diff --git a/DESCRIPTION b/DESCRIPTION
index c84bddf..f2ce9bc 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -48,9 +48,7 @@ Suggests:
     knitr,
     magick,
     matrixStats,
-    raster,
     rmarkdown,
-    sp,
     spThin,
     stars,
     testthat (>= 3.0.0),
diff --git a/R/data.R b/R/data.R
index bec11a6..496da89 100644
--- a/R/data.R
+++ b/R/data.R
@@ -127,30 +127,6 @@
 "sample_filt_data"
 
 
-#' @name sample_biome_data
-#' @title Biome classification raster for southern Africa.
-#' @description
-#' Categorical RasterLayer cropped to southern Africa at 0.08 degree resolution from OpenLandMap
-#' Potential Distribution of Biomes (Hengl et al., 2018).
-#' @format A RasterLayer
-#' \describe{
-#'   \item{class}{RasterLayer}
-#'   \item{dimensions}{4839, 3243, 15692877  (nrow, ncol, ncell)}
-#'   \item{resolution}{0.008983153, 0.008983153  (x, y)}
-#'   \item{extent}{11.71845, 40.85081, -47.89832, -4.428839  (xmin, xmax, ymin, ymax)}
-#'   \item{crs}{"+proj=longlat +datum=WGS84 +no_defs"}
-#'   \item{source}{biome_type_2001-01-01.tif }
-#'   \item{names}{biome_type_2001-01-01.tif }
-#'   \item{values}{Numerical categorical value ranging from 0, 255  (min, max).}
-#' }
-#' @docType data
-#' @references
-#' Hengl T, Walsh MG, Sanderman J, Wheeler I, Harrison SP, Prentice IC. (2018) Global Mapping of
-#' Potential Natural Vegetation: An Assessment of Machine Learning Algorithms for Estimating Land
-#' Potential. PeerJ Preprints.
-"sample_biome_data"
-
-
 
 #' @name sample_events_data
 #' @title  Sample e-Bird sampling event records
@@ -176,7 +152,6 @@
 #'  \doi{10.2173/ebirdst.2020}
 #'
 #' GBIF.org (12 July 2021) GBIF Occurrence Download \doi{10.15468/dl.ppcu6q}
-
 "sample_events_data"
 
 
@@ -212,25 +187,3 @@
 #' hazards infrared precipitation with stations-a new environmental record for monitoring extremes".
 #' Scientific Data 2, 150066.
 "sample_cov_data"
-
-#' @name sample_proj_rast
-#' @title Distribution suitability projections for red-billed quelea in southern Africa
-#' @description
-#' RasterBrick of distribution suitability projections for red-billed quelea across southern at one
-#' degree resolution for the dates: "2018-01-01", "2018-04-01", "2018-07-01" and "2018-10-01". A
-#' value of 1 represents high suitability and a value of 0 low suitability.
-#' @format A RasterBrick
-#' \describe{
-#'   \item{class}{RasterBrick}
-#'   \item{dimensions}{31, 30, 930, 4  (nrow, ncol, ncell, nlayers)}
-#'   \item{resolution}{1, 1  (x, y)}
-#'   \item{extent}{11.6901, 41.6901, -35.43802, -4.438023  (xmin, xmax, ymin, ymax)}
-#'   \item{crs}{"+proj=longlat +datum=WGS84 +no_defs"}
-#'   \item{source}{memory} \item{names}{ X2018.01.01_proportional, X2018.04.01_proportional,
-#'   X2018.07.01_proportional, X2018.10.01_proportional }
-#'   \item{min values}{Numerical minimum distribution suitability projected in each layer.}
-#'   \item{max values}{Numerical maximum distribution suitability projected in each layer.}
-#' }
-#' @docType data
-"sample_proj_rast"
-
diff --git a/R/dynamic_proj.R b/R/dynamic_proj.R
index a73fcb3..2a55d18 100644
--- a/R/dynamic_proj.R
+++ b/R/dynamic_proj.R
@@ -34,7 +34,7 @@
 #'  Default is "+proj=longlat +datum=WGS84".
 #'@param proj.prj a character string, the coordinate reference system desired for output projection
 #'  rasters. Default is assumed to be the same as prj.
-#'@param spatial.mask an object of class `Raster`, `sf` or `Spatial`, representing a mask in which
+#'@param spatial.mask an object of class `SpatRaster` or`sf` polygon, representing a mask in which
 #'  NA cells in the mask layer are removed from the projection covariates.
 #'@details Function projects a model object or list of model objects onto projection covariate data
 #'  frames for each projection date given.
@@ -190,8 +190,13 @@ dynamic_proj <-  function(dates,
   # Convert mask to SPDF for use with raster::mask
 
   if (!missing(spatial.mask)) {
-    spatial.mask<-convert_to_sf(spatial.mask,prj)
-   # spatial.mask<-sf::as_Spatial(spatial.mask)
+
+    spatial.mask <- convert_to_sf(spatial.mask, prj)
+
+    if(inherits(spatial.mask, "sfc_POLYGON")){
+      spatial.mask <- terra::vect(spatial.mask)
+    }
+
   }
 
 
@@ -499,7 +504,7 @@ dynamic_proj <-  function(dates,
 
           binaryrast <- terra::rast(as.matrix(cbind(projection_df[, "x"],
                                                     projection_df[, "y"],
-                                                    SDMbinary), crs = prj),
+                                                    SDMbinary)), crs = prj,
                                     type="xyz")
 
         }
@@ -528,7 +533,7 @@ dynamic_proj <-  function(dates,
         if (cov.file.type == "csv") {
           abundancerast <-  terra::rast(as.matrix(cbind(projection_df[, "x"],
                                                         projection_df[, "y"],
-                                                        SAMpred), crs = prj),
+                                                        SAMpred)), crs = prj,
                                         type="xyz")
 
         }
@@ -554,8 +559,8 @@ dynamic_proj <-  function(dates,
         if (cov.file.type == "csv") {
           proportionalrast <- terra::rast(as.matrix(cbind(projection_df[, "x"],
                                                           projection_df[, "y"],
-                                                          SDMpred), crs = prj),
-                                          type="xyz")
+                                                          SDMpred)), crs = prj,
+                                          type = "xyz")
         }
 
         if (!missing(spatial.mask)) {
@@ -579,7 +584,7 @@ dynamic_proj <-  function(dates,
         if (cov.file.type == "csv") {
           stackedrast <- terra::rast(as.matrix(cbind(projection_df[, "x"],
                                                     projection_df[, "y"],
-                                                    stacked), crs = prj),
+                                                    stacked)), crs = prj,
                                     type="xyz")
         }
 
diff --git a/R/dynamic_proj_covariates.R b/R/dynamic_proj_covariates.R
index eedddaa..8882ddd 100644
--- a/R/dynamic_proj_covariates.R
+++ b/R/dynamic_proj_covariates.R
@@ -4,9 +4,9 @@
 #'stacked and then exported as a covariate data frame or raster stack for each projection date.
 #'
 #'@param dates a character string, vector of dates in format "YYYY-MM-DD".
-#'@param spatial.ext optional; the spatial extent to crop explanatory variable rasters to. Object of
-#'  class `Extent`, `RasterLayer`, `sf`, `SpatialPolygonsDataFrame`, `polygon` or numeric vector
-#'  listing xmin, xmax, ymin and ymax in order.
+#'@param spatial.ext optional; the spatial extent to crop explanatory variable
+#'  rasters to. Object of class `SpatExtent`, `SpatRaster`, an `sf` polygon or
+#'  numeric vector listing xmin, xmax, ymin and ymax in order.
 #'@param varnames a character string, the unique names for each explanatory variable.
 #'@param spatial.res.degrees optional; a numeric value, the spatial resolution in degrees for
 #'  projection rasters to be resampled to. Required if `spatial.ext` given.
@@ -24,7 +24,7 @@
 #'@param save.drive.folder optional; a character string, Google Drive folder to save projection
 #'  covariates to. Folder must be uniquely named within Google Drive. Do not provide path.
 #'@param cov.file.type a character string, the type of file to export projection covariates as. One
-#'  of: `tif` (raster stack) or `csv`(data frame).
+#'  of: `tif` (SpatRaster with multiple layers) or `csv`(data frame).
 #'@param prj a character string, the coordinate reference system desired for projection covariates.
 #'  Default is "+proj=longlat +datum=WGS84".
 #'@param cov.prj a character string, the coordinate reference system desired for output projection
@@ -54,9 +54,10 @@
 #'date and explanatory variable name, then the function will error.
 #'
 #'# Processing rasters
-#'If required, rasters are cropped and resampled to the same spatial extent and resolution. If
-#'`spatial.mask` is given, then cells with NA in this mask layer are removed from the returned
-#'projection covariates. See `raster::mask()` in R package `raster` for details.
+#'If required, rasters are cropped and resampled to the same spatial extent and
+#'resolution. If `spatial.mask` is given, then cells with NA in this mask layer
+#'are removed from the returned projection covariates. See `terra::mask()` in R
+#'package `terra` for details (Hijmans et al., 2022).
 #'
 #'Rasters are then stacked and reprojected if `cov.prj` is different to `prj`.
 #'
@@ -105,7 +106,7 @@
 #'
 #'#' # Spatial buffering of static rasters (optional)
 #'
-#'  Using the `focal` function from `raster` R package (Hijmans et al., 2015),
+#'  Using the `focal` function from `terra` R package (Hijmans et al., 2022),
 #'  `GEE.math.fun` is calculated across the spatial buffer area from the record
 #'  co-ordinate. The spatial buffer area used is specified by the argument
 #'  `moving.window.matrix`, which dictates the neighbourhood of cells
@@ -129,6 +130,9 @@
 #'@returns Exports combined covariates in "csv" or "tif" file for each projection date to the local
 #'directory or Google Drive folder.
 #'@export
+#'@references
+#'Hijmans, R.J., Bivand, R., Forner, K., Ooms, J., Pebesma, E. and Sumner, M.D.,
+#'2022. Package 'terra'. Maintainer: Vienna, Austria.
 #'@examplesIf googledrive::drive_has_token()
 #'
 #'data("sample_extent_data")
@@ -238,12 +242,6 @@ dynamic_proj_covariates <- function(dates,
     stop("Provide save.directory or save.drive.folder to export data.frame.")
   }
 
