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4.2 Applications and skills

4.2.1 Practical 5Sustainable mesocosms 
4.2.2 Chi-square statistics and quadrat samples
4.2.3 Nature of science: The missing sink
4.2.4 Assessing claims about climate change

Site: Philpot Education
Course: Biology Support Site
Book: 4.2 Applications and skills
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Date: Sunday, 27 September 2020, 5:17 PM

4.2.1 Practical 5: Sustainable mesocosms

A mesocosm is an experimental tool designed to mimic, as closely as possible, the conditions of a closed ecosystem. An ecosystem is closed when:

  • Energy in the form of heat and light can enter and exit the system. In most cases, this involves sunlight being added constantly, and heat energy being released through respiration.
  • Matter in the form of nutrients and biomass stay enclosed in the system. The amount of matter is constant in a closed system, but it is cycled through the system in various forms.

Natural ecosystems are open, in that they can exchange matter and energy with other systems in the environment.

Building a mesocosm is useful to demonstrate that ecosystems develop and change over time. This process is called ecological succession. Some of the key features of succession are:

  • Systems become more complex over time. The abundance of species tends to increase as a community develops.
  • Systems become more stable over time. Natural systems rely on feedback loops, which stabilise the structure of a community.

It is difficult to predict whether an experimental mesocosm will be self-sustaining over long periods of time. You may be creative in your design of a sustainable mesocosm, but the design must:

  • include photosynthetic organisms to provide oxygen for cellular respiration, as well as heterotrophic organisms (i.e. decomposers)
  • include relevant nutrients required for growth
  • not include any vertebrate or invertebrate animals (microbes are acceptable according to the IB’s animal experimentation policy).

Activity 1: A sealed aquatic mesocosm 

Pond water contains algae, microbes and plants, and makes a very simple, but possibly odorous, mesocosm in a bottle. For this activity each group needs:

  • a large pail or bucket
  • a wooden spoon
  • clear plastic or glass bottles with lids
  • pond water, weeds and mud from the same pond
  • sieves of varying-sized pores
  • paper towel.

Instructions

  1. Collect pond water and mud in the bucket. Mix it to create a muddy suspension.
  2. Pour some of the suspension through the sieve to remove large animal organisms. Return these to the pond.
  3. Use successively smaller sieves to remove smaller organisms.
  4. Fill the bottles about three-quarters full of the filtered muddy suspension. Wait for the mud to settle at the bottom. You may add pond weed or plant fertiliser at this point.
  5. Place a layer of paper towel under the lid, then close the lid on your aquatic mesocosm. The paper towel will absorb condensation at the top of the bottle, and regulate moisture in the air above the ‘pond’.
  6. Place your mesocosm in a well-lit and cool area. Observe changes over a period of weeks.

mixed mesocosmFigure 4.2.1a – Mixed mesocosm
The top half of the bottle at the bottom is used to join the two parts together.

Activity 2: A mixed mesocosm

This mesocosm has terrestrial and aquatic elements.

  • 2 transparent 2l plastic bottles
  • clear adhesive tape
  • cheesecloth or pantyhose
  • aquarium pebbles or gravel
  • elastic bands
  • bucket and shovel
  • pond mud and water
  • grass seeds or seedlings
  • soil from the school field or garden
  • dry leaves or litter

Instructions:

  1. Follow steps 1–3 from Activity 1. Keep the filtered suspension aside.
  2. Cut the top off one bottle. Invert the top, and cover the spout with the cheesecloth. Fix the cheesecloth in place with an elastic band. Save the bottom.
  3. Put about 3cm of pebbles in the inverted bottle. Layer the garden soil on top of the pebbles. (Make sure to remove any invertebrate animals like slugs and worms and return them to the garden.) Add the grass seeds, a bit of water and leaf litter to the top of the soil.
  4. Tape the bottom of the bottle to the inverted top.
  5. Cut the top off the second bottle. Fill the bottom about halfway with the muddy suspension from step 1.
  6. Cut the top of the second bottle again at a point before it begins to taper. Use this piece to couple the terrestrial part of your mesocosm to the aquatic part. Use tape to fix the part together as shown in Figure 4.2.1a.
  7. Leave your mesocosm in a well-lit place at room temperature for at least a few weeks. 

Open/closed systemFigure 4.2.1b – Open/closed system
In an open system, matter and energy are exchanged with the environment. In a closed system, energy is exchanged, but not matter. In real systems, the boundaries of systems are often difficult to identify.

