Interspecific competition lab report

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Imran Rafi Ahmed Abdullah Punekar

Bio 3255 – Fall 2004

Interspecific competition lab report

Competitiveness and effects of differing densities of rye-grass (Lolium multiflorum) on combined leaf length of soybean (Glycine max) plants does not differ between potting soil and 33% sand environments.

Plant growth can be measured by several different variables, and is controlled by many more. Interspecific competition can inhibit plant growth by exploiting shared resources or having a direct influence on the growth of the target plants. Experiments were conducted with differing planted seed densities of rye-grass (Lolium multiflorum) which competed against soybean plants (Glycine max) in a potting-soil environment as well as a sandy, 33% potting soil environment. It was hypothesized that rye-grass, being a more widely common plant, would be a stronger competitor against soybean plants in the sandy environment which t was expected that soybean would have a harder time adapting to. The results indicated that there was no significant difference in the competitiveness of rye-grass against soybean between the two environments. This suggests that the difference in environments was not severe enough to elicit a difference in competition.

Plant performance in terms of growth is limited by many factors. These may include the availability of resources such as nutrients, water, space, and sunlight which may be controlled by the presence of intra- or inter-specific competitors. Intraspecific competition is typically critical only in high-density populations and works to limit population size by reducing resources overall. Interspecific competition, on the other hand, can be more complicated in that different species of plants may have greater affinities for, and demands on, specific classes of resources. Therefore, the effects of interspecific competition are more difficult to predict because they may rely on the restriction of shared resources or direct interference between specimens of different species (Ricklefs 2001:365-76).

In this experiment, the competitiveness and effects of differing densities of perennial rye-grass (Lolium multiflorum) on the combined leaf length of target soybean (Glycine max) plants were tested and measured. The aim of this study was to determine what effect, if any, the higher competitor density would have on the growth of the target specimens. Specifically, the effect, in relation to soil quality, on target combined leaf length—a parameter measured by calculating the sum of the lengths of each leaf on the plant—was examined, along with the dependent variables of target plant height, mass, and number of leaves.

Higher densities of rye-grass were expected to result in inversely proportional performance of the target soybean plants. In order to analyze the effect of soil composition, trials were repeated in both potting soil and a 67% potting soil – 33% sand environments. It was predicted that soybean growth would be inhibited, in terms of combined leaf length, to a greater degree by high densities of rye-grass in the sandy environment than in the potting soil environment. Such a relationship, if true, may be explained by the fact that rye-grass, common across most of the United States as well as Europe and adapted to climates as varied as those of Texas, Maine, and Alaska, is able to adapt well and flourish in different environments successfully, whereas soybean plants, whose region of origin is much smaller and which are much more dry-intolerant, presumably do not have the same level of adaptability (Hannaway et al. 1999).


This study was conducted in close collaboration with K. Shemanski, of Villanova University, PA (Shemanski 2004). In order to test the above hypotheses, an experiment was conducted in which target four soybean plants per pot were subjugated to interspecific competition from rye-grass in five different competitor densities, ranging from 0 seeds to 80 seeds. A total of 30 pots were used—split into two trays, one with potting soil, and the other with a 33% sand mixture. Within each tray, three pots were dedicated to each competitor seed density, thus providing multiple trials for the same test. The potting soil material was obtained from the potting soil supply at the Villanova University Biology Department greenhouse, whereas the sandy mixture was custom made by thoroughly mixing approximately 33% by volume coarse grained sand and 67% of the same potting soil. The pots were arranged in both trays in the following manner, with the order of the pots within each row having been determined by the use of a random number table, where A1, B1, and C1 are identical trials:




