Effect of sampling scenario on FD index sensitivity.

<p>Barplots showing the results of linear mixed effects model, specifically the effect of the two sampling scenarios on the sensitivity of indices for three different types of organisms. The more negative the regression slope, the more sensitive the particular index is to missing trait information. The error bars denote the 95% confidence intervals. (A) plant community (n = 12 plots), (B) ant community (n = 58 plots), and (C) bird community (n = 8 plots).</p

Flow diagram of the consecutive methodological steps.

<p>Upper left corner—in each plot species are ordered by their relative abundance and FD index is calculated for each plot of a community. Upper right corner—0.5% of the species relative abundance is removed in consecutive steps, starting with the least abundant species and FD index is then calculated again for each plot at each reduction step. Upper middle columns—plots are ranked based on the values of the FD index and the ranks of original data and data at each reduction step are correlated. Figure in the middle—regression slopes from fitting the linear model represent the robustness of FD index to missing trait data; in this example FD index is (A) less robust and (B) more robust to missing trait data (example RaoQ on head length of ants).</p

Effect of sampling method and abundance transformation on FD index sensitivity.

<p>Barplot depicting the results of linear mixed effects models, specifically the interaction between abundance transformation and the different abundance measures used in plant ecology (all three abundance measures were used for the same plant dataset in order to make their effects comparable). The effect of down-weighting the dominant species by log-transformation of their abundance was most pronounced in the biomass abundance measure. When log transformed, all three sampling methods have a very similar effect on the sensitivity of indices to missing trait data. Error bars denote the 95% confidence intervals.</p

Effect of trait transformation on FD index sensitivity.

<p>The effect of trait transformation on the improvement in slope (transformed—untransformed trait data)—the bigger the improvement in slope, the more robust the index becomes to missing trait data (y axis). The right panels illustrate the different improvements in trait skewness, depicting examples of trait distribution before and after transformation, which correspond to the x axis of the main figure (matching colours).</p

Plot-wise and pool-wise trait data thresholds.

<p>Schematic figure depicting plot-wise and pool-wise scenarios for setting the thresholds for trait data sampling. (A) species from all plots make up the pool of species; (B) species can be ordered by their abundance in each plot or in the whole pool; (C) the least abundant species in the whole pool of species are removed until reaching the desired threshold for trait sampling; (D) the least abundant species in each plot are removed until reaching the desired threshold for trait sampling.</p

Effect of abundance transformation on FD index sensitivity.

<p>Barplots showing the results of linear mixed effects models, specifically the effect of the abundance transformation on the slopes for the three different types of organisms. The more negative the regression slope, the more sensitive the particular index is to missing trait information. The error bars denote the 95% confidence intervals. (A) plant community (n = 12 plots), (B) ant community (n = 58 plots), and (C) bird community (n = 8 plots). The right panels depict dominance-diversity curves for the respective organism dataset before and after log-transformation.</p

Effect of sampling scenario and abundance transformation on FD index sensitivity.

<p>Effect of sampling scenario and abundance transformation on FD index sensitivity.</p

Ant assemblage species richness and PIE

This data file includes details of species richness and PIE from pitfall-trapped ant assemblages across the globe. The data includes the following information for 1128 assemblages: Locality_ID - A unique code for each locality; Source - Details of reference source for each dataset; Cluster - Geographic cluster used as a random factor in analyses; Latitude - Latitude provided by authors; Longitude - Longitude provided by authors; Mean annual temperature - Derived from WorldClim; Total annual precipitation - Derived from WorldClim; Temperature range - Derived from WorldClim; Disturbance - Disturbance described by authors; Hemisphere - Derived from Latitude; Continent - Continent provided by authors; Pitfall days - Number of pitfalls multiplied by trapping days; Transect length - Distance from first to last trap in transect; Species richness - Total number of species collected; PIE - Probability of Interspecific Encounter, a measure of species evenness. Further details are provided in the paper