• Identify features of data that drive analyses

• Think critically about what the data could tell you

Question \(\rightarrow\) Data \(\rightarrow\) Model \(\rightarrow\) Inference \(\rightarrow\) Prediction

Question \(\rightarrow\) Data \(\rightarrow\) Model \(\rightarrow\) Inference \(\rightarrow\) Prediction

Sex?

Fecundity?

Growth?

Survival?

Movement?

Abundance?

Survival?

Spatial distribution?

Movement/migration?

Question \(\rightarrow\) Data \(\rightarrow\) Model \(\rightarrow\) Inference \(\rightarrow\) Prediction

1 Detection \(\rightarrow\) presence/absence

2+ Detections \(\rightarrow\) survival, movement

1 Detection \(\rightarrow\) presence/absence

2+ Detections \(\rightarrow\) survival, movement

1 Measurement \(\rightarrow\) fecundity, age, size

2+ Measurements \(\rightarrow\) growth

Detections \(\rightarrow\) presence/absence

Counts \(\rightarrow\) density or survival/movement

Nonexhaustive counts

Exhaustive counts

(Non)exhaustive surveys

Depletions

(Non)exhaustive surveys

Depletions

Capture/Tag/Recapture

Sex

Age

Fecundity

Counts/Census

Survival (individual)

Size (length, mass)

Density

Survival (population)

Survival (7 of 9 survived \(\approx\) 0.78)

Composition (4 age-3, 18 age-4, 11 age-5 \(\rightarrow\) ~55% age-4)

Density (3 animals in 21 ha plot \(\approx\) 0.14 per ha)

Which of these give you more confidence?

A) 3 / 9 \(\approx\) 0.33

B) 300 / 900 \(\approx\) 0.33

Binary (0,1) \(\rightarrow\) Bernoulli

Binary (0,1) \(\rightarrow\) Bernoulli

Count \(\left(\mathbb{Z}^{\geq}\right)\) \(\rightarrow\) Poisson or Negative-Binomial

Binary (0,1) \(\rightarrow\) Bernoulli

Count \(\left(\mathbb{Z}^{\geq}\right)\) \(\rightarrow\) Poisson or Negative-Binomial

Composition \(\left(\mathcal{S}^D\right)\) \(\rightarrow\) Binomial (D = 2) or Multinomial (D > 2)

Density \(\left(\mathbb{R}^{\geq}\right)\) \(\rightarrow\) log-Normal or Gamma

Density \(\left(\mathbb{R}^{\geq}\right)\) \(\rightarrow\) log-Normal or Gamma

Proportion \(\left(\mathcal{C}^D\right)\) \(\rightarrow\) Beta (D = 2) or Dirichlet (D > 2)

Density \(\left(\mathbb{R}^{\geq}\right)\) \(\rightarrow\) log-Normal or Gamma

Proportion \(\left(\mathcal{C}^D\right)\) \(\rightarrow\) Beta (D = 2) or Dirichlet (D > 2)

Transformations \(\left(\mathbb{R}\right)\) \(\rightarrow\) Normal