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
Abstract:Prunus serotina Ehrh. is an alien invasive neophyte widespread in European forests. So far, no effective methods of its elimination have been developed. For this reason, the aim of our study was to determine how herbicides affect the morphological characteristics of pollen grains. This knowledge may be crucial to control this invasive species. The current study was carried out in a research area of 2.7 ha located in the Zielonka Forest near Poznań, Poland (N 52°31′58.016″, E 17°05′55.588″). We tested morpholog… Show more
“…Effect of long-term herbicide influence on Prunus serotina pollen studied Wrońska-Pilarek et al (2023).…”
Section: Resultsmentioning
confidence: 99%
“…The data of the authors on the size of the pollen grains of same plant species are variable and may vary, often depending on environmental factors or pollen treatment in the analyses. Various external factors such as air temperature, precipitation, desiccation, flood stress, drought stress, plant nutrition and effect of herbicides can influence on the size of pollen grains (Dajoz et al, 1991;Delph et al, 1997;Ejsmond et al, 2011Ejsmond et al, , 2015Yamburov et la., 2014;Mehmood et al, 2023;Wrońska-Pilarek et al, 2023).…”
Palynology as a science creates many opportunities for its practical use. The study of symmetry, polarity, shape, size, structure, sculpture and apertures of spores can be very useful for many other sciences such as botany, oceanography, limnology, pedology,
“…Effect of long-term herbicide influence on Prunus serotina pollen studied Wrońska-Pilarek et al (2023).…”
Section: Resultsmentioning
confidence: 99%
“…The data of the authors on the size of the pollen grains of same plant species are variable and may vary, often depending on environmental factors or pollen treatment in the analyses. Various external factors such as air temperature, precipitation, desiccation, flood stress, drought stress, plant nutrition and effect of herbicides can influence on the size of pollen grains (Dajoz et al, 1991;Delph et al, 1997;Ejsmond et al, 2011Ejsmond et al, , 2015Yamburov et la., 2014;Mehmood et al, 2023;Wrońska-Pilarek et al, 2023).…”
Palynology as a science creates many opportunities for its practical use. The study of symmetry, polarity, shape, size, structure, sculpture and apertures of spores can be very useful for many other sciences such as botany, oceanography, limnology, pedology,
“…Rank correlation is less sensitive to outliers than linear correlation because it is based on the order of the ranks rather than the exact values of the data. Correlation analysis is very often performed on forest stand data (Ahmadi et al 2020;Hyyppä et al 2000;Puliti et al 2020;Moradi et al 2022;Wrońska-Pilarek et al 2023a).…”
The Spearman rank correlation coefficient is a non-parametric (distribution-free) rank statistic proposed by Charles Spearman as a measure of the strength of the relationship between two variables. It is a measure of a monotonic relationship that is used when the distribution of the data makes Pearson's correlation coefficient undesirable or misleading. The Spearman coefficient is not a measure of the linear relationship between two variables. It assesses how well an arbitrary monotonic function can describe the relationship between two variables, without making any assumptions about the frequency distribution of the variables. Unlike Pearson's product-moment (linear) correlation coefficient, it does not require the assumption that the relationship between variables is linear, nor does it require that the variables be measured on interval scales; it can be applied to variables measured at the ordinal level. The purpose of this study is to compare the values of Pearson's product-moment correlation coefficient and Spearman's rank correlation coefficient and their statistical significance for six Pinus sylvestris L. traits (original – for Pearson's coefficient and ranked – for Spearman's coefficient) estimated from all observations, object means (for trees) and medians. The results show that the linear and rank correlation coefficients are consistent (as to direction and strength). In cases of divergence in the direction of correlation, the correlation coefficients were not statistically significant, which does not imply consistency in decision-making. Estimation of correlation coefficients based on medians is robust to outlier observations and factors that linear correlation is then very similar to rank correlation.
“…Rank correlation is less sensitive to outliers than linear correlation because it is based on the order of the ranks rather than the exact values of the data. Correlation analysis is very often performed on forest stand data [15][16][17][18][19].…”
The Spearman rank correlation coefficient is a non-parametric (distribution-free) rank statistic proposed by Charles Spearman as a measure of the strength of the relationship between two variables. It is a measure of a monotonic relationship that is used when the distribution of the data makes Pearson's correlation coefficient undesirable or misleading. The Spearman coefficient is not a measure of the linear relationship between two variables. It assesses how well an arbitrary monotonic function can describe the relationship between two variables, without making any assumptions about the frequency distribution of the variables. Unlike Pearson's product-moment (linear) correlation coefficient, it does not require the assumption that the relationship between variables is linear, nor does it require that the variables be measured on interval scales; it can be applied to variables measured at the ordinal level. The purpose of this study is to compare the values of Pearson's product-moment correlation coefficient (treating the data in a quantitative way) and Spearman's rank correlation coefficient (treating the same data in a somewhat "qualitative" way) and their statistical significance for six Pinus sylvestris L. traits (original – for Pearson's coefficient and ranked – for Spearman's coefficient) estimated from all observations, object means (for trees) and medians. The results show that the linear and rank correlation coefficients are consistent (as to direction and strength). In cases of divergence in the direction of correlation, the correlation coefficients were not statistically significant, which does not imply consistency in decision-making. Estimation of correlation coefficients based on medians is robust to outlier observations and factors that linear correlation is then very similar to rank correlation.
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