Philippa Kaur

1.Kuypers MMM, Marchant HK, Kartal B. The microbial nitrogen-cycling network. Nat Rev Microbiol. 2018;16:263–76.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Stein LY, Klotz MG. The nitrogen cycle. Curr Biol. 2016;26:R94–R98.CAS 
PubMed 
Article 

Google Scholar 
3.Erguder TH, Boon N, Wittebolle L, Marzorati M, Verstraete W. Environmental factors shaping the ecological niches of ammonia-oxidizing archaea. FEMS Microbiol Rev. 2009;33:855–69.CAS 
PubMed 
Article 

Google Scholar 
4.Nicol GW, Leininger S, Schleper C, Prosser JI. The influence of soil pH on the diversity, abundance and transcriptional activity of ammonia oxidizing archaea and bacteria. Environ Microbiol. 2008;10:2966–78.CAS 
PubMed 
Article 

Google Scholar 
5.Lehtovirta-Morley LE, Ge C, Ross J, Yao H, Nicol GW, Prosser JI. Characterisation of terrestrial acidophilic archaeal ammonia oxidisers and their inhibition and stimulation by organic compounds. FEMS Microbiol Ecol. 2014;89:542–52.CAS 
PubMed 
Article 

Google Scholar 
6.Lehtovirta-Morley LE, Stoecker K, Vilcinskas A, Prosser JI, Nicol GW. Cultivation of an obligate acidophilic ammonia oxidizer from a nitrifying acid soil. Proc Natl Acad Sci USA. 2011;108:15892–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Hayatsu M, Tago K, Uchiyama I, Toyoda A, Wang Y, Shimomura Y, et al. An acid-tolerant ammonia-oxidizing γ-proteobacterium from soil. ISME J. 2017;11:1130–41.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Prosser JI, Nicol GW. Archaeal and bacterial ammonia-oxidisers in soil: the quest for niche specialisation and differentiation. Trends Microbiol. 2012;20:523–31.CAS 
PubMed 
Article 

Google Scholar 
9.Gubry-Rangin C, Hai B, Quince C, Engel M, Thomson BC, James P, et al. Niche specialization of terrestrial archaeal ammonia oxidizers. Proc Natl Acad Sci USA. 2011;108:21206–11.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Aigle A, Prosser JI, Gubry-Rangin C. The application of high-throughput sequencing technology to analysis of amoA phylogeny and environmental niche specialisation of terrestrial bacterial ammonia-oxidisers. Environ Microbiome. 2019;14:3.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
11.Antony CP, Kumaresan D, Hunger S, Drake HL, Murrell JC, Shouche YS. Microbiology of Lonar Lake and other soda lakes. ISME J. 2013;7:468–76.PubMed 
Article 
CAS 

Google Scholar 
12.Montanarella L, Chude V, Yagi K, Krasilnikov P, Panah SKA, Mendonca-Santos MDL, et al. Status of the World’s Soil Resources (SWSR) – Main Report. 2015.13.Vera-Gargallo B, Chowdhury TR, Brown J, Fansler SJ, Durán-Viseras A, Sánchez-Porro C, et al. Spatial distribution of prokaryotic communities in hypersaline soils. Sci Rep. 2019;9:1769.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
14.Hollister EB, Engledow AS, Hammett AJM, Provin TL, Wilkinson HH, Gentry TJ. Shifts in microbial community structure along an ecological gradient of hypersaline soils and sediments. ISME J. 2010;4:829–938.CAS 
PubMed 
Article 

Google Scholar 
15.Metternicht GI, Zinck JA. Remote sensing of soil salinity: potentials and constraints. Remote Sens Environ. 2003;85:1–20.Article 

Google Scholar 
16.Shi YL, Liu XR, Zhang QW. Effects of combined biochar and organic fertilizer on nitrous oxide fluxes and the related nitrifier and denitrifier communities in a saline-alkali soil. Sci Total Environ. 2019;686:199–211.CAS 
PubMed 
Article 

Google Scholar 
17.Konneke M, Bernhard AE, de la Torre JR, Walker CB, Waterbury JB, Stahl DA. Isolation of an autotrophic ammonia-oxidizing marine archaeon. Nature. 2005;437:543–6.PubMed 
Article 
CAS 

Google Scholar 
18.Bayer B, Vojvoda J, Offre P, Alves RJE, Elisabeth NH, Garcia JAL, et al. Physiological and genomic characterization of two novel marine thaumarchaeal strains indicates niche differentiation. ISME J. 2016;10:1051–63.CAS 
PubMed 
Article 

Google Scholar 
19.Santoro AE, Dupont CL, Richter RA, Craig MT, Carini P, McIlvin MR, et al. Genomic and proteomic characterization of “Candidatus Nitrosopelagicus brevis”: An ammonia-oxidizing archaeon from the open ocean. Proc Natl Acad Sci USA. 2015;112:1173–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Pan KL, Gao JF, Li DC, Fan XY. The dominance of non-halophilic archaea in autotrophic ammonia oxidation of activated sludge under salt stress: a DNA-based stable isotope probing study. Bioresour Technol. 2019;291:8.Article 
CAS 

Google Scholar 
21.Nejidat A. Nitrification and occurrence of salt-tolerant nitrifying bacteria in the Negev desert soils. FEMS Microbiol Ecol. 2005;52:21–29.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Ward BB, O’Mullan GD. Worldwide distribution of Nitrosococcus oceani, a marine ammonia-oxidizing gamma-proteobacterium, detected by PCR and sequencing of 16S rRNA and amoA genes. Appl Environ Micro. 2002;68:4153–7.CAS 
Article 

Google Scholar 
23.Koops HP, Böttcher B, Möller UC, Pommerening-Röser A, Stehr G. Description of a new species of Nitrosococcus. Arch Microbiol. 1990;154:244–8.CAS 
Article 

Google Scholar 
24.Fumasoli A, Bürgmann H, Weissbrodt DG, Wells GF, Beck K, Mohn J, et al. Growth of Nitrosococcus-related ammonia oxidizing bacteria coincides with extremely low pH values in wastewater with high ammonia content. Environ Sci Technol. 2017;51:6857–66.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Olivera NL, Prieto L, Bertiller MB, Ferrero MA. Sheep grazing and soil bacterial diversity in shrublands of the Patagonian Monte, Argentina. J Arid Environ. 2016;125:16–20.Article 

Google Scholar 
26.Pérez-Hernandez V, Hernandez-Guzman M, Serrano-Silva N, Luna-Guido M, Navarro-Noya YE, Montes-Molina JA, et al. Diversity of amoA and pmoA genes in extremely saline alkaline soils of the former lake Texcoco. Geomicrobiol J. 2020;37:785–97.Article 
CAS 

Google Scholar 
27.Picone N, Pol A, Mesman R, van Kessel MAHJ, Cremers G, van Gelder AH. et al. Ammonia oxidation at pH 2.5 by a new gammaproteobacterial ammonia-oxidizing bacterium. ISME J. 2020;15:1150–64.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
28.Pan H, Liu HY, Liu YW, Zhang QC, Luo Y, Liu XM, et al. Understanding the relationships between grazing intensity and the distribution of nitrifying communities in grassland soils. Sci Total Environ. 2018;634:1157–64.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Santos JP, Mendes D, Monteiro M, Ribeiro H, Baptista MS, Borges MT, et al. Salinity impact on ammonia oxidizers activity and amoA expression in estuarine sediments. Estuar Coast Shelf Sci. 2018;211:177–87.CAS 
Article 

Google Scholar 
30.Ye L, Zhang T. Ammonia-oxidizing bacteria dominates over ammonia-oxidizing archaea in a saline nitrification reactor under low DO and high nitrogen loading. Biotechnol Bioeng. 2011;108:2544–52.CAS 
PubMed 
Article 

Google Scholar 
31.Luo S, Wang S, Tian L, Shi S, Xu S, Yang F, et al. Aggregate-related changes in soil microbial communities under different ameliorant applications in saline-sodic soils. Geoderma. 2018;329:108–17.CAS 
Article 

Google Scholar 
32.Wang WJ, He HS, Zu YG, Guan Y, Liu ZG, Zhang ZH, et al. Addition of HPMA affects seed germination, plant growth and properties of heavy saline-alkali soil in northeastern China: comparison with other agents and determination of the mechanism. Plant Soil. 2011;339:177–91.CAS 
Article 

Google Scholar 
33.Xia W, Zhang C, Zeng X, Feng Y, Jia Z. Autotrophic growth of nitrifying community in an agricultural soil. ISME J. 2011;5:1226–36.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Francis CA, Roberts KJ, Beman JM, Santoro AE, Oakley BB. Ubiquity and diversity of ammonia-oxidizing archaea in water columns and sediments of the ocean. Proc Natl Acad Sci USA. 2005;102:14683–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Holmes AJ, Costello A, Lidstrom ME, Murrell JC. Evidence that participate methane monooxygenase and ammonia monooxygenase may be evolutionarily related. FEMS Microbiol Lett. 1995;132:203–8.CAS 
PubMed 
Article 

Google Scholar 
36.Fowler SJ, Palomo A, Dechesne A, Mines PD, Smets BF. Comammox Nitrospira are abundant ammonia oxidizers in diverse groundwater-fed rapid sand filter communities. Environ Microbiol. 2018;20:1002–15.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Zhao ZR, Huang GH, He SS, Zhou N, Wang MY, Dang CY, et al. Abundance and community composition of comammox bacteria in different ecosystems by a universal primer set. Sci Total Environ. 2019;691:145–55.Article 
CAS 

Google Scholar 
38.Alves RJE, Minh BQ, Urich T, von Haeseler A, Schleper C. Unifying the global phylogeny and environmental distribution of ammonia-oxidising archaea based on amoA genes. Nat Commun. 2018;9:1517.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
39.Richter M, Rossello-Mora R. Shifting the genomic gold standard for the prokaryotic species definition. Proc Natl Acad Sci USA. 2009;106:19126–31.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Konstantinidis KT, Rosselló-Móra R, Amann R. Uncultivated microbes in need of their own taxonomy. ISME J. 2017;11:2399–406.PubMed 
PubMed Central 
Article 

