Table 2 Prior distributions and hyperparameters in our statewide Bayesian state-space model using age-at-harvest data, split into recruitment and survival parameters.

From: A Bayesian state-space model using age-at-harvest data for estimating the population of black bears (Ursus americanus) in Wisconsin

Recruitment Parameters

Variable

Parameter

Mean

Distribution

LS-a

Litter Size 2.5-year-olds

2.00

Gamma (20,10)

LS-b

Litter Size 3.5-year-olds

2.00

Gamma (20,10)

LS-c

Litter Size 4.5-year-olds

2.00

Gamma (20,10)

LS-d

Litter Size 5.5+ year-olds

2.74

Gamma (16.4,6)

PR-a

Pregnancy Rate 2.5-year-olds

0.003

Beta (2.61,1000)

PR-b

Pregnancy Rate 3.5-year-olds

0.25

Beta (34,100)

PR-c

Pregnancy Rate 4.5-year-olds

0.53

Beta (54,48)

PR-d

Pregnancy Rate 5.5+ year-olds

0.48

Beta (47,50)

SP

Sex Proportion (female)

0.46

Beta (426, 500)

Survival Parameters

Variable

Parameter

Mean

Long-Term Precision

Annual Precision

HSm

Male Harvest Survival

0.77

3

Gamma (20,0.5)

HSf

Female Harvest Survival

0.85

3

Gamma (20,0.5)

NS

Non-harvest Survival

0.95

4

Gamma (20,0.5)

CubSa

Cub Survival years 0.0–0.5

0.84

4

n/a

CubSb

Cub Survival years 0.5–1.5

0.71

4

n/a

Rep

Recovery Rate

0.98

2

n/a

  1. We include the variable, parameter description (for gamma distributions these are the shape and rate), mean and distribution used. For survival prior distributions the means are given at the real parameter scale and long-term and annuals precisions (1/variance) are at the link scale (loglog).
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