(A3) 7 soybean seeds, 40 rye-grass seeds

(B3) 7 soybean seeds, 40 rye-grass seeds

(C4) 7 soybean seeds, 60 rye-grass seeds

(A4) 7 soybean seeds, 60 rye-grass seeds

(B2) 7 soybean seeds, 20 rye-grass seeds

(C1) 7 soybean seeds, 0 rye-grass seeds

(A2) 7 soybean seeds, 20 rye-grass seeds

(B1) 7 soybean seeds, 0 rye-grass seeds

(C5) 7 soybean seeds, 80 rye-grass seeds

(A5) 7 soybean seeds, 80 rye-grass seeds

(B4) 7 soybean seeds, 60 rye-grass seeds

(C4) 7 soybean seeds, 60 rye-grass seeds

(A1) 7 soybean seeds, 0 rye-grass seeds

(B5) 7 soybean seeds, 80 rye-grass seeds

(C2) 7 soybean seeds, 20 rye-grass seeds

Table 1 – Layout of 15 pots within each tray. A1, B1, C1 are identical trials, and so on. Order of pots within each column determined by use of random number table.
The seeds were planted on 21 Sep., 2004 and were allowed to grow in a greenhouse setting (Villanova University Biology Dept. Greenhouse, Villanova, PA) for 8 weeks during which time they were exposed to daytime lighting and regular watering. Aside from one visit on 6 Oct., during which the target plants were “thinned-out” to 4-5 per pot, the plants were undisturbed. The local weather during this time was variable, temperatures ranging from a high of 26 ˚C to a low of -5 ˚C with an average of 11˚C. The precipitation during this time period for this region was minimal at only 1.99cm (Weather Underground 2004). On 16 Nov., 2004, destructive sampling commenced and the plants were removed from the pots for measurement and disposal. The soil from the experiment was duly collected for reuse. During sampling, first the rye-grass plants were cut off at soil level using a blade, and subsequently collectively weighed then disposed of. Then, the soybean plants were similarly cut off; each was weighed, its height determined, leaves counted, and combined leaf length measured. The combined leaf length was an innovative measure carefully performed according to the following protocol:

Figure 1 – Protocol showing method of measurement of combined leaf length, an innovative measurement taken for target soybean plants. Under this protocol, the leaves of a particular plant are removed, lined up end-to-end, and the total length measured using a standard centimeter-scale ruler. In the example above, the total leaf length for this soybean plant with three leaves is approximately 8cm.

Data from these measurements were recorded and transcribed into JMP IN 4 (v. 4.0.2, SAS Institute 2000). Further analysis and of the data was performed using various statistical tests using JMP as well as Microsoft Excel (Microsoft Corp. 2003).

Figure 2 – Effects of rye-grass seed density on target soybean combined leaf length in potting soil and 33% sand mixture. Regression equations:

Leaf length in Potting soil= 241 - 5.10 (Rye-grass Seed Density); F1,13=23.4, P =0.0003, R2=0.643

Leaf length in Sandy soil= 213 - 5.55 (Rye-grass Seed Density); F1,13=10.7, P = 0.0061, R2=0.451

Figure 3 – Comparison of slopes of linear fit of target mass vs competitor seed density plots in sandy and Potting soil trays. Showing Standard Error bars for each.

Figure 4 – Bivariate Fit analysis (P=0.950) of dependent variables: Soybean Mass and Soybean Combined Leaf Length for Potting soil and 33% Sand Mixture Trays.

Combined leaf length vs. Mass correlation in Potting Soil= 91.5%.
Combined leaf length vs. Mass correlation in Sandy soil= 96.2%.

Analysis showed greater correlation between Soybean Leaf Length and Mass than between Leaf Length and Height (76.7%, 72.7%).

Observations at time of sampling showed few plants which had not grown well. Most of the poor plants were found in the pots with high rye-grass seed density (60-80 seeds) or were located at the periphery of the pots, indicating that perhaps neighboring plants had overshadowed them and thus restricted their growth. In every pot, at least 4 soybean plants were healthy enough to be measured (Table 1), however in the few cases that there were more than 4 soybean plants, all were measured.. Visual analysis confirmed the presence of intended plant densities of rye-grass in each pot for both trays (Table 1).

Results from both trays indicated a strong relationship between rye-grass density and target performance as measured by combined leaf lengths (Fig. 1). The relationships between these two variables were linear and appropriate linear fits were added (Fig. 1). The same was true of target performance and rye-grass combined mass, however, the R2 values for these were much lower, indicating a weaker relationship. Counting the actual number of rye-grass specimen alive was not feasible, and so that variable was ignored.

The visual representation of the difference in the values of the slopes for each tray is seen in Fig. 3, which also shows the standard error for each slope. An ANCOVA test of the two slopes gave the following values: F = 0.0514, df = 29, P = 0.823.

A bivariate fit test was conducted on Soybean Leaf Length vs. Height, however, the level of correlation obtained was rather low, especially when compared to the correlation percentages of Soybean Leaf Length vs. Soybean Plant Mass (Fig. 4). The high correlation values obtained indicated a very strong relationship between Soybean leaf length and plant mass (Fig. 4).


