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    Microbial transfers from permanent grassland ecosystems to milk in dairy farms in the Comté cheese area

    1.Mauchamp, L., Mouly, A., Badot, P.-M. & Gillet, F. Impact of nitrogen inputs on multiple facets of plant biodiversity in mountain grasslands: Does nutrient source matter?. Appl. Veg. Sci. 19, 206–217 (2016).Article 

    Google Scholar 
    2.Mesbahi, G., Michelot-Antalik, A., Goulnik, J. & Plantureux, S. Permanent grassland classifications predict agronomic and environmental characteristics well, but not ecological characteristics. Ecol. Indic. 110, 105956 (2020).Article 

    Google Scholar 
    3.Karimi, B. et al. Biogeography of soil microbial habitats across France. Glob. Ecol. Biogeogr. 29, 1399–1411 (2020).Article 

    Google Scholar 
    4.Mahaut, L., Fort, F., Violle, C. & Freschet, G. T. Multiple facets of diversity effects on plant productivity: Species richness, functional diversity, species identity and intraspecific competition. Funct. Ecol. 34, 287–298 (2020).Article 

    Google Scholar 
    5.Tilman, D. The ecological consequences of changes in biodiversity: A search for general principles. Ecology 80, 1455–1474 (1999).
    Google Scholar 
    6.van der Heijden, M. G. A., Bardgett, R. D. & van Straalen, N. M. The unseen majority: Soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol. Lett. 11, 296–310 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.Tardy, V. et al. Stability of soil microbial structure and activity depends on microbial diversity: Linking microbial diversity and stability. Environ. Microbiol. Rep. 6, 173–183 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    8.Philippot, L. et al. Loss in microbial diversity affects nitrogen cycling in soil. ISME J. 7, 1609–1619 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Fierer, N., Barberan, A. & Laughlin, D. C. Seeing the forest for the genes: Using metagenomics to infer the aggregated traits of microbial communities. Front. Microbiol. 5, 614 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Loreau, M. Linking biodiversity and ecosystems: Towards a unifying ecological theory. Philos. Trans. R. Soc. B 365, 49–60 (2010).Article 

    Google Scholar 
    11.Buchin, S., Martin, B., Dupont, D., Bornard, A. & Achilleos, C. Influence of the composition of Alpine highland pasture on the chemical, rheological and sensory properties of cheese. J. Dairy Res. 66, 579–588 (1999).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Bugaud, C., Buchin, S., Hauwy, A. & Coulon, J.-B. Flavour and texture of cheeses according to grazing type: The Abundance cheese. INRA Prod. Anim. 15, 31–36 (2002).Article 

    Google Scholar 
    13.Monnet, J. C., Berodier, F. & Badot, P. M. Characterization and localization of a cheese georegion using edaphic criteria (Jura Mountains, France). J. Dairy Sci. 83, 1692–1704 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    14.Mariotte, P., Vandenberghe, C., Kardol, P., Hagedorn, F. & Buttler, A. Subordinate plant species enhance community resistance against drought in semi-natural grasslands. J. Ecol. 101, 763–773 (2013).Article 

    Google Scholar 
    15.Montel, M.-C. et al. Traditional cheeses: Rich and diverse microbiota with associated benefits. Int. J. Food Microbiol. 177, 136–154 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Bouton, Y., Guyot, P., Berthier, F., & Beuvier, E. Investigation of bacterial community development from raw milk and starter to curd and mature Comte cheese. in Cheese ripening and technology: abstracts of IDF symposium held in Banff, Canada, March 2000 (ed. International Dairy Federation) 85 (Brussel, Belgium, 2000).17.Demarigny, Y., Beuvier, E., Buchin, S., Pochet, S. & Grappin, R. Influence of raw milk microflora on the characteristics of Swiss-type cheese. Lait 77, 151–167 (1997).CAS 
    Article 

    Google Scholar 
    18.Bouton, Y., Buchin, S., Duboz, G., Pochet, S. & Beuvier, E. Effect of mesophilic lactobacilli and enterococci adjunct cultures on the final characteristics of a microfiltered milk Swiss-type cheese. Food Microbiol. 26, 183–191 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.Vacheyrou, M. et al. Cultivable microbial communities in raw cow milk and potential transfers from stables of sixteen French farms. Int. J. Food Microbiol. 146, 253–262 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Verdier-Metz, I. et al. Cow teat skin, a potential source of diverse microbial populations for cheese production. Appl. Environ. Microbiol. 78, 326–333 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    21.Doyle, C. J., Gleeson, D., O’Toole, P. W. & Cotter, P. D. Impacts of seasonal housing and teat preparation on raw milk microbiota: A high-throughput sequencing study. Appl. Environ. Microbiol. 83(e02694–16), e02694-e2716 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    22.Frétin, M. et al. Bacterial community assembly from cow teat skin to ripened cheeses is influenced by grazing systems. Sci. Rep. 8, 200 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    23.Falentin, H. et al. Bovine teat microbiome analysis revealed reduced alpha diversity and significant changes in taxonomic profiles in quarters with a history of mastitis. Front. Microbiol. 7, 480 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Vorholt, J. A. Microbial life in the phyllosphere. Nat. Rev. Microbiol. 10, 828–840 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    26.Dequiedt, S. et al. Biogeographical patterns of soil bacterial communities. Environ. Microbiol. Rep. 1, 251–255 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Sadet-Bourgeteau, S. et al. Lasting effect of repeated application of organic waste products on microbial communities in arable soils. Appl. Soil Ecol. 125, 278–287 (2018).Article 

    Google Scholar 
    28.Nacke, H. et al. Pyrosequencing-based assessment of bacterial community structure along different management types in german forest and grassland soils. PLoS ONE 6, e17000 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Coolon, J. D., Jones, K. L., Todd, T. C., Blair, J. M. & Herman, M. A. Long-term nitrogen amendment alters the diversity and assemblage of soil bacterial communities in Tallgrass Prairie. PLoS ONE 8, e67884 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Toyota, K. & Kuninaga, S. Comparison of soil microbial community between soils amended with or without farmyard manure. Appl. Soil Ecol. 33, 39–48 (2006).Article 

    Google Scholar 
    31.Garnier, E. & Navas, M.-L. A trait-based approach to comparative functional plant ecology: Concepts, methods and applications for agroecology: A review. Agron. Sustain. Dev. 32, 365–399 (2012).Article 

    Google Scholar 
    32.Mauchamp, L., Mouly, A., Badot, P.-M. & Gillet, F. Impact of management type and intensity on multiple facets of grassland biodiversity in the French Jura Mountains. Appl. Veg. Sci. 17, 645–657 (2014).Article 

    Google Scholar 
    33.Chytrý, M. et al. European map of alien plant invasions based on the quantitative assessment across habitats. Divers. Distrib. 15, 98–107 (2009).Article 

    Google Scholar 
    34.Klaudisová, M., Hejcman, M. & Pavlů, V. Long-term residual effect of short-term fertilizer application on Ca, N and P concentrations in grasses Nardus stricta L. and Avenella flexuosa L. Nutr. Cycl. Agroecosyst. 85, 187–193 (2009).Article 
    CAS 

    Google Scholar 
    35.Terrat, S. et al. Mapping and predictive variations of soil bacterial richness across France. PLoS ONE 12, e0186766 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    36.Terrat, S. et al. Improving soil bacterial taxa–area relationships assessment using DNA meta-barcoding. Heredity 114, 468–475 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    37.Navrátilová, D. et al. Diversity of fungi and bacteria in species-rich grasslands increases with plant diversity in shoots but not in roots and soil. FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiy208 (2018).Article 

    Google Scholar 
    38.Zhang, Q. et al. Niche differentiation in the composition, predicted function, and co-occurrence networks in bacterial communities associated with antarctic vascular plants. Front. Microbiol. 11, 1036 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    39.Falardeau, J., Keeney, K., Trmčić, A., Kitts, D. & Wang, S. Farm-to-fork profiling of bacterial communities associated with an artisan cheese production facility. Food Microbiol. 83, 48–58 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    40.Plassart, P. et al. Soil parameters, land use, and geographical distance drive soil bacterial communities along a European transect. Sci. Rep. 9, 605 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    41.Rastogi, G., Coaker, G. L. & Leveau, J. H. J. New insights into the structure and function of phyllosphere microbiota through high-throughput molecular approaches. FEMS Microbiol. Lett. 348, 1–10 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    42.Andrews, T., Neher, D. A., Weicht, T. R. & Barlow, J. W. Mammary microbiome of lactating organic dairy cows varies by time, tissue site, and infection status. PLoS ONE 14, e0225001 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    43.Karimi, B. et al. Biogeography of soil bacteria and archaea across France. Sci. Adv. 4, 1808 (2018).ADS 
    Article 

    Google Scholar 
    44.Lewin, G. R. et al. Evolution and ecology of Actinobacteria and their bioenergy applications. Annu. Rev. Microbiol. 70, 235–254 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    45.Li, N. et al. Variation in raw milk microbiota throughout 12 months and the impact of weather conditions. Sci. Rep. 8, 2371 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    46.Lavoie, K., Touchette, M., St-Gelais, D. & Labrie, S. Characterization of the fungal microflora in raw milk and specialty cheeses of the province of Quebec. Dairy Sci. Technol. 92, 455–468 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Verdier-Metz, I. & Monsallier, F. Place des pâturages des bovins dans les flux microbiens laitiers. Fourrages 6, 1–10 (2012).
    Google Scholar 
    48.Laforest-Lapointe, I., Paquette, A., Messier, C. & Kembel, S. W. Leaf bacterial diversity mediates plant diversity and ecosystem function relationships. Nature 546, 145–147 (2017).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    49.Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. USA 103, 626–631 (2006).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Mallet, A. et al. Quantitative and qualitative microbial analysis of raw milk reveals substantial diversity influenced by herd management practices. Int. Dairy J. 27, 13–21 (2012).Article 

    Google Scholar 
    51.Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World Map of the Köppen-Geiger climate classification updated. Metz 15, 259–263 (2006).ADS 
    Article 

    Google Scholar 
    52.Beck, H. E. et al. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    53.QGIS.org. QGIS Geographic Information System. QGIS Association. http://www.qgis.org (2021).54.Homburger, H. & Hofer, G. Diversity change of mountain hay meadows in the Swiss Alps. Basic Appl. Ecol. 13, 132–138 (2012).Article 

    Google Scholar 
    55.Gillet, F., Mauchamp, L., Badot, P.-M. & Mouly, A. Recent changes in mountain grasslands: a vegetation resampling study. Ecol. Evol. 6, 2333–2345 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    56.Maabel, E. Transformation of cover-abundance values in phytosociology and its effects on community similarity. Vegetatio 39, 97–114 (1979).Article 

    Google Scholar 
    57.Jost, L. The relation between evenness and diversity. Diversity 26, 207–230 (2010).Article 

    Google Scholar 
    58.ChemidlinPrévost-Bouré, N. et al. Validation and application of a PCR primer set to quantify fungal communities in the soil environment by real-time quantitative PCR. PLoS ONE 6, e24166 (2011).ADS 
    Article 
    CAS 

    Google Scholar 
    59.Djemiel, C. et al. BIOCOM-PIPE: A new user-friendly metabarcoding pipeline for the characterization of microbial diversity from 16S, 18S and 23S rRNA gene amplicons. BMC Bioinform. 21, 492. https://doi.org/10.1186/s12859-020-03829-3 (2020).CAS 
    Article 

    Google Scholar 
    60.Cole, J. R. et al. The ribosomal database project: Improved alignments and new tools for rRNA analysis. Nucleic Acids Res. 37, D141–D145 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    61.Bardou, P., Mariette, J., Escudié, F., Djemiel, C. & Klopp, C. jvenn: An interactive Venn diagram viewer. BMC Bioinform. 15, 293 (2014).Article 

    Google Scholar 
    62.Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5–7. https://CRAN.R-project.org/package=vegan (2020).63.Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    64.Asnicar, F., Weingart, G., Tickle, T. L., Huttenhower, C. & Segata, N. Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ 3, e1029. https://doi.org/10.7717/peerj.1029 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    65.Yekutieli, D. & Benjamini, Y. The control of the false discovery rate in multiple testing under dependency. Ann. Statist. 29, 1165–1188 (2001).MathSciNet 
    MATH 

    Google Scholar 
    66.Gysi, D. M., Voigt, A., Fragoso, T. M., Almaas, E. & Nowick, K. wTO: an R package for computing weighted topological overlap and a consensus network with integrated visualization tool. BMC Bioinform. 19, 392 (2018).Article 

    Google Scholar 
    67.Kursa, M. B. & Rudnicki, W. R. Feature Selection with the Boruta Package. J. Stat. Soft. 36, 11 (2010).Article 

    Google Scholar  More

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    Prokaryotic viruses impact functional microorganisms in nutrient removal and carbon cycle in wastewater treatment plants

