Zooplankton salinity relationships in Suisun
Sam Bashevkin
8/26/2022
Load packages
library(conflicted)
library(MASS)
library(dplyr)
library(zooper)
library(lubridate)
library(readr)
library(tidyr)
library(ggplot2)
library(sf)
library(readxl)
library(stringr)
library(mgcv)
library(purrr)
library(deltamapr)
library(scales)
library(here)
conflict_prefer("filter", "dplyr")
conflict_prefer("select", "dplyr")Load and wrangle data
zoop_data<-Zoopsynther(Data_type="Community", Sources=c("EMP", "STN", "20mm", "FMWT"), Time_consistency = FALSE)## [1] "These species have no relatives in their size class common to all datasets and have been removed from one or more size classes: Ostracoda Adult (Meso), Cumacea Undifferentiated (Meso), Annelida Adult (Meso), Gammarus Adult (Meso), Orientomysis aspera Adult (Meso), Chironomidae Larva (Meso), Insecta Larva (Meso)"Read in zoop mass conversions
zoop_mass_conversions<-read_excel(here("Data/SMSCG salinity modeling/Biomass conversions.xlsx"), sheet="Micro and Meso-zooplankton")%>%
mutate(Taxname=case_when(Taxname=="Sinocalanus"~"Sinocalanus doerrii", # Change to help this match to zoop data
TRUE ~ Taxname),
Taxlifestage=paste(Taxname, Lifestage))%>%
select(Taxlifestage, CarbonWeight_ug)Read in zoop groupings
zoop_groups<-read_csv(here("Data/zoopcrosswalk2.csv"), col_types=cols_only(Taxlifestage="c", IBMR="c"))%>%
distinct()Load Mysid biomass data
zoop_mysid<-read_excel(here("Data/1972-2020MysidBPUEMatrix.xlsx"), # EMP
sheet="Mysid_BPUE_matrix_1972-2020", na = "NA",
col_types = c(rep("numeric", 4), "date", "text", "text", rep("text", 7), rep("numeric", 8)))%>%
select(Date=SampleDate, Station=StationNZ, BPUE=`Hyperacanthomysis longirostris`)%>% # Only select Hyperacanthomysis longirostris
mutate(Source="EMP")%>%
bind_rows(read_csv(here("Data/FMWT STN 2007to2019 Mysid BPUE.csv"), # FMWT/STN
col_types=cols_only(Station="c", SampleDate="c", Project="c", `Hyperacanthomysis longirostris`="d"))%>%
rename(Date=SampleDate, Source=Project, BPUE=`Hyperacanthomysis longirostris`)%>% # Only select Hyperacanthomysis longirostris
mutate(Date=mdy(Date),
Station=recode(Station, MONT="Mont", HONK="Honk")))%>% #Get station names to match to main dataset
mutate(BPUE_mysid=BPUE*1000, # Convert to ug
Taxlifestage="Hyperacanthomysis longirostris Adult",
SampleID=paste(Source, Station, Date),
SizeClass="Macro")%>%
select(SampleID, Taxlifestage, SizeClass, BPUE_mysid)Start processing the zoop data
zoop_data_mass<-zoop_data%>%
mutate(Taxlifestage=str_remove(Taxlifestage, fixed("_UnID")))%>%
filter(
!(SizeClass=="Meso" & #eliminating species which are counted in meso and micro and retained better in the micro net from the meso calcs
Taxlifestage%in%c("Asplanchna Adult", "Copepoda Larva","Cyclopoida Juvenile", "Eurytemora Larva", "Harpacticoida Undifferentiated",
"Keratella Adult", "Limnoithona Adult", "Limnoithona Juvenile", "Limnoithona sinenesis Adult", "Limnoithona tetraspina
Adult", "Oithona Adult", "Oithona Juvenile", "Oithona davisae Adult", "Polyarthra Adult","Pseudodiaptomus Larva",
"Rotifera Adult", "Sinocalanus doerrii Larva", "Synchaeta Adult", "Synchaeta bicornis Adult", "Trichocerca Adult")) &
!(SizeClass=="Micro" &Taxlifestage%in%c("Cirripedia Larva", "Cyclopoida Adult", "Oithona similis")) & #removing categories better retained in meso net from micro net matrix
(is.na(Order) | Order!="Amphipoda") & # Remove amphipods
(is.na(Order) | Order!="Mysida" | Taxlifestage=="Hyperacanthomysis longirostris Adult"))%>% #Only retain Hyperacanthomysis longirostris
mutate(Taxlifestage=recode(Taxlifestage, `Synchaeta bicornis Adult`="Synchaeta Adult", # Change some names to match to biomass conversion dataset
`Pseudodiaptomus Adult`="Pseudodiaptomus forbesi Adult",
`Acanthocyclops vernalis Adult`="Acanthocyclops Adult"))%>%
left_join(zoop_mass_conversions, by="Taxlifestage")%>% # Add biomass conversions
left_join(zoop_mysid, by=c("SampleID", "Taxlifestage", "SizeClass"))%>% # Add mysid biomass
left_join(zoop_groups, by="Taxlifestage")%>% # Add IBMR categories
mutate(BPUE=if_else(Taxlifestage=="Hyperacanthomysis longirostris Adult", BPUE_mysid, CPUE*CarbonWeight_ug))%>% # Create 1 BPUE variable
filter(!is.na(BPUE) & !is.na(Latitude) & !is.na(Longitude) & !is.na(SalSurf))%>% # Removes any data without BPUE, which is currently restricted to Rotifera Adult, Copepoda Larva, and H. longirostris from STN. Also removes 20mm and EMP EZ stations without coordinates
group_by(IBMR)%>%
mutate(flag=if_else(all(c("Micro", "Meso")%in%SizeClass), "Remove", "Keep"))%>% # This and the next 2 lines are meant to ensure that all categories are consistent across the surveys. Since only EMP samples microzoops, only EMP data can be used for categories that include both micro and mesozoops.
ungroup()%>%
filter(!(flag=="Remove" & Source!="EMP"))%>%
select(SampleID, Station, Latitude, Longitude, SalSurf, Date, Year, IBMR, BPUE)%>%
group_by(across(-BPUE))%>%
summarise(BPUE=sum(BPUE), .groups="drop")%>% # Sum each IBMR categories
st_as_sf(coords=c("Longitude", "Latitude"), crs=4326)%>%
st_transform(crs=st_crs(deltamapr::R_DSIBM)) %>%
st_join(deltamapr::R_DSIBM %>%
select(SUBREGION)) %>%
st_drop_geometry() %>%
filter(SUBREGION %in% c("NW Suisun","SW Suisun","NE Suisun","SE Suisun","Confluence", "Suisun Marsh"))%>%
mutate(doy=yday(Date), #Day of year
Month=month(Date), # Month
Year_fac=factor(Year), # Factor year for model random effect
Station_fac=factor(Station), # Factor station for model random effect
across(c(SalSurf, doy), list(s=~(.x-mean(.x))/sd(.x))), # Center and standardize predictors
BPUE_log1p=log(BPUE+1)) # log1p transform BPUE for modelCheck sample size
zoop_sample_size <- zoop_data_mass %>%
group_by(SampleID,Year,Month,SUBREGION,Station) %>%
summarise(BPUE=sum(BPUE)) %>%
mutate(Samplesize=1) %>%
group_by(Year, Month, SUBREGION) %>%
summarise(mean_BPUE=mean(BPUE),Samplesize=sum(Samplesize)) %>%
filter(Year>=1995)
ggplot(zoop_sample_size, aes(x=Year, y=Month, fill=Samplesize))+
geom_tile()+
scale_y_continuous(breaks=1:12, labels=month(1:12, label=T))+
scale_fill_viridis_c(breaks=c(1,5,10,15,20))+
facet_wrap(~SUBREGION)+
theme_bw()
All the remaining brackish regions have sufficient sample size with the exception of NE Suisun. As such, NE Suisun is to be combined with SE Suisun while the rest of the regions are to be analyzed on their own.
