Philippa Kaur

Predictors of microbial carbon stocksWe used a machine learning modeling approach to predict soil microbial carbon from a set of environmental covariates. To account for stochastic variability, we ran a set of models to assess the importance of environmental factors, which showed that the contribution of each variable to the model fit differed between runs, with some overlap between a number of them (Fig. 2b). Mean annual temperature was always the most important variable, with soil organic carbon and soil pH following. Clay content, precipitation, land-cover type, nitrogen content, and sand content contributed roughly equally to explaining variations in microbial carbon. Finally, NDVI and elevation had the lowest variable importance. Coniferous forests had the highest and most variable predicted values of microbial carbon (Supplementary Figs. 1, 2), which can be explained by high soil organic matter and a thick litter layer26. Tropical forests also had fairly high values of microbial carbon, while shrublands and croplands had the lowest values26. We used partial prediction response curves to evaluate the direction and range of effect of the predictor variables (Supplementary Figs. 1, 2). In agreement with the variable importance measure, variables that scored high often showed strong effects on the predicted microbial carbon values, while variables with a low variable importance score (e.g., elevation, NDVI, and sand content) only showed smaller responses. The only exception was for precipitation, which had a relatively high variable importance, although the response curves only showed a weak effect of precipitation for forests and grasslands, with limited effect on other land-cover types (Supplementary Fig. 2). The importance of precipitation might also indicate that this relationship involves interactions with other variables7,28. Overall, the differences in microbial carbon between land-cover types showed mostly similar patterns across the range of variables. Soil organic carbon and nitrogen content had a positive and mostly linear effect on microbial carbon (Supplementary Fig. 1). In contrast, clay content, soil pH, and mean temperature had non-linear relationships, with high microbial carbon in the low range of these variables and a rapid decrease that reached an asymptote at low microbial carbon values for the higher portion of the range. Soil pH patterns showed a decrease in microbial carbon for values between 4.1 and 5.8, and a constant pattern between 5.8 and 8.6. Contrary to our expectations, we did not find a parabolic effect of soil pH on microbial carbon26. Instead, our model predicted higher values in very acidic soils with a pH below 5.2, which are rare globally and almost only found in central Amazonia. Similarly, locations with a clay content lower than 16.9% had higher values in microbial carbon, and then stabilized until 51.0%.Fig. 2: Microbial carbon stock spatial predictions and temporal trends.a Microbial carbon stock predictions for 2013. b Variable importance from 100 random forest model runs, calculated by the mean decrease in accuracy after variable permutation. Variables were ordered by the median variable importance. SOC soil organic carbon, NDVI normalized difference vegetation index. Center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; points, outliers. c Relative microbial carbon stocks rate of change in percentage per year.Full size imageMean temperature showed an interesting shift with much higher microbial carbon values with a mean annual temperature below zero, but had otherwise a limited effect on microbial carbon values in the rest of the range above zero up to 28.9 °C. Based on partial predictions (Supplementary Figs. 1–2), microbial carbon decreased monotonically with an increase in temperature (with all other variables fixed to their median), with the relationship being mostly stable for parts of the range. We observed an especially sharp decrease at around 0°C, which is in agreement with the patterns observed in the data. The reason for sites with a mean annual temperature below the freezing point to have higher microbial carbon stocks is not fully understood. This could be due to a regime shift in which microbial communities are in a semi-dormant state for a major part of the year35. Moreover, it could also be in part explained by the soil organic carbon content that follows a similar trend and accumulates in higher latitude soils9, thus promoting higher microbial carbon stocks. Within these cold, high organic carbon soils, large microbial populations can be maintained, due to the low temperature that reduces metabolic requirements35. In contrast, at higher temperatures, metabolic activity increases and requires more resources and nutrients to maintain microorganisms alive. Experimental evidence is divided about the effects of warming on microbial carbon18,36, highlighting the strong context-dependency of this relationship, although global observations show a clear pattern, where low-temperature sites have higher soil microbial carbon stocks. Despite this uncertainty, there is a strong indication that a warming soil would tend to lose organic carbon17,37, and subsequent patterns in microbial carbon can also be expected, because of the dependency on organic substrate9,26,38. These dynamics were observed in Melillo et al.39, where the warming of sites in a mid-latitude forest ecosystem led to a decrease in soil carbon, followed by a decrease in microbial carbon12.Even with predictions being made for each grid location separately, microbial carbon values showed distinctive patterns and transitions over the globe (Fig. 2a). While temporal changes took place, broad spatial patterns were relatively constant over the range of years studied (Supplementary Movie 1). The highest microbial carbon stock values ranging from 1.50 to 7.00 t ha−1 were found at high latitudes in the Northern Hemisphere in areas of coniferous forest. Tropical humid regions also showed high microbial carbon values between 0.50 and 1.50 t ha−1 in the Amazon Rainforest and Central Africa. The main regions with low microbial carbon below 0.30 t ha−1 were in Eastern South America, areas directly south of the Sahara Desert, East Africa, and most of Australia, all of which mostly correspond to shrublands. Cropland areas as seen in India were also predicted with low microbial carbon values ranging from 0.06 to 0.38 t ha−1. A strong latitudinal gradient was visible for North America and Eurasia, with the highest microbial carbon stocks at high latitude, medium values in temperate ecosystems, and decreasing values towards the Equator. Positive coastal effects can also be observed, mostly on the Eastern South American and Australian coasts. In total, we estimated that there is 4.34 Gt of microbial carbon in the 5 to 15 cm layer for the predicted areas. Using the coefficient of variation calculated from the variability assessment set of models, we found that predictions made for the Amazon Basin, Northern Canada, and South-East Russia were more variable than for other regions (Supplementary Fig. 3a). Especially Western Europe, Central North America, and South-East Asia, however, showed high stability in the predictions between model runs.Drivers of changeThe analysis of the rate of change of microbial carbon stocks over time revealed that large regions of the globe experienced important changes in soil microbial carbon stocks between 1992 and 2013, with contrasting patterns across areas, and overall larger regions showed a decrease rather than an increase in microbial carbon stocks (Fig. 2c and Supplementary Fig. 3b). To account for spatial differences in microbial carbon stocks, we calculated the relative rate of change in percentage for each location (Fig. 2c). When considering all predictable regions together, microbial carbon stocks in the 5–15 cm layer showed a decrease of 7.09 Mt per year, summing to 148.80 Mt between 1992 and 2013, or 3.4% of the global microbial carbon pool predicted (Supplementary Fig. 4a; p = 0.038). The main regions with a microbial carbon loss higher than 0.7 kg ha−1 y−1 were in Northern Canada and a large continuous region in North-Eastern Europe. These northern regions accounted for an important part of the global loss in microbial carbon stocks, with large areas that had both a high soil microbial carbon stock and a fast decrease (Figs. 3 and 4). Other areas of high loss were in the Amazon basin, Western Argentina, the USA East Coast, Southern South Africa, and South-East Russia. The main continuous region of microbial carbon increase above 0.7 kg ha−1 y−1 was in central Russia, with smaller regions present in India, Europe, Central North America, and parts of Africa. Besides these general patterns, predictions vary at the local scale, and they consider the effects of parameters including soil properties, elevation, and land-cover type, which change between neighbor locations and affect the observed patterns. This is especially visible in the Americas, where both increases and decreases happen side-by-side.Fig. 3: Status of microbial carbon stocks between 1992 and 2013.Bivariate plot comparing the relative microbial carbon stock rate of change (% per year) with the amount of microbial carbon stock. The status groups were allocated using quantile distributions.Full size imageFig. 4: Distribution and classification of point values from the locations in Fig. 3.The assignment of points into the 9 groups was performed using quantile distributions. Areas in dark red are especially vulnerable to climate and land-cover change.Full size imagePatterns in the relative rate of change have a lot in common with that of absolute change, with a few notable differences (Fig. 2c and Supplementary Fig. 3b). Both positive and negative stock changes in tropical and subtropical regions are more prominent in relative terms, as these regions typically have low microbial carbon stocks. Similarly, regions in Central Russia with high microbial carbon stocks show less decrease in relative terms. To assess how stable these trends are over time, we show the p values of the rate of change for the 22 years (Supplementary Fig. 3c). The largest region with low p values is associated with more significant trends in Western Russia, and corresponds to an area with a fast loss of microbial carbon. India and Central Russia show high p values, and are informative of high variability compared to the strength of the signal. Considering that only up to 22 data points are available for each grid location and that especially climatic conditions vary considerably from year to year, p values are only provided as a complementary assessment. We can summarize the global situation by combining the two maps of microbial carbon stocks and relative rate of change to categorize and define vulnerable locations that experienced a high loss of microbial carbon (Figs. 3 and 4), and where the provision of soil functions is potentially at risk.It is informative to look at regional trends, by grouping grid locations using the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) sub-regions, and assessing regional-scale changes in microbial carbon stocks (Fig. 5, Supplementary Table 1). The main regions that contributed to microbial carbon loss were North America with a decrease of 62.49 Mt of microbial carbon and Eastern Europe with 60.88 Mt over the studied period, although both trends had high yearly variability and were non-significant. The region with the highest increase was North-East Asia with a gain of 4.49 Mt, but this change was also non-significant. The Caribbean was the only region to show a significant increase in soil microbial carbon stocks over time (+2.1% over 22 y, p = 0.017), while significant decreases in stocks were found in North Africa (−4.1%, p  More

Laxminarayan R, Duse A, Wattal C, Zaidi AKM, Wertheim HFL, Sumpradit N, et al. Antibiotic resistance—the need for global solutions. Lancet Infect Dis. 2013;13:1057–98.PubMed 
Article 

Google Scholar 
Murray CJ, Ikuta KS, Sharara F, Swetschinski L, Robles Aguilar G, Gray A, et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet. 2022;399:629–55.CAS 
Article 

Google Scholar 
O’Neil J. Antimicrobial resistance: tackling a crisis for the health and wealth of nations. The review on antimicrobial resistance. 2014. https://amr-review.org/sites/default/files/AMRReviewPaper-Tacklingacrisisforthehealthandwealthofnations_1.pdf.Pang Z, Raudonis R, Glick BR, Lin T-J, Cheng Z. Antibiotic resistance in Pseudomonas aeruginosa: mechanisms and alternative therapeutic strategies. Biotechnol Adv. 2019;37:177–92.CAS 
PubMed 
Article 

Google Scholar 
Vandeplassche E, Tavernier S, Coenye T, Crabbé A. Influence of the lung microbiome on antibiotic susceptibility of cystic fibrosis pathogens. Eur Respir Rev. 2019;28:190041.PubMed 
Article 

Google Scholar 
Wheatley R, Diaz Caballero J, Kapel N, de Winter FHR, Jangir P, Quinn A, et al. Rapid evolution and host immunity drive the rise and fall of carbapenem resistance during an acute Pseudomonas aeruginosa infection. Nat Commun. 2021;12:2460.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Adamowicz EM, Flynn J, Hunter RC, Harcombe WR. Cross-feeding modulates antibiotic tolerance in bacterial communities. ISME J. 2018;12:2723–35.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Allison DG, Matthews MJ. Effect of polysaccharide interactions on antibiotic susceptibility of Pseudomonas aeruginosa. J Appl Bacteriol. 1992;73:484–8.CAS 
PubMed 
Article 

Google Scholar 
Beaudoin T, Yau YCW, Stapleton PJ, Gong Y, Wang PW, Guttman DS, et al. Staphylococcus aureus with Pseudomonas aeruginosa biofilm enhances tobramycin resistance. Npj Biofilms Microbiomes. 2017;3:25.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bottery MJ, Matthews JL, Wood AJ, Johansen HK, Pitchford JW, Friman V-P. Inter-species interactions alter antibiotic efficacy in bacterial communities. ISME J. 2022;16:812–21.CAS 
PubMed 
Article 

Google Scholar 
Elias S, Banin E. Multi-species biofilms: living with friendly neighbors. FEMS Microbiol Rev. 2012;36:990–1004.CAS 
PubMed 
Article 

Google Scholar 
Hoffman LR, Deziel E, D’Argenio DA, Lepine F, Emerson J, McNamara S, et al. Selection for Staphylococcus aureus small-colony variants due to growth in the presence of Pseudomonas aeruginosa. Proc Natl Acad Sci USA. 2006;103:19890–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Molina-Santiago C, Daddaoua A, Fillet S, Duque E, Ramos J-L. Interspecies signalling: Pseudomonas putida efflux pump TtgGHI is activated by indole to increase antibiotic resistance: Antibiotic resistance. Environ Microbiol. 2014;16:1267–81.CAS 
PubMed 
Article 

Google Scholar 
Orazi G, O’Toole GA. Pseudomonas aeruginosa alters Staphylococcus aureus sensitivity to vancomycin in a biofilm model of cystic fibrosis infection. mBio. 2017;8:e00873–17. https://doi.org/10.1128/mBio.00873-17.Article 
PubMed 
PubMed Central 

