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BRT_Space_nochl.R
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### Fishery-dependent SDM (L^3) Estimation Model Code
## Function to run BRTs on data from OM, with all Covariates (temp, chla, mld)
## Function returns only the fitted and predicted values
## modified my M.Karp 1/15/21
#############################
# Full Models- with Chla and Space component #
#############################
#
###Random sampling
#
if("ran" %in% sampling) {
print("Fitting BRT-Ran_nochl_S")
brt_R_P_nochl_S <- gbm.step(data=dat_hist_random,
gbm.x = c(1:2, 25, 27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_R_N_nochl_S <- gbm.step(data=dat_hist_random[dat_hist_random$abundance>0,],
gbm.x = c(1:2, 25, 27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presRx_nochl_S <- predict(brt_R_P_nochl_S, dat_hist, n.trees=brt_R_P_nochl_S$gbm.call$best.trees, type="response")
abundRx_nochl_S <- exp(predict(brt_R_N_nochl_S, dat_hist, n.trees=brt_R_N_nochl_S$gbm.call$best.trees, type="response"))
dat_hist$brt_Ran_nochl_S <- presRx_nochl_S * abundRx_nochl_S
presRx_nochl_S <- predict(brt_R_P_nochl_S, dat_fcast, n.trees=brt_R_P_nochl_S$gbm.call$best.trees, type="response")
abundRx_nochl_S <- exp(predict(brt_R_N_nochl_S, dat_fcast, n.trees=brt_R_N_nochl_S$gbm.call$best.trees, type="response"))
dat_fcast$brt_Ran_nochl_S <- presRx_nochl_S * abundRx_nochl_S
}
#
### Preferential (target) sampling
#
#Target sampling-0.5
if("tar_0.5" %in% sampling){
print("Fitting BRT-Pref0.5_nochl_S")
brt_T_P_1_nochl_S <- gbm.step(data=dat_hist_Tar_1,
gbm.x = c(1:2, 25, 27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_T_N_1_nochl_S <- gbm.step(data=dat_hist_Tar_1[dat_hist_Tar_1$abundance>0,],
gbm.x = c(1:2, 25, 27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presTx_1_nochl_S <- predict(brt_T_P_1_nochl_S, dat_hist, n.trees=brt_T_P_1_nochl_S$gbm.call$best.trees, type="response")
abundTx_1_nochl_S <- exp(predict(brt_T_N_1_nochl_S, dat_hist, n.trees=brt_T_N_1_nochl_S$gbm.call$best.trees, type="response"))
dat_hist$brt_Tar_0.5_nochl_S <- presTx_1_nochl_S * abundTx_1_nochl_S
presTx_1_nochl_S <- predict(brt_T_P_1_nochl_S, dat_fcast, n.trees=brt_T_P_1_nochl_S$gbm.call$best.trees, type="response")
abundTx_1_nochl_S <- exp(predict(brt_T_N_1_nochl_S, dat_fcast, n.trees=brt_T_N_1_nochl_S$gbm.call$best.trees, type="response"))
dat_fcast$brt_Tar_0.5_nochl_S <- presTx_1_nochl_S * abundTx_1_nochl_S
}
#Target sampling-0.6
if("tar_0.6" %in% sampling){
print("Fitting BRT-Pref0.6_nochl_S")
brt_T_P_2_nochl_S <- gbm.step(data=dat_hist_Tar_2,
gbm.x = c(1:2, 25, 27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_T_N_2_nochl_S <- gbm.step(data=dat_hist_Tar_2[dat_hist_Tar_2$abundance>0,],
