Philippa Kaur

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

Among the 325 municipalities in Greece during the period 2010–2021, WNV events, defined as the occurrence of at least one laboratory-confirmed human WNV case during a specific year, were reported in 154 (47%) municipalities, while the remaining 171 did not report any WNV case. WNV events were reported for a period ranging from one to eight years: 54 (35%) municipalities reported laboratory-confirmed WNV cases in only one year, 38 (25%) in two years, 30 (19%) in three years, 12 (8%) in four years, 10 (6%) in five years, 6 (4%) in six years, 1 (1%) in seven years, and 3 (2%) in eight years. This means that in 60% of the positive areas (82 municipalities out of 154), WNV appeared at most for two years, in 27% (42 out of 154) between three and four years, and in the remaining 13% (20 out of 154) for five years or more. Considering the total number of reported laboratory-confirmed human WNV cases across the twelve years (Fig. 1), in approximately 50% of the positive municipalities (78 out of 154), at most 4 cases were reported: 1, 2, 3, and 4 WNV cases were reported in 24, 32, 11, and 11 municipalities, respectively. Overall, 39 municipalities recorded a number of WNV cases ranging from 5 to 10 (third quartile), 34 a number ranging from 11 to 46, while the remaining 3 municipalities recorded a number of WNV cases equal to 56, 71 and 94.Figure 1(a) Map of Greece with total numbers of laboratory-confirmed human WNV cases throughout the 12-year period 2010–2021, with breakdowns by municipality. White denotes municipalities where no human cases were observed. (b) Map of Greece with total numbers of modelled human WNV cases throughout the 12-year period 2010–2021, with breakdowns by municipality. White denotes municipalities where no human cases were modelled.Full size imageModel evaluation and comparison with MIMESISWe investigated the ability of the MIMESIS-2 model to correctly identify the occurrence of WNV events, both in space and time, and its capacity to quantify the annual number of human WNV cases and the timing of the first WNV event in the year. The performance of many quantities of interest, such as the severity and timing of occurrence of human WNV cases, was also compared output from the original MIMESIS model26.Occurrence of WNV eventsStarting with the spatial analysis, we considered the fit of the model to replicate the observed 385 WNV events out of 3,900 (325*12) possible events across municipalities. MIMESIS-2 was able to correctly identify 356 of them, generated only one false alarm, and correctly modelled 3,514 true negatives.The performance of MIMESIS-2 was then evaluated according to four indices: the probability of detection (POD), false alarm rate (FAR), miss rate (MIS), and critical success index (CSI), described in the Methods section. For the POD, MIS and CSI, we considered the 154 municipalities with at least one reported and laboratory-confirmed human WNV case over the 12-year period, while for the FAR, we considered the 153 municipalities where at least one human WNV case was modelled over the same period. We split the (0.0–1.0) index interval into five equally sized bins to derive for each index, the fraction of municipalities falling into each bin. Both the POD and CSI were above 0.8 for 139 municipalities out of 154, while the MIS was below 0.2 for 142 municipalities (out of 154) and the FAR was always below 0.2, with one false alarm produced in a municipality where WNV events were observed in eight out of twelve years (Table 1).Table 1 Capacity of the MIMESIS-2 model to correctly model laboratory-confirmed human WNV cases.Full size tableWe also analysed how the model performed in different years by studying the multiannual evolution of the indices. Both the aggregated POD and CSI were equal to 0.92, with annual variations ranging from 0.72 (2021) to 1 (2011 and 2014). The aggregated MIS was 0.08, ranging from 0.0 (2011 and 2014) to 0.28 (2021). The FAR was virtually 0, being always equal to 0.0, with the only exception being 2017, when it was 0.1 (Table 2).Table 2 Capacity of the MIMESIS-2 model to correctly model laboratory-confirmed human WNV cases by year and in the whole observed time period.Full size tableMagnitude and timing of WNV events: performance and comparison with MIMESISTo evaluate the ability of MIMESIS-2 to capture the magnitude and timing of WNV events, we first considered the discrepancy between the overall number of observed and modelled WNV cases during the 12-year period for each municipality. Out of the 153 municipalities where at least one case was modelled across the 12 years, 76 (50%) had at most 4 modelled cases of WNV: 1, 2, 3, and 4 WNV cases were modelled in 22, 31, 13, and 10 municipalities, respectively. In 42 municipalities, the number of modelled cases ranged from 5 to 10 (which, as for the observed WNV cases, coincided with the third quartile), and in 32 municipalities, the number ranged from 11 to 47, while the remaining 3 municipalities had 55, 70, and 99 modelled cases (Fig. 1).The MIMESIS-2 model closely replicated the total number of laboratory-confirmed WNV cases during the 12-year period. When considering only the 154 municipalities that recorded at least one WNV event during the considered period (excluding the true negatives), for 140 of them, the modelled number of cases fell within a ± 10% error range of the observed value, whereas for 149 the modelled number of cases fell within the ± 25% error margin. Only two municipalities showed a percent error above 50%. These were particular instances where only one WNV case was reported throughout the considered period, while MIMESIS-2 fitted zero human cases. For the original MIMESIS model, 63 and 84 municipalities fell within the ± 10% and ± 25% error margins, respectively, while 31 municipalities—mainly those where few cases were observed— had a relative error ≥ 100% (Fig. 2).Figure 2(a) For MIMESIS-2, modelled (IHMOD) vs. observed (IHOBS) human WNV cases in each municipality in the period 2010–2021. The inner black line represents the main diagonal where ideally the points would lie in case of perfect fit, while the dashed green, black and red lines represent, respectively, the ± 10%, ± 25% and ± 50% error margin. (b) Same quantities for MIMESIS. (c). Breakdown of the week of first WNV incidence by year. Plotted are the modelled quantities (WYMOD) for each of the 325 municipalities on the y-axis and the observed quantities (WYOBS) on the x-axis. The continuous line represents the main diagonal where ideally the points would lie in case of perfect fit, while the dashed lines represent the ± 4-week error margins.Full size imageTo further evaluate the bias