Philippa Kaur

Colpaert, J. V. In Stress in Yeasts and Filamentous Fungi Vol. 27 (eds Avery, S. V. et al.) 157–173 (Elsevier, Academic Press, 2008).Chapter 

Google Scholar 
Dalvi, A. A. & Bhalerao, S. A. Response of plants towards heavy metal toxicity: An overview of avoidance, tolerance and uptake mechanism. Ann. Plant Sci. 2, 362–368 (2013).
Google Scholar 
Langer, I. et al. Ectomycorrhizal impact on Zn accumulation of Populus tremula L. grown in metalliferous soil with increasing levels of Zn concentration. Plant Soil 355, 283–297 (2012).Article 

Google Scholar 
Agrahar-Murugkar, D. & Subbulakshmi, G. Nutritional value of edible wild mushrooms collected from the Khasi hills of Meghalaya. Food Chem. 89, 599–603 (2005).Article 

Google Scholar 
Das, N., Charumathi, D. & Vimala, R. Effect of pretreatment on Cd2+ biosorption by mycelial biomass of Pleurotus florida. Afr. J. Biotechnol. 6, 2555–2558 (2007).Article 

Google Scholar 
Dulay, R. M. R. et al. Effects and myco-accumulation of lead (Pb) in five Pleurotus mushrooms. Int. J. Biol. Pharm. Allies Sci. 4, 1664–1677 (2015).
Google Scholar 
Prasad, A. A., Varatharaju, G., Anushri, C. & Dhivyasree, S. Biosorption of lead by Pleurotus florida and Trichoderma viride. Br. Biotechnol. J. 3, 66–78 (2013).Article 

Google Scholar 
Tay, C. C. et al. Biosorption of cadmium ions using Pleurotus ostreatus: Growth kinetics, isotherm study and biosorption mechanism. Korean J. Chem. Eng. 28, 825–830 (2011).Article 

Google Scholar 
Kulshreshtha, S., Mathur, N. & Bhatnagar, P. Mushroom as a product and their role in mycoremediation. AMB Express 4, 1–7 (2014).Article 

Google Scholar 
Khan, I. et al. Mycoremediation of heavy metal (Cd and Cr)-polluted soil through indigenous metallotolerant fungal isolates. Environ. Monit. Assess. 191, 1–11 (2019).
Google Scholar 
Mosa, K. A., Saadoun, I., Kumar, K., Helmy, M. & Dhankher, O. P. Potential biotechnological strategies for the cleanup of heavy metals and metalloids. Front. Plant Sci. 7, 303 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Velásquez, L. & Dussan, J. Biosorption and bioaccumulation of heavy metals on dead and living biomass of Bacillus sphaericus. J. Hazard. Mater. 167, 713–716 (2009).Article 
PubMed 

Google Scholar 
Fawzy, E. M., Abdel-Motaal, F. F. & El-zayat, S. A. Biosorption of heavy metals onto different eco-friendly substrates. J. Toxicol. Environ. Health Sci. 9, 35–44 (2017).
Google Scholar 
Kumar, V. & Dwivedi, S. K. Mycoremediation of heavy metals: Processes, mechanisms, and affecting factors. Environ. Sci. Pollut. Res. 28, 10375–10412 (2021).Article 

Google Scholar 
Barros, L., Baptista, P., Estevinho, L. M. & Ferreira, I. C. Bioactive properties of the medicinal mushroom Leucopaxillus giganteus mycelium obtained in the presence of different nitrogen sources. Food Chem. 105, 179–186 (2007).Article 

Google Scholar 
Kim, H. G. et al. Phellinus linteus inhibits inflammatory mediators by suppressing redox-based NF-κB and MAPKs activation in lipopolysaccharide-induced RAW 264.7 macrophage. J. Ethnopharmacol. 114, 307–315 (2007).Article 
PubMed 

Google Scholar 
Sarikurkcu, C., Tepe, B. & Yamac, M. Evaluation of the antioxidant activity of four edible mushrooms from the Central Anatolia, Eskisehir–Turkey: Lactarius deterrimus, Suillus collitinus, Boletus edulis, Xerocomus chrysenteron. Bioresour. Technol. 99, 6651–6655 (2008).Article 
PubMed 

Google Scholar 
Giannopolitis, C.N. & Ries, S.K. Superoxide Dismutases II. Purification and quantitative relationship with water-soluble protein in seedlings.Plant Physiol. 59, 315–318 (1977).Article 
PubMed 
PubMed Central 

Google Scholar 
Dhindsa, R.S., Plumb-Dhindsa, P. & Thorpe, T.A. Leaf senescent: correlated with increased levels of membrane permeability and lipid peroxidation, and decreased levels of superoxide dismutase and catalase. J. Exp. Bot. 32, 93–101 (1981).Article 

Google Scholar 
Marx, D. H. Ectomycorrhizae as biological deterrents to pathogenic root infections. Annu. Rev. Phytopathol. 10, 429–454 (1972).Article 
PubMed 

Google Scholar 
Calvillo-Medina, R. P. Determination of fungal tolerance index to heavy metals and heavy metal resistance tests. Bio-Protoc. 11, e4218 (2021).PubMed 
PubMed Central 

Google Scholar 
Ouzounidou, G., Eleftheriou, E. & Karataglis, S. Ecophysical and ultrastructural effects of copper in Thlaspi ochroleucum (Cruciferae). Can. J. Bot. 70, 947–957 (1992).Article 

Google Scholar 
Carrillo-González, R., González-Chávez, M. & del Carmen, A. Tolerance to and accumulation of cadmium by the mycelium of the fungi Scleroderma citrinum and Pisolithus tinctorius. Biol. Trace Elem. Res. 146, 388–395 (2012).Article 
PubMed 

Google Scholar 
Mkhize, S. S., Simelane, M. B. C., Gasa, N. L. & Pooe, O. J. Evaluating the antioxidant and heavy metal content of Pleurotus ostreatus mushrooms cultivated using sugar cane agro-waste. Pharmacogn. J. 13, 1–9 (2021).Article 

Google Scholar 
Kumar, V. In Microbial Cell Factories (eds Sharma, D. & Saharan, B. S.) 149–174 (CRC Press, 2018).Chapter 

Google Scholar 
El-Sayed, M. T., Ezzat, S. M., Taha, A. S. & Ismaiel, A. A. Iron stress response and bioaccumulation potential of three fungal strains isolated from sewage-irrigated soil. J. Appl. Microbiol. 132, 1936–1953 (2022).Article 
PubMed 

Google Scholar 
Li, X. et al. Mechanisms of Cd and Cr removal and tolerance by macrofungus Pleurotus ostreatus HAU-2. J. Hazard. Mater. 330, 1–8 (2017).Article 
ADS 
PubMed 

Google Scholar 
Chuang, H.-W., Wang, I.-W., Lin, S.-Y. & Chang, Y.-L. Transcriptome analysis of cadmium response in Ganoderma lucidum. FEMS Microbiol. Lett. 293, 205–213 (2009).Article 
PubMed 

Google Scholar 
Jones, D. & Muehlchen, A. Effects of the potentially toxic metals, aluminium, zinc and copper on ectomycorrhizal fungi. J. Environ. Sci. Health A Environ. Sci. Eng. Technol. 29, 949–966 (1994).
Google Scholar 
Tam, P. C. Heavy metal tolerance by ectomycorrhizal fungi and metal amelioration by Pisolithus tinctorius. Mycorrhiza 5, 181–187 (1995).Article 

Google Scholar 
Purkayastha, R., Mitra, A. K. & Bhattacharyya, B. Uptake and toxicological effects of some heavy metals on Pleurotus sajor-caju (Fr.) Singer. Ecotoxicol. Environ. Saf. 27, 7–13 (1994).Article 
PubMed 

Google Scholar 
Akkin, N. Mycoremediation by oyster mushroom. Acta Sci. Agric. 5, 47–48 (2021).Article 

Google Scholar 
Ogbo, E. & Okhuoya, J. Bio-absorption of some heavy metals by Pleurotus tuber-regium Fr. Singer (an edible mushroom) from crude oil polluted soils amended with fertilizers and cellulosic wastes. Int. J. Soil Sci. 6, 34–48 (2011).Article 

Google Scholar 
Brunnert, H. & Zadražil, F. The translocation of mercury and cadmium into the fruiting bodies of six higher fungi. Eur. J. Appl. Microbiol. Biotechnol. 17, 358–364 (1983).Article 

Google Scholar 
Singh, V., Singh, M. P. & Mishra, V. Bioremediation of toxic metal ions from coal washery effluent. Desalin. Water Treat. 197, 300–318 (2020).Article 

Google Scholar 
Lazarova, N., Krumova, E., Stefanova, T., Georgieva, N. & Angelova, M. The oxidative stress response of the filamentous yeast Trichosporon cutaneum R57 to copper, cadmium and chromium exposure. Biotechnol. Biotechnol. Equip. 28, 855–862 (2014).Article 
PubMed 
PubMed Central 

