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    More than skin deep

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    Predators buffer impacts

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    Predation increases multiple components of microbial diversity in activated sludge communities

    1.Seviour RJ, Kragelund C, Kong Y, Eales K, Nielsen JL, Nielsen PH. Ecophysiology of the Actinobacteria in activated sludge systems. Antonie Van Leeuw J Microb. 2008;94:21–33.
    Google Scholar 
    2.Jiang X-T, Ye L, Ju F, Wang Y-L, Zhang T. Toward an intensive longitudinal understanding of activated sludge bacterial assembly and dynamics. Environ Sci Technol. 2018;52:8224–32.CAS 
    PubMed 

    Google Scholar 
    3.Fiałkowska E, Pajdak-Stós A. The role of Lecane rotifers in activated sludge bulking control. Water Res. 2008;42:2483–90.PubMed 

    Google Scholar 
    4.Madoni P. Protozoa in wastewater treatment processes: a minireview. Ital J Zool. 2011;78:3–11.
    Google Scholar 
    5.Ye L, Mei R, Liu W-T, Ren H, Zhang X-X. Machine learning-aided analyses of thousands of draft genomes reveal specific features of activated sludge processes. Microbiome. 2020;8:16.PubMed 
    PubMed Central 

    Google Scholar 
    6.Peces M, Astals S, Jensen P, Clarke W. Deterministic mechanisms define the long-term anaerobic digestion microbiome and its functionality regardless of the initial microbial community. Water Res. 2018;141:366–76.CAS 
    PubMed 

    Google Scholar 
    7.Wu L, Ning D, Zhang B, Li Y, Zhang P, Shan X, et al. Global diversity and biogeography of bacterial communities in wastewater treatment plants. Nat Microbiol. 2019;4:1183–95.CAS 
    PubMed 

    Google Scholar 
    8.Cox HH, Deshusses MA. Biomass control in waste air biotrickling filters by protozoan predation. Biotechnol Bioeng. 1999;62:216–24.CAS 
    PubMed 

    Google Scholar 
    9.Madoni P. A sludge biotic index (SBI) for the evaluation of the biological performance of activated sludge plants based on the microfauna analysis. Water Res. 1994;28:67–75.CAS 

    Google Scholar 
    10.Ratsak C, Maarsen K, Kooijman S. Effects of protozoa on carbon mineralization in activated sludge. Water Res. 1996;30:1–12.CAS 

    Google Scholar 
    11.Pogue AJ, Gilbride KA. Impact of protozoan grazing on nitrification and the ammonia- and nitrite-oxidizing bacterial communities in activated sludge. Can J Microbiol. 2007;53:559–71.CAS 
    PubMed 

    Google Scholar 
    12.Esteban G, Tellez C, Bautista LM. Dynamics of ciliated protozoa communities in activated-sludge process. Water Res. 1991;25:967–72.
    Google Scholar 
    13.Madoni P, Davoli D, Chierici E. Comparative analysis of the activated sludge microfauna in several sewage treatment works. Water Res. 1993;27:1485–91.CAS 

    Google Scholar 
    14.Otto S, Harms H, Wick LY. Effects of predation and dispersal on bacterial abundance and contaminant biodegradation. FEMS Microbiol Ecol. 2017;93:fiw241.PubMed 

    Google Scholar 
    15.Peralta-Maraver I, Reiss J, Robertson AL. Interplay of hydrology, community ecology and pollutant attenuation in the hyporheic zone. Sci Total Environ. 2018;610:267–75.PubMed 

    Google Scholar 
    16.Yang JW, Wu W, Chung C-C, Chiang K-P, Gong G-C, Hsieh C-H. Predator and prey biodiversity relationship and its consequences on marine ecosystem functioning—interplay between nanoflagellates and bacterioplankton. ISME J. 2018;12:1532–42.PubMed 
    PubMed Central 

    Google Scholar 
    17.Seiler C, van Velzen E, Neu TR, Gaedke U, Berendonk TU, Weitere M. Grazing resistance of bacterial biofilms: a matter of predators’ feeding trait. FEMS Microbiol Ecol. 2017;93:fix112.
    Google Scholar 
    18.Burian A, Nielsen JM, Winder M. Food quantity-quality interactions and their impact on consumer behavior and trophic transfer. Ecol Monogr. 2020;90:e01395.
    Google Scholar 
    19.Schmitz OJ. Effects of predator functional diversity on grassland ecosystem function. Ecology. 2009;90:2339–45.PubMed 

    Google Scholar 
    20.Estes JA, Terborgh J, Brashares JS, Power ME, Berger J, Bond WJ, et al. Trophic downgrading of planet Earth. Science. 2011;333:301–6.CAS 
    PubMed 

    Google Scholar 
    21.Cardinale BJ, Duffy JE, Gonzalez A, Hooper DU, Perrings C, Venail P, et al. Biodiversity loss and its impact on humanity. Nature. 2012;486:59–67.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    22.Isbell F, Calcagno V, Hector A, Connolly J, Harpole WS, Reich PB, et al. High plant diversity is needed to maintain ecosystem services. Nature. 2011;477:199–202.CAS 
    PubMed 

