| Type: | Package |
| Title: | Calculate the Latitudinal Bias Index |
| Version: | 1.0.0 |
| Description: | Studies that report shifts in species distributions may be biased by the shape of the study area. The main functionality of this package is to calculate the Latitudinal Bias Index (LBI) for any given shape. The LBI is bounded between +1 (100% probability to exclusively record latitudinal shifts, i.e., range shifts data sampled along a perfectly South-North oriented straight line) and -1 (100% probability to exclusively record longitudinal shifts, i.e., range shifts data sampled along a perfectly East-West oriented straight line). |
| Depends: | R (≥ 3.5.3) |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| Imports: | dplyr, geosphere, ggplot2, psych, RColorBrewer, reshape2, sf, sp, terra, tidyr, units |
| RoxygenNote: | 7.3.3 |
| Suggests: | elevatr, knitr, progress, rmarkdown, rnaturalearth, rnaturalearthdata, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| URL: | https://github.com/pierredenelle/latbias, |
| BugReports: | https://github.com/pierredenelle/latbias/issues |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2025-10-21 16:01:25 UTC; Pierre |
| Author: | Pierre Denelle 
[aut, cre],
Pieter Sanczuk
[aut] |
| Maintainer: | Pierre Denelle <pierre.denelle@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-10-25 12:40:02 UTC |
LBI
Description
Computes the Latitudinal Bias Index (LBI) for a given shapefile, by calculating the distance between two random locations within the shape multiple times (see details).
Usage
LBI(
study_area_id,
study_area_polygon,
nobs = 250,
nboot = 1000,
fact_location = 10,
elevation = NULL,
raw_output = FALSE
)
Arguments
study_area_id |
Character string. Name ID of the study case area or country name. |
study_area_polygon |
Polygon shapefile. It should be a sfc object, of class POLYGON or MULTIPOLYGON. |
nobs |
Numeric. Number of random observations in each sample. 250 by default. |
nboot |
Numeric. Determines how many times the random shifts are calculated. 1,000 by default. |
fact_location |
Numeric. fact_location x nobs determine all the possible coordinates that can be sampled within the provided polygon for all bootstraps (faster than to generate a set of random location at each bootstrap) |
elevation |
Elevation raster. elevation in wgs84; if not provided, NA will be returned for null-model elevational shifts. |
raw_output |
Logical. FALSE by default. If TRUE, all bootstraps are returned as a data.frame. => say that the raw outputs are accessible |
Details
The main output contains the following columns:
study_area_id: ID or name of the study case region
distance_km: average expected geographic distance shift between t1 and t2
null_mod_SN_shift: average expected South-North shift between t1 and t2, in absolute values
null_mod_EW_shift: average expected East-West shift between t1 and t2, in absolute values
null_mod_elevation_shift: average expected elevation shift between t1 and t2, in absolute values
LBI: the Latitudinal Bias Index value
LBI formula is
LBI = 2\times(\frac{mean(|\frac{A_nlat}{A_nlon}|)}{1+mean(|\frac{A_nlat}{A_nlon}|)}-0.5)
with A_nlat and A_nlon denoting the geographic displacement of
the centroid positions of both sets of observation in the nth iteration by
means in the latitudinal and the longitudinal dimension.
Value
A data.frame or a list of two data.frames if raw_output is set to TRUE.
References
Sanczuk et al. submitted.
Examples
study_area <- rnaturalearth::ne_countries(scale = 110, continent = "Europe",
country = "Sweden", type = "map_units", returnclass = "sf")
study_area <- sf::st_union(study_area)
LBI(study_area_id = "Sweden", study_area_polygon = study_area,
nobs = 10, nboot = 10, fact_location = 5, elevation = NULL)
# With elevation
elevation_df <- elevatr::get_elev_raster(
locations = sf::st_as_sf(study_area), z = 5)
LBI(study_area_id = "Sweden", study_area_polygon = study_area,
nobs = 10, nboot = 10, fact_location = 5, elevation = elevation_df)
windrose
Description
Plot the directional shifts for a given bootstrap.
Usage
windrose(
data,
spd,
dir,
spdres = 30,
dirres = 30,
spdmin = 0,
spdmax = 150,
spdseq = NULL,
palette = "Spectral",
countmax = NA,
debug = 0
)
Arguments
data |
ddata frame with two columns including the bearing and distance or speed of shifts |
spd |
distance or speed column |
dir |
bearing column |
spdres |
30 by default. Numeric; resolution for plotting distance of speed categories; adapt here the legend and breaks. |
dirres |
30 by default. Numeric, resolution for plotting bearings on windrose; adapt here the number of bars plotted on the windrose. |
spdmin |
0 by default. Numeric, minimal distance/speed for plotting. |
spdmax |
150 by default. Numeric, maximal distance/speed for plotting. |
spdseq |
NULL |
palette |
Character string defining the color palette to be used. Available options are taken from rownames(RColorBrewer::brewer.pal.info). |
countmax |
NA by default. Numeric, optional, adjust the y-axis limit (maximum) |
debug |
0 by default. Numeric, if >0, run debug to find optimal bins of spd and dir. |
Details
This is intended to plot raw simulation output (that is, bearing and distance of random shifts) on a windrose.
Value
Windrose plot, ggplot object.
References
Sanczuk et al. submitted.
Examples
study_area <- rnaturalearth::ne_countries(scale = 110, continent = "Europe",
country = "Sweden", type = "map_units", returnclass = "sf")
study_area <- sf::st_union(study_area)
po <- LBI(study_area_id = "Sweden", study_area_polygon = study_area,
nobs = 10, nboot = 10, fact_location = 5, elevation = NULL,
raw_output = TRUE)
test <- as.data.frame(po$all[, c("study_area_id", "distance_km",
"bearing", "rep")])
test$distance_km <- as.numeric(test$distance_km)
pop <- windrose(data = test, spd = "distance_km", dir = "bearing")