The Cedar Hollow Case File: Deer, Cattle and the Next Dry Year

Investigation question: Twelve years of monitoring records show rain, forage, cattle and deer in one valley. What limits the deer herd, did adding cattle change it, and what should the county do before a dry year that forecasters expect next spring?

Scientific purpose

Students act as a county wildlife team reading a fictional twelve-year case file for a mule deer range. They calculate how much forage each millimeter of rain grows and how many deer each unit of forage supports, graph deer against forage before and after cattle arrived, and test five rival explanations in a skeptic's round. Then they use two prediction methods to forecast the deer count and fawn mass for a coming dry year under several plans, check each plan against criteria and a budget, and commit to a recommendation. The lab assesses the chapter's main data skill on records that include both population size and the growth of individual animals.

Why this helps students with this chapter

Real wildlife decisions are made from records like these: messy, lagging, and full of more than one thing changing at once. The textbook's two graphs show a tidy pattern and call it cause and effect; this case file makes students earn that claim by naming the mechanism, finding the years that act as natural comparisons, and testing rival explanations one at a time. Two prediction methods that disagree show students that a forecast has a range, and a budget forces them to choose rather than do everything. The decision is honest about uncertainty, which is what makes it science rather than a guess.

Standards connections

MS-LS2-1 (assesses, the whole PE: analyzing records from abundant and scarce years for effects on deer numbers and on individual fawn growth): Students analyze and interpret a simulated twelve-year record of rainfall, forage, deer counts and fawn mass, and use it as evidence that forage availability changes both the size of the population and how large individual fawns grow. The clarification statement's emphasis on cause and effect during abundant and scarce periods is met by the dry-year and wet-year comparisons and the skeptic's round.

MS-LS2-4 (practice, making warranted inferences that adding a biological component (cattle) changed the deer population): Students argue from patterns in the data that a new competitor lowered the deer herd's carrying capacity, and must rule out rainfall, recovery lag, water, predators and counting error. This practices the evidence-based argument the PE requires; Lesson 22 assesses it with real ecosystem changes.

MS-LS1-5 (supports, an environmental factor (food supply) influencing the growth of individual animals): Average fawn mass falls in scarce years and after cattle arrive, which is evidence that food supply affects individual growth. In the California integrated sequence MS-LS1-5 is a Grade 6 PE, so it is background here, not a target.

MS-ETS1-2 (practice, the plan check only): Groups check plans A to E against two criteria and two constraints in a table, and may design plan E, before recommending one. The plans, criteria and prediction methods are supplied, so this practices the systematic evaluation the PE describes rather than assessing it.

Roles (rotate each session)

• Data analyst: Computes the forage-per-rain and deer-per-forage columns and the two averages; every number is read aloud so the others can check it.
• Grapher: Plots deer against forage with a different symbol before and after cattle, and draws a trend line by eye through each set.
• Skeptic: Leads the skeptic's round: reads each rival explanation and will not let the group move on until it names the years and numbers that support or rule it out.
• Planner and spokesperson: Runs the prediction and criteria tables, keeps track of costs, drafts the recommendation from the group's numbers and presents it to the class in one minute.

Materials

Estimated cost: 2 to 4 per class (estimate) (an estimate; Printing only; calculators, rulers, colored pencils and sticky notes are classroom stock.)

Safety

• Paper, pencil and calculator only; there are no physical hazards. Keep the discussion about the fictional valley rather than about any named local ranch or family.

Teacher preparation and setup

1. Work the computations yourself with the answer key beside you: the twelve indexes, the two averages (35.0 and 27.0) and the planning table. The most common slip is including year 7 in an average.
2. Print the case file. The dataset, skeptic cards, plan menu and planning tables are all on the recording sheet.
3. Post the criteria and constraints where every group can see them: C1, at least 180 deer in the fall count of year 13; C2, average fawn mass at least 24 kg; K1, the plan costs at most $10,000; K2, the cattle lease cannot be ended for good.
4. Decide how you will run the skeptic's round: within groups, or with each group taking one card and reporting to the class.
5. Some students come from ranching or hunting families. Tell the class the valley is fictional, the cattle are not the villains, and the job is to read the evidence fairly.

