Can Metabarcoding Resolve Intraspecific Genetic Diversity Changes to Environmental Stressors?

bioRxiv preprint doi: https://doi.org/10.1101/2020.03.08.982561; this version posted March 9, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 Can metabarcoding resolve intraspecific genetic diversity changes to 2 environmental stressors? A test case using river macrozoobenthos 3 4 Vera Marie Alida Zizkaa,c*, Martina Weissa,b and Florian Leesea,b 5 6 a) Aquatic Ecosystem Research, Faculty of Biology, University of Duisburg-Essen, Universitätsstraße 7 5, 45141 Essen, North Rhine-Westphalia, Germany 8 b) Centre for Water and Environmental Research (ZWU) Essen, University of Duisburg-Essen, 9 Universitätsstraße 2, 45141 Essen, North Rhine-Westphalia, Germany 10 c) Present address: Zoological Research Museum Alexander Koenig, Leibniz Institute for Animal 11 Biodiversity, Adenauerallee 160, 53113 Bonn, North Rhine-Westphalia, Germany 12 13 * Corresponding author: [email protected] 14 15 16 Key words 17 haplotypes, denoising, genetic erosion, environmental impact, pollution 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.08.982561; this version posted March 9, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 38 Abstract 39 Genetic diversity is the most basal level of biodiversity and determines the evolutionary capacity of 40 species to adapt to changing environments, yet it is typically neglected in routine biomonitoring and 41 stressor impact assessment. For a comprehensive analysis of stressor impacts on genetic diversity, it is 42 necessary to assess genetic variants simultaneously in many individuals and species. Such an assessment 43 is not as straight-forward and usually limited to one or few individual species. However, nowadays 44 species diversity can be assessed by analysing thousands of individuals of a community simultaneously 45 with DNA metabarcoding. Recent bioinformatic advances also allow for the extraction of exact 46 sequence variants (ESVs or haplotypes) in addition to Operational Taxonomic Units (OTUs). By using 47 this new capability, we here evaluated if the analysis of mitochondrial genetic diversity in addition to 48 species diversity can provide insights into responses of stream macrozoobenthic communities to 49 environmental stressors. For this purpose, we analysed macroinvertebrate bulk samples of three German 50 river systems with different stressor levels using DNA metabarcoding. While OTU and haplotype 51 number were negatively correlated with stressor impact, this association was not as clear when looking 52 at haplotype diversity. Here, stressor responses were only found for sensitive EPT (Ephemeroptera, 53 Plecoptera, Trichoptera) taxa, and those exceedingly resistant to organic stress. An increase in haplotype 54 number per OTU and haplotype diversity of sensitive taxa was observed with an increase in ecosystem 55 quality and stability, while the opposite pattern was detected for pollution resistant taxa. However, this 56 pattern was less prominent than expected based on the strong differences in stressor intensity between 57 sites. To compare genetic diversity among river systems, only OTUs could be used, which were present 58 in all systems. As OTU composition differed strongly between the rivers, this led to the exclusion of a 59 high number of OTUs, especially in diverse river systems of good quality, which potentially diminished 60 the genetic diversity patterns. To better understand responses of genetic diversity to environmental 61 stressors for example in river ecosystems, it would be important to increase OTU overlap between sites 62 of comparisons, e.g. by sampling a narrower stressor gradient, and to perform calibrated studies 63 controlling for the number and individual genotypes. However, this pioneer study shows that the 64 extraction of haplotypes from DNA metabarcoding datasets is a promising tool to simultaneously assess 65 mitochondrial genetic diversity changes in response to environmental impacts for a metacommunity. 