Google Scholar 
Perlin MH, Clark DR, McKenzie C, Patel H, Jackson N, Kormanik C, et al. Protection of Salmonella by ampicillin-resistant Escherichia coli in the presence of otherwise lethal drug concentrations. Proc R Soc B Biol Sci. 2009;276:3759–68.CAS 
Article 

Google Scholar 
Ryan RP, Fouhy Y, Garcia BF, Watt SA, Niehaus K, Yang L, et al. Interspecies signalling via the Stenotrophomonas maltophilia diffusible signal factor influences biofilm formation and polymyxin tolerance in Pseudomonas aeruginosa. Mol Microbiol. 2008;68:75–86.CAS 
PubMed 
Article 

Google Scholar 
Sherrard LJ, McGrath SJ, McIlreavey L, Hatch J, Wolfgang MC, Muhlebach MS, et al. Production of extended-spectrum β -lactamases and the potential indirect pathogenic role of Prevotella isolates from the cystic fibrosis respiratory microbiota. Int J Antimicrob Agents. 2016;47:140–5.CAS 
PubMed 
Article 

Google Scholar 
Tognon M, Köhler T, Gdaniec BG, Hao Y, Lam JS, Beaume M, et al. Co-evolution with Staphylococcus aureus leads to lipopolysaccharide alterations in Pseudomonas aeruginosa. ISME J. 2017;11:2233–43.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Adamowicz EM, Muza M, Chacón JM, Harcombe WR. Cross-feeding modulates the rate and mechanism of antibiotic resistance evolution in a model microbial community of Escherichia coli and Salmonella enterica. PLOS Pathog. 2020;16:e1008700.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bottery MJ, Pitchford JW, Friman V-P. Ecology and evolution of antimicrobial resistance in bacterial communities. ISME J. 2021;15:939–48.PubMed 
Article 

Google Scholar 
Estrela S, Brown SP. Community interactions and spatial structure shape selection on antibiotic resistant lineages. PLOS Comput Biol. 2018;14:e1006179.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Klümper U, Recker M, Zhang L, Yin X, Zhang T, Buckling A, et al. Selection for antimicrobial resistance is reduced when embedded in a natural microbial community. ISME J. 2019;13:2927–37.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Scheuerl T, Hopkins M, Nowell RW, Rivett DW, Barraclough TG, Bell T. Bacterial adaptation is constrained in complex communities. Nat Commun. 2020;11:754.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sorg RA, Lin L, van Doorn GS, Sorg M, Olson J, Nizet V, et al. Collective resistance in microbial communities by intracellular antibiotic deactivation. PLOS Biol. 2016;14:e2000631.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kulczycki LL, Kostuch M, Bellanti JA. A clinical perspective of cystic fibrosis and new genetic findings: relationship of CFTR mutations to genotype-phenotype manifestations. Am J Med Genet. 2003;116A:262–7.PubMed 
Article 

Google Scholar 
Flume PA, Mogayzel PJ, Robinson KA, Rosenblatt RL, Quittell L, Marshall BC. Cystic fibrosis pulmonary guidelines: pulmonary complications: hemoptysis and pneumothorax. Am J Respir Crit Care Med. 2010;182:298–306.PubMed 
Article 

Google Scholar 
Belkin RA, Henig NR, Singer LG, Chaparro C, Rubenstein RC, Xie SX, et al. Risk factors for death of patients with cystic fibrosis awaiting lung transplantation. Am J Respir Crit Care Med. 2006;173:659–66.PubMed 
Article 

Google Scholar 
Martin C, Hamard C, Kanaan R, Boussaud V, Grenet D, Abély M, et al. Causes of death in French cystic fibrosis patients: the need for improvement in transplantation referral strategies! J Cyst Fibros. 2016;15:204–12.PubMed 
Article 

Google Scholar 
Döring G, Conway SP, Heijerman HGM, Hodson ME, Høiby N, Smyth A, et al. Antibiotic therapy against Pseudomonas aeruginosa in cystic fibrosis: a European consensus. Eur Respir J. 2000;16:749.PubMed 
Article 

Google Scholar 
Marshall B, Faro A, Brown W, Elbert A, Fink A, Cromwell E, et al. Patient registry, annual data report. Bethesda, Maryland: Cystic Fibrosis Foundation; 2020. https://www.cff.org/sites/default/files/2021-11/Patient-Registry-Annual-Data-Report.pdf.Vongthilath R, Richaud Thiriez B, Dehillotte C, Lemonnier L, Guillien A, Degano B, et al. Clinical and microbiological characteristics of cystic fibrosis adults never colonized by Pseudomonas aeruginosa: analysis of the French CF registry. PLOS ONE. 2019;14:e0210201.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zolin A, Orenti A, Jung A, van Rens J. ECFSPR annual report 2019. Denmark: European Cystic Fibrosis Society Patient Registry; 2021. https://www.ecfs.eu/sites/default/files/general-content-files/working-groups/ecfs-patient-registry/ECFSPR_Report_2019_v1_16Feb2022.pdf.Conrad D, Haynes M, Salamon P, Rainey PB, Youle M, Rohwer F. Cystic fibrosis therapy: a community ecology perspective. Am J Respir Cell Mol Biol. 2013;48:150–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Filkins LM, Graber JA, Olson DG, Dolben EL, Lynd LR, Bhuju S, et al. Coculture of Staphylococcus aureus with Pseudomonas aeruginosa drives S. aureus towards fermentative metabolism and reduced viability in a cystic fibrosis model. J Bacteriol. 2015;197:2252–64.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhao J, Schloss PD, Kalikin LM, Carmody LA, Foster BK, Petrosino JF, et al. Decade-long bacterial community dynamics in cystic fibrosis airways. Proc Natl Acad Sci USA. 2012;109:5809–14.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ballestero S, Vírseda I, Escobar H, Suárez L, Baquero F. Stenotrophomonas maltophilia in cystic fibrosis patients. Eur J Clin Microbiol Infect Dis. 1995;14:728–9.CAS 
PubMed 
Article 

Google Scholar 
Gladman G, Connor PJ, Williams RF, David TJ. Controlled study of Pseudomonas cepacia and Pseudomonas maltophilia in cystic fibrosis. Arch Dis Child. 1992;67:192–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Goss CH. Association between Stenotrophomonas maltophilia and lung function in cystic fibrosis. Thorax. 2004;59:955–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Parkins MD, Floto RA. Emerging bacterial pathogens and changing concepts of bacterial pathogenesis in cystic fibrosis. J Cyst Fibros. 2015;14:293–304.CAS 
PubMed 
Article 

Google Scholar 
Goss CH, Otto K, Aitken ML, Rubenfeld GD. Detecting Stenotrophomonas maltophilia does not reduce survival of patients with cystic fibrosis. Am J Respir Crit Care Med. 2002;166:356–61.PubMed 
Article 

Google Scholar 
Alonso A, Martínez JL. Multiple antibiotic resistance in Stenotrophomonas maltophilia. Antimicrob Agents Chemother. 1997;41:1140–2.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang L, Li XZ, Poole K. Multiple antibiotic resistance in Stenotrophomonas maltophilia: involvement of a multidrug efflux system. Antimicrob Agents Chemother. 2000;44:287–93.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Abda EM, Krysciak D, Krohn-Molt I, Mamat U, Schmeisser C, Förstner KU, et al. Phenotypic heterogeneity affects Stenotrophomonas maltophilia K279a colony morphotypes and β-lactamase expression. Front Microbiol. 2015;6:1373.Okazaki A, Avison MB. Induction of L1 and L2 β-lactamase production in Stenotrophomonas maltophilia is dependent on an AmpR-type regulator. Antimicrob Agents Chemother. 2008;52:1525–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Walsh TR, Hall L, Assinder SJ, Nichols WW, Cartwright SJ, MacGowan AP, et al. Sequence analysis of the L1 metallo-β-lactamase from Xanthomonas maltophilia. Biochim Biophys Acta. 1994;1218:199–201.CAS 
PubMed 
Article 

Google Scholar 
Yang Z, Liu W, Cui Q, Niu W, Li H, Zhao X, et al. Prevalence and detection of Stenotrophomonas maltophilia carrying metallo-I2-lactamase blaL1 in Beijing, China. Front Microbiol. 2014;5:692.Kataoka D, Fujiwara H, Kawakami T, Tanaka Y, Tanimoto A, Ikawa S, et al. The indirect pathogenicity of Stenotrophomonas maltophilia. Int J Antimicrob Agents. 2003;22:601–6.CAS 
PubMed 
Article 

Google Scholar 
Winstanley C, O’Brien S, Brockhurst MA. Pseudomonas aeruginosa evolutionary adaptation and diversification in cystic fibrosis chronic lung infections. Trends Microbiol. 2016;24:327–37.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McGuigan L, Callaghan M. The evolving dynamics of the microbial community in the cystic fibrosis lung. Environ Microbiol. 2015;17:16–28.PubMed 
Article 

Google Scholar 
Wistrand-Yuen E, Knopp M, Hjort K, Koskiniemi S, Berg OG, Andersson DI. Evolution of high-level resistance during low-level antibiotic exposure. Nat Commun. 2018;9:1599.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mahrt N, Tietze A, Künzel S, Franzenburg S, Barbosa C, Jansen G, et al. Bottleneck size and selection level reproducibly impact evolution of antibiotic resistance. Nat Ecol Evol. 2021;5:1233–1242.Govaert L, Altermatt F, De Meester L, Leibold MA, McPeek MA, Pantel JH, et al. Integrating fundamental processes to understand eco‐evolutionary community dynamics and patterns. Funct Ecol. 2021;35:2138–55.Article 

Google Scholar 
Palmer KL, Aye LM, Whiteley M. Nutritional cues control Pseudomonas aeruginosa multicellular behavior in cystic fibrosis sputum. J Bacteriol. 2007;189:8079–87.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Souza Barbosa F, Capra Pezzi L, Tsao M, Oliveira TF, Manoela Dias Macedo S, Schapoval EES, et al. Stability and degradation products of imipenem applying High‐Resolution Mass Spectrometry: an analytical study focused on solutions for infusion. Biomed Chromatogr. 2018;33:4471.Verpooten G, Verbist L, Buntinx A, Entwistle L, Jones K, Broe M. The pharmacokinetics of imipenem (thienamycin-formamidine) and the renal dehydropeptidase inhibitor cilastatin sodium in normal subjects and patients with renal failure. Br J Clin Pharmacol. 1984;18:183–93.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li H, Luo Y-F, Williams BJ, Blackwell TS, Xie C-M. Structure and function of OprD protein in Pseudomonas aeruginosa: from antibiotic resistance to novel therapies. Int J Med Microbiol. 2012;302:63–8.CAS 
PubMed 
Article 

Google Scholar 
Kousser C, Clark C, Sherrington S, Voelz K, Hall RA. Pseudomonas aeruginosa inhibits Rhizopus microsporus germination through sequestration of free environmental iron. Sci Rep. 2019;9:5714.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Schalk IJ, Guillon L. Pyoverdine biosynthesis and secretion in Pseudomonas aeruginosa: implications for metal homeostasis: pyoverdine biosynthesis. Environ Microbiol. 2013;15:1661–73.CAS 
PubMed 
Article 

Google Scholar 
Duan X, Pan Y, Cai Z, Liu Y, Zhang Y, Liu M, et al. rpoS-mutation variants are selected in Pseudomonas aeruginosa biofilms under imipenem pressure. Cell Biosci. 2021;11:138.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhu K, Chen S, Sysoeva TA, You L. Universal antibiotic tolerance arising from antibiotic-triggered accumulation of pyocyanin in Pseudomonas aeruginosa. PLOS Biol. 2019;17:e3000573.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
El-Fouly MZ, Sharaf AM, Shahin AAM, El-Bialy HA, Omara AMA. Biosynthesis of pyocyanin pigment by Pseudomonas aeruginosa. J Radiat Res Appl Sci. 2015;8:36–48.CAS 
Article 

Google Scholar 
Baron SS, Rowe JJ. Antibiotic action of pyocyanin. Antimicrob Agents Chemother. 1981;20:814–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kimura M, Ohta T. The average number of generations until fixation of a mutant gene in a finite population. Genetics. 1969;61:763–71.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Meirelles LA, Perry EK, Bergkessel M, Newman DK. Bacterial defenses against a natural antibiotic promote collateral resilience to clinical antibiotics. PLOS Biol. 2021;19:e3001093.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hall JPJ, Harrison E, Brockhurst MA. Competitive species interactions constrain abiotic adaptation in a bacterial soil community. Evol Lett. 2018;2:580–9.PubMed 
PubMed Central 
Article 

