| Literature DB >> 32265485 |
Marianne Ellegaard1,2, Martha R J Clokie3, Till Czypionka4, Dagmar Frisch5, Anna Godhe6, Anke Kremp7,8, Andrey Letarov9, Terry J McGenity10, Sofia Ribeiro11, N John Anderson12.
Abstract
DNA can be preserved in marine and freshwater sediments both in bulk sediment and in intact, viable resting stages. Here, we assess the potential for combined use of ancient, environmental, DNA and timeseries of resurrected long-term dormant organisms, to reconstruct trophic interactions and evolutionary adaptation to changing environments. These new methods, coupled with independent evidence of biotic and abiotic forcing factors, can provide a holistic view of past ecosystems beyond that offered by standard palaeoecology, help us assess implications of ecological and molecular change for contemporary ecosystem functioning and services, and improve our ability to predict adaptation to environmental stress.Entities:
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Year: 2020 PMID: 32265485 PMCID: PMC7138834 DOI: 10.1038/s42003-020-0899-z
Source DB: PubMed Journal: Commun Biol ISSN: 2399-3642
A summary of the strength and weaknesses of the different approaches to reconstructing aquatic ecosystems and pheno- and genotypic variability over time in aquatic systems.
| Method | Strengths | Weaknesses | References |
|---|---|---|---|
| Palaeoecology based on fossil organisms | • >107 year time scales | • No genotype information | [ |
| • Large datasets are available | • Labour intensive | ||
| • Potentially quantitative | • Only some groups/species preserved | ||
| Geochemical (bio)markers | • >107 year time scales | • No genotype information | [ |
| • Large databases available | • Potential for porewater mobility | ||
| • High throughput | • Lacks the taxonomic specificity of DNA sequences | ||
| • Potentially quantitative | |||
| Sedimentary eDNA timeseries | • So far, ~105 year time scales | • No direct phenotype information | See references in text |
| • Cover all domains of life | • So far, above population level | ||
| • High throughput | • Few reference sequences | ||
| • Sequence data has the potential to link specifically to taxa or traits | • Potential for porewater mobility | ||
| • Potentially quantitative (qPCR; so far only ~100 years) | • New bacterial and archaeal signals overprint the paleo-sequences, due to in situ growth | ||
| • Risk of chimeras & contamination | |||
| • Risk of bias in extraction | |||
| Resurrection ecology | • So far, 101-102 time scales (much longer for Bacteria and Archaea) | • Labour intensive | See references in text |
| • Linking genotype and phenotype directly | • Only some species preserved | ||
| • Applicable at population level | • Potential bias in survivability, but single-cell approaches possible | ||
| • Potentially quantitative |
Fig. 1A schematic food-web in a limnic and coastal marine system.
This figure shows where we have data on resurrection ecology series and indicates the food-web and interaction gaps in in the palaeoecological record, which sed-eDNA has the potential to fill in, to reconstruct food-webs, possibly through association networks, as suggested by[16] for lake ecosystems. Extracting, respectively, DNA and live propagules from dated sediment core has the potential to greatly enhance both the amount and types of information that we can gain about evolutionary and adaptive processes, by filling different information gaps in the paleo-ecological record. Green stars indicate the organism types for which genetic and phenotypic time-series have already been established from resurrected resting stages; the more stars, the larger the existing dataset. A green star in parenthesis indicates that viable resting stages have been recorded from old sediment layers, but no timeseries data published. Red circles indicate organism types for which there is information based on morphological remains (“traditional” palaeoecology); the more circles, the larger the existing dataset.
Fig. 2Schematic illustration of change in relative abundance of DNA due to taphonomic processes.
This figure illustrates the processes affecting DNA distribution, degradation and/or preferential preservation during the transitions from the pelagic to the benthic zones, and from the surface sediment to the deeper sediment. The approximate timescales of preservation of different fractions of the sediment record is also illustrated.
Fig. 3Resurrection ecology can be used to generate time series of population genetic data to test hypotheses of adaptation to temporal stressors.
The same strains, from which the DNA was extracted, can be used for side-by-side, or common-garden, tests of phenotypic/physiological response to the same stressors. Here we show modified versions of figures from Lundholm et al.[29] (a) and Frisch et al.[5] (b), both showing population structure plots. a Analysis of population genetic response of the phytoplankton Pentapharsodinium dalei in a Swedish fjord to environmental change associated with changes in the index of the North Atlantic Oscillation (NAO), which affects, among other things, salinity and water-column stability. b Analysis of population genetic response of the herbivore Daphnia pulicaria in a lake to changing phosphorus concentrations through time.