Create a new column with IBMR edited regions to accomodate combination of NE and SE Suisun regions.
zoop_data_mass$Subregion_edit<-ifelse(zoop_data_mass$SUBREGION%in%c("NE Suisun", "SE Suisun"), "East Suisun", zoop_data_mass$SUBREGION)Model
Prediction data
Set up prediction data for model
# Min year to start models
year_min<-1995
newdata_function<-function(region, data=zoop_data_mass, quant=0.99){
lower<-(1-quant)/(2)
upper<-1-lower
data_filt<-data%>%
filter(Subregion_edit%in%region & Year >= year_min)
# Calculate monthly quantiles of salinity
month_sal<-data_filt%>%
group_by(Month)%>%
summarise(l=quantile(SalSurf, lower),
u=quantile(SalSurf, upper), .groups="drop")
newdata<-expand_grid(date=mdy(paste(1:12, 15, 2001, sep="/")), # The 15th of each month on a non-leap year
SalSurf=seq(round(min(data_filt$SalSurf), 1),
round(max(data_filt$SalSurf), 1), by=0.1))%>% # Salinity sequence nicely rounded to 1 decimal
mutate(Month=month(date),
doy=yday(date), # Day of year
SalSurf_s=(SalSurf-mean(data$SalSurf))/sd(data$SalSurf), # center and standardize salinity to match data
doy_s=(doy-mean(data$doy))/sd(data$doy))%>% # center and standardize doy to match data
left_join(month_sal, by="Month")%>%
filter(SalSurf >= l & SalSurf <= u)%>% # Remove any salinity values outside the quantiles for each month
select(Month, doy, doy_s, SalSurf, SalSurf_s)
}
newdata<-map(set_names(unique(zoop_data_mass$Subregion_edit)), newdata_function)Posterior prediction function
# Function to generate posterior predictions from a gam model
# From https://stats.stackexchange.com/questions/190348/can-i-use-bootstrapping-to-estimate-the-uncertainty-in-a-maximum-value-of-a-gam
predict_posterior<-function(model, newdata, exclude, n=1e3, seed=999){
Xp <- predict(model, newdata=newdata, type="lpmatrix", exclude=exclude, newdata.guaranteed=TRUE) ## map coefs to fitted curves
beta <- coef(model)
Vb <- vcov(model) ## posterior mean and cov of coefs
set.seed(seed)
mrand <- mvrnorm(n, beta, Vb) ## simulate n rep coef vectors from posterior
pred<-matrix(nrow=nrow(newdata), ncol=n)
ilink <- family(model)$linkinv
for (i in seq_len(n)) {
pred[,i] <- ilink(Xp %*% mrand[i, ])
}
colnames(pred)<-paste("draw", 1:n, sep="_")
pred<-as_tibble(pred)
return(pred)
}Model fitting
model
sal_model<-function(group,region,new_data=newdata){
cat("<<<<<<<<<<<<<<<<<<<<<<< modeling", group, region, ">>>>>>>>>>>>>>>>>>>>>>>>>\n\n")
new_data<-new_data[[region]]
data<-filter(zoop_data_mass, IBMR==group & Subregion_edit==region & Year>=year_min)
par(mfrow=c(2,2))
if(length(unique(data$Station_fac))>1){
model<-gam(BPUE_log1p ~ te(SalSurf_s, doy_s, k=c(5,5), bs=c("cs", "cc")) +
s(Year_fac, bs="re") + s(Station_fac, bs="re"),
data=data,
method="REML")
random_effects<-c("s(Year_fac)", "s(Station_fac)")
}else{
model<-gam(BPUE_log1p ~ te(SalSurf_s, doy_s, k=c(5,5), bs=c("cs", "cc")) +
s(Year_fac, bs="re"),
data=data,
method="REML")
random_effects<-c("s(Year_fac)")
}
cat("-------------gam check-------------\n")
gam.check(model)
cat("\n\n-------------summary-------------\n")
print(summary(model))
sal<-predict_posterior(model, new_data, random_effects)%>%
bind_cols(new_data%>% # Add covariate columns before these columns
select(-doy_s, -SalSurf_s),
.)
return(sal)
}Apply model to all groups and regions
model_factors<-expand_grid(IBMR=unique(zoop_data_mass$IBMR),
Subregion_edit=unique(zoop_data_mass$Subregion_edit))%>%
mutate(IBMR=set_names(IBMR, paste(IBMR, Subregion_edit)))
sal_conversions<-pmap_dfr(model_factors, function(IBMR, Subregion_edit) sal_model(IBMR, Subregion_edit), .id = "IBMR_region")%>%
mutate(IBMR=sapply(IBMR_region, function(x) str_split(x, " ", n=2)[[1]][1]),
Region=factor(sapply(IBMR_region, function(x) str_split(x, " ", n=2)[[1]][2]),
levels=c("Confluence", "Suisun Marsh", "East Suisun",
"NW Suisun", "SW Suisun")),
Month=as.integer(Month))%>%
select(-IBMR_region, -doy)%>%
relocate(Region, Month, IBMR, SalSurf)## <<<<<<<<<<<<<<<<<<<<<<< modeling acartela SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.0009170929,0.0007875523]
## (score 1730.771 & scale 2.616006).
## Hessian positive definite, eigenvalue range [1.342423,439.8853].
## Model rank = 53 / 53
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 13.11 0.98 0.24
## s(Year_fac) 28.00 23.72 NA NA
## s(Station_fac) 5.00 3.62 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.7122 0.7226 3.754 0.000186 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 13.11 19 794.836 < 0.0000000000000002 ***
## s(Year_fac) 23.72 27 9.916 < 0.0000000000000002 ***
## s(Station_fac) 3.62 4 12.070 0.0000012 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.478 Deviance explained = 50.2%
## -REML = 1730.8 Scale est. = 2.616 n = 880
## <<<<<<<<<<<<<<<<<<<<<<< modeling acartela NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 11 iterations.
## Gradient range [-0.000001206145,0.0000006103146]
## (score 2086.456 & scale 2.462597).
## Hessian positive definite, eigenvalue range [1.408909,536.8763].
## Model rank = 53 / 53
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.02 0.95 0.04 *
## s(Year_fac) 28.00 25.52 NA NA
## s(Station_fac) 5.00 3.66 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.6972 0.4851 5.56 0.0000000344 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.015 19 236.92 <0.0000000000000002 ***
## s(Year_fac) 25.516 27 21.41 <0.0000000000000002 ***
## s(Station_fac) 3.656 4 31.08 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.611 Deviance explained = 62.8%
## -REML = 2086.5 Scale est. = 2.4626 n = 1074
## <<<<<<<<<<<<<<<<<<<<<<< modeling acartela East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.00001940901,0.00001586249]
## (score 4148.471 & scale 2.40992).
## Hessian positive definite, eigenvalue range [2.728215,1085.712].
## Model rank = 58 / 58
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 17.46 0.87 <0.0000000000000002 ***
## s(Year_fac) 28.00 26.44 NA NA
## s(Station_fac) 10.00 7.69 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.149 0.352 11.79 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 17.460 19 902.10 <0.0000000000000002 ***
## s(Year_fac) 26.435 27 50.54 <0.0000000000000002 ***
## s(Station_fac) 7.691 9 14.66 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.699 Deviance explained = 70.7%
## -REML = 4148.5 Scale est. = 2.4099 n = 2172
## <<<<<<<<<<<<<<<<<<<<<<< modeling acartela Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.0007115767,0.0005337382]
## (score 3830.95 & scale 1.893154).
## Hessian positive definite, eigenvalue range [2.64436,1071.708].
## Model rank = 58 / 58
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.86 0.92 <0.0000000000000002 ***
## s(Year_fac) 28.00 26.12 NA NA
## s(Station_fac) 10.00 7.34 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.0351 0.2285 17.66 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.860 19 3458.87 <0.0000000000000002 ***
## s(Year_fac) 26.122 27 34.63 <0.0000000000000002 ***
## s(Station_fac) 7.343 9 12.75 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.814 Deviance explained = 81.8%
## -REML = 3830.9 Scale est. = 1.8932 n = 2144
## <<<<<<<<<<<<<<<<<<<<<<< modeling acartela Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.00003692586,0.00002931655]
## (score 3154.719 & scale 1.803668).
## Hessian positive definite, eigenvalue range [1.979754,892.7428].
## Model rank = 56 / 56
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 17.03 0.93 <0.0000000000000002 ***
## s(Year_fac) 28.00 26.10 NA NA
## s(Station_fac) 8.00 5.53 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.5635 0.2191 16.27 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 17.030 19 1717.891 <0.0000000000000002 ***
## s(Year_fac) 26.103 27 28.071 <0.0000000000000002 ***
## s(Station_fac) 5.528 7 9.201 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.793 Deviance explained = 79.9%
## -REML = 3154.7 Scale est. = 1.8037 n = 1786
## <<<<<<<<<<<<<<<<<<<<<<< modeling daphnia SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.00005313728,0.00002650896]
