Intraspecific Competition in Plants - Lab Report Example

Intraspecific competition in plants Introduction Intraspecific competition is the conflict over resources between members of the same species, in which case the individuals may compete over mates, water, sunlight, and territory or food energy sources. Thus intraspecific competition is an important factor in limiting the population size of many species, as it can cause populations to stop growing when they reach their carrying capacity (Begon 2009). In plants, competition is evident in dense populations’ shortly after germination and thereafter becomes operative progressively in populations of lower and lower density. In plants, an isolated seedling in fertile soil may have a very high chance of surviving to reproductive maturity. it will probably exhibit an extensive amount of module growth and will eventually produce a large number of seeds (Pujol 2006). However, a seedling that is closely surrounded by neighbours ,shading it with their leaves and depleting the water and nutrients of its soil with their roots, will be very unlikely to survive, and if it does, it will almost certainly form few modules and set few seeds (Wang 2005). Thus, it’s evident that the ultimate effect of competition on an individual is a decreased contribution to the next generation compared with what would have happened had there been no competitors. Thus the main aim of this report will be to test the null hypothesis that there is no statistically significant evidence in the data to show that the data provided is normally distributed. Methodology Group plants were collected and put into six pots per group while being planted at densities of 1,2,4,8,16,32 plants for every pot. Then a tray was used for storing tap water. The roots were keenly and carefully washed out in water while ensuring that the whole plant is kept intact. The next step involved separating the root from the hypocotyls as well as from the leaves of the plant. After separation process, the total number of plants in every pot were counted and recorded. For each pot, the total root weight was taken, total shoot weight as well as total hypocotyls weight and the data recorded in an Excel table. The next step entailed calculating the mean weight (of shoots or roots or hypocotyls) per plant. Calculation of mean weight per plant was done by dividing the weights by the number of plants under study (Bossdorf 2005). Finally, all the contents of each pot were put into one bag and send to the oven – labelling the bag with the group name and plant density. There were a total of 6 bags. All the six bags were put inside another bag to keep them together. One group measured the dried plant parts and the data recorded to be used for analysis. Then graphs of the data were drawn to facilitate evaluation of the relationship between growth of the individual plant and sowing density. Many parametric statistical methods (ANOVA, discriminant analysis, linear regression, Pearson correlation, F-test and t-test) require that the dependent variable is approximately normally distributed for each category of the independent variable. Results, tests and analysis Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent Total fresh mass g/pot 47 48.5% 50 51.5% 97 100.0% Descriptive Statistic Std. Error Total fresh mass g/pot Mean 22.890638297872442 1.950306159799732 95% Confidence Interval for Mean Lower Bound 18.964875612266948 Upper Bound 26.816400983477937 5% Trimmed Mean 22.734669030732956 Median 23.640000000000100 Variance 178.774 Std. Deviation 13.370625396620941 Minimum 1.3600000000000998 Maximum 46.9600000000001000 Range 45.6000000000001000 Interquartile Range 21.4600000000000970 Skewness .223 .347 Kurtosis -.934 .681 In data analysis, skewness and kurtosis Z-values should always be somewhere in the span of -1.96 to +1.96, the skewness and kurtosis measures should be as close to zero as possible, however, in reality, data are always skewed and kurtotic.A small departure from zero has no problem, as long as the measures are not too large compared to their standard errors (SE) and as a consequence, one must divide the measures by its standard error manually using a calculator. This will give the Z-value, which should be somewhere between -1.96 to +1.96. Skewness z-value of the above data thus results to 0.223/0.347 =0.643 with a kurtosis z-value of -0.934/0.681= -1.372. The skewness z-value 0.643 is neither below -1.96 nor above 1.96, which is within the