Ecology

We analyse weekly reported counts of suspected and confirmed human cases and deaths attributed to LF (as defined in Supplementary Table 1), between 1 January 2012 and 30 December 2019, from across the entire of Nigeria. The weekly counts were reported from 774 LGAs in 36 Federal states and the Federal Capital Territory, under Integrated Disease Surveillance and Response (IDSR) protocols, and collated by the NCDC. All suspected cases, confirmed cases and deaths from notifiable infectious diseases (including viral haemorrhagic fevers; VHFs) are reported weekly to the LGA Disease Surveillance and Notification Officer (DSNO) and State Epidemiologist (SE). IDSR routine data on priority diseases are collected from inpatient and outpatient registers in health facilities, and forwarded to each LGA’s DSNO using SMS or paper form. Subsequently, individual LGA DSNOs collate and forward the data to their respective SE, also by SMS and paper form, for weekly and monthly reporting respectively to NCDC. From mid-2017 onwards, data entry in 18 states has been conducted using a mobile phone-based electronic reporting system called mSERS, with the data entered using a customised Excel spreadsheet that is used to manually key into NCDC-compatible spreadsheets. Data from this surveillance regime (WERs) were collated by epidemiologists at NCDC throughout the period 2012 to March 2018 (Supplementary Fig. 1).Throughout the study period, within-country LF surveillance and response has been strengthened under NCDC coordination2,20,33. LGAs are now required to notify immediately any suspected case to the state-level, which in turn reports to NCDC within 24 h, and also sends a cumulative weekly report of all reported cases. A dedicated, multi-sectoral NCDC LF TWG was set up in 2016 with the responsibility of coordinating all LF preparedness and response activities across states. Further capacity building occurred in 2017 to 2019, with the opening of three additional LF diagnostic laboratories in Abuja (Federal Capital Territory), Abakaliki (Ebonyi state) and Owo (Ondo state) (to a total of five; Fig. 2) and the rollout of intensive country-wide training on surveillance, clinical case management and diagnosis. We note that, due to the rapid expansion in a test capacity, the definition of a suspected case in our data has subtly changed over the surveillance period: from 2012 to 2016, suspected cases include probable cases that were not lab-tested, whereas from 2017 to 2019, all suspected cases were tested and confirmed to be negative.In addition to the WERs data, since 2017 LF case reporting data has also been collated by the LF TWG and used to inform the weekly NCDC LF Situation Reports (SitRep data; https://ncdc.gov.ng/diseases/sitreps). This regime includes post hoc follow-ups to ensure more accurate case counts, so our analyses use WER-derived case data from 2012 to 2016, and SitRep-derived case data from 2017 to 2019 (see Fig. 1 for full time series). A visual comparison of the data from each separate time series, including the overlap period (2017 to March 2018) is provided in Supplementary Fig. 1, and all statistical models considered random intercepts for the different surveillance regimes. Where other studies of recent Nigeria LF incidence have been more spatially and temporally restricted34,35, the extended monitoring period and fine spatial granularity of these data provide the opportunity for a detailed empirical perspective on the local drivers of LF at a country-wide scale and their relationship to changes in reporting effort.Recent trends in LF surveillance in NigeriaWe visualised temporal and seasonal trends in suspected and confirmed LF cases within and between years, for both surveillance datasets. Weekly case counts were aggregated to country-level and visualised as both annual case accumulation curves, and aggregated weekly case totals (Fig. 1 and Supplementary Fig. 1). We also mapped annual counts of suspected and confirmed cases across Nigeria at the LGA-level to examine spatial changes in reporting over the surveillance period (Fig. 2). State and LGA shapefiles used for modelling and mapping were obtained from Humanitarian Data Exchange under a CC-BY-IGO license (https://data.humdata.org/dataset/nga-administrative-boundaries).Analyses of aggregated district data are sensitive to differences in scale and shape of aggregation (the modifiable areal unit problem; MAUP36), and LGA geographical areas in Nigeria are highly skewed and vary over >3 orders of magnitude (median 713 km2, mean 1175 km2, range 4–11,255 km2). We therefore also aggregated all LGAs across Nigeria into 130 composite districts with a more even distribution of geographical areas, using distance-based hierarchical clustering on LGA centroids (implemented using hclust in R), with the constraint that each new cluster must contain only LGAs from within the same state (to preserve potentially important state-level differences in surveillance regime). Weekly and annual suspected and confirmed LF case totals were then calculated for each aggregated district. We used these spatially aggregated districts to test for the effects of scale on spatial drivers of LF occurrence and incidence.Statistical analysisWe analysed the full case time series (Fig. 1) to characterise the spatiotemporal incidence and drivers of LF in Nigeria, while controlling for year-on-year increases and expansions of surveillance effort. We firstly modelled annual LF occurrence and incidence at a country-wide scale, to identify the spatial, climatic and socio-ecological correlates of disease risk across Nigeria. Secondly, we modelled seasonal and temporal trends in weekly LF incidence within hyperendemic areas in the north and south of Nigeria, to identify the seasonal climatic conditions associated with LF risk dynamics and evaluate the scope for forecasting. All data processing and modelling was conducted in R v.3.4.1 with the packages R-INLA v.20.03.1737, raster v.3.4.1338 and velox v0.2.039. Statistical modelling was conducted using hierarchical regression in a Bayesian inference framework (integrated nested Laplace approximation (INLA)), which provides fast, stable and accurate posterior approximation for complex, spatially and temporally-structured regression models37,40, and has been shown to outperform alternative methods for modelling environmental phenomena with evidence of spatially biased reporting41.Processing climatic and socio-ecological covariatesWe collated geospatial data on socio-ecological and climatic factors that are hypothesised to influence either M. natalensis distribution and population ecology (rainfall, temperature and vegetation patterns), frequency and mode of human–rodent contact (poverty and improved housing prevalence), both of the above (agricultural and urban land cover) or likelihood of LF reporting (travel time to nearest laboratory with LF diagnostic capacity and travel time to nearest hospital). For each LGA we extracted the mean value for each covariate across the LGA polygon. The full suite of covariates tested across all analyses, data sources and associated hypotheses are described in Supplementary Table 5.We collated climate data spanning the full monitoring period and up until the date of analysis (July 2011 to January 2021). We obtained daily precipitation rasters for Africa42 from the Climate Hazards Infrared Precipitation with Stations (CHIRPS) project; this dataset is based on combining sparse weather station data with satellite observations and interpolation techniques, and is designed to support hydrologic forecasts in areas with poor weather station coverage (such as tropical West Africa)42. A recent study ground-truthing against weather station data showed that CHIRPS provides greater overall accuracy than other gridded precipitation products in Nigeria43. Air temperature daily minimum and maximum rasters were obtained from NOAA and were also averaged to calculate daily mean temperature. EVI, a measure of vegetation quality, was obtained from processing 16-day composite layers from NASA (National Aeronautics and Space Administration) (excluding all grid cells with unreliable observations due to cloud cover and linearly interpolating between observations to give daily values; Supplementary Table 5).We derived several spatial bioclimatic variables to capture conditions across the full monitoring period (Jan 2012 to Dec 2019): mean precipitation of the driest annual month, mean precipitation of the wettest annual month, precipitation seasonality (coefficient of variation), annual mean air temperature, air temperature seasonality, annual mean EVI and EVI seasonality. We also calculated monthly total precipitation, 3-month SPI44, average daily mean (Tmean), minimum (Tmin) and maximum (Tmax) temperature and EVI variables at sequential time lags prior to reporting week for seasonal modelling (described below in Temporal drivers). SPI is a standardised measure of drought or wetness conditions relative to the historical average conditions for a given period of the year. SPI was calculated within a rolling 3-month window across the full 40-year historical CHIRPS rainfall time series (1981–2020) using the R package SPEI v.1.744.We accessed annual human population rasters at 100 m resolution from WorldPop. We accessed the proportion of the population living in poverty in 2010 ( More

1.Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 259, 660–684 (2010).
Google Scholar 
2.Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).CAS 

Google Scholar 
3.Zhao, M. & Running, S. W. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329, 940–943 (2010).CAS 

Google Scholar 
4.Orth, R. & Destouni, G. Drought reduces blue-water fluxes more strongly than green-water fluxes in Europe. Nat. Commun. 9, 3602 (2018).PubMed 
PubMed Central 

Google Scholar 
5.Doughty, C. E. et al. Drought impact on forest carbon dynamics and fluxes in Amazonia. Nature 519, 78–82 (2015).CAS 

Google Scholar 
6.Schwalm, C. R. et al. Reduction in carbon uptake during turn of the century drought in western North America. Nat. Geosci. 5, 551–556 (2012).CAS 

Google Scholar 
7.Bastos, A. et al. Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity. Sci. Adv. 6, eaba2724 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
8.Wolf, S. et al. Warm spring reduced carbon cycle impact of the 2012 US summer drought. Proc. Natl Acad. Sci. USA 113, 5880–5885 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Jump, A. S. et al. Structural overshoot of tree growth with climate variability and the global spectrum of drought‐induced forest dieback. Glob. Change Biol. 23, 3742–3757 (2017).
Google Scholar 
10.Buermann, W. et al. Widespread seasonal compensation effects of spring warming on northern plant productivity. Nature 562, 110–114 (2018).CAS 

Google Scholar 
11.Goulden, M. L. & Bales, R. C. California forest die-off linked to multi-year deep soil drying in 2012–2015 drought. Nat. Geosci. 12, 632–637 (2019).CAS 

Google Scholar 
12.West, M. & Harrison, J. Bayesian Forecasting and Dynamic Models (Springer, 1997).13.Pinzon, J. E. & Tucker, C. J. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 6, 6929–6960 (2014).
Google Scholar 
14.Nemani, R. R. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
15.Zhang, X., Friedl, M. A., Schaaf, C. B. & Strahler, A. H. Climate controls on vegetation phenological patterns in northern mid and high latitudes inferred from MODIS data. Glob. Change Biol. 10, 1133–1145 (2004).
Google Scholar 
16.Zeng, Z. et al. Impact of earth greening on the terrestrial water cycle. J. Clim. 31, 2633–2650 (2018).
Google Scholar 
17.Vicente-Serrano, S. M. et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl Acad. Sci. USA 110, 52–57 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
18.Anderegg, W. R. L. et al. Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 561, 538–541 (2018).CAS 

Google Scholar 
19.Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).CAS 
PubMed 