    AS systems display many novel and a high fraction of shared virusesWe used metagenomic sequencing of 30 Gb of sequences per sample to characterize the composition of viral concentrates across six WWTPs (see Methods). After assembly and mapping of reads, 24–34% of the total sequence information could be classified as viral using two different identification pipelines (see Methods). By combining the results of these two pipelines, the final data set consisted of 50,037 viral contigs with an N50 >20 kb. To evaluate whether our sequencing effort sufficiently sampled the viromes, all six WWTP samples were subsampled iteratively to evaluate the saturation dynamics. Rarefaction curves of the number of viral contigs, reached a plateau ~15 Gb of sequencing data for all six samples (Fig. 1a), indicating adequate recovery of prokaryotic viruses in these AS systems at the sequencing depth (30 Gb per sample) in this study.Fig. 1: Compositional variation in viromes among wastewater treatment plants.a Rarefaction curve of each sample. b Rank-abundance curve of each sample. c PCoA analysis of viromes based on the relative abundance of viral genera. d Relative abundance and appearance of dominant viral genera in each WWTP. White color denotes no appearance in this WWTP. Source data are provided as a Source Data file.Full size imageViral contigs were further classified into 8756 viral clusters (VC, equivalent to viral genera) using vConTACT2 by calculating the gene-content-based distance between viral contigs (~40% proteome similarity)11, and each VC was assigned an ID for identification (mean length = 15.2 kb, mean genera size = 3). Compared with the current number of viral sequences from AS systems, our sequencing data increase the AS virome database (N = 2103 in the IMG/VR database v.2.0)10 by 12-fold at the genus level and by 23-fold at the species-level (95% identity, 80% coverage). Comparison with NCBI RefSeq viral genome database showed that across the six AS systems, only 0.4–1.6% of total viral contigs (coverage percentage) could be assigned to a known viral family. Similar to previously described viral metagenomes from the soil, freshwater, and marine system7,12,13, this limited annotation highlights substantial uncharacterized viral diversity in AS communities. Among these recognizable viruses, members of the family Podoviridae (short-tailed phages from the Caudovirales order) were the most prevalent, comprising on average 41.3% of these viral contigs (coverage percentage) across the six WWTPs.All samples displayed high but variable diversity of viral genera with Shannon’s diversity index H’ ranging from 5.22 to 7.14 and Pielou’s evenness index J’ ranging from 0.71 to 0.86 (Supplementary Data 1). These differences are evident in rank-abundance curves, which show that each sample has different viral frequency patterns (Fig. 1b). Most viruses occurred at low frequency with the relative abundance of individual genera diminishing below 0.1% after counting the top 138 viral genera.Principal coordinate analysis (PCoA) of the Bray–Curtis dissimilarity based on the relative abundance of viral genera suggested that most samples are divergent from each other (Fig. 1c), with only two pairs of AS viromes, ST and STL as well as SK and SWH, displaying higher similarity to each other.The overall variability in the viromes is also reflected in different dominance patterns. Each AS sample yielded a different dominant viral genus, and while these were also abundant in some WWTPs, they were below the detection limit in others (Fig. 1d). Although high relative abundance across all WWTPs indicates linkage to consistently abundant hosts, highly variable occurrence suggests that host populations are also more dynamic.Although the viromes appear overall variable in rank abundance, many viral genera were shared across the WWTPs. Fifty-three viral genera were detected in all samples and were thus considered to be common members of the AS viromes, accounting for 1.7–5.4% of viral contigs (coverage percentage) in each WWTP (Fig. 2). Thirteen of these common viral genera were also present in AS viromes in the IMG/VR database v.2.010. Of the total of 8756 unique viral genera collected across samples, STL and ST contained the largest fraction (5245 and 5149 genera, respectively) and shared most viral genera (N = 2885) with each other, far exceeding the number of all the other shared or unique viral genera (Fig. 2). These two WWTPs also had only 45 and 64 site-specific viral genera, consistent with the pattern in the PCoA virome profile. On the other hand, SWH possessed the most unique viral genera (N = 323), making up 11.0% of the total (Fig. 2). In fact, only relatively few viral genera were found exclusively in one of the WWTPs.Fig. 2: Shared viral genera in each WWTP.The UpSet55 chart shows the total number of viral genera and their sharedness in each WWTP. The bar chart on the top right shows the distribution of predicted hosts for common viral genera in all WWTPs. Shared viral genera between ST and STL were labeled orange and shared viral genera in all WWTPs were labeled red. Source data are provided as a Source Data file.Full size imageOverall, these results suggest that the virome across WWTPs consists of many shared genera. The lack of detection of some viral genera in the AS virome of one WWTP may be primarily due to the biological variation in the grab samples and/or the technical variation. Hence, if such technical and biological variations are taken into account, the virome shared among all AS maybe even more diverse.Viruses infect a broad spectrum of bacteria and archaeaTo examine putative host associations of all 50,037 viral contigs in the six WWTPs, we amassed a database of approximately three million CRISPR-Cas spacers from the NCBI prokaryotic complete genomes and metagenomes database (https://www.ncbi.nlm.nih.gov/assembly/). Host prediction was performed by matching CRISPR-Cas spacers at a sequence identity above 97%, sequence coverage over 90% in length, and mismatches 1000 ORFs in each sample were found in the following categories, L: replication, recombination, and repair, M: cell wall/membrane/envelope biogenesis, and K: transcription, confirming that the main functions sustain viral reproduction and transcription. It is noteworthy that viruses encode on average 541 ORFs (1.4% of annotated viral ORFs) in each sample in category G: carbohydrate transport and metabolism. After removing redundant viral ORFs, most unique ORFs (N = 1610) were classified into the glycoside hydrolases (GH) module24 (Fig. 5b). These GHs may be involved in the digestion of capsules to allow the viral tail to reach its membrane receptor on the host. The high representation of this function may be explained by the prevalence of biofilm formation among AS microbes and has previously also been noted among mangrove sediment viruses25.Fig. 5: Distribution of auxiliary metabolic genes (AMGs) relevant to the carbon cycle and nutrient removal.a Boxplot of the overall gene profile for six WWTPs was summarized both as viral ORF hits and viral ORF relative abundance (ORF hits to each COG function class/total ORFs that have hits to eggNOG database). Data in a are presented as mean values (center) and 25%, 75% percentiles (bounds of box). The minima and maxima represent the range of the data. b Number of unique viral ORFs related to each CAZy function class was shown in a descendant order. Source data are provided as a Source Data file.Full size imagePrevious work has shown that many prokaryotic viruses carry auxiliary metabolic genes (AMGs), which can modulate host energy metabolism to provide an energetic advantage during viral genome and protein synthesis26. Broadly speaking, AMGs refer to all metabolic genes in lytic phages27, i.e., all genes in categories C, E, F, G, H, I, and P. Though only about a quarter of the viral ORFs have annotations in the eggNOG database, our data reveal a large repertoire of potential AMGs in the AS viromes (Fig. 5a). For example, 72 ORFs in total are potentially involved with carbon fixation pathways and 35 of them annotate as photosynthetic carbon fixation pathways in KEGG28. Moreover, 10 viral ORFs belong to CobS genes, which are essential for the biosynthesis of cobalamin. Cobalamin biosynthesis pathway is usually not complete in bacterial genomes29, and these viral encoded CobS genes could possibly assist the host metabolic capability. Seven viral genes encode adenylyl-sulfate kinase (CysC), which could facilitate host’s assimilatory sulfate reduction. By modulating host metabolism during infection, AMGs could alter the specific functions of their hosts in WWTPs and therefore influence carbon cycling and the removal of nutrients.Viromes are shared between WWTPs and the water environmentTo investigate whether the WWTP viral genera also occur in other habitats, we compared all viral sequences recovered here with the IMG/VR database v.2.0, which consists of 735,112 viral contigs predicted from metagenomic data10. Viral sequences from five ecosystems (AS, AD, solid waste, freshwater, marine) were included for comparison. For AS (N = 2103), AD (N = 8580), and solid waste (N = 5760), all viral sequences were subjected to our viral clustering pipeline, whereas for freshwater and marine environments, we each randomly selected 50,037 viral sequences to match the number in our samples (N = 50,037).Results showed that viral genera in our samples were shared among multiple environments. There was considerable overlap between WWTPs and freshwater (N = 402) and marine (N = 172) environments (Fig. 6). Moreover, 200, 273, and 244 viral genera were shared with AS, AD, and solid waste, respectively (Fig. 6). This represents a higher number than with marine viromes, and if normalized against the dataset size, there is also a larger fraction of connections than with freshwater viromes. When hosts were predicted for these shared viral genera, Proteobacteria was the most shared host phylum, followed by Firmicutes, Actinobacteria, Bacteroidetes, and Cyanobacteria (Supplementary Data 2). At the genus level, Bacillus was the most abundant between our samples and marine, AD, and AS environments, while Streptomyces displayed a higher prevalence between our samples and freshwater and solid waste environments (Supplementary Data 3). Although it is difficult to identify the source and sink dynamics, AS and AD are typical processes in WWTPs, and solid waste viral sequences mainly stem from compost and leachate microbial communities. The considerable sharing of viromes between WWTPs and marine water may be caused by Hong Kong’s extensive application of marine water for toilet flushing, causing the influent sewage of WWTPs to contain a sizable amount of marine water. These data thus suggest that viruses are extensively shared and that the same viral genera may manipulate microbial communities in these different environments.Fig. 6: Connections between viral genera in AS samples and in five ecosystems from IMG/VR database.Pairwise connections were shown in a Circos56 plot between samples and different ecosystems, including AS, AD, solid waste, freshwater, and marine water. Source data are provided as a Source Data file.Full size imageHi-C validation of virus–host interactions in AS systemHigh throughput chromosome conformation capture (Hi-C) method was used to validate the virus–host connections predicted by our CRISPR-based methods using an additional sample in December 2020 at ST WWTP, by referring to viral contigs and host genome bins obtained from direct sequencing using Illumina and Nanopore metagenomic sequencing (Fig. 7).Fig. 7: General workflow to validate the precision of CRISPR-based methods in the present study.For Illumina data, 91% precision was observed. For Nanopore data, 94% precision was observed. Source data are provided as a Source Data file.Full size imageAs for Illumina metagenomic sequencing, 4578 viral contigs were identified and 1695 of them were deconvoluted in Hi-C data to have virus–host interactions with 197 host bins (Supplementary Data 9). To compare the Hi-C results with the CRISPR-based methods, 21 viruses were predicted by BLASTn-short to link with spacers in eight bins (Supplementary Data 10).As for hybrid assembly using both Nanopore and Illumina reads, 2593 viral contigs were identified and 989 of them were deconvoluted in Hi-C data to have virus–host interactions with 144 host bins (Supplementary Data 11). To compare the Hi-C results with the CRISPR-based methods, 28 viruses were predicted by BLASTn-short to link with spacers in 10 bins (Supplementary Data 12).Results show that CRISPR-based results have very high accuracy. For Illumina data, of the 21 virus–host connections predicted using CRISPR spacers, 11 are simultaneously found in Hi-C data and 10 are not detected in Hi-C data. Of 11 detected connections, only 1 is different in Hi-C data and 10 are the same (91% precision) (Supplementary Data 13). Also for the Nanopore/Illumina hybrid data, of the 28 virus–host connections predicted using CRISPR spacers, 16 are simultaneously found in Hi-C data and 12 are not detected in Hi-C data. Of 16 detected connections, only 1 is different in Hi-C data, 15 are the same (94% precision) (Supplementary Data 14).It should be noticed that some of the predicted CRISPR-based virus–host interactions are undetected in Hi-C data. CRISPR spacers represent a collection of memories regarding past virus invasions, whereas Hi-C data provide a snapshot of ongoing virus–host interactions. Also, Hi-C crosslinking may not be 100% efficient and might miss some of the virus–host interactions. More

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    Marauding elephants, menacing macaques and epicurean bears

    BOOK REVIEW
    13 September 2021

    Marauding elephants, menacing macaques and epicurean bears

    As humans encroach on the habitat of wild animals, is it any surprise that they advance upon ours?

    Josie Glausiusz

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    Josie Glausiusz

    Josie Glausiusz is a science journalist in Israel.Twitter: @josiegz

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    An adult male elephant wanders through the town of Siliguri, India, in February 2016.Credit: Diptendu Dutta/AFP/Getty

    Fuzz: When Nature Breaks the Law Mary Roach W. W. Norton (2021)On 8 September 1488, the French fiefdom of Beaujeu issued an unusual order. Curates were charged with warning slugs three times “to cease from vexing the people by corroding and consuming the herbs of the fields and the vines, and to depart”. Mary Roach cites this episode in her introduction to Fuzz: When Nature Breaks the Law — eliciting the first laugh of many in a book in which she turns her deft wit on the destruction and death that results from human–wildlife conflict. It is a fitting sequel to her previous ‘science-ofs’: Stiff (about cadavers), Spook (the afterlife), Bonk (sex), Gulp (eating) and Grunt (combat).Beyond medieval proceedings against slugs, caterpillars and weevils, Roach addresses more modern resolutions to our rivalry with species that “regularly commit acts that put them at odds with humans”. Travelling from the alleyways of Aspen, Colorado, where epicurean bears forage among restaurant dustbins, to “leopard-terrorized hamlets” in the Himalayas, she investigates how wild creatures from cougars to crows menace humans, their crops and their property.Fundamentally, she asks: when we encroach on the habitat of wild creatures, is it any surprise that they advance on ours? Perhaps nowhere is this collision clearer than in the Indian region of North Bengal, where, each year, dozens of people die after elephant attacks. Elephants there forage at night and sleep by day in patches of teak and red sandalwood trees, the remnants of forests that once stretched from the state of Assam to the eastern border of Nepal. This elephant corridor was fractured by imperialist-era tea estates and more recently by military bases. As the population of elephants in the remaining pockets spikes, the animals are wandering into villages, eating crops and grain stores.
    The long goodbye
    A bull elephant in must — the periodic hormonal tumult signified by frequent erections and ogling eyeballs — is highly aggressive and can crush people. Roach accompanies researchers from the Wildlife Institute of India in Dehradun to visit “awareness camps”, where they teach villagers to stay calm and call the local Elephant Squad so that rangers can herd roving elephants back into the forest. Even better, conservationist Dipanjan Naha tells her, would be to install seismic sensors to warn of approaching elephant footfalls. But, as one officer notes: “We are disturbing them.”In India, where in Hindu tradition elephants are the incarnation of the god Ganesha, it is customary to offer compensation to the families of those killed by elephants, and by leopards. In the United States, by contrast, the focus is not on compensation but on euthanizing the few bears who attack and kill humans. With bears, too, habitat fragmentation as well as climate change appear to play a major part in the conflict with humans. Major highways on the US–Canada border might restrict the movement of black bears. In California, drought is pushing bears into urban areas and, during a record-breaking heatwave earlier this year, into the waters of Lake Tahoe.

