Reading Lab

IELTS Academic Reading Practice Pack 43

A full 60-minute Academic Reading mock with three source-grounded passages, 40 questions, answer key coverage, and doctrine QA traceability.

Question count

Time allowed

60 min

Passages

Academic ReadingFull MockIELTS PracticeQA Approved

Exam panel

You have 60 minutes including answer transfer time. Submit once at the end or let the timer finish the exam automatically.

Time remaining

60:00

0 / 40 answers filled

Write only what the question requires. One extra word can still lose the mark.

After submission, you will see your raw score, estimated Academic Reading band, and the correct answers for every question.

What this reading pack trains

This set is built around from fungal threads to functional materials, measuring forest structure from space, planning when prediction is not enough with 7 official IELTS Reading task types spread across three passages.

IELTS Academic Reading Practice Pack 43 is designed as a full Academic Reading simulation, not just a passage archive. The three texts move from a more accessible opener into denser, more inference-heavy material so the burden rises in the same direction students expect in a real test.

Across this pack, you work through roughly 2,492 words on From Fungal Threads to Functional Materials; Measuring Forest Structure from Space; Planning When Prediction Is Not Enough. That mix matters because IELTS Reading rewards candidates who can adjust between topic vocabulary, paraphrase recognition, and question-discipline rather than relying on one search habit.

Use this pack when you want one serious timed session, then review every wrong answer against the exact trap type. A strong post-test habit is to check whether the miss came from rushing, weak paraphrase tracking, unstable Not Given logic, or ignoring the word-limit instruction.

Inside the pack

Use the pack as one timed attempt, then return for deliberate review.

Domains

from fungal threads to functional materials · measuring forest structure from space · planning when prediction is not enough

Question types

Matching Headings · Matching Sentence Endings · Multiple Choice · Sentence Completion · Summary Completion · True/False/Not Given · Yes/No/Not Given

If you want more full mocks after this one, go back to the Reading pack library. If you need a broader exam routine, pair one reading session with Listening practice or IELTS Writing repair work.

Passage 1

From Fungal Threads to Functional Materials

An academic IELTS passage on from fungal threads to functional materials, opening with in recent years, materials scientists have shown renewed interest in mycelium, the network of fine fungal threads that normally spreads throug....

A.A. In recent years, materials scientists have shown renewed interest in mycelium, the network of fine fungal threads that normally spreads through soil, wood and other organic matter. When a suitable fungus is grown through chopped agricultural residues, such as straw, sawdust or hemp fibres, the threads bind the particles together into a light but coherent mass. After the desired form has developed, heat treatment can stop further growth and stabilise the material. The resulting composite does not behave like a conventional plastic foam, yet it can perform some similar functions in packaging, insulation and acoustic panels. Unlike a sheet material manufactured in a factory line, the composite is partly assembled by biological growth. This gives the process an unusual character: the organism is not merely an ingredient but also a temporary worker that links loose particles before being deactivated.

B.B. The basic attraction of mycelium composites is that their production begins with low-value biological waste rather than with petroleum or mineral extraction. A grower places a selected fungal species in contact with a cleaned substrate and controls moisture, temperature and air supply. The fungus digests parts of the substrate while leaving enough fibrous structure to give the composite body. Because the material grows into a mould, it can take the shape of a packaging corner, a decorative tile or an insulation block without extensive machining. This does not mean that the process is effortless; contamination, drying time and consistency remain practical difficulties. In laboratory and small commercial settings, trays or moulds must be kept clean enough to favour the intended fungus over competing microorganisms. The pace of growth can be changed by adjusting humidity and by selecting substrates with different particle sizes, but these decisions also affect the final texture and strength. This makes production control more similar to cultivation than to ordinary assembly.

C.C. Compared with many synthetic foams, mycelium products are often praised for their low density and their ability to break down after use. Some studies also report useful fire behaviour, partly because fungal and plant matter can form a char layer rather than melt rapidly. However, these properties vary widely. The species of fungus, particle size of the substrate, growth period and post-processing method all influence strength, water absorption and thermal performance. A sample grown from fine hemp fibres may not resemble one made from coarse straw, even if both are called mycelium composite. For designers, this is both useful and inconvenient. It means that a product can be tuned for a purpose, such as cushioning or sound absorption, but it also means that test results from one recipe cannot automatically be transferred to another. Standardisation is therefore a central research problem.