Google Scholar 
20.Lian, X. et al. Partitioning global land evapotranspiration using CMIP5 models constrained by observations. Nat. Clim. Change 8, 640–646 (2018).
Google Scholar 
21.Zscheischler, J. et al. Future climate risk from compound events. Nat. Clim. Change 8, 469–477 (2018).
Google Scholar 
22.Zscheischler, J. et al. A typology of compound weather and climate events. Nat. Rev. Earth Environ. 1, 333–347 (2020).
Google Scholar 
23.Zhou, S., Zhang, Y., Williams, A. P. & Gentine, P. Projected increases in intensity, frequency, and terrestrial carbon costs of compound drought and aridity events. Sci. Adv. 5, eaau5740 (2019).PubMed 
PubMed Central 

Google Scholar 
24.Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
Google Scholar 
25.Sungmin, O. & Orth, R. Global soil moisture data derived through machine learning trained with in-situ measurements. Sci. Data 8, 170 (2021).
Google Scholar 
26.Pendergrass, A. G. et al. Flash droughts present a new challenge for subseasonal-to-seasonal prediction. Nat. Clim. Change 10, 191–199 (2020).
Google Scholar 
27.Otkin, J. A. et al. Flash droughts: a review and assessment of the challenges imposed by rapid-onset droughts in the United States. Bull. Am. Meteorol. Soc. 99, 911–919 (2018).
Google Scholar 
28.Lian, X. et al. Summer soil drying exacerbated by earlier spring greening of northern vegetation. Sci. Adv. 6, eaax0255 (2020).PubMed 
PubMed Central 

Google Scholar 
29.Green, J. K. et al. Regionally strong feedbacks between the atmosphere and terrestrial biosphere. Nat. Geosci. 10, 410–414 (2017).CAS 

Google Scholar 
30.Keenan, T. F. & Richardson, A. D. The timing of autumn senescence is affected by the timing of spring phenology: implications for predictive models. Glob. Change Biol. 21, 2634–2641 (2015).
Google Scholar 
31.Zani, D., Crowther, T. W., Mo, L., Renner, S. S. & Zohner, C. M. Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees. Science 370, 1066–1071 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
32.Buitenwerf, R., Rose, L. & Higgins, S. I. Three decades of multi-dimensional change in global leaf phenology. Nat. Clim. Change 5, 364–368 (2015).
Google Scholar 
33.Douville, H., Ribes, A., Decharme, B., Alkama, R. & Sheffield, J. Anthropogenic influence on multidecadal changes in reconstructed global evapotranspiration. Nat. Clim. Change 3, 59–62 (2013).
Google Scholar 
34.Asrar, G., Fuchs, M., Kanemasu, E. T. & Hatfield, J. L. Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agron. J. 76, 300–306 (1984).
Google Scholar 
35.Chen, J. M. & Cihlar, J. Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sens. Environ. 55, 153–162 (1996).
Google Scholar 
36.Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).
Google Scholar 
37.Becker, A. et al. A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present. Earth Syst. Sci. Data 5, 71–99 (2013).
Google Scholar 
38.Sun, Q. et al. A review of global precipitation data sets: data sources, estimation, and intercomparisons. Rev. Geophys. 56, 79–107 (2018).
Google Scholar 
39.Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).PubMed 
PubMed Central 

Google Scholar 
40.Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).
Google Scholar 
41.Harrison, P. J. & Stevens, C. F. Bayesian forecasting. J. R. Stat. Soc. B 38, 205–247 (1976).
Google Scholar 
42.Liu, Y., Kumar, M., Katul, G. G. & Porporato, A. Reduced resilience as an early warning signal of forest mortality. Nat. Clim. Change 9, 880–885 (2019).
Google Scholar 
43.Humphrey, V., Gudmundsson, L. & Seneviratne, S. I. Assessing global water storage variability from grace: trends, seasonal cycle, subseasonal anomalies and extremes. Surv. Geophys. 37, 357–395 (2016).PubMed 
PubMed Central 

Google Scholar 
44.Schwalm, C. R. et al. Global patterns of drought recovery. Nature 548, 202–205 (2017).CAS 

Google Scholar  More

1.Leach JE, Tringe SG. Plant–microbiome interactions: from community assembly to plant health. Nat Rev Microbiol. 2020;18:607–21.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
2.Pii Y, Mimmo T, Tomasi N, Terzano R, Cesco S, Crecchio C. Microbial interactions in the rhizosphere: beneficial influences of plant growth-promoting rhizobacteria on nutrient acquisition process. A Rev Biol Fertil Soils. 2015;51:403–21.CAS 
Article 

Google Scholar 
3.Backer R, Rokem JS, Ilangumaran G, Lamont J, Praslickova D, Ricci E, et al. Plant growth-promoting rhizobacteria: context, mechanisms of action, and roadmap to commercialization of biostimulants for sustainable agriculture. Front Plant Sci. 2018;871:1473–89.Article 

Google Scholar 
4.Lugtenberg B, Kamilova F. Plant-growth-promoting rhizobacteria. Annu Rev Microbiol. 2009;63:541–56.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Bhattacharyya PN, Jha DK. Plant growth-promoting rhizobacteria (PGPR): emergence in agriculture. World J Microbiol Biotechnol. 2012;28:1327–50.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Leach JE, Triplett LR, Argueso CT, Trivedi P. Communication in the phytobiome. Cell. 2017;169:587–96.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Vorholt JA, Vogel C, Carlström CI, Müller DB. Establishing causality: opportunities of synthetic communities for plant microbiome research. Cell Host Microbe. 2017;22:142–55.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Douglas AE. The microbial exometabolome: ecological resource and architect of microbial communities. PTRBAE. 2020;375:20190250.CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Turroni F, Milani C, Duranti S, Mahony J, van Sinderen D, Ventura M. Glycan utilization and cross-feeding activities by Bifidobacteria. Trends Microbiol. 2018;26:339–50.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Evans CR, Kempes CP, Price-Whelan A, Dietrich LEP. Metabolic heterogeneity and cross-feeding in bacterial multicellular systems. Trends Microbiol. 2020;28:732–43.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Smith NW, Shorten PR, Altermann E, Roy NC, McNabb WC. The classification and evolution of bacterial cross-feeding. Front Ecol Evol. 2019;7:153.Article 

Google Scholar 
12.Santoyo G, del Orozco-Mosqueda MC, Govindappa M. Mechanisms of biocontrol and plant growth-promoting activity in soil bacterial species of Bacillus and Pseudomonas: a review. Biocontrol Sci Technol. 2012;22:855–72.Article 

Google Scholar 
13.Borriss R. Use of plant-associated Bacillus strains as biofertilizers and biocontrol agents in agriculture. Bacteria in agrobiology: plant growth responses. Springer: Berlin; 2011. 41–76.14.Xiong W, Guo S, Jousset A, Zhao Q, Wu H, Li R, et al. Bio-fertilizer application induces soil suppressiveness against Fusarium wilt disease by reshaping the soil microbiome. Soil Biol Biochem. 2017;114:238–47.CAS 
Article 

Google Scholar 
15.Qin Y, Shang Q, Zhang Y, Li P, Chai Y. Bacillus amyloliquefaciens L-S60 reforms the rhizosphere bacterial community and improves growth conditions in cucumber plug seedling. Front Microbiol. 2017;8:2620.PubMed 
PubMed Central 
Article 

Google Scholar 
16.Compant S, Samad A, Faist H, Sessitsch A. A review on the plant microbiome: ecology, functions, and emerging trends in microbial application. J Adv Res. 2019;19:29–37.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Cao Y, Zhang Z, Ling N, Yuan Y, Zheng X, Shen B, et al. Bacillus subtilis SQR9 can control Fusarium wilt in cucumber by colonizing plant roots. Biol Fertil Soils. 2011;47:495–506.CAS 
Article 

Google Scholar 
18.Xu Z, Shao J, Li B, Yan X, Shen Q, Zhang R. Contribution of bacillomycin D in Bacillus amyloliquefaciens SQR9 to antifungal activity and biofilm formation. Appl Environ Microbiol. 2013;79:808–15.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Chen L, Liu Y, Wu G, Veronican Njeri K, Shen Q, Zhang N, et al. Induced maize salt tolerance by rhizosphere inoculation of Bacillus amyloliquefaciens SQR9. Physiol plant. 2016;158:34–44.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Blake C, Nordgaard Christensen M, Kovács ÁT. Molecular aspects of plant growth promotion and protection by Bacillus subtilis. Mol Plant Microbe Interact. 2020;34:15–25.PubMed 
Article 
PubMed Central 

Google Scholar 
21.Al-Ali A, Deravel J, Krier F, Béchet M, Ongena M, Jacques P. Biofilm formation is determinant in tomato rhizosphere colonization by Bacillus velezensis FZB42. Environ Sci Pollut Res. 2018;25:29910–20.CAS 
Article 

Google Scholar 
22.Xu Z, Mandic-Mulec I, Zhang H, Liu Y, Sun X, Feng H, et al. Antibiotic bacillomycin D affects iron acquisition and biofilm formation in Bacillus velezensis through a Btr-mediated FeuABC-dependent pathway. Cel Rep. 2019;29:1192–202.CAS 
Article 

Google Scholar 
23.Murashige T, Skoog F. A revised medium for rapid growth and bioassays with tobacco tissue cultures. Physiol Plant. 1962;15:473–97.CAS 
Article 

Google Scholar 
24.Bai Y, Müller DB, Srinivas G, Garrido-Oter R, Potthoff E, Rott M, et al. Functional overlap of the Arabidopsis leaf and root microbiota. Nature. 2015;528:364–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Zhou C, Shi L, Ye B, Feng H, Zhang J, Zhang R, et al. pheS *, an effective host-genotype-independent counter-selectable marker for marker-free chromosome deletion in Bacillus amyloliquefaciens. Appl Microbiol Biotechnol. 2017;101:217–27.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Feng H, Zhang N, Fu R, Liu Y, Krell T, Du W, et al. Recognition of dominant attractants by key chemoreceptors mediates recruitment of plant growth-promoting rhizobacteria. Environ Microbiol. 2019;21:402–15.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Lambertsen L, Sternberg C, Molin S. Mini-Tn7 transposons for site-specific tagging of bacteria with fluorescent proteins. Environ Microbiol. 2004;6:726–32.CAS 
PubMed 
Article 
PubMed Central 

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

Google Scholar 
29.Machado D, Andrejev S, Tramontano M, Patil KR. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 2018;46:7542–53.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Lieven C, Beber ME, Olivier BG, Bergmann FT, Ataman M, Babaei P, et al. MEMOTE for standardized genome-scale metabolic model testing. Nat Biotechnol. 2020;38:272–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Zelezniak A, Andrejev S, Ponomarova O, Mende DR, Bork P, Patil KR. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc Natl Acad Sci USA. 2015;112:6449–54.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Parks DH, Tyson GW, Hugenholtz P, Beiko RG. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics. 2014;30:3123–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Branda SS, González-Pastor JE, Ben-Yehuda S, Losick R, Kolter R. Fruiting body formation by Bacillus subtilis. Proc Natl Acad Sci USA. 2001;98:11621–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S, Holden MTG, et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics. 2015;31:3691–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Langdon WB. Performance of genetic programming optimised Bowtie2 on genome comparison and analytic testing (GCAT) benchmarks. BioData Min. 2015;8:1–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21.Article 
CAS 

