Designing & Evaluating a Habitat Solution — Print · Grade 7 Science
Chapter: Ecological Evidence & Environmental DesignDesigning & Evaluating a Habitat Solution
Learning goals
- I can explain designing & evaluating a habitat solution.
- I can use evidence, a model, and stated assumptions to support a biological explanation.
Before this unit
Use the supplied vocabulary, distinguish an observation from an explanation, and identify any units or model assumptions. Revisit a connected earlier unit if needed; new terms are explained here.
Use three flexible 40–45-minute sessions: core background and Parts 1–2 with their checks; Parts 3–4 and the worked application; then remaining practice, a labeled model, revision, and the exit check. Checks completed during reading can count toward the practice set. Read one short part at a time. Investigations and chapter review/quiz have separate sessions, and flexible weeks allow more time.
An ecological decision almost never has a correct answer waiting to be discovered. It has options, each doing some things well and costing something, and a choice that depends on what you are trying to achieve. The discipline that makes such a choice defensible is simple to state: write down what counts as success and what you are limited by, before looking at the options.
A criterion is a statement of what the solution must achieve, expressed so that it can be measured — reduce runoff from this area by at least half during a rainfall event of this size. A constraint is a limit you must work within: a budget, an area of available land, a rule about what can be built, a requirement that a space stay usable. Criteria say what good looks like; constraints say what is allowed. Both have to be written before options are compared, or the criteria quietly become a description of whichever option was already preferred.
Then comes building and testing. A prototype is a first version made to be tested rather than to be final — a single planted strip rather than a whole site, a model basin, a trial on one corner. Iteration is the cycle of testing it, learning something, and changing it, and a design that has not been through at least one iteration has not been tested. Cost effectiveness is the comparison that stops a decision being made on total performance alone: how much benefit each unit of cost buys, which can rank options differently from raw effect and is the reason both figures should be calculated.
Build your understanding
Where this fits
This chapter has covered what interacts with what, what makes a community resilient, how human activity changes habitats, and how the evidence is actually gathered. The last unit puts them to work on a decision — which is where ecology usually ends up — and closes the chapter with the method for comparing options honestly.
Use three flexible 40–45-minute sessions: core background and Parts 1–2 with their checks; Parts 3–4 and the worked application; then remaining practice, a labeled model, revision, and the exit check. Checks completed during reading can count toward the practice set. Read one short part at a time. Investigations and chapter review/quiz have separate sessions, and flexible weeks allow more time.
Part 1 · Writing a problem down so it can be solved
A vague problem produces an unevaluable solution. "Improve the schoolyard for wildlife" cannot be succeeded at or failed at. A written version names what will change, by how much, measured how, by when, and within what limits — which is the same discipline as turning a broad question into a testable one in Chapter 1.
Criteria should be measurable with the means available. If you cannot measure invertebrate abundance, do not make it the criterion; choose something you can measure, such as area of flowering plants or number of structures provided, and say plainly that it stands in for the thing you care about. A measurable proxy honestly labelled beats an unmeasurable ideal.
Constraints are easy to leave implicit and expensive to discover late. Budget, space, who maintains it and how often, what the area is also used for, what is allowed. A design that meets every criterion and violates a constraint is not a solution, and finding that out after building it is the common failure.
Pause and explain
Turn "make the yard better for wildlife" into a statement with a criterion and a constraint.
Check your reasoning after answering
Something like: increase the area of flowering plants in the yard from 0 to at least 20 m² by June, within a budget of £100 and without reducing the usable play area. The criterion names a measurable target with a deadline; the constraints name the budget and the competing use. Both are written before options are compared.
Part 2 · Comparing options on the same evidence
Options must be assessed against the same criteria using comparable evidence. If one option's performance is a measurement and another's is a manufacturer's claim, the comparison is not between the options but between two kinds of statement. Where evidence differs in quality, say so, and treat the weaker figure with more doubt rather than averaging them as equals.
Set the comparison out as a table: each option against each criterion and constraint, with the figures. A table makes the trade-offs visible and prevents the common move of comparing option A on its best criterion with option B on its worst.
Work the model. Within a budget of 100 tokens, plan A costs 80 and reduces runoff from 20 to 8 L; plan B costs 60 and reduces it from 20 to 10 L. Both meet the same stated space requirement. A reduces runoff by 12 L and B by 10 L, so A performs better in total — and both fit the budget, so the constraint does not decide it.
Pause and explain
Plan A costs 80 and takes runoff from 20 to 8 L; plan B costs 60 and takes it from 20 to 10 L. Which reduces runoff more?
Check your reasoning after answering
Plan A. It reduces runoff by 20 − 8 = 12 L, while plan B reduces it by 20 − 10 = 10 L, so A achieves 2 L more reduction. Both fit a 100-token budget, so affordability does not separate them — which is exactly when a second comparison, such as reduction per token spent, becomes worth calculating.
