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Spatio-temporal patterns of multi-trophic biodiversity and food-web characteristics uncovered across a river catchment using environmental DNA

  • Whittaker, R. H. Vegetation of the Siskiyou mountains, Oregon and California. Ecol. Monogr. 30, 279–338 (1960).

    Google Scholar 

  • Wilson, R. J., Thomas, C. D., Fox, R., Roy, D. B. & Kunin, W. E. Spatial patterns in species distributions reveal biodiversity change. Nature 432, 393–396 (2004).

    CAS 
    PubMed 

    Google Scholar 

  • Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).

    CAS 
    PubMed 

    Google Scholar 

  • Ings, T. C. et al. Ecological networks—beyond food webs. J. Anim. Ecol. 78, 253–269 (2009).

    PubMed 

    Google Scholar 

  • Dunne, J. A. & Williams, R. J. Cascading extinctions and community collapse in model food webs. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 364, 1711–1723 (2009).

    Google Scholar 

  • Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).

    PubMed 

    Google Scholar 

  • Vellend, M. The Theory of Ecological Communities Vol. 57 229 (Princeton University Press, 2016).

  • Altermatt, F. Diversity in riverine metacommunities: a network perspective. Aquat. Ecol. 47, 365–377 (2013).

    Google Scholar 

  • Peterson, E. E. et al. Modelling dendritic ecological networks in space: an integrated network perspective. Ecol. Lett. 16, 707–719 (2013).

    PubMed 

    Google Scholar 

  • Tonkin, J. D. et al. The role of dispersal in river network metacommunities: patterns, processes, and pathways. Freshw. Biol. 63, 141–163 (2018).

    Google Scholar 

  • Muneepeerakul, R. et al. Neutral metacommunity models predict fish diversity patterns in Mississippi-Missouri basin. Nature 453, 220–222 (2008).

    CAS 
    PubMed 

    Google Scholar 

  • Besemer, K. et al. Headwaters are critical reservoirs of microbial diversity for fluvial networks. Proc. Biol. Sci. 280, 20131760 (2013).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Finn, D. S., Bonada, N., Múrria, C. & Hughes, J. M. Small but mighty: headwaters are vital to stream network biodiversity at two levels of organization. J. North Am. Benthol. Soc. 30, 963–980 (2011).

    Google Scholar 

  • Altermatt, F., Seymour, M. & Martinez, N. River network properties shape α-diversity and community similarity patterns of aquatic insect communities across major drainage basins. J. Biogeogr. 40, 2249–2260 (2013).

    Google Scholar 

  • Harvey, E., Gounand, I., Fronhofer, E. A. & Altermatt, F. Disturbance reverses classic biodiversity predictions in river-like landscapes. Proc. R. Soc. B: Biol. Sci. 285, 20182441 (2018).

    Google Scholar 

  • Tylianakis, J. M., Laliberté, E., Nielsen, A. & Bascompte, J. Conservation of species interaction networks. Biol. Conserv. 143, 2270–2279 (2010).

    Google Scholar 

  • Thompson, R. M. et al. Food webs: reconciling the structure and function of biodiversity. Trends Ecol. Evol. 27, 689–697 (2012).

    PubMed 

    Google Scholar 

  • Woodward, G. & Hildrew, A. G. Food web structure in riverine landscapes. Freshw. Biol. 47, 777–798 (2002).

    Google Scholar 

  • Williams, R. J. & Martinez, N. D. Limits to trophic levels and omnivory in complex food webs: theory and data. Am. Nat. 163, 458–468 (2004).

    PubMed 

    Google Scholar 

  • Thompson, R. M. & Townsend, C. R. The effect of seasonal variation on the community structure and food-web attributes of two streams: implications for food-web science. Oikos 87, 75–88 (1999).

    Google Scholar 

  • Wood, S. A., Russell, R., Hanson, D., Williams, R. J. & Dunne, J. A. Effects of spatial scale of sampling on food web structure. Ecol. Evol. 5, 3769–3782 (2015).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Tylianakis, J. M. & Morris, R. J. Ecological networks across environmental gradients. Annu. Rev. Ecol., Evolution, Syst. 48, 25–48 (2017).

    Google Scholar 

  • Romanuk, T. N. et al. The structure of food webs along river networks. Ecography 29, 3–10 (2006).

    Google Scholar 

  • Olivier, P. et al. Exploring the temporal variability of a food web using long‐term biomonitoring data. Ecography 42, 2107–2121 (2019).

    Google Scholar 

  • Poisot, T., Canard, E., Mouillot, D., Mouquet, N. & Gravel, D. The dissimilarity of species interaction networks. Ecol. Lett. 15, 1353–1361 (2012).

    PubMed 

    Google Scholar 

  • Delmas, E. et al. Analysing ecological networks of species interactions. Biol. Rev. Camb. Philos. Soc. https://doi.org/10.1111/brv.12433 (2018).