of the model across all municipalities and years, we explored the difference between the yearly modelled and observed human WNV cases both with MIMESIS-2 and the original MIMESIS (IHMOD-IHOBS) across municipalities. In MIMESIS-2, we excluded 3,514 true negative cases to avoid distorted conclusions. For the remaining 386 cases, the mean bias was -0.04 indicating a possibly unbiased model, with the standard deviation (SD) of the residuals equal to 0.66 (original MIMESIS: mean bias 0.33, SD 2.07, after removing 3,387 true negatives) (Supplementary Fig. 1).Across the 325 municipalities and the 12 years, 385 WNV events were observed, while on 3,515 occasions, no laboratory-confirmed human WNV cases were reported; on 162 occurrences, 1 case was reported, and on 67 and 39 occasions, 2 and 3 cases were reported, respectively. The maximum yearly number of human WNV cases observed in a single municipality was 38. Considering the modelled human WNV cases with MIMESIS-2, the distribution of the 356 hits ranged between 1 and 37 modelled cases, closely mimicking the distribution of the observed cases, since 1, 2 and 3 human WNV cases were modelled on 129, 72 and 37 occasions, respectively. For the 29 misses, the observed numbers of human cases were 1 (24 times), 2 (3 times), or 3 (2 times). The only false alarm was produced in the Pellas municipality, where WNV events were observed in 8 out of the 12 years.We evaluated the timing of the first occurrence of WNV in humans for any municipality and year. Ignoring the municipalities with zero cases, the observed and MIMESIS-2-modelled first WNV cases occurred between weeks 22 and 44 and weeks 24 and 36, respectively. Modelled values tended to be dispersed around the observed ones: excluding the 3514 true negatives, 290 (75.13%) of the remaining 386 cases fell into the ± 4-week error margins from the observed cases (Fig. 2). This translated into a much lower bias of the week of first appearance (WYMOD-WYOBS) with respect to MIMESIS (Supplementary Fig. 2).Case study: The Pellas municipalityIn addition to presenting the overall performance of the model throughout different years and Greek municipalities, we highlight here the capacity of the model to capture population-specific behaviour and epidemiological features, such as the force of infection, that is, the rate at which susceptible humans, birds, and mosquitoes become infected, by presenting a single municipality case study for the municipality of Pellas. The Pellas municipality had the highest number of observed WNV cases over the 12-year period with a total of 94 human WNV cases, 38 in 2010, 16 in 2018, and 13 in 2021, no cases from 2014 to 2017, and between 4 to 8 cases in the remaining years.We considered the impact arising from the changes in parameters defining the forces of infection. In addition to the introduction of bird (({psi }_{B})) and human (({psi }_{H})) host selections, changes included modifications for the mosquito-to-bird (({p}_{M})) and bird-to-mosquito (({p}_{B})) probabilities of transmission, whose values were made temperature-dependent following Vogels et al.21, and the replacement of the mosquito-to-bird (({varphi }_{B})) and mosquito-to-human (({varphi }_{H})) ratios with their dynamic counterparts, ({N}_{M}/{N}_{B}) and ({N}_{M}/{N}_{H}), respectively (Fig. 3). We used the May–October period for the 12 years that were considered, because this is the part of the year when Culex pipiens mosquitoes are reproductively active and the majority of human WNV cases are reported. In each year of the 12-year period, ({p}_{M}) started from 0.02, reached its peak—ranging from 0.16 to 0.25—in midsummer, and then decreased to the initial values (in the original model, ({p}_{M}=0.9)). Similarly, ({p}_{B}) started from 0.28, peaked in the same time interval—with maximal values ranging from 0.51 to 0.56—and then returned to the initial values (in the original model, ({p}_{B}=0.125)). Additionally, the dynamic specifications of ({varphi }_{B}) and ({varphi }_{H}) were shown to play an important role. Whereas in MIMESIS ({varphi }_{B}=30), in MIMESIS-2 the values started at approximately 8.6 and peaked in late summer when more human WNV cases are reported, reaching values of approximately 57, before decreasing to values ranging from 31.15 to 41.60 in late October. In MIMESIS, ({varphi }_{H}) was calibrated at the municipality level, and for Pellas municipality, it was 0.0001, whereas the dynamic counterpart in MIMESIS-2 showed a temporal evolution with a shape (but different scale) similar to that of ({varphi }_{B}), starting from values of approximately 1, peaking in late summer to values of approximately 7, and then decreasing to values of approximately 4 in late October.Figure 3The temporal evolution during May to October of the (a) mosquito-to-bird probability of transmission, ({p}_{M}), (b) bird-to-mosquito probability of transmission, ({p}_{B},) (c) mosquito-to-bird ratio, ({varphi }_{B},) and (d) mosquito-to-human ratio, ({varphi }_{H},) for both MIMESIS-2 across different years and MIMESIS for each of the 12 years from 2010 to 2021. The plots refer to the simulations for the municipality of Pella.Full size imageChanges in these parameters enter into the expression for the forces of infection. It is of major practical interest to investigate how the values for the forces of infection resulting from MIMESIS-2 may vary for different values of the relative abundance of the vectors with respect to the corresponding carrying capacity and the temperature in different months (Fig. 4). As expected, all forces of infection increased with both the temperature and the relative abundance of the infectious vertebrate hosts. It is worth noting the importance of day length, as this affects the fraction of nondiapausing mosquitoes, ({delta }_{M}), and causes the forces of infection, all other things being equal, to be potentially higher in June and July than in the other months. However, in these two months, the modelled forces of infection tend to be smaller than those in August due to the lower abundance of infectious hosts.Figure 4Contour plots of the forces of infection for May to September for different values of the relative abundance of infected hosts/vectors with respect to the carrying capacity and the temperature. All the other quantities were fixed to the amounts obtained in the simulations for Pellas municipality for 2021 at the end of the corresponding month. (a) Bird-to-mosquito force of infection (({lambda }_{BM})) as a function of the relative abundance of infected birds (({I}_{B})) with respect to the bird carrying capacity (({K}_{B})) and temperature. (b) Mosquito-to-bird force of infection (({lambda }_{MB})) as a function of the relative