Takes a CompadreDB object and calculates a grand mean fecundity matrix for each unique population (a mean of all population-specific fecundity matrices, including fecundity matrices for which MatrixComposite == 'Mean').

Populations are defined based on unique combinations of the columns 'SpeciesAuthor', 'MatrixPopulation', and 'MatrixDimension', (or optionally, a different set of columns supplied by the user).

The main purpose of this function is to identify stage classes that are potentially reproductive (i.e. the absence of fecundity in a given stage class and year does not necessarily indicate that the stage in question is non-reproductive).

cdb_mean_matF(cdb, columns = c("SpeciesAuthor", "MatrixPopulation",
  "MatrixDimension"))

Arguments

cdb

A CompadreDB object

columns

Vector of column names from which unique populations should be identified. Defaults to c("SpeciesAuthor", "MatrixPopulation", "MatrixDimension").

Value

Returns a list of matrices, representing the mean fecundity matrix associated with each row of the database.

Examples

# print matF associated with row 16 of database Compadre$mat[[16]]
#> A compadre matrix object with 7 stages. #> #> MatrixClassOrganized #> 1 prop #> 2 active #> 3 active #> 4 active #> 5 active #> 6 active #> 7 active #> MatrixClassAuthor #> 1 Seeds in the seedbank #> 2 Seedling: <1 year old #> 3 Small saplings: ≤ 0,072 cm² #> 4 Medium saplings: 0,072 < d ≤ 0,787 cm² #> 5 Large saplings: 0,787 < d ≤ 19,643 cm² #> 6 Small adults: (with reproduction) 19,643 < d ≤ 63,643 cm² #> 7 Large adults: (with reproduction) > 63,643 cm² #> #> matA: #> 1 2 3 4 5 6 7 #> 1 0.586 0.000 0.000 0.000 0.000 73.25 87.800 #> 2 0.015 0.000 0.000 0.000 0.000 1.24 1.490 #> 3 0.000 0.384 0.411 0.063 0.000 0.00 0.000 #> 4 0.000 0.384 0.429 0.785 0.000 0.00 0.000 #> 5 0.000 0.000 0.000 0.090 0.923 0.00 0.000 #> 6 0.000 0.000 0.000 0.000 0.020 0.88 0.000 #> 7 0.000 0.000 0.000 0.000 0.000 0.02 0.962 #> #> matU: #> 1 2 3 4 5 6 7 #> 1 0.586 0.000 0.000 0.000 0.000 0.00 0.000 #> 2 0.015 0.000 0.000 0.000 0.000 0.00 0.000 #> 3 0.000 0.384 0.411 0.063 0.000 0.00 0.000 #> 4 0.000 0.384 0.429 0.785 0.000 0.00 0.000 #> 5 0.000 0.000 0.000 0.090 0.923 0.00 0.000 #> 6 0.000 0.000 0.000 0.000 0.020 0.88 0.000 #> 7 0.000 0.000 0.000 0.000 0.000 0.02 0.962 #> #> matF: #> 1 2 3 4 5 6 7 #> 1 0 0 0 0 0 73.25 87.80 #> 2 0 0 0 0 0 1.24 1.49 #> 3 0 0 0 0 0 0.00 0.00 #> 4 0 0 0 0 0 0.00 0.00 #> 5 0 0 0 0 0 0.00 0.00 #> 6 0 0 0 0 0 0.00 0.00 #> 7 0 0 0 0 0 0.00 0.00 #> #> matC: #> 1 2 3 4 5 6 7 #> 1 0 0 0 0 0 0 0 #> 2 0 0 0 0 0 0 0 #> 3 0 0 0 0 0 0 0 #> 4 0 0 0 0 0 0 0 #> 5 0 0 0 0 0 0 0 #> 6 0 0 0 0 0 0 0 #> 7 0 0 0 0 0 0 0 #>
# create list of meanMatFs meanF <- cdb_mean_matF(Compadre) # print meanMatF associated with row 16 of database meanF[[16]]
#> F1 F2 F3 F4 F5 F6 F7 #> [1,] 0 0 0 0 0 77.316667 261.666667 #> [2,] 0 0 0 0 0 1.306667 4.196667 #> [3,] 0 0 0 0 0 0.000000 0.000000 #> [4,] 0 0 0 0 0 0.000000 0.000000 #> [5,] 0 0 0 0 0 0.000000 0.000000 #> [6,] 0 0 0 0 0 0.000000 0.000000 #> [7,] 0 0 0 0 0 0.000000 0.000000