desired range. The same applies to the Kurtosis z-value which is -1.372. From the above data and calculations, one can conclude that regarding skewness and kurtosis, the t data is a little skewed and kurtotic for the total fresh mass in g/pot but it does not differ significantly from normality. We can therefore assume that, the data are approximately normally distributed, in terms of skewness and kurtosis. Tests of Normality Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig. Total fresh mass g/pot .097 47 .200* .957 47 .080 *. This is a lower bound of the true significance. a. Lilliefors Significance Correction In testing for Normality, the Shapiro-Wilk test p-value should be above 0.05. The null hypothesis in Shapiro-Wilk test for normality is that the data for the total fresh mass in g/pot are normally distributed. This null hypothesis will be rejected if the p-value is below 0.05. In SPSS, the p-value is labelled “sig”.Since the p-value in the above table. 0.080 is above 0.05, we fail to reject the null hypothesis above. It can therefore be concluded that in terms of Shapiro-Wilk test, the data above on the total fresh mass in g/pot are approximately normally distributed. The histograms, Normal Q-Q plots and Box plots should visually indicate that the data are approximately normally distributed. However, data do not have to be perfectly normally distributed, the main thing is that they are approximately normally distributed; this can be done by checking each category of the independent variable (Zhou et al 2012). The data plots (dots) should be along the line (best fit); this indicates that the data is normally distributed. For the above graph, the data on the total fresh mass in g/pot is approximately distributed along the line, from this; we can conclude that total fresh mass in g/pot data is approximately normally distributed (Ayres 2006). Statistics Total fresh mass g/pot N Valid 47 Missing 50 After inspecting the box plots, it should be as symmetrical as possible. By inspection method, the above Box plot chart data are not perfectly symmetrical but they are good enough to conclude that the total fresh mass in g/pot is approximately normally distributed. For the given data, inspection must be done on the histogram for the total fresh mass in g/pot. The histogram should have the approximate shape of the general curve (normal curve) for the data to be validated to be normally distributed with mean equal to median and mode. The graph above for the total fresh mass in g/pot exhibits a normal curve by inspection. It can therefore be concluded that the data is approximately normally distributed. ANOVA Number sown Sum of Squares df Mean Square F Sig. Between Groups 5535.830 46 120.344 . . Within Groups .000 0 . Total 5535.830 46 Conclusion In conclusion, a Shapiro-Wilk’s test (p>0.05) and a visual inspection of their histograms, Normal Q-Q plots and Box plots showed that the total fresh mass in g/pot data were approximately normally distributed with a skewness of 0.223 (Standard Error = 0.347) and a kurtosis of -0.934 (Standard Error = 0.681). Parametric methods help in checking if a dependent variable is approximately normally distributed for each category of an independent variable. References Ayres, R. L., Gange, A. C., & Aplin, D. M. 2006. Interactions between arbuscular mycorrhizal fungi and intraspecific competition affect size, and size inequality, of Plantago lanceolata L. Journal of Ecology, 94(2), 285-294. Begon, M., Mortimer, M., & Thompson, D. J. 2009. Population ecology: a unified study of animals and plants. John Wiley & Sons. Bossdorf, O., Auge, H., Lafuma, L., Rogers, W. E., Siemann, E., & Prati, D. 2005. Phenotypic and genetic differentiation between native and introduced plant populations. Oecologia, 144(1), 1-11. Pujol, B., & McKey, D. 2006. Size asymmetry in intraspecific competition and the density‐dependence of inbreeding depression in a natural plant population: a case study in cassava (Manihot esculenta Crantz, Euphorbiaceae). Journal of Evolutionary Biology, 19(1), 85-96. Wang, L. W., Showalter, A. M., & Ungar, I. A. 2005. Effects of intraspecific competition on growth and photosynthesis of Atriplex prostrata. Aquatic botany, 83(3), 187-192. Zhou, J., Dong, B. C., Alpert, P., Li, H. L., Zhang, M. X., Lei, G. C., & Yu, F. H. 2012. Effects of soil nutrient heterogeneity on intraspecific competition in the invasive, clonal plant Alternanthera philoxeroides. Annals of Botany, 109(4), 813-818. Read More