Google Scholar  More

Study settingUganda covers 241,037 km2 (4° N, 2° S, 29° E, 35° E) and averages 1100 m above sea level25. Uganda has a total population of 34.6 million people, with 7.4 and 27.2 million living in urban and rural areas, respectively26. It borders Lake Victoria and has an equatorial climate. The study area is mainly plateau, with a few mountains. About 20 percent of Uganda’s area is covered by swamps and water bodies, including the four Great Lakes of East Africa (Lake Edward, Lake Victoria, Lake Albert, and Lake Kyoga). The country has ten national parks housing a high diversity of wildlife and endangered species26. By 2009, agriculture ranked as the second leading contributor to the country’s Gross Domestic Product27.Surveillance data of Bacillus anthracis infectionSurveillance data of livestock and human cases from 2018 were provided by the Ministry of Health (MOH) in Uganda through the Field Epidemiology and Laboratory Training Program and the Uganda Ministry of Agriculture, Animal Industry and Fisheries (MAAIF). Geographical coordinates of anthrax cases among wildlife (from 2004 to 2010) in Queen Elizabeth National Park were obtained from a recently published study4. These cases were mapped to show the distribution of anthrax across Uganda (Fig. 1). It is only recently that Uganda has mandated systematic anthrax surveillance across the country following the outbreak that started in 2018. GPS coordinates gathered by field personnel during outbreak responses were used to map outbreak locations. The human anthrax cases were defined based on the CDC’s clinical criteria (signs and symptoms), presumptive laboratory diagnosis (Gram staining), and confirmatory laboratory diagnosis (bacterial culture, immunohistochemistry, ELISA, and PCR)28. The animal anthrax cases were also defined based on the clinical presentation, presumptive laboratory diagnosis, and confirmatory laboratory diagnosis. All cases were classified as either ‘probable’ or ‘confirmed,’ with probable defined as cases that met both the clinical and presumptive laboratory diagnostic criteria and confirmed defined as cases that met the clinical and confirmatory laboratory diagnostic criteria. A total of 497 livestock (n = 171), humans (n = 32), and wildlife cases (n = 294), both confirmed (n = 32) and probable (n = 465), occurring from 2004 to 2018 were compiled. All methods were performed in accordance with the relevant guidelines and regulations.Figure 1Distribution of anthrax presence and pseudo-absence locations across Uganda. The navy-blue circles show wildlife cases (n = 294) used for model training, the blue triangles show livestock cases (n = 171), and the red diamonds represent human cases (n = 32). The blue polygons show the locations of the 50 km, 75 km, and 100 km buffers which were constructed around the wildlife cases with a distance of 10 km between the buffers and the presence locations. The pink dots show the pseudo-absence points selected within the 50 km buffer, the orange dots show the pseudo-absence points selected within the 75 km buffer, and the white dots show the pseudo-absence points selected within the 100 km buffer. Prediction maps were developed using the Quantum Geographic Information System software (QGIS). URL: https://qgis.osgeo.org (2020).Full size imageEnvironmental variable processingCorrelative studies of environmental risk factors for anthrax outbreaks suggest that temperature6,29,30,31,32,33,34,35,36,37, precipitation6,29,30,31,32,33,34,35,36,37,38,39, elevation6,29,31,32,34,35,37,38,39, soil (type, calcium concentration, pH, carbon content, and moisture)6,30,31,33,34,36,37,39,40,41,42,43, vegetation6,29,31,34,36,37,38,39,40,42,43, and hydrology37,44 are some of the major drivers of B. anthracis suitability. A total of 26 environmental predictors (Fig. 2) were selected for this study based on these known variables. These comprised 19 bioclimatic variables (the mean for the years 1970–2000) collected from the WorldClim database version 2 (https://www.worldclim.org/data/worldclim21.html) at a resolution of 30 s (~ 1 km2)45. Four soil variables, including exchangeable calcium at a depth of 0–20 cm, soil water availability, soil pH (10×) in H2O at a depth of 0 cm, and soil organic carbon at a depth of 0–5 cm, were retrieved from the International Soil Reference and Information Centre (ISRIC) data hub at a resolution of 250 m (https://data.isric.org/geonetwork/srv/eng/catalog.search#/home). Distance to permanent water bodies was derived from a global hydrology map provided by ArcGIS online version 10.6.146. Elevation data of 1 km2 in resolution was obtained from the Global Multi-resolution Terrain Elevation Data (GMTED2010) dataset available from the United States Geological Service. Finally, the monthly Enhanced Vegetation Index (EVI) data for the years 2004, 2005, and 2010 (36 tiles in total) were obtained from The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13A3 v.6) at a spatial resolution of 1 km (https://lpdaac.usgs.gov/products/myd13a3v006/). The single variable, mean EVI, was calculated in QGIS by averaging all 36 tiles. The EVI minimizes variations in the canopy background and maintains precision over conditions with dense vegetation.Figure 2Results of correlation between covariates using Pearson’s correlation test. Correlation between covariates was shown by red numbers (negative correlation) and blue numbers (positive correlation). BIO1 = Annual Mean Temperature, BIO2 = Mean Diurnal Range, BIO3 = Isothermality, BIO4 = Temperature Seasonality, BIO5 = Max Temperature of Warmest Month, BIO6 = Min Temperature of Coldest Month, BIO7 = Temperature Annual Range, BIO8 = Mean Temperature of Wettest Quarter, BIO9 = Mean Temperature of Driest Quarter, BIO10 = Mean Temperature of Warmest Quarter, BIO11 = Mean Temperature of Coldest Quarter, BIO12 = Annual Precipitation, BIO13 = Precipitation of Wettest Month, BIO14 = Precipitation of Driest Month, BIO15 = Precipitation Seasonality, BIO16 = Precipitation of Wettest Quarter, BIO17 = Precipitation of Driest Quarter, BIO18 = Precipitation of Warmest Quarter, BIO19 = Precipitation of Coldest Quarter.Full size imageAll environmental variables were resampled using the R package ‘Resample’47 to a resolution of 1 km and clipped to the extent of Uganda. Since data sampling for wildlife data (used in model training) was not done systematically across the study area, target backgrounds buffers (polygon buffers created at certain radii from the training points and used for the random selection of pseudo-absences) were created for model calibration to reduce sampling bias. As there was no information on the sampling radius, a sensitivity analysis was conducted by creating target backgrounds using circular buffers of radius 50 km, 75 km, and 100 km around the presence points used for model training, leaving 10 km between the presence points and the various buffers (Fig. 1). Quantum Global Information System (QGIS) version 3.16 (https://qgis.org) software was then used to add 294 random pseudo-absence points within each buffer polygon giving a ratio of 1:1 for the training presences to pseudo-absences. A recent study explored how four approaches of pseudo-absence creation affect the performance of models across different species and three model types by building both terrestrial and marine models using boosted regression trees, generalised additive mixed models, and generalised linear mixed models48. They then tested four methods for generating pseudoabsences across all the different model types: (1) correlated random walks (RW); (2) reverse correlated RW; (3) sampling pseudoabsences within a buffer area surrounding the presence points; (4) background sampling48. The findings of the study suggested that the separation or distance in the environmental space between the presence locations and the pseudoabsences was the most significant driver of the model predictive ability and explanatory power, and thus finding was consistent across the three model types (boosted regression trees, generalised additive mixed models, and generalised linear mixed models) and both the terrestrial and marine habitats48.The values of the environmental variables were then extracted for each presence and pseudo-absence location using the raster package in R. With these, we did an initial data exploration to check for outliers within the covariates, collinearity, and to explore the relationships between the covariates and the response variables (presence or absence of anthrax). Cleveland dot plots were used to check for possible outliers. Following the outlier checks, variance inflation factors (VIF), pairwise plots, and Pearson correlation coefficients with correction for multiple comparisons were used to measure the statistically significant correlation between the covariates (Fig. 2). For variables that were highly correlated (correlation greater than 0.6) or those with high variance inflation (VIF  > 3), only one was used in the modelling process. Five variables were selected following this analysis: Temperature seasonality (BIO4), elevation, distance to water, soil calcium, and soil water (Table 1).Table 1 Summary of the environmental variables used.Full size tableModelling anthrax suitability across UgandaQGIS v.3.16 (https://qgis.org) and the R statistical package version 4.1.049 were used to conduct data visualization, cleaning, and model analysis (R code used available in a Github repository: https://github.com/valentinandolo/Uganda-Spatial_Model/tree/master). The wildlife cases (n = 294) were used for model training and testing. The remaining human (n = 32) and livestock (n = 171) cases were used for model evaluation. Since the wildlife case locations were recorded from 2004 to 2010, while the livestock and human cases were recorded in 2018, the latter locations were both spatially and temporally distinct from the wildlife cases, making an excellent basis for block cross-validation of the final model performance50. Random partitioning of the data into training and testing sets can inflate the performance of a model and underestimate the error in the spatial prediction evaluation50. Block cross-validation uses spatial blocks that separate the testing and training datasets; thus, the method has been suggested to be a good technique for error estimation and a robust approach for measuring a model’s predictive performance50.The INLA package in R was applied to model the suitability of B. anthracis across Uganda. INLA calculates the spatial interaction effects using a Stochastic Partial Differential Equation (SPDE) method, which estimates a continuous Gaussian Markov Random Field (GMRF) where the correlation between two locations in space is specified by the Matérn correlation which is explained in more detail elsewhere51. The initial step in fitting an SPDE model is the creation of a Constrained Refined Delaunay Triangulation or a mesh to illustrate the spatial process51. R-INLA uses a function called ‘inla.mesh.2d()’ that applies a variety of arguments to build the mesh, these include: loc, loc.domain, boundary, max.edge, and cutoff51. The loc argument contains the point locations which provide information about the area of study and are used to create the triangulation nodes. Alternatively, a polygon of the study area can be used to identify the extent of the domain via loc.domain. We applied the point locations using the loc argument. We then specified the boundary of the mesh as a convex hull. We used the max.edge argument to specify the maximum edge length for the inner mesh domain/triangles and the outer triangles. We did this by first studying the distribution of distances between the point locations for the training presences and pseudo-absences. Most points were within a distance of about 90–100 km away from each other, thus, a possible guess for the range at which spatial autocorrelation persists was 100 km. We used a distance of 20 km as a range guess to create a finer mesh which has been shown to produce more precise models. We specified the maximum edge length for the inner triangles as 20 km divided by 5 (4 km) and the maximum edge length for the outer triangles as 20 km. Finally, the cutoff argument sets the minimum distance allowed between point locations. We divided the maximum edge length for the inner triangles by 5 (4 km divided by 5 = 0.8 km), meaning that points that were closer in distance than 0.8 km were replaced by one vertex to avoid the occurrence of small triangles.The spatial effect, which is a numeric vector, then links each observation within the data to a spatial location, thus, accounting for region-specific variation that cannot be accounted for by the covariates. Following the recommendation by Lindgren and Rue52, multivariate Gaussian distributions with means of zero and a spatially defined covariance matrix were used to model the spatial effect. Several versions of Bayesian hierarchical additive models were created by estimating a Bernoulli generalized additive model (GAM) with and without spatially correlated random effects. The Bernoulli GAM is defined as shown in Eqs. (1) and (2)$${C}_{i} sim Bernoulli left({p}_{i}right),$$
(1)
$$logit left({p}_{i}right)= alpha +sum_{j=1}^{m}{beta }_{j}left({X}_{j,i}right)+ sum_{k=1}^{l}{f}_{k}left({X}_{k,i}right)+ {mu }_{i},$$
(2)
where ({C}_{i}) denotes the observed value, such that: B. anthracis presence or absence at a given location i (i = 1, …, n; n = 588) is given as ({C}_{i}), where ({C}_{i}) =1 if B. anthracis was present, and ({C}_{i})=0 if absent. Logit is the link function for binomial family, ({p}_{i}) is the expected value of the response variable (the probability of B. anthracis suitability) at location i, α is the intercept, ({X}_{j,i}) and ({X}_{k,i}) are the j th and the k th covariates at a location i, ({beta }_{j}) are the beta coefficients, ({f}_{k}) are the smooth functions (cubic regression splines) for k th covariates, and ({mu }_{i}) is the spatial random effect at the location i53. The number of variables in linear term (m) and the non-liner term (l) are different because the variables employed in each term are different. We estimated both linear and non-linear effects for the covariates. Our overall database had 294 B. anthracis pseudo-absences generated randomly across the target background buffers to match the number of species presences recorded. Because we had no prior information, a Gaussian prior distribution with a mean of zero (default no effect prior unless data is informative) was applied for all the model parameters. The posterior mean, standard deviation, and 95% credible intervals were estimated for all the parameters.Model selectionSeveral different candidate models were examined. First, a baseline model was built using only the intercept. A second baseline model was then built, which included the intercept and spatial random effects only. Covariates were added to the second baseline model (intercept plus spatial effects model) without any smoothing function (i.e., only linear effects). The contribution of the spatial random effect was then re-examined by taking it out from the model. Smoothing functions were then added to all covariates, and the same procedure was repeated. Model selection was done using this forward stepwise approach. The final model was run using the three different target background buffers to identify the buffer distance with the lowest Deviance Information Criterion (DIC). The various options were assessed using the DIC54, Watanabe-Akaike information criterion (WAIC), and the Conditional Predictive Ordinate (CPO). The DIC and WAIC were chosen because they are commonly used to assess model performance by measuring the compromise between the goodness of fit and complexity. For CPO, the logarithmic score (− mean(log (CPO)) (LCPO) was calculated and used55. CPO can also be used to conduct internal cross-validation of models using a leave-one-out approach to evaluate the predictive performance of the model. Lower LCPO, DIC, and WAIC estimates suggest superior model performance. Thus, the favoured model had the lowest values across the 3 metrics.Model validation and evaluationModel validation for the favoured model was conducted using an independent evaluation dataset comprising of livestock and human outbreaks occurring at different spatial locations and 8 years after the training data. The omission rate, which indicates the proportion of positive test locations that end up in pixels predicted to be unsuitable for B. anthracis56, was used to validate the model. A low omission rate indicates good model performance. The threshold for suitability was the probability threshold that maximized the sensitivity and specificity of the model. The sensitivity was then derived by calculating the proportion of positive test locations that end up in pixels predicted to be suitable for B. anthracis56. A high sensitivity indicates good model performance.Model predictionThe favoured model selected using the criteria described above was used to generate countrywide prediction maps showing the posterior mean values, standard deviation, and the 95% credible intervals of the probability of B. anthracis suitability. The raster package in R was used to make the prediction maps. Bayesian kriging was done by treating all model parameters as random variables to include uncertainty in the prediction57. This kriging is built into the INLA framework via the SPDE, which allows a Delaunay triangulation to be constructed around the presence and absence locations within the sampling frame52. INLA then conducts model inference and prediction at the same time by considering the prediction points as locations missing the response variable (set to NA)51. Following successful model prediction, additional linear interpolation functions then generate the output for the whole study area scaled from 0 to 1.Ethical approvalEthical approval for this study was provided by the Human Biology Research Ethics Committee, University of Cambridge, UK (Ref: HBREC.2019.02) the School of Veterinary Medicine and Animal Resources Institutional Animal Care and Use Committee, Makerere University, Uganda (Ref: SVAREC/21/2019); and the Uganda National Council for Science and Technology. Informed consent was obtained from all subjects and/or their legal guardian(s). All methods were performed in accordance with the relevant guidelines and regulations. More

Thrash JC, Boyd A, Huggett MJ, Grote J, Carini P, Yoder RJ, et al. Phylogenomic evidence for a common ancestor of mitochondria and the SAR11 clade. Sci Rep. 2011;1:9.
Google Scholar 
Ferla MP, Thrash JC, Giovannoni SJ, Patrick WM. New rRNA gene-based phylogenies of the alphaproteobacteria provide perspective on major groups, mitochondrial ancestry and phylogenetic instability. PLoS One. 2013;8:e83383.
Google Scholar 
Giovannoni SJ. SAR11 bacteria: the most abundant plankton in the oceans. Annu Rev Mar Sci. 2017;9:231–55.
Google Scholar 
Zhao X, Schwartz CL, Pierson J, Giovannoni SJ, McIntosh RJ, Nicastro D. Three-dimensional structure of the ultraoligotrophic marine bacterium “Candidatus pelagibacter ubique”. Appl Environ Microbiol. 2017;83:807–16.
Google Scholar 
Giovannoni SJ, DeLong EF, Schmidt TM, Pace NR. Tangential flow filtration and preliminary phylogenetic analysis of marine picoplankton. Appl Environ Microbiol. 1990;56:4.
Google Scholar 
Morris RM, Rappé MS, Connon SA, Vergin KL, Siebold WA, Carlson CA, et al. SAR11 clade dominates ocean surface bacterioplankton communities. Nature. 2002;420:806–10.CAS 

Google Scholar 
Rappé MS, Connon SA, Vergin KL, Giovannoni SJ. Cultivation of the ubiquitous SAR11 marine bacterioplankton clade. Nature. 2002;418:630–3.
Google Scholar 
Grote J, Thrash JC, Huggett MJ, Landry ZC, Carini P, Giovannoni SJ, et al. Streamlining and core genome conservation among highly divergent members of the SAR11 clade. mBio. 2012;3:e00252–12.CAS 

Google Scholar 
Field KG, Gordon D, Wright T, Rappé M, Urback E, Vergin K, et al. Diversity and depth-specific distribution of SAR11 cluster rRNA genes from marine planktonic bacteria. Appl Environ Microbiol. 1997;63:63–70.CAS 

Google Scholar 
Suzuki MT, Beja O, Taylor LT, DeLong EF. Phylogenetic analysis of ribosomal RNA operons from uncultivated coastal marine bacterioplankton. Environ Microbiol. 2001;3:323–31.CAS 

Google Scholar 
Carlson CA, Morris R, Parsons R, Treusch AH, Giovannoni SJ, Vergin K. Seasonal dynamics of SAR11 populations in the euphotic and mesopelagic zones of the northwestern Sargasso Sea. ISME J. 2009;3:283–95.CAS 

Google Scholar 
Brown MV, Lauro FM, DeMaere MZ, Muir L, Wilkins D, Thomas T, et al. Global biogeography of SAR11 marine bacteria. Mol Syst Biol. 2012;8:595.
Google Scholar 
Haro‐Moreno JM, Rodriguez‐Valera F, Rosselli R, Martinez‐Hernandez F, Roda‐Garcia JJ, Gomez ML, et al. Ecogenomics of the SAR11 clade. Environ Microbiol. 2020;22:1748–63.
Google Scholar 
Carini P, White AE, Campbell EO, Giovannoni SJ. Methane production by phosphate-starved SAR11 chemoheterotrophic marine bacteria. Nat Commun. 2014;5:4346.CAS 

