How much carbon is stored in each tree?
How does the relative carbon content stored in trees differ between a managed and an unmanaged wood?
Carbon Content In Trees HOW DOES THE TREE DENSITY DIFFER BETWEEN A MANAGED AND AN UNMANAGED WOOD? HOW DOES THE RELATIVE CARBON CONTENT STORED IN DEAD ORGANIC MATTER (DOM) DIFFER BETWEEN A MANAGED AND AN UNMANAGED WOOD? HOW DOES THE BIODIVERSITY OF TREE SPECIES DIFFER BETWEEN A MANAGED AND AN UNMANAGED WOOD?
Fieldwork for this investigation was undertaken in two separate woodland areas, France Wood and Slapton Wood. Slapton Wood is a managed woodland – The Forestry Stewardship Council (FSC) regularly fell trees of the Sycamore species. France Wood is unmanaged – the FSC does not currently manage the forest in any way. This project compares whether the tree density, relevant carbon content stored in DOM, biodiversity of tree species, and the relevant carbon content stored in trees, differs between France Wood and Slapton Wood. This project’s primary research involves collecting data relevant to the three Sub-Questions and the Overall Question. This project is also well-supported by secondary research; a variety of literature sources are utilized to provide the project with context and specific details pertinent to the investigation.
Data collection techniques, chosen to investigate each Sub-Question and the Overall Question, were carefully and consistently executed. Time constraints limited this project’s data collection to one quadrant in each Wood. Several multi-step data calculations followed soon after data had been collected. This data was then presented using various methods to highlight whether parts of the data are similar or divergent between the two Woods. This data presentation aided scrupulous and coherent data analysis to ensue, which scrutinized any anomalies and linked the data to the predictions made prior to data collection.
This project eventually reached conclusions for each Sub-Question. Regarding the tree density, more trees are identified in the Slapton Wood quadrant than in the France Wood quadrant. As for the relative carbon content stored in DOM, this was concluded (using a Mann Whitney U statistical test) to be higher in Slapton Wood than in France Wood. The tree species in the France Wood quadrant was dominated by Ash, whereas Sycamore and Silver Birch were found to be the most prevalent Slapton Wood quadrant species.
Relating to the Overall Question, this project concluded that the carbon content stored in trees was very similar between the two Woods. This project then aimed to relate these findings to secondary research and geographic theory. Finally, among the strengths of the investigation, the main project limitation, of data being collected in only one quadrant in each Wood, was recognized. This impeded the project from confidently being able to extrapolate the data as being representative of the whole of Slapton Wood and France Wood.
Location of sample sites:
This project’s research location was in the Slapton Ley Nature Reserve around the village of Slapton, in the South Hams district of Devon, England. Slapton is located near the A379 road between Kingsbridge and Dartmouth, and lies within the South Devon Area of Outstanding Natural Beauty (AONB). The Slapton Ley National Nature Reserve has been named a National Nature Reserve (NNR) since 1991, and this status was renewed until 2035 on 1 April 2011 (FSC Slapton Ley, 2018). The NNR has both national and regional importance for wildlife and attracts many thousands of visitors each year for its wildlife, educational opportunities, amenity and stunning landscape setting (FSC Slapon Ley, 2018). Slapton Ley was also designated as a Site of Special Scientific Interest (SSSI) in 2004 (FSC Slapton Ley, 2018). The NNR covers an area of 209.3ha (FSC Slapton Ley, 2018).
This project collected data in the NNR’s two woods: Slapton Wood and France Wood.
Slapton Wood is a 28ha ancient broadleaved woodland which occupies a 1.5km section of the valley of Slapton Wood Stream at its confluence with the Gara (FSC Slapton Ley, 2018). Slapton Wood is broken up into a number of compartments, some of which are relatively recent secondary woodland on abandoned fields and others that are plantations of beech Fagus sylvatica and sweet chestnut Castanea sativa (FSC Slapton Ley, 2018). Roughly 18ha
Slapton Wood is considered ‘ancient’1, and this ancient part is on the southern valley-side of the Wood (FSC Slapton Ley, 2018). There are some compartments, such as at Eastergrounds and an area of Loworthy Brake, where trees of the beech species dominate. These areas are assumed to also be plantations (FSC Slapton Ley, 2018). It was decided that this project’s fieldwork would be undertaken in the ‘ancient’ part of Slapton Wood, to enable data to be collected in the natural woodland rather than in man-made plantation areas.
France Wood is a secondary broadleaved woodland dating back to the 18th century (FSC Slapton Ley, 2018). It is hence a newer Wood than Slapton Wood.
1In England and Wales, a woodland area is deemed ‘ancient’ if it has persisted since 1600 (woodland Trust, 2019)
Figure 1: Slapton on a national scale map (Google Maps, 2020)
Figure 2: Slapton on a regional scale map (Google Maps, 2020)
Figure 3: The location of Slapton Ley NNR on a county-scale map (FSC Slapton Ley, 2018)
Figure 4: Slapton Wood (blue) and France Wood (red) on a local scale map (Google Maps, 2020)
Figure 5: Main habitat types of Slapton Ley NNR (FSC Slapton Ley, 2018) Slapton Wood and France Wood comprise the Broadleaf woodland areas
Figure 6: Public rights of way and access in Slapton Ley NNR (FSC Slapton Ley, 2018) Both Slapton Wood and France Wood are easily accessible on foot for data collection to be conducted
Figure 7: Place names for the north of the Slapton Ley NNR (FSC Slapton Ley, 2018)
Figure 8: Place names for the south of the Slapton Ley NNR (FSC Slapton Ley, 2018)
Justification for why the sample sites were chosen:
The two sample sites, Slapton Wood and France Wood, were chosen because they are similar woodland areas, in similar locations – only 3.4 miles away from each other (FSC Slapton Ley, 2018) – but carry a major distinction.
The major distinction between Slapton Wood and France Wood is the differing levels of management occurring in each Wood. Sycamore removal (management) takes place in Slapton Wood, but no management occurs in France Wood. Since 2010, sycamore removal has taken place as a form of management in Slapton Wood (Forest Research, 2018). This management is ongoing and planned to take place annually during the next ten years until 2030, when the next ten year plan will be decided by several stakeholders including the Field Studies Council (FSC) (Forest Research, 2018). On the other hand, management of France Wood is not currently ongoing, nor is it planned to take place in the future (Forest Research, 2018). According to past 10-year management plans, France Wood has never been managed before at all (Forest Research, 2018).
In Slapton Wood, “low-level Sycamore removal has recently been carried out in the ancient woodland and compartments” as well as “coppicing at Loworthy Brake”, according to the Slapton Ley National Nature Reserve Management Plan (FSC Slapton Ley, 2018). Furthermore, the Slapton Ley National Nature Reserve Management Plan (2020-2030) states that Sycamore removal is ongoing, and is planned to occur annually for the next ten years until 2030 (FSC Slapton Ley, 2018). The specifics of the current management in Slapton Wood include:
• To coppice 0.07ha every other year in Slapton Wood (FSC Slapton Ley, 2018) • To remove 1-2 larch trees from Slapton Wood annually (FSC Slapton Ley, 2018) • To coppice 200 m2 every other year around Loworthy Brake (FSC Slapton Ley, 2018). Figure 7 shows the location of Loworthy Brake.