Google Scholar 
38.Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, von Mering C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol. 2017;34:2115–22.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔ C(T) method. Methods. 2001;25:402–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Ling N, Raza W, Ma J, Huang Q, Shen Q. Identification and role of organic acids in watermelon root exudates for recruiting Paenibacillus polymyxa SQR-21 in the rhizosphere. Eur J Soil Biol. 2011;47:374–9.CAS 
Article 

Google Scholar 
41.Gordillo F, Chávez FP, Jerez CA. Motility and chemotaxis of Pseudomonas sp. B4 towards polychlorobiphenyls and chlorobenzoates. FEMS Microbiol Ecol. 2007;60:322–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Dragoš A, Kiesewalter H, Martin M, Hsu CY, Hartmann R, Wechsler T, et al. Division of labor during biofilm matrix production. Curr Biol. 2018;28:1903–.e5.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
43.Ahmad F, Ahmad I, Khan MS. Screening of free-living rhizospheric bacteria for their multiple plant growth promoting activities. Microbiol Res. 2008;163:173–81.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Lynne AM, Haarmann D, Louden BC. Use of blue agar CAS assay for siderophore detection. J Microbiol Biol Educ. 2011;12:51–53.PubMed 
PubMed Central 
Article 

Google Scholar 
45.Nautiyal CS. An efficient microbiological growth medium for screening phosphate solubilizing microorganisms. FEMS Microbiol Lett. 1999;170:265–70.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Ansari FA, Ahmad I. Fluorescent Pseudomonas -FAP2 and Bacillus licheniformis interact positively in biofilm mode enhancing plant growth and photosynthetic attributes. Sci Rep. 2019;9:1–12.
Google Scholar 
47.Santhanam R, Menezes RC, Grabe V, Li D, Baldwin IT, Groten K. A suite of complementary biocontrol traits allows a native consortium of root‐associated bacteria to protect their host plant from a fungal sudden‐wilt disease. Mol Ecol. 2019;28:1154–69.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Santhanam R, Luu VT, Weinhold A, Goldberg J, Oh Y, Baldwin IT. Native root-associated bacteria rescue a plant from a sudden-wilt disease that emerged during continuous cropping. Proc Natl Acad Sci USA. 2015;112:E5013–E5120.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Berendsen RL, Vismans G, Yu K, Song Y, de Jonge R, Burgman WP, et al. Disease-induced assemblage of a plant-beneficial bacterial consortium. ISME J. 2018;12:1496–507.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Ren D, Madsen JS, Sørensen SJ, Burmølle M. High prevalence of biofilm synergy among bacterial soil isolates in cocultures indicates bacterial interspecific cooperation. ISME J. 2015;9:81–89.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Oliveira NM, Martinez-Garcia E, Xavier J, Durham WM, Kolter R, Kim W, et al. Biofilm formation as a response to ecological competition. PLoS Biol. 2015;13:1–23.
Google Scholar 
52.Gallegos-Monterrosa R, Mhatre E, Kovács ÁT. Specific Bacillus subtilis 168 variants form biofilms on nutrient-rich medium. Microbiology. 2016;162:1922–32.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Shao J, Xu Z, Zhang N, Shen Q, Zhang R. Contribution of indole-3-acetic acid in the plant growth promotion by the rhizospheric strain Bacillus amyloliquefaciens SQR9. Biol Fertil Soils. 2015;51:321–30.CAS 
Article 

Google Scholar 
54.Stopnisek N, Shade A. Persistent microbiome members in the common bean rhizosphere: an integrated analysis of space, time, and plant genotype. ISME J. 2021;15:2708–22.PubMed 
PubMed Central 
Article 

Google Scholar 
55.Sivasakthi S, Usharani G, Saranraj P. Biocontrol potentiality of plant growth promoting bacteria (PGPR)—Pseudomonas fluorescens and Bacillus subtilis: a review. Afr J Agric Res. 2014;9:1265–77.
Google Scholar 
56.Dardanelli MS, Manyani H, González-Barroso S, Rodríguez-Carvajal MA, Gil-Serrano AM, Espuny MR, et al. Effect of the presence of the plant growth promoting rhizobacterium (PGPR) Chryseobacterium balustinum Aur9 and salt stress in the pattern of flavonoids exuded by soybean roots. Plant Soil. 2010;328:483–93.CAS 
Article 

Google Scholar 
57.Gómez Expósito R, Postma J, Raaijmakers JM, de Bruijn I. Diversity and activity of Lysobacter species from disease suppressive soils. Front Microbiol. 2015;6:1243.PubMed 
PubMed Central 
Article 

Google Scholar 
58.Han Q, Ma Q, Chen Y, Tian B, Xu L, Bai Y, et al. Variation in rhizosphere microbial communities and its association with the symbiotic efficiency of rhizobia in soybean. ISME J. 2020;14:1915–28.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Peterson SB, Dunn AK, Klimowicz AK, Handelsman J. Peptidoglycan from Bacillus cereus mediates commensalism with rhizosphere bacteria from the Cytophaga-Flavobacterium group. Appl Environ Microbiol. 2006;72:5421–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Tao C, Li R, Xiong W, Shen Z, Liu S, Wang B, et al. Bio-organic fertilizers stimulate indigenous soil Pseudomonas populations to enhance plant disease suppression. Microbiome. 2020;8:137.PubMed 
PubMed Central 
Article 

Google Scholar 
61.Kumar A, Singh J. Biofilms forming microbes: diversity and potential application in plant-microbe interaction and plant growth. Springer: Cham; 2020. 173−97.62.Tabassum B, Khan A, Tariq M, Ramzan M, Iqbal Khan MS, Shahid N, et al. Bottlenecks in commercialisation and future prospects of PGPR. Appl Soil Ecol. 2017;121:102–17.Article 

Google Scholar 
63.Madsen JS, Røder HL, Russel J, Sørensen H, Burmølle M, Sørensen SJ. Coexistence facilitates interspecific biofilm formation in complex microbial communities. Environ Microbiol. 2016;18:2565–74.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Burmølle M, Webb JS, Rao D, Hansen LH, Sørensen SJ, Kjelleberg S. Enhanced biofilm formation and increased resistance to antimicrobial agents and bacterial invasion are caused by synergistic interactions in multispecies biofilms. Appl Environ Microbiol. 2006;72:3916–23.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
65.Lee KWK, Periasamy S, Mukherjee M, Xie C, Kjelleberg S, Rice SA. Biofilm development and enhanced stress resistance of a model, mixed-species community biofilm. ISME J. 2014;8:894–907.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
66.Yannarell SM, Grandchamp GM, Chen SY, Daniels KE, Shank EA. A dual-species biofilm with emergent mechanical and protective properties. J Bacteriol. 2019;201:e00670–18.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Preussger D, Giri S, Muhsal LK, Oña L, Kost C. Reciprocal fitness feedbacks promote the evolution of mutualistic cooperation. Curr Biol. 2020;30:1–11.Article 
CAS 

Google Scholar 
68.Nadell CD, Drescher K, Foster KR. Spatial structure, cooperation, and competition in biofilms. Nat Rev Microbiol. 2016;14:589–600.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
69.Estrela S, Sanchez-Gorostiaga A, Vila JCC, Sanchez A. Nutrient dominance governs the assembly of microbial communities in mixed nutrient environments. eLife. 2021;10:e65948.PubMed 
PubMed Central 
Article 

Google Scholar 
70.Molina-Santiago C, Pearson JR, Navarro Y, Berlanga-Clavero MV, Caraballo-Rodriguez AM, Petras D, et al. The extracellular matrix protects Bacillus subtilis colonies from Pseudomonas invasion and modulates plant co-colonization. Nat Commun. 2019;10:1919.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
71.Yan Q, Lopes LD, Shaffer BT, Kidarsa TA, Vining O, Philmus B, et al. Secondary metabolism and interspecific competition affect accumulation of spontaneous mutants in the GacS-GacA regulatory system in Pseudomonas protegens. mBio. 2018;9:e01845–17.CAS 
PubMed 
PubMed Central 

Google Scholar 
72.Mee MT, Collins JJ, Church GM, Wang HH. Syntrophic exchange in synthetic microbial communities. Proc Natl Acad Sci USA. 2014;111:E2149–E2156.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.D’Souza G, Shitut S, Preussger D, Yousif G, Waschina S, Kost C. Ecology and evolution of metabolic cross-feeding interactions in bacteria. Nat Prod Rep. 2018;35:455–88.PubMed 
Article 
PubMed Central 

Google Scholar 
74.Goldford JE, Lu N, Bajić D, Estrela S, Tikhonov M, Sanchez-Gorostiaga A, et al. Emergent simplicity in microbial community assembly. Science. 2018;361:469–74.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
75.Lawrence D, Fiegna F, Behrends V, Bundy JG, Phillimore AB, Bell T, et al. Species interactions alter evolutionary responses to a novel environment. PLoS Biol. 2012;10:e1001330.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Evans R, Beckerman AP, Wright RCT, McQueen-Mason S, Bruce NC, Brockhurst MA. Eco-evolutionary dynamics set the tempo and trajectory of metabolic evolution in multispecies communities. Curr Biol. 2020;30:1–5.Article 
CAS 

Google Scholar 
77.Gamez RM, Ramirez S, Montes M, Cardinale M. Complementary dynamics of banana root colonization by the plant growth-promoting rhizobacteria Bacillus amyloliquefaciens Bs006 and Pseudomonas palleroniana Ps006 at spatial and temporal scales. Micro Ecol. 2020;80:656–68.CAS 
Article 

Google Scholar 
78.Feng H, Zhang N, Fu R, Liu Y, Krell T, Du W, et al. Recognition of dominant attractants by key chemoreceptors mediates recruitment of plant growth-promoting rhizobacteria. Environ Microbiol. 2019;21:402–15.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Feng H, Zhang N, Du W, Zhang H, Liu Y, Fu R, et al. Identification of chemotaxis compounds in root exudates and their sensing chemoreceptors in plant-growth-promoting rhizobacteria Bacillus amyloliquefaciens SQR9. Mol Plant Microbe interact. 2018;31:995–1005.CAS 
PubMed 
Article 