Google Scholar 
Sun J, Steindler L, Thrash JC, Halsey KH, Smith DP, Carter AE, et al. One carbon metabolism in SAR11 Pelagic marine bacteria. PLoS One. 2011;6:e23973.CAS 

Google Scholar 
Schwalbach MS, Tripp HJ, Steindler L, Smith DP, Giovannoni SJ. The presence of the glycolysis operon in SAR11 genomes is positively correlated with ocean productivity. Environ Microbiol. 2010;12:490–500.CAS 

Google Scholar 
Sun J, Todd JD, Thrash JC, Qian Y, Qian MC, Temperton B, et al. The abundant marine bacterium Pelagibacter simultaneously catabolizes dimethylsulfoniopropionate to the gases dimethyl sulfide and methanethiol. Nat Microbiol. 2016;1:16065.CAS 

Google Scholar 
Halsey KH, Giovannoni SJ, Graus M, Zhao Y, Landry Z, Thrash JC, et al. Biological cycling of volatile organic carbon by phytoplankton and bacterioplankton: VOC cycling by marine plankton. Limnol Oceanogr. 2017;62:2650–61.CAS 

Google Scholar 
Carlson CA, Giovannoni SJ, Hansell DA, Goldberg SJ, Parsons R, Vergin K. Interactions among dissolved organic carbon, microbial processes, and community structure in the mesopelagic zone of the northwestern Sargasso Sea. Limnol Oceanogr. 2004;49:1073–83.CAS 

Google Scholar 
Wagner S, Schubotz F, Kaiser K, Hallmann C, Waska H, Rossel PE, et al. Soothsaying DOM: a current perspective on the future of oceanic dissolved organic carbon. Front Mar Sci. 2020;7:341.
Google Scholar 
Quinn PK, Bates TS. The case against climate regulation via oceanic phytoplankton sulphur emissions. Nature. 2011;480:51–6.CAS 

Google Scholar 
Bolaños LM, Choi CJ, Worden AZ, Baetge N, Carlson CA, Giovannoni S. Seasonality of the microbial community composition in the North Atlantic. Front Mar Sci. 2021;8:624164.
Google Scholar 
Tucker SJ, Freel KC, Monaghan EA, Sullivan CES, Ramfelt O, Rii YM, et al. Spatial and temporal dynamics of SAR11 marine bacteria across a nearshore to offshore transect in the tropical Pacific Ocean. PeerJ. 2021;9:e12274.
Google Scholar 
Giovannoni SJ, Vergin KL. Seasonality in ocean microbial communities. Science. 2012;335:671–6.CAS 

Google Scholar 
Eren AM, Maignien L, Sul WJ, Murphy LG, Grim SL, Morrison HG, et al. Oligotyping: differentiating between closely related microbial taxa using 16S RRNA gene data. Methods Ecol Evol. 2013;4:1111–9.
Google Scholar 
Vergin K, Done B, Carlson C, Giovannoni S. Spatiotemporal distributions of rare bacterioplankton populations indicate adaptive strategies in the oligotrophic ocean. Aquat Microb Ecol. 2013;71:1–13.
Google Scholar 
Salter I, Galand PE, Fagervold SK, Lebaron P, Obernosterer I, Oliver MJ, et al. Seasonal dynamics of active SAR11 ecotypes in the oligotrophic Northwest Mediterranean Sea. ISME J. 2015;9:347–60.CAS 

Google Scholar 
Ortmann AC, Santos TTL. Spatial and temporal patterns in the Pelagibacteraceae across an estuarine gradient. FEMS Microbiol Ecol. 2016;92:fiw133.
Google Scholar 
Vergin KL, Beszteri B, Monier A, Cameron Thrash J, Temperton B, Treusch AH, et al. High-resolution SAR11 ecotype dynamics at the Bermuda Atlantic Time-series Study site by phylogenetic placement of pyrosequences. ISME J. 2013;7:1322–32.CAS 

Google Scholar 
Needham DM, Fichot EB, Wang E, Berdjeb L, Cram JA, Fichot CG, et al. Dynamics and interactions of highly resolved marine plankton via automated high-frequency sampling. ISME J. 2018;12:2417–32.CAS 

Google Scholar 
Benway HM, Lorenzoni L, White AE, Fiedler B, Levine NM, Nicholson DP, et al. Ocean time series observations of changing marine ecosystems: an era of integration, synthesis, and societal applications. Front Mar Sci. 2019;12:6–393.
Google Scholar 
Steinberg DK, Carlson CA, Bates NR, Johnson RJ, Michaels AF, Knap AH. Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): a decade-scale look at ocean biology and biogeochemistry. Deep Sea Res Part II Top Stud Oceanogr. 2001;48:1405–47.CAS 

Google Scholar 
Southward AJ, Langmead O, Hardman-Mountford NJ, Aiken J, Boalch GT, Dando PR, et al. Long-term oceanographic and ecological research in the Western English Channel. In: Advances in marine biology. Elsevier. 2005;47:1–105.Gilbert JA, Field D, Swift P, Newbold L, Oliver A, Smyth T, et al. The seasonal structure of microbial communities in the Western English Channel. Environ Microbiol. 2009;11:3132–9.CAS 

Google Scholar 
Gilbert JA, Steele JA, Caporaso JG, Steinbrück L, Reeder J, Temperton B, et al. Defining seasonal marine microbial community dynamics. ISME J. 2012;6:298–308.CAS 

Google Scholar 
Caporaso JG, Paszkiewicz K, Field D, Knight R, Gilbert JA. The Western English Channel contains a persistent microbial seed bank. ISME J. 2012;6:1089–93.CAS 

Google Scholar 
Warwick-Dugdale J, Solonenko N, Moore K, Chittick L, Gregory AC, Allen MJ, et al. Long-read viral metagenomics captures abundant and microdiverse viral populations and their niche-defining genomic islands. PeerJ. 2019;7:e6800.
Google Scholar 
Vergin KL, Done B, Carlson CA, Giovannoni SJ. Spatiotemporal distributions of rare bacterioplankton populations indicate adaptive strategies in the oligotrophic ocean. Aquat Microb Ecol. 2013;71:1–3.
Google Scholar 
Choi CJ, Jimenez V, Needham DM, Poirier C, Bachy C, Alexander H, et al. Seasonal and geographical transitions in eukaryotic phytoplankton community structure in the Atlantic and Pacific Oceans. Front Microbiol. 2020;11:542372.
Google Scholar 
Bolaños LM, Karp-Boss L, Choi CJ, Worden AZ, Graff JR, Haëntjens N, et al. Small phytoplankton dominate western North Atlantic biomass. ISME J. 2020;14:1663–74.
Google Scholar 
Matsen FA, Kodner RB, Armbrust E. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform. 2010;11:1–6.
Google Scholar 
Treusch AH, Vergin KL, Finlay LA, Donatz MG, Burton RM, Carlson CA, et al. Seasonality and vertical structure of microbial communities in an ocean gyre. ISME J. 2009;3:1148–63.
Google Scholar 
Giovannoni SJ, Rappe MS, Vergin KL, Adair NL. 16S rRNA genes reveal stratified open ocean bacterioplankton populations related to the Green Non-Sulfur bacteria. Proc Natl Acad Sci. 1996;93:7979–84.CAS 

Google Scholar 
Morris RM, Vergin KL, Cho J-C, Rappé MS, Carlson CA, Giovannoni SJ. Temporal and spatial response of bacterioplankton lineages to annual convective overturn at the Bermuda Atlantic Time-series Study site. Limnol Oceanogr. 2005;50:1687–96.CAS 

Google Scholar 
Daims H, Brühl A, Amann R, Schleifer K-H, Wagner M. The domain-specific probe EUB338 is insufficient for the detection of all bacteria: development and evaluation of a more comprehensive probe set. Syst Appl Microbiol. 1999;22:434–44.CAS 

Google Scholar 
Lane DJ. Nucleic acid techniques in bacterial systematics. In: Nucleic acid techniques in bacterial systematics. New York: Wiley; p. 115–75.Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 2017;11:2639–43.
Google Scholar 
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8:e61217.CAS 

Google Scholar 
Eren AM, Borisy GG, Huse SM, Mark Welch JL. Oligotyping analysis of the human oral microbiome. Proc Natl Acad Sci. 2014;111:E2875–84.CAS 

Google Scholar 
Buchholz HH, Michelsen ML, Bolaños LM, Browne E, Allen MJ, Temperton B. Efficient dilution-to-extinction isolation of novel virus–host model systems for fastidious heterotrophic bacteria. ISME J. 2021;15:1585–98.CAS 

Google Scholar 
R Core Team. R: a language and environment for statistical computing. Vienna, Austria; https://www.R-project.org/Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. Package “vegan”.Wickham H. ggplot2: ggplot2. Wiley Interdiscip Rev Comput Stat. 2011;3:180–5.
Google Scholar 
Wang W, Yan J. Shape-restricted regression splines with R package splines2. J Data Sci. 2021;19:498–517.
Google Scholar 
Auladell A, Sánchez P, Sánchez O, Gasol JM, Ferrera I. Long-term seasonal and interannual variability of marine aerobic anoxygenic photoheterotrophic bacteria. ISME J. 2019;13:1975–87.CAS 

Google Scholar 
Ahdesmaki M, Fokianos K, Strimmer K, Ahdesmaki MM. Package ‘GeneCycle’ 2015.Roesch A, Schmidbauer H and Roesch MA. Package ‘WaveletComp.’ 2014.Lomas MW, Bates NR, Johnson RJ, Knap AH, Steinberg DK, Carlson CA. Two decades and counting: 24-years of sustained open ocean biogeochemical measurements in the Sargasso Sea. Deep Sea Res Part II Top Stud Oceanogr. 2013;93:16–32.CAS 

Google Scholar 
Lomas MW, Bates NR, Johnson RJ, Steinberg DK, Tanioka T. Adaptive carbon export response to warming in the Sargasso Sea. Nature Commun. 2022;13:1–0.
Google Scholar 
Sargeant SL, Murrell JC, Nightingale PD, Dixon JL. Basin-scale variability of microbial methanol uptake in the Atlantic Ocean. Biogeosciences. 2018;15:5155–67.CAS 

Google Scholar 
Smyth TJ, Allen I, Atkinson A, Bruun JT, Harmer RA, Pingree RD, et al. Ocean net heat flux influences seasonal to interannual patterns of plankton abundance. PLoS One. 2014;9:e98709.
Google Scholar 
Van de Peer Y. A quantitative map of nucleotide substitution rates in bacterial rRNA. Nucleic Acids Res. 1996;24:3381–91.
Google Scholar 
Baker GC, Smith JJ, Cowan DA. Review and re-analysis of domain-specific 16S primers. J Microbiol Methods. 2003;55:541–55.CAS 

Google Scholar 
Vasileiadis S, Puglisi E, Arena M, Cappa F, Cocconcelli PS, Trevisan M. Soil bacterial diversity screening using single 16S rRNA gene V regions coupled with multi-million read generating sequencing technologies. PLoS ONE. 2012;7:e42671.CAS 

Google Scholar 
Stingl U, Tripp HJ, Giovannoni SJ. Improvements of high-throughput culturing yielded novel SAR11 strains and other abundant marine bacteria from the Oregon coast and the Bermuda Atlantic Time-series study site. ISME J. 2007;1:361–71.CAS 

Google Scholar 
Delmont TO, Kiefl E, Kilinc O, Esen OC, Uysal I, Rappé MS, et al. Single-amino acid variants reveal evolutionary processes that shape the biogeography of a global SAR11 subclade. eLife. 2019;8:e46497.
Google Scholar 
Lévy M, Jahn O, Dutkiewicz S, Follows MJ, d’Ovidio F. The dynamical landscape of marine phytoplankton diversity. J R Soc Interface. 2015;12:20150481.
Google Scholar 
Hellweger FL, van Sebille E, Calfee BC, Chandler JW, Zinser ER, Swan BK, et al. The role of ocean currents in the temperature selection of plankton: insights from an individual-based model. PLoS ONE. 2016;11:e0167010.
Google Scholar 
Giovannoni SJ, Tripp HJ, Givan S, Podar M, Vergin KL, Baptista D, et al. Genome streamlining in a cosmopolitan oceanic bacterium. Science. 2005;309:1242–5.CAS 

Google Scholar 
Brown SN, Giovannoni S, Cho JC. Polyphasic taxonomy of marine bacteria from the SAR11 group Ia: Pelagibacter ubiquis (strain HTCC1062) & Pelagibacter bermudensis (strain HTCC7211). Oregon State University; 2012.Auladell A, Barberán A, Logares R, Garcés E, Gasol JM, Ferrera I. Seasonal niche differentiation among closely related marine bacteria. ISME J. 2022;16:178–89.CAS 

Google Scholar 
Tsementzi D, Wu J, Deutsch S, Nath S, Rodriguez-R LM, Burns AS, et al. SAR11 bacteria linked to ocean anoxia and nitrogen loss. Nature. 2016;536:179–83.CAS 

Google Scholar 
Ruiz-Perez CA, Bertagnolli AD, Tsementzi D, Woyke T, Stewart FJ, Konstantinidis KT. Description of Candidatus Mesopelagibacter carboxydoxydans and Candidatus Anoxipelagibacter denitrificans: nitrate-reducing SAR11 genera that dominate mesopelagic and anoxic marine zones. Syst Appl Microbiol. 2021;44:126185.CAS 

Google Scholar 
Yeh YC, Fuhrman JA. Contrasting diversity patterns of prokaryotes and protists over time and depth at the San-Pedro Ocean Time series. ISME Commun. 2022;13:1–12.
Google Scholar 
McCarthy M, Spillane S, Walsh S, Kendon M. The meteorology of the exceptional winter of 2015/2016 across the UK and Ireland. Weather. 2016;71:305–13.
Google Scholar 
Met Office. UK Climate Projections: Headline Findings. 2021. More

United Nations. Transforming our world: the 2030 Agenda for Sustainable Development. General Assembly https://doi.org/10.5040/9781782257790.part-008 (2015).Article 

