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results_habitat_association

Source: R/results_habitat_association.R
results_habitat_association.Rd

Results habitat association

Usage

results_habitat_association(
  pattern,
  raster,
  significance_level = 0.05,
  breaks = NULL,
  digits = NULL,
  verbose = TRUE
)

Arguments

pattern

ppp object with original point pattern data or rd_pat or rd_mar object with randomized point pattern.

raster

SpatRaster with original discrete habitat data or rd_ras object with randomized environmental data.

significance_level

Double with significance level.

breaks

Vector with breaks of habitat classes.

digits

Integer with digits used during rounding.

verbose

Logical if messages should be printed.

Value

data.frame

Details

The functions shows significant habitat associations by comparing the number of points within a habitat between the observed data and randomized data as described in Plotkin et al. (2000) and Harms et al. (2001). Significant positive or associations are present if the observed count in a habitat is above or below a certain threshold of the randomized count, respectively.

In case the SpatRaster contains NA cells, this needs to be reflected in the observation window of the point pattern as well (i.e., no point locations possible in these areas).

If breaks = NULL (default), only habitat labels (but not breaks) will be returned. If a vector with breaks is provided (same order as increasing habitat values), the breaks will be included as well.

References

Harms, K.E., Condit, R., Hubbell, S.P., Foster, R.B., 2001. Habitat associations of trees and shrubs in a 50-ha neotropical forest plot. Journal of Ecology 89, 947–959. <https://doi.org/10.1111/j.1365-2745.2001.00615.x>

Plotkin, J.B., Potts, M.D., Leslie, N., Manokaran, N., LaFrankie, J.V., Ashton, P.S., 2000. Species-area curves, spatial aggregation, and habitat specialization in tropical forests. Journal of Theoretical Biology 207, 81–99. <https://doi.org/10.1006/jtbi.2000.2158>

See also

reconstruct_pattern
fit_point_process

Examples

landscape_classified <- classify_habitats(terra::rast(landscape), n = 5, style = "fisher")
species_a_random <- fit_point_process(species_a, n_random = 199)
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results_habitat_association(pattern = species_a_random, raster = landscape_classified)
#> > Input: randomized pattern
#> > Quantile thresholds: negative < 0.025 || positive > 0.975
#>   habitat breaks count    lo hi significance
#> 1       1     NA    35 12.00 26     positive
#> 2       2     NA    44 25.00 44         n.s.
#> 3       3     NA    36 22.95 41         n.s.
#> 4       4     NA     4 29.00 52     negative
#> 5       5     NA    73 54.00 80         n.s.