Philippa Kaur

Flemming, H. C. & Wuertz, S. Bacteria and archaea on Earth and their abundance in biofilms. Nat. Rev. Microbiol. 17, 247–260 (2019).CAS 
PubMed 
Article 

Google Scholar 
Bulgarelli, D., Schlaeppi, K., Spaepen, S., Ver Loren van Themaat, E. & Schulze-Lefert, P. Structure and functions of the bacterial microbiota of plants. Annu Rev. Plant Biol. 64, 807–838 (2013).CAS 
PubMed 
Article 

Google Scholar 
Turnbaugh, P. J. et al. The human microbiome project. Nature 449, 804–810 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Venturelli, O. S. et al. Deciphering microbial interactions in synthetic human gut microbiome communities. Mol. Syst. Biol. 14, e8157 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Foster, K. R. & Bell, T. Competition, not cooperation, dominates interactions among culturable microbial species. Curr. Biol. 22, 1845–1850 (2012).CAS 
PubMed 
Article 

Google Scholar 
Helfrich, E. J. N. et al. Bipartite interactions, antibiotic production and biosynthetic potential of the Arabidopsis leaf microbiome. Nat. Microbiol. 3, 909–919 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Coyte, K. Z. & Rakoff-Nahoum, S. Understanding competition and cooperation within the mammalian gut microbiome. Curr. Biol. 29, 538–544 (2019).Article 
CAS 

Google Scholar 
Turner, T. R. et al. Comparative metatranscriptomics reveals kingdom level changes in the rhizosphere microbiome of plants. ISME J. 7, 2248–2258 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Trivedi, P., Leach, J. E., Tringe, S. G., Sa, T. & Singh, B. K. Plant–microbiome interactions: from community assembly to plant health. Nat. Rev. Microbiol. 18, 607–621 (2020).CAS 
PubMed 
Article 

Google Scholar 
Müller, D. B., Vogel, C., Bai, Y. & Vorholt, J. A. The plant microbiota: systems-level insights and perspectives. Annu. Rev. Genet. 50, 211–234 (2016).PubMed 
Article 
CAS 

Google Scholar 
Lugtenberg, B. & Kamilova, F. Plant-growth-promoting Rhizobacteria. Annu. Rev. Microbiol. 63, 541–556 (2009).CAS 
PubMed 
Article 

Google Scholar 
Berendsen, R. L., Pieterse, C. M. J. & Bakker, P. A. H. M. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).CAS 
PubMed 
Article 

Google Scholar 
Innerebner, G., Knief, C. & Vorholt, J. A. Protection of Arabidopsis thaliana against leaf-pathogenic Pseudomonas syringae by Sphingomonas strains in a controlled model system. Appl. Environ. Microbiol. 77, 3202–3210 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shekhawat, K. et al. Root endophyte induced plant thermotolerance by constitutive chromatin modification at heat stress memory gene loci. EMBO Rep. 22, e51049 (2021).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
Bodenhausen, N., Bortfeld-Miller, M., Ackermann, M. & Vorholt, J. A. A synthetic community approach reveals plant genotypes affecting the phyllosphere microbiota. PLoS Genet. 10, e1004283 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Reisberg, E. E., Hildebrandt, U., Riederer, M. & Hentschel, U. Distinct phyllosphere bacterial communities on Arabidopsis wax mutant leaves. PLoS ONE 8, e78613 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kniskern, J. M., Traw, M. B. & Bergelson, J. Salicylic acid and jasmonic acid signaling defense pathways reduce natural bacterial diversity on Arabidopsis thaliana. Mol. Plant Microbe Interact. 20, 1512–1522 (2007).CAS 
PubMed 
Article 

Google Scholar 
Pfeilmeier, S. et al. The plant NADPH oxidase RBOHD is required for microbiota homeostasis in leaves. Nat. Microbiol. 6, 852–864 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, T. et al. A plant genetic network for preventing dysbiosis in the phyllosphere. Nature 580, 653–657 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hassani, M. A., Duran, P. & Hacquard, S. Microbial interactions within the plant holobiont. Microbiome 6, 58 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Lidicker, W. Z. Clarification of interactions in ecological systems. Bioscience 29, 475–477 (1979).Article 

Google Scholar 
Schlechter, R. O., Miebach, M. & Remus-Emsermann, M. N. P. Driving factors of epiphytic bacterial communities: a review. J. Adv. Res. 19, 57–65 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Faust, K. & Raes, J. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).CAS 
PubMed 
Article 

Google Scholar 
Grosskopf, T. & Soyer, O. S. Synthetic microbial communities. Curr. Opin. Microbiol. 18, 72–77 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Blair, P. M. et al. Exploration of the biosynthetic potential of the Populus microbiome. mSystems 3, e00045-00018 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Suda, W., Nagasaki, A. & Shishido, M. Powdery mildew-infection changes bacterial community composition in the phyllosphere. Microbes Environ. 24, 217–223 (2009).PubMed 
Article 

Google Scholar 
Manching, H. C., Balint-Kurti, P. J. & Stapleton, A. E. Southern leaf blight disease severity is correlated with decreased maize leaf epiphytic bacterial species richness and the phyllosphere bacterial diversity decline is enhanced by nitrogen fertilization. Front. Plant Sci. 5, 403 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Agler, M. T. et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 14, 100235 (2016).Article 
CAS 

Google Scholar 
Layeghifard, M., Hwang, D. M. & Guttman, D. S. Disentangling interactions in the microbiome: a network perspective. Trends Microbiol. 25, 217–228 (2017).CAS 
PubMed 
Article 

Google Scholar 
Faust, K. et al. Microbial co-occurrence relationships in the human microbiome. PLoS Comput. Biol. 8, e1002606 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Carr, A., Diener, C., Baliga, N. S. & Gibbons, S. M. Use and abuse of correlation analyses in microbial ecology. ISME J. 13, 2647–2655 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Vorholt, J. A., Vogel, C., Carlström, C. I. & Müller, D. B. Establishing causality: opportunities of synthetic communities for plant microbiome research. Cell Host Microbe 22, 142–155 (2017).CAS 
PubMed 
Article 

Google Scholar 
Bai, Y. et al. Functional overlap of the Arabidopsis leaf and root microbiota. Nature 528, 364–369 (2015).CAS 
PubMed 
Article 

Google Scholar 
Knief, C., Frances, L. & Vorholt, J. A. Competitiveness of diverse Methylobacterium strains in the phyllosphere of Arabidopsis thaliana and identification of representative models, including M. extorquens PA1. Microb. Ecol. 60, 440–452 (2010).PubMed 
Article 

Google Scholar 
Fan, J., Crooks, C. & Lamb, C. High-throughput quantitative luminescence assay of the growth in planta of Pseudomonas syringae chromosomally tagged with Photorhabdus luminescens luxCDABE. Plant J. 53, 393–399 (2008).CAS 
PubMed 
Article 

Google Scholar 
Carlström, C. I. et al. Synthetic microbiota reveal priority effects and keystone strains in the Arabidopsis phyllosphere. Nat. Ecol. Evol. 3, 1445–1454 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Vogel, C. M., Potthoff, D. M., Schäfer, M., Barandun, N. & Vorholt, J. A. Protective role of the Arabidopsis leaf microbiota against a bacterial pathogen. Nat. Microbiol. 6, 1537–1548 (2021).CAS 
PubMed 
Article 

Google Scholar 
Chen, I.-M. A. et al. The IMG/M data management and analysis system v.6.0: new tools and advanced capabilities. Nucleic Acids Res. 49, 751–763 (2020).Article 
CAS 

Google Scholar 
Ortiz, A., Vega, N. M., Ratzke, C. & Gore, J. Interspecies bacterial competition regulates community assembly in the C. elegans intestine. ISME J. 15, 2131–2145 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Goberna, M. & Verdú, M. Predicting microbial traits with phylogenies. ISME J. 10, 959–967 (2016).PubMed 
Article 

Google Scholar 
Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).Article 

Google Scholar 
Cahill, J. F., Kembel, S. W., Lamb, E. G. & Keddy, P. A. Does phylogenetic relatedness influence the strength of competition among vascular plants? Perspect. Plant Ecol. 10, 41–50 (2008).Article 

Google Scholar 
Maherali, H. & Klironomos, J. N. Influence of phylogeny on fungal community assembly and ecosystem functioning. Science 316, 1746–1748 (2007).CAS 
PubMed 
Article 

Google Scholar 
Duncan, R. P. & Williams, P. A. Ecology – Darwin’s naturalization hypothesis challenged. Nature 417, 608–609 (2002).CAS 
PubMed 
Article 

Google Scholar 
Slingsby, J. A. & Verboom, G. A. Phylogenetic relatedness limits co-occurrence at fine spatial scales: evidence from the schoenoid sedges (Cyperaceae: Schoeneae) of the Cape Floristic Region, South Africa. Am. Nat. 168, 14–27 (2006).PubMed 
Article 

Google Scholar 
Mayfield, M. M. & Levine, J. M. Opposing effects of competitive exclusion on the phylogenetic structure of communities. Ecol. Lett. 13, 1085–1093 (2010).PubMed 
Article 

Google Scholar 
Teixeira, P. J. P. L., Colaianni, N. R., Fitzpatrick, C. R. & Dangl, J. L. Beyond pathogens: microbiota interactions with the plant immune system. Curr. Opin. Microbiol. 49, 7–17 (2019).CAS 
PubMed 
Article 

Google Scholar 
Maier, B. A. et al. A general non-self response as part of plant immunity. Nat. Plants 7, 696–705 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Friedman, J., Higgins, L. M. & Gore, J. Community structure follows simple assembly rules in microbial microcosms. Nat. Ecol. Evol. 1, 0109 (2017).Article 

Google Scholar 
Kehe, J. et al. Positive interactions are common among culturable bacteria. Sci. Adv. 7, eabi7159 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lindow, S. E. & Brandl, M. T. Microbiology of the phyllosphere. Appl. Environ. Microbiol. 69, 1875–1883 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Remus-Emsermann, M. N. P. et al. Spatial distribution analyses of natural phyllosphere-colonizing bacteria on Arabidopsis thaliana revealed by fluorescence in situ hybridization. Environ. Microbiol. 16, 2329–2340 (2014).CAS 
PubMed 
Article 

Google Scholar 
Billick, I. & Case, T. J. Higher-order interactions in ecological communities – what are they and how can they be detected. Ecology 75, 1529–1543 (1994).Article 

Google Scholar 
Grilli, J., Barabas, G., Michalska-Smith, M. J. & Allesina, S. Higher-order interactions stabilize dynamics in competitive network models. Nature 548, 210–213 (2017).CAS 
PubMed 
Article 

Google Scholar 
Levine, J. M., Bascompte, J., Adler, P. B. & Allesina, S. Beyond pairwise mechanisms of species coexistence in complex communities. Nature 546, 56–64 (2017).CAS 
PubMed 
Article 

Google Scholar 
Sundarraman, D. et al. Higher-order interactions dampen pairwise competition in the zebrafish gut microbiome. mBio 11, e01667-20 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Morris, C. in Encyclopedia for Life Sciences (National Publishing Group, 2002).Raaijmakers, J. M. & Mazzola, M. Diversity and natural functions of antibiotics produced by beneficial and plant pathogenic bacteria. Annu. Rev. Phytopathol. 50, 403–424 (2012).CAS 
PubMed 
Article 

Google Scholar 
Iversen, O. J. & Grov, A. Studies on lysostaphin – separation and characterization of 3 enzymes. Eur. J. Biochem. 38, 293–300 (1973).CAS 
PubMed 
Article 

Google Scholar 
Recsei, P. A., Gruss, A. D. & Novick, R. P. Cloning, sequence, and expression of the lysostaphin gene from Staphylococcus simulans. Proc. Natl Acad. Sci. USA 84, 1127–1131 (1987).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kessler, E., Safrin, M., Abrams, W. R., Rosenbloom, J. & Ohman, D. E. Inhibitors and specificity of Pseudomonas aeruginosa LasA. J. Biol. Chem. 272, 9884–9889 (1997).CAS 
PubMed 
Article 

Google Scholar 
Trayer, H. R. & Buckley, C. E. Molecular properties of lysostaphin, a bacteriolytic agent specific for Staphylococcus aureus. J. Biol. Chem. 245, 4842–4846 (1970).CAS 
PubMed 
Article 

Google Scholar 
Heymer, B. & Schmidt, W. C. Purification and characterization of a Streptomyces albus endo-N-acetylmuramidase lytic for group A and other beta hemolytic streptococci. Microbios 12, 51–66 (1975).CAS 
PubMed 

Google Scholar 
Vollmer, W., Joris, B., Charlier, P. & Foster, S. Bacterial peptidoglycan (murein) hydrolases. FEMS Microbiol. Rev. 32, 259–286 (2008).CAS 
PubMed 
Article 

Google Scholar 
Peyraud, R. et al. Demonstration of the ethylmalonyl-CoA pathway by using C-13 metabolomics. Proc. Natl Acad. Sci. USA 106, 4846–4851 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schlesier, B., Breton, F. & Mock, H. P. A hydroponic culture system for growing Arabidopsis thaliana plantlets under sterile conditions. Plant Mol. Biol. Rep. 21, 449–456 (2003).CAS 
Article 

Google Scholar 
Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).CAS 
PubMed 
Article 

Google Scholar 
Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 
Article 

Google Scholar 
Pruesse, E. et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 35, 7188–7196 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Integrated Development Environment for R (R Studio, 2020).R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. R package v. 2.5-7 (2020).Armenteros, J. J. A. et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat. Biotechnol. 37, 420–423 (2019).Article 
CAS 

Google Scholar 
Gasteiger, E. et al. in The Proteomics Protocols Handbook 571–607 (ed Walker, J. M.) (Humana Press, 2005).Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).CAS 
PubMed 
Article 

Google Scholar 
Bushnell, B. BBMap short read aligner, and other bioinformatic tools (SourceForge, version 38.87); https://sourceforge.net/projects/bbmapDeatherage, D. E. & Barrick, J. E. Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq. Methods Mol. Biol. 1151, 165–188 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nat. Biotechnol. 37, 540–546 (2019).CAS 
PubMed 
Article 

Google Scholar 
Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).CAS 
PubMed 
Article 

Google Scholar  More

Stability analysis of the deterministic modelSolving (left( x(t) times left( r_{p}x(t)^{(alpha )}left( 1-big (frac{x(t)}{K}big )^{beta }right) – nx(t)^{(delta )} right) right) =0), we obtain two stable and one unstable equilibrium points for the model. One stable equilibrium is trivial, i.e., (x(t)=0), another stable equilibrium point being the non-zero satisfying (left( r_{p}x(t)^{(alpha )}left( 1-big (frac{x(t)}{K}big )^{beta }right) – nx(t)^{(delta )} right) =0). Figure 1a shows three different equilibrium points of the model. In addition to the equilibrium, the model has two inflection points (Fig. 1a). At these inflection points the absolute growth rates are minimum and maximum. The density vs relative proliferation rate (RPR) profile of the model shows that the model can attain negative RPR for a positive cell density, suggesting that the model can portray the Allee phenomenon (Fig. 1b). Figure 1c,d portray the proliferation and decay phases, respectively through the model.Figure 1Growth dynamics of the proposed model: (a) Absolute proliferation rate (APR) profile considering (r_{p}=0.13), (K=1.43), (n=0.0095), (alpha =1.15), (beta =0.99) and (delta =0.2); (b) RPR profiles for different n and other same constant model parameters; (c) Cell population survive for (r_{p}=0.13), (K=1.43), (n=0.0095), (alpha =1.15), (beta =0.99) and (delta =0.2) with the initial cell density 0.1; (d) The population goes to extinction for the initial cell density 0.06 with the same constant parameters.Full size imageThe solution of the deterministic model finally provides two theorems.
Theorem 1

(x^{*}approx K -Kleft( frac{Big (beta r_{p}K^{alpha }+n delta K^{delta }Big )-sqrt{Big (beta r_{p}K^{alpha }+n delta K^{delta }Big )^{2}-2 left( 2 alpha beta r_{p}K^{alpha } +beta (beta -1)r_{p}K^{alpha }+delta (delta -1)nK^{delta } right) nK^{delta }}}{left( 2 alpha beta r_{p}K^{alpha } +beta (beta -1)r_{p}K^{alpha }+delta (delta -1)nK^{delta } right) }right)) is the conditional MSSCD for the intercellular-interaction-induced proliferative cells. The conditional threshold density for cell-proliferation upon interaction is (x^{*}=K -Kleft( frac{Big (beta r_{p}K^{alpha }+n delta K^{delta }Big )+sqrt{Big (beta r_{p}K^{alpha }+n delta K^{delta }Big )^{2}-2 left( 2 alpha beta r_{p}K^{alpha } +beta (beta -1)r_{p}K^{alpha }+delta (delta -1)nK^{delta } right) nK^{delta }}}{left( 2 alpha beta r_{p}K^{alpha } +beta (beta -1)r_{p}K^{alpha }+delta (delta -1)nK^{delta } right) }right)) (proof is in the supplementary information).
Allee and cooperation models are the only extended logistic law other than our model to provide a threshold population size for growth or proliferation. Our proposed model is superior to the Allee and cooperation model as it can detect the conditional threshold cell density for proliferation and regulate the density by its different parameters. For example, One may reduce the conditional threshold density by either regulating the interaction between growth-inhibiting molecules and cells ((delta)) or reducing the inhibiting molecule concentration (n).The conditional MSSCD from Theorem 1 is lower than the carrying capacity of the conventional logistic model due to growth-inhibiting molecules; it provides the expected cell density during culture in a given environment. Theorem 1 also states the set of parameters to control the cell proliferation and get the desired density during such cell cultures. A further question arises knowing this set of parameters: which one of the parameters in the expression is crucial in terms of application purpose? Since the (r_{p}) is the constant proliferation rate for a given cell line, controlling the conditional MSSCD is not possible through (r_{p}). We simulate the distribution of conditional MSSCD for other parametric planes to answer this question. For this, we use the parameter values obtained from the data.