    Google Scholar 
    23.Delgado-Baquerizo M, Maestre FT, Reich PB, Jeffries TC, Gaitan JJ, Encinar D, et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat Commun. 2016;7:10541.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.McCann KS. The diversity–stability debate. Nature. 2000;405:228.CAS 
    PubMed 

    Google Scholar 
    25.Pennekamp F, Pontarp M, Tabi A, Altermatt F, Alther R, Choffat Y, et al. Biodiversity increases and decreases ecosystem stability. Nature. 2018;563:109–12.CAS 
    PubMed 

    Google Scholar 
    26.Saikaly PE, Oerther DB. Diversity of dominant bacterial taxa in activated sludge promotes functional resistance following toxic shock loading. Microb Ecol. 2011;61:557–67.CAS 
    PubMed 

    Google Scholar 
    27.Worm B, Lotze HK, Hillebrand H, Sommer U. Consumer versus resource control of species diversity and ecosystem functioning. Nature. 2002;417:848–51.CAS 
    PubMed 

    Google Scholar 
    28.Gauzens B, Legendre S, Lazzaro X, Lacroix G. Intermediate predation pressure leads to maximal complexity in food webs. Oikos. 2016;125:595–603.
    Google Scholar 
    29.Chase JM, Biro EG, Ryberg WA, Smith KG. Predators temper the relative importance of stochastic processes in the assembly of prey metacommunities. Ecol Lett. 2009;12:1210–8.PubMed 

    Google Scholar 
    30.Paine RT. Food web complexity and species diversity. Am Nat. 1966;100:65–75.
    Google Scholar 
    31.Gliwicz ZM, Wursbaugh WA, Szymanska E. Absence of predation eliminates coexistence: experience from the fish–zooplankton interface. Fifty years after the “Homage to Santa Rosalia”: old and new paradigms on biodiversity in aquatic ecosystems. Springer; 2010. p. 103–17.32.Terborgh JW. Toward a trophic theory of species diversity. Proc Natl Acad Sci USA. 2015;112:11415–22.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Kondoh M. Unifying the relationships of species richness to productivity and disturbance. Proc R Soc B-Biol Sci. 2001;268:269–71.CAS 

    Google Scholar 
    34.Hutchinson GE. The paradox of the plankton. Am Nat. 1961;95:137–45.
    Google Scholar 
    35.Al-Shahwani S, Horan N. The use of protozoa to indicate changes in the performance of activated sludge plants. Water Res. 1991;25:633–8.CAS 

    Google Scholar 
    36.Torsvik V, Øvreås L, Thingstad TF. Prokaryotic diversity-magnitude, dynamics, and controlling factors. Science. 2002;296:1064–6.CAS 
    PubMed 

    Google Scholar 
    37.Papadimitriou C, Papatheodoulou A, Takavakoglou V, Zdragas A, Samaras P, Sakellaropoulos G, et al. Investigation of protozoa as indicators of wastewater treatment efficiency in constructed wetlands. Desalination. 2010;250:378–82.CAS 

    Google Scholar 
    38.Rossberg AG. Food webs and biodiversity: foundations, models, data. John Wiley & Sons; 2013.39.Vage S, Bratbak G, Egge J, Heldal M, Larsen A, Norland S, et al. Simple models combining competition, defence and resource availability have broad implications in pelagic microbial food webs. Ecol Lett. 2018;21:1440–52.PubMed 

    Google Scholar 
    40.Landry M, Hassett R. Estimating the grazing impact of marine micro-zooplankton. Mar Biol. 1982;67:283–8.
    Google Scholar 
    41.Dolan J, Gallegos C, Moigis A. Dilution effects on microzooplankton in dilution grazing experiments. Mar Ecol Prog Ser. 2000;200:127–39.CAS 

    Google Scholar 
    42.Dottorini G, Michaelsen TY, Kucheryavskiy S, Andersen KS, Kristensen JM, Peces M, et al. Mass-immigration determines the assembly of activated sludge microbial communities. Proc Natl Acad Sci USA; 2021;118:e2021589118.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.Stevens-Garmon J, Drewes JE, Khan SJ, McDonald JA, Dickenson ERV. Sorption of emerging trace organic compounds onto wastewater sludge solids. Water Res. 2011;45:3417–26.CAS 
    PubMed 

    Google Scholar 
    44.Gasol JM, Morán XAG. Flow cytometric determination of microbial abundances and its use to obtain indices of community structure and relative activity. Hydrocarbon and lipid microbiology protocols. Springer; 2015. p. 159–87.45.Ram AP, Chaibi-Slouma S, Keshri J, Colombet J, Sime-Ngando T. Functional responses of bacterioplankton diversity and metabolism to experimental bottom-up and top-down forcings. Microb Ecol. 2016;72:347–58.
    Google Scholar 
    46.Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci USA. 2011;108:4516–22.CAS 
    PubMed 

    Google Scholar 
    47.Hugerth LW, Muller EE, Hu YO, Lebrun LA, Roume H, Lundin D, et al. Systematic design of 18S rRNA gene primers for determining eukaryotic diversity in microbial consortia. Plos One. 2014;9:e95567.PubMed 
    PubMed Central 

