Documenting DNA in the dust

I bought a robotic vacuum cleaner this summer and set it to work. Although my initial expectations were not high, my robot (christened Buddy) finished its cleaning cycle, and then insistently demanded that I empty its dust collection box. As I took the box out, my jaw dropped. I live in a modern house, we don't have pets, and I like to think that I keep it reasonably dust free. But, there was much dust in that box. And when I ran it again 2 days later, the same thing happened. And indeed, every 2 days, Buddy dutifully goes to work, and sucks up a similarly impressive quantity. It's remarkable, and naturally begs the question of where it all comes from? Some is externally derived, entering the house with us or through open windows. Some is clearly fibres shed from clothes, furniture etc. Then there's the skin cells and hair we shed. But at least part is derived from the host of smaller organisms that live in and around our homes, many of which are arthropods (Butte & Heinzow 2002 ). I suspect almost all readers are aware that some smaller animals live in our houses – even those who live in the modern urban houses will have occasionally encountered the odd drosophila, silverfish or spider. But I suspect that prior to reading Madden et al.'s article in this issue of Molecular Ecology (Madden et al. 2017 ), few of you will have appreciated the true diversity, which, it turns out, is huge.     

动物食性分析在生态学中的应用研究进展——基于DNA宏条形码技术

动物食性分析是动物营养生态学的重要研究手段,可用于解析动物与环境因素的关联性、捕食者与猎物之间的关系,以及动物物种多样性等科学问题。近年来,基于新一代测序技术的DNA宏条形码技术被广泛应用到生态学多个研究领域,极大地促进了生命科学交叉学科的发展。其中,DNA宏条形码技术在动物食性分析中具有高分辨、高效率、低样本量等优势,具有重要的应用前景。综述了基于DNA宏条形码技术的动物食性分析在生态学中的应用研究进展,并进一步总结了DNA宏条形码技术原理和食性分析方法,着重探讨了基于DNA宏条形码技术的动物食性分析在珍稀濒危动物保护、生物多样性监测、农业害虫防治等生态学研究领域中的应用,并对DNA宏条形码技术在动物食性分析中存在的问题及应用前景进行小结与展望。     

Comparative study of the validity of three regions of the 18S‐rRNA gene for massively parallel sequencing‐based monitoring of the planktonic eukaryote community

The nuclear 18S‐rRNA gene has been used as a metabarcoding marker in massively parallel sequencing (MPS)‐based environmental surveys for plankton biodiversity research. However, different hypervariable regions have been used in different studies, and their utility has been debated among researchers. In this study, detailed investigations into 18S‐rRNA were carried out; we investigated the effective number of sequences deposited in international nucleotide sequence databases (INSDs), the amplification bias, and the amplicon sequence variability among the three variable regions, V1–3, V4–5 and V7–9, using in silico polymerase chain reaction (PCR) amplification based on INSDs. We also examined the primer universality and the taxonomic identification power, using MPS‐based environmental surveys in the Sea of Okhotsk, to determine which region is more useful for MPS‐based monitoring. The primer universality was not significantly different among the three regions, but the number of sequences deposited in INSDs was markedly larger for the V4–5 region than for the other two regions. The sequence variability was significantly different, with the highest variability in the V1–3 region, followed by the V7–9 region, and the lowest variability in the V4–5 region. The results of the MPS‐based environmental surveys showed significantly higher identification power in the V1–3 and V7–9 regions than in the V4–5 region, but no significant difference was detected between the V1–3 and V7–9 regions. We therefore conclude that the V1–3 region will be the most suitable for future MPS‐based monitoring of natural eukaryote communities, as the number of sequences deposited in INSDs increases.     

A 3-year plankton DNA metabarcoding survey reveals marine biodiversity patterns in Australian coastal waters

Aim

To use a long-term collection of bulk plankton samples to test the capacity of DNA metabarcoding to characterize the spatial and seasonal patterns found within a range of zooplankton communities, and investigate links with concurrent abiotic data collected as part of Australia's Integrated Marine Observing System (IMOS) programme.

Location

Samples were sourced seasonally for 3 years from nine Pan-Australian marine sites (n = 90).

Methods

Here, we apply a multi-assay metabarcoding approach to environmental DNA extracted from bulk plankton samples. Six assays (targeting 16SrRNA and COI genes) were used to target, amplify and sequence the zooplankton diversity found within each sample. The data generated from each assay were filtered and clustered into OTUs prior to analysis. Abiotic IMOS data collected contemporaneously enabled us to explore the physical and chemical drivers of community composition.