abundance of infected mosquitoes (({I}_{M})) with respect to the mosquito carrying capacity (({K}_{M})) and temperature. (c) Mosquito-to-human force of infection (({lambda }_{MH})) as a function of the relative abundance of infected mosquitoes (({I}_{M})) with respect to the mosquito carrying capacity (({K}_{M})) and temperature. The ranges for ({I}_{B}/{K}_{B}) and ({I}_{M}/{K}_{M}) were fixed, increasing the maximum modelled value by 20% for the considered period, while the range for the temperature was chosen considering that in the period of interest, the average daily temperature ranged from 16.6 to 27.1 degrees Celsius. Black crosses represent the modelled values for 2021.Full size imageThe bird-to-mosquito force of infection, ({uplambda }_{BM}), took values on the order of 10–4, with possible peaks of approximately 7 × 10–4 in the case of high temperature and high prevalence of birds in June and July, which were nevertheless not reached due to a low abundance of infected birds in that period. Considering the months of July and August 2021 for illustrative purposes, the resulting modelled values were 1.20 × 10–4 and 2.19 × 10–4, respectively, with the increase in August explained by a higher abundance of infected birds in that period. It is worth noting that if the infection across birds had a lead period of two weeks, the resulting ({uplambda }_{BM}) in July would become 3.91 × 10–4 (+ 226%), while an increase in the average temperature in August by 1 °C would result in ({uplambda }_{BM})= 2.36 × 10–4 (+ 8%). The mosquito-to-bird, ({uplambda }_{MB}), and mosquito-to-human, ({uplambda }_{MH}), forces of infection showed similar qualitative behaviours, albeit at different scales, and in this case, they were higher in August due to a higher prevalence of infected Culex mosquitoes in that month. More specifically, ({uplambda }_{MB}) equalled 1.06 × 10–3 and 1.22 × 10–3 at the end of July and August, respectively, and an expected two weeks for the infection of mosquitoes would result in ({uplambda }_{MB})=4.15 × 10–3 (+ 292%) at the end of July, while an increase in the average temperature in August by 1 °C would result in ({uplambda }_{MB})= 1.31 × 10–3 (+ 7%) at the end of August. Finally, ({uplambda }_{MH})=2.86 × 10–6 at the end of July, while ({uplambda }_{MH})=3.26 × 10–6 at the end of August, with the anticipation of the infection among mosquitoes by two weeks resulting in ({uplambda }_{MH})=1.12 × 10–5 (+ 290%) and an increase in the average August temperature by 1 °C leading to ({uplambda }_{MH})=3.51 × 10–6 (+ 8%). It is worth recalling that since we calibrated the model on the number of reported laboratory-confirmed human WNV cases, ({uplambda }_{MH}) represents the rate at which susceptible humans contract infection and become symptomatic leading to a recorded human WNV case.We explored changes in the populations of infectious hosts and the total population number for both mosquitoes and birds over 2010–2021 for the period spanning from May to October (Figs. 5 and 6). The population of infected mosquitoes (({I}_{M})) was initialised by calibration (see the Methods section). Each year, after a short period in which the population of infected mosquitoes slightly decreased due to a very small number of infectious birds (({I}_{B})) that prevented the infection from spreading, it started growing substantially during summer, reaching its peak in late summer, coinciding with the period when most human cases were recorded. The observed increase in ({I}_{M}) was combined with the growth ({I}_{B}) at approximately the same time (with a slightly anticipated peak), which had an amplification effect on the spread of the infection. Both ({I}_{M}) and ({I}_{B}) showed significant yearly variation, with higher modelled numbers in years where more human WNV cases were reported. The modelled total population of mosquitoes (({N}_{M})) did not show significant interannual variability, always peaking in late summer. Finally, the overall population of birds (({N}_{B})) did not show any variability in the first part of the year, when an increase due to immigration and offspring generation was observed, whereas it had a moderate interannual variability in the second half of the year. These differences may be due to heterogeneous numbers of observed infected, dead and immune birds.Figure 5The temporal evolution during May to October of (a) the number of infected mosquitoes modelled by MIMESIS-2 (({I}_{M})), (b) the total number of mosquitoes modelled by MIMESIS-2 (({N}_{M}))(,) (c) the number of infected birds modelled by MIMESIS-2 (({I}_{B}))(,) and (d) the total number of birds modelled by MIMESIS-2 (({N}_{B})) for each of the 12 years from 2010 to 2021. The plots refer to the simulations for the municipality of Pella.Full size imageFigure 6The temporal evolution during May to October of (a) the ratio between the number of WNV-infected mosquitoes modelled by MIMESIS-2 (({I}_{M,MIM-2})) and the ratio modelled by MIMESIS (({I}_{M,MIM})), (b) the ratio between the total number of mosquitoes modelled by MIMESIS-2 (({N}_{M,MIM-2})) and the ratio modelled by MIMESIS (({N}_{M,MIM}))(,) (c) the ratio between the number of infected birds modelled by MIMESIS-2 (({I}_{B,MIM-2})) and the ratio modelled by MIMESIS (({I}_{B,MIM}))(,) and (d) the ratio between the total number of birds modelled by MIMESIS-2 (({N}_{B,MIM-2})) and the ratio modelled by MIMESIS (({N}_{B,MIM})) for each of the years from 2010 to 2021. The plots refer to the simulations for the municipality of Pella.Full size imageComparison of these population dynamics with those of MIMESIS revealed interesting patterns (Fig. 6). Considering the relative number of mosquitoes in MIMESIS-2 with respect to MIMESIS, the populations in MIMESIS tended to grow faster due to a higher mosquito carrying capacity (({K}_{M})) in the original model (({K}_{M}) ≈ 8.3 × 105 in MIMESIS versus ({K}_{M}) ≈ 2.4 × 105 in MIMESIS-2), resulting in a decrease in the ratio between the amounts modelled by MIMESIS-2 and the ones modelled by MIMESIS. Significant interannual variability could be seen in the first part of the year for infectious mosquitoes, where different initial calibration values played an important role. For the populations of birds, until midsummer, the overall number modelled by MIMESIS-2 tended to be approximately 1/4 that of MIMESIS, while as of July, different patterns were observed due to the higher mortality of birds in the original MIMESIS model. In years with higher virus spread, higher mortality was reflected in a sharper decrease in bird populations; therefore, the ratio between the population modelled by MIMESIS-2 and that modelled by MIMESIS increased up to approximately 0.6 (2010). More

