Takes a OrganizedBirds-object and a SpatialPolygons*-grid and summarizes it in spatial and temporal dimensions.
summarizeBirds(x, grid, spillOver = NULL) # S3 method for OrganizedBirds summarizeBirds(x, grid, spillOver = NULL) summariseBirds(x, grid, spillOver = NULL)
| x | An OrgnanizedBirds-object created by |
|---|---|
| grid | A sf, SpatialPolygons or SpatialPolygonsDataFrame-object with grid cells for the study area. This variable is optional and can be set to NULL, which will treat the area for all observations as one single grid cell. |
| spillOver | Specifies if the function should search for observations done
during the same visit (identified by visit UID) but that fall outside the grid
cell. Default is |
A SummarizedBirds-object
If spillOver = NULL the splitting is done spatially according to the
overlay of observations and grid cells, without further consideration of
coherence for visits (visit UID). If spillOver = c("unique", "duplicate")
the splitting will be done spatially in a first step, and then:
if (spillOver = "unique") assigns (and moves) all observations with same
visitUID to the grid cell with most observations (or picks one grid cell at
random if there is a tie); or if (spillOver = "duplicate") duplicates
all observations with same visitUID across all grid cells containing at least
one observation with that visitUID.
The later approach is useful when the amount of observations spilled over neighbouring cells is minimal and information over the spatial extent of the sampling effort is more important than sample independence.
# \donttest{ ob <- organizeBirds(bombusObsShort) grid <- makeGrid(gotaland, gridSize = 10) SB <- summarizeBirds(ob, grid)#>nObsG <- rowSums(SB$spatioTemporal[,,13,"nObs"], na.rm = FALSE) nObsG2 <- SB$spatial$nObs any(nObsG != nObsG2, na.rm = TRUE) ## Check, two ways to obtain the same#> [1] FALSE# }