Mediterranean forest- The Mediterranean forests, located 40S latitude are common in places such as the South of France where our experiments will be undertaken.
Observations of the local area shows considerable variation in the level of vegetation varies considerably from very little plant life to complete coverage of lush vegetation. The research question to be investigated was whether the variation in levels of vegetation coverage were associated with differences in pH level in the soil.
Given that the availability of various plant nutrients changes as the soil becomes more acid (low pH) or more alkali (high pH), different nutrients are more or less available based on pH. In addition, plants in an area will show attraction to certain levels of alkalinity. Thus, it can be expected that changes in the pH of the soil in various patches of ground might be associated with changes in the degree of vegetative cover in that patch of ground.
The hypothesis, that differences in soil pH are affecting vegetation levels in the Mediterranean area of southern France, will be investigated by researching differences in the soil alkalinity of vegetation cover. More specifically, the hypothesis question to be tested is:
A higher level of vegetation cover will be positively correlated to greater alkalinity of the soil in that area.
Since there is no way in conducting this experiment to determine which of these measures is the cause and which the effect, all that can be determined from the planned experiment is whether there is an association between pH level and percent of vegetation cover.
Variables and Control of Variables
The independent variable is the level of vegetation coverage as measured by a percentage of the area of the patch being measured. This allows for minor differences in sizes of patches due to terrain, measurement error, or similar variations. The dependent variable is the pH of the soil in each patch of ground measured. The controlled variables are the specific locations used to define the patches of ground and the time of day in which the data is collected. The uncontrolled variables are the specific variations in climate such as sunlight, soil moisture, rainfall, and temperature, as well as the species of plants in the various samples used. Therefore, the patches will be chosen to be as similar as possible in exposure to these variables.
The apparatus used in this experiment include
Quadrats of 25 cm. x 25 cm., corresponding to near fairly uniform levels of vegetation;
A bottle of distilled water 1.25liter;
20 clean beakers;
Nine pH test probes;
Electronic pH meter;
A digital camera to record each patch used;
A 50 cm. ruler;
Plastic bags; and
A stratified sampling method was used to investigate the research question, as both random sampling but standardization of those samples were required. The process used summarized in the following:
A 100 cm by 100 cm area was staked out as an initial sampling space;
Within this area, a specific patch 25 cm. by 25 cm. was identified with 20% vegetative coverage.
This small patch was photographed to determine an actual percent of vegetative coverage, and marked off with sticks.
This small area was then dug up, and the soil sample was placed in a plastic bag.
Two additional patches of similar size were located by specifically choosing a larger patch and identifying a smaller patch of 25cm by 25cm within it.
one with between 35 percent and 70 percent vegetative coverage, and a second with vegetative coverage of between 70 percent and 100 percent coverage.
These two additional patches were similarly photographed, marked off with sticks, dug up, and samples placed in plastic bags.
Once the three sample patches from this 100 cm. by 100 cm. area were collected as noted above, the entire procedure was repeated in two other 100 cm. by 100 cm. areas, making a total of three larger areas, each with three patches that were photographed and samples collected. The total number of samples taken was thus nine.
Once the sample collection process was complete, the nine samples were taken to the lab for further data collection. In the lab, the following data collection procedure was used for each of the nine samples:
20 g. of soil were taken from the sample, and 100 ml. of distilled water was added and mixed for 15 minutes.
A filter was positioned on top of an unused beaker (a different beaker for each of the nine samples).
The soil-and-water mixture was poured through the filters into the beakers.
The pH of the resulting filtered sample was tested with a pH test probe, then read by the electronic pH reader.
All data was recorded and is presented in the next section
The collected data was placed in an Excel spreadsheet for analysis. The correlation between the two measures was calculated, and the resulting graph is provided Figure 1 above.
A correlation measures whether a variable (vegetation coverage) rises or falls in accord with the alkalinity of the soil. For example, if a rise in vegetative cover percentage is generally accompanied by a rise in pH levels, the correlation is a positive one, with a maximum value of 1.0. If the vegetative cover percentage generally falls when the pH rises (i.e., goes in the opposite direction), the correlation is a negative one, with a minimum value of –1.0. When changes in one variable are not consistent with changes in the other variable, the correlation is very close to 0.0.
The correlation of the data collected in this experiment is -0.36635.
In other words, there is no significant association between the pH of the soil in the samples collected and the amount of vegetative cover in their corresponding patches.
Discussion Evaluation and Conclusion
Figure A scattrgram of the data collected in this experiment.
As noted in the previous section, the data refutes any significant correlation between vegetative cover and soil alkalinity as predicted in the hypothesis, and other investigations would be necessary to determine why vegetation levels were higher in some patches rather than others.
The hypothesis was based on the presumption that changes in pH levels would result in changing availability of nutrients in the soil. Higher alkalinity is associated with higher nutrient levels dissolved in water; it was expected that this would be associated with greater levels of vegetation in that soil. However, this was not supported by the limited data collected here. Several factors might cause this difference between expected outcome and the data collected.
First, it may be that the limited range of variability of the pH levels is too small to make much difference in nutrient availability. If the soil samples had come from substantially different environments—a swamp, a river delta, a grassy field, etc.—there may have been enough difference to matter to the vegetation.
Second, it may also be that there were simply too few samples collected to show much.
Nine samples is very few on which to base any kind of hard numerical results. If there were more like a hundred samples rather than only nine, the results might be more conclusive.
Third, while all efforts were made to properly isolate the various samples by using separate containers, pH probes, and so on, it is possible that a procedure error might have contaminated the results.
Finally, there was insufficient attention paid to other factors that may also impact vegetation coverage, items such as amount of sunlight, the species of plants in each patch, the soil moisture, and so on. It is very possible that some of those factors might have overwhelmed the effect of pH on the vegetation growth, and thus made it impossible to determine any correlation with that factor.
The biggest improvement to this field study of alkalinity and vegetation levels in a natural setting would be replicate the findings in the controlled environment of a laboratory. Using controlled patches of soil in which factors such as temperature, pressure, light, and so on could be controlled would remove variation and complexity from affecting the results. In particular, it would be important to ensure that all patches had the same type of plants in them. This was completely uncontrolled in this experiment, and since different plants want different nutrients, a changing pH level might simply be more attractive to different species of plants. One example would be evergreens, which prefer slightly acidic soil.
This experiment generated no significant correlation in the level of vegetation and the alkalinity of soil in the nine samples assessed. Thus, the hypothesis that such a correlation exists is refuted by this data and was not supported. Future investigations into this question should use more tightly controlled samples, and include measures of light exposure, temperature, relative humidity, and other factors that may be influences to plant growth. In particular, however, it would also be important to ensure that all patches used had the same mixture of species in the same proportions.