Figure 4.2.1cFigure 4.2.1c – The Earth, as a whole, is a closed ecosystem 
Heat and light are reflected back to space, while matter is not exchanged past the boundary of the atmosphere. The concept of a closed system is conceptually useful. In reality, all ecosystems are open.

ecosphereFigure 4.2.1d – Ecosphere
This self-sufficient ecosphere is a closed system containing algae, microbes and shrimp. It was sealed in 1999 and is on display at the American Natural History Museum in New York. 

Did you know?

Biosphere 2 is now a museum and research centre owned by the University of Arizona, but it was originally designed as an experiment to determine if humans could live in a closed mesocosm, for the purpose of one-day colonising Mars?

biosphere 2 - internalFigure 4.2.1e – The internal facilities of Biosphere 2

biosphere 2 - externalFigure 4.2.1f – The external facilities of Biosphere 2

In the lab 1

For either mesocosm, try some or all of these variations and make hypotheses about which system is more sustainable:

  • Fill bottles with different combinations of organisms (based on the level of filtration) or change the ratio of mud:water.
  • Add different amounts of pond weed and fertiliser.
  • Place the bottles in different locations based on temperature and light exposure.

In the lab 2

Poke holes in the sides of your plastic bottles, so you can take sensor readings of oxygen and carbon dioxide levels in the air of your mesocosm. Be sure to seal the holes with transparent film and/or tape. Track the changes in gas concentrations over time.

In the lab 3

You can dechlorinate tap water by leaving it in a shallow pan for 24 hours. Chlorine will evaporate.

Course link

Learn more about succession and stability of ecosystems in 14.1.2 Communities and ecosystems.

4.2.2 Chi-square statistics and quadrat samples

  • A quadrat sample is a method used to collect random data in order to estimate population size or distribution of species in an ecological community.
  • Quadrats are square or rectangular frames, like picture frames, which are placed on the ground so that scientists can count organisms within the frame.
  • Quadrats are useful for counting plants, fungi or very slow-moving animals. The dimensions of a quadrat can range from 10 x 10cm when studying lichens or moss, to 10 x 10m when studying trees.

How to set up a quadrat sample

  1. Choose or make suitably sized quadrats for your sample. 1 x 1m squares are appropriate for most samples.
  2. Measure the test site and divide it into a grid of zones equal to the size of your quadrat. For example, if your quadrat is 1 x 1m, and your test site is 8 x 10m, you should have 80 zones. Number the zones.
  3. Use a random method to generate numbers, for example roll a die, or use the MS Excel RAND function.
  4. Place your quadrats in the areas represented by the numbers generated and start counting species. Make a note of which species are present or absent in each quadrat.
  5. Your sample is complete when you have counted in about 5% of the zones, for example 4 zones out of 80, or when you are consistently finding the same species in all quadrats (i.e. no new species). Record the species as either present or absent for each quadrat as shown in the table below.
Species Quadrat 1 Quadrat 2 Quadrat 3 ... and so on
A + + +  
B  
… and so on        

Figure 4.2.2a – Results table for quadrat sample

Testing for association using a chi-square (x2) test

A chi-squared test is a statistical tool used to determine whether there is an association between two sets of frequency data. The test involves calculating the probability (p) of an association by comparing observed values to values expected if there was no association. It is important that the data used is categorical, not continuous.

Categorical data
(can be used in a chi-square test)
Continuous data
(cannot be used in a chi-square test)
  • Species name
  • Sex
  • Presence of a certain gene or trait
  • Yes/No category
  • Height
  • Abundance
  • Density

For example, let’s say you wanted to know if the presence of one species is associated with the presence of another. This could mean either that the two species tend to appear together, or that the presence of one species is associated with the presence or absence of another. Either way, you have two possible hypotheses:

  • There is no association between the two species. This is the null hypothesis. If there is no association, at the end of the test we will see that p >0.05.
  • There is an association between the two species. If there is an association, at the end of the test we will see that p <0.05. 

Let’s assume we have collected data about many species, as shown in Figure 4.2.2a. Highlighting the relevant data about the two species we are interested in, leaves us with a table like Figure 4.2.2b.

  Species 2 present Speciesnot present Total
Species 1 present 12 5 17
Species 1 not present 8 13 21
Total 20 18 38

Figure 4.2.2b – Observed frequencies of species in a quadrat sample
This is called a 2x2 contingency table. It shows the different combinations of outcomes and the frequency observed of each.

