Occupacy model with space-varying time trends

This code reproduces the case study “Occupancy model with spatially-varying coefficients for the Gray Catbird” in “Spatio-temporal Occupancy Models with INLA”. The dataset and part of the code for preparing the data, is taken from the supplementary material of (Doser et al. 2022)

library(spOccupancy)
library(tidyverse)
library(INLA)
library(inlabru)
library(scico)
library(sf)
library(stars)
library(patchwork)
library(kableExtra)
library(scales)
library(tidyterra)

rm(list = ls())


theme_maps = theme(axis.title.x=element_blank(),
                  axis.text.x=element_blank(),
                  axis.ticks.x=element_blank(),
                  axis.title.y=element_blank(),
                  axis.text.y=element_blank(),
                  axis.ticks.y=element_blank(),)

# load data ---------------------------------------------
load(here::here("data_SCV_model/spOcc-bbs-data.rda"))
data.list_orig  = data.list
# Loads the data.list object, which is formatted for spOccupancy.
# Species info
sp.names <- dimnames(data.list$y)[[1]]

# Create an index for the observers
# this is created as
N_species_obs = apply(data.list$y,c(1,2,3), sum, na.rm = T) 
N_species_counts = apply(data.list$y, c(1,2,3), function(x)
  all(!is.na(x))) 


N_species_obs[!N_species_counts] = NA
N_species_obs2 = apply(N_species_obs,c(2,3), function(x) 
{if((sum(x)==0 | all(is.na(x)))) return(NA)
  else return(sum(x[x>0]))}) 


species_observed = apply(data.list$y, c(1,2,3),function(x) ifelse(!all(x==0),1,0))
species_observed = apply(species_observed,c(2,3), sum)
obs_index = data.frame(n_species = as.vector(N_species_obs2)/5,
                       tot_species = as.vector(species_observed),
           obs = as.vector(data.list$det.covs$obs),
           year = as.vector(data.list$det.covs$year.det),
           site = rep(1:1846,20))%>% 
  dplyr::filter(!is.na(n_species)) %>%
  mutate(score = n_species/tot_species) %>%
  group_by(obs) %>% summarise(score = mean(score)) 

# choose the species

# choose the species
curr.sp <- 'GRCA'
print(curr.sp)

[1] "GRCA"

sp.indx <- which(sp.names == curr.sp)
data.list$y <- data.list$y[sp.indx, , , ]
# Only use locations within the breeding range of the species.
# Load in species range information based on bird life data. 
load(here::here("data_SCV_model/bird-life-processed.rda"))
#load("C:/Users/jb538u/NTNU/Occupancy_model_Git/Occupancy-Models-in-INLA-/docs/paper/data_SCV_model/bird-life-processed.rda")

data.list$y <- data.list$y[range.ind[sp.indx, ] == 1, , ]
data.list$occ.covs$years <- data.list$occ.covs$years[range.ind[sp.indx, ] == 1, ]
data.list$occ.covs$BCR <- data.list$occ.covs$BCR[range.ind[sp.indx, ] == 1]
data.list$det.covs$day <- data.list$det.covs$day[range.ind[sp.indx, ] == 1, ]
data.list$det.covs$obs <- data.list$det.covs$obs[range.ind[sp.indx, ] == 1, ]
data.list$det.covs$obs.first.year <- data.list$det.covs$obs.first.year[range.ind[sp.indx, ] == 1, ]
data.list$det.covs$year.det <- data.list$det.covs$year.det[range.ind[sp.indx, ] == 1, ]
data.list$coords <- data.list$coords[range.ind[sp.indx, ] == 1, ]

