Sediment core DNA-Metabarcoding and chitinous remain identification:
Integrating complementary methods to characterise Chironomidae
biodiversity in lake sediment archives
Abstract
Chironomidae, so-called non-biting midges, are considered key
bioindicators of aquatic ecosystem variability. Data derived from
morphologically identifying their chitinous remains in sediments
documents chironomid larvae assemblages, which are studied to
reconstruct ecosystem changes over time. Recent developments in
sedimentary DNA (sedDNA) research have demonstrated that molecular
techniques are suitable for determining past and present occurrences of
organisms. Nevertheless, sedDNA records documenting alterations in
chironomid assemblages remain largely unexplored. To close this gap, we
examined the applicability of sedDNA metabarcoding to identify
Chironomidae assemblages in lake sediments by sampling and processing
three 21 – 35 cm long sediment cores from Lake Sempach in Switzerland.
With a focus on developing analytical approaches, we compared an
invertebrate-universal (FWH) and a newly designed Chironomidae-specific
metabarcoding primer set (CH) to assess their performance in detecting
Chironomidae DNA. We isolated and identified chitinous larval remains
and compared the morphotype assemblages with the data derived from
sedDNA metabarcoding. Results showed a good overall agreement of the
morphotype assemblage-specific clustering among the chitinous remains
and the metabarcoding datasets. Both methods indicated higher chironomid
assemblage similarity between the two littoral cores in contrast to the
deep lake core. Moreover, we experienced a pronounced primer bias effect
resulting in more Chironomidae detections with the CH primer combination
compared to the FWH combination. Overall, we conclude that sedDNA
metabarcoding can supplement traditional remain identifications and
potentially provide independent reconstructions of past chironomid
assemblage changes. Furthermore, it has the potential of more efficient
workflows, better sample standardisation and species-level resolution
datasets.