Using high-throughput sequencing for precise genotyping of multi-locus gene families, such as the Major Histocompatibility Complex (MHC), remains challenging, due to the complexity of the data and difficulties in distinguishing genuine from erroneous variants. Several dedicated genotyping pipelines for data from high-throughput sequencing, such as next-generation sequencing (NGS), have been developed to tackle the ensuing risk of artificially inflated diversity. Here, we thoroughly assess three such multi-locus genotyping pipelines for NGS data, using MHC class IIβ datasets of three-spined stickleback gDNA, cDNA, and “artificial” plasmid samples with known allelic diversity. We show that genotyping of gDNA and plasmid samples at optimal pipeline parameters was highly accurate and reproducible across methods. However, for cDNA data, the same configuration yielded decreased overall genotyping precision and consistency between pipelines. Further adjustments of key clustering parameters were required tο account for higher error rates and larger variation in sequencing depth per allele, highlighting the importance of template-specific pipeline optimization for reliable genotyping of multi-locus gene families. Through accurate paired gDNA-cDNA genotyping and MHC-II haplotype inference, we show that MHC-II allele-specific expression levels correlate negatively with allele number across haplotypes. Lastly, sibship-assisted cDNA genotyping of MHC-I revealed novel variants and haplotype-based allelic segregation with a higher-than-previously-reported individual allelic diversity for MHC-I in sticklebacks. In conclusion, we here provide novel genotyping protocols for MHC-I and -II genes of the three-spined stickleback, but also evaluate the performance of popular NGS-genotyping pipelines and highlight the need for template-specific optimization for reliable multi-locus genotyping.

Our limited knowledge about the ecological drivers of global arthropod decline highlights the urgent need for more effective biodiversity monitoring approaches. Monitoring of arthropods is commonly performed using passive trapping devices, which reliably recover diverse communities, but provide little ecological information on the sampled taxa. Especially the manifold interactions of arthropods with plants are barely understood. A promising strategy to overcome this shortfall is environmental DNA (eDNA) metabarcoding of arthropods from plant material they have interacted with. However, the accuracy of this approach has not been sufficiently tested. In four experiments, we exhaustively test the comparative performance of plant-derived eDNA from surface washes of plants and homogenized plant material against traditional monitoring approaches. We show that the recovered communities of plant-derived eDNA and traditional approaches only partly overlap, with eDNA recovering various additional cryptic taxa. This suggests eDNA as a useful complementary tool to traditional monitoring. Despite the differences in recovered taxa, estimates of community α- and β-diversity between both approaches are well correlated, highlighting the utility of eDNA as a broad scale tool for community monitoring. Last, eDNA outperforms traditional approaches in the recovery of plant-specific arthropod communities. Unlike traditional monitoring, eDNA revealed fine-scaled community differentiation between individual plants and even within plant compartments. Especially specialized herbivores are better recovered with eDNA. Our results highlight the value of plant derived eDNA analysis for large-scale biodiversity assessments that include information about community level interactions.

Seaweeds are colonized by a microbial community which can be directly linked to their performance. This community is shaped by an interplay of stochastic and deterministic processes, including mechanisms which the holobiont host deploys to manipulate its associated microbiota. The Anna Karenina Principle predicts that when a holobiont is exposed to suboptimal or stressful conditions, these host mechanisms may be compromised. This leads to a relative increase of stochastic processes that may potentially result in the succession of a microbial community harmful to the host. Based on this principle, we used the variability in microbial communities (i.e., beta diversity) as a proxy for stability within the invasive holobiont Gracilaria vermiculophylla during a simulated invasion in a common garden experiment. At elevated temperature (22 °C), host performance declined and disease incidence and beta diversity increased. At optimal temperature (15 °C), beta diversity did not differ between native and non-native populations. However, under thermally stressful conditions beta diversity increased more in epibiota from native populations. This suggests that epibiota associated with holobionts from non-native populations are under thermal stress more stable than holobionts from native populations. This pattern reflects an increase of deterministic processes acting on epibiota associated with non-native hosts, which in the setting of a common garden can be assumed to originate from the host itself. Therefore, these experimental data suggest that the invasion process may have selected for hosts better able to maintain stable microbiota during stress. Future studies are needed to identify the underlying host mechanisms.

Diatoms (Bacillariophyceae) are widely used as bioindicators of present and past water quality because they inhabit the vast majority of aquatic ecosystems, are very diverse, highly sensitive to a variety of environmental conditions, and are characterized by silicified cell walls that favor their long-term preservation in sediments. Alongside with traditional morphological analyses, metabarcoding has become a valuable tool to study the community structures of various organisms, including diatoms. Here, we aimed to test whether the quantity of sediment sample used for DNA extraction is affecting the results obtained from high-throughput sequencing (metabarcoding) of the diatom rbcL region by isolating DNA from 10 g and 0.5 g (wet weight) of lake surface sediment samples. Because bioinformatics processing of metabarcoding data may affect the outcome, we also tested the consistency of the results from three different pipelines. Additionally, the agreement between metabarcoding data and morphological inventories of corresponding samples were compared. Our results demonstrate highly uniform patterns between the diatom rbcL amplicons from 10 g and 0.5 g of DNA extracts (HTS 10 and HTS 0.5, respectively). Furthermore, metabarcoding results were highly consistent among the data sets produced by different bioinformatics pipelines. Comparing results from metabarcoding and microscopy, we identified some taxonomic mismatches, which are related to the common issue of incompleteness of the sequence databases, but also to inconsistencies in diatom taxonomy in general and potential dissolution effects of diatom valves caused by high alkalinity of the investigated lake waters. Nevertheless, multivariate community analysis demonstrated highly similar results between data sets identified by microscopy and metabarcoding, further confirming that metabarcoding is a viable alternative for identifying diatom-environment relationships.