The power of metabarcoding: Can we improve bioassessment and biodiversity surveys of stream macroinvertebrate communities?

From DNA to diagnostics: A case study using macroinvertebrate metabarcoding to assess the effectiveness of restoration measures in a Dutch stream

Stream ecosystems are under pressure due to multiple stressors. Restoration measures can halt further degradation and improve their ecological status. However, assessment of the effectiveness of the implemented measures is often insufficient because of logistic and financial constraints. DNA-metabarcoding has been proposed to scale up sample processing, although its application as a diagnostic tool has received less attention. The aim of our study was to evaluate if DNA-metabarcoding of stream macroinvertebrates can be used to compute a stressor-specific index to assess the effectiveness of a stream restoration project. For this purpose, we sampled the upstream, restored, and downstream section of a recently restored lowland stream in the Netherlands. At each site, we applied three different methods of macroinvertebrate identification: morphological identification of bulk samples (morphology), DNA-metabarcoding of the same bulk samples (DNA) and metabarcoding of eDNA extracted from the water (eDNA). First, we compared the community composition identified by each method. The communities identified by morphology and DNA were highly similar, whereas the communities generated by the eDNA differed. Second, we analysed whether the identification methods could be used to assess the effectiveness of the restoration project, focussing on a stressor-specific index for flow as the restoration measures aimed at improving flow conditions. Both the morphology and bulk DNA samples indicated improved flow conditions in the restored section of the stream (i.e., less stress from the reduction or absence of flow than in the unrestored sections). Contrary, the eDNA-water samples did not differentiate the amount of stress throughout the catchment, although applying recent developments in eDNA sampling could lead to more robust results. In conclusion, this study forms proof of concept that DNA from bulk samples can be utilized to assess the effectiveness of restoration measures, showing the added value of this approach for water managers.

Introducing an rbcL and a trnL reference library to aid in the metabarcoding analysis of foraged plants from two semi-arid eastern South African savanna bioregions

Success of a metabarcoding study is determined by the extent of taxonomic coverage and the quality of records available in the DNA barcode reference database used. This study aimed to create an rbcL and a trnL (UAA) DNA barcode sequence reference database of plant species that are potential herbivore foraging targets and commonly found in semi-arid savannas of eastern South Africa. An area-specific species list of 765 species was compiled according to plant collection records available and areas comparable to an eastern semi-arid South African savanna. Thereafter, rbcL and trnL sequences of species from this list were mined from GenBank and BOLD sequence databases according to specific quality criteria to ensure accurate taxonomic coverage and resolution. These were supplemented with sequences of 24 species sequenced for this study. A phylogenetic approach, employing Neighbor-Joining, was used to verify the topology of the reference libraries to known angiosperm phylogeny. The taxonomic reliability of these reference libraries was evaluated by testing for the presence of a barcode gap, identifying a data-appropriate identification threshold, and determining the identification accuracy of reference sequences via primary distance-based criteria. The final rbcL reference dataset consisted of 1238 sequences representing 318 genera and 562 species. The final trnL dataset consisted of 921 sequences representing 270 genera and 461 species. Barcode gaps were found for 76% of the taxa in the rbcL barcode reference dataset and 68% of the taxa in the trnL barcode reference dataset. The identification success rate, calculated with the k-nn criterion was 85.86% for the rbcL dataset and 73.72% for the trnL dataset. The datasets for rbcL and trnL combined during this study are not presented as complete DNA reference libraries, but rather as two datasets that should be used in unison to identify plants present in the semi-arid eastern savannas of South Africa.

Wild Bee Nutritional Ecology: Integrative Strategies to Assess Foraging Preferences and Nutritional Requirements

Bees depend on flowering plants for their nutrition, and reduced availability of floral resources is a major driver of declines in both managed and wild bee populations. Understanding the nutritional needs of different bee species, and how these needs are met by the varying nutritional resources provided by different flowering plant taxa, can greatly inform land management recommendations to support bee populations and their associated ecosystem services. However, most bee nutrition research has focused on the three most commonly managed and commercially reared bee taxa—honey bees, bumble bees, and mason bees—with fewer studies focused on wild bees and other managed species, such as leafcutting bees, stingless bees, and alkali bees. Thus, we have limited information about the nutritional requirements and foraging preferences of the vast majority of bee species. Here, we discuss the approaches traditionally used to understand bee nutritional ecology: identification of floral visitors of selected focal plant species, evaluation of the foraging preferences of adults in selected focal bee species, evaluation of the nutritional requirements of focal bee species (larvae or adults) in controlled settings, and examine how these methods may be adapted to study a wider range of bee species. We also highlight emerging technologies that have the potential to greatly facilitate studies of the nutritional ecology of wild bee species, as well as evaluate bee nutritional ecology at significantly larger spatio-temporal scales than were previously feasible. While the focus of this review is on bee species, many of these techniques can be applied to other pollinator taxa as well.