spatiotemp_weights function

Calculate sampling effort across spatial and temporal buffer from species occurrence records

Calculates the total number of sampling events across a given spatial and temporal buffer from each occurrence record’s co-ordinate and date.

spatiotemp_weights(
  occ.data,
  samp.events,
  spatial.dist = 0,
  temporal.dist = 0,
  prj = "+proj=longlat +datum=WGS84"
)

Arguments

• 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".
• samp.events: a data.frame, sampling events with column names as follows; record longitude as "x", latitude as "y", year as "year", month as "month", and day as "day".
• spatial.dist: a numeric value, the spatial distance in metres representing the radius from occurrence record co-ordinate to sum sampling events across.
• temporal.dist: a numeric value, the temporal distance in days, representing the period before and after the occurrence record date to sum sampling events across.
• prj: a character string, the coordinate reference system of input occ.data co-ordinates Default is "+proj=longlat +datum=WGS84".

Returns

Returns input occurrence record data frame with additional columns for sampling effort "SAMP_EFFORT" and relative sampling effort "REL_SAMP_EFFORT".

Details

For each occurrence record, this function calculates the total number of sampling events within given radius (spatial.dist) from each record co-ordinate and days (temporal.dist) both prior and post record date.

In addition to total sampling events, the function also calculates relative sampling effort, scaling from 0 (least sampled) to 1 (most sampled).

Output could be used to calculate model weights to correct spatial and temporal biases in occurrence record collections (Stolar and Nielsen, 2015).

Examples

data("sample_explan_data")
data("sample_events_data")

spatiotemp_weights(
 occ.data = sample_explan_data,
 samp.events = sample_events_data,
 spatial.dist = 200000,
 temporal.dist = 20
)

References

Stolar, J. & Nielsen, S. E. 2015. Accounting For Spatially Biased Sampling Effort In Presence-Only Species Distribution Modelling. Diversity And Distributions, 21, 595-608.