Case File: Two Kelp Coves, One Heat Wave

Investigation question: Two fictional kelp coves on the California coast went through the same sea star die-off and the same marine heat wave. Why did one keep its forest and most of its species while the other became an urchin barren?

Scientific purpose

Students act as a survey team handed six years of dive data from two fictional coves. They compare the coves' species richness and key populations, test five competing explanations (the 'suspects') against eight clue cards, and build a causal chain that explains why only one kelp forest collapsed. The case is patterned on what happened on California's north and central coasts after 2013, so students meet keystone species, multiple stressors and the power of a comparison site in one investigation.

Why this helps students with this chapter

The textbook tells the otter story as one cause with a happy ending. Working a realistic case shows students that ecosystems often change because of several stressors at once, that the most useful evidence is a comparison between two places that shared most conditions, and that timing can rule a suspect out. It also exposes a trade-off they will need in Lab 3: the cove with sea otters has far fewer abalone.

Standards connections

MS-LS2-4 (practice, constructing an argument from evidence that changes to physical and biological components affect populations): Students argue from survey data and clue cards that a warmer ocean (physical) and the loss of urchin predators (biological) changed urchin, kelp, abalone and community-wide species numbers, and they rule competing explanations in or out. The data are simulated, so this builds the Grade 7 argument practice rather than assessing the PE with empirical evidence.

MS-LS2-5 (supports, diagnosing why biodiversity and ecosystem services were lost before evaluating solutions): Knowing which change caused the collapse is the first step in choosing a fix; q8 asks which change would most likely have protected the cove. Lab 3 continues at Driftwood Cove and evaluates competing restoration plans.

Roles (rotate each session)

• Case lead: Runs the suspect table, keeps the group on one suspect at a time, and makes sure every verdict has a written reason.
• Data analyst: Computes richness totals, percent change and urchin increase, and reads numbers from the datasets when the group needs them.
• Clue reader: Reads each clue card aloud and asks the group whether it supports, rules out or says nothing about each suspect.
• Chain builder: Draws the causal chain for Driftwood Cove and checks that every arrow is backed by data or a clue.

Materials

Estimated cost: 2 to 5 per class (an estimate; Estimate for printing a case file per student and one set of clue cards per group. Envelopes, highlighters and chart paper are usually classroom stock.)

Safety

• Paper-based activity with no physical hazards; if students cut their own cards, use classroom scissors at their desks.
• Keep the case clearly fictional: the coves and every number are invented teaching data, so students should not quote them as facts about real California places.

Teacher preparation and setup

1. Print a case file for every student and one set of clue cards per group; cut the cards and put cards 1-4 in an envelope marked Round 1 and cards 5-8 in one marked Round 2.
2. Read the suspect verdicts in the answer key. Decide when to reveal the real events behind the case; the debrief is recommended, so groups reason from the evidence rather than from what they have heard.
3. Find and project photos of a healthy kelp forest, an urchin barren and a sunflower sea star from a public source such as NOAA or a California aquarium.
4. Write the rule of evidence on the board: a suspect is 'needed' only if it helps explain why the two coves ended differently, or when the change happened.

Investigation design

Student procedure

1. Read the case introduction and both cove datasets. In q1, write your first hunch about the biggest difference between the coves by 2018.
2. Data analyst totals the species for each cove and year in the totals table and computes the percent change from 2012 to 2018 for each cove (q2). The others check the arithmetic.
3. Data analyst computes how many times purple urchin density grew at each cove from 2012 to its highest survey value (q3). Highlight in one color every change that happened at both coves and in the other color every change that happened at only one.
4. Open Round 1 (clue cards 1-4). The Clue reader reads each card aloud; the group decides which suspects it supports and which it rules out or weakens, and records the card number in the suspect table.
5. Open Round 2 (cards 5-8) and repeat. Rule a suspect out if its timing is wrong or if it cannot explain why only one cove collapsed.
6. Give every suspect a verdict: needed to explain the difference, contributed but cannot explain the difference, or ruled out. Each verdict needs one written reason.
7. Chain builder draws the causal chain for Driftwood Cove from the first stressors to the loss of species, labeling each arrow with its evidence, and adds a box showing what was different at Lupine Cove.
8. Answer q4 as a group. Then answer the analysis questions individually.

Expected observations

• Total species: Lupine 58, 56, 50, 53 (change −8.6 percent); Driftwood 57, 53, 31, 24 (change −57.9 percent).
• Purple urchins grew about 48 times at Driftwood Cove (1.0 to 48 per m²) but only 3 times at Lupine Cove at their peak (2.0 to 6.0), and fell again there by 2018.
• Both coves lost their sunflower sea stars and had the same warm water; only the presence of sea otters differs.
• Groups rule out S4 (harvest steady while the forest was healthy; abalone declined after the kelp) and S5 (no change in the creek, which reaches both coves), and judge S1 and S2 as contributing but not explaining the difference.