    Google Scholar 
    48.D’Amore R, Ijaz UZ, Schirmer M, Kenny JG, Gregory R, Darby AC, et al. A comprehensive benchmarking study of protocols and sequencing platforms for 16S rRNA community profiling. BMC Genom. 2016;17:55.
    Google Scholar 
    49.Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    50.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–D596.PubMed 
    PubMed Central 

    Google Scholar 
    52.Price MN, Dehal PS, Arkin AP. FastTree 2-approximately maximum-likelihood trees for large alignments. Plos One. 2010;5:10.
    Google Scholar 
    53.Faith DP. Conservation evaluation and phylogenetic diversity. Biol Conserv. 1992;61:1–10.
    Google Scholar 
    54.Tsirogiannis C, Sandel B. PhyloMeasures: a package for computing phylogenetic biodiversity measures and their statistical moments. Ecography. 2016;39:709–14.
    Google Scholar 
    55.Wobbrock JO, Findlater L, Gergle D, Higgins JJ, Acm. The aligned rank transform for nonparametric factorial analyses using only ANOVA procedures. Association Computing Machinery: New York; 2011.56.Burnham KP, Anderson DR. Model selection and multimodel interference: a practical information—theoretic approach. Springer: New York, USA; 2002.57.Arndt D, Xia J, Liu Y, Zhou Y, Guo AC, Cruz JA, et al. METAGENassist: a comprehensive web server for comparative metagenomics. Nucleic Acids Res. 2012;40:W88–W95.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.R Development Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2015. ISBN 3-900051-07-0, http://wwwR-projectorg.59.Calbet A, Landry MR. Phytoplankton growth, microzooplankton grazing, and carbon cycling in marine systems. Limnol Oceanogr. 2004;49:51–57.CAS 

    Google Scholar 
    60.Kiorboe T. How zooplankton feed: mechanisms, traits and trade-offs. Biol Rev. 2011;86:311–39.PubMed 

    Google Scholar 
    61.Juergens K, Matz C. Predation as a shaping force for the phenotypic and genotypic composition of planktonic bacteria. Antonie Van Leeuw J Microb. 2002;81:413–34.
    Google Scholar 
    62.Hammill E, Kratina P, Beckerman A, Anholt BR. Precise time interactions between behavioural and morphological defences. Oikos. 2010;119:494–9.
    Google Scholar 
    63.Pernthaler J. Predation on prokaryotes in the water column and its ecological implications. Nat Rev Microbiol. 2005;3:537–46.CAS 
    PubMed 

    Google Scholar 
    64.Visser MD, Muller‐Landau HC, Wright SJ, Rutten G, Jansen PA. Tri‐trophic interactions affect density dependence of seed fate in a tropical forest palm. Ecol Lett. 2011;14:1093–1100.PubMed 

    Google Scholar 
    65.Bagchi R, Gallery RE, Gripenberg S, Gurr SJ, Narayan L, Addis CE, et al. Pathogens and insect herbivores drive rainforest plant diversity and composition. Nature. 2014;506:85–88.CAS 
    PubMed 

    Google Scholar 
    66.Kratina P, Vos M, Anholt BR. Species diversity modulates predation. Ecology. 2007;88:1917–23.PubMed 

    Google Scholar 
    67.Jaworski CC, Bompard A, Genies L, Amiens-Desneux E, Desneux N. Preference and prey switching in a generalist predator attacking local and invasive alien pests. Plos One. 2013;8:e82231.PubMed 
    PubMed Central 

    Google Scholar 
    68.Coblentz KE, DeLong JP. Predator‐dependent functional responses alter the coexistence and indirect effects among prey that share a predator. Oikos. 2020;129:1404–14.
    Google Scholar 
    69.Madoni P. Estimates of ciliated protozoa biomass in activated sludge and biofilm. Bioresour Technol. 1994;48:245–9.CAS 

    Google Scholar 
    70.Tilman D, Knops J, Wedin D, Reich P, Ritchie M, Siemann E. The influence of functional diversity and composition on ecosystem processes. Science. 1997;277:1300–2.CAS 

    Google Scholar 
    71.Sato Y, Hori T, Navarro RR, Habe H, Ogata A. Functional maintenance and structural flexibility of microbial communities perturbed by simulated intense rainfall in a pilot-scale membrane bioreactor. Appl Microbiol Biot. 2016;100:6447–56.CAS 

    Google Scholar 
    72.Cardinale BJ, Wright JP, Cadotte MW, Carroll IT, Hector A, Srivastava DS, et al. Impacts of plant diversity on biomass production increase through time because of species complementarity. Proc Natl Acad Sci USA. 2007;104:18123–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    73.Srivastava DS, Cadotte MW, MacDonald AAM, Marushia RG, Mirotchnick N. Phylogenetic diversity and the functioning of ecosystems. Ecol Lett. 2012;15:637–48.PubMed 

    Google Scholar 
    74.Yachi S, Loreau M. Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis. Proc Natl Acad Sci USA. 1999;96:1463–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    75.Mori AS, Isbell F, Seidl R. β-diversity, community assembly, and ecosystem functioning. Trends Ecol Evol. 2018;33:549–64.PubMed 

