IA2 Student Experiment PDF

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The Impact of Disturbances and Changes of Sunlight in a Sclerophyll Forest IA2 STUDENT EXPERIMENT BRIGIDINE COLLEGE INDOOROOPILLY Patricia Consoli | Biology Unit 3 | 24/03/2021 Word count: 1894

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Table of Contents List of Figures......................................................................................................................................................................2 P Value identification table.................................................................................................................................................2 1.0

Rationale..................................................................................................................................................................3

2.0

Research Question..................................................................................................................................................4

3.0

Original Experiment...............................................................................................................................................4

4.0

Modifications of the Methodology.........................................................................................................................5

4.1

Refined by:............................................................................................................................................................5

4.2

Extended by:.........................................................................................................................................................5

5.0

Safety and Ethical Considerations..........................................................................................................................5

6.0

Processed Data........................................................................................................................................................6

6.1

Calcuations Table................................................................................................................................................6

6.2

FIGURE 1 – Diversity...........................................................................................................................................7

6.2.1 6.3

FIGURE 2 – Incident Sunlight............................................................................................................................8

6.3.1 6.4 7.0 7.1 8.0 8.1 9.0

Interpretation and Analysis........................................................................................................................7

Interpretation and Analysis........................................................................................................................8 Discussion............................................................................................................................................................9

Evaluation................................................................................................................................................................9 Limitations...........................................................................................................................................................9 Sources of Error.......................................................................................................................................................9 Reliability and Vaildity.......................................................................................................................................10 Improvements and Extensions..............................................................................................................................10

9.1

Improvements....................................................................................................................................................10

9.2

Extensions...........................................................................................................................................................10

10.0

Conclusion...............................................................................................................................................................11

Reference List.....................................................................................................................................................................12 Appendix.............................................................................................................................................................................12

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List of Figure Figure 1: Location Map of Sites A & B................................................................................................................................4 Figure 2: Diversity between Sites A and B (ns = p>0.05). Error bars represent standard error......................................7 Figure 3: Incident Sunlight (W/m2) at Sites A and B (ns = p>0.05). Error bars represent standard error....................8 Y Table 1: Calculations used within this experiment...........................................................................................................6

P Value identification table

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1.0

Rationale

Humans disturbing natural ecosystems has been occurring for hundreds of years, however it has become more of a pressing issue in our world today. Entire species are going extinct because of anthropogenic processes, rather than allowing for biophysical processes to continue their natural ways. Therefore, the effect of human disturbances on an ecosystem is demonstrated in our immediate surroundings. If there is a correlation between diversity and sunlight within an Australian forest, ways can be created to conserve endangered areas due to these human impacts. Biodiversity is the variety of biotic and abiotic factors – all living organisms and non-living factors respectively – found within a certain area. The abiotic factors include pH levels, sunlight, salinity levels, etc. Biodiversity can be measured by looking at the species abundance and richness of the organisms in an area. Abundance is defined as the number of individuals per species whereas the richness is the number of different species in an ecosystem [ CITATION MWa19 \l 3081 ]. Simpson’s Diversity Index (SDI) is another method of measuring diversity, which combines finding the mean of the richness and abundance of a site. Investigating the richness and abundance of trees and incident sunlight within a sample size of 30 subsites at two separate sites, one disturbed (Site A) and one undisturbed (Site B), was performed to fulfill the specific research question. The investigation was undertaken at the University of Queensland, St Lucia (Figure 1). The difference between disturbed and undisturbed is recognised by the impacts of both anthropogenic and biophysical measures (footpaths, drain systems, planting non-native trees, clearing of land). Site A is defined as a disturbed site, as it is placed along the beginning of a footpath. The vegetation type of the two sites is characterized as a dry sclerophyll ecosystem. Sclerophyll forests, the typical Australian vegetation type, are mainly occupied with hard, soft and spiky leaved trees with a low soil fertility level. These include eucalypts, wattles and banksia trees [ CITATION NSW21 \l 3081 ]. It is hypothesised that the more sunlight there is in an ecosystem, the presence of diversity of trees will be less. This is achieved by changing the independent variable (disturbed against undisturbed forest), it will alter the dependent variables (sunlight and diversity).

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Figure 1: Location Map of Sites A & B

2.0 Research Question Is there a correlation in the amount of sunlight present and the diversity of trees (richness and abundance) in both a disturbed and undisturbed dry sclerophyll ecosystem?