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

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

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

ProductivityWith regard to productivity, in the summer harvest of the 2016–2017 crop year, in which all grain production systems had soybean in common, there were significant differences among crop rotations with species diversification and the double-cropped corn–soybean rotation; performance was better in AS-II, AS-III, AS-IV, AS-V and AS-VI and worst in AS-I. There was no significant difference in productivity among the crop rotations with species diversification (Table 2).Table 2 Productivity (kg ha−1) of the crop rotation systems for the 2014–2015 to 2019–2020 crop years in Londrina, state of Paraná, Brazil.Full size tableFor the summer harvest of the 2019–2020 crop year, in which all the grain production systems again had soybean in common, significant differences were also observed among the production systems. AS-I and AS-V had the lowest productivities, differing from AS-IV and AS-VI, which had the highest productivities. Conversely, the productivities of AS-II and AS-III did not differ significantly from those of the other evaluated systems (Table 2).In the cycle that ended in crop year 2019–2020, compared to the cycle that ended in crop year 2016–2017, there was a reduction in soybean productivity in all the analyzed grain production systems (Table 2). There was also a decrease in the productivity of corn grown in the summer in the 2015–2016 and 2018–2020 crop years. This decrease in productivity observed between the production cycles may be associated with climatic conditions because from 2014–2015 to 2016–2017, there was a good rainfall distribution and few water deficit peaks, while from 2017–2018 to 2019–2020, the water deficit peaks were more constant, especially in 2018–2019 and 2019–2020 (Fig. 1). Notably, there was a greater influence of the El Niño phenomenon on the first production cycle (2014–2017) and of the La Niña phenomenon on the second (2017–2020)28. In southern Brazil, these phenomena correspond to periods of weaker droughts under El Niño conditions and a higher frequency of severe and moderate droughts under La Niña conditions29. The occurrence of a water deficit may limit plant growth and development, particularly during the flowering and grain filling stages. Systems that employ crop rotation with species diversification are less susceptible to production losses due to water deficits30. The results of this study show that crop rotation systems with species diversification, by providing a longer soil cover time for soil protection, either with live plants or from the input of surface straw, together with the respective increase in the soil water storage capacity, can mitigate productivity losses resulting from periods of drought (Fig. 1, Table 2).Another finding is that soybean has higher productivity when grown in systems with greater species diversification, as was the case for AS-IV and AS-VI (Table 2). In general, grain production systems that employ crop rotation with species diversification produce more than those that are not diversified31,32, especially in atypical growing seasons affected by climatic factors limiting crop development33.AS-I and AS-V showed the lowest soybean productivity at the end of the second crop rotation cycle, in the 2019–2020 crop year (Table 2). AS-I had the lowest soybean productivity at the end of the two crop cycles, i.e., in 2016–2017 and 2019–2020, a result that is directly related to corn–soybean double cropping. In the southern region of Brazil, for example, soybean productivity in crop rotation systems with species diversification is 6.2% higher than that in double-crop systems22. In this sense, the results of this study indicate that production systems with little species diversification have lower soybean productivity than those that employ crop rotation with species diversification.At the end of the second crop rotation cycle, in 2019–2020, AS-II and AS-III also showed good soybean productivity, i.e., 3864 kg ha−1 and 3848 kg ha−1, respectively. AS-III had one of the highest grain yields in the summer crops, which may be associated with the use of cover crops in the previous winter. The use of cover crops in the winter growing seasons results in a number of benefits from permanent soil cover because cover crops can improve chemical, physical and biological soil attributes, favoring the accumulation of biomass and organic carbon in the soil34 and prevent soil erosion35. In addition, cover crops control pests, diseases and weeds36 and contribute to weed37 and nematode38 control.Regarding crop dry matter, AS-III, AS-IV, AS-V and AS-VI (Table 3) deposited the most dry matter in the system; the crop dry matter in these systems was greater than that in AS-I and showed no significant difference in relation to that in AS-II. The lower production of dry matter in AS-I is explained by the lack of corn cultivation in the summer. Corn grown in the summer was the crop that most contributed to the accumulation of dry matter in AS-III, AS-IV and AS-VI, compensating for the low averages obtained with beans in AS-V and AS-VI and with safflower in AS-IV. The higher dry matter inputs in AS-IV and AS-VI are because these are the only systems in which corn was grown in the summer for two consecutive years. The average dry matter contributed by corn grown in the summer is 9.9 Mg ha−1, while that from off-season corn and soybeans is 6.5 Mg ha−1 and 4.35 Mg ha−1, respectively.Table 3 Dry matter (Mg ha−1) of the grain production systems for the 2014–2015 to 2019–2020 crop years in Londrina, state of Paraná, Brazil.Full size tableStudies carried out in the Cerrado, Mato Grosso, showed that the minimum amount of plant dry matter deposited by crop rotation systems needed to obtain a balance of C in the soil in the region is between 11.7 and 13.3 Mg ha−139. Therefore, we can deduce that AS-III, AS-IV, AS-V and AS-VI would enter equilibrium; that is, over time, there will be neither accumulation of nor loss of C from the soil. For AS-I and AS-II, we can conclude that over time, C stocks in the soil will be reduced, causing a loss of soil fertility and, consequently, productivity, as shown in Table 2, where the yield of AS-I was lower than that of the most diversified treatments.The results show that crop diversification in grain production systems with the cultivation of commercial or cover crops in the winter benefited soybean and corn production in the summer. In similar studies, species diversification is reported to have increased summer crop productivity over time; specifically, in the U.S. and Canada, corn productivity increased by an average of 28.1%40, and in Canada, corn yield increased by 9.9% and soybean productivity increased by 11.8%41.Economic analysisThe highest mean annual revenue was found for AS-VI, while the lowest was found for AS-III. Regarding the mean annual cost, AS-VI demanded the greatest investment, while AS-III showed the lowest production cost. The highest mean annual profit was also observed for AS-VI, highlighting that the revenue more than offset the costs. As expected, the lowest mean annual profit was found for AS-I, that is, the corn–soybean double-crop system (Fig. 2).Figure 2(a) Mean