    Google Scholar 
    76.Hammill E, Hawkins CP, Greig HS, Kratina P, Shurin JB, Atwood TB. Landscape heterogeneity strengthens the relationship between β‐diversity and ecosystem function. Ecology. 2018;99:2467–75.PubMed 

    Google Scholar 
    77.Ellingsen KE, Yoccoz NG, Tveraa T, Frank KT, Johannesen E, Anderson MJ, et al. The rise of a marine generalist predator and the fall of beta diversity. Glob Change Biol. 2020;26:2897–907.
    Google Scholar 
    78.Weisse T. The significance of inter-and intraspecific variation in bacterivorous and herbivorous protists. Antonie Van Leeuw J Microb. 2002;81:327–41.
    Google Scholar 
    79.Nierychlo M, Andersen KS, Xu Y, Green N, Jiang C, Albertsen M, et al. MiDAS 3: an ecosystem-specific reference database, taxonomy and knowledge platform for activated sludge and anaerobic digesters reveals species-level microbiome composition of activated sludge. Water Res. 2020;182:115955.CAS 
    PubMed 

    Google Scholar  More

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    Microplastics pollution in salt pans from the Maheshkhali Channel, Bangladesh

    MPs abundanceIn Table 1 MP abundance (mean value ± standard deviation) values are presented by shape, size range, color and polymer type categories for each sampling site. MP were found in all analyzed salt samples including pellets, fibers, fragments, films and lines (Fig. 3). MP total abundance values per site ranged from 74.7 to 136.7 particles kg−1 in the following order of increasing abundance: S3  black (17%)  > blue (15%)  > green and transparent (10% each)  > pink (6%)  > colorless (5%). In terms of size, most particles were in the category 500–1000 µm, except for S3 (1000–5000 µm) (Table 1). The distribution of MP particles based on size range was: 500–1000 µm (40%)  > 1000–5000 µm (34%)  > 250–500 µm (26%). For salts from the Atlantic and the Pacific Ocean, originating from Brazil, the United Kingdom, and the USA, Kim et al.12 reported a higher abundance of MP in size range 100–1000 µm while sizes in the range 100–5000 µm were reported for salt samples from the Black Sea. Seth and Shriwastay20 found that 80% of fibers found in salt samples from the Indian Sea were smaller than 2000 μm in length. MP size range differences among the various studies are suggested to depend on the degree of weathering for a given environment30, different climatic conditions such as wind, rain, temperature, salinity, and waves influencing size range composition. Also, for runoff, rivers, and atmospheric fallout transportation, smaller MP size ranges can be expected to be associated with a longer range from the initial plastic sources31,32,33. Nevertheless, more detailed information about MP polymer/color features within the size ranges are needed to achieve stronger conclusions about potential long/short-range sources.Figure 6Microplastics abundance (particles kg−1) by color in sea salt samples from stations S1 to S8.Full size imageFigure 7Microplastics abundance (particles kg−1) by size range in sea salt samples from stations S1 to S8.Full size imageMP polymer compositionFour types of polymer, namely polypropylene (PP), polystyrene (PS), polyethylene (PE), and polyethylene terephthalate (PET), were identified with FT-MIR-NIR (Supplementary Figure S1). These results are in accordance with those reported for salt samples in other studies worldwide (Table 1). These polymer types are widely used in daily life products, packaging, single-use plastics, and clothes, contributing to plastic pollution worldwide21. PET presented the highest contribution at all sampling sites, at ~ 48%, whereas PS was found to be least, at ~ 15% (Fig. 8, Table 1). Iñiguez et al.34 also reported PET predominance (83.3%) in Spanish table salt samples. PET predominance could be explained by its high density (1.30 g cm−3), making particles prone to sedimentation during the salt crystallization process19. PE (0.94 g cm−3), PP (0.90 g cm−3), and PS (1.05 cm−3) presented lower or similar densities to seawater (~ 1.02 g cm−3), making these more prone to flotation and possible loss due to wind during desiccation.Figure 8Microplastics abundances (particles kg−1) by polymer composition in sea salt samples from stations S1 to S8.Full size imageRisks assessmentDuring degradation, MP tends to emit monomers and different types of additives, these having the potential to cause harm to ecological systems and health18, 35. Results for the polymeric risks indices are presented in Fig. 9. According to polymer risk classification, all salts samples showed low risks, being similar to the entire study area. To date, none of the published studies have applied chemometric models in evaluating MP pollution in salts, posing difficulties when comparing our results. Information on the hazards of MP from ingestion to human health is still highly unclear. Other than exposure, the destiny and transit of ingested MP in the human body, including intestinal digestion and biliary discharge, have not been determined in previous research and remained largely unclear36. Some studies conducted impact assessments based on in vitro models37,38. However, whether the exposure concentrations used in such studies indicate the MP consumed and collected in humans is inconclusive. Previous studies found that toxicity, oxidative stress, and inflammation could result from MP exposure, including immune disruption and neurotoxicity effects, among others39. Therefore, an immediate effort is required to assess the health consequences of these MP when they reach the human body.Figure 9Polymeric risk indices for MP types in salts from stations S1 to S8.Full size image More

