Background Freshwaters are exposed to multiple anthropogenic stressors, leading to habitat degradation and biodiversity decline. In particular, agricultural stressors are known to result in decreased abundances and community shifts towards more tolerant taxa. However, the combined effects of stressors are difficult to predict as they can interact in complex ways, leading to enhanced (synergistic) or decreased (antagonistic) response patterns. Furthermore, stress responses may remain undetected if only the abundance changes in ecological experiments are considered, as organisms may have physiological protective pathways to counteract stressor effects. Therefore, we here used transcriptome-wide sequencing data to quantify single and combined effects of elevated fine sediment deposition, increased salinity and reduced flow velocity on the gene expression of the amphipod Gammarus fossarum in a mesocosm field experiment. Results Stressor exposure resulted in a strong transcriptional suppression of genes involved in metabolic and energy consuming cellular processes, indicating that G. fossarum responds to stressor exposure by directing energy to vitally essential processes. Treatments involving increased salinity induced by far the strongest transcriptional response, contrasting the observed abundance patterns where no effect was detected. Specifically, increased salinity induced the expression of detoxification enzymes and ion transporter genes, which control the membrane permeability of sodium, potassium or chloride. Stressor interactions at the physiological level were mainly antagonistic, such as the combined effect of increased fine sediment and reduced flow velocity. The compensation of the fine sediment induced effect by reduced flow velocity is in line with observations based on specimen abundance data. Conclusions Our findings show that gene expression data provide new mechanistic insights in responses of freshwater organisms to multiple anthropogenic stressors. The assessment of stressor effects at the transcriptomic level and its integration with stressor effects at the level of specimen abundances significantly contribute to our understanding of multiple stressor effects in freshwater ecosystems.

Mitochondrial DNA (mtDNA) sequences are often found as byproducts in next‐generation sequencing (NGS) datasets that were originally created to capture genomic or transcriptomic information of an organism. These mtDNA sequences are often discarded, wasting this valuable sequencing information. We developed MitoGeneExtractor, an innovative tool which allows to extract mitochondrial protein coding genes (PCGs) of interest from NGS libraries through multiple sequence alignments of sequencing reads to amino acid references. General references, for example on order level are sufficient for mining mitochondrial PCGs. In a case study, we applied MitoGeneExtractor to recently published genomic datasets of 1993 birds and were able to extract complete or nearly complete sequences for all 13 mitochondrial PCGs for a large proportion of libraries. Compared to an existing assembly guided sequence reconstruction algorithm, MitoGeneExtractor was faster and substantially more sensitive. We compared COI sequences mined with MitoGeneExtractor to COI databases. Mined sequences show a high sequence similarity and correct taxonomic assignment between the recovered sequence and the assigned morphospecies in most samples. In some cases of incongruent taxonomic assignments, we found evidence for contamination in NGS libraries. MitoGeneExtractor allows a fast extraction of mitochondrial PCGs from a wide range of NGS datasets. We recommend to routinely harvest and curate mitochondrial sequence information from genomic resources. MitoGeneExtractor output can be used to identify contaminated NGS libraries and to validate the species identity of the sequenced animal based on the extracted COI sequences.

Reliable biodiversity data are crucial for environmental research and management. Unfortunately, data paucity prevails for many regions and organismal groups such as aquatic invertebrates. High-throughput DNA-based identification, in particular DNA metabarcoding, has accelerated biodiversity data generation. However, in the process of metabarcoding, specimens are usually destroyed, precluding later specimen-based analyses. Metabarcoding of DNA released into the preservative ethanol has been proposed as a non-destructive alternative, but proof-of-principle studies have yielded ambiguous results, reporting variance in detection probability for various taxa and methodological biases. In this study, we tested the performance of preservative-based metabarcoding of aquatic invertebrates in comparison to a standard morpho-taxonomic assessment based on samples from one of Europe’s last free-flowing rivers, the Vjosa. Multi-habitat samples were collected at 43 sites in two seasons and stored in ethanol, after fixation in formaldehyde for morpho-taxonomic analyses. Preservative-based DNA metabarcoding detected three times more taxa but failed to detect other taxa found using the standard method. In addition to incomplete reference data and primer bias that likely precluded the detection of specific taxa, preservative-based DNA metabarcoding cannot provide accurate abundance estimates. However, the metabarcoding data revealed distinct small-scale and large-scale community patterns in the Vjosa river network, which were also recovered by quantitative data of the standard approach. Overall, our results indicate that preservative-based metabarcoding provides important biodiversity data, which could be further improved by quantitative validation. The method is robust and reliable, even though samples were taken under harsh field-conditions and stored without cooling. Further, our results emphasise the need for reliable DNA barcoding reference libraries. Building those may be supported by preservative-based metabarcoding that maintains intact vouchers for subsequent specimen-based analyses.

Mitochondrial DNA sequences (mtDNA) are often found as byproduct in hybrid enrichment data sets originally created to capture anchored hybrid enrichment (AHE) or ultra-conserved element (UCE) nuclear loci. The mtDNA sequences in these data sets are currently rarely used, even though mitochondrial genes such as COI, ND5, CytB, and 16S are of general interest and often not yet known and deposited in public databases. We developed MitoGeneExtractor to extract mitochondrial genes of interest from genomic libraries. Gene sequences are reconstructed through multiple sequence alignments of sequencing reads to an amino acid reference. We applied MitoGeneExtractor to recently published data created for UCE enrichment and were able to extract complete or nearly complete COI and ND5 sequences for a large proportion of the sequencing libraries. MitoGeneExtractor can be used to extract mitochondrial protein coding genes from a wide range of next generation sequencing data sets.