-  # If mask if sf polygon, convert to spatial polygons dataframe for raster:: mask
-  #if (!missing(spatial.mask)) {
- ##   if (any(class(spatial.mask) == "sf")) {
-  #    spatial.mask<-sf::as_Spatial(spatial.mask)
-  #  }
- # }
 
   # Process spatial extent given for cropping rasters before stacking into covariate data frame
 
@@ -251,20 +249,6 @@ dynamic_proj_covariates <- function(dates,
 
   if (!missing(spatial.ext)) {
 
-    if (!any(class(spatial.ext) %in% c("numeric",
-                                       "SpatialPolygonsDataFrame",
-                                       "Extent",
-                                       "RasterLayer",
-                                       "Polygon",
-                                       "SpatRaster",
-                                       "SpatExtent",
-                                       "sf"))) {
-
-      stop("Please check spatial.ext is of the correct class")
-
-    }
-
-
 
     # Numeric extent to co-ords
     if (is.numeric(spatial.ext) && length(spatial.ext) != 4) {
@@ -436,9 +420,18 @@ dynamic_proj_covariates <- function(dates,
       r <- terra::setValues(r, 1:terra::ncell(r))
 
       if(!missing(spatial.mask)) {
+
+        if(inherits(spatial.mask, "sfc_POLYGON")){
+          spatial.mask <- terra::vect(spatial.mask)
+        }
+
+        if("sf" %in% class(spatial.mask)){
+          spatial.mask <- terra::vect(spatial.mask)
+        }
+
         # Mask to original spatial.ext, if fails keep original. Depending on spatial.ext type.
         tryCatch({
-          r <- terra::mask(r, spatial.ext)
+          r <- terra::mask(r, spatial.mask)
         }, error = function(error_message) {
           r <- r
           message("spatial.mask could not be used. Check valid input type.")
@@ -453,6 +446,16 @@ dynamic_proj_covariates <- function(dates,
       if (!length(static.resample.method) == 1) {
         raster <- terra::resample(raster, r, method = static.resample.method[sr])
       }
+
+      if(!missing(spatial.mask)) {
+        tryCatch({
+          raster <- terra::mask(raster, spatial.mask)
+        }, error = function(error_message) {
+          raster <- raster
+          message("spatial.mask could not be used. Check valid input type.")
+        })}
+
+
     }
 
     terra::crs(raster) <- prj
@@ -522,10 +525,15 @@ dynamic_proj_covariates <- function(dates,
         terra::res(r) <- spatial.res.degrees
         r <- terra::setValues(r, 1:terra::ncell(r))
 
+
         if(!missing(spatial.mask)) {
+
+          if(inherits(spatial.mask, "sfc_POLYGON")){
+            spatial.mask <- terra::vect(spatial.mask)
+          }
           # Mask to original spatial.ext, if fails keep original. Depending on spatial.ext type.
           tryCatch({
-            r <- terra::mask(r, spatial.ext)
+            r <- terra::mask(r, spatial.mask)
           }, error = function(error_message) {
             r <- r
             message("spatial.mask could not be used. Check valid input type.")
@@ -540,6 +548,17 @@ dynamic_proj_covariates <- function(dates,
         if (!length(resample.method) == 1) {
           raster <- terra::resample(raster, r, method = resample.method[v])
         }
+
+        if(!missing(spatial.mask)) {
+        # Mask to original spatial.ext, if fails keep original. Depending on spatial.ext type.
+        tryCatch({
+          raster <- terra::mask(raster, spatial.mask)
+        }, error = function(error_message) {
+          raster <- raster
+          message("spatial.mask could not be used. Check valid input type.")
+        })}
+
+
       }
 
       terra::crs(raster) <- prj
@@ -557,7 +576,7 @@ dynamic_proj_covariates <- function(dates,
     names(stack) <- varnames_all # Label each layer in stack as variable
 
     if (!prj == cov.prj) {
-      stack <- terra::project(stack, crs = cov.prj)
+      stack <- terra::project(stack, cov.prj)
     }
 
     cov.file.type <- match.arg(cov.file.type, choices = c("tif", "csv"))
diff --git a/R/extract_buffered_coords.R b/R/extract_buffered_coords.R
index aafbe0e..1275368 100644
--- a/R/extract_buffered_coords.R
+++ b/R/extract_buffered_coords.R
@@ -52,7 +52,7 @@
 #'
 #'  # Spatial dimension
 #'
-#'  Using the `focal` function from `raster` R package (Hijmans et al., 2015), `GEE.math.fun` is
+#'  Using the `focal` function from `terra` R package (Hijmans et al., 2022), `GEE.math.fun` is
 #'  calculated across the spatial buffer area from the record co-ordinate. The spatial buffer area
 #'  used is specified by the argument `moving.window.matrix`, which dictates the neighbourhood of
 #'  cells surrounding the cell containing the occurrence record to include in this calculation.
@@ -101,7 +101,7 @@
 #'  # Aggregation factor
 #'
 #'  `agg.factor` given represents the factor to aggregate `RasterLayer` data with function
-#'  `aggregate` in `raster` R package (Hijmans et al., 2015). Aggregation uses the `GEE.math.fun` as
+#'  `aggregate` in `terra` R package (Hijmans et al., 2022). Aggregation uses the `GEE.math.fun` as
 #'  the function. Following aggregation spatial buffering using the moving window matrix occurs.
 #'  This is included to minimise computing time if data are of high spatial resolution and a large
 #'  spatial buffer is needed. Ensure to calculate `get_moving_window()` with the spatial resolution
@@ -151,9 +151,8 @@
 #'  D'Agostino McGowan L., and Bryan J., 2022. googledrive: An Interface to Google Drive.
 #'  <https://googledrive.tidyverse.org>, <https://github.com/tidyverse/googledrive>.
 #'
-#'  Hijmans, R. J., Van Etten, J., Cheng, J., Mattiuzzi, M., Sumner, M., Greenberg, J. A.,
-#'  Lamigueiro, O. P., Bevan, A., Racine, E. B. & Shortridge, A. 2015. Package ‘raster’. R package,
-#'  734.
+#'Hijmans, R.J., Bivand, R., Forner, K., Ooms, J., Pebesma, E. and Sumner, M.D.,
+#'2022. Package 'terra'. Maintainer: Vienna, Austria.
 #'@return Returns details of successful explanatory variable extractions.
 #'@export
 #'@examplesIf googledrive::drive_has_token()
diff --git a/R/extract_buffered_raster.R b/R/extract_buffered_raster.R
index fcc10e9..378933f 100644
--- a/R/extract_buffered_raster.R
+++ b/R/extract_buffered_raster.R
@@ -3,8 +3,8 @@
 #'Extract rasters for spatially buffered and temporally dynamic explanatory variables at each
 #'projection date using Google Earth Engine.
 #'@param dates a character string, vector of dates in format "YYYY-MM-DD".
-#'@param spatial.ext the spatial extent for the extracted raster. Object from which extent can be
-#'  extracted of class `Extent`, `RasterLayer`,`SpatialPolygonsDataFrame`, `sf` or `polygon` or
+#'@param spatial.ext the spatial extent for the extracted raster. Object from which extent
+#'  can be extracted of class `SpatExtent`, `SpatRaster`, `sf` polygon or
 #'  numeric vector listing xmin, xmax, ymin and ymax in order.
 #'@param datasetname a character string, the Google Earth Engine dataset to extract data from.
 #'@param bandname a character string, the Google Earth Engine dataset bandname to extract data for.
@@ -62,7 +62,7 @@
 #'
 #'  # Spatial dimension
 #'
-#'  Using the `focal` function in `raster` R package (Hijmans et al., 2015), `GEE.math.fun` is
+#'  Using the `focal` function in `terra` R package (Hijmans et al., 2022), `GEE.math.fun` is
 #'  calculated across the spatial buffer area from each cell in `spatial.ext`. The spatial buffer
 #'  area used is defined by `moving.window matrix`, which dictates the neighbourhood of cells
 #'  surrounding each cell in `spatial.ext` to include in the calculation.
@@ -90,8 +90,8 @@
 #'
 #'  # Aggregation factor
 #'
-#'  `agg.factor` given represents the factor to aggregate `RasterLayer` data with function
-#'  `aggregate` in `raster` R package (Hijmans et al., 2015). Aggregation uses the `GEE.math.fun` as
+#'  `agg.factor` given represents the factor to aggregate `SpatRaster` data with function
+#'  `aggregate` in `terra` R package (Hijmans et al., 2022). Aggregation uses the `GEE.math.fun` as
 #'  the function. Following aggregation spatial buffering using the moving window matrix occurs.
 #'  This is included to minimise computing time if data are of high spatial resolution and a large
 #'  spatial buffer is needed. Ensure to calculate `get_moving_window()` with the spatial resolution
@@ -158,9 +158,8 @@
 #'  D'Agostino McGowan L., and Bryan J., 2022. googledrive: An Interface to Google Drive.
 #'  <https://googledrive.tidyverse.org>, <https://github.com/tidyverse/googledrive>.
 #'
-#'  Hijmans, R. J., Van Etten, J., Cheng, J., Mattiuzzi, M., Sumner, M., Greenberg, J. A.,
-#'  Lamigueiro, O. P., Bevan, A., Racine, E. B. & Shortridge, A. 2015. Package ‘raster’. R package,
-#'  734.
+#'Hijmans, R.J., Bivand, R., Forner, K., Ooms, J., Pebesma, E. and Sumner, M.D.,
+#'2022. Package 'terra'. Maintainer: Vienna, Austria.
 #'@return Returns details of successful explanatory variable raster extractions for each projection
 #'  date.
 #'@export
diff --git a/R/extract_dynamic_raster.R b/R/extract_dynamic_raster.R
index c7714d2..b368e8c 100644
--- a/R/extract_dynamic_raster.R
+++ b/R/extract_dynamic_raster.R
@@ -4,7 +4,7 @@
 #'Earth Engine.
 #'@param dates a character string, vector of dates in format "YYYY-MM-DD".
 #'@param spatial.ext the spatial extent for the extracted raster. Object from which extent can be
-#'  extracted of class `Extent`, `RasterLayer`, `SpatialPolygonsDataFrame`, `sf` or `polygon` or
+#'  extracted of class `SpatExtent`, `SpatRaster`, an `sf` polygon or a
 #'  numeric vector listing xmin, xmax, ymin and ymax in order.
 #'@param datasetname a character string, the Google Earth Engine dataset to extract data from.
 #'@param bandname a character string, the Google Earth Engine dataset bandname to extract data for.
diff --git a/R/extract_static_coords.R b/R/extract_static_coords.R
index f9a7675..beef3ad 100644
--- a/R/extract_static_coords.R
+++ b/R/extract_static_coords.R
@@ -6,15 +6,15 @@
 #'@param occ.data a data frame, with columns for occurrence record co-ordinates
 #'  and dates with column names as follows; record longitude as "x", latitude as
 #'  "y", and associated explanatory variable data.
-#'@param static.rasters a RasterStack of one or more rasters to extracted data
-#'  from.
+#'@param static.rasters a `SpatRaster` containing one or more SpatRaster layers
+#'  to extract data from.
 #'@param extraction.method a character string or vector, the methods to extract
-#'  data from raster using `raster` package `extract` function. One of `simple`
+#'  data from SpatRaster using `terra` package `extract` function. One of `simple`
 #'  or `bilinear`. If `simple` values for the cell a point falls in are
 #'  returned. If `bilinear` the returned values are interpolated from the values
 #'  of the four nearest raster cells.
 #'@param varnames a character string or vector, the unique names for each
-#'  explanatory variable in order of rasters in stack.
+#'  explanatory variable in order of layers in the SpatRaster.
 #'@param GEE.math.fun optional; a character string, the mathematical function to
 #'  compute across the specified spatial matrix for each record.
 #'@param moving.window.matrix optional; a matrix of weights with an odd number
@@ -27,14 +27,13 @@
 #'
 #'
 #'Note:
-#' * `varnames` must be in the order of raster layers within the provided stack.
-#' * `extraction.method` must be of length one to use for all layers, or the
-#' length of nlayers(static.rasters) and in the same order as the appropriate
-#' layers within the provided stack.
+#' * `varnames` must be in the order of raster layers within the SpatRaster.
+#' * `extraction.method` must be of length one to apply to all layers, or
+#' length equal to the number of layers in `static.rasters`.
 #'
 #' # Spatial buffering (optional)
 #'
-#'  Using the `focal` function from `raster` R package (Hijmans et al., 2015),
+#'  Using the `focal` function from `terra` R package (Hijmans et al., 2022),
 #'  `GEE.math.fun` is calculated across the spatial buffer area from the record
 #'  co-ordinate. The spatial buffer area used is specified by the argument
 #'  `moving.window.matrix`, which dictates the neighbourhood of cells
@@ -59,15 +58,20 @@
 #'    data.
 #'
 #'@export
-#'
+#'@references
+#'Hijmans, R.J., Bivand, R., Forner, K., Ooms, J., Pebesma, E. and Sumner, M.D.,
+#'2022. Package ‘terra’. Maintainer: Vienna, Austria.
 #'@examples
 #'\donttest{
 #'data("sample_explan_data")
-#'data("sample_biome_data")
+#'random_cat_layer <- terra::rast(sample_extent_data)
+#'random_cat_layer <- terra::setValues(random_cat_layer,
+#'                                     sample(0:10, terra::ncell(random_cat_layer),
+#'                                            replace = TRUE))
 #'
 #'extract_static_coords(occ.data = sample_explan_data,
-#'                      varnames = "biome_type",
-#'                      static.rasters = raster::stack(sample_biome_data))
+#'                      varnames = "random_cat_layer",
+#'                      static.rasters = random_cat_layer)
 #'
 #'}
 