Investigation design

Student procedure

1. Read the background and the dataset. Answer q1 and q2: find the scarce years and describe how forage relates to rainfall.
2. Fill the work table. For every year, divide forage by rainfall (one decimal is enough) and compute the deer index: deer ÷ forage × 100, which is the number of deer counted for every 100 kg/ha of forage. Round to one decimal.
3. Average the deer index for years 1 to 6 (no cattle) and for years 8 to 12 (200 cattle). Leave year 7 out of both averages. Answer q3 and q4.
4. Graph deer counted against spring forage. Plot years 1 to 6 as dots in one color, years 8 to 12 as crosses in the other color, and year 7 as a triangle. Draw a trend line by eye through each set of points.
5. Skeptic's round. The skeptic reads each card aloud. For each one, the group finds the years and numbers that test it, decides whether the data support it or rule it out, and fills one row of the skeptic table.
6. Read the forecast and the plan menu. Predict the year-13 forage (2 kg/ha for every mm of rain). Fill the planning table with both methods for 0, 100 and 200 cattle kept, and the fawn mass column.
7. Fill the criteria table for plans A to D. If no plan meets every criterion and constraint, design plan E by choosing how many cattle to keep, compute its predictions and cost, and add it to both tables.
8. Decide. Write the recommendation (q5) using numbers from your tables, and present it to the class in one minute. If time runs short, finish the written recommendation as homework.
9. Answer the analysis questions on your own.

Expected observations

• Forage is close to 2.0 kg/ha for every mm of rain in every year (1.93 to 2.06).
• The deer index averages 35.0 for years 1 to 6 and 27.0 for years 8 to 12. On the graph the crosses (years 8 to 12) sit below the dots (years 1 to 6) at similar forage.
• Matched comparisons: year 3 (no cattle, 560 kg/ha) had 230 deer and 25 kg fawns; year 9 (200 cattle, 520 kg/ha) had 160 deer and 23 kg fawns. Years 5 and 6 (no cattle, about 1,000 kg/ha) had 350 and 330 deer and 31 to 32 kg fawns; years 11 and 12 (200 cattle, about 1,000 kg/ha) had 260 deer and 29 to 30 kg fawns.
• Year-13 forage = 2 × 280 = 560 kg/ha. Predicted deer, Method 1 / Method 2: 0 cattle 230 / 196; 100 cattle 201 / 174; 200 cattle 172 / 151. Fawn mass 25, 24 and 23 kg.
• Plan A meets C1 and C2 but costs $12,000; plan B meets C1 only by Method 1; plans C and D fail both criteria. A plan E keeping about 35 to 70 cattle (for example 50 kept: 216 / 185 deer, 24.5 kg, $9,000) meets every criterion and constraint.

Likely difficulties

• Students divide forage by deer instead of deer by forage, or forget to multiply by 100. Have the data analyst read year 1 aloud (300 ÷ 840 × 100 = 35.7) before anyone does the rest.
• Groups include year 7 in an average or average the raw deer counts instead of the indexes. Ask what changed partway through year 7.
• Interpolating for 100 or 50 cattle is new to many students. Model the 100-cattle row on the board: halfway between 41.1 and 30.8 is (41.1 + 30.8) ÷ 2 = 35.95.
• Students want to pick plan A because it is best for the deer and ignore the budget. Point to K1 and ask what a real county does when the best plan costs more than it has.
• The skeptic's round can become a debate about ranching. Keep it on the data: each card is tested with named years and numbers, and the verdict comes from the numbers.

Structured debrief

1. Collect the averages from every group (35.0 before cattle, 27.0 after). Ask what the drop means in words: after the cattle arrived, every 100 kg/ha of forage supported about 8 fewer deer. Point to the two clouds of points on the graph: same forage, lower herd.
2. Run the skeptic's round as a class. The strongest rival is usually S2 (recovery lag). Show why the data rule it out: the herd grew 25 and 30 percent in years 10 and 11 but did not grow at all in year 12 in a wet year, so it was leveling off at a lower ceiling, not still climbing. S1 is answered by the matched wet years, S3 by the creek note and fawn mass, S4 by the steady lion counts, S5 by the size of the difference.
3. Put the plans on the board with both predictions. Ask which plans every group rejected (C and D) and why. Then ask whether anyone found a plan E that meets every criterion and the budget under both methods (keeping about 35 to 70 cattle). Discuss why plan B, which passes by one method and fails by the other, is risky.
4. Ask what the county should measure in April of year 13. Forage plots: if the spring forage comes in well below 560 kg/ha, the forecast was too hopeful and the plan should change before the herd pays for it.
5. Close on q12: 200 cattle eat as much as about 1,200 deer, yet the herd dropped by only about 70. The two species overlap only partly in what they eat, which is the textbook's point about using a shared resource in different ways, now with numbers.

