Gbol Iii: Dark Taxa

Hausmann, Axel; Krogmann, Lars; Peters, Ralph S.; Rduch, Vera; Schmidt, Stefan

doi:10.21083/ibol.v10i1.6242

Cited by 32 publications

(31 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of the species-level diversity and a significant proportion of biomass are concentrated in taxonomically poorly known taxa that are nowadays often referred to as "dark taxa" (e.g. bacteria, fungi, invertebrates; Hausmann et al, 2020). Such groups are usually avoided by taxonomists because they combine high diversity with high abundance.…”

Section: Introductionmentioning

confidence: 99%

A re‐analysis of the data in Sharkey et al.’s (2021) minimalist revision reveals that BINs do not deserve names, but BOLD Systems needs a stronger commitment to open science

et al. 2021

View full text Add to dashboard Cite

Halting biodiversity decline is one of the most critical challenges for humanity, but monitoring biodiversity is hampered by taxonomic impediments. One impediment is the large number of undescribed species (here called "dark taxon impediment") whereas another is caused by the large number of superficial species descriptions, that can only be resolved by consulting type specimens ("superficial description impediment"). Recently, Sharkey et al. (2021) proposed to address the dark taxon impediment for Costa Rican braconid wasps by describing 403 species based on COI barcode clusters ("BINs") computed by BOLD Systems. More than 99% of the BINs (387 of 390) were converted into species by assigning binominal names (e.g. BIN "BOLD: ACM9419" becomes Bracon federicomatarritai) and adding a minimal diagnosis (consisting only of a consensus barcode for most species). We here show that many of Sharkey et al.'s species are unstable when the underlying data are analyzed using different species delimitation algorithms. Add the insufficiently informative diagnoses, and many of these species will become the next "superficial description impediment" for braconid taxonomy because they will have to be tested and redescribed after obtaining sufficient evidence for confidently delimiting species. We furthermore show that Sharkey et al.'s approach of using consensus barcodes as diagnoses is not functional because it cannot be applied consistently. Lastly, we reiterate that COI alone is not suitable for delimiting and describing species, and voice concerns over Sharkey et al.'s uncritical use of BINs because they are calculated by a proprietary algorithm (RESL) that uses a mixture of public and private data. We urge authors, reviewers and editors to maintain high standards in taxonomy by only publishing new species that are rigorously delimited with openaccess tools and supported by publicly available evidence.

show abstract

Section: Introductionmentioning

confidence: 99%

A re‐analysis of the data in Sharkey et al.’s (2021) minimalist revision reveals that BINs do not deserve names, but BOLD Systems needs a stronger commitment to open science

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Such clades used to be called “open-ended taxa” (Bickel 2009), but recently the term “dark taxa” is more commonly used, although it was originally coined for the growing number of sequences in GenBank that were not linked to formal scientific names (Page 2011, 2016). As used today, “dark taxa” refers to species-rich taxa of small body size for whom most of the species-level diversity is undescribed (Hausmann et al 2020). Here, we accept the current usage, but also propose that it should only be applied to taxa for which the undescribed fauna is estimated to exceed the described fauna by at least one order of magnitude and the total diversity exceeds 1000 species.…”

Section: Introductionmentioning

confidence: 99%

Large-scale Integrative Taxonomy (LIT): resolving the data conundrum for dark taxa

Hartop

Srivathsan

Ronquist

et al. 2021

Preprint

View full text Add to dashboard Cite

New, rapid, accurate, scalable, and cost-effective species discovery and delimitation methods are needed for tackling 'dark taxa', that we here define as clades for which <10% of all species are described and the estimated diversity exceeds 1000 species. Species delimitation should be based on multiple data sources ('integrative taxonomy') but collecting several types of data for the same specimens risks impeding the discovery process that is already too slow. We here show how this can be avoided with Large-scale Integrative Taxonomy (LIT). Preliminary species hypotheses are generated based on inexpensive data that are obtained quickly and cost-effectively in a technical exercise. The validation step is then based on a more expensive type of data that are only obtained for few specimens selected based on objective criteria. We here use this approach to sort 18 000 scuttle flies (Diptera: Phoridae) from Sweden into 315 preliminary species hypotheses based on NGS barcode (313bp) clusters. These clusters went through subsequent validation based on morphology and were then used to develop quantitative indicators for predicting which barcode clusters are in conflict with morphospecies. For this purpose, we first randomly selected 100 clusters for in-depth validation with morphology. Afterwards, we used a linear model to demonstrate that the best predictors for conflict between barcode clusters and morphology are maximum p-distance within the cluster and cluster stability across different clustering thresholds. A test of these indicators using the 215 remaining clusters reveals that these predictors correctly identify all clusters that conflict with morphology. The morphological validation step in our study involved just 1 039 specimens (5.8% of all specimens), but a newly proposed simplified protocol would only require the study of 915 (5.1%: 2.5 specimens per species) as we show that clusters without signatures of incongruence can be validated by only studying two specimens representing the most divergent haplotypes. To test the generality of our results across different barcode clustering techniques, we establish that the levels of conflict are similar across Objective Clustering (OC), Automatic Barcode Gap Discovery (ABGD), Poisson Tree Processes (PTP) and Refined Single Linkage (RESL) (used by Barcode of Life Data System (BOLD) to assign Barcode Index Numbers (BINs)). OC and ABGD achieved a maximum match score with morphology of 89% while PTP was slightly less effective (84%). RESL could only be tested for a subset of the specimens because the algorithm is not public. BINs based on 277 of the original 1 714 haplotypes were 86% congruent with morphology while the values were 89% for OC, 74% for PTP, and 72% for ABGD.