Google Scholar 
80.Xu Z, Xie J, Zhang H, Wang D, Shen Q, Zhang R. Enhanced control of plant wilt disease by a xylose-inducible degQ gene engineered into Bacillus velezensis strain SQR9XYQ. Phytopathology. 2019;109:36–43.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

Yield definitionsYield potential (Yp; megagrams per harvested hectare) is defined as the yield of a cultivar in an environment to which it is adapted, when grown with sufficient water and nutrients in the absence of abiotic and biotic stress40. In irrigated fields, Yp is determined by solar radiation, temperature, atmospheric CO2 concentration and management practices that influence crop cycle duration and light interception, such as sowing date, cultivar maturity and plant density. In rainfed systems where water supply from stored soil water at sowing and in-season precipitation is not enough to meet crop water requirements, water-limited Yp (Yw) is determined by water supply amount and its distribution during the growing season, as well as by soil properties influencing the crop–water balance, such as the rootable soil depth, texture and terrain slope. Actual yield is defined as the average grain yield (megagrams per harvested hectare) obtained by farmers for a given crop with a given water regime. The difference between Yp (or Yw) and farmer actual yield is known as the yield gap11. In the case of irrigated crops, Yp is the proper benchmark to estimate yield gaps, while Yw is the meaningful benchmark for rainfed crops. With good, cost-effective crop management, reaching 70–80% of Yp (or Yw) is a reasonable target for farmers with good access to markets, inputs and extension services, which is usually referred to as ‘attainable yield’41,42. Beyond this yield level, the small return to extra input requirement and labour does not justify the associated financial and environmental costs and level of sophistication in crop and soil management practices.Sources of Yp data derived from top-down and bottom-up approachesWe retrieved data generated from two initiatives following a top-down approach: (1) the GAEZ (http://www.fao.org/nr/gaez/en/; refs. 18,19) and (2) the AgMIP (https://agmip.org/data-and-tools-updated/; refs. 20,21). As the bottom-up approach, we used results from the GYGA (www.yieldgap.org; refs. 11,31,43). The main features of these databases are summarized elsewhere (Supplementary Table 1 and Supplementary Section 1). In the process of selecting the specific dataset, we explicitly attempted to reduce biases in the comparisons to the extent this was possible. For example, in all cases, we used simulations that meet the yield definitions provided in the previous section. We also tried to be consistent in terms of the time period for which Yp (or Yw) was simulated; however, this was not always possible, because while GAEZ and AgMIP use weather datasets that cover the time period between 1961 and 1990 and between 1980 and 2010, respectively, GYGA uses more recent weather data (Supplementary Table 1). Similarly, comparisons between databases were limited to those regions for which there were estimates of Yp (or Yw) for each of the top-down and bottom-up approaches. More detailed information about the three approaches can be found in Supplementary Section 1. We acknowledge that, when assessing different approaches, it is conceivable that there would be an inherent bias depending on who performs it and his/her preference. Although the authors of this current study have all contributed to the development of GYGA, we have maintained neutrality when conducting the analysis and made inferences solely based on the results shown here, avoiding any inherent bias. Additionally, methods and data sources are fully documented and publicly accessible for other researchers who may be interested in replicating our comparison.Comparison of bottom-up and top-down approaches at different spatial levelsComparison of the three databases needs to account for the different spatial resolution at which the data are reported (grid in GAEZ and AgMIP versus buffer in GYGA). In the present study, we compared Yp (or Yw) among the three databases at three spatial levels: local (also referred to as buffer), climate zone (CZ) and country (or subcontinent). An example of the three spatial levels evaluated in this study as well as the Yw estimated by each of the three databases for rainfed maize is shown in Extended Data Fig. 4. We note that buffer is the lowest spatial level at which Yp and Yw are reported in GYGA. For a country such as the United States, where maize production is concentrated on flat geographic areas, the average size of buffers and CZs selected by GYGA is 17,000 and 60,000 km2, respectively; the size is smaller for countries with greater terrain and climate heterogeneity, such as Ethiopia, where the average size of buffers and CZs selected for maize by GYGA is a respective 4,000 and 21,000 km2, or for smaller countries, such as in Europe.The GYGA already provides estimates of Yp (or Yw) and yield gaps at those three spatial levels. Following a bottom-up approach, GYGA estimates the Yp (or Yw) at the buffer level based on the Yp (or Yw) simulated for each crop cycle and soil type (within a given buffer) and their associated harvested area (within that same buffer) using a weighted average. Subsequently, Yp (or Yw) at buffer levels are upscaled to CZ, national or subcontinental levels using a weighted average based on harvested area retrieved from the Spatial Production Allocation Model (SPAM) 201044. Details on the GYGA upscaling method can be found in van Bussel et al.13 In the case of top-down approaches, for comparison purposes, it was necessary to aggregate Yp (or Yw) reported for each individual grid into buffers, CZs and countries in order to make them comparable to those reported by GYGA. To do so, Yp (or Yw) from GAEZ and AgMIP was scaled up to buffer, climate zone and country (or subnational levels) considering the crop-specific area within each pixel, as reported by SPAM 201044. For example, for a given buffer, the average Yp (or Yw) was estimated using a weighted average, in which the value of Yp (or Yw) reported for each of the GAEZ or AgMIP grids located within the GYGA buffer was ‘weighted’ according to the SPAM crop-specific area within that grid. The same approach was used to estimate average Yp (or Yw) at the CZ and country (or subcontinental) levels for GAEZ and AgMIP.For a given buffer, CZ or country (or subcontinent), the yield gap was calculated as the difference between Yp (or Yw) and the average farmer yield (actual yield, Ya). The Yp and Yw were taken as the appropriate benchmarks to estimate yield gaps for irrigated and rainfed crops, respectively. To avoid biases due to the source of average actual yield in the estimation of yield gap, we used the average actual yield dataset from GYGA, because it provides estimates of average actual yield disaggregated by water regime for the most recent time period. Actual yield data from GYGA were retrieved from official statistics available at subnational administrative units such as municipalities, counties, departments and subdistrict. The exact number of years of data to calculate average yield is determined by GYGA on a case-by-case basis, following the principle of including as many recent years of data as possible to account for weather variability while avoiding the bias due to a technological time trend and long-term climate change31. Using the GYGA database on average actual yield for estimation of yield gaps does not bias the results from our study, as GYGA favours the use of official sources of average yields at the finer available spatial resolution, which is the same source of actual yield data used by other databases such as FAO and SPAM22,44. In this study, we opted not to use actual yield data from GAEZ, because they derived from FAOSTAT statistics of the years 2000 and 2005, and thus, they could lead to an overestimation of the yield gap in those regions where actual yields have increased over the past two decades19. Finally, extra production potential was calculated based on the yield gap estimated by each approach and the SPAM crop-specific harvested area reported for each buffer, CZ and country (or subcontinent). The top-down and bottom-up approaches were compared in a total of 67 countries, which together account for 74%, 67% and 43% of global maize, rice and wheat harvested areas, respectively (Extended Data Fig. 2). Overall, our comparison included a total of 1,362 buffers located within 870 CZs, with 422 buffers (within 249 CZs) for rainfed maize, 160 buffers (116 CZs) for irrigated maize, 93 buffers (66 CZs) for rainfed rice, 209 buffers (114 CZs) for irrigated rice, 400 buffers (274 CZs) for rainfed wheat and 78 buffers (49 CZs) for irrigated wheat. In all cases, Yp (or Yw), yield gaps and extra production potential were expressed at standard commercial moisture content (that is, 15.5% for maize, 14% for rice and 13.5% for wheat).We assessed the agreement in Yp (or Yw), yield gap, and extra production potential between GYGA and the two databases that follow a top-down approach (GAEZ and AgMIP) separately for each of the spatial levels (buffer, CZ, country or subcontinent) by calculating root-mean-square error (RMSE) and absolute mean error (ME):$${mathrm{RMSE}} = sqrt {frac{{{sum} {(Y_{{mathrm{TD}}} – Y_{{mathrm{BU}}})^2} }}{n}}$$
(1)
$${mathrm{ME}} = frac{{{sum} {left( {Y_{{mathrm{TD}}} – Y_{{mathrm{BU}}}} right)} }}{n}$$
(2)
where YTD and YBU are the estimated Yp (or Yw), yield gap, or extra production potential for database i following a top-down approach and for GYGA, respectively, and n is the number of paired YTD versus YBU comparisons at a given spatial scale for a given crop in a given country. Separate comparisons were performed for irrigated and rainfed crops.Impact of Yp estimates on food self-sufficiency analysisWe assessed the impact of discrepancies in Yp (or Yw) between top-down and bottom-up approaches on the SSR, which is an important indicator for food security. To do so, we focused on cereal crops in sub-Saharan Africa, and we calculated the SSR for the five main cereal crops in this region (that is, maize, millet, rice, sorghum and wheat) following van Ittersum et al.23. Millet and sorghum were included in the analysis of SSR in sub-Saharan Africa, because together they account for ca. 25% of the total cereal production and ca. 40% of the total cereal harvested area in this region (average over the 2015–2019 period)22. Briefly, we computed current national demand (assumed equal to the 2015 consumption) and the 2015 production of the five cereals to estimate the baseline SSR (that is, in 2015) in ten countries for which Yw (or Yp) data were available in GYGA. Current total cereal demand per country were calculated as the product of current population size derived from United Nations population prospects and cereal demand per capita based on the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT)35,45. The annual per-capita demand for the five cereals was expressed in maize yield equivalents by using the crop-specific grain caloric contents, with caloric contents based on FAO food balances46. Current domestic grain production per cereal crop per country (approximately 2015) was calculated as mean actual crop yield (2003–2012) as estimated in GYGA times the 2015 harvested area per crop by FAO22. Total future annual cereal demand per capita (2050), for each of the five cereals and each country, was retrieved from IMPACT modelling results35 using the shared socioeconomic pathway (SSP2, no climate change) from the Intergovernmental Panel on Climate Change fifth assessment47. Total cereal demand per country in 2050 was calculated based on projected 2050 population (medium-fertility variant of United Nations population prospects; https://population.un.org/wpp/) multiplied by the per-capita cereal demand in 2050 from the SSP2 scenario. In our study, we assumed an attainable yield of 80% of Yw for rainfed crops, which is consistent with the original approach followed by van Ittersum et al.23, but, in our study, we also used 80% of Yp for irrigated crops as an estimate of the attainable yield, instead of 85% as in van Ittersum et al.23, to be slightly more conservative. Because the goal was to understand the level of SSR on existing cropland, we assumed no expansion of rainfed or irrigated cropland and no change in net planted area for each of the cereal crops. Our calculations for sub-Saharan Africa may be too pessimistic if genetic progress to increase Yp is achieved. Historically, genetic progress in Yp has contributed to progress in farm yields, although the magnitude of Yp increase is debatable. Progress in elevating Yp of the major cereals would imply, however, that even larger yield gaps need to be overcome than the already large gaps reported herein. Hence, we did not account for changes in genetic Yp in our calculation of SSR by 2050, also because climate change is likely to have a negative effect on Yp and Yw in sub-Saharan Africa.Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.Kang CS, Liang X, Dershwitz P, Gu W, Schepers A, Flatley A, et al. Evidence for methanobactin “theft” and novel chalkophore production in methanotrophs: impact on methanotrophic-mediated methylmercury degradation. ISME J. 2021;https://doi.org/10.1038/s41396-021-01062-1.2.Semrau JD, DiSpirito AA, Obulisamy PK, Kang-Yun CS. Methanobactin from methanotrophs: genetics, structure, function and potential applications. FEMS Microbiol Lett. 2020;367:fnaa045.CAS 
Article 

