Murray State University · Part four · Example projects

What the Survey Missed

Murray State University
Demo 16 of 17 · Part four · Ecology · Stage 1

Run the fixed round.

When you survey every one of 48 fixed sites once, what have you actually measured?

Before you start. Occupancy means a site is used by the species in this model — it is not abundance, the number of animals present. Every round revisits the same 48 fixed sites, 24 in Habitat A and 24 in Habitat B, rather than sampling new ones.

Predict

Which habitat will show more detections after this first round: A, B, or about the same?

Try

Press Survey round 1 · 48 site-visits.

Takeaway

A fixed round returns a detection history for every site, not a count of animals — that is the only kind of evidence this investigation produces.

Illustrative the generating scenario is invented to make one point, not measured

Fixed survey plan

Every enabled round visits all 24 Habitat A and all 24 Habitat B sites — 48 fixed site-visits. The site set never adapts to what was detected.

Check yourself

After round 1, Habitat B shows more detections than Habitat A. Does that mean Habitat B has more occupied sites?

In the declared model, Habitat A has the higher occupancy probability (ψ = 0.8) but the lower detection probability (p = 0.25); Habitat B has lower occupancy (ψ = 0.5) but higher detection (p = 0.8).

Demo 16 of 17 · Part four · Ecology · Stage 2

Interpret the first result.

Is a detection difference between habitats, by itself, enough to conclude an occupancy difference?

Before you start. Stage 1 gave you a detection history for every site — whether each site was ever detected across the rounds run so far — not a realized occupancy map. This stage asks what that history can justify before any map is shown.

Predict

Yes or no: does a detection difference alone prove an occupancy difference?

Try

Under Choose the justified claim, pick the option that stays inside the round-1 evidence, then read why.

Takeaway

A difference in detections can come from occupancy, from detection probability, or from both — one round cannot tell you which.

Live the observed fraction ever detected, computed from your round-1 histories

What round 1 actually showed

Run round 1 in the Survey stage first.

Check yourself

Once you have chosen the cautious claim, what should you look for before trusting any occupancy ranking from one round?

Demo 16 of 17 · Part four · Ecology · Stage 3

Repeat the same fixed survey.

Does adding a round change which habitat shows the higher observed fraction, and does it have to?

Before you start. A later round visits the exact same 48 sites again; it is not a new, larger sample. Detections accumulate per site as "ever detected across K rounds," so the observed fraction can only rise or stay level as K grows, in either habitat.

Predict

Will adding round 2 flip which habitat shows the higher observed fraction?

Try

Press Survey round 2, then Survey round 3 if you want a third.

Takeaway

More fixed rounds improve the evidence, but a finite realization can still disagree with the true occupancy ranking.

Live the same seeded scenario, revisited

Add a fixed round

Check yourself

Adding rounds narrows the gap between the observed fraction and its expected value. Does enough rounds guarantee your finite sample ranks the habitats correctly?

Demo 16 of 17 · Part four · Ecology · Stage 4

Compare evidence with hidden truth.

Which habitat actually has more occupied sites in this seeded scenario — the one with more detections, or the other one?

Before you start. Revealing shows this scenario's realized occupancy (which sites actually generated as occupied) and the generating values ψ and p behind it. Neither number is estimated from your rounds; both were fixed the moment the scenario was seeded.

Predict

Before revealing, which habitat has the higher realized occupancy?

Try

Answer the transfer question, then press Reveal synthetic map and model parameters.

Takeaway

The habitat with more detections is not always the habitat with more occupied sites — and one visit alone cannot separate occupancy from detection probability.

Illustrative the generating ψ and p are invented to make one point

Reveal the synthetic model

Optional: relative likelihood from observed histories Live computed here from the histories your rounds produced — not a posterior probability

Uses observed K-visit histories for one habitat only. Darker cells have greater relative likelihood on this bounded ψ/p grid. It is not a posterior probability or a calibrated confidence interval. With one visit, ψ and p are not separately identified.

0p .51

Survey at least one round to view histories.

Choose ψ and p to inspect one grid cell.

Check yourself

The reveal marks some sites "occupied but missed." What does that category show about detection?

