BBI: an R package for the computation of Benthic Biotic Indices from composition data

Cordier, Tristan; Pawłowski, Jan

doi:10.3897/mbmg.2.25649

Cited by 7 publications

(6 citation statements)

References 16 publications

(15 reference statements)

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“…We consider, however, that eDNA should be used as a general term, to designate any DNA extracted from an environmental sample. It is commonly made up of a mix of intraorganismal (in the form of small organisms relative to the sample size) and extra-organismal or trace eDNA shed from large organisms (Creer et al, 2016;Pawlowski et al, 2018;Porter & Hajibabaei, 2018;Salter, 2018;Taberlet, Coissac, Hajibabaei, et al, 2012). The relative amount of both components is highly variable, though, and it depends on the sampling method and the target group, and hence the primers used.…”

Section: Discussionmentioning

confidence: 99%

“…Alpha‐ and beta‐diversity estimates, as well as biotic indices, can be reliably obtained using metabarcoding (Aylagas et al., 2018; Bani et al., 2020; Hering et al., 2018; Pawlowski et al., 2018). The amount of data typically generated in metabarcoding data sets also allows bioassessments based on taxonomy‐free and machine learning techniques (Cordier & Pawlowski, 2018; Gerhard & Gunsch, 2019), or the analysis of diversity at the within‐species level (Turon et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

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Marine biomonitoring with eDNA: Can metabarcoding of water samples cut it as a tool for surveying benthic communities?

et al. 2020

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In the marine realm, biomonitoring using eDNA of benthic communities requires destructive direct sampling or the setting-up of settlement structures. Comparatively much less effort is required to sample the water column, which can be accessed remotely. In this study we assess the feasibility of obtaining information from the eukaryotic benthic communities by sampling the adjacent water layer. We studied two different rocky-substrate benthic Accepted Article This article is protected by copyright. All rights reserved communities with a technique based on quadrat sampling. We also took replicate water samples at four distances (0, 0.5, 1.5, and 20 m) from the benthic habitat. Using broad range primers to amplify a ca. 313 bp fragment of the cytochrome oxidase subunit I gene, we obtained a total of 3,543 molecular operational taxonomic units (MOTUs). The structure obtained in the two environments was markedly different, with Metazoa, Archaeplastida and Stramenopiles being the most diverse groups in benthic samples, and Hacrobia, Metazoa and Alveolata in the water. Only 265 MOTUs (7.5%) were shared between benthos and water samples and, of these, 180 (5.1%) were identified as benthic taxa that left their DNA in the water. Most of them were found immediately adjacent to the benthos, and their number decreased as we moved apart from the benthic habitat. It was concluded that water eDNA, even in the close vicinity of the benthos, was a poor proxy for the analysis of benthic structure, and that direct sampling methods are required for monitoring these complex communities via metabarcoding.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Marine biomonitoring with eDNA: Can metabarcoding of water samples cut it as a tool for surveying benthic communities?

et al. 2020

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“…In the case of the three eukaryotic markers, we compared our results obtained using SML with the ones obtained using taxonomy‐based assessment, that is, metazoan‐assigned OTUs, to compute BI values, by retrieving the ecological weight of the taxa present in the reference morphological database and computing BI values using their respective formula (Borja et al., ; Rygg & Norling, ) using the BBI r package (Cordier & Pawlowski, ).…”

Section: Methodsmentioning

confidence: 99%

Supervised machine learning outperforms taxonomy‐based environmental DNA metabarcoding applied to biomonitoring

Cordier

Forster

Dufresne

et al. 2018

Molecular Ecology Resources

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128

106

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Biodiversity monitoring is the standard for environmental impact assessment of anthropogenic activities. Several recent studies showed that high-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) could overcome many limitations of the traditional morphotaxonomy-based bioassessment. Recently, we demonstrated that supervised machine learning (SML) can be used to predict accurate biotic indices values from eDNA metabarcoding data, regardless of the taxonomic affiliation of the sequences. However, it is unknown to which extent the accuracy of such models depends on taxonomic resolution of molecular markers or how SML compares with metabarcoding approaches targeting well-established bioindicator species. In this study, we address these issues by training predictive models upon five different ribosomal bacterial and eukaryotic markers and measuring their performance to assess the environmental impact of marine aquaculture on independent data sets. Our results show that all tested markers are yielding accurate predictive models and that they all outperform the assessment relying solely on taxonomically assigned sequences. Remarkably, we did not find any significant difference in the performance of the models built using universal eukaryotic or prokaryotic markers. Using any molecular marker with a taxonomic range broad enough to comprise different potential bioindicator taxa, SML approach can overcome the limits of taxonomy-based eDNA bioassessment.

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“…For direct comparison with morphological values from the standard monitoring surveys in the area ( Hatlen et al, 2019 ), Shannon diversity estimates were calculated using the base 2 rather than the natural logarithm. Shannon diversity values were calculated for both 18S and COI data using the R BBI package ( Cordier & Pawlowski, 2018 ). For COI, BBI was also used to calculate several macrofaunal biotic indices based on taxonomic classification of the macrofauna reads in the metazoan data, including the AZTI Marine Biotic Index (AMBI) ( Borja, Franco & Pérez, 2000 ), and the local Norwegian biotic indices NQI1 ( Rygg, 2006 ), ISI2012, and NSI2012 ( Rygg & Norling, 2013 ).…”

Section: Methodsmentioning

confidence: 99%

Grab what you can—an evaluation of spatial replication to decrease heterogeneity in sediment eDNA metabarcoding

Hestetun

Lanzén

Dahlgren

2021

PeerJ

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Environmental DNA methods such as metabarcoding have been suggested as possible alternatives or complements to the current practice of morphology-based diversity assessment for characterizing benthic communities in marine sediment. However, the source volume used in sediment eDNA studies is several magnitudes lower than that used in morphological identification. Here, we used data from a North Sea benthic sampling station to investigate to what extent metabarcoding data is affected by sampling bias and spatial heterogeneity. Using three grab parallels, we sampled five separate sediment samples from each grab. We then made five DNA extraction replicates from each sediment sample. Each extract was amplified targeting both the 18S SSU rRNA V1–V2 region for total eukaryotic composition, and the cytochrome c oxidase subunit I (COI) gene for metazoans only. In both datasets, extract replicates from the same sediment sample were significantly more similar than different samples from the same grab. Further, samples from different grabs were less similar than those from the same grab for 18S. Interestingly, this was not true for COI metabarcoding, where the differences within the same grab were similar to the differences between grabs. We also investigated how much of the total identified richness could be covered by extract replicates, individual sediment samples and all sediment samples from a single grab, as well as the variability of Shannon diversity and, for COI, macrofaunal biotic indices indicating environmental status. These results were largely consistent with the beta diversity findings, and show that total eukaryotic diversity can be well represented using 18S metabarcoding with a manageable number of biological replicates. Based on these results, we strongly recommend the combination of different parts of the surface of single grabs for eDNA extraction as well as several grab replicates, or alternatively box cores or similar. This will dilute the effects of dominating species and increase the coverage of alpha diversity. COI-based metabarcoding consistency was found to be lower compared to 18S, but COI macrofauna-based indices were more consistent than direct COI alpha diversity measures.

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BBI: an R package for the computation of Benthic Biotic Indices from composition data

Cited by 7 publications

References 16 publications

Marine biomonitoring with eDNA: Can metabarcoding of water samples cut it as a tool for surveying benthic communities?

Marine biomonitoring with eDNA: Can metabarcoding of water samples cut it as a tool for surveying benthic communities?

Supervised machine learning outperforms taxonomy‐based environmental DNA metabarcoding applied to biomonitoring

Grab what you can—an evaluation of spatial replication to decrease heterogeneity in sediment eDNA metabarcoding

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