## (score 1380.78 & scale 1.240564).
## Hessian positive definite, eigenvalue range [0.00005313543,439.7202].
## Model rank = 53 / 53
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000000 13.889105 0.85 <0.0000000000000002 ***
## s(Year_fac) 28.000000 16.907612 NA NA
## s(Station_fac) 5.000000 0.000212 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.69553 0.06477 10.74 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 13.8891050 19 82.272 < 0.0000000000000002 ***
## s(Year_fac) 16.9076119 27 1.847 0.00000155 ***
## s(Station_fac) 0.0002124 4 0.000 0.611
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.578 Deviance explained = 59.3%
## -REML = 1380.8 Scale est. = 1.2406 n = 880
## <<<<<<<<<<<<<<<<<<<<<<< modeling daphnia NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.0001443584,0.000101751]
## (score 1665.672 & scale 1.174687).
## Hessian positive definite, eigenvalue range [1.057985,536.769].
## Model rank = 53 / 53
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.03 0.9 <0.0000000000000002 ***
## s(Year_fac) 28.00 20.87 NA NA
## s(Station_fac) 5.00 2.41 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.9385 0.1135 8.271 0.000000000000000406 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.027 19 213.090 < 0.0000000000000002 ***
## s(Year_fac) 20.873 27 3.220 < 0.0000000000000002 ***
## s(Station_fac) 2.412 4 4.442 0.000168 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.697 Deviance explained = 70.8%
## -REML = 1665.7 Scale est. = 1.1747 n = 1074
## <<<<<<<<<<<<<<<<<<<<<<< modeling daphnia East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.002776364,0.01614734]
## (score 3544.361 & scale 1.425606).
## Hessian positive definite, eigenvalue range [0.003529646,1085.663].
## Model rank = 58 / 58
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.0000 17.0556 0.87 <0.0000000000000002 ***
## s(Year_fac) 28.0000 25.0323 NA NA
## s(Station_fac) 10.0000 0.0903 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.3460 0.1052 12.8 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 17.05563 19 901.45 <0.0000000000000002 ***
## s(Year_fac) 25.03229 27 11.58 <0.0000000000000002 ***
## s(Station_fac) 0.09034 9 0.01 0.444
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.704 Deviance explained = 71%
## -REML = 3544.4 Scale est. = 1.4256 n = 2172
## <<<<<<<<<<<<<<<<<<<<<<< modeling daphnia Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 10 iterations.
## Gradient range [-0.00125077,0.001992025]
## (score 3950.097 & scale 2.17853).
## Hessian positive definite, eigenvalue range [0.001251247,1071.671].
## Model rank = 58 / 58
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00000 16.25757 0.85 <0.0000000000000002 ***
## s(Year_fac) 28.00000 24.52987 NA NA
## s(Station_fac) 10.00000 0.00652 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8308 0.1149 15.93 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.257572 19 523.50 <0.0000000000000002 ***
## s(Year_fac) 24.529871 27 10.26 <0.0000000000000002 ***
## s(Station_fac) 0.006523 9 0.00 0.71
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.615 Deviance explained = 62.3%
## -REML = 3950.1 Scale est. = 2.1785 n = 2144
## <<<<<<<<<<<<<<<<<<<<<<< modeling daphnia Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.003115112,0.002776406]
## (score 3060.218 & scale 1.685018).
## Hessian positive definite, eigenvalue range [1.650321,892.6881].
## Model rank = 56 / 56
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 14.98 0.92 <0.0000000000000002 ***
## s(Year_fac) 28.00 22.90 NA NA
## s(Station_fac) 8.00 4.17 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.8766 0.1040 8.425 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 14.98 19 124.955 < 0.0000000000000002 ***
## s(Year_fac) 22.90 27 5.095 < 0.0000000000000002 ***
## s(Station_fac) 4.17 7 3.259 0.0000612 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.495 Deviance explained = 50.7%
## -REML = 3060.2 Scale est. = 1.685 n = 1786
## <<<<<<<<<<<<<<<<<<<<<<< modeling eurytem SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.00009026664,0.0000770262]
## (score 1678.902 & scale 2.436944).
## Hessian positive definite, eigenvalue range [1.454748,439.6365].
## Model rank = 53 / 53
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 13.27 0.95 0.05 *
## s(Year_fac) 28.00 11.62 NA NA
## s(Station_fac) 5.00 3.66 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.9045 0.7167 4.053 0.0000553 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 13.273 19 1232.626 <0.0000000000000002 ***
## s(Year_fac) 11.616 27 0.777 0.0101 *
## s(Station_fac) 3.657 4 12.011 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.65 Deviance explained = 66.1%
## -REML = 1678.9 Scale est. = 2.4369 n = 880
## <<<<<<<<<<<<<<<<<<<<<<< modeling eurytem NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.0004186128,0.0003007133]
## (score 1995.305 & scale 2.168362).
## Hessian positive definite, eigenvalue range [0.1234876,536.7675].
## Model rank = 53 / 53
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.58 0.95 0.035 *
## s(Year_fac) 28.00 20.70 NA NA
## s(Station_fac) 5.00 0.81 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8810 0.1069 17.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.5840 19 163.928 <0.0000000000000002 ***
## s(Year_fac) 20.7040 27 3.057 <0.0000000000000002 ***
## s(Station_fac) 0.8097 4 0.314 0.212
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.645 Deviance explained = 65.8%
## -REML = 1995.3 Scale est. = 2.1684 n = 1074
## <<<<<<<<<<<<<<<<<<<<<<< modeling eurytem East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 9 iterations.
## Gradient range [-0.0001771457,0.0001698712]
## (score 3948.074 & scale 2.068058).
## Hessian positive definite, eigenvalue range [1.367214,1085.668].
## Model rank = 58 / 58
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 17.13 0.77 <0.0000000000000002 ***
## s(Year_fac) 28.00 23.05 NA NA
## s(Station_fac) 10.00 6.53 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.2095 0.1504 14.69 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 17.13 19 457.714 < 0.0000000000000002 ***
## s(Year_fac) 23.05 27 5.086 < 0.0000000000000002 ***
## s(Station_fac) 6.53 9 3.605 0.0000351 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.684 Deviance explained = 69.1%
## -REML = 3948.1 Scale est. = 2.0681 n = 2172
## <<<<<<<<<<<<<<<<<<<<<<< modeling eurytem Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 16 iterations.
## Gradient range [-0.002872656,0.003927675]
## (score 3986.964 & scale 2.250248).
## Hessian positive definite, eigenvalue range [0.09529293,1071.656].
## Model rank = 58 / 58
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 17.28 0.74 <0.0000000000000002 ***
## s(Year_fac) 28.00 23.05 NA NA
## s(Station_fac) 10.00 1.04 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.29381 0.09321 24.61 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 17.280 19 282.876 <0.0000000000000002 ***
## s(Year_fac) 23.051 27 6.104 <0.0000000000000002 ***
## s(Station_fac) 1.042 9 0.142 0.289
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.669 Deviance explained = 67.5%
## -REML = 3987 Scale est. = 2.2502 n = 2144
## <<<<<<<<<<<<<<<<<<<<<<< modeling eurytem Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.002713304,0.001997683]
## (score 3455.755 & scale 2.60385).
## Hessian positive definite, eigenvalue range [1.184171,892.7007].
## Model rank = 56 / 56
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 14.41 0.77 <0.0000000000000002 ***
## s(Year_fac) 28.00 23.57 NA NA
## s(Station_fac) 8.00 5.88 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.2309 0.1955 16.53 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 14.408 19 484.643 <0.0000000000000002 ***
## s(Year_fac) 23.569 27 6.138 <0.0000000000000002 ***
## s(Station_fac) 5.885 7 13.745 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.709 Deviance explained = 71.6%
## -REML = 3455.8 Scale est. = 2.6039 n = 1786
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcalad SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.00000110061,0.0000009558118]
## (score 1765.855 & scale 2.931646).
## Hessian positive definite, eigenvalue range [1.445007,439.7816].
## Model rank = 53 / 53
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 13.21 1.01 0.59
## s(Year_fac) 28.00 19.84 NA NA
## s(Station_fac) 5.00 2.87 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.5363 0.3642 17.95 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 13.206 19 115.558 <0.0000000000000002 ***
## s(Year_fac) 19.836 27 3.367 <0.0000000000000002 ***
## s(Station_fac) 2.865 4 29.474 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.403 Deviance explained = 42.7%
## -REML = 1765.9 Scale est. = 2.9316 n = 880
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcalad NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.0005205248,0.0003439632]