Google Scholar 
Scanlan PD, Hall AR, Blackshields G, Friman V-P, Davis MR, Goldberg JB, et al. Coevolution with bacteriophages drives genome-wide host evolution and constrains the acquisition of abiotic-beneficial mutations. Mol Biol Evol. 2015;32:1425–35.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Finkel SE. Long-term survival during stationary phase: evolution and the GASP phenotype. Nat Rev Microbiol. 2006;4:113–20.CAS 
PubMed 
Article 

Google Scholar 
Gefen O, Fridman O, Ronin I, Balaban NQ. Direct observation of single stationary-phase bacteria reveals a surprisingly long period of constant protein production activity. Proc Natl Acad Sci. 2014;111:556–61.CAS 
PubMed 
Article 

Google Scholar 
Fang Z, Zhang L, Huang Y, Qing Y, Cao K, Tian G, et al. OprD mutations and inactivation in imipenem-resistant Pseudomonas aeruginosa isolates from China. Infect Genet Evol. 2014;21:124–8.CAS 
PubMed 
Article 

Google Scholar 
Hirabayashi A, Kato D, Tomita Y, Iguchi M, Yamada K, Kouyama Y, et al. Risk factors for and role of OprD protein in increasing minimal inhibitory concentrations of carbapenems in clinical isolates of Pseudomonas aeruginosa. J Med Microbiol. 2017;66:1562–72.CAS 
PubMed 
Article 

Google Scholar 
Huang H, Jeanteur D, Pattus F, Hancock REW. Membrane topology and site-specific mutagenesis of Pseudomonas aeruginosa porin OprD. Mol Microbiol. 1995;16:931–41.CAS 
PubMed 
Article 

Google Scholar 
Fournier D, Richardot C, Müller E, Robert-Nicoud M, Llanes C, Plésiat P, et al. Complexity of resistance mechanisms to imipenem in intensive care unit strains of Pseudomonas aeruginosa. J Antimicrob Chemother. 2013;68:1772–80.CAS 
PubMed 
Article 

Google Scholar 
Kao C-Y, Chen S-S, Hung K-H, Wu H-M, Hsueh P-R, Yan J-J, et al. Overproduction of active efflux pump and variations of OprD dominate in imipenem-resistant Pseudomonas aeruginosa isolated from patients with bloodstream infections in Taiwan. BMC Microbiol. 2016;16:107.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Ocampo-Sosa AA, Cabot G, Rodríguez C, Roman E, Tubau F, Macia MD, et al. Alterations of OprD in carbapenem-intermediate and -susceptible strains of Pseudomonas aeruginosa isolated from patients with bacteremia in a Spanish multicenter study. Antimicrob Agents Chemother. 2012;56:1703–13.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shu J-C, Kuo A-J, Su L-H, Liu T-P, Lee M-H, Su I-N, et al. Development of carbapenem resistance in Pseudomonas aeruginosa is associated with OprD polymorphisms, particularly the amino acid substitution at codon 170. J Antimicrob Chemother. 2017;72:2489–95.CAS 
PubMed 
Article 

Google Scholar 
Pernet E, Guillemot L, Burgel P-R, Martin C, Lambeau G, Sermet-Gaudelus I, et al. Pseudomonas aeruginosa eradicates Staphylococcus aureus by manipulating the host immunity. Nat Commun. 2014;5:5105.CAS 
PubMed 
Article 

Google Scholar 
Briaud P, Camus L, Bastien S, Doléans-Jordheim A, Vandenesch F, Moreau K. Coexistence with Pseudomonas aeruginosa alters Staphylococcus aureus transcriptome, antibiotic resistance and internalization into epithelial cells. Sci Rep. 2019;9:16564.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Khare A, Tavazoie S. Multifactorial competition and resistance in a two-species bacterial system. PLOS Genet. 2015;11:e1005715.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mashburn LM, Jett AM, Akins DR, Whiteley M. Staphylococcus aureus serves as an iron source for Pseudomonas aeruginosa during in vivo coculture. J Bacteriol. 2005;187:554–66.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cirz RT, O’Neill BM, Hammond JA, Head SR, Romesberg FE. Defining the Pseudomonas aeruginosa SOS response and its role in the global response to the antibiotic ciprofloxacin. J Bacteriol. 2006;188:7101–10.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
García-Contreras R, Nuñez-López L, Jasso-Chávez R, Kwan BW, Belmont JA, Rangel-Vega A, et al. Quorum sensing enhancement of the stress response promotes resistance to quorum quenching and prevents social cheating. ISME J. 2015;9:115–25.PubMed 
Article 
CAS 

Google Scholar 
Moradali MF, Ghods S, Rehm BHA. Pseudomonas aeruginosa lifestyle: a paradigm for adaptation, survival, and persistence. Front Cell Infect Microbiol 2017;7:39.Vogt SL, Green C, Stevens KM, Day B, Erickson DL, Woods DE, et al. The stringent response is essential for Pseudomonas aeruginosa virulence in the rat lung agar bead and Drosophila melanogaster feeding models of infection. Infect Immun. 2011;79:4094–104.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Baron SS, Terranova G, Rowe JJ. Molecular mechanism of the antimicrobial action of pyocyanin. Curr Microbiol. 1989;18:223–30.CAS 
Article 

Google Scholar 
Castañeda-Tamez P, Ramírez-Peris J, Pérez-Velázquez J, Kuttler C, Jalalimanesh A, Saucedo-Mora MÁ, et al. Pyocyanin restricts social cheating in Pseudomonas aeruginosa. Front Microbiol. 2018;9:1348.PubMed 
PubMed Central 
Article 

Google Scholar 
Fontoura R, Spada JC, Silveira ST, Tsai SM, Brandelli A. Purification and characterization of an antimicrobial peptide produced by Pseudomonas sp. strain 4B. World J Microbiol Biotechnol. 2009;25:205–13.CAS 
Article 

Google Scholar 
Hassan HM, Fridovich I. Mechanism of the antibiotic action pyocyanine. J Bacteriol. 1980;141:156–63.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Machan ZA, Pitt TL, White W, Watson D, Taylor GW, Cole PJ, et al. Interaction between Pseudomonas aeruginosa and Staphylococcus aureus: description of an antistaphylococcal substance. J Med Microbiol. 1991;34:213–7.CAS 
PubMed 
Article 

Google Scholar 
Raji El Feghali PA, Nawas T. Pyocyanin: a powerful inhibitor of bacterial growth and biofilm formation. Madridge J Case Rep Stud. 2018;3:101–7.Article 

Google Scholar 
Saha S, Thavasi R, Jayalakshmi S. Phenazine pigments from Pseudomonas aeruginosa and their application as antibacterial agent and food colourants. Res J Microbiol. 2008;3:122–8.CAS 
Article 

Google Scholar 
Schiessl KT, Hu F, Jo J, Nazia SZ, Wang B, Price-Whelan A, et al. Phenazine production promotes antibiotic tolerance and metabolic heterogeneity in Pseudomonas aeruginosa biofilms. Nat Commun. 2019;10:762.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jagmann N, Brachvogel H-P, Philipp B. Parasitic growth of Pseudomonas aeruginosa in co-culture with the chitinolytic bacterium Aeromonas hydrophila: parasitic growth of Pseudomonas aeruginosa. Environ Microbiol. 2010;12:1787–802.CAS 
PubMed 
Article 

Google Scholar 
Noto MJ, Burns WJ, Beavers WN, Skaar EP. Mechanisms of pyocyanin toxicity and genetic determinants of resistance in Staphylococcus aureus. J Bacteriol. 2017;199:00221–17.Venkataraman A, Rosenbaum MA, Perkins SD, Werner JJ, Angenent LT. Metabolite-based mutualism between Pseudomonas aeruginosa PA14 and Enterobacter aerogenes enhances current generation in bioelectrochemical systems. Energy Environ Sci. 2011;4:4550.CAS 
Article 

Google Scholar 
Waite RD, Qureshi MR, Whiley RA. Modulation of behaviour and virulence of a high alginate expressing Pseudomonas aeruginosa strain from cystic fibrosis by oral commensal bacterium Streptococcus anginosus. PLOS ONE. 2017;12:e0173741.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Whooley MA, McLoughlin AJ. The regulation of pyocyanin production in Pseudomonas aeruginosa. Eur J Appl Microbiol Biotechnol. 1982;15:161–6.CAS 
Article 

Google Scholar 
Elbargisy RM. Optimization of nutritional and environmental conditions for pyocyanin production by urine isolates of Pseudomonas aeruginosa. Saudi J Biol Sci. 2021;28:993–1000.CAS 
PubMed 
Article 

Google Scholar 
Gupta S, Laskar N, Kadouri DE. Evaluating the effect of oxygen concentrations on antibiotic sensitivity, growth, and biofilm formation of human pathogens. Microbiol Insights. 2016;9. https://doi.org/10.4137/MBI.S40767.Article 
PubMed 
PubMed Central 

Google Scholar 
Worlitzsch D, Tarran R, Ulrich M, Schwab U, Cekici A, Meyer KC, et al. Effects of reduced mucus oxygen concentration in airway Pseudomonas infections of cystic fibrosis patients. J Clin Investig. 2002;109:317–25.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Skurnik D, Roux D, Cattoir V, Danilchanka O, Lu X, Yoder-Himes DR, et al. Enhanced in vivo fitness of carbapenem-resistant oprD mutants of Pseudomonas aeruginosa revealed through high-throughput sequencing. Proc Natl Acad Sci USA. 2013;110:20747–52.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Higgins S, Heeb S, Rampioni G, Fletcher MP, Williams P, Cámara M. Differential regulation of the phenazine biosynthetic operons by quorum sensing in Pseudomonas aeruginosa PAO1-N. Front Cell Infect Microbiol. 2018;8:252.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Dragoš A, Martin M, Falcón García C, Kricks L, Pausch P, Heimerl T, et al. Collapse of genetic division of labour and evolution of autonomy in pellicle biofilms. Nat Microbiol. 2018;3:1451–60.PubMed 
Article 
CAS 

Google Scholar 
Cuthbertson L, Walker AW, Oliver AE, Rogers GB, Rivett DW, Hampton TH, et al. Lung function and microbiota diversity in cystic fibrosis. Microbiome. 2020;8:45.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rogers GB, Carroll MP, Serisier DJ, Hockey PM, Jones G, Bruce KD. Characterization of bacterial community diversity in cystic fibrosis lung infections by use of 16S ribosomal DNA terminal restriction fragment length polymorphism profiling. J Clin Microbiol. 2004;42:5176–83.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Workentine ML, Sibley CD, Glezerson B, Purighalla S, Norgaard-Gron JC, Parkins MD, et al. Phenotypic heterogeneity of Pseudomonas aeruginosa populations in a cystic fibrosis patient. PLoS ONE. 2013;8:e60225.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Valdezate S. Persistence and variability of Stenotrophomonas maltophilia in cystic fibrosis patients, Madrid, 1991-8. Emerg Infect Dis. 2001;7:113–22.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dalbøge CS, Hansen CR, Pressler T, Høiby N, Johansen HK. Chronic pulmonary infection with Stenotrophomonas maltophilia and lung function in patients with cystic fibrosis. J Cyst Fibros. 2011;10:318–25.PubMed 
Article 

Google Scholar 
Jeon YD, Jeong WY, Kim MH, Jung IY, Ahn MY, Ann HW, et al. Risk factors for mortality in patients with Stenotrophomonas maltophilia bacteremia. Medicine. 2016;95:e4375.PubMed 
PubMed Central 
Article 

Google Scholar 
Sherrard LJ, Tunney MM, Elborn JS. Antimicrobial resistance in the respiratory microbiota of people with cystic fibrosis. Lancet Lond Engl. 2014;384:703–13.CAS 
Article 

Google Scholar 
Choi K-H, Schweizer HP. Mini-Tn7 insertion in bacteria with single attTn7 sites: example Pseudomonas aeruginosa. Nat Protoc. 2006;1:153–61.CAS 
PubMed 
Article 

Google Scholar 
Jelsbak L, Johansen HK, Frost A-L, Thøgersen R, Thomsen LE, Ciofu O, et al. Molecular epidemiology and dynamics of Pseudomonas aeruginosa populations in lungs of cystic fibrosis patients. Infect Immun. 2007;75:2214–24.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yeung ATY, Parayno A, Hancock REW. Mucin promotes rapid surface motility in Pseudomonas aeruginosa. mBio. 2012;3:300073–12.Kirchner S, Fothergill JL, Wright EA, James CE, Mowat E, Winstanley C. Use of artificial sputum medium to test antibiotic efficacy against Pseudomonas aeruginosa in conditions more relevant to the cystic fibrosis lung. J Vis Exp. 2012;64:3857.Hill DB, Long RF, Kissner WJ, Atieh E, Garbarine IC, Markovetz MR, et al. Pathological mucus and impaired mucus clearance in cystic fibrosis patients result from increased concentration, not altered pH. Eur Respir J. 2018;52:1801297.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Benoni G, Cuzzolin L, Bertrand C, Puchetti V, Velo G. Imipenem kinetics in serum, lung tissue and pericardial fluid in patients undergoing thoracotomy. J Antimicrob Chemother. 1987;20:725–8.CAS 
PubMed 
Article 