Recording sheet: what students fill in

Lab 3 recording sheet: The Cedar Hollow case file

Cedar Hollow is a fictional valley of dry foothill rangeland, and every number in this file is a teaching dataset built to behave like real rangeland records. Mule deer live there all year. Each spring the county clips and weighs the grasses and leafy plants on sample plots to estimate spring forage in kilograms per hectare (kg/ha: the mass of plants grown on a square 100 m on a side). Each fall a team walks the same survey route to count the deer and weighs fawns caught for ear-tagging. Starting in year 7 the county leased the valley to a ranch for 200 cattle. A creek and two springs held water in every year of the record. Biologists think each fall count is within about 10 percent of the true number.

The forecast. Forecasters expect year 13 to be dry, with about 280 mm of rain. The herd at the end of year 12 was 260 deer. Two ways to predict the year-13 fall count. Method 1, the closest dry year: use the deer index from the most similar dry year (year 3, index 41.1, with no cattle; year 9, index 30.8, with 200 cattle), because those years started from a herd of similar size. Method 2, the long-run average: use your average index for that cattle level (years 1-6 or years 8-12). Either way, predicted deer = index × forage ÷ 100. For 100 cattle, use the index halfway between the 0-cattle and 200-cattle values. For any other number, move from the 0-cattle index toward the 200-cattle index by one quarter of the gap for every 50 cattle kept. Predict fawn mass the same way, from 25 kg (year 3) with no cattle to 23 kg (year 9) with 200. Criteria: C1, at least 180 deer in the year-13 fall count. C2, average fawn mass at least 24 kg (in real deer herds heavier fawns are more likely to survive their first winter; this 24 kg cutoff is part of the fictional case). Constraints: K1, the plan may cost the county at most $10,000. K2, the lease cannot be ended for good; cattle can be reduced or removed for year 13 only. The lease pays the county $60 per head of cattle per year, so every head kept off the range for year 13 costs $60 in refunds to the rancher.

Analysis questions: answer key

Individual evidence of learning

Each student submits their own completed work table, graph, skeptic table, planning and criteria tables, the q5 recommendation and answers to q6 to q12. The work table with q1, q4 and q6 is the evidence for analyzing data on resource availability and its effects on the population and on individual fawns; q7 and the skeptic table are the evidence for an argument about a biological change to the ecosystem; q5 with q10 and q11 shows whether the student can use data to make and qualify a prediction.

Assessment criteria

Accessibility adaptations

• Give students who need support a work table with years 1 and 8 already computed as worked examples, and a calculator key sequence (deer ÷ forage × 100).
• Let a student who struggles with graphing plot only years 3, 5, 9 and 11, which carry the main comparison, and add the rest with a partner.
• Read the background and skeptic cards aloud for students who need reading support, and provide the sentence frame 'The data rule out ___ because in year ___ the ___ was ___.'
• Accept a one-minute recorded spoken recommendation in place of the written q5, as long as it includes the numbers and the trigger.

Low-cost alternative

No printing: project the dataset and have students copy only the columns they need (year, forage, cattle, deer) into notebooks, and run the skeptic's round and the plan vote as a whole class on the board. The calculations and decision are unchanged.

Model limitations

Cedar Hollow is fictional and every number was written for this lab, so it has not been tested against a real herd, and the activity itself has not been physically tested with a class by the authors. The dataset was built so that forage tracks rain closely and the cattle effect is clear; real records are noisier and often have missing years. Both prediction methods assume that next year will behave like the past, that the cattle effect grows in proportion to the number of cattle, and that nothing else changes: a hard winter, a disease outbreak, a change in hunting, or a dry year that is also hot could each push the real count below the prediction. The deer index lumps together everything about forage quality, deer movement and fawn survival into one number. Use the case to practice reading evidence and making a qualified decision, not as a guide to managing any real range.

Teacher notes

Deer indexes, one decimal: 35.7, 37.0, 41.1, 29.6, 33.3, 33.3, 33.7, 31.9, 30.8, 22.2, 24.5, 25.7; averages 35.0 (years 1-6) and 27.0 (years 8-12). Forage ÷ rain: 2.00, 2.03, 1.93, 2.04, 2.06, 2.02, 2.02, 2.00, 1.93, 2.00, 2.04, 2.02. Year-13 forage 560 kg/ha. Planning table, Method 1 index / deer then Method 2 index / deer: 0 kept 41.1 / 230 and 35.0 / 196; 100 kept 35.95 (36.0) / 201 and 31.0 / 174; 200 kept 30.8 / 172 and 27.0 / 151; fawn mass 25, 24, 23 kg. Accept predictions within about 2 deer of these when students round the index first. Plan E keeping 50: 38.5 / 216 and 33.0 / 185, 24.5 kg, $9,000. Feasible plan E range: keep about 35 to 70 cattle. The six of these twelve years shown on presentation slide 17 match this table.