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    Correction: The rate and fate of N2 and C fixation by marine diatom-diazotroph symbioses

    AffiliationsDepartment of Ocean Sciences, University of California, Santa Cruz, CA, USARachel A. FosterDepartment of Biogeochemistry, Max Planck Institute for Marine Microbiology, Bremen, GermanyRachel A. Foster, Daniela Tienken, Sten Littmann & Marcel M. M. KuypersDepartment of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, SwedenRachel A. FosterDepartment of Geosciences, Swedish Museum of Natural History, Stockholm, SwedenMartin J. WhitehouseDepartment of Oceanography, University of Hawai’i at Mānoa, Honolulu, HI, USAAngelicque E. WhiteAuthorsRachel A. FosterDaniela TienkenSten LittmannMartin J. WhitehouseMarcel M. M. KuypersAngelicque E. WhiteCorresponding authorCorrespondence to
    Rachel A. Foster. More

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    Modeling a primate technological niche

    1.Stiner, M. C. The challenges of documenting coevolution and niche construction: The example of domestic spaces. Evol. Anthropol. https://doi.org/10.1002/evan.21878 (2020).Article 
    PubMed 

    Google Scholar 
    2.Potts, R. Why the Oldowan? Plio-Pleistocene toolmaking and the transport of resources. J. Anthropol. Res. 47, 153–176 (1991).
    Google Scholar 
    3.Kuhn, S. L., Raichlen, D. A. & Clark, A. E. What moves us? How mobility and movement are at the center of human evolution. Evol. Anthropol. 25, 86–97 (2016).PubMed 

    Google Scholar 
    4.Haas, R. & Kuhn, S. L. Forager mobility in constructed environments. Curr. Anthropol. 60, 499–535 (2019).
    Google Scholar 
    5.Iovita, R. et al. Operationalizing niche construction theory with stone tools. Evol. Anthropol. https://doi.org/10.1002/evan.21881 (2021).Article 
    PubMed 

    Google Scholar 
    6.Reeves, J. S., Braun, D. R., Finestone, E. M. & Plummer, T. W. Ecological perspectives on technological diversity at Kanjera South. J. Hum. Evol. 158, 103029 (2021).PubMed 

    Google Scholar 
    7.Finestone, E. M., Braun, D. R., Plummer, T. W., Bartilol, S. & Kiprono, N. Building ED-XRF datasets for sourcing rhyolite and quartzite artifacts: A case study on the Homa Peninsula, Kenya. J. Archaeol. Sci. 33, 102510 (2020).
    Google Scholar 
    8.Braun, D. R. et al. Oldowan behavior and raw material transport: Perspectives from the Kanjera Formation. J. Archaeol. Sci. 35, 2329–2345 (2008).
    Google Scholar 
    9.Potts, R. Home bases and early hominids. Am. Sci. 72, 338–347 (1984).ADS 

    Google Scholar 
    10.Schick, K. D. Modeling the formation of Early Stone Age artifact concentrations. J. Hum. Evol. 16, 789–807 (1987).
    Google Scholar 
    11.Binford, L. R. Willow smoke and dogs’ Tails: Hunter-gatherer settlement systems and archaeological site formation. Am. Antiq. 45, 4–20 (1980).
    Google Scholar 
    12.Schiffer, M. B. Archaeology as behavioral science. Am. Anthropol. 77, 836–848 (1975).
    Google Scholar 
    13.Schiffer, M. B. Formation Processes of the Archaeological Record (University of New Mexico Press, 1987).
    Google Scholar 
    14.Binford, L. R. Behavioral Archaeology and the ‘Pompeii Premise’. J. Anthropol. Res. 37, 195–208 (1981).
    Google Scholar 
    15.Binford, L. R. The archaeology of place. J. Anthropol. Archaeol. 1, 5–31 (1982).
    Google Scholar 
    16.Braun, D. R. et al. Ecosystem engineering in the Quaternary of the West Coast of South Africa. Evol. Anthropol. 30, 50–62 (2020).
    Google Scholar 
    17.Yellen, J. E. Archaeological Approaches to the Present: Models for Reconstructing the Past (Academic Press, 1977).
    Google Scholar 
    18.Isaac, G. L. L. The Harvey Lecture Series, 1977–1978. Food sharing and human evolution: Archaeological Evidence from the Plio-Pleistocene of East Africa Author (s): Glynn Ll Isaac Published by: The University of Chicago Press Stable. http://www.jstor.org/sta. 34, 311–325 (1978).19.Brooks, A. S. & Yellen, J. E. The preservation of activity areas in the archaeological record: Ethnoarchaeological and archaeological work in NOrthwest Ngamiland, Botswana. In Methog and Theory for Activity Area Research: An Ethnoarchaeological Approach 63–106 (Columbia University Press, 1987).
    Google Scholar 
    20.Binford, L. R. Nunamiut Ethnoarchaeology (Percheron Press, 2012).
    Google Scholar 
    21.Hawkes, K. Ethnoarchaeology and Plio-Pleistocene sites: Some lessons from the Hadza. J. Anthropol. Archaeol. 44, 158–165 (2016).
    Google Scholar 
    22.McGrew, W. Chimpanzee Material Culture: Implications for Human Evolution (Cambridge University Press, 1992).
    Google Scholar 
    23.Carvalho, S., Cunha, E., Sousa, C. & Matsuzawa, T. Chaînes opératoires and resource-exploitation strategies in chimpanzee (Pan troglodytes) nut cracking. J. Hum. Evol. 55, 148–163 (2008).PubMed 