Google Scholar 
Radhakrishnan M, Jaganath A, Rao GSU, Kumari HBV. Nebulized imipenem to control nosocomial pneumonia caused by Pseudomonas aeruginosa. J Crit Care. 2008;23:148–50.CAS 
PubMed 
Article 

Google Scholar 
Wenzler E, Fraidenburg DR, Scardina T, Danziger LH. Inhaled antibiotics for gram-negative respiratory infections. Clin Microbiol Rev. 2016;29:581–632.PubMed 
PubMed Central 
Article 

Google Scholar 
The European Committee on Antimicrobial Susceptibility Testing. Breakpoint tables for interpretation of MICs and zone diameters.Version 12.0, 2022. http://www.eucast.org.Kang D, Revtovich AV, Chen Q, Shah KN, Cannon CL, Kirienko NV. Pyoverdine-dependent virulence of Pseudomonas aeruginosa isolates from cystic fibrosis patients. Front Microbiol. 2019;10:2048.PubMed 
PubMed Central 
Article 

Google Scholar 
Martin LW, Reid DW, Sharples KJ, Lamont IL. Pseudomonas siderophores in the sputum of patients with cystic fibrosis. BioMetals. 2011;24:1059–67.CAS 
PubMed 
Article 

Google Scholar 
Caldwell CC, Chen Y, Goetzmann HS, Hao Y, Borchers MT, Hassett DJ, et al. Pseudomonas aeruginosa exotoxin pyocyanin causes cystic fibrosis airway pathogenesis. Am J Pathol. 2009;175:2473–88.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
O’Loughlin CT, Miller LC, Siryaporn A, Drescher K, Semmelhack MF, Bassler BL. A quorum-sensing inhibitor blocks Pseudomonas aeruginosa virulence and biofilm formation. Proc Natl Acad Sci USA. 2013;110:17981–6.PubMed 
PubMed Central 
Article 

Google Scholar 
Sass G, Nazik H, Penner J, Shah H, Ansari SR, Clemons KV, et al. Studies of Pseudomonas aeruginosa mutants indicate pyoverdine as the central factor in inhibition of Aspergillus fumigatus biofilm. J Bacteriol. 2018;200:00345–17.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.CAS 
PubMed 
Article 

Google Scholar 
Deatherage DE, Barrick JE. Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq. In: Sun L, Shou W, editors. Engineering and Analyzing Multicellular Systems. New York, NY: Springer New York; 2014. p. 165–88.Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G, et al. Integrative genomics viewer. Nat Biotechnol. 2011;29:24–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Thorvaldsdottir H, Robinson JT, Mesirov JP. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform. 2013;14:178–92.CAS 
PubMed 
Article 

Google Scholar  More

Xie, Y., Fang, M. & Shauman, K. STEM education. Annu. Rev. Sociol. 41, 331–357 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, X. STEM attrition: College students’ paths into and out of STEM fields. National Center for Education Statistics. Retrieved from http://ies.ed.gov/pubsearch/pubsinfo.asp?pubid=2014001rev. Accessed 22 September 2021.Huang G, Taddese N, Walter E (2000) Entry and persistence of women and minorities in college science and engineering education. National Center for Education Statistics. Retrieved from https://eric.ed.gov/?id=ED566411. Accessed 22 September 2021.Hurtado, S., Eagan, K., & Chang, M. Degrees of Success: Bachelor’s Degree Completion Rates among Initial STEM Majors (Higher Education Research Institute, Los Angeles, CA) (2010).National Science Foundation, Broadening Participation Working Group (2014) Pathways to broadening participation in response to the CEOSE 2011–2012 recommendation. National Science Foundation. Retrieved from https://www.nsf.gov/pubs/2015/nsf15037/nsf15037.pdf. Accessed 22 Sep 2021.James, S. M. & Singer, S. R. From the NSF: The National Science Foundation’s investments in broadening participation in science, technology, engineering, and mathematics education through research and capacity building. CBE Life Sci. Educ. 15(3), 1–8 (2016).Article 

Google Scholar 
Smith, B. L., MacGregor, J., Matthews, R. & Gabelnick, F. Learning communities: Reforming undergraduate education (Jossey-Bass, 2004).
Google Scholar 
Andrade, M. S. Learning communities: Examining positive outcomes. J. Coll. Stud. Ret. 9(1), 1–20 (2007).Article 

Google Scholar 
Maton, K. I., Pollard, S. A., McDougall Weise, T. V. & Hrabowski, F. A. Meyerhoff Scholars Program: A strengths-based, institution-wide approach to increasing diversity in science, technology, engineering, and mathematics. Mt Sinai J. Med. 79(5), 610–623 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Dagley, M., Georgiopoulos, M., Reece, A. & Young, C. Increasing retention and graduation rates through a STEM learning community. J. Coll. Stud. Ret. 18(2), 167–182 (2016).Article 

Google Scholar 
National Survey of Student Engagement (2015) Engagement Insights: Survey Findings on the Quality of Undergraduate Education—Annual Results 2015 (Bloomington, IN).Tinto, V. Leaving college: Rethinking the causes and cures of student attrition (University of Chicago Press, 1987).
Google Scholar 
Tinto, V. Learning better together: The impact of learning communities on student success. Higher Educ. Monogr. Ser. 1(8), 1–8 (2003).
Google Scholar 
Otto, S., Evins, M. A., Boyer-Pennington, M. & Brinthaupt, T. M. Learning communities in higher education: Best practices. Journal of Student Success and Retention 2(1), 1–20 (2015).
Google Scholar 
Boda, Z., Elmer, T., Vörös, A. & Stadtfeld, C. Short-term and long-term effects of a social network intervention on friendships among university students. Sci. Rep. 10(1), 1–2 (2020).Article 
CAS 

Google Scholar 
Hotchkiss, J. L., Moore, R. E. & Pitts, M. M. Freshman learning communities, college performance, and retention. Educ. Econ. 14(2), 197–210 (2006).Article 

Google Scholar 
Whalen, D. F. & Shelley, M. C. Academic success for STEM and non-STEM majors. J. STEM Educ. 11(1), 45–60 (2010).
Google Scholar 
Xu, D., Solanki, S., McPartlan, P. & Sato, B. EASEing students into college: The impact of multidimensional support for underprepared students. Educ. Res. 47(7), 435–450 (2018).Article 

Google Scholar 
Jaffee, D., Carle, A., Phillips, R. & Paltoo, L. Intended and unintended consequences of first-year learning communities: An initial investigation. J. First-Year Exp. Stud. Trans. 20(1), 53–70 (2008).
Google Scholar 
Tinto, V. & Goodsell, A. Freshman interest groups and the first-year experience: Constructing student communities in a large university. J. First Year Exp. Stud. Trans. 6(1), 7–28 (1994).
Google Scholar 
Domizi, D. Student perceptions about their informal learning experiences in a first-year residential learning community. J. First Year Exp. Stud. Transit. 20(1), 97–110 (2008).
Google Scholar 
Lee, D. S. & Lemieux, T. Regression discontinuity designs in economics. J. Econ. Lit. 2, 281–355 (2010).Article 

Google Scholar 
Jacob, R., Zhu, P., Somers, M.A., & Bloom, H. A Practical Guide to Regression Discontinuity (MDRC, New York, NY, 2012).Hays, R. B. & Oxley, D. Social network development and functioning during a life transition. J. Pers. Soc. Psychol. 50(2), 305–313 (1986).CAS 
PubMed 
Article 

Google Scholar 
Freeman, T. M., Anderman, L. H. & Jensen, J. M. Sense of belonging in college freshmen at the classroom and campus levels. J. Exp. Educ. 75(3), 203–220 (2007).Article 

Google Scholar 
Zumbrunn, S., McKim, C., Buhs, E. & Hawley, L. R. Support, belonging, motivation, and engagement in the college classroom: A mixed method study. Instr. Sci. 42(5), 661–684 (2014).Article 

Google Scholar 
Hasan, S. & Bagde, S. The mechanics of social capital and academic performance in an Indian college. Am. Sociol. Rev. 78(6), 1009–1032 (2013).Article 

Google Scholar 
Stadtfeld, C., Vörös, A., Elmer, T., Boda, Z. & Raabe, I. J. Integration in emerging social networks explains academic failure and success. Proc. Natl. Acad. Sci. USA 116(3), 792–797 (2019).CAS 
PubMed 
Article 

Google Scholar 
Kraemer, B. A. The academic and social integration of Hispanic students into college. Rev. High Educ. 20(2), 163–179 (1997).Article 

Google Scholar 
Nora, A. Two-year colleges and minority students’ educational aspirations: Help or hindrance. Higher Educ. Handb. Theory Res. 9(3), 212–247 (1993).
Google Scholar 
McCabe, J.M. Connecting in College: How Friendship Networks Matter for Academic and Social Success (University of Chicago Press, Chicago, IL, 2016).Felten, P., & Lambert, L. M. Relationship-rich Education: How Human Connections Drive Success in College (Johns Hopkins University Press, Baltimore, MD, 2020).Hallinan, M. T. The peer influence process. Stud. Educ. Eval. 7(3), 285–306 (1981).Article 

Google Scholar 
Thomas, S. L. Ties that bind: A social network approach to understanding student integration and persistence. J. Higher Educ. 71(5), 591–615 (2000).
Google Scholar 
Turetsky, K. M., Purdie-Greenaway, V., Cook, J. E., Curley, J. P. & Cohen, G. L. A psychological intervention strengthens students’ peer social networks and promotes persistence in STEM. Sci. Adv. 6(45), 1–10 (2020).Article 

Google Scholar 
Dokuka, S., Valeeva, D. & Yudkevich, M. How academic achievement spreads: The role of distinct social networks in academic performance diffusion. PLoS ONE 15(7), 1–16 (2020).Article 
CAS 

Google Scholar 
Epstein, J. L. & Karweit, N. (eds) Friends in school: Patterns of selection and influence in secondary schools (Academic Press, 1983).
Google Scholar 
Feld, S. L. The focused organization of social ties. AJS 86(5), 1015–1035 (1981).
Google Scholar 
Rivera, M. T., Soderstrom, S. B. & Uzzi, B. Dynamics of dyads in social networks: Assortative, relational, and proximity mechanisms. Annu. Rev. Sociol. 36, 91–115 (2010).Article 

Google Scholar 
Mollenhorst, G., Volker, B. & Flap, H. Changes in personal relationships: How social contexts affect the emergence and discontinuation of relationships. Soc. Netw. 37, 65–80 (2014).Article 

Google Scholar 
Thomas, R. J. Sources of friendship and structurally induced homophily across the life course. Sociol Perspect 62(6), 822–843 (2019).Article 

Google Scholar 
Kubitschek, W. N. & Hallinan, M. T. Tracking and students’ friendships. Soc. Psychol. Q 46, 1–5 (1998).Article 

Google Scholar 
Frank, K. A., Muller, C. & Mueller, A. S. The embeddedness of adolescent friendship nominations: The formation of social capital in emergent network structures. AJS 119(1), 216–253 (2013).PubMed 
PubMed Central 

Google Scholar 
Kossinets, G. & Watts, D. J. Origins of homophily in an evolving social network. AJS 115(2), 405–450 (2009).
Google Scholar 
Wimmer, A. & Lewis, K. Beyond and below racial homophily: ERG models of a friendship network documented on Facebook. AJS 116(2), 583–642 (2010).PubMed 

Google Scholar 
Hallinan, M. T. & Sørensen, A. B. Ability grouping and student friendships. Am. Educ. Res. J. 51, 485–499 (1985).Article 

Google Scholar 
Leszczensky, L. & Pink, S. Ethnic segregation of friendship networks in school: Testing a rational-choice argument of differences in ethnic homophily between classroom-and grade-level networks. Soc. Netw. 42, 18–26 (2015).Article 

Google Scholar 
DiMaggio, P. & Garip, F. Network effects and social inequality. Annu. Rev. Sociol. 54, 93–118 (2012).Article 

Google Scholar 
Johnson, A. M. ‘“I can turn it on when i need to”’: Pre-college Integration, culture, and peer academic engagement among black and Latino/a engineering Students. Sociol. Educ. 56, 1–20 (2019).Article 

Google Scholar 
Perry, B. L., Pescosolido, B. A. & Borgatti, S. P. Egocentric network analysis: Foundations, methods, and models (Cambridge University Press, 2018).Book 

Google Scholar 
Wasserman, S. & Faust, K. Social network analysis: Methods and applications (Cambridge University Press, 1994).MATH 
Book 

Google Scholar 
Hartup, W. W. & Stevens, N. Friendships and adaptation in the life course. Psychol. Bull. 121(3), 355 (1997).Article 

Google Scholar 
Vaquera, E. & Kao, G. Do you like me as much as I like you? Friendship reciprocity and its effects on school outcomes among adolescents. Soc. Sci. Res. 37(1), 55–72 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
Imbens, G. W. & Lemieux, T. Regression discontinuity designs: A guide to practice. J. Econom. 142(2), 615–635 (2008).MathSciNet 
MATH 
Article 

Google Scholar 
Imbens, G. W. & Angrist, J. D. Identification and estimation of local average treatment effects. Econometrica 62(2), 467–475 (1994).MATH 
Article 

Google Scholar 
Robins, G., Pattison, P., Kalish, Y. & Lusher, D. An introduction to exponential random graph (p*) models for social networks. Soc. Netw. 29(2), 173–191 (2007).Article 

Google Scholar 
Handcock, M. S., Hunter, D. R., Butts, C. T., Goodreau, S. M. & Morris, M. Statnet: Software tools for the representation, visualization, analysis and simulation of network data. J. Stat. Softw. 24(1), 1548–7660 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
Calonico, S., Cattaneo, M. D. & Titiunik, R. Optimal data-driven regression discontinuity plots. J. Am. Stat. Assoc. 110(512), 1753–1769 (2015).MathSciNet 
CAS 
MATH 
Article 

Google Scholar 
Duxbury, S. W. The problem of scaling in exponential random graph models. Sociol. Methods Res. https://doi.org/10.1177/0049124120986178:1-39 (2021).MathSciNet 
Article 

Google Scholar 
McPherson, M., Smith-Lovin, L. & Cook, J. M. Birds of a feather: Homophily in social networks. Annu. Rev. Sociol. 27(1), 415–444 (2001).Article 

Google Scholar 
Kadushin, C. Understanding social networks: Theories, concepts, and findings (Oxford University Press, 2012).
Google Scholar 
Flashman, J. Academic achievement and its impact on friend dynamics. Sociol. Educ. 85(1), 61–80 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Carrell, S. E., Sacerdote, B. I. & West, J. E. From natural variation to optimal policy? The importance of endogenous peer group formation. Econometrica 81(3), 855–882 (2013).MathSciNet 
MATH 
Article 

Google Scholar 
Cox, A. B. Cohorts, ‘“siblings”,’ and mentors: Organizational structures and the creation of social capital. Sociol. Educ. 90(1), 47–63 (2017).Article 

Google Scholar 
Valente, T. W. Network interventions. Science 337(6090), 49–53 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Nunn, L. M. College belonging: How first-year and first-generation students navigate campus life (Rutgers University Press, 2021).Book 

Google Scholar 
Garlick, R. Academic peer effects with different group assignment policies: Residential tracking versus random assignment. Am. Econ. J. Appl. Econ. 10(3), 345–369 (2018).Article 

Google Scholar 
Carrell, S. E., Fullerton, R. L. & West, J. E. Does your cohort matter? Measuring peer effects in college achievement. J. Labor. Econ. 27(3), 439–464 (2009).Article 

Google Scholar 
Lomi, A., Snijders, T. A., Steglich, C. E. & Torló, V. J. Why are some more peer than others? Evidence from a longitudinal study of social networks and individual academic performance. Soc. Sci. Res. 40(6), 1506–1520 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Poldin, O., Valeeva, D. & Yudkevich, M. Which peers matter: How social ties affect peer-group effects. Res. High Educ. 57(4), 448–468 (2016).Article 

Google Scholar 
Raabe, I. J., Boda, Z. & Stadtfeld, C. The social pipeline: How friend influence and peer exposure widen the STEM gender gap. Sociol. Educ. 92(2), 105–123 (2019).Article 

Google Scholar 
Burt, R. S. Structural holes and good ideas. AJS 110(2), 349–399 (2004).
Google Scholar 
Oakes, J. Keeping track: How schools structure inequality (Yale University Press, 2005).
Google Scholar 
Park JJ et al. (2021) Who are you studying with? The role of diverse friendships in STEM and corresponding inequality. Res. High Educ. https://doi.org/10.1007/s11162-021-09638-8.Marsden, P. V. & Campbell, K. E. Measuring tie strength. Soc. Forces 63(2), 482–501 (1984).Article 

Google Scholar 
Mattie, H., Engø-Monsen, K., Ling, R. & Onnela, J. P. Understanding tie strength in social networks using a local “bow tie” framework. Sci. Rep. 8(1), 1–9 (2018).CAS 
Article 

Google Scholar 
Sørensen, A. B. Organizational differentiation of students and educational opportunity. Sociol. Educ. 43(4), 355–376 (1970).Article 

Google Scholar  More

Massive drift of pumice along the northeastern coast of Okinawa IslandA large amount of pumice stones reached and was deposited along the northeastern coast of Okinawa Island, that were brought by strong seasonal northeasterly winds (Supplementary Video 1). The pumice was thought to be brought by the Kuroshio countercurrent from sites near the Ogasawara Archipelago 1300 km away. Because the Kuroshio countercurrent is composed of various medium-sized eddies in the ocean, the current does not always flow in one direction and as a continuous flow27,28. The pumice drift was more strongly controlled by the seasonal northwesterly winds to be transported to Okinawa across the Philippine Sea (Fig. 1a). The pumice raft reached the northern part of Okinawa approximately 2 months after the eruption (Figs. 2, 3 and 4). According to a very recent report, the pumice clasts were drifting ashore in Thailand (traveling 4000 km-long distance) across the South China Sea within half a year of this eruption29. Most pumice stones were gray, but some pumice was banded, and others were black reflecting some compositional variation25,29 (Fig. 2d,e). The Kuroshio Current is faster than the Kuroshio countercurrent27, so some pumice clasts have already reached the main island of Japan25. Tracking the dispersal of the pumice will allow a better forecasting model based on observed raft trajectories by considering exact wind effects in the Philippine Sea30.Figure 2An example of a natural beach on Okinawa Island where pumice has washed ashore. (a) Appearance of natural sandy beaches on the northern part of Okinawa Island (Ibu beach, Kunigami Village, 26°75′57.88″ N, 128°32′23.32″ E). Photo was taken on 24 October 2021. Pumice drifted onto the sandy beach and formed a striped pattern. The white-capped waves indicate on the place where the reef edge exist. The white arrow points to the mangrove river estuary corresponding to Fig. 9. (b) Estimation of the pumice sedimentation depth on the original sand beach surface. (c) The high tide zone of the natural sandy beach is covered with pumice pebbles and stones. Yellow arrows indicate black pumice stones. Scale bar: 10 cm. (d, e) Front and back of examples of relatively large pumice stones from the same beach. The left image is mostly light brown, whereas the right image is almost black. Scale bars: 5 cm.Full size imageFigure 3Short-term migration of pumice from beaches as revealed by stationary observations. These four photos were taken at two sites on northern Okinawa Island on two consecutive days, 23 and 24 October 2021. (a, b) A sandy beach along the Sate Coast (26°78′84.56″ N, 128°22′30.57″ E). It was windy on the first day, and pumice stones were washed up with the waves. Almost all the pumice stones were removed from the beach and transported offshore on the following day. The black arrow in photo (a) indicates Cape Hedo, the northernmost tip of Okinawa Island. (c, d) At this gravelly beach (26°80′83.25″ N, 128°23′38.56″ E), pumice fully covers the seawall on the first day, but all of the pumice stones washed away, leaving the original gravels, on the following day. The white arrow in each photo indicates an identical marker stone placed on the beach. Weather data of northern Okinawa (https://www.data.jma.go.jp/obd/stats/etrn/view/daily_a1.php?prec_no=91&block_no=0901&year=2021&month=10&day=23&view=g_wsp) and tidal data (Naha: 26°13′ N, 127°40′ E) (https://www.data.jma.go.jp/gmd/kaiyou/db/tide/genbo/genbo.php) are provided by Japan Meteorological Agency.Full size imageFigure 4Pumice stones settled by marine organisms. (a) Pumice collected from Ibu beach on 31 October 2021. Two marine benthos coexist close together on a pumice stone. Scale bar: 1 cm. (b) Enlarged image of the Lepas barnacle. Scale bar: 3 mm. (c) Enlarged image of the bryozoan. Scale bar: 3 mm. (d) Stereo microscopic image of pumice pebbles of a few millimeters in diameter collected from Ibu beach on 15 January 2022. The light brown coloration indicates some algal/cyanobacterial growth on the pumice. Scale bar 1 mm. (e) Red autofluorescence was detected from pumice pebbles. Image corresponds to (d). Autofluorescence from microalgae was confirmed by Supplementary Fig. 2. Scale bar 1 mm. (f) Enlarged image of the center of the figure of (e) shows red autofluorescent signals with a diameter of 10–30 µm. Scale bar: 200 µm.Full size imageChanges in the coastal landscape: natural beaches and estuariesMarine calcifiers, including corals, calcareous algae, and foraminifers, produce white sandy beaches on Okinawa Island. However, the gray pumice drifting ashore changed the white sand beach, especially along the northeastern coastline. We observed several lines of pumice aggregations, suggesting that pumice was brought ashore by wavefronts several times produced by a strong north wind at the tide lines (Supplementary Video 1; Fig. 2a). At the same sampling site, the thickest depth of beached pumice was more than 30 cm (Fig. 2b; Supplementary Video 2). Most of the pumice stones were from 0.5 cm to 3 cm in diameter, with a few black pumice stones included (Fig. 2c: yellow arrow). Pumice stones arrived at the estuaries of some brackish rivers (Fig. 8, Supplementary Fig. 1a) and mangrove forests in northwest Okinawa (Fig. 9).Pumice stones and pumice rafts show dynamic behavior in a short period. We captured photographs 24 h apart at two positions on the shore of Okinawa, which allowed us to compare the pumice dynamics during this period (Fig. 3). Within that time frame, there were two high tides, and the tide level changed by up to 170 cm. As seen in Fig. 3a, on the first day, the coast was covered with pumice, and floating pumice could be seen on the seafront. The north wind was strong that day, as shown by the relatively high waves near the shore as well as white‐crested waves near the reef edge. By the following day, most of the pumice had been moved offshore by tides and winds (Fig. 3b), indicating that newly beached pumice raft deposits were removed quickly from open beach areas. At another site on a gravelly beach, pumice fully covered the seawall on the first day, but almost all of the pumice stones were washed away, leaving the original gravels, on the following day (Fig. 3c,d). Japan Meteorological Agency (Oku station: 232 m above sea level, latitude 26°50.1, longitude 128°16.3′) reported that northerly winds were blowing (mean wind speed: 3.4 m/s) on 23rd October in northern Okinawa. The following day, the wind direction changed to the east-southeast; blowing offshore (mean wind speed: 2.9 m/s), resulting in the dramatic removal of pumice form the coast (Fig. 3). These observations indicate that surface winds rather than ocean currents had a strong influence on the raft trajectory and residence time on beaches, and are consistent with past research5. These observations lead us to expect that the pumice rafts will disappear from the coast of Okinawa fairly quickly, but in fact, there have been many cases where they have come back again in a few days. Although the overall amount of pumice drifting has been decreasing, a small amount of pumice has been drifting in coastal area of Okinawa in May, 202231. It is unlikely that large amounts of pumice will drift repeatedly throughout Okinawa Prefecture as reported in this report, but it should be noted that detached pumice material remains in beach and river runoff.Biofouling of sessile organisms on pumice arriving to OkinawaIt is noteworthy that the pumice rafts traveled over the deep Philippine Sea for over 2 months, and on arrival in Okinawa there was little to no biofouling of the pumice (Fig. 2). Some stranded pumices observed on Okinawa beaches had become habitats for sessile organisms (Fig. 4), as reported in previous studies1,2,3,4,5,6,29. Goose barnacles (Lepas sp.) without external damage to the shell were the most abundant species observed on the pumice (Fig. 4b). Lepas is a common biofouling taxon distributed globally and plays a role in biofouling as a foundation organism. The shell growth rate is more than 1 mm/day in some Lepas species32 suggesting that the Lepas had been growing on the pumice for about two weeks. Measurements of the shell size of Lepas attached to the pumice collections conducted in the same area (Supplementary Video 2) showed a bias toward larger sizes in the second collection (5.92 ± 3.86 mm (average ± S.D.), n = 75, 13 November 2021) than in the first one (3.43 ± 1.08 mm, n = 21, 31 October 2021), and significant differences were detected between the measurement periods (Mann–Whitney U test, p  More

Harun, N., Chaudhry, A. S., Shaheen, S., Ullah, K. & Khan, F. Ethnobotanical studies of fodder grass resources for ruminant animals, based on the traditional knowledge of indigenous communities in Central Punjab Pakistan. J. Ethnobiol. Ethnomed. 13(1), 56. https://doi.org/10.1186/s13002-017-0184-5 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Shaheen, H., Qureshi, R., Qaseem, M. F. & Bruschi, P. The fodder grass resources for ruminants: A indigenous treasure of local communities of Thal desert Punjab, Pakistan. PLoS One 15(3), e0224061. https://doi.org/10.1371/journal.pone.0224061 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Huston, J. E. Forage utilization and nutrient requirements of the goat1. J. Dairy Sci. 61(7), 988–993. https://doi.org/10.3168/jds.S0022-0302(78)83679-0 (1978).Article 

Google Scholar 
Wilson, A. D., Leigh, J. H., Hindley, N. L. & Mulham, W. E. Comparison of the diets of goats and sheep on a Casuarina cristata–Heterodendrum oleifolium woodland community in western New South Wales. Aust. J. Exp. Agric. 15(72), 45–53. https://doi.org/10.1071/EA9750045 (1975).CAS 
Article 

Google Scholar 
Grünwaldt, E. G., Pedrani, A. R. & Vich, A. I. Goat grazing in the arid piedmont of Argentina. Small Ruminants Res. 13(3), 211–216. https://doi.org/10.1016/0921-4488(94)90066-3 (1994).Article 

Google Scholar 
Aganga, A. A., Omphile, U. J., Thema, T. & Baitshotlhi, J. C. Chemical composition of napier grass (Pennisetum purpureum) at different stages of growth and napier grass silages with additives. J. Biosci. 5(4), 493–496. https://doi.org/10.3923/jbs.2005.493.496 (2005).Article 

Google Scholar 
Ganskopp, D. & Bohnert, D. Nutritional dynamics of 7 Northern Great Basin grasses. J. Range Manage. 54, 640–647. https://doi.org/10.2307/4003664 (2001).Article 

Google Scholar 
Capstaff, N. M. & Miller, A. J. Improving the yield and nutritional quality of forage crops. Front. Plant Sci. 9, 535. https://doi.org/10.3389/fpls.2018.00535 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Arzani, H., Basiri, M., Khatibi, F. & Ghorbani, G. Nutritive value of some Zagros Mountain rangeland species. Small Ruminants Res. 65(1–2), 128–135. https://doi.org/10.1016/j.smallrumres.2005.05.033 (2006).Article 

Google Scholar 
Keba, H. T., Madakadze, I. C., Angassa, A. & Hassen, A. Nutritive value of grasses in semi-arid rangelands of Ethiopia, Local experience based herbage preference evaluation versus laboratory analysis. Asian-Aust. J. Anim. Sci. 26(3), 366. https://doi.org/10.5713/ajas.2012.12551 (2013).Article 

Google Scholar 
Dhungana, S., Tripathee, H. P., Puri, L., Timilsina, Y. P. & Devkota, K. P. Nutritional analysis of locally preferred fodder trees of Middle Hills of Nepal, a case study from Hemja VDC, Kaski District, Nepal. J. Sci. Technol. 13(2), 39–44. https://doi.org/10.3126/njst.v13i2.7712 (2012).Article 

Google Scholar 
Talore, D. G. Evaluation of major feed resources in crop-livestock mixed farming systems, southern Ethiopia, Indigenous knowledge versus laboratory analysis results. J. Agric. Rural Dev. 116(2), 157–166 (2015). http://nbn-resolving.de/urn:nbn:de:hebis:34-2015061048507.Geng, Y. et al. Nutrient value of wild fodder species and the implications for improving the diet of mithun (Bos frontalis) in Dulongjiang area, Yunnan Province, China. Plant Diversity 42(6), 455–463. https://doi.org/10.1016/j.pld.2020.09.007 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Sayed, M. A. I., Kulkarni, S., Kulkarni, D., Pande, A. & Kauthale, V. Nutritional study of local fodder species in Ahmednagar district of western Maharashtra. Agric. Sci. Digest A Res. J. 37(2), 154–156. https://doi.org/10.18805/asd.v37i2.7979 (2017).Article 

Google Scholar 
Evitayani, L. W., Fariani, A., Ichinohe, T. & Fujihara, T. Study on nutritive value of tropical forages in North Sumatra, Indonesia. Asian-Aust. J. Anim. Sci. 17(11), 1518–1523. https://doi.org/10.5713/ajas.2004.1518 (2004).Article 

Google Scholar 
Kanak, A. R., Khan, M. J., Debi, M. R., Pikar, M. K. & Aktar, M. Nutritive value of three fodder species at different stages of maturity, Bangladesh. J. Anim. Sci. 41(2), 90–95. https://doi.org/10.3329/bjas.v41i2.14123 (2012).Article 

Google Scholar 
Rahim, I., Maselli, D., Rueff, H. & Wiesmann, U. Indigenous fodder trees can increase grazing accessibility for landless and mobile pastoralists in northern Pakistan. Pastoral. Res. Policy Pract. 1(2), 1–2. https://doi.org/10.1186/2041-7136-1-2 (2011).Article 

Google Scholar 
Sultan, J., Inam-ur-rahim, I., Nawaz, H., Yaqoob, M. & Javed, I. Mineral composition, palatability and digestibility of free rangeland grasses of northern grasslands of Pakistan. Pak. J. Bot. 40(5), 2059–2070 (2008).CAS 

Google Scholar 
Bano, G., Islam, M., Ahmad, S., Aslam, S. & Koukab, S. Seasonal variation in nutritive value of Chrysopogon aucheri (boiss) stapf., and Cymbopogon jwarancusa (jones) schult., in highland Balochistan, Pakistan. Pak. J. Bot. 41(2), 511–517 (2009).CAS 

Google Scholar 
Rafay, M., Khan, R. A., Yaqoob, S. & Ahmad, M. Nutritional evaluation of major range grasses from Cholistan Desert. Pak. J. Nutr. 12(1), 23–29. https://doi.org/10.3923/pjn.2013.23.29 (2013).CAS 
Article 

Google Scholar 
Sultan, J. I., Manzoor, M. N., Shahzad, M. A. & Nisa M. Nutritional profile and in situ digestion kinetics of some irrigated grasses at pre-bloom stage. In International Conference on Biology, Environment and Chemistry 455–463 (2011). https://doi.org/10.3923/pjn.2013.23.29.Ahmed, K. et al. Proximate analysis, Relative feed values of various forage plants for ruminants investigated in a semi-arid region of Punjab, Pakistan. J. Agric. Sci. 27(6), 302. https://doi.org/10.4236/as.2013.46043 (2013).Article 

Google Scholar 
Manzoor, M. N., Sultan, J. I., Nisa, M. U. & Bilal, M. Q. Nutritive evaluation and in-situ digestibility of irrigated grasses. J. Anim. Plant Sci. 23, 1223–1227 (2013).CAS 

Google Scholar 
Sultan, J. I., Rahim, I. U., Nawaz, H. & Yaqoob, M. Nutritive value of marginal land grasses of northern grasslands of Pakistan. Pak. J. Bot. 39(4), 1071–1082 (2007).
Google Scholar 
Khan, R. I., Alam, M. R. & Amin, M. R. Effect of season and fertilizer on species composition and nutritive value of native grasses. Asian-Aust. J. Anim. Sci. 12(8), 1222–1227. https://doi.org/10.5713/ajas.1999.1222 (1999).Article 

Google Scholar 
Grant, K., Kreyling, J., Dienstbach, L. F. H., Beierkuhnlein, C. & Jentsch, A. Water stress due to increased intra-annual precipitation variability reduced forage yield but raised forage quality of a temperate grassland. Agric. Ecosyst. Environ. 186, 11–22. https://doi.org/10.1016/j.agee.2014.01.013 (2014).Article 

Google Scholar 
Ray, D. K., Gerber, J. S., MacDonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6(1), 1–9. https://doi.org/10.1038/ncomms6989 (2015).CAS 
Article 

Google Scholar 
Egeru, A. et al. Land cover and soil properties influence on forage quantity in a semiarid region in East Africa. Appl. Environ. Soil Sci. https://doi.org/10.1155/2019/6874268 (2019).Article 

Google Scholar 
Mertens, D. R. Interpretation of forage analysis reports. In 30th National Alfalfa symposium. Las vegas, NV. (2000).Hussain, F. & Durrani, M. J. Nutritional evaluation of some forage plants from Harboi Rangeland, Kalat, Pakistan. Pak. J. Bot. 41(3), 1137–1154 (2009).CAS 

Google Scholar 
Ammar, H., López, S., Bochi-Brum, O., García, R. & Ranilla, M. J. Composition and in vitro digestibility of leaves and stems of grasses and legumes harvested from permanent mountain meadows at different stages of maturity. J. Anim. Feed Sci. 8(4), 599–610. https://doi.org/10.22358/jafs/69184/1999 (1999).Article 

Google Scholar 
Faichney, G. J., Gordon, G. L. R., Welch, R. J. & Rintoul, A. J. Effect of dietary free lipid on anaerobic fungi and digestion in the rumen of sheep. Aust. J. Agric. Res. 53(5), 519–527. https://doi.org/10.1071/AR01143 (2002).CAS 
Article 

Google Scholar 
Khan, S., Anwar, K., Kalim, K., Saeed, A. & Shah, S. Z. Nutritional evaluation of some top fodder tree leaves and shrubs of District Dir (Lower), Pakistan as a quality livestock feed. Int. J. Curr. Microbiol. Appl. Sci. 3(5), 941–947 (2014).
Google Scholar 
Tudsri, S. & Kaewkunya, C. Effect of leucaena row spacing and cutting intensity on the growth of leucaena and three associated grasses in Thailand. Asian Aust. J. Anim. Sci. 15(7), 986–991 (2002).
Google Scholar 
Nasrullah, M., Niimi, R., Akashi, X. & Kawamura, O. Nutritive evaluation of forage plants grown in South Sulawesi, Indonesia. Asian Aust. J. Anim. Sci. 16(5), 693–701. https://doi.org/10.5713/ajas.2004.63 (2003).CAS 
Article 

Google Scholar 
Yahaya, M. S., Kawai, M., Takahashi, J. & Matsuoka, S. The effects of different moisture content and ensiling time on silo degradation of structural carbohydrate of orchard grass. Asian Aust. J. Anim. Sci. 15(2), 213–217. https://doi.org/10.5713/ajas.2002.213 (2002).Article 

Google Scholar 
Norton, B. W. Differences between species in forage quality. In Nutritional Limits to Animal Production from Pastures, proceedings of an international symposium held at St. Lucia, Queensland, Australia, UK. Commonwealth Agricultural Bureaux, (1982).National Research council. Nutrient Requirements of Dairy Cattle 7th edn. (National Academy Press, 2001).
Google Scholar 
Nogueira Filho, J. C. M., Fondevila, M., Urdaneta, A. B. & Ronquillo, M. G. In vitro microbial fermentation of tropical grasses at an advanced maturity stage. Anim. Feed Sci. Technol. 83(2), 145–157. https://doi.org/10.1016/S0377-8401(99)00123-6 (2000).CAS 
Article 

Google Scholar 
National Research Council. Nutrient Requirements of Sheep, Vol ***5 (National Academies Press, 1985).
Google Scholar 
Erickson, P. S. & Kalscheur, K. F. Nutrition and feeding of dairy cattle. In Animal Agriculture pp 157–180 (2020).Holechek, J. L., Pieper, R. D. & Herbel, C. H. Range Management Principles and Practices 5th edn. (Prentice-Hall, 2004).
Google Scholar 
Saro, C. et al. Effect of dietary crude protein on animal performance, blood biochemistry profile, ruminal fermentation parameters and carcass and meat quality of heavy fattening Assaf lambs. Animals 10(11), 2177 (2020).PubMed Central 

Google Scholar 
Buckmaster, D. R. Forage Looses, Equal Economic Looses Agricultural Engineer Fact Shell PM-107 (The Pennsylvania State University, 1990).
Google Scholar 
Paulson, J., Jung, H., Raeth-Knight, M. & Linn, J. Grass vs. legume forages for dairy cattle (2008). https://conservancy.umn.edu/bitstream/handle/11299/204154/SF95_M658a-69-2008_magr56173.pdf?sequence=1.Lüscher, A., Mueller-Harvey, I., Soussana, J. F., Rees, R. M. & Peyraud, J. L. Potential of legume-based grassland–livestock systems in Europe: A review. Grass Forage Sci. 69(2), 206–228. https://doi.org/10.1111/gfs.12124 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Van Soest, P. J. Nutritional Ecology of the Ruminant 2nd edn. (Cornell University Press, 1994).
Google Scholar 
Tucak, M., Ravlic, M., Horvat, D. & Cupic, T. Improvement of forage nutritive quality of alfalfa and red clover through plant breeding. Agronomy 11(11), 2176. https://doi.org/10.3390/agronomy11112176 (2021).CAS 
Article 

Google Scholar 
Harper, K. & McNeill, D. The role iNDF in the regulation of feed intake and the importance of its assessment in subtropical ruminant systems (the role of iNDF in the regulation of forage intake). Agriculture 5(3), 778–790. https://doi.org/10.3390/agriculture5030778 (2015).CAS 
Article 

Google Scholar 
Singh, G. P. & Oosting, S. J. A model for describing the energy value of straws. Indian Dairyman XLI 322–327 (1992). https://agris.fao.org/agris-search/search.do?recordID=NL2012083374.Reed, J. A. & Goe, M. R. Estimating the Nutritive Value of Cereal Crop Residues, Implications for developing feeding standards for draught animals. ILCA Bulletin (1989). https://hdl.handle.net/10568/4610.Kumar, K. & Soni, A. Nutrient evaluation of common vegetation of Rajasthan, Pennisetum typholdenum, Cenchrus ciliaris, Cenchrus setigerus and Lasiurus sindicus. Int. J. Plant Anim. Environ. Sci. 4(1), 177–183 (2014).CAS 

Google Scholar 
Kramberger, B. & Klemenčič, S. Effect of harvest date on the chemical composition and nutritive value of Cerastium holosteoides. Grass Forage Sci. 58(1), 12–16. https://doi.org/10.1046/j.1365-2494.2003.00346.x (2003).CAS 
Article 

Google Scholar 
Raffrenato, E. et al. Effect of lignin linkages with other plant cell wall components on in vitro and in vivo neutral detergent fiber digestibility and rate of digestion of grass forages. J. Dairy Sci. 100(10), 8119–8131. https://doi.org/10.3168/jds.2016-12364 (2017).CAS 
Article 
PubMed 

Google Scholar 
McDonald, P. et al. Animal nutrition. Pearson UK https://doi.org/10.1088/1755-1315/951/1/012013 (2022).Article 

Google Scholar 
Brown, P. H., Graham, R. D. & Nicholas, D. G. D. The effect of manganese and nitrate supply on the level of phenolics and lignin in young wheat plant. Plant Soil 81, 437–440 (1984).CAS 

Google Scholar 
Mbwile, R. P. & Uden, P. Effects of age and season on growth and nutritive value of Rhodes grass (Chloris gayana cv. Kunthi). Anim. Feed Sci. Technol. 65, 87–98 (1997).
Google Scholar 
Hameed, M., Naz, N., Ahmad, M. S. A. & Islam-ud-Din, R. A. Morphological adaptations of some grasses from the salt range, Pakistan. Pak. J. Bot. 40(4), 1571–1578 (2008).
Google Scholar 
Makkar, H. P. S. Effects and fate of tannins in ruminant animals, adaptation to tannins, and strategies to overcome detrimental effects of feeding tannin-rich feeds. Small Rumin. Res. 49(3), 241–256. https://doi.org/10.1016/S0921-4488(03)00142-1 (2003).ADS 
Article 

Google Scholar 
Patra, A. K. Nutritional management in organic livestock farming for improved ruminant health and production—an overview. Livestock Res. Rural Dev. 19(3), 41 (2007).
Google Scholar 
Akande, K. E., Doma, U. D., Agu, H. O. & Adamu, H. M. Major antinutrients found in plant protein sources: Their effect on nutrition. Pak. J. Nutr. 9(8), 827–832 (2010).CAS 

Google Scholar 
Tadele, Y. Important anti-nutritional substances and inherent toxicants of feeds. Food Sci. Qual. Manage. 36, 40–47 (2015).
Google Scholar 
D’Mello, J.F. Farm animal metabolism and nutrition. Cabi Publishing. UK. (2000). https://www.researchgate.net/profile/Adegbola-Adesogan/publication/242151831_What_are_feeds_worth_A_critical_evaluation_of_selected_nutritive_value_methods/links/5852780c08aef7d030a4e95b/What-are-feeds-worth-A-critical-evaluation-of-selected-nutritive-value-methods.pdf.Panhwar, F. Anti-nutritional Factors in Oil Seeds as Aflatoxin in Ground Nut (Digitalverlag GmbH, 2005).
Google Scholar 
Huang, J. et al. Tree defence and bark beetles in a drying world: Carbon partitioning, functioning and modelling. New Phytol. 225(1), 26–36. https://doi.org/10.1111/nph.16173 (2020).Article 
PubMed 

Google Scholar 
Min, B. R., Barry, T. N., Attwood, G. T. & McNabb, W. C. The effect of condensed tannins on the nutrition and health of ruminants fed fresh temperate forages, a review. Anim. Feed Sci. Technol. 106(1–4), 3–19 (2003).CAS 

Google Scholar 
Muetzel, S., Hoffmann, E. M. & Becker, K. Supplementation of barley straw with Sesbania pachycarpa leaves in vitro: Effects on fermentation variables and rumen microbial population structure quantified by ribosomal RNA targeted probes. Br. J. Nutr. 89(4), 445–453 (2003).CAS 
PubMed 

Google Scholar 
Yao, L. H. et al. Flavonoids in food and their health benefits. Plant Foods Hum. Nutr. 59(3), 113–122 (2004).CAS 
PubMed 

Google Scholar 
Tracy, B. F. et al. Resilience in forage and grazinglands. Crop Sci. 58(1), 31–42 (2018).
Google Scholar 
Ehsen, S. et al. Secondary metabolites as anti-nutritional factors in locally used halophytic forage/fodder. Pak. J. Bot. 48(2), 629–636 (2016).CAS 

Google Scholar 
Mudzwiri, M. Evaluation of traditional South African leafy plants for their safety in human consumption. Doctoral Dissertation (2007).Francis, G., Kerem, Z., Makkar, H. P. & Becker, K. The biological action of saponins in animal systems, A review. Brit. J. Nutr. 88(6), 587–605 (2002).CAS 
PubMed 

Google Scholar 
Duke, J. Phytochemical and ethnobotanical databases (2000).Terrill, T. H., Rowan, A. M., Douglas, G. B. & Barry, T. N. Determination of extractable and bound condensed tannin concentrations in forage plants, protein concentrate meals and cereal grains. J. Sci. Food Agric. 58(3), 321–329. https://doi.org/10.1002/jsfa.2740580306 (1992).CAS 
Article 

Google Scholar 
Barry, T. N. & McNabb, W. C. The implications of condensed tannins on the nutritive value of temperate forages fed to ruminants. Br. J. Nutr. 81(4), 263–272 (1999).CAS 
PubMed 

Google Scholar 
Kallah, S. K., Bale, J. D., Abdullahi, U. S., Mohammed, I. R. & Lawai, R. Nutrient composition of native forms of semi-arid and dry-humid savannahs of Nigeria. Anim. Feed Sci. Technol. 84, 137–145 (2000).CAS 

Google Scholar 
Megersa, E., Mengistu, A. & Asebe, G. Nutritional characterization of selected fodder species in Abol and Lare Districts of Gambella Region, Ethiopia. J. Nutr. Food Sci. 7(2), 2–6 (2017).
Google Scholar 
Van Soest, P. J. & Robertson, J. B. Analysis of Forages and Fibrous Foods (Cornell University, 1985).
Google Scholar 
Moore, K. J. & Jung, H. G. Lignin and fiber digestion. J. Range Manag. 54(4), 420–430 (2001).
Google Scholar 
Ramirez, R. G., Haenlein, G. F. W., Garcia-Castillo, C. G. & Nunez-Gonzalez, M. A. Protein, lignin and mineral contents and In-Situ dry matter digestibility of native Mexican grasses consumed by range goats. Small Ruminant Resour. 52(3), 261–269 (2004).
Google Scholar 
Ronquillo, M. G., Fondevila, M., Urdaneta, A. B. & Newman, Y. In vitro gas production from buffel grass Cenchrus ciliaris L. fermentation in relation to the cutting interval, the level of nitrogen fertilisation and the season of growth. Anim. Feed Sci. Technol. 72(1–2), 19–32 (1998).
Google Scholar 
Mlay, P. S. et al. Feed value of selected tropical grasses, legumes and concentrates. Vet. Arch. 76(1), 53–63 (2006).
Google Scholar 
Arif, M. et al. In vitro digestibility of selected forages in Sargodha district, Pakistan. In Vitro 6(3), 62–72 (2016).CAS 

Google Scholar 
Revell, D. K., Baker, S. K. & Purser, B. B. Estimates of the intake and digestion of nitrogen by sheep grazing a Mediterranean pasture as it matures senesces. Aust. Soc. Anim. Prod. 20, 217–220 (1994).
Google Scholar 
Cherney, D. J. R., Mertens, D. R. & Moore, J. E. Intake and digestibility by withers as influenced by forage morphology at three levels of forage offering. J. Anim. Sci. 68(12), 4387–4399. https://doi.org/10.2527/1990.68124387x (1990).CAS 
Article 
PubMed 

Google Scholar 
Lichtenberg, V. L. & Hemken, R. W. Hay quality. In: Grazing Management: An Ecological Perspective. Timber Press, Portland, Oregon USA (1985). https://www.pakbs.org/pjbot/PDFs/40(1)/PJB40(1)249.pdf.de Oliveira, C. V. et al. Urea supplementation in rumen and post-rumen for cattle fed a low-quality tropical forage. Brit. J. Nutr. 124(11), 1166–1178. https://doi.org/10.1017/S0007114520002251 (2020).CAS 
Article 
PubMed 

Google Scholar 
Rufino, L. M. et al. Effects of the amount and frequency of nitrogen supplementation on intake, digestion, and metabolism in cattle fed low-quality tropical grass. Anim. Feed Sci. Technol. 260, 114367 (2020).CAS 

Google Scholar 
Njidda, A. A. Determining dry matter degradability of some semi-arid browse species of north-eastern Nigeria using the in vitro technique. Nigerian J. Basic Appl. Sci. 18(2), 160–167. https://doi.org/10.4314/njbas.v18i2.64306 (2014).Article 

Google Scholar 
Rakib-Uz-Zaman, S. M. et al. Ethnobotanical study and phytochemical profiling of Heptapleurum hypoleucum leaf extract and evaluation of its antimicrobial activities against diarrhea-causing bacteria. J. Genet. Engl. Biotechnol. https://doi.org/10.1186/s43141-020-00030-0 (2020).Article 

Google Scholar 
Rodrigues, E. & de Oliveira, D. R. Ethnopharmacology: A laboratory science?. Rodriguésia 71, 25 (2020).
Google Scholar 
Kellogg, E. A. Poaceae. In The Families and Genera of Vascular Plants (ed. Kubtizki, K.) (Springer, 2014).
Google Scholar 
Horwitz W. & Latimer G. W. Official methods of analysis of AOAC International. 18th Ed. Gaithersburg, Md. AOAC International (2005). https://doi.org/10.1071/EA9750045.Makkar, H. P., Siddhuraju, P. & Becker, K. Plant Secondary Metabolites (Humana Press, 2007).
Google Scholar 
Tilley, J. M. & Terry, R. A. A two stage technique for the in vitro digestion of forage crops. Grass Forage Sci. 18(2), 104–111. https://doi.org/10.1111/j.1365-2494.1963.tb00335.x (1963).CAS 
Article 

Google Scholar  More

Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).ADS 
Article 

Google Scholar 
Céréghino, R. et al. Constraints on the functional trait space of aquatic invertebrates in bromeliads. Funct. Ecol. 32, 2435–2447 (2018).Article 

Google Scholar 
Winemiller, K. O., Fitzgerald, D. B., Bowler, L. & Pianka, E. R. Functional traits, convergent evolution, and periodic tables of niches. Ecol. Lett. 18, 737–751 (2015).Article 

Google Scholar 
Díaz, S. et al. Functional traits, the phylogeny of function, and ecosystem service vulnerability. Ecol. Evol. 3, 2958–2975 (2013).Article 

Google Scholar 
Leimar, O. Evolutionary change and Darwinian demons. Selection 2, 65–72 (2001).Article 

Google Scholar 
Cummins, K. W. & Klug, M. J. Feeding ecology of stream invertebrates. Annu. Rev. Ecol. Syst. 10, 147–172 (1979).Article 

Google Scholar 
Pianka, E. R., Vitt, L. J., Pelegrin, N., Fitzgerald, D. B. & Winemiller, K. O. Toward a periodic table of niches, or exploring the lizard niche hypervolume. Am. Nat. 190, 601–616 (2017).Article 

Google Scholar 
Rosenberg, D. M. & Resh, V. H. Freshwater Biomonitoring and Benthic Macroinvertebrates (Chapman and Hall, 1993).
Google Scholar 
Allan, J. D. & Castillo, M. M. Stream Ecology. Structure and Function of Running Waters 2nd edn. (Springer, 2007).
Google Scholar 
Wallace, J. B. & Webster, J. R. The role of macroinvertebrates in stream ecosystem function. Annu. Rev. Entomol. 41, 115–139 (1996).CAS 
Article 

Google Scholar 
Southwood, T. R. E. Habitat, the templet for ecological strategies?. J. Anim. Ecol. 46, 336–365 (1977).Article 

Google Scholar 
Townsend, C. R. & Hildrew, A. G. Species traits in relation to a habitat templet for river systems. Freshw. Biol. 31, 265–275 (1994).Article 

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

Google Scholar 
Schmera, D., Heino, J., Podani, J., Erős, T. & Dolédec, S. Functional diversity: A review of methodology and current knowledge in freshwater macroinvertebrate research. Hydrobiologia 787, 27–44 (2017).Article 

Google Scholar 
Diehl, S. Fish predation and benthic community structure: The role of omnivory and habitat complexity. Ecology 73, 1646–1661 (1992).Article 

Google Scholar 
Brucet, S. et al. Effects of temperature, salinity and fish in structuring the macroinvertebrate community in shallow lakes: Implications for the effects of climate change. PLoS ONE 7, e30877 (2012).ADS 
CAS 
Article 

Google Scholar 
Usseglio-Polatera, P., Bournaud, M., Richoux, P. & Tachet, H. Biological and ecological traits of benthic freshwater macroinvertebrates: Relationships and definition of groups with similar traits. Freshw. Biol. 43, 175–205 (2000).Article 

Google Scholar 
Poff, N. L. et al. Functional trait nichees of North American lotic insects: Trait-based ecological applications in light of phylogenetic relationships. J. North Am. Soc. 25, 730–755 (2006).Article 

Google Scholar 
Bonada, N., Dolédec, S. & Statzner, B. Taxonomic and biological trait differences of stream macroinvertebrate communities between Mediterranean and temperate regions: Implications for future climatic scenarios. Glob. Change Biol. 13, 1658–1671 (2007).ADS 
Article 

Google Scholar 
Stazner, B., Bonada, N. & Dolédec, S. Biological attributes discriminating invasive from native European stream macroinvertebrates. Biol. Invasions 10, 517–530 (2008).Article 

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

Google Scholar 
Verberk, W. C. E. P., Siepel, H. & Esselink, H. Life-history strategies in freshwater macroinvertebrates. Freshw. Biol. 53, 1722–1738 (2008).Article 

Google Scholar 
Dolédec, S., Statzner, B. & Frainay, V. Accurate description of functional community structure: Identifying stream invertebrates to species-level?. Bull. North Am. Benthol. Soc. 15, 154–155 (1998).
Google Scholar 
Podani, J., Kalapos, T., Barta, B. & Schmera, D. Principal component analysis of incomplete data—A simple solution to an old problem. Ecol. Inform. 61, 101235 (2021).Article 

Google Scholar 
Podani, J. Introduction to the Exploration of Multivariate Biological Data (Backhuys Publishers, 2000).MATH 

Google Scholar 
Tachet, H., Richoux, P., Bournaud, M. & Usseglio-Polatera, P. Invertébrés d’eau douce—systématique, biologie, écologie 600 (CNRS Editions, 2010).
Google Scholar 
Chevenet, F., Dolédec, S. & Chessel, D. A fuzzy coding approach for the analysis of long-term ecological data. Freshw. Biol. 31, 295–309 (1994).Article 

Google Scholar 
Schmidt-Kloiber A. & Hering, D. www.freshwaterecology.info—The Taxa and Autecology Database for Freshwater Organisms, Version 7.0. (Accessed on 12.09.2019) (2019).Schmera, D., Podani, J., Heino, J., Erős, T. & Poff, N. L. A proposed unified terminology of species traits in stream ecology. Freshw. Sci. 34, 823–830 (2015).Article 

Google Scholar 
Schmera, D., Podani, J., Erős, T. & Heino, J. Combining taxon-by-trait and taxon-by-site matrices for analysing trait patterns of macroinvertebrate communities: A rejoinder to Monaghan & Soares (2014). Freshw. Biol. 59, 1551–1557 (2014).Article 

Google Scholar 
Bonada, N. et al. Do Mediterranean genera not included in Tachet et al. 2002 have Mediterranean characteristics?. Limnetica 30, 129–142 (2011).Article 

Google Scholar 
de Jong, Y. et al. Fauna Europaea—All European animal species on the web. Biodivers. Data J. 2, e4034 (2014).Article 

Google Scholar 
de Bello, F., Botta-Dukát, Z., Leps, J. & Fibich, P. Towards a more balanced combination of multiple traits when computing functional differences between species. Methods Ecol. Evol. 12, 443–448 (2021).Article 

Google Scholar 
Legendre, P. & Legendre, L. Numerical Ecology 2 English. (Elsevier, 1998).MATH 

Google Scholar 
Cornwell, W. K., Schwilk, D. W. & Ackerly, D. A trait-based test for habitat filtering: Convex hull volume. Ecology 87, 1465–1471 (2006).Article 

Google Scholar 
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral. Ecol. 26, 32–46 (2001).
Google Scholar 
Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253 (2006).MathSciNet 
Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).
Google Scholar 
Kindt, R. & Coe, R. Tree Diversity Analysis. A Manual and Software for Common Statistical Methods for Ecological and Biodiversity Studies (World Agroforestry Centre (ICRAF), 2005).
Google Scholar 
Habel, K., Grasman, R., Gramacy, R. G., Mozharovskyi, P. & Sterratt, D. C. Geometry: Mesh Generation and Surface Tessellation. R package version 0.4.5. https://CRAN.R-project.org/package=geometry (2019).Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P. R., O’Hara, R. G., Simpson, G. L., Solymos, P., Stevens, M. H. H., Szoecs, E. & Wagner, H. vegan: Community Ecology Package. R package version 2.5-6. https://CRAN.R-project.org/package=vegan (2019). More

After the successful protection of Himalayan areas on the border of China and Nepal, we propose that the two nations should create the world’s highest international peace park by combining the Qomolangma and Sagarmatha national parks. This would align with United Nations Sustainable Development Goal 17, to achieve sustainable development through international cooperation (see go.nature.com/3ixmini).
Competing Interests
The authors declare no competing interests. More

Climate change drove evolution, biogeography, and demographyPhylogenetic results (Fig. 1 and Supplementary Fig. 2) confirm previous findings, recovering Aptenodytes (king and emperor penguins) as the sister clade to all other crown penguins, with brush-tailed (Pygoscelis) penguins in turn sister to two clades uniting the banded (Spheniscus) and little (Eudyptula) penguins and the yellow-eyed (Megadyptes) and crested (Eudyptes) penguins6,7,9. Biogeographical reconstructions (Fig. 1, Supplementary Figs. 3–4 and Supplementary Data 1) support a Zealandian origin for penguins6,7. Stem penguins radiated extensively in Zealandia before dispersing to South America and Antarctica multiple times, following the eastward-flowing direction of the Antarctic Circumpolar Current (ACC) (Fig. 1). Crown penguins most likely arose from descendant lineages in South America, before dispersing back to Zealandia at least three times. Interestingly, at least two such dispersals occurred before the inferred onset of the ACC system, suggesting that early stem penguins were not dependent on currents to disperse over long distances. A second pulse of speciation coincides with the onset of the ACC, though understanding whether this pattern is real or an artifact of fossil sampling requires more collecting from early Eocene localities. We infer an age of ~14 Ma for the origin of crown penguins, which is more recent than the ~24 Ma age recovered in genomic analyses, not including fossil taxa7 (Supplementary Fig. 2b) and coincides with the onset of global cooling during the middle Miocene climate transition4,10 (Supplementary Fig. 3a). This young age suggests that expansion of Antarctic ice sheets and the onset of dispersal vectors such as the Benguela Current11 during the middle to late Miocene facilitated crown penguin dispersal and speciation, as hinted at by fossil evidence12.Incongruences between species trees and gene trees were identified, e.g., alternate topologies occurred at high frequencies ( >10%) for several internal branches (Fig. 1c; Supplementary Fig. 5). These patterns indicate that gene tree discordance may be caused by incomplete lineage sorting (ILS) or introgression events. By quantifying ILS and introgression via branch lengths from over 10,000 gene trees, we found that the rapid speciation within crown penguins was accompanied by >5% ILS content within the ancestors of Spheniscus, Eudyptula, Eudyptes, and several subgroups within Eudyptes (Fig. 2a). Our dated tree provides a temporal framework for this rapid radiation: the four extant Spheniscus taxa are all inferred to have split from one another within the last ~3 Ma, and likewise the nine extant Eudyptes taxa likely split from one another in that same time (Fig. 1b). Many closely related penguin species/lineages are known to hybridize in the wild (see supplementary methods). Consistent with this, multiple analyses suggest that introgression also contributes to species tree—gene tree incongruence (Supplementary Figs. 6–9 and Supplementary Data 2; also see Supplementary Methods for further details). This could explain the most notable conflict in previous phylogenetic results, which showed inconsistency over whether Aptenodytes alone7 or Aptenodytes and Pygoscelis together4,5 represent the sister clade to all other extant penguins. Introgression was detected between the ancestor of Aptenodytes and the ancestor of other extant penguins, and is inferred to have occurred when the range of these ancestors overlapped in South America (Fig. 2a and Supplementary Data 2). Introgression ( >9%) was also detected between Eudyptula novaehollandiae and Eudyptula minor, and several introgression events were especially pervasive in Eudyptes (Fig. 2a and Supplementary Fig. 6).Fig. 2: Incomplete lineage sorting, introgression events, and demographic history among penguins.a Model of incomplete lineage sorting (ILS) and introgression events estimated from QuIBL and hybrid pairwise sequentially Markovian coalescent (hPSMC) results. hPSMC was only run for 20 species pairs (see b). Numbers on branches represent the proportion (%) of ILS (orange branches) or introgression (blue lines, blue dashed lines, and blue dotted lines) detected by QuIBL. Proportions 50 km; see Supplementary Data 117), while taxa that decreased towards the end of the LGP (e.g., S. humboldti, S. demersus, M. a. antipodes and likely M. a. richdalei) tend to be residential, and forage inshore; see Supplementary Data 1. Taxa that disperse farther may have overcome local impacts of global climate cooling during the LGP (e.g., changes in sea-ice extent, prey abundance and terrestrial glaciation, however see18) largely by relocating to lower latitudes (e.g.,14), whereas locally-restricted taxa may have been more prone to sudden population collapses.Penguins have the slowest evolutionary rates among birdsThe integrated evolutionary speed hypothesis (IESH) proposes that temperature, water availability, population size, and spatial heterogeneity influence evolutionary rate19. Life history traits also impact the evolutionary rate, but such relationships remain incompletely understood in birds20. Penguins are long-lived, large-bodied, and produce few offspring, thus providing an ideal case study in how life history may impact evolutionary rate. We tested the IESH using three proxies for evolutionary rate: substitution rate, P and K2P distances between lineages and their ancestors (Supplementary Fig. 12 and Supplementary Data 3). We found that penguins and their sister group (Procellariiformes) had the lowest evolutionary rates of the 17 avian orders sampled by21 (Fig. 3a, Supplementary Fig. 13, and Supplementary Data 3). Because other aquatic orders also show slow rates (e.g., the aquatic Anseriformes show a significantly slower rate than their terrestrial sister group Galliformes), we hypothesize that the rate in penguins represents the culmination of a gradual slowdown associated with increasingly aquatic ecology. Intriguingly, we detected a trend toward decreasing rate over the first ~10 Ma of crown penguin evolution, followed by a marked uptick ~2 Ma, which suggests the onset of glacial-interglacial cycles contributed to a recent increase in evolutionary rates in penguins (Fig. 3b).Fig. 3: Evolutionary rates in birds.a Evolutionary rate in avian orders based on a ~19 Mbp alignment of highly conserved genome regions. Sphenisciformes and Procellariiformes have the lowest evolutionary rate among modern bird orders (One-sided Wilcoxon Rank sum test, P values  0.1)). Numbers at the tips represent the sample size in each group. Numbers at nodes represent the divergence times (Ma) between each order and its sister taxon and red dots within the boxplots indicate average values. We did not attempt to estimate the evolutionary rates for orders containing less than three sampled species (gray font; Musophagiformes, Mesitornithiformes, and Struthioniformes). Boxplots show the median with hinges at the 25th and 75th percentile and whiskers extending 1.5 times the interquartile range. Some bird images were downloaded from phylopic.org and were licensed under the Creative Commons (CC0) 1.0 Universal Public Domain Dedication. b Evolutionary rates inferred for extant penguin lineages at internal nodes from the maximum clade credibility tree, calculated using a 500 Mbp genome alignment. Gray shadows represent the 95% credible intervals. c–e Correlations between c, body mass and generation time (P value  More