What the Survey Missed · Bryant Harrison · Murray State University
One HTML file · runs offline · source-backed occupancy investigation
Printable guide

Guidehow to use this demo

Can fewer sightings mean fewer occupied sites? Survey two fixed synthetic habitats and find out what a detection difference can and cannot prove.

Where this sits: the tenth of ten AI-built classroom tools in Part four — an ecology investigation whose goal, engine and assumptions are all one tap away, the same discipline every other Part-four demo follows.

  1. Survey. Predict a habitat, then press Survey round 1 to visit all 48 fixed sites.
  2. Interpret. Choose the claim that is actually justified by the round-1 detection difference.
  3. Revisit. Optionally repeat the fixed survey with Survey round 2 and Survey round 3.
  4. Compare. Answer the transfer question, then press Reveal synthetic map and model parameters.

Getting around

The stage tabs switch stages; Interpret, Revisit and Compare unlock as you progress. Details opens the assumptions and sources for the current stage. Guide reopens this card; Settings holds Open Presenter Notes, Presentation mode, Reset, and two scenario controls.

Time

About ten minutes for all four stages.

Settings

Presenter notes

The printable session plan, on screen: what to say at each stage, the answer key, the misconceptions, and a short-of-time cut. This is the printable guide itself, not a summary of it.

Presentation mode

Scales the whole page up one notch for projecting to a room, and puts a Notes button in the header.

Reset

Back to the start: the first seeded scenario, no rounds run, no predictions or interpretation chosen, nothing revealed, the likelihood view closed and re-centred. The page does not reload. Presentation mode stays as you set it.

Scenario

Replay keeps the scenario on screen but clears your progress through it; a new seed draws the next scenario in sequence.

DetailsSurvey

What is live, and what is invented

Every detection outcome is computed here; the generating scenario is invented

Live the detection outcome for every one of 48 sites

The moment you press Survey round 1, this page draws a detection outcome for every fixed site from the seeded scenario. Nothing is looked up or pre-recorded.

Illustrative the generating ψ and p values

Habitat A's occupancy probability (ψ = 0.8) and detection probability (p = 0.25), and Habitat B's (ψ = 0.5, p = 0.8), are invented to make one point — that a lower-occupancy habitat can show more detections — not measured from a real survey.

How it works

Site occupied with probability ψ; if occupied, detected each round with probability p

A site is drawn occupied or not once per seeded scenario, and stays that way for every round (closure). Given occupancy, each round's detection is an independent coin flip at probability p.

Assumptions and simplifications

  • Closure across visits. No site changes occupancy status between rounds within one scenario.
  • No false positives. A detection never occurs at an unoccupied site.
  • Conditionally independent detections. Given occupancy, one round's detection outcome does not depend on any other round's.
  • Constant detection probability within a habitat. No per-site heterogeneity in p is modeled.

Sources

Boundary: this demo does not estimate or claim anything about a real wildlife population. It is a synthetic teaching model with invented generating parameters, chosen to illustrate one confound.

Site detail

Presenter Notesthe printable guide, on screen

Demo 16 of 17 · Part four · Ecology · about 10 min

What the Survey Missed — presenter guide

One HTML file, offline, no AI at runtime. You need: a screen to project or share; no other materials.

Learning goals

Not stated objectives — what the activity itself demonstrably shows, one per stage:

  1. A fixed round returns a detection history for every site, not a count of animals — that is the only kind of evidence this investigation produces.
  2. A difference in detections can come from occupancy, from detection probability, or from both — one round cannot tell you which.
  3. More fixed rounds improve the evidence, but a finite realization can still disagree with the true occupancy ranking.
  4. The habitat with more detections is not always the habitat with more occupied sites — and one visit alone cannot separate occupancy from detection probability.

Where this sits on the path

The tenth of ten AI-built classroom tools in Part four, and the last demo in the tour. Like the four "investigation" demos before it (wavelet, ion flight, inhibitor, missing time), it teaches a discipline through the same grammar: predict, run, then explain before reveal. Its own discipline is ecology — survey design under imperfect detection — and its assumptions are one tap away, the same standard every earlier demo in the tour is held to.

One question for the session

Can fewer sightings mean fewer occupied sites? Not necessarily — a detection difference between two habitats can come from a difference in occupancy, a difference in how easy the species is to detect, or both, and one fixed round of surveying cannot tell you which.

The one sentence to land: a detection is not an animal, and a difference in detections is not by itself a difference in occupancy. In this scenario, the habitat with the higher true occupancy (Habitat A, ψ = 0.8) is EXPECTED to show fewer detections after one round than the habitat with lower occupancy but higher detectability (Habitat B, ψ = 0.5, p = 0.8).

The shape of the session

StageWhat happensTime
1 — SurveyPredict a habitat, then run the one fixed round across all 48 sites.2 min
2 — InterpretChoose the claim actually justified by the round-1 detection difference.2–3 min
3 — RevisitOptionally repeat the fixed survey with rounds 2 and 3.2 min
4 — CompareAnswer the transfer question, then reveal the synthetic model and, optionally, the likelihood view.3 min

Stage 1 — Survey

Question. When you survey every one of 48 fixed sites once, what have you actually measured?

Predict prompt. Guess which habitat, A or B, will show more detections after this first round — before pressing Survey round 1.

Activity. Have the room commit to a habitat by show of hands before you press Survey round 1 · 48 site-visits. The maps fill in live; ask what a colored site means before revealing that it is only a detection, not a confirmed absence of the species elsewhere.

Expected observation. With the declared values (A: ψ = 0.8, p = 0.25; B: ψ = 0.5, p = 0.8), Habitat B is EXPECTED to show more detections despite having the LOWER occupancy probability — the room's habit of predicting the higher-occupancy habitat to "win" the detection count is the misconception this stage exists to surface.

Check-yourself answer key. "Does more detections mean more occupied sites?" — No: a detection difference can come from occupancy, detection probability, or both.

Teaching limit. The generating ψ and p values are invented to make this one point; they are not measured from a real survey and should not be read as typical field values.

Misconception: more detections means more animals were found. A detection history counts site-visits where the species was seen, not individuals. A habitat can produce more detections while having fewer occupied sites, if its detection probability is higher.

Stage 2 — Interpret

Question. Is a detection difference between habitats, by itself, enough to conclude an occupancy difference?

Predict prompt. A quick yes/no vote before the three options: does a detection difference alone prove an occupancy difference?

Activity. Read the round-1 recap aloud, then have the room choose among the three claims. Only the cautious claim ("could reflect occupancy, detection probability, or both") unlocks the rest of the demo; the other two are genuine, plausible-sounding overreaches, not strawmen.

Expected observation. The cautious claim is the only one the model actually supports; "the habitat with fewer detections must have lower occupancy" is exactly backwards for the declared scenario.

Check-yourself answer key. "What should you look for before trusting an occupancy ranking from one round?" — Evidence about detection probability, not only the raw detection count.

Teaching limit. This stage tests recognition of the correct claim; it does not by itself estimate ψ or p from data — that is what the optional likelihood view in Stage 4 illustrates, and even it cannot separate the two from one visit.

Misconception: a detection difference already settles which habitat has more occupied sites. It settles nothing about occupancy alone. The same detection difference is equally consistent with an occupancy difference, a detection-probability difference, or a mix of both.

Stage 3 — Revisit

Question. Does adding a round change which habitat shows the higher observed fraction, and does it have to?

Predict prompt. Before running round 2, ask whether the room thinks the round-1 ranking can flip.

Activity. Run round 2, then round 3 if time allows. Watch the observed fractions in the metric cards move toward (but not exactly reach) their theoretical expectations from the Details drawer.

Expected observation. The observed fractions get closer to their K = 3 expectations (A 0.4625, B 0.496) but a real seeded run can still land either side of the expected ranking — the exact outcome the room sees will vary by which scenario is loaded.

Check-yourself answer key. "Does enough rounds guarantee the ranking is correct?" — No: more rounds improve the evidence but do not guarantee it, because the two expected fractions stay close.

Teaching limit. Only three rounds are modeled; the demo does not claim what happens at ten or a hundred rounds, where the law of large numbers would narrow this gap further.

Misconception: enough repeated surveys always produce the "correct" ranking. More visits improve the evidence but a finite sample can still disagree with the underlying model, especially when the two habitats' expected values are close, as they are here by K = 3.

Stage 4 — Compare

Question. Which habitat actually has more occupied sites in this seeded scenario — the one with more detections, or the other one?

Predict prompt. Before revealing, guess which habitat has the higher REALIZED occupancy, not the higher detected fraction.

Activity. Answer the transfer question (a colleague's overreach) as a room, then press Reveal synthetic map and model parameters. Point out the newly visible "occupied but missed" sites — they were occupied the whole time and simply were not detected in the rounds run. If time allows, open the optional likelihood view and note that it never produces a single confident answer from one visit.

Expected observation. Habitat A (the higher-occupancy habitat) reveals MORE occupied sites despite having shown fewer detections throughout — the inversion the whole demo has been building toward.

Check-yourself answer key. "What does 'occupied but missed' show about detection?" — A truly occupied site can produce zero detections across every round run, because detection probability is less than one.

Teaching limit. The likelihood view uses only observed histories from one chosen habitat at a time and a coarse 11-by-11 grid; it illustrates the ψ/p ambiguity, it does not fit a model.

Misconception: revealing the map is the "real" measurement and the survey rounds were only practice. The reveal is not a measurement either — it is the generating truth of a synthetic scenario, shown for comparison. In a real field study, no such reveal ever happens; the survey rounds ARE the only evidence a researcher gets.

Discussion questions

  1. If you could only run one more round in ONE habitat, which would you choose to best distinguish "Habitat A has lower detection probability" from "Habitat A has lower occupancy"? Why does a single extra round in either habitat still leave you short of a clean answer?
  2. The reveal shows Habitat A has more occupied sites but fewer detections. Name one real-world reason a species might be more occupant-rich but harder to detect (habitat structure, behavior, timing).
  3. The optional likelihood view uses only ONE habitat's histories at a time. What would combining both habitats' histories into one estimate assume that this demo does not?
  4. This demo never estimates ψ or p with a number and a confidence interval, the way RPresence or a similar tool would. What would a real analysis need that this demo's single seeded scenario cannot supply?

Presenter controls

The stage tabs switch stages; Interpret, Revisit and Compare unlock as the room progresses and re-lock on Reset. Details (in each stage's intro) opens that stage's assumptions and sources. Guide reopens the orientation card; Settings holds Open Presenter Notes (this document, on screen), Presentation mode (scales the page for a projector), Reset (the whole demo back to first load, in place — no page reload), and two scenario controls: Replay as practice (same seed, clears progress) and New seeded scenario (draws the next one). Site squares in either map are clickable at any time to read that one site's own detection history.

Sources and attribution

MacKenzie, D. I., et al. (2002), “Estimating Site Occupancy Rates When Detection Probabilities Are Less Than One,” Ecology, 83(8), 2248–2255. U.S. Geological Survey: Estimating site occupancy rates when detection probabilities are less than one. This is the source context for separating occupancy probability and detection probability, and for the detection-history likelihood structure this model implements.

Donovan, T. M., Hines, J. E., & MacKenzie, D. I. (2024), occupancyTuts: Occupancy modelling tutorials with RPresence, U.S. Geological Survey. Current USGS teaching materials on occupancy/detection-history workflows with RPresence, cited here for occupancy-modeling teaching context. This replaces a 2023 RPresence workshop page previously cited at mbr-pwrc.usgs.gov/workshops/occupancy2023online.html, which as of a 2026-09-15 fleet-wide citation sweep now blanket-redirects (confirmed against a second path on the same subdomain) to a generic USGS center homepage with no occupancy-modeling content.

Neither source establishes a current Murray State assignment or endorsement.

What the Survey Missed · Bryant Harrison · Murray State University · Offline synthetic teaching model. This HTML file is the canonical guide source: the printable sheet, the PDF, and the in-app presenter notes all come from it.

Open the printable copy (teacher-guide.html) — the same document, laid out for print; the PDF beside it is a render of that file.
Bryant Harrison · Murray State University