Now we can calculate the value we expect if the null hypothesis were true, using this formula:

Expected space frequency space equals fraction numerator Column space total space straight x space Row space total over denominator Total space number space of space quadrats end fraction

  Species 2 present Species 2 not present Total
Species 1 present 8.95 8.05 17
Species 1 not present 11.05 9.95 21
Total 20 18 38

Figure 4.2.2c – Expected frequencies if no association (null hypothesis)

At this point, you may choose to use an online chi-square calculation tool to determine the p value. (You can also use the ‘CHITEST’ function on Excel, to compare the expected to observed values.)

Alternatively, you can calculate the chi-square ( x2 )value by using the following formula:

begin mathsize 20px style X squared equals sum subscript blank superscript blank fraction numerator left parenthesis straight O subscript straight n minus straight E subscript straight n right parenthesis squared over denominator straight E subscript straight n end fraction

end style

Where O is the observed frequency (from Figure 4.2.2b) and E is the expected frequency (from Figure 4.2.2c) for each outcome (n).

For the data set above, the calculated chi-squared value is 3.98. We can now compare the chi-square value to a table of critical values to see if our result is significant. A 2x2 contingency table has one degree of freedom, so we read the first line of the table.

table of critical valuesFigure 4.2.2d – Table of critical values for the chi-square test
The calculated chi-squared value falls between 95% and 97% confidence. This means our null hypothesis is void.

In our example, we can conclude, with more than 95% confidence, that there is an association between the presence of species 1 and the presence of species 2.

homemade quadratFigure 4.2.2e – Quadrats are very easy to make using wood or plastic tubing

In the lab

  • The most important thing to remember when setting up a quadrat is that you should be sampling randomly. Don’t look for areas that appear to be ‘good’ test sites.
  • Make sure you define very clearly what you are sampling. Are you counting the number of individuals of each species to estimate population size? Are you counting the presence of different species? Be clear in your aim and method.

underwater quadratFigure 4.2.2f – Underwater quadrat
This researcher has divided his quadrat into 100 zones in order to make it easier for him to calculate the percentage of the sample area where different species are found.

Concept tip

  • The aim of every statistical test is to prove that the null hypothesis is wrong. The null hypothesis predicts that the experimental values we observed are completely random.
  • An association between the two species means that the probability of these numbers being random is less than 5%. We can state our conclusion (i.e. that there is an association) with 95% confidence. 

Tools

An example of a free on-line statistical tool can be found at:

http://vassarstats.net/tab2x2.html

Using this tool, our data has a p value of 0.046.

plant associationsFigure 4.2.2g – Plant associations
Is the diversity in this field a coincidence or are these plants species associated with each other? Use a chi-square test to find out!

Extended essay

Think about ways you could determine if there are associations of different abiotic factors on species diversity and/or populations. A good way to do this would be to use quadrats or transects at different locations. Learn about transects in 14.2.4 Field techniques: Using transects.

Course link

There are different circumstances in which to use chi-square tests. HL students will use chi-squared values to compare observed values to expected theoretical ratios of genes in 10.1.2 Inheritance.

4.2.3 Nature of science: The missing sink

Evidence, whether gathered by direct observation or experimentation, is fundamental to a common understanding of science. When systems are small and involve only a few variables, controlled experimentation yields very reliable data. For example, one can get very significant results testing the effect of seasonal variations of carbon dioxide concentration on photosynthetic output of a specific plant at a specific altitude by mimicking those conditions in a laboratory.

When scientists make predictions about systems on a larger scale, it is much less straightforward to devise controlled experiments, so scientists rely on observational data. It is important that the data is:

  • quantitative – the common language of all the sciences is mathematics; mathematical analysis should be objective and easily interpreted by other scientists
  • repeated – to improve reliability and to make predictions more accurate
  • available – scientists rely on cooperation and sharing of data in databases in order to improve their own models.

Calculating carbon flux

  • Meteorological flux towers take readings of important greenhouse gases including carbon dioxide, water vapour and methane at various locations around the world.
  • Scientists then transform those readings, using complex algorithms, to generate flux ratios at different locations.
  • Although the mathematics involved is complex, the collection method is based on a simple principle, as shown in Figure 4.2.3a1. Air currents travel upwards and downwards. The flux calculation takes into consideration the concentration of gases, and wind speed in vertical air movements, to calculate carbon flux ratio.

flux principleFigure 4.2.3a – Simplified principle of a flux tower1

Where did the carbon go?

