diff --git a/man/FitGAM.Rd b/man/FitGAM.Rd
index abc56c7..e3da3d8 100644
--- a/man/FitGAM.Rd
+++ b/man/FitGAM.Rd
@@ -41,3 +41,13 @@ Fit a GAM that predicts abundance based on the data. One should  supply a formul
 \details{
 If verbose=T, printouts of the model selection process are produced.
 }
+\examples{
+gam.form <- formula("a_atf ~ s(lon,lat,bs = 'ds',m=c(1,.5), k=10) +
+s(bdepth, bs='tp',m=1,k=4) + s(btemp, bs='tp',m=1,k=4) +
+s(slope, bs='tp',m=1,k=4) + offset(logarea)")
+data("region_data_all")
+region_data_all$sponge <- as.integer(region_data_all$sponge > 0)
+region_data_all$logarea <- log(region_data_all$area)
+
+poisson.model <- FitGAM(gam.formula = gam.form, data = region_data_all, family.gam = "poisson")
+}
diff --git a/man/GAMStats.Rd b/man/GAMStats.Rd
index bfc0cc3..5ce1914 100644
--- a/man/GAMStats.Rd
+++ b/man/GAMStats.Rd
@@ -12,7 +12,7 @@ GAMStats(model, data)
 \item{data}{a data frame; usually the same one used to fit the GAM model}
 }
 \value{
-a named vector of decimal values indicating the percent contribution
+a named vector of decimal values indicating the percent contribution to deviance explained
 }
 \description{
 Make a quick and dirty jackknife estimate of the deviance explained by each variable
diff --git a/man/GOA_bathy.Rd b/man/GOA_bathy.Rd
index 044e9f3..b340f9a 100644
--- a/man/GOA_bathy.Rd
+++ b/man/GOA_bathy.Rd
@@ -9,7 +9,6 @@ A raster layer with 12 slots
 \describe{
 \item{name}{character string with path to raster layer}
 \item{datanotation}{Raster file native format}
-...
 }
 }
 \source{
diff --git a/man/MakeAKGFDotplot.Rd b/man/MakeAKGFDotplot.Rd
index 8695f57..c8e7e8a 100644
--- a/man/MakeAKGFDotplot.Rd
+++ b/man/MakeAKGFDotplot.Rd
@@ -86,3 +86,23 @@ makes a pretty good dotplot; needs additional testing with areas other than "bs.
 \details{
 Settings are set to work with 8x8 in 300 res output, so results may vary when making different size figures.
 }
+\examples{
+data("region_data_all")
+region.data <- region_data_all
+region.data$sponge <- as.integer(region.data$sponge > 0)
+region.data$coral <- as.integer(region.data$coral > 0)
+region.data$pen <- as.integer(region.data$pen > 0)
+region.data$logarea <- log(region.data$area)
+data("raster_stack")
+raster.stack <- terra::rast(raster_stack)
+species.data <- subset(region.data, year >= 2012)
+species <- "a_atf"
+hd <- stats::quantile(species.data[species.data[, species] > 0, species], .9)
+MakeAKGFDotplot(
+  presence = species.data[species.data[, species] > 0, ],
+  absence = species.data[species.data[, species] == 0, ],
+  highdensity = species.data[species.data[, species] >= hd, ],
+  dataCRS = terra::crs(raster.stack), region = "goa",
+  title.name = "Adult arrowtooth flounder"
+)
+}
diff --git a/man/raster_stack.Rd b/man/raster_stack.Rd
new file mode 100644
index 0000000..bfc85f0
--- /dev/null
+++ b/man/raster_stack.Rd
@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{raster_stack}
+\alias{raster_stack}
+\title{GOA covariate data}
+\format{
+A terra PackedSpatRaster with 6 slots
+\describe{
+\item{lon}{Longitude}
+\item{lat}{Latitude}
+\item{bdepth}{Bottom depth in meters}
+\item{btemp}{slope}{The rate of change in bathymetry over a defined area, measured as the first derivative of the bathymetric surface, in degrees of incline (Horn 1981, Dolan and Lucieer 2014.)}
+\item{sponge}{CPUE of sponge from bottom trawl surveys. Usually, this is transformed into sponge presence-absence when fitting EFH SDMs.}
+}
+}
+\source{
+Compiled by Jeremy Harris using 1-km resolution data from Mark Zimmerman
+}
+\usage{
+data(raster_stack)
+}
+\description{
+A terra SpatRaster containing environmental covariates for the GOA,
+as used in the 2023 EFH review.
+}
+\keyword{datasets}
diff --git a/man/region_data_all.Rd b/man/region_data_all.Rd
index 8618672..3b67b69 100644
--- a/man/region_data_all.Rd
+++ b/man/region_data_all.Rd
@@ -3,9 +3,17 @@
 \docType{data}
 \name{region_data_all}
 \alias{region_data_all}
-\title{Region data for abundance for all the EFH species included in the 2022 Review}
+\title{GOA region data for abundance for EFH species and lifestages included in the 2023 Review}
 \format{
-A data frame with 15592 rows and 184 variables
+a data frame with 8,507 rows and 184 variables (each species/lifestage gets a column):
+\describe{
+\item{hauljoin}
+\item{region}{EFH area (EBS, GOA, or AI)}
+\item{subregion}{Subregion.}
+\item{vessel}{Vessel ID from the survey; matches RACEBASE, FOSS, others}
+\item{cruise}{Cruise ID number}
+\item{haul}{Haul number}
+}
 }
 \usage{
 data(region_data_all)
@@ -13,15 +21,4 @@ data(region_data_all)
 \description{
 The variables are as follows:
 }
-\details{
-\itemize{
-\item hauljoin.
-\item region. EFH area (EBS, GOA, or AI)
-\item subregion.
-\item vessel. Vessel ID from the survey; matches RACEBASE, FOSS, others
-\item cruise. Cruise ID number.
-\item haul. Haul number.
-...
-}
-}
 \keyword{datasets}