    Pushed into urban areas by encroaching development, bears raid bins for food.Credit: Tomas Hulik ARTpoint/Shutterstock

    Once upon a time, bears in the forests around Aspen, Colorado, dined well on acorns, chokecherries and “the outrageous fecundity of crabapple trees”. Roach watches them in the wee hours gorging instead on crab legs and cabbage leaves, tossed out by the city’s restaurants. Stewart Breck at the National Wildlife Research Center in Fort Collins, Colorado, argues that limiting the availability of human food can reduce the need to kill or ward off marauding bears. But replacing busted bear-resistant dumpsters, hiring staff to enforce bin-locking laws, and issuing tickets to restaurants and “alpha residents” who ignore local waste-disposal ordinances isn’t easy: “the county is home to about as many billionaires as bears,” Roach writes.Complex trade-offsOften, it’s our meddling that created the threat in the first place, as when humans introduce animals that inflict unbridled harm upon native species. Case in point: carnivorous stoat (Mustela erminea), that were shipped from Europe to New Zealand in the late nineteenth century to control rabbits, themselves originally imported for food and sport. Stoats, which are agile climbers and swimmers, now prey upon New Zealand’s birds, eating eggs and chicks of tree-trunk-nesting mohua (Mohoua ochrocephala), kākā (Nestor meridionalis) and yellow-crowned kākāriki (Cyanoramphus auriceps), as well as coastal-dwelling endangered hoiho (Megadyptes antipodes).
    Conservation: Backyard jungles
    New Zealand launched the Predator Free 2050 programme to protect native biodiversity by eradicating stoats and two other invasive predators, rats and brushtail possums (Trichosurus vulpecula). The effort relies on humane trapping as well as helicopter drops of a biodegradable toxin called 1080. The programme has led to some small predator-free havens such as Tiritiri Matangi island, but 1080 also kills deer and native kea birds (Nestor notabilis).Such trade-offs are complex, and Roach does a fine job of weighing human needs against those of pests and predators. After all, it can be ruinous for Indian villagers to have their granaries looted by elephants and dangerous for people in Delhi to be attacked by hordes of macaques. (Roach is at her most entertaining when she attempts to track down Ishwar Singh, chief wildlife warden for the Delhi government and an expert on macaque contraception. He finally answers her call with two words, “laparoscopic sterilization”, before slamming down the phone.)But the biggest pest is clearly us. As a 2020 report by the conservation group WWF shows, populations of wild mammals, birds, fish, amphibians and reptiles have dropped by 68% on average since 1970, and one million wildlife species are in danger of extinction, because of burned forests, overfished seas, and the destruction of wild areas. There’s no mirth in that.

    Nature 597, 325-326 (2021)
    doi: https://doi.org/10.1038/d41586-021-02484-9

    Competing Interests
    The author declares no competing interests.

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    Climate change and the increase of human population will threaten conservation of Asian cobras

    1.Reading, C. J. et al. Are snake populations in widespread decline?. Biol. Lett. 6, 777–780 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    2.Gibbons, J. W. et al. The Global Decline of Reptiles, Déja Vu Amphibians. Bioscience 50, 653–666 (2000).Article 

    Google Scholar 
    3.Wilcove, D. S., Rothstein, D., Dubow, J., Phillips, A. & Losos, E. Quantifying threats to imperiled species in the United States. Bioscience 48, 607–615 (1998).Article 

    Google Scholar 
    4.Stenseth, N. C. et al. Ecological effects of climate fluctuations. Science 297, 1292–1296 (2002).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    5.IUCN. Wildlife in a Changing World: An Analysis of the 2008 IUCN Red List of Threatened Species. (IUCN, 2009).6.Needleman, R. K., Neylan, I. P. & Erickson, T. Potential environmental and ecological effects of global climate change on venomous terrestrial species in the wilderness. Wilderness Environ. Med. 29, 226–238 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.Segura, C., Feriche, M., Pleguezuelos, J. M. & Santos, X. Specialist and generalist species in habitat use: Implications for conservation assessment in snakes. J. Nat. Hist. 41, 2765–2774 (2007).Article 

    Google Scholar 
    8.Moreno-Rueda, G., Pleguezuelos, J. M. & Alaminos, E. Climate warming and activity period extension in the Mediterranean snake Malpolon monspessulanus. Clim. Change 92, 235–242 (2009).ADS 
    Article 

    Google Scholar 
    9.Brown, G. P. & Shine, R. Effects of nest temperature and moisture on phenotypic traits of hatchling snakes (Tropidonophis mairii, Colubridae) from tropical Australia. Biol. J. Linn. Soc. 89, 159–168 (2006).Article 

    Google Scholar 
    10.Lourenço-de-Moraes, R. et al. Climate change will decrease the range size of snake species under negligible protection in the Brazilian Atlantic Forest hotspot. Sci. Rep. 9, 1–14 (2019).Article 
    CAS 

    Google Scholar 
    11.Uetz, P., Freed, P. & Hošek, J. The Reptile Database. http://www.reptile-database.org (2019).12.Wallach, V., Wüster, W. & Broadley, D. G. In praise of subgenera: Taxonomic status of cobras of the genus. Zootaxa 2236, 26–36 (2009).Article 

    Google Scholar 
    13.Wüster, W. Taxonomic changes and toxinology: Systematic revisions of the Asiatic cobras (Naja naja species complex). Toxicon 34, 399–406 (1996).PubMed 
    Article 

    Google Scholar 
    14.Wüster, W., Thorpe, R. S., Cox, M., Jintakune, P. & Nabhitabhata, J. Population systematics of the snake genus Naja (Reptilia: Serpentes: Elapidae) in Indochina: Multivariate morphometrics and comparative mitochondrial DNA sequencing (cytochrome oxidase I). J. Evol. Biol. 8, 493–510 (1995).Article 

    Google Scholar 
    15.Smith, M. A. The Fauna of British India Vol. 3 (Taylor and Francis, 1943).
    Google Scholar 
    16.Wüster, W. & Thorpe, R. S. Asiatic Cobras: Population systematics of the Naja naja Species Complex (Serpentes: Elapidae) in India and Central Asia. Herpetologica 48, 69–85 (1992).
    Google Scholar 
    17.IUCN. The IUCN Red List of Threatened Species. Version 2019-3. https://www.iucnredlist.org (2019).18.Tittensor, D. P. et al. A mid-term analysis of progress toward international biodiversity targets. Science 346, 241–243 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    19.Vitousek, P. M., Mooney, H. A., Lubchenco, J. & Melillo, J. M. Human domination of Earth’s ecosystems. Science 277, 494–499 (1997).CAS 
    Article 

    Google Scholar 
    20.United Nations. World Population Prospects 2019. Department of Economic and Social Affairs. World Population Prospects 2019. (2019).21.UNESCAP. Factsheet: Urbanization trends in Asia and the Pacific 4 (2013).22.Zhou, Z. & Jiang, Z. International trade status and crisis for snake species in China. Conserv. Biol. 18, 1386–1394 (2004).Article 

    Google Scholar 
    23.Li, Y. & Li, D. The dynamics of trade in live wildlife across the Guangxi border between China and Vietnam during 1993–1996 and its control strategies. Biodivers. Conserv. 7, 895–914 (1998).Article 

    Google Scholar 
    24.CITES. CITES Appendices I, II, and III. (2019).25.Gutiérrez, J. M., Williams, D., Fan, H. W. & Warrell, D. A. Snakebite envenoming from a global perspective: Towards an integrated approach. Toxicon 56, 1223–1235 (2010).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    26.Kasturiratne, A. et al. The global burden of snakebite: A literature analysis and modelling based on regional estimates of envenoming and deaths. PLoS Med. 5, 1591–1604 (2008).Article 

    Google Scholar 
    27.Longbottom, J. et al. Vulnerability to snakebite envenoming: A global mapping of hotspots. Lancet 392, 673–684 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    28.Warrell, D. A. Clinical toxicology of snakebite in Asia. In Handbook of Clinical Toxicology of Animal Venoms and Poisons (eds Meier, J. & White, J.) 493–594 (CRC Press, 1995).
    Google Scholar 
    29.Seto, K. C., Güneralp, B. & Hutyra, L. R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl. Acad. Sci. USA 109, 16083–16088 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Yue, S., BoneBrake, T. C. & GiBSon, L. Human-snake conflict patterns in a dense urban-forest mosaic landscape. Herpetol. Conserv. Biol. 14, 143–154 (2019).
    Google Scholar 
    31.Yousefi, M., Kafash, A., Khani, A. & Nabati, N. Applying species distribution models in public health research by predicting snakebite risk using venomous snakes’ habitat suitability as an indicating factor. Sci. Rep. 10, 1–11 (2020).Article 
    CAS 

    Google Scholar 
    32.Slowinski, J. B. & Wüster, W. A New Cobra (Elapidae: Naja) from Myanmar (Burma). Herpetologica 56, 257–270 (2000).
    Google Scholar 
    33.Wüster, W. & Thorpe, R. S. Population affinities of the asiatic cobra (Naja naja) species complex in south-east Asia: Reliability and random resampling. Biol. J. Linn. Soc. 36, 391–409 (1989).Article 

    Google Scholar 
    34.Wüster, W., Warrell, D. A., Cox, M. J., Jintakune, P. & Nabhitabhata, J. Redescription of Naja siamensis (Serpentes: Elapidae), a widely overlooked spitting cobra from S.E. Asia: Geographic variation, medical importance and designation of a neotype. J. Zool. 243, 771–788 (1997).Article 

    Google Scholar 
    35.Kuch, U. et al. A new species of krait (Squamata: Elapidae) from the Red River System of Northern Vietnam. Copeia 2005, 818–833 (2005).Article 

    Google Scholar 
    36.Journé, V., Barnagaud, J. Y., Bernard, C., Crochet, P. A. & Morin, X. Correlative climatic niche models predict real and virtual species distributions equally well. Ecology 101, 1–14 (2020).Article 

    Google Scholar 
    37.Kelly, M. Adaptation to climate change through genetic accommodation and assimilation of plastic phenotypes. Philos. Trans. R. Soc. B 374, 20180176 (2019).Article 

    Google Scholar 
    38.Siqueira, L. H. C. & Marques, O. A. V. Effects of Urbanization on Bothrops jararaca Populations in São Paulo Municipality, Southeastern Brazil. J. Herpetol. 52, 299–306 (2018).Article 

    Google Scholar 
    39.Santra, V. et al. Confirmation of Naja oxiana in Himachal Pradesh, India. Herpetol. Bull. https://doi.org/10.33256/hb150.2628 (2019).Article 

    Google Scholar 
    40.IUCN Standards and Petitions Committee. Guidelines for Using the IUCN Red List Categories and Criteria, Vol. 1 (2019).41.IUCN. Guidelines for Application of IUCN Red List Criteria At Regional And National Levels. (IUCN, 2012).42.Colwell, R. K., Brehm, G., Cardelús, C. L., Gilman, A. C. & Longino, J. T. Global warming, elevational range shifts, and lowland biotic attrition in the wet tropics. Science 322, 258–261 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    43.Sahlean, T. C., Gherghel, I., Papeş, M., Strugariu, A. & Zamfirescu, ŞR. Refining climate change projections for organisms with low dispersal abilities: A case study of the Caspian whip snake. PLoS ONE 9, e91994 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    44.Wolfe, A. K., Fleming, P. A. & Bateman, P. W. Impacts of translocation on a large urban-adapted venomous snake. Wildl. Res. 45, 316–324 (2018).Article 

    Google Scholar 
    45.Visser, M. E. Keeping up with a warming world; assessing the rate of adaptation to climate change. Proc. R. Soc. B Biol. Sci. 275, 649–659 (2008).Article 

    Google Scholar 
    46.Chen, C., Qu, Y., Zhou, X. & Wang, Y. Human overexploitation and extinction risk correlates of Chinese snakes. Ecography (Cop.) 42, 1777–1788 (2019).Article 

    Google Scholar 
    47.CITES. Full CITES Trade Database 2000–2018. https://trade.cites.org/ (2018).48.Braimoh, A. K., Subramanian, S. M., Elliot, W. & Gasparatos, A. Climate and Human-Related Drivers of Biodiversity Decline in Southeast Asia. (United Nations University Institute of Advanced Studies, 2010) https://unu.edu/publications/articles/unraveling-the-drivers-of-southeast-asia-biodiversity-loss.html#info.49.Wood, S., Sebastian, K. & Scherr, S. Pilot Analysis of Global Ecosystems: Agroecosystems: A Joint Study (International Food Policy Research Institute and World Resources Institute, 2000).
    Google Scholar 
    50.Castelletta, M., Sodhi, N. S. & Subaraj, R. Heavy extinctions of forest avifauna in Singapore: Lessons for biodiversity conservation in Southeast Asia. Conserv. Biol. 14, 1870–1880 (2000).Article 

    Google Scholar 
    51.Zhao, S. et al. Land use change in Asia and the ecological consequences. Ecol. Res. 21, 890–896 (2006).Article 

    Google Scholar 
    52.Estoque, R. C. & Murayama, Y. Trends and spatial patterns of urbanization in Asia and Africa: A comparative analysis. In Urban Development in Asia and Africa 393–414 (2017).53.Shankar, P. G., Singh, A., Ganesh, S. R. & Whitaker, R. Factors influencing human hostility to King Cobras (Ophiophagus hannah) in the Western Ghats of India. Hamadryad 36, 91–100 (2013).
    Google Scholar 
    54.United Nations. Progress Towards the Sustainable Development Goals. https://undocs.org/en/E/2020/57 (2020).55.Nori, J., Carrasco, P. A. & Leynaud, G. C. Venomous snakes and climate change: Ophidism as a dynamic problem. Clim. Change 122, 67–80 (2014).ADS 
    Article 

    Google Scholar 
    56.Organization, W. H. Snakebite Envenoming: A Strategy for Prevention and Control (World Health Organization, 2019).
    Google Scholar 
    57.Zancolli, G. et al. When one phenotype is not enough: Divergent evolutionary trajectories govern venom variation in a widespread rattlesnake species. Proc. R. Soc. B Biol. Sci. 286, 20182735 (2019).CAS 
    Article 

    Google Scholar 
    58.Wüster, W. & Thorpe, R. S. Systematics and biogeography of the Asiatic cobra (Naja naja) species complex in the Philippine Islands. In Vertebrates in the Tropics (eds Peters, G. & Hutterer, R.) 333–344 (Museum Alexander Koenig, 1990).
    Google Scholar 
    59.Kazemi, E., Kaboli, M., Khosravi, R. & Khorasani, N. Evaluating the importance of environmental variables on spatial distribution of caspian cobra naja oxiana (Eichwald, 1831) in Iran. Asian Herpetol. Res. 10, 129–138 (2019).
    Google Scholar 
    60.Khan, M. The snakebite problem in Pakistan. Bull. Chicago Herp. Soc 49, 165–167 (2014).
    Google Scholar 
    61.Showler, D. A. A Checklist of the Amphibians and Reptiles of the Republic of Uzbekistan with a Review and Summary of Species Distribution. https://www.sustainablehoubaramanagement.org/wp-content/uploads/2018/09/Uzbekistan-Amphibian-Reptile-Checklist-14Sept2018-PDF.pdf (2018).62.Prakash, S., Kumar Mishra, A. & Raziuddin, M. A new record of cream coloured morph of Naja kaouthia Lesson, 1831 (Reptilia, Serpentes, Elapidae) from Hazaribag, Jharkhand, India. Biodivers. J. 3, 153–155 (2012).
    Google Scholar 
    63.Kazemi, E., Nazarizadeh, M., Fatemizadeh, F., Khani, A. & Kaboli, M. The phylogeny, phylogeography, and diversification history of the westernmost Asian cobra (Serpentes: Elapidae: Naja oxiana) in the Trans-Caspian region. Ecol. Evol. https://doi.org/10.1002/ece3.7144 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    64.Bivand, R. et al. Tools for Handling Spatial Objects. (2019).65.Lima-Ribeiro, M. et al. The ecoClimate Database. http://ecoclimate.org.66.Rangel, T. F. & Loyola, R. D. Labeling ecological niche models. Nat. Conserv. 10, 119–126 (2012).Article 

