29 worked examples

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Each example is a realistic study with simulated data, so the true answer is known. Open one to see the prompt, download the data, and compare the results with the truth.

20 examples are DONE: every quantity the report interprets passed planted truth at the example's design. 9 are OPEN: at least one did not, and the page says which results can still be used. OPEN examples are kept on purpose; they show how CascadiaTools tells you when something can't be trusted. See the verdict table.

Figure: Conditional effects of every predictor on the expected Wood Thrush count per 10 survey minutes (typical station).

Regression and causal inference

Wood Thrush point counts: negative-binomial GLMM with survey-effort offset and station random intercepts (simulated example)

Per 10 percentage points of canopy cover (%) this is associated with a 39% higher expected Wood Thrush count (rate ratio 1.39, 95% CI 1.18 to 1.64).

✓ DONEglm

Figure: What-if comparison: the same average causal effect estimated under different adjustment choices, with 95% confidence intervals.

Regression and causal inference

Does riparian canopy cover cause more juvenile trout? Causal effect from 140 observational stream reaches (DAG + g-computation, simulated data with known truth)

Average causal effect: increasing riparian canopy cover by 10 percentage points at every site would change the expected number of juvenile trout per 100 m reach by 1.18 (95% CI 0.258 to 2.10)…

✓ DONEcausal

Figure: Path diagram with standardised coefficients (piecewiseSEM conventions: SD scaling; latent-theoretic for binary, observation-empirical for count responses).

Regression and causal inference

Fire severity, vegetation and songbirds after a wildfire (piecewise SEM with a Poisson GLMM; simulated data with known truth)

Global fit: Fisher's C = 11.06 (df = 8), P = 0.198: no evidence that the model leaves out a needed link.

! OPENsem

Occupancy

Pileated woodpecker occupancy and forest cover (simulated call-playback surveys, 120 stands)

The species was detected at least once at 34 of 120 sites (naive occupancy 0.28).

✓ DONEoccu-single

Occupancy

Do gray foxes avoid coyotes, and does housing density change that? Multispecies occupancy with species interactions (Rota et al. 2016)

Allowing for missed detections, the estimated probability that each species occupies a site with average covariate values is: coyote 0.60 (95% CI 0.50 to 0.69); gray fox 0.25 (95% CI 0.18 to 0.34).

✓ DONEoccu-rota

Occupancy

Do invasive Cuban treefrogs push out native green treefrogs? Two-species conditional occupancy from PVC-pipe surveys (simulated data)

Where Cuban treefrog is present, green treefrog occupies 0.31 (95% CrI 0.21 to 0.43) of sites; where Cuban treefrog is absent, 0.67 (95% CrI 0.56 to 0.78).

✓ DONEoccu-waddle

Occupancy

Fisher occupancy, canopy cover and elevation with residual spatial clustering (simulated camera-trap survey, 160 stations)

Occupancy probability at average covariate values and zero spatial effect is 0.50 (95% CrI 0.36 to 0.65).

✓ DONEoccu-spatial

Occupancy

Abundance-mediated species interactions: pine marten abundance and red squirrel occupancy (simulated camera-trap example)

Each 1-SD increase in woodland_pct (1 SD = 17.4 units) is associated with 1.70 (95% CI 1.47 to 1.96) times the expected Pine marten abundance (a +70% change) (per 10 units: rate ratio 1.36 (95% CI 1.25 to…

! OPENabund-interactions

Abundance

Hermit warbler abundance from repeated point counts (static binomial N-mixture model)

At a site with average covariate values the model expects 2.00 (95% CI 1.69 to 2.38) Hermit warblers.

✓ DONEnmixture

Figure: Expected mule deer density versus shrub cover from the hierarchical distance-sampling model (road distance at its mean), with 95% and 50% bands; points are rough per-transect densities.

Abundance

Mule deer density on a winter range from line-transect distance sampling, with shrub-cover and road-distance effects (simulated data)

A group within 400 m of the line had probability 0.44 (95% CI 0.38 to 0.50) of being detected.

! OPENdistance

Figure: Posterior mean realised bobcat density (per km2) over the habitat; white dots = cameras; orange crosses = most likely activity centre of each of the 33 detected cats; blank = reservoir (not habitat).

Spatial capture-recapture

Bobcat density from camera traps: closed spatial capture-recapture with a canopy-cover density covariate (simulated data)

Density was estimated at 0.24 (95% CrI 0.16 to 0.34) animals per km², i.e. 23.7 per 100 km2.

✓ DONEscr

Figure: Expected density versus Canopy cover (%) (other density covariates at their state-space mean), with 95% and 50% credible bands.

Spatial capture-recapture

Fisher density from ear tags and trail cameras: generalized spatial mark-resight (simulated data)

Density was estimated at 0.30 (95% CrI 0.24 to 0.38) fishers per km² (30.1 per 100 km2).

✓ DONEscr-smr

Figure: Expected density versus Forest cover (%) (other density covariates at their state-space mean), with 95% and 50% credible bands.

Spatial capture-recapture

Fisher density from a scat-detection-dog survey with GPS-logged search routes (search-encounter SCR, simulated data with known truth)

Density was estimated at 0.070 (95% CrI 0.045 to 0.10) animals per km², i.e. 7.04 per 100 km2.

✓ DONEscr-search

Figure: Realised abundance per period (posterior mean, line) with 95% and 50% credible bands.

Spatial capture-recapture

Pacific marten abundance, survival, recruitment and canopy association from five years of DNA hair snares (open-population spatial capture-recapture, simulated data with known truth)

Abundance in the 144 km² state space (traps plus buffer) was 119 (95% CrI 85.0 to 161) in period 2020 and 186 (95% CrI 144 to 238) in period 2024 (density 0.83 and 1.3 animals per km²).

✓ DONEscr-open