Google Scholar 
European Commission. Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Off. J. Eur. Communities L327, 1–72 (2000).
Google Scholar 
Kelly, R. P. et al. Harnessing DNA to improve environmental management. Science (80-) 344, 1455–1456 (2014).ADS 
CAS 

Google Scholar 
Bálint, M. et al. Cryptic biodiversity loss linked to global climate change. Nat. Clim. Chang. 1, 313–318 (2011).ADS 

Google Scholar 
Bohmann, K. et al. Environmental DNA for wildlife biology and biodiversity monitoring. Trends Ecol. Evol. 29, 358–367 (2014).PubMed 

Google Scholar 
Deiner, K. et al. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol. Ecol. 26, 5872–5895 (2017).PubMed 

Google Scholar 
Ficetola, G. F., Miaud, C., Pompanon, F. & Taberlet, P. Species detection using environmental DNA from water samples. Biol. Lett. 4, 423–425 (2008).PubMed 
PubMed Central 

Google Scholar 
Yang, J., Jeppe, K., Pettigrove, V. & Zhang, X. Environmental DNA metabarcoding supporting community assessment of environmental stressors in a field-based sediment microcosm study. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.8b04903 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
DiBattista, J. D. et al. Environmental DNA can act as a biodiversity barometer of anthropogenic pressures in coastal ecosystems. Sci. Rep. 10, 1–15 (2020).
Google Scholar 
Ministry of the Environment. Manual for Water Quality Assessment Method by Aquatic Organisms -Japanese version of average score method-. (2017).Mayama, S. Taxonomic revisions to the differentiating diatom groups for water quality evaluation and some comments for taxa with new designations. Diatom 15, 1–9 (1994).
Google Scholar 
Kobayashi, H. & Mayama, S. Evaluation of river water quality by diatoms. Korean J. Phycol. 4, 121–133 (1989).
Google Scholar 
European Commission. Technical Guidance Document on Risk Assessment Part II. (2003).Wang, P. et al. Environmental DNA: An emerging tool in ecological assessment. Bull. Environ. Contam. Toxicol. 103, 651–656 (2019).CAS 
PubMed 

Google Scholar 
Zhang, X. Environmental DNA shaping a new era of ecotoxicological research. Environ. Sci. Technol. 53, 5605–5612 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Cristescu, M. E. & Hebert, P. D. N. Uses and misuses of environmental DNA in biodiversity science and conservation. Annu. Rev. Ecol. Evol. Syst. 49, 209–230 (2018).
Google Scholar 
Ficetola, G. F., Taberlet, P. & Coissac, E. How to limit false positives in environmental DNA and metabarcoding?. Mol. Ecol. Resour. 16, 604–607 (2016).CAS 
PubMed 

Google Scholar 
Yates, M. C., Derry, A. M. & Cristescu, M. E. Environmental RNA: A revolution in ecological resolution?. Trends Ecol. Evol. 36, 601–609 (2021).CAS 
PubMed 

Google Scholar 
Veilleux, H. D., Misutka, M. D. & Glover, C. N. Environmental DNA and environmental RNA: Current and prospective applications for biological monitoring. Sci. Total Environ. 782, 146891 (2021).ADS 
CAS 
PubMed 

Google Scholar 
Cristescu, M. E. Can Environmental RNA Revolutionize Biodiversity Science?. Trends Ecol. Evol. 34, 694–697 (2019).PubMed 

Google Scholar 
Qian, T., Shan, X., Wang, W. & Jin, X. Effects of Temperature on the Timeliness of eDNA/eRNA: A Case Study of Fenneropenaeus chinensis. Water (Switzerland) 14, 1155 (2022).CAS 

Google Scholar 
Jo, T., Tsuri, K., Hirohara, T., Yamanaka, H. & Toshiaki Jo, C. Warm temperature and alkaline conditions accelerate environmental RNA degradation. Environ. DNA 00, 1–13 (2022).
Google Scholar 
Miyata, K. et al. Fish environmental RNA enables precise ecological surveys with high positive predictivity. Ecol. Indic. 128, 107796 (2021).
Google Scholar 
Littlefair, J. E., Rennie, M. D. & Cristescu, M. E. Environmental nucleic acids: a field-based comparison for monitoring freshwater habitats using eDNA and eRNA. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.13671 (2022).Article 
PubMed 

Google Scholar 
Broman, E., Bonaglia, S., Norkko, A., Creer, S. & Nascimento, F. J. A. High throughput shotgun sequencing of eRNA reveals taxonomic and derived functional shifts across a benthic productivity gradient. Mol. Ecol. https://doi.org/10.1111/mec.15561 (2020).Article 
PubMed 

Google Scholar 
Miya, M. et al. Use of a filter cartridge for filtration of water samples and extraction of environmental DNA. J. Vis. Exp. https://doi.org/10.3791/54741 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Oe, S., Sashika, M., Fujimoto, A., Shimozuru, M. & Tsubota, T. Predation impacts of invasive raccoons on rare native species. Sci. Rep. 10, 1–12 (2020).
Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
MLIT (Ministry of Land Infrastructure and Transport). IV Benthic invertebrate. Manual of National Census of the River Environment (River Edition) (in Japanese). http://www.nilim.go.jp/lab/fbg/ksnkankyo/mizukokuweb/system/DownLoad/H28KK_manual_river/H28KK_02.teisei.pdf (2016).Hleap, J. S., Littlefair, J. E., Steinke, D., Hebert, P. D. N. & Cristescu, M. E. Assessment of current taxonomic assignment strategies for metabarcoding eukaryotes. Mol. Ecol. Resour. 21, 2190–2203 (2021).PubMed 

Google Scholar 
Jones, E. P. et al. Guidance for end users on DNA methods development and project assessment. JNCC Report (2020).Littlefair, J. E., Rennie, M. D. & Cristescu, M. E. Environmental nucleic acids: A field-based comparison for monitoring freshwater habitats using eDNA and eRNA. Mol. Ecol. Resour. 22, 2928–2940. https://doi.org/10.1111/1755-0998.13671 (2022).Article 
CAS 
PubMed 

Google Scholar 
MLIT (Ministry of Land Infrastructure and Transport). River Environmental Database (in Japanese). http://www.nilim.go.jp/lab/fbg/ksnkankyo/ (2018).Kitahashi, T. et al. Meiofaunal diversity at a seamount in the Pacific Ocean: A comprehensive study using environmental DNA and RNA. Deep. Res. Part I Oceanogr. Res. Pap. 160, 103253. https://doi.org/10.1016/j.dsr.2020.103253 (2020).Article 

Google Scholar 
Brandt, M. I. et al. An assessment of environmental metabarcoding protocols aiming at favoring contemporary biodiversity in inventories of deep-sea communities. Front. Mar. Sci. 7, 234 (2020).
Google Scholar 
Laroche, O. et al. A cross-taxa study using environmental DNA / RNA metabarcoding to measure biological impacts of off shore oil and gas drilling and production operations. Mar. Pollut. Bull. 127, 97–107 (2018).CAS 
PubMed 

Google Scholar 
Laroche, O. et al. Metabarcoding monitoring analysis: The pros and cons of using co-extracted environmental DNA and RNA data to assess offshore oil production impacts on benthic communities. PeerJ 5, e3347 (2017).PubMed 
PubMed Central 

Google Scholar 
Pochon, X. et al. Wanted dead or alive ? Using metabarcoding of environmental DNA and RNA to distinguish living assemblages for biosecurity applications. PLoS ONE 12, 1–19 (2017).
Google Scholar 
Foley, C. J., Bradley, D. L. & Höök, T. O. A review and assessment of the potential use of RNA: DNA ratios to assess the condition of entrained fish larvae. Ecol. Indic. 60, 346–357 (2016).CAS 

Google Scholar 
Guardiola, M. et al. Spatio-temporal monitoring of deep-sea communities using metabarcoding of sediment DNA and RNA. PeerJ 4, 1–31 (2016).
Google Scholar 
Keeley, N., Wood, S. A. & Pochon, X. Development and preliminary validation of a multi-trophic metabarcoding biotic index for monitoring benthic organic enrichment. Ecol. Indic. 85, 1044–1057 (2018).CAS 

Google Scholar 
Whangbo, J. S. & Hunter, C. P. Environmental RNA interference. Trends Genet. 24, 297–305 (2008).CAS 
PubMed 

Google Scholar 
Sidova, M., Tomankova, S., Abaffy, P., Kubista, M. & Sindelka, R. Effects of post-mortem and physical degradation on RNA integrity and quality. Biomol. Detect. Quantif. 5, 3–9 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wood, S. A. et al. Release and degradation of environmental DNA and RNA in a marine system. Sci. Total Environ. 704, 135314 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Watanabe, T. Picture Book and Ecology of the Freshwater Diatoms (UCHIDA ROKAKUHO PUBLISHING CO., LTD., 2005).
Google Scholar 
Laroche, O. et al. First evaluation of foraminiferal metabarcoding for monitoring environmental impact from an offshore oil drilling site. Mar. Environ. Res. 120, 225–235 (2016).CAS 
PubMed 

Google Scholar 
Andruszkiewicz Allan, E. et al. Environmental DNA shedding and decay rates from diverse animal forms and thermal regimes. Environ. DNA 3, 492–514 (2021).
Google Scholar 
Hajibabaei, M. et al. Watered-down biodiversity? A comparison of metabarcoding results from DNA extracted from matched water and bulk tissue biomonitoring samples. PLoS ONE 14, e0225409 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
the Orthopterological Society of Japan. Orthoptera of the Japanese archipelago in color (Hokkaido University Press, 2006).Li, Z. H. et al. Enzymatic alterations and RNA/DNA ratio in intestine of rainbow trout, Oncorhynchus mykiss, induced by chronic exposure to carbamazepine. Ecotoxicology 19, 872–878 (2010).CAS 
PubMed 

Google Scholar 
Chícharo, M. A. & Chícharo, L. RNA:DNA ratio and other nucleic acid derived indices in marine ecology. Int. J. Mol. Sci. 9, 1453–1471 (2008).PubMed 
PubMed Central 

Google Scholar 
Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).CAS 
PubMed 

Google Scholar 
Hebert, P. D. N., Stoeckle, M. Y., Zemlak, T. S. & Francis, C. M. Identification of birds through DNA barcodes. PLoS Biol. 2, e312 (2004).PubMed 
PubMed Central 

Google Scholar 
Takenaka, M., Yano, K., Suzuki, T. & Tojo, K. Development of novel PCR primer sets for DNA metabarcoding of aquatic insects, and the discovery of some cryptic species. bioRxiv https://doi.org/10.1101/2021.11.05.467390 (2021).Article 

Google Scholar 
Elbrecht, V. et al. Testing the potential of a ribosomal 16S marker for DNA metabarcoding of insects. PeerJ 4, 1966 (2016).
Google Scholar 
Leese, F. et al. Improved freshwater macroinvertebrate detection from environmental DNA through minimized nontarget amplification. Environ. DNA 3, 261–276 (2021).CAS 

Google Scholar 
Hajibabaei, M., Porter, T. M., Wright, M. & Rudar, J. COI metabarcoding primer choice affects richness and recovery of indicator taxa in freshwater systems. PLoS ONE 14, e0220953 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Elbrecht, V. et al. Validation of COI metabarcoding primers for terrestrial arthropods. PeerJ 7, 7745 (2019).
Google Scholar 
Elbrecht, V. & Leese, F. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass-sequence relationships with an innovative metabarcoding protocol. PLoS ONE 10, e0130324 (2015).PubMed 
PubMed Central 

Google Scholar 
Marquina, D., Andersson, A. F. & Ronquist, F. New mitochondrial primers for metabarcoding of insects, designed and evaluated using in silico methods. Mol. Ecol. Resour. 19, 90–104 (2019).CAS 
PubMed 

Google Scholar 
Tochigi prefectural government. Results of continuous monitoring and measurement [Water quality] in Japanese. https://www.pref.tochigi.lg.jp/d03/eco/kankyou/hozen/joujikanshikekka-mizu.html (2020).Emilson, C. E. et al. DNA metabarcoding and morphological macroinvertebrate metrics reveal the same changes in boreal watersheds across an environmental gradient. Sci. Rep. 7, 1–11 (2017).CAS 

Google Scholar 
Uchida, N., Kubota, K., Aita, S. & Kazama, S. Aquatic insect community structure revealed by eDNA metabarcoding derives indices for environmental assessment. PeerJ 2020, e9176 (2020).
Google Scholar 
Baird, D. J. & Hajibabaei, M. Biomonitoring 2.0: a new paradigm in ecosystem assessment made possible by next-generation DNA sequencing. Mol. Ecol. 21, 2039–2044 (2012).PubMed 

Google Scholar  More

Gorb, E. V. & Gorb, S. N. Anti-adhesive effects of plant wax coverage on insect attachment. J. Exp. Bot. 68, 5323–5337 (2017).CAS 
PubMed 

Google Scholar 
Agrawal, A. A. & Konno, K. Latex: A model for understanding mechanisms, ecology, and evolution of plant defense against herbivory. Annu. Rev. Ecol. Evol. Syst. 40, 311–331 (2009).
Google Scholar 
Langenheim, J. H. Plant resins. Am. Sci. 78, 16–24 (1990).
Google Scholar 
Ben-Mahmoud, S. et al. Acylsugar amount and fatty acid profile differentially suppress oviposition by western flower thrips, Frankliniella occidentalis, on tomato and interspecific hybrid flowers. PLoS ONE 13, 1–20 (2018).
Google Scholar 
LoPresti, E. F., Pearse, I. S. & Charles, G. K. The siren song of a sticky plant: Columbines provision mutualist arthropods by attracting and killing passerby insects. Ecology 96, 2862–2869 (2015).CAS 
PubMed 

