The influence of macroinvertebrate abundance on the assessment of freshwater quality in The Netherlands

Abstract

The use of molecular tools for the detection and identification of invertebrate species enables the development of more easily standardisable inventories of biological elements for water quality assessments, as it circumvents human-based bias and errors in species identifications. Current Ecological Quality Ratio (EQR) assessments methods, however, often rely on abundance data. Translating metabarcoding sequence data into biomass or specimen abundances has proven difficult, as PCR amplification bias due to primer mismatching often provides skewed proportions of read abundances. While some potential solutions have been proposed in previous research, we instead looked at the necessity of abundance data in EQR assessments. In this study, we used historical monitoring data from natural (lakes, rivers and streams) and artificial (ditches and canals) water bodies to assess the impact of species abundances on the EQR scores for macroinvertebrates in the Water Framework Directive (WFD) monitoring programme of The Netherlands. By removing all the abundance data from the taxon observations, we simulated presence/absencebased monitoring, for which EQRs were calculated according to traditional methods. Our results showed a strong correlation between abundance-based and presence/absence-based EQRs. EQR scores were generally higher without abundances (75.8% of all samples), which resulted in 9.1% of samples being assigned to a higher quality class. The majority of the samples (89.7%) were assigned to the same quality class in both cases. These results are valuable for the incorporation of presence/absence metabarcoding data into water quality assessment methodology, potentially eliminating the need to translate metabarcoding data into biomass or absolute specimen counts for EQR assessments.

Studying Ecosystems With DNA Metabarcoding: Lessons From Biomonitoring of Aquatic Macroinvertebrates

“…These approaches have proved acceptable to practitioners precisely because count data provide little or no incremental improvement to detecting differences among sites. Moreover, approaches based on occurrence data illustrate a direct pathway to implement DNA metabarcoding in routine biomonitoring programs (Beentjes et al, 2018).…”

Advancing the use of molecular methods for routine freshwater macroinvertebrate biomonitoring – the need for calibration experiments

“…Metabarcoding studies have so far been unable to relate read number precisely to abundance, relative abundance or biomass of macroinvertebrates. A recent study by Beentjes et al (2018) demonstrated a strong correlation between ecological quality ratios of presence/absence data and abundance-based data on historical macroinvertebrate monitoring data from a wide range of waterbody types for the Netherlands. Such comparisons to validate the importance of abundance measures for EQS assessments are underway in the Nordic countries, which is important to determine if methods can be calibrated without including abundance information.…”

Decision making and best practices for taxonomy-free eDNA metabarcoding in biomonitoring using Hill numbers

“…The first three strategies are based on consistent appearance of ZOTUs across replicates (Alberdi et al,197 2018; Beentjes et al, 2018;Mächler et al, 2019). We assessed how often each individual ZOTU was 198 detected across the three filter replicates of an individual site and differentiated between additive (counting 199 all ZOTUs found in any of the three replicates), relaxed (counting only ZOTUs with a minimal presence in 200 two replicates), and strict (counting only ZOTUs with presence in all three replicates).…”

“…Especially errors or biases in PCR reactions can contribute to an inconsistency across 343 true replicates (Alberdi et al, 2018;Leray & Knowlton, 2017;Murray, Coghlan, & Bunce, 2015), which 344 cannot be eliminated during bioinformatic data preparation. The use of technical (e.g., PCR reactions, 345 Alberdi et al, 2018;Beentjes et al, 2018) or biological (e.g., Macher & Leese, 2017;Mächler et al, 2019) 346 replicates allows to apply such stringency treatments that are capable to infer validity of ZOTUs. Our 347 study highlights, consistent with other studies, the importance of taking multiple replicates, and to use 348 14 these replicates to get more coherent and robust conclusions on the diversity assessed (Alberdi et al,349 2018; Leray & Knowlton, 2017).…”

“…However, it is yet unclear whether the focus on the abundant (z)OTUs 383 only is an eligible option for biomonitoring studies. Recent research showed that, for classical biomon-384 itoring studies and subsequently derived indices on ecological state, presence-absence data of classical 385 approaches are resulting mostly in the same conclusions then when including abundance information 386 (Beentjes et al, 2018;Buchner et al, 2019). In contrast, our study shows that the eDNA metabarcoding 387 results will differ to quite some extent depending on the used stringency treatment in the data analysis, 388 and thus needs critical considerations of the selected criteria and may be most robust when including 389 information on abundance data.…”