    Google Scholar 
    24.Whiten, A. Archaeology meets primate technology. Nature 498, 303–305 (2013).ADS 
    CAS 
    PubMed 

    Google Scholar 
    25.Haslam, M. et al. Primate archaeology evolves. Nat. Ecol. Evol. 1, 1431–1437 (2017).PubMed 

    Google Scholar 
    26.Biro, D., Haslam, M. & Rutz, C. Tool use as adaptation. Philos. Trans. R. Soc. B 368, 20120408 (2013).
    Google Scholar 
    27.Carvalho, S., Biro, D., McGrew, W. C. & Matsuzawa, T. Tool-composite reuse in wild chimpanzees (Pan troglodytes): Archaeologically invisible steps in the technological evolution of early hominins?. Anim. Cogn. 12, 103–114 (2009).
    Google Scholar 
    28.Haslam, M. et al. Primate archaeology. Nature 460, 339–344 (2009).ADS 
    CAS 
    PubMed 

    Google Scholar 
    29.Boesch, C. & Boesch, H. Mental map in wild chimpanzees: An analysis of hammer transports for nut cracking. Primates 25, 160–170 (1984).
    Google Scholar 
    30.Hannah, A. C. & McGrew, W. C. Chimpanzees using stones to crack open oil palm nuts in Liberia. Primates 28, 31–46 (1987).
    Google Scholar 
    31.Luncz, L. V., Proffitt, T., Kulik, L., Haslam, M. & Wittig, R. M. Distance-decay effect in stone tool transport by wild chimpanzees. Proc. R. Soc. B 283, 20161607 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    32.Braun, D. R., Harris, J. W. K. & Maina, D. N. Oldowan raw material procurement and use: Evidence from the koobi fora formation. Archaeometry 51, 26–42 (2009).CAS 

    Google Scholar 
    33.Plummer, T. W. Flaked stones and old bones: Biological and cultural evolution at the dawn of technology. Yearb. Phys. Anthropol. 47, 118–164 (2004).
    Google Scholar 
    34.Isaac, G. The archaeology of human origins: Studies of the Lower Pleistocene in East Africa, 1971–1981. Adv. World Archaeol. 3, 1–86 (1984).
    Google Scholar 
    35.Blumenschine, R. J., Masao, F. T., Tactikos, J. C. & Ebert, J. I. Effects of distance from stone source on landscape-scale variation in Oldowan artifact assemblages in the Paleo-Olduvai Basin, Tanzania. J. Archaeol. Sci. 35, 76–86 (2008).
    Google Scholar 
    36.Blumenschine, R. J. et al. Landscape distribution of Oldowan stone artifact assemblages across the fault compartments of the eastern Olduvai Lake Basin during early lowermost Bed II times. J. Hum. Evol. 63, 384–394 (2012).PubMed 

    Google Scholar 
    37.Visalberghi, E. et al. Distribution of potential suitable hammers and transport of hammer tools and nuts by wild capuchin monkeys. Primates 50, 95–104 (2009).PubMed 

    Google Scholar 
    38.Fragaszy, D. M. et al. The fourth dimension of tool use: Temporally enduring artefacts aid primates learning to use tools. Philos Trans R Soc Lond B 368, 20120410 (2013).CAS 

    Google Scholar 
    39.Stern, N. et al. The structure of the lower pleistocene archaeological record: A case study From the Koobi Fora Formation [and Comments and Reply]. Curr. Anthropol. 34, 201–225 (1993).
    Google Scholar 
    40.Stern, N. The implications of time-averaging for reconstructing the land-use patterns of early tool-using hominids. J. Hum. Evol. 27, 89–105 (1994).
    Google Scholar 
    41.Blumenschine, R. J. et al. Environments and hominin activities across the FLK Peninsula during Zinjanthropus times (1.84 Ma), Olduvai Gorge, Tanzania. J. Hum. Evol. 63, 364–383 (2012).PubMed 

    Google Scholar 
    42.Ferraro, J. V. et al. Earliest archaeological evidence of persistent hominin carnivory. PLoS ONE 8, e62174 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.Dibble, H. L. et al. Major fallacies surrounding stone artifacts and assemblages. J. Archaeol. Method Theory 24, 813–851 (2017).
    Google Scholar 
    44.Wilson, M. L. Long-term studies of the chimpanzees of Gombe National Park, Tanzania. In Long-Term Field Studies of Primates (eds Kappeler, P. M. & Watts, D. P.) 357–384 (Springer, 2012).
    Google Scholar 
    45.Proffitt, T., Haslam, M., Mercader, J. F., Boesch, C. & Luncz, L. V. Revisiting Panda 100, the first archaeological chimpanzee nut-cracking site. J. Hum. Evol. 124, 117–139 (2018).CAS 
    PubMed 