gbm.x = c(1:2, 25, 27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presTx_2_nochl_S <- predict(brt_T_P_2_nochl_S, dat_hist, n.trees=brt_T_P_2_nochl_S$gbm.call$best.trees, type="response")
abundTx_2_nochl_S <- exp(predict(brt_T_N_2_nochl_S, dat_hist, n.trees=brt_T_N_2_nochl_S$gbm.call$best.trees, type="response"))
dat_hist$brt_Tar_0.6_nochl_S <- presTx_2_nochl_S* abundTx_2_nochl_S
presTx_2_nochl_S <- predict(brt_T_P_2_nochl_S, dat_fcast, n.trees=brt_T_P_2_nochl_S$gbm.call$best.trees, type="response")
abundTx_2_nochl_S <- exp(predict(brt_T_N_2_nochl_S, dat_fcast, n.trees=brt_T_N_2_nochl_S$gbm.call$best.trees, type="response"))
dat_fcast$brt_Tar_0.6_nochl_S <- presTx_2_nochl_S * abundTx_2_nochl_S
}
#Target sampling-0.7
if("tar_0.7" %in% sampling){
print("Fitting BRT-Pref0.7_nochl_S")
brt_T_P_3_nochl_S <- gbm.step(data=dat_hist_Tar_3,
gbm.x = c(1:2, 25, 27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_T_N_3_nochl_S <- gbm.step(data=dat_hist_Tar_3[dat_hist_Tar_3$abundance>0,],
gbm.x = c(1:2, 25, 27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presTx_3_nochl_S <- predict(brt_T_P_3_nochl_S, dat_hist, n.trees=brt_T_P_3_nochl_S$gbm.call$best.trees, type="response")
abundTx_3_nochl_S <- exp(predict(brt_T_N_3_nochl_S, dat_hist, n.trees=brt_T_N_3_nochl_S$gbm.call$best.trees, type="response"))
dat_hist$brt_Tar_0.7_nochl_S <- presTx_3_nochl_S * abundTx_3_nochl_S
presTx_3_nochl_S <- predict(brt_T_P_3_nochl_S, dat_fcast, n.trees=brt_T_P_3_nochl_S$gbm.call$best.trees, type="response")
abundTx_3_nochl_S <- exp(predict(brt_T_N_3_nochl_S, dat_fcast, n.trees=brt_T_N_3_nochl_S$gbm.call$best.trees, type="response"))
dat_fcast$brt_Tar_0.7_nochl_S <- presTx_3_nochl_S * abundTx_3_nochl_S
}
#Target sampling-0.8
if("tar_0.8" %in% sampling){
print("Fitting BRT-Pref0.8_nochl_S")
brt_T_P_4_nochl_S <- gbm.step(data=dat_hist_Tar_4,
gbm.x = c(1:2, 25, 27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_T_N_4_nochl_S <- gbm.step(data=dat_hist_Tar_4[dat_hist_Tar_4$abundance>0,],
gbm.x = c(1:2, 25, 27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presTx_4_nochl_S <- predict(brt_T_P_4_nochl_S, dat_hist, n.trees=brt_T_P_4_nochl_S$gbm.call$best.trees, type="response")
abundTx_4_nochl_S <- exp(predict(brt_T_N_4_nochl_S, dat_hist, n.trees=brt_T_N_4_nochl_S$gbm.call$best.trees, type="response"))
dat_hist$brt_Tar_0.8_nochl_S <- presTx_4_nochl_S * abundTx_4_nochl_S
presTx_4_nochl_S <- predict(brt_T_P_4_nochl_S, dat_fcast, n.trees=brt_T_P_4_nochl_S$gbm.call$best.trees, type="response")
abundTx_4_nochl_S <- exp(predict(brt_T_N_4_nochl_S, dat_fcast, n.trees=brt_T_N_4_nochl_S$gbm.call$best.trees, type="response"))
dat_fcast$brt_Tar_0.8_nochl_S <- presTx_4_nochl_S * abundTx_4_nochl_S
}
#Target sampling-0.9
if("tar_0.9" %in% sampling){
print("Fitting BRT-Pref0.9_nochl_S")