Google Scholar 
Weinhold, A. & Baldwin, I. T. Trichome-derived O-acyl sugars are a first meal for caterpillars that tags them for predation. Proc. Natl. Acad. Sci. 108, 7855–7859 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Krimmel, B. A. & Wheeler, A. G. Host-plant stickiness disrupts novel ant–mealybug association. Arthropod. Plant. Interact. 9, 187–195 (2015).
Google Scholar 
Simmons, A. T., Gurr, G. M., McGrath, D., Martin, P. M. & Nicol, H. I. Entrapment of Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) on glandular trichomes of Lycopersicon species. Aust. J. Entomol. 43, 196–200 (2004).
Google Scholar 
Carter, C. D., Gianfagna, T. J. & Sacalis, J. N. Sesquiterpenes in glandular trichomes of a wild tomato species and toxicity to the colorado potato beetle. J. Agric. Food Chem. 37, 1425–1428 (1989).CAS 

Google Scholar 
Van Dam, N. M. & Hare, J. D. Biological activity of Datura wrightii glandular trichome exudate against Manduca sexta larvae. J. Chem. Ecol. 24, 1529–1549 (1998).
Google Scholar 
Kessler, A. & Heil, M. The multiple faces of indirect defences and their agents of natural selection. Funct. Ecol. 25, 348–357 (2011).
Google Scholar 
Karban, R., LoPresti, E., Pepi, A. & Grof-Tisza, P. Induction of the sticky plant defense syndrome in wild tobacco. Ecology 100, 1–9 (2019).
Google Scholar 
Krimmel, B. A. & Pearse, I. S. Sticky plant traps insects to enhance indirect defence. Ecol. Lett. 16, 219–224 (2013).CAS 
PubMed 

Google Scholar 
Eisner, T. & Aneshansley, D. J. Adhesive strength of the insect-trapping glue of a plant (Befaria racemosa). Ann. Entomol. Soc. Am. 76, 295–298 (1983).
Google Scholar 
Spomer, G. G. Evidence of protocarnivorous capabilities in Geranium viscosissimum and Potentilla arguta and other sticky plants. Int. J. Plant Sci. 160, 98–101 (1999).
Google Scholar 
Darnowski, D. W., Carroll, D. M., Płachno, B., Kabanoff, E. & Cinnamon, E. Evidence of protocarnivory in triggerplants (Stylidium spp.; Stylidiaceae). Plant Biol. 8, 805–812 (2006).CAS 
PubMed 

Google Scholar 
Givnish, T. J., Burkhardt, E. L., Happel, R. E. & Weintraub, J. D. Carnivory in the bromeliad Brocchinia reducta, with a cost/benefit model for the general restriction of carnivorous plants to sunny, moist nutrient-poor habitats. Am. Nat. 124, 479–497 (1984).
Google Scholar 
Jürgens, N. Psammophorous plants and other adaptations to desert ecosystems with high incidence of sandstorms. Feddes Repert. 107, 345–359 (1996).
Google Scholar 
Lopresti, E. F. & Karban, R. Chewing sandpaper: Grit, plant apparency, and plant defense in sand-entrapping plants. Ecology 97, 826–833 (2016).PubMed 

Google Scholar 
Krupnick, G. A. & Weis, A. E. The effect of floral herbivory on male and female reproductive success in Isomeris arborea. Ecology 80, 135–149 (1999).
Google Scholar 
McCall, A. C. Florivory affects pollinator visitation and female fitness in Nemophila menziesii. Oecologia 155, 729–737 (2008).ADS 
PubMed 

Google Scholar 
Bandeili, B. & Müller, C. Folivory versus florivory-adaptiveness of flower feeding. Naturwissenschaften 97, 79–88 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Lai, D. et al. Lotus japonicus flowers are defended by a cyanogenic β-glucosidase with highly restricted expression to essential reproductive organs. Plant Mol. Biol. 89, 21–34 (2015).CAS 
PubMed 

Google Scholar 
Kessler, A. & Halitschke, R. Testing the potential for conflicting selection on floral chemical traits by pollinators and herbivores: Predictions and case study. Funct. Ecol. 23, 901–912 (2009).
Google Scholar 
Kessler, D., Diezel, C., Clark, D. G., Colquhoun, T. A. & Baldwin, I. T. Petunia flowers solve the defence/apparency dilemma of pollinator attraction by deploying complex floral blends. Ecol. Lett. 16, 299–306 (2013).PubMed 

Google Scholar 
Li, J. et al. Defense of pyrethrum flowers: Repelling herbivores and recruiting carnivores by producing aphid alarm pheromone. New Phytol. 223, 1607–1620 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kennedy, G. G. Tomato, pests, parasitoids, and predators: tritrophic interactions involving the genus Lycopersicon. Annu. Rev. Entomol. 48, 51–72 (2003).CAS 
PubMed 

Google Scholar 
McCarren, S., Coetzee, A. & Midgley, J. Corolla stickiness prevents nectar robbing in Erica. J. Plant Res. https://doi.org/10.1007/s10265-021-01299-z (2021).Article 
PubMed 

Google Scholar 
Matulevich Peláez, J. A., Gil Archila, E. & Ospina Giraldo, L. F. Estudio fitoquímico de hojas, flores y frutos de Bejaria resinosa mutis ex linné filius (ericaceae) y evaluación de su actividad antiinflamatoria. Rev. Cuba. Plantas Med. 21, 332–345 (2016).
Google Scholar 
Kraemer, M. On the pollination of Bejaria resinosa Mutis ex Linne f. ( Ericaceae ), an ornithophilous Andean paramo shrub. Flora 196, 59–62 (2001).
Google Scholar 
Melampy, A. M. N. Flowering phenology, pollen flow and fruit production in the Andean Shrub Befaria resinosa. Oecologia 73, 293–300 (1987).ADS 
CAS 
PubMed 

Google Scholar 
LoPresti, E. F., Robinson, M. L., Krimmel, B. A. & Charles, G. K. The sticky fruit of manzanita: potential functions beyond epizoochory. Ecology 99, 2128–2130 (2018).PubMed 

Google Scholar 
Kessler, A. & Chautá, A. The ecological consequences of herbivore-induced plant responses on plant-pollinator interactions. Emerg. Topics Life Sci. 4, 33–43 (2020).
Google Scholar 
Lucas-Barbosa, D. Integrating studies on plant-pollinator and plant-herbivore interactions. Trends Plant Sci. 21, 125–133 (2016).CAS 
PubMed 

Google Scholar 
Leckie, B. M. et al. Differential and synergistic functionality of acylsugars in suppressing oviposition by insect herbivores. PLoS ONE 11, 1–19 (2016).
Google Scholar 
Monteiro, R. F. & Macedo, M. V. First report on the diversity of insects trapped by a sticky exudate of the inflorescences of Vriesea bituminosa Wawra (Bromeliaceae: Tillandsioideae). Arthropod. Plant. Interact. 8, 519–523 (2014).
Google Scholar 
Chatzivasileiadis, E. A. & Sabelis, M. W. Toxicity of methyl ketones from tomato trichomes to Tetranychus urticae Koch. Exp. Appl. Acarol. 21, 473–484 (1997).CAS 

Google Scholar 
Avé, D. A., Gregory, P. & Tingey, W. M. Aphid repellent sesquiterpenes in glandular trichomes of Solanum berthaultii and S. tuberosum. Entomol. Exp. Appl. 44, 131–138 (1987).
Google Scholar 
LoPresti, E. Columbine pollination success not determined by a proteinaceous reward to hummingbird pollinators. J. Pollinat. Ecol. 20, 35–39 (2017).
Google Scholar 
Krimmel, B. A. & Pearse, I. S. Generalist and sticky plant specialist predators suppress herbivores on a sticky plant. Arthropod. Plant. Interact. 8, 403–410 (2014).
Google Scholar 
Adlassnig, W., Lendl, T., Peroutka, M. & Lang, I. Deadly glue- Adhesive traps of carnivorous plants. in Biological Adhesive Systems (eds. von Byren, J. & Grunwald, I.) 15–28 (2010).Ellison, A. M. & Gotelli, N. J. Evolutionary ecology of carnivorous plants. Trends Ecol. Evol. 16, 623–629 (2001).
Google Scholar 
Maloof, J. E. & Inouye, D. W. Are nectar robbers cheaters or mutualists?. Ecology 81, 2651–2661 (2000).
Google Scholar 
Asai, T., Hirayama, Y. & Fujimoto, Y. Epi-α-bisabolol 6-deoxy-β-d-gulopyranoside from the glandular trichome exudate of Brillantaisia owariensis. Phytochem. Lett. 5, 376–378 (2012).CAS 

Google Scholar 
Asai, T., Hara, N. & Fujimoto, Y. Fatty acid derivatives and dammarane triterpenes from the glandular trichome exudates of Ibicella lutea and Proboscidea louisiana. Phytochemistry 71, 877–894 (2010).CAS 
PubMed 

Google Scholar 
Ohkawa, A., Sakai, T., Ohyama, K. & Fujimoto, Y. Malonylated glycerolipids from the glandular trichome exudate of Ceratotheca triloba. Chem. Biodivers. 9, 1611–1617 (2012).CAS 
PubMed 

Google Scholar 
Omosa, L. K. et al. Antimicrobial flavonoids and diterpenoids from Dodonaea angustifolia. S. Afr. J. Bot. 91, 58–62 (2014).CAS 

Google Scholar 
Kessler, A. The information landscape of plant constitutive and induced secondary metabolite production. Curr. Opin. Insect Sci. 8, 47–53 (2015).PubMed 

Google Scholar 
Knudsen, J. T., Tollsten, L., Groth, I., Bergström, G. & Raguso, R. A. Trends in floral scent chemistry in pollination syndromes: Floral scent composition in hummingbird-pollinated taxa. Bot. J. Linn. Soc. 146, 191–199 (2004).
Google Scholar 
Pearse, I. S., Gee, W. S. & Beck, J. J. Headspace volatiles from 52 oak species advertise induction, species identity, and evolution, but not defense. J. Chem. Ecol. 39, 90–100 (2013).CAS 
PubMed 

Google Scholar 
El-Sayed, A. M., Byers, J. A. & Suckling, D. M. Pollinator-prey conflicts in carnivorous plants: When flower and trap properties mean life or death. Sci. Rep. 6, 1–11 (2016).
Google Scholar 
Greenaway, W., May, J. & Whatley, F. R. Analysis of phenolics of bud exudate of Populus tristis by GC/MS. Zeitschrift fur Naturforsch.. Sect C J. Biosci. 47, 512–515 (1992).
Google Scholar 
Urzua, A. & Cuadra, P. Acylated flavonoid aglycones from Gnaphalium robustum. Phytochem. Divers. Redundancy Ecol. Interact. 29, 1342–1343 (1990).CAS 

Google Scholar 
Drewes, S. E., Mudau, K. E., Van Vuuren, S. F. & Viljoen, A. M. Antimicrobial monomeric and dimeric diterpenes from the leaves of Helichrysum tenax var tenax. Phytochemistry 67, 716–722 (2006).CAS 
PubMed 

Google Scholar 
Midiwo, J. O. et al. Bioactive compounds from some Kenyan ethnomedicinal plants: Myrsinaceae, Polygonaceae and Psiadia punctulata. Phytochem. Rev. 1, 311–323 (2002).CAS 

Google Scholar 
Jiménez-Pomárico, A. et al. Chemical and morpho-functional aspects of the interaction between a Neotropical resin bug and a sticky plant. Rev. Biol. Trop. 67, 454–465 (2019).
Google Scholar 
Linhart, Y. B., Thompson, J. D., Url, S. & John, D. Terpene-based selective herbivory by Helix aspersa (Mollusca) on Thymus vulgaris (Labiatae). Oecologia 102, 126–132 (2012).
Google Scholar 
Kessler, A., Halitschke, R. & Poveda, K. Herbivory-mediated pollinator limitation: Negative impacts of induced volatiles on plant-pollinator interactions. Ecology 92, 1769–1780 (2011).PubMed 

Google Scholar 
Sletvold, N., Moritz, K. K. & Ågren, J. Additive effects of pollinators and herbivores result in both conflicting and reinforcing selection on floral traits. Ecology 96, 214–221 (2015).PubMed 

Google Scholar 
Ramos, S. E. & Schiestl, F. P. Rapid plant evolution driven by the interaction of pollination and herbivory. Science (80-). 364, 193–196 (2019).ADS 
CAS 

Google Scholar 
Rojas-Nossa, S. V. Estrategias de extracción de néctar por pinchaflores (Aves: Diglossa y Diglossopis) y sus efectos sobre la polinización de plantas de los altos Andes. Ornitol. Colomb. 5, 21–39 (2007).
Google Scholar 
R Team Core. R: A language and environment for statistical computing. R Foundation for Statistical Computing. (2021).Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
Google Scholar 
Diaz-Uriarte, R. Package ‘ varSelRF ’. Compr. R Arch. Netw. 1–23 (2015). More