coords.sf <- st_as_sf(as.data.frame(data.list$coords),
                      coords = c("X", "Y"),
                      crs = "+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=37.5 +lon_0=-96 +x_0=0 +y_0=0 +datum=NAD83 +units=km +no_defs")
usa <- st_as_sf(maps::map("state", fill = TRUE, plot = FALSE))
usa.no.states <- st_as_sf(maps::map("usa", fill = TRUE, plot = FALSE))
# Restrict to east of the 100th meridian
usa.bbox <- st_bbox(usa)
usa.bbox[1] <- -100
usa.bbox <- as.vector(usa.bbox)
sf_use_s2(FALSE)
# Full data
east.us <- st_crop(st_make_valid(usa), xmin = usa.bbox[1], ymin = usa.bbox[2], 
                   xmax = usa.bbox[3], ymax = usa.bbox[4])
east.us <- east.us %>%
  st_transform(st_crs(coords.sf))
east.us.no.states <- st_crop(st_make_valid(usa.no.states), xmin = usa.bbox[1], ymin = usa.bbox[2], 
                             xmax = usa.bbox[3], ymax = usa.bbox[4])
east.us.no.states <- east.us.no.states %>%
  st_transform(st_crs(coords.sf))

# BCRS, in case you want to visualize the maps with these
bcrs <- st_read(dsn = here::here("data_SCV_model/BCR_Terrestrial/"), layer = "BCR_Terrestrial_master")

Reading layer `BCR_Terrestrial_master' from data source 
  `C:\Users\jaf_i\OneDrive\Documentos\GitHub\Occupancy-Models-in-INLA-\docs\website\data_SCV_model\BCR_Terrestrial' 
  using driver `ESRI Shapefile'
Simple feature collection with 373 features and 11 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -179.1413 ymin: 14.53287 xmax: 179.7785 ymax: 83.11063
Geodetic CRS:  NAD83

#bcrs <- st_read(dsn = here::here("C:/Users/jb538u/NTNU/Occupancy_model_Git/Occupancy-Models-in-INLA-/docs/paper/data_SCV_model/BCR_Terrestrial/"), layer = "BCR_Terrestrial_master")



my.proj <- "+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=37.5 +lon_0=-96 +x_0=0 +y_0=0 +datum=NAD83 +units=km +no_defs"
coords.sf <- st_as_sf(as.data.frame(data.list$coords),
                      coords = c("X", "Y"),
                      crs = my.proj)

bcrs.albers <- bcrs %>%
  st_transform(crs = my.proj)

# Clip bcrs to region of ne.states
bcrs.ne <- st_intersection(st_make_valid(bcrs.albers), st_make_valid(east.us))

bcrs.ne.grouped <- bcrs.ne %>%
  group_by(BCR) %>%
  summarize(geometry = st_union(geometry))


all_bcr = c(data.list$occ.covs$BCR, bcrs.ne.grouped$BCR )
all_bcr_idx = as.numeric(as.factor(all_bcr))

Data for INLA

nsites = dim(data.list$y)[1]
Y = c()
year = c()
for(i in 1:20)
{
  Y = rbind(Y, data.list$y[,i,])
  year = c(year, rep(1999+i, nsites))
  #cat(length(counts), length(year), length(n_samples), "\n")
}

Xocc = data.frame(
           year = year,
           year_idx = year-min(year)+1,
           obs = as.vector(data.list$det.covs$obs),
           bcr = rep(data.list$occ.covs$BCR, 20),
           bcr_idx = rep(all_bcr_idx[1:length(data.list$occ.covs$BCR)], 20),
           obs = as.vector(data.list$det.covs$obs),
           obs_first_year = as.vector(data.list$det.covs$obs.first.year),
           Int_occ = 1,
           scale_year = scale(year),
           x  = rep(data.list$coords[,1],20),
           y  = rep(data.list$coords[,2],20),
           spatialfield = NA)  
Xocc = left_join(Xocc, obs_index, by = "obs")

# we have 3 covariates in the detection part

Xdet = cbind(1, 
             scale(as.vector(data.list$det.covs$day)), 
             scale(as.vector(data.list$det.covs$day))^2,
             1, 
             scale(as.vector(data.list$det.covs$day)), 
             scale(as.vector(data.list$det.covs$day))^2,
             1, 
             scale(as.vector(data.list$det.covs$day)), 
             scale(as.vector(data.list$det.covs$day))^2,
             1, 
             scale(as.vector(data.list$det.covs$day)), 
             scale(as.vector(data.list$det.covs$day))^2,
             1, 
             scale(as.vector(data.list$det.covs$day)), 
             scale(as.vector(data.list$det.covs$day))^2
             )

keep = !apply(Y,1,function(x)all(is.na(x)))
Y = Y[keep,]
Xocc = Xocc[keep,]
Xdet = Xdet[keep,]

data_list = as.list(Xocc)
data_list$Y = Y
data_list$X = Xdet


Xocc %>% 
  ggplot()  +
  geom_point(aes(x,y), size  = 0.5)  +
 scale_color_viridis_c(na.value = "transparent") +
  geom_sf(data = bcrs.ne.grouped, alpha = 0, col = 'grey') +
  facet_wrap(.~year) +
  xlab("") + ylab("") + theme_maps + 
  ggtitle("Yearly detections per site")