In making predictions and models, scientists need to keep fundamental laws of nature in mind. A natural law describes patterns in observed phenomena, and any observation that does not obey the law must be explained.

  •  The law of conservation of mass states that matter is neither created nor destroyed in any transformation or process. In other words, the amount of carbon released and the amount of carbon accumulated in the various carbon sinks on Earth, must be equal.
  • When all the air-monitoring data from flux towers and data collected from the sea were compiled to generate a picture of global carbon flux, scientists could only account for some of the carbon emissions. The carbon cycle does not appear to be in balance.
  • This was a very unexpected result, and is demonstrated in Figure 4.2.3b. Anthropogenic carbon emissions (releases) are shown on the top half of the graph. The bottom half of the graph shows where carbon accumulated. The carbon in the orange section is unaccounted for, and presumably accumulated in, a ‘missing sink’.

flux of carbonFigure 4.2.3b – Annual carbon flux, 1850–2000

  • The missing carbon is a concern for scientists because it is difficult to make predictions on the consequences of the carbon emissions without a clear picture of where carbon is accumulating. They do not know where the carbon is, or how long it will stay stored; if all the missing carbon suddenly reappears in the atmosphere, it could be catastrophic.

The story of the missing sink provides an example of how natural law, evidence and theory work together to improve scientific models.

Key questions

  • One of the key themes in the Nature of Science is objectivity. How do scientists ensure objectivity in data collection and reporting?
  • What is the relationship between law, theory and hypothesis?
  • Why is the missing sink a concern to scientists?

flux towerFigure 4.2.3c – A flux tower

Concept help

Recall from Page: 4.1.3 Carbon cycling that carbon flux is a measurement of carbon exchange between processes in the carbon cycle. Scientists have used recent data from flux towers to estimate that human activity, especially the burning of fossil fuels and redirecting land use to agriculture, is responsible for putting approximately 7–8 gigatonnes of carbon dioxide into the atmosphere annually.

Further reading

  • The mathematics of calculating carbon flux is quite complex, but relies on a statistical tool called eddy covariance.
  • Many hypothetical missing sinks have been proposed by scientists. One such proposal is forests in northern regions, which are known to be expanding because of higher temperatures. For more information on possible missing sinks, see:
    Schindler, D.W. ‘The mysterious missing sink’ in Nature 398: 105–107, 11 March 1999.

forests north americaFigure 4.2.3d – Expanding forests in northern North America
Forests in northern latitudes have shown increases in productivity due to higher temperatures. Could northern forests be the missing sink?

Language tools

The words ‘law’, ‘theory’ and ‘hypothesis’ have scientific meanings that should be distinguished from their non-scientific meanings.

  • A natural law describes phenomena without necessarily explaining them.
  • Theories provide models and mechanisms explaining how natural phenomena operate
  • Hypotheses provide testable and falsifiable explanations for observed phenomena.

Sources

  1. Image credit: Burba, G.G., 2013, Eddy Covariance Method For Scientific, Industrial, Agricultural and Regulatory Applications, LI-COR, Biosciences, USA, 12-13pp., Copyright, LI-COR, Inc.

4.2.4 Assessing claims about climate change

As you read through this page, consider the following:

  • When is skepticism appropriate?
  • In what circumstances are correlative links as powerful as causal links?
  • What are some ways in which bias might be introduced unintentionally into discussions about climate change?

Evidence for climate change

The National Oceanic and Atmospheric Agency (NOAA) has been collecting data on atmospheric carbon dioxide levels at Mauna Loa, in Hawaii, for over 60 years. A summary of the Agency’s data is shown in Figure 4.2.4a.

Atmospheric carbon dioxide concentration at Mauna Loa, Hawaii, 1957-2006Figure 4.2.4a – Atmospheric carbon dioxide concentration at Mauna Loa, Hawaii, 1957–2006

Methane concentration measured through ice core samples and air monitoring stationsFigure 4.2.4b – Methane concentration measured through ice core samples and by air monitoring stations

Methane is another very important greenhouse gas, which is produced when coal is burned, and in agriculture. Atmospheric levels of methane have been increasing rapidly since the Industrial Revolution, as shown in Figure 4.2.4b.

All the data from Figures 4.2.2a and 4.2.2b provides strong correlative evidence that increases in carbon emissions, and subsequently climate change, are caused by human activity.

Correlation and causation

Correlative and observational studies are very important lines of evidence in the debate on climate change. It would be impossible to demonstrate experimentally that climate change is not caused, or at least amplified, by human activities. Attempting to do so would require that scientists reduce carbon dioxide emissions to pre-industrial levels.