## (score 1947.714 & scale 2.036778).
## Hessian positive definite, eigenvalue range [1.695345,536.609].
## Model rank = 53 / 53
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 14.57 0.94 0.01 **
## s(Year_fac) 28.00 10.50 NA NA
## s(Station_fac) 5.00 3.91 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.4338 0.7873 6.902 0.00000000000889 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 14.569 19 547.093 <0.0000000000000002 ***
## s(Year_fac) 10.501 27 0.688 0.0141 *
## s(Station_fac) 3.906 4 27.279 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.42 Deviance explained = 43.5%
## -REML = 1947.7 Scale est. = 2.0368 n = 1074
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcalad East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.000004831961,0.000004008069]
## (score 4223.403 & scale 2.70773).
## Hessian positive definite, eigenvalue range [1.788059,1085.647].
## Model rank = 58 / 58
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 14.46 0.92 <0.0000000000000002 ***
## s(Year_fac) 28.00 22.15 NA NA
## s(Station_fac) 10.00 6.04 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.3118 0.1453 36.57 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 14.457 19 127.342 <0.0000000000000002 ***
## s(Year_fac) 22.153 27 5.375 <0.0000000000000002 ***
## s(Station_fac) 6.035 9 7.178 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.347 Deviance explained = 36%
## -REML = 4223.4 Scale est. = 2.7077 n = 2172
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcalad Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.000008710303,0.0000002761191]
## (score 4347.677 & scale 3.123892).
## Hessian positive definite, eigenvalue range [1.080052,1071.689].
## Model rank = 58 / 58
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.35 0.87 <0.0000000000000002 ***
## s(Year_fac) 28.00 25.46 NA NA
## s(Station_fac) 10.00 4.65 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.2327 0.1868 22.66 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.355 19 279.785 < 0.0000000000000002 ***
## s(Year_fac) 25.456 27 17.278 < 0.0000000000000002 ***
## s(Station_fac) 4.649 9 1.663 0.00463 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.569 Deviance explained = 57.9%
## -REML = 4347.7 Scale est. = 3.1239 n = 2144
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcalad Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.0001103899,0.0001007544]
## (score 3524.54 & scale 2.793997).
## Hessian positive definite, eigenvalue range [2.300091,892.7047].
## Model rank = 56 / 56
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.78 0.92 <0.0000000000000002 ***
## s(Year_fac) 28.00 23.49 NA NA
## s(Station_fac) 8.00 5.54 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.2402 0.1807 29 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.776 19 218.092 <0.0000000000000002 ***
## s(Year_fac) 23.488 27 8.372 <0.0000000000000002 ***
## s(Station_fac) 5.538 7 10.652 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.521 Deviance explained = 53.3%
## -REML = 3524.5 Scale est. = 2.794 n = 1786
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcaljuv SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 9 iterations.
## Gradient range [-0.0008135116,0.0002538109]
## (score 1445.234 & scale 1.391752).
## Hessian positive definite, eigenvalue range [1.0725,439.793].
## Model rank = 53 / 53
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 14.91 1 0.45
## s(Year_fac) 28.00 19.59 NA NA
## s(Station_fac) 5.00 3.36 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.2326 0.3777 19.15 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 14.912 19 119.399 0.000000897 ***
## s(Year_fac) 19.591 27 2.631 < 0.0000000000000002 ***
## s(Station_fac) 3.358 4 13.487 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.303 Deviance explained = 33.3%
## -REML = 1445.2 Scale est. = 1.3918 n = 880
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcaljuv NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.0000004921892,0.0000004548085]
## (score 1650.722 & scale 1.119079).
## Hessian positive definite, eigenvalue range [0.1972083,536.8415].
## Model rank = 53 / 53
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 15.83 0.96 0.03 *
## s(Year_fac) 28.00 24.24 NA NA
## s(Station_fac) 5.00 2.71 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.696 0.153 43.76 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 15.828 19 81.948 < 0.0000000000000002 ***
## s(Year_fac) 24.237 27 8.338 < 0.0000000000000002 ***
## s(Station_fac) 2.708 4 3.784 0.00201 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.476 Deviance explained = 49.7%
## -REML = 1650.7 Scale est. = 1.1191 n = 1074
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcaljuv East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 9 iterations.
## Gradient range [-0.000008148751,0.000008008423]
## (score 3436.878 & scale 1.305814).
## Hessian positive definite, eigenvalue range [1.953746,1085.632].
## Model rank = 58 / 58
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.44 0.88 <0.0000000000000002 ***
## s(Year_fac) 28.00 19.66 NA NA
## s(Station_fac) 10.00 6.43 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.2333 0.1053 59.22 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.443 19 212.842 <0.0000000000000002 ***
## s(Year_fac) 19.658 27 2.829 <0.0000000000000002 ***
## s(Station_fac) 6.429 9 6.833 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.486 Deviance explained = 49.6%
## -REML = 3436.9 Scale est. = 1.3058 n = 2172
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcaljuv Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 13 iterations.
## Gradient range [-0.0003478319,0.0002293254]
## (score 3175.041 & scale 1.053499).
## Hessian positive definite, eigenvalue range [2.569585,1071.668].
## Model rank = 58 / 58
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.06 0.76 <0.0000000000000002 ***
## s(Year_fac) 28.00 23.12 NA NA
## s(Station_fac) 10.00 7.18 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.2521 0.1129 55.39 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.056 19 803.570 <0.0000000000000002 ***
## s(Year_fac) 23.117 27 6.683 <0.0000000000000002 ***
## s(Station_fac) 7.182 9 14.445 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.654 Deviance explained = 66.1%
## -REML = 3175 Scale est. = 1.0535 n = 2144
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcaljuv Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.0003452575,0.0002809761]
## (score 2765.36 & scale 1.195782).
## Hessian positive definite, eigenvalue range [2.237014,892.6731].
## Model rank = 56 / 56
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.68 0.92 <0.0000000000000002 ***
## s(Year_fac) 28.00 20.67 NA NA
## s(Station_fac) 8.00 6.76 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.4269 0.2605 24.67 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.678 19 177.887 <0.0000000000000002 ***
## s(Year_fac) 20.670 27 3.598 <0.0000000000000002 ***
## s(Station_fac) 6.762 7 55.419 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.458 Deviance explained = 47.1%
## -REML = 2765.4 Scale est. = 1.1958 n = 1786
## <<<<<<<<<<<<<<<<<<<<<<< modeling othclad SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.0002475734,0.0001421556]
## (score 1333.246 & scale 1.094582).
## Hessian positive definite, eigenvalue range [0.0002475605,439.8176].
## Model rank = 53 / 53
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000000 11.746823 1.01 0.56
## s(Year_fac) 28.000000 21.837978 NA NA
## s(Station_fac) 5.000000 0.000933 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.08664 0.08748 12.42 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 11.7468229 19 230.715 <0.0000000000000002 ***
## s(Year_fac) 21.8379776 27 3.944 <0.0000000000000002 ***
## s(Station_fac) 0.0009334 4 0.000 0.628
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.71 Deviance explained = 72.1%
## -REML = 1333.2 Scale est. = 1.0946 n = 880
## <<<<<<<<<<<<<<<<<<<<<<< modeling othclad NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.001109391,0.0009161378]
## (score 1565.784 & scale 0.9757542).
## Hessian positive definite, eigenvalue range [0.8527283,536.7975].
## Model rank = 53 / 53
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 13.93 0.86 <0.0000000000000002 ***
## s(Year_fac) 28.00 22.74 NA NA
## s(Station_fac) 5.00 2.28 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.2128 0.1085 11.18 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 13.94 19 480.262 < 0.0000000000000002 ***
## s(Year_fac) 22.74 27 5.213 < 0.0000000000000002 ***
## s(Station_fac) 2.28 4 3.358 0.00132 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.773 Deviance explained = 78.1%
## -REML = 1565.8 Scale est. = 0.97575 n = 1074
## <<<<<<<<<<<<<<<<<<<<<<< modeling othclad East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.00003101132,0.00002714871]