    Google Scholar 
    67.Beaumont, L. J., Hughes, L. & Poulsen, M. Predicting species distributions: Use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions. Ecol. Model. 186, 251–270 (2005).Article 

    Google Scholar 
    68.Hijmans, R. J. et al. Geographic Data Analysis and Modeling. https://cran.r-project.org/package=raster (2019).69.Bivand, R. et al. Bindings for the ‘Geospatial’ Data Abstraction Library Version. Cran (2019).70.Pebesma, E. et al. Classes and Methods for Spatial Data. (R News, 2019).71.Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. Species Distribution Modeling. (2017).72.Sharma, S. K. et al. Venomous Snakes of Nepal. (2013).73.Whitaker, R. & Captain, A. Snakes of India: The Field Guide. Draco Books (Chennai), (2008).74.Gao, J. Downscaling Global Spatial Population Projections from 1/8-degree to 1-km Grid Cells. NCAR Technical Note NCAR/TN-537+STR https://sedac.ciesin.columbia.edu/data/set/popdynamics-pop-projection-ssp-downscaled-1km-2010-2100. https://doi.org/10.5065/D60Z721H (2017).75.van Vuuren, D. P. et al. A new scenario framework for Climate Change Research: Scenario matrix architecture. Clim. Change 122, 373–386 (2014).Article 

    Google Scholar  More

  • in

    Warming climate challenges breeding

    1.Falconer, D. S. & Mackay, T. F. C. Introduction to Quantitative Genetics, 4th edn (Longman, 1996).2.Xiong, W. et al. Nat. Plants https://doi.org/10.1038/s41477-021-00988-w (2021).3.Tadesse, W. et al. Crop Breed Genet Genom. 1, e190005 (2019).
    Google Scholar 
    4.Li, X., Guo, T., Mu, Q., Li, X. & Yu, J. Proc. Natl Acad. Sci. USA 115, 6679–6684 (2018).CAS 
    Article 

    Google Scholar 
    5.Anderson, D. R. Model Based Inference in the Life Sciences (Springer, 2008).6.Zhao, Y. et al. Sci. Adv. 7, eabf9106 (2021).CAS 
    Article 

    Google Scholar 
    7.Shi, L., Li, B., Kim, C., Kellnhofer, P. & Matusik, W. Nature 591, 234–239 (2021).CAS 
    Article 

    Google Scholar 
    8.Li, J. et al. Mol. Ecol. 28, 3544–3560 (2019).CAS 
    Article 

    Google Scholar 
    9.CGIAR. One CGIAR, https://www.cgiar.org/food-security-impact/one-cgiar/ More

  • in

    Ectomycorrhizal access to organic nitrogen mediates CO2 fertilization response in a dominant temperate tree

    1.Campbell, J. E. et al. Large historical growth in global terrestrial gross primary production. Nature 544, 84–87 (2017).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    2.Schwalm, C. R. et al. Modeling suggests fossil fuel emissions have been driving increased land carbon uptake since the turn of the 20th Century. Sci. Rep. 10, 9059 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Wenzel, S., Cox, P. M., Eyring, V. & Friedlingstein, P. Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric CO2. Nature 538, 499–501 (2016).ADS 
    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    4.Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).ADS 
    Article 

    Google Scholar 
    5.Ellsworth, D. S. et al. Elevated CO2 does not increase eucalypt forest productivity on a low-phosphorus soil. Nat. Clim. Change 7, 279–282 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    6.Hararuk, O., Campbell, E. M., Antos, J. A. & Parish, R. Tree rings provide no evidence of a CO2 fertilization effect in old-growth subalpine forests of western Canada. Glob. Change Biol. 25, 1222–1234 (2019).ADS 
    Article 

    Google Scholar 
    7.Jiang, M. et al. The fate of carbon in a mature forest under carbon dioxide enrichment. Nature 580, 227–231 (2020).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    8.Friedlingstein, P. et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim. 27, 511–526 (2014).ADS 
    Article 

    Google Scholar 
    9.Koven, C. D. et al. Controls on terrestrial carbon feedbacks by productivity versus turnover in the CMIP5 earth system models. Biogeosciences 12, 5211–5228 (2015).ADS 
    Article 

    Google Scholar 
    10.Norby, R. J. & Zak, D. R. Ecological lessons from free-air CO2 enrichment (FACE) experiments. Annu. Rev. Ecol. Evol. Syst. 42, 181–203 (2011).Article 

    Google Scholar 
    11.Sigurdsson, B. D., Medhurst, J. L., Wallin, G., Eggertsson, O. & Linder, S. Growth of mature boreal Norway spruce was not affected by elevated [CO2] and/or air temperature unless nutrient availability was improved. Tree Physiol. 33, 1192–1205 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Walker, A. P. et al. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2. N. Phytol. 229, 2413–2445 (2021).CAS 
    Article 

    Google Scholar 
    13.Gedalof, Z. & Berg, A. A. Tree ring evidence for limited direct CO2 fertilization of forests over the 20th century. Glob. Biogeochem. Cycles 24, (2010).14.van der Sleen, P. et al. No growth stimulation of tropical trees by 150 years of CO2 fertilization but water-use efficiency increased. Nat. Geosci. 8, 24–28 (2015).ADS 
    Article 
    CAS 

    Google Scholar 
    15.Girardin, M. P. et al. No growth stimulation of Canada’s boreal forest under half-century of combined warming and CO2 fertilization. Proc. Natl Acad. Sci. USA 113, E8406–E8414 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Giguère-Croteau, C. et al. North America’s oldest boreal trees are more efficient water users due to increased [CO2], but do not grow faster. Proc. Natl Acad. Sci. USA 116, 2749–2754 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    17.Walker, A. P. et al. Decadal biomass increment in early secondary succession woody ecosystems is increased by CO2 enrichment. Nat. Commun. 10, 454 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Du, E. et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nat. Geosci. https://doi.org/10.1038/s41561-019-0530-4 (2020).19.Schimel, J. P. & Bennett, J. Nitrogen mineralization: challenges of a changing paradigm. Ecology 85, 591–602 (2004).Article 

    Google Scholar 
    20.Vitousek, P. M. & Howarth, R. W. Nitrogen limitation on land and in the sea: how can it occur? Biogeochemistry 13, 87–115 (1991).Article 

    Google Scholar 
    21.Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).ADS 
    Article 

    Google Scholar 
    22.Näsholm, T., Kielland, K. & Ganeteg, U. Uptake of organic nitrogen by plants. N. Phytol. 182, 31–48 (2009).Article 
    CAS 

    Google Scholar 
    23.Lindahl, B. D. & Tunlid, A. Ectomycorrhizal fungi – potential organic matter decomposers, yet not saprotrophs. N. Phytol. 205, 1443–1447 (2015).CAS 
    Article 

    Google Scholar 
    24.Terrer, C., Vicca, S., Hungate, B. A., Phillips, R. P. & Prentice, I. C. Mycorrhizal association as a primary control of the CO2 fertilization effect. Science 353, 72–74 (2016).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Terrer, C. et al. Ecosystem responses to elevated CO2 governed by plant–soil interactions and the cost of nitrogen acquisition. N. Phytol. 217, 507–522 (2018).CAS 
    Article 

    Google Scholar 
    26.Sulman, B. N. et al. Diverse Mycorrhizal associations enhance terrestrial C storage in a global model. Glob. Biogeochem. Cycles 33, 501–523 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    27.Terrer, C. et al. A trade-off between plant and soil carbon storage under elevated CO2. Nature 591, 599–603 (2021).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Wieder, W. R., Cleveland, C. C., Smith, W. K. & Todd-Brown, K. Future productivity and carbon storage limited by terrestrial nutrient availability. Nat. Geosci. 8, 441–444 (2015).ADS 
    CAS 
    Article 

    Google Scholar 
    29.Smith, S. E. & Read, D. J. Mycorrhizal symbiosis. (Academic Press, 2010).30.Pellitier, P. T. & Zak, D. R. Ectomycorrhizal fungi and the enzymatic liberation of nitrogen from soil organic matter: why evolutionary history matters. N. Phytol. 217, 68–73 (2018).CAS 
    Article 

    Google Scholar 
    31.Phillips, R. P. et al. Roots and fungi accelerate carbon and nitrogen cycling in forests exposed to elevated CO2. Ecol. Lett. 15, 1042–1049 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    32.Terrer, C. et al. Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass. Nat. Clim. Change 9, 684–689 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    33.Christian, N. & Bever, J. D. Carbon allocation and competition maintain variation in plant root mutualisms. Ecol. Evol. 8, 5792–5800 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Hortal, S. et al. Role of plant–fungal nutrient trading and host control in determining the competitive success of ectomycorrhizal fungi. ISME J. 11, 2666–2676 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Bogar, L. et al. Plant-mediated partner discrimination in ectomycorrhizal mutualisms. Mycorrhiza 29, 97–111 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Bödeker, I. T. M., Nygren, C. M. R., Taylor, A. F. S., Olson, Å. & Lindahl, B. D. ClassII peroxidase-encoding genes are present in a phylogenetically wide range of ectomycorrhizal fungi. ISME J. 3, 1387–1395 (2009).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    37.Hobbie, E. A. & Agerer, R. Nitrogen isotopes in ectomycorrhizal sporocarps correspond to belowground exploration types. Plant Soil 327, 71–83 (2010).CAS 
    Article 

    Google Scholar 
    38.Koide, R. T., Fernandez, C. & Malcolm, G. Determining place and process: functional traits of ectomycorrhizal fungi that affect both community structure and ecosystem function. N. Phytol. 201, 433–439 (2014).Article 

    Google Scholar 
    39.Lindahl, B. D. et al. A group of ectomycorrhizal fungi restricts organic matter accumulation in boreal forest. Ecol. Lett. 24, 1341–1351 (2021).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    40.van der Linde, S. et al. Environment and host as large-scale controls of ectomycorrhizal fungi. Nature 558, 243–248 (2018).ADS 
    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    41.Read, D. J. & Perez-Moreno, J. Mycorrhizas and nutrient cycling in ecosystems: a journey towards relevance? N. Phytol. 157, 475–492 (2003).CAS 
    Article 

    Google Scholar 
    42.Bödeker, I. T. M. et al. Ectomycorrhizal Cortinarius species participate in enzymatic oxidation of humus in northern forest ecosystems. N. Phytol. 203, 245–256 (2014).Article 
    CAS 

    Google Scholar 
    43.Bogar, L. & Peay, K. Processes maintaining the coexistence of ectomycorrhizal fungi at a fine spatial scale. in Biogeography of Mycorrhizal Symbiosis (ed. Tedersoo, L.) vol. 230 79–105 (Springer, 2017).44.Xu, K. et al. Tree-ring widths are good proxies of annual variation in forest productivity in temperate forests. Sci. Rep. 7, 1945 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    45.Nehrbass‐Ahles, C. et al. The influence of sampling design on tree-ring-based quantification of forest growth. Glob. Change Biol. 20, 2867–2885 (2014).ADS 
    Article 

    Google Scholar 
    46.Mathias, J. M. & Thomas, R. B. Disentangling the effects of acidic air pollution, atmospheric CO2, and climate change on recent growth of red spruce trees in the Central Appalachian Mountains. Glob. Change Biol. 24, 3938–3953 (2018).ADS 
    Article 

    Google Scholar 
    47.Fierer, N., Barberán, A. & Laughlin, D. C. Seeing the forest for the genes: using metagenomics to infer the aggregated traits of microbial communities. Front. Microbiol. 5, 614 (2014).48.Zak, D. R. & Pregitzer, K. S. Spatial and temporal variability of nitrogen cycling in northern lower Michigan. Science 36, 367–380 (1990).
    Google Scholar 
    49.Zak, D. R., Pregitzer, K. S. & Host, G. E. Landscape variation in nitrogen mineralization and nitrification. Can. J. Res. 16, 1258–1263 (1986).Article 

    Google Scholar 
    50.Chen, J. & Gupta, A. K. Parametric Statistical Change Point Analysis: With Applications to Genetics, Medicine, and Finance. (Springer Science & Business Media, 2011).51.Thomas, R. Q., Canham, C. D., Weathers, K. C. & Goodale, C. L. Increased tree carbon storage in response to nitrogen deposition in the US. Nat. Geosci. 3, 13–17 (2010).ADS 
    Article 
    CAS 

    Google Scholar 
    52.Pellitier, P. T., Zak, D. R., Argiroff, W. A. & Upchurch, R. A. Coupled shifts in ectomycorrhizal communities and plant uptake of organic nitrogen along a soil gradient: an isotopic perspective. Ecosystems (2021).53.Sterkenburg, E., Clemmensen, K. E., Ekblad, A., Finlay, R. D. & Lindahl, B. D. Contrasting effects of ectomycorrhizal fungi on early and late stage decomposition in a boreal forest. ISME J. 12, 2187–2197 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    54.Lilleskov, E. A., Hobbie, E. A. & Fahey, T. J. Ectomycorrhizal fungal taxa differing in response to nitrogen deposition also differ in pure culture organic nitrogen use and natural abundance of nitrogen isotopes. N. Phytol. 154, 219–231 (2002).CAS 
    Article 