show abstract

“…Any quantitative analysis of the problem reveals that most of the species-level diversity and biomass is concentrated in taxonomically poorly known microbial, fungal, and invertebrate clades. Within invertebrates are pockets of light (e.g., butterflies, dragonflies, bees), but also very large areas occupied by “dark taxa” with unknown numbers of species (Hausmann, A., Krogmann, L., et al 2020). These dark taxa are avoided by taxonomists and other biologists alike because they are not only hyperdiverse but also includes hyper-abundant species.…”

Section: Many Bins Are Unstablementioning

confidence: 99%

“…This heightened interest has highlighted once more that one of the most important tasks in science is unfinished: the discovery and description of earth’s biodiversity (May, 2011). Most of the species-level diversity and a significant proportion of biomass are concentrated in taxonomically poorly known taxa that are nowadays often referred to as “dark taxa” (e.g., bacteria, fungi, invertebrates) (Hausmann et al, 2020). Such groups are usually avoided by taxonomists because they combine high diversity with high abundance.…”

mentioning

confidence: 99%

A re-analysis of the data in Sharkey et al.’s (2021) minimalist revision reveals that BINs do not deserve names, but BOLD Systems needs a stronger commitment to open science

Meier

Blaimer

Buenaventura

et al. 2021

Preprint

View full text Add to dashboard Cite

Halting biodiversity decline is one of the most critical challenges for humanity, but biodiversity assessment and monitoring are hampered by taxonomic impediments. We here distinguish between a "dark taxon impediment" caused by a large number of undescribed species and a "superficial description impediment" caused by species descriptions so imprecise that type specimens have to be consulted in order to resolve species identities. Recently, Sharkey et al. (2021) proposed to address the dark taxon impediment for Costa Rican braconid wasps by describing 403 species based on barcode clusters ("BINs") computed by BOLD Systems. The default assumption of the revision is that BIN=Species (e.g., BOLD:ACM9419 becomes Bracon federicomatarritai Sharkey, sp. nov.) and therefore the diagnoses of most species consist only of a consensus barcode. We here argue that this type of "minimalist revision" is unnecessary and undesirable. It is unnecessary because barcode clusters (e.g. BINs) already provide grouping statements that overcome many of the obstacles associated with dark taxon impediments. However, minimalist revisions are also undesirable and problematic because the diagnoses are only based on one character system - that in the case of Sharkey et al. was poorly analyzed. Furthermore, the revision relies on units that violate basic rules of reproducibility because the BINs were delimited by a proprietary algorithm (RESL) that is applied to a mixture of public and private data. Here, we illustrate that many of the BINs described as species are unstable when the available public data are reanalyzed, reiterate that COI mostly measures time of divergence, and that BOLD Systems violates key principles of open science. We conclude by urging authors, reviewers, editors, and grantors to only publish and fund projects that adhere to modern standards of reproducibility.

show abstract

Gbol Iii: Dark Taxa

Cited by 32 publications

References 0 publications

A re‐analysis of the data in Sharkey et al.’s (2021) minimalist revision reveals that BINs do not deserve names, but BOLD Systems needs a stronger commitment to open science

A re‐analysis of the data in Sharkey et al.’s (2021) minimalist revision reveals that BINs do not deserve names, but BOLD Systems needs a stronger commitment to open science

Large-scale Integrative Taxonomy (LIT): resolving the data conundrum for dark taxa

A re-analysis of the data in Sharkey et al.’s (2021) minimalist revision reveals that BINs do not deserve names, but BOLD Systems needs a stronger commitment to open science

Contact Info

Product

Resources

About