diff --git a/R/extract_xy_min_max.R b/R/extract_xy_min_max.R
index a778b02..3023c34 100644
--- a/R/extract_xy_min_max.R
+++ b/R/extract_xy_min_max.R
@@ -1,5 +1,5 @@
 #' extract_xy_min_max Extracts xmin,xmax,ymin,ymax from spatial extent object
-#' @param x spatial extent object. Object of class "Extent", "raster" or "polygon" or numeric vector
+#' @param x spatial extent object.
 #'   listing xmin, xmax, ymin and ymax in order.
 #' @noRd
 
@@ -15,10 +15,11 @@ extract_xy_min_max <- function(x) {
   ### Extent object to co-ords
 
   if ("Extent" %in% class(x)) {
-    xmin <- sp::bbox(x)[1, 1]
-    xmax <- sp::bbox(x)[1, 2]
-    ymin <- sp::bbox(x)[2, 1]
-    ymax <- sp::bbox(x)[2, 2]
+    x <- terra::ext(x)
+    xmin <-  x[1]
+    xmax <-  x[2]
+    ymin <-  x[3]
+    ymax <-  x[4]
   }
 
 
@@ -37,10 +38,14 @@ extract_xy_min_max <- function(x) {
   ### Polygon object to co-ords
 
   if ("Polygon" %in% class(x)) {
-    xmin <- sp::bbox(x)[1, 1]
-    xmax <- sp::bbox(x)[1, 2]
-    ymin <- sp::bbox(x)[2, 1]
-    ymax <- sp::bbox(x)[2, 2]
+    stop("Please provide sf polygon.")
+  }
+
+  if ("POLYGON" %in% class(x)) {
+    xmin <- as.numeric(sf::st_bbox(x)[1])
+    xmax <- as.numeric(sf::st_bbox(x)[3])
+    ymin <- as.numeric(sf::st_bbox(x)[2])
+    ymax <- as.numeric(sf::st_bbox(x)[4])
   }
 
   if ("sf" %in% class(x)) {
@@ -50,12 +55,9 @@ extract_xy_min_max <- function(x) {
     ymax <- as.numeric(sf::st_bbox(x)[4])
   }
 
-  if ("SpatialPolygonsDataFrame" %in% class(x)) {
-    xmin <- sp::bbox(raster::extent(x))[1, 1]
-    xmax <- sp::bbox(raster::extent(x))[1, 2]
-    ymin <- sp::bbox(raster::extent(x))[2, 1]
-    ymax <- sp::bbox(raster::extent(x))[2, 2]
 
+  if ("SpatialPolygonsDataFrame" %in% class(x)) {
+    stop("Please provide spatial object as an sf polygon or terra extent/r")
   }
 
 
@@ -84,6 +86,13 @@ extract_xy_min_max <- function(x) {
 
   }
 
+  if ("sfc_POLYGON" %in% class(x)) {
+    xmin <- as.numeric(sf::st_bbox(x)[1])
+    xmax <- as.numeric(sf::st_bbox(x)[3])
+    ymin <- as.numeric(sf::st_bbox(x)[2])
+    ymax <- as.numeric(sf::st_bbox(x)[4])
+  }
+
   return(c(xmin, xmax, ymin, ymax))
 }
 
@@ -100,17 +109,23 @@ convert_to_sf <- function(x,prj) {
 
 
   if ("numeric" %in% class(x) && length(x) == 4) {
-    raster<-raster::raster(raster::extent(x[1],x[2],x[3],x[4]),crs=prj)
-    spatial.ext <- methods::as(raster, 'SpatialPolygons')
+    raster<-terra::rast(terra::ext(x[1],x[2],x[3],x[4]),crs=prj)
+    spatial.ext <- terra::as.polygons(raster)
     spatial.ext<-sf::st_as_sf(spatial.ext,CRS = sf::st_crs(prj))
-
   }
 
   ### Extent object to co-ords
-
   if ("Extent" %in% class(x)) {
-    raster <- raster::raster(raster::extent(x), crs = prj)
-    spatial.ext <- methods::as(raster, 'SpatialPolygons')
+    raster <- terra::rast(terra::ext(x), crs = prj)
+    spatial.ext <- terra::as.polygons(raster)
+    spatial.ext <- sf::st_as_sf(spatial.ext, CRS = sf::st_crs(prj))
+  }
+
+
+
+  if ("SpatExtent" %in% class(x)) {
+    raster <- terra::rast(terra::ext(x), crs = prj)
+    spatial.ext <- terra::as.polygons(raster)
     spatial.ext <- sf::st_as_sf(spatial.ext, CRS = sf::st_crs(prj))
 
   }
@@ -119,45 +134,49 @@ convert_to_sf <- function(x,prj) {
   ### RasterLayer object to co-ords
 
   if ("RasterLayer" %in% class(x)) {
-    raster::crs(x) <- prj
-    spatial.ext <- methods::as(x, 'SpatialPolygons')
+    raster <- terra::rast(x, crs = prj)
+    terra::crs(raster) <- prj
+    spatial.ext <- terra::as.polygons(raster)
     spatial.ext <- sf::st_as_sf(spatial.ext)
     spatial.ext <-  sf::st_transform(spatial.ext, 7801)
     spatial.ext <- sf::st_union(spatial.ext)
     spatial.ext <-  sf::st_transform(spatial.ext, prj)
-
   }
 