There was a strong effect of interspecific competition by the rye-grass on the combined leaf length of the target soybean plants in this experiment. This effect was clearly density dependent and increased according to rye-grass seed density (Fig. 2). The data was approximated by linear fits reasonably well (and gave significant P values), although the R2 values were nonetheless rather small (Fig. 2). This simply indicated that the linear fits and any conclusions formed from, although maybe significant, did not overwhelmingly represent the entire sample of study. Visual analysis of the slopes of the two lines reveals that they are almost equal (Fig. 2). This suggests that, in fact, the null hypothesis that there was no overarching effect of soil quality on the effect of interspecific competition on soybean combined leaf length, was true. The two slopes are represented visually with SE bars in Fig 3, and the major overlap confirms the fact that the slopes are not significantly different, and that there was no effect of soil composition on the competitiveness of rye-grass against soybean plants. This was further corroborated by the results of the ANCOVA test, which showed revealed a P-value of 0.823, much higher than the .05 or less needed to show that the two slopes are significantly different. This result is surprising, as it was expected that there would be an effect of soil composition, especially considering the wide origin and adaptability of the rye-grass but not the soybean. Visual examination showed that, in general, the plants of the sandy tray did worse than those in the potting-soil tray, however, the effect of competition by the rye-grass and the resultant inhibition of soybean combined leaf length growth, seems to have been equivalent in both trays. This disproves the hypothesis that rye-grass would be a better competitor against soybean in the sandy soil. One reason that the hypothesis did not hold may be that the difference in soil quality, while it may seem large, did not affect the nutrient content and draining ability of the environment as much as might be necessary to elicit significant differences. The data also suggest, by the fact that the slopes for both trays came out to be essentially the same, that, instead, the effect on soil quality was so great that the decrease in the ability of rye-grass to compete was fully compensated by a similar decrease in survivorship of the target plants. However, the conditions as determined by visual inspection, while they do support this theory, were not severely different enough to provide conclusive evidence.

Analysis of the data, and in particular, the bivariate fit presented in Fig. 4 showed that height and combined leaf length were not highly correlated variables, with correlation percentages of 76.7% and 72.7% for the potting soil tray and the sandy tray, respectively. However, as is shown in Fig. 4, combined leaf length and plant mass was very highly correlated, with correlation percentages of 91.5% for the potting soil tray and the extremely high 96.5% for the sandy tray. This is expected, because, whereas it is clear that leaf length and height are not intimately related variables, mass and leaf length must be simply because it is not possible to increase leaf length alone without also increasing the mass of the plant. On the other hand, it is possible to increase leaf length without affecting the plants height. It is interesting that there is a higher correlation in the sandy, and presumably, nutrient-deficient soil than in the pure potting soil, however, this may be explained by the fact that, when nutrients are scarce, plants would be more likely to limit, and control to a greater degree, growth in both terms of leaf-length and mass. These results may not revolutionize the world of ecology today, and they not even have a lasting impact on this course… but the methods innovated here surely will. Combined leaf length, with its high correlation to biomass and accuracy may soon become a new standard in ecological ventures world wide. In other aspects, the data is not terribly remarkable, nor does it bespeak any special endowment of knowledge. However, it may be useful in the innovation of experimental design and the exploration of new methods, variables and outlooks which may prove fruitful to this or any scientific endeavor.


Hannaway, D., S. Fransen, J. Cropper, M. Teel, M. Chaney, T. Griggs, R. Halse, J. Hart, P.

Cheeke, D. Hansen, R. Klinger, and W. Lane. Annual Ryegrass. PNW 501: April 1999.

Microsoft Corporation. 2003. Microsoft Excel v11.6355.6360. Microsoft Office Professional Edition 2003. USA.

Ricklefs, R. E. 2001. The economy of nature, Fifth edition. W. H. Freeman, New York, New York, USA.

Sall, J., Lehman, A., and Creighton, L. 2001. JMP v5.1.0.2. Duxbury Press, Belmont, California, USA.

Shemanski, K., 2004. Untitled. Unpublished laboratory project report, Biology 3255, Villanova University, Fall 2004.

Weather Underground, 2004. History for Philadelphia Wings Airport, Pennsylvania.

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