However, correlative studies are not enough. The link between human activities and climate change is supported by a theoretical understanding of how the enhanced greenhouse effect leads to increasing global temperatures. It is further supported by localised studies and predictions from computer models. 

Here is an example. Based on our understanding of the carbon cycle and global climate change, scientists have predicted the following consequences for Arctic ecosystems:

  • Increased release of carbon dioxide due to decomposition in soils previously covered by permafrost.
  • Increased pathogens and insect pests on newly exposed soils.
  • Habitat loss for large carnivores, resulting in redistribution of prey species.

Monitoring data from different sites confirms the relationship between loss of ice in the Arctic, and carbon dioxide levels. Correlations can be as powerful as experimental evidence when there is a scientifically sound explanation linking variables.

Skepticism and public understanding of science

Scientists display skepticism when making claims. Skepticism is an important part of the scientific process, since it encourages strong evidence and research. There are many legitimate reasons why scientists are skeptical, for example:

  • Environmental chemists, biologists and physicists normally have a very narrow range of expertise, so experts in one field may disagree with the methodologies and goals of another field.
  • Predictions based on computer modelling programs vary depending on whether individuals choose to be conservative, or to overestimate possible long-term effects of climate change. Models and predictions vary widely for another important reason: that natural systems tend to display emergent properties. The carbon cycle involves both living and non-living systems on a global scale and their interactions. It is very difficult to understand whether correlations between parts of the system are relevant or not.

Critics of climate change often exaggerate the legitimate skepticism shown by scientists in order to heighten the public debate on climate change in the following ways:

  • The straw man fallacy – while there is no consensus among scientists about the extent of damage or long-term consequences of climate change, there is consensus that climate change is occurring and that human activity is involved. Critics will premise their arguments by saying that scientists can’t agree on anything, or by misrepresenting important aspects of the debate. These ‘straw men’ scientists simply don’t exist.
  • Confirmation bias – scientists collect data on a large scale, analyse anomalies and look for patterns. Critics are selective in their representation of the data. Only data that supports their theory is included in the debate.

When the public form opinions on climate change, it is difficult to distinguish between well-researched science on the one hand, and political or economic ideology on the other. It is important that we recognise logical fallacies when analysing claims about the anthropogenic causes of climate change.

cartoonFigure 4.2.4c – The reductionist approach to science 
This cartoon illustrates, ironically, the reductionist approach to science. The mathematician on the right side believes every other academic discipline can be redefined simply as applied maths.

Check your understanding

Suggest reasons for the monthly variation in carbon dioxide levels illustrated in the data from Mauna Loa (Figure 4.2.4a).

Activity

Use the NOAA database to analyse concentrations of greenhouse gases from different sources NOAA database: http://www.esrl.noaa.gov/psd/data/

polar bearFigure 4.2.4d – Stranded polar bear
Was this polar bear stranded on this iceberg because of human sources of carbon emissions? It’s important to look at all the data, not just a few anecdotes.

cowFigure 4.2.4e – Device wearing cow
Novel ways to collect data: this cow is wearing a device that captures methane gas released in flatulence.

Did you know?

A significant source of methane – an important greenhouse gas – comes from livestock. Scientists in Argentina are trying to capture waste gases in an attempt to understand how much is being produced, and whether it might be used as a source of fuel.

Concept help

Which of these two correlations can be explained using a causal theory? Explain how you would provide further evidence for that theory.

  • As the outside temperature rises, ice cream sales increase.
  • Brown-eyed students get better IB exam scores than blue-eyed students.

International mindedness

The precautionary principle guides decision-making processes in the absence of scientific consensus. If an action or decision has the potential to do harm, policy-makers have the social responsibility to err on the side of caution and make decisions that limit potential harm. There seems to be no consensus among scientists about the speed and extent of damage on ecosystems related to climate change. Why should policy-makers use conservative models to make decisions on carbon reduction strategies?

TOK

The concept of emergent properties is consistent with a holistic, or systems, approach to science.

  • The guiding philosophy of the systems approach is that nothing makes sense unless it is considered in the context of the whole system.
  • At the other end of the spectrum is the reductionist approach: the idea that everything in nature can be explained by simple fundamental laws that apply to everything including living and non-living systems.

Reductionism dominates most scientific endeavour, but is there a limit to what we can learn about nature using this philosophy? Where do different scientific sub-disciplines fall on the spectrum?