-
   ### Polygon object to co-ords
 
   if ("Polygon" %in% class(x)) {
-    xmin <- sp::bbox(x)[1, 1]
-    xmax <- sp::bbox(x)[1, 2]
-    ymin <- sp::bbox(x)[2, 1]
-    ymax <- sp::bbox(x)[2, 2]
+    stop("Please provide sf polygon")
+  }
 
-    raster<-raster::raster(raster::extent(xmin,xmax,ymin,ymax),crs=prj)
-    spatial.ext <- methods::as(raster, 'SpatialPolygons')
+  if ("sf" %in% class(x)) {
 
-    message("Only using extent of sp Polygon. Provide sf polygon for specific shape")
+    x <- sf::st_set_crs(x, prj)
 
-    spatial.ext<-sf::st_as_sf(spatial.ext)
+    return(x)
+  }
 
+  if ("POLYGON" %in% class(x)) {
+    polygon <- polygon %>%
+      sf::st_sfc(crs = prj)
+    x <- sf::st_set_crs(polygon, prj)
+    return(x)
   }
 
-  if ("sf" %in% class(x)) {
 
+  if ("sfc_POLYGON" %in% class(x)) {
     x <- sf::st_set_crs(x, prj)
-
     return(x)
   }
 
+
   if ("SpatialPolygonsDataFrame" %in% class(x)) {
     spatial.ext <- sf::st_as_sf(x)
 
   }
 
+
+
   return(spatial.ext)
 }
 
diff --git a/R/get_moving_window.R b/R/get_moving_window.R
index 98080c8..4acd49c 100644
--- a/R/get_moving_window.R
+++ b/R/get_moving_window.R
@@ -8,9 +8,9 @@
 #'  variable data.
 #'@param spatial.res.metres a numeric value, the spatial resolution in metres of explanatory
 #'  variable data.
-#'@param spatial.ext the spatial extent of the study. Object from which extent can be extracted of
-#'  class `Extent`, `RasterLayer`, `sf` or `polygon` or numeric vector listing xmin, xmax, ymin and
-#'  ymax in order.
+#'@param spatial.ext the spatial extent of the study. Object from which extent
+#'  can be extracted of class `SpatExtent`, `SpatRaster`, `sf` polygon or
+#'  numeric vector listing xmin, xmax, ymin and ymax in order.
 #'@details
 #'
 #'# Importance for other functions in `dynamicSDM` To extract spatially buffered explanatory
@@ -22,10 +22,13 @@
 #'variable would be calculated across the nine grid cells neighbouring the cell of interest and the
 #'cell of interest.
 #'
-#'# Why use a moving window matrix instead of circular buffer? The benefit of using a “moving
-#'window” over calculating explanatory variable values across a set radius from each record
-#'co-ordinate, is that when generating projection rasters at high spatial and temporal resolution,
-#'these can be generated much faster as the “moving windows” standardise the calculation.
+#'# Why use a moving window matrix instead of circular buffer?
+#'
+#'The benefit of using a “moving window” over calculating explanatory
+#'variable values across a set radius from each record co-ordinate, is that when
+#'generating projection rasters at high spatial and temporal resolution, these
+#'can be generated much faster as the “moving windows” standardise the
+#'calculation.
 #'
 #'# Function calculation
 #'
@@ -72,12 +75,7 @@ get_moving_window <-  function(radial.distance,
         stop("spatial.ext is missing. Required when spatial.res given in degrees.")
       }
 
-    # Check spatial.ext is valid
-      if (!any(class(spatial.ext) %in% c("numeric", "Extent","sf", "RasterLayer", "Polygon","SpatRaster"))) {
-        stop("spatial.ext must be of class numeric, Extent, sf, RasterLayer, SpatRaster or Polygon")
-      }
-
-      if (any(class(spatial.ext) == "numeric") && !length(spatial.ext) == 4) {
+         if (any(class(spatial.ext) == "numeric") && !length(spatial.ext) == 4) {
         stop("spatial.ext numeric vector should be of length four c(xmin, xmax, ymin and ymax)")
       }
 
diff --git a/R/spatiotemp_bias.R b/R/spatiotemp_bias.R
index 5e44e8d..cb54b0d 100644
--- a/R/spatiotemp_bias.R
+++ b/R/spatiotemp_bias.R
@@ -33,7 +33,7 @@
 #'
 #'# Spatial bias
 #'
-#' To assess spatial sampling bias, the function returns a scatterplot of the spatial
+#' To assess spatial sampling bias, the function returns a scatter plot of the spatial
 #'distribution of occurrence records to illustrate any spatial clustering (if `plot = TRUE`). The
 #'average nearest neighbour distance of record co-ordinates is then compared to that of records
 #'randomly generated at same density using a t-test, following the nearest neighbour index
diff --git a/R/spatiotemp_block.R b/R/spatiotemp_block.R
index 9b7afd5..72f38e8 100644
--- a/R/spatiotemp_block.R
+++ b/R/spatiotemp_block.R
@@ -9,7 +9,7 @@
 #'  "month", and day as "day", and associated explanatory variable data.
 #'@param vars.to.block.by a character string or vector, the explanatory variable column names to
 #'  group sampling units based upon.
-#'@param spatial.layer optional; a `RasterLayer` object, a categorical spatial layer for sample
+#'@param spatial.layer optional; a `SpatRaster` object, a categorical spatial layer for sample
 #'  unit splitting.
 #'@param spatial.split.degrees a numeric value, the grid cell resolution in degrees to split
 #'  `spatial.layer` by. Required if `spatial.layer` given.
diff --git a/R/spatiotemp_extent.R b/R/spatiotemp_extent.R
index e6ab79a..e9dcfff 100644
--- a/R/spatiotemp_extent.R
+++ b/R/spatiotemp_extent.R
@@ -9,9 +9,9 @@
 #'@param temporal.ext optional; a character vector, two dates in format "YYYY-MM-DD". First date
 #'  represents start of temporal extent and second date represents end of temporal extent for
 #'  inclusion.
-#'@param spatial.ext the spatial extent to filter by. Object from which extent can be extracted of
-#'  class `Extent`, `RasterLayer`, `SpatialPolygonsDataFrame`,`sf` or `polygon` or numeric vector
-#'  listing xmin, xmax, ymin and ymax in order.
+#'@param spatial.ext the spatial extent to filter by. Object from which extent
+#'  can be extracted of class `SpatExtent`, `SpatRaster`, `sf` polygon or
+#'  numeric vector listing xmin, xmax, ymin and ymax in order.
 #'@param prj a character string, the coordinate reference system of input `occ.data` co-ordinates.
 #'  Default is "+proj=longlat +datum=WGS84".
 #'@details
@@ -23,10 +23,9 @@
 #'outside of this extent.
 #'
 #'
-#'If `spatial.ext` object can be used as a mask by `raster::mask()` then the mask is used to filter
-#'records in a more targetted way. If not, then the rectangular extent of the `spatial.ext` object
-#'is used. If an `sf` polygon object is provided, this is first transformed into a `Spatial` object
-#'for use by `raster::mask()`.
+#'If `spatial.ext` object can be used as a mask by `terra::mask()` then the mask is used to filter
+#'records in a more targeted way. If not, then the rectangular extent of the `spatial.ext` object
+#'is used.
 #'
 #'# Temporal extent
 #'
@@ -113,31 +112,22 @@ spatiotemp_extent <- function(occ.data,
 
     # Convert sf object to Spatial object that can be tranformed into raster
     if("sf" %in% class(spatial.ext)){
-      v <- terra::vect(spatial.ext)
-      r <- terra::rast(v,ncols=75, nrows=100)
-      spatial.ext <- terra::rasterize(v, r)}
-
+      spatial.ext <- terra::vect(spatial.ext)
+     }
 
     # Convert polygon object to Extent object that can be transformed into raster
     if ("Polygon" %in% class(spatial.ext)) {
-      xmin <- sp::bbox(spatial.ext)[1, 1]
-      xmax <- sp::bbox(spatial.ext)[1, 2]
-      ymin <- sp::bbox(spatial.ext)[2, 1]
-      ymax <- sp::bbox(spatial.ext)[2, 2]
-      r<- terra::ext(xmin,xmax,ymin,ymax)}
-
+      stop("Please provide sf polygon as spatial.ext object.")
+    }
 
     if("RasterLayer" %in% class(spatial.ext)){
       spatial.ext <- terra::rast(spatial.ext)
     }
 
-    if("SpatRaster" %in% class(spatial.ext)){
-      spatial.ext <- terra::as.polygons(spatial.ext)
+    if(inherits(spatial.ext, "sfc_POLYGON")){
+      spatial.ext <- terra::vect(spatial.ext)
     }
 