Google Scholar 
3.Kim HJ, Graham DW, DiSpiito AA, Alterman MA, Galeva N, Larive CK, et al. Methanobactin: a copper-acquisition compound from methane-oxidizing bacteria. Science. 2004;305:1612–5.CAS 
Article 

Google Scholar 
4.Lu X, Gu W, Zhao L, Farhan Ul Haque M, DiSpirito AA, Semrau JD, et al. Methylmercury uptake and degradation by methanotrophs. Sci Adv. 2017;3:e1700041.Article 

Google Scholar 
5.Ve T, Mathisen K, Helland R, Karlsen OA, Fjellbirkeland A, Røhr ÅK, et al. The Methylococcus capsulatus (Bath) secreted protein, MopE*, binds both reduced and oxidized copper. PLoS ONE. 2012;7:e43146.CAS 
Article 

Google Scholar 
6.DiSpirito AA, Semrau JD, Murrell JC, Gallagher WH, Dennison C, Vuilleumier S. Methanobactin and the link between copper and bacterial methane oxidation. Microbiol Mol Biol Rev. 2016;80:387–409.CAS 
Article 

Google Scholar 
7.Kenney GE, Rosenzweig AC. Genome mining for methanobactins. BMC Biol. 2013;11:17.CAS 
Article 

Google Scholar 
8.Yu Z, Zheng Y, Huang J, Chistoserdova L. Systems biology meets enzymology: recent insights into communal metabolism of methane and the role of lanthanides. Curr Issues Mol Biol. 2019;33:183–96.Article 

Google Scholar 
9.Gwak J-H, Jung M-Y, Hong HY, Kim J-G, Quan Z-X, Reinfelder JR, et al. Archaeal nitrification is constrained by copper complexation with organic matter in municipal wastewater treatment plants. ISME J. 2020;14:335–46.CAS 
Article 

Google Scholar 
10.Chang J, Kim DD, Semrau JD, Lee J, Heo H, Gu W, et al. Enhancement of nitrous oxide emissions in soil microbial consortia via copper competition between proteobacterial methanotrophs and denitrifiers. Appl Environ Microbiol. 2020;87:e02301–20.
Google Scholar  More

Targeted amplicon sequencing (TAS) or targeted analysis sequencing is a method which addresses the sequencing of specific amplicons and genes. The approach is technologically rooted in next-generation sequencing (NGS), also called high-throughput sequencing (HTS) or massively parallel sequencing and offers the possibility to read millions of sequences in one sequencing run. The rapid evolution of NGS technology with constant increases in sample numbers, data output per sequencing run and associated decreases in costs, has led to this approach becoming widely used in various areas of research. With epigenome, genome and transcriptome sequencing, NGS extends over a wide field, regardless of the different biological disciplines (e.g., botany, ecology, evolutionary biology, genetics, medical sciences, microbiology, zoology, etc.)1,2,3,4,5,6,7,8. In addition to the use of NGS runs in studies to research gene regulation and expression, the characterization of mRNA during transcriptome analyses, the development of molecular markers and genome assembly, another possible application in the context of TAS is the investigation of genetic variation. There is a large range of possible TAS applications including variant detection and tumour profiling in cancer research, the detection of somatic mutations or those associated with susceptibility to disease, new findings in the field of phylogeny and taxonomy studies or the discovery of useful genes for applications in molecular breeding2,3,9,10. In the field of environmental sciences, TAS is becoming increasingly important, as it facilitates the assessment of the taxonomic composition of environmental samples with the help of metabarcoding approaches such as environmental DNA (eDNA) based biomonitoring or food web studies11,12,13.Although NGS-based TAS is a powerful approach, different errors and biases can be introduced in such data sets. Sequencing errors have already been documented in medical studies, wherein factors such as sample handling, polymerase errors and PCR enrichment steps were identified as potential biases14,15. Similarly, other factors such as the variation in sequencing depth between individual samples, sequencing errors rates and index hopping can also play an important role within the analysis of NGS data. The difficulty is that there are currently no general standards requiring detailed reports and explanations to correct such potential errors, and very few studies have addressed this issue. Moreover, there is ever increasing access to NGS platforms, provided by sequencing companies, core facilities and research institutes16,17. NGS services often only provide the sequencing data while general information on the particular NGS run, demultiplexing-efficiency of individual samples and other relevant parameters are usually not passed on. The lack of such information and of a precise description of bioinformatic data processing makes it difficult to assess how the respective NGS run and the subsequent data processing went, which in turn complicates the comparison of results from different studies. Here, we show that specific aspects of library and data preparation have a critical influence on the assignment of sequencing results and how these problems can be addressed using a carabid beetle trophic data set as a case study system.Currently, a widely used approach to study large sample numbers is the analysis of pooled samples, by combining DNA from multiple individuals into one sample of the NGS library, thereby excluding the opportunity of backtracking specific sequences to an individual sample (no individual tagging)18,19,20. In ecological studies (e.g., in biodiversity research and functional ecology), the analysis of such pooled samples may then lead to a decreased estimate of the diversity of the identified species compared to an individual-based analysis21. Aside from the potential loss of information, pooled samples make it impossible to assign a given sample to its specific collection site and thus, the ability to refer to habitat related differences. For individual-level analyses, the ‘nested metabarcoding approach’22 offers a promising solution to problems of complexity and cost. It is both a cost-efficient NGS protocol and one that is scalable to hundreds of individual samples, making it ideal for any study that relies on high sample numbers or that analyses samples which need to be tagged individually, such as in the medical field for patient samples. Using the nested metabarcoding approach, each sample is tagged with four indexes defining a sample. The presence of sequencing errors within the index region can complicate the demultiplexing process and thus the identification of the sample affiliation of individual reads. For a precise assignment of reads to each sample using the index combinations, sequencing errors must be considered in the analysis in order to be able to assign a maximum number of reads.Besides sequencing errors within the different index regions that renders the read assignment difficult, a well-known, but at the same time often ignored problem is ‘index hopping’. This phenomenon, also called index switching/swapping, describes the index mis-assignment between multiplexed libraries and its rate rises as more free adapters or primers are present in the prepared NGS library23,24. Illumina therefore differentiates between combinatorial dual indexing and unique dual indexing. Special kits are offered with unique dual index sequences (set of 96 primer pairs) to counter the problem of index hopping and pitfalls of demultiplexing. This is an option for low sample numbers, as these can still be combined with unique dual indexes (UDIs). If several hundred samples are to be individually tagged in one run, it can be difficult to implement unique dual indexing due to the high number of samples and for cost reasons. Here, the nested metabarcoding approach offers a convenient solution for analysing a large number of individual samples at comparatively low costs. However, it is important to be careful regarding index hopping since more indexes are used in the nested metabarcoding approach than for pooling approaches. For instance, in silico cross-contamination between samples from different studies and altered or falsified results can occur if a flow cell lane is shared and the reads were incorrectly assigned. Even where samples are run exclusively on a single flow cell, index hopping may result in barcode switching events between samples that lead to mis-assignment of reads.For library preparations of Illumina NGS runs, two indexes are usually used to tag the individual samples (dual indexing)25. Illumina offers the option to do the demultiplexing and convert the sequenced data into FASTQ file formats using the supplied ‘bcl2fastq’ or ‘bcl2fastq2’ conversion software tool26. This demultiplexing is a crucial step, as it is here that the generated DNA sequences are assigned to the samples. In most cases, the data is already provided demultiplexed after the NGS run by the sequencing facility, especially if runs were shared between different studies/sample sets. Researchers starting the bioinformatic analysis with demultiplexed data assume that the assignment of the sequences to samples was correct. Verifying this is extremely difficult because the provided data sets lack all the information on the demultiplexing settings and, above all, on the extent of sequencing errors within indexes and index hopping. As a consequence, sequences can be incorrectly assigned to samples and, in case of a shared flow cell, even across sample sets. These steps of bioinformatic analysis are very often outsourced to companies and details on demultiplexing are seldom reported, showing that the problem of read mis-assignment has received little attention so far. However, it is known that demultiplexing errors occur and depend on various factors such as the Illumina sequencing platform, the library type used and index combinations23,24,25,27,28,29,30. The few existing studies investigating index hopping in more detail give rates of 0.2–10%24,31,32,33,34. This indicates the importance of being able to estimate the extent of index hopping for a specific library. The problem of sequencing errors within indexes and index hopping can become particularly significant if, due to the large number of individual samples, libraries were constructed with two instead of one index pair, such as it is the case in the nested metabarcoding approach35. Then, one is inevitably confronted with the effect of sequencing errors and index hopping on demultiplexing and subsequently on the data output.After each NGS run, the combination of computational power and background knowledge in bioinformatics are needed to ensure time-efficient and successful data analysis36. But even for natural scientists with considerable bioinformatic experience, there is a lack of know-how or even rules-of-thumb in this still nascent field. It is well known that specific decisions have a marked impact on the outcome of a study, with both the sequencing platform and software tools significantly affecting the results and thereby the interpretation of the sequencing information37. Knowledge of the individual data processing steps, such as for the demultiplexing, is also often missing or poorly described. Information on how to minimize data loss within the individual steps for data preparation of the NGS data is also mostly not explained. Given this lack of detail, it is a challenge to understand what was done during sample processing and data analysis, and impossible to compare the outcomes of different studies. To date, published NGS studies, such as TAS or DNA metabarcoding studies, are difficult to compare or evaluate because of the lack of this essential information on data processing. This is particularly important as NGS is increasingly being done by external service providers. As a consequence, there is a pressing need for comprehensive protocols that detail the aspects that need to be considered during analysis.Using a case study on the dietary choice of carabid beetles (Coleoptera: Carabidae) in arable land, we detail a comprehensive protocol that describes an entire workflow targeting ITS2 fragments, using an Illumina HiSeq 2500 system and applying the nested metabarcoding approach22 to identify those species of weed seeds consumed by carabid individuals. We demonstrate a concept that employs bioinformatic tools for targeted amplicon sequencing in a defined order. By analysing the effects of sequencing errors and index hopping on demultiplexing and data trimming, we show the importance of describing the software and pipeline used and its version, as well as specifying software configurations and thresholds settings for each TAS data set to receive a realistic data output per sample. Without this information, there is the possibility of incorrectly assigning samples or not receiving the maximum or at least a sufficient number of sequences which in turn would hamper the results.The concept described below can be used to analyse a large number of samples, here to identify food items on species-specific level, and to address the possible problems that may arise in NGS data processing. We identify problems to overcome and potential solutions by examining: (i) the variation in sequencing depth of individually tagged samples and the effect of library preparation on the data output; (ii) the influence of sequencing errors within index regions and its consequences for demultiplexing; and, (iii) the effect of index hopping. By doing this, we highlight the benefits of a detailed protocol for bioinformatic analysis of a given data set, and the importance of the reporting of bioinformatic parameters, especially for the demultiplexing, and thresholds to be used for meaningful data interpretation. More

