It is often the case that effort, distances and prediction area are
collected in different units in the field. Functions in
allow for an argument to convert between these and provide an answer that
makes sense. This function calculates that conversion factor, given
knowledge of the units of the quantities used.
convert_units(distance_units, effort_units, area_units)
units distances were measured in.
units that effort were measured in. Set as
units for the prediction area.
convert_units expects particular names for its inputs – these should
be singular names of the unit (e.g., "metre" rather than "metres"). You can
view possible options with
units_table. Both UK and US
spellings are acceptable, case does not matter. For density estimation, area
must still be provided ("objects per square ???"). Note that for cue counts
(or other multiplier-based methods) one will still have to ensure that the
rates are in the correct units for the survey.
David L Miller
# distances measured in metres, effort in kilometres and # abundance over an area measured in hectares: convert_units("Metre", "Kilometre", "Hectare") # all SI units, so the result is 1 convert_units("Metre", "metre", "square metre") # for points ignore effort convert_units("Metre", NULL, "Hectare")
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