The coastline of Sub-Saharan Africa hosts highly diverse fish communities of high conservation value, which are also key resources for local livelihoods. However, many costal ecosystems are threatened by overexploitation and their conservation state is frequently unknown due to limited monitoring budgets and challenges associated with their vast spatial extents. Here, we evaluated the potential of citizen science-based eDNA surveys to alleviate such chronic data deficiencies and assessed fish communities in Mozambique using two 12S metabarcoding primer sets. Samples were either collected by scientific personnel or trained local community members and results from the two metabarcoding primer sets were combined using a newly created data merging approach. Irrespective of the background of sampling personnel, a high average fish species richness was recorded (38±20 OTUs sample-1). Individual sections of the coastline largely differed in the occurrence of threatened and commercially important species, highlighting the need for regionally differentiated management strategies. A detailed comparison of the two applied primer sets revealed an important trade-off in primer choice with MiFish primers amplifying a higher number of species but Riaz primers performing better in the detection of threatened fish species. This trade-off could be partly resolved by applying our data-merging approach, which has the potential to provide a more robust baseline-data for decision-making processes. Overall, our study provides encouraging results but also highlights that eDNA-based monitoring will require further improvements of e.g., reference databases and local analytical infrastructure to facilitate routine applications in Sub-Saharan Africa.

The promotion of responsible and sustainable trade in biological resources is widely proposed as one solution to mitigate currently high levels of global biodiversity loss. Various molecular identification methods have been proposed as appropriate tools for monitoring global supply chains of commercialized animals and plants. We demonstrate the efficacy of target capture genomic barcoding in identifying and establishing the geographic origin of samples traded as Anacyclus pyrethrum, a medicinal plant assessed as globally vulnerable in the IUCN Red List. Samples collected from national and international supply chains were identified through target capture sequencing of 443 low-copy nuclear makers and compared to results derived from genome skimming of plastome, standard plastid barcoding regions and ITS. Both target capture and genome skimming provided approximately 3.4 million reads per sample, but target capture largely outperformed standard plant DNA barcodes and entire plastid genome sequences. Despite the difficulty of distinguishing among closely related species and infraspecific taxa of Anacyclus using conventional taxonomic methods, we succeeded in identifying 89 of 110 analysed samples to subspecies level without ambiguity through target capture. Furthermore, we were able to discern the geographical origin of Anacyclus samples collected in Moroccan, Indian and Sri Lankan markets, differentiating between plant materials originally harvested from diverse populations in Algeria and Morocco. With a recent drop in the cost of analysing samples, target capture offers the potential to routinely identify commercialized plant species and determine their geographic origin. It promises to play an important role in monitoring and regulation of plant species in trade, supporting biodiversity conservation efforts, and in ensuring that plant products are unadulterated, contributing to consumer protection.

In a recent paper, “Environmental DNA: What’s behind the term? Clarifying the terminology and recommendations for its future use in biomonitoring”, Pawlowski et al. argue that the term eDNA should be used to refer to the pool of DNA isolated from environmental samples, as opposed to only extra-organismal DNA from macro-organisms. We agree with this view. However, we are concerned that their proposed two-level terminology specifying sampling environment and targeted taxa is overly simplistic and might hinder rather than improve clear communication about environmental DNA and its use in biomonitoring. Not only is this terminology based on categories that are often difficult to assign and uninformative, but it ignores what is in our opinion the most important distinction within eDNA: the type of DNA (organismal or extra-organismal) from which ecological interpretations are derived.

Automatic pollen image identification using neural networks increases accuracy for hay fever forecasts