Set up mesh and SPDE

# set up mesh -------------------------------------------------------------

mesh = inla.mesh.2d(loc= data.list$coords,
                    max.edge = 300,
                    cutoff = 30)
mesh = inla.mesh.2d(loc= data.list$coords,
              max.edge = c(300,600),
              offset = c(100,500),
              cutoff = 40)

spde <- inla.spde2.pcmatern(
  mesh = mesh, 
  prior.range = c(800, 0.5),
  prior.sigma = c(1, 0.5)) 

ggplot() + inlabru::gg(mesh) + theme_maps + coord_equal()

Model 1: fixed time trend

Model for occurrences: \[ \text{logit}(\pi_{st}) = \beta_0 + \beta_t\ t + \omega(s) \] Model for detection \[ \text{logit}(p_s) = \alpha_0 + \alpha X(s) \]

# model with fixed covariate ----------------------------------------------

A_sp <- inla.spde.make.A(mesh = mesh, 
                         loc = cbind(Xocc$x, Xocc$y))

formula = inla.mdata(Y,X) ~ 
  -1 + Int_occ + scale_year + 
  f(spatialfield, model=spde,A.local = A_sp) 

time1 = system.time(model <- inla(formula, #the formula
                             data=data_list, 
                             family= 'occupancy',  
                             control.fixed =  list(prec = 1, prec.intercept = 1),
                             control.compute = list(dic = TRUE, waic = TRUE, 
                                                    config = TRUE), 
                             control.inla = list(int.strategy = "eb"), 
                             control.family = list(control.link = list(model = "logit"), 
                                                   link.simple = "logit",
                                                   hyper = list(beta1 = list(param = c(0,1),
                                                                             initial = 0),
                                                                beta2 = list(param = c(0,1),
                                                                             initial = 0),
                                                                beta3 = list(param = c(0,1),
                                                                             initial = 0)))))

Model 2: Continuously varying time trend

Model for occurrences: \[ \text{logit}(\pi_{st}) = \beta_0 + \beta_t(s)\ t + \omega(s) \] Model for detection \[ \text{logit}(p_s) = \alpha_0 + \alpha X(s) \]

A_sp1 <- inla.spde.make.A(mesh = mesh, 
                         loc = cbind(Xocc$x, Xocc$y))
A_sp2 <- inla.spde.make.A(mesh = mesh, 
                          loc = cbind(Xocc$x, Xocc$y),
                          weights = Xocc$scale_year)

data_list$i1 <- rep(NA,length(data_list$spatialfield))
data_list$i2 <- rep(NA,length(data_list$spatialfield))


formula2 = inla.mdata(Y,X) ~ 
  -1 + Int_occ + 
  scale_year + 
  f(i1, model=spde,A.local = A_sp1)  +
  f(i2, model=spde, A.local = A_sp2) 
 # f(time, model =  "iid") + 


time2 = system.time(model2 <- inla(formula2, #the formula
              data=data_list,  
              family= 'occupancy',  
              control.fixed =  list(prec = 1, prec.intercept = 1),
              control.compute = list(dic = TRUE, waic = TRUE, 
                                     config = TRUE), 
              # verbose = TRUE,
              control.inla = list(int.strategy = "eb"),
              control.family = list(control.link = list(model = "logit"), 
                                    link.simple = "logit",
                                    hyper = list(beta1 = list(param = c(0,1/3),
                                                              initial = 0),
                                                 beta2 = list(param = c(0,1/3),
                                                              initial = 0),
                                                 beta3 = list(param  = c(0,1/3),
                                                              initial= 0)))))