    Google Scholar 
    55.Kohler, A. et al. Convergent losses of decay mechanisms and rapid turnover of symbiosis genes in mycorrhizal mutualists. Nat. Genet. 47, 410–415 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Moeller, H. V., Peay, K. G. & Fukami, T. Ectomycorrhizal fungal traits reflect environmental conditions along a coastal California edaphic gradient. FEMS Microbiol. Ecol. 87, 797–806 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    57.Defrenne, C. E. et al. Shifts in Ectomycorrhizal fungal communities and exploration types relate to the environment and fine-root traits across interior douglas-fir forests of Western Canada. Front. Plant Sci. 10, 643 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Fawal, N. et al. PeroxiBase: a database for large-scale evolutionary analysis of peroxidases. Nucleic Acids Res. 41, D441–D444 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    59.Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P. M. & Henrissat, B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 42, D490–D495 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    60.Garajova, S. et al. Single-domain flavoenzymes trigger lytic polysaccharide monooxygenases for oxidative degradation of cellulose. Sci. Rep. 6, 28276 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    61.Janusz, G. et al. Lignin degradation: microorganisms, enzymes involved, genomes analysis and evolution. FEMS Microbiol. Rev. 41, 941–962 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    62.Shah, F. et al. Ectomycorrhizal fungi decompose soil organic matter using oxidative mechanisms adapted from saprotrophic ancestors. N. Phytol. 209, 1705–1719 (2016).CAS 
    Article 

    Google Scholar 
    63.Baldrian, P. Fungal laccases – occurrence and properties. FEMS Microbiol. Rev. 30, 215–242 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    64.Fernandez, C. W. & Kennedy, P. G. Revisiting the ‘Gadgil effect’: do interguild fungal interactions control carbon cycling in forest soils? N. Phytol. 209, 1382–1394 (2016).CAS 
    Article 

    Google Scholar 
    65.Reich, P. B. et al. Nitrogen limitation constrains sustainability of ecosystem response to CO2. Nature 440, 922–925 (2006).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Oren, R. et al. Soil fertility limits carbon sequestration by forest ecosystems in a CO2-enriched atmosphere. Nature 411, 469–472 (2001).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    67.Norby, R. J., Warren, J. M., Iversen, C. M., Medlyn, B. E. & McMurtrie, R. E. CO2 enhancement of forest productivity constrained by limited nitrogen availability. Proc. Natl Acad. Sci. USA 107, 19368–19373 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    68.Andrew, C. & Lilleskov, E. A. Productivity and community structure of ectomycorrhizal fungal sporocarps under increased atmospheric CO2 and O3. Ecol. Lett. 12, 813–822 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    69.Näsholm, T. et al. Are ectomycorrhizal fungi alleviating or aggravating nitrogen limitation of tree growth in boreal forests? N. Phytol. 198, 214–221 (2013).Article 
    CAS 

    Google Scholar 
    70.Finzi, A. C. et al. Increases in nitrogen uptake rather than nitrogen-use efficiency support higher rates of temperate forest productivity under elevated CO2. Proc. Natl Acad. Sci. USA 104, 14014–14019 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    71.Merkel, D. Soil Nutrients in Glaciated Michigan Landscapes: Distribution of Nutrients and Relationships with Stand Productivity. (Doctoral Thesis Submitted to Michigan State University, 1988).72.Host, G. E. & Pregitzer, K. S. Geomorphic influences on ground-flora and overstory composition in upland forests of northwestern lower Michigan. Can. J. Res. 22, 1547–1555 (1992).Article 

    Google Scholar 
    73.Edwards, I. P. & Zak, D. R. Phylogenetic similarity and structure of Agaricomycotina communities across a forested landscape. Mol. Ecol. 19, 1469–1482 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    74.Schimel, D., Stephens, B. B. & Fisher, J. B. Effect of increasing CO2 on the terrestrial carbon cycle. Proc. Natl Acad. Sci. USA 112, 436–441 (2015).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    75.Medlyn, B. E. et al. Using ecosystem experiments to improve vegetation models. Nat. Clim. Change 5, 528–534 (2015).ADS 
    Article 

    Google Scholar 
    76.Peñuelas, J. et al. Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evol. 1, 1438–1445 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    77.McClaugherty, C. A., Pastor, J., Aber, J. D. & Melillo, J. M. Forest litter decomposition in relation to soil nitrogen dynamics and litter quality. Ecology 66, 266–275 (1985).Article 

    Google Scholar 
    78.Pastor, J., Aber, J. D., McClaugherty, C. A. & Melillo, J. M. Aboveground production and N and P cycling along a nitrogen mineralization gradient on Blackhawk Island, Wisconsin. Ecology 65, 256–268 (1984).CAS 
    Article 

    Google Scholar 
    79.Serra-Maluquer, X., Mencuccini, M. & Martínez-Vilalta, J. Changes in tree resistance, recovery and resilience across three successive extreme droughts in the northeast Iberian Peninsula. Oecologia 187, 343–354 (2018).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    80.Vitousek, P. Nutrient cycling and nutrient use efficiency. Am. Nat. 119, 553–572 (1982).Article 

    Google Scholar 
    81.Darrouzet-Nardi, A., Ladd, M. P. & Weintraub, M. N. Fluorescent microplate analysis of amino acids and other primary amines in soils. Soil Biol. Biochem. 57, 78–82 (2013).CAS 
    Article 

    Google Scholar 
    82.Ibáñez, I., Zak, D. R., Burton, A. J. & Pregitzer, K. S. Anthropogenic nitrogen deposition ameliorates the decline in tree growth caused by a drier climate. Ecology 99, 411–420 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    83.Lines, E. R., Zavala, M. A., Purves, D. W. & Coomes, D. A. Predictable changes in aboveground allometry of trees along gradients of temperature, aridity and competition. Glob. Ecol. Biogeogr. 21, 1017–1028 (2012).Article 

    Google Scholar 
    84.Taylor, D. L. et al. Accurate estimation of fungal diversity and abundance through improved lineage-specific primers optimized for illumina amplicon sequencing. Appl. Environ. Microbiol. 82, 7217–7226 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    85.Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    86.Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    87.Konar, A. et al. High-quality genetic mapping with ddRADseq in the non-model tree Quercus rubra. BMC Genomics 18, 417 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    88.Sork, V. L. et al. First draft assembly and annotation of the genome of a California Endemic oak. Genes|Genomes|Genet. 6, 3485–3495 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    89.Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    90.Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    91.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    92.Treiber, M. L., Taft, D. H., Korf, I., Mills, D. A. & Lemay, D. G. Pre- and post-sequencing recommendations for functional annotation of human fecal metagenomes. BMC Bioinforma. 21, 74 (2020).CAS 
    Article 

    Google Scholar 
    93.Peng, M. et al. Comparative analysis of basidiomycete transcriptomes reveals a core set of expressed genes encoding plant biomass degrading enzymes. Fungal Genet. Biol. 112, 40–46 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    94.Floudas, D. et al. Uncovering the hidden diversity of litter-decomposition mechanisms in mushroom-forming fungi. ISME J. https://doi.org/10.1038/s41396-020-0667-6 (2020).95.Kriventseva, E. V. et al. OrthoDB v10: sampling the diversity of animal, plant, fungal, protist, bacterial and viral genomes for evolutionary and functional annotations of orthologs. Nucleic Acids Res. 47, D807–D811 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    96.Quinn, T. P., Erb, I., Richardson, M. F. & Crowley, T. M. Understanding sequencing data as compositions: an outlook and review. Bioinformatics 34, 2870–2878 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    97.Ferrier, S., Manion, G., Elith, J. & Richardson, K. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Divers. Distrib. 13, 252–264 (2007).Article 

    Google Scholar 
    98.Duhamel, M. et al. Plant selection initiates alternative successional trajectories in the soil microbial community after disturbance. Ecol. Monogr. 89, e01367 (2019).Article 

    Google Scholar 
    99.Qin, C., Zhu, K., Chiariello, N. R., Field, C. B. & Peay, K. G. Fire history and plant community composition outweigh decadal multi-factor global change as drivers of microbial composition in an annual grassland. J. Ecol. 108, 611–625 (2020).CAS 
    Article 

    Google Scholar 
    100.Oksanen, J., et al. Package vegan.101.Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).ADS 
    Article 

    Google Scholar 
    102.Spiegelhalter, D. J., Best, N. G., Carlin, B. P. & Linde, A. V. D. Bayesian measures of model complexity and fit. J. R. Stat. Soc. Ser. B Stat. Methodol. 64, 583–639 (2002).MathSciNet 
    MATH 
    Article 

    Google Scholar  More

  • in

    The critical role of natural history museums in advancing eDNA for biodiversity studies: a case study with Amazonian fishes

    1.Lundberg, J. G., Kottelat, M., Smith, G. R., Stiassny, M. L. J. & Gill, A. C. So many fishes, so little time: An overview of recent ichthyological discovery in continental waters. Ann. Mo. Bot. Gard. 87, 26–62 (2000).Article 

    Google Scholar 
    2.Relyea, R. A. The impact of insecticides and herbicides on the biodiversity and productivity of aquatic communities. Ecol. Appl. 15, 618–627 (2005).Article 

    Google Scholar 
    3.Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: Detection of more than 230 subtropical marine species. R. Soc. Open Sci. 2, 150088 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    4.Clare, A. I. M. et al. Beyond biodiversity: Can environmental DNA (eDNA) cut it as a population genetics tool?. Genes 10, 192 (2019).Article 
    CAS 

    Google Scholar 
    5.Tsuji, S., Shibata, N., Sawada, H. & Ushio, M. Quantitative evaluation of intraspecific genetic diversity in a natural fish population using environmental DNA. Mol. Ecol. Resour. 20, 1323–1332 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    6.Miya, M., Gotoh, R. O. & Sado, T. MiFish metabarcoding: A high-throughput approach for simultaneous detection of multiple fish species from environmental DNA and other samples. Fish. Sci. 86, 939–970 (2020).CAS 
    Article 

    Google Scholar 
    7.Dagosta F. C. P. & de Pinna, M. C. C. The fishes of the Amazon: Distribution and biogeographical patterns, with a comprehensive list of species. Bull. Am. Mus. Nat. Hist, 431, 1–163 (2019).8.Jézéquel, C., Tedesco, P. A. & Bigorne, R. A database of freshwater fish species of the Amazon Basin. Sci. Data 7, 96 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Reis, R. E., Kullander, S. O. & Ferraris, C. J. Check List of the Freshwater Fishes of South and Central America. (Edipucrs, 2003).10.Tedesco, P. et al. A global database on freshwater fish species occurrence in drainage basins. Sci. Data 4, 170141 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Brito, P. M., Meunier, F. J. & Leal, M. E. C. Origine et diversification de líchthyofaune Neotropical: Une revue. Cybium 31, 139–153 (2007).
    Google Scholar 
    12.Lowe-McConnell, R. H. Ecological Studies in Tropical Fish Communities (Cambridge University Press, 1987).Book 

    Google Scholar 
    13.Bloom, D. D. & Lovejoy, N. R. On the origins of marine derived fishes in South America. J. Biogeogr. 44, 1927–1938 (2017).Article 

    Google Scholar 
    14.de Santana, C. D. et al. Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator. Nat. Commun. 10, 4000 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    15.Carvalho, L. N., Zuanon, J. & Sazima, I. Natural history of Amazon fishes. In Tropical Biology and Natural Resources Theme (ed. Del-Claro, K.), K. Del-Claro & R. J. Marquis (Session Eds. the Natural History Session), Encyclopedia of Life Support Systems (EOLSS) (Eolss Publishers, 2007).16.Cardoso, Y. P. et al. A continental-wide molecular approach unraveling mtDNA diversity and geographic distribution of the Neotropical genus Hoplias. PLoS ONE 13, e0202024 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    17.Hebert, P. D. N., Cywinska, A., Ball, S. L. & de Waard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. Ser. B Biol. Sci. 270, 313–321 (2003).CAS 
    Article 

    Google Scholar 
    18.Baldwin, C. C., Castillo, C. I., Weigt, L. A. & Victor, B. C. Seven new species within western Atlantic Starksia atlantica, S. lepicoelia, and S. sluiteri (Teleostei, Labrisomidae), with comments on congruence of DNA barcodes and species. ZooKeys 79, 21–27 (2011).Article 

    Google Scholar 
    19.Robertson, D. R. et al. Deep-water bony fishes collected by the B/O Miguel Oliver on the shelf edge of Pacific Central America: An annotated, illustrated and DNA-barcoded checklist. Zootaxa 4348, 1–125 (2017).PubMed 
    Article 

    Google Scholar 
    20.Weigt, L. A. et al. Using DNA barcoding to assess Caribbean reef fish biodiversity: Expanding taxonomic and geographic coverage. PLoS ONE 7, e41059 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    21.Seberg, O. et al. Global genome biodiversity network: Saving a blueprint of the tree of life—a botanical perspective. Ann. Bot. 118, 393–399 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    22.Parenti, L. R. et al. Fishes collected during the 2017 MarineGEO assessment of Kāne‘ohe Bay, O‘ahu, Hawai‘i. J. Mar. Biol. Assoc. UK 100, 607–637 (2020).Article 

    Google Scholar 
    23.Droege, G. et al. The Global Genome Biodiversity Network (GGBN) Data Standard specification. Database https://doi.org/10.1093/database/baw125 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Marques, V. et al. Blind assessment of vertebrate taxonomic diversity across spatial scales by clustering environmental DNA metabarcoding sequences. Ecography 43, 1779–1790 (2020).Article 

    Google Scholar 
    25.Leray, M., Knowlton, N., Shien-Lei, H., Nguyen, B. N. & Machida, R. J. GenBank is a reliable resource for 21st biodiversity research. Proc. Natl. Acad. Sci. U.S.A. 116, 22651–22656 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Dillman, C. B. et al. Forensic investigations into a GenBank anomaly: Endangered taxa and the importance of voucher specimens in molecular studies. J. Appl. Ichthyol. 30, 1300–1309 (2014).CAS 
    Article 

    Google Scholar 
    27.Locatelli, N. S., McIntyre, P. B., Therkildsen, N. O. & Baetscher, D. S. GenBank’s reliability is uncertain for biodiversity researchers seeking species-level assignment for eDNA. Proc. Natl. Acad. Sci. U.S.A. 117, 32211–32212 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    28.Jerde, C. L., Wilson, E. A. & Dressler, T. L. Measuring global fish species richness with eDNA metabarcoding. Mol. Ecol. Resour. 19, 19–22 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    29.Nobile, A. B. et al. DNA metabarcoding of Neotropical ichthyoplankton: Enabling high accuracy with lower cost. Metabarcoding Metagenom. 3, 35060 (2019).Article 

    Google Scholar 
    30.Cilleros, K. et al. Unlocking biodiversity and conservation studies in high diversity environments using environmental DNA (eDNA): A text with Guianese freshwater fishes. Mol. Ecol. Resour. 19, 27–46 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    31.Sales, N. G., Wangensteen, O. S., Carvalho, D. C. & Mariani, S. Influence of preservation methods, sample medium and sampling time on eDNA recovery in a neotropical river. Environ. DNA 1, 119–130 (2019).Article 

    Google Scholar 
    32.Jackman, J. M. C. et al. eDNA in a bottleneck: Obstacles to fish metabarcoding studies in megadiverse freshwater systems. Environ. DNA https://doi.org/10.1002/edna3.191 (2021).Article 

    Google Scholar 
    33.Valentini, A. et al. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol. Ecol. 25, 929–942 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    34.McElroy, M. E. et al. Calibrating environmental DNA metabarcoding to conventional surveys for measuring fish species richness. Front. Ecol. Evol. 8, 276 (2020).Article 

    Google Scholar 
    35.Dudgeon, D. Freshwater Biodiversity: Status (Cambridge University Press, 2020).Book 

    Google Scholar 
    36.Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Austral Ecol. 18, 117–143 (1993).Article 

    Google Scholar 
    37.Milan, D. T., Mendes, I. S. & Carvalho, D. C. New 12S metabarcoding primers for enhanced Neotropical freshwater fish biodiversity assessment. Sci. Rep. 10, 17966 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Deagle, B. E., Jarman, S. N., Coissac, E., Pompanon, F. & Taberlet, P. DNA metabarcoding and the cytochrome c oxidase subunit I marker: Not a perfect match. Biol. Lett. 10, 20140562 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    39.Collins, R. A. et al. Non-specific amplification compromises environmental DNA metabarcoding with COI. Methods Ecol. Evol. 10, 1985–2001 (2019).Article 

    Google Scholar 
    40.Antich, A. et al. To denoise or to cluster, that is not the question: optimizing pipelines for COI metabarcoding and metaphylogeography. BMC Bioinf. 22, 177 (2021).CAS 
    Article 

    Google Scholar 
    41.Vieira, T. B. et al. A multiple hypothesis approach to explain species richness patterns in neotropical stream-dweller fish communities. PLoS ONE 13, e0204114 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    42.Zuanon, J., Bockmann, F. A. & Sazima, I. A remarkable sand-dwelling fish assemblage from central Amazonia, with comments on the evolution of psammophily in South American freshwater fishes. Neotrop. Ichthyol. 4, 107–118 (2006).Article 

    Google Scholar 
    43.Sazima, I., Carvalho, L. N., Mendonça, F. P. & Zuanon, J. Fallen leaves on the water-bed: Diurnal camouflage of three night-active fish species in an Amazonian streamlet. Neotrop. Ichthyol. 4, 119–122 (2006).Article 

    Google Scholar 
    44.Espírito-Santo, H. M. V. & Zuanon, J. Temporary pools provide stability to fish assemblages in Amazon headwater streams. Ecol. Freshw. Fish 26, 475–483 (2017).Article 

    Google Scholar 
    45.de Pinna, M. C. C., Zuanon, J., Rapp-Py-Daniel, L. R. & Petry, P. A new family of neotropical freshwater fishes from deep fossorial Amazonian habitat, with a reappraisal of morphological characiform phylogeny (Teleostei: Ostariophysi). Zool. J. Linn. Soc. 182, 76–106 (2018).Article 

    Google Scholar 
    46.López-Rojas, H., Lundberg, J. G. & Marsh, E. Design and operation of a small trawling apparatus for use with dugout canoes. N. Am. J. Fish. Manag. 4, 331–334 (1984).Article 

    Google Scholar 
    47.Marrero, C. & Taphorn, D. C. Notas sobre la historia natural y la distribution de los peces Gymnotiformes in la cuenca del Rio Apure y otros rios de la Orinoquia. Biollania 8, 123–142 (1991).
    Google Scholar 
    48.Cox-Fernandes, C., Podos, J. & Lundberg, J. G. Amazonian ecology: Tributaries enhance the diversity of electric fishes. Science 305, 1960–1962 (2004).ADS 
    Article 
    CAS 

    Google Scholar 
    49.Peixoto, L. A. W., Dutra, G. M. & Wosiack, W. B. The electric. Glassknife fishes of the Eigenmannia trilineata group (Gymnotiformes: Sternopygidae): Monophyly and description of seven new species. Zool. J. Linn. Soc. 175, 384–414 (2015).Article 

    Google Scholar 
    50.de Santana, C. D. & Vari, R. P. Electric fishes of the genus Sternarchorhynchus (Teleostei, Ostariophysi, Gymnotiformes); phylogenetic and revisionary studies. Zool. J. Linn. Soc. 159, 223–371 (2010).Article 

    Google Scholar 
    51.Castro, R. M. C. Evolução da ictiofauna de riachos sul-americanos: Padrões gerais e possíveis processos causais. In Ecologia de peixes de riachos (eds Caramaschi, E. P., Mazzoni, R., & Peres-Neto, P. R.) Série Oecologia Brasiliensis volume VI, PPGE-UFRJ, Rio de Janeiro, 139–155 (1999).52.Mojica, J. I., Castellanos, C. & Lobón-Cerviá, J. High temporal species turnover enhances the complexity of fish assemblages in Amazonian Terra firme streams. Ecol. Freshw. Fish 18, 518–526 (2009).Article 

    Google Scholar 
    53.de Oliveira, R. R., Rocha, M. M., Anjos, M. B., Zuanon, J. & Rapp Py-Daniel, L. H. Fish fauna of small streams of the Catua-Ipixuna Extractive Reserve, State of Amazonas, Brazil. Check List 5, 154–172 (2009).Article 

    Google Scholar 
    54.Caramaschi E., Mazzoni, P. R., Bizerril, C. R. S. F. & Peres-Neto, P. R. Ecologia de Peixes de Riachos: Estado Atual e Perspectivas. Oecologia Brasiliensis, v. VI, Rio de Janeiro (1999).55.Anjos, M. B. & Zuanon, J. Sampling effort and fish species richness in small Terra firme forest streams of central Amazonia, Brazil. Neotrop. Ichthyol. 5, 45–52 (2007).Article 

    Google Scholar 
    56.Mojica, J. I., Lobón-Cerviá, J. & Castellanos, C. Quantifying fish species richness and abundance in Amazonian streams: Assessment of a multiple gear method suitable for Terra firme stream fish assemblages. Fish. Manag. Ecol. 21, 220–233 (2014).Article 

    Google Scholar 
    57.Barros, D. F. et al. The fish fauna of streams in the Madeira-Purus interfluvial region, Brazilian Amazon. Check List 7, 768–773 (2011).Article 

    Google Scholar 
    58.Escobar-Camacho, D., Barriga, R. & Ron, S. R. Discovering hidden diversity of characins (Teleostei: Characiformes) in Ecuador’s Yasuní National Park. PLoS ONE 10, e0135569 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    59.Ramirez, J. L. et al. Revealing hidden diversity of the underestimated neotropical ichthyofauna: DNA barcoding in the recently described genus Megaleporinus (Characiformes: Anostomidae). Front. Genet. 8, 149 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    60.Crampton, W. G. R., de Santana, C. D., Waddell, J. C. & Lovejoy, N. R. The Neotropical electric fish genus Brachyhypopomus (Ostariophysi: Gymnotiformes: Hypopomidae): taxonomy and biology, with descriptions of 15 new species. Neotrop. Ichthyol. 14, 639–790 (2016).Article 

    Google Scholar 
    61.Abel, R. Conservation biology for the biodiversity crisis: A freshwater follow-up. Conserv. Biol. 5, 1435–1437 (2002).Article 

    Google Scholar 
    62.Dudgeon, D. Prospects for sustaining freshwater biodiversity in the 21st century: Linking ecosystem structure and function. Curr. Opin. Environ. Sustain. 5, 422–430 (2010).Article 

    Google Scholar 
    63.Jenkins, M. Prospects for biodiversity. Science 302, 1175–1177 (2003).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    64.Bunn, S. E. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010).ADS 
    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    65.Albert, J. S. et al. Scientists’ warning to humanity on the freshwater biodiversity crisis. Ambio 50, 85–94 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Gilbert, M. T. P. et al. The isolation of nucleic acids from fixed, paraffin-embedded tissues–which methods are useful when?. PLoS ONE 2, e537 (2007).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    67.Campos, P. F. & Gilbert, T. M. DNA extraction from formalin-fixed material. In Ancient DNA 81–85 (Humana Press, 2012).68.Hykin, S. M., Bi, K. & McGuire, J. A. Fixing formalin: A method to recover genomic-scale DNA sequence data from formalin-fixed museum specimens using high-throughput sequencing. PLoS ONE 10, e0141579 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    69.Hagedorn, M. M. et al. Cryopreservation of fish spermatogonial cells: The future of natural history collections. Sci. Rep. 8, 6149 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    70.Albert, J. & Reis, R. E. Historical Biogeography of Neotropical Freshwater Fishes (University of California Press, 2011).Book 

    Google Scholar 
    71.Sabaj Pérez, M. H. Where the Xingu bends and will soon break. Am. Sci. 103, 395–403 (2015).Article 

    Google Scholar 
    72.Amigo, I. When will the Amazon hit a tipping point?. Nature 578, 505–507 (2020).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    73.Murienne, J. et al. Aquatic DNA for monitoring French Guiana biodiversity. Biodivers. Data J. 7, 37518 (2019).Article 

    Google Scholar 
    74.McDevitt, A. D. et al. Environmental DNA metabarcoding as an effective and rapid tool for fish monitoring in canals. J. Fish Biol. 95, 679–682 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    75.Fernandes, G. W. et al. Dismantling Brazil’s science threatens global biodiversity heritage. Perspect. Ecol. Conserv. 15, 239–243 (2017).
    Google Scholar 
    76.Alves, R. J. V. et al. Brazilian legislation on genetic heritage harms Biodiversity Convention goals and threatens basic biology research and education. An. Acad. Bras. Ciênc. 90, 1279–1284 (2018).PubMed 
    Article 

    Google Scholar 
    77.Overbeck, G. E. et al. Global biodiversity threatened by science budget cuts in Brazil. Bioscience 68, 11–12 (2018).PubMed 
    Article 

    Google Scholar 
    78.Miya, M. et al. Use of a filter cartridge for filtration of water samples and extraction of environmental DNA. J. Vis. Exp. 117, 54741 (2016).
    Google Scholar 
    79.Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
    Article 

    Google Scholar 
    80.Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    81.Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    82.Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    83.Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    84.Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: More models, new heuristics and parallel computing. Nat. Methods 9, 772 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    85.Ronquist, F. et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).Article 

    Google Scholar 
    86.Miller, M. A. et al. A RESTful API for access to phylogenetic tools via the CIPRES science gateway. Evol. Bioinf. 11, 43–48 (2015).CAS 
    Article 

    Google Scholar 
    87.Ciccarelli, F. D. et al. Toward automatic reconstruction of a highly resolved tree of life. Science 311, 1283–1287 (2006).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    88.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020). https://www.Rproject.org/.89.Wickham, H. Ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 
    Book 

    Google Scholar 
    90.Oksanen, J., Kindt, R. & O’Hara, B. Package VEGAN. Community Ecology Package, Version 2 (2013).91.Fox, J. & Weisberg, S. An R Companion to Applied Regression 3rd edn. (Sage, 2019).
    Google Scholar 
    92.Adler D., Nenadic, O. & Zucchini, W. rgl: 3D visualization device system (OpenGL). R package version 0.93.945. http://CRAN.R-project.org/package=rgl (2013).93.Gu, Z. Circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    94.Schiettekatte, N. M. D., Brandl, S. J. & Casey, J. M. Fishualize: Color Palettes Based On Fish Species. CRAN version 0.2.0 (2019).95.Chao, A. Estimating population size for sparse data in capture-recapture experiments. Biometrics 45, 427 (1989).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    96.Hsieh T. C., Ma, K. H. & Chao, A. iNEXT: Interpolation and Extrapolation for Species Diversity. R package version 2.0.20 (2020).97.Chao, A., Chazdon, R. L., Colwell, R. K. & Shen, T.-J. A new statistical approach for assessing compositional similarity based on incidence and abundance data. Ecol. Lett. 8, 148–215 (2005).Article 

    Google Scholar 
    98.Olds, B. P. et al. Estimating species richness using environmental DNA. Ecol. Evol. 6, 4214–4226 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    99.Chao A., Ma, K. H., Hsieh, T. C. & Chiu, C. H. SpadeR (Species-richness Prediction and Diversity Estimation in R): An R package in CRAN. Program and User’s Guide also published at http://chao.stat.nthu.edu.tw/wordpress/software_download/ (2016). More

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    The time course of molecular acclimation to seawater in a euryhaline fish

    1.Edwards, S. L. & Marshall, W. S. In Euryhaline Fishes. Fish Physiology Vol. 32 (eds Farrell Stephen, A. P. et al.) 1–44 (Academic Press, 2012).Chapter 

    Google Scholar 
    2.Evans, D. H., Piermarini, P. M. & Choe, K. P. The multifunctional fish gill: Dominant site of gas exchange, osmoregulation, acid-base regulation, and excretion of nitrogenous waste. Physiol. Rev. 85, 97–177. https://doi.org/10.1152/physrev.00050.2003 (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    3.Kultz, D. Physiological mechanisms used by fish to cope with salinity stress. J. Exp. Biol. 218, 1907–1914. https://doi.org/10.1242/jeb.118695 (2015).Article 
    PubMed 

    Google Scholar 
    4.Schultz, E. T. & McCormick, S. D. In Euryhaline Fishes. Fish Physiology Vol. 32 (eds Farrell, A. P. et al.) 477–533 (Academic Press, 2012).Chapter 

