Freshwater diatom biomonitoring through benthic kick-net metabarcoding

Abstract

Biomonitoring is an essential tool for assessing ecological conditions and informing management strategies. The application of DNA metabarcoding and high throughput sequencing has improved data quantity and resolution for biomonitoring of taxa such as macroinvertebrates, yet, there remains the need to optimise these methods for other taxonomic groups. Diatoms have a longstanding history in freshwater biomonitoring as bioindicators of water quality status. However, multi-substrate periphyton collection, a common diatom sampling practice, is time-consuming and thus costly in terms of labour. This study examined whether the benthic kick-net technique used for macroinvertebrate biomonitoring could be applied to bulk-sample diatoms for metabarcoding. To test this approach, we collected samples using both conventional multi-substrate microhabitat periphyton collections and bulk-tissue kick-net methodologies in parallel from replicated sites with different habitat status (good/fair). We found there was no significant difference in community assemblages between conventional periphyton collection and kick-net methodologies or site status, but there was significant difference between diatom communities depending on site (P = 0.042). These results show the diatom taxonomic coverage achieved through DNA metabarcoding of kick-net is suitable for ecological biomonitoring applications. The shift to a more robust sampling approach and capturing diatoms as well as macroinvertebrates in a single sampling event has the potential to significantly improve efficiency of biomonitoring programmes that currently only use the kick-net technique to sample macroinvertebrates.

Fig 1. Sampling schematic of periphyton collection.

A benthic kick-net sample (dotted line) was collected first by kicking benthic sediment into a D-net, from downstream to upstream in a zig-zag across the sampling reach. Microhabitats (rocks, leaf littler, macrophytes and sediment) from the same reach were sampled second for periphyton collection following previously published, standardised protocols [24].

Fig 2. ESV richness varies across different sample types.

Methods refer to the different sampling approaches analyzed (i.e. Kick-net, Macrophyte, Leaf Litter, Rock and Sediment). ESV scores are listed in replicate order (1–3), top to bottom of each microhabitat bar. Based on rarefied data.

Fig 3. A majority of diatom families were detected in both microhabitat and kick-net samples.

Left to right: Phylum (Bacillariophyta), Class, Order, Family.

Fig 4. Number of ESVs detected from genera detected from kick-net versus conventionally sampled diatoms are similar.

The points are color-coded for the orders detected in this study. A 1:1 correspondence line (dotted) is also shown. A log10 scale is shown on each axis to improve the spread of points with small values. Based on rarefied data.

Fig 5. Non-metric multi-dimensional scaling plots show clustering mainly due to site and status.

Specifically, a) binary Bray Curtis (Sorensen) dissimilarities overlapping across different sampling approaches, b) clustering by site, c) overlap between replicates, and d) clustering based on habitat quality status (stress = 0.109, R² = 0.98). Based on rarefied data.

Fig 6. Samples detect similar diatom species across sampling methods and site status.

Only ESVs taxonomically assigned to species with high confidence (bootstrap support > = 0.90 for 90% accuracy) are included. Plots are faceted by site + site status. Sampling methods: K = kick-net; R = rock scraping; L = leaf litter; M = macrophyte; S = sediment. White lanes indicate the corresponding microhabitat was not present at the site; grey lanes indicate species was not present within the particular site. For each site, three replicates for each sampling method are pooled. Based on normalized data.

Fig 7. Frequency plots for diatom species identified in this study and present on the North American Diatom Database.

a) Biological Condition Gradient (BCG) scores (number of diatom species = 73, 1 = specialist species, 2 = highly sensitive species, 3 = sensitive species, 4 = indiscriminate species and 5 = tolerant species); b) Habitat types (number of diatom species = 84); c) Eastern Canadian Diatom Index classes (number of diatom species = 14, A = reference condition, B = slightly altered, C = altered, D = severely altered conditions).