Theorem 2

The RPR is maximum at the cell density (x^{*}= K-Kleft( frac{r_{p}beta K^{alpha -1}+ndelta K^{delta -1}}{2r_{p}alpha beta K^{alpha -1}+r_{p}beta (beta -1)K^{alpha -1}+ndelta (delta -1)K^{delta -1}}right)) for the concave downward profile under the condition (r_{p}alpha (alpha -1){x^{*}}{}^{(alpha -2)}-frac{r_{p}}{K^{beta }}(alpha +beta )(alpha +beta -1){x^{*}}{}^{(alpha +beta -2)}-ndelta (delta -1){x^{*}}{}^{(delta -2)}n) (see the supplementary information). The cell population sustain with any positive initial cell density x(t) and try to stabilize at (x(t)= K(1-frac{n}{r_{p}})^frac{1}{beta }). Therefore, bimodality vanishes and unimodality is observed for the case (alpha =delta) (r_{p} >n). The RPR profile will be concave downward always with the maximum RPR value is at the inflection point (x(t)= K(frac{(r_{p}-n)alpha }{r_{p}(alpha +beta )})^frac{1}{beta }). The deterministic potential function in this case is (U(x)=-Big [(r_{p}-n)frac{x^{(alpha +2)}}{(alpha +2)}-frac{r_{p}}{K^{beta }}frac{x^{(alpha +beta +2)}}{(alpha +beta +2)} Big ]). The minima of this effective potential function will be at (x(t)= K(1-frac{n}{r_{p}})^frac{1}{beta }) which is the maximum stable cell density for (r_{p} >n).
Parameter estimationThe density-RPR and time-density fitting to the scratch assay datasets show a lower RSS for our model than the logistic one for each of the three seeding conditions. The estimated parameters from the RPR fitting through the grid-search are in Table 2. Although the RSS for the RPR fitting of the seeding 2 is very low, the data itself is too scattered in both the upper and lower range for the small cell density. Therefore, there is a chance that regardless of the low RSS value, the fitting for seeding 2 may not reflect the actual estimates of the parameters with the bias in the data set (Fig. 2b). Nevertheless, the density-RPR fittings to the other two seeding density datasets do not suffer from bias (Fig. 2a,c).Table 2 Estimated model parameters from density-RPR fitting of our model.Full size table
Figure 2Our proposed model best fitted the cell density-RPR datasets for all of the seeding conditions generated through the grid-search method.Full size image
Jin et al.1 suggested that their two phase logistic model may share similarities with the Allee effect. However, they did not fit the Allee model stating seeding 2 and 3 were large enough seeding densities. We calculated the conditional threshold density, conditional MSSCD, density at the minimum and maximum RPR for the model from our estimated parameters (Table 3). The conditional threshold cell density calculated from our estimated parameters confirms that the smallest initial seeding density of the dataset was greater than the conditional threshold cell density.Table 3 Calculated cell densities from estimated parameters from our model fitting.Full size tableFigure 3 compares the portrayal of the data through our model with the fitting by Jin et al.1. The blue dashed line is the time-series fitting of the proposed model, and the red-colored line is the time-series fitting of the logistic model to the scratch assay data sets in the Fig. 3. The carrying capacity values are unexpectedly very high in the logistic fit, keeping the model near the exponential phase for the entire dataset. Thus the overall and two phase logistic fits are unrealistic compared to the highest cell density observed in the assay. Also, logistic fitting of the RPR profiles to the data after 18 h does not capture the whole scenario. The green solid and the violet dashed line represent the logistic time-density fit after and before 18 h density profiles respectively. The orange-colored lines in the Fig. 3 are the expected population density as per estimated parameters from the RPR fitting after 18 h data sets. Table 4 enlists all parameters for a comparison between logistic and our model fitting.Figure 3Time series solution of the proposed model and logistic law with comparative RSS for all three seeding conditions.Full size imageTable 4 Logistic model fitting with the Jin et al.1 estimates used in Fig. 3 with the specific colors.Full size tableTrends in cell densities under deterministic set upThe (r_{p}) is fixed for a cell line among all the determining parameters of the conditional MSSCD. n and K vary together with the culture media, flask, and environmental setup. On the other hand, the (alpha), (beta), and (delta) vary together with intercellular-interactions and cellular-interaction with growth-inhibitory molecules, which depend on the medium’s initial cell density per well and fluidity. We observe that the distribution of the conditional MSSCD depends more on the K than the n. There is a chance of overproliferation in the deterministic setup under low n but high K. The cells may die under high n. The cell density at maximum RPR also depends more on K than n (Fig. 4). So the cells should be cultured in the larger flask to achieve maximum proliferativeness.Figure 4The distribution of conditional MSSCD and cell density at maximum RPR in n-K parametric plane.Full size imageThe conditional MSSCD depends more on (beta) than (alpha) (Fig. 5a). The cells may tend to overproliferate under both high (alpha) and (beta). The conditional MSSCD does not exist for a high (delta) and low (beta) depending more on (delta) than (beta). The cells may overproliferate only under a high (beta) and low (delta) (Fig. 5b). The conditional MSSCD also depends more on (delta) than (alpha) showing mostly underproliferation of cells in the (delta ~-alpha) parametric plane. Therefore, the proliferation can be controlled via regulating the interaction between the growth-inhibitory molecules and cells followed by density-regulation through contact-inhibition and cell-cell cooperation (Fig. 5c).Figure 5The distribution of the conditional MSSCD in parametric plane of regulators in the growth law: (a) dependence of the conditional MSSCD on (alpha) and (beta) parameters; (b) dependence of the conditional MSSCD on (delta) and (beta) parameters; (c) dependence of the conditional MSSCD on (alpha) and (delta) parameters.Full size imageThe new cell fitness measure, i.e. cell density at maximum RPR depends more on the (alpha) than the (beta) (Fig. 6a). The cells achieve maximum RPR at a great cell density under the high value of these two parameters. Figure 6b,c suggest that cell density depends only a little on the (delta) under high (alpha) and (beta). Under the low value of these two regulators, a high (delta) always reduces the cell density attaining the maximum RPR, resulting a poor cell-fitness.Figure 6The distribution of cell density at maximum RPR in parametric plane of regulators in the growth law: (a) dependence on (alpha) and (beta) parameters; (b) dependence on (alpha) and (delta) parameters; (c) dependence on (delta) and (beta) parameters.Full size imageStochastic model analysisOur proposed stochastic model (3) can be compared with the general stratonovich stochastic differential equation (frac{dx}{dt}=f(x)+g_{1}(x)epsilon (t)+g_{2}(x)Gamma (t)). Comparing it with our proposed stochastic model we obtain (g_{1}(x)=-x^{delta +1}) and (g_{2}(x)=1). Using the help of47, we get noise induced drift (A(x)=r_{p}x^{alpha +1}left( 1-Big (frac{x}{K}Big )^{beta } right) -nx^{(delta +1)}+D(delta +1)x^{(2delta +1)}-lambda sqrt{DQ}(delta +1)x^{delta }) and noise induced diffusion coefficient (B(x)=Dx^{(2delta +2)}-2lambda sqrt{DQ}x^{(delta +1)}+Q). The cell density at long run can be obtained from the steady state probability density function (SSPDF). The analytical expression of the SSPDF is obtained from the Fokker-Planck equation. The Fokker-Planck equation is (frac{partial P(x, t)}{partial t} =- frac{partial big [ A(x) P(x, t)big ]}{partial x}+ frac{partial ^{2} big [B(x) P(x, t)big ]}{partial x^{2}}), where P(x,t) is the probability density function of the cell population at the time point t. Solving the Fokker-Planck equation we get the SSPDF as (P_{st} (x)= frac{N^{prime }}{B(x)} exp left( int _{x} frac{A(x^{prime })}{B(x^{prime })} dx^{prime }right)) with the normalizing constant (N^{prime }). The value of (N^{prime }) can be obtained from (int _{0}^{infty } P_{st} (x)dx=1).This SSPDF (P_{st} (x)) helps to understand the validity of the proposed stochastic model. Since the number of the data points is too low to fit the stochastic model to the data directly, validation of the stochastic model is challenging in this case. The dataset we used is a time series with 15 data points with three replicates only. An experiment must have many replicates to have a sample with a large sample size so that the SSPDF of cell densities obtained from theoretical findings can be validated with the real observation of cell densities at the steady state. Such datasets with many replicates are rare.So, we generate 2000 sample paths with the help of numerical simulation based on stochastic model 3. We use the parameter values estimated from the fittings of the deterministic model to the seeding condition 1, and we consider some particular values for the two noise intensities and correlation strength ((lambda)) to get a simulated dataset. To achieve the stationary state, we consider sufficiently large time points, and the cell densities at the final time point are used as the data set for the stationary state. We compare the frequency density of cell densities at steady-state of a simulated dataset of 2000 sample paths with the SSPDF obtained from the analytical solution. This comparison shows that the cell density distribution at the steady state matches the steady state probability density function obtained analytically (Fig. 7).In addition, we illustrated the time series generated with the help of stochastic model 3 through numerical technique (Fig. 8). We have plotted the time series data thus obtained for each of the three seeding conditions and in the same figure we also plotted the observed cell densities. The red dots (o) represent the original/experimental dataset of Jin et al.1. The blue dots ((*)) represent the simulated dataset obtained from the stochastic model. This Fig. 8 clarifies our claim that the proposed stochastic model is in good agreement with the actual observation.Figure 7The histogram shows the distribution of cell densities at steady state under additive and multiplicative noises. The blue curve is the SSPDF. The function SSPDF and the distribution of cell densities matches to each other.Full size imageFigure 8The red dots (o) in each sub-figures represent the experimental data of Jin et al.1. The blue dots ((*)) are obtained from the stochastic model (3) considering: (a) The seeding 1 estimated model parameters with (D= 0.002), (Q= 0.06) and (lambda = 0.4). (b) The seeding 2 estimated model parameters with (D= 0.01), (Q= 0.15) and (lambda = 0.6). (c) The seeding 3 estimated model parameters with (D= 0.002), (Q= 0.2) and (lambda = 0.4).Full size imageFigures 7 and 8 suggest that the stochastic model is valid. So the model can be further analyzed to meet the first objective. Differentiating (P_{st} (x)), we obtain (frac{dP_{st} (x)}{dx}=frac{N^{prime }}{[B(x)]^2} exp left( int frac{A(x)}{B(x)}dx right) left( A(x)-frac{dB(x)}{dx} right)) and (frac{d^{2}P_{st} (x)}{dx^{2}}= frac{N^{prime }}{[B(x)]^{2}}exp left( int frac{A(x)}{B(x)}dx right) left( frac{dA(x)}{dx}-frac{d^{2}B(x)}{dx^{2}} right) +frac{N^{prime }}{[B(x)]^{2}} left( A(x)-frac{dB(x)}{dx} right) exp left( int frac{A(x)}{B(x)}dx right) frac{A(x)}{B(x)}-frac{2}{[B(x)]^3}N^{prime } exp left( int frac{A(x)}{B(x)}dx right) left( A(x)-frac{dB(x)}{dx} right) frac{dB(x)}{dx}). At the extrema of the SSPDF, we must have (frac{dP_{st} (x)}{dx}=0) i.e. (left( A(x)-frac{dB(x)}{dx} right) =0).