The Sycamore removal is carried out in Slapton Wood for two main reasons: • “To maintain and enhance the important habitats and species of the site to good condition whilst recognizing climate change is likely to influence the status of some of these habitats” (FSC Slapton Ley, 2018)
• “Conserve the distinctive landscape character and spirit of place the site has” (FSC Slapton Ley, 2018)
Conversely, in France Wood, the FSC’s plan of action is “no active intervention…to allow natural vegetation” (FSC Slapton Ley, 2018). Thus, no management has recently occurred in France Wood, no management currently occurs in France Wood, and no management is planned to take place during the next ten years (until 2030) in France Wood (FSC Slapton Ley, 2018).
Consequently, it is highly likely that any trends inferred from the data collected in the two woods is as a result of the differing levels of management in each wood. While there are other variables between the two woods – specifically, Slapton Wood and France Wood are larger and older than France Wood – this is less likely than the differing levels of management to impact the data trends, according to John Wright, the fieldwork expert at the FSC Slapton Ley Field Center.
Choosing two locations within close proximity to each other carries multiple benefits for this project. Firstly, both Woods are situated less than a mile away from the Slapton Ley Field Centre – the place where this project was planned and equipment was provided from. Secondly, choosing to collect data in very similar locations helps to eliminate many of the possible differentiating variables between the two Woods. Physical factors – such as the terrain, altitude, rock/soil type and quality, climate, water supply, and natural threats (pests, threatening animals, and diseases) – are all likely similar in Slapton Wood as they are in France Wood.
These factors thus become control variables for this project. It could be argued that the independent variable during this project is the level of management between the two Woods, as it is the main difference that distinguishes Slapton Wood from France Wood. However, natural variation and the differing age of the two Woods may also cause the data to differ between Slapton Wood and France Wood, and thus – like the vast majority of fieldwork – this is not a perfectly controlled investigation
Justification for the importance of the investigation:
This project involves the carbon cycle on a global scale as well as the carbon cycle on a local scale.
On a global scale, this project relates to the process of carbon offsetting through tree planting. Carbon offsetting is the action or process of compensating for carbon dioxide emissions arising from industrial or other human activity, by participating in schemes designed to make equivalent reductions of carbon dioxide in the atmosphere (www.dictionary.com, 2020). Through photosynthesis, trees can be planted to absorb carbon dioxide to produce oxygen and wood. The Conservative government has pledged to plant 30 million trees per year in projects across the UK, mainly for carbon offsetting purposes, and to help the UK achieve its target to be net-zero in carbon by 2050 (Sharma, 2019). Large-scale tree-planting is already underway, and a new £10 million fund was announced in mid-April 2020 by the UK government to boost the scheme (DEFRA, 2019).
This project relates to carbon offsetting as the data collected can be used to indicate how to manage woodlands to maximize the carbon stored within the woodland. By comparing the carbon stored in trees (and DOM) in a managed woodland (Slapton Wood) and an unmanaged woodland (France Wood), this project will investigate whether it is more effective to remove trees of the invasive Sycamore species or not, from a carbon storage perspective.
Overall, carbon offsetting through tree planting is an important and current issue (Igoe, 2020), and hence a worthwhile topic for this project to investigate.
On a more local scale, this project relates to the sustainable conservation of Slapton Wood and France Wood by comparing the biodiversity of tree species in Slapton Wood and France Wood. With Slapton Wood being an ancient woodland2, and France Wood being a younger ‘secondary woodland’ – dating back to the 19th century (Forest Research, 2019) – the sustainable conservation of Slapton Wood and France Wood is a pertinent issue. Covering 2.4% of the UK, ancient and secondary woods are threatened (Woodland Trust, 2019). “Much of what we have left is being damaged and, once it’s gone, it can’t be replaced” a spokesperson from the Woodland Trust (2019) explains.
In short, this project relates to the sustainable conservation of ancient and secondary woodlands, which is a pressing environmental concern.
2 The Forest and Stewardship Council (FSC) classifies Slapton Wood as an ‘ancient woodland’, because it has been continuously Wooded for more than 400 years (Forest Research, 2019)
Relevance of each Sub-Question to Overall Question:
SUB-QUESTION 1: How does the tree density differ between a managed and unmanaged wood?
• Before collecting data for trees, this project must first work out the number of trees in a given area within each Wood. If all other factors were constant, the higher the density of trees, the more carbon stored in a given Wood. During data collection, this project will compare the number of trees in a quadrant in Slapton Wood to the number of trees in a quadrant in France Wood.
SUB-QUESTION 2: How does the relative carbon content stored in dead organic matter (DOM) differ between a managed and unmanaged wood?
• While the Overall Question refers to the relative carbon content stored in trees, it is important to explore the relative carbon content stored in DOM too, as this can prove to be a significant carbon store within each wood that should not be ignored.
SUB-QUESTION 3: How does the biodiversity of tree species vary between a managed and unmanaged wood?
• Tree species identification of each tree measured during data collection is necessary to determine whether a tree is a broadleaf or conifer. When calculating the carbon content of a tree, the biomass of the tree must first be calculated. To calculate the biomass of the tree, the volume of the tree will be multiplied by a ‘nominal specific gravity’; the nominal specific gravity is 0.53 for broadleaf trees and 0.39 for conifer trees. Ultimately, if the species of each tree isn’t determined (and thus whether it is a broadleaf or conifer tree), the carbon store of each tree cannot be calculated.
Context for each Sub-Question:
This section explores the wider world relevance of the topic that each Sub-Question investigates.
SUB-QUESTION 1: How does the tree density differ between a managed and an unmanaged wood?
• As development takes place in rural areas, it is of the government’s interest to maximize the density of trees in woodland areas (Sharma, 2019). Dense forestry brings numerous advantages, and can: maximize carbon storage, maximize flood mitigation (through maximizing interception), and maximize noise absorption. If a trend is discovered – e.g. unmanaged woods tend to have a higher tree density than managed woods – this can inform government decisions.
SUB-QUESTION 2: How does the relative carbon content stored in dead organic matter (DOM) differ between a managed and an unmanaged wood?
• Research trends tend to focus on the carbon stored in trees rather than in DOM. However, DOM stores carbon too, and prevents all of a tree’s carbon store being released into the atmosphere when a tree dies or is cut down (Stolte, 2013). ThereforeIt is important to measure the carbon stored in DOM as it can comprise a significant biosphere carbon store.
SUB-QUESTION 3: How does the biodiversity of tree species differ between a managed and an unmanaged wood?
• Maximizing the biodiversity of tree species in a Wood maximizes the sustainability for all life forms by providing an ecosystem for many plants and animals, and boosts ecosystem productivity, as each species has a unique role to play.