Indigenous territories, long a bulwark against deforestation in the Amazon, are under increasing threat in Brazil, according to an analysis of 36 years’ worth of satellite imagery. The data show that illicit mining operations on Indigenous lands and in other areas formally protected by law have hit a record high in the past few years, under the administration of President Jair Bolsonaro, underscoring fears that his policies and rhetoric are undermining both human rights and environmental protection across the world’s largest rainforest. These operations strip the land of vegetation and pollute waterways with mercury.
When will the Amazon hit a tipping point?
The analysis, released in late August, comes as scientists and environmentalists warn of a deteriorating situation in Brazil; Indigenous groups have frequently found themselves in violent clashes with miners since Bolsonaro took office in 2019 — and they are demanding more protection for their land. Although Indigenous territories are legally protected, Bolsonaro has openly called for mining and other development in them.“This is definitely the worst it’s been for Indigenous peoples since the constitution was signed in 1988,” says Glenn Shepard, an anthropologist with the Emílio Goeldi Museum in Belém. Before this, Brazil was ruled by a military dictatorship.Researchers at MapBiomas, a consortium of academic, business and non-governmental organizations that has been conducting geospatial studies across Brazil, developed algorithms that they used in conjunction with Google Earth Engine to conduct the analysis. After training the algorithms on images of mining operations — desolate landscapes where forests have been converted into a collection of sand dunes pockmarked by mining ponds — the team ran its analysis on a freely available archive of imagery captured by the US Landsat programme, and then analysed trends on Indigenous lands and other formally protected areas where mining is not allowed.Over the past decade, illegal mining incursions — mostly small-scale gold extraction operations — have increased fivefold on Indigenous lands and threefold in other protected areas of Brazil such as parks, the data show (see ‘Mining incursions’). The findings agree broadly with reports from Brazil’s National Institute for Space Research (INPE) in São José dos Campos, which monitors the country’s forests and has been issuing alerts about mining incursions for several years. “We kind of knew that this was happening, but to see numbers like this is scary even for us,” says Cesar Diniz, a geologist with the geospatial-analysis company Solved in Belém, Brazil, who led the analysis for MapBiomas.Clashes on multiple frontsAside from being home to their people, Indigenous territories play a part in protecting the Amazon’s biodiversity and the enormous pool of carbon that is locked away in its trees and soils. Numerous studies have found that Indigenous lands, as well as other conservation areas, are effective buffers against tropical deforestation in the Amazon1,2, which is responsible for around 8% of global carbon emissions.Earlier this month, the International Union for Conservation of Nature (IUCN) approved a motion, put forward by Indigenous groups, calling on governments to protect 80% of the Amazon basin by 2025. Indigenous representatives say they plan to fight for implementation across the Amazon, but the proposal faces a particularly tough sell in Brazil under Bolsonaro, whose pro-business conservative government has scaled back enforcement of existing environmental laws and halted efforts to demarcate new Indigenous territories.

Sources: MapBiomas/Amazon Geo-Referenced Socio-Environmental Information Network/Terrabrasilis

Indigenous groups have also taken their case to the International Criminal Court in The Hague, the Netherlands. On 9 August, the Articulation of Indigenous Peoples of Brazil (APIB), which represents Indigenous groups across the country, filed a complaint with the court accusing the Bolsonaro administration of violating human rights and, they claim, paving a path for genocide by undermining Indigenous rights, reducing environmental protections and inciting incursions and violence through calls for mining and land development. APIB also made it clear that it’s not just Indigenous rights at stake, drawing a direct link between the protection of their territories and of the globe.

Members of the Munduruku people sit in front of equipment from an illegal mining operation on their land.Credit: Meridith Kohut/The New York Times/eyevine

“Defending the traditional territories of Amazonian communities is the best way to save the forest,” says Luiz Eloy Terena, an anthropologist and lawyer from the village of Ipegue who coordinates legal affairs for APIB. “What is needed is a state commitment on the demarcation and protection of Indigenous lands, which are the last barrier against deforestation and forest degradation.”During an address to the United Nations General Assembly on 21 September, Bolsonaro said he was committed to protecting the Amazon and emphasized that 600,000 Indigenous people live “in freedom” on reserves totalling 1.1 million square kilometres of land, equivalent to 14% of Brazil’s territory. In the past, Bolsonaro has publicly said that Indigenous peoples have too much land given their sparse population, and at times called for their “integration”. The Bolsonaro administration did not respond to Nature’s requests for comment regarding illegal mining in the Amazon, its Indigenous and environmental policies or the accusations filed with the International Criminal Court.Existential threatBrazil earned recognition as a leader in sustainable development during the 2000s. Former president Luiz Inácio ‘Lula’ da Silva and his Workers’ Party put in place policies that helped to curb deforestation in the Amazon by more than 80% between 2004 and 2012.

Source: Brazilian National Institute for Space Research

But the party was dogged by corruption charges that would later land Lula in jail, and its environmental agenda ultimately faltered. In 2012, the increasingly conservative Brazilian Congress weakened a once-vaunted forest-protection law. With each successive government, funding for the country’s main environmental enforcement agency, the Institute of Environment and Renewable Natural Resources (IBAMA), has decreased: IBAMA had 1,500 enforcement agents in 2012, compared with just 600 today, says Suely Araújo, a political scientist in Brasília who spent nearly three decades working in the Brazilian Congress and led IBAMA from 2016 to 2018.The rate of deforestation in the Amazon, which includes land converted for mining, agriculture and other development, began rising anew after 2012 and shot up by 44% during Bolsonaro’s first two years in office, according to INPE (see ‘Razing the rainforest’). Many expect yet another increase when the numbers for 2021 are released later this year.But the biggest threats are yet to come, says Araújo. The current government is now pushing legislation in Congress — as well as arguments in a case that is pending before Brazil’s Supreme Court — that would make it harder to establish new Indigenous lands and could even allow the government to repossess existing lands. Other legislation that has been advanced by Bolsonaro’s supporters in Congress would open up Indigenous lands to industrial development, grant amnesty to people who have illegally invaded public lands and gut regulations governing major infrastructure projects such as mines, roads and dams.
The scientists restoring a gold-mining disaster zone in the Peruvian Amazon
“It’s painful,” says Araújo, who decided to forgo retirement and join Brazil’s Climate Observatory, a coalition of activist and academic groups fighting to preserve the country’s social and environmental protections. “This has become my mission.”For Indigenous tribes, the growing damage to their lands and the rainforest pose an existential threat. More than 6,000 Indigenous people descended on Brasília, the country’s capital, in August and September in protest against Bolsonaro’s policies on land demarcation and the environment. They also travelled to Marseille, France, for the IUCN’s World Conservation Congress earlier this month to promote their motion to protect the Amazon basin.“We will not give up,” says José Gregorio Diaz Mirabal, a member of the Wakueni Kurripaco people of Venezuela and the elected leader of the Congress of Indigenous Organizations of the Amazon Basin. “Science supports us, and the world is waking up.”

doi: https://doi.org/10.1038/d41586-021-02644-x

References1.Blackman, A., Corral, L., Lima, E. S. & Asner, G. P. Proc. Natl Acad. Sci. USA 114, 4123–4128 (2017).PubMed 
Article 

Google Scholar 
2.Walker, W. S. et al. Proc. Natl Acad. Sci. USA 117, 3015–3025 (2020).PubMed 
Article 

Google Scholar 
Download references

Related Articles

The scientists restoring a gold-mining disaster zone in the Peruvian Amazon

When will the Amazon hit a tipping point?

To save Brazil’s rainforest, boost its science

Subjects

Anthropology

Politics

Government

Climate change

Biodiversity

Latest on:

Anthropology

Ancient Maya capital housed a copy of a rival city’s pyramid
Research Highlight 30 SEP 21

Ancient footprints could be oldest traces of humans in the Americas
News 23 SEP 21

Modern Polynesian genomes offer clues to early eastward migrations
News & Views 22 SEP 21

Politics

Climate change to loom large in talks to form new German government
News 27 SEP 21

Indonesia’s science super-agency must earn researchers’ trust
Editorial 08 SEP 21

The global research community must not abandon Afghanistan
Editorial 01 SEP 21

Government

Climate change to loom large in talks to form new German government
News 27 SEP 21

Sustainable Development Goals research speaks to city strengths and priorities
Nature Index 24 SEP 21

University under pressure to rehire scientist acquitted of hiding China links
News 24 SEP 21

Jobs

Project manager target identification and validation for Alzheimer’s disease

Flanders Institute for Biotechnology (VIB)
Leuven, Belgium

PhD Positions in the Wisnovsky Lab, UBC Pharmaceutical Sciences

The University of British Columbia (UBC)
Vancouver, Canada

Post-doctoral Fellow – NAD Metabolism in Heart Disease

Oklahoma Medical Research Foundation (OMRF)
Oklahoma City, United States

Research Scientist – High Performance Computing (HPC) / Machine Learning (ML)

Jülich Research Centre (FZJ)
Jülich, Germany More

1.Wasser, S. K. et al. Population growth is limited by nutritional impacts on pregnancy success in endangered Southern resident killer whales (Orcinus orca). PLoS ONE 12, e0179824. https://doi.org/10.1371/journal.pone.0179824 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
2.Hoare, J. et al. Conservation implications of a long-term decline in body condition of the Brothers Island tuatara (Sphenodon guntheri). Anim. Conserv. 9, 456–462 (2006).Article 