Rule-based mean sowing and maturity datesLocation- and climate-specific mean crop calendars are computed by combining two rule-based approaches published by19 and22 to simulate sowing and physiological maturity dates of grain crops, respectively. The assumption is that farmers select growing seasons based on the mean climatic characteristics of their specific location and on the physiological limitations (base and optimum temperatures for reproductive growth; sensitivity to terminal water stress) of the respective crop species. Accordingly, they select sowing dates and cultivars with phenologies that, on average, meet these adapted maturity dates.The climate is classified into (i) seasonality types, based on the coefficient of variation of monthly mean temperature and precipitation and (ii) temperature levels, based on the temperature of the warmest month as compared to the base and the optimum temperatures for the crop reproductive growth. Optimal temperatures for sowing, optimal temperature ranges for grain filling, as well as indicators of soil moisture conditions (based on precipitation/potential-evapotranspiration ratio (P/PET)), are defined as global parameters for each crop (Supplementary Table 1) and used as thresholds to identify the best timing for sowing and for the start or end of the crop grain-filling phase. To cope with fluctuations of daily values around these thresholds, mean daily temperature, precipitation and potential evapotranspiration are derived by linear interpolation between monthly values.We distinguish between spring and winter crop types. Maize, rice, sorghum, and soybean are simulated as spring crops only, for wheat we simulate both types. For spring crops, farmers sow the crops at the onset of the wet season (first day of the wettest 120 consecutive days), in case of prevailing precipitation seasonality, or on the day of the year when temperatures increase above crop-specific temperature threshold19 (Supplementary Table 1), in case of temperature-driven seasonality.For wheat, we distinguish three types: winter wheat with vernalization is chosen if monthly temperatures fall below 0 °C, but winter is neither too harsh (temperature of the coldest month is higher than −10 °C), nor too long (temperatures fall below the sowing temperature threshold (12 °C) after 15th September (North hemisphere) or 31st March (South hemisphere)19). Winter wheat without vernalization is grown if winters are mild (the temperature of the coldest month is higher than 0 °C) without dormancy. In this case, wheat is sown 75 days before the coldest month of the year. This rule was arbitrarily chosen based on observed wheat sowing dates in mild winter regions. If the conditions for growing any of the winter-wheat types are not met (winter too harsh and too long), then spring wheat (without vernalization) is chosen. Note that the computed sowing dates do not differ between rainfed and irrigated for any of the crops.The mean maturity date is chosen so that the crop grain-filling phase, the most critical for yield formation, occurs under the least stressful conditions possible in that location and climate as follows. Under precipitation seasonality, grain filling starts towards the end of the rainy season, when a P/PET threshold is crossed. Under temperature seasonality, (a) grain filling of spring crops starts in the warmest month of the year (if summer temperatures are optimal), or right after temperatures return within an optimal range; (b) grain filling of winter crops ends in the warmest month of the year (if summer temperatures are optimal), or right before temperatures exceed the optimal range; (c) eventually, maturity is advanced to escape terminal water stress. Note that the grain-filling phase has a static duration of 60 days for maize and 40 days for all the other crops. This assumption is based on empirical relationships between the total growth period and the post-flowering reproductive phase, showing that the partition between the vegetative and reproductive phase of grain crops follows a saturation curve that levels off after 90–100 days of total growth duration54. Different crops are assumed to have only one crop cycle (sowing-to-maturity) per year, therefore neither multi-cropping systems nor crop rotations are accounted for in the decision-making rules. A detailed description of the rules and parameterization can be found in refs. 19, 22.Simulated crop calendars reflect current farmers’ managementSimulated historical crop calendars, driven by the bias-corrected climate dataset WFDEI23, largely agree with observations11,12,13. We compare results both at the country and grid-cell level because, although the observed crop calendars used here are gridded datasets, their underlying sources are often reported per country. The country-level comparison highlights that the agreement is good for most countries, importantly, including those with large cropland area. The area-weighted Mean Absolute Error (MAE) is close or well below 30 days for all considered crops (Fig. 4). The simulated crop calendars compare well with the observed data also at the grid-cell level. Large areas, including major agricultural regions of importance for global yields, show deviations within ±15 days for both sowing and maturity dates (Supplementary Table 2 and Supplementary Figs. 21–24). However, evaluating the accuracy below 30 days is limited by the time resolution of the observations, which is either (i) monthly11 and converted by us into daily values, by taking the mid-day of the reported month, or (ii) daily12,13, but resulting from averages over large time windows (often  > 1 month). Overall, the accuracy of the model is in line with the original evaluations of this rule-base method19,22, as well as with other studies simulating average growing periods across large regions18,20.Fig. 4: Evaluation of simulated crop calendars.Country-level comparison of simulated and observed sowing (A) and maturity (B) dates (day of the year) for five crops. Each circle refers to a country and a crop, the size of the circle is scaled according to the cropland area per country. The area-weighted Mean Absolute Error (MAE, days) is reported for each crop. Crop-calendar simulations are based on WFDEI reanalysis climate forcing23 for the period 1979–2012. The observed crop calendar includes different sources11,12,13.Full size imageSimulation of daily crop phenology and yields with the LPJmL crop modelWe perform a modeling experiment across the global land grid at 0.5° × 0.5° resolution. We used the LPJmL5 crop model24,25 to simulate daily growth and phenological development of five crops, driven by climate projections from four General Circulation Models (GCMs) GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR and MIROC5 under the Representative Concentration Pathways 6.0 (RCP6.0) as provided in bias-adjusted form from the CMIP5 archive by the ISIMIP2b project42. Irrigated and rainfed production systems are simulated separately on their current harvested areas11, which is also used to compute total crop yields at grid-cell and global scale, as the product of yield by crop-specific area. A first 5000-year spin-up simulation is used to initialize all model pools (e.g., soil carbon and nitrogen content). A second spin-up simulation of 390 years is used to introduced effects of historical human-driven land-use change on these pools. A change in cropping area for the future scenarios is not considered in this study.Phenological development is simulated based on the thermal-time model, including the effect of vernalization. All crops are assumed to be insensitive to photoperiod, due to a lack of parameters for multiple-crops and global-scale simulations. Previous global studies15,18 that have focused on maize and wheat only, have found lower performances in the growing-period simulations when using a photo-thermal model, compared to a temperature-only driven approach and thus recommend caution when using the photoperiodic response. State-of-the-art global crop models13,16 also typically do not consider sensitivity to photoperiod or assume that the photoperiodic response of the cultivars chosen in each location are perfectly tuned to the given conditions.Sowing dates are prescribed based on the external rule-based algorithm. Crop cultivars are parametrized based on the phenological units required to reach the corresponding maturity dates (TUreq, °C days). In line with15, TUreq are derived consistently with the phenological module of the crop model LPJmL for each grid cell, crop, and rule-based computed growing period from the respective climate input. They are calculated as the sum of daily mean air temperature increments above a crop-specific base temperature (TU) (Supplementary Table 1) between rule-based sowing and maturity. In addition, winter-wheat cultivars require effective vernalization days (VUreq), that range between 0 (mild winters) and 70 (cold winters), depending on the temperature of the 5 coldest months (Eq. (1))15,18.$${{{{{mathrm{V}}}}}}{{{{{{mathrm{U}}}}}}}_{{{{{{{mathrm{req}}}}}}}}=frac{70}{5}times left(1-frac{{T}_{m}-3}{10-3}right)$$
(1)
where Tm is the mean temperature of the month.From the day of sowing, effective TU for phenological development are accumulated daily, as the difference between the mean air temperature on that day and the crop-specific base temperature for phenological development (Eq. (2)). The vernalization effectiveness is computed daily by a scaling factor (0–1), which is then multiplied to the TU (Eq. (2)). For crops that are insensitive to vernalization, VUd is set equal one.$${{{{{mathrm{T}}}}}}{{{{{{mathrm{U}}}}}}}_{{{{{{{mathrm{req}}}}}}}}=mathop{sum }_{d=1}^{{ndays}}left({max }left(0,{T}_{d}-{T}_{{base}}right)times mathop{sum }_{0}^{d}{{{{{mathrm{V}}}}}}{{{{{{mathrm{U}}}}}}}_{d}right)$$
(2)
where the scaling factor VUd is computed by a three-stage linear response function with a range of optimal temperatures (Eq. 3). Temperature for effective vernalization range between −4 °C and +17 °C, with an optimum range between 3 °C and 10 °C.$${{{{{{{mathrm{VU}}}}}}}}_{d}=left{begin{array}{cc}left({T}_{d}-left(-4right)right)/left(3-10right) & {{{{{{mathrm{if}}}}}}}-4 , < ,{T}_{d} , < , 3\ 1 & {{{{{{mathrm{if}}}}}}};3,le ,{T}_{d},le, 10\ left(17-{T}_{d}right)/left(17-10right) & {{{{{{mathrm{if}}}}}}};10 , < ,{T}_{d} , < , 17\ 0 & {{{{{{mathrm{otherwise}}}}}}}end{array}right}$$ (3) In this study, we have removed the effect of vernalization on slowing down TU accumulation until 10% of the total vernalization requirements is reached. In this way, the crop can accumulate both vernalization units and heat units in fall, so that there is some leaf growth before winter (in LPJmL, the LAI curve depends on accumulated heat units).The LPJmL model simulates phenology as one single phase from emergence to maturity. Although the flowering stage is not simulated as an explicit break point, the fraction of above-ground biomass that is allocated to the storage organs (fHI) depends on the phenological progress (fTUreq, fraction of TUreq that have been fulfilled), with the bulk of the storage organs start filling up after 40% of TUreq have been reached (Eq. (4)). In line with this, the LAI curve reaches a plateau when 45% (wheat) or 50% (other crops) of the TUreq are fulfilled, which could be considered a proxy of the flowering stage.$${{{{{{mathrm{fHI}}}}}}}=100times frac{{{{{{{{mathrm{fTU}}}}}}}}_{{{{{{{mathrm{req}}}}}}}}}{100times {{{{{{{mathrm{fTU}}}}}}}}_{{{{{{{mathrm{req}}}}}}}}+{{exp }}^{11.1-10.0times {{{{{{{mathrm{fTU}}}}}}}}_{{{{{{{mathrm{req}}}}}}}}}}$$ (4) Crop biomass growth is simulated by daily carbon accumulation and allocation to different plant organs (roots, leaves, storage organs, mobile reserves, and stem). The fraction of carbon allocated to each pool is a function of the fraction of completed phenological progress. Water stress increases allocation to the roots and reduces allocation to the leaves. The daily Net Primary Production (NPP) is the result of the Gross Primary Production (daily gross photosynthesis) reduced by the respiration costs. Gross photosynthesis is simulated as a function of absorbed photosynthetically active radiation, CO2 atmospheric mixing ratio, air temperature, day length, and canopy conductance. Photosynthesis rate is given by the minimum between light-limited and Rubisco-limited photosynthesis rates, with distinguished pathways for C3 and C4 crops. Respiration is tissue-specific and it is also driven by temperature. If accumulated NPP is insufficient to satisfy all organ demands, allocation follows a hierarchical order from roots, to leaves, to storage organs, and consequently penalizing the harvest index. Crops are subject to yield failure due to frost events (daily minimum temperature More

Marra, P., Hobson, K. A. & Holmes, R. T. Linking winter and summer events in a migratory bird by using stable-carbon isotopes. Science 282, 1884–1886 (1998).ADS 
CAS 
PubMed 

Google Scholar 
Korslund, L. & Steen, H. Small rodent winter survival: Snow conditions limit access to food resources. J. Anim. Ecol. 75, 423–436 (2009).
Google Scholar 
Both, C., Bouwhuis, S., Lessells, C. M. & Visser, M. E. Climate change and population declines in a long-distance migratory bird. Nature 441, 81–83 (2006).ADS 
CAS 
PubMed 

Google Scholar 
Rughetti, M. & Festa-Bianchet, M. Effects of spring–summer temperature on body mass of chamois. J. Mammal. 93, 1301–1307 (2012).
Google Scholar 
Davidson, J. & Andrewartha, H. The influence of rainfall, evaporation and atmospheric temperature on fluctuations in the size of a natural population of Thrips imaginis (Thysanoptera). J. Anim. Ecol. 17, 200–222 (1948).
Google Scholar 
Sillett, T. S., Holmes, R. T. & Sherry, T. W. Impacts of a global climate cycle on population dynamics of a migratory songbird. Science 288, 2040–2043 (2000).ADS 
CAS 
PubMed 

Google Scholar 
SÆther, B. E., Sutherland, W. J. & Engen, S. Climate influences on avian population dynamics. Adv. Ecol. Res. 35, 185–209 (2004).
Google Scholar 
Frederiksen, M., Daunt, F., Harris, M. & Wanless, S. The demographic impact of extreme events: Stochastic weather drives survival and population dynamics in a long-lived seabird. J. Anim. Ecol. 77, 1020–1029 (2008).CAS 
PubMed 

Google Scholar 
Cox, A. R., Robertson, R. J., Rendell, W. B. & Bonier, F. Population decline in tree swallows (Tachycineta bicolor) linked to climate change and inclement weather on the breeding ground. Oecologia 192, 713–722 (2020).ADS 
PubMed 

Google Scholar 
Peach, W., Baillie, S. & Underhill, L. Survival of British Sedge Warblers in relation to west African rainfall. Ibis 133, 300–305 (1991).
Google Scholar 
Altwegg, R., Dummermuth, S., Anholt, B. R. & Flatt, T. Winter weather affects asp viper Vipera aspis population dynamics through susceptible juveniles. Oikos 110, 55–66 (2005).
Google Scholar 
Woodworth, B. K., Wheelwright, N. T., Newman, A. E., Schaub, M. & Norris, D. R. Winter temperatures limit population growth rate of a migratory songbird. Nat. Commun. 8, 14812 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ådahl, E., Lundberg, P. & Jonzén, N. From climate change to population change: The need to consider annual life cycles. Glob. Change Biol. 12, 1627–1633 (2006).ADS 

Google Scholar 
Marra, P. P., Cohen, E. B., Loss, S. R., Rutter, J. E. & Tonra, C. M. A call for full annual cycle research in animal ecology. Biol. Lett. 11, 20150552 (2015).PubMed 
PubMed Central 

Google Scholar 
Telenský, T., Klvaňa, P., Jelínek, M., Cepák, J. & Reif, J. The influence of climate variability on demographic rates of avian Afro-palearctic migrants. Sci. Rep. 10, 17592 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Dybala, K. E., Eadie, J. M., Gardali, T., Seavy, N. E. & Herzog, M. P. Projecting demographic responses to climate change: Adult and juvenile survival respond differently to direct and indirect effects of weather in a passerine population. Glob. Chang. Biol. 19, 2688–2697 (2013).ADS 
PubMed 

Google Scholar 
Gullett, P., Evans, K. L., Robinson, R. A. & Hatchwell, B. J. Climate change and annual survival in a temperate passerine: Partitioning seasonal effects and predicting future patterns. Oikos 123, 389–400 (2014).
Google Scholar 
Selwood, K. E., McGeoch, M. A. & Mac Nally, R. The effects of climate change and land-use change on demographic rates and population viability. Biol. Rev. 90, 837–853 (2015).PubMed 