## (score 3308.63 & scale 1.140333).
## Hessian positive definite, eigenvalue range [0.9003783,1085.69].
## Model rank = 58 / 58
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.31 0.84 <0.0000000000000002 ***
## s(Year_fac) 28.00 25.56 NA NA
## s(Station_fac) 10.00 4.69 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7657 0.1194 14.79 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.310 19 2347.378 < 0.0000000000000002 ***
## s(Year_fac) 25.555 27 16.898 < 0.0000000000000002 ***
## s(Station_fac) 4.694 9 2.048 0.00167 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.776 Deviance explained = 78.1%
## -REML = 3308.6 Scale est. = 1.1403 n = 2172
## <<<<<<<<<<<<<<<<<<<<<<< modeling othclad Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.001310917,0.001036225]
## (score 3541.261 & scale 1.485982).
## Hessian positive definite, eigenvalue range [1.684147,1071.681].
## Model rank = 58 / 58
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 13.08 0.9 <0.0000000000000002 ***
## s(Year_fac) 28.00 25.01 NA NA
## s(Station_fac) 10.00 6.82 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.8440 0.1409 20.18 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 13.077 19 1759.84 <0.0000000000000002 ***
## s(Year_fac) 25.006 27 14.33 <0.0000000000000002 ***
## s(Station_fac) 6.818 9 11.79 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.695 Deviance explained = 70.1%
## -REML = 3541.3 Scale est. = 1.486 n = 2144
## <<<<<<<<<<<<<<<<<<<<<<< modeling othclad Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.001111214,0.0007122973]
## (score 2874.657 & scale 1.355514).
## Hessian positive definite, eigenvalue range [1.748993,892.7126].
## Model rank = 56 / 56
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 13.63 0.96 0.045 *
## s(Year_fac) 28.00 24.83 NA NA
## s(Station_fac) 8.00 5.82 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.3241 0.1519 8.717 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 13.631 19 501.82 <0.0000000000000002 ***
## s(Year_fac) 24.830 27 11.76 <0.0000000000000002 ***
## s(Station_fac) 5.824 7 15.51 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.565 Deviance explained = 57.5%
## -REML = 2874.7 Scale est. = 1.3555 n = 1786
## <<<<<<<<<<<<<<<<<<<<<<< modeling pdiapfor SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.0007664217,0.0006716275]
## (score 1695.621 & scale 2.443762).
## Hessian positive definite, eigenvalue range [0.6693566,439.8466].
## Model rank = 53 / 53
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 13.62 0.96 0.045 *
## s(Year_fac) 28.00 22.23 NA NA
## s(Station_fac) 5.00 3.06 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.079 0.395 7.794 0.0000000000000192 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 13.620 19 642.702 < 0.0000000000000002 ***
## s(Year_fac) 22.229 27 4.258 < 0.0000000000000002 ***
## s(Station_fac) 3.059 4 5.272 0.000812 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.585 Deviance explained = 60.3%
## -REML = 1695.6 Scale est. = 2.4438 n = 880
## <<<<<<<<<<<<<<<<<<<<<<< modeling pdiapfor NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 10 iterations.
## Gradient range [-0.00002930937,0.00003251366]
## (score 1988.072 & scale 2.17128).
## Hessian positive definite, eigenvalue range [0.00002931045,536.7931].
## Model rank = 53 / 53
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000000 10.268134 0.95 0.01 **
## s(Year_fac) 28.000000 23.320477 NA NA
## s(Station_fac) 5.000000 0.000199 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.3028 0.1325 24.93 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 10.268134 19 196.490 <0.0000000000000002 ***
## s(Year_fac) 23.320477 27 5.405 <0.0000000000000002 ***
## s(Station_fac) 0.000199 4 0.000 0.508
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.616 Deviance explained = 62.8%
## -REML = 1988.1 Scale est. = 2.1713 n = 1074
## <<<<<<<<<<<<<<<<<<<<<<< modeling pdiapfor East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 10 iterations.
## Gradient range [-0.001154106,0.001110304]
## (score 4065.752 & scale 2.317953).
## Hessian positive definite, eigenvalue range [1.629925,1085.675].
## Model rank = 58 / 58
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 13.90 0.81 <0.0000000000000002 ***
## s(Year_fac) 28.00 24.48 NA NA
## s(Station_fac) 10.00 6.42 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.2228 0.1703 24.8 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 13.901 19 905.582 <0.0000000000000002 ***
## s(Year_fac) 24.476 27 9.701 <0.0000000000000002 ***
## s(Station_fac) 6.423 9 7.460 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.667 Deviance explained = 67.3%
## -REML = 4065.8 Scale est. = 2.318 n = 2172
## <<<<<<<<<<<<<<<<<<<<<<< modeling pdiapfor Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 12 iterations.
## Gradient range [-0.00001822404,0.00001686391]
## (score 3989.853 & scale 2.259605).
## Hessian positive definite, eigenvalue range [1.479661,1071.676].
## Model rank = 58 / 58
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 14.20 0.69 <0.0000000000000002 ***
## s(Year_fac) 28.00 24.44 NA NA
## s(Station_fac) 10.00 6.52 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.9444 0.1525 38.99 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 14.203 19 527.558 <0.0000000000000002 ***
## s(Year_fac) 24.439 27 9.954 <0.0000000000000002 ***
## s(Station_fac) 6.523 9 7.142 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.699 Deviance explained = 70.6%
## -REML = 3989.9 Scale est. = 2.2596 n = 2144
## <<<<<<<<<<<<<<<<<<<<<<< modeling pdiapfor Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 9 iterations.
## Gradient range [-0.0002335782,0.0001741052]
## (score 3253.216 & scale 2.022225).
## Hessian positive definite, eigenvalue range [1.188887,892.7374].
## Model rank = 56 / 56
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.47 0.9 <0.0000000000000002 ***
## s(Year_fac) 28.00 25.62 NA NA
## s(Station_fac) 8.00 6.44 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.1801 0.2655 19.51 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.47 19 2202.52 <0.0000000000000002 ***
## s(Year_fac) 25.62 27 17.25 <0.0000000000000002 ***
## s(Station_fac) 6.44 7 30.42 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.698 Deviance explained = 70.6%
## -REML = 3253.2 Scale est. = 2.0222 n = 1786
## <<<<<<<<<<<<<<<<<<<<<<< modeling allcopnaup SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.0001218611,0.0001338777]
## (score 626.4337 & scale 2.627281).
## Hessian positive definite, eigenvalue range [0.0001218792,158.517].
## Model rank = 50 / 50
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000000 7.458863 1.14 1
## s(Year_fac) 27.000000 16.996577 NA NA
## s(Station_fac) 3.000000 0.000514 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.3653 0.1588 8.599 0.000000000000000502 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 7.458863 19 12.277 < 0.0000000000000002 ***
## s(Year_fac) 16.996577 26 1.952 0.00000371 ***
## s(Station_fac) 0.000514 2 0.000 0.55
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.39 Deviance explained = 43.7%
## -REML = 626.43 Scale est. = 2.6273 n = 317
## <<<<<<<<<<<<<<<<<<<<<<< modeling allcopnaup NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.000009243147,0.000001259638]
## (score 646.3905 & scale 3.1024).
## Hessian positive definite, eigenvalue range [0.8984986,155.2844].
## Model rank = 47 / 47
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.0 7.8 0.95 0.12
## s(Year_fac) 27.0 20.8 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7455 0.2292 7.615 0.000000000000406 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 7.80 19 13.147 <0.0000000000000002 ***
## s(Year_fac) 20.79 26 4.061 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.44 Deviance explained = 49.2%
## -REML = 646.39 Scale est. = 3.1024 n = 310
## <<<<<<<<<<<<<<<<<<<<<<< modeling allcopnaup East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.000007499786,-0.0000001106973]
## (score 1323.751 & scale 2.697066).
## Hessian positive definite, eigenvalue range [0.3087737,334.4007].
## Model rank = 51 / 51
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 11.94 0.95 0.025 *
## s(Year_fac) 27.00 21.21 NA NA
## s(Station_fac) 4.00 1.52 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.9571 0.1994 9.817 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 11.939 19 41.805 <0.0000000000000002 ***
## s(Year_fac) 21.212 26 4.360 <0.0000000000000002 ***
## s(Station_fac) 1.521 3 1.538 0.0487 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.502 Deviance explained = 52.8%