annual revenue, (b) mean annual cost and (c) mean annual profit of grain production systems with varied levels of species diversity in Londrina, state of Paraná, Brazil.Full size imageThe higher profitability observed for AS-VI indicates that the practice of crop rotation with species diversification in grain production systems increased the grain productivity and economic gains. In this system, the productivity of the commercial crops was positively impacted, and the crops showed excellent yields compared to those in the production systems with lower species diversification. In addition, the winter crops played a key role in the composition of the revenues, especially wheat and bean. As previously noted, the highest mean annual costs of inputs (US$ 685), agricultural operations (US$ 353) and other costs (US$ 177) were found for this system. Within the inputs, the highest cost was for fertilizers (K2O, P2O5, and N), accounting for approximately 22% of the total cost (US$ 280). The higher cost may be related to higher energy demands because in a grain production system, a greater energy volume represents a greater use of inputs42. However, although the cost was the highest, the system was found to be more capable of converting investments into higher productivity and, consequently, into higher revenue and profit. Other studies conducted in Brazil also found economic benefits in crop rotation systems with species diversification, for example, in areas with a predominance of Caiuá sandstone, a region with low-fertility soils, in which the highest profitability was obtained in diversified systems that adopted the highest number of commercial crops, both in the winter and summer growing seasons21. Similarly, in another study in southern Brazil, higher productivities were obtained for more diversified crop rotation systems23. In a long-term study involving soybean, corn, wheat and tropical forage grasses in southern Brazil, higher profits were also found for more diversified production systems22.AS-II had the second highest mean annual profit; this system is characterized by the cultivation of cereals in the winter. The results show that this grain production system is promising, as the use of winter cereal crops had a positive effect on the productivity of the summer crops, leading to increased revenue and profit from the sale of soybean and corn (Supplementary Table S2). With regard to costs, the items that generated the highest expenses in AS-II were inputs, accounting for an average of 54% of the total cost, followed by agricultural operations, which represented an average of 31% of the total, and other costs, accounting for an average of 15% of the total cost (Supplementary Table S2). Studies conducted in other locations also recommend crop rotation systems with the use of cereals, as in the semiarid Northern Great Plains, Canada, where higher productivity and greater profit were found with these cultivation systems compared to a system without species diversification43.AS-V had the third highest mean annual profit. This system is composed of six different crops, and its profitability results were also relevant. Regarding the revenues obtained in the winter growing seasons, beans stood out, accounting for 21% of the total (Supplementary Table S2). One of the problems with AS-V was the cultivation of buckwheat, which, in addition to having a low market price and generating little revenue, also had a high production cost, negatively impacting the entire production system. Thus, if buckwheat had not been cultivated, AS-V could have achieved higher profitability than that observed. With regard to the costs for AS-V, the cost of inputs represented an average of 53% of the total cost, followed by agricultural operations (on average, 31% of the total cost) and other costs (on average, 15% of the total). The cultivation of legumes such as beans in the winter is beneficial for grain production systems because it can favor increased production and, consequently, the profit obtained with subsequent crops44.AS-III had the fourth highest mean annual profit. Although this system did not have the best profitability, it should not be disregarded. This system is focused on the production of straw in the winter and on the revenue generated by the summer crops. However, although cover crops do not generate income for the producer, they indirectly promote gains in subsequent crops. With the maintenance of soil cover, productivity gains and increased revenue are expected in production systems in the medium and long terms21. Cover crops, in general, control pests, diseases and weeds and improve soil conditions36 because they prevent soil compaction and improve soil water infiltration and retention, density, and hydraulic conductivity45. AS-III also had the lowest mean annual production cost; the cost with inputs was on average 35% lower than that observed in the other systems. The lower costs are because the cover crops were not harvested because their benefits are obtained from the biomass generated; thus, the cost is lower than that for systems for which the purpose is to sell grains. One of the great benefits of adopting this system is that the cultivation of cover crops in the winter can reduce the cost of the crop that follows because the amount of inputs involved in the production of the next crop can decrease, as can fuel expenses46. In addition, the lower demand for pesticides makes the system more economical and sustainable and less risky. The quantification and analysis of the items composing the costs of each system are extremely important for producers’ decision-making. However, this analysis requires extreme caution because higher production costs do not necessarily mean lower yields, and similarly, lower costs do not necessarily mean higher profits20,21.AS-IV had the second lowest mean annual profit. This system included winter agroenergy crops. With the exception of canola, the other agroenergy crops grown in this production system showed low profitability. Despite having one of the lowest production costs, the low revenue obtained with agroenergy crops compromised the profitability of AS-IV. Even with the sale of crambe, safflower and canola, the revenues were not sufficient to cover the production costs. Although this system did not show one of the best results, studies with bioenergy crops are being conducted in various regions of the world, and these crops may become an option for southern Brazil, as in the case of Italy, where plants of the family Brassicaceae are being introduced in rotation with cereals as a source of income diversification47.The lowest mean annual profit was observed for AS-I. The low profit is related to the high production costs. Despite having the second highest mean annual revenue, the high production cost compromised the profitability of the system. This result is associated with the lower grain productivity observed in this production system and the fact that it specialized in few crops and focused only on commodities, which are subject to changes in their sale price due to seasonality and market uncertainties, or with the increased susceptibility of this system to problems caused by climatic variations. The crops grown in this system are traded in the international market, and in this case, the