    Google Scholar 
    46.Panger, M. A., Brooks, A. S., Richmond, B. G. & Wood, B. Older than the Oldowan? Rethinking the emergence of hominin tool use. Evol. Anthropol. 11, 235–245 (2003).
    Google Scholar 
    47.Premo, L. Agent-based models as behavioral laboratories for evolutionary anthropological research. Ariz. Anthropol. 17, 91–113 (2006).
    Google Scholar 
    48.Premo, L. S. Exploratory agent-based models: Towards an experimental ethnoarchaeology. In Digital Discovery. Exploring New Frontiers in Human Heritage. CAA2006. Computer Applications and Quantitative Methods in Archaeology. Proceedings of the 34th Conference 22–29 (Archaeolingua, 2007).49.Faith, J. T. et al. Rethinking the ecological drivers of hominin evolution. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2021.04.011 (2021).Article 
    PubMed 

    Google Scholar 
    50.Wurzer, G., Kowarik, K. & Reschreiter, H. Agent-Based Modeling and Simulation in Archaeology Vol. 7 (Springer, 2015).
    Google Scholar 
    51.Boesch, C. Wild cultures a comparison between chimpanzee and human cultures. (Cambridge University Press, 2014).52.Masad, D. & Kazil, J. MESA: An agent-based modeling framework. Proceedings of the 14th Python in Science Conference (SCIPY 2015) 53–60 (2015).53.Grimm, V. et al. The ODD protocol: A review and first update. Ecol. Model. 221, 2760–2768 (2010).
    Google Scholar 
    54.Koops, K., McGrew, W. C. & Matsuzawa, T. Ecology of culture: Do environmental factors influence foraging tool use in wild chimpanzees, Pan troglodytes verus?. Anim. Behav. 85, 175–185 (2013).
    Google Scholar 
    55.Visalberghi, E., Sirianni, G., Fragaszy, D. & Boesch, C. Percussive tool use by Taï Western chimpanzees and Fazenda Boa Vista bearded capuchin monkeys: A comparison. Phil. Trans. R. Soc. B 370, 20140351 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    56.Whiten, A. et al. Cultures in chimpanzees. Nature 399, 682–685 (1999).ADS 
    CAS 
    PubMed 

    Google Scholar 
    57.Potts, R. Variables versus models of early Pleistocene hominid land use. J. Hum. Evol. 27, 7–24 (1994).
    Google Scholar 
    58.Potts, R., Behrensmeyer, A. K. & Ditchfield, P. Paleolandscape variation and early Pleistocene hominid activities: Members 1 and 7, Olorgesailie formation, Kenya. J. Hum. Evol. 37, 747–788 (1999).CAS 
    PubMed 

    Google Scholar 
    59.Foley, R. A model of regional archaeological structure. Proc. Prehist. Soc 47, 1–17 (1981).ADS 

    Google Scholar 
    60.Maurin, T., Bertran, P., Delagnes, A. & Boisserie, J.-R. Early hominin landscape use in the Lower Omo Valley, Ethiopia: Insights from the taphonomical analysis of Oldowan occurrences in the Shungura Formation (Member F). J. Hum. Evol. 111, 33–53 (2017).PubMed 

    Google Scholar 
    61.Binford, L. R. Constructing Frames of Reference (University of California Press, 2001).
    Google Scholar  More

  • in

    Wheat (Triticum aestivum) adaptability evaluation in a semi-arid region of Central Morocco using APSIM model

    1.FAO. Food and Agriculture Organization of the United Nations. FAOSTAT Data; www.faostat.fao.org (last access 15.06.21), (2016).2.Gomez, D., Salvador, P., Sanz, J. & Casanova, J. L. Modelling wheat yield with antecedent information, satellite and climate data using machine learning methods in Mexico. Agric. For. Meteorol. 300, 108317. https://doi.org/10.1016/j.agrformet.2020.108317 (2021).ADS 
    Article 

    Google Scholar 
    3.Wrigley, C. W. Wheat: A unique grain for the world. In Wheat chemistry and technology 4th edn (eds Khan, K. & Shewry, P. R.) 1–17 (AACC Int. Inc, St Paul, 2009).
    Google Scholar 
    4.Awika, J. M. Major cereal grains production and use around the world. In Advances in Cereal Science: Implications to Food Processing and Health Promotion, Vol. 1089 (eds Awika, J. M., Piironen, V. & Bean, S.) 1–13 (American Chemical Society, 2011).5.Gupta, R., Meghwal, M. & Prabhakar, P. K. Bioactive compounds of pigmented wheat (Triticum aestivum): Potential benefits in human health. Trends Food Sci. Technol. 110, 240–252. https://doi.org/10.1016/j.tifs.2021.02.003 (2021).CAS 
    Article 