• Policy makers and Geographers must consider the biodiversity of tree species and avoid becoming preoccupied with meeting climate targets. Tree planting techniques that overlook the importance of biodiversity – such as monoculture plantations – are unsustainable, being more susceptible to pests and diseases (Sharma, 2019). Woodlands with greater biodiversity of tree species are healthier, function more efficiently, and are more productive, because organisms (particularly fungi and invertebrates) carry out the essential ecological processes that keep the woodland ecosystem running (Forestry Commission, 2015). It is therefore important that woodland areas are biodiverse as well as carbon stores.
This project has chosen three Sub-Questions that are relevant to the Overall Question, and relevant to important wider world issues.
Predictions prior to data collection:
SUB-QUESTION 1: How does the density of trees vary between a managed and an unmanaged wood?
• The prediction is for France Wood to have more trees per 10x10m quadrant than Slapton Wood, because Sycamore trees (an invasive species) were cut down in Slapton Wood as part of the Slapton Ley National Nature Reserve Management Plan (2020-2030). In France Wood, however, every tree has been allowed to grow.
SUB-QUESTION 2: How does the relative carbon content stored in dead organic matter (DOM) vary between a managed and an unmanaged wood?
• The prediction is for Slapton Wood to have a higher relative carbon content stored in DOM than France Wood, because there will likely be more DOM in Slapton Wood. This project predicts that there will be more DOM in Slapton Wood because there may be several felled Sycamore trees that lie on the forest floor as dead trees/DOM.
RESEARCH SUB-QUESTION 3: How does the biodiversity of tree species vary between a managed and an unmanaged wood?
• The prediction is for Slapton Wood to have more tree biodiversity than France Wood, as one of the main purposes of the Sycamore removal is to ensure other tree species can grow, rather than merely the invasive Sycamore species. With France Wood being unmanaged, its trees may be dominated by an invasive tree species.
OVERALL QUESTION: How does the relevant carbon content stored in trees differ between a managed and unmanaged wood?
• The prediction is for France Wood to have a higher relevant carbon content stored in trees than in Slapton Wood. This is because no management has occurred there, meaning all of its trees have been allowed to grow freely, maximizing the number and size of trees, and thus carbon storage.
Unfortunately, there isn’t a wealth of literature regarding Slapton Wood and France Wood. However, a key source used during this project’s secondary research is the Slapton Ley National Nature Reserve Management Plan 2020-30. This provided this project with additional information on details such as what actions will be undertaken to conserve different areas of the Slapton Ley NNR, including an outline as to how Slapton Wood and France Wood will be protected from threats to its existence.
Other secondary research during this project involved: comparing photos of tree leaves with tree species identification handbooks, using online dictionaries to define specialist key terms, and exploring the reading news articles which provide wider context to the topics that this project investigates.
Secondary research sources are referenced using the Harvard Referencing style. The sources are included in the Bibliography section of this project.
SECTION 2: METHODOLOGY / DATA COLLECTION
Prior to undertaking data collection, this project completed a phase of planning. Over the course of three weeks, the methodology, required equipment, and data collection techniques were finalized to formulate a thorough project plan. The tables for recording data were also created. Simultaneously, this project collated over ten secondary research sources. These sources proved invaluable for providing context and specific details regarding the data collection locations, and how to collect, present, and analyze data most effectively. The final step involved consultation with the Field Studies supervisor, and a Geography teacher. This allowed them to approve that this project was feasible within the time constraints, and explicitly relevant to the AQA A Level Geography specification.
Step-by-step summary of primary data collection (in chronological order) and justification:
1. The first step involved walking from the fieldwork center to Slapton Wood with the necessary equipment.
• It was decided to walk directly to Slapton Wood, then France Wood, as this was determined to be the shortest and least-time consuming route. This avoided unnecessary walking with heavy equipment.
2. Once in Slapton Wood, a random number generator on Google was used to derive two numbers from 1 to 9. These two numbers were combined to determine how far to walk inside the forest – i.e. the first number was 7 and the second number was 1, creating a distance of 71 paces to arrive at a random area within the forest. • This ensured that the quadrant was located in a random part of Slapton Wood.
Avoiding bias is necessary to avoid collecting unreliable data that fail to reflect the typical characteristics (such as the number of trees, size of trees, number of different tree species) of Slapton Wood.
• One random quadrant location was chosen rather than systematically measuring multiple quadrant locations because there was only sufficient time to accurately collect data in one quadrant location.
• Using a random number generator on Google was preferred instead of alternatives, because it avoided the need to carry any extra equipment such as a dice.
3. In Slapton Wood, the Compass app on iPhone was used to direct an easterly direction.
• Even though the chosen quadrant location is ‘random’, it was necessary to walk in that direction so that the chosen quadrant location was situated in part of the 14ha of ancient woodland, rather than one of the tree plantations towards the West of the main Slapton Wood entrance.
4. Upon walking 71 paces eastwards, a 10-meter-long square quadrant was measured. Four tape measures were left on the ground to mark the sides of the quadrant. Orange cones(for maximal visibility) marked out the corners of the quadrant.
• A 10-meter long quadrant was the largest quadrant size that could be measured, in each Wood, within the time constraints (this project was given only five hours to complete the data collection).
• The tape measures ensured that the quadrant was measured accurately. If the quadrant in Slapton Wood was a different size to the one in France Wood, which would produce invalid data.
5. The first tree was selected, and a photo of it was taken on a mobile phone • This removed the possibility of the same tree being measured twice inside the quadrant.
• This technique was chosen as an alternative to marking the trees with a felt-tip pen, to minimize the impact on the environment.
6. A pistol clinometer was used to measure the angle from eye level to the top and bottom of the tree.
• The same person completed this measurement so that the technique is consistent across all trees in each wood.
• Measuring the angle from eye level ensured accurate clinometer measurements.
• Measuring angle from eye-level to the bottom of the tree to ensure that the tree’s height is calculated from ground-level rather than eye-level.
• Each angle was measured three times to avoid the potential for random errors.
7. A tape measure was used to measure the distance from the person with the clinometer to the tree.
• Practicing this method beforehand concluded that being too close to the tree can distort the angles measured with the clinometer, resulting in unreliable data collection. Therefore, it was ensured that the clinometer was from a sizable and appropriate distance from the tree and that the top of the tree could be seen easily without having to look up at too large an angle.
8. Next, a tape measure was used to measure exactly 137 centimeters (4.5 feet) from the ground on the tree’s uphill side (if not on even ground).
• If the tree forks or bulged at or below 137 cm, we measured the circumference where the tree reaches normal size or tapers below the 137 cm point. • This technique follows the instructions in the American Trees Measuring Guideline Handbook (Leverrett ,2015). Refer to Figure 9 – below.
Figure 9: For the tree on the left-hand side, the circumference of the tree trunk would be measured at a height of 137 cm (4.5 feet). For the tree on the right-hand side, the circumference of the tree trunk would be measured where the tree trunk reaches normal size or tapers – where the arrows are labeled (Leverrett, 2015)
9. At 137 centimeters (4.5 feet), the circumference of the tree’s trunk was measured, or at the point the tree reaches normal size or tapers (if the tree forks or bulged at or below 137 cm)
10. All of the data collected was recorded in a table on the ‘Notes’ app on iPhone • If the tree was notably atypical in that the trunk didn’t reduce its thickness up the tree, this was noted in the table with an asterisk. During our data
calculations, the assumption – that the tree trunk’s radius (at the top of the tree) is a third of the length it is at 137 centimeters – would be ignored for the trees marked with an asterisk.
11. Corresponding with the Woodland Trust Guide to Tree Species Identification (2019), along with consultation with a local expert, the species of the tree was determined and recorded
12. Steps 5-11 were repeated for every tree that appeared within the quadrant, even if parts of the tree were outside the quadrant.
13. Once every tree in the quadrant had been measured, the circumference and length of every piece of DOM deemed a significantly large size – i.e. with a circumference of 0.05 m or larger – was measured using a tape measure.
• It would have been too time-consuming to measure pieces of DOM with a circumference smaller than 0.05m, as the volumes would have been so small that it would make a negligible overall impact on results.
14. Having completed the data collection in Slapton Wood, the equipment was gathered.
15. The next step involved walking to France Wood
• An Ordinance Survey map was used for directional purposes
• Designated footpaths were used when possible to minimize the impact left on the woodland ecosystem
16. Steps 2-14 were repeated in France Wood before returning to the fieldwork center and returning any borrowed equipment.
17. Data collected was recorded on an Excel document
• This document was saved on a personal OneDrive account (cloud storage) to avoid the risk of the document being lost
18. Data calculations commenced (explained in Section 3: Data Calculations)
19. A range of secondary research was conducted
• This consisted of newspaper articles, online articles, official council documents, and nature journals.
• Much of this secondary data collection is included in this project and referenced in the bibliography
Primary data collected for each Sub-Question:
SUB-QUESTION 1: How does the tree density differ between a managed and an unmanaged wood?
• All trees within the 10x10m quadrant were measured
SUB-QUESTION 2: How does the relative carbon content stored in dead organic matter (DOM) differ between a managed and an unmanaged wood?
• The length and circumference of any DOM (with a circumference larger than 5 cm) were measured.
• This data is used to calculate the volume, and in turn the carbon content, of each piece of DOM
SUB-QUESTION 3: how does the biodiversity of tree species differ between a managed and an unmanaged wood?
• The tree species of every tree within the 10x10m quadrant was identified
OVERALL QUESTION: How does the relevant carbon content stored in trees differ between a managed and an unmanaged wood?
• The circumference, and the angle to the top and bottom of the tree from a given distance, was measured for every tree in the quadrant
• This data is used to calculate the volume of the tree, and (by multiplying the volume by a constant) the overall tree biomass
• The overall tree biomass is divided by two to calculate the carbon content of each tree
Justification of how each primary data set links to Sub-Questions and Overall Question:
Carbon stored in trees in 10x10m quadrant (method of calculation is explained in Section 3: Data Calculations of this project):
• Necessary to answer the Overall Question
Carbon stored in DOM (of circumference > 0.05 m) in 10x10m quadrant (method of calculation is explained in Section 3: Data Calculations of this project):: • Necessary to answer Sub-Question 2
• Necessary to answer Sub-Question 3
• Must determine the species of each tree – to determine whether it is coniferous or broadleaf – in order to calculate the carbon stored in trees (to answer the Overall Question)
Number of trees:
• Necessary to count the number of trees in each quadrant to ensure that data is collected for every tree in the quadrant (for Sub-Question 1, Sub-Question 3, and Overall Question)
• Random sampling
o Sampling is categorized as ‘random’, as the location within the main forest areas were chosen randomly using a Google random number generator
Number of samples:
• 1 x 10x10m quadrant in each wood
How sample sites were chosen:
• Refer to Steps 2-4 in the Step-by-step summary of primary data collection (above)
Frequency of data collection:
• Data was collected on only one instance on one specific day, due to time constraints
Figure 10: The 10×10 quadrants were measured using tape measures
Figure 11: photos of tree leaves were taken to aid species identification
Figure 12: tree trunk circumference was measured using a tape-measure
Figure 13: Walkways were used to enter and leave forests to minimize the impact on the ecosystem
Figure 14: Evidence of management – tree felling – in the managed woodland, Slapton Wood. These pieces of DOM are likely of the Sycamore species, however, this could not be confirmed
Figure 15: further evidence of management in Slapton Wood
Figure 16: Taking photos was an alternative to taking tree cuttings. This was a way that this project could minimize the impact on the woodland ecosystem.
|Hazard and risks||Precautions to reduce the risk from this hazard|
|Weather – risks of sunburn, or hypothermia||Check weather forecast before day of fieldwork Bring sufficient supplies of food and drinking water Wear appropriate clothing – insulating layers, waterproofs Wear sun cream if necessary|
|Slips, trips, and falls – risks of injury and damaging equipment||Wear sturdy footwear Watch where you’re going Enter and leave forests using public access routes Formulate a contingency plan with the supervisor in the case that someone injures themself|
|Traffic on roads – risks of accident||Be careful when walking near roads Always use pavements and crossings Cross with care|
|Farmland – risks of disturbing ecosystem or animals||Leave animals alone Maintain at least a 3-metre distance away from animals whenever possible|
|Fungi – risks of fungal infections||Avoid skin contact with toadstools Wash hands upon return to the field centre and before eating or drinking; bring and use hand sanitiser Wear gloves when touching forest floor and trees|
|River – risks of slipping into river||Avoid going in river Wear suitable shoes with soles that have grip to reduce risk of slipping Avoid walking too close to river edge|
|Other people – risks of disturbing others||Be aware of other forest users Keep equipment away from others Be polite and respectful|
|Water-borne diseases||Avoid putting hands in the stream Use hand sanitiser regularly|
|Trees falling down||Avoid walking under or near any trees that look unstable|
|Hazel coppice – risk to people with nut allergies||No one who entered the woods suffered from any nut allergies|
|Getting lost||Plan route within the woods in advance Notify supervisor of planned whereabouts Bring a hard copy of a detailed, local-scale map|
Impacts on other participants:
• Two other students chose to record data in Slapton and France Wood. This provided the opportunity to cooperate in navigating the walk to each Woods, and sharing and carrying equipment from the Slapton Ley Field Center to the Woods and back. • Photographs were taken of the environment and not of people
• Dedicated footpaths were used as much as possible, when walking to, from and around the Woods. This minimized the risk to damaging the NNR environment by stepping on fragile natural areas
• Data collection techniques that do not require removal of samples from the natural environments were chosen deliberately to minimize the impact on the NNR
Figure 17: Human contact with toadstools was avoided
SECTION 3: DATA CALCULATIONS
This section includes explanations and justifications for the data calculations that were conducted in this project.
Explanation of calculations of the carbon stored in trees in 10x10m quadrant:
1. Calculate the height of the tree (using trigonometry)
o Step 1:
Stand so you can easily see both the top and bottom of the tree.
It does not matter if the base of the tree is slightly below you but you must be able to measure the horizontal distance (D) from your eye to the tree. Measure this distance in metres.
You cannot be down the slope from your tree.
o Step 2:
Use the clinometer to measure the angle from your eye to the top of the main trunk of the tree (angle ‘a’), not the very top of the canopy.
o Step 3:
Use the clinometer to measure the angle from your eye to the base of the trunk (angle ‘b’)
Record this as a positive angle
o Use the formulas:
A = tan a x D
B = tan b x D
Tree height = A + B
To work out the tree’s height
2. Calculate tree trunk volume
o Using the formula V=1/3πr2h (considering the tree trunk to be shaped like a cone)
o However, if the trunk circumference was deemed to not significantly become smaller between chest height and the top of the tree, this was noted. In this case, the tree volume was calculated using V= πr2h instead (considering the tree trunk to be a cylinder)
3. Calculate tree trunk biomass
o Biomass = volume x nominal specific gravity
o Nominal specific gravity = 0.53 for broadleaved trees
o Nominal specific gravity = 0.39 for conifer trees
4. Estimate the crown biomass
o Double the radius by two to get the diameter at breast height (DBH), multiply it by 100 to get it in centimeters
o If DBH is 7-50cm then Crown biomass=a×DBHb
o If DBH > 50cm then Crown biomass=c+(d×DBH)
o A, b, c, d are species-specific constants shown on a table based on analysis used by Forest Research (see Figure 18)
Figure 18: Table of species-specific constants used for calculating the Crown biomass for each tree (Forest Research, 2018)
5. Estimate the root biomass
o If DBH is 7-50cm then Root biomass=e×DBH2.5
o If DBH > 50cm then Root biomass=f+(g×DBH)
o e, f and g are species-specific constants shown in a table based on analysis by Forest Research (see Figure 19)
Figure 19: Table of species-specific constants used for calculating the root biomass for each tree (Forest Research, 2018)
*some trees had a DBH of less than 7cm. Therefore, the crown and root biomass for these trees were ignored, as they couldn’t be calculated accurately. The trunk biomass was thus stated as the total biomass for these trees.
6. Calculate biomass of the whole tree
o TOTAL Biomass of whole tree = trunk biomass + crown biomass + root biomass
7. Calculate the carbon stored
O Total biomass / 2 (carbon stored is considered to be 50% total biomass, so divide total biomass by 2)
O This is the conventional method used by the FSC (Forest Research, 2018)
Explanation of calculations of the carbon stored in DOM in 10x10m quadrant:
1. Calculate volume of the piece of DOM
o Use the formula V= πr2h (considering the piece of DOM to be a cylinder)
2. Calculate the biomass of DOM
O Biomass = volume x nominal specific gravity
o Nominal specific gravity = 0.53 for broadleaved species
o Nominal specific gravity = 0.39 for conifer species
O If species identification was difficult with the piece of DOM (they often no longer have any leaves, so they lack any distinguishing, species-specific features), it was considered to be a broadleaved species, because every alive tree in both the Slapton Wood quadrant and the France Wood quadrant were identified to be broadleaved species trees
3. Calculate the carbon stored
O Biomass / 2
Statistical test(s): Mann Whitney U test:
Justification of why this project has chosen to utilise two Mann Whitney U tests:
• The first Mann Whitney U test is calculated to determine whether – at any confidence level – there is sufficient evidence to conclude that there is a difference between the carbon content in DOM in the quadrant in Slapton Wood and that of France Wood (helping to answer Sub-Question 2).
• The second Mann Whitney U test is calculated to determine whether – at any confidence level – there is sufficient evidence to conclude that there is a difference between the carbon content in the quadrant in Slapton Wood and that of France Wood (helping to answer the Overall Question).
Explanation of how a Mann Whitney U test is calculated:
• Mann Whitney U is a statistical test that is used to test whether there is a significant difference between the medians of two independent sets of data. • The Mann Whitney U test can only be used if there are at least 6 pairs of data. It does not require a normal distribution.
Use the following formulas:
• U1 = n1×n2+0.5n2(n2+1)−∑R2U1 = n1×n2+0.5n2(n2+1)-∑R2
• U2 = n1×n2+0.5n1(n1+1)−∑R1U2 = n1×n2+0.5n1(n1+1)-∑R1
o n1 is the number of values of x1 (the first data set is labeled ‘x1’)
o n2 is the number of values of x2 (the second data set is labeled ‘x2’)
o R1 is the ranks given to x1
o R2 is the ranks given to x2
Step 2: Test the significance of the result
• Compare the value of U against the critical value for U at a confidence level of 95% / significance value of P = 0.05.
• If U is equal to or smaller than the critical value, then REJECT the null hypothesis. Conclude: there is a SIGNIFICANT difference between the 2 data sets. • If U is greater than the critical value, then ACCEPT the null hypothesis. Conclude: There is NOT a significant difference between the 2 data sets. o In this case, compare the value with the significance value of P = 0.1 and P = 0.2
SECTION 3: DATA PRESENTATION AND DATA ANALYSIS AND CONCLUSIONS
Sub-Question 1: Does the density of trees vary between Slapton Wood and France Wood? Sub-Question 1 – Data presentation:
Figure 20: The number of trees in 10×10 meter quadrants in Slapton Wood and France Wood
Sub-Question 1 – Data analysis:
As shown in Figure 20, sixteen trees were identified in the Slapton Wood quadrant whereas twelve trees were identified in the France Wood quadrant – a difference of four trees. There are 33.3% (3 significant figures [s.f.]) more trees in Slapton Wood than in France Wood. This data answers Sub-Question 1 as the number of trees in a 10x10m quadrant can also be expressed as the density of trees within the Wood, with the units being ‘number of trees per 100 square meters’.
This project predicted that France Wood would have a higher tree density than Slapton Wood. The rationale for this prediction was that the management outlined in the Slapton Ley National Nature Reserve Management Plan (2020-2030) involves cutting down Sycamore trees (an invasive species). This project predicted that the recently removed Sycamore trees would have reduced the number of trees in the quadrant to a level below the number of trees measured in the France Wood quadrant.
With four more trees being identified in the Slapton Wood quadrant than in the France Wood quadrant, the data is not as predicted. One possible reason for this could be the fact that the Sycamore removal is ongoing, and will occur annually until 2020-30 according to the management plan, and thus Sycamore removal hasn’t occurred specifically in the location of the Slapton Wood quadrant.
Regardless of whether this possible reason is true, it is important to highlight that the data collected is from a very small size. Due to time constraints – and the fact that this is an independent investigation with only one person collecting data – data was collected from only one 10×10 m quadrant in each Wood. It would be inaccurate to extrapolate the trend detected – that there is a higher tree density in the France Wood quadrant than in the Slapton Wood quadrant – and assume that this is reflective for the whole of France Wood and the whole of Slapton Wood. Rather, it would be necessary to measure the number of trees in multiple quadrants, either by employing stratified sapling (setting up a quadrant at regular intervals along a transect from one end of the Wood to the other end), or by random sampling (as this project chose to use) many times.
Either of these methods to collect data from multiple quadrants would have been used had this project been allotted more time to collect data (e.g. three days for data collection rather than only one). Alternatively, had there been more resources – i.e. a team of people to collect data rather than only one person – this project could have been able to collect data from multiple quadrants rather than just the one in each wood.
Moreover, collecting data over a period of time – e.g. the number of trees in Slapton Wood and France Wood quadrants every 4 years over a 20-year time period – could also improve the usefulness of results by demonstrating the changes in the number of trees within the Wood quadrants over time. A trend over time could suggest that this project’s prediction holds true, and that the ongoing Sycamore removal is reducing the tree density within Slapton Wood.
The reliability of the data refers to the consistency of a measure, and whether the results can be reproduced under the same conditions. Regarding the reliability of the tree density
data collected, it is highly likely that the data is reliable. This is due to multiple reasons.
Firstly, counting the number of trees within a quadrant is a very easy task for any geographer to do, regardless of their experience in data collection or fieldwork. Secondly, control variables were considered to ensure that the data was collected using the same method in each Wood – for example, it was decided before entering either Wood that any tree that was partly inside the edge of the quadrant would be counted as being inside it.
Thirdly, the data collected will generally stay the same, and not fluctuate on a day-to-day basis – i.e. the number of trees within a given quadrant does not change frequently, and it is highly likely that the number of trees within the quadrants has not changed between the time of writing (March 2021) and the time of data collection (March 2020). Fourthly, a photo of every tree within each quadrant was taken to avoid any tree being counted twice or a tree being accidentally forgotten. These four reasons provide justification that the data collected for the number of trees within the quadrants is reliable.
The validity of the data refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure. It Is highly likely that this tree density data is valid, as the data truly reflects the number of trees within the quadrant in each Wood.
Ethically, collecting the tree density inside each quadrant leaves minimal impact on the wood ecosystem. One way that this project minimized the environmental impact was by taking several photos of trees and findings of interest rather than physically marking the trees. Photos were taken of the trees that had been managed, rather than an alternative method of physically writing on the tree trunk (in felt tip pen).
Sub-Question 1 – Conclusions
• There were more trees in the quadrant in Slapton Wood than in the quadrant in France
• This contrasts with this project’s prediction
• There is an insufficient amount of data to conclude that there is a difference between the tree density across the whole of Slapton Wood and across the whole of France Wood, because data was collected in only one quadrant in each Wood
Sub-Question 2: Does the carbon content in dead organic matter (DOM) differ between the two forests?
Sub-Question 2 – Data presentation:
Figure 21: Mann Whitney U Statistical Test of the carbon content of DOM in each Wood
Figure 22: Total carbon content (oven dry tonnes) of DOM; Blue = France Wood, Orange = Slapton Wood
Sub-Question 2 – Data analysis:
From Figure 21, the critical values for the two-tailed Mann Whitney U statistical test are:
• 0.20 significance level = 26
• 0.10 significance level = 21
• 0.05 significance level = 17
• 0.01 significance level = 11
The U-value is 25. The critical value of U at p < 0.2 is 26. Therefore, the result is significant at p < 0.20.
Therefore, the conclusion of this Mann Whitney U statistical test (Figure 21) is that there is sufficient evidence to prove that there is a difference between the total carbon content in DOM in the quadrant in Slapton Wood and the quadrant in France Wood, with a 20% significance level. [The 20% significance level means that, if the Mann Whitney U statistical test concluded there to be a difference between the carbon content in DOM in the two quadrants, there would be a 20% chance of a difference not actually existing.]
In addition to the conclusion from the Mann Whitney U statistical test, the pie chart in Figure 22 demonstrates a clear difference between the total carbon content in DOM between the Slapton Wood quadrant and the France Wood quadrant. Of all of the DOM found in the two quadrants, the sum of the pieces of DOM in Slapton Wood contained 0.1848 (4 s.f.) oven dry tonnes of carbon, whereas the sum of the pieces of DOM in France Wood contained only 0.01083 (4 s.f.) oven dry tonnes.
The prediction for Sub-Question 2 was for Slapton Wood to have a higher relative carbon content stored in DOM than France Wood, because there may be several felled Sycamore trees that lie on the forest floor as DOM. Figures 21 and 22 suggest that this is true for the data collected in the two quadrants. Furthermore, this project noticed large pieces of DOM that appeared to be felled trees throughout Slapton Wood (see Figure 14 and Figure 15). These were situated outside the borders of this project’s quadrant. Unfortunately, without any leaves or distinguishing features, this project could not determine whether the felled trees were of the Sycamore species. This would have provided more evidence that supports this project’s prediction.
Moreover, in Figure 23, there is a clear difference between the mean average carbon content per piece of DOM in Slapton Wood and France Wood. The mean average carbon content per piece of DOM in Slapton Wood is 0.001354 (4 s.f.) oven dry tonnes, whereas the value in France Wood is significantly lower – 0.0004963 (4 s.f.) oven dry tonnes. It was noticed that the largest piece of DOM within Slapton Wood had a significantly higher carbon store than the other pieces of DOM, so a separate mean average was carried out – one that excludes the largest piece of DOM to discover whether this large piece of DOM was distorting the results. Slapton Wood’s average DOM carbon content remains higher than the mean in France Wood even with the largest piece of DOM excluded.
Like all inferences made from the data collected during this project, it is difficult for the project to extrapolate, with any certainty, to conclude that there is a higher relative carbon content stored in DOM across the whole of Slapton Wood than across the whole of France Wood.
The data collection for the carbon content is likely fairly reliable as the DOM measurements are easy for this project to accurately measure. The tape measures used are accurate to an ample degree (to the nearest millimeter) when measuring the circumference and the length of each piece of DOM. The data calculations are also easy-to-make with the aid of a calculator, following the formula pi x (radius)2x length. As these calculations were made, and checked, by an A Level Math student, it is unlikely that any calculation errors were made. The final calculation involved multiplying the volume of the DOM by a specific coefficient to estimate the carbon stored in each piece of DOM. Again, this involves fairly elementary multiplication, and as a result the data is likely reliable.
Regarding the validity of the results, one potential pitfall is that the data calculations assume pieces of DOM to be of a uniform cylindrical shape which is not necessarily accurate for the most unconventionally-shaped pieces of DOM measured in both France Wood and Slapton Wood. However, on the whole, pieces of DOM tended to follow either a cylindrical shape or a conical shape (to which the formula for the volume of a cone, 1/3 x pi x (radius)2 x length, was used alternatively). Another issue to consider when assessing the validity of the results involves the estimation of a piece of DOM’s carbon storage based on its volume.
The method for this conversion involved multiplying the volume (in cubic meters) by the ‘nominal specific gravity’, before dividing by 2. A potential pitfall within this process is that the nominal specific gravity was the same value (0.53) for every piece of DOM (because each one was detected to be broadleaf rather than a conifer) when the nominal specific gravity is likely slightly different and dependent on the specific species of the DOM.
On the other hand, experts remain uncertain regarding the amount of carbon stored by specific species per cubic meter of volume, so it makes sense to use a general broadleaf multiplier during the data calculations. This issue was also discussed with the carbon Cycle expert at the Slapton Field Centre, who confirmed that using a general broadleaf multiplier was the best method, especially as it can be extremely difficult to determine the species of a piece of DOM when it does not have any leaves to aid species detection.
Sub-Question 2 – Conclusions:
• A higher relative carbon content stored in DOM – in terms of the number of pieces of DOM, the average carbon content of each piece of DOM, and the total carbon content in DOM – was found in the Slapton Wood quadrant than in the Frane Wood quadrant.
• This trend was confirmed, with a 20% significance level, by the Mann Whitney U statistical test, and was as
• More data is required to be collected to determine whether there is a higher density of carbon content in DOM across the whole of Slapton Wood when compared to the whole of France Wood.
Sub-Question 3: Does the biodiversity of tree species vary between Slapton Wood and France Wood?
Sub-Question 3 – Data presentation:
Sub-Question 3 – Data analysis:
• Slapton Wood: four species (5 Sycamore, 5 Silver birch, 1 Elder, 1 Ash) • France Wood: five species (1 Elder, 9 Ash, 2 Holly, 3 Sycamore, 1 Silver birch)
Figure 24 and Figure 25, the tree maps, reveal there to have been four tree species in the managed Slapton Wood quadrant, but five species in France Wood’s quadrant. The most common tree species in the Slapton Wood quadrant are Sycamore and Silver Birch. This indicates that the Sycamore removal that has taken place in the Wood has failed to remove the invasive Sycamore species thus far, and that Sycamore remains an invasive species in Slapton Wood. This data suggests that the ongoing Sycamore removal over the next ten years (until 2030), outlined in the Slapton Ley National Nature Reserve Management Plan, is necessary.
Ash Tree found in the UK
On the other hand, in France Wood, nine of the sixteen trees measured in the quadrant were of the Ash species. Secondary research supports this prevalence of Ash trees; the Forestry Commission (2019) states that there are 80 million Ash trees in the UK, and it is the third most common tree species in Britain after Oak and Birch. In Devon specifically, Ash-dominated woodland covers about 11,000ha, 22% of all broadleaved woodland (Forest Research, 2018). There are also estimated to be at least 1.9 million mature Ash trees outside of woodlands throughout Devon – for example, in hedgerows (Forest Research, 2018). The data, and the background secondary research, suggest that Ash is a dominant species throughout France Wood.
The four species in the Slapton Wood quadrant also appeared as four of the five species in the France Wood quadrant. Ash, Elder, Sycamore, and Silver Birch trees appeared in both the Slapton Wood and France Wood quadrants. With the two woods being situated next to each other within the Slapton Ley NNR, the similar locations contribute to the two woods being of similar tree species. Hence this data is unsurprising. Holly trees were the only species to have appeared in one wood and not the other – two Holly trees were identified in the France Wood quadrant but not the Slapton Wood quadrant.
This project predicted Slapton Wood to have more tree biodiversity than France Wood, as one of the main purposes of the Sycamore removal is to ensure that other tree species can grow as well as the invasive species. There is insufficient data to determine whether this prediction holds true even if only for inside the quadrants. Although one more species was identified in France Wood than in Slapton Wood, there were also four more trees in France Wood. Thus, it is difficult to state whether there was more diversity of tree species in the France Wood quadrant or not, or whether the fifth tree species is due to the extra trees being found.
This project also predicted that, with France Wood being unmanaged, it may be dominated by its own invasive species. The data collected in the quadrants appear to support this prediction: Ash appears to be a dominant species within France Wood, if the quadrant is reflective of the whole Wood.
This project expected there to be a lack of oak trees, after completing secondary research. Kitchin (1983) showed that there is almost no oak regenerating in Slapton Wood. The prevalence of Sycamore (acer pseudoplatanus) was also unsurprising, after Kitchin’s (1983) research also concluded that Sycamore trees fill canopy gaps following wind-throw. (Wind throw is the uprooting and overthrowing of trees by the wind (merriam-webster.com, 2020)).
It is also worth noting that every tree species identified in both France and Slapton Wood were broadleaf trees. Jenkins et al. (2001) has demonstrated that broadleaf trees have a higher biomass than coniferous species, meaning that they store more carbon than coniferous tree species do. It seems that the management has not induced a prevalence of coniferous trees in Slapton Wood.
These results are likely both reliable and valid. Tree species identification involved corresponding with the Woodland Trust Guide to Tree Species Identification (2019) as well as consultation with a local expert. The sizes and appearances of the leaves, needles (or lack of), leaf buds, bark, flowers, fruits, and twigs all aided tree species identification. Accurate identification is paramount for investigating Sub-Question 3; this project is confident in its species identification.
Sub-Question 3 – Conclusions:
• There is a difference between the distribution of tree species within the quadrant in Slapton Wood and the quadrant in France Wood. The trees in the France Wood quadrant are dominated by trees of the Ash species while, in the Slapton Wood
quadrant trees of the Sycamore and Silver Birch species were most prevalent, despite the ongoing Sycamore removal.
• This project requires more data to be collected in more quadrants to determine whether this trend is unique to the two quadrants, or whether the pattern persists throughout the whole of Slapton Wood and the whole of France Wood
Overall Question: Does the relative carbon content stored in trees differ between Slapton Wood and France Wood in Slapton, Devon? If so, how?
Overall Question – Data presentation:
Figure 26: Box and whisker chart of the carbon content stored in each tree in the France Wood quadrant
Figure 27: Box and whisker chart of the carbon content stored in each tree in the France Wood quadrant
Figure 27: Box and whisker chart of the carbon content stored in each tree in the France Wood quadrant
Figure 28: Mann Whitney U Statistical Test of the relative carbon content stored in trees in Slapton Wood and France Wood
Overall Question – Data analysis:
From Figure 28, the critical values for this two-tailed Mann Whitney U statistical test are:
• 0.20 significance level = 67
• 0.10 significance level = 60
• 0.05 significance level = 53
• 0.01 significance level = 41
The U-value is 91. The critical value of U at p < 0.2 is 67. Therefore, the result is not significant at p < 0.2.
The results from Figure 28 concludes that there is insufficient evidence to prove that there is a difference between the median carbon content in the trees in the Slapton Wood quadrant and the France Wood quadrant, with a 20% significance level.
This conclusion from Figure 28 is furthered by Figure 29, which presents the total carbon content of trees in Slapton Wood and France Wood. The total carbon contents stored in trees is calculated by adding the carbon content of every tree. Figure 29 shows Slapton
Wood and France Wood to have extremely similar total carbon contents of trees – with a difference of merely 0.03064 (4 s.f.) oven dry tonnes between the two Woods. Ultimately, despite 16 trees being measured in France (compared to 12 in Slapton Wood), the cumulative carbon content in trees in each quadrant is relatively similar.
Regarding the spread of carbon content stored by each tree, Figure 26 and Figure 27 shows there to be an evident range of sizes in the quadrant in each Wood. In Slapton Wood, the carbon content of the largest tree was 1.016 (4 s.f.) oven dry tonnes whereas the carbon content of the smallest tree was 0.0002441 (4 s.f.) oven dry tonnes, creating a range of 1.016 (4 s.f.) oven dry tonnes. On the other hand, in France Wood, the carbon content of the largest tree was 0.4587 (4 s.f.) oven dry tonnes whereas the carbon content of the smallest tree was 0.00001547 (4 s.f.), creating a range of 0.4587 (4 s.f.) oven dry tonnes.
Despite the Slapton Wood quadrant’s wide range in the carbon storage of the trees – over twice as wide a range as that of the France Wood quadrant – this is heavily affected by one exceedingly large tree that was measured in the Slapton Wood quadrant. The volume calculated for the largest tree measured in the Slapton Wood quadrant was 1.827 cubic meters (4 s.f.), whereas the volume for the second largest tree was only 0.04283 cubic meters (4 s.f.). Thus, the largest tree in Slapton Wood is 42 times larger (to 2 s.f.) than the second largest tree.
Figure 30 attempts to convey the significant effect of the largest tree on the mean average carbon content per tree in each Wood. When every measured tree is included, the mean average carbon content is 0.09643 oven dry tonnes (4 s.f.) per tree in Slapton Wood, but 0.7040 oven dry tonnes (4 s.f.) in France Wood. However, when the largest tree from each Wood is excluded from results, the mean average decreases to 0.01280 oven dry tonnes (4 s.f.) in Slapton Wood, and to 0.04452 oven dry tonnes (4 s.f.) in France Wood.
If the largest tree in each Wood was discounted – perhaps by labeling it an ‘anomalous’ value – the mean average carbon content per tree decreases by 86.73% (4 s.f.) in Slapton Wood, but only by 36.76% in France Wood. When all trees are considered, the Slapton Wood quadrant’s mean average carbon content per tree is notably higher than that of France Wood; when the largest tree of each quadrant is removed, the mean average carbon content per tree is higher in the France Wood quadrant than in the Slapton Wood quadrant.
Figure 30 emphasizes the fact that there is insufficient data for a conclusion to be confidently made by demonstrating how one tree (the largest tree in Slapton Wood) heavily influences the trends inferred from the data. Collecting more data in more quadrants would improve this project’s capacity to make more accurate conclusions from the data, reduce the potential impact of anomalous results, and may make anomalous results easier to notice and investigate or discount, when analyzing the data
Overall Question – conclusions:
• The prediction for the Overall Question was for France Wood to have a higher relative carbon content stored in trees than in Slapton Wood. This is because no management has occurred there, meaning all of its trees have been allowed to grow freely, maximizing the number and size of trees, and thus carbon storage.
• However – with Figure 28 concluding that there is insufficient evidence to declare a difference between the two data sets, Figure 29 demonstrating extremely similar total carbon content in trees in both Woods, and Figure 30 hinting at how any trends inferred are heavily dependent on one specific tree – the evidence points towards the prediction being untrue.
• From the data collected, it is likely that the density of carbon content in trees does not vary significantly between Slapton Wood and France Wood. Nonetheless, more data must be collected to reveal whether this data is similar across the whole of each Wood and not merely in the two quadrants where data was collected.
Overall Project – Conclusions:
The main limitation of the data collected is the fact that data was collected only in one quadrant in each Wood. This is a severe limitation on the results of this project as it is not a sufficient amount of data for the project to extrapolate the data and confidently claim for the trends in the data to be reflective across the whole of each Wood. For example – while the Mann Whitney U Statistical Test concluded that, with a 20% significance level, there is a difference between the carbon content in DOM when comparing the quadrant in France Wood and the quadrant in Slapton Wood, this project cannot conclude that this is true when comparing France Wood and Slapton Wood in general. More data must be collected from more quadrants in each Wood to enable this project to confidently reach that conclusion.
Another, albeit more minor, limitation of the data relates to the equipment used during the data collection. For the purposes of convenience, this project chose to use a light, portable, easy-to-use handheld clinometer. This clinometer only measures to the nearest degree, and the general consensus among the Slapton Field Centre experts is that the clinometer is only accurate to the nearest degree. While some digital clinometers exist, that are renowned to
be more accurate as well as more precise (some measure angles up to the nearest 0.01 degree) these are significantly more expensive. It would have been inappropriate for a more accurate clinometer to have been purchased specifically for this project, as it would have had a negligible impact overall on the data conclusion.
Potential human error – such as when measuring angles with a clinometer and reading circumference values off a tape measure – could also limit the validity of results. However, as the data was collected in a careful, thorough manner by an A Level Geography student, with occasional supervision from either an experienced A Level Geography teacher or a member of the Field Studies Council, the likelihood of human error occurring that significantly affects the data is unlikely.
There are multiple ways that this study could be extended. One of the most obvious extensions to the study would be to allocate more time to allow additional data to be collected. This project estimates that over 15 quadrants could have been measured in each wood if another two days was given for data to be collected. Although data for only quadrants in each wood was collected in the whole of one day for this project, the person collecting the data would be much more accustomed to the data collection process, and would be able to accurately collect the data a lot more quickly if given another opportunity for it.
An increase in the amount of data collected – specifically the number of data collection quadrants completed in each Wood – would tackle the main limitation of the data, and allow for the data to be extrapolated as being reflective for each Wood.
Another way to extend the study would be to conduct the data collection in more than just Slapton Wood and France Wood. There are very few differences between the location of Slapton Wood and France Wood, and they are very similar overall, other than the differing levels of Sycamore removal. However, the two Woods also vary in their age. As summarized in Section 1, according to the Woodland Trust (2019), Slapton Wood is classified as an ‘ancient woodland’ (as it has been continuously wooded for more than 400 years).
On the other hand, France Wood is a younger ‘secondary woodland’, dating back to the 19th century. This project could be extended to collect data in several different ‘ancient’ and ‘secondary’ woods. This would enable the project to investigate whether there is a correlation between the age of the wood, and the characteristics within the wood. These characteristics could involve the density of trees, carbon storage volume in DOM, tree species biodiversity, and the carbon storage volume in trees, which this project chose to investigate.
If this project were to be completed again with the same time constraints – only one day to collect data – it may be more effective to focus on only Sub-Question 1 (investigating the density of trees) and Sub-Question 3 (investigating the biodiversity of tree species) – rather than all three Sub-Questions and the Overall Question. Measuring the angles and circumference required to calculate the volume of trees and DOM (and in turn the carbon content) for Sub-Question 2 and the Overall Question was the most time-consuming part of data collection.
If Sub-Question 2 and the Overall Question were disregarded, collecting data in each quadrant would be a significantly faster process, and this would facilitate the project to collect data in multiple quadrants in each Wood. Having data from multiple quadrants across the whole of each Wood would provide the project with a certain level of confidence to extrapolate the trends from the data as being true for the whole of each Wood. Overall, redoing the project focussing on only Sub-Question 1 and Sub-Question 3 would partially remove the project limitation of a lack of data.
Whilst the small amount of data collected is evidently a weakness of the project, one strength of the project is the reliability of results. This project ensured that data was collected carefully and thoroughly, and that the risk for measuring or human errors were minimized during the data collection process. This project fully considered the risk for bias and inaccuracies, and generated methods to minimize this risk. Techniques were practiced before the day of data collection, and decisions – such as deciding how far away to stand away from the tree when measuring the angle towards the top of the tree – followed any advice offered from the Field Studies Council expert. This project is confident that no major errors were made when conducting data collection, and hence the reliability and validity of data collection can be viewed as a success of this project.
Written by Edred Opie
SECTION 6: BIBLIOGRAPHY
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How much carbon is stored in each tree?