## -REML = 1323.8 Scale est. = 2.6971 n = 669
## <<<<<<<<<<<<<<<<<<<<<<< modeling allcopnaup Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 9 iterations.
## Gradient range [-0.0003215838,0.001616097]
## (score 1404.98 & scale 2.964505).
## Hessian positive definite, eigenvalue range [0.0003265852,347.3799].
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.0000 10.9212 0.93 0.01 **
## s(Year_fac) 27.0000 21.4066 NA NA
## s(Station_fac) 5.0000 0.0044 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.4085 0.1607 14.99 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 10.921152 19 34.558 <0.0000000000000002 ***
## s(Year_fac) 21.406627 26 4.658 <0.0000000000000002 ***
## s(Station_fac) 0.004396 4 0.001 0.391
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.477 Deviance explained = 50.1%
## -REML = 1405 Scale est. = 2.9645 n = 695
## <<<<<<<<<<<<<<<<<<<<<<< modeling allcopnaup Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 9 iterations.
## Gradient range [-0.000297875,0.00006039964]
## (score 1366.52 & scale 4.182628).
## Hessian positive definite, eigenvalue range [0.0002977048,310.4781].
## Model rank = 49 / 49
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000000 7.676102 0.94 0.04 *
## s(Year_fac) 27.000000 23.310881 NA NA
## s(Station_fac) 2.000000 0.000717 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.1816 0.2619 8.331 0.000000000000000563 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 7.6761019 19 14.44 <0.0000000000000002 ***
## s(Year_fac) 23.3108813 26 8.87 <0.0000000000000002 ***
## s(Station_fac) 0.0007166 1 0.00 0.681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.392 Deviance explained = 42.2%
## -REML = 1366.5 Scale est. = 4.1826 n = 621
## <<<<<<<<<<<<<<<<<<<<<<< modeling limno SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 5 iterations.
## Gradient range [-0.00005651404,0.00005859625]
## (score 681.6302 & scale 3.422221).
## Hessian positive definite, eigenvalue range [0.00004508318,161.4539].
## Model rank = 50 / 50
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000000 9.333769 0.88 0.005 **
## s(Year_fac) 27.000000 15.551463 NA NA
## s(Station_fac) 3.000000 0.000172 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.6536 0.1665 33.96 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 9.3337692 19 7.302 < 0.0000000000000002 ***
## s(Year_fac) 15.5514628 26 1.494 0.000107 ***
## s(Station_fac) 0.0001718 2 0.000 0.583339
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.343 Deviance explained = 39.4%
## -REML = 681.63 Scale est. = 3.4222 n = 323
## <<<<<<<<<<<<<<<<<<<<<<< modeling limno NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.0003405178,0.0002167835]
## (score 600.3333 & scale 2.201127).
## Hessian positive definite, eigenvalue range [1.592647,156.5486].
## Model rank = 47 / 47
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.0 11.7 0.98 0.3
## s(Year_fac) 27.0 16.2 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.4074 0.1397 45.85 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 11.75 19 46.76 < 0.0000000000000002 ***
## s(Year_fac) 16.17 26 1.67 0.0000324 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.703 Deviance explained = 73%
## -REML = 600.33 Scale est. = 2.2011 n = 313
## <<<<<<<<<<<<<<<<<<<<<<< modeling limno East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 10 iterations.
## Gradient range [-0.000000239289,0.0000001464564]
## (score 1236.472 & scale 1.965841).
## Hessian positive definite, eigenvalue range [0.8025186,335.9462].
## Model rank = 51 / 51
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 15.16 0.85 <0.0000000000000002 ***
## s(Year_fac) 27.00 21.43 NA NA
## s(Station_fac) 4.00 2.32 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.765 0.245 27.62 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 15.158 19 123.851 < 0.0000000000000002 ***
## s(Year_fac) 21.432 26 4.426 < 0.0000000000000002 ***
## s(Station_fac) 2.319 3 5.541 0.000576 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.703 Deviance explained = 72%
## -REML = 1236.5 Scale est. = 1.9658 n = 672
## <<<<<<<<<<<<<<<<<<<<<<< modeling limno Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.00004884064,0.000008055068]
## (score 1346.053 & scale 2.325187).
## Hessian positive definite, eigenvalue range [1.009779,350.4133].
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 14.78 0.95 0.09 .
## s(Year_fac) 27.00 21.16 NA NA
## s(Station_fac) 5.00 2.34 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.4573 0.2244 24.32 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 14.784 19 187.882 < 0.0000000000000002 ***
## s(Year_fac) 21.155 26 4.447 < 0.0000000000000002 ***
## s(Station_fac) 2.337 4 5.369 0.0000309 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.748 Deviance explained = 76.2%
## -REML = 1346.1 Scale est. = 2.3252 n = 701
## <<<<<<<<<<<<<<<<<<<<<<< modeling limno Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.000001770403,0.000001619472]
## (score 1260.551 & scale 2.853194).
## Hessian positive definite, eigenvalue range [0.472618,313.8114].
## Model rank = 49 / 49
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000 13.889 0.96 0.13
## s(Year_fac) 27.000 16.907 NA NA
## s(Station_fac) 2.000 0.973 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.8838 0.4269 13.78 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 13.8895 19 92.212 < 0.0000000000000002 ***
## s(Year_fac) 16.9071 26 1.801 0.00000818 ***
## s(Station_fac) 0.9731 1 36.476 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.693 Deviance explained = 70.8%
## -REML = 1260.6 Scale est. = 2.8532 n = 628
## <<<<<<<<<<<<<<<<<<<<<<< modeling mysid SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 5 iterations.
## Gradient range [-0.0003850967,0.0002974669]
## (score 800.589 & scale 4.432278).
## Hessian positive definite, eigenvalue range [0.1906857,177.1153].
## Model rank = 50 / 50
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000 10.192 1.03 0.71
## s(Year_fac) 26.000 19.153 NA NA
## s(Station_fac) 4.000 0.968 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.3767 0.3914 11.18 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 10.1924 19 25.218 <0.0000000000000002 ***
## s(Year_fac) 19.1528 25 3.255 <0.0000000000000002 ***
## s(Station_fac) 0.9676 3 0.933 0.12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.544 Deviance explained = 58.3%
## -REML = 800.59 Scale est. = 4.4323 n = 354
## <<<<<<<<<<<<<<<<<<<<<<< modeling mysid NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.0000008772582,0.0000008332563]
## (score 905.1353 & scale 3.788971).
## Hessian positive definite, eigenvalue range [0.927108,208.44].
## Model rank = 50 / 50
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 9.72 0.93 0.045 *
## s(Year_fac) 26.00 17.38 NA NA
## s(Station_fac) 4.00 2.94 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.450 1.089 4.084 0.0000538 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 9.717 19 12.094 0.00571 **
## s(Year_fac) 17.383 25 2.461 < 0.0000000000000002 ***
## s(Station_fac) 2.942 3 93.494 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.498 Deviance explained = 53.4%
## -REML = 905.14 Scale est. = 3.789 n = 417
## <<<<<<<<<<<<<<<<<<<<<<< modeling mysid East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 5 iterations.
## Gradient range [-0.0009278945,0.0005095288]
## (score 1797.217 & scale 3.738696).
## Hessian positive definite, eigenvalue range [2.018086,417.8264].
## Model rank = 56 / 56
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 14.78 0.92 0.01 **
## s(Year_fac) 26.00 19.00 NA NA
## s(Station_fac) 10.00 8.11 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.2058 0.4966 10.48 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 14.785 19 66.931 <0.0000000000000002 ***
## s(Year_fac) 18.997 25 3.894 <0.0000000000000002 ***
## s(Station_fac) 8.105 9 15.436 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.52 Deviance explained = 54.4%
## -REML = 1797.2 Scale est. = 3.7387 n = 836
## <<<<<<<<<<<<<<<<<<<<<<< modeling mysid Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 5 iterations.
## Gradient range [-0.0005055385,0.0003473337]
## (score 1647.357 & scale 3.17049).
## Hessian positive definite, eigenvalue range [2.158402,397.3703].
## Model rank = 55 / 55
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 15.08 0.92 <0.0000000000000002 ***
## s(Year_fac) 26.00 20.56 NA NA
## s(Station_fac) 9.00 5.98 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.2493 0.3046 13.95 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 15.081 19 94.522 <0.0000000000000002 ***
## s(Year_fac) 20.563 25 4.866 <0.0000000000000002 ***
## s(Station_fac) 5.975 8 8.510 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.564 Deviance explained = 58.7%
## -REML = 1647.4 Scale est. = 3.1705 n = 795
## <<<<<<<<<<<<<<<<<<<<<<< modeling mysid Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.000000295379,0.0000002097751]
## (score 1484.766 & scale 3.363607).
## Hessian positive definite, eigenvalue range [1.725115,350.4737].
## Model rank = 53 / 53
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 14.96 0.92 0.005 **
## s(Year_fac) 26.00 22.50 NA NA
## s(Station_fac) 7.00 5.46 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.2432 0.6501 8.065 0.00000000000000347 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 14.965 19 73.968 <0.0000000000000002 ***
## s(Year_fac) 22.501 25 8.827 <0.0000000000000002 ***
## s(Station_fac) 5.455 6 18.013 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.482 Deviance explained = 51.4%
## -REML = 1484.8 Scale est. = 3.3636 n = 701
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcyc SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 13 iterations.
## Gradient range [-0.0002764003,0.001354068]
## (score 544.7388 & scale 1.639363).
## Hessian positive definite, eigenvalue range [0.0002765043,161.0236].
## Model rank = 50 / 50
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00000 4.94606 0.98 0.29
## s(Year_fac) 27.00000 0.00327 NA NA
## s(Station_fac) 3.00000 0.34272 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.7176 0.1956 34.35 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 4.946064 19 2.578 <0.0000000000000002 ***
## s(Year_fac) 0.003272 26 0.000 0.694
## s(Station_fac) 0.342718 2 0.215 0.263
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.132 Deviance explained = 14.6%
## -REML = 544.74 Scale est. = 1.6394 n = 323
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcyc NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.000003658726,0.00000007542045]
## (score 521.3249 & scale 1.443972).
## Hessian positive definite, eigenvalue range [0.3782888,156.3477].
## Model rank = 47 / 47
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 7.57 0.96 0.2
## s(Year_fac) 27.00 13.57 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.12207 0.09958 71.52 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 7.566 19 2.91 0.00000323 ***
## s(Year_fac) 13.572 26 1.12 0.0012 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.2 Deviance explained = 25.4%
## -REML = 521.32 Scale est. = 1.444 n = 313
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcyc East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.0000003757979,0.00000004154148]
## (score 950.8421 & scale 0.8857205).
## Hessian positive definite, eigenvalue range [0.1253222,335.8621].
## Model rank = 51 / 51
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 10.64 0.91 0.005 **
## s(Year_fac) 27.00 20.44 NA NA
## s(Station_fac) 4.00 1.13 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.69141 0.09706 68.94 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 10.64 19 10.156 <0.0000000000000002 ***
## s(Year_fac) 20.44 26 3.932 <0.0000000000000002 ***
## s(Station_fac) 1.13 3 0.795 0.122
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.29 Deviance explained = 32.4%
## -REML = 950.84 Scale est. = 0.88572 n = 672
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcyc Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.000001215975,0.0000006698351]
## (score 1185.185 & scale 1.558843).
## Hessian positive definite, eigenvalue range [0.84159,350.3071].
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 9.80 1 0.56
## s(Year_fac) 27.00 19.25 NA NA
## s(Station_fac) 5.00 1.92 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.1623 0.1413 43.62 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 9.798 19 13.331 < 0.0000000000000002 ***
## s(Year_fac) 19.254 26 2.877 < 0.0000000000000002 ***
## s(Station_fac) 1.918 4 3.211 0.00061 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.278 Deviance explained = 31%
## -REML = 1185.2 Scale est. = 1.5588 n = 701
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcyc Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 9 iterations.
## Gradient range [-0.000007110302,0.000002166729]
## (score 949.5123 & scale 1.100501).
## Hessian positive definite, eigenvalue range [0.440909,313.748].
## Model rank = 49 / 49
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000 9.614 0.96 0.21
## s(Year_fac) 27.000 16.120 NA NA
## s(Station_fac) 2.000 0.946 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.3649 0.1898 38.8 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 9.6144 19 5.962 < 0.0000000000000002 ***
## s(Year_fac) 16.1204 26 1.615 0.0000270 ***
## s(Station_fac) 0.9463 1 17.689 0.0000184 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.191 Deviance explained = 22.6%
## -REML = 949.51 Scale est. = 1.1005 n = 628
## <<<<<<<<<<<<<<<<<<<<<<< modeling other SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 10 iterations.
## Gradient range [-0.00004598597,0.00001909747]
## (score 485.8023 & scale 1.015095).
## Hessian positive definite, eigenvalue range [0.00004598205,161.4582].
## Model rank = 50 / 50
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000000 7.595815 0.98 0.32
## s(Year_fac) 27.000000 15.630590 NA NA
## s(Station_fac) 3.000000 0.000146 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.70223 0.09046 85.15 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 7.5958152 19 4.167 0.000000516 ***
## s(Year_fac) 15.6305898 26 1.607 0.000032234 ***
## s(Station_fac) 0.0001457 2 0.000 0.654
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.262 Deviance explained = 31.6%
## -REML = 485.8 Scale est. = 1.0151 n = 323
## <<<<<<<<<<<<<<<<<<<<<<< modeling other NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 19 iterations.
## Gradient range [-0.00001402973,0.0009424418]
## (score 546.4557 & scale 1.847335).
## Hessian positive definite, eigenvalue range [0.00001414472,156.0349].
## Model rank = 47 / 47
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00000 4.70287 0.91 0.075 .
## s(Year_fac) 27.00000 0.00265 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.98191 0.07683 90.88 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 4.702875 19 2.1 <0.0000000000000002 ***
## s(Year_fac) 0.002646 26 0.0 0.473
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.113 Deviance explained = 12.7%
## -REML = 546.46 Scale est. = 1.8473 n = 313
## <<<<<<<<<<<<<<<<<<<<<<< modeling other East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.00008235776,0.00005099789]
## (score 1209.185 & scale 1.895939).
## Hessian positive definite, eigenvalue range [0.6033779,335.8206].
## Model rank = 51 / 51
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.0 13.2 0.87 <0.0000000000000002 ***
## s(Year_fac) 27.0 18.3 NA NA
## s(Station_fac) 4.0 2.0 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.5466 0.1817 36.04 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 13.157 19 14.052 < 0.0000000000000002 ***
## s(Year_fac) 18.259 26 2.230 0.000000972 ***
## s(Station_fac) 2.001 3 4.279 0.000956 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.295 Deviance explained = 33%
## -REML = 1209.2 Scale est. = 1.8959 n = 672
## <<<<<<<<<<<<<<<<<<<<<<< modeling other Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 9 iterations.
## Gradient range [-0.0003385313,0.00009444794]
## (score 1358.238 & scale 2.444334).
## Hessian positive definite, eigenvalue range [0.000338371,350.3844].
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000000 15.040901 0.89 <0.0000000000000002 ***
## s(Year_fac) 27.000000 20.384204 NA NA
## s(Station_fac) 5.000000 0.000866 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.8291 0.1319 44.18 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 15.0409012 19 21.001 <0.0000000000000002 ***
## s(Year_fac) 20.3842041 26 3.124 <0.0000000000000002 ***
## s(Station_fac) 0.0008657 4 0.000 0.821
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.379 Deviance explained = 41.1%
