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Abstract Environmental DNA (eDNA) is frequently used to infer distributions of microorganisms in Antarctica. Their distributions relative to environmental variables are, in turn, sometimes used to infer their physiological range (and a relationship between the two is generally assumed for conservation purposes). We sought to determine whether ecological inferences based on distributions accurately reflect tolerances of the organisms concerned, using 249 legacy non-marine samples from a latitudinal gradient between 72 and 86°S, Antarctica. A cyanobacterium, a heterotrophic bacterium, two eukaryotic algae, two fungi, and a moss were isolated into culture, and their field distributions inferred using eDNA analysis of the samples above. Tolerances of each organism with respect to environmental predictors were then inferred from the eDNA distribution and metadata using Generalised Additive Models. We then measured growth of the cultured isolates in response to a set of these predictors. Laboratory responses were then compared to inferences from the eDNA/metadata. Predictions from eDNA/metadata agreed with the results of physiological laboratory experiments for strains that were detected at high taxonomic resolution in the field samples. However, errors were never completely eliminated, and direct contradictions occurred when strains were represented at lower taxonomic resolution in the field data. We found that accurate ecological inference from eDNA studies would be best achieved via maximising both taxonomic resolution (through marker choice/read length) and ecological signal (through careful sampling design and rigorous metadata collection).
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Landscape analyses using eDNA metabarcoding and Earth observation predict community biodiversity in California
Abstract Ecosystems globally are under threat from ongoing anthropogenic environmental change. Effective conservation management requires more thorough biodiversity surveys that can reveal system‐level patterns and that can be applied rapidly across space and time. Using modern ecological models and community science, we integrate environmental DNA and Earth observations to produce a time snapshot of regional biodiversity patterns and provide multi‐scalar community‐level characterization. We collected 278 samples in spring 2017 from coastal, shrub, and lowland forest sites in California, a complex ecosystem and biodiversity hotspot. We recovered 16,118 taxonomic entries from eDNA analyses and compiled associated traditional observations and environmental data to assess how well they predicted alpha, beta, and zeta diversity. We found that local habitat classification was diagnostic of community composition and distinct communities and organisms in different kingdoms are predicted by different environmental variables. Nonetheless, gradient forest models of 915 families recovered by eDNA analysis and using BIOCLIM variables, Sentinel‐2 satellite data, human impact, and topographical features as predictors, explained 35% of the variance in community turnover. Elevation, sand percentage, and photosynthetic activities (NDVI32) were the top predictors. In addition to this signal of environmental filtering, we found a positive relationship between environmentally predicted families and their numbers of biotic interactions, suggesting environmental change could have a disproportionate effect on community networks. Together, these analyses show that coupling eDNA with environmental predictors including remote sensing data has capacity to test proposed Essential Biodiversity Variables and create new landscape biodiversity baselines that span the tree of life.
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- Award ID(s):
- 1759756
- PAR ID:
- 10375741
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Ecological Applications
- Volume:
- 31
- Issue:
- 6
- ISSN:
- 1051-0761
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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