Part 3 · Cost effectiveness, and the downside you have to name
Total performance is one comparison and benefit per unit cost is another, and they can disagree. Divide each plan's reduction by its cost. Plan A gives 12 ÷ 80 = 0.15 litres reduced per token. Plan B gives 10 ÷ 60 = 0.167 litres per token. So A achieves more overall and B achieves slightly more for each token spent.
Neither figure settles it, and recognising that is the point. If the goal is the largest reduction available within the budget, A wins. If the goal is efficient use of funds, or if the remaining 40 tokens could be spent on something else worthwhile, B has a claim. The decision follows from which criterion was stated as primary, which is why that had to be written down first.
Every option also has a downside, and naming it is part of the proposal rather than an argument against it. A costs most of the budget, leaving little for anything else or for repair. B leaves 40 tokens and achieves less. Both need maintenance that the model says is unknown, which is a real gap: a plan that performs well and is never maintained can end up worse than a modest one that is.
Pause and explain
Calculate the reduction per token for both plans, and say what the comparison shows.
Check your reasoning after answering
Plan A gives 12 L ÷ 80 = 0.15 litres per token; plan B gives 10 L ÷ 60 = 0.167 litres per token. B is slightly more cost effective while A delivers the larger total reduction. The two measures disagree, so the choice depends on which criterion was stated as primary — maximum reduction within budget, or best value per token.
Part 4 · Proposing a next test rather than a verdict
A recommendation is stronger when it includes the test that would confirm or overturn it. "Build plan B on the eastern corner first, measure runoff over the next three rainfall events against the untreated western corner, and extend it if the reduction holds" commits to a decision and keeps it revisable, which is what a prototype is for.
The test should target the biggest uncertainty rather than the easiest measurement. Here the model says maintenance is unknown, so a trial running long enough to need maintenance is worth more than one that measures peak performance in week one. The weakest part of the evidence is where the next effort should go.
That is the end of this chapter and most of the method of the course. Define the problem measurably, state the criteria and constraints before looking, compare options on the same evidence, calculate more than one measure, name the trade-off and the downside, choose under a stated criterion, and say what would change your mind. It applies to a schoolyard and it applies to a national decision, and the difference is scale rather than kind.
Pause and explain
Why should a recommendation include a next test, and what should the test target?
Check your reasoning after answering
Because a recommendation based on model figures is a decision under uncertainty, and a test makes it revisable rather than final. The test should target the biggest uncertainty rather than the easiest measurement — here, maintenance is unknown, so a trial long enough to need maintenance is worth more than one measuring peak performance immediately after installation.
Apply the whole unit · Recommend a model habitat plan
Illustrative plans within a 100-token budget, not real costings: plan A costs 80 and reduces runoff from 20 to 8 L; plan B costs 60 and reduces it from 20 to 10 L. Both satisfy the same stated space requirement, and later maintenance needs are unknown for both.
Your task
Calculate each plan's reduction and its reduction per token, recommend one under an explicitly stated criterion, and give a next test.
What your response should show
- Explain the relevant biological process accurately.
- Use the supplied evidence and show calculation steps with units where needed.
- Connect a labeled model or mechanism with the claim.
- State an assumption, evidence limitation, or useful next observation.
Response guidance — after your attempt
Calculate the reductions: A gives 20 − 8 = 12 L; B gives 20 − 10 = 10 L.
Calculate reduction per token: A gives 12 ÷ 80 = 0.15 L per token; B gives 10 ÷ 60 = 0.167 L per token.
State the criterion before recommending, since the two measures disagree — maximum total reduction favours A, best value per token favours B.
Propose a next test aimed at the largest unknown, which the model says is maintenance.
Plan A reduces runoff by 20 − 8 = 12 L and plan B by 20 − 10 = 10 L, so A achieves 2 L more in total. On cost effectiveness the ranking reverses: A delivers 12 ÷ 80 = 0.15 litres of reduction per token and B delivers 10 ÷ 60 = 0.167 litres per token, so B buys slightly more for each token spent. Both fit the 100-token budget and both meet the stated space requirement, so neither constraint decides it, and the two performance measures point different ways — which is exactly why the criterion has to be stated rather than discovered. Under a criterion of maximum runoff reduction within the budget, plan A is the recommendation. Under a criterion of best value per token, or where the remaining 40 tokens have another worthwhile use such as maintenance or a second site, plan B is. I would recommend B under a stated criterion of value per token plus reserve for upkeep, precisely because the model says maintenance needs are unknown for both, and a plan that consumes 80 of 100 tokens leaves almost nothing to repair or adjust it. Each option's downside should be named: A leaves little budget for anything else, and B accepts 2 L less reduction. The next test should target that largest unknown rather than the easiest measurement. Build plan B on one section, leave a comparable section untreated, measure runoff from both over several rainfall events across at least one season — long enough for maintenance needs to appear — and extend, revise or switch to A depending on whether the reduction holds up once the installation is no longer new.
Scientific references for this unit
Parent / teacher teaching notes
Ask for a because-statement and a labeled relationship, not just a memorized word. Work through one calculation step at a time, permit oral or diagram responses, and use flexible sessions for difficult reading. Compare the learner’s actual evidence with the supplied model; do not reward copying or agreement with a personal preference. No private family, genetic, or medical records are required.