Google Scholar 
3.Castrillon, J. & Bengtson Nash, S. Evaluating cetacean body condition; a review of traditional approaches and new developments. Ecol. Evol. (2020).4.Peig, J. & Green, A. J. New perspectives for estimating body condition from mass/length data: the scaled mass index as an alternative method. Oikos 118, 1883–1891 (2009).Article 

Google Scholar 
5.Labrada-Martagón, V., Méndez-Rodríguez, L. C., Gardner, S. C., Cruz-Escalona, V. H. & Zenteno-Savín, T. Health indices of the green turtle (Chelonia mydas) along the Pacific coast of Baja California Sur, Mexico. II. Body condition index. Chelonian Conserv. Biol. 9, 173–183 (2010).6.Weber, L., Higgins, P., Carlson, R. & Janz, D. Development and validation of methods for measuring multiple biochemical indices of condition in juvenile fishes. J. Fish Biol. 63, 637–658 (2003).CAS 
Article 

Google Scholar 
7.Fokidis, H. B., Hurley, L., Rogowski, C., Sweazea, K. & Deviche, P. Effects of captivity and body condition on plasma corticosterone, locomotor behavior, and plasma metabolites in curve-billed thrashers. Physiol. Biochem. Zool. 84, 595–606 (2011).CAS 
PubMed 
Article 

Google Scholar 
8.Labocha, M. K. & Hayes, J. P. Morphometric indices of body condition in birds: a review. J. Ornithol. 153, 1–22. https://doi.org/10.1007/s10336-011-0706-1 (2012).Article 

Google Scholar 
9.Labocha, M. K., Schutz, H. & Hayes, J. P. Which body condition index is best?. Oikos 123, 111–119. https://doi.org/10.1111/j.1600-0706.2013.00755.x (2014).Article 

Google Scholar 
10.Bonnet, X. & Naulleau, G. Estimation of body reserves in living snakes using a Body Condition Index (BCI). Scientia Herpetologica 3, 237 (1995).
Google Scholar 
11.Lubbe, A., Underhill, L., Waller, L. & Veen, J. A condition index for African penguin Spheniscus demersus chicks. Afr. J. Mar. Sci. 36, 143–154 (2014).Article 

Google Scholar 
12.Santoro, M. et al. Parasitic infection by larval helminths in Antarctic fishes: pathological changes and impact on the host body condition index. Dis. Aquat. Org. 105, 139–148 (2013).CAS 
Article 

Google Scholar 
13.Rossi, S. et al. Monitoring green sea turtles in Brazilian feeding areas: relating body condition index to fibropapillomatosis prevalence. J. Mar. Biol. Assoc. U.K. 99, 1879–1887 (2019).14.Mubiana, V. K., Vercauteren, K. & Blust, R. The influence of body size, condition index and tidal exposure on the variability in metal bioaccumulation in Mytilus edulis. Environ. Pollut. 144, 272–279 (2006).CAS 
PubMed 
Article 

Google Scholar 
15.Lippold, A. et al. Temporal trends of persistent organic pollutants in Barents Sea Polar bears (Ursus maritimus) in relation to changes in feeding habits and body condition. Environ. Sci. Technol. 53, 984–995 (2018).ADS 
PubMed 
Article 
CAS 

Google Scholar 
16.Delciellos, A. C. et al. Habitat fragmentation affects individual condition: evidence from small mammals of the Brazilian Atlantic Forest. J. Mammal. 99, 936–945 (2018).Article 

Google Scholar 
17.Burgess, E. A., Brown, J. L. & Lanyon, J. M. Sex, scarring, and stress: understanding seasonal costs in a cryptic marine mammal. Conserv. Physiol. 1, https://doi.org/10.1093/conphys/cot014 (2013).18.McKinney, M. A. et al. Validation of adipose lipid content as a body condition index for polar bears. Ecol. Evol. 4, 516–527 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
19.Noren, S. R. et al. Identifying a reliable blubber measurement site to assess body condition in a marine mammal with topographically variable blubber, the Pacific walrus. Mar. Mamm. Sci. 31, 658–676 (2015).Article 

Google Scholar 
20.Arnould, J. P. Indices of body condition and body composition in female Antarctic fur seals (Arctocephalus gazella). Mar. Mamm. Sci. 11, 301–313 (1995).Article 

Google Scholar 
21.Pitcher, K., Calkins, D. & Pendleton, G. Steller sea lion body condition indices. Mar. Mamm. Sci. 16, 427–436 (2000).Article 

Google Scholar 
22.Harshaw, L. T., Larkin, I. V., Bonde, R. K., Deutsch, C. J. & Hill, R. C. Morphometric body condition indices of wild Florida manatees (Trichechus manatus latirostris). Aquat. Mamm. 42, 428 (2016).Article 

Google Scholar 
23.Castelblanco-Martínez, D. N., Nourisson, C., Quintana-Rizzo, E., Padilla-Saldivar, J. A. & Schmitter-Soto, J. J. Potential effects of human pressure and habitat fragmentation on population viability of the Antillean manatee Trichechus manatus manatus: a predictive model. Endanger. Spec. Res. 18, 129–145. https://doi.org/10.3354/esr00439 (2012).Article 

Google Scholar 
24.IUCN. The IUCN Red List of Threatened Species. Version 2020–2. https://www.iucnredlist.org. Downloaded on 09 Dec 2020. (2020).25.Quintana-Rizzo, E. & Reynolds III, J. E. Regional management plan for the West Indian manatee. 178 (United Nations Environment Programme. United Nations Environment Programme. CEP Technical Report, Kingston, Jamaica, 2008).26.Morrison, M. L. The habitat sampling and analysis paradigm has limited value in animal conservation: a prequel. J. Wildl. Manag. 76, 438–450 (2012).Article 

Google Scholar 
27.Wong, A. W. et al. Monitoring oral temperature, heart rate, and respiration rate of West Indian manatees (Trichechus manatus) during capture and handling in the field. Aquat. Mamm. 38, 1–16. https://doi.org/10.1578/AM.38.1.2012.1 (2012).Article 

Google Scholar 
28.Castelblanco-Martínez, D. N., Morales-Vela, B. & Padilla-Saldívar, J. A. Using craniometrical predictors to infer body size of Antillean manatees. Mammalia 78, 109–115 (2014).Article 

Google Scholar 
29.QGIS.org. QGIS Geographic Information System. QGIS Association. http://www.qgis.org. (2021).30.Bonde, R. K. et al. Biomedical health assessments of the Florida manatee in Crystal River – providing opportunities for training during the capture, handling, and processing of this endangered aquatic mammal. J. Mar. Anim. Ecol. 5 (2012).31.Mignucci-Giannoni, A. A. et al. Manatee mortality in Puerto Rico. Environ. Manage. 25, 189–198 (2000).CAS 
PubMed 
Article 

Google Scholar 
32.Susanti, Y., Pratiwi, H., Sulistijowati H., S., & Liana, T. M estimation, S estimation, and MM estimation in robust regression. Int. J. Pure Appl. Math. 91, 349–360 (2014).33.R Core Team. R: A language and environment for statistical computing: version 3.6.0. URL: https://www.R-project.org (2019).34.Maechler, M. et al. robustbase: Basic robust statistics. R package version 0.4–3, http://CRAN.R-project.org/package=robustbase (2021).35.Koller, M. & Stahel, W. A. Nonsingular subsampling for regression S estimators with categorical predictors. Comput. Stat. 32, 631–646 (2017).MathSciNet 
MATH 
Article 

Google Scholar 
36.Wickham, H. ggplot2-Elegant Graphics for Data Analysis (Springer International Publishing. Cham, 2016).MATH 

Google Scholar 
37.Converse, L. J., Fernandes, P. J., Macwilliams, P. S. & Bossart, G. D. Hematology, serum chemistry, and morphometric reference values for Antillean Manatees (Trichechus manatus manatus). J. Zoo Wildl. Med. 25, 423–431 (1994).
Google Scholar 
38.O’Shea, T. J. & Reep, R. L. Encephalization quotients and life-history traits in the Sirenia. J. Mammal. 71, 534–543 (1990).Article 

Google Scholar 
39.Chirachevin, P. The relationship between cold stress syndrome mortality and body shape in Florida manatees.  Undergraduate thesis, Andrews University (2017). 
Google Scholar 
40.Johnson, J. Is natural selection shaping Florida manatees? An investigation into the body shapes between the subspecies of the West Indian manatee. Undergraduate thesis, Andrews University, (2019).41.Torres-Romero, E. J., Morales-Castilla, I. & Olalla-Tárraga, M. Á. Bergmann’s rule in the oceans? Temperature strongly correlates with global interspecific patterns of body size in marine mammals. Global Ecol. Biogeogr. 25, 1206–1215 (2016).Article 

Google Scholar 
42.Alvarez-Alemán, A., Beck, C. A. & Powell, J. A. First report of a Florida manatee (Trichechus manatus latirostris) in Cuba. Aquat. Mamm. 36, 148–153. https://doi.org/10.1578/AM.36.2.2010.148 (2010).Article 

Google Scholar 
43.Alvarez-Alemán, A., Austin, J. D., Jacoby, C. A. & Frazer, T. K. Cuban connection: regional role for Florida’s manatees. Front. Mar. Sci. 5, 294 (2018).Article 

Google Scholar 
44.Rood, K., Teague, A., Barton, S., Alvarez-Alemán, A. & Hieb, E. First documentation of return movement from Cuba to the continental United States by a Florida manatee. Sirenews 71, 24–25 (2020).
Google Scholar 
45.Konishi, K. Characteristics of blubber distribution and body condition indicators for Antarctic minke whales (Balaenoptera bonaerensis). Mamm. Study 31, 15–22 (2006).Article 

Google Scholar 
46.Viblanc, V. A. et al. Body girth as an alternative to body mass for establishing condition indexes in field studies: a validation in the king penguin. Physiol. Biochem. Zool. 85, 533–542 (2012).PubMed 
Article 

Google Scholar 
47.Ramos, E. A., Landeo-Yauri, S., Castelblanco-Martinez, N., Quade, A. & Rieucau, G. Drone-based photogrammetry assessments of body size and body condition of Antillean manatees. Mamm. Biol. (In prep. ).48.Lanyon, J. M., Sneath, H. L., Long, T. & Bonde, R. K. Physiological response of wild dugongs (Dugong dugon) to out-of-water sampling for health assessment. Aquat. Mamm. 36, 46–58 (2010).Article 