-    if(inherits(spatial.ext,"SpatExtent")){
-      spatial.ext <- terra::as.polygons(terra::ext(spatial.ext))}
-
     # Convert spatial.ext to raster in same projection
     r <- terra::rast(r)
     terra::crs(r) <- prj
diff --git a/R/spatiotemp_pseudoabs.R b/R/spatiotemp_pseudoabs.R
index 1fe32a6..89d6bfa 100644
--- a/R/spatiotemp_pseudoabs.R
+++ b/R/spatiotemp_pseudoabs.R
@@ -22,10 +22,11 @@
 #'  represents the start of the temporal extent and the second represents the end of temporal extent
 #'  to randomly generate pseudo-absences dates within. Required if `temporal.method` is `random`,
 #'  and optionally used if `buffer`. See details.
-#'@param spatial.ext the spatial extent to randomly generate pseudo-absences within. Object from
-#'  which extent can be extracted of class `Extent`, `RasterLayer`,`SpatialPolygonsDataFrame`, `sf`
-#'  or `polygon` or numeric vector listing xmin, xmax, ymin and ymax in order. Required if
-#'  `spatial.method` is `random`, and optionally used if `buffer`. See details.
+#'@param spatial.ext the spatial extent to randomly generate pseudo-absences
+#'  within. Object from which extent can be extracted of class `SpatExtent`,
+#'  `SpatRaster`, an `sf` polygon or a numeric vector listing xmin, xmax, ymin
+#'  and ymax in order. Required if `spatial.method` is `random`, and optionally
+#'  used if `buffer`. See details.
 #'@param prj a character string, the coordinate reference system of input `occ.data` co-ordinates.
 #'  Default is "+proj=longlat +datum=WGS84".
 #'@details
@@ -61,12 +62,12 @@
 #'If `spatial.method` is `random`, then the pseudo-absence record co-ordinates are randomly
 #'generated across `spatial.ext` object given.
 #'
-#'If `spatial.ext` is a `sf` polygon or multipolygon object, `RasterLayer` (mask if possible before
-#'input) or `SpatialPolygonsDataFrame`, then these shapes are used, instead of a simple rectangular
-#'extent. Therefore, inputting one of these objects will allow for more specific pseudo-absence
+#'If `spatial.ext` is a `sf` polygon or `SpatRaster` (mask if possible before
+#'input) then these shapes are used, instead of a simple rectangular
+#'extent (`SpatExtent`). Therefore, inputting one of these objects will allow for more specific pseudo-absence
 #'generation.
 #'
-#'For example, inputting a `SpatialPolygonsDataFrame` of specific countries will ensure co-ordinates
+#'For example, inputting an `sf` polygon of a specific countries will ensure co-ordinates
 #'are terrestrial, whereas an extent (xmin, xmax, ymin, ymax) that encompasses these countries may
 #'result in the generation of pseudo-absence records in inappropriate areas, such as oceans or
 #'non-study-area countries.
diff --git a/README.Rmd b/README.Rmd
index 31c2656..01ae78d 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -20,7 +20,7 @@ knitr::opts_chunk$set(
 [![Codecov test coverage](https://codecov.io/gh/r-a-dobson/dynamicSDM/branch/main/graph/badge.svg)](https://app.codecov.io/gh/r-a-dobson/dynamicSDM?branch=main)
 [![R-CMD-check](https://github.com/r-a-dobson/dynamicSDM/workflows/R-CMD-check/badge.svg)](https://github.com/r-a-dobson/dynamicSDM/actions)
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7405906.svg)](https://doi.org/10.5281/zenodo.7405906)
-[![Journal article](https://img.shields.io/badge/Published in-Methods in Ecology and Evolution-purple.svg)](https://doi.org/10.1111/2041-210X.14101)
+[![Journal article](https://img.shields.io/badge/Published_in-Methods_in_Ecology_and_Evolution-purple.svg)](\doi{10.1111/2041-210X.14101})
 [![CRAN status](https://www.r-pkg.org/badges/version/dynamicSDM)](https://CRAN.R-project.org/package=dynamicSDM)
 <!-- badges: end -->
 
@@ -36,7 +36,7 @@ knitr::opts_chunk$set(
 
 * The package has since been published in the **Open Access journal "Methods in Ecology and Evolution"**
 
-[*Dobson, R., Challinor, A.J., Cheke, R.A., Jennings, S., Willis, S.G. and Dallimer, M., 2023. dynamicSDM: An R package for species geographical distribution and abundance modelling at high spatiotemporal resolution. Methods in Ecology and Evolution.*](https://doi.org/10.1111/2041-210X.14101)
+[*Dobson, R., Challinor, A.J., Cheke, R.A., Jennings, S., Willis, S.G. and Dallimer, M., 2023. dynamicSDM: An R package for species geographical distribution and abundance modelling at high spatiotemporal resolution. Methods in Ecology and Evolution.*](\doi{10.1111/2041-210X.14101})
 
 <a href='https://github.com/r-a-dobson/dynamicSDM'><img src="https://github.com/raw/r-a-dobson/dynamicSDM/main/man/figures/MEE_title.png" align="centre" height="500" width="100%" /></a>
 
diff --git a/README.md b/README.md
index 8242b65..898538d 100644
--- a/README.md
+++ b/README.md
@@ -11,7 +11,7 @@ coverage](https://codecov.io/gh/r-a-dobson/dynamicSDM/branch/main/graph/badge.sv
 [![R-CMD-check](https://github.com/r-a-dobson/dynamicSDM/workflows/R-CMD-check/badge.svg)](https://github.com/r-a-dobson/dynamicSDM/actions)
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7405906.svg)](https://doi.org/10.5281/zenodo.7405906)
 [![Journal
-article](https://img.shields.io/badge/Published%20in-Methods%20in%20Ecology%20and%20Evolution-purple.svg)](https://doi.org/10.1111/2041-210X.14101)
+article](https://img.shields.io/badge/Published_in-Methods_in_Ecology_and_Evolution-purple.svg)](\doi%7B10.1111/2041-210X.14101%7D)
 [![CRAN
 status](https://www.r-pkg.org/badges/version/dynamicSDM)](https://CRAN.R-project.org/package=dynamicSDM)
 <!-- badges: end -->
@@ -36,7 +36,7 @@ status](https://www.r-pkg.org/badges/version/dynamicSDM)](https://CRAN.R-project
 and Dallimer, M., 2023. dynamicSDM: An R package for species
 geographical distribution and abundance modelling at high spatiotemporal
 resolution. Methods in Ecology and
-Evolution.*](https://doi.org/10.1111/2041-210X.14101)
+Evolution.*](\doi%7B10.1111/2041-210X.14101%7D)
 
 <a href='https://github.com/r-a-dobson/dynamicSDM'><img src="https://github.com/raw/r-a-dobson/dynamicSDM/main/man/figures/MEE_title.png" align="centre" height="500" width="100%" /></a>
 
diff --git a/data/sample_biome_data.rda b/data/sample_biome_data.rda
deleted file mode 100644
index 0b88476..0000000
Binary files a/data/sample_biome_data.rda and /dev/null differ
diff --git a/data/sample_proj_rast.rda b/data/sample_proj_rast.rda
deleted file mode 100644
index 48d36cb..0000000
Binary files a/data/sample_proj_rast.rda and /dev/null differ
diff --git a/man/dynamic_proj.Rd b/man/dynamic_proj.Rd
index 229521b..5ae0d9d 100644
--- a/man/dynamic_proj.Rd
+++ b/man/dynamic_proj.Rd
@@ -70,7 +70,7 @@ Default is "+proj=longlat +datum=WGS84".}
 \item{proj.prj}{a character string, the coordinate reference system desired for output projection
 rasters. Default is assumed to be the same as prj.}
 
-\item{spatial.mask}{an object of class \code{Raster}, \code{sf} or \code{Spatial}, representing a mask in which
+\item{spatial.mask}{an object of class \code{SpatRaster} or\code{sf} polygon, representing a mask in which
 NA cells in the mask layer are removed from the projection covariates.}
 }
 \value{
diff --git a/man/dynamic_proj_covariates.Rd b/man/dynamic_proj_covariates.Rd
index dd2daf0..1f9eebf 100644
--- a/man/dynamic_proj_covariates.Rd
+++ b/man/dynamic_proj_covariates.Rd
@@ -41,9 +41,9 @@ if \code{drive.folder} or \code{save.drive.folder} used.}
 \item{local.directory}{optional; a character string or vector, path to local directory or
 directories to read projection covariate rasters from.}
 
-\item{spatial.ext}{optional; the spatial extent to crop explanatory variable rasters to. Object of
-class \code{Extent}, \code{RasterLayer}, \code{sf}, \code{SpatialPolygonsDataFrame}, \code{polygon} or numeric vector
-listing xmin, xmax, ymin and ymax in order.}
+\item{spatial.ext}{optional; the spatial extent to crop explanatory variable
+rasters to. Object of class \code{SpatExtent}, \code{SpatRaster}, an \code{sf} polygon or
+numeric vector listing xmin, xmax, ymin and ymax in order.}
 
 \item{spatial.mask}{an object of class \code{Raster}, \code{sf} or \code{Spatial}, representing a mask in which
 NA cells in the mask layer are removed from the projection covariates.}
@@ -55,7 +55,7 @@ projection rasters to be resampled to. Required if \code{spatial.ext} given.}
 method to use. One of \code{ngb} and \code{bilinear}. See details for more information.}
 
 \item{cov.file.type}{a character string, the type of file to export projection covariates as. One
-of: \code{tif} (raster stack) or \code{csv}(data frame).}
+of: \code{tif} (SpatRaster with multiple layers) or \code{csv}(data frame).}
 
 \item{prj}{a character string, the coordinate reference system desired for projection covariates.
 Default is "+proj=longlat +datum=WGS84".}
@@ -104,9 +104,10 @@ date and explanatory variable name, then the function will error.
 }
 
 \section{Processing rasters}{
-If required, rasters are cropped and resampled to the same spatial extent and resolution. If
-\code{spatial.mask} is given, then cells with NA in this mask layer are removed from the returned
-projection covariates. See \code{raster::mask()} in R package \code{raster} for details.
+If required, rasters are cropped and resampled to the same spatial extent and
+resolution. If \code{spatial.mask} is given, then cells with NA in this mask layer
+are removed from the returned projection covariates. See \code{terra::mask()} in R
+package \code{terra} for details (Hijmans et al., 2022).
 
 Rasters are then stacked and reprojected if \code{cov.prj} is different to \code{prj}.
 
