View source: R/spatiotemp_bias.R
spatiotemp_bias | R Documentation |
Generates plots for visual assessment of spatial and temporal biases in occurrence records. Tests whether the spatiotemporal distribution of records is significantly different from the distribution from random sampling.
spatiotemp_bias(
occ.data,
temporal.level,
plot = FALSE,
spatial.method = "simple",
centroid,
radius,
prj = "+proj=longlat +datum=WGS84"
)
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", year as "year", month as "month", and day as "day". |
temporal.level |
a character string or vector, the time step(s) to test for temporal bias at.
One or multiple of |
plot |
a logical indicating whether to generate plots of spatial and temporal bias. See
details for plot descriptions. Default = |
spatial.method |
a character string, the method to calculate the spatial bias statistic. One
of; |
centroid |
a numeric vector of length two, specifying the centroid co-ordinates in the order
of longitude then latitude. Only required if |
radius |
a numeric value, the radial distance in metres from the given centroid co-ordinate
to measure spatial bias within. Only required if |
prj |
a character string, the coordinate reference system of occ.data co-ordinates. Default is "+proj=longlat +datum=WGS84". |
Returns list containing chi-squared and t-test results, and plots if specified.
To assess temporal sampling bias, the function returns a histogram plot
of the frequency distribution of records across the given time step specified by temporal.level
(if plot = TRUE
). The observed frequency of sampling across the categorical time steps are
compared to the distribution expected from random sampling, using a chi-squared test (Greenwood
and Nikulin, 1996) .
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
established by Clark and Evans (1954).
Below we outline the methods for which these tests for biases can be applied. dynamicSDM
offers
the additional functionality of the core
approach. This enables users to explore sampling biases
in set areas of a species range. This may be valuable if periphery-core relationships could lead
to inaccurate inferences of sampling bias. For instance, if species are expanding or shifting
their ranges through space and time.
#'
simple
- generates the random points within a rectangle created using
the minimum and maximum longitude and latitude of occurrence co-ordinates.
convex_hull
- generates the random points within the convex hull of occurrence record
co-ordinates (i.e. the smallest convex set that contains all records).
core
- generates the random points within specified circular area generated from a centroid
point and radius. If these arguments ( centroid
and radius
) are not provided then centroid
is calculated by averaging co-ordinates of all occurrence records, and radius
is the mean
distance away of all records from the centroid.
For each method, only occurrence records within the specified area are tested for spatial and temporal sampling biases.
As the spatial bias test involves the calculation of a distance matrix. To reduce computation time, it is recommended that only a representative sample of large occurrence datasets are input.
Clark, P. J. & Evans, F. C. J. E. 1954. Distance To Nearest Neighbor As A Measure Of Spatial Relationships In Populations. 35, 445-453.
Greenwood, P. E. & Nikulin, M. S. 1996. A Guide To Chi-Squared Testing, John Wiley & Sons.
data(sample_explan_data)
bias_simple <- spatiotemp_bias(
occ.data = sample_explan_data,
temporal.level = c("year"),
spatial.method = "simple",
plot = FALSE
)
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