brt_T_P_5_nochl_S <- gbm.step(data=dat_hist_Tar_5,
gbm.x = c(1:2, 25, 27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_T_N_5_nochl_S <- gbm.step(data=dat_hist_Tar_5[dat_hist_Tar_5$abundance>0,],
gbm.x = c(1:2, 25, 27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presTx_5_nochl_S <- predict(brt_T_P_5_nochl_S, dat_hist, n.trees=brt_T_P_5_nochl_S$gbm.call$best.trees, type="response")
abundTx_5_nochl_S <- exp(predict(brt_T_N_5_nochl_S, dat_hist, n.trees=brt_T_N_5_nochl_S$gbm.call$best.trees, type="response"))
dat_hist$brt_Tar_0.9_nochl_S <- presTx_5_nochl_S * abundTx_5_nochl_S
presTx_5_nochl_S <- predict(brt_T_P_5_nochl_S, dat_fcast, n.trees=brt_T_P_5_nochl_S$gbm.call$best.trees, type="response")
abundTx_5_nochl_S <- exp(predict(brt_T_N_5_nochl_S, dat_fcast, n.trees=brt_T_N_5_nochl_S$gbm.call$best.trees, type="response"))
dat_fcast$brt_Tar_0.9_nochl_S <- presTx_5_nochl_S * abundTx_5_nochl_S
}
#
### Distance from Port Sampling
#
#Northern Offshore
if("npo" %in% sampling){
print("Fitting BRT-NPO_nochl_S")
brt_dist_P_npo_nochl_S <- gbm.step(data=dat_hist_Dist_npo,
gbm.x = c(1:2, 25, 27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_dist_N_npo_nochl_S <- gbm.step(data=dat_hist_Dist_npo[dat_hist_Dist_npo$abundance>0,],
gbm.x = c(1:2, 25, 27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presDx_npo_nochl_S <- predict(brt_dist_P_npo_nochl_S, dat_hist, n.trees=brt_dist_P_npo_nochl_S$gbm.call$best.trees, type="response")
abundDx_npo_nochl_S <- exp(predict(brt_dist_N_npo_nochl_S, dat_hist, n.trees=brt_dist_N_npo_nochl_S$gbm.call$best.trees, type="response"))
dat_hist$brt_Dist_npo_nochl_S <- presDx_npo_nochl_S * abundDx_npo_nochl_S
presDx_npo_nochl_S <- predict(brt_dist_P_npo_nochl_S, dat_fcast, n.trees=brt_dist_P_npo_nochl_S$gbm.call$best.trees, type="response")
abundDx_npo_nochl_S <- exp(predict(brt_dist_N_npo_nochl_S, dat_fcast, n.trees=brt_dist_N_npo_nochl_S$gbm.call$best.trees, type="response"))
dat_fcast$brt_Dist_npo_nochl_S <- presDx_npo_nochl_S * abundDx_npo_nochl_S
}
#Northern Nearshore
if("npn" %in% sampling){
print("Fitting BRT-NPN_nochl_S")
brt_dist_P_npn_nochl_S <- gbm.step(data=dat_hist_Dist_npn,
gbm.x = c(1:2, 25, 27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_dist_N_npn_nochl_S <- gbm.step(data=dat_hist_Dist_npn[dat_hist_Dist_npn$abundance>0,],
gbm.x = c(1:2, 25, 27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presDx_npn_nochl_S <- predict(brt_dist_P_npn_nochl_S, dat_hist, n.trees=brt_dist_P_npn_nochl_S$gbm.call$best.trees, type="response")
abundDx_npn_nochl_S <- exp(predict(brt_dist_N_npn_nochl_S, dat_hist, n.trees=brt_dist_N_npn_nochl_S$gbm.call$best.trees, type="response"))
dat_hist$brt_Dist_npn_nochl_S <- presDx_npn_nochl_S * abundDx_npn_nochl_S
