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College intro · Seasons & change

Seasonal Signal: testing detection probability across a term

An intro lab section runs a repeated-measures eDNA study to test how season changes detection probability.

Working as a single lab section, students formalize a null and alternative hypothesis about seasonal detection probability for up to five aquatic target species, run a composite batching field protocol on one vial per round, and use the returned Field Journal to evaluate their hypothesis with a short lab report rather than a lecture-style write-up.

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Grade band

College intro

Time required

5 lab sessions across a term

Class size

16–24 students, lab section, one kit per sampling round

Sample type

Water, one vial

Standards and outcomes

  • Vision & Change Core Competency — Ability to apply the process of science: formulate hypotheses, design an investigation, and interpret data
  • Vision & Change Core Competency — Ability to use quantitative reasoning to interpret detection and relative-signal data
  • Vision & Change Core Concept — Systems: understand ecosystems in terms of component parts and their interactions across time
  • Course Learning Outcome — Communicate scientific findings in a structured written lab report with clearly separated methods, results, and discussion

How one kit serves a whole class

The $200 Classroom Starter Kit holds one collection vial per round and targets up to five species. Because seasonal comparison requires multiple sampling rounds, the section pools its budget across the term for two or three separate kit orders (fall/winter/spring or similar), always sampling the same site with the same composite batching protocol so any signal change reflects season, not method drift. Every student rotates through a sampling role at least once across the term.

Choose one

Research tracks

Track A — Migratory Presence

Seasonal habitat use

Driving question. Do migratory or seasonally active aquatic species show a detectable seasonal signal at our site?

Species to pick. Up to five species with known seasonal life-history patterns at most temperate sites: a migratory or spawning fish, an amphibian with a breeding-season aquatic phase, a seasonally active turtle, a waterfowl species that uses the site part of the year, and a year-round resident used as a seasonal control.

What students take away. Students test whether detection signal for seasonal species rises and falls with the expected life-history calendar while the year-round control stays roughly stable.

Track B — Temperature-Linked Activity

Physiology and detection probability

Driving question. Does water temperature predict which species we detect and how strong the signal is?

Species to pick. Species chosen in pairs across a warm-water/cool-water preference gradient: a warm-tolerant fish, a cold-water-associated fish or amphibian, a temperature-sensitive invertebrate, and up to two additional species chosen by the section from local field guides.

What students take away. Students build a regression-style argument (qualitative or simple correlation) linking recorded water temperature at each round to relative signal strength.

Track C — False Negative Risk Across Seasons

Method limitations and statistical reasoning

Driving question. Does the risk of a false negative change across the term, and can we quantify our confidence in a non-detection?

Species to pick. A small set of species expected to be present year-round based on prior local records or a preliminary track A/B round, used specifically to interrogate non-detections rather than confirm new presence.

What students take away. Students distinguish 'true absence' from 'false negative' using degradation rate, shedding rate, and sampling effort as explanatory variables, and propose a confidence statement rather than a binary claim.

The lesson sequence

Step by step, with teacher notes

  1. 01

    Formulating a testable hypothesis and experimental design

    75 minutes

    Before any sample is collected, lab groups draft a formal null hypothesis (H0: detection probability does not vary by season) and alternative hypothesis (H1: detection probability varies by season) for their chosen track. Groups then design the sampling calendar, control variables, and justify species selection against the five-species, one-vial constraint.

    Teacher notes

    • Open with: "This is not a nature walk — you are running a repeated-measures study with an n of one classroom kit per round. Design accordingly."
    • Require every group to write H0 and H1 in formal statistical language before discussing species, not after.
    • Push back on any design that changes the site, the sampling depth, or the protocol between rounds — confounding variables should be flagged and eliminated in this phase, not discovered in week 10.
    • Introduce the term 'detection probability' explicitly and distinguish it from 'population size' before students choose species.
    • Have each group submit a one-page pre-registration of their hypothesis and design before field work begins.

    Materials

    • Pre-registration template
    • Regional species life-history references
    • Site map with fixed sampling coordinates
    • Course lab notebook

    Student prompts

    • State your null and alternative hypotheses in formal, falsifiable language.
    • What variable are you holding constant across rounds, and how will you enforce that?
    • Which of your five species acts as a control, and why?
    • What result would cause you to fail to reject the null hypothesis?
  2. 02

    Round one: baseline field collection

    75 minutes

    The section executes its first composite batching round exactly as designed. Four crews pull sub-samples from fixed micro-habitat points, pool them into one vial, and log full environmental metadata that will later be regressed against detection results.

    Teacher notes

    • Say clearly: "Whatever coordinates and depth you use today are the coordinates and depth you use every round. Write them down precisely — you will not remember in eight weeks."
    • Assign a data manager role responsible for keeping the master environmental log consistent in units and format across all rounds.
    • Model chain-of-custody labeling with round number, date, and site ID so future rounds don't get confused in shipping.
    • Remind students that this baseline round has no 'right' result — a low-detection baseline is still valid data for a seasonal comparison.

    Materials

    • 1 classroom eDNA collection vial and prepaid mailer
    • Nitrile gloves
    • 4 sterile sub-sample containers
    • Calibrated thermometer
    • Master environmental log spreadsheet or sheet

    Student prompts

    • Sampling crew — draw and pool four sub-samples from the fixed coordinates established in Phase 1.
    • Data crew — record temperature, flow, weather, and any change in site conditions since scouting.
    • Chain-of-custody crew — label with round number and prepare the return mailer.
  3. 03

    Round two (and three): repeated measures and interim analysis

    75 minutes per round, spaced 6–10 weeks apart

    The section repeats the identical protocol at a later point in the term, ideally a different season or a meaningful temperature shift. Between rounds, students maintain a running data table and begin sketching what a supported versus unsupported hypothesis will look like once all rounds return.

    Teacher notes

    • Before the second round leaves for the field, have groups re-read their own Phase 1 pre-registration aloud — memory of the original design fades over a term.
    • Use the gap between rounds to cover PCR specificity and primer design at a level appropriate for non-majors, connecting it to why the kit can distinguish target species from background DNA.
    • Ask: "If round two looks identical to round one, is that a failed experiment or a real result?" — push students past the assumption that a null result is a bad result.
    • If a scheduling conflict pushes a round, document the deviation explicitly in the shared log rather than quietly absorbing it.

    Materials

    • Running data table (shared doc)
    • Second and/or third eDNA collection vial and mailer
    • Primer specificity handout
    • Updated environmental log

    Student prompts

    • What has changed in the environmental log since round one, and by how much?
    • Restate your original hypothesis — has your confidence in H1 gone up or down before seeing new results?
    • What would a primer that lacked specificity do to your seasonal comparison?
  4. 04

    Lab methods: primer specificity and error types

    50 minutes

    Students work through how PCR primers are designed to bind target-species DNA regions while avoiding cross-reaction with non-target or background organisms, and connect primer specificity directly to the two error types relevant to their study: false positives and false negatives.

    Teacher notes

    • Diagram a primer binding site next to a mismatched sequence and ask students to predict whether amplification succeeds.
    • Define false positive and false negative in this context specifically, not just in the abstract statistical sense: false positive = detecting a species not truly present (contamination, cross-reactivity); false negative = failing to detect a species that is present (degradation, low shedding, poor primer match).
    • Say: "A false negative in your seasonal study could get misread as 'the species left for winter' when it actually means 'the DNA degraded before we sampled.' You have to rule that out before you claim a seasonal effect."
    • Have students individually classify five short scenario cards as most likely false positive, false negative, or true result.

    Materials

    • Primer binding-site diagrams
    • Scenario classification cards
    • Vocabulary handout (specificity, amplicon, false positive/negative, Type I/II error)

    Student prompts

    • How does primer specificity reduce the chance of a false positive?
    • Give one seasonal-study-specific reason a true detection could still come back as a false negative.
    • Which error type is more damaging to your team's specific hypothesis, and why?
  5. 05

    Full data reveal and lab report

    75 minutes

    With all rounds returned, the section compiles the complete Field Journal series, tests each group's hypothesis against the pooled detection and relative-signal data, and drafts a structured lab report evaluating whether the null hypothesis can be rejected.

    Teacher notes

    • Project all rounds side by side as one table before any single group looks at just their own track's data — the whole-section view matters for the discussion.
    • Require the lab report to explicitly state whether H0 was rejected or failed to be rejected — 'inconclusive' is not an acceptable substitute for that statement.
    • Ask each group to identify one alternative explanation for their result that isn't the seasonal hypothesis, and address it in the discussion section.
    • Grade the report on structure (methods/results/discussion separation) as heavily as on the conclusion itself.
    • Close by asking what a fourth round, one year later, would need to confirm the pattern found this term.