    Google Scholar 
    5.Scott, G. R., Richards, J. G., Forbush, B., Isenring, P. & Schulte, P. M. Changes in gene expression in gills of the euryhaline killifish Fundulus heteroclitus after abrupt salinity transfer. Am. J. Physiol. Cell Physiol. 287, C300–C309. https://doi.org/10.1152/ajpcell.00054.2004 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    6.Deane, E. E. & Woo, N. Y. Differential gene expression associated with euryhalinity in sea bream (Sparus sarba). Am. J. Physiol. Regul. Integr. Comp. Physiol. 287, R1054–R1063. https://doi.org/10.1152/ajpregu.00347.2004 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    7.Scott, G. R., Claiborne, J. B., Edwards, S. L., Schulte, P. M. & Wood, C. M. Gene expression after freshwater transfer in gills and opercular epithelia of killifish: Insight into divergent mechanisms of ion transport. J. Exp. Biol. 208, 2719–2729. https://doi.org/10.1242/jeb.01688 (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    8.Dymowska, A. K., Hwang, P. P. & Goss, G. G. Structure and function of ionocytes in the freshwater fish gill. Respir. Physiol. Neurobiol. 184, 282–292. https://doi.org/10.1016/j.resp.2012.08.025 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    9.Hiroi, J. & McCormick, S. D. New insights into gill ionocyte and ion transporter function in euryhaline and diadromous fish. Respir. Physiol. Neurobiol. 184, 257–268. https://doi.org/10.1016/j.resp.2012.07.019 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    10.Hsu, H. H., Lin, L. Y., Tseng, Y. C., Horng, J. L. & Hwang, P. P. A new model for fish ion regulation: Identification of ionocytes in freshwater- and seawater-acclimated medaka (Oryzias latipes). Cell Tissue Res. 357, 225–243. https://doi.org/10.1007/s00441-014-1883-z (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    11.Hwang, P. P. & Lin, L. Y. In The Physiology of Fishes Vol. 4 (eds Evans, D. H. et al.) 205–233 (CRC Press, 2013).
    Google Scholar 
    12.Evans, T. G. & Somero, G. N. A microarray-based transcriptomic time-course of hyper- and hypo-osmotic stress signaling events in the euryhaline fish Gillichthys mirabilis: Osmosensors to effectors. J. Exp. Biol. 211, 3636–3649. https://doi.org/10.1242/jeb.022160 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    13.Fiol, D. F. & Kultz, D. Osmotic stress sensing and signaling in fishes. FEBS J. 274, 5790–5798. https://doi.org/10.1111/j.1742-4658.2007.06099.x (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    14.Kultz, D. The combinatorial nature of osmosensing in fishes. Physiology (Bethesda) 27, 259–275. https://doi.org/10.1152/physiol.00014.2012 (2012).CAS 
    Article 

    Google Scholar 
    15.Komoroske, L. M. et al. Sublethal salinity stress contributes to habitat limitation in an endangered estuarine fish. Evol. Appl. 9, 963–981. https://doi.org/10.1111/eva.12385 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Foskett, J. K., Logsdon, C. D., Turner, T., Machen, T. E. & Bern, H. A. Differentiation of the chloride extrusion mechanism during seawater adaptation of a teleost fish, the cichlid Sarotherodon mossambicus. J. Exp. Biol. 93, 209–224 (1981).Article 

    Google Scholar 
    17.Katoh, F. & Kaneko, T. Short-term transformation and long-term replacement of branchial chloride cells in killifish transferred from seawater to freshwater, revealed by morphofunctional observations and a newly established “time-differential double fluorescent staining” technique. J. Exp. Biol. 206, 4113–4123. https://doi.org/10.1242/jeb.00659 (2003).Article 
    PubMed 

    Google Scholar 
    18.Uchida, K., Kaneko, T., Miyazaki, H., Hasegawa, S. & Hirano, T. Excellent salinity tolerance of mozambique tilapia (Oreochromis mossambicus): Elevated chloride cell activity in the branchial and opercular epithelia of the fish adapted to concentrated seawater. Zool. Sci. 17, 149–160. https://doi.org/10.2108/zsj.17.149 (2000).Article 

    Google Scholar 
    19.Whitehead, A., Roach, J. L., Zhang, S. & Galvez, F. Salinity- and population-dependent genome regulatory response during osmotic acclimation in the killifish (Fundulus heteroclitus) gill. J. Exp. Biol. 215, 1293–1305. https://doi.org/10.1242/jeb.062075 (2012).Article 
    PubMed 

    Google Scholar 
    20.Mundy, P. C., Jeffries, K. M., Fangue, N. A. & Connon, R. E. Differential regulation of select osmoregulatory genes and Na+/K+-ATPase paralogs may contribute to population differences in salinity tolerance in a semi-anadromous fish. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 240, 110584. https://doi.org/10.1016/j.cbpa.2019.110584 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    21.Jeffries, K. M. et al. Divergent transcriptomic signatures in response to salinity exposure in two populations of an estuarine fish. Evol. Appl. 12, 1212–1226. https://doi.org/10.1111/eva.12799 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    22.Lam, S. H. et al. Differential transcriptomic analyses revealed genes and signaling pathways involved in iono-osmoregulation and cellular remodeling in the gills of euryhaline Mozambique tilapia, Oreochromis mossambicus. BMC Genomics 15, 921. https://doi.org/10.1186/1471-2164-15-921 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Evans, T. G. & Kultz, D. The cellular stress response in fish exposed to salinity fluctuations. J. Exp. Zool. A Ecol. Integr. Physiol. 333, 421–435. https://doi.org/10.1002/jez.2350 (2020).Article 
    PubMed 

    Google Scholar 
    24.Burg, M. B., Ferraris, J. D. & Dmitrieva, N. I. Cellular response to hyperosmotic stresses. Physiol. Rev. 87, 1441–1474. https://doi.org/10.1152/physrev.00056.2006 (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    25.Tine, M., Bonhomme, F., McKenzie, D. J. & Durand, J. D. Differential expression of the heat shock protein Hsp70 in natural populations of the tilapia, Sarotherodon melanotheron, acclimatised to a range of environmental salinities. BMC Ecol. 10, 11. https://doi.org/10.1186/1472-6785-10-11 (2010).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    26.Whitehead, A., Zhang, S., Roach, J. L. & Galvez, F. Common functional targets of adaptive micro- and macro-evolutionary divergence in killifish. Mol. Ecol. 22, 3780–3796. https://doi.org/10.1111/mec.12316 (2013).Article 
    PubMed 

    Google Scholar 
    27.Brennan, R. S., Galvez, F. & Whitehead, A. Reciprocal osmotic challenges reveal mechanisms of divergence in phenotypic plasticity in the killifish Fundulus heteroclitus. J. Exp. Biol. 218, 1212–1222. https://doi.org/10.1242/jeb.110445 (2015).Article 
    PubMed 

    Google Scholar 
    28.Kultz, D. Molecular and evolutionary basis of the cellular stress response. Annu. Rev. Physiol. 67, 225–257. https://doi.org/10.1146/annurev.physiol.67.040403.103635 (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    29.Takei, Y. & Hwang, P.-P. In Biology of Stress in Fish—Fish Physiology Vol. 35 (eds Schreck, C. B. et al.) 207–249 (Academic Press, 2016).Chapter 

    Google Scholar 
    30.Tseng, Y. C. & Hwang, P. P. Some insights into energy metabolism for osmoregulation in fish. Comp. Biochem. Physiol. C Toxicol. Pharmacol. 148, 419–429. https://doi.org/10.1016/j.cbpc.2008.04.009 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    31.Chen, X. L., Lui, E. Y., Ip, Y. K. & Lam, S. H. RNA sequencing, de novo assembly and differential analysis of the gill transcriptome of freshwater climbing perch Anabas testudineus after 6 days of seawater exposure. J. Fish Biol. 93, 215–228. https://doi.org/10.1111/jfb.13653 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    32.Nguyen, T. V., Jung, H., Nguyen, T. M., Hurwood, D. & Mather, P. Evaluation of potential candidate genes involved in salinity tolerance in striped catfish (Pangasianodon hypophthalmus) using an RNA-Seq approach. Mar. Genomics 25, 75–88. https://doi.org/10.1016/j.margen.2015.11.010 (2016).Article 
    PubMed 

    Google Scholar 
    33.Bœuf, G. & Payan, P. How should salinity influence fish growth?. Comp. Biochem. Physiol. C Toxicol. Pharmacol. 130, 411–423. https://doi.org/10.1016/s1532-0456(01)00268-x (2001).Article 
    PubMed 

    Google Scholar 
    34.Makrinos, D. L. & Bowden, T. J. Natural environmental impacts on teleost immune function. Fish Shellfish Immunol. 53, 50–57. https://doi.org/10.1016/j.fsi.2016.03.008 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    35.Morgan, J. D. & Iwama, G. K. Effects of salinity on growth, metabolism, and ion regulation in juvenile rainbow and steelhead trout (Oncorhynchus mykiss) and fall chinook salmon (Oncorhynchus tshawytscha). Can. J. Fish. Aquat. Sci. 48, 2083–2094. https://doi.org/10.1139/f91-247 (1991).Article 

    Google Scholar 
    36.Whitehead, A., Roach, J. L., Zhang, S. & Galvez, F. Genomic mechanisms of evolved physiological plasticity in killifish distributed along an environmental salinity gradient. Proc. Natl. Acad. Sci. U.S.A. 108, 6193–6198. https://doi.org/10.1073/pnas.1017542108 (2011).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    37.Kozak, G. M., Brennan, R. S., Berdan, E. L., Fuller, R. C. & Whitehead, A. Functional and population genomic divergence within and between two species of killifish adapted to different osmotic niches. Evolution 68, 63–80. https://doi.org/10.1111/evo.12265 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    38.Hrbek, T. & Meyer, A. Closing of the Tethys Sea and the phylogeny of Eurasian killifishes (Cyprinodontiformes: Cyprinodontidae). J. Evol. Biol. 16, 17–36. https://doi.org/10.1046/j.1420-9101.2003.00475.x (2003).CAS 
    Article 
    PubMed 

    Google Scholar 
    39.Schunter, C. et al. Desert fish populations tolerate extreme salinity change to overcome hydrological constraints. bioRxiv. https://doi.org/10.1101/2021.05.14.444120 (2021).Article 

    Google Scholar 
    40.Marshall, J. C. et al. Go with the flow: The movement behaviour of fish from isolated waterhole refugia during connecting flow events in an intermittent dryland river. Freshw. Biol. 61, 1242–1258. https://doi.org/10.1111/fwb.12707 (2016).Article 

    Google Scholar 
    41.Kerezsy, A., Balcombe, S. R., Tischler, M. & Arthington, A. H. Fish movement strategies in an ephemeral river in the Simpson Desert, Australia. Austral Ecol. 38, 798–808. https://doi.org/10.1111/aec.12075 (2013).Article 

    Google Scholar 
    42.Martin, C. H., Crawford, J. E., Turner, B. J. & Simons, L. H. Diabolical survival in Death Valley: Recent pupfish colonization, gene flow and genetic assimilation in the smallest species range on earth. Proc. Biol. Sci. 283, 20152334. https://doi.org/10.1098/rspb.2015.2334 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.Mossop, K. D. et al. Dispersal in the desert: Ephemeral water drives connectivity and phylogeography of an arid-adapted fish. J. Biogeogr. 42, 2374–2388. https://doi.org/10.1111/jbi.12596 (2015).Article 

    Google Scholar 
    44.Collins, J. P., Young, C., Howell, J. & Minckley, W. L. Impact of flooding in a Sonoran desert stream, including elimination of an endangered fish population (Poeciliopsis O. occidentalis, Poeciliidae). Southwest. Nat. 26, 415–423. https://doi.org/10.2307/3671085 (1981).Article 

    Google Scholar 
    45.Meffe, G. K. Effects of abiotic disturbance on coexistence of predator–prey fish species. Ecology 65, 1525–1534. https://doi.org/10.2307/1939132 (1984).Article 

    Google Scholar 
    46.Lotan, R. Sodium, chloride and water balance in the euryhaline teleost Aphanius dispar (Rüppell) (Cyprinodontidae). Z. Vgl. Physiol. 65, 455–462. https://doi.org/10.1007/bf00299054 (1969).Article 

    Google Scholar 
    47.Lotan, R. Osmotic adjustment in the euryhaline teleost Aphanius dispar (Cyprinodontidae). Z. Vgl. Physiol. 75, 383–387. https://doi.org/10.1007/bf00630558 (1971).CAS 
    Article 

    Google Scholar 
    48.Plaut, I. Resting metabolic rate, critical swimming speed, and routine activity of the euryhaline cyprinodontid, Aphanius dispar, acclimated to a wide range of salinities. Physiol. Biochem. Zool. 73, 590–596. https://doi.org/10.1086/317746 (2000).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    49.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    50.Andrews, S. FastQC: a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).51.Song, L. & Florea, L. Rcorrector: Efficient and accurate error correction for Illumina RNA-seq reads. Gigascience 4, 48. https://doi.org/10.1186/s13742-015-0089-y (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Grabherr, M. G. et al. Trinity: Reconstructing a full-length transcriptome without a genome from RNA-Seq data. Nat. Biotechnol. 29, 644 (2011).CAS 
    Article 

    Google Scholar 
    53.Lafond-Lapalme, J., Duceppe, M. O., Wang, S., Moffett, P. & Mimee, B. A new method for decontamination of de novo transcriptomes using a hierarchical clustering algorithm. Bioinformatics 33, 1293–1300. https://doi.org/10.1093/bioinformatics/btw793 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    54.Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512. https://doi.org/10.1038/nprot.2013.084 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    55.Finn, R. D., Clements, J. & Eddy, S. R. HMMER web server: Interactive sequence similarity searching. Nucleic Acids Res. 39, W29–W37. https://doi.org/10.1093/nar/gkr367 (2011).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    56.Camacho, C. et al. BLAST+: Architecture and applications. BMC Bioinform. 10, 421. https://doi.org/10.1186/1471-2105-10-421 (2009).CAS 
    Article 

    Google Scholar 
    57.Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: Accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152. https://doi.org/10.1093/bioinformatics/bts565 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359. https://doi.org/10.1038/nmeth.1923 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.Simao, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: Assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212. https://doi.org/10.1093/bioinformatics/btv351 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    60.BioBam Bioinformatics. OmicsBox – Bioinformatics Made Easy. https://www.biobam.com/omicsbox (2019).61.Huerta-Cepas, J. et al. eggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314. https://doi.org/10.1093/nar/gky1085 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    62.Gotz, S. et al. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 36, 3420–3435. https://doi.org/10.1093/nar/gkn176 (2008).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    63.Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419. https://doi.org/10.1038/nmeth.4197 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    64.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550. https://doi.org/10.1186/s13059-014-0550-8 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    65.Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: Transcript-level estimates improve gene-level inferences. F1000Research https://doi.org/10.12688/f1000research.7563.1 (2015).Article 
    PubMed 

    Google Scholar 
    66.Zhu, A., Ibrahim, J. G. & Love, M. I. Heavy-tailed prior distributions for sequence count data: Removing the noise and preserving large differences. Bioinformatics 35, 2084–2092. https://doi.org/10.1093/bioinformatics/bty895 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    67.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate—A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 57, 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x (1995).MathSciNet 
    Article 
    MATH 