Theorem 3

(x^{*}approx K-K left( frac{nK^{delta +1}+D(delta +1) K^{2delta +1}-lambda sqrt{DQ}(delta +1)K^{delta }}{beta r K^{alpha +1}+n(delta +1) K^{(delta +1)}+D(delta +1) (2delta +1)K^{(2delta +1)}-lambda sqrt{DQ}delta (delta +1)K^{delta }} right)) is the conditional MSSCD due to the correlated additive and multiplicative noises under the condition (r_{p}(alpha +1)x^{*}{}^{alpha }-frac{r_{p}}{K^{beta }}(alpha +beta +1)x^{*}{}^{(alpha +beta )} -n(delta +1)x^{*}{}^{delta }-D(delta +1)(2delta +1)x^{*}{}^{(2delta )}+lambda sqrt{Dalpha }delta (delta +1)x^{*}{}^{(delta -1)} < 0) (proof is in the supplementary information). Figure 9 visualizes the effect of noise strength and correlation strength on the conditional MSSCD. The conditional MSSCD increases with the additive noise strength (Q) and decreases with the multiplicative noise strength (D) when the other model parameters are fixed (Fig. 9a). There is a high chance of overproliferation for a low D and a high Q (Fig. 9a). Again, there is a high chance of extinction for the low Q and high D. The conditional MSSCD depends more on D than (lambda) (Fig. 9b), and more on (lambda) than Q (Fig. 9c). The conditional MSSCD increases with (lambda) and Q; there is a high chance of overproliferation for high (lambda) and Q. The extinction risk of cells from the culture increases with low (lambda) and Q.Figure 9The change in the conditional MSSCD value for different noise strengths and correlation strength using the parameters estimated for seeding 1: (a) the conditional MSSCD values in (D-Q) noise strength plane with highest correlation ((lambda =1)); (b) the conditional MSSCD values in (D-lambda) noise plane with (Q=0.01); (c) the conditional MSSCD values in (Q-lambda) noise plane with (D=0.01).Full size imageDue to the difficulty and complicated expression of the analytical expression of the SSPDF, we use numerical simulation to study the steady-state behavior in the long run under correlated noises. We draw a histogram of the cell densities based on 500 normal sample paths at the final time points. We use seeding 1 fitting estimates as the initial parameter values for this simulation. The cell population is stable and steady at either 0 cell density or at the conditional MSSCD. The distribution is symmetric around the conditional MSSCD for (lambda =1) (Fig. 10a). There is a loss in the symmetry for the decreasing (lambda). For (lambda =0.5), there is a mode at the zero states with another mode at conditional MSSCD (Fig. 10b). The histogram shows a bi-modality for low values of (lambda). The mode at the zero state is highest for (lambda =0) (Fig. 10c). Therefore, the extinction chance increases for zero noise correlation between the additive and the multiplicative noises.Figure 10Distribution of cell density for (r_{p}=0.13), (K=1.43), (n=0.0095), (alpha =1.15), (beta =0.99), (delta =0.2), (D=0.01), (Q=0.01), and variable correlation between additive and multiplicative noises: (a) (lambda =1), (b) (lambda =0.5) and (c) (lambda =0).Full size imageThe sustainability of the cell population depends on the strength of the two noises, like the correlation strength between them. For the zero strength multiplicative noise, the population has the mode at around the conditional MSSCD value (Fig. 11). Therefore, the population sustains in this case and tries to stabilize at the conditional MSSCD value. For (D=0.02), there is a bimodality, where the highest mode is at the zero cell density. For (D=0.05), we observe only one mode at (x=0). Therefore, with the increasing values of the multiplicative noise strengths (D), the chance of extinction increases for (lambda =0.5), (Q=0.01), and other constant model parameters for the seeding condition 1. Similar things happen for increasing Q values considering (D=0.01), (lambda =0.5), and other constant model parameters (Fig. 12).Figure 11Distribution of cell density for (r_{p}=0.13), (K=1.43), (n=0.0095), (alpha =1.15), (beta =0.99), (delta =0.2), (lambda =0.5), (Q=0.01), and variable strength of multiplicative noise: (a) (D=0.05), (b) (D=0.02) and (c) (D=0).Full size imageFigure 12Distribution of cell density for (r_{p}=0.13), (K=1.43), (n=0.0095), (alpha =1.15), (beta =0.99), (delta =0.2), (lambda =0.5), (D=0.01), and variable correlation between multiplicative noise: (a) (Q=0.05), (b) (Q=0.02) and (c) (Q=0).Full size image Remark 5 We have previously discussed the scenario for (alpha =delta) for deterministic case in Remark 4. It is important to understand the scenario under stochastic case too. For (alpha =delta) the proposed stochastic model 3 becomes (frac{dx(t)}{dt}=r_{p}x(t)^{(alpha +1)}left( 1-big (frac{x(t)}{K}big )^{beta }right) - nx(t)^{(alpha +1)}-x(t)^{(alpha +1)} epsilon (t)+ Gamma (t)). For this stochastic model (g_{1}(x)=-x^{alpha +1}) and (g_{2}(x)=1). We get, (A(x)=r_{p}x^{alpha +1}left( 1-Big (frac{x}{K}Big )^{beta } right) -nx^{(alpha +1)}+D(alpha +1)x^{(2alpha +1)}-lambda sqrt{DQ}(alpha +1)x^{alpha }) and (B(x)=Dx^{(2alpha +2)}-2lambda sqrt{DQ}x^{(alpha +1)}+Q). The extrema of the SPDF (big (x(t)=x^{*}big )) must satisfy the growth equation (r_{p}{x^{*}}^{alpha +1}-frac{r_{p}}{K^{beta }}(x^{*})^{alpha +beta +1}-n(x^{*})^{alpha +1}-D(alpha +1)(x^{*})^{2alpha +1}+lambda sqrt{D~Q}(alpha +1)(x^{*})^{alpha }=0). Therefore, for (alpha =delta) the conditional MSSCD is (x^{*}= K-Kfrac{nK^{(alpha +1)}+D(alpha +1)K^{(2alpha +1)}-lambda sqrt{DQ}(alpha +1)K^{alpha }}{beta r_{p}K^{(alpha +1)}+nK^{(alpha +1)}(alpha +1)+D(alpha +1)(2alpha +1)K^{(2alpha +1)}-alpha lambda sqrt{DQ}(alpha +1)K^{alpha }}) under the condition ((r_{p}-n)(alpha +1)(x^{*})^{alpha }-frac{r_{p}}{K^{beta }}(alpha +beta +1)(x^{*})^{(alpha + beta )}-(alpha +1)(2alpha +1)D(x^{*})^{2alpha }+lambda sqrt{DQ}(alpha +1)alpha (x^{*})^{(alpha -1)} More

Rendell, L. & Whitehead, H. Culture in whales and dolphins. Behav. Brain Sci. 24, 309–324 (2001).CAS 
Article 

Google Scholar 
Krützen, M. et al. Cultural transmission of tool use in bottlenose dolphins. Proc. Natl. Acad. Sci. U.S.A. 102, 8939–8943 (2005).ADS 
Article 

Google Scholar 
Kawai, M. Newly-acquired pre-cultural behavior of the natural troop of Japanese monkeys on Koshima Islet. Primates 6, 1–30 (1965).Article 

Google Scholar 
Slater, P. The cultural transmission of bird song. Trends Ecol. Evol. 1, 94–97 (1986).CAS 
Article 

Google Scholar 
Whitehead, H. & Rendell, L. The cultural lives of whales and dolphins. (University of Chicago Press, 2014).Whiten, A. The identification and differentiation of culture in chimpanzees and other animals: from natural history to diffusion experiments. The question of animal culture, 99–124 (2009).Allen, J. A. Community through culture: from insects to whales: How social learning and culture manifest across diverse animal communities. BioEssays 41, 1900060 (2019).Article 

Google Scholar 
Allen, J., Weinrich, M., Hoppitt, W. & Rendell, L. Network-based diffusion analysis reveals cultural transmission of lobtail feeding in humpback whales. Science 340, 485–488 (2013).ADS 
CAS 
Article 

Google Scholar 
Baker, C. Migratory movement and population structure of humpback whales (Megaptera novaeangliae) in the central and eastern North Pacific. Mar Ecol Prog Ser 31, 105–119 (1986).ADS 
Article 

Google Scholar 
Garrigue, C. et al. Movement of individual humpback whales between wintering grounds of Oceania (South Pacific), 1999 to 2004. J. Cetacean Res. Manage 3, 275–281 (2011).
Google Scholar 
Rosenbaum, H. C. et al. First circumglobal assessment of Southern Hemisphere humpback whale mitochondrial genetic variation and implications for management. Endang. Spec. Res. 32, 551–567 (2017).Article 

Google Scholar 
Noad, M. J., Cato, D. H., Bryden, M. M., Jenner, M. N. & Jenner, K. C. Cultural revolution in whale songs. Nature 408, 537. https://doi.org/10.1038/35046199 (2000).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Payne, R. S. & McVay, S. Songs of humpback whales. Science 173, 585–597 (1971).ADS 
CAS 
Article 

Google Scholar 
Garland, E. C. et al. Dynamic horizontal cultural transmission of humpback whale song at the ocean basin scale. Curr. Biol. 21, 687–691. https://doi.org/10.1016/j.cub.2011.03.019 (2011).CAS 
Article 
PubMed 

Google Scholar 
Payne, R. & Guinee, L. N. Humpback whale (Megaptera novaeangliae) songs as an indicator of “stocks”. Communication and behavior of whales, 333–358 (1983).Garrigue, C. et al. Movements of humpback whales in Oceania, South Pacific. J. Cetac. Res. Manage. 4, 255–260 (2002).
Google Scholar 
Derville, S., Torres, L. G., Zerbini, A. N., Oremus, M. & Garrigue, C. Horizontal and vertical movements of humpback whales inform the use of critical pelagic habitats in the western South Pacific. Sci. Rep. 10, 1–13 (2020).Article 

Google Scholar 
Garrigue, C. et al. First assessment of interchange of humpback whales between Oceania and the East coast of Australia. J. Cetac. Res. Manage. 3, 269–274 (2011).
Google Scholar 
Steel, D. et al. Migratory connections between humpback whales from South Pacific breeding grounds and Antarctic feeding areas based on genotype matching. Int. Whal. Comm. (2008).Constantine, R., Russell, K., Gibbs, N., Childerhouse, S. & Baker, C. S. Photo-identification of humpback whales (Megaptera novaeangliae) in New Zealand waters and their migratory connections to breeding grounds of Oceania. Mar. Mam. Sci. 23, 715–720 (2007).Article 

Google Scholar 
Garland, E. C. et al. Humpback whale song on the southern ocean feeding grounds: implications for cultural transmission. PLoS ONE 8, e79422 (2013).ADS 
Article 

Google Scholar 
Garland, E. C. et al. Population structure of humpback whales in the western and central South Pacific Ocean as determined by vocal exchange among populations. Conserv. Biol. 29, 1198–1207 (2015).Article 

Google Scholar 
Cholewiak, D. M., Sousa-Lima, R. S. & Cerchio, S. Humpback whale song hierarchical structure: Historical context and discussion of current classification issues. Mar. Mam. Sci. 29, E312–E332. https://doi.org/10.1111/mms.12005 (2013).Article 

Google Scholar 
Payne, K., Tyack, P. & Payne, R. Progressive changes in the songs of humpback whales (Megaptera novaeangliae): a detailed analysis of two seasons in Hawaii. Communication and behavior of whales, 9–57 (1983).Payne, K. & Payne, R. Large scale changes over 19 years in songs of humpback whales in Bermuda. Ethology 68, 89–114 (1985).
Google Scholar 
Allen, J. A., Garland, E. C., Dunlop, R. A. & Noad, M. J. Cultural revolutions reduce complexity in the songs of humpback whales. Proc. R. Soc. B: Biol. Sci. 285, 20182088. https://doi.org/10.1098/rspb.2018.2088 (2018).Article 

Google Scholar 
Allen, J. A., Garland, E. C., Murray, A., Noad, M. J. & Dunlop, R. Using self-organizing maps to classify humpback whale song units and quantify their similarity. J. Acoust. Soc. Am. 142, 1943–1952 (2017).ADS 
Article 

Google Scholar 
Murray, A., Dunlop, R. A., Noad, M. J. & Goldizen, A. W. Stereotypic and complex phrase types provide structural evidence for a multi-message display in humpback whales (Megaptera novaeangliae). J Acoust Soc Am. 143, 980–994 (2018).ADS 
Article 

Google Scholar 
Garland, E. C. et al. Redefining western and central South Pacific humpback whale population structure based on vocal cultural exchange. (2013).Rekdahl, M. Humpback whale vocal communication: Use and stability of social calls and revolutions in the songs of east Australian whales. (2012).Templeton, C. N., Laland, K. N. & Boogert, N. J. Does song complexity correlate with problem-solving performance in flocks of zebra finches?. Anim. Behav. 92, 63–71 (2014).Article 

Google Scholar 
Boogert, N. J., Giraldeau, L.-A. & Lefebvre, L. Song complexity correlates with learning ability in zebra finch males. Anim. Behav. 76, 1735–1741 (2008).Article 

Google Scholar 
Winn, H. & Winn, L. The song of the humpback whale Megaptera novaeangliae in the West Indies. Mar. Biol. 47, 97–114 (1978).Article 

Google Scholar 
Girola, E., Noad, M. J., Dunlop, R. A. & Cato, D. H. Source levels of humpback whales decrease with frequency suggesting an air-filled resonator is used in sound production. The Journal of the Acoustical Society of America (In Review).Warren, V. E., Constantine, R., Noad, M., Garrigue, C. & Garland, E. C. Migratory insights from singing humpback whales recorded around central New Zealand. R. Soc. Open Sci. 7, 201084 (2020).ADS 
Article 

Google Scholar 
Garland, E. C. et al. Quantifying humpback whale song sequences to understand the dynamics of song exchange at the ocean basin scale. J. Acoust. Soc. Am. 133, 560–569. https://doi.org/10.1121/1.4770232 (2013).ADS 
Article 
PubMed 

Google Scholar 
Owen, C. et al. Migratory convergence facilitates cultural transmission of humpback whale song. R. Soc. Open Sci. 6, 190337 (2019).ADS 
Article 

Google Scholar 
Garland, E. C., Rendell, L., Lamoni, L., Poole, M. M. & Noad, M. Song hybridization events during revolutionary song change provide insights into cultural transmission in humpback whales. Proc. Natl. Acad. Sci. 114, 7822–7829 (2017).CAS 
Article 

Google Scholar 
Noad, M. & Cato, D. A combined acoustic and visual survey of humpback whales off southeast Queensland. Mem. Qld. Mus. 47, 507–523 (2001).
Google Scholar 
Spierings, M., de Weger, A. & Ten Cate, C. Pauses enhance chunk recognition in song element strings by zebra finches. Anim. Cogn. 18, 867–874 (2015).Article 

Google Scholar 
Doupe, A. J. & Kuhl, P. K. Birdsong and human speech: common themes and mechanisms. Annu. Rev. Neurosci. 22, 567–631 (1999).CAS 
Article 

Google Scholar 
Allen, J. A., Garland, E. C., Dunlop, R. A. & Noad, M. J. Network analysis reveals underlying syntactic features in a vocally learnt mammalian display, humpback whale song. Proc. R. Soc. B 286, 20192014 (2019).Article 

Google Scholar 
Barón Birchenall, L. Animal communication and human language: An overview. International Journal of Comparative Psychology 29 (2016).Noad, M. J. The use of song by humpback whales (Megaptera novaeangliae) during migration off the east coast of Australia (doctoral dissertation) Doctor of Philosophy thesis, University of Sydney, (2002).Catchpole, C. Song and female choice: good genes and big brains?. Trends Ecol. Evol. 11, 358–360. https://doi.org/10.1016/0169-5347(96)30042-6 (1996).CAS 
Article 
PubMed 

Google Scholar 
Nowicki, S., Hasselquist, D., Bensch, S. & Peters, S. Nestling growth and song repertoire size in great reed warblers: evidence for song learning as an indicator mechanism in mate choice. Proc. R. Soc. London B: Biol. Sci. 267, 2419–2424 (2000).CAS 
Article 

Google Scholar 
NOAA. Vol. 81 (ed National Marine Fisheries Service) 62260–62320 (Department of Commerce, Federal Register, 2016).Noad, M. J., Kniest, E. & Dunlop, R. A. Boom to bust? Implications for the continued rapid growth of the eastern Australian humpback whale population despite recovery. Popul. Ecol. 61(2), 198–209 (2019).Article 

Google Scholar 
Garrigue, C., Albertson, R. & Jackson, J. A. An anomalous increase in the New Caledonia humpback whales breeding sub-stock E2. Scientific Committee of the International Whaling Commission, Paper (2012).Garland, E. C. & McGregor, P. K. Cultural transmission, evolution, and revolution in vocal displays: Insights from bird and whale song. Front. Psychol. 11, 2387 (2020).Article 

Google Scholar 
Zandberg, L., Lachlan, R. F., Lamoni, L. & Garland, E. C. Global cultural evolutionary model of humpback whale song. Philos. Trans. R. Soc. B 376, 20200242 (2021).Article 

Google Scholar 
Crates, R. et al. Loss of vocal culture and fitness costs in a critically endangered songbird. Proc. R. Soc. B 288, 20210225 (2021).Article 

Google Scholar 
Garland, E. C., Garrigue, C. & Noad, M. J. When does cultural evolution become cumulative culture? A case study of humpback whale song. Philos. Trans. R. Soc. B 377, 20200313 (2022).Article 

Google Scholar 
Garland, E. C. et al. Improved versions of the Levenshtein distance method for comparing sequence information in animals’ vocalisations: tests using humpback whale song. Behaviour 149, 1413–1441 (2012).Article 

Google Scholar 
Garland, E. C. et al. The devil is in the detail: quantifying vocal variation in a complex, multileveled, and rapidly evolving display. J. Acoust. Soc. Am. 142, 460–472 (2017).ADS 
Article 

Google Scholar 
Rekdahl, M. L. et al. Culturally transmitted song exchange between humpback whales (Megaptera novaeangliae) in the southeast Atlantic and southwest Indian Ocean basins. R. Soc. Open Sci. 5, 172305 (2018).ADS 
Article 