Google Scholar 
Bridge, E. S. et al. Technology on the move: Recent and forthcoming innovations for tracking migratory birds. Bioscience 61, 689–698 (2011).
Google Scholar 
van Bemmelen, R. S. A. et al. Red-necked phalaropes in the Western Palearctic reveals contrasting migration and wintering movement strategies. Front. Ecol. Evol. 7, 86 (2019).
Google Scholar 
Jiguet, F. et al. Unravelling migration connectivity reveals unsustainable hunting of the declining ortolan bunting. Sci. Adv. 5, eaau2642 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Stutchbury, B. J. M. et al. Tracking long-distance songbird migration by using geolocators. Science 323, 896 (2009).ADS 
CAS 
PubMed 

Google Scholar 
Sanderson, F. J., Donald, P. F., Pain, D. J., Burfield, I. J. & Van Bommel, F. P. J. Long-term population declines in Afro-Palearctic migrant birds. Biol. Conserv. 131, 93–105 (2006).
Google Scholar 
Sandvik, H., Erikstad, K. E., Barrett, R. T. & Yoccoz, N. G. The effect of climate on adult survival in five species of North Atlantic seabirds. J. Anim. Ecol. 74, 817–831 (2005).
Google Scholar 
BirdLife International and NatureServe. Bird species distribution maps of the world. (2014).Hedenström, A., Klaassen, R. H. G. & Åkesson, S. Migration of the little ringed plover Charadrius dubius breeding in South Sweden tracked by geolocators. Bird Study 60, 466–474 (2013).
Google Scholar 
Fransson, T., Österblom, H. & Hall-Karlsson, S. Svensk ringmärkningsatlas. (2008).Pakanen, V., Lampila, S., Arppe, H. & Valkama, J. Estimating sex specific apparent survival and dispersal of Little Ringed Plovers (Charadrius dubius). Ornis Fenn. 92, 52 (2015).
Google Scholar 
Jarošík, V., Honěk, A., Magarey, R. & Skuhrovec, J. Developmental database for phenology models: Related insect and mite species have similar thermal requirements. J. Econ. Entomol. 104, 1870–1876 (2011).PubMed 

Google Scholar 
Cramp, J. Handbook of the Birds of Europe, the Middle East and North Africa (Oxford University Press, 1992).
Google Scholar 
Leyrer, J. et al. Mortality within the annual cycle: Seasonal survival patterns in Afro-Siberian Red Knots Calidris canutus canutus. J. Ornithol. 154, 933–943 (2013).
Google Scholar 
Norris, R. D. & Marra, P. P. Seasonal interactions, habitat quality, an population dynamics in migratory birds. Condor 109, 535–547 (2007).
Google Scholar 
Schmaljohann, H., Eikenaar, C. & Sapir, N. Understanding the ecological and evolutionary function of stopover in migrating birds. Biol. Rev. 97, 1231–1252 (2022).PubMed 

Google Scholar 
Doyle, S. et al. Temperature and precipitation at migratory grounds influence demographic trends of an Arctic-breeding bird. Glob. Change Biol. 26, 5447–5458 (2020).ADS 

Google Scholar 
Rockwell, S. M. et al. Seasonal survival estimation for a long-distance migratory bird and the influence of winter precipitation. Oecologia 183, 715–726 (2017).ADS 
PubMed 

Google Scholar 
Insley, H., Peach, W., Swann, B. & Etheridge, B. Survival rates of Redshank Tringa totanus wintering on the Moray Firth. Bird Study 44, 277–289 (1997).
Google Scholar 
Duriez, O., Ens, B. J., Choquet, R., Pradel, R. & Klaassen, M. Comparing the seasonal survival of resident and migratory oystercatchers: Carry-over effects of habitat quality and weather conditions. Oikos 121, 862–873 (2012).
Google Scholar 
Cook, A. S. C. P. et al. Temperature and density influence survival in a rapidly declining migratory shorebird. Biol. Conserv. 260, 109198 (2021).
Google Scholar 
Pearce-Higgins, J. W., Yalden, D., Dougall, T. & Beale, C. M. Does climate change explain the decline of a trans-Saharan Afro-Palaearctic migrant?. Oecologia 159, 649–659 (2009).ADS 
CAS 
PubMed 

Google Scholar 
Weiser, E. L. et al. Environmental and ecological conditions at Arctic breeding sites have limited effects on true survival rates of adult shorebirds. Auk 135, 29–43 (2018).
Google Scholar 
Piersma, T. & Baker, A. Life history characteristics and the conservation of migratory shorebirds. In Behaviour and Conservation (eds Gosling, L. & Sutherland, W.) 105–124 (Cambridge University Press, 2000).
Google Scholar 
Conklin, J. R., Senner, N. R., Battley, P. F. & Piersma, T. Extreme migration and the individual quality spectrum. J. Avian Biol. 48, 19–36 (2017).
Google Scholar 
Méndez, V., Alves, J. A., Gill, J. A. & Gunnarsson, T. G. Patterns and processes in shorebird survival rates: A global review. Ibis (Lond.) 160, 723–741 (2018).
Google Scholar 
Roche, E. A. et al. Range-wide piping plover survival: Correlated patterns and temporal declines. J. Wildl. Manage. 74, 1784–1791 (2010).
Google Scholar 
Skagen, S. K. & Knopf, F. L. Toward conservation of midcontinental shorebird migrations. Conserv. Biol. 7, 533–541 (1993).
Google Scholar 
Kasahara, S., Moritomo, G., Kitamura, W., Imanishi, S. & Azuma, N. Rice fields along the East Asian-Australasian flyway are important habitats for an inland wader’s migration. Sci. Rep. 10, 4118 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Studds, C. E. & Marra, P. P. Linking fluctuations in rainfall to nonbreeding season performance in a long-distance migratory bird, Setophaga ruticilla. Clim. Res. 35, 115–122 (2007).
Google Scholar 
Newton, I. Can conditions experienced during migration limit the population levels of birds?. J. Ornithol. 147, 146–166 (2006).
Google Scholar 
Anderson, A. M. et al. Drought at a coastal wetland affects refuelling and migration strategies of shorebirds. Oecologia 197, 661–674 (2021).ADS 
PubMed 
PubMed Central 

Google Scholar 
Rakhimberdiev, E. et al. Fuelling conditions at staging sites can mitigate Arctic warming effects in a migratory bird. Nat. Commun. 9, 4263 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Wikelski, M. et al. Costs of migration in free-flying songbirds. Nature 423, 704 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Meissner, W. Ageing and sexing the curonicus subspecies of the Little Ringed Plover Charadrius dubius. Wader Study Gr. Bull. 113, 28–31 (2007).
Google Scholar 
Giorgi, F. Climate change hot-spots. Geophys. Res. Lett. 33, L08707 (2006).ADS 

Google Scholar 
Almazroui, M., Saeed, S., Saeed, F., Islam, M. N. & Ismail, M. Projections of precipitation and temperature over the South Asian countries in CMIP6. Earth Syst. Environ. 4, 297–320 (2020).ADS 

Google Scholar 
Lisovski, S. et al. The Indo-European flyway: Opportunities and constraints reflected by Common Rosefinches breeding across Europe. J. Biogeogr. 48, 1255–1266 (2021).
Google Scholar 
Lislevand, T. et al. Red-spotted Bluethroats Luscinia s. svecica migrate along the Indo-European flyway: A geolocator study. Bird Study 62, 508–515 (2015).
Google Scholar 
Brlík, V., Ilieva, M., Lisovski, S., Voigt, C. C. & Procházka, P. First insights into the migration route and migratory connectivity of the Paddyfield Warbler using geolocator tagging and stable isotope analysis. J. Ornithol. 159, 879–882 (2018).
Google Scholar 
Wernham, C. et al. The Migration Atlas: Movements of the Birds of Britain and Ireland (Poyser, 2002).
Google Scholar 
Saurola, P., Valkama, J. & Velmala, W. The Finnish Bird Ringing Atlas (Finnish Museum of Natural History and the Ministry of Environment, 2013).
Google Scholar 
Bairlein, F. et al. Atlas des Vogelzugs—Ringfunde Deutscher Brut- und Gastvögel (AULA-Verlag GmbH, 2014).
Google Scholar 
Salewski, V., Hochachka, W. M. & Fiedler, W. Multiple weather factors affect apparent survival of European Passerine birds. PLoS One 8, e59110 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schaub, M., Jakober, H. & Stauber, W. Demographic response to environmental variation in breeding, stopover and non-breeding areas in a migratory passerine. Oecologia 167, 445–459 (2011).ADS 
PubMed 

Google Scholar 
Brlík, V. et al. Weak effects of geolocators on small birds: A meta-analysis controlled for phylogeny and publication bias. J. Anim. Ecol. 89, 207–220 (2020).PubMed 

Google Scholar 
Weiser, E. L. et al. Effects of geolocators on hatching success, return rates, breeding movements, and change in body mass in 16 species of Arctic-breeding shorebirds. Mov. Ecol. 4, 12 (2016).PubMed 
PubMed Central 

Google Scholar 
Lisovski, S., Sumner, M. D., & Wotherspoon, S. J. TwGeos: Basic data processing for light based geolocation archival tags. 2015. https://github.com/slisovski/TwGeosLisovski, S. & Hahn, S. GeoLight—processing and analysing light-based geolocator data in R. Methods Ecol. Evol. 3, 1055–1059 (2012).
Google Scholar 
Ekstrom, P. A. An advance in geolocation by light. Mem. Natl Inst. Polar Res. 58, 210–226 (2004).
Google Scholar 
Brunsdon, C. & Chen, H. GISTools: Some further GIS capabilities for R. (2014).Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).PubMed 
PubMed Central 

Google Scholar 
Wang, B. The Asian Monsoon (Springer, 2006).
Google Scholar 
R Core Team. A Language and Environment for Statistical Computing (2021).Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).ADS 

Google Scholar 
Lebreton, J., Burnham, K. P., Clobert, J. & Anderson, D. R. Modeling survival and testing biological hypotheses using marked animals: A unified approach with case studies. Ecol. Monogr. 62, 67–118 (1992).
Google Scholar 
White, G. C. & Burnham, K. P. Program MARK: Survival estimation from populations of marked animals. Bird Study 46, S120–S139 (1999).
Google Scholar 
Pradel, R. Flexibility in survival analysis from recapture data: Handling trap-dependence. In Marked Individuals in the Study of Bird Population (eds Lebreton, J.-D. & North, P.) (Birkhäuser-Verlag, 1993).
Google Scholar 
Choquet, R., Lebreton, J. D., Gimenez, O., Reboulet, A. M. & Pradel, R. U-CARE: Utilities for performing goodness of fit tests and manipulating CApture-REcapture data. Ecography (Cop.) 32, 1071–1074 (2009).
Google Scholar 
Pakanen, V. M. et al. Natal dispersal does not entail survival costs but is linked to breeding dispersal in a migratory shorebird, the southern dunlin Calidris alpina schinzii. Oikos 2022, ee08951 (2022).Article 

Google Scholar 
Burnham, K. & Anderson, D. Model Selection and Multimodel Inference: A Practical in-Formation-Theoretic Approach (Springer, 2002).MATH 

Google Scholar 
Grosbois, V. et al. Assessing the impact of climate variation on survival in vertebrate populations. Biol. Rev. 83, 357–399 (2008).CAS 
PubMed 

Google Scholar 
Brlík, V. et al. Survival fluctuations linked to variation in the South Asian monsoon in a Palearctic migratory shorebird. Zenodo https://doi.org/10.5281/zenodo.7026440 (2022). More

C40 Cities. 700+ cities in 53 countries now committed to halve emissions by 2030 and reach net zero by 2050. C40 Cities https://www.c40.org/news/cities-committed-race-to-zero/ (2021).Watts, M. Cities spearhead climate action. Nat. Clim. Change 7, 537–538 (2017).
Google Scholar 
Brownlie, W. J. et al. Global actions for a sustainable phosphorus future. Nat. Food 2, 71–74 (2021).CAS 

Google Scholar 
El Wali, M., Golroudbary, S. R. & Kraslawski, A. Circular economy for phosphorus supply chain and its impact on social sustainable development goals. Sci. Total Environ. 777, 146060 (2021).CAS 

Google Scholar 
Bai, X. et al. Defining and advancing a systems approach for sustainable cities. Curr. Opin. Environ. Sustain. 23, 69–78 (2016).
Google Scholar 
De Boer, M. A., Wolzak, L. & Slootweg, J. C. Phosphorus: reserves, production, and applications. in Phosphorus Recovery and Recycling. (eds. Ohtake, H. & Tsuneda, S.) 75–100 (Springer, 2019).Brownlie, W. J. et al. Chapter 2. Phosphorus reserves, resources and uses. In Our Phosphorus Future (eds. Brownlie, W. J., Sutton, M. A., Heal, K. V., Reay, D. S. & Spears, B. M.) (UK Centre for Ecology & Hydrology, 2022). https://doi.org/10.13140/RG.2.2.25016.83209.Chow, E. China issues phosphate quotas to rein in fertiliser exports – analysts. Reuters (2022).Klesty, V. Global food supply at risk from Russian invasion of Ukraine, Yara says. Reuters (2022).Dumas, M., Frossard, E. & Scholz, R. W. Modeling biogeochemical processes of phosphorus for global food supply. Chemosphere 84, 798–805 (2011).CAS 