Model 3: Regional varying time trend

Model for occurrences:

\[ \text{logit}(\pi_{st}) = \beta_0 + \beta_t(\text{bcr}_s)\ t + \omega(s) + \epsilon_t \]

Model for detection

\[ \text{logit}(p_s) = \alpha_0 + \alpha X(s) \]

A_sp <- inla.spde.make.A(mesh = mesh,

                         loc = cbind(Xocc$x, Xocc$y))


formula3 = inla.mdata(Y,X) ~
  -1 + Int_occ +
  #f(time, model =  "iid") +
  + scale_year + 
  f(bcr_idx,scale_year, model="iid", values = sort(unique(all_bcr_idx)))  +
  f(spatialfield,    model = spde, A.local = A_sp)

model3 <- inla(formula3, #the formula

               data=data_list,  #the data stack

               family= 'occupancy',   #which family the data comes from

               control.fixed =  list(prec = 1, prec.intercept = 1),

               control.compute = list(dic = TRUE, waic = TRUE,

                                      config = TRUE), #model diagnostics and config = TRUE gives you the GMRF

               # verbose = TRUE,

               control.inla = list(int.strategy = "eb"),#, cmin =0),

               control.family = list(control.link = list(model = "logit"),

                                     link.simple = "logit",

                                     hyper = list(beta1 = list(param = c(0,1),

                                                               initial = 0.5),

                                                  beta2 = list(param = c(0,1),

                                                               initial = 0),

                                                  beta3 = list(param  = c(0,1),

                                                               initial= 0),

                                                  beta4 = list(param  = c(0,1),

                                                               initial= 0))))

Detection parameters

tab1 = rbind(model$summary.hyperpar[1:3,c(1,3,5)],
      model2$summary.hyperpar[1:3,c(1,3,5)],
      model3$summary.hyperpar[1:3,c(1,3,5)])
rownames(tab1) = c (c("Int detection1", "day1", "day^2_1"),
                     c("Int detection2", "day2", "day^2_2"),
                     c("Int detection3", "day3", "day^2_3"))

kable(tab1, booktabs = TRUE, digits = 2) %>% pack_rows(
  index = c("Model 1" = 3, "Model 2" = 3, "Model 3" = 3)
)

	mean	0.025quant	0.975quant
Model 1
Int detection1	0.57	0.55	0.59
day1	0.14	0.13	0.16
day^2_1	-0.11	-0.12	-0.10
Model 2
Int detection2	0.58	0.47	0.69
day2	0.14	0.12	0.16
day^2_2	-0.11	-0.12	-0.10
Model 3
Int detection3	0.57	0.55	0.59
day3	0.14	0.13	0.16
day^2_3	-0.11	-0.12	-0.10

Model Comparison

# compute LGOCV scores

df_sf <- Xocc %>% mutate(id = 1:nrow(Xocc)) %>% st_as_sf(coords =c("x","y"))
buffer_25 <- st_buffer(df_sf, dist = 150) 

loocv_m1 <- inla.group.cv(result = model,num.level.sets = 3)
ULOOCV = mean(log(loocv_m1$cv),na.rm=T)

loocv_m2 <- inla.group.cv(result = model2,num.level.sets = 3)
ULOOCV2 = mean(log(loocv_m2$cv),na.rm=T)

loocv_m3 <- inla.group.cv(result = model3,num.level.sets = 3)
ULOOCV3 = mean(log(loocv_m3$cv),na.rm=T)

score = c(ULOOCV,
ULOOCV2,
ULOOCV3)

	WAIC	MLIK	LGOCV_score
Model 1	135149.7	-67999.81	-2.57
Model 2	134970.7	-67950.04	-2.56
Model 3	135105.3	-67990.05	-2.57