presDx_npn_nochl_S <- predict(brt_dist_P_npn_nochl_S, dat_fcast, n.trees=brt_dist_P_npn_nochl_S$gbm.call$best.trees, type="response")
abundDx_npn_nochl_S <- exp(predict(brt_dist_N_npn_nochl_S, dat_fcast, n.trees=brt_dist_N_npn_nochl_S$gbm.call$best.trees, type="response"))
dat_fcast$brt_Dist_npn_nochl_S <- presDx_npn_nochl_S * abundDx_npn_nochl_S
}
#Middle Offshore
if("mpo" %in% sampling){
print("Fitting BRT-MPO_nochl_S")
brt_dist_P_mpo_nochl_S <- gbm.step(data=dat_hist_Dist_mpo,
gbm.x = c(1:2, 25, 27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_dist_N_mpo_nochl_S <- gbm.step(data=dat_hist_Dist_mpo[dat_hist_Dist_mpo$abundance>0,],
gbm.x = c(1:2, 25, 27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presDx_mpo_nochl_S <- predict(brt_dist_P_mpo_nochl_S, dat_hist, n.trees=brt_dist_P_mpo_nochl_S$gbm.call$best.trees, type="response")
abundDx_mpo_nochl_S <- exp(predict(brt_dist_N_mpo_nochl_S, dat_hist, n.trees=brt_dist_N_mpo_nochl_S$gbm.call$best.trees, type="response"))
dat_hist$brt_Dist_mpo_nochl_S <- presDx_mpo_nochl_S * abundDx_mpo_nochl_S
presDx_mpo_nochl_S <- predict(brt_dist_P_mpo_nochl_S, dat_fcast, n.trees=brt_dist_P_mpo_nochl_S$gbm.call$best.trees, type="response")
abundDx_mpo_nochl_S <- exp(predict(brt_dist_N_mpo_nochl_S, dat_fcast, n.trees=brt_dist_N_mpo_nochl_S$gbm.call$best.trees, type="response"))
dat_fcast$brt_Dist_mpo_nochl_S <- presDx_mpo_nochl_S * abundDx_mpo_nochl_S
}
#Middle nearshore
if("mpn" %in% sampling){
print("Fitting BRT-MPN_nochl_S")
brt_dist_P_mpn_nochl_S <- gbm.step(data=dat_hist_Dist_mpn,
gbm.x = c(1:2, 25, 27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_dist_N_mpn_nochl_S <- gbm.step(data=dat_hist_Dist_mpn[dat_hist_Dist_mpn$abundance>0,],
gbm.x = c(1:2, 25, 27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presDx_mpn_nochl_S <- predict(brt_dist_P_mpn_nochl_S, dat_hist, n.trees=brt_dist_P_mpn_nochl_S$gbm.call$best.trees, type="response")
abundDx_mpn_nochl_S <- exp(predict(brt_dist_N_mpn_nochl_S, dat_hist, n.trees=brt_dist_N_mpn_nochl_S$gbm.call$best.trees, type="response"))
dat_hist$brt_Dist_mpn_nochl_S <- presDx_mpn_nochl_S * abundDx_mpn_nochl_S
presDx_mpn_nochl_S <- predict(brt_dist_P_mpn_nochl_S, dat_fcast, n.trees=brt_dist_P_mpn_nochl_S$gbm.call$best.trees, type="response")
abundDx_mpn_nochl_S <- exp(predict(brt_dist_N_mpn_nochl_S, dat_fcast, n.trees=brt_dist_N_mpn_nochl_S$gbm.call$best.trees, type="response"))
dat_fcast$brt_Dist_mpn_nochl_S <- presDx_mpn_nochl_S * abundDx_mpn_nochl_S
}
#Southern Offshore
if("spo" %in% sampling){
print("Fitting BRT-SPO_nochl_S")
brt_dist_P_spo_nochl_S <- gbm.step(data=dat_hist_Dist_spo,
gbm.x = c(1:2, 25, 27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_dist_N_spo_nochl_S <- gbm.step(data=dat_hist_Dist_spo[dat_hist_Dist_spo$abundance>0,],