Google Scholar 
Cordell, D., Turner, A. & Chong, J. The hidden cost of phosphate fertilizers: mapping multi-stakeholder supply chain risks and impacts from mine to fork. Glob. Change Peace Secur. 27, 1–21 (2015).
Google Scholar 
Metson, G. S., Bennett, E. M. & Elser, J. J. The role of diet in phosphorus demand. Environmental Research Letters 7, 044043 (2012).
Google Scholar 
Oita, A., Wirasenjaya, F., Liu, J., Webeck, E. & Matsubae, K. Trends in the food nitrogen and phosphorus footprints for Asia’s giants: China, India, and Japan. Resour. Conserv. Recycl. 157, 104752 (2020).
Google Scholar 
Chen, M. & Graedel, T. E. A half-century of global phosphorus flows, stocks, production, consumption, recycling, and environmental impacts. Glob. Environ. Chang. 36, 139–152 (2016).
Google Scholar 
Johnes, P. J. et al. Chapter 5. Phosphorus and water quality. in Our Phosphorus Future (eds. Brownlie, W. J., Sutton, M. A., Heal, K. V., Reay, D. S. & Spears, B. M.) (UK Centre for Ecology & Hydrology, 2022). https://doi.org/10.13140/RG.2.2.14950.50246.Dodds, W. K. et al. Eutrophication of US freshwaters: analysis of potential economic damages. Environ. Sci. Technol. 43, 12–19 (2008).
Google Scholar 
Watson, S. B. et al. The re-eutrophication of Lake Erie: Harmful algal blooms and hypoxia. Harmful Algae 56, 44–66 (2016).CAS 

Google Scholar 
Rabalais, N. N. & Turner, R. E. Gulf of Mexico Hypoxia: Past, Present, and Future. Limnol. Oceanogr. Bull. 28, 117–124 (2019).
Google Scholar 
Carstensen, J. & Conley, D. J. Baltic Sea Hypoxia Takes Many Shapes and Sizes. Limnol. Oceanog. Bull. 28, 125–129 (2019).
Google Scholar 
Kanter, D. R. & Brownlie, W. J. Joint nitrogen and phosphorus management for sustainable development and climate goals. Environ. Sci. Policy 92, 1–8 (2019).CAS 

Google Scholar 
Hamilton, D. P., Salmaso, N. & Paerl, H. W. Mitigating harmful cyanobacterial blooms: strategies for control of nitrogen and phosphorus loads. Aquat. Ecol. 50, 351–366 (2016).CAS 

Google Scholar 
Brownlie, W. J. et al. Chapter 9. Towards our phosphorus future. In Our Phosphorus Future (eds. Brownlie, W. J., Sutton, M. A., Heal, K. V., Reay, D. S. & Spears, B. M.) (UK Centre for Ecology & Hydrology, 2022). https://doi.org/10.13140/RG.2.2.16995.22561.MacDonald, G. K. et al. Guiding phosphorus stewardship for multiple ecosystem services. Ecosyst. Health Sustain. 2, e01251 (2016).
Google Scholar 
Withers, P. J. A. et al. Stewardship to tackle global phosphorus inefficiency: The case of Europe. Ambio 44, 193–206 (2015).CAS 

Google Scholar 
Withers, P. J. A. et al. Towards resolving the phosphorus chaos created by food systems. Ambio 49, 1076–1089 (2020).CAS 

Google Scholar 
Withers, P. J. A. Closing the phosphorus cycle. Nat. Sustain. 2, 1001–1002 (2019).
Google Scholar 
Langhans, C., Beusen, A. H. W., Mogollón, J. M. & Bouwman, A. F. Phosphorus for Sustainable Development Goal target of doubling smallholder productivity. Nat. Sustain. 5, 57–63 (2022).
Google Scholar 
Kuss, P. & Nicholas, K. A. A dozen effective interventions to reduce car use in European cities: Lessons learned from a meta-analysis and transition management. Case Stud. Transp. Policy. 10, 1494–1513 (2022).
Google Scholar 
Hobbie, S. E. et al. Contrasting nitrogen and phosphorus budgets in urban watersheds and implications for managing urban water pollution. Proc. Natl. Acad. Sci. USA 114, E4116–E4116 (2017).
Google Scholar 
Seto, K. C. et al. From low- to net-zero carbon cities: the next global agenda. Annu. Rev. Environ. Resour. 46, 377–415 (2021).
Google Scholar 
Zhang, Y. Urban metabolism: A review of research methodologies. Environ. Pollut. 178, 463–473 (2013).CAS 

Google Scholar 
Kissinger, M. & Stossel, Z. An integrated, multi-scale approach for modelling urban metabolism changes as a means for assessing urban sustainability. Sustain. Cities Soc. 67, 102695 (2021).
Google Scholar 
Li, H. & Kwan, M.-P. Advancing analytical methods for urban metabolism studies. Resour. Conserv. Recycl. 132, 239–245 (2018).
Google Scholar 
Goldstein, B., Birkved, M., Quitzau, M.-B. & Hauschild, M. Quantification of urban metabolism through coupling with the life cycle assessment framework: concept development and case study. Environ. Res. Lett. 8, 035024 (2013).CAS 

Google Scholar 
Kovac, A. et al. Global Protocol for Community-Scale Greenhouse Gas Inventories— An Accounting and Reporting Standard for Cities Version 1.1. 190 https://ghgprotocol.org/greenhouse-gas-protocol-accounting-reporting-standard-cities.Rogelj, J., Geden, O., Cowie, A. & Reisinger, A. Net-zero emissions targets are vague: three ways to fix. Nature 591, 365–368 (2021).CAS 

Google Scholar 
Wiedmann, T. et al. Three-scope carbon emission inventories of global cities. J. Ind. Ecol. 25, 735–750 (2021).CAS 

Google Scholar 
Metson, G. S. et al. Urban phosphorus sustainability: Systemically incorporating social, ecological, and technological factors into phosphorus flow analysis. Environ. Sci. Policy 47, 1–11 (2015).CAS 

Google Scholar 
Harseim, L., Sprecher, B. & Zengerling, C. Phosphorus governance within planetary boundaries: the potential of strategic local resource planning in The Hague and Delfland, The Netherlands. Sustainability 13, 10801 (2021).CAS 

Google Scholar 
Coutard, O. & Florentin, D. Resource ecologies, urban metabolisms, and the provision of essential services. J. Urban Technol. 29, 49–58 (2022).
Google Scholar 
UDG at COP26 | Urban Design Events. Urban Design Group https://www.udg.org.uk/events/2021/udg-cop26 (2021).Ramaswami, A., Russell, A. G., Culligan, P. J., Sharma, K. R. & Kumar, E. Meta-principles for developing smart, sustainable, and healthy cities. Science 352, 940–943 (2016).CAS 

Google Scholar 
McPhearson, T. et al. A social-ecological-technological systems framework for urban ecosystem services. One Earth 5, 505–518 (2022).
Google Scholar 
McPhearson, T., Haase, D., Kabisch, N. & Gren, Å. Advancing understanding of the complex nature of urban systems. Ecol. Indic. 70, 566–573 (2016).
Google Scholar 
Metson, G. S. et al. Socio-environmental consideration of phosphorus flows in the urban sanitation chain of contrasting cities. Regional Environmental Change 18, 1387–1401 (2018).
Google Scholar 
Iwaniec, D. M., Metson, G. S. & Cordell, D. P-FUTURES: Towards urban food & water security through collaborative design and impact. Curr. Opin. Environ. Sustain. 20, 1–7 (2016).
Google Scholar 
Bulkeley, H. et al. Urban living laboratories: Conducting the experimental city? Eur. Urban. Reg. Stud. 26, 317–335 (2019).
Google Scholar 
Beukers, E. & Bertolini, L. Learning for transitions: An experiential learning strategy for urban experiments. Environ. Innov. Soc. Transit. 40, 395–407 (2021).
Google Scholar 
Ramaswami, A. et al. Carbon analytics for net-zero emissions sustainable cities. Nat. Sustain. 4, 460–463 (2021).
Google Scholar 
Petit-Boix, A., Apul, D., Wiedmann, T. & Leipold, S. Transdisciplinary resource monitoring is essential to prioritize circular economy strategies in cities. Environ. Res. Lett. 17, 021001 (2022).
Google Scholar 
WWAP. Wastewater: The Untapped Resource. https://www.unwater.org/publications/un-world-water-development-report-2017 (2017).van Puijenbroek, P. J. T. M., Beusen, A. H. W. & Bouwman, A. F. Global nitrogen and phosphorus in urban waste water based on the Shared Socio-economic pathways. J. Environ. Manage. 231, 446–456 (2019).
Google Scholar 
Kovacs, A. & Zavadsky, I. Success and sustainability of nutrient pollution reduction in the Danube River Basin: recovery and future protection of the Black Sea Northwest shelf. Water Int. 46, 176–194 (2021).
Google Scholar 
Trimmer, J. T. & Guest, J. S. Recirculation of human-derived nutrients from cities to agriculture across six continents. Nat. Sustain. 1, 427–435 (2018).
Google Scholar 
Powers, S. M. et al. Global opportunities to increase agricultural independence through phosphorus recycling. Earths Future 7, 370–383 (2019).
Google Scholar 
Metson, G. S., Cordell, D., Ridoutt, B. & Mohr, S. Mapping phosphorus hotspots in Sydney’s organic wastes: a spatially-explicit inventory to facilitate urban phosphorus recycling. J. Urban Ecol. 4, 1–19 (2018).
Google Scholar 
Hu, Y., Sampat, A. M., Ruiz-Mercado, G. J. & Zavala, V. M. Logistics Network Management of Livestock Waste for Spatiotemporal Control of Nutrient Pollution in Water Bodies. ACS Sustain. Chem. Eng. 7, 18359–18374 (2019).CAS 

Google Scholar 
Mayer, B. K. et al. Total value of phosphorus recovery. Environ. Sci. Technol. 50, 6606–6620 (2016).CAS 

Google Scholar 
van Hessen, J. An Assessment of Small-Scale Biodigester Programmes in the Developing World: The SNV and Hivos Approach. (Vrije Universiteit Amsterdam, 2014).Harder, R., Wielemaker, R., Larsen, T. A., Zeeman, G. & Öberg, G. Recycling nutrients contained in human excreta to agriculture: Pathways, processes, and products. Crit. Rev. Environ. Sci. Technol. 49, 695–743 (2019).
Google Scholar 
Metson, G. S. et al. Chapter 8. Consumption: the missing link towards phosphorus security. In Our Phosphorus Future (eds. Brownlie, W. J., Sutton, M. A., Heal, K. V., Reay, D. S. & Spears, B. M.) (UK Centre for Ecology & Hydrology, 2022). https://doi.org/10.13140/RG.2.2.36498.73925.Qiao, M., Zheng, Y. M. & Zhu, Y. G. Material flow analysis of phosphorus through food consumption in two megacities in northern China. Chemosphere 84, 773–778 (2011).CAS 

Google Scholar 
Forber, K. J., Rothwell, S. A., Metson, G. S., Jarvie, H. P. & Withers, P. J. A. Plant-based diets add to the wastewater phosphorus burden. Environ. Res. Lett. 15, 094018 (2020).CAS 

Google Scholar 
UN Population Division. The World’s cities in 2018. https://digitallibrary.un.org/record/3799524 (2018).Klöckner, C. A. A comprehensive model of the psychology of environmental behaviour-A meta-analysis. Glob. Environ. Change 23, 1028–1038 (2013).
Google Scholar 
Nyborg, K. et al. Social norms as solutions. Science 354, 42–43 (2016).CAS 

Google Scholar 
Vermeir, I. & Verbeke, W. Sustainable Food Consumption: Exploring the Consumer “Attitude – Behavioral Intention” Gap. J. Agric. Environ. Ethics 19, 169–194 (2006).
Google Scholar 
Ullström, S., Stripple, J. & Nicholas, K. A. From aspirational luxury to hypermobility to staying on the ground: changing discourses of holiday air travel in Sweden. J. Sustain. Tour. https://doi.org/10.1080/09669582.2021.1998079 (2021).Morris, T. H. Experiential learning—a systematic review and revision of Kolb’s model. Interact. Learn. Environ. 28, 1064–1077 (2020).
Google Scholar 
Metson, G. S. & Bennett, E. M. Facilitators & barriers to organic waste and phosphorus re-use in Montreal. Elementa 3, 000070 (2015).
Google Scholar 
Winkler, B., Maier, A. & Lewandowski, I. Urban gardening in germany: cultivating a sustainable lifestyle for the societal transition to a bioeconomy. Sustainability 11, 801 (2019).
Google Scholar 
Kim, J. E. Fostering behaviour change to encourage low-carbon food consumption through community gardens. Int. J. Urban Sci. 21, 364–384 (2017).
Google Scholar 
Fuhr, H., Hickmann, T. & Kern, K. The role of cities in multi-level climate governance: local climate policies and the 1.5 °C target. Curr. Opin. Environ. Sustain. 30, 1–6 (2018).
Google Scholar 
Steffen, W. et al. Planetary boundaries: Guiding human development on a changing planet. Science 347, 1259855 (2015).
Google Scholar 
Santos, A. F., Almeida, P. V., Alvarenga, P., Gando-Ferreira, L. M. & Quina, M. J. From wastewater to fertilizer products: Alternative paths to mitigate phosphorus demand in European countries. Chemosphere 284, 131258 (2021).CAS 

Google Scholar 
UNFCCC. Race To Zero Campaign. https://unfccc.int/climate-action/race-to-zero-campaign.Locsin, J. A., Hood, K. M., Doré, E., Trueman, B. F. & Gagnon, G. A. Colloidal lead in drinking water: Formation, occurrence, and characterization. Crit. Rev. Environ. Sci. Technol. https://doi.org/10.1080/10643389.2022.2039549 (2022).Li, Y. et al. The role of freshwater eutrophication in greenhouse gas emissions: A review. Sci. Total Environ. 768, 144582 (2021).CAS 

Google Scholar 
Gong, H. et al. Synergies in sustainable phosphorus use and greenhouse gas emissions mitigation in China: Perspectives from the entire supply chain from fertilizer production to agricultural use. Sci. Total Environ. 838, 155997 (2022).CAS 

Google Scholar  More