producers are only “price takers,”, i.e., they are not able to influence the price of the products48. The prices of commodities may vary; thus, producers may obtain higher or lower revenue due to market fluctuations or volatility. In turn, market fluctuations or volatility are caused by, among other factors, production or external factors, such as exchange rate variations or increased food consumption49,50. AS-I had the highest mean annual pesticide costs, approximately 21% of the total cost (US$ 254). In addition to economic factors, the double-crop system has also generated problems such as the proliferation of pests, diseases and weeds because, in contrast to crop rotation, it does not interrupt the life cycles of pests and diseases51. To control the proliferation of pests, diseases and weeds, the increased use of inputs and an increase in the number of agricultural operations are required52, with a consequent increase in production costs20. This increase in production costs can be observed for winter corn crops, which were more expensive than summer soybean crops. In this system, the mean cost to produce soybean in the summer was US$ 567 per ha, and that to produce corn in the winter was US$ 648. Compared to the other systems studied, the average investment required for the winter growing season was US$ 448 and that for the growing season was US$ 640; that is, the winter crops required 30% less investment than the summer crops (Supplementary Table S3).When considering the real selling price of grains, the highest accumulated profit was observed in AS-VI (Fig. 3); however, in a scenario in which the price of soybeans fluctuates (Fig. 3a) both upward and downward, sensitivity analysis revealed different behaviors. If there was a 44% increase in the selling price of soybeans, the ranking order of the systems would change, making AS-I more profitable. AS-I is the most sensitive to soybean price variations, since in this system, the crop is mainly responsible for generating income and is cultivated in all summers. Thus, the opposite results are also expected. A negative variation in the selling price of soybeans will make AS-I the system with the highest accumulated loss. Price changes can significantly increase or decrease the profitability of producers. Thus, the choice of crops and the number of times a crop appears in each agricultural system determines the profitability of the system as the sale price of the crops varies.Figure 3Price sensitivity analysis (accumulated profit of 6 crop years on the y-axis) of six agricultural systems in Londrina, state of Paraná, Brazil. (a) Soybean; (b) corn; (c) wheat; and (d) bean.Full size imageCorn showed some changes in the order of classification of the systems (Fig. 3b). If the corn sale prices were increased by up to 50%, AS-VI would continue to be the system with the highest accumulated profit. In this scenario, AS-I, composed solely of the corn crop in winter, would cease to be the system with the lowest accumulated profit, occupying the position of AS-III. Different from what happened with the soybean crop, the fluctuations in the corn sale price had less impact on AS-I in terms of accumulated profit. This was because the corn produced in this system accounted for a smaller share of profits and, in some cases, even resulted in losses.Regarding the wheat crop (Fig. 3c), changes in the sale price led to little change in the accumulated profit. Wheat was grown only in AS-II and AS-VI, and in a scenario that considered only the variation in the price of this grain, if its selling price was reduced by up to 47%, AS-VI would continue to be the system with the highest accumulated profit. Changes in the selling price of the bean crop (Fig. 3d) had greater impacts. A 50% increase in the sale price of beans led to a 47% increase in profit in AS-VI.In addition to variations in sale prices, another possible scenario is that crops are stored and sold at later dates. This is possible, as cooperatives are able to provide producers with storage and future sale of grains, extending the time for decision-making. Thus, producers can market products at an optimal time, e.g., when sale prices are better than those on the day of harvest. In this scenario, if corn and soybeans were stored and sold at peak prices recorded each quarter, over the 12 months following the harvest date, the evaluated agricultural systems would show even greater profits. Figure 4 shows the evolution of real prices in tons (USD) of corn and soybeans from July 2014 to March 2021.Figure 4Evolution of corn and soybean prices from July 2014 to March 2020. Data were obtained from the Department of Rural Economy of the Paraná State Secretariat of Agriculture and Supply (DERAL-SEAB). The monetary values are corrected for inflation according to the Brazilian Extended National Consumer Price Index (IPCA) to December 2021.Full size imageIf the sale of soybean and corn was carried out at times of price peaks, the accumulated profit of the systems would vary (Table 4). AS-I, composed exclusively of corn and soybean crops, would become the highest profit system (US$ 3,683). AS-VI, although no longer the highest profit system, would still be one of the systems with the best economic results (US$ 3479). In this scenario, AS-IV would occupy the last position, with the lowest accumulated profit (US$ 2732).Table 4 Profit (USD ha−1) of the grain production systems for the 2014–2015 to 2019–2020 crop years, considering quarterly price peaks in Londrina, state of Paraná, Brazil. .Full size tableIn this scenario, driven by the devaluation of the real against the dollar, the increase in domestic consumption and exports influenced the supply of grains in the market, and agricultural commodities such as soybeans and corn reached high sale values. Thus, it is evident that the market is able to condition the farmer’s profitability, which can influence the results of the analysis, both positively and negatively, according to the daily variations in grain commercialization prices53.From the results, it is evident that species diversification in crop rotation has enabled an increase in both grain productivity and economic gains. It is not enough to simply adopt no-till practices without species diversification in grain production systems31,32; it is necessary for the systems to be aligned with the no-tillage system and conservation agriculture principles. The main reasons for investing in crop diversification are as follows: production of roots and straw to cover the soil surface; improved soil structure and sustained soil biology; nutrient cycling; breaking the cycles of pests, diseases, and weeds; productivity gains; and increased profitability. Thus, the challenge lies in the diffusion of production systems aligned with the principles of the no-tillage system and conservation agriculture, that is, to diversify without failing to produce and obtain gains from grain production. Information on the benefits of grain production systems that employ crop rotation with species diversification, tested and with demonstrated economicity, such as those presented in this study, can therefore be decisive for producers’ decision-making and the adoption of practices aligned with sustainability in agriculture. 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