Google Scholar 
Suzuki, R. & Shimodaira, H. Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics 22, 1540–1542 (2006).CAS 
Article 

Google Scholar 
Sokal, R. R. & Rohlf, F. J. The comparison of dendrograms by objective methods. Taxon 11(2), 33–40 (1962).Article 

Google Scholar 
R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2015. URL http (R Core Development Team, 2016). More

Larson, D. W., Matthes, U. & Kelly, P. E. Cliff Ecology (Cambridge University Press, 2000).Book 

Google Scholar 
Cooper, A. Plant species coexistence in cliff habitats. J. Biogeogr. 24, 483–494 (1997).Article 

Google Scholar 
Davis, P. H. Cliff vegetation in the eastern Mediterranean. J. Ecol. 39, 63–93 (1951).Article 

Google Scholar 
Snogerup, S. Evolutionary and plant geographical aspects of chasmophytic communities. In Plant life of South-West Asia (eds Davis, P. H. et al.) 157–170 (Bot. Soc. Edinb, 1971).
Google Scholar 
Baskin, J. M. & Baskin, C. C. Endemism in rock outcrop plant communities of unglaciated eastern United States: An evaluation of the roles of the edaphic, genetic and light factors. J. Biogeogr. 15, 829–840 (1988).Article 

Google Scholar 
Medina, B. M. O. & Fernandes, G. W. The potential of natural regeneration of rocky outcrop vegetation on rupestrian field soils in Serra do Cipo, Brazil. Braz. J. Bot. 30, 665–678 (2007).Article 

Google Scholar 
Alves, R. J. V., Cardin, L. & Kropf, M. S. Angiosperm disjunction “Campos Rupestres-Restingas”: Are-evaluation. Acta Bot. Bras. 2, 675–685 (2007).Article 

Google Scholar 
Harley, R. M. Introduction. In Flora of the Pico das Almas, Chapada Diamantina, Bahia, Brazil (eds Stannard, B. L., Harvey, Y. B. & Harley, R. M) 1–42 (Royal Botanic Gardens, 1995).Hubbell, S. P. Neutral theory in ecology and the evolution of ecological equivalence. Ecology 87, 1387–1398 (2006).PubMed 
Article 

Google Scholar 
Conceição, A. A., Pirani, J. R. & Meirelles, S. T. Floristics, structure and soil of insular vegetation in four quartzite-sandstone outcrops of “Chapada Diamantina”, Northeast Brazil. Rev. Bras. Bot. 30, 641–656 (2007).Article 

Google Scholar 
Le Stradic, S., Buisson, E. & Wilson, F. G. Vegetation composition and structure of some Neotropical mountain grasslands in Brazil. J Mt Sci 12:864–77. An. Acad. Bras. Ciênc. 87(4), 2097–2110 (2015).Article 
CAS 

Google Scholar 
Nunes, J. A. et al. Soil–vegetation relationships on a banded ironstone ‘island’, Carajás Plateau, Brazilian Eastern Amazonia. An. Acad. Bras. Cienc. 87(4), 2097–2110 (2015).CAS 
PubMed 
Article 

Google Scholar 
Silva, W. A. Gradiente vegetacional e pedológico em complexo rupestre de quartzito no Quadrilátero Ferrífero, Minas Gerais, Brasil. MSc Thesis. (Universidade Federal de Viçosa, 2013).Vincent, R. C. & Meguro, M. Influence of soil properties on the abundance of plant species in ferruginous rocky soils vegetation, southeastern Brazil. Braz. J. Bot. 31, 377–388 (2008).Article 

Google Scholar 
Porembski, S. Tropical inselbergs: Habitat types, adaptive strategies and diversity patterns. Rev. Bras. de Bot. 30, 579–586 (2007).Article 

Google Scholar 
De Paula, L. F. A., Forzza, R. C., Neri, A. V., Bueno, M. L. & Porembski, S. Sugar Loaf Land in south-eastern Brazil: A center of diversity for mat-forming bromeliads on inselbergs. Bot. J. Linn. Soc. 181, 459–476 (2016).Article 

Google Scholar 
Rezende, M. G., Elias, R. C. L., Salimena, F. R. G. & Neto, L. M. Flora vascular da Serra da Pedra Branca, Caldas, Minas Gerais e relações florísticas com áreas de altitude da Região Sudeste do Brasil. Biota Neotrop. 13, 201–224 (2013).Article 

Google Scholar 
Sarthou, C., Villiers, J. F. & Ponge, J. F. Shrub vegetation on tropical granitic inselbergs in French Guiana. J. Veg. Sci. 14, 645–652 (2003).Article 

Google Scholar 
Tinti, B. V. et al. Plant diversity on granite/gneiss rock outcrop at Pedra do Pato, Serra do Brigadeiro State Park, Brazil. Check List 11, 1780 (2015).Article 

Google Scholar 
Barbara, T., Martinelli, G., Fay, M. F., Mayo, S. J. & Lexer, C. Population differentiation and species cohesion in two closely related plants adapted to neotropical high-altitude “inselbergs”, Alcantarea imperialis and Alcantarea geniculata (Bromeliaceae). Mol. Ecol. 16, 1981–1992 (2007).CAS 
PubMed 
Article 

Google Scholar 
Boisselier-Dubayle, M. C., Leblois, R., Samadi, S., Lambourdière, J. & Sarthou, C. Genetic structure of the xerophilous bromeliad Pitcairnia geyskesii on inselbergs in French Guiana—A test of the forest refuge hypothesis. Ecography 33, 175–184 (2010).Article 

Google Scholar 
Domingues, R. et al. Genetic variability of an endangered Bromeliaceae species (Pitcairnia albiflos) from the Brazilian Atlantic rainforest. Genet. Mol. Res. 10, 2482–2491 (2011).CAS 
PubMed 
Article 

Google Scholar 
Hmeljevski, K. V. et al. Conservation assessment of an extremely restricted bromeliad highlights the need for population-based conservation on granitic inselbergs of the Brazilian Atlantic Forest. Flora Morpho. Distribut. Funct. Ecolo. Plants. 209, 250–259 (2014).Article 

Google Scholar 
Palma-Silva, C. et al. Sympatric bromeliad species (Pitcairnia spp.) facilitate tests of mechanisms involved in species cohesion and reproductive isolation in Neotropical inselbergs. Mol. Ecol. 20, 3185–3201 (2011).CAS 
PubMed 
Article 

Google Scholar 
Gomes, P. & Alves, M. Floristic diversity of two crystalline rocky outcrops in the Brazilian northeast semi-arid region. Rev. Bras. Bot. 33(4), 661–676 (2010).Article 

Google Scholar 
Nunes, J. A., Villa, P. M., Neri, A. V., Silva, W. A. & Schaefer, C. E. G. R. Seasonality drives herbaceous community beta diversity in lithologically different rocky outcrops in Brazil. Plant. Ecol. Evol. 153(2), 208–218 (2020).Article 

Google Scholar 
Speziale, K. L. & Ezcurra, C. The role of outcrops in the diversity of Patagonian vegetation: Relicts of glacial palaeofloras?. Flora Morphol. Distrib. Funct. Ecol. Plant. 207, 141–149 (2012).
Google Scholar 
Speziale, K. L., Ruggiero, A. & Ezcurra, C. Plant species richness–environment relationships across the Subantarctic-Patagonian transition zone. J. Biogeogr. 37, 449–464 (2010).Article 

Google Scholar 
Yates, C. J. et al. High species diversity and turnover in granite inselberg floras highlight the need for a conservation strategy protecting many outcrops. Ecol. Evol. 9, 7660–7675 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Gaston, K. J. Geographic range limits: Achieving synthesis. Proc. R. Soc. B Biol. Sci. 276, 1395–1406 (2009).Article 

Google Scholar 
McGann, T. D. How insular are ecological ‘islands’? An example from the granitic outcrops of the New England Batholith of Australia. Proc. R. Soc. Queensland. 110, 1–13 (2002).
Google Scholar 
Parmentier, I., Stévart, T. & Hardy, O. J. The inselberg flora of Atlantic Central Africa. I. Determinants of species assemblages. J. Biogeogr. 32, 685–696 (2005).Article 

Google Scholar 
Changwe, K. & Balkwill, K. Floristics of the Dunbar Valley serpentinite site, Songimvelo Game Reserve, South Africa. Bot. J. Linn. Soc. 143, 271–285 (2003).Article 

Google Scholar 
Clarke, P. J. Habitat islands in fire-prone vegetation: Do landscape features influence community composition?. J. Biogeogr. 29, 677–684 (2002).Article 

Google Scholar 
De Bello, F., Leps, J. & Sebastia, M. T. Variations in species and functional plant diversity along climatic and grazing gradients. Ecography 29(6), 801–810 (2006).Article 

Google Scholar 
Porembski, S., Martinelli, G., Ohlemüller, R. & Barthlott, W. Diversity and ecology of saxicolous vegetation mats on inselbergs in the Brazilian Atlantic rainforest. Divers. Distrib. 4, 107–119 (1998).Article 

Google Scholar 
Porembski, S., Szarzynski, J., Mund, J. P. & Barthlott, W. Biodiversity and vegetation of small-sized inselbergs in a West African rain forest (Taï, Ivory Coast). J. Biogeogr. 23, 47–55 (1996).Article 

Google Scholar 
Rahmanian, S. et al. Effects of livestock grazing on soil, plant functional diversity, and ecological traits vary between regions with different climates in northeastern Iran. Ecol. Evol. 9, 8225–8237 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Speziale, K. L. & Ezcurra, C. Patterns of alien plant invasions in northwestern Patagonia, Argentina. J. Arid Environ. 75, 890–897 (2011).ADS 
Article 

Google Scholar 
Qian, H., Chen, S. H. & Zhang, J. L. Disentangling environmental and spatial effects on phylogenetic structure of angiosperm tree communities in China. Sci. Rep. 7, 5864 (2017).ADS 
Article 
CAS 

Google Scholar 
Farzam, M. & Ejtehadi, H. Effects of drought and canopy facilitation on plant diversity and abundance in a semiarid mountainous rangeland. J. Plant. Ecol. 10(4), 626–633 (2016).
Google Scholar 
Heino, J. & Tolonen, K. T. Ecological drivers of multiple facets of beta diversity in a lentic macroinvertebrate metacommunity. Limnol. Oceanogr. 62, 2431–2444. https://doi.org/10.1002/lno.10577 (2017).ADS 
Article 

Google Scholar 
Miranda, J. D., Armas, C., Padilla, F. M. & Pugnaire, F. I. Climatic change and rainfall patterns: Effects on semi-arid plant communities of the Iberian Southeast. J. Arid. Environ. 75, 1302–1309 (2011).ADS 
Article 

Google Scholar 
Pashirzad, M., Ejtehadi, H., Vaezi, J. & Shefferson, R. P. Multiple processes at different spatial scales determine beta diversity patterns in a mountainous semi-arid rangeland of Khorassan-Kopet Dagh floristic province, NE Iran. Plant. Ecol. 220(9), 829–844 (2019).Article 

Google Scholar 
Victorero, L., Robert, K., Robinson, L. F., Taylor, M. L. & Huvenne, V. A. I. Species replacement dominates megabenthos beta diversity in a remote seamount setting. Sci. Rep. 8, 4152 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Deil, U. Rock communities in tropical Arabia. Flora et Vegetation Mundi 9, 175–187 (1991).
Google Scholar 
Dimopoulos, P., Sýkora, K. V., Mucina, L. & Georgiadis, T. The high-rank syntaxa of the rock-cliff and scree vegetation of the mainland Greece and Crete. Folia Geobot. 32, 313–334 (1997).Article 

Google Scholar 
Hein, P., Kürschner, H. & Parolly, G. Phytosociological studies on high mountain plant communities of the Taurus Mountains (Turkey) 2. Rock communities. Phytocoenologia 28, 465–563 (1998).Article 

Google Scholar 
Nowak, A., Nowak, S., Nobis, M. & Nobis, A. Vegetation of rock clefts and ledges in the Pamir Alai Mts, Tajikistan (Middle Asia). Cent. Eur. J. Biol. 9, 444–460 (2014).
Google Scholar 
Urbis, A. & Blazyca, B. Rock vascular plant species of the Kraków-Częstochowa, Uplands. Thaiszia J. Bot. 21, 207–214 (2011).
Google Scholar 
Wiser, S. K., Peet, R. K. & White, P. S. High-elevation rock outcrop vegetation of the Southern Appalachian Mountains. J. Veg. Sci. 7, 703–722 (1996).Article 

Google Scholar 
Cadotte, M. W. Experimental evidence that evolutionarily diverse assemblages result in higher productivity. PNAS 110(22), 8996–9000 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Swenson, G.N. Functional and Phylogenetic Ecology in R (Use R!) Kindle Edition (2014).Cadotte, M. W. & Davies, P. R. Why phylogenies do not always predict ecological differences. Ecol. Monogr. 87(4), 535–551 (2016).Article 

Google Scholar 
De Bello, F., LepŠ, J. A. N. & Sebastià, M. T. Predictive value of plant traits to grazing along a climatic gradient in the Mediterranean. J. Appl. Ecol. 42(5), 824–833 (2005).Article 

Google Scholar 
Funk, J. et al. Revisiting the Holy Grail: Using plant functional traits to understand ecologica processes. Biol. Rev. 92(2), 1156–1173 (2017).PubMed 
Article 

Google Scholar 
Lavorel, S. & Garnier, É. Predicting changes in community composition and ecosystem functioning from plant traits: Revisiting the Holy Grail. Funct. Ecol. 16(5), 545–556 (2002).Article 

Google Scholar 
Violle, C. et al. Let the concept of trait be functional!. Oikos 116, 882–892 (2007).Article 

Google Scholar 
Zheng, S., Li, W., Lan, Z., Ren, H. & Wang, K. Functional trait responses to grazing are mediated by soil moisture and plant functional group identity. Sci. Rep. 5, 18163 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gillison, A. N. Plant functional types and traits at the community, ecosystem and world level. In Vegetation Ecology (eds van der Maarel, E. & Franklin, J.) 347–386 (Wiley, 2013).Chapter 

Google Scholar 
Loreau, M. Biodiversity and ecosystem functioning: Recent theoretical advances. Oikos 91, 3–17 (2000).Article 

Google Scholar 
Akhani, H., Djamali, M., Ghorbanalizadeh, A. & Ramezani, E. Plant biodiversity of Hyrcanian relict forests, N Iran: An overview of the flora, vegetation, paleoecology and conservation. Pak. J. Bot. 42, 231–258 (2010).
Google Scholar 
Hamzehee, B. et al. Phytosociological survey of remnant Alnus glutinosa ssp. barbata communities in the lowland Caspian forests of northern Iran. Pytocoenologia. 38, 117–132 (2008).Article 

Google Scholar 
Moradi, H. et al. Elevational gradient and vegetation-environmental relationships in the central Hyrcanian forests of northern Iran. Nord. J. Bot. 34, 1–14 (2016).Article 

Google Scholar 
Naqinezhad, A., Esmailpoor, A. & Jafari, N. A new record of Pyrola minor (Pyrolaceae) for the flora of Iran as well as a description of its surrounding habitats. Taxon. Biosyst. 22, 71–80 (2015).
Google Scholar 
Naqinezhad, A., Zare-Maivan, H. & Gholizadeh, H. A floristic survey of the Hyrcanian forests in Northern Iran, using two lowland-mountain transects. J. For. Res. 26, 187–199 (2015).CAS 
Article 

Google Scholar 
Sagheb-Talebi, K., Sajedi, T. & Pourhashemi, M. Forests of Iran (Springer Sci, 2014).Book 

Google Scholar 
Siadati, S. et al. Botanical diversity of Hyrcanian forests; a case study of a transect in the Kheyrud protected lowland mountain forests in northern Iran. Phytotaxa 7, 1–18 (2010).Article 

Google Scholar 
Akhani, H. & Ziegler, H. Photosynthetic pathways and habitats of grasses in Golestan National Park (NE Iran), with an emphasis on the C 4-grass dominated rock communities. Phytocoenologia 32, 455–501 (2002).Article 

Google Scholar 
Akhani, H., Mahdavi, P., Noroozi, J. & Zarrinpour, V. Vegetation patterns of the Irano-Turanian steppe along a 3,000 m altitudinal gradient in the Alborz Mountains of Northern Iran. Folia Geobot. 48, 229–255 (2013).Article 

Google Scholar 
Klein, J. C. The altitudinal vegetation Alborez The Central (Iran) between the Iranian-Turanian and Euro-Siberian regions (French) (Institut Français de Recherche en Iran, 2001).
Google Scholar 
Noroozi, J. Case study: High Mountain Regions in Iran 255–260. of Chapter 7 (Endemism in mainland regions-case studies). In Endemism in Vascular plants. Plant. Veg. (ed Hobohm, C.) 9. (Springer, 2014).Noroozi, J., Akhani, H. & Willner, W. Phytosociological and ecological study of the high alpine vegetation of Tuchal Mountains (Central Alborz, Iran). Phytocoenologia 40, 293–321 (2010).Article 

Google Scholar 
Do Carmo, F. F. & Jacobi, C. M. Diversity and plant trait-soil relationships among rock outcrops in the Brazilian Atlantic rainforest. Plant Soil. 403, 7–20 (2015).Article 
CAS 

Google Scholar 
Cavender-Bares, J., Kozak, K. H., Fine, P. V. A. & Kembel, S. The merging of community ecology and phylogenetic biology. Ecol Lett. 12, 693–715 (2009).PubMed 
Article 

Google Scholar 
Heydari, M., Poorbabaei, H., Esmailzadeh, O., Salehi, A. & EshaghiRad, J. Indicator plant species in monitoring forest soil conditions using logistic regression model in Zagros Oak (Quercus brantii var. persica) forest ecosystems. Ilam city. J. Plant Res. 27(5), 811–828 (2014).
Google Scholar 
Speziale, K. L. & Ezcurra, C. Rock outcrops as potential biodiversity refugia under climate change in North Patagonia. Plant Ecol. Diver. 8, 353–361 (2014).Article 

Google Scholar 
Rahmanian, S. et al. Effects of livestock grazing on plant species diversity vary along a climatic gradient in northeastern Iran. Appl. Veg. Sci. 23, 551–561 (2020).Article 

Google Scholar 
Huston, M. A. Biological Diversity: The Coexistence of Species in Changing Landscape (Cambridge University, 1994).
Google Scholar 
Mason, N. W., Mouillot, D. & Lee, W. G. Functional richness, functional evenness and functional divergence: The primary components of functional diversity. Oikos 111, 112–118 (2005).Article 

Google Scholar 
Stubbs, W. J. & Wilson, J. B. Evidence for limiting similarity in a sand dune community. J. Ecol. 92, 557567 (2004).Article 

Google Scholar 
Stanisci, A. et al. Functional composition and diversity of leaf traits in subalpine versus alpine vegetation in the Apennines. Ann. Bot. Comp. plants. 12, plaa004 (2020).CAS 

Google Scholar 
Chesson, P. et al. Resource pulses, species interactions, and diversity maintenance in arid and semi-arid environments. Oecologia 141, 236–253 (2004).ADS 
PubMed 
Article 

Google Scholar 
Rosbakh, S. et al. Contrasting effects of extreme drought and snowmelt patterns on mountain plants along an elevation gradient. Front. Plant Sci. 8, 1478 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Korner, C. Alpine Treelines: Functional Ecology of the Global High Elevation tree Limits (Springer Sci. & Business Media, 2012).Book 

Google Scholar 
Reich, P. B. et al. Generality of leaf trait relationships: A test across six biomes. Ecology 80, 1955–1969 (1999).Article 

Google Scholar 
Westoby, M., Falster, D. S., Moles, A. T., Vesk, P. A. & Wright, I. J. Plant ecological strategies: Some leading dimensions of variation between species. Ann. Rev. Ecol. Syst. 33, 125–159 (2002).Article 

Google Scholar 
Hautier, Y., Niklaus, P. A. & Hector, A. Competition for light causes plant biodiversity loss after eutrophication. Science 324, 636–638 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
De Bello, F. D. et al. Hierarchical effects of environmental filters on the functional structure of plant communities: A case study in the French Alps. Ecography 36, 393–402 (2013).Article 

Google Scholar 
Korner, C., Neumayer, M., Menendez-Riedl, S. P. & Smeets-Scheel, A. Functional morphology of mountain plants. Flora 182, 353–383 (1989).Article 

Google Scholar 
Rosbakh, S., Römermann, C. & Poschlod, P. Specific leaf area correlates with temperature new evidence of trait variation at the population, species and community levels. Alp. Bot. 125, 79–86 (2015).Article 

Google Scholar 
Ordonez, J. C. et al. Global study of relationships between leaf traits, climate and soil measures of nutrient fertility. Glob. Ecol. Biogeogr. 18, 137–149 (2009).Article 

Google Scholar 
Li, W. et al. Community-weighted mean traits but not functional diversity determine the changes in soil properties during wetland drying on the Tibetan Plateau. Solid Earth. 8, 137–147 (2017).ADS 
Article 

Google Scholar 
Bardgett, R. D., Mommer, L. & De Vries, F. T. Going underground: Root traits as drivers of ecosystem processes. Trends Ecol. Evol. 29, 692–699 (2014).PubMed 
Article 

Google Scholar 
Lane, D. R., Coffin, D. P. & Lauenroth, W. K. Effects of soil texture and precipitation on above-ground net primary productivity and vegetation structure across the Central Grassland region of the United States. J. Veg. Sci. 9, 239–250 (1998).Article 

Google Scholar 
Noy-Meir, I. Multivariate analysis of the semi-arid vegetation of southern Australia. II. Vegetation catenae an environmental gradients. Aust. J. Bot. 22, 40–115 (1973).
Google Scholar 
Moura, M. R., Villalobos, F., Costa, G. C. & Garcia, P. C. A. Disentangling the role of climate, topography and vegetation in species richness gradients. PLoS ONE 11(3), 0152468 (2016).Article 
CAS 

Google Scholar 
Neri, A. V. et al. Soil and altitude drives diversity and functioning of Brazilian Páramos (Campo de Altitude). J. plant. Ecol. 10(5), 771–779 (2016).
Google Scholar 
Benites, V. M., Schaefer, C. E. G. R., Simas, F. N. B., Santos, H. G. & Mendonca, B. A. F. Soils associated to rock outcrops in the Brazilian mountain ranges Mantiqueira and Espinhaço. Rev. Bras. Bot. 30, 569–577 (2007).Article 

Google Scholar 
Flynn, D. F. B. et al. Loss of functional diversity under land use intensification across multiple taxa. Ecol. Lett. 12, 22–33 (2009).PubMed 
Article 

Google Scholar 
Zuo, X. A. et al. Testing associations of plant functional diversity with along a restoration gradient of sandy grassland. Front. Plant. Sci. 7, 1–11 (2016).ADS 
Article 

Google Scholar 
Myers-Smith, I. H. et al. Shrub expansion in tundra ecosystems: Dynamics, impacts and research priorities. Environ. Res. Lett. 6, 045509 (2011).ADS 
Article 

Google Scholar 
Vankoughnett, M. R. & Grogan, P. Nitrogen isotope tracer acquisition in low and tall birch tundra plant communities: A 2-year test of the snow–shrub hypothesis. Biogeochemistry 118, 291–306 (2014).CAS 
Article 

Google Scholar 
Pescador, D. S., de Bello, F., Valladares, F. & Escudero, A. Plant trait variation along an altitudinal gradient in Mediterranean high mountain grasslands: Controlling the species turnover effect. PLoS ONE 10, e0118876 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Pescador, D. S., Sierra-Almeida, A., Torres, P. J. & Escudero, A. Summer freezing resistance: A critical filter for plant community assemblies in Mediterranean high mountains. Front. Plant. Sci. 7, 194 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Heydarnejad, S. & Ranjbar, A. Investigation of the effect of salinity stress on growth characteristic and ion accumulation in plants. J. Desert Ecos. Eng. 3(4), 1–10 (2013).
Google Scholar 
Perez-Harguindeguy, N. et al. New handbook for standardized measurement of plant functional traits worldwide. Aust. J. Bot. 61, 167–234 (2013).Article 

Google Scholar 
Cornelissen, J. H. C. et al. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust. J. Bot. 51, 335–380 (2003).Article 

Google Scholar 
Raunkiaer, C. The Life Forms of Plants and Statistical Plant Geography (Oxford University Press, 1934).
Google Scholar 
Gee, G. W. & Bauder, J. W. Particle size analysis. In Methods of Soil Analysis. Part 1, 2nd ed. (ed Klute, A.) Agronomy Monographs, Vol. 9, 383–409 (Am. Soc. Agr., 1986).Bremner, J. M. In Nitrogen-Total Methods of Soil Analysis. (eds Sparks, D. L.) Soil Sci Soc Am J. 1085–1122 (Am Soc Agr. Inc, 1996).Walkley, A. & Black, I. A. An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Sci. 37, 29–38 (1934).ADS 
CAS 
Article 

Google Scholar 
Nelson, D. W. & Sommers, L. Total carbon, organic carbon, and organic matter 1. Methods of soil analysis. Part 2. Chemical and microbi‐ological properties, (methodsofsoilan2), 539–579 (1982).Miller, R. H. & Keeney, D. R. Methods of soil analysis, 2nd ed. In Part 2. Chemical and Microbiological Properties (eds Page, A. L. et al.) 1–129 (ASA, SSSA, 1982).
Google Scholar 
Food and Agriculture Organization-FAO. Management of gypsiferous soils. Soil Bulletin, 62, (FAO, 1990).Chao, A. et al. Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67 (2014).Article 

Google Scholar 
Shipley, B., Vile, D. & Garnier, É. from plant traits to plant communities: A statistica mechanistic approach to biodiversity. Science 314(5800), 812–814 (2006).ADS 
MathSciNet 
CAS 
PubMed 
MATH 
Article 

Google Scholar 
Zhu, J., Jiang, L. & Zhang, Y. Relationships between functional diversity and aboveground biomass production in the Northern Tibetan alpine grasslands. Sci. Rep. 6, 34105 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Laliberte, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91(1), 299–305 (2010).PubMed 
Article 

Google Scholar 
Wheeler, D. & Tiefelsdorf, M. Multicollinearity and correlation among local regression coefficients in geographically weighted regression. J. Geogr. Syst. 7, 161–187 (2005).Article 

Google Scholar 
Fox, J. & Weisberg, S. A review of: an R companion to applied regression, second edition. J. Biopharm. Stat. 22, 418–419 (2011).
Google Scholar 
Brien, R. M. A caution regarding rules of thumb for variance inflation factors. Qual. Quant. 41, 673–690 (2007).Article 

Google Scholar 
Dray, S., Legendre, P. & Blanchet, F. G. packfor: forward selection with permutation (Canoco p. 46). (2011) http://R-Forge.R-project.org/projects/sedar (Accessed 7 Nov 2016).Blanchet, F. G., Legendre, P. & Borcard, D. Forward selection of explanatory variables. Ecology 89, 2623–2632 (2008).PubMed 
Article 

Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package (2017).Wickham, H. et al. Ggplot2: Elegant Graphics for Data Analysis 2nd edn. (Springer International Publishing, 2016).MATH 
Book 

Google Scholar  More

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Choice of biowaste treatment plants and sample identifiersCompost samples were collected from four central municipal biowaste treatment plants (denominated as #1 to #4) in Baden-Wurttemberg, Germany (Table 1). All plants used a state-of-the-art two-stage biowaste treatment process comprising of (a) anaerobic digestion/biogas production and (b) subsequent composting of the solid digestate to produce a high-quality mature compost sold for direct use as fertilizer in agriculture. The composts were regularly analyzed by an independent laboratory for quality and residual contamination and consistently fulfilled the quality requirements of the label RAL-GZ 251 Gütezeichen Kompost of the German Bundesgütegemeinschaft Kompost e.V. (www.gz-kompost.de). Plants #1 and #3 produce in addition a liquid fertilizer, which is separated from the solid digestate at the end of stage a) by press filtration and which is also intended for direct use on agricultural soil (replacement of liquid manure). In case of plants #1, #3, and #4 up to 25 wt% of shrub/tree cuttings were added to the solid digestate for composting. All plants used sieving (typically with a 12 or a 20 mm mesh) at the end of the process to assure the necessary purity of their finished composts. Whenever technically possible, we as well took samples of the pre-compost immediately before this final sieving step to evaluate its contribution to the removal of residual BPD fragments. For analysis, composts were passed consecutively through two sieves with mesh sizes of 5 mm and 1 mm, yielding two fragment preparations for IR-analysis namely a > 5 mm fraction corresponding to the contamination by residual “macroplastic” (5 mm is a commonly used upper size limit for “microplastic”, anything larger is macroplastic) and a 1–5 mm fraction corresponding to the regulatory relevant residual contamination by microplastic. The lower limit of 1 mm rather than 2 mm was chosen in anticipation of the expected changes in regulation, where the replacement of the 2 mm limit by a 1 mm limit is imminent.Table 1 Technical data of the investigated plants and incidence of BDP fragments in the sampled composts.Full size tableOccurrence of plastic fragments  > 1 mm in the sampled compostsComposting times of 5–9 weeks were used in the investigated plants (Table 1), which is shorter than the 12 weeks indicated in EN 13432 for the 90% disintegration of a compostable plastic material, but a realistic time span for state-of-the-art technical waste treatment. Since we were not in a position to estimate the quantity of BDP entering the plants, since for technical reasons we were unable to obtain a representative sample, we cannot say, whether any residual BDP detected by us in the finished composts was due to a yet incomplete disintegration process or whether it corresponds to the 10% material still permissible by EN 13432 even after the full composting step. However, in 7 out of the 12 sampled composts and pre-composts fragments with chemical signatures corresponding to the BDPs poly (lactic acid) (PLA) and poly (butylene-adipate-co-terephthalate) (PBAT) were identified in the > 5 mm and/or the 1–5 mm sieving fractions using FTIR analysis3 (Fig. 1; Table 1). All recovered fragments appeared to stem from foils, bags or packaging, since they were thin compared to their length and width (see Suppl Figure S1 for typical examples). Fragments with overlapping signatures, most likely PBAT/PLA mixtures or blends, were also found (see Suppl Figure S2 for the interpretation of the spectra). In addition, the recorded BDP fragment spectra (Fig. 1A) showed high similarity to the FTIR spectra of commercial compostable bags sold in the vicinity of the biowaste treatment plants (Fig. 1B), which together with the geometry of the recovered fragments led us to assuming that the majority of the BDP entered the biowaste in the form of such bags.Figure 1FTIR spectra of BDP fragments from composts and commercial bags. (A) BDP fragments recovered from the composts and (B) the commercial compostable bags. Fragments were coded as follows: p or f for pre-compost or finished compost, followed by the plant number (#1 to #4), an indication of the size fraction ( > 5 mm or 1–5 mm) in which the fragment was found, and finally, the fragment number. Fragment F#1_5mm_4 therefore represents the 4th fragment collected in the  > 5 mm size fraction from the finished compost of plant number 1. Bags were arbitrarily numbered 1–10, see Suppl Table S1 for supplier information. The spectra (in grey) of the reference materials for PLA and PBAT are given as basis for the interpretation. Spectra in red refer to test samples consisting only of PBAT, while those in blue indicate samples composed of PBAT/PLA mixtures.Full size imageThe BDP fragments were found alongside fragments of commodity plastics (mostly PE) in all cases. Finished composts tended to contain fewer and smaller fragments than the corresponding pre-composts. The final sieving of the pre-composts to prepare the finished composts hence appears to be quite effective in removing such fragments, in particular those from the > 5 mm size fraction (Table 1) and for that reason has become state-of-the-art in preparing quality composts (contamination by plastic fragments > 2 mm of less than 0.1 wt%). Given that the size of the fragments is a crucial factor regarding ecological risk, we analyzed the sizes (length Î width) of the BDP fragments in comparison to that of the plastic fragments with signatures of commodity plastics such as PE (Fig. 2). BDP fragments found in a given compost sample tended to be smaller than the fragments stemming from non-BDP materials, which may indicate that BDPs degrade faster or tend to disintegrate into tinier particles than commodity plastics. This may also explain why in the compost from plant #2, no BDP fragments were found in the particle fraction retained by the 5 mm sieve ( > 5 mm fraction), while 19 such particles were found in the fraction then retained by the 1 mm sieve (1–5 mm fraction). Interestingly, plant #2 is the only one included in our study that uses no mechanical breakdown of the incoming biowaste. This reduces the mechanical stress on the incoming material. Mechanical stress can alter the properties of plastic foils such as the crystallinity whereby crystallinity has been shown to influence the biological degradation of BDP such as PLA7.Figure 2Size distribution of plastic fragments  > 1 mm. (A) Fragments found in the finished compost from plant #1, (B) in the finished compost from plant #2, and (C) in the pre-compost from plant #3. For reasons of statistical relevance, only samples containing more than 20 BDP fragments per kg of compost were included in the analysis.Full size imageMaterial characteristics of BDP fragments in comparison to those of commercial biodegradable bagsIn order to verify whether the BDP fragments recovered from the composts differed from the compostable bags in any parameter with possible relevance for biodegradation and environmental impact16, the physico-chemical properties of bags and fragments were studied in detail. Since we wanted to have a maximum of information of the BDP fragments, size/weight was a limiting factor in selecting fragments for analysis. Fragments of at least 1 mg were required for the FT-IR analysis. 5 mg-fragments could be analyzed in addition by 1H-NMR, while the full set of analytics (FT-IR, 1H-NMR, and DSC) required at least 10 mg of sample.For insight into the chemical composition, 1H-NMR spectra of the commercial bags and all suitable BDP fragments were compared (Fig. 3). In case of material mixtures and blends, the 1H-NMR analysis allows quantification of the PBAT/PLA weight ratio in the materials and also of the ratio of the butylene terephthalate (BT) and butylene adipate (BA) units in the involved PBAT polyesters.Figure 31H NMR spectra of BDP fragments from composts and commercial bags. (A) BDP fragments recovered from the composts and (B) the commercial compostable bags. Fragments were coded as follows: p or f for pre-compost or finished compost, followed by the plant number (#1 to #4), an indication of the size fraction ( > 5 mm or 1–5 mm) in which the fragment was found, and finally, the fragment number. Bags were arbitrarily numbered 1–10, see Suppl Table S1 for supplier information. The spectra (in grey) of the reference materials for PLA and PBAT are given as basis for the interpretation. Spectra in red refer to test samples consisting only of PBAT, while those in blue indicate samples composed of PBAT/PLA mixtures. (C) Chemical structures of PLA and PBAT, chemical shifts of the protons are assigned as indicated in the reference spectra in (B).Full size imageThe 1H-NMR spectra corroborate the FTIR measurements in that all investigated commercial bags were made from PBAT/PLA mixtures of varied composition (Table 2). By comparison, some of the fragments, for instance, f#1_5mm_4, appeared to consist of only PBAT. Other fragments, e.g., f#1_1mm_9, were mixtures of PLA and PBAT (Table 2). However, even in the case of PBAT/PLA mixtures, the average PBAT content tended to be higher in the fragments than in the bags, while the BT/BA monomer ratio in the respective PBATs, was also significantly higher in the fragments than in the bags. If we assume the fragments to stem from similar compostable bags as the ones included in our comparison, this would mean that during composting of such a bag, the PLA degrades more quickly than the PBAT, whereas within a given PBAT polyester, the BA unit is more easily degraded than the BT unit. Evidence can indeed be found in the pertinent literature that PLA has faster biodegradation kinetics than PBAT, while BT is more resistant to mineralization than BA17,18.Table 2 Composition of commercial compostable bags and BDP fragments recovered from the composts as analyzed by 1H-NMR.Full size tableNext, differential scanning calorimetry (DSC) was used to analyze fragments compared to commercial bags in regard to the presence of amorphous vs. crystalline domains, a parameter expected to affect biodegradation kinetics and therefore the putative environmental impact of the produced microplastic16 upon release into the environment with the composts. Whereas amorphous domains show glass transition, crystalline domains show melting, both of which can be discerned by the respective phase transition enthalpy in the DSC curves (Fig. 4).Figure 4DSC curves of BDP fragments and compostable bags #1 and #7. Curves for the reference materials (in grey) for PLA and PBAT are given for comparison. Curves were recorded during the first heating run (temperature range: − 50 °C to 200 °C, heating rate: 10 °C min−1). (A) and (B) curves in red refer to test samples consisting only of PBAT, while those in blue indicate samples composed of PBAT/PLA mixtures. Fragments were coded as follows: p or f for pre-compost or finished compost, followed by the plant number (#1 to #4), an indication of the size fraction ( > 5 mm or 1–5 mm) in which the fragment was found, and finally, the fragment number.Full size imageThe curve for the reference PBAT shows a glass transition temperature (Tg) of − 29 °C and a broad melting range between 100 and 140 °C for the crystalline domains, while that of the PLA reference shows a glass transition temperature of 58 °C and a narrower melting peak between 144 °C and 162 °C. The curve for commercial bag #1, which had a comparatively high PLA content, shows a pronounced melting peak in the expected range; the same is the case for fragment p#3_5mm_1 and to a lesser extent for fragment p#3_5mm_9, two fragments, which also have high PLA contents. The DSC curves of the other fragments and bag #1 are undefined in comparison, which is due to their high PBAT content. According to the DSC curves, most of the investigated materials are semicrystalline, i.e., contain both amorphous (glass transition) and crystalline (melting) domains. However, the DCS data alone allow only a qualitative discussion of the differences between fragments and bags.To obtain quantitative data on the crystallinity differences, wide angle X-ray scattering (WAXS) spectra were recorded. WAXS requires fragments at least 3 cm long, which restricted the number of fragment samples to three, all of which were found in pre-compost samples. The corresponding curves are shown in Fig. 5A–C. The spectra of the commercial biodegradable bags are shown in Suppl Figure S3. Foils were in addition prepared by heat pressing from the reference materials for PLA and PBAT in order to include them into the WAXS measurements (Fig. 5D). While the foils produced from the PBAT reference material produced crystallinity peaks at 16.2°, 17.3°, 20.4°, 23.2°, and 24.8°, the foil prepared from the PLA reference material showed only an amorphous halo at 15.5° and 31.5°, which is in accordance with values published in the literature19. A more pronounced crystallinity peak was obtained in the case of an additionally annealed PLA foil.Figure 5WAXS curves with Lorenz fitting for (A) fragment p#3_5mm_1, (B) fragment p#3_5mm_9, and (C) fragment p#4_5mm_2. (D) WAXS curves for foils produced from the PBAT and PLA reference materials; the percent values indicate the crystallinity. The dash lines are the fitting peak curves for the XRD spectrum. Crystallinity can be obtained by dividing the integration area of the fitted peaks by the integration area of the entire spectrum. Fragments were coded as follows: p or f for pre-compost or finished compost, followed by the plant number (#1 to #4), an indication of the size fraction ( > 5 mm or 1–5 mm) in which the fragment was found, and finally, the fragment number.Full size imageIn case of the fragments and bags, the peaks of PLA and PBAT overlapped to some extent in the WAXS spectra, but by conducting Lorenz fitting using Origin software, the overall crystallinity could be calculated as follows:$$chi = { 1}00% , *{text{ Aa}}/left( {{text{Aa }} + {text{ Ac}}} right)$$where χ is the crystallinity and Aa and Ac represent the areas of the amorphous and crystalline peaks.Using this equation, crystallinities of 55% (fragments p#3_5mm_1), 34% (p#3_5mm_9), and 34% (p#4_5mm_2) were calculated for the fragments. The foils prepared in house for the reference materials had similar crystallinities (43% in case of the annealed PLA foil and 26% of the PBAT foil), while the simple PLA foil was amorphous. By comparison, for eight of the commercial bags, crystallinities in the range from 1% to 7% were calculated, whereas these values were 14% and 15% for the remaining two bag types (Suppl Figure S3).The high crystallinity of the larger fragments recovered from the pre-compost samples suggests that crystalline domains of BDP materials may indeed disintegrate more slowly than the amorphous ones, as prior studies on microbial biodegradation have suggested7,8. Admittedly, such large fragments per se would not enter the environment, since the final sieving step used to prepare the finished composts is quite efficient at removing them. However, it is tempting to extrapolate that residual BDP in general are remnants of the more crystal domains of the original material, even though experimental proof of this assumption is at present not possible. 10 wt% of a BDP bag is allowed to remain after standard composting. It is usually assumed that any such residues continue to degrade with comparable speed. However, should these residues correspond to the more crystalline domains, rather than degrading with similar speed as the bulk material, the more crystalline fragments can be expected to persist for a much longer and at present unpredictable length of time in the environment, e.g. when applied to the soil with the composts; in particular, when they are also enriched in PBAT and BT units as suggested by our analysis of the chemical composition. Data from the use of biodegradable foils in agriculture show that the degradation in the environment may take years20. Altogether this may have unforeseen economic and environmental consequences, especially when considering the high fraction of BDP fragments < 5 mm. Putative consequences include changes in soil properties, the soil microbiome and therefore in plant performance21, a factor indispensable for worldwide nutrition.Residues of BDP fragments  1 mm were found in the collected LF samples. This is hardly surprising, given that the LF is produced by press filtration of the digestate after the anaerobic stage. Such a filtration step can be expected to retain fragments > 1 mm in the produced filter cake, which goes into the composting step, leaving the filtrate, i.e. the LF, essentially free of such particles. Anaerobic digestion is currently not assumed to contribute significantly to the degradation of BDP17,22, but the process conditions (mixing, pumping) may promote breakdown of larger fragments, particularly when additives such as plasticizers23 leach out of the material.Since the residual solids content of the LF is low (plant #1: 8.6 wt%, plant #3: 5.8 wt%), a combination of enzymatic-oxidative treatment and µFTIR imaging originally developed for environmental samples from aqueous systems24,25 could be adapted for the analysis (size and chemical signature) of particles in the LF down to a size of 10 µm. The corresponding data are compiled in Table 3. In all cases, residual fragments from PBAT-based polymers represented the dominant plastic fraction in the investigated samples; i.e. approximately 53% of all plastic particles in the LF from plant #1 (11,520 BDP particles per liter) and 65% in the case of plant #3 (12,480 BDP particles per liter). Liquid manure is applied several times a year to fields at a concentration of 2–3 L m−2. According to our analysis > 20,000 BDP microparticles of a size ranging from 10 µm to 500 µm enter each m2 of agricultural soil whenever LF is applied on agricultural surfaces.Table 3 Microplastic fragments (BDP/all) found per liter of liquid fertilizer.Full size tableDue to the complexity of the matrix, a similar analysis of individual plastic fragments  1 mm. Six compost samples representing the more contaminated ones based on the content of fragments > 1 mm, namely, f#1, f#2, p#3, f#3, p#4 and f#4 (nomenclature: f or p for finished or pre-compost, followed by plant number), were extracted with a 90/10 vol% chloroform/methanol mixture. The amounts of PBAT and PLA in the obtained extracts were then quantified via 1H-NMR (Table 4). Briefly, the intensity of characteristic signals in the extract spectra of the compost samples (see Suppl Figure S4) were compared to peak intensities produced by calibration standards of the pure polymer dissolved at a known concentration in the chloroform/methanol. All samples and standards were normalized using the 1,2-dichloroethan signal at 3.73 ppm as internal standard. See also Suppl Figure S5 for an exemplification of the quantification of the PBAT/PLA ratios. Based on the amounts of PBAT and PLA extracted from a known amount of compost, the total mass concentration (wt% dry weight) of these polymers in the composts was calculated.Table 4 Evidence of PBAT and PLA residues caused by fragments  2 mm. Moreover, residues of PBAT and PLA were found in all investigated compost samples, including the finished compost from plant #4, which had shown no contamination by larger BPD fragments (Table 1). The pre-compost from that plant had shown a few contaminating BDP fragments in the > 5 mm fraction. However, in regard to the fragments More

Jones, J. W. et al. The DSSAT cropping system model. Eur. J. Agron. 18, 235–265 (2003).Article 

Google Scholar 
van Diepen, C. A., Wolf, J., van Keulen, H. & Rappoldt, C. WOFOST: a simulation model of crop production. Soil Use Manag. 5, 16–24 (1989).Article 

Google Scholar 
Cao, J. et al. Integrating multi-source data for rice yield prediction across China using machine learning and deep learning approaches. Agric. For. Meteorol. 297, 108275 (2021).ADS 
Article 

Google Scholar 
Khanal, S., Kushal, K. C., Fulton, J. P., Shearer, S. & Ozkan, E. Remote sensing in agriculture—accomplishments, limitations, and opportunities. Remote Sens. 12, 3783 (2020).ADS 
Article 

Google Scholar 
Maas, S. J. Parameterised model of gramineous crop growth: II. within-season simulation calibration. Agron. J. 85, 354–358 (1993).Article 

Google Scholar 
Nguyen, V., Jeong, S., Ko, J., Ng, C. & Yeom, J. Mathematical integration of remotely-sensed information into a crop modelling process for mapping crop productivity. Remote Sens. 11, 2131 (2019).Article 

Google Scholar 
Huang, J. et al. Assimilation of remote sensing into crop growth models: current status and perspectives. Agric. For. Meteorol. 276–277, 107609 (2019).ADS 
Article 

Google Scholar 
Jin, X. et al. A review of data assimilation of remote sensing and crop models. Eur. J. Agron. 92, 141–152 (2018).Article 

Google Scholar 
Shawon, A. R. et al. Assessment of a proximal sensing-integrated crop model for simulation of soybean growth and yield. Remote Sens. 12, 410 (2020).ADS 
Article 

Google Scholar 
Shawon, A. R. et al. Two-dimensional simulation of barley growth and yield using a model integrated with remote-controlled aerial imagery. Remote Sens. 12, 3766 (2020).ADS 
Article 

Google Scholar 
Shin, T. et al. Simulation of wheat productivity using a model integrated with proximal and remotely controlled aerial sensing information. Front. Plant Sci. https://doi.org/10.3389/fpls.2021.649660 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Huang, J. et al. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agric. For. Meteorol. 216, 188–202 (2016).ADS 
Article 

Google Scholar 
Khaki, S., Wang, L. & Archontoulis, S. V. A CNN-RNN framework for crop yield prediction. Front. Plant Sci. https://doi.org/10.3389/fpls.2019.01750 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Kim, N. et al. An artificial intelligence approach to prediction of corn yields under extreme weather conditions using satellite and meteorological data. Appl. Sci. 10, 3785 (2020).CAS 
Article 

Google Scholar 
Kumar, P. et al. Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data. Geocarto Int. 34, 1022–1041 (2019).Article 

Google Scholar 
Everingham, Y., Sexton, J., Skocaj, D. & Inman-Bamber, G. Accurate prediction of sugarcane yield using a random forest algorithm. Agron. Sustain. Dev. 36, 27 (2016).Article 

Google Scholar 
Feng, P., Wang, B., Li Liu, D., Waters, C. & Yu, Q. Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia. Agric. For. Meteorol. 275, 100–113 (2019).ADS 
Article 

Google Scholar 
Shahhosseini, M., Hu, G., Huber, I. & Archontoulis, S. V. Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt. Sci. Rep. 11, 1606 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cai, Y. et al. Detecting in-season crop nitrogen stress of corn for field trials using UAV- and CubeSat-based multispectral sensing. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 12, 5153–5166 (2019).ADS 
Article 

Google Scholar 
van Klompenburg, T., Kassahun, A. & Catal, C. Crop yield prediction using machine learning: a systematic literature review. Comput. Electron. Agric. 177, 105709 (2020).Article 

Google Scholar 
Kamilaris, A. & Prenafeta-Boldú, F. X. Deep learning in agriculture: a survey. Comput. Electron. Agric. 147, 70–90 (2018).Article 

Google Scholar 
Bui, D. T., Tsangaratos, P., Nguyen, V.-T., Liem, N. V. & Trinh, P. T. Comparing the prediction performance of a deep learning neural network model with conventional machine learning models in landslide susceptibility assessment. CATENA 188, 104426 (2020).Article 

Google Scholar 
Sahoo, A. K., Pradhan, C. & Das, H. Performance evaluation of different machine learning methods and deep-learning based convolutional neural network for health decision making. In Nature Inspired Computing for Data Science (eds Rout, M. et al.) (Springer International Publishing, 2020).
Google Scholar 
Jeong, S. et al. Development of Variable Threshold Models for detection of irrigated paddy rice fields and irrigation timing in heterogeneous land cover. Agric. Water Manag. 115, 83–91 (2012).Article 

Google Scholar 
Peng, D., Huete, A. R., Huang, J., Wang, F. & Sun, H. Detection and estimation of mixed paddy rice cropping patterns with MODIS data. Int. J. Appl. Earth Obs. Geoinf. 13, 13–23 (2011).ADS 

Google Scholar 
Jeong, S., Ko, J. & Yeom, J.-M. Nationwide projection of rice yield using a crop model integrated with geostationary satellite imagery: a case study in South Korea. Remote Sens. 10, 1665 (2018).ADS 
Article 

Google Scholar 
Xiao, X. et al. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sens. Environ. 100, 95–113 (2006).ADS 
Article 

Google Scholar 
Ozdogan, M. & Gutman, G. A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: an application example in the continental US. Remote Sens. Environ. 112, 3520–3537 (2008).ADS 
Article 

Google Scholar 
Yeom, J.-M., Jeong, S., Deo, R. C. & Ko, J. Mapping rice area and yield in northeastern Asia by incorporating a crop model with dense vegetation index profiles from a geostationary satellite. GISci. Remote Sens. 58, 1–27 (2021).Article 

Google Scholar 
Yeom, J.-M. et al. Monitoring paddy productivity in North Korea employing geostationary satellite images integrated with GRAMI-rice model. Sci. Rep. 8, 16121 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Jeong, S., Ko, J., Choi, J., Xue, W. & Yeom, J.-M. Application of an unmanned aerial system for monitoring paddy productivity using the GRAMI-rice model. Int. J. Remote Sens. 39, 2441–2462 (2018).Article 

Google Scholar 
Jeong, S. et al. Geographical variations in gross primary production and evapotranspiration of paddy rice in the Korean Peninsula. Sci. Total Environ. 714, 136632 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Roger, P., Vermote, E. & Ray, J. MODIS Surface Reflectance User’s Guide. Collection 6 (2015).Scharlemann, J. P. W. et al. Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data. PLoS ONE 3, e1408 (2008).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pede, T. & Mountrakis, G. An empirical comparison of interpolation methods for MODIS 8-day land surface temperature composites across the conterminous Unites States. ISPRS J. Photogramm. Remote Sens. 142, 137–150 (2018).ADS 
Article 

Google Scholar 
Kilibarda, M. et al. Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution. J. Geophys. Res. Atmos. 119, 2294–2313 (2014).ADS 
Article 

Google Scholar 
Nunez, M. The development of a satellite-based insolation model for the tropical western Pacific Ocean. Int. J. Climatol. 13, 607–627 (1993).Article 

Google Scholar 
Otkin, J. A., Anderson, M. C., Mecikalski, J. R. & Diak, G. R. Validation of GOES-based insolation estimates using data from the U.S. Climate reference network. J. Hydrometeorol. 6, 460–475 (2005).ADS 
Article 

Google Scholar 
Pinker, R. & Laszlo, I. Modeling surface solar irradiance for satellite applications on a global scale. J. Appl. Meteorol. 31, 194–211 (1992).ADS 
Article 

Google Scholar 
Kawamura, H., Tanahashi, S. & Takahashi, T. Estimation of insolation over the Pacific Ocean off the Sanriku coast. J. Oceanogr. 54, 457–464 (1998).Article 

Google Scholar 
Yeom, J.-M., Seo, Y.-K., Kim, D.-S. & Han, K.-S. Solar radiation received by slopes using COMS imagery, a physically based radiation model, and GLOBE. J. Sens. 2016, 1–15 (2016).Article 

Google Scholar 
Yeom, J.-M., Han, K.-S. & Kim, J.-J. Evaluation on penetration rate of cloud for incoming solar radiation using geostationary satellite data. Asia-Pac. J. Atmos. Sci. 48, 115–123 (2012).ADS 
Article 

Google Scholar 
Kawai, Y. & Kawamura, H. Validation and improvement of satellite-derived surface solar radiation over the Northwestern Pacific Ocean. J. Oceanogr. 61, 79–89 (2005).Article 

Google Scholar 
Tanahashi, S., Kawamura, H., Matsuura, T., Takahashi, T. & Yusa, H. A system to distribute satellite incident solar radiation in real-time. Remote Sens. Environ. 75, 412–422 (2001).ADS 
Article 

Google Scholar 
Elbern, H., Schmidt, H., Talagrand, O. & Ebel, A. 4D-variational data assimilation with an adjoint air quality model for emission analysis. Environ. Model. Softw. 15, 539–548 (2000).Article 

Google Scholar 
Press, W. H., Teukolsky, S. A., Vetterling, W. T. & Flannery, B. P. Numerical Recipes: The Art of Scientific Computing (Cambridge University Press, 1992).MATH 

Google Scholar 
Ko, J. et al. Simulation and mapping of rice growth and yield based on remote sensing. J. Appl. Remote Sens. 9, 096067 (2015).Article 

Google Scholar 
Emami Javanmard, M., Ghaderi, S. F. & Hoseinzadeh, M. Data mining with 12 machine learning algorithms for predict costs and carbon dioxide emission in integrated energy-water optimization model in buildings. Energy Convers. Manag. 238, 114153 (2021).CAS 
Article 

Google Scholar 
Diebold, F. X. & Shin, M. Machine learning for regularized survey forecast combination: partially-egalitarian LASSO and its derivatives. Int. J. Forecast. 35, 1679–1691 (2019).Article 

Google Scholar 
Khosla, E., Dharavath, R. & Priya, R. Crop yield prediction using aggregated rainfall-based modular artificial neural networks and support vector regression. Environ. Dev. Sustain. 22, 5687–5708 (2020).Article 

Google Scholar 
Wang, S., Azzari, G. & Lobell, D. B. Crop type mapping without field-level labels: random forest transfer and unsupervised clustering techniques. Remote Sens. Environ. 222, 303–317 (2019).ADS 
Article 

Google Scholar 
Ustuner, M. & Balik, S. F. Polarimetric target decompositions and light gradient boosting machine for crop classification: a comparative evaluation. ISPRS Int. J. Geo Inf. 8, 97 (2019).Article 

Google Scholar 
Jeong, S., Ko, J. & Yeom, J.-M. Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea. Sci. Total Environ. 802, 149726 (2022).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Nash, J. E. & Sutcliffe, J. V. River flow forecasting through conceptual models part I: a discussion of principles. J. Hydrol. 10, 282–290 (1970).ADS 
Article 

Google Scholar  More

Murray, C. J. et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 399, 629–655 (2022).CAS 
Article 

Google Scholar 
Nelson, R. E. et al. National estimates of healthcare costs associated with multidrug-resistant bacterial infections among hospitalized patients in the United States. Clin. Infect. Dis. 72, S17–S26 (2021).PubMed 
Article 

Google Scholar 
Ludden, C. et al. One Health genomic surveillance of Escherichia coli demonstrates distinct lineages and mobile genetic elements in isolates from humans versus livestock. mBio 10, e02693-18 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Gouliouris, T. et al. Genomic surveillance of Enterococcus faecium reveals limited sharing of strains and resistance genes between livestock and humans in the United Kingdom. mBio 9, e01780-18 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Labar, A. S. et al. Regional dissemination of a trimethoprim-resistance gene cassette via a successful transposable element. PLoS ONE 7, e38142 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lamikanra, A. et al. Rapid evolution of fluoroquinolone-resistant Escherichia coli in Nigeria is temporally associated with fluoroquinolone use. BMC Infect. Dis. 11, 312 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kunhikannan, S. et al. Environmental hotspots for antibiotic resistance genes. MicrobiologyOpen 10, e1197 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sulis, G., Sayood, S. & Gandra, S. Antimicrobial resistance in low- and middle-income countries: current status and future directions. Expert Rev. Anti Infect. Ther. 20, 147–160 (2022).CAS 
PubMed 
Article 

Google Scholar 
Okeke, I. N. & Nwoko, E. in Urban Crisis and Management in Africa: A Festschrift (eds Albert, I. O. & Mabogunje, A.) 125–148 (Pan-African Univ. Press, 2019).Doron, A. & Jeffrey, R. Waste of a Nation: Garbage and Growth in India (Harvard Univ. Press, 2018).Nadimpalli, M. L. et al. Urban informal settlements as hotspots of antimicrobial resistance and the need to curb environmental transmission. Nat. Microbiol. 5, 787–795 (2020).CAS 
PubMed 
Article 

Google Scholar 
Okeke, I. & Lamikanra, A. A study of the effect of the urban/rural divide on the incidence of antibiotic resistance in Escherichia coli. Biomed. Lett. 55, 91–97 (1997).
Google Scholar 
Aijuka, M., Charimba, G., Hugo, C. J. & Buys, E. M. Characterization of bacterial pathogens in rural and urban irrigation water. J. Water Health 13, 103–117 (2015).PubMed 
Article 

Google Scholar 
Hendriksen, R. S. et al. Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage. Nat. Commun. 10, 1124 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mahmud, Z. H. et al. Presence of virulence factors and antibiotic resistance among Escherichia coli strains isolated from human pit sludge. J. Infect. Dev. Ctries 13, 195–203 (2019).CAS 
PubMed 
Article 

Google Scholar 
Beukes, L. S., King, T. L. B. & Schmidt, S. Assessment of pit latrines in a peri-urban community in KwaZulu-Natal (South Africa) as a source of antibiotic resistant E. coli strains. Int. J. Hyg. Environ. Health 220, 1279–1284 (2017).PubMed 
Article 

Google Scholar 
Zhang, H., Gao, Y. & Chang, W. Comparison of extended-spectrum β-lactamase-producing Escherichia coli isolates from drinking well water and pit latrine wastewater in a rural area of China. Biomed. Res. Int. 2016, 4343564 (2016).PubMed 
PubMed Central 

Google Scholar 
Nji, E. et al. High prevalence of antibiotic resistance in commensal Escherichia coli from healthy human sources in community settings. Sci. Rep. 11, 3372 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ramblière, L., Guillemot, D., Delarocque-Astagneau, E. & Huynh, B. T. Impact of mass and systematic antibiotic administration on antibiotic resistance in low- and middle-income countries? A systematic review. Int. J. Antimicrob. Agents 58, 106396 (2021).PubMed 
Article 
CAS 

Google Scholar 
Hlashwayo, D. F. et al. A systematic review and meta-analysis reveal that Campylobacter spp. and antibiotic resistance are widespread in humans in sub-Saharan Africa. PLoS ONE 16, e0245951 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Van Boeckel, T. P. et al. Global trends in antimicrobial resistance in animals in low- and middle-income countries. Science 365, eaaw1944 (2019).PubMed 
Article 
CAS 

Google Scholar 
Argudín, M. A. et al. Genotypes, exotoxin gene content, and antimicrobial resistance of Staphylococcus aureus strains recovered from foods and food handlers. Appl. Environ. Microbiol. 78, 2930–2935 (2012).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Sivagami, K., Vignesh, V. J., Srinivasan, R., Divyapriya, G. & Nambi, I. M. Antibiotic usage, residues and resistance genes from food animals to human and environment: an Indian scenario. J. Environ. Chem. Eng. 8, 102221 (2020).CAS 
Article 

Google Scholar 
Wall, B. A. et al. Drivers, Dynamics and Epidemiology of Antimicrobial Resistance in Animal Production (FAO, 2016).Hassani, A. & Khan, G. Human–animal interaction and the emergence of SARS-CoV-2. JMIR Public Health Surveill. 6, e22117 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Madoshi, B. P. et al. Characterisation of commensal Escherichia coli isolated from apparently healthy cattle and their attendants in Tanzania. PLoS ONE 11, e0168160 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Guetiya Wadoum, R. E. et al. Abusive use of antibiotics in poultry farming in Cameroon and the public health implications. Br. Poult. Sci. 57, 483–493 (2016).CAS 
PubMed 
Article 

Google Scholar 
Rousham, E. K., Unicomb, L. & Islam, M. A. Human, animal and environmental contributors to antibiotic resistance in low-resource settings: integrating behavioural, epidemiological and One Health approaches. Proc. Biol. Sci. 285, 20180332 (2018).PubMed 
PubMed Central 

Google Scholar 
Jibril, A. H., Okeke, I. N., Dalsgaard, A. & Olsen, J. E. Association between antimicrobial usage and resistance in Salmonella from poultry farms in Nigeria. BMC Vet. Res. 17, 234 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tiseo, K., Huber, L., Gilbert, M., Robinson, T. P. & Van Boeckel, T. P. Global trends in antimicrobial use in food animals from 2017 to 2030. Antibiotics 9, 918 (2020).PubMed Central 
Article 

Google Scholar 
Schar, D., Sommanustweechai, A., Laxminarayan, R. & Tangcharoensathien, V. Surveillance of antimicrobial consumption in animal production sectors of low- and middle-income countries: optimizing use and addressing antimicrobial resistance. PLoS Med. 15, e1002521 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Liu, Y. Y. et al. Emergence of plasmid-mediated colistin resistance mechanism MCR-1 in animals and human beings in China: a microbiological and molecular biological study. Lancet Infect. Dis. 16, 161–168 (2016).PubMed 
Article 
CAS 

Google Scholar 
Sun, J., Zhang, H., Liu, Y. H. & Feng, Y. Towards understanding MCR-like colistin resistance. Trends Microbiol. 26, 794–808 (2018).CAS 
PubMed 
Article 

Google Scholar 
Wang, C. et al. Identification of novel mobile colistin resistance gene mcr-10. Emerg. Microbes Infect. 9, 508–516 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
He, T. et al. Emergence of plasmid-mediated high-level tigecycline resistance genes in animals and humans. Nat. Microbiol. 4, 1450–1456 (2019).CAS 
PubMed 
Article 

Google Scholar 
Sun, C. et al. Plasmid-mediated tigecycline-resistant gene tet(X4) in Escherichia coli from food-producing animals, China, 2008–2018. Emerg. Microbes Infect. 8, 1524–1527 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lowder, B. V. et al. Recent human-to-poultry host jump, adaptation, and pandemic spread of Staphylococcus aureus. Proc. Natl Acad. Sci. USA 106, 19545–19550 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bachiri, T. et al. First report of the plasmid-mediated colistin resistance gene mcr-1 in Escherichia coli ST405 isolated from wildlife in Bejaia, Algeria. Microb. Drug Resist. 24, 890–895 (2018).CAS 
PubMed 
Article 

Google Scholar 
Roberts, M. C. et al. The human clone ST22 SCCmec IV methicillin-resistant Staphylococcus aureus isolated from swine herds and wild primates in Nepal: is man the common source? FEMS Microbiol. Ecol. 94, fiy052 (2018).PubMed Central 
Article 
CAS 

Google Scholar 
Aliyu, A. B., Saleha, A. A., Jalila, A. & Zunita, Z. Risk factors and spatial distribution of extended spectrum β-lactamase-producing-Escherichia coli at retail poultry meat markets in Malaysia: a cross-sectional study. BMC Public Health 16, 699 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Alam, M. U. et al. Human exposure to antimicrobial resistance from poultry production: assessing hygiene and waste-disposal practices in Bangladesh. Int. J. Hyg. Environ. Health 222, 1068–1076 (2019).PubMed 
Article 

Google Scholar 
Donado-Godoy, P. et al. Prevalence, risk factors, and antimicrobial resistance profiles of Salmonella from commercial broiler farms in two important poultry-producing regions of Colombia. J. Food Prot. 75, 874–883 (2012).CAS 
PubMed 
Article 

Google Scholar 
Moser, K. A. et al. The role of mobile genetic elements in the spread of antimicrobial-resistant Escherichia coli from chickens to humans in small-scale production poultry operations in rural Ecuador. Am. J. Epidemiol. 187, 558–567 (2018).PubMed 
Article 

Google Scholar 
Songe, M. M., Hang’ombe, B. M., Knight-Jones, T. J. D. & Grace, D. Antimicrobial resistant enteropathogenic Escherichia coli and Salmonella spp. in houseflies infesting fish in food markets in Zambia. Int. J. Environ. Res. Public Health 14, (2017).Alves, T. S., Lara, G. H. B., Maluta, R. P., Ribeiro, M. G. & Leite, D. S. Carrier flies of multidrug-resistant Escherichia coli as potential dissemination agent in dairy farm environment. Sci. Total Environ. 633, 1345–1351 (2018).CAS 
PubMed 
Article 

Google Scholar 
Allen, H. K. et al. Call of the wild: antibiotic resistance genes in natural environments. Nat. Rev. Microbiol. 8, 251–259 (2010).CAS 
PubMed 
Article 

Google Scholar 
Hasan, B. et al. Antimicrobial drug–resistant Escherichia coli in wild birds and free-range poultry, Bangladesh. Emerg. Infect. Dis. 18, 2055–2058 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Blanco, G. Supplementary feeding as a source of multiresistant Salmonella in endangered Egyptian vultures. Transbound. Emerg. Dis. 65, 806–816 (2018).CAS 
PubMed 
Article 

Google Scholar 
Matias, C. A. R. et al. Frequency of zoonotic bacteria among illegally traded wild birds in Rio de Janeiro. Braz. J. Microbiol. 47, 882–888 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Brealey, J. C., Leitão, H. G., Hofstede, T., Kalthoff, D. C. & Guschanski, K. The oral microbiota of wild bears in Sweden reflects the history of antibiotic use by humans. Curr. Biol. 31, 4650–4658.e6 (2021).CAS 
PubMed 
Article 

Google Scholar 
Liu, C. M. et al. Escherichia coli ST131-H22 as a foodborne uropathogen. mBio 9, e00470-18 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Randad, P. R. et al. Transmission of antimicrobial-resistant Staphylococcus aureus clonal complex 9 between pigs and humans, United States. Emerg. Infect. Dis. 27, 740–748 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jørgensen, S. L. et al. Diversity and population overlap between avian and human Escherichia coli belonging to sequence type 95. mSphere 4, e00333-18 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Ludden, C. et al. A One Health study of the genetic relatedness of Klebsiella pneumoniae and their mobile elements in the east of England. Clin. Infect. Dis. 70, 219–226 (2020).PubMed 
Article 

Google Scholar 
Thorpe, H. et al. One Health or Three? Transmission modelling of Klebsiella isolates reveals ecological barriers to transmission between humans, animals and the environment. Preprint at bioRxiv https://doi.org/10.1101/2021.08.05.455249 (2021).Ingham, A. C. et al. Dynamics of the human nasal microbiota and Staphylococcus aureus cc398 carriage in pig truck drivers across one workweek. Appl. Environ. Microbiol. 87, e0122521 (2021).PubMed 
Article 

Google Scholar 
Hickman, R. A. et al. Exploring the antibiotic resistance burden in livestock, livestock handlers and their non-livestock handling contacts: a One Health perspective. Front. Microbiol. 12, 65161 (2021).Article 

Google Scholar 
Okeke, I. N. African biomedical scientists and the promises of ‘big science’. Can J. Afr. Stud. https://doi.org/10.1080/00083968.2016.1266677 (2017).Nadimpalli, M. L. & Pickering, A. J. A call for global monitoring of WASH in wet markets. Lancet Planet. Health 4, e439–e440 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Grace, D. & Little, P. Informal trade in livestock and livestock products. Rev. Sci. Tech. 39, 183–192 (2020).CAS 
PubMed 
Article 

Google Scholar 
Caudell, M. A. et al. Towards a bottom-up understanding of antimicrobial use and resistance on the farm: a knowledge, attitudes, and practices survey across livestock systems in five African countries. PLoS ONE 15, e0220274 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Adekanye, U. O. et al. Knowledge, attitudes and practices of veterinarians towards antimicrobial resistance and stewardship in Nigeria. Antibiotics 9, 453 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
Mangesho, P. E. et al. ‘We are doctors’: drivers of animal health practices among Maasai pastoralists and implications for antimicrobial use and antimicrobial resistance. Prev. Vet. Med. 188, 105266 (2021).PubMed 
Article 

Google Scholar 
Essack, S. Water, sanitation and hygiene in national action plans for antimicrobial resistance. Bull. World Health Organ. 99, 606–608 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Aarestrup, F. M. et al. Effect of abolishment of the use of antimicrobial agents for growth promotion on occurrence of antimicrobial resistance in fecal enterococci from food animals in Denmark. Antimicrob. Agents Chemother. 45, 2054–2059 (2001).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Funtowicz, S. & Ravetz, J. in Handbook of Transdisciplinary Research (eds Hadorn, G. H. et al.) 361–368 (Springer, 2008); https://doi.org/10.1007/978-1-4020-6699-3Theuretzbacher, U., Outterson, K., Engel, A. & Karlén, A. The global preclinical antibacterial pipeline. Nat. Rev. Microbiol. 185, 275–285 (2019).
Google Scholar 
Lacotte, Y., Årdal, C. & Ploy, M. C. Infection prevention and control research priorities: what do we need to combat healthcare-associated infections and antimicrobial resistance? Results of a narrative literature review and survey analysis. Antimicrob. Resist. Infect. Control 9, 142 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Kennedy, D. A. & Read, A. F. Why the evolution of vaccine resistance is less of a concern than the evolution of drug resistance. Proc. Natl Acad. Sci. USA 115, 12878 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vekemans, J. et al. Leveraging vaccines to reduce antibiotic use and prevent antimicrobial resistance: a World Health Organization action framework. Clin. Infect. Dis. 73, E1011–E1017 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Micoli, F., Bagnoli, F., Rappuoli, R. & Serruto, D. The role of vaccines in combatting antimicrobial resistance. Nat. Rev. Microbiol. 195, 287–302 (2021).Article 
CAS 

Google Scholar 
Massella, E. et al. Antimicrobial resistance profile and ExPEC virulence potential in commensal Escherichia coli of multiple sources. Antibiotics 10, 351 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Huttner, A. et al. Safety, immunogenicity, and preliminary clinical efficacy of a vaccine against extraintestinal pathogenic Escherichia coli in women with a history of recurrent urinary tract infection: a randomised, single-blind, placebo-controlled phase 1b trial. Lancet Infect. Dis. 17, 528–537 (2017).CAS 
PubMed 
Article 

Google Scholar 
Frenck, R. W. et al. Safety and immunogenicity of a vaccine for extra-intestinal pathogenic Escherichia coli (ESTELLA): a phase 2 randomised controlled trial. Lancet Infect. Dis. 19, 631–640 (2019).CAS 
PubMed 
Article 

Google Scholar 
Patel, R. & Fang, F. C. Diagnostic stewardship: opportunity for a laboratory-infectious diseases partnership. Clin. Infect. Dis. 67, 799–801 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Okeke, I. N. Divining Without Seeds: The Case for Strengthening Laboratory Medicine in Africa (Cornell Univ. Press, 2011).Loosli, K., Davis, A., Muwonge, A. & Lembo, T. Addressing antimicrobial resistance by improving access and quality of care—a review of the literature from East Africa. PLoS Negl. Trop. Dis. 15, e0009529 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Chokshi, A., Sifri, Z., Cennimo, D. & Horng, H. Global contributors to antibiotic resistance. J. Glob. Infect. Dis. 11, 36–42 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Adedapo, A. D. & Akunne, O. O. Patterns of antimicrobials prescribed to patients admitted to a tertiary care hospital: a prescription quality audit. Cureus 13, e15896 (2021).PubMed 
PubMed Central 

Google Scholar 
Kumarasamy, K. K. et al. Emergence of a new antibiotic resistance mechanism in India, Pakistan, and the UK: a molecular, biological, and epidemiological study. Lancet Infect. Dis. 10, 597–602 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Davenport, M. et al. New and developing diagnostic technologies for urinary tract infections. Nat. Rev. Urol. 14, 298–310 (2017).Article 

Google Scholar 
van Dongen, J. E. et al. Point-of-care CRISPR/Cas nucleic acid detection: recent advances, challenges and opportunities. Biosens. Bioelectron. 166, 112445 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Nielsen, T. B. et al. Monoclonal antibody therapy against Acinetobacter baumannii. Infect. Immun. 89, e0016221 (2021).PubMed 
Article 

Google Scholar 
Dwivedi, P., Narvi, S. S. & Tewari, R. P. Application of polymer nanocomposites in the nanomedicine landscape: envisaging strategies to combat implant associated infections. J. Appl. Biomater. Funct. Mater. 11, 129–142 (2013).
Google Scholar 
Song, M., Wu, D., Hu, Y., Luo, H. & Li, G. Characterization of an Enterococcus faecalis bacteriophage vB_EfaM_LG1 and its synergistic effect with antibiotic. Front. Cell. Infect. Microbiol. 11, 636 (2021).
Google Scholar 
Dhama, K. et al. Growth promoters and novel feed additives improving poultry production and health, bioactive principles and beneficial applications: the trends and advances—a review. Int. J. Pharmacol. 10, 129–159 (2014).CAS 
Article 

Google Scholar 
Vieco-Saiz, N. et al. Benefits and inputs from lactic acid bacteria and their bacteriocins as alternatives to antibiotic growth promoters during food-animal production. Front. Microbiol. 10, 57 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Ng, W. K. & Koh, C. B. The utilization and mode of action of organic acids in the feeds of cultured aquatic animals. Rev. Aquac. 9, 342–368 (2017).Article 

Google Scholar 
Mattioli, G. A. et al. Effects of parenteral supplementation with minerals and vitamins on oxidative stress and humoral immune response of weaning calves. Animals 10, 1298 (2020).PubMed Central 
Article 

Google Scholar 
Mwangi, S., Timmons, J., Fitz-Coy, S. & Parveen, S. Characterization of Clostridium perfringens recovered from broiler chicken affected by necrotic enteritis. Poult. Sci. 98, 128–135 (2019).CAS 
PubMed 
Article 

Google Scholar 
Prendergast, A. J. et al. Putting the ‘A’ into WaSH: a call for integrated management of water, animals, sanitation, and hygiene. Lancet Planet. Health 3, e336–e337 (2019).PubMed 
Article 

Google Scholar 
Martinelli, M. et al. Probiotics’ efficacy in paediatric diseases: which is the evidence? A critical review on behalf of the Italian Society of Pediatrics. Ital. J. Pediatr. 46, 104 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Rasko, D. A. & Sperandio, V. Anti-virulence strategies to combat bacteria-mediated disease. Nat. Rev. Drug Discov. 9, 117–128 (2010).CAS 
PubMed 
Article 

Google Scholar 
Rodrigues, M., McBride, S. W., Hullahalli, K., Palmer, K. L. & Duerkop, B. A. Conjugative delivery of CRISPR–Cas9 for the selective depletion of antibiotic-resistant enterococci. Antimicrob. Agents Chemother. 63, e01454-19 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Casu, B., Arya, T., Bessette, B. & Baron, C. Fragment-based screening identifies novel targets for inhibitors of conjugative transfer of antimicrobial resistance by plasmid pKM101. Sci. Rep. 7, 14907 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Denyer Willis, L. & Chandler, C. Quick fix for care, productivity, hygiene and inequality: reframing the entrenched problem of antibiotic overuse. BMJ Glob. Health 4, e001590 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Wilkinson, A., Ebata, A. & Macgregor, H. Interventions to reduce antibiotic prescribing in LMICs: a scoping review of evidence from human and animal health systems. Antibiotics 8, 2 (2018).Torres, N. F., Chibi, B., Middleton, L. E., Solomon, V. P. & Mashamba-Thompson, T. P. Evidence of factors influencing self-medication with antibiotics in low and middle-income countries: a systematic scoping review. Public Health 168, 92–101 (2019).CAS 
PubMed 
Article 

Google Scholar 
Potgieter, N., Banda, N. T., Becker, P. J. & Traore-Hoffman, A. N. WASH infrastructure and practices in primary health care clinics in the rural Vhembe District municipality in South Africa. BMC Fam. Pract. 22, 8 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Humphreys, G. Reinventing the toilet for 2.5 billion in need. Bull. World Health Organ. 92, 470–471 (2014).PubMed 
Article 

Google Scholar 
Yam, P., Fales, D., Jemison, J., Gillum, M. & Bernstein, M. Implementation of an antimicrobial stewardship program in a rural hospital. Am. J. Health Syst. Pharm. 69, 1142–1148 (2012).PubMed 
Article 

Google Scholar 
Sartelli, M. et al. Antibiotic use in low and middle-income countries and the challenges of antimicrobial resistance in surgery. Antibiotics 9, 497 (2020).PubMed Central 
Article 

Google Scholar 
Büdel, T. et al. On the island of Zanzibar people in the community are frequently colonized with the same MDR Enterobacterales found in poultry and retailed chicken meat. J. Antimicrob. Chemother. 75, 2432–2441 (2020).PubMed 
Article 
CAS 

Google Scholar 
Finch, M. J., Morris, J. G., Kaviti, J., Kagwanja, W. & Levine, M. M. Epidemiology of antimicrobial resistant cholera in Kenya and East Africa. Am. J. Trop. Med. Hyg. 39, 484–490 (1988).CAS 
PubMed 
Article 

Google Scholar 
Mutreja, A. et al. Evidence for several waves of global transmission in the seventh cholera pandemic. Nature 477, 462–465 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Weill, F. X. et al. Genomic history of the seventh pandemic of cholera in Africa. Science 358, 785–789 (2017).CAS 
PubMed 
Article 

Google Scholar 
Opintan, J. A., Newman, M. J., Nsiah-Poodoh, O. A. & Okeke, I. N. Vibrio cholerae O1 from Accra, Ghana carrying a class 2 integron and the SXT element. J. Antimicrob. Chemother. 62, 929–933 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Garbern, S. C. et al. Clinical and socio-environmental determinants of multidrug-resistant Vibrio cholerae 01 in older children and adults in Bangladesh. Int. J. Infect. Dis. 105, 436–441 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mintz, E. D. & Guerrant, R. L. A lion in our village—the unconscionable tragedy of cholera in Africa. N. Engl. J. Med. https://doi.org/10.1056/NEJMp0810559 (2009).Gibani, M. M. et al. The impact of vaccination and prior exposure on stool shedding of Salmonella typhi and Salmonella paratyphi in 6 controlled human infection studies. Clin. Infect. Dis. 68, 1265–1273 (2019).CAS 
PubMed 
Article 

Google Scholar  More