@@ -154,7 +155,7 @@ to be specified too using \code{static.resample.method}. Please also provide
 
 #' # Spatial buffering of static rasters (optional)
 
-Using the \code{focal} function from \code{raster} R package (Hijmans et al., 2015),
+Using the \code{focal} function from \code{terra} R package (Hijmans et al., 2022),
 \code{GEE.math.fun} is calculated across the spatial buffer area from the record
 co-ordinate. The spatial buffer area used is specified by the argument
 \code{moving.window.matrix}, which dictates the neighbourhood of cells
@@ -232,3 +233,7 @@ dynamic_proj_covariates(dates = projectiondates,
                        save.directory = tempdir())
 \dontshow{\}) # examplesIf}
 }
+\references{
+Hijmans, R.J., Bivand, R., Forner, K., Ooms, J., Pebesma, E. and Sumner, M.D.,
+2022. Package 'terra'. Maintainer: Vienna, Austria.
+}
diff --git a/man/extract_buffered_coords.Rd b/man/extract_buffered_coords.Rd
index c73f587..20940ed 100644
--- a/man/extract_buffered_coords.Rd
+++ b/man/extract_buffered_coords.Rd
@@ -99,7 +99,7 @@ occurrence record date.
 }
 
 \section{Spatial dimension}{
-Using the \code{focal} function from \code{raster} R package (Hijmans et al., 2015), \code{GEE.math.fun} is
+Using the \code{focal} function from \code{terra} R package (Hijmans et al., 2022), \code{GEE.math.fun} is
 calculated across the spatial buffer area from the record co-ordinate. The spatial buffer area
 used is specified by the argument \code{moving.window.matrix}, which dictates the neighbourhood of
 cells surrounding the cell containing the occurrence record to include in this calculation.
@@ -147,7 +147,7 @@ same raster. For the former, \code{temporal.level} argument should be \code{year
 
 \section{Aggregation factor}{
 \code{agg.factor} given represents the factor to aggregate \code{RasterLayer} data with function
-\code{aggregate} in \code{raster} R package (Hijmans et al., 2015). Aggregation uses the \code{GEE.math.fun} as
+\code{aggregate} in \code{terra} R package (Hijmans et al., 2022). Aggregation uses the \code{GEE.math.fun} as
 the function. Following aggregation spatial buffering using the moving window matrix occurs.
 This is included to minimise computing time if data are of high spatial resolution and a large
 spatial buffer is needed. Ensure to calculate \code{get_moving_window()} with the spatial resolution
@@ -229,7 +229,6 @@ interacting with Google Earth Engine. Journal of Open Source Software, 5(51), p.
 D'Agostino McGowan L., and Bryan J., 2022. googledrive: An Interface to Google Drive.
 \url{https://googledrive.tidyverse.org}, \url{https://github.com/tidyverse/googledrive}.
 
-Hijmans, R. J., Van Etten, J., Cheng, J., Mattiuzzi, M., Sumner, M., Greenberg, J. A.,
-Lamigueiro, O. P., Bevan, A., Racine, E. B. & Shortridge, A. 2015. Package ‘raster’. R package,
-734.
+Hijmans, R.J., Bivand, R., Forner, K., Ooms, J., Pebesma, E. and Sumner, M.D.,
+2022. Package 'terra'. Maintainer: Vienna, Austria.
 }
diff --git a/man/extract_buffered_raster.Rd b/man/extract_buffered_raster.Rd
index 18c4234..ce4b87e 100644
--- a/man/extract_buffered_raster.Rd
+++ b/man/extract_buffered_raster.Rd
@@ -27,8 +27,8 @@ extract_buffered_raster(
 \arguments{
 \item{dates}{a character string, vector of dates in format "YYYY-MM-DD".}
 
-\item{spatial.ext}{the spatial extent for the extracted raster. Object from which extent can be
-extracted of class \code{Extent}, \code{RasterLayer},\code{SpatialPolygonsDataFrame}, \code{sf} or \code{polygon} or
+\item{spatial.ext}{the spatial extent for the extracted raster. Object from which extent
+can be extracted of class \code{SpatExtent}, \code{SpatRaster}, \code{sf} polygon or
 numeric vector listing xmin, xmax, ymin and ymax in order.}
 
 \item{datasetname}{a character string, the Google Earth Engine dataset to extract data from.}
@@ -109,7 +109,7 @@ more relevant that "sum" of land cover classification numbers.
 }
 
 \section{Spatial dimension}{
-Using the \code{focal} function in \code{raster} R package (Hijmans et al., 2015), \code{GEE.math.fun} is
+Using the \code{focal} function in \code{terra} R package (Hijmans et al., 2022), \code{GEE.math.fun} is
 calculated across the spatial buffer area from each cell in \code{spatial.ext}. The spatial buffer
 area used is defined by \verb{moving.window matrix}, which dictates the neighbourhood of cells
 surrounding each cell in \code{spatial.ext} to include in the calculation.
@@ -136,8 +136,8 @@ For example, this function could return the sum of suitable land cover classifie
 }
 
 \section{Aggregation factor}{
-\code{agg.factor} given represents the factor to aggregate \code{RasterLayer} data with function
-\code{aggregate} in \code{raster} R package (Hijmans et al., 2015). Aggregation uses the \code{GEE.math.fun} as
+\code{agg.factor} given represents the factor to aggregate \code{SpatRaster} data with function
+\code{aggregate} in \code{terra} R package (Hijmans et al., 2022). Aggregation uses the \code{GEE.math.fun} as
 the function. Following aggregation spatial buffering using the moving window matrix occurs.
 This is included to minimise computing time if data are of high spatial resolution and a large
 spatial buffer is needed. Ensure to calculate \code{get_moving_window()} with the spatial resolution
@@ -235,7 +235,6 @@ interacting with Google Earth Engine. Journal of Open Source Software, 5(51), p.
 D'Agostino McGowan L., and Bryan J., 2022. googledrive: An Interface to Google Drive.
 \url{https://googledrive.tidyverse.org}, \url{https://github.com/tidyverse/googledrive}.
 
-Hijmans, R. J., Van Etten, J., Cheng, J., Mattiuzzi, M., Sumner, M., Greenberg, J. A.,
-Lamigueiro, O. P., Bevan, A., Racine, E. B. & Shortridge, A. 2015. Package ‘raster’. R package,
-734.
+Hijmans, R.J., Bivand, R., Forner, K., Ooms, J., Pebesma, E. and Sumner, M.D.,
+2022. Package 'terra'. Maintainer: Vienna, Austria.
 }
diff --git a/man/extract_dynamic_raster.Rd b/man/extract_dynamic_raster.Rd
index 57ed485..7ad29d4 100644
--- a/man/extract_dynamic_raster.Rd
+++ b/man/extract_dynamic_raster.Rd
@@ -24,7 +24,7 @@ extract_dynamic_raster(
 \item{dates}{a character string, vector of dates in format "YYYY-MM-DD".}
 
 \item{spatial.ext}{the spatial extent for the extracted raster. Object from which extent can be
-extracted of class \code{Extent}, \code{RasterLayer}, \code{SpatialPolygonsDataFrame}, \code{sf} or \code{polygon} or
+extracted of class \code{SpatExtent}, \code{SpatRaster}, an \code{sf} polygon or a
 numeric vector listing xmin, xmax, ymin and ymax in order.}
 
 \item{datasetname}{a character string, the Google Earth Engine dataset to extract data from.}
diff --git a/man/extract_static_coords.Rd b/man/extract_static_coords.Rd
index c1a71f5..1ec78e3 100644
--- a/man/extract_static_coords.Rd
+++ b/man/extract_static_coords.Rd
@@ -19,16 +19,16 @@ and dates with column names as follows; record longitude as "x", latitude as
 "y", and associated explanatory variable data.}
 
 \item{varnames}{a character string or vector, the unique names for each
-explanatory variable in order of rasters in stack.}
+explanatory variable in order of layers in the SpatRaster.}
 
 \item{extraction.method}{a character string or vector, the methods to extract
-data from raster using \code{raster} package \code{extract} function. One of \code{simple}
+data from SpatRaster using \code{terra} package \code{extract} function. One of \code{simple}
 or \code{bilinear}. If \code{simple} values for the cell a point falls in are
 returned. If \code{bilinear} the returned values are interpolated from the values
 of the four nearest raster cells.}
 
-\item{static.rasters}{a RasterStack of one or more rasters to extracted data
-from.}
+\item{static.rasters}{a \code{SpatRaster} containing one or more SpatRaster layers
+to extract data from.}
 
 \item{moving.window.matrix}{optional; a matrix of weights with an odd number
 of sides, representing the spatial neighbourhood of cells (“moving
@@ -52,14 +52,13 @@ record co-ordinates or spatially buffered using a moving window matrix.
 
 Note:
 \itemize{
-\item \code{varnames} must be in the order of raster layers within the provided stack.
-\item \code{extraction.method} must be of length one to use for all layers, or the
-length of nlayers(static.rasters) and in the same order as the appropriate
-layers within the provided stack.
+\item \code{varnames} must be in the order of raster layers within the SpatRaster.
+\item \code{extraction.method} must be of length one to apply to all layers, or
+length equal to the number of layers in \code{static.rasters}.
 }
 }
 \section{Spatial buffering (optional)}{
-Using the \code{focal} function from \code{raster} R package (Hijmans et al., 2015),
+Using the \code{focal} function from \code{terra} R package (Hijmans et al., 2022),
 \code{GEE.math.fun} is calculated across the spatial buffer area from the record
 co-ordinate. The spatial buffer area used is specified by the argument
 \code{moving.window.matrix}, which dictates the neighbourhood of cells
@@ -84,11 +83,18 @@ analogous R function is available. This includes: "allNonZero","anyNonZero",
 \examples{
 \donttest{
 data("sample_explan_data")
-data("sample_biome_data")
+random_cat_layer <- terra::rast(sample_extent_data)
+random_cat_layer <- terra::setValues(random_cat_layer,
+                                    sample(0:10, terra::ncell(random_cat_layer),
+                                           replace = TRUE))
 
 extract_static_coords(occ.data = sample_explan_data,
-                     varnames = "biome_type",
-                     static.rasters = raster::stack(sample_biome_data))
+                     varnames = "random_cat_layer",
+                     static.rasters = random_cat_layer)
 
 }
 }
+\references{
+Hijmans, R.J., Bivand, R., Forner, K., Ooms, J., Pebesma, E. and Sumner, M.D.,
+2022. Package ‘terra’. Maintainer: Vienna, Austria.
+}
diff --git a/man/figures/dynamicSDM_1.3.pdf b/man/figures/dynamicSDM_1.3.pdf
index e892a7f..c150d43 100644
Binary files a/man/figures/dynamicSDM_1.3.pdf and b/man/figures/dynamicSDM_1.3.pdf differ
diff --git a/man/get_moving_window.Rd b/man/get_moving_window.Rd
index 39b1a8d..dc1d4f1 100644
--- a/man/get_moving_window.Rd
+++ b/man/get_moving_window.Rd
@@ -20,9 +20,9 @@ variable data.}
 \item{spatial.res.metres}{a numeric value, the spatial resolution in metres of explanatory
 variable data.}
 
-\item{spatial.ext}{the spatial extent of the study. Object from which extent can be extracted of
-class \code{Extent}, \code{RasterLayer}, \code{sf} or \code{polygon} or numeric vector listing xmin, xmax, ymin and
-ymax in order.}
+\item{spatial.ext}{the spatial extent of the study. Object from which extent
+can be extracted of class \code{SpatExtent}, \code{SpatRaster}, \code{sf} polygon or
+numeric vector listing xmin, xmax, ymin and ymax in order.}
 }
 \value{
 Returns "moving window" matrix with an odd number of sides and equal weights.
@@ -42,10 +42,12 @@ variable would be calculated across the nine grid cells neighbouring the cell of
 cell of interest.
 }
 
-\section{Why use a moving window matrix instead of circular buffer? The benefit of using a “moving}{
-window” over calculating explanatory variable values across a set radius from each record
-co-ordinate, is that when generating projection rasters at high spatial and temporal resolution,
-these can be generated much faster as the “moving windows” standardise the calculation.
+\section{Why use a moving window matrix instead of circular buffer?}{
+The benefit of using a “moving window” over calculating explanatory
+variable values across a set radius from each record co-ordinate, is that when
+generating projection rasters at high spatial and temporal resolution, these
+can be generated much faster as the “moving windows” standardise the
+calculation.
 }
 
 \section{Function calculation}{
diff --git a/man/sample_biome_data.Rd b/man/sample_biome_data.Rd
deleted file mode 100644
index ab911cd..0000000
--- a/man/sample_biome_data.Rd
+++ /dev/null
@@ -1,32 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data.R
-\docType{data}
-\name{sample_biome_data}
-\alias{sample_biome_data}
-\title{Biome classification raster for southern Africa.}
-\format{
-A RasterLayer
-\describe{
-\item{class}{RasterLayer}
-\item{dimensions}{4839, 3243, 15692877  (nrow, ncol, ncell)}
-\item{resolution}{0.008983153, 0.008983153  (x, y)}
-\item{extent}{11.71845, 40.85081, -47.89832, -4.428839  (xmin, xmax, ymin, ymax)}
-\item{crs}{"+proj=longlat +datum=WGS84 +no_defs"}
-\item{source}{biome_type_2001-01-01.tif }
-\item{names}{biome_type_2001-01-01.tif }
-\item{values}{Numerical categorical value ranging from 0, 255  (min, max).}
-}
-}
-\usage{
-sample_biome_data
-}
-\description{
-Categorical RasterLayer cropped to southern Africa at 0.08 degree resolution from OpenLandMap
-Potential Distribution of Biomes (Hengl et al., 2018).
-}
-\references{
-Hengl T, Walsh MG, Sanderman J, Wheeler I, Harrison SP, Prentice IC. (2018) Global Mapping of
-Potential Natural Vegetation: An Assessment of Machine Learning Algorithms for Estimating Land
-Potential. PeerJ Preprints.
-}
-\keyword{datasets}
diff --git a/man/sample_proj_rast.Rd b/man/sample_proj_rast.Rd
deleted file mode 100644
index f8126f5..0000000
--- a/man/sample_proj_rast.Rd
+++ /dev/null
@@ -1,29 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data.R
-\docType{data}
-\name{sample_proj_rast}
-\alias{sample_proj_rast}
-\title{Distribution suitability projections for red-billed quelea in southern Africa}
-\format{
-A RasterBrick
-\describe{
-\item{class}{RasterBrick}
-\item{dimensions}{31, 30, 930, 4  (nrow, ncol, ncell, nlayers)}
-\item{resolution}{1, 1  (x, y)}
-\item{extent}{11.6901, 41.6901, -35.43802, -4.438023  (xmin, xmax, ymin, ymax)}
-\item{crs}{"+proj=longlat +datum=WGS84 +no_defs"}
-\item{source}{memory} \item{names}{ X2018.01.01_proportional, X2018.04.01_proportional,
-X2018.07.01_proportional, X2018.10.01_proportional }
-\item{min values}{Numerical minimum distribution suitability projected in each layer.}
-\item{max values}{Numerical maximum distribution suitability projected in each layer.}
-}
-}
-\usage{
-sample_proj_rast
-}
-\description{
-RasterBrick of distribution suitability projections for red-billed quelea across southern at one
-degree resolution for the dates: "2018-01-01", "2018-04-01", "2018-07-01" and "2018-10-01". A
-value of 1 represents high suitability and a value of 0 low suitability.
-}
-\keyword{datasets}
diff --git a/man/spatiotemp_bias.Rd b/man/spatiotemp_bias.Rd
index 161d124..bea1767 100644
--- a/man/spatiotemp_bias.Rd
+++ b/man/spatiotemp_bias.Rd
@@ -56,7 +56,7 @@ and Nikulin, 1996) .
 }
 
 \section{Spatial bias}{
-To assess spatial sampling bias, the function returns a scatterplot of the spatial
+To assess spatial sampling bias, the function returns a scatter plot of the spatial
 distribution of occurrence records to illustrate any spatial clustering (if \code{plot = TRUE}). The
 average nearest neighbour distance of record co-ordinates is then compared to that of records
 randomly generated at same density using a t-test, following the nearest neighbour index
diff --git a/man/spatiotemp_block.Rd b/man/spatiotemp_block.Rd
index e72c806..4bb308e 100644
--- a/man/spatiotemp_block.Rd
+++ b/man/spatiotemp_block.Rd
@@ -22,7 +22,7 @@ column names as follows; record longitude as "x", latitude as "y", year as "year
 \item{vars.to.block.by}{a character string or vector, the explanatory variable column names to
 group sampling units based upon.}
 
-\item{spatial.layer}{optional; a \code{RasterLayer} object, a categorical spatial layer for sample
+\item{spatial.layer}{optional; a \code{SpatRaster} object, a categorical spatial layer for sample
 unit splitting.}
 
 \item{spatial.split.degrees}{a numeric value, the grid cell resolution in degrees to split
diff --git a/man/spatiotemp_extent.Rd b/man/spatiotemp_extent.Rd
index b7c5313..beb30d8 100644
--- a/man/spatiotemp_extent.Rd
+++ b/man/spatiotemp_extent.Rd
@@ -20,9 +20,9 @@ column names as follows; record longitude as "x", latitude as "y", year as "year
 represents start of temporal extent and second date represents end of temporal extent for
 inclusion.}
 
-\item{spatial.ext}{the spatial extent to filter by. Object from which extent can be extracted of
-class \code{Extent}, \code{RasterLayer}, \code{SpatialPolygonsDataFrame},\code{sf} or \code{polygon} or numeric vector
-listing xmin, xmax, ymin and ymax in order.}
+\item{spatial.ext}{the spatial extent to filter by. Object from which extent
+can be extracted of class \code{SpatExtent}, \code{SpatRaster}, \code{sf} polygon or
+numeric vector listing xmin, xmax, ymin and ymax in order.}
 
 \item{prj}{a character string, the coordinate reference system of input \code{occ.data} co-ordinates.
 Default is "+proj=longlat +datum=WGS84".}
@@ -40,10 +40,9 @@ If \code{spatial.ext} is provided, \code{spatiotemp_extent()} checks whether spe
 co-ordinates are within the given spatial extent of the study (\code{spatial.ext}) and excludes any
 outside of this extent.
 
-If \code{spatial.ext} object can be used as a mask by \code{raster::mask()} then the mask is used to filter
-records in a more targetted way. If not, then the rectangular extent of the \code{spatial.ext} object
-is used. If an \code{sf} polygon object is provided, this is first transformed into a \code{Spatial} object
-for use by \code{raster::mask()}.
+If \code{spatial.ext} object can be used as a mask by \code{terra::mask()} then the mask is used to filter
+records in a more targeted way. If not, then the rectangular extent of the \code{spatial.ext} object
+is used.
 }
 
 \section{Temporal extent}{
diff --git a/man/spatiotemp_pseudoabs.Rd b/man/spatiotemp_pseudoabs.Rd
index 21e96a6..f6401fa 100644
--- a/man/spatiotemp_pseudoabs.Rd
+++ b/man/spatiotemp_pseudoabs.Rd
@@ -27,10 +27,11 @@ of \code{buffer} or \code{random}: can be abbreviated.}
 with column names as follows; record longitude as "x", latitude as "y", year as "year", month as
 "month", and day as "day". Required if either \code{temporal.method} or \code{spatial.method} is \code{buffer}.}
 
-\item{spatial.ext}{the spatial extent to randomly generate pseudo-absences within. Object from
-which extent can be extracted of class \code{Extent}, \code{RasterLayer},\code{SpatialPolygonsDataFrame}, \code{sf}
-or \code{polygon} or numeric vector listing xmin, xmax, ymin and ymax in order. Required if
-\code{spatial.method} is \code{random}, and optionally used if \code{buffer}. See details.}
+\item{spatial.ext}{the spatial extent to randomly generate pseudo-absences
+within. Object from which extent can be extracted of class \code{SpatExtent},
+\code{SpatRaster}, an \code{sf} polygon or a numeric vector listing xmin, xmax, ymin
+and ymax in order. Required if \code{spatial.method} is \code{random}, and optionally
+used if \code{buffer}. See details.}
 
 \item{temporal.ext}{optional; a character vector, two dates in format "YYYY-MM-DD". The first
 represents the start of the temporal extent and the second represents the end of temporal extent
@@ -89,12 +90,12 @@ want to clip buffers to only terrestrial regions using a country polygon given i
 If \code{spatial.method} is \code{random}, then the pseudo-absence record co-ordinates are randomly
 generated across \code{spatial.ext} object given.
 
-If \code{spatial.ext} is a \code{sf} polygon or multipolygon object, \code{RasterLayer} (mask if possible before
-input) or \code{SpatialPolygonsDataFrame}, then these shapes are used, instead of a simple rectangular
-extent. Therefore, inputting one of these objects will allow for more specific pseudo-absence
+If \code{spatial.ext} is a \code{sf} polygon or \code{SpatRaster} (mask if possible before
+input) then these shapes are used, instead of a simple rectangular
+extent (\code{SpatExtent}). Therefore, inputting one of these objects will allow for more specific pseudo-absence
 generation.
 
-For example, inputting a \code{SpatialPolygonsDataFrame} of specific countries will ensure co-ordinates
+For example, inputting an \code{sf} polygon of a specific countries will ensure co-ordinates
 are terrestrial, whereas an extent (xmin, xmax, ymin, ymax) that encompasses these countries may
 result in the generation of pseudo-absence records in inappropriate areas, such as oceans or
 non-study-area countries.
diff --git a/tests/testthat/test-dynamic_proj_covariates.R b/tests/testthat/test-dynamic_proj_covariates.R
index 3a966f4..45c8f4d 100644
--- a/tests/testthat/test-dynamic_proj_covariates.R
+++ b/tests/testthat/test-dynamic_proj_covariates.R
@@ -138,7 +138,7 @@ test_that("Works if spatial.ext = RasterLayer", {
 
 test_that("Works if spatial.ext = polygon", {
   dates <- c("2010-01-01")
-  polygon <- sp::Polygon(cbind(c(12, 12, 36, 36), c(-35, -12, -35, -12)))
+  polygon <-  sf::st_polygon(list(cbind(c(20, 12, 36, 36,20), c(-35, -12, -35, -12,-35))))
   results <- dynamic_proj_covariates(
       dates = dates,
       varnames = c("precipitation_10_prior_sum", "NDVI_5_post_max"),
diff --git a/tests/testthat/test-get_moving_window.R b/tests/testthat/test-get_moving_window.R
index 80f016c..753ae09 100644
--- a/tests/testthat/test-get_moving_window.R
+++ b/tests/testthat/test-get_moving_window.R
@@ -35,16 +35,6 @@ test_that("stops if spatial.res.degrees given but spatial.ext not given", {
   ))
 })
 
-test_that("stops if spatial.res.degrees and spatial.ext class invalid", {
-  expect_error(
-    get_moving_window(
-      radial.distance = 100,
-      spatial.res.degrees = 0.5,
-      spatial.ext = "southernafrica"
-    )
-  )
-})
-
 test_that(
   "stops if spatial.res.degrees and spatial.ext class numeric and incorrect length invalid",
   {
@@ -82,7 +72,7 @@ test_that("works with spatial.ext as numeric", {
 })
 
 test_that("works with spatial.ext as Extent", {
-  Extent <- raster::extent(c(12, 36, -35, -12))
+  Extent <- terra::ext(c(12, 36, -35, -12))
   results <- get_moving_window(
       radial.distance = 100,
       spatial.res.degrees = 0.5,
@@ -93,7 +83,7 @@ test_that("works with spatial.ext as Extent", {
 
 test_that("works with spatial.ext as raster", {
   numeric <- c(12, 36, -35, -12)
-  raster <- raster::raster(raster::extent(numeric))
+  raster <- terra::rast(terra::ext(numeric))
   results <- get_moving_window(
       radial.distance = 100000,
       spatial.res.degrees = 0.5,
@@ -103,7 +93,7 @@ test_that("works with spatial.ext as raster", {
 })
 
 test_that("works with spatial.ext as polygon", {
-  polygon <- sp::Polygon(cbind(c(12, 12, 36, 36), c(-35, -12, -35, -12)))
+  polygon <-  sf::st_polygon(list(cbind(c(20, 12, 36, 36,20), c(-35, -12, -35, -12,-35))))
   results <- get_moving_window(
       radial.distance = 100000,
       spatial.res.degrees = 0.5,
@@ -123,15 +113,6 @@ test_that("works with spatial.ext as sf", {
   expect_equal(class(results)[1], "matrix")
 })
 
-test_that("stops if spatial.res.degrees and spatial.ext class invalid", {
-  expect_error(
-    get_moving_window(
-      radial.distance = 100000,
-      spatial.res.degrees = 0.5,
-      spatial.ext = "southernafrica"
-    )
-  )
-})
 
 test_that("Error if wrong class spatial.res.metres", {
   expect_error(get_moving_window(radial.distance = 100000, spatial.res.metres =
diff --git a/tests/testthat/test-spatiotemp_block.R b/tests/testthat/test-spatiotemp_block.R
index 7dfac30..3d230a9 100644
--- a/tests/testthat/test-spatiotemp_block.R
+++ b/tests/testthat/test-spatiotemp_block.R
@@ -1,9 +1,9 @@
 data("sample_explan_data")
 sample_model_data2 <- dplyr::sample_n(sample_explan_data, 50)
-biome_layer <- raster::raster()
-raster::extent(biome_layer) <-  c(11.71845, 40.85081,-47.89832,-4.428839)
-raster::res(biome_layer) <- 10
-raster::values(biome_layer) <- 1:raster::ncell(biome_layer)
+biome_layer <- terra::rast(sample_extent_data)
+terra::res(biome_layer) <- 10
+biome_layer <- terra::setValues(biome_layer,
+                                     1:terra::ncell(biome_layer))
 
 test_that("stops if no occ.data provided", {
   expect_error(
diff --git a/tests/testthat/test-spatiotemp_extent.R b/tests/testthat/test-spatiotemp_extent.R
index 33ffc1e..9bc77da 100644
--- a/tests/testthat/test-spatiotemp_extent.R
+++ b/tests/testthat/test-spatiotemp_extent.R
@@ -95,7 +95,7 @@ test_that("Filters by spatial.ext but all within extent, raster ", {
 })
 
 test_that("Filters by spatial.ext polygon ", {
-  polygon <- sp::Polygon(cbind(c(12, 12, 36, 36), c(-35, -12, -35, -12)))
+  polygon <-  sf::st_polygon(list(cbind(c(20, 12, 36, 36,20), c(-35, -12, -35, -12,-35))))
   results <- spatiotemp_extent(occ.data = sample_explan_data, spatial.ext = polygon)
   expect_equal(nrow(results), 327)
 })
diff --git a/tests/testthat/test-spatiotemp_pseudoabs.R b/tests/testthat/test-spatiotemp_pseudoabs.R
index c4fc542..8fcbc63 100644
--- a/tests/testthat/test-spatiotemp_pseudoabs.R
+++ b/tests/testthat/test-spatiotemp_pseudoabs.R
@@ -323,7 +323,7 @@ test_that("Success if spatial extent numeric", {
 })
 
 test_that("Success if spatial extent extent", {
-  Extent <- raster::extent(c(20, 36, -35, -12))
+  Extent <- terra::ext(c(20, 36, -35, -12))
   results <- spatiotemp_pseudoabs(
       occ.data = sample_explan_data,
       n.pseudoabs = 10,
@@ -336,7 +336,7 @@ test_that("Success if spatial extent extent", {
 })
 
 test_that("Success if spatial extent raster", {
-  raster <- raster::raster(raster::extent(c(20, 36, -35, -12)))
+  raster <- terra::rast(terra::ext(c(20, 36, -35, -12)))
   results <- spatiotemp_pseudoabs(
       occ.data = sample_explan_data,
       n.pseudoabs = 10,
@@ -349,7 +349,7 @@ test_that("Success if spatial extent raster", {
 })
 
 test_that("Success if spatial extent polygon", {
-  polygon <- sp::Polygon(cbind(c(20, 12, 36, 36), c(-35, -12, -35, -12)))
+  polygon <-  sf::st_polygon(list(cbind(c(20, 12, 36, 36,20), c(-35, -12, -35, -12,-35))))
   results <-spatiotemp_pseudoabs(
       occ.data = sample_explan_data,
       n.pseudoabs = 10,
diff --git a/vignettes/vignette4_projecting.Rmd b/vignettes/vignette4_projecting.Rmd
index 08c7f49..3803fb5 100644
--- a/vignettes/vignette4_projecting.Rmd
+++ b/vignettes/vignette4_projecting.Rmd
@@ -209,10 +209,10 @@ dynamic_proj(dates = projectiondates,
 Let’s read in and plot our projections!
 
 ```{r plot projections, eval = FALSE}
-sp::plot(terra::rast(list.files(projection_directories)[1]))
-sp::plot(terra::rast(list.files(projection_directories)[2]))
-sp::plot(terra::rast(list.files(projection_directories)[3]))
-sp::plot(terra::rast(list.files(projection_directories)[4]))
+terra::plot(terra::rast(list.files(projection_directories)[1]))
+terra::plot(terra::rast(list.files(projection_directories)[2]))
+terra::plot(terra::rast(list.files(projection_directories)[3]))
+terra::plot(terra::rast(list.files(projection_directories)[4]))
 ```