Environmental DNA metabarcoding of rivers: Not all eDNA is everywhere, and not all the time

Macher, Jan‐Niklas; Leese, Florian

doi:10.1101/164046

Cited by 28 publications

(35 citation statements)

References 59 publications

(65 reference statements)

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“…Surprisingly, the 7th stream order sites diverge from this pattern, potentially because we could only access part of these wide river cross sections, and sampling was restricted to the river edge where less mixing occurs, where more extensive sampling may be needed with eDNA (Bylemans et al, ). Also, recent studies indicate that eDNA is not evenly distributed in the water column (Macher & Leese, ), and it is still unclear how spatial variance in structures, such as riffles and ponds, is affecting the mixing of the water column and thus the equal detection of eDNA along the water column. We speculate that only in smaller streams (1st to 5th order), one or two samples from the edge or in the center adequately reflect the eDNA distribution across the river transect, while in larger rivers, multiple samples across the cross section may be recommended.…”

Section: Discussionmentioning

confidence: 99%

Assessing different components of diversity across a river network using eDNA

et al. 2019

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Assessing individual components of biodiversity, such as local or regional taxon richness, and differences in community composition is a long‐standing challenge in ecology. It is especially relevant in spatially structured and diverse ecosystems. Environmental DNA (eDNA) has been suggested as a novel technique to detect taxa and therefore may allow to accurately measure biodiversity. However, we do not yet fully understand the comparability of eDNA‐based assessments to classical morphological approaches. We assessed may‐, stone‐, and caddisfly genera with two contemporary methods, namely eDNA sampling followed by molecular identification and kicknet sampling followed by morphological identification. We sampled 61 sites distributed over a large river network, allowing a comparison of various diversity measures from the catchment to site levels and providing insights into how these measures relate to network properties. We extended our data with historical morphological records of total diversity at the catchment level. At the catchment scale, identification based on eDNA and kicknet samples detected similar proportions of the overall and cumulative historically documented richness (gamma diversity), 42% and 46%, respectively. We detected a good overlap (62%) between genera identified from eDNA and kicknet samples at the regional scale. At the local scale, we found highly congruent values of local taxon richness (alpha diversity) between eDNA and kicknet samples. Richness of eDNA was positively related to discharge, a descriptor of network position, while kicknet was not. Beta diversity, a measure of dissimilarity between sites, was comparable for the two contemporary methods and is driven by species replacement and not by nestedness. Although eDNA approaches are still in their infancy and optimization regarding sampling design and laboratory work is still needed, our results indicate that it can capture different components of diversity, proving its potential utility as a new tool for large sampling campaigns across hitherto understudied complete river catchments.

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Section: Discussionmentioning

confidence: 99%

Assessing different components of diversity across a river network using eDNA

et al. 2019

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“…Another non‐harmful method that could be used with underwater cameras, and is becoming increasingly popular for detecting rare aquatic species, is environmental DNA (eDNA; Balasingham, Walter, Mandrak, & Heath, ; Boothroyd, Mandrak, Fox, & Wilson, ; Janosik & Johnson, ). This emerging method detects species based on collected genetic material from water samples (Janosik & Johnson, ), but also has some limitations, such as the heterogeneous distribution of eDNA across temporal and spatial scales in a river, reducing the detection of species even at small spatial scales (Macher & Leese, ). This method could be combined with underwater cameras to obtain higher detection probabilities of rare fishes without adverse effects on the species and habitats.…”

Section: Discussionmentioning

confidence: 99%

Using occupancy models to assess the effectiveness of underwater cameras to detect rare stream fishes

Castañeda

Weyl

Mandrak

2020

Aquatic Conservation

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Many conservation efforts for freshwater fishes have been undertaken; however, continuing the monitoring of both the distribution and the abundance of species to determine the effectiveness of these actions can be difficult. As species increase in rarity, they are more difficult to detect in the field, making inferences on occupancy less reliable. Conventional sampling methods, such as electrofishing and seining, require the physical handling of rare fishes, which may cause stress and mortality and, consequently, compromise conservation goals and limit monitoring programmes. Non‐invasive surveillance methods, including underwater video, are playing an increasingly important role. In this study, occupancy models were used to estimate the detection probability of underwater cameras as an alternative to the conventional sampling methods for rare stream fishes. Redside dace (Clinostomus elongatus), a small minnow listed as Endangered, was used as a model organism for rarity. A total of 69 historical redside dace sites were sampled using three sampling methods to determine the effect of gear type on detecting and identifying the habitat preferences of this rare minnow. On average, using multiple underwater cameras is as effective at detecting a rare minnow as conventional sampling methods (backpack electrofisher and seine) and causes no harm. The detection probability of both underwater cameras and backpack electrofishing were adversely affected by turbidity, whereas seining was positively affected by stream velocity. The probability of occupancy of redside dace is driven by open channels and sediment size, and this provides a strong basis for informing stream restoration projects. The use of multiple underwater cameras over conventional sampling methods is recommended when sampling for rare and endangered minnows in systems with low turbidity.

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“…In part, this is due to the fact that the samples and lakes included in this analysis are limited in number and are geographically close to each other [22, 24, 25, 36]. Therefore, for a more thorough analysis, larger datasets from more variable sites would be neccessary, as currently only available from large-scale environmental sequencing efforts such as the Earth Microbiome Project [37] or the 1000 Springs Project [28, 38].…”

Section: Discussionmentioning

confidence: 99%

SEDE-GPS: socio-economic data enrichment based on GPS information

Sperlea¹,

Füser²,

Boenigk³

et al. 2018

BMC Bioinformatics

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BackgroundMicrobes are essentail components of all ecosystems because they drive many biochemical processes and act as primary producers. In freshwater ecosystems, the biodiversity in and the composition of microbial communities can be used as indicators for environmental quality. Recently, some environmental features have been identified that influence microbial ecosystems. However, the impact of human action on lake microbiomes is not well understood. This is, in part, due to the fact that environmental data is, albeit theoretically accessible, not easily available.ResultsIn this work, we present SEDE-GPS, a tool that gathers data that are relevant to the environment of an user-provided GPS coordinate. To this end, it accesses a list of public and corporate databases and aggregates the information in a single file, which can be used for further analysis. To showcase the use of SEDE-GPS, we enriched a lake microbial ecology sequencing dataset with around 18,000 socio-economic, climate, and geographic features. The sources of SEDE-GPS are public databases such as Eurostat, the Climate Data Center, and OpenStreetMap, as well as corporate sources such as Twitter. Using machine learning and feature selection methods, we were able to identify features in the data provided by SEDE-GPS that can be used to predict lake microbiome alpha diversity.ConclusionThe results presented in this study show that SEDE-GPS is a handy and easy-to-use tool for comprehensive data enrichment for studies of ecology and other processes that are affected by environmental features. Furthermore, we present lists of environmental, socio-economic, and climate features that are predictive for microbial biodiversity in lake ecosystems. These lists indicate that human action has a major impact on lake microbiomes. SEDE-GPS and its source code is available for download at http://SEDE-GPS.heiderlab.deElectronic supplementary materialThe online version of this article (10.1186/s12859-018-2419-4) contains supplementary material, which is available to authorized users.

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Environmental DNA metabarcoding of rivers: Not all eDNA is everywhere, and not all the time

Cited by 28 publications

References 59 publications

Assessing different components of diversity across a river network using eDNA

Assessing different components of diversity across a river network using eDNA

Using occupancy models to assess the effectiveness of underwater cameras to detect rare stream fishes

SEDE-GPS: socio-economic data enrichment based on GPS information

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