Length–Dry Mass Relationships of Aquatic Insects: Geographic and Taxonomic Variation in a Digital Database

ABSTRACT Aquatic insects are an abundant, yet declining, taxonomically heterogeneous group with special importance in both aquatic and terrestrial ecosystems. Accurate estimations of insect biomass during their aquatic life stages are essential to advance our fundamental knowledge about insects, their roles in ecosystems, and their vulnerability to human impact. However, assessing insect biomass from samples using classical drying techniques is time‐consuming and prohibits the use of samples for other analyses. A widely applied method is therefore to use length–dry mass power regressions to obtain dry mass (DM) from body lengths (BL) using literature‐derived parameter values. However, the application of this method relies on reliable and accessible parameter values, preferably matching the studied specimens both taxonomically and geographically. Here, we aimed to increase parameter accessibility in the literature to (1) facilitate researchers in employing more appropriate length–mass regressions in their studies, (2) identify knowledge gaps that can direct future research towards unexplored regions and understudied taxonomic groups, and (3) visualise the relative contribution of geographic variation (differences among continents) and taxonomic variation (differences among families within each order) to regression lines. We compiled a parameter dataset based on 25 publications for eight insect orders with aquatic life stages: Coleoptera, Diptera, Ephemeroptera, Hemiptera, Megaloptera/Neuroptera, Odonata, Plecoptera, and Trichoptera, and made the dataset available digitally. This parameter dataset is derived from over 15,000 measured insects of 84 (sub)families and 233 genera from all continents, except Africa and Antarctica. We found parameter values to be widely available at the order level, but at the resolution of family and genus levels, values were missing for 65% and 94% of the taxa, respectively. Identified knowledge gaps were the need for (1) more data on variation among families that is collected standardised within the same geographic regions, (2) targeted collections of data for different orders within the same study areas, to reveal variation among families and genera, and (3) careful reporting of the exact methodologies used, to identify variation introduced by methodological dissimilarities. Geographic and taxonomic variation is visually presented in figures for further interpretation. We conclude that length‐mass regressions can be a powerful method, but due to data shortage at the genus and family taxonomic levels, order‐level regressions with less reliability are necessarily applied. By providing parameters in a new digital dataset, we hope to facilitate users in more efficient assessment of parameter availability for studied taxa in any geographic region. The identified knowledge gaps can be used to direct future research efforts. More accessible parameter data will facilitate more reliable assessments of aquatic insect biomass and benefit future studies on this important and abundant group of organisms, bridging aquatic and terrestrial ecosystems.