Tackling inconsistencies among freshwater invertebrate trait databases: harmonising across continents and aggregating taxonomic resolution

Use of invertebrate traits rather than species composition may facilitate large‐scale comparisons of community structure and responses to disturbance in freshwater ecology because the same traits potentially occur everywhere. In recent years, comprehensive invertebrate trait databases have been established at different scales (e.g., regions, continents). The wide availability of invertebrate trait data supports large‐scale studies. However, a number of data‐related issues complicate the use of invertebrate traits for ecological studies. It is uncertain how harmonising varying trait definitions among databases might influence subsequent identification of trait–environment relationships. Furthermore, there have been few comparisons of trait aggregation approaches with expert‐assigned trait affinities. We describe inconsistencies in the definitions of traits used to create freshwater invertebrate trait databases in Europe, North America, New Zealand, and Australia. Based on our comparisons of these databases, we established four novel trait datasets by harmonising definitions of commonly used traits. Next, we used two of these datasets to compare aggregated traits obtained by different aggregation methods with traits assigned by experts, both at the family level. The trait aggregation methods that we compared used either the mean or the median and different weightings. We further explored the effects of harmonisation and trait aggregation by re‐analysing data from a case study. We found that among databases, trait definitions often differed because varying numbers of traits were used to describe particular functions (e.g., respiration traits) and the way those functions were described also varied (e.g., for feeding mode some databases focused on the food source, whereas others focused on mouthpart morphology). The coding to describe traits (binary, fuzzy) also varied among databases. Our comparison of different aggregation methods showed that family‐level aggregated and expert‐assigned traits were similar, especially when traits were aggregated based on the median of trait values of taxa within a family. The case study showed that harmonised and aggregated data identified similar trait–environment relationships to non‐aggregated data. However, harmonised and aggregated data yielded only partially similar values for functional diversity metrics when compared to the case study results. By identifying inconsistencies in trait definitions we hope to motivate the