D.D. This variability explains why most commercial uses remain in non-load-bearing roles. A packaging insert only needs to cushion and protect an object for a limited period; a wall panel must meet more demanding rules for durability, moisture control and fire safety. Researchers are therefore experimenting with additives, coatings and densification methods to make the material more predictable. Others are investigating whether mycelium can be combined with textiles or printed pastes to create more complex shapes than simple moulds allow. Building regulations add another layer of caution. A material used inside a wall or ceiling must continue to perform after years of seasonal humidity, occasional leaks and contact with other construction products. Short demonstrations can show promise, but long service lives require repeatable evidence.

E.E. The strongest argument for mycelium materials is not that they can replace all plastics or mineral products. Rather, they may occupy a useful middle ground where short service life, low weight and biodegradability matter more than maximum mechanical strength. If the substrate is locally available and the energy used for drying is controlled, the environmental case becomes stronger. If the material travels long distances or needs heavy coatings to survive moisture, the advantage becomes less clear. Mycelium composites are therefore best understood as a promising family of materials whose success depends on matching the biology to a realistic application. This is why the field has moved away from simple claims that a biological material is automatically sustainable. Life-cycle thinking asks where the residue came from, how much energy was used for drying, what additives were required and what actually happens at disposal.

True/False/Not Given

Questions 1-6

Do the following statements agree with the information given in Reading Passage 1?

Write TRUE if the statement agrees with the information, FALSE if the statement contradicts the information, and NOT GIVEN if there is no information on this.

1. Mycelium composites are formed when fungal threads bind pieces of organic material together.

2. Heat treatment is used to make the fungus grow more quickly.

3. The passage states that mycelium composites are already cheaper than all synthetic foams.

4. The material can take a moulded shape without extensive machining.

5. All mycelium composites have similar strength and water absorption.

6. The writer suggests that the environmental value of mycelium materials depends partly on transport and processing choices.

Sentence Completion

Questions 7-13

Complete the sentences below. Choose NO MORE THAN TWO WORDS from the passage for each answer.