Google Scholar 
49.Baker, J., Meade, A., Pagel, M. & Venditti, C. Adaptive evolution toward larger size in mammals. Proc. Natl. Acad. Sci. 112, 5093–5098 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Noren, S. R. & Williams, T. M. Body size and skeletal muscle myoglobin of cetaceans: adaptations for maximizing dive duration. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 126, 181–191, https://doi.org/10.1016/S1095-6433(00)00182-3 (2000).51.Sibly, R. M. & Brown, J. H. Effects of body size and lifestyle on evolution of mammal life histories. Proc. Natl. Acad. Sci. 104, 17707–17712 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Cassini, M. H. A mixed model of the evolution of polygyny and sexual size dimorphism in mammals. Mammal Rev. 50, 112–120 (2020).Article 

Google Scholar 
53.Fokidis, H. B., Risch, T. S. & Glenn, T. C. Reproductive and resource benefits to large female body size in a mammal with female-biased sexual size dimorphism. Anim. Behav. 73, 479–488 (2007).Article 

Google Scholar 
54.Deutsch, C. J. et al. Seasonal movements, migratory behavior and site fidelity of West Indian manatees along the Atlantic Coast of the United States. Wildl. Monogr. 151, 1–77 (2003).
Google Scholar 
55.Flamm, R. O., Weigle, B. L., Wright, I. E., Ross, M. & Aglietti, S. Estimation of manatee (Trichechus manatus latirostris) places and movement corridors using telemetry data. Ecol. Appl. 15, 1415–1426 (2005).Article 

Google Scholar 
56.Puc-Carrasco, G., Morales-Vela, B., Olivera-Gomez, L. D. & González-Solís, D. First field-based estimate of Antillean manatee abundance in the San Pedro River system suggests large errors in current estimates for Mexico. Cienc. Mar. 43, 285–299 (2017).Article 

Google Scholar 
57.Puc-Carrasco, G., Olivera-Gómez, L. D., Arriaga-Hernández, S. & Jiménez-Domínguez, D. Relative abundance of Antillean manatees in the Pantanos de Centla Biosphere Reserve in the coastal plain of Tabasco Mexico. Cienc. Mar. 42, 261–270 (2016).Article 

Google Scholar 
58.Castelblanco-Martínez, D. N., Kendall, S., Orozco, D. L. & Arévalo-González, K. La conservación de los manatíes (Trichechus inunguis y Trichechus manatus) en áreas no protegidas de Colombia in Conservación de grandes vertebrados en áreas no protegidas de Colombia, (eds Payán, E. et al.) 81–98 (Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, 2015).59.Castelblanco-Martínez, D. N. et al. Movement patterns of Antillean manatees in Chetumal Bay (Mexico) and coastal Belize: A challenge for regional conservation. Mar. Mamm. Sci. 29, 166–182. https://doi.org/10.1111/j.1748-7692.2012.00602.x (2013).Article 

Google Scholar 
60.Corona-Figueroa, M. et al. Searching for manatees in the dark waters of a transboundary river between Mexico and Belize: a predictive distribution model. Aquat. Ecol., 1–16 (2020).61.Alvarez-Alemán, A., Angulo-Valdés, J. A., Alfonso, E. G., Powell, J. A. & Taylor, C. R. Occurrence of the Endangered Antillean manatee Trichechus manatus manatus in a marine protected area, Isla de la Juventud, Cuba. Oryx 51, 324–331 (2017).Article 

Google Scholar 
62.Castelblanco-Martínez, D. N., dos Reis, V. & de Thoisy, B. How to detect an elusive aquatic mammal in complex environments? A study of the Endangered Antillean manatee Trichechus manatus manatus in French Guiana. Oryx 52, 382–392. https://doi.org/10.1017/S0030605316000922 (2017).Article 

Google Scholar 
63.Castelblanco-Martínez, D. N., Holguín, V. E. & Zapata, M. Conservación y manejo del manatí en la Ciénaga de Paredes (Santander). In Programa Nacional de manejo y conservación de manatíes en Colombia, (eds Caicedo, D. et al.) 105–113 (Ministerio de Ambiente, Vivienda y Desarrollo Territorial – Fundación Omacha, 2005).64.Gonzalez-Socoloske, D. & Olivera-Gomez, L. Food choice by a free-ranging Antillean manatee (Trichechus manatus manatus) in Tabasco, Mexico. J. Mar. Anim. Ecol. 11, 19–32 (2019).
Google Scholar 
65.Jimenez-Dominguez & Olivera Gómez, L. D. Características del hábitat del manatí antillano (Trichechus manatus manatus) en sistemas fluvio-lagunares del sur del Golfo de México. Therya 5, 601–6014 (2014).66.Best, R. C. Apparent dry-season fasting in Amazonian manatees (Mammalia, Sirenia). Biotropica 15, 61–64 (1983).Article 

Google Scholar 
67.Florant, G. L. & Healy, J. E. The regulation of food intake in mammalian hibernators: a review. J. Comp. Physiol., B 182, 451–467 (2012).68.Tighe, R. L., Bonde, R. K. & Avery, J. P. Seasonal response of ghrelin, growth hormone, and insulin-like growth factor I in the free-ranging Florida manatee (Trichechus manatus latirostris). Mamm. Biol. 81, 247–254. https://doi.org/10.1016/j.mambio.2016.02.006 (2016).Article 

Google Scholar 
69.Castelblanco-Martínez, D. N., Morales-Vela, B., Hernández-Arana, H. A. & Padilla-Saldívar, J. Diet of the manatees Trichechus manatus manatus in Chetumal Bay, Mexico. Latin Am. J. Aquat. Mammals 7, 39–46 (2009).
Google Scholar 
70.Allen, A. C., Beck, C. A., Bonde, R. K., Powell, J. A. & Gomez, N. A. Diet of the Antillean manatee (Trichechus manatus manatus) in Belize, Central America. J. Mar. Biol. Assoc. U.K., 1–10 (2017).71.Garcés-Cuartas, N. Ecología trófica del manatí del Caribe: una herramienta de conservación para ecosistemas estratégicos en el Caribe mexicano Ph.D. thesis, Universidad de Quintana Roo, (2020).72.Rodrigues, F. M. et al. Nutritional composition of food items consumed by Antillean manatees (Trichechus manatus manatus) along the coast of Paraíba Northeastern Brazil. Aquat. Bot. 168, 103324. https://doi.org/10.1016/j.aquabot.2020.103324 (2021).Article 

Google Scholar 
73.Navarro-Martínez, Z., Alvarez-Alemán, A. & Castelblanco-Martínez, D. N. Diet components in three manatees in Cuba. Rev. Invest. Mar. 34, 1–11 (2014).
Google Scholar 
74.Ponce-García, G., Olivera-Gómez, L. D. & Solano, E. Analysis of the plant composition of manatee (Trichechus manatus manatus) faeces in a lake in south-eastern Mexico. Aquat. Conserv.: Mar. Freshwat. Ecosyst. (2017).75.Pablo-Rodríguez, N., Olivera-Gómez, L. D., Aurioles-Gamboa, D. & Vega-Cendejas, M. E. Seasonal differences in the feeding habits of the Antillean manatee population (Trichechus manatus manatus) in the fluvial-lagoon systems of Tabasco Mexico. Mar. Mamm. Sci. 32, 363–375 (2016).Article 

Google Scholar 
76.Siegal-Willott, J. L. et al. Proximate nutrient analyses of four species of submerged aquatic vegetation consumed by Florida manatee (Trichechus manatus latirostris) compared to romaine lettuce (Lactuca sativa var. longifolia). J. Zoo Wildl. Med. 41, 594–602, https://doi.org/10.1638/2009-0118.1 (2010).77.Lomolino, M. V. & Ewel, K. C. Digestive efficiencies of the West Indian manatee (Trichechus manatus). Fla. Sci. 47, 176–179 (1984).
Google Scholar 
78.Larkin, I. L. V., Fowler, V. F. & Reep, R. L. Digesta passage rates in the Florida manatee Trichechus manatus latirostris. Zoo Biol. 26, 503–515 (2007).PubMed 
Article 

Google Scholar 
79.Reynolds, J. E. III. & Rommel, S. A. Structure and function of the gastrointestinal tract of the Florida manatee Trichechus manatus latirostris. Anat. Rec. 245, 539–558 (1996).PubMed 
Article 
PubMed Central 

Google Scholar 
80.Arévalo-González, K. Aspectos de la ecología y fisiología alimentaria del manatí antillano. M.Sc. thesis, Universidad Veracruzana (2020).81.Harshaw, L. T., Larkin, I. V., Staples, C. R., Scott, K. C. & Hill, R. C. In vivo apparent digestibility of fiber in Florida manatees (Trichechus manatus latirostris) under human care. Aquat. Mamm. 45, 513–524 (2019).Article 

Google Scholar 
82.Alves-Stanley, C. D. & Worthy, G. A. J. Carbon and nitrogen stable isotope turnover rates and diet-tissue discrimination in Florida manatees (Trichechus manatus latirostris). J. Exp. Biol. 212, 2349–2355 (2009).CAS 
PubMed 
Article 

Google Scholar 
83.Alves-Stanley, C. D., Worthy, G. A. J. & Bonde, R. K. Feeding preferences of the West Indian manatee in Florida, Belize, and Puerto Rico as indicated by stable isotope analysis. Mar. Ecol. Prog. Ser. 402, 255–267 (2010).ADS 
Article 

Google Scholar 
84.West-Eberhard, M. J. Phenotypic plasticity and the origins of diversity. Annu. Rev. Ecol. Syst. 20, 249–278 (1989).Article 

Google Scholar 
85.Stamper, M. A. & Bonde, R. Health assessment of captive and wild-caught West Indian manatees. In Sirenian conservation: Issues and strategies in developing countries, (eds Hines, E. et al.) (University Press of Florida, 2012).86.Wilder, S. M., Raubenheimer, D. & Simpson, S. J. Moving beyond body condition indices as an estimate of fitness in ecological and evolutionary studies. Funct. Ecol. 30, 108–115 (2016).Article 

Google Scholar 
87.Santos, N. et al. Protein metabolism and physical fitness are physiological determinants of body condition in Southern European carnivores. Sci. Rep. 10, 15755. https://doi.org/10.1038/s41598-020-72761-6 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
88.Mumby, H. S. et al. Stress and body condition are associated with climate and demography in Asian elephants. Conserv. Physiol. 3, cov030 (2015).89.Abujanra, F., Agostinho, A. & Hahn, N. Effects of the flood regime on the body condition of fish of different trophic guilds in the Upper Paraná River floodplain Brazil. Braz. J. Biol. 69, 459–468 (2009).Article 

Google Scholar  More