    Materials

    • Compiled multi-round Field Journal data table
    • Lab report template
    • Statistical reasoning checklist

    Student prompts

    • Report your detection and relative-signal results by round in a clearly labeled table.
    • State whether your data support rejecting the null hypothesis, and to what degree of confidence.
    • Identify at least one confound or alternative explanation and address it directly.
    • Propose one specific change to the design if this study were repeated next year.

Student handout

For every student

Thinking prompts

  • Write your null and alternative hypotheses in formal statistical language before field work begins.
  • Justify your five species selections against seasonal life-history evidence.
  • Describe your fixed sampling protocol in enough detail that another lab section could replicate it exactly.
  • Explain the difference between a false positive and a false negative in the context of your specific hypothesis.
  • Predict, before the final reveal, which of your species is most likely to show a real seasonal signal and which is most likely to be a false negative.
  • After the reveal, state clearly whether you reject or fail to reject your null hypothesis.

Data sheet

  • Round number, date, and time of each collection:
  • Fixed site coordinates and sampling depth for each round:
  • Water temperature, flow, and weather at each round:
  • Sub-sample sources and pooling method used in the composite batch:
  • Target species list with per-round pre-reveal predictions:
  • Chain-of-custody label and shipping date for each round:
  • Any deviations from the original protocol, noted at the time they occurred:

Discussion questions

  • Why must sampling coordinates, depth, and method stay identical across rounds in a seasonal comparison study?
  • What does it mean, statistically, to 'fail to reject the null hypothesis,' and why is that different from 'proving the null is true'?
  • How does primer specificity affect your ability to trust a seasonal change in relative signal?
  • If two of your three rounds agreed but one didn't, how would you decide whether that's real variation or a false negative/positive?

Field checklist

  • Null and alternative hypotheses written and pre-registered before round one.
  • Identical site coordinates, depth, and sampling method used every round.
  • Nitrile gloves used and equipment kept upstream/uncontaminated at every round.
  • Full environmental log (temperature, flow, weather) completed each round.
  • Chain-of-custody labeling includes round number and date on every vial.
  • Deviations from protocol documented in the shared log at the time they occur.

Analysis

Hypothesis test: does detection probability vary by season?

  1. 1.Restate H0 and H1 exactly as pre-registered before any results were known.
  2. 2.Tabulate detection and relative-signal values for each target species across all rounds.
  3. 3.Identify which species show a pattern consistent with H1 and which are stable (consistent with H0 for that species).
  4. 4.Evaluate each non-detection: classify it as more consistent with true absence or with a false negative, citing environmental log evidence.
  5. 5.State a conclusion on H0/H1 with an explicit confidence level, and name the strongest alternative explanation for your pattern.

Assessment

20-point rubric

Hypothesis formulation and pre-registration

4 pts

H0 and H1 are stated in clear, falsifiable statistical language before data collection begins.

Experimental design consistency

4 pts

Site, depth, and protocol remain genuinely fixed across rounds, with any deviations documented.

Field discipline and data logging

4 pts

Environmental metadata is complete and consistent in format across every round.

Error-type reasoning

4 pts

Non-detections are correctly evaluated as true absence versus false negative using specific evidence.

Lab report structure and conclusion

4 pts

Methods, results, and discussion are clearly separated, and the H0/H1 conclusion is explicitly stated with a confidence level.

Go further

Extensions

  • Compare the section's multi-round dataset against a public phenology or citizen-science database for the same species.
  • Have a second lab section run the identical protocol at a different site as a spatial control on the seasonal comparison.
  • Introduce a simple chi-square or proportion test on the pooled detection data for sections that have covered basic statistics.
  • Ask students to design next year's sampling calendar using this term's false-negative patterns to choose better collection windows.

For the teacher

Answer key and misconceptions

  • Expected: species with strong seasonal life histories (e.g., a spawning fish or breeding amphibian) typically show the clearest round-to-round signal change; year-round residents typically stay comparatively stable — this contrast is the core evidence for H1.
  • Common misconception: a non-detection in round two automatically confirms the species left the site. Correct framing: it must first be evaluated against false-negative risk (temperature, degradation, sampling effort) before being read as a true absence.
  • Common misconception: 'failing to reject the null hypothesis' means the study failed. Correct framing: a stable, non-significant result across rounds is still valid data and answers the research question.
  • Expected: students should be able to name at least one confound (e.g., a storm event between rounds, a change in personnel technique, or a missed calibration) and explain how it could produce a false seasonal signal.
  • Common misconception: primer specificity issues only cause false positives. Correct framing: a poorly matched primer can also cause a false negative if it fails to amplify a present but genetically divergent local population.

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