7. The fungal network normally spreads through soil, wood and other ___ .

8. A selected fungus is grown through agricultural residues such as straw, sawdust or ___ .

9. During production, growers control temperature, air supply and ___ .

10. Some mycelium materials may form a protective ___ rather than melt quickly.

11. Most present commercial uses are in roles that are not ___ .

12. Researchers are testing coatings and densification methods to make the material more ___ .

13. The passage argues that mycelium composites work best when biology is matched to a realistic ___ .

A. Forests are often described by their area, but two forests with the same outline on a map may differ greatly in height, density and internal arrangement. These vertical features influence habitat quality, carbon storage and the way disturbances such as logging or storms affect an ecosystem. Traditional satellite images are valuable for mapping land cover, yet they mainly show the surface from above. To understand forest structure, scientists need information about how vegetation is distributed from the ground to the canopy. A plantation of young trees, a selectively logged forest and an old-growth stand may each appear as vegetation in a two-dimensional image, although their ecological value and carbon stocks differ. The missing dimension is often the one most relevant to animals and to biomass estimation.
B. Light detection and ranging, usually shortened to LiDAR, addresses this problem by sending laser pulses towards the Earth and measuring the returning signal. Airborne LiDAR has been used for years to produce detailed three-dimensional maps, but aircraft surveys are expensive and rarely cover the globe at regular intervals. NASA's Global Ecosystem Dynamics Investigation, or GEDI, placed a full-waveform LiDAR instrument on the International Space Station. Instead of producing a continuous photograph-like image, GEDI samples narrow tracks, recording information about canopy height, vertical foliage distribution and ground elevation. Full-waveform systems are especially useful because they record more than a single return from the canopy top. Parts of the signal can represent upper leaves, lower branches and the ground surface, allowing scientists to infer the vertical distribution of vegetation rather than only the maximum height.
C. The distinction between continuous imagery and sampled measurements is important. GEDI does not observe every tree. Its strength lies in providing carefully calibrated vertical profiles across many ecosystems. Researchers can combine those profiles with other satellite data, such as radar or optical imagery, to estimate forest height and structural complexity over wider areas. In this sense, GEDI functions less like a camera and more like a set of reference measurements that improve broader mapping models. This combination is sometimes called data fusion. The sampled LiDAR observations provide reliable vertical anchors, while other sensors supply frequent or continuous coverage. Statistical or machine-learning models can then estimate structural features between the sampled tracks, although uncertainty remains higher where field validation is scarce.
D. Forest structure matters because many ecological processes depend on it. Tall, multi-layered canopies can store substantial biomass and offer different microhabitats from low, even-aged stands. Some birds, insects and mammals rely on particular canopy layers, while other species respond to gaps, edges or dense understory vegetation. Structural information can therefore help conservation planners identify habitat conditions that a simple green-or-brown land-cover map would miss. The same information can also support restoration monitoring. A recovering forest may regain green cover quickly, while complex vertical layering develops more slowly. If managers measure only area, they may overestimate recovery; structural indicators can reveal whether habitat conditions are actually returning. This is particularly important where restoration projects are judged by numerical targets that may reward planting without measuring later ecological function.
E. There are also limitations. Laser pulses may be affected by steep terrain, dense vegetation or cloud-related observation gaps in supporting data. The orbit of the space station gives strong coverage in many tropical and temperate regions but does not sample the highest latitudes. Moreover, translating a waveform into an ecological conclusion requires field knowledge. A high canopy does not automatically mean an undisturbed forest, and a shorter forest may be naturally appropriate in some environments. Sampling design also affects interpretation. A footprint may fall on a forest edge, a small clearing or a slope, and the result may not represent a larger surrounding area. Analysts must therefore consider scale before turning a physical measurement into a conservation conclusion.
F. For these reasons, the value of spaceborne LiDAR is greatest when it is treated as one part of a measurement system. Field plots, local ecological expertise, radar data and repeated satellite observations all help interpret the structural signal. The goal is not simply to create more detailed maps, but to link physical measurements to decisions about restoration, carbon accounting and biodiversity protection. As forest monitoring becomes more data-rich, the challenge is increasingly to decide which structural indicators are meaningful for a particular management question. For policy users, this matters because different decisions require different levels of certainty. A national carbon estimate may tolerate some local error, while a reserve boundary or restoration payment may require more precise evidence. The same dataset can be powerful in one context and insufficient in another. Good interpretation therefore requires users to state the decision first and then choose the indicators, instead of assuming that every new measurement automatically answers every ecological question.

Passage 2

Measuring Forest Structure from Space

An academic IELTS passage on measuring forest structure from space, opening with forests are often described by their area, but two forests with the same outline on a map may differ greatly in height, density and internal a....

A.A. Forests are often described by their area, but two forests with the same outline on a map may differ greatly in height, density and internal arrangement. These vertical features influence habitat quality, carbon storage and the way disturbances such as logging or storms affect an ecosystem. Traditional satellite images are valuable for mapping land cover, yet they mainly show the surface from above. To understand forest structure, scientists need information about how vegetation is distributed from the ground to the canopy. A plantation of young trees, a selectively logged forest and an old-growth stand may each appear as vegetation in a two-dimensional image, although their ecological value and carbon stocks differ. The missing dimension is often the one most relevant to animals and to biomass estimation.

B.B. Light detection and ranging, usually shortened to LiDAR, addresses this problem by sending laser pulses towards the Earth and measuring the returning signal. Airborne LiDAR has been used for years to produce detailed three-dimensional maps, but aircraft surveys are expensive and rarely cover the globe at regular intervals. NASA's Global Ecosystem Dynamics Investigation, or GEDI, placed a full-waveform LiDAR instrument on the International Space Station. Instead of producing a continuous photograph-like image, GEDI samples narrow tracks, recording information about canopy height, vertical foliage distribution and ground elevation. Full-waveform systems are especially useful because they record more than a single return from the canopy top. Parts of the signal can represent upper leaves, lower branches and the ground surface, allowing scientists to infer the vertical distribution of vegetation rather than only the maximum height.