Likely difficulties

• Treating every suspect as guilty because each 'did something'. Push the rule of evidence: what explains the difference between coves?
• Reading the abalone decline as a cause rather than an effect. Ask which came first in the data, the kelp loss or the abalone loss, and what abalone eat.
• Percent change errors: dividing by the 2018 value instead of the 2012 value, or reporting the difference in species instead of the percent.
• Concluding from Lupine's low abalone that otters are harmful. Use q6 to separate 'bad for abalone harvests' from 'bad for the ecosystem'.

Structured debrief

1. Collect each group's verdicts on the board. Most groups agree that S4 and S5 are ruled out and that S3 is needed; the debate is usually whether S1 and S2 'count'. Settle it with the rule of evidence: both stressors hit both coves, so they set the stage, but only the missing otters explain why the stage collapsed at one cove. Ecologists call this multiple stressors.
2. Reveal the real events. North of San Francisco, where there are no sea otters, sea star wasting disease from 2013 and a marine heat wave from 2014 to 2016 were followed by an explosion of purple urchins and the loss of more than 90 percent of the bull kelp canopy along more than 350 km of coast. Red abalone starved, and the recreational fishery closed in 2018; in December 2025 the closure was extended to 2036. On the central coast, researchers in Monterey Bay found that sea otters ate many more urchins after the boom, mostly plump urchins near remaining kelp, which helped those forest patches hold on.
3. Point out what the fenced plot (clue 8) added: it was a small experiment. Kelp grew in warm water when urchins were kept out, so warmth alone did not stop kelp. Ask what other experiment would strengthen the case.
4. Raise the abalone trade-off (q6) and carry it into Lab 3: bringing otters back could help kelp, but otters eat abalone, crabs and urchins that people harvest. Which criteria would a fair plan need?

Recording sheet: what students fill in

Lab 2 case file: Two Kelp Coves, One Heat Wave

Lupine Cove and Driftwood Cove are fictional rocky coves on the California coast, and every number in this file is a teaching dataset patterned on real events. Divers surveyed the same underwater transects at both coves every two years from 2012. By 2018, Driftwood Cove's kelp forest was nearly gone and the rock was carpeted with purple sea urchins, a habitat called an urchin barren. Lupine Cove still had a forest. Your team must work out why, using the data and the clue cards.

Analysis questions: answer key

Individual evidence of learning

Each student submits their own completed totals table, suspect table, causal-chain drawing and answers to q1 through q10. The suspect table and q4 are the argument from evidence; q5 and q10 show reasoning about what the evidence can and cannot support; q6 and q7 show understanding of keystone effects and trade-offs.

Assessment criteria

Accessibility adaptations

• Provide a version of the datasets with the two coves side by side in one table and the 'both coves' changes already highlighted.
• Read clue cards aloud for the whole class in each round, and offer cards with key words in bold and a picture icon for each organism.
• Give the suspect table with sentence starters: 'Clue __ supports this because...' and 'Clue __ rules this out because...'.
• Allow the causal chain to be built from pre-printed boxes and arrows that students arrange and glue, or as a spoken explanation recorded on a device.

Low-cost alternative

No materials beyond printing are needed. If printing is limited, project the datasets and read the clue cards aloud one at a time, with students keeping the suspect table in their notebooks.

Model limitations

Lupine Cove and Driftwood Cove are fictional and every number is a simulated teaching dataset, written so that the evidence points to one key difference. Real coves differ in many more ways (depth, wave exposure, rock type, fishing, distance from the otter range), real surveys have measurement error, and real investigations rarely have such a tidy comparison site. The case simplifies the science: otters are one of several urchin predators, kelp responds to nutrients and storms as well as grazing, and researchers still debate how quickly barrens can recover. A two-site comparison shows a pattern consistent with a cause; it cannot prove the cause the way a replicated experiment could.

Teacher notes

Verdicts: S1 heat wave, contributed (thinned kelp at both coves, clue 1) but cannot explain the difference, and kelp grew inside the urchin-proof plot in the warm year (clue 8). S2 sea star die-off, contributed (removed a predator at both coves, clue 2) but shared by both. S3 no otters, needed: the one difference between the coves, with otters eating far more urchins at Lupine (clue 4) and urchins exploding only at Driftwood. S4 abalone harvest, ruled out: harvest was steady while the forest was healthy (clue 6), and the abalone decline followed the kelp loss and came from starvation (clue 5). S5 creek pollution, ruled out: no change in the water, and it reaches both coves (clue 7). Accept 'S1 and S2 were needed too' if the student explains that the collapse needed the shared stressors plus the missing otters; the key distinction is between causes shared by both coves and the cause that explains the difference.