    Article 
    PubMed 

    Google Scholar 

  • Tavares-Cromar, A. F. & Williams, D. D. The importance of temporal resolution in food web analysis: Evidence from a detritus-based stream. Ecol. Monogr. 66, 91–113 (1996).

    Google Scholar 

  • Poisot, T., Stouffer, D. B. & Gravel, D. Beyond species: why ecological interaction networks vary through space and time. Oikos 124, 243–251 (2015).

    Google Scholar 

  • Thomsen, P. F. & Willerslev, E. Environmental DNA—an emerging tool in conservation for monitoring past and present biodiversity. Biol. Conserv. 183, 4–18 (2015).

    Google Scholar 

  • Deiner, K., Fronhofer, E. A., Mächler, E., Walser, J.-C. & Altermatt, F. Environmental DNA reveals that rivers are conveyer belts of biodiversity information. Nat. Commun. 7, 12544 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Dunne, J. A. In Ecological Networks: Linking Structure and Dynamics (eds. Pascual, J. A. & Dunne, J. A.) 27–86 (University Press, 2006).

  • Neff, F. et al. Changes in plant-herbivore network structure and robustness along land-use intensity gradients in grasslands and forests. Sci Adv 7, eabf3985 (2021).

  • O’Connor, M. J. et al. Unveiling the food webs of tetrapods across Europe through the prism of the Eltonian niche. J. Biogeogr. 47, 181–192 (2020).

    Google Scholar 

  • Pellissier, L. et al. Comparing species interaction networks along environmental gradients. Biol. Rev. Camb. Philos. Soc. 93, 785–800 (2018).

    PubMed 

    Google Scholar 

  • Saravia, L. A. et al. Ecological network assembly: how the regional metaweb influences local food webs. BioRxiv, https://doi.org/10.1101/340430 (2021).

  • Blackman, R. C. et al. Mapping biodiversity hotspots of fish communities in subtropical streams through environmental DNA. Sci. Rep. 4, e65352 (2021).

    Google Scholar 

  • Baselga, A. & Orme, C. D. L. betapart: an R package for the study of beta diversity: Betapart package. Methods Ecol. Evol. 3, 808–812 (2012).

    Google Scholar 

  • Seymour, M. et al. Executing multi-taxa eDNA ecological assessment via traditional metrics and interactive networks. Sci. Total Environ. 729, 138801 (2020).

    CAS 
    PubMed 

    Google Scholar 

  • D’Alessandro, S. & Mariani, S. Sifting environmental DNA metabarcoding data sets for rapid reconstruction of marine food webs. Fish Fish 22, 822–833 (2021).

    Google Scholar 

  • Zhang, Y. et al. Holistic pelagic biodiversity monitoring of the Black Sea via eDNA metabarcoding approach: From bacteria to marine mammals. Environ. Int. 135, 105307 (2020).

    PubMed 

    Google Scholar 

  • Altermatt, F. et al. Uncovering the complete biodiversity structure in spatial networks: the example of riverine systems. Oikos 129, 607–618 (2020).

    Google Scholar 

  • Widder, S. et al. Fluvial network organization imprints on microbial co-occurrence networks. Proc. Natl Acad. Sci. USA 111, 12799–12804 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Seymour, M. et al. Environmental DNA provides higher resolution assessment of riverine biodiversity and ecosystem function via spatio-temporal nestedness and turnover partitioning. Commun. Biol. 4, 512 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Mächler, E. et al. Assessing different components of diversity across a river network using eDNA. Environ. DNA 1, 290–301 (2019).

    Google Scholar 

  • Peralta-Maraver, I., López-Rodríguez, M. J. & de Figueroa, J. M. T. Structure, dynamics and stability of a Mediterranean river food web. Mar. Freshw. Res. 68, 484–495 (2017).

    Google Scholar 

  • Woodward, G. et al. Ecological networks in a changing climate. Ecol. Netw. 42, 71–138 (2010).

    Google Scholar 

  • Kondoh, M., Kato, S. & Sakato, Y. Food webs are built up with nested subwebs. Ecology 91, 3123–3130 (2010).

    PubMed 

    Google Scholar 

  • Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R. & Cushing, C. E. The River Continuum Concept. Can. J. Fish. Aquat. Sci. 37, 130–137 (1980).

    Google Scholar 

  • Power, M. E. & Dietrich, W. E. Food webs in river networks. Ecol. Res. https://doi.org/10.1046/j.0912-3814.2002.00503.x (2002).

  • Montoya, D., Yallop, M. L. & Memmott, J. Functional group diversity increases with modularity in complex food webs. Nat. Commun. 6, 7379 (2015).