The key idea
Engineering begins with a defined problem, criteria for success, and constraints such as available space, resources, time, and safety. An ecological proposal should connect a mechanism with measurements. “Help nature” is too broad unless we define which conditions or functions are being improved.
Worked examples
Example 1 · Make an unevaluable goal evaluable
- Name a measurable outcome within reach: area of flowering plants in bloom, or number of nesting structures installed, as a stated proxy for pollinator support.
- Attach a target and a date so success or failure is decidable.
- Name the constraints: budget, available area, who maintains it, and any competing use of the space.
A measurable proxy, labelled as one, beats an unmeasurable ideal.
Example 2 · Decide when two measures disagree
- Compare totals: P removes 30 kg against Q's 18 kg, so P does more.
- Compare value: P gives 30 ÷ 200 = 0.15 kg per token; Q gives 18 ÷ 90 = 0.2 kg per token, so Q buys more per token.
- Recognise that the question is unanswerable as asked, because better depends on which criterion was stated.
When totals and value per cost disagree, the criterion decides. Name it before calculating.
Avoid these mistakes
- Mistake: criteria can be settled after seeing the options. Then they describe the preferred option rather than testing it; write them before comparing.
- Mistake: the option with the biggest effect wins. A reduces 12 L against B's 10, and B gives 0.167 L per token against A's 0.15 — the measures disagree and the stated criterion decides.
- Mistake: naming a downside weakens a proposal. An unnamed downside is discovered later by someone else; naming it is what makes the recommendation trustworthy.
- Mistake: a recommendation ends the work. A prototype and a next test aimed at the biggest unknown — here maintenance — is what makes a decision revisable.
Check your understanding
How is a criterion different from a constraint?
Discuss the answer
A criterion evaluates success; a constraint limits allowable options, such as a maximum budget or available area.
Why not compare one design’s best trial with another’s worst?
Discuss the answer
It selects evidence unfairly and can misrepresent typical performance; compare the full relevant records.
Words to know
- criterion
- A measurable statement of what a solution must achieve, written before options are compared so it cannot be shaped to fit a favourite.
- constraint
- A limit the solution must work within — budget, space, maintenance, competing uses; a design that violates one is not a solution.
- prototype
- A first version built to be tested rather than to be final, such as one planted section instead of a whole site.
- iteration
- The cycle of testing, learning and changing; a design that has not been through one has not been tested.
- cost effectiveness
- Benefit per unit of cost, which can rank options differently from total benefit — 0.15 against 0.167 litres per token here.
Extend your scientific explanation
Compare a second supplied example or the linked educational reference. Identify a connection, a model limit, and an additional observation that would test the explanation.
In the real world
Use a clear question, reliable observations, and a biological mechanism to evaluate a claim. State what remains uncertain before applying a classroom model to a different setting.
Exit check
What makes a proposed revision useful?
Check your explanation after answering
It targets an identified limitation or tradeoff and can be evaluated with a defined measurement.
Explain your reasoning without notes. If a key idea is missing, revisit the lesson and try again.
Problem 1
Answer and explanation
A criterion evaluates success; a constraint limits allowable options, such as a maximum budget or available area.
Problem 2
Answer and explanation
It selects evidence unfairly and can misrepresent typical performance; compare the full relevant records.
Problem 3
Answer and explanation
No. Cost is one criterion and feasibility limit; outcomes and other requirements also matter.
Problem 4
Answer and explanation
It targets an identified limitation or tradeoff and can be evaluated with a defined measurement.
Problem 5
Answer and explanation
A provides the larger total reduction in the model, while B gives a slightly larger reduction per token. Both fit the initial budget. Either can be supported under an explicit priority, but maintenance, repeated performance, and real-setting transfer remain unknown. Recommend a matched repeated test and a maintenance estimate; fictional tokens are not actual prices.
A reduction: 12 L; B: 10 L.
A ratio: 12/80=0.15 L/token; B: 10/60≈0.167 L/token.
Choose using total reduction or cost efficiency and identify maintenance uncertainty.
Response criteria
- Explain the relevant biological process accurately.
- Use the supplied evidence and show calculation steps with units where needed.
- Connect a labeled model or mechanism with the claim.
- State an assumption, evidence limitation, or useful next observation.
Problem 6
Answer and explanation
No. It exceeds the stated constraint unless the budget or plan changes. A desirable result does not remove a feasibility limit.
Response criteria
- Explain the relevant biological process accurately.
- Use the supplied evidence and show calculation steps with units where needed.
- Connect a labeled model or mechanism with the claim.
- State an assumption, evidence limitation, or useful next observation.
Problem 7
- Maximum budget of 100 tokens
- Wanting more cover
- Liking a color
- A general hope
Answer and explanation
Maximum budget of 100 tokens
The budget limits feasible options; criteria measure desired performance.
Problem 8
Answer and explanation
15 L
24−9=15 L under the supplied comparison.