Mahmoodi, S. et al. The current and future potential geographical distribution of Nepeta crispa Willd., an endemic, rare and threatened aromatic plant of Iran: Implications for ecological conservation and restoration. Ecol. Indic. 137, 108752 (2022).
Google Scholar 
Behroozian, M., Ejtehadi, H., Peterson, A. T., Memariani, F. & Mesdaghi, M. Climate change influences on the potential distribution of Dianthus polylepis Bien. ex Boiss.(Caryophyllaceae), an endemic species in the Irano-Turanian region. PLoS ONE 15, e0237527 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Khanal, S. et al. Potential impact of climate change on the distribution and conservation status of Pterocarpus marsupium, a Near Threatened South Asian medicinal tree species. Ecol. Inform. 70, 101722 (2022).
Google Scholar 
Dyderski, M. K., Paź, S., Frelich, L. E. & Jagodziński, A. M. How much does climate change threaten European forest tree species distributions?. Glob. Change Biol. 24, 1150–1163 (2018).ADS 

Google Scholar 
Sanjerehei, M. M. & Rundel, P. W. The impact of climate change on habitat suitability for Artemisia sieberi and Artemisia aucheri (Asteraceae)—A modeling approach. Pol. J. Ecol. 65, 97–109 (2017).
Google Scholar 
Erfanian, M. B., Sagharyan, M., Memariani, F. & Ejtehadi, H. Predicting range shifts of three endangered endemic plants of the Khorassan-Kopet Dagh floristic province under global change. Sci. Rep. 11, 1–13 (2021).
Google Scholar 
Zhang, J. M. et al. Effects of climate change on the distribution of wild Akebia trifoliata. Ecol. Evol. 12, e8714 (2022).PubMed 
PubMed Central 

Google Scholar 
Li, J., Fan, G. & He, Y. Predicting the current and future distribution of three Coptis herbs in China under climate change conditions, using the MaxEnt model and chemical analysis. Sci. Total Environ. 698, 134141 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Yang, X.-Q., Kushwaha, S., Saran, S., Xu, J. & Roy, P. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills. Ecol. Eng. 51, 83–87 (2013).CAS 

Google Scholar 
Greiser, C., Hylander, K., Meineri, E., Luoto, M. & Ehrlén, J. Climate limitation at the cold edge: Contrasting perspectives from species distribution modelling and a transplant experiment. Ecography 43, 637–647 (2020).
Google Scholar 
Guisan, A. & Thuiller, W. Predicting species distribution: Offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).PubMed 

Google Scholar 
Thuiller, W. et al. Predicting global change impacts on plant species’ distributions: Future challenges. Plant Ecol. Evol. Syst. 9, 137–152 (2008).
Google Scholar 
Menke, S., Holway, D., Fisher, R. & Jetz, W. Characterizing and predicting species distributions across environments and scales: Argentine ant occurrences in the eye of the beholder. Glob. Ecol. Biogeogr. 18, 50–63 (2009).
Google Scholar 
Warren, D. L. & Seifert, S. N. Ecological niche modeling in Maxent: The importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342 (2011).PubMed 

Google Scholar 
Celenk, S., Dirmenci, T., Malyer, H. & Bicakci, A. A palynological study of the genus Nepeta L.(Lamiaceae). Plant Syst. Evol. 276, 105–123 (2008).
Google Scholar 
Zargari, A. Medicinal Plants Vol. 2 (University of Tehran Pub, 1990).
Google Scholar 
Javidnia, K., Miri, R., Rezazadeh, S. R., Soltani, M. & Khosravi, A. R. Essential oil composition of two subspecies of Nepeta glomerulosa Boiss. from Iran. Nat. Prod. Commun. 3, 1934578X0800300530 (2008).
Google Scholar 
Jamzad, Z. Flora of Iran, no 76, Lamiaceae. Res. Inst. For. Rangel. Tehran 76, 542–544 (2012).
Google Scholar 
Talebi, S. M., Nohooji, M. G., Yarmohammadi, M., Azizi, N. & Matsyura, A. Trichomes morphology and density analysis in some Nepeta species of Iran. Mediterr. Bot. 39, 51–62 (2018).
Google Scholar 
Amirmohammadi, F., Azizi, M., Nemati, S. H., Memariani, F. & Murphy, R. Nutlet micro‐morphology of selected species of Nepeta (Lamiaceae) in Iran. Nord. J. Bot. (2019).Jamzad, Z., Chase, M. W., Ingrouille, M., Simmonds, M. S. & Jalili, A. Phylogenetic relationships in Nepeta L.(Lamiaceae) and related genera based on ITS sequence data. Taxon 52, 21–32 (2003).
Google Scholar 
Emami, S. A., Yazdian, R., Arab, A., Sadeghi, M. & Tayarani-Najaran, Z. Anti-melanogenic activity of different extracts from aerial parts of Nepeta glomeruloasin on murine melanoma B16F10 cells. Iran. J. Pharm. Sci. 13, 61–74 (2017).
Google Scholar 
Narimani, R., Moghaddam, M., Ghasemi Pirbalouti, A. & Mojarab, S. Essential oil composition of seven populations belonging to two Nepeta species from Northwestern Iran. Int. J. Food Prop. 20, 2272–2279 (2017).CAS 

Google Scholar 
Hosseini, A., Forouzanfar, F. & Rakhshandeh, H. Hypnotic effect of Nepeta glomerulosa on pentobarbital-induced sleep in mice. Jundishapur J. Nat. Pharm. Prod. https://doi.org/10.17795/jjnpp-25063 (2016).Article 