    CAS 
    PubMed 

    Google Scholar 

  • Gravel, D., Albouy, C. & Thuiller, W. The meaning of functional trait composition of food webs for ecosystem functioning. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 371, 20150268 (2016).

  • Ruppert, K. M., Kline, R. J. & Rahman, M. S. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: a systematic review in methods, monitoring, and applications of global eDNA. Glob. Ecol. Conserv. 17, e00547 (2019).

    Google Scholar 

  • Carraro, L., Mächler, E., Wüthrich, R. & Altermatt, F. Environmental DNA allows upscaling spatial patterns of biodiversity in freshwater ecosystems. Nat. Commun. 11, 3585 (2020).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Barnes, M. A. & Turner, C. R. The ecology of environmental DNA and implications for conservation genetics. Conserv. Genet. 17, 1–17 (2016).

    CAS 

    Google Scholar 

  • Bista, I. et al. Annual time-series analysis of aqueous eDNA reveals ecologically relevant dynamics of lake ecosystem biodiversity. Nat. Commun. 8, 14087 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Erickson, R. A., Merkes, C. M., Jackson, C. A., Goforth, R. R. & Amberg, J. J. Seasonal trends in eDNA detection and occupancy of bigheaded carps. J. Gt. Lakes Res. 43, 762–770 (2017).

    Google Scholar 

  • Troth, C. R., Sweet, M. J., Nightingale, J. & Burian, A. Seasonality, DNA degradation and spatial heterogeneity as drivers of eDNA detection dynamics. Sci. Total Environ. 768, 144466 (2021).

    CAS 
    PubMed 

    Google Scholar 

  • Thalinger, B. et al. The effect of activity, energy use, and species identity on environmental DNA shedding of freshwater fish. Front. Ecol. Evolution 9, 73 (2021).

    Google Scholar 

  • Kelly, R. P., Port, J. A., Yamahara, K. M. & Crowder, L. B. Using environmental DNA to census marine fishes in a large mesocosm. PLoS ONE 9, e86175 (2014).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front. Zool. 10, 34 (2013).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Geller, J., Meyer, C., Parker, M. & Hawk, H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13, 851–861 (2013).

    CAS 
    PubMed 

    Google Scholar 

  • Liu, C. M. et al. BactQuant: An enhanced broad-coverage bacterial quantitative real-time PCR assay. BMC Microbiol. 12, 56 (2012).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Mansfeldt, C. et al. Microbial community shifts in streams receiving treated wastewater effluent. Sci. Total Environ. 709, 135727 (2020).

    CAS 
    PubMed 

    Google Scholar 

  • Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

    CAS 
    PubMed 

    Google Scholar 

  • Andrews, S. FASTQC A Quality Control tool for High Throughput Sequence Data (Babraham Institute, 2015).

  • Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Hänfling, B. et al. Environmental DNA metabarcoding of lake fish communities reflects long-term data from established survey methods. Mol. Ecol. 25, 3101–3119 (2016).

    PubMed 

    Google Scholar 

  • Csárdi, G. & Nepusz, T. The igraph software package for complex network research. Int. J. Complex Syst. 1695, 1–9 (2006).

    Google Scholar 

  • Oksanen, J. et al. vegan: Community Ecology Package 2.5-6. https://CRAN.Rproject.org/package=vegan (2019).

  • Tachet, H., Bournaud, M., Richoux, P. & Usseglio-Polatera, P. Invertébrés d’eau douce—systématique, biologie, écologie (CNRS Editions, 2010).

  • Schmidt-Kloiber, A. & Hering, D. www.freshwaterecology.info—an online tool that unifies, standardises and codifies more than 20,000 European freshwater organisms and their ecological preferences. Ecol. Indic. 53, 271–282 (2015).

  • Newton, R. J., Jones, S. E., Eiler, A., McMahon, K. D. & Bertilsson, S. A guide to the natural history of freshwater lake bacteria. Microbiol. Mol. Biol. Rev. 75, 14–49 (2011).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Fortuna, M. A. et al. Nestedness versus modularity in ecological networks: two sides of the same coin? J. Anim. Ecol. 79, 811–817 (2010).

    PubMed 

    Google Scholar 

  • Johnson, S., Domínguez-García, V., Donetti, L. & Muñoz, M. A. Trophic coherence determines food-web stability. Proc. Natl Acad. Sci. USA 111, 17923–17928 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wootton, K. L. Omnivory and stability in freshwater habitats: Does theory match reality? Freshw. Biol. 62, 821–832 (2017).

    Google Scholar 

  • Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw., Artic. 82, 1–26 (2017).

    Google Scholar 

  • Lenth, R. V. Estimated Marginal Means, aka Least-Squares Means [R package emmeans version 1.6.1] (2021).

  • RStudio Team RStudio: Integrated development for R. RStudio, PBC, Boston, MA. R version 4.0.4 Retrieved from http://www.rstudio.com/ (2021)


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