exposeIgnorance

an object of any class (mainly resulting from <code>summariseBirds</code> or
<code>exportBirds</code> with the number of observations, or visits in your desired analysis unit.

nObs

the number of unique species observed

nSpp

the half ignorance parameter value.

Ignorance scores are a proxy for the lack of sampling effort, computed by making
the number of observations relative to a reference number of observations that
is considered to be enough to reduce the ignorance score by half (henceforth
the Half-ignorance approach). The algorithm behind the Ignorance Score is
designed for comparison of bias and gaps in primary biodiversity data across
taxonomy, time and space

It helps making the evaluation and preparation of biodiversity data
easy, systematic and reproducible. It also helps the users to overlay the
point observations into a custom grid that is useful for further analysis.
The review summarise statistics that helps evaluate whether a set of species
observations is fit-for-use and take decisions upon its use of on further
analyses. It does so by quantifying the sampling effort (amount of effort
expended during an event) and data completeness (data gaps) to help judge
whether the data is representative, valid and fit for any intended purpose.
The 'BIRDS' package is most useful when working with heterogeneous data sets
with variation in the sampling process, i.e. where data have been collected
and reported in various ways and therefore varying in sampling effort
and data completeness (i.e. how well the reported observations describe the
true state). Primary biodiversity data (PBD) combining data from different
data sets, like e.g. Global Biodiversity Information Facility (GBIF) mediated
data, commonly vary in the ways data has been generated - containing
opportunistically collected presence-only data together with and data from
systematic monitoring programs. The set of tools provided is aimed at
understanding the process that generated the data (i.e. observing, recording
and reporting species into databases). References:
Ruete (2015) <doi:10.3897/BDJ.3.e5361>; Szabo, Vesk, Baxter & Possingham (2010)
<doi:10.1890/09-0877.1>; Telfer, Preston 6 Rothery (2002) <doi:10.1016/S0006-3207(02)00050-2>.

exposeIgnorance: Create ignorance scores

Description

Usage

Arguments

Value

References

See Also

Examples