Google Scholar 
41.Luo C, Rodriguez-R LM, Konstantinidis KT. MyTaxa: an advanced taxonomic classifier for genomic and metagenomic sequences. Nucleic Acids Res. 2014;42:e73.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2020;36:1925–7.CAS 

Google Scholar 
43.Kuroda T, Mizushima T, Tsuchiya T. Physiological roles of three Na+/H+ antiporters in the halophilic bacterium Vibrio parahaemolyticus. Microbiol Immunol. 2005;49:711–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Daebeler A, Kitzinger K, Koch H, Herbold CW, Steinfeder M, Schwarz J, et al. Exploring the upper pH limits of nitrite oxidation: diversity, ecophysiology, and adaptive traits of haloalkalitolerant. Nitrospira ISME J. 2020;14:2967–79.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Padan E, Venturi M, Gerchman Y, Dover N. Na+/H+ antiporters. Biochim Biophys Acta. 2001;1505:144–57.CAS 
PubMed 
Article 

Google Scholar 
46.Kraegeloh A, Amendt B, Kunte HJ. Potassium transport in a halophilic member of the bacteria domain: identification and characterization of the K+ uptake systems TrkH and TrkI from Halomonas elongata DSM 2581T. J Bacteriol. 2005;187:1036–43.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Becker EA, Seitzer PM, Tritt A, Larsen D, Krusor M, Yao AI, et al. Phylogenetically driven sequencing of extremely halophilic archaea reveals strategies for static and dynamic osmo-response. PloS Genet. 2014;10:e1004784.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
48.Cardoso FS, Castro RF, Borges N, Santos H. Biochemical and genetic characterization of the pathways for trehalose metabolism in Propionibacterium freudenreichii, and their role in stress response. Microbiology. 2007;153:270–80.CAS 
PubMed 
Article 

Google Scholar 
49.Sadeghi A, Soltani BM, Nekouei MK, Jouzani GS, Mirzaei HH, Sadeghizadeh M. Diversity of the ectoines biosynthesis genes in the salt tolerant Streptomyces and evidence for inductive effect of ectoines on their accumulation. Microbiol Res. 2014;169:699–708.CAS 
PubMed 
Article 

Google Scholar 
50.Ngugi DK, Blom J, Alam I, Rashid M, Ba-Alawi W, Zhang G, et al. Comparative genomics reveals adaptations of a halotolerant thaumarchaeon in the interfaces of brine pools in the Red Sea. ISME J. 2015;9:396–411.Article 
CAS 

Google Scholar 
51.Spang A, Poehlein A, Offre P, Zumbragel S, Haider S, Rychlik N, et al. The genome of the ammonia-oxidizing Candidatus Nitrososphaera gargensis: insights into metabolic versatility and environmental adaptations. Environ Microbiol. 2012;14:3122–45.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Glover HE. The relationship between inorganic nitrogen oxidation and organic carbon production in batch and chemostat cultures of marine nitrifying bacteria. Arch Microbiol. 1985;142:45–50.CAS 
Article 

Google Scholar 
53.Lehtovirta-Morley LE, Ross J, Hink L, Weber EB, Gubry-Rangin C, Thion C, et al. Isolation of ‘Candidatus Nitrosocosmicus franklandus’, a novel ureolytic soil archaeal ammonia oxidiser with tolerance to high ammonia concentration. FEMS Microbiol Ecol. 2016;92:fiw057.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
54.Kits KD, Sedlacek CJ, Lebedeva EV, Han P, Bulaev A, Pjevac P, et al. Kinetic analysis of a complete nitrifier reveals an oligotrophic lifestyle. Nature. 2017;549:269–72.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Hink L, Gubry-Rangin C, Nicol GW, Prosser JI. The consequences of niche and physiological differentiation of archaeal and bacterial ammonia oxidisers for nitrous oxide emissions. ISME J. 2018;12:1084–93.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Shen JP, Zhang LM, Zhu YG, Zhang JB, He JZ. Abundance and composition of ammonia-oxidizing bacteria and ammonia-oxidizing archaea communities of an alkaline sandy loam. Environ Microbiol. 2008;10:1601–11.CAS 
PubMed 
Article 

Google Scholar 
57.Jia Z, Conrad R. Bacteria rather than archaea dominate microbial ammonia oxidation in an agricultural soil. Environ Microbiol. 2009;11:1658–71.CAS 
PubMed 
Article 

Google Scholar 
58.Millero FJ, Feistel R, Wright DG, McDougall TJ. The composition of Standard Seawater and the definition of the Reference-Composition Salinity Scale. Deep-Sea Res Part I-Oceanogr Res Pap. 2008;55:50–72.Article 

Google Scholar 
59.Mosier AC, Allen EE, Kim M, Ferriera S, Francis CA. Genome sequence of “Candidatus Nitrosopumilus salaria” BD31, an ammonia-oxidizing archaeon from the San Francisco bay estuary. J Bacteriol. 2012;194:2121–2.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Matsutani N, Nakagawa T, Nakamura K, Takahashi R, Yoshihara K, Tokuyama T. Enrichment of a novel marine ammonia-oxidizing archaeon obtained from sand of an eelgrass zone. Microbes Environ. 2011;26:23–29.PubMed 
Article 
PubMed Central 

Google Scholar 
61.Park BJ, Park SJ, Yoon DN, Schouten S, Damste JSS, Rhee SK. Cultivation of autotrophic ammonia-oxidizing archaea from marine sediments in coculture with sulfur-oxidizing bacteria. Appl Environ Micro. 2010;76:7575–87.CAS 
Article 

Google Scholar 
62.Parada AE, Fuhrman JA. Marine archaeal dynamics and interactions with the microbial community over 5 years from surface to seafloor. ISME J. 2017;11:2510–25.PubMed 
PubMed Central 
Article 

Google Scholar 
63.Wu YJ, Whang LM, Fukushima T, Chang SH. Responses of ammonia-oxidizing archaeal and betaproteobacterial populations to wastewater salinity in a full-scale municipal wastewater treatment plant. J Biosci Bioeng. 2013;115:424–32.CAS 
PubMed 
Article 

Google Scholar 
64.Cardarelli EL, Bargar JR, Francis CA. Diverse Thaumarchaeota dominate subsurface ammonia-oxidizing communities in semi-arid floodplains in the western United States. Micro Ecol. 2020;80:778–92.CAS 
Article 

Google Scholar 
65.Wang HT, Gilbert JA, Zhu YG, Yang XR. Salinity is a key factor driving the nitrogen cycling in the mangrove sediment. Sci Total Environ. 2018;631-2:1342–9.Article 
CAS 

Google Scholar 
66.Oren A. Thermodynamic limits to microbial life at high salt concentrations. Environ Microbiol. 2011;13:1908–23.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Ito M, Guffanti AA, Oudega B, Krulwich TA. mrp, a multigene, multifunctional locus in Bacillus subtilis with roles in resistance to cholate and to Na+ and in pH homeostasis. J Bacteriol. 1999;181:2394–402.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Krulwich TA, Sachs G, Padan E. Molecular aspects of bacterial pH sensing and homeostasis. Nat Rev Microbiol. 2011;9:330–43.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
69.Swartz TH, Ikewada S, Ishikawa O, Ito M, Krulwich TA. The Mrp system: a giant among monovalent cation/proton antiporters? Extremophiles. 2005;9:345–54.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Oren A. Bioenergetic aspects of halophilism. Microbiol Mol Biol R. 1999;63:334–48.CAS 
Article 

Google Scholar 
71.Mackay MA, Norton RS, Borowitzka LJ. Organic osmoregulatory solutes in Cyanobacteria. J Gen Microbiol. 1984;130:2177–91.CAS 

Google Scholar 
72.Sadler M, McAninch M, Alico R, Hochstein LI. The intracellular Na+ and K+ composition of the moderately halophilic bacterium, Paracoccus halodenitrificans. Can J Microbiol. 1980;26:496–502.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Brown AD. Compatible solutes and extreme water stress in eukaryotic micro-organisms. Adv Micro Physiol. 1978;17:181–243.CAS 
Article 

Google Scholar 
74.Reed RH, Warr SRC, Richardson DL, Moore DJ, Stewart WDP. Multiphasic osmotic adjustment in a euryhaline cyanobacterium. FEMS Microbiol Lett. 1985;28:225–9.CAS 
Article 

Google Scholar 
75.Welsh DT, Herbert RA. Osmoadaptation of Thiocapsa roseopersicina OP-1 in batch and continuous culture: Accumulation of K+ and sucrose in response to osmotic stress. FEMS Microbiol Ecol. 1993;13:151–7.CAS 
Article 

Google Scholar 
76.Sauvage D, Hamelin J, Larher F. Glycine betaine and other structurally related compounds improve the salt tolerance of Rhizobium meliloti. Plant Sci Lett. 1983;31:291–302.CAS 
Article 

Google Scholar 
77.Campbell MA, Chain PSG, Dang H, Sheikh EI, Norton AF, Ward JM, et al. MG. Nitrosococcus watsonii sp. nov., a new species of marine obligate ammonia-oxidizing bacteria that is not omnipresent in the world’s oceans: calls tovalidate the names’Nitrosococcus halophilus’ and ‘Nitrosomonas mobilis’. FEMS Microbiol Ecol. 2011;76:39–48.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
78.Arguelles JC. Physiological roles of trehalose in bacteria and yeasts: a comparative analysis. Arch Microbiol. 2000;174:217–24.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Widderich N, Czech L, Elling FJ, Könneke M, Stöveken N, Pittelkow M, et al. Strangers in the archaeal world: osmostress-responsive biosynthesis of ectoine and hydroxyectoine by the marine thaumarchaeon Nitrosopumilus maritimus. Environ Microbiol. 2016;18:1227–48.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Bursy J, Pierik AJ, Pica N, Bremer E. Osmotically induced synthesis of the compatible solute hydroxyectoine is mediated by an evolutionarily conserved ectoine hydroxylase. J Biol Chem. 2007;282:31147–55.CAS 
PubMed 
Article 

Google Scholar 
81.Kol S, Merlo ME, Scheltema RA, de Vries M, Vonk RJ, Kikkert NA, et al. Metabolomic characterization of the salt stress response in Streptomyces coelicolor. Appl Environ Micro. 2010;76:2574–81.CAS 
Article 

Google Scholar 
82.Csonka LN. Physiological and genetic responses of bacteria to osmotic stress. Microbiol Rev. 1989;53:121–47.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
83.Saum SH, Sydow JF, Palm P, Pfeiffer F, Oesterhelt D, Muller V. Biochemical and molecular characterization of the biosynthesis of glutamine and glutamate, two major compatible solutes in the moderately halophilic bacterium Halobacillus halophilus. J Bacteriol. 2006;188:6808–15.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
84.Ventosa A, Nieto JJ, Oren A. Biology of moderately halophilic aerobic bacteria. Microbiol Mol Biol R. 1998;62:504–44.CAS 
Article 

Google Scholar 
85.Mahan MJ, Csonka LN. Genetic analysis of the proBA genes of Salmonella typhimurium: physical and genetic analysis of the cloned proB+A+ genes of Escherichia coli and of a mutant allele that confers proline overproduction and enhanced osmotolerance. J Bacteriol. 1983;156:1249–62.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
86.Empadinhas N, Pereira PJB, Albuquerque L, Costa J, Sa-Moura B, Marques AT, et al. Functional and structural characterization of a novel mannosyl-3-phosphoglycerate synthase from Rubrobacter xylanophilus reveals its dual substrate specificity. Mol Microbiol. 2011;79:76–93.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
87.Santos H, da Costa MS. Compatible solutes of organisms that live in hot saline environments. Environ Microbiol. 2002;4:501–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
88.Koops HP, Purkhold U, Pommerening-Röser A, Timmermann G, Wagner M. The Lithoautotrophic Ammonia-Oxidizing Bacteria. In: Dworkin M, Falkow S, Rosenberg E, Schleifer KH, Stackebrandt E (eds). The Prokaryotes: a Handbook on the Biology of Bacteria, 3rd edn. New York, USA: Springer Science+Business Media; 2006, pp 778–811. More

Our results suggest that certain plant functional traits can be retrieved from simple RGB photographs. The key for this trait retrieval are deep learning-based Convolutional Neural Networks in concert with Big Data derived from open and citizen science projects. Although these models are subject to some noise, there is a wealth of applications for this approach, such as global trait maps, monitoring of trait shifts in time and the identification of large-scale ecological gradients. This way, the problem of limited data that still impedes us to picture global gradients7 could be alleviated by harnessing the exponentially growing iNaturalist database16. The performance of the CNN models across traits varied strongly, but revealed a clear trend: As expected, the more a trait referred to morphological features, the more accurate the predictions were. The models of the Baseline setup explained a substantial amount of the variance for LA and GH, whereas traits that are partly related to morphological features, SLA and SM, show moderate (R^2) values. The predictions of LNC and SSD explain almost none of the variance, suggesting that tissue constituents are not directly expressed or related to visible features. It also indicates that the strong covariance among these traits13 does not suffice in supporting their predictions from photographs. If the RGB images do not contain relevant information, the model will minimise the prediction errors by the regression-to-the-mean bias seen in Fig. 3 (especially lower panels).The value of informing the model on the known trait variability through an augmentation of the target values (Plasticity setup) depended on the results of the Baseline setup of the trait. That is, the better the predictive performance of the Baseline setup, the more the trait seemed to profit from the Plasticity setup, rendering it ineffective for SSD, LNC and SM (Fig. 3). Refraining to cling to species mean values by considering within-species trait variation has been applied before using conventional methods26, but to our knowledge has never been tried in CNN models, yet. We expected that providing a distribution of trait values rather than a single mean for each species can convey to the CNN that different trait realisations can be expected from the same species. Obviously, this idea can only work if a distribution rather than a single value is available for each species. The SM dataset, for instance, contained only one image per species (Table 1). In this case, the Plasticity setup reduced the predictive performance compared to the Baseline setup, possibly by increasing the discrepancy to the true trait value. Since the traits with more accurate predictions profited most from the Plasticity setup, we assume that it supports the model in learning to predict the trait expressions themselves rather than extracting them indirectly through taxa-specific morphological features. Given that we restricted the number of images per species to a maximum of 8, while successful deep learning-based plant species identification usually requires thousands of images12,22, it seems very unlikely that the models inferred traits from species-specific plant features visible in the imagery. The latter was underpinned by our finding that the predictions of most traits are void of phylogenetic autocorrelation (Supplementary Information 1 and Supplementary Table 5), indicating that taxonomic relationships were insignificant for the trait predictions. The absence of phylogenetic autocorrelation of the prediction errors underlines that the models did not learn species-specific features for most traits, as this would imply similar trait predictions for related species.On the contrary, the SSD model predictions express a phylogenetic signal (Supplementary Information 1 and Supplementary Table 5). Trait expressions are generally clustered under similar climatic conditions29,30,31. Simultaneously, climatic conditions constrain the geographic distribution of species and growth forms29,30,31,32. The SSD dataset is biased towards woody species (Table 1), which confines it to a smaller taxonomic range. Hence, the phylogenetic signal of SSD might result from its phylogenetic clustering and predominant dependence on bioclimatic information rather than on RGB imagery (Fig. 2).Nevertheless, the benefit of including climate information on temperature, precipitation and their seasonality8,20,21,26 on predicting trait expressions was confirmed for all traits in this study, which underlines the value of contextual constraints in CNN models10 (see below for a discussion of the relevance of climate vs. image data). This also highlights the general flexibility of deep learning frameworks in adapting to variable input data from different scales and sensors10, which makes them a promising tool for ecological research. Our results particularly revealed this effect for SSD, SLA and LA, whereas it was smaller for GH, LNC and SM (Fig. 2). For the latter traits, other physical constraints such as disturbance33,34, seasonal variation35,36 and soil conditions6,26,28 come into consideration. As the focus of the Worldclim setup was to show that contextual cues can improve the trait retrieval from photographs rather than identifying the best set of auxiliary data, we confined the analysis to the most promising20,26 data source (WorldClim37).In the Worldclim setup, a single model accumulated knowledge about the trait learning task. Combined predictions of different CNN models, however, have shown to surpass the predictive performance of single CNN, e.g. in plant species identification tasks22. Each of the CNN models is prone to literally ‘look’ at different aspects of the learning task by focusing on different image features. Previous research also showed increased model performance in a trait prediction task in case of ensembles of regression and machine learning models26. Accordingly, and as demonstrated by our results, an ensemble approach seems promising to further enhance predictive performance of CNN models concerning trait prediction.The predictive performance of these Ensembles has shown to be reproducible with different sets of training images (cp. Figs. 2, 3, Supplementary Fig. 2). In our heterogeneous dataset, model performance was not affected by different growth forms, image qualities and image-target distances (Fig. 4). Different growth forms and plant functional types show their own characteristic trait spectrum13. Possibly, contextual cues within the image might have supported the CNN in inferring the plant functional type of a species, e.g. by a long-distance image being indicative of a tree species. Yet, since the majority of the images only shows single plant organs on close-up photographs (Fig. 4), we assume that the trait predictions are not confounded by the identification of growth forms. Furthermore, the absence of a phylogenetic signal in the prediction errors for most traits highlights the model’s ability to generalise by extracting trait information independent of taxonomic relationships, meaning that the models (except for SSD) did not learn species-specific mean trait expressions (see Supplementary Information 1 and Supplementary Table 5).Additionally, we disclosed the high generalisability of these results by investigating the datasets’ underlying distributions both spatially (Supplementary Fig. 3) and across biomes (Supplementary Fig. 4). Although some regions such as Central Europe and North America show higher data coverage, the datasets used for this study contain data across all biomes and regions on Earth. Therefore and despite this clustering, we expect the models to be applicable for all biomes around the globe. This was highlighted by an additional analysis showing that the predictive performance of the models is reasonably constant across biomes (Supplementary Fig. 5). As suggested by refs.38,39, we tolerated a certain spatial bias in favor of larger datasets. Although the SSD dataset predominantly contained woody species, neither of the six datasets expressed an exclusion of either growth form (Table 1, Fig. 4).The application of our models to global gradients of traits revealed that our GTDM indeed cover macroecological patterns and trends known from other publications: The latitudinal distributions could roughly be confirmed for GH26, LNC26,27 and SM6,8 (Fig. 5). Predicted trends for maximum leaf size hint at the applicability of our GTDM of LA40. The trait gradients for North America were confirmed for SLA6,8,26,27,28, SM6,8, LNC27 and SSD6 alike. Although based on different input data and modelling methods, the major global latitudinal gradients found in previous studies could be reproduced by our GTDM, which indicates the plausibility of the latter6,8,26,27.We further validated the GTDM quantitatively by means of correlations with other GTDM. Regarding SSD, the detected high correlations might be due to method similarity, as our GTDM product of SSD primarily builds upon climate data (see above), just as ref.6,26. For GH, SLA and SM, however, the high correlations are unlikely to result from climate data exclusively, as the explained variances of the RGB imagery ((R^2) of Plasticity setup) are higher than the additional contributions of the Worldclim setup (approx. 94%, 70% and 79% share of imagery on total explained variance, respectively; Supplementary Table 2). We decoupled the GTDM products from bioclimatic information in an additional analysis (Supplementary Fig. 6). Remarkably, the macroecological patterns could roughly be reproduced when the GTDM were based exclusively on RGB imagery, which shows that the bioclimatic information merely serves to smooth the macroecological trait patterns for most of the traits.Despite of all GTDM being at least partly build upon climate data and using trait data from the same source (TRY database), some GTDM of SLA and all GTDM of LNC vary strongly in their correlations (Supplementary Fig. 1). On the one hand, this might indicate that LNC varies at a different scale, e.g. on account of its seasonal and within-species variation35,36. On the other hand, other GTDM products are based on mean trait values weighted by abundances of plant functional types27,28 rather than single trait predictions, which might explain negative or non-significant correlations as well.Hence, a potential pitfall of the presented approach is that it is prone to express an observation bias, e.g. by citizen scientists only taking pictures of the most striking species. The sampling design underlying the GTDM does not account for plant community composition, meaning that we cannot tell if plant photographs at a certain location represent the actual community structure. Since many images contain more than one individual plant and different species, the CNN model predictions, however, might be based on more than one species, thereby partly resembling trait expressions of the community. The representativeness of trait data for plant communities, though, remains an ubiquitous problem of global trait maps, including those fully based on trait data from the TRY database7, since every available dataset is far from representing the actual plant community composition7. Hence, at present our GTDM have to be considered a plausibility check of the model predictions rather than an application-ready trait map product, not least because the sampling of images might not be representative of the respective plant community.Nevertheless, our results indicate that exploiting a Big Data approach is viable to reveal macroecological trait patterns, maybe because the most striking species of an ecosystem are likely to suffice in describing its functional footprint5. Since the strong growth of the iNaturalist database leads to a steadily increasing geographic coverage, the representativeness of these data is likely to grow as well. A recent study investigating the records of FloraIncognita12, a citizen-science and deep learning-based application for identifying plant species from photographs, suggested that such crowd-sourced data can reproduce primary dimensions in plant community composition41. This underlines the future potential of harnessing citizen science databases for identifying these patterns. Here, we demonstrated the practical value and applicability of the CNN models by producing GTDM that were able to reproduce known macroecological trait patterns while displaying one anticipated application of this method. Additionally, in these GTDM we bypass the issue of spatial error analysis that is challenging for most GTDM products26 by obtaining a potentially arbitrary number of observations in light of the strongly increasing number of observations in iNaturalist, almost rendering an extrapolation obsolete. Our GTDM are based on individual trait measurements rather than estimated on behalf of a small set of covariates, which is typical for climate-based GTDM26. Since plant traits vary strongly within species17,18,19, these measurements express a high practical relevance. As the iNaturalist plant photograph database is witnessing an exponential growth of data inputs, the potential of exploiting this data source for plant trait predictions is growing rapidly. It is worth mentioning that this approach also led to the first publication of a GTDM of mean LA (available for download under https://doi.org/10.6084/m9.figshare.13312040), since former publications were limited to modelling upper limits of LA based on climatic constraints40.Future studies building on our work, which benefit from the ever-growing data accumulation of both the iNaturalist and TRY database, might not face restrictions of dataset size as we did in our study. This might allow for more representative samples in future studies, e.g. enabling to stratify training data by species while simultaneously balancing the trait distribution. This might support a reduction of the regression-to-the-mean bias seen in all of the results (Fig. 3) by avoiding to overrepresent common trait expressions. Another possible approach would be to select only species with particularly low variability for model training, since it decreases the chance of incorporating images showing plants with an extreme trait expression that differ strongly from the chosen mean trait values from TRY. By that, we might be able to derive more reliable and accurate predictions in the context of weakly supervised learning by reducing noise in the training data.Although weakly supervised learning approaches generally have shown to be an effective way of compensating a shortage of individually labeled data42,43, an image dataset including in-field trait measurements under natural conditions representing the global trait spectrum would be necessary for a conclusive validation. In our study, it even remains unclear to what extent the trait values actually refer to the individual plant shown in an image, particularly as the images sometimes show more than one individual plant and more than one species (Supplementary Fig. 7). This may hinder the model from predicting a trait value corresponding to the dominant species in the image (but might also partly resemble the community composition, see above). Although we attempted to compensate the lack of a dataset that enables a conclusive validation by eliminating possible biases concerning image settings (Fig. 4), growth forms (Fig. 4), phylogenetic autocorrelation (Supplementary Information 1, Supplementary Table 5), predictions based only on climate data (Supplementary Fig. 6), predictive performance across biomes (Supplementary Fig. 5), a training dataset subject to limited geographic or climatic coverage (Supplementary Figs. 3, 4) and effects of a specific set of training data (Supplementary Fig. 2), we cannot conclusively prove that the model predictions are based on causal relationships. Our model results suggest that the trait predictions reflect the feature space of natural trait expressions (Fig. 3), but an in-depth analysis of the image features the models learned for inferring the respective traits will be necessary to rule out any possible biases in future studies. An explicit analysis might involve investigating which plant organs are relevant for the trait predictions by means of feature attribution techniques and could ultimately provide clear evidence. This may not only enable to build trust in such artificial intelligence (AI) models, but also to generate new knowledge from them in order to deepen our understanding of plant morphology and trait covariance.Nevertheless, this study can only be considered a pioneering work testing the feasibility of the approach, as application-ready models require a conclusive and explicit validation. A dataset enabling this has to incorporate image-trait pairs measured and photographed on the same individuals. One possible solution would be to generate a database of plant traits including respective photographs, which then can serve as a benchmark for future studies. More

Log normal concentrations in the environmentEmpirical concentration distributions in the environment exhibit a wide variability, ranging from familiar parametric forms, e.g., normal and log-normal distributions, to more complex shapes9,10,11,12. Overall, the concentration distribution reflects the different processes that govern environmental fate, e.g., emissions/formation, transport processes, source mixing, sinks and dependences on other variables and processes. In principle, we may therefore extract mechanistic insights into the lifecycle of an environmental component from analysis of the concentration distribution. Although such deconvolution is intangible in the general case, there are specific cases where there are known mechanistic origins for certain distributions. For instance, source mixing in the environment will tend the distribution towards normal.In this paper we present a model for how log-normal distributions may emerge in the environment (Fig. 1A). This topic has been addressed in a few earlier studies6,7,8. A joint argument in these is the implication of the ‘Multiplicative Central Limit Theorem’ (or ‘Gibrat’s law’), which applies to processes that include the product of many random variables. If we take the logarithm of the product of many random variables, we have the sum of many random variables, by which the Central Limit Theorem applies, which predict a normal distribution. Back-transforming the logarithm, yields a log-normal distribution. Although this heuristic argumentation does not provide specific physico/chemical mechanisms as to how the multiplication of multiple random variables may commonly appear in the environment, it does provide an intuitive framework.The present contribution is based on a physico/chemical first order exponential kinetics model (Fig. 1B). Starting with an ordinary differential equation (Eq. 1), we introduce a stochastically variable rate, resulting in a stochastic differential equation (Eq. 2). By solving the corresponding Fokker–Planck equation (FPE) (see Theory section and SI for details) we derive the log-normal distribution (Eq. 3), under certain assumptions about stochastic variability. Since exponential kinetics are commonly observed for many processes in the environment, this model provides a potential explanation for the relative ubiquity of lognormal distributions in the environment.Observations of lognormal-like concentration distributions in the environment include a wide array of components (e.g., aerosols; bulk organic carbon; heavy metals; organic pollutants; minerals; trace gases; inorganic ions; humic matter; biomarkers; radionuclides; microplastics; pharmaceuticals and pesticides) in various environmental sub-compartments (e.g., groundwater; watersheds; urban air; precipitation; rocks; stormwater; indoor air; soils; wastewater; marine sediments; landfills; lake sediments; peat; glaciers; background air; biota and humans)2,3,12,13,14,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38. However, even though log normality is relatively common in the environment, we emphasize that it by no means is generally observed10,11,12: see discussion in the next section.Ascribing a data set to a specific parametric form, e.g., log-normal, may provide mechanistic insights, but also typically simplifies statistical treatment11. Among multiple approaches, a straight-forward approach to test for log-normality is by log-transforming the data and then use one of the many well-established tests for normality, e.g., the Shapiro-Wilks test9,39,40. Specific aspects of analysis of log-normal data sets, e.g., the impact of non-detects or data averaging, or data sampling strategies, have been discussed in some detail in the literature12,41,42,43. However, even though we may attribute a certain data set to a parametric distribution, there are many empirical situations where the data appears to follow a complex/un-known distribution. Can we identify some general principles that may help disentangle observed variability, even beyond common parametric forms?Three mechanisms influencing concentration distributionsAny environmental system is governed by a multitude of different processes, occurring simultaneously. Fortunately, some mechanisms appears to be more prevalent than others, and we can make simplifications, allowing modeling, e.g., the currently presented model for log-normal dependence. To attempt a more general description for concentration distributions, it may prove useful to consider a few different principal mechanisms. Here we explore three such mechanisms:KineticsThe model presented here is a common first order approximation of kinetics, e.g., of source and sink processes (Fig. 2A–B). In reality, dynamical systems may be highly complex, beyond the present formulation with a constant deterministic part (μk, Eq. 2), with multiple coupled components, non-linearities, feedbacks and heterogenous interactions. How such more complex kinetics influence concentration distributions is a topic for further scientific discovery.Figure 2Numerical simulations (random sampling) of time-series concentration data and corresponding distributions, illustrating how different processes may influence concentration distributions. Panels (A)–(B): Log normally-distributed data (Eq. 3), e.g., influenced by exponential kinetics. Panels (C)–(D): Data reflecting the random fluctuation between three different log-normally distributed states (green, blue and red), yielding a multi-modal (mixture) distribution. Panels (E)–(F): Random mixing of the three distributions from panels (C)–(D), yielding a convolution distribution, tending towards a normal distribution. Panels (G)–(H): The log-normal distribution of panels (A)–(B), modulated by an oscillatory function (here sine function), illustrating the impact of, e.g. diurnal or seasonal cycles on observed distributions. The simulations were conducted using Matlab (ver. R2019b).Full size imageMixingThe concentration of a component in the environment is typically the sum of emissions from many sources. If these combine randomly, the concentration distribution approach normality (Fig. 2E–F). However, this type represents one limit of mixing—where the resulting distribution at each time point is the sum distribution (convolution) of many variables. Another limit is where the contributions from different sources are separated, such that the concentration at any given measurement point (e.g., time point) reflects one individual source (Fig. 2C–D). An example could be an atmospheric measurement site located between two cities: by wind direction each time point is mainly dominated by either city. In this limit the concentration data will tend to a multi-modal (or ‘mixture’) distribution.External variablesIn the model described in the Theory section the non-stochastic part of Eq. (2) is a constant (μk). But this parameter may also vary with external parameters, e.g., state variables. For instance, diurnal or seasonal variations of temperature, pressure or sunlight, including phase transitions, may strongly influence concentration distributions (Fig. 2G–H).These three classes of mechanisms influence the lifecycle of a component in the environment, but their relative importance regarding how concentration distributions are affected may be highly variable. For certain instances it may be possible to ascribe a certain parametric function to the data, e.g., a normal distribution for well-mixed sources or a log-normal suggesting a kinetic domain. However, for situations with more complex, multi-modal or broad distributions both statistical treatment and interpretations are more challenging. For instance, quite similarly looking distributions may emerge from rather different mechanisms, e.g., dependence on an externally oscillating parameter or a stochastically jumping mixture distribution (e.g., compare Fig. 2C–D with Fig. 2G–H).OutlookThe shape of empirical concentration distributions is determined by the lifecycle dynamics and thereby contain information of environmental fate. In this paper we show that log-normality suggests influence by first order exponential kinetics (Eqs. 1 and 5; Fig. 1A–B). Reflecting the overall non-equilibrium state of environmental systems, there are a number of different processes/mechanisms that may exhibit exponential kinetics and thereby drive concentrations towards log-normality, e.g., emissions/chemical formation (e.g., of primary or secondary pollutants), degradation/decomposition (e.g., chemical reactions or deposition), kinetic transfer between different pools/layers/reservoirs (e.g., between the troposphere and the stratosphere) or kinetic partitioning (e.g., between gas and liquid phases). Some of these processes, e.g., sink kinetics, are active throughout the lifecycle of a component, and thereby continuously push concentrations towards log-normality.Overall, the implications of log-normal concentration distributions span a broad spectrum of potential applications, ranging from data analysis methodologies, sampling strategies, emissions estimation, source apportionment calculations, modelling of chemical fate, estimation of toxic exposure to future climate scenarios9,25,29,41,42,43,44,45,46,47,48,49,50,51,52. Given the general mathematical formulation of the model (Eq. 2), we note that the applicability extends to log-normal concentration distributions also beyond Environmental systems53,54.A specific example, where mechanistic insights may be derived from analysis of concentration distributions is the estimation of lifetimes (τ = 1/k, Eq. 1)55,56. Our present findings suggest that observation of log-normally distributed concentration data, perhaps especially at remote or receptor sites, may indicate sink kinetics, and may therefore provide insights into the sink rate (Eqs. 4a–b and 5). Another specific situation where concentration distribution analysis is of importance is the common challenge of trend detection in environmental data11. For certain time-series data, log-transformation is commonly used prior to statistical analysis51,57. The present analysis may provide a physico-chemical motivation for such transformations, potentially contributing to interpretations. A third specific example regards the analysis of ratios of diagnostic markers, which are common tools to assess sources and processes across Earth and Environmental sciences. Examples include ratios of chemical markers and isotope signatures, where the latter commonly are reported as concentration ratios. The ratio of two log-normal random variables is another log-normal variable58. We may then predict that if the overall concentrations are log-normal, then, e.g., isotope signatures should also be log-normal, with potential implications for data analysis methodology and interpretation.Finally, we note that rates in environmental systems often are proportional to concentrations, e.g., reactants. As an example, the sink rate for CO is proportional to the OH concentration (see Theory). Consequently, if the concentration is log-normal, so is the rate. Furthermore, fluxes (e.g., emission or sink fluxes) are often defined as the product of a rate and a concentration (or at least the amount, by some proportional measure). Taken together, this suggests that log-normal concentration distributions may imply log-normal distributions also for rates and/or fluxes for certain systems, with implications for, e.g., emission estimation or box models47,49,59.All-in-all, this paper presents a mechanistic model for how log-normal concentrations may emerge in the environment, which in turn suggests an explanation for the relative abundance. More

1.Wilcove, D. S., McLellan, C. H. & Dobson, A. P. Habitat fragmentation in the temperate zone. Conserv. Biol. 6, 237–256 (1986).
Google Scholar 
2.Crooks, K. R. et al. Quantification of habitat fragmentation reveals extinction risk in terrestrial mammals. Proc. Natl. Acad. Sci. USA 114, 7635–7640 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2011).Article 

Google Scholar 
4.Okie, J. G. & Brown, J. H. Niches, body sizes, and the disassembly of mammal communities on the Sunda Shelf islands. Proc. Natl. Acad. Sci. USA 106, 19679–19684 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Viveiros De Castro, E. B. & Fernandez, F. A. S. Determinants of differential extinction vulnerabilities of small mammals in Atlantic forest fragments in Brazil. Biol. Conserv. 119, 73–80 (2004).Article 

Google Scholar 
6.Feeley, K. J. & Terborgh, J. W. Direct versus indirect effects of habitat reduction on the loss of avian species from tropical forest fragments. Anim. Conserv. 11, 353–360 (2008).Article 

Google Scholar 
7.Prugh, L. R., Hodges, K. E., Sinclair, A. R. E. & Brashares, J. S. Effect of habitat area and isolation on fragmented animal populations. Proc. Natl. Acad. Sci. USA 105, 20770–20775 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Crooks, K. R., Burdett, C. L., Theobald, D. M., Rondinini, C. & Boitani, L. Global patterns of fragmentation and connectivity of mammalian carnivore habitat. Philos. Trans. R. Soc. B Biol. Sci. 366, 2642–2651 (2011).Article 

Google Scholar 
9.Janecka, J. E. et al. Genetic differences in the response to landscape fragmentation by a habitat generalist, the bobcat, and a habitat specialist, the ocelot. Conserv. Genet. 17, 1093–1108 (2016).Article 

Google Scholar 
10.Creel, S. Four factors modifying the effect of competition on Carnivore population dynamics as illustrated by African wild dogs. Conserv. Biol. 15, 271–274 (2001).Article 

Google Scholar 
11.Crooks, K. R. Relative sensitivities of mammalian carnivores to habitat fragmentation. Conserv. Biol. 16, 488–502 (2002).Article 

Google Scholar 
12.Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343 (2014).13.Sanderson, C. E., Jobbins, S. E. & Alexander, K. A. With Allee effects, life for the social carnivore is complicated. Popul. Ecol. 56, 417–425 (2014).Article 

Google Scholar 
14.Kamler, J. F. et al. Cuon alpinus. The IUCN Red List of Threatened Species 2015: e.T5953A72477893. https://doi.org/10.2305/IUCN.UK.2015-4.RLTS.T5953A72477893.en (2015).15.Bashir, T., Bhattacharya, T., Poudyal, K., Roy, M. & Sathyakumar, S. Precarious status of the endangered dhole cuon alpinus in the high elevation eastern himalayan habitats of khangchendzonga biosphere reserve, Sikkim, India. Oryx 48, 125–132 (2014).Article 

Google Scholar 
16.Pal, R., Thakur, S., Arya, S., Bhattacharya, T. & Sathyakumar, S. Recent records of dhole (Cuon alpinus, Pallas 1811) in Uttarakhand, Western Himalaya, India. Mammalia 82, 614–617 (2018).Article 

Google Scholar 
17.Karanth, K. K., Nichols, J. D., UllasKaranth, K., Hines, J. E. & Christensen, N. L. The shrinking ark: Patterns of large mammal extinctions in India. Proc. R. Soc. B Biol. Sci. 277, 1971–1979 (2010).Article 

Google Scholar 
18.Keyghobadi, N. The genetic implications of habitat fragmentation for animals. Can. J. Zool. 85, 1049–1064 (2007).Article 

Google Scholar 
19.Lourenço, A., Álvarez, D., Wang, I. J. & Velo-Antón, G. Trapped within the city: Integrating demography, time since isolation and population-specific traits to assess the genetic effects of urbanization. Mol. Ecol. 26, 1498–1514 (2017).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
20.Ghaskadbi, P., Habib, B. & Qureshi, Q. A whistle in the woods: An ethogram and activity budget for the dhole in central India. J. Mammal. 97, 1745–1752 (2016).Article 

Google Scholar 
21.Karanth, K. U. & Sunquist, M. E. Behavioural correlates of predation by tiger (Panthera tigiris), leopard (Panthera pardus) and dhole (Cuon alpinus) in Nagarahole, India. J. Zool. Lond. 250, 255–265 (2000).Article 

Google Scholar 
22.Johnsingh, A. J. T. Reproduction and social behaviour of the dhole, Cuon alpinus (Canidae). J. Zool. 198, 443–463 (1982).Article 

Google Scholar 
23.Ngoprasert, D. & Gale, G. A. Tiger density, dhole occupancy, and prey occupancy in the human disturbed Dong Phayayen—Khao Yai Forest Complex, Thailand. Mammal. Biol. 95, 51–58 (2019).Article 

Google Scholar 
24.Selvan, K. M., Lyngdoh, S., Habib, B. & Gopi, G. V. Population density and abundance of sympatric large carnivores in the lowland tropical evergreen forest of Indian Eastern Himalayas. Mammal. Biol. 79, 254–258 (2014).Article 

Google Scholar 
25.Jenks, K. E. et al. Comparative movement analysis for a sympatric dhole and golden jackal in a human-dominated landscape. Raffles Bull. Zool. 63, 546–554 (2015).
Google Scholar 
26.Modi, S., Habib, B., Ghaskadbi, P., Nigam, P. & Mondol, S. Standardization and validation of a panel of cross-species microsatellites to individually identify the Asiatic wild dog (Cuon alpinus). PeerJ 7, e7453 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
27.Modi, S. et al. Noninvasive DNA-based species and sex identification of Asiatic wild dog (Cuonalpinus). J. Genet. 97, 1457–1461 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Iyengar, A. et al. Phylogeography, genetic structure, and diversity in the dhole (Cuon alpinus). Mol. Ecol. 14, 2281–2297 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Durbin, L., Venkataraman, A. & Hedges, S. D. J. Dhole (Cuon alpinus). In Status Survery and Conservation Action Plan. Canids: Foxes, Wolves, Jackals and Dogs (eds. Sillero-Zubiri, C., Hoffman, M. & Macdonald, D. W.) 210–219 (2004).30.Smith, O. & Wang, J. When can noninvasive samples provide sufficient information in conservation genetics studies?. Mol. Ecol. Resour. 14, 1011–1023 (2014).CAS 
PubMed 

Google Scholar 
31.Godinho, R. et al. Real-time assessment of hybridization between wolves and dogs: Combining noninvasive samples with ancestry informative markers. Mol. Ecol. Resour. 15, 317–328 (2015).CAS 
PubMed 
Article 

Google Scholar 
32.Venkataraman, A. B., Arumugam, R. & Sukumar, R. The foraging ecology of dhole (Cuon alpinus) in Mudumalai Sanctuary, southern India. J. Zool. 237, 543–561 (1995).Article 

Google Scholar 
33.Srivathsa, A., Karanth, K. U., Kumar, N. S. & Oli, M. K. Insights from distribution dynamics inform strategies to conserve a dhole Cuon alpinus metapopulation in India. Sci. Rep. 9, 1–12 (2019).ADS 
CAS 
Article 

Google Scholar 
34.Reddy, C. S., Sreelekshmi, S., Jha, C. S. & Dadhwal, V. K. National assessment of forest fragmentation in India: Landscape indices as measures of the effects of fragmentation and forest cover change. Ecol. Eng. 60, 453–464 (2013).Article 

Google Scholar 
35.Dutta, T., Sharma, S. & DeFries, R. Targeting restoration sites to improve connectivity in a tiger conservation landscape in India. PeerJ 6, e5587 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
36.Mondal, I., Habib, B., Talukdar, G. & Nigam, P. Triage of means: Options for conserving tiger corridors beyond designated protected lands in India. Front. Ecol. Evol. 4, 2–7 (2016).ADS 
Article 

Google Scholar 
37.Lowther, A. D., Harcourt, R. G., Goldsworthy, S. D. & Stow, A. Population structure of adult female Australian sea lions is driven by fine-scale foraging site fidelity. Anim. Behav. 83, 691–701 (2012).Article 

Google Scholar 
38.Marsden, C. D. et al. Spatial and temporal patterns of neutral and adaptive genetic variation in the endangered African wild dog (Lycaon pictus). Mol. Ecol. 21, 1379–1393 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Yumnam, B. et al. Prioritizing tiger conservation through landscape genetics and habitat linkages. PLoS ONE 9 (2014).40.Dutta, T. et al. Fine-scale population genetic structure in a wide-ranging carnivore, the leopard (Panthera pardus fusca) in central India. Divers. Distrib. 19, 760–771 (2013).Article 

Google Scholar 
41.Thatte, P. et al. Human footprint differentially impacts genetic connectivity of four wide-ranging mammals in a fragmented landscape. Divers. Distrib. 26, 299–314 (2020).Article 

Google Scholar 
42.Slatkin M. Gene flow and population structure. Ecol. Genet. 3–17 (1994).43.Bhandari, A., Ghaskadbi, P., Nigam, P. & Habib, B. Dhole pack size variation: Assessing effect of Prey availability and Apex predator. Ecol. Evol. 00, 1–12 (2021).
Google Scholar 
44.Davies, K. F., Margules, C. R. & Lawrence, J. F. Which traits of species predict population declines in experimental forest fragments?. Ecology 81, 1450–1461 (2000).Article 

Google Scholar 
45.Bhatt, S., Biswas, S., Karanth, K., Pandav, B. & Mondol, S. Genetic analyses reveal population structure and recent decline in leopards (Panthera pardus fusca) across the Indian subcontinent. PeerJ 8, e8482 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
46.Mondol, S., Karanth, K. U. & Ramakrishnan, U. Why the Indian subcontinent holds the key to global tiger recovery. PLoS Genet. 5 (2009).47.Nijman, V. et al. Illegal wildlife trade–surveying open animal markets and online platforms to understand the poaching of wild cats. Biodiversity 20, 58–61 (2019).Article 

Google Scholar 
48.Srivathsa, A., Sharma, S., Singh, P., Punjabi, G. A. & Oli, M. K. A strategic road map for conserving the Endangered dhole Cuon alpinus in India. Mammal. Rev. 50, 399–412 (2020).Article 

Google Scholar 
49.Richards, J. F. & Elizabeth, P. F. A century of land-use change in South and Southeast Asia. In Effects of land-use change on atmospheric CO2 concentrations 15–66 (1994).50.Goldewijk, K. K. & Ramankutty, N. Land use changes during the past 300 years (EOLSS Publisher Co., 2009).
Google Scholar 
51.Sharma, S. et al. Forest corridors maintain historical gene flow in a tiger metapopulation in the highlands of central India. Proc. R. Soc. B Biol. Sci. 280, 14 (2013).
Google Scholar 
52.Rangarajan, M. Fencing the forest: Conservation and ecological change in India’s central provinces 1860–1914 (1999).53.Gadgil, M. Towards an ecological history of India. Econ. Pol. Wkly. 20, 1909–1911 (2011).
Google Scholar 
54.Bebarta, K. C. Teak; ecology, silviculture, management and profitability (International Book Distributors, 1999).
Google Scholar 
55.Waples, R. S. & England, P. R. Estimating contemporary effective population size on the basis of linkage disequilibrium in the face of migration. Genetics 189, 633–644 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
56.Frankham, R., Bradshaw, C. J. A. & Brook, B. W. Genetics in conservation management: Revised recommendations for the 50/500 rules, Red List criteria and population viability analyses. Biol. Conserv. 170, 56–63 (2014).Article 

Google Scholar 
57.de Manuel, M. et al. The evolutionary history of extinct and living lions. Proc. Natl. Acad. Sci. USA 117, 10927–10934 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
58.Creel, S. Social organization and effective population size in carnivores. Behav. Ecol. Conserv. Biol. 264–265 (1998).59.Lande, R. & Barrowclough, G. Effective population size, genetic variation, and their use in population. Viable Popul. Conserv. 87–123 (1987).60.Neel, M. C. et al. Estimation of effective population size in continuously distributed populations: There goes the neighborhood. Heredity 111, 189–199 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Girman, D. J. et al. Patterns of population subdivision, gene flow and genetic variability in the African wild dog (Lycaon pictus). Mol. Ecol. 10, 1703–1723 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Sacks, B. N., Mitchell, B. R., Williams, C. L. & Ernest, H. B. Coyote movements and social structure along a cryptic population genetic subdivision. Mol. Ecol. 14, 1241–1249 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Stronen, A. V. et al. Population genetic structure of gray wolves (Canis lupus) in a marine archipelago suggests island-mainland differentiation consistent with dietary niche. BMC Ecol. 14, 1–9 (2014).Article 

Google Scholar 
64.Wolf, C. & Ripple, W. J. Range contractions of the world’s large carnivores. R. Soc. Open Sci. 4 (2017).65.Walston, J. et al. Bringing the tiger back from the brink-the six percent solution. PLoS Biol. 8, 6–9 (2010).Article 
CAS 

Google Scholar 
66.Champion, H. G. & Seth, S. K. A revised survey of the forest types of India. (Manager of Publications, 1968).67.Biswas, S. et al. A practive faeces collection protocol for multidisciplinary research in wildlife science. Curr. Sci. 116, 1878 (2019).CAS 
Article 

Google Scholar 
68.Hallsworth, J. E., Nomura, Y. & Iwahara, M. Ethanol-induced water stress and fungal growth. J. Ferment. Bioeng. 86, 451–456 (1998).CAS 
Article 

Google Scholar 
69.van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).Article 
CAS 

Google Scholar 
70.Broquet, T. & Petit, E. Quantifying genotyping errors in noninvasive population genetics. Mol. Ecol. 13, 3601–3608 (2004).CAS 
PubMed 
Article 

Google Scholar 
71.Kalinowski, S. T., Taper, M. L. & Marshall, T. C. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol. Ecol. 16, 1099–1106 (2007).PubMed 
Article 

Google Scholar 
72.Waits, L., Taberlet, P. & Luikart, G. Estimating the probability of identity among genotypesin natural populations: Cautions and guidelines. Mol. Ecol. 10, 249–256 (2001).CAS 
PubMed 
Article 

Google Scholar 
73.Valière, N. GIMLET: A computer program for analysing genetic individual identification data. Mol. Ecol. Notes 2, 377–379 (2002).
Google Scholar 
74.Excoffier, L., Laval, G. & Schneider, S. Arlequin (version 3.0): An integrated software package for population genetics data analysis. Evol. Bioinf. 1, 117693430500100 (2005).75.Pritchard, J. K. & Stephens, M. D. M. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
PubMed 
Article 

Google Scholar 
77.Earl, D. A. & vonHoldt, B. M. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).78.Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
79.Caye, K., Deist, T. M., Martins, H., Michel, O. & François, O. TESS3: Fast inference of spatial population structure and genome scans for selection. Mol. Ecol. Resour. 16, 540–548 (2016).CAS 
PubMed 
Article 

Google Scholar 
80.Jombart, T. et al. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
81.Jombart, T. Adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
82.Jombart, T., Devillard, S., Dufour, A. B. & Pontier, D. Revealing cryptic spatial patterns in genetic variability by a new multivariate method. Heredity 101, 92–103 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
83.Thioulouse, J., Chessel, D. & Champely, S. Multivariate analysis of spatial patterns: a unified approach to local and global structures. Environ. Ecol. Stat. 2, 1–14 (1995).Article 

Google Scholar 
84.Moran, P. The interpretation of statistical maps. J. R. Stat. Soc. Ser. B Stat. Methodol. 10, 243–251 (1948).85.Hedrick, P. W. A standardized genetic differentiation measure. Evolution 59, 1633–1638 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
86.Jost, L. GST and its relatives do not measure differentiation. Mol. Ecol. 17, 4015–4026 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
87.Keenan, K., Mcginnity, P., Cross, T. F., Crozier, W. W. & Prodöhl, P. A. DiveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol. Evol. 4, 782–788 (2013).Article 

Google Scholar 
88.Sundqvist, L., Keenan, K., Zackrisson, M., Prodöhl, P. & Kleinhans, D. Directional genetic differentiation and relative migration. Ecol. Evol. 6, 3461–3475 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
89.Ryman, N. & Leimar, O. GST is still a useful measure of genetic differentiation—A comment on Jost’s D. Mol. Ecol. 18, 2084–2087 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
90.Meirmans, P. G. & Hedrick, P. W. Assessing population structure: FST and related measures. Mol. Ecol. Resour. 11, 5–18 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
91.Wilson, G. A. & Rannala, B. Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163, 1177–1191 (2003).PubMed 
PubMed Central 
Article 

Google Scholar 
92.Faubet, P., Waples, R. S. & Gaggiotti, O. E. Evaluating the performance of a multilocus Bayesian method for the estimation of migration rates. Mol. Ecol. 16, 1149–1166 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
93.Do, C. et al. NeEstimator v2: Re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 14, 209–214 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
94.Waples, R. S. & Do, C. LDNE: A program for estimating effective population size from data on linkage disequilibrium. Mol. Ecol. Resour. 8, 753–756 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
95.Piry, S., Luikart, G. & Cornuet, J. M. BOTTLENECK: A computer program for detecting recent reductions in the effective population size using allele frequency data. J. Hered. 90, 502–503 (1999).Article 

Google Scholar 
96.Nikolic, N. & Chevalet, C. Detecting past changes of effective population size. Evol. Appl. 7, 663–681 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
97.Kimura, M. & Ohta, T. Stepwise mutation model and distribution of allelic frequencies in a finite population. Proc. Natl. Acad. Sci. USA 75, 2868–2872 (1978).ADS 
CAS 
PubMed 
PubMed Central 
MATH 
Article 

Google Scholar 
98.Ruiz-Garcia, M. et al. Determination of microsatellite DNA mutation rates, mutation models and mutation bias in four main Felidae lineages (European wild cat, F. silvestris; ocelot, Leopardus pardalis; puma, Puma concolor; jaguar, Panthera onca). In Molecular Population Genetics, Evolutionary Biology & Biological Conservation of Neotropical Carnivores. (Nova Science Publishers Inc., New York, 2013).99.Xu, X., Peng, M., Fang, Z. & Xu, X. The direction of microsatellite mutations is dependent upon allele length. Nat. Genet. 24, 396–399 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

Understanding the spatial patterns and drivers of animal movement is a crucial first step to controlling disease spread4. Our study provides novel information about where, how and when cattle move in a region beset by endemic pathogens2,39,40. Because contacts occur heterogeneously through time and space, interventions targeting areas and times of high contact risk could effectively break the chain of transmission across wide areas. We found that cattle herds had the highest probability of contact at dipping sites, far from their bomas, in small herds and during periods of low rainfall, indicating that transmission of all pathogens may be particularly elevated under these conditions (Figs. 5, 6). Nonetheless, cattle spent most of their time in other areas (i.e. near bomas or in grazing areas) where the direction and magnitude of effect of spatiotemporal scale on contact rates varies. This suggests that interventions for different pathogens in these systems will likely require a consideration of scale of transmission and be tailored to particular pathogens. Overall, our study provides a framework for risk-based livestock disease control approaches for the most dominant management systems in sub-Saharan Africa.Daily movement patterns of cattle in pastoral and agropastoral settings in sub-Saharan Africa largely reflect the distribution of shared resources, which determines the distance animals move each day and the probability of contacting each other. Our results are similar to those reported in other regions of Africa, suggesting broadly comparable patterns of daily displacement. For instance, cattle in our agropastoral study area travel to grazing, watering and dipping locations that are ~ 4 km from their bomas and primarily during daylight hours (Fig. 2). Similarly, in Kenya, cattle in the pastoral Mara and Ol Pajeta regions move less than 6 km from their bomas and movements peak around 12:00–14:00 h each day9,41. Despite the predominance of short-distance daily movements, we observed occasional long-distance movements (i.e. up to 12 km), particularly by larger herds. Transhumant cattle in Cameroon also moved up to 23 km/day for short periods, while relocating to seasonal grazing areas on the edge of the Sahel, though in most observations (86%) they moved less than 5 km/day8. Although we observed no contacts among cattle from bomas  > 17 km apart (Supplementary Fig. S5), regardless of how contact was defined, infrequent long-distance movements by large herds may provide a conduit for disease transmission between villages42. Indeed, larger herds actually had a lower relative probability of contact across spatiotemporal scales (Fig. 5), which may reflect the fact that large herds were more likely to move to areas away from other collared cattle, either because they were moving outside the study area, or because they had exclusive use of particular areas, whereas smaller herds that were mostly moved around bomas mixed more frequently. While interventions (e.g. vaccination or quarantine) targeting small herds would address local disease events, particularly within villages, halting larger-scale transmission requires an understanding of livestock pathways enabling inter-village connectivity and strategies tailored to herds driving these processes.A key difference between the movement of cattle in agropastoral and pastoral systems lies in the seasonal variation of daily movement. In our study, agropastoralists move their herds farther in the wet compared to the dry season, while the opposite has been reported for pastoralists8,9,41. During the wet season, agropastoralists cultivate crops near their homesteads, which increases competition for space and displaces cattle to reserved grazing areas far from cultivated land11. During the dry season, particularly in the early period, cattle graze harvested fields around the homestead and tend to move short distances each day. In our study, although individual herds travelled more (marginally) in the wet compared to the dry season, there were more contacts following low rainfall periods when resources were typically scarce (Fig. 5). Similarly, a previous study has shown that more villages were connected at shared resource areas during dry spells, which resulted in higher contacts11. This suggests a higher disease risk in the dry compared to wet seasons in agropastoral management systems.Translating movements into contact between individuals is challenging because the definition of a “contact” depends on the distance at which pathogens can travel in space, and the time period that pathogens survive, or mature to an infectious state, in the environment. Most studies that attempt to measure contact, however, focus only on a single scale. Here, we show that pairwise contact rates between cattle herds generally increase with broader spatiotemporal definitions of contact. Yet, there was no difference at spatial scales between 50 m, 100 m and 200 m for a temporal scale of one hour, suggesting these scales are functionally equivalent definitions of contact. Thus, we define “close contact” as proximity of livestock herds within 200 m in any given hour, which would be applicable to multiple disease systems and vital for understanding infectious disease spread in traditionally managed herds. However, given that herds tracked in our study ranged in size from 30 to 500 cattle, for households with herds of  More

Soil physicochemical propertiesThe test results of physicochemical factors of the soil are shown in Table 2. In the soil with 15-year vines, the average contents of TK and SK were highest and the contents of SOM and TN were lowest. In the soil with 5-year vines, the contents of XN and SK were relatively higher, and the soil pH was between 7.86 and 7.98, thus it is alkaline soil.Table 2 Determined results of soil physicochemical properties.Full size tableThe analysis of variance showed that grape planting year had significant effect on TK and SP (P  More

1.Palumbi, S. R. Humans as the world’s greatest evolutionary force. Science 293, 1786–1790 (2001).ADS 
PubMed 
Article 
CAS 

Google Scholar 
2.Hendry, A. P., Gotanda, K. M. & Svensson, E. I. Human Influences on Evolution, and the Ecological and Societal Consequences (The Royal Society, 2017).Book 

Google Scholar 
3.Otto, S. P. Adaptation, speciation and extinction in the Anthropocene. Proc. R. Soc. B 285, 20182047 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Dynesius, M. & Nilsson, C. Fragmentation and flow regulation of river systems in the northern third of the world. Science 266, 753–762 (1994).ADS 
PubMed 
Article 
CAS 

Google Scholar 
5.Gibson, L., Wilman, E. N. & Laurance, W. F. How green is ‘green’energy?. Trends Ecol. Evol. 32, 922–935 (2017).PubMed 
Article 

Google Scholar 
6.Calles, O. & Greenberg, L. Connectivity is a two-way street—the need for a holistic approach to fish passage problems in regulated rivers. River Res. Appl. 25, 1268–1286 (2009).Article 

Google Scholar 
7.Poff, N. L. et al. The natural flow regime. Bioscience 47, 769–784 (1997).Article 

Google Scholar 
8.Haraldstad, T. et al. Anthropogenic and natural size-related selection act in concert during brown trout (Salmo trutta) smolt river descent. Hydrobiologia, 1–14 (2020).9.Limburg, K. E. & Waldman, J. R. Dramatic declines in North Atlantic diadromous fishes. Bioscience 59, 955–965 (2009).Article 

Google Scholar 
10.Belletti, B. et al. More than one million barriers fragment Europe’s rivers. Nature 588, 436–441 (2020).ADS 
PubMed 
Article 
CAS 

Google Scholar 
11.Klemetsen, A. et al. Atlantic salmon Salmo salar L., brown trout Salmo trutta L. and Arctic charr Salvelinus alpinus (L.): a review of aspects of their life histories. Ecol. Freshw. Fish 12, 1–59 (2003).Article 

Google Scholar 
12.Thorstad, E. B., Økland, F., Aarestrup, K. & Heggberget, T. G. Factors affecting the within-river spawning migration of Atlantic salmon, with emphasis on human impacts. Rev. Fish Biol. Fish. 18, 345–371 (2008).Article 

Google Scholar 
13.Parrish, D. L., Behnke, R. J., Gephard, S. R., McCormick, S. D. & Reeves, G. H. Why aren’t there more Atlantic salmon (Salmo salar)?. Can. J. Fish. Aquat. Sci. 55, 281–287 (1998).Article 

Google Scholar 
14.Larinier, M. Fish passage experience at small-scale hydro-electric power plants in France. Hydrobiologia 609, 97–108 (2008).Article 

Google Scholar 
15.Coutant, C. C. & Whitney, R. R. Fish behavior in relation to passage through hydropower turbines: a review. Trans. Am. Fish. Soc. 129, 351–380 (2000).Article 

Google Scholar 
16.Montèn, E. Fish and Turbines: Fish Injuries During Passage Through Power Station Turbines (Nordsteds Tryckeri, 1985).
Google Scholar 
17.Pracheil, B. M., DeRolph, C. R., Schramm, M. P. & Bevelhimer, M. S. A fish-eye view of riverine hydropower systems: the current understanding of the biological response to turbine passage. Rev. Fish Biol. Fisheries 26, 153–167 (2016).Article 

Google Scholar 
18.Calles, O., Rivinoja, P. & Greenberg, L. A Historical perspective on downstream passage at hydroelectric plants in swedish rivers. In: Ecohydraulics. Wiley (2013).19.Silva, A. T. et al. The future of fish passage science, engineering, and practice. Fish Fish. 19, 340–362 (2017).Article 

Google Scholar 
20.Noonan, M. J., Grant, J. W. A. & Jackson, C. D. A quantitative assessment of fish passage efficiency. Fish Fish. 13, 450–464 (2012).Article 

Google Scholar 
21.Scruton, D. A., McKinley, R. S., Kouwen, N., Eddy, W. & Booth, R. K. Improvement and optimization of fish guidance efficiency (FGE) at a behavioural fish protection system for downstream migrating Atlantic salmon (Salmo salar) smolts. River Res. Appl. 19, 605–617 (2003).Article 

Google Scholar 
22.Mallen-Cooper, M. & Brand, D. A. Non-salmonids in a salmonid fishway: what do 50 years of data tell us about past and future fish passage?. Fish. Manage. Ecol. 14, 319–332 (2007).Article 

Google Scholar 
23.Bunt, C., Castro-Santos, T. & Haro, A. Performance of fish passage structures at upstream barriers to migration. River Res. Appl. 28, 457–478 (2012).Article 

Google Scholar 
24.Haugen, T. O., Aass, P., Stenseth, N. C. & Vøllestad, L. A. Changes in selection and evolutionary responses in migratory brown trout following the construction of a fish ladder. Evol. Appl. 1, 319–335 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
25.Mallen-Cooper, M. & Stuart, I. G. Optimising Denil fishways for passage of small and large fishes. Fish. Manage. Ecol. 14, 61–71 (2007).Article 

Google Scholar 
26.Maynard, G. A., Kinnison, M. & Zydlewski, J. D. Size selection from fishways and potential evolutionary responses in a threatened Atlantic salmon population. River Res. Appl. 33, 1004–1015 (2017).Article 

Google Scholar 
27.Lothian, A. J. et al. Are we designing fishways for diversity? Potential selection on alternative phenotypes resulting from differential passage in brown trout. J Environ Manag 262, 110317 (2020).Article 

Google Scholar 
28.Haraldstad, T., Haugen, T. O., Kroglund, F., Olsen, E. M. & Höglund, E. Migratory passage structures at hydropower plants as potential physiological and behavioural selective agents. R. Soc. Open Sci. 6, 190 (2019).Article 

Google Scholar 
29.Conrad, J. L., Weinersmith, K. L., Brodin, T., Saltz, J. B. & Sih, A. Behavioural syndromes in fishes: a review with implications for ecology and fisheries management. J. Fish Biol. 78, 395–435 (2011).PubMed 
Article 
CAS 

Google Scholar 
30.Dochtermann, N. A., Schwab, T. & Sih, A. The contribution of additive genetic variation to personality variation: heritability of personality. Proc. R. Soc. B: Biol. Sci. 282, 20142201 (2015).Article 

Google Scholar 
31.Réale, D. et al. Personality and the emergence of the pace-of-life syndrome concept at the population level. Philos. Trans. R. Soc. B: Biol. Sci. 365, 4051–4063 (2010).Article 

Google Scholar 
32.Haraldstad, T., Höglund, E., Kroglund, F., Haugen, T. O. & Forseth, T. Common mechanisms for guidance efficiency of descending Atlantic salmon smolts in small and large hydroelectric power plants. River Res. Appl. 34, 1179–1185 (2018).Article 

Google Scholar 
33.Larsen, M. H., Thorn, A. N., Skov, C. & Aarestrup, K. Effects of passive integrated transponder tags on survival and growth of juvenile Atlantic salmon Salmo salar. Anim. Biotelem. 1, 19 (2013).Article 

Google Scholar 
34.Vollset, K. W. et al. Systematic review and meta-analysis of PIT tagging effects on mortality and growth of juvenile salmonids. Rev. Fish Biol. Fish, 1–16 (2020).35.Adriaenssens, B. & Johnsson, J. I. Natural selection, plasticity and the emergence of a behavioural syndrome in the wild. Ecol. Lett. 16, 47–55 (2013).PubMed 
Article 

Google Scholar 
36.Dingemanse, N. J. et al. Behavioural syndromes differ predictably between 12 populations of three-spined stickleback. J. Anim. Ecol. 76, 1128–1138 (2007).PubMed 
Article 

Google Scholar 
37.Larsen, M. H. et al. Effects of emergence time and early social rearing environment on behaviour of Atlantic salmon: consequences for juvenile fitness and smolt migration. PLoS ONE 10, e0119127 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
38.Castanheira, M. F., Herrera, M., Costas, B., Conceição, L. E. & Martins, C. I. Can we predict personality in fish? Searching for consistency over time and across contexts. PLoS ONE 8, e62037 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
39.Huntingford, F. et al. Coping strategies in a strongly schooling fish, the common carp Cyprinus carpio. J. Fish Biol. 76, 1576–1591 (2010).PubMed 
Article 
CAS 

Google Scholar 
40.Brown, C., Jones, F. & Braithwaite, V. Correlation between boldness and body mass in natural populations of the poeciliid Brachyrhaphis episcopi. J. Fish Biol. 71, 1590–1601 (2007).Article 

Google Scholar 
41.R Development Core Team. R: A language and environment for statistical computing.). R Foundation for Statistical Computing (2016).42.Akaike, H. A. new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716–723 (1974).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
43.Anderson, D. R. Model-Based Interference in the Life Sciences: A Primer on Evidence (Springer, 2008).MATH 
Book 

Google Scholar 
44.Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.17. https://CRAN.R-project.org/package=MuMIn (2020).45.Brunham, A. & Anderson D, R. Model selection and multimodel inference: A practical information-theoretic approach. 2nd edn (Springer-Verlag, New York 2002).46.Fjeldstad, H. P., Alfredsen, K. & Boissy, T. Optimising Atlantic salmon smolt survival by use of hydropower simulation modelling in a regulated river. Fish. Manage. Ecol. 21, 22–31 (2014).Article 

Google Scholar 
47.Calles, O. et al. Anordning för upp- och nedströmspassage av fisk vid vattenanläggningar (2013).48.Larinier, M., Travade, F. The development and evaluation of downstream bypasses for juvenile salmonids at small hydroelectric plants in France. Innov. Fish Passage Technol. 25–42 (1999).49.Turnpenny, A. W. H., O`Keeffe, N. Screening for intake and Outfalls: a best practice guide (2005).50.Réale, D., Reader, S. M., Sol, D., McDougall, P. T. & Dingemanse, N. J. Integrating animal temperament within ecology and evolution. Biol. Rev. 82, 291–318 (2007).PubMed 
Article 

Google Scholar 
51.Taylor, M. K. & Cooke, S. J. Repeatability of movement behaviour in a wild salmonid revealed by telemetry. J. Fish Biol. 84, 1240–1246 (2014).PubMed 
Article 
CAS 

Google Scholar 
52.Odling-Smee, L. & Braithwaite, V. A. The role of learning in fish orientation. Fish Fish. 4, 235–246 (2003).Article 

Google Scholar 
53.Lucon-Xiccato, T., Montalbano, G. & Bertolucci, C. Personality traits covary with individual differences in inhibitory abilities in 2 species of fish. Curr. Zool. 66, 187–195 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
54.Endler, J. A. Natural Selection in the Wild (Princeton University Press, 1986).
Google Scholar 
55.Bell, A. M., Hankison, S. J. & Laskowski, K. L. The repeatability of behaviour: a meta-analysis. Anim. Behav. 77, 771–783 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
56.Sih, A., Bell, A. & Johnson, J. C. Behavioral syndromes: an ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378 (2004).PubMed 
Article 

Google Scholar 
57.Wuerz, Y. & Krüger, O. Personality over ontogeny in zebra finches: long-term repeatable traits but unstable behavioural syndromes. Front. Zool. 12, S9 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
58.Wolf, M. & Weissing, F. J. Animal personalities: consequences for ecology and evolution. Trends Ecol. Evol. 27, 452–461 (2012).PubMed 
Article 

Google Scholar 
59.Cordero-Rivera, A. Behavioral diversity (ethodiversity): a neglected level in the study of biodiversity. Front. Ecol. Evol. 5, 7 (2017).ADS 
Article 

Google Scholar 
60.Biro, P. A. & Post, J. R. Rapid depletion of genotypes with fast growth and bold personality traits from harvested fish populations. Proc. Natl. Acad. Sci. 105, 2919–2922 (2008).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Uusi-Heikkilä, S., Wolter, C., Klefoth, T. & Arlinghaus, R. A behavioral perspective on fishing-induced evolution. Trends Ecol. Evol. 23, 419–421 (2008).PubMed 
Article 

Google Scholar 
62.Cooke, S. J., Suski, C. D., Ostrand, K. G., Wahl, D. H. & Philipp, D. P. Physiological and behavioral consequences of long-term artificial hselection for vulnerability to recreational angling in a teleost fish. Physiol. Biochem. Zool. 80, 480–490 (2007).PubMed 
Article 

Google Scholar  More

Host and mutual symbionts decline at different rates following consecutive cyclones and bleachingBefore and after disturbances, we surveyed Acropora corals known to host Gobiodon coral gobies along line (30 m) and cross (two 4-m by 1-m belt) transects. In February 2014, prior to cyclones and bleaching events, most of these Acropora corals were inhabited by Gobiodon coral gobies. Gobies were not found in corals under 7-cm average diameter, therefore we only sampled bigger corals. The vast majority of transects (95%) had Acropora corals. On average there were 3.24 ± 0.25 (mean ± standard error) Acropora coral species per transect (Fig. 2a) and a total of 17 species were observed among all 2014 transects. Average coral diameter was 25.4 ± 1.0 cm (Fig. 2b), with some corals reaching over 100 cm. Only 4.1 ± 1.4% of corals lacked any goby inhabitants (Fig. 2c). On average there were 3.37 ± 0.26 species of gobies per transect (Fig. 2d) and a total of 13 species among all 2014 transects. In each occupied coral there were 2.20 ± 0.14 gobies (Fig. 2e), with a maximum of 11 individuals of the same species.Figure 2Effects of consecutive climate disturbances on coral and goby populations. Changes in Acropora (a) richness (n = 279), and (b) average diameter (n = 244), (c) percent goby occupancy (n = 244) and Gobiodon (d) richness (n = 279), and (e) group size (n = 230) per transect (n = sample size per variable) before and after each cyclone (black cyclone symbols) and after two consecutive heatwaves/bleaching events (white coral symbols) around Lizard Island, Great Barrier Reef, Australia. Error bars are standard error. Fish and coral symbols above each graph illustrate the change in means for each variable among sampling events from post-hoc tests. Figures were illustrated in R (v3.5.2)33 and Microsoft Office PowerPoint 2016.Full size imageIn January–February 2015, 9 months after Cyclone Ita (category 4) struck from the north (Supplementary Fig. 1), follow-up surveys revealed no changes to coral richness (p = 0.986, see Supplementary Table 1 for all statistical outputs) relative to February 2014, but corals were 19% smaller (p  More