gbm.x = c(1:2, 25, 27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presDx_spo_nochl_S <- predict(brt_dist_P_spo_nochl_S, dat_hist, n.trees=brt_dist_P_spo_nochl_S$gbm.call$best.trees, type="response")
abundDx_spo_nochl_S <- exp(predict(brt_dist_N_spo_nochl_S, dat_hist, n.trees=brt_dist_N_spo_nochl_S$gbm.call$best.trees, type="response"))
dat_hist$brt_Dist_spo_nochl_S <- presDx_spo_nochl_S * abundDx_spo_nochl_S
presDx_spo_nochl_S <- predict(brt_dist_P_spo_nochl_S, dat_fcast, n.trees=brt_dist_P_spo_nochl_S$gbm.call$best.trees, type="response")
abundDx_spo_nochl_S <- exp(predict(brt_dist_N_spo_nochl_S, dat_fcast, n.trees=brt_dist_N_spo_nochl_S$gbm.call$best.trees, type="response"))
dat_fcast$brt_Dist_spo_nochl_S <- presDx_spo_nochl_S * abundDx_spo_nochl_S
}
#Southern Nearshore
if("spn" %in% sampling){
print("Fitting BRT-SPN_nochl_S")
brt_dist_P_spn_nochl_S <- gbm.step(data=dat_hist_Dist_spn,
gbm.x = c(1:2, 25, 27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_dist_N_spn_nochl_S <- gbm.step(data=dat_hist_Dist_spn[dat_hist_Dist_spn$abundance>0,],
gbm.x = c(1:2, 25, 27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presDx_spn_nochl_S <- predict(brt_dist_P_spn_nochl_S, dat_hist, n.trees=brt_dist_P_spn_nochl_S$gbm.call$best.trees, type="response")
abundDx_spn_nochl_S <- exp(predict(brt_dist_N_spn_nochl_S, dat_hist, n.trees=brt_dist_N_spn_nochl_S$gbm.call$best.trees, type="response"))
dat_hist$brt_Dist_spn_nochl_S <- presDx_spn_nochl_S * abundDx_spn_nochl_S
presDx_spn_nochl_S <- predict(brt_dist_P_spn_nochl_S, dat_fcast, n.trees=brt_dist_P_spn_nochl_S$gbm.call$best.trees, type="response")
abundDx_spn_nochl_S <- exp(predict(brt_dist_N_spn_nochl_S, dat_fcast, n.trees=brt_dist_N_spn_nochl_S$gbm.call$best.trees, type="response"))
dat_fcast$brt_Dist_spn_nochl_S <- presDx_spn_nochl_S * abundDx_spn_nochl_S
}
#All Offshore
if("allo" %in% sampling){
print("Fitting BRT-ALLO_nochl_S")
brt_dist_P_allo_nochl_S <- gbm.step(data=dat_hist_Dist_allo,
gbm.x = c(1:2, 25, 27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_dist_N_allo_nochl_S <- gbm.step(data=dat_hist_Dist_allo[dat_hist_Dist_allo$abundance>0,],
gbm.x = c(1:2, 25, 27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presDx_allo_nochl_S <- predict(brt_dist_P_allo_nochl_S, dat_hist, n.trees=brt_dist_P_allo_nochl_S$gbm.call$best.trees, type="response")
abundDx_allo_nochl_S <- exp(predict(brt_dist_N_allo_nochl_S, dat_hist, n.trees=brt_dist_N_allo_nochl_S$gbm.call$best.trees, type="response"))
dat_hist$brt_Dist_allo_nochl_S <- presDx_allo_nochl_S * abundDx_allo_nochl_S
presDx_allo_nochl_S <- predict(brt_dist_P_allo_nochl_S, dat_fcast, n.trees=brt_dist_P_allo_nochl_S$gbm.call$best.trees, type="response")