## -REML = 1358.2 Scale est. = 2.4443 n = 701
## <<<<<<<<<<<<<<<<<<<<<<< modeling other Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 16 iterations.
## Gradient range [-0.0008651282,0.0005539619]
## (score 1257.6 & scale 2.949765).
## Hessian positive definite, eigenvalue range [0.000009983549,313.8467].
## Model rank = 49 / 49
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.0000000 5.2664342 0.87 <0.0000000000000002 ***
## s(Year_fac) 27.0000000 19.9108455 NA NA
## s(Station_fac) 2.0000000 0.0000254 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.1741 0.1432 43.12 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 5.2664342 19 6.771 <0.0000000000000002 ***
## s(Year_fac) 19.9108455 26 3.289 <0.0000000000000002 ***
## s(Station_fac) 0.0000254 1 0.000 0.644
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.245 Deviance explained = 27.5%
## -REML = 1257.6 Scale est. = 2.9498 n = 628sal_conversions## # A tibble: 88,836 × 1,004
## Region Month IBMR SalSurf draw_1 draw_2 draw_3 draw_4 draw_5 draw_6 draw_7
## <fct> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 SW Suis… 1 acar… 0.1 4.37 3.52 6.17 4.09 3.46 3.69 3.43
## 2 SW Suis… 1 acar… 0.2 4.38 3.54 6.18 4.13 3.47 3.72 3.43
## 3 SW Suis… 1 acar… 0.3 4.38 3.57 6.20 4.18 3.48 3.74 3.44
## 4 SW Suis… 1 acar… 0.4 4.39 3.59 6.21 4.22 3.50 3.77 3.44
## 5 SW Suis… 1 acar… 0.5 4.39 3.61 6.23 4.26 3.51 3.80 3.44
## 6 SW Suis… 1 acar… 0.6 4.40 3.63 6.24 4.30 3.52 3.83 3.44
## 7 SW Suis… 1 acar… 0.7 4.41 3.65 6.26 4.35 3.53 3.86 3.45
## 8 SW Suis… 1 acar… 0.8 4.41 3.67 6.27 4.39 3.54 3.89 3.45
## 9 SW Suis… 1 acar… 0.9 4.42 3.69 6.29 4.43 3.55 3.92 3.45
## 10 SW Suis… 1 acar… 1 4.42 3.71 6.30 4.47 3.56 3.95 3.45
## # ℹ 88,826 more rows
## # ℹ 993 more variables: draw_8 <dbl>, draw_9 <dbl>, draw_10 <dbl>,
## # draw_11 <dbl>, draw_12 <dbl>, draw_13 <dbl>, draw_14 <dbl>, draw_15 <dbl>,
## # draw_16 <dbl>, draw_17 <dbl>, draw_18 <dbl>, draw_19 <dbl>, draw_20 <dbl>,
## # draw_21 <dbl>, draw_22 <dbl>, draw_23 <dbl>, draw_24 <dbl>, draw_25 <dbl>,
## # draw_26 <dbl>, draw_27 <dbl>, draw_28 <dbl>, draw_29 <dbl>, draw_30 <dbl>,
## # draw_31 <dbl>, draw_32 <dbl>, draw_33 <dbl>, draw_34 <dbl>, …Plot salinity-biomass relationships
sal_conversions_sum<-apply(select(sal_conversions, starts_with("draw_")), 1,
function(x) quantile(x, c(0.025, 0.5, 0.975)))
sal_conversions_plot<-sal_conversions%>%
select(-starts_with("draw_"))%>%
bind_cols(tibble(l95=sal_conversions_sum["2.5%",],
median=sal_conversions_sum["50%",],
u95=sal_conversions_sum["97.5%",]))
plot_sal_conversions<-function(group, data=sal_conversions_plot){
if(group!="All"){
data<-filter(data, IBMR%in%group)
ggplot(data, aes(x=SalSurf, y=median, ymin=l95, ymax=u95))+
geom_ribbon(alpha=0.4, fill="chartreuse4")+
ylab("Zooplankton biomass (log scale)")+
facet_grid(Region~month(Month, label=T))+
theme_bw()+
theme(axis.text.x=element_text(angle=45, hjust=1))
}else{
ggplot(data, aes(x=SalSurf, y=median, ymin=l95, ymax=u95, fill=IBMR))+
geom_ribbon(alpha=0.4)+
ylab("Zooplankton biomass (log scale)")+
facet_grid(Region~month(Month, label=T))+
scale_fill_viridis_d()+
theme_bw()+
theme(axis.text.x=element_text(angle=45, hjust=1))
}
}# Create plots for each IBMR group
sal_conversion_plots <- tibble(group=c("All", unique(model_factors$IBMR)))%>%
mutate(plot=map(group, plot_sal_conversions))Salinity-biomass plots
All
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
Apply model
Load in SMSCG modeled salinity
scenario_file<-here("Data/CSAMP_DS_SDM_salinity_scenarios.csv")
scenario_names<-tibble(name=colnames(read.csv(scenario_file)))%>%
filter(str_detect(name, "sal_"))%>%
rev()
scenario_sal<-read_csv(scenario_file, guess_max=2800)%>%
select(region, year, month, starts_with("sal_"))%>%
mutate(across(c(year, month), as.integer),
across(starts_with("sal_"), ~if_else(is.na(.x), sal_base, .x)))%>%
filter(region%in%unique(zoop_data_mass$SUBREGION))%>%
mutate(region=factor(region,
levels=c("Confluence", "Suisun Marsh", "NE Suisun",
"SE Suisun", "NW Suisun", "SW Suisun")))%>%
pivot_longer(cols=starts_with("sal_"), names_to="Scenario", values_to="Salinity")%>% # Prepare data for easier plotting
mutate(Scenario=factor(Scenario,
levels=scenario_names$name),
Salinity=round(Salinity, 1))Plot SMSCG modeled salinity
ggplot(scenario_sal,
aes(x=year, y=Salinity, color=Scenario))+
geom_line()+
scale_color_viridis_d(direction=-1)+
facet_grid(region ~ month(month, label=T))+
theme_bw()+
theme(legend.position = "bottom", axis.text.x=element_text(angle=45, hjust=1))
Calculate zoop abundance difference between each scenario and the baseline
zoop_saladjusted<-scenario_sal%>%
mutate(Salinity=as.character(Salinity),
IBMR=unique(model_factors$IBMR)[1])%>%
complete(region, year, month, Scenario, IBMR=unique(model_factors$IBMR))%>%
group_by(region, year, month, Scenario)%>%
mutate(Salinity=na.exclude(Salinity),
region2=if_else(region%in%c("NE Suisun", "SE Suisun"), "East Suisun", as.character(region)))%>%
ungroup()%>%
left_join(sal_conversions%>%
mutate(SalSurf=as.character(SalSurf)),
by=c("region2"="Region",
"month"="Month",
"Salinity"="SalSurf",
"IBMR"="IBMR"))%>%
select(-Salinity, -region2)%>%
mutate(across(starts_with("draw_"), ~exp(.x)-1))%>%
pivot_longer(starts_with("draw_"), names_prefix="draw_", names_to="draw", values_to="fit")%>%
mutate(fit=if_else(fit<0, 0, fit))%>%
pivot_wider(names_from="Scenario", values_from="fit")%>%
mutate(across(starts_with("sal_"), ~.x/sal_base))%>%
group_by(region, year, month, IBMR)%>%
summarise(across(starts_with("sal_"),
list(median=~median(.x, na.rm=T),
l95=~quantile(.x, 0.025, na.rm=T),
u95=~quantile(.x, 0.975, na.rm=T))),
.groups="drop")
write_csv(zoop_saladjusted, here("Outputs", "CSAMP zoop sal adjustments.csv"))You can find the final zoop salinity adjustments here
Prepare plots
Plot the missing model results resulting from out-of-range salinity values in the inputs
missing_adjusted_data<-zoop_saladjusted%>%
select(-ends_with("l95"), -ends_with("u95"))%>%
filter(IBMR=="acartela")%>%
pivot_longer(cols=starts_with("sal_"), names_to="Scenario", values_to="zoop_change")%>%
mutate(Scenario=str_remove(Scenario, fixed("_median")))
ggplot(missing_adjusted_data,
aes(x=year, y=Scenario, fill=is.na(zoop_change)))+
geom_tile()+
scale_fill_viridis_d(name="Are the model results missing due to out-of-range salinity values?")+
facet_grid(region ~ month(month, label=T))+
theme_bw()+
theme(legend.position = "bottom", axis.text.x=element_text(angle=45, hjust=1))
Plot the result
Create some plotting functions
neglop1p<-trans_new("neglop1p", transform=function(x) sign(x)*log(abs(x)+1), inverse=function(x) sign(x)*(exp(abs(x))-1))
plot_scenario_result <- function(scenario, group) {
plot_data<-zoop_saladjusted%>%
filter(IBMR%in%group)
ggplot(plot_data,
aes(x=year, y=.data[[paste0(scenario, "_median")]], ymin=.data[[paste0(scenario, "_l95")]], ymax=.data[[paste0(scenario, "_u95")]]))+
geom_ribbon(alpha=0.4, fill="darkorchid4")+
geom_line(alpha=0.4, color="darkorchid4")+
scale_y_continuous(trans=neglop1p, breaks=c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))+
ylab("Scenario/baseline (log scale)")+
facet_grid(region ~ month(month, label=T))+
theme_bw()+
theme(legend.position = "bottom", axis.text.x=element_text(angle=45, hjust=1))
}# Create plots for each Parameter
scenario_result_plots <- expand_grid(Scenario=unique(scenario_sal$Scenario)[-1],
IBMR=unique(model_factors$IBMR))%>%
mutate(plot=map2(Scenario, IBMR, ~plot_scenario_result(.x, .y)))Result plots
sal_fallX2_hi
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_fallX2_low
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_sumX2_hi
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_sumX2_low
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_SMSCG
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_sumX2_WAN_low
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_sumX2_WAN_1
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_sumX2_WAN_2
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_sumX2_WAN_3
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_sumX2_WAN_high
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_fallX2_WAN_low
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_fallX2_WAN_1
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_fallX2_WAN_2
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_fallX2_WAN_3
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_fallX2_WAN_high
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_Port2a1
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_Port2a2
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