66 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.08.982561; this version posted March 9, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 67 Introduction 68 Degradation, pollution, and exploitation of freshwater ecosystems has resulted in a drastic decline of 69 biodiversity (Vörösmarty et al., 2010; WWF, 2018). The magnitude of biodiversity loss depends on 70 stressor intensities as well as on resistance and resilience of the biotic communities (Dobson et al., 2006; 71 Elmqvist et al., 2003; Vörösmarty et al., 2010). So far, degradation and recovery processes have mostly 72 been studied at the level of species diversity (alpha diversity). However, the underlying genetic diversity 73 within species is an essential variable to consider in this context, as it determines the evolutionary 74 capacity of a species to adapt to changing environments. Genetic diversity in ecosystems is influenced 75 by four major processes: mutation, drift, migration, and selection (Vellend and Geber, 2005). A high 76 level of genetic variation is assumed to occur in intact and stable ecosystems, where effective population 77 sizes are large and relatively constant over time. Under stressor impacts, genetic diversity declines 78 (‘genetic erosion hypothesis’) primarily due to reduced population sizes leading to enhanced genetic 79 drift (Amos and Balmford, 2001; Reusch et al., 2005; Reynolds et al., 2012; Ribeiro and Lopes, 2013; 80 van Straalen and Timmermans, 2002). Genetic diversity is the most basal level of biodiversity and 81 typically the first to decrease under, and the last to regenerate after stressor impact. It consequently 82 provides a proxy for ecological processes long before, or even if never visible on the species diversity 83 level (Bazin et al., 2006; Guttman, 1994; Hughes et al., 2008; Reynolds et al., 2012; Vellend and Geber, 84 2005). In conservation biology, genetic diversity is therefore used as a measure for population and 85 habitat stability. This information is typically neglected in the legally binding species assessment, or 86 species diversity of a habitat and is regarded as a proxy for genetic diversity (Vellend, 2005; Vellend 87 and Geber, 2005). However, this concept has been rarely tested and has been questioned for example by 88 Taberlet et al. (2012) who showed that for alpine plant species genetic diversity is not correlated with 89 alpha diversity. 90 Nowadays, alpha diversity can be assessed with great resolution using DNA metabarcoding (e.g. Deiner 91 et al., 2016; Hänfling et al., 2016; Macher et al., 2018). With these data, stressor impacts can be analysed 92 simultaneously for many taxa not distinguishable by morphological determination methods (Bagley et 93 al., 2019; Beermann et al., 2018; Pfrender et al., 2010; Theissinger et al., 2019). Typically, DNA 94 metabarcoding infers responses at Operational Taxonomic Unit (OTU) level. In most cases, distance- 95 based thresholds are used to define OTUs with the aim that these reflect as closely as possible biological 96 species. While OTU classification can drastically improve the taxonomic and ecological resolution in 97 comparison to classical morphological taxa assignment (Beermann et al., 2018; Macher et al., 2016; 98 Sturmbauer et al., 1999), the level of intraspecific genetic diversity still goes unnoticed, when distance- 99 based clustering is used to analyse the sequencing data. As an alternative, bioinformatic denoising 100 approaches can be used to obtain ‘exact sequence variants’ (ESVs) (Callahan et al., 2016; Frøslev et al., 101 2017). Here, additional bioinformatic filtering steps are used, that allow to separate biological template 102 sequences from noisy reads caused by PCR and sequencing errors. With ESVs it is possible to explore 103 intraspecific genetic diversity patterns in eukaryotes (Elbrecht et al., 2018; Tsuji et al., 2019; Turon et bioRxiv preprint doi: https://doi.org/10.1101/2020.03.08.982561; this version posted March 9, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 104 al., 2019). For animals, commonly a 658-bp fragment of the mitochondrial cytochrome c oxidase I gene 105 (COI) is used for DNA barcoding and a shorter part of this for metabarcoding (Hebert et al., 2003). 106 While the use of haploid, maternally-inherited mitochondrial markers has limitations for detailed 107 population genetic analyses (Ballard and Whitlock, 2004; Leese and Held, 2011), its utility to infer 108 insights into geographic structure and population diversity, and thereby ecological processes acting at 109 local or regional level, has often been demonstrated (e.g. Pauls et al., 2006; Weiss and Leese, 2016; Witt 110 and Hebert, 2000). Furthermore, extracting intraspecific haplotype variation data from COI 111 metabarcoding datasets that typically

Load more