3.0

Original Experiment

The original mandatory practicals that were conducted in Robertson Park Indooroopilly included calculation of SDI using a pitfall trap (container with ethanol designed to trap any motile species that comes across the trap) a at a random location within each site. Specht’s Vegetation Classification Matrix was used to identify the vegetation type, using visual estimation for tree height and a line transect (10m tape measure placed at the site) for percentage tree cover. A point transect (10m tape measure with trees recorded when they are seen within a 3m radius on either side) was used as the surveying technique, as it was best fit for the area and we measured in both a disturbed and undisturbed area. The sample size measured n=4 subsites per site. The two sites were identified as a wet and dry sclerophyll forest. PAGE 4

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4.0 Modifications of the Methodology 4.1

REFINED BY:

Only certain pieces of the data collected were used to support the research question. The main and most important modifications include the following: -

Measuring the diversity using species richness and abundance

-

Measuring the incident sunlight at a random spot at each site

-

Larger sampling size: n=9 subsites per site

-

Only observing a dry sclerophyll forest (not both a wet and dry one) 4.2

EXTENDED BY:

The point transect stayed as the surveying technique, with readings done from within a 3m radius on either side of the tape measure – like practised during the mandatory practicals. However, there is a more obvious difference between the disturbed and undisturbed sites as well as a greater distance between the two. The disturbed site, for example, is the beginning of a footpath used by the university students. By measuring both the richness and abundance of the species present at each site allows for a more practical and illustrative understanding of these ecosystems. Simply using SDI limits the variety and exactly what contributes to the diversity of the space.

5.0 Safety and Ethical Considerations Overall, the safety and risks involved for this practical were low (see Appendix 1) and there was no pressing alarms of safety and ethics. Aspects such as sun and insect protection (sunscreen and insect repellent) were a necessary precaution to take, however this was the biggest and most pressing issue for this investigation.

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6.0 Processed Data The diversity – measured by richness and abundance – and incident sunlight at both Sites A and B will determine if there is a relationship between these factors that contributes to the diversity of dry sclerophyll forests. All measurements were calculated as mean ± standard error (SE) and the data was statistically analysed using a two-tailed t test to yield a p value. All calculations were performed using Excel (see Appendix 3, 5-7). 6.1

CALCUATIONS TABLE

Standard Error (SE)

SE=σ ÷ √ n

Mean

m=∑ of the terms ÷ num

Outliers

IQR=Q 3−Q 1

Excel

- Descriptive Statistics - t-test: Two Sample Assuming Unequal Variances)

Table 1: Calculations used within this experiment.

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6.2

FIGURE 1 – DIVERSITY

8

ns

7

6

Axis Title

5

ns

4

3

2

1

0

Richness

Abundance

Axis Title A

B

Figure 2: Diversity between Sites A and B (ns = p>0.05). Error bars represent standard error.

6.2.1

Interpretation and Analysis

The mean richness at Site A (2.77 ± 0.43) is lower than at Site B (3.33 ± 0.41) – as seen in Figure 2 – indicating the increased presence of the number of different species in the undisturbed site. This corresponds with the higher number of individuals per species observed in Site B (6.78 ± 1.58) compared to Site A (4.33 ± 1.09). A higher abundance rate also means there is more vegetation in this location. The area was physically denser – as a noted observation – meaning it was harder to walk through and manoeuvre around both trees and arthropods alike, compared to the clear trodden path found at Site A. The error bars in Figure 2 state that the richness provides a more concentrated data result compared to the abundance (with a longer error bar compared to the richness). The p values of the two sets illustrate that the richness fulfills the null hypothesis (p value = 0.46; ns), meaning there is no difference in the PAGE 7

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data set. The abundance also has no statistical significance in the data, providing a p value that is greater than 0.05 (p value = 0.23; ns). Collectively with both the richness and abundance of trees at Sites A and B observed, Site B demonstrates a general trend as the p values suggest the there is no statistical significance. 6.3

FIGURE 2 – INCIDENT SUNLIGHT

920

ns 900

Watts per second (W/m²)

880

860

840

820

800

780

A

B

Sites

Figure 3: Incident Sunlight (W/m2) at Sites A and B (ns = p>0.05). Error bars represent standard error.

6.3.1

Interpretation and Analysis

The scale of watts per second shows that 0 is dark, with 2000 being the brightest. Site A (910 ± 131.41 W/m2) and Site B (831.44 ± 171.21 W/m2) have a similar incident sunlight level (p = 0.72; ns), indicating that there is a slight difference between the disturbed and undisturbed sites within this variable. They both have very similar canopy space. As Site A is a disturbed site, the human impacts to the area are obvious, meaning that there will be naturally less trees because of the development of the path and interactions with the area. Site B does not get as affected by trees needing/forcing to be cleared, so it has a lower incident sunlight reading.

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6.4

DISCUSSION

From the evidence brought forward, the incident sunlight does not have an effective correlation with either of the diversity (richness and abundance of trees) variables. This supports the null hypothesis. The relation that these data points have with the research question allows for all factors of this investigation to be observed to make a correct and evident conclusion. The common conception that sunlight and the growth/health of a vegetation-based ecosystem relies heavily on the sun is contradicted when exploring the relationship between sunlight and diversity.

7.0 Evaluation 7.1

LIMITATIONS

An outlier that presented itself during data collection is an error in the species richness and abundance in trees data (Appendix 2: green highlighter: abundance). However, as the data is very similar, this would not affect the outcome too much. Data such as the p values and mean may change slightly. The outlier effects the SE, therefore demonstrating the large variance of the data. As the sample size was larger than the original experiment, it would be assumed that the SE would decrease as a result, however this was contradicted by the SE being large. This then proceeds to increase the uncertainty of the conclusions that can be made. Time constraints are a major factor in limitations. If more time was spent at each site, for an extended amount of time (days, months, etc.), then more data would be presented to possibly find a conclusion different to the one already made. As it was the middle of an Australian summer as well, seasonal restrictions can possibly have a large effect on the sunlight reading compared to a day in winter for example.

8.0 Sources of Error Whilst conducting the experiments, there were no physical errors that were made. As there was many people conducting the same experiments at the same time, tools were shared and passed around allowing for everyone to work cohesively without damage to anyone’s data, however as stated there is the presence of human error.

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8.1

RELIABILITY AND VAILDITY

The evaluations of the data are heavily dependent on the reliability of the instruments used to test the sunlight. The use of a floor meter that measures incident sunlight, moisture and pH level because of the ease of finding the necessary factors to record and reliability of the device. Excel is a reliable function due to its ability to calculate and generate tables of equations with ease. The data’s legitimacy relies on a combination of the reliability of testing and sources of error, which was good. As mentioned, the tool that was used for these variables was the floor meter, which correctly and accurately gave the needed results. An approach to consider is the validity of collecting all the data in one day.

9.0 Improvements and Extensions 9.1

IMPROVEMENTS

A worthy improvement would be to conduct recording of data once a month across 1 to 2 years at the same locations. This allows for both the seasonal restrictions and futuristic review to be resolved (as discussed in 7.1). This could ultimately assist with the sunlight data as well, as the weather/conditions of the day that the data is recorded effects the data in the graphs. For example, one month it may be a hot but wet day, therefore it being naturally less sunny, but the possible temperature and time of year contradicts this due to cloud coverage. 9.2

EXTENSIONS

Possibly by adding the canopy percentage cover as another abiotic factor into the research question would have given extensive information and data to the analysis. To see if there was a correlation between diversity (richness and abundance) and sunlight (incident sunlight and canopy cover) would have further given more data to analysis and prove the same research question. Other biotic and abiotic factors were recorded during the testing period so by incorporating some of those variables it could further support the research question.

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10.0 Conclusion There is no correlation between the diversity of a sclerophyll forest and its incident sunlight readings. It was hypothesised that there would be a relationship between sunlight with the abundance and richness of the trees in two different dry sclerophyll forests because of plants being reliant on sunlight. The disturbance itself is an obvious assumption, however the sunlight must be reflected through another variable. The hypothesis stated that the more sunlight in an ecosystem there is, the presence of diversity of trees will be less. Part of this is proven correct – within the diversity aspect. Human disruptions to natural environments forces these places to be disturbed, turning them into opportunities that local councils can use for development. This ultimately has negative impacts onto the ecosystems and species. Entire habitats have been destroyed with countless species lost because of these anthropogenic impacts. Studies such as these demonstrate how vital it is to preserve Australian forests.

Reference List PAGE 11

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