-
Notifications
You must be signed in to change notification settings - Fork 0
/
model_cov_reduced_crit.txt
145 lines (113 loc) · 7.89 KB
/
model_cov_reduced_crit.txt
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
# Critical factors expected to reflect moose population best.
# 19 betas
model
{
# Priors
p00 ~ dunif(0,1)
pInt = log(p00/(1-p00)) # Intercept for p on logit scale
theta00 ~ dunif(-20,5) #prior for theta intercept
lambda0 ~ dunif(-20,5) #prior for lambda intercept
# Priors for landscape fixed effects
beta_lam_hab_softwood ~ dnorm(0,0.01) # prior for habitat effect
beta_lam_hab_hardwood ~ dnorm(0,0.01) # prior for habitat effect
beta_lam_hab_wetland ~ dnorm(0,0.01) # prior for habitat effect
beta_lam_hab_mixed ~ dnorm(0,0.01) # prior for habitat effect
beta_lam_elev ~ dnorm(0,0.01) # prior for elevation effect
#beta_lam_highway ~ dnorm(0,0.01) # prior for highway effect
#beta_lam_minor_road ~ dnorm(0,0.01) # prior for an index of human effect, local road density
beta_lam_northing ~ dnorm(0,0.01) # prior for northing effect
#beta_lam_easting ~ dnorm(0,0.01) # prior for easting effect
beta_theta_hab_softwood ~ dnorm(0,0.01) # prior for habitat effect
beta_theta_hab_hardwood ~ dnorm(0,0.01) # prior for habitat effect
beta_theta_hab_wetland ~ dnorm(0,0.01) # prior for habitat effect
beta_theta_hab_mixed ~ dnorm(0,0.01) # prior for habitat effect
beta_theta_elev ~ dnorm(0,0.01) # prior for elevation effect
#beta_theta_highway ~ dnorm(0,0.01) # prior for highway effect
#beta_theta_minor_road ~ dnorm(0,0.01) # prior for an index of human effect, local road density
beta_theta_northing ~ dnorm(0,0.01) # prior for northing effect
#beta_theta_easting ~ dnorm(0,0.01) # prior for easting effect
# Priors for detection fixed effects
#beta_detect_skye ~ dnorm(0,0.01) # prior for dog effect
#beta_detect_scooby ~ dnorm(0,0.01) # prior for dog effect
#beta_detect_ranger ~ dnorm(0,0.01) # prior for dog effect
#beta_detect_max ~ dnorm(0,0.01) # prior for dog effect
#beta_detect_hiccup ~ dnorm(0,0.01) # prior for dog effect
#beta_detect_suzie ~ dnorm(0,0.01) # prior for suzie effect
#beta_detect_jennifer ~ dnorm(0,0.01) # prior for suzie effect
#beta_detect_justin ~ dnorm(0,0.01) # prior for suzie effect
beta_detect_dist ~ dnorm(0,0.01) # prior for distance effect
for(i in 1:nSites){
# Model for deposition ---------------------------------------------------------------------------------------------------------------------------------------
# This iteration of the model has a slightly different structure for N due to changes in simulation.
# Before, y_t ~ Bin(N_t-1, p)
# Now, y_t ~ Bin(N_t, p)
# Initial deposition is Poisson random, and occurs on June 1, 2016 (arbitrary selection, but is the first visit to any site).
N1[i] ~ dpois(lambda[i])
# Time 1 is visit 1, but indexed by 2, since we need to model the initial N. I choose to call that period before any visits time 0.
# Linear model for lambda. Include fixed/random effects here later
lambda[i] = exp(lambda0 * gridCovariates[i,1] +
beta_lam_northing * gridCovariates[i,2] +
#beta_lam_easting * gridCovariates[i,3] +
beta_lam_elev * gridCovariates[i,4] +
#beta_lam_highway * gridCovariates[i,5] +
#beta_lam_minor_road * gridCovariates[i,6] +
beta_lam_hab_softwood * gridCovariates[i,7] +
beta_lam_hab_hardwood * gridCovariates[i,8] +
beta_lam_hab_mixed * gridCovariates[i,9] +
beta_lam_hab_wetland * gridCovariates[i,10]
) # somewhat immaterial except for mechanistic model of deposition process and observation.
# Initial deposition of scats. There are no collections, and assumed to be no degradation until we visit, so N[i,1,v] is equal over all v.
for(v in 1:maxV){
N[i,1,v] = N1[i]
}
# Deposition between time 0 and first visit is found in days[i,1]
for(t in 1:(maxT - 1)){
R[i,t] ~ dpois(theta[i]*days[i,t]) # Every round has some added deposition after we leave. It is dependent upon the DAYS in between visits.
# For instance, R[i,2] ~ dpois(theta[i]*days[i,2]), where days[i,2] is the intervening time
}
# Linear model for theta. Include fixed/random effects here later
theta[i] = exp(theta00 * gridCovariates[i,1] +
beta_theta_northing * gridCovariates[i,2] +
#beta_theta_easting * gridCovariates[i,3] +
beta_theta_elev * gridCovariates[i,4] +
#beta_theta_highway * gridCovariates[i,5] +
#beta_theta_minor_road * gridCovariates[i,6] +
beta_theta_hab_softwood * gridCovariates[i,7] +
beta_theta_hab_hardwood * gridCovariates[i,8] +
beta_theta_hab_mixed * gridCovariates[i,9] +
beta_theta_hab_wetland * gridCovariates[i,10]
)
# Proceeding N's add new recruits and remove current counts from the previous time step's N.
# Recruits are random poisson variates with mean theta.
# Mechanism for scat removals/deposition ------------------------------------------------------------------------------------------
for(t in 2:maxT){
# First time going to a sampling unit what's there is what was there last time, minus what we picked up last time, plus what deposition happened last time.
N[i,t,1] = N[i,t-1,maxV] - y[i,t-1,maxV] + R[i,t-1]
# On subsequent visits within a sample occasion to a sampling unit, what's there is what was there after the first visit, minus what we picked up, all the way to maxV (20 in 2016).
for(v in 2:maxV){
N[i,t,v] = N[i,t,v-1] - y[i,t,v-1]
}
}
# Observation likelihood. Counts are conditional on population size at previous time step (after recruits and removals), and detection (which also depends on having visited the site). ------------------
for(t in 2:maxT){
for(v in 1:maxV){
# Covariates on detection
logit(p0[i,t,v]) = pInt +
Dcov[i,t-1,v]*beta_detect_dist #+
#dogCov[i,t-1,1]*beta_detect_skye +
#dogCov[i,t-1,2]*beta_detect_scooby +
#dogCov[i,t-1,3]*beta_detect_ranger +
#dogCov[i,t-1,4]*beta_detect_max +
#dogCov[i,t-1,5]*beta_detect_hiccup +
#humCov[i,t-1,1]*beta_detect_suzie +
#humCov[i,t-1,2]*beta_detect_jennifer +
#humCov[i,t-1,3]*beta_detect_justin
# adjust p0 such that those sites not visited are set to 0. Initially, all sites are p0[i,t] = 0.8
# vis is a matrix of binary indicators with '1' being 'visited', and '0' being 'not visited'. Multiply by p0 to fix 'not visited' sites to p = 0, and obtain a new matrix.
p[i,t,v] = p0[i,t,v] * vis[i,t,v]
y[i,t,v] ~ dbin(p[i,t,v], N[i,t,v])
}
}
#density[i] = (theta[i] / per_moose_deposition) / 2500 # density of moose per grid cell (50m x 50m = 2500m)
}
}