Predictions

# prediction dataset


samples = inla.posterior.sample(1000, model)
samples2 = inla.posterior.sample(1000, model2)
samples3 = inla.posterior.sample(1000, model3)


load(here::here('data_SCV_model/pred-coordinates.rda'))

coords.0.sf <- st_as_sf(as.data.frame(coords.0),
                        coords = c("X", "Y"),
                        crs = st_crs(coords.sf))


aa = (st_intersects(coords.0.sf, bcrs.ne.grouped, sparse = FALSE))
idx = apply(aa,1,function(x) 
   ifelse((!all(is.na(x))), which((x)),NA))
coords.0.sf$bcr  = bcrs.ne.grouped$BCR[idx]
coords.0.sf$bcr_idx  = idx

coords.0.sf = coords.0.sf %>% filter(!is.na(bcr_idx))
A_pred = inla.spde.make.A(mesh, st_coordinates(coords.0.sf))

Temporal trends

Here are plots of the spatial temporal trends according to the three models.

func = function(..., year)
{
  fix = scale_year
  return(c(fix))
}

func2 = function(..., year)
{
  fix =  scale_year 
  spat2 = (A_pred %*% i2)[,1] 
  return((fix  + spat2))
}


func3 = function(..., year)
{
  fix =  scale_year 
  time = bcr_idx[coords.0.sf$bcr_idx]

  return((fix  +time))
  
}
year = 1
aa = inla.posterior.sample.eval(func, samples, year = year)
aa2 = inla.posterior.sample.eval(func2, samples2, year = year)
aa3 = inla.posterior.sample.eval(func3, samples3, year = year)


dd = data.frame(x = st_coordinates(coords.0.sf)[,1],
                y = st_coordinates(coords.0.sf)[,2],
                z = apply(aa,1,mean),
                z1_1 = apply(aa,1,quantile, 0.025),
               z1_2 = apply(aa,1,quantile, 0.975),

                z2 = apply(aa2,1,mean),
                z2_1 = apply(aa2,1,quantile, 0.025),
                z2_2 = apply(aa2,1,quantile, 0.975),

                z3 = apply(aa3,1,mean),
                z3_1 = apply(aa3,1,quantile, 0.025),
                z3_2 = apply(aa3,1,quantile, 0.975) ) 


dd = dd %>%
  mutate(check1 = factor(case_when(z1_1>0 ~ 1,
                            z1_2<0 ~ -1,
                            z1_1<0 & z1_2 >0 ~ 0), levels = c("-1","0","1")),
         check2 = factor(case_when(z2_1>0 ~ 1,
                            z2_2<0 ~ -1,
                            z2_1<0 & z2_2 >0 ~ 0), levels = c("-1","0","1")),
         check3 = factor(case_when(z3_1>0 ~ 1,
                            z3_2<0 ~ -1,
                            z3_1<0 & z3_2 >0 ~ 0), levels = c("-1","0","1")))
         
pred.stars <- st_as_stars(data.frame(dd), dims = c('x', 'y'))
lims = range(c(dd$z,dd$z2, dd$z3), na.rm = T)




trend1 <-  dd |> 
  dplyr::select(c(x,y,z,z2,z3)) |>
  dplyr::rename(`SVCM 1` = z,
         `SVCM 2` = z2,
         `SVCM 3` = z3) %>%
  pivot_longer(-c(x,y))
  
trend2 <-  dd |> 
  dplyr::select(c(x,y,check1,check2,check3)) |>
    dplyr::rename(`SVCM 1` = check1,
         `SVCM 2` = check2,
         `SVCM 3` = check3) %>%
  pivot_longer(-c(x,y))
  


trend1 %>% ggplot() + geom_tile(aes(x,y,fill = value)) +
  facet_wrap(.~name) + coord_equal() +
  scale_fill_scico(name= "Mean \ntemporal trend" , limits = lims) + 
   theme_maps 

trend2 %>% ggplot() + geom_tile(aes(x,y,fill = value)) +
  facet_wrap(.~name) + coord_equal() +
   scale_fill_manual(name="Change in trend",
                    values = c("-1" = "maroon",
                               "0" = "grey",
                               "1" = "cyan4"),
                    labels = c("Negative", "No change", "Positive"), na.translate = F)+
   theme_maps 

References

Doser, Jeffrey W., Andrew O. Finley, Marc Kéry, and Elise F. Zipkin. 2022. “spOccupancy: An R Package for Single-Species, Multi-Species, and Integrated Spatial Occupancy Models.” Methods in Ecology and Evolution 13 (8): 1670–78. https://doi.org/10.1111/2041-210x.13897.