ACT Science Practice Passages

The peak of the curve in Figure 1 occurs at 37°C, where activity reaches 100 units/min. This is a direct reading question — locate the highest point on the curve and read its x-axis value.

At temperatures above 60°C, Figure 1 shows activity approaching zero. High temperatures denature (unfold) enzyme proteins, destroying the active site. Choice D is wrong because the problem is heat, not cold. Choice A contradicts the downward trend shown in the graph.

The Figure 2 curve is bell-shaped and symmetric around pH 7. Reading the graph at 10 units/min shows two points: pH 4 (on the rising side) and pH 10 (on the falling side). At pH 2 and pH 12, activity is approximately 0, not 10 units/min.

pH 7 is the definition of neutral. Figure 2 directly shows that amylase peak activity occurs at pH 7. Figure 1 is about temperature, not acidity/basicity. Figure 3 is about substrate concentration. Only Figure 2 speaks to pH conditions.

Figure 3 shows the curve flattening to a plateau at about 110 units/min from 80 mmol/L onward. Between 60 and 100 mmol/L, the increase continues but slows dramatically. This plateau is called Vmax — maximum velocity, occurring when all enzyme active sites are occupied.

Figure 1 shows that at 50°C, amylase activity is approximately 40 units/min compared to 100 units/min at 37°C. Temperature affects the overall rate (through enzyme structure), so Vmax at 50°C would be lower. Some enzymes are partially denatured at 50°C.

This question integrates all three figures. Figure 1 peak: 37°C. Figure 2 peak: pH 7. Figure 3 plateau: ≥80 mmol/L substrate. Choice B satisfies all three optimal conditions simultaneously. Choice C uses 50°C (below optimal) and pH 7. Choice D uses pH 9 (below optimal).

The interglacial maxima visible in Figure 1 reach approximately 280 ppm. The 420 ppm value at the right end of the graph represents the modern spike, which is far above any natural value in the 400,000-year record. 150 ppm is the glacial minimum. 220 ppm is between extremes.

Calculate decade-to-decade changes: 1970s vs 1960s: 0.05 − 0.01 = 0.04°C; 1980s vs 1970s: 0.26 − 0.05 = 0.21°C; 2000s vs 1990s: 0.56 − 0.40 = 0.16°C; 2010s vs 2000s: 0.80 − 0.56 = 0.24°C. Wait — 2010s: 0.24°C increase. Let me recheck: 1980s increase is 0.21, 2010s is 0.24. Answer is 2010s (D). Let me verify: 2010s (+0.80) vs 2000s (+0.56) = +0.24°C, which is larger than 1980s (+0.21°C). The 2010s showed the greatest single-decade increase.

Both Figure 1 (CO₂) and Figure 2 (temperature) show the same oscillating cycle pattern over 400,000 years, with highs and lows occurring at approximately the same times. This is a positive correlation. They are not identical in magnitude (different units and scales), but their patterns mirror each other.

From 2000s to 2010s: CO₂ increased from 379 to 401 ppm, a gain of 22 ppm per decade. From 2010s to 2020s: 418 − 401 = 17 ppm. The average rate ≈ 17–22 ppm per decade. Applying approximately 17–22 ppm to the 2020s baseline of ~418 ppm gives approximately 435–440 ppm for the 2030s. Answer C (435 ppm) is the most reasonable estimate.

418/280 ≈ 1.49, so current CO₂ is about 49% higher than the natural maximum. This is expressed accurately in choice D. Choice C says "50% higher" but phrases it ambiguously. Choice B is directly contradicted by Figure 1 — 418 ppm is well above the natural maximum of 280 ppm.

Acceleration means the rate is increasing over time. Table 1 shows sea level rise of 31 mm (1990s), 33 mm (2000s), 38 mm (2010s), and 45 mm (2020s*). Each decade's rise is larger than the previous, directly supporting acceleration. Choice D is about CO₂, not sea level.

The passage shows a strong correlation between CO₂ and temperature, but correlation alone does not establish causation. The passage does not provide a mechanistic explanation for why CO₂ changes cause temperature changes. A, B, and D are all directly readable from the figures and table without inferring causation.

Experiment 1 data shows a clear trend: period increases from 0.63 s (at 10 cm) to 2.53 s (at 160 cm) as string length increases. This is a monotonically increasing relationship.

In Experiment 2, the only variable changed was bob mass (the independent variable). All other factors (string length and angle) were held constant. Therefore, the purpose was to test how bob mass affects period.

The periods in Experiment 2 range from 1.79 to 1.80 s — essentially constant across all masses tested (10 g to 200 g). The 0.01 s difference at 100 g is within measurement uncertainty (the timing of 10 swings has inherent variability). The conclusion is well supported. C is wrong because string length was kept constant at 80 cm in Experiment 2.