Google Scholar 
Layeghhaghighi, M., Hassanpour Asil, M., Abbaszadeh, B., Sefidkon, F. & Matinizadeh, M. Investigation of altitude on morphological traits and essential oil composition of Nepeta pogonosperma Jamzad and Assadi from Alamut region. J. Med. Plants Prod. 6, 35–40 (2017).
Google Scholar 
Sefidkon, F. Essential oil of Nepeta glomerulosa Boiss. from Iran. J. Essent. Oil Res. 13, 422–423 (2001).CAS 

Google Scholar 
Djamali, M. et al. Application of the global bioclimatic classification to Iran: Implications for understanding the modern vegetation and biogeography. Ecol. Mediterr. 37, 91–114 (2011).
Google Scholar 
Djamali, M., Brewer, S., Breckle, S. W. & Jackson, S. T. Climatic determinism in phytogeographic regionalization: a test from the Irano-Turanian region, SW and Central Asia. Flora Morphol. Distrib. Funct. Ecol. Plants 207, 237–249 (2012).
Google Scholar 
Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).
Google Scholar 
Escobar, L. E., Lira-Noriega, A., Medina-Vogel, G. & Peterson, A. T. Potential for spread of the white-nose fungus (Pseudogymnoascus destructans) in the Americas: Use of Maxent and NicheA to assure strict model transference. Geospat. Health 9, 221–229 (2014).PubMed 

Google Scholar 
Valencia-Rodríguez, D., Jiménez-Segura, L., Rogéliz, C. A. & Parra, J. L. Ecological niche modeling as an effective tool to predict the distribution of freshwater organisms: The case of the Sabaleta Brycon henni (Eigenmann, 1913). PLoS ONE 16, e0247876 (2021).PubMed 
PubMed Central 

Google Scholar 
Merow, C., Smith, M. J. & Silander, J. A. Jr. A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography 36, 1058–1069 (2013).
Google Scholar 
Peterson, A. T., Cobos, M. E. & Jiménez-García, D. Major challenges for correlational ecological niche model projections to future climate conditions. Ann. N. Y. Acad. Sci. 1429, 66–77 (2018).ADS 
PubMed 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
Google Scholar 
Raghavan, R. K., Peterson, A. T., Cobos, M. E., Ganta, R. & Foley, D. Current and future distribution of the lone star tick, Amblyomma americanum (L.)(Acari: Ixodidae) in North America. PLoS ONE 14, e0209082 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Muscarella, R. et al. ENM eval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods Ecol. Evol. 5, 1198–1205 (2014).
Google Scholar 
Ramírez Villegas, J. & Jarvis, A. Downscaling global circulation model outputs: The delta method decision and policy analysis Working Paper No. 1 (2010).Liu, C., Newell, G. & White, M. On the selection of thresholds for predicting species occurrence with presence-only data. Ecol. Evol. 6, 337–348 (2016).PubMed 

Google Scholar 
Austin, M. Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecol. Model. 200, 1–19 (2007).
Google Scholar 
Rahmanian, S., Pouyan, S., Karami, S. & Pourghasemi, H. R. In Computers in Earth and Environmental Sciences 245–254 (Elsevier, 2022).Rahmanian, S., Pourghasemi, H. R., Pouyan, S. & Karami, S. Habitat potential modelling and mapping of Teucrium polium using machine learning techniques. Environ. Monit. Assess. 193, 1–21 (2021).
Google Scholar 
Domroes, M., Kaviani, M. & Schaefer, D. An analysis of regional and intra-annual precipitation variability over Iran using multivariate statistical methods. Theor. Appl. Climatol. 61, 151–159 (1998).ADS 

Google Scholar 
Prevéy, J. et al. Greater temperature sensitivity of plant phenology at colder sites: Implications for convergence across northern latitudes. Glob. Change Biol. 23, 2660–2671 (2017).ADS 

Google Scholar 
Rousta, I. et al. Impacts of drought on vegetation assessed by vegetation indices and meteorological factors in Afghanistan. Remote Sens. 12, 2433 (2020).ADS 

Google Scholar 
Wang, Y. et al. Contrasting effects of temperature and precipitation on vegetation greenness along elevation gradients of the Tibetan Plateau. Remote Sens. 12, 2751 (2020).ADS 

Google Scholar 
Zhang, Y. et al. Vegetation change and its relationship with climate factors and elevation on the Tibetan plateau. Int. J. Environ. Res. Public Health 16, 4709 (2019).PubMed Central 

Google Scholar 
Vanneste, T. et al. Impact of climate change on alpine vegetation of mountain summits in Norway. Ecol. Res. 32, 579–593 (2017).
Google Scholar 
Rodriguez, C., Navarro, T. & El-Keblawy, A. Covariation in diaspore mass and dispersal patterns in three Mediterranean coastal dunes in southern Spain. Turk. J. Bot. 41, 161–170 (2017).
Google Scholar 
Zona, S. Fruit and seed dispersal of Salvia L.(Lamiaceae): A review of the evidence. Bot. Rev. 83, 195–212 (2017).
Google Scholar 
Ryding, O. Myxocarpy in the Nepetoideae (Lamiaceae) with notes on myxodiaspory in general. Syst. Geogr. Plants 71, 503–514 (2001).
Google Scholar 
Tanaka, K., Ogata, K., Mukai, H., Yamawo, A. & Tokuda, M. Adaptive advantage of myrmecochory in the ant-dispersed herb Lamium amplexicaule (Lamiaceae): Predation avoidance through the deterrence of post-dispersal seed predators. PLoS ONE 10, e0133677 (2015).PubMed 
PubMed Central 

Google Scholar 
Ferreira, P. M. et al. Long-term ecological research in southern Brazil grasslands: Effects of grazing exclusion and deferred grazing on plant and arthropod communities. PLoS ONE 15, e0227706 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar  More