DOI: 10.5061/dryad.1fn46
title: Data from: Sensitivity of functional diversity metrics to sampling intensity

description: 1. Functional diversity (FD) metrics are increasingly used in ecological
 research, particularly in studies of community assembly and ecosystem
 functioning. However, studies using FD metrics vary greatly in the
 intensity by which ecological communities were sampled and it is largely
 unknown how sensitive these metrics are to low sampling intensity
 (undersampling). 2. Here, we used a combination of simulations with
 theoretically assembled communities and three comprehensive, independent,
 empirical datasets on plant, ground beetle and bird communities to
 investigate the sensitivity of nine commonly used FD metrics to
 undersampling. 3. Simulations with both theoretical communities and
 empirical data showed that in a wide range of contexts, the measurement of
 various FD metrics requires a much higher sampling effort to reach an
 ‘adequate’ precision (defined as an r2 of at least 0.7 between different
 subsets of the same population), than that required for commonly used
 taxonomic diversity metrics (e.g. species richness), although the
 ‘accuracy’ (their deviation from the diversity value of a completely
 sampled community) of their measurements is not more sensitive to
 undersampling than species richness. We also found that some FD metrics
 (e.g. Functional Dispersion) are consistently less sensitive to
 undersampling than others (e.g. nearest neighbour distance-metrics).
 Problems of undersampling were generally most severe in datasets with high
 overall species richness and low overall abundances. 4. We found that the
 precision of many FD metrics is highly sensitive to undersampling, and
 more so than commonly used taxonomic diversity metrics. Therefore, to
 ensure reproducible results in functional biodiversity research, we
 recommend that thorough sampling designs are used to sample communities
 and that datasets originally collected for studying taxonomic diversity
 should only be used for FD when it can be shown that undersampling is not
 a major issue. In cases where undersampling is suspected or logistically
 unavoidable, FD metrics that are relatively insensitive to its effects
 (e.g. Functional Dispersion) should be prioritized.
description: South African grass
 communitiesdata on grass
 observations in 10 savannah plots in Hluhluwe iMfolozi Park, South Africa.
 First column: plot name. Second column: location (grid cell intersection
 point) within plot. Third column: species name.hip grass communities
 2010.txtcarabids
 2011-2014Data on carabid
 observations in pitfall traps in Drenthe, Netherlands. Colum names:
 Blik_nr. = pitfall number; Datum = Date; Jaar = year; serie = location;
 soort_kever = species name; Totaal = number of individuals; Seriejaar =
 location-year combination; PosVal = position pitfall (each location
 contained three pitfalls, only second included in this study, see
 publication); week = week of the year (ranginging from
 1-53).carabid traits
 finalCarabid morphological
 traits. Column names: name_correct = species name; source_measurement =
 researcher who performed measurement; body_length = body length in mm;
 antenna_length = antenna length in mm; metafemur_length = length metafemur
 in mm; eyewith_corr = eye width in mm; note = note; resid_femur = residual
 femur length in mm (i.e. residual from linear model in which femur length
 is explained by body length); resid_eye = residual eye length in mm (i.e.
 residual from linear model in which eye length is explained by body
 length) resid_antenna = residual antenna length in mm (i.e. residual from
 linear model in which antenna length is explained by body
 length)undersampling ground
 beetlesfile to analyse how
 accuracy and precision of various FD metrics depends on undersampling.
 This specific file was used for the analysis of ground beetle data. Files
 with small modifications were used to analyse South African grass data and
 breeding bird data of Vermont. In principle, with some modifications, this
 file can be used to study undersampling in for any kind of community data,
 if community and corresponding trait data are provided as
 input.ground beetles
 20161123.Rundersampling of
 artificial communitiesR file to investigate
 how the accuracy and precision of FD metrics is affected by undersampling,
 and how this depends on the species richness, number of individuals and
 species abundance distribution of the 'region,' 'species
 pool' or 'landscape'theory_Cardozo
 20161124.txt