    Google Scholar 
    68.Cui, W. et al. Comparative transcriptomic analysis reveals mechanisms of divergence in osmotic regulation of the turbot Scophthalmus maximus. Fish Physiol. Biochem. 46, 1519–1536. https://doi.org/10.1007/s10695-020-00808-6 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    69.Lee, S. Y., Lee, H. J. & Kim, Y. K. Comparative transcriptome profiling of selected osmotic regulatory proteins in the gill during seawater acclimation of chum salmon (Oncorhynchus keta) fry. Sci. Rep. 10, 1987. https://doi.org/10.1038/s41598-020-58915-6 (2020).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    70.Su, H., Ma, D., Zhu, H., Liu, Z. & Gao, F. Transcriptomic response to three osmotic stresses in gills of hybrid tilapia (Oreochromis mossambicus female × O. urolepis hornorum male). BMC Genomics 21, 110. https://doi.org/10.1186/s12864-020-6512-5 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    71.Fischer, D. S., Theis, F. J. & Yosef, N. Impulse model-based differential expression analysis of time course sequencing data. Nucleic Acids Res. 46, e119. https://doi.org/10.1093/nar/gky675 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    72.Hwang, P. P., Lee, T. H. & Lin, L. Y. Ion regulation in fish gills: Recent progress in the cellular and molecular mechanisms. Am. J. Physiol. Regul. Integr. Comp. Physiol. 301, R28–R47. https://doi.org/10.1152/ajpregu.00047.2011 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    73.Marshall, W. S. Mechanosensitive signalling in fish gill and other ion transporting epithelia. Acta Physiol. (Oxf.) 202, 487–499. https://doi.org/10.1111/j.1748-1716.2010.02189.x (2011).CAS 
    Article 

    Google Scholar 
    74.Lema, S. C., Carvalho, P. G., Egelston, J. N., Kelly, J. T. & McCormick, S. D. Dynamics of gene expression responses for ion transport proteins and aquaporins in the gill of a euryhaline pupfish during freshwater and high-salinity acclimation. Physiol. Biochem. Zool. 91, 1148–1171. https://doi.org/10.1086/700432 (2018).Article 
    PubMed 

    Google Scholar 
    75.Flemmer, A. W. et al. Phosphorylation state of the Na+–K+–Cl− cotransporter (NKCC1) in the gills of Atlantic killifish (Fundulus heteroclitus) during acclimation to water of varying salinity. J. Exp. Biol. 213, 1558–1566. https://doi.org/10.1242/jeb.039644 (2010).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    76.Delpire, E. & Gagnon, K. B. SPAK and OSR1: STE20 kinases involved in the regulation of ion homoeostasis and volume control in mammalian cells. Biochem. J. 409, 321–331. https://doi.org/10.1042/BJ20071324 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    77.Rinehart, J. et al. WNK2 kinase is a novel regulator of essential neuronal cation-chloride cotransporters. J. Biol. Chem. 286, 30171–30180. https://doi.org/10.1074/jbc.M111.222893 (2011).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    78.Li, J. et al. Gill transcriptomes reveal expression changes of genes related with immune and ion transport under salinity stress in silvery pomfret (Pampus argenteus). Fish Physiol. Biochem. 46, 1255–1277. https://doi.org/10.1007/s10695-020-00786-9 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    79.Yancey, P. H., Clark, M. E., Hand, S. C., Bowlus, R. D. & Somero, G. N. Living with water stress: Evolution of osmolyte systems. Science 217, 1214–1222. https://doi.org/10.1126/science.7112124 (1982).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    80.Kalujnaia, S., McVee, J., Kasciukovic, T., Stewart, A. J. & Cramb, G. A role for inositol monophosphatase 1 (IMPA1) in salinity adaptation in the euryhaline eel (Anguilla anguilla). FASEB J. 24, 3981–3991. https://doi.org/10.1096/fj.10-161000 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    81.Cui, W. X. et al. myo-inositol facilitates salinity tolerance by modulating multiple physiological functions in the turbot Scophthalmus maximus. Aquaculture 527, 735451. https://doi.org/10.1016/j.aquaculture.2020.735451 (2020).CAS 
    Article 

    Google Scholar 
    82.Ma, A. et al. Osmoregulation by the myo-inositol biosynthesis pathway in turbot Scophthalmus maximus and its regulation by anabolite and c-Myc. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 242, 110636. https://doi.org/10.1016/j.cbpa.2019.110636 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    83.Wang, Y. F., Yan, J. J., Tseng, Y. C., Chen, R. D. & Hwang, P. P. Molecular physiology of an extra-renal Cl− uptake mechanism for body fluid Cl− homeostasis. Int. J. Biol. Sci. 11, 1190–1203. https://doi.org/10.7150/ijbs.11737 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    84.Leguen, I., Le Cam, A., Montfort, J., Peron, S. & Fautrel, A. Transcriptomic analysis of trout gill ionocytes in fresh water and sea water using laser capture microdissection combined with microarray analysis. PLoS One 10, e0139938. https://doi.org/10.1371/journal.pone.0139938 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    85.Richards, J. G., Semple, J. W., Bystriansky, J. S. & Schulte, P. M. Na+/K+-ATPase alpha-isoform switching in gills of rainbow trout (Oncorhynchus mykiss) during salinity transfer. J. Exp. Biol. 206, 4475–4486. https://doi.org/10.1242/jeb.00701 (2003).CAS 
    Article 
    PubMed 

    Google Scholar 
    86.McCormick, S. D., Regish, A. M. & Christensen, A. K. Distinct freshwater and seawater isoforms of Na+/K+-ATPase in gill chloride cells of Atlantic salmon. J. Exp. Biol. 212, 3994–4001. https://doi.org/10.1242/jeb.037275 (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    87.Bystriansky, J. S., Richards, J. G., Schulte, P. M. & Ballantyne, J. S. Reciprocal expression of gill Na+/K+-ATPase alpha-subunit isoforms alpha1a and alpha1b during seawater acclimation of three salmonid fishes that vary in their salinity tolerance. J. Exp. Biol. 209, 1848–1858. https://doi.org/10.1242/jeb.02188 (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    88.Tipsmark, C. K. et al. Switching of Na+, K+-ATPase isoforms by salinity and prolactin in the gill of a cichlid fish. J. Endocrinol. 209, 237–244. https://doi.org/10.1530/JOE-10-0495 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    89.Urbina, M. A., Schulte, P. M., Bystriansky, J. S. & Glover, C. N. Differential expression of Na+, K+-ATPase alpha-1 isoforms during seawater acclimation in the amphidromous galaxiid fish Galaxias maculatus. J. Comp. Physiol. B 183, 345–357. https://doi.org/10.1007/s00360-012-0719-y (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    90.Velotta, J. P. et al. Transcriptomic imprints of adaptation to fresh water: Parallel evolution of osmoregulatory gene expression in the Alewife. Mol. Ecol. 26, 831–848. https://doi.org/10.1111/mec.13983 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    91.Ip, Y. K. et al. Roles of three branchial Na+-K+-ATPase alpha-subunit isoforms in freshwater adaptation, seawater acclimation, and active ammonia excretion in Anabas testudineus. Am. J. Physiol. Regul. Integr. Comp. Physiol. 303, R112–R125. https://doi.org/10.1152/ajpregu.00618.2011 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    92.Birrer, S. C., Reusch, T. B. & Roth, O. Salinity change impairs pipefish immune defence. Fish Shellfish Immunol. 33, 1238–1248. https://doi.org/10.1016/j.fsi.2012.08.028 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    93.Delamare-Deboutteville, J., Wood, D. & Barnes, A. C. Response and function of cutaneous mucosal and serum antibodies in barramundi (Lates calcarifer) acclimated in seawater and freshwater. Fish Shellfish Immunol. 21, 92–101. https://doi.org/10.1016/j.fsi.2005.10.005 (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    94.Koppang, E. O., Kvellestad, A. & Fischer, U. In Mucosal Health in Aquaculture (eds Beck, B. H. & Peatman, E.) 93–133 (Academic Press, 2015).Chapter 

    Google Scholar 
    95.Poulin, R., Blanar, C. A., Thieltges, D. W. & Marcogliese, D. J. The biogeography of parasitism in sticklebacks: Distance, habitat differences and the similarity in parasite occurrence and abundance. Ecography 34, 540–551. https://doi.org/10.1111/j.1600-0587.2010.06826.x (2011).Article 

    Google Scholar 
    96.Takemura, A. F., Chien, D. M. & Polz, M. F. Associations and dynamics of Vibrionaceae in the environment, from the genus to the population level. Front. Microbiol. 5, 38. https://doi.org/10.3389/fmicb.2014.00038 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    97.Nitzan, S., Shwartsburd, B. & Heller, E. D. The effect of growth medium salinity of Photobacterium damselae subsp. piscicida on the immune response of hybrid bass (Morone saxatilis × M. chrysops). Fish Shellfish Immunol. 16, 107–116. https://doi.org/10.1016/s1050-4648(03)00045-7 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    98.Zheng, D. H. et al. Effect of temperature and salinity on virulence of Edwardsiella tarda to Japanese flounder, Paralichthys olivaceus (Temminck et Schlegel). Aquac. Res. 35, 494–500. https://doi.org/10.1111/j.1365-2109.2004.01044.x (2004).Article 

    Google Scholar 
    99.Dominguez, M., Takemura, A., Tsuchiya, M. & Nakamura, S. Impact of different environmental factors on the circulating immunoglobulin levels in the Nile tilapia, Oreochromis niloticus. Aquaculture 241, 491–500. https://doi.org/10.1016/j.aquaculture.2004.06.027 (2004).CAS 
    Article 

    Google Scholar 
    100.Mozanzadeh, M. T. et al. The effect of salinity on growth performance, digestive and antioxidant enzymes, humoral immunity and stress indices in two euryhaline fish species: Yellowfin seabream (Acanthopagrus latus) and Asian seabass (Lates calcarifer). Aquaculture 534, 736329. https://doi.org/10.1016/j.aquaculture.2020.736329 (2021).CAS 
    Article 

    Google Scholar 
    101.Gao, Y., Tang, X., Sheng, X., Xing, J. & Zhan, W. Antigen uptake and expression of antigen presentation-related immune genes in flounder (Paralichthys olivaceus) after vaccination with an inactivated Edwardsiella tarda immersion vaccine, following hyperosmotic treatment. Fish Shellfish Immunol. 55, 274–280. https://doi.org/10.1016/j.fsi.2016.05.042 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    102.Salinas, I. The mucosal immune system of teleost fish. Biology 4, 525–539. https://doi.org/10.3390/biology4030525 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    103.Reverter, M., Tapissier-Bontemps, N., Lecchini, D., Banaigs, B. & Sasal, P. Biological and ecological roles of external fish mucus: A review. Fishes 3, 41. https://doi.org/10.3390/fishes3040041 (2018).Article 

    Google Scholar 
    104.Shephard, K. L. Functions for fish mucus. Rev. Fish Biol. Fish. 4, 401–429. https://doi.org/10.1007/Bf00042888 (1994).Article 

    Google Scholar 
    105.Wong, M. K. S. et al. A sodium binding system alleviates acute salt stress during seawater acclimation in eels. Zool. Lett. 3, 22. https://doi.org/10.1186/s40851-017-0081-8 (2017).Article 

    Google Scholar 
    106.Malachowicz, M., Wenne, R. & Burzynski, A. De novo assembly of the sea trout (Salmo trutta m. trutta) skin transcriptome to identify putative genes involved in the immune response and epidermal mucus secretion. PLoS One 12, e0172282. https://doi.org/10.1371/journal.pone.0172282 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    107.Roberts, S. D. & Powell, M. D. Comparative ionic flux and gill mucous cell histochemistry: Effects of salinity and disease status in Atlantic salmon (Salmo salar L.). Comp. Biochem. Physiol. A Mol. Integr. Physiol. 134, 525–537. https://doi.org/10.1016/s1095-6433(02)00327-6 (2003).Article 
    PubMed 

    Google Scholar 
    108.Mylonas, C. C. et al. Growth performance and osmoregulation in the shi drum (Umbrina cirrosa) adapted to different environmental salinities. Aquaculture 287, 203–210. https://doi.org/10.1016/j.aquaculture.2008.10.024 (2009).CAS 
    Article 

    Google Scholar 
    109.Roberts, S. D. & Powell, M. D. The viscosity and glycoprotein biochemistry of salmonid mucus varies with species, salinity and the presence of amoebic gill disease. J. Comp. Physiol. B 175, 1–11. https://doi.org/10.1007/s00360-004-0453-1 (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    110.Kalujnaia, S. et al. Transcriptomic approach to the study of osmoregulation in the European eel Anguilla anguilla. Physiol. Genomics 31, 385–401. https://doi.org/10.1152/physiolgenomics.00059.2007 (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    111.Shaw, J. R. et al. The role of SGK and CFTR in acute adaptation to seawater in Fundulus heteroclitus. Cell Physiol. Biochem. 22, 69–78. https://doi.org/10.1159/000149784 (2008).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    112.Kammerer, B. D., Sardella, B. A. & Kultz, D. Salinity stress results in rapid cell cycle changes of tilapia (Oreochromis mossambicus) gill epithelial cells. J. Exp. Zool. A Ecol. Genet. Physiol. 311, 80–90. https://doi.org/10.1002/jez.498 (2009).Article 
    PubMed 

    Google Scholar 
    113.Ronkin, D., Seroussi, E., Nitzan, T., Doron-Faigenboim, A. & Cnaani, A. Intestinal transcriptome analysis revealed differential salinity adaptation between two tilapiine species. Comp. Biochem. Physiol. Part D Genomics Proteomics 13, 35–43. https://doi.org/10.1016/j.cbd.2015.01.003 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    114.Dong, Y. W. et al. Genomic and physiological mechanisms underlying skin plasticity during water to air transition in an amphibious fish. J. Exp. Biol. 224, jeb235515. https://doi.org/10.1242/jeb.235515 (2021).Article 
    PubMed 

    Google Scholar 
    115.Inokuchi, M. & Kaneko, T. Recruitment and degeneration of mitochondrion-rich cells in the gills of Mozambique tilapia Oreochromis mossambicus during adaptation to a hyperosmotic environment. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 162, 245–251. https://doi.org/10.1016/j.cbpa.2012.03.018 (2012).CAS 
    Article 
    PubMed 

    Google Scholar  More