C.C. The distinction between continuous imagery and sampled measurements is important. GEDI does not observe every tree. Its strength lies in providing carefully calibrated vertical profiles across many ecosystems. Researchers can combine those profiles with other satellite data, such as radar or optical imagery, to estimate forest height and structural complexity over wider areas. In this sense, GEDI functions less like a camera and more like a set of reference measurements that improve broader mapping models. This combination is sometimes called data fusion. The sampled LiDAR observations provide reliable vertical anchors, while other sensors supply frequent or continuous coverage. Statistical or machine-learning models can then estimate structural features between the sampled tracks, although uncertainty remains higher where field validation is scarce.

D.D. Forest structure matters because many ecological processes depend on it. Tall, multi-layered canopies can store substantial biomass and offer different microhabitats from low, even-aged stands. Some birds, insects and mammals rely on particular canopy layers, while other species respond to gaps, edges or dense understory vegetation. Structural information can therefore help conservation planners identify habitat conditions that a simple green-or-brown land-cover map would miss. The same information can also support restoration monitoring. A recovering forest may regain green cover quickly, while complex vertical layering develops more slowly. If managers measure only area, they may overestimate recovery; structural indicators can reveal whether habitat conditions are actually returning. This is particularly important where restoration projects are judged by numerical targets that may reward planting without measuring later ecological function.

E.E. There are also limitations. Laser pulses may be affected by steep terrain, dense vegetation or cloud-related observation gaps in supporting data. The orbit of the space station gives strong coverage in many tropical and temperate regions but does not sample the highest latitudes. Moreover, translating a waveform into an ecological conclusion requires field knowledge. A high canopy does not automatically mean an undisturbed forest, and a shorter forest may be naturally appropriate in some environments. Sampling design also affects interpretation. A footprint may fall on a forest edge, a small clearing or a slope, and the result may not represent a larger surrounding area. Analysts must therefore consider scale before turning a physical measurement into a conservation conclusion.

F.F. For these reasons, the value of spaceborne LiDAR is greatest when it is treated as one part of a measurement system. Field plots, local ecological expertise, radar data and repeated satellite observations all help interpret the structural signal. The goal is not simply to create more detailed maps, but to link physical measurements to decisions about restoration, carbon accounting and biodiversity protection. As forest monitoring becomes more data-rich, the challenge is increasingly to decide which structural indicators are meaningful for a particular management question. For policy users, this matters because different decisions require different levels of certainty. A national carbon estimate may tolerate some local error, while a reserve boundary or restoration payment may require more precise evidence. The same dataset can be powerful in one context and insufficient in another. Good interpretation therefore requires users to state the decision first and then choose the indicators, instead of assuming that every new measurement automatically answers every ecological question.

Matching Headings

Questions 14-19

Reading Passage 2 has six paragraphs, A-F. Choose the correct heading for each paragraph from the list of headings below.

14. Paragraph A

i. Why vertical information changes forest assessment
ii. The financial benefits of aircraft surveys
iii. How a spaceborne laser samples forest structure
iv. Why sampled data need supporting information
v. Structural features as ecological evidence
vi. The danger of replacing fieldwork completely
vii. Technical and interpretive limits of the method
viii. A measurement system for management decisions
ix. How tree species are named from space

15. Paragraph B

i. Why vertical information changes forest assessment
ii. The financial benefits of aircraft surveys
iii. How a spaceborne laser samples forest structure
iv. Why sampled data need supporting information
v. Structural features as ecological evidence
vi. The danger of replacing fieldwork completely
vii. Technical and interpretive limits of the method
viii. A measurement system for management decisions
ix. How tree species are named from space

16. Paragraph C

i. Why vertical information changes forest assessment
ii. The financial benefits of aircraft surveys
iii. How a spaceborne laser samples forest structure
iv. Why sampled data need supporting information
v. Structural features as ecological evidence
vi. The danger of replacing fieldwork completely
vii. Technical and interpretive limits of the method
viii. A measurement system for management decisions
ix. How tree species are named from space

17. Paragraph D

i. Why vertical information changes forest assessment
ii. The financial benefits of aircraft surveys
iii. How a spaceborne laser samples forest structure
iv. Why sampled data need supporting information
v. Structural features as ecological evidence
vi. The danger of replacing fieldwork completely
vii. Technical and interpretive limits of the method
viii. A measurement system for management decisions
ix. How tree species are named from space

18. Paragraph E

i. Why vertical information changes forest assessment
ii. The financial benefits of aircraft surveys
iii. How a spaceborne laser samples forest structure
iv. Why sampled data need supporting information
v. Structural features as ecological evidence
vi. The danger of replacing fieldwork completely
vii. Technical and interpretive limits of the method
viii. A measurement system for management decisions
ix. How tree species are named from space

19. Paragraph F

i. Why vertical information changes forest assessment
ii. The financial benefits of aircraft surveys
iii. How a spaceborne laser samples forest structure
iv. Why sampled data need supporting information
v. Structural features as ecological evidence
vi. The danger of replacing fieldwork completely
vii. Technical and interpretive limits of the method
viii. A measurement system for management decisions
ix. How tree species are named from space

Summary Completion

Questions 20-23

Complete the summary below. Choose ONE WORD ONLY from the passage for each answer.

GEDI is a space-based system for measuring forest structure.

20. GEDI uses laser-based _____ from the International Space Station.

21. Unlike a normal image, GEDI records narrow _____ rather than continuous coverage.

22. Scientists combine these profiles with radar or _____ imagery to extend interpretation across wider regions.

23. A high canopy alone does not prove that a forest is _____.

Multiple Choice

Questions 24-26

Choose the correct letter, A, B, C or D.

24. What is the main purpose of paragraph C?

25. According to paragraph D, why is forest structure important for conservation?

26. What warning does the writer give in paragraph F?

Passage 3

Planning When Prediction Is Not Enough

An academic IELTS passage on planning when prediction is not enough, opening with public decisions are often expected to rest on prediction.

A.A. Public decisions are often expected to rest on prediction. A transport authority estimates future demand before building a rail line; a water agency estimates rainfall before expanding storage; a coastal city estimates sea-level rise before redesigning drainage. Prediction is necessary, but it becomes less sufficient when the future contains deep uncertainty. In such cases, experts may agree on broad risks yet disagree about probabilities, timing or the relative importance of interacting forces. Climate adaptation has made this problem especially visible, because infrastructure choices made today may have to perform under social, technological and environmental conditions that cannot be known with confidence. The difficulty is not simply that the numbers are uncertain. It is that the future may be shaped by choices people have not yet made: migration patterns, energy systems, construction standards, insurance markets and political priorities. These factors interact with climate hazards rather than merely adding to them.

B.B. One response is to demand better forecasts before acting. This is reasonable when uncertainty can be reduced quickly and cheaply. However, waiting for precision may itself be a risky choice if roads, hospitals or water systems already face stress. Decision-making under deep uncertainty therefore begins from a different question: not 'Which future is most likely?' but 'Which choices remain acceptable across many plausible futures?' This shift does not reject science. It changes how scientific information is used. Models become tools for stress-testing options rather than machines for selecting a single best future. In this framework, uncertainty is not treated as an embarrassment to be hidden in technical appendices. It becomes part of the design problem. A plan that performs well only when one forecast proves correct may be less attractive than a plan that performs adequately under several futures, even if it is not optimal in any single one.

C.C. Scenario planning illustrates the change. Instead of treating one projection as a target, planners examine several coherent futures, such as a hotter but wetter region, a drier region with rapid population growth, or a moderate climate change pathway with severe economic constraints. The purpose is not to guess which story will occur. It is to expose the assumptions that make a plan fragile. If a drainage project fails only under an extreme rainfall pathway, the risk may be tolerable; if it fails under several ordinary combinations of population growth and rainfall variability, the design needs reconsideration. The method is most useful when scenarios are distinct enough to challenge a plan, yet plausible enough to command attention. A weak scenario exercise simply decorates a preferred policy with alternative stories. A stronger one forces decision-makers to ask what evidence would make them change course.

D.D. A related approach, robust decision making, uses models to search for vulnerabilities. Analysts test strategies against hundreds or thousands of combinations of future conditions, then identify the circumstances in which each strategy performs poorly. The result is not necessarily the strategy with the highest expected benefit in one forecast. It may be a strategy that avoids unacceptable failure in a wide range of futures. This can frustrate organisations that prefer a ranked list of options, but it is useful when the cost of being confidently wrong is high. Robustness therefore differs from efficiency. An efficient strategy may use resources very well under expected conditions, but collapse when those conditions shift. A robust strategy may look less elegant on paper, yet protect essential services when several pressures arrive together.

E.E. Adaptive pathways add a time dimension. Rather than committing immediately to the largest possible intervention, planners identify near-term actions, warning signs and later choices. A city might improve maintenance and protect critical sites now, while reserving land or legal authority for a larger flood barrier if sea-level rise or storm damage crosses a defined threshold. This approach can prevent overbuilding, but it depends on monitoring and institutional memory. A pathway written in a report is weak if no agency is responsible for tracking the signposts. The timing of thresholds is crucial. If the warning sign is detected too late, later options may no longer be available; if it is too sensitive, authorities may spend heavily before the threat justifies it. Adaptive planning therefore requires both technical monitoring and governance rules.

F.F. Critics argue that these methods can make public decisions appear more technical than they really are. A choice that is robust for a whole city may still distribute costs unfairly between neighbourhoods. A flexible pathway may protect future budgets while leaving current residents exposed. Scenario exercises can also be manipulated if inconvenient futures are excluded. Deep uncertainty methods therefore do not remove politics from adaptation. They make some trade-offs more visible, but elected officials and communities still have to decide which failures are unacceptable and who should bear the cost of avoiding them. This criticism is not an argument against analysis. It is a warning against presenting analysis as neutral when the choice of objectives, indicators and acceptable losses already contains values. A model may clarify trade-offs, but it cannot decide whether temporary disruption, relocation or higher taxes are socially acceptable.

G.G. The most defensible use of these methods is neither paralysis nor false certainty. It is disciplined preparation. Prediction remains valuable for near-term design and for detecting trends, while robustness and adaptation help planners avoid pretending that uncertainty has disappeared. In practice, the quality of a decision depends less on the elegance of the model than on whether the organisation can learn, revise and explain its choices over time. Planning under deep uncertainty is therefore not a way to escape judgement. It is a way to make judgement more explicit before events impose it. This is also why transparency matters. Citizens do not need every equation behind a climate model, but they do need to know which risks were considered, which were postponed, and what evidence would trigger a change in policy. Without that explanation, flexibility can look like evasion rather than preparation.

Yes/No/Not Given

Questions 27-31

Do the following statements agree with the claims of the writer in Reading Passage 3?

Write YES if the statement agrees with the claims of the writer, NO if the statement contradicts the claims of the writer, and NOT GIVEN if it is impossible to say what the writer thinks about this.

27. The writer believes that prediction is useful but insufficient in some long-term public decisions.

28. The writer claims that waiting for better forecasts is always irresponsible.

29. Scenario planning is presented as a method for identifying fragile assumptions rather than choosing the most likely future.

30. The writer says robust decision making always produces the cheapest strategy.

31. The writer suggests that deep uncertainty methods cannot by themselves settle political questions about fairness.

Matching Sentence Endings

Questions 32-36

Complete each sentence with the correct ending, A-G, below.

32. Decision-making under deep uncertainty changes the role of models by

33. Scenario planning can help planners by

34. Robust decision making works by

35. Adaptive pathways depend on

36. The writer concludes that good planning requires

A. treating models as stress-testing tools rather than prediction machines.
B. removing the need for political judgement in adaptation.
C. identifying thresholds that trigger later action.
D. choosing the strategy with the highest benefit in a single forecast.
E. revealing which assumptions cause a plan to fail.
F. making organisational learning and explanation part of decision quality.
G. testing strategies across many combinations of future conditions.

Multiple Choice

Questions 37-40

Choose the correct letter, A, B, C or D.

37. What does the writer imply about demanding better forecasts before acting?

38. What is the writer’s main point about adaptive pathways?

39. Why does paragraph F mention neighbourhoods and current residents?

40. Which statement best captures the writer’s overall position?