The small-angle period is 1.79 s. The threshold for "more than 0.05 s deviation" is 1.79 + 0.05 = 1.84 s. At 30°: 1.82 (deviation = 0.03, not enough). At 45°: 1.87 (deviation = 0.08 > 0.05). So 45° is the first angle that exceeds the 0.05 s threshold.

Experiment 1: varying string length changed period from 0.63 to 2.53 s (a 4× change). Experiment 2: varying mass had no measurable effect. Experiment 3: varying angle from 5° to 75° changed period from 1.79 to 2.15 s (a 20% change, and only at large angles). String length causes the largest effect by far.

From Experiment 1, interpolate between the data points: at 80 cm, period = 1.79 s; at 160 cm, period = 2.53 s. For a period of 2.00 s, the length is between 80 and 160 cm, closer to 80 cm. Physically, T = 2π√(L/g); for T = 2 s and g ≈ 9.8 m/s²: L = g(T/2π)² = 9.8 × (2/2π)² ≈ 0.993 m ≈ 99 cm ≈ 100 cm.

To test only one variable (string material), all other potentially influential factors must be controlled (kept constant): bob mass, string length, and release angle. The three previous experiments showed that both length and angle affect period, so these must be standardized. Even mass should be controlled as good experimental practice.

When hare populations increase (providing more food for lynx), lynx reproduction and survival improve. However, it takes a generation or breeding season for this to translate into measurable population growth. This time delay is a classic feature of predator-prey dynamics. Choice D would be a methodological artifact, not an ecological explanation.

Even without lynx, hare populations grew until they overgrazed their food supply (vegetation cover decreased significantly after year 3), then declined. This demonstrates bottom-up regulation: food resources (vegetation) limit hare populations independently of predation. Choice A is unsupported by the passage.

% increase = (150,000 − 90,000) / 90,000 × 100 = 60,000 / 90,000 × 100 = 66.7% ≈ 67%. Choice C (60%) would be if you calculated 60,000/100,000 — using the wrong denominator.

Study 2 directly addresses this: even with predators removed, hare populations eventually declined due to food limitation. This proves that predation is not the only control mechanism. Study 3 shows food matters but doesn't specifically demonstrate that predation is not the sole factor.

This is a classic bottom-up cascade in food webs: more food → more hares → more food for lynx → more lynx. The researchers fed hares only, but the benefit propagated up the food chain. Choice D is possible but not supported by the passage.

Study 1 establishes a repeating historical pattern: hare peaks of ~90,000 every ~10 years, with lynx lagging 1–2 years. Choice B uses the historical pattern to make a prediction about future peak magnitude. Choice A claims the cycle is exactly 10 years (the text says "approximately"). Choice C contradicts the lag described in the text.

To isolate disease as a variable, you must control the other factors (food and predation) while comparing populations that differ only in disease exposure. Vaccination is a standard way to manipulate disease. Choice A tests predation, not disease. Choice D is observational only and doesn't test whether disease causes population changes.

The passage explicitly states that "both accept that evolution occurs and that DNA mutation provides the raw material for change." Choice A is Scientist 1's position. Choice C is not stated by either scientist. Choice D is Scientist 2's position, not agreed upon by Scientist 1.

Scientist 1 explicitly states: "The apparent 'gaps' in the fossil record are simply sampling artifacts — fossilization is rare, and intermediate forms existed but have not been preserved or discovered." Choice B is Scientist 2's explanation. Choice D is Scientist 2's theory.

Scientist 2 states: "rapid transitions occurred when small, geographically isolated populations underwent intense selection pressures." Small populations also amplify genetic drift. Choice B describes Scientist 1's view (molecular clock / gradual change). Choice D is irrelevant to Scientist 2's mechanism.

Scientist 1 specifically claims that gradual fossil transitions exist and that gaps are just sampling artifacts. A complete series showing gradual change directly supports the gradualist view. Scientist 2 predicts stasis punctuated by rapid change — not a smooth gradual series. Choice C contradicts what the evidence shows.

Scientist 2 argues that regulatory mutations "cause large phenotypic changes with few underlying mutations." This supports rapid change (punctuated equilibrium) by showing how big evolutionary jumps could occur without requiring thousands of gradual mutational steps. Choice D contradicts Scientist 2's position.

Scientist 1 states the molecular clock is "the consistent rate at which DNA accumulates neutral mutations" and that it is "consistent with gradual evolutionary change over millions of years." A clock that ticks steadily implies steady, gradual accumulation of change — not bursts of rapid change followed by stasis.

Scientist 1 argues gaps are just sampling artifacts and that gradual transitions existed. If a body plan appeared instantaneously (with no predecessors) AND then remained static for 50 million years, this would perfectly fit punctuated equilibrium and directly undermine the gradualist prediction of slow, steady change. Choices A and B support Scientist 1's position. Choice D is neutral — both scientists accept natural selection.