abundDx_allo_nochl_S <- exp(predict(brt_dist_N_allo_nochl_S, dat_fcast, n.trees=brt_dist_N_allo_nochl_S$gbm.call$best.trees, type="response"))
dat_fcast$brt_Dist_allo_nochl_S <- presDx_allo_nochl_S * abundDx_allo_nochl_S
}
#All Nearshore
if("alln" %in% sampling){
print("Fitting BRT-ALLN_nochl_S")
brt_dist_P_alln_nochl_S <- gbm.step(data=dat_hist_Dist_alln,
gbm.x = c(1:2, 25, 27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_dist_N_alln_nochl_S <- gbm.step(data=dat_hist_Dist_alln[dat_hist_Dist_alln$abundance>0,],
gbm.x = c(1:2, 25, 27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presDx_alln_nochl_S <- predict(brt_dist_P_alln_nochl_S, dat_hist, n.trees=brt_dist_P_alln_nochl_S$gbm.call$best.trees, type="response")
abundDx_alln_nochl_S <- exp(predict(brt_dist_N_alln_nochl_S, dat_hist, n.trees=brt_dist_N_alln_nochl_S$gbm.call$best.trees, type="response"))
dat_hist$brt_Dist_alln_nochl_S <- presDx_alln_nochl_S * abundDx_alln_nochl_S
presDx_alln_nochl_S <- predict(brt_dist_P_alln_nochl_S, dat_fcast, n.trees=brt_dist_P_alln_nochl_S$gbm.call$best.trees, type="response")
abundDx_alln_nochl_S <- exp(predict(brt_dist_N_alln_nochl_S, dat_fcast, n.trees=brt_dist_N_alln_nochl_S$gbm.call$best.trees, type="response"))
dat_fcast$brt_Dist_alln_nochl_S <- presDx_alln_nochl_S * abundDx_alln_nochl_S
}
#
## Bycatch + Opt Target Sampling
#
if("BY" %in% sampling){
print("Fitting BRT-BY_nochl_S")
brt_B_P_nochl_S <- gbm.step(data=dat_hist_BY,
gbm.x = c(1:2, 25, 27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_B_N_nochl_S <- gbm.step(data=dat_hist_BY[dat_hist_BY$abundance>0,],
gbm.x = c(1:2, 25, 27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presBx_nochl_S <- predict(brt_B_P_nochl_S, dat_hist, n.trees=brt_B_P_nochl_S$gbm.call$best.trees, type="response")
abundBx_nochl_S <- exp(predict(brt_B_N_nochl_S, dat_hist, n.trees=brt_B_N_nochl_S$gbm.call$best.trees, type="response"))
dat_hist$brt_BY_nochl_S <- presBx_nochl_S * abundBx_nochl_S
presBx_nochl_S <- predict(brt_B_P_nochl_S, dat_fcast, n.trees=brt_B_P_nochl_S$gbm.call$best.trees, type="response")
abundBx_nochl_S <- exp(predict(brt_B_N_nochl_S, dat_fcast, n.trees=brt_B_N_nochl_S$gbm.call$best.trees, type="response"))
dat_fcast$brt_BY_nochl_S<- presBx_nochl_S * abundBx_nochl_S
}
#
###Closed Areas + Opt Target Species
#
# Small Closed Area
if("CA_sm" %in% sampling){
print("Fitting BRT-CASM_nochl_S")
brt_CA_P_sm_nochl_S <- gbm.step(data=dat_hist_CA_sm,
gbm.x = c(1:2, 25, 27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_CA_N_sm_nochl_S <- gbm.step(data=dat_hist_CA_sm[dat_hist_CA_sm$abundance>0,],
gbm.x = c(1:2, 25, 27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presCAx_sm_nochl_S <- predict(brt_CA_P_sm_nochl_S, dat_hist, n.trees=brt_CA_P_sm_nochl_S$gbm.call$best.trees, type="response")
abundCAx_sm_nochl_S <- exp(predict(brt_CA_N_sm_nochl_S, dat_hist, n.trees=brt_CA_N_sm_nochl_S$gbm.call$best.trees, type="response"))
dat_hist$brt_CA_sm_nochl_S <- presCAx_sm_nochl_S * abundCAx_sm_nochl_S
presCAx_sm_nochl_S <- predict(brt_CA_P_sm_nochl_S, dat_fcast, n.trees=brt_CA_P_sm_nochl_S$gbm.call$best.trees, type="response")
abundCAx_sm_nochl_S <- exp(predict(brt_CA_N_sm_nochl_S, dat_fcast, n.trees=brt_CA_N_sm_nochl_S$gbm.call$best.trees, type="response"))
dat_fcast$brt_CA_sm_nochl_S<- presCAx_sm_nochl_S * abundCAx_sm_nochl_S
}
# Medium Closed Area
if("CA_med" %in% sampling){
print("Fitting BRT-CAMED_nochl_S")
brt_CA_P_med_nochl_S <- gbm.step(data=dat_hist_CA_med,
gbm.x = c(1:2, 25, 27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_CA_N_med_nochl_S <- gbm.step(data=dat_hist_CA_med[dat_hist_CA_med$abundance>0,],
gbm.x = c(1:2, 25, 27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presCAx_med_nochl_S <- predict(brt_CA_P_med_nochl_S, dat_hist, n.trees=brt_CA_P_med_nochl_S$gbm.call$best.trees, type="response")
abundCAx_med_nochl_S <- exp(predict(brt_CA_N_med_nochl_S, dat_hist, n.trees=brt_CA_N_med_nochl_S$gbm.call$best.trees, type="response"))
dat_hist$brt_CA_med_nochl_S <- presCAx_med_nochl_S * abundCAx_med_nochl_S
presCAx_med_nochl_S <- predict(brt_CA_P_med_nochl_S, dat_fcast, n.trees=brt_CA_P_med_nochl_S$gbm.call$best.trees, type="response")
abundCAx_med_nochl_S <- exp(predict(brt_CA_N_med_nochl_S, dat_fcast, n.trees=brt_CA_N_med_nochl_S$gbm.call$best.trees, type="response"))
dat_fcast$brt_CA_med_nochl_S<- presCAx_med_nochl_S * abundCAx_med_nochl_S
}
# Large Closed Area
if("CA_lar" %in% sampling){
print("Fitting BRT-CALAR_nochl_S")
brt_CA_P_lar_nochl_S <- gbm.step(data=dat_hist_CA_lar,
gbm.x = c(1:2, 25, 27),
gbm.y = 'pres',
family = "bernoulli",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
brt_CA_N_lar_nochl_S <- gbm.step(data=dat_hist_CA_lar[dat_hist_CA_lar$abundance>0,],
gbm.x = c(1:2, 25, 27),
gbm.y = 'log_abundance',
family = "gaussian",
tree.complexity = 3, learning.rate = 0.01, bag.fraction = 0.6,
plot.main=FALSE, verbose = FALSE)
presCAx_lar_nochl_S <- predict(brt_CA_P_lar_nochl_S, dat_hist, n.trees=brt_CA_P_lar_nochl_S$gbm.call$best.trees, type="response")
abundCAx_lar_nochl_S <- exp(predict(brt_CA_N_lar_nochl_S, dat_hist, n.trees=brt_CA_N_lar_nochl_S$gbm.call$best.trees, type="response"))
dat_hist$brt_CA_lar_nochl_S <- presCAx_lar_nochl_S * abundCAx_lar_nochl_S
presCAx_lar_nochl_S <- predict(brt_CA_P_lar_nochl_S, dat_fcast, n.trees=brt_CA_P_lar_nochl_S$gbm.call$best.trees, type="response")
abundCAx_lar_nochl_S <- exp(predict(brt_CA_N_lar_nochl_S, dat_fcast, n.trees=brt_CA_N_lar_nochl_S$gbm.call$best.trees, type="response"))
dat_fcast$brt_CA_lar_nochl_S<- presCAx_lar_nochl_S * abundCAx_lar_nochl_S
}