    Google Scholar 
    6.FAO. Food and Agriculture Organization of the United Nations. FAOSTAT Data; www.faostat.fao.org (last access 15.06.21), (2020).7.USDA. Grain and Feed Annual. United States Department of Agriculture (USDA), Foreign Agricultural Service (FAS), MO2020-0007; https://www.fas.usda.gov/data/morocco-grain-and-feed-annual-3 (last access 15.06.21), (2020).8.McIntyre, C. L. et al. Molecular detection of genomic regions associated with grain yield and yield-related components in an elite bread wheat cross evaluated under irrigated and rainfed conditions. Theor. Appl. Genet. 120, 527–541. https://doi.org/10.1007/s00122-009-1173-4 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    9.UN. World population prospects. United Nations (UN), Department of Economic and Social Affairs (DESA); https://www.un.org/development/desa/en/news/population/world-population-prospects-2017.html (last access 15.06.21), (2017).10.Gomez-Macpherson, H. & Richards, R. A. Effect of sowing time on yield and agronomic characteristics of wheat in south-eastern Australia. Aust. J. Agric. Res. 46, 1381–1399. https://doi.org/10.1071/AR9951381 (1995).Article 

    Google Scholar 
    11.Stone, P. J. & Nicolas, M. E. Effect of timing of heat stress during grain filling on two wheat varieties differing in heat tolerance. I. Grain growth. Aust. J. Plant Physiol. 22, 927–934. https://doi.org/10.1071/PP9950927 (1995).Article 

    Google Scholar 
    12.Mahdi, L., Bell, C. J. & Ryan, J. Establishment and yield of wheat (Triticum turgidum L.) after early sowing at various depths in a semi-arid Mediterranean environment. Field Crops Res. 58, 187–196. https://doi.org/10.1016/S0378-4290(98)00094-X (1998).Article 

    Google Scholar 
    13.Radmehr, M., Ayeneh, G. A. & Mamghani, R. Responses of late, medium and early maturity bread wheat genotypes to different sowing date. I. Effect of sowing date on phonological, morphological, and grain yield of four breed wheat genotypes. Iran. J. Seed. Sapling 21, 175–189 (2003).
    Google Scholar 
    14.Turner, N. C. Agronomic options for improving rainfall use efficiency of crops in dryland farming systems. J. Exp. Bot. 55, 2413–2425. https://doi.org/10.1093/jxb/erh154 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    15.Pickering, P. A. & Bhave, M. Comprehensive analysis of Australian hard wheat cultivars shows limited puroindoline allele diversity. Plant Sci. 172, 371–379. https://doi.org/10.1016/j.plantsci.2006.09.013 (2007).CAS 
    Article 

    Google Scholar 
    16.Zheng, B., Chenu, K., Fernanda Dreccer, M. & Chapman, S. C. Breeding for the future: What are the potential impacts of future frost and heat events on sowing and flowering time requirements for Australian bread wheat (Triticum aestivium) varieties?. Glob. Change Biol. 18, 2899–2914. https://doi.org/10.1111/j.1365-2486.2012.02724.x (2012).ADS 
    Article 

    Google Scholar 
    17.Wu, X. S., Chang, X. P. & Jing, R. L. Genetic insight into yield-associated traits of wheat grown in multiple rain-fed environments. PLoS ONE 7, e31249. https://doi.org/10.1371/journal.pone.0031249 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Mueller, B. et al. Lengthening of the growing season in wheat and maize producing regions. Weather Clim. Extrem. 9, 47–56. https://doi.org/10.1016/j.wace.2015.04.001 (2015).Article 

    Google Scholar 
    19.Hunt, J. R., Hayman, P. T., Richards, R. A. & Passioura, J. B. Opportunities to reduce heat damage in rainfed wheat crops based on plant breeding and agronomic management. Field Crops Res. 224, 126–138. https://doi.org/10.1016/j.fcr.2018.05.012 (2018).Article 

    Google Scholar 
    20.Ababaei, B. & Chenu, K. Heat shocks increasingly impede grain filling but have little effect on grain setting across the Australian wheatbelt. Agric. For. Meteorol. 284, 107889. https://doi.org/10.1016/j.agrformet.2019.107889 (2020).ADS 
    Article 

    Google Scholar 
    21.Anderson, W. K. & Smith, W. R. Yield advantage of two semi-dwarf compared with two tall wheats depends on sowing time. Aust. J. Agric. Res. 41, 811–826. https://doi.org/10.1071/AR9900811 (1990).Article 

    Google Scholar 
    22.Connor, D. J., Theiveyanathan, S. & Rimmington, G. M. Development, growth, water-use and yield of a spring and a winter wheat in response to time of sowing. Aust. J. Agric. Res. 43, 493–516. https://doi.org/10.1071/AR9920493 (1992).Article 

    Google Scholar 
    23.Owiss, T., Pala, M. & Ryan, J. Management alternatives for improved durum wheat production under supplemental irrigation in Syria. Eur. J. Agron. 11, 255–266. https://doi.org/10.1016/S1161-0301(99)00036-2 (1999).Article 

    Google Scholar 
    24.Bassu, S., Asseng, A., Motzo, R. & Giunta, F. Optimizing sowing date of durum wheat in a variable Mediterranean environment. Field Crops Res. 111, