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Record W3134390644 · doi:10.3897/aca.4.e65062

From microbes to mammals: agriculture homogenizes pond biodiversity across different land-use types

2021· article· en· W3134390644 on OpenAlexaboutno aff
Danny Ionescu, Mina Bižić, Rajat Karnatak, Camille Musseau, Gabriela Onandía, Stella A. Berger, Jens C. Nejstgaard, Gunnar Lischeid, Mark O. Gessner, Sabine Wollrab, Hans‐Peter Grossart

Bibliographic record

VenueARPHA Conference Abstracts · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiodiversityGeographyArable landEcologyLand useSpecies richnessAgricultureEcosystemIntensive farmingBiologyAgroforestry

Abstract

fetched live from OpenAlex

The diversity and composition of biological communities in ecosystems is typically linked to land use. Consequently, intensive agriculture has a strong influence on these patterns, including a reduction and homogenization of species diversity. Kettle holes (KH; also known as potholes) are small water bodies (<1 ha) typical to formerly glaciated land on the northern hemisphere, reaching densities up to several dozen per km 2 . They often function as drainage centers, linking various landscape elements in their surroundings and are hotspots of aquatic biodiversity in terrestrial landscapes. In the 1950s, the land use surrounding KH in Europe (e.g., Germany, Poland, The Netherlands) and North America (USA and Canada) shifted towards intensive agriculture, likely affecting the local and regional biodiversity of the KH. Reports on the biodiversity of ponds in agricultural landscapes are scarce, and mostly focus on single taxa. We used deep eDNA amplicon sequencing of eukaryotic, bacterial, and archaeal SSU rRNA genes to assess effects of land use on the overall species biodiversity of 67 KH in either arable fields, grasslands, or forests, all embedded in a landscape subjected to intensive agriculture for decades. These were later contrasted with Berlin city ponds. Metatranscriptomic analyses were conducted in parallel to investigate the taxonomic diversity and functionality of the active communities. The amplicon data, >300 million reads, were analyzed with phyloFlash and Kraken2 using the SILVA_SSU_Ref database. Subsequently, the sequences were grouped based on taxonomy. The use of different annotation pipelines resulted in similar patterns. A subset of the data was analyzed using DADA2, generating amplicon sequence variants. Clustering the sequences as variants or according to taxonomy resulted in identical patterns, suggesting that broad taxonomical groupings provide sufficient resolution for general overviews. Annotation of the eukaryotic community was challenging, as none of the currently available databases contains sufficient high-quality sequences to cover the entire phylogenetic breadth. To overcome this limitation, we used the SILVA_SSU_Parc database, which also contains short sequences excluded from SILVA_SSU_Ref, thus extending the taxonomic base of the analysis. The curated, high-quality PR2 eukaryotic database did not provide better results than the SILVA_SSU_Ref database. An incompatibility between the SILVA and classical taxonomic nomenclature resulted in a ‘language barrier,’ which could be partly resolved by using the parallel EMBL taxonomy provided in the SILVA databases. Nevertheless, we propose that short-read SSU-based eukaryotic taxonomic annotations should be validated by other means. Despite such methodological limitations, our study demonstrates that deep amplicon sequencing of eDNA return a reliable picture of the biodiversity in complex ecosystems. Such information is sufficient to identify biodiversity patterns across the three domains of life and can serve to pinpoint taxonomic groups which should be investigated by a more detailed approach. Our study concluded that long-term exposure to intensive agriculture results in biodiversity homogenization across diverse taxonomic groups, removing most differences in biodiversity patterns among land-use types. This outcome contrasts with biodiversity patterns associated with sediments of the KH, where temporal coverage by the eDNA analyses extends back to at least the onset of intensive agriculture. However, metatranscriptomic analyses, reflecting the distribution of activities rather than relative abundance, revealed temporal differences in the structure of the active community in KH of the investigated land-use types, matching times of field fertilization. Thus, even in a landscape where biodiversity has been homogenized, inputs from the surroundings result in short-term activity changes of different organisms. This needs to be considered when developing new management schemes needed to counteract the current biodiversity loss.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.007

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.222
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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