Results

From over 25 million sequences, we identified in excess of 500 distinct taxa and detected clear spatial differences. We found that site and sea surface temperature are the most consistent predictors of differences between zooplankton communities. We detected endangered and invasive species such as the bryozoan Membranipora membranacea and the mollusc Maoricolpus roseus, and seasonal occurrences of species such as humpback whales (Megaptera novaeangliae). We also estimated the number of samples required to detect any significant seasonal changes. For OTU richness, this was found to be assay dependent and for OTU assemblage, a minimum of nine samples per season would be required.

Main Conclusion

Our results demonstrate the ability of DNA to capture and map zooplankton community changes in response to seasonal and spatial stressors and provide vital evidence to environmental stakeholders. We confirm that a metabarcoding method offers a practical opportunity for an ecosystem-wide approach to long-term biomonitoring and understanding marine biomes where morphological analysis is not feasible.     

Laboratory conditions can change the complexity and composition of the natural aquatic mycobiome on Alnus glutinosa leaf litter

Community structure is of major interest when aquatic fungi are studied, particularly in leaf decomposition experiments. Although such studies are often conducted as laboratory experiments with microbial communities taken from the field, it remains unclear to what extent natural fungal communities can be sustained under experimental conditions. Here, we used DNA metabarcoding to investigate the development of fungal communities on alder leaves both under laboratory and field conditions. Five leaf conditioning treatments were compared by colonizing leaves in a stream, exposing stream colonized leaves to a defined medium or filtered stream water and using stream colonized leaves to inoculate sterile leaves in the defined medium or stream water. Fewer species were found on leaves that were inoculated under laboratory conditions, whereas differences in fungal community composition were comparably low in the other treatments, irrespective of the chosen medium. Possible shifts in fungal communities should therefore be considered in laboratory experiments.     

An appetite for pests: Synanthropic insectivorous bats exploit cotton pest irruptions and consume various deleterious arthropods

Conservation biological control (CBC) seeks to minimize the deleterious effects of agricultural pests by enhancing the efficiency of natural enemies. Despite the documented potential of insectivorous bats to consume pests, many synanthropic bat species are still underappreciated as beneficial species. We investigated the diet of Kuhl's pipistrelle (Pipistrellus kuhlii), a common synanthropic insectivorous bat that forages in urban and agricultural areas, to determine whether it may function as a natural enemy in CBC. Faecal samples of P. kuhlii were collected throughout the cotton‐growing season from five roost sites near cotton fields located in a Mediterranean agroecosystem, Israel, and analyzed using DNA metabarcoding. Additionally, data on estimated abundance of major cotton pests were collected. We found that the diet of P. kuhlii significantly varied according to sites and dates and comprised 27 species of agricultural pests that were found in 77.2% of the samples, including pests of key economic concern. The dominant prey was the widespread cotton pest, the pink bollworm, Pectinophora gossypiella, found in 31% of the samples and in all the roosts. Pink bollworm abundance was positively correlated with its occurrence in the bat diet. Furthermore, the bats’ dietary breadth narrowed, while temporal dietary overlap increased, in relation to increasing frequencies of pink bollworms in the diet. This suggests that P. kuhlii exploits pink bollworm irruptions by opportunistic feeding. We suggest that synanthropic bats provide important pest suppression services, may function as CBC agents of cotton pests and potentially contribute to suppress additional deleterious arthropods found in their diet in high frequencies.     

Efficient and sensitive identification and quantification of airborne pollen using next‐generation DNA sequencing

Pollen monitoring is an important and widely used tool in allergy research and creation of awareness in pollen‐allergic patients. Current pollen monitoring methods are microscope‐based, labour intensive and cannot identify pollen to the genus level in some relevant allergenic plant groups. Therefore, a more efficient, cost‐effective and sensitive method is needed. Here, we present a method for identification and quantification of airborne pollen using DNA sequencing. Pollen is collected from ambient air using standard techniques. DNA is extracted from the collected pollen, and a fragment of the chloroplast gene trnL is amplified using PCR. The PCR product is subsequently sequenced on a next‐generation sequencing platform (Ion Torrent). Amplicon molecules are sequenced individually, allowing identification of different sequences from a mixed sample. We show that this method provides an accurate qualitative and quantitative view of the species composition of samples of airborne pollen grains. We also show that it correctly identifies the individual grass genera present in a mixed sample of grass pollen, which cannot be achieved using microscopic pollen identification. We conclude that our method is more efficient and sensitive than current pollen monitoring techniques and therefore has the potential to increase the throughput of pollen monitoring.     

