tada T., Kimura T., Duvivier R., van der Vleuten C. (2020): Measuring social interdependence in collaborative learning: instrument development and validation. BMC Med Educ 20: 177. https://doi.org/10.1186/s12909-020-02088-3Search in Google Scholar

Solon J., Borzyszkowski J., Bidłasik M., Richling A., Badora K., Balon J., Brzezińska-Wójcik T., Chabudziński Ł., Dobrowolski R., Grzegorczyk I., Jodłowski M., Kistowski M., Kot R., Krąż P., Lechnio J., Macias A., Majchrowska A., Malinowska E., Migoń P., Myga-Piątek U., Nita J., Papińska E., Rodzik J., Strzyż M., Terpiłowski S., Ziaja W. (2018): Physico-geographical mesoregions of Poland: Verification and adjustment of boundaries on the basis of contemporary spatial data. Geogr Pol 91: 143–170. DOI:10.7163/GPol.0115Search in Google Scholar

Song H.Y., Park S. (2020): An Analysis of Correlation between Personality and Visiting Place using Spearman’s Rank Correlation Coefficient. KSII Trans Internet Inf Syst 14: 1951–1966. http://doi.org/10.3837/tiis.2020.05.005Search in Google Scholar

Spearman C. (1904): The Proof and Measurement of Association between Two Things. The Amer J Psychol 15: 72–101. https://doi.org/10.2307/1412159Search in Google Scholar

Stigler S.M. (1988): Francis Galton’s account of the invention of correlation. Stat Sci 4: 73–86.Search in Google Scholar

Thielmann I., Spadaro G., Balliet D. (2020): Personality and prosocial behavior: A theoretical framework and meta-analysis Psychological Bulletin 146: 30–90. https://doi.org/10.1037/bul0000217Search in Google Scholar

Tortella G.R., Rubilar O., Durán N., Diez M.C., Martínez M., Parada J., Seabra A.B. (2020): Silver nanoparticles: Toxicity in model organisms as an overview of its hazard for human health and the environment. J Hazard Mater 390: 121974. https://doi.org/10.1016/j.jhazmat.2019.121974Search in Google Scholar

Tundys B., Bretyn A., Urbaniak M. (2021): Energy Poverty and Sustainable Economic Development: An Exploration of Correlations and Interdependencies in European Countries. Energies 14: 7640. https://doi.org/10.3390/en14227640Search in Google Scholar

Udovičić M., Baždarić K., Bilić-Zulle L., Petrovečki M. (2007): What we need to know when calculating the coefficient of correlation? Biochemia Medica 17: 10–15.Search in Google Scholar

VSN International Genstat for Windows (2023): VSN International Genstat for Windows., 23rd Edition; VSN International: Hemel Hempstead, UK.Search in Google Scholar

Walker H.M. (1928): The relation of Plana and Bravais to theory of correlation. Isis 10: 466–484.Search in Google Scholar

Waszak N., Robertson I., Puchałka R., Przybylak R., Pospieszyńska A., Koprowski M. (2021): Investigating the Climate-Growth Response of Scots Pine (Pinus sylvestris L.) in Northern Poland. Atmosphere 12: 1690. https://doi.org/10.3390/atmos12121690Search in Google Scholar

Weida F.M. (1927): On various conceptions of correlation. Ann Math 29: 276–312.Search in Google Scholar

Wright I.J., Ackerly D.D., Bongers F., Harms K.E., Ibarra-Manriquez G., Martinez-Ramos M., Mazer S.J., Muller-Landau H.C., Paz H., Pitman N.C.A., Poorter L., Silman M.R., Vriesendorp C.F., Webb C.O., Westoby M., Wright S.J. (2007): Relationships Among Ecologically Important Dimensions of Plant Trait Variation in Seven Neotropical Forests. Ann Bot 99: 1003–1015. https://doi.org/10.1093/aob/mcl066Search in Google Scholar

Wrońska-Pilarek D., Krysztofiak-Kaniewska A., Matusiak K., Bocianowski J., Wiatrowska B., Okoński B. (2023b): Does distance from a sand mine affect needle features in Pinus sylvestris L.? For Ecol Manage 546: 121276. https://doi.org/10.1016/j.foreco.2023.121276Search in Google Scholar

Wrońska-Pilarek D., Maciejewska–Rutkowska I., Lechowicz K., Bocianowski J., Hauke–Kowalska M., Baranowska M., Korzeniewicz R. (2023a): The effect of herbicides on morphological features of pollen grains in Prunus serotina Ehrh. in the context of elimination of this invasive species from European forests. Sci Rep 13: 4657. https://doi.org/10.1038/s41598-023-31010-2Search in Google Scholar