Ecosystem type drives soil eukaryotic diversity and composition in Europe

Soil eukaryotes play a crucial role in maintaining ecosystem functions and services, yet the factors driving their diversity and distribution remain poorly understood. While many studies focus on some eukaryotic groups (mostly fungi), they are limited in their spatial scale. Here, we analyzed an unprecedented amount of observational data of soil eukaryomes at continental scale (787 sites across Europe) to gain further insights into the impact of a wide range of environmental conditions (climatic and edaphic) on their community composition and structure. We found that the diversity of fungi, protists, rotifers, tardigrades, nematodes, arthropods, and annelids was predominantly shaped by ecosystem type (annual and permanent croplands, managed and unmanaged grasslands, coniferous and broadleaved woodlands), and higher diversity of fungi, protists, nematodes, arthropods, and annelids was observed in croplands than in less intensively managed systems, such as coniferous and broadleaved woodlands. Also in croplands, we found more specialized eukaryotes, while the composition between croplands was more homogeneous compared to the composition of other ecosystems. The observed high proportion of overlapping taxa between ecosystems also indicates that DNA has accumulated from previous land uses, hence mimicking the land transformations occurring in Europe in the last decades. This strong ecosystem-type influence was linked to soil properties, and particularly, soil pH was driving the richness of fungi, rotifers, and annelids, while plant-available phosphorus drove the richness of protists, tardigrades, and nematodes. Furthermore, the soil organic carbon to total nitrogen ratio crucially explained the richness of fungi, protists, nematodes, and arthropods, possibly linked to decades of agricultural inputs. Our results highlighted the importance of long-term environmental variables rather than variables measured at the time of the sampling in shaping soil eukaryotic communities, which reinforces the need to include those variables in addition to ecosystem type in future monitoring programs and conservation efforts.     

Multiscale climate change impacts on plant diversity in the Atacama Desert

Comprehending ecological dynamics requires not only knowledge of modern communities but also detailed reconstructions of ecosystem history. Ancient DNA (aDNA) metabarcoding allows biodiversity responses to major climatic change to be explored at different spatial and temporal scales. We extracted aDNA preserved in fossil rodent middens to reconstruct late Quaternary vegetation dynamics in the hyperarid Atacama Desert. By comparing our paleo‐informed millennial record with contemporary observations of interannual variations in diversity, we show local plant communities behave differentially at different timescales. In the interannual (years to decades) time frame, only annual herbaceous expand and contract their distributional ranges (emerging from persistent seed banks) in response to precipitation, whereas perennials distribution appears to be extraordinarily resilient. In contrast, at longer timescales (thousands of years) many perennial species were displaced up to 1,000 m downslope during pluvial events. Given ongoing and future natural and anthropogenically induced climate change, our results not only provide baselines for vegetation in the Atacama Desert, but also help to inform how these and other high mountain plant communities may respond to fluctuations of climate in the future.     

Optimal sequence similarity thresholds for clustering of molecular operational taxonomic units in DNA metabarcoding studies

Clustering approaches are pivotal to handle the many sequence variants obtained in DNA metabarcoding data sets, and therefore they have become a key step of metabarcoding analysis pipelines. Clustering often relies on a sequence similarity threshold to gather sequences into molecular operational taxonomic units (MOTUs), each of which ideally represents a homogeneous taxonomic entity (e.g., a species or a genus). However, the choice of the clustering threshold is rarely justified, and its impact on MOTU over-splitting or over-merging even less tested. Here, we evaluated clustering threshold values for several metabarcoding markers under different criteria: limitation of MOTU over-merging, limitation of MOTU over-splitting, and trade-off between over-merging and over-splitting. We extracted sequences from a public database for nine markers, ranging from generalist markers targeting Bacteria or Eukaryota, to more specific markers targeting a class or a subclass (e.g., Insecta, Oligochaeta). Based on the distributions of pairwise sequence similarities within species and within genera, and on the rates of over-splitting and over-merging across different clustering thresholds, we were able to propose threshold values minimizing the risk of over-splitting, that of over-merging, or offering a trade-off between the two risks. For generalist markers, high similarity thresholds (0.96–0.99) are generally appropriate, while more specific markers require lower values (0.85–0.96). These results do not support the use of a fixed clustering threshold. Instead, we advocate careful examination of the most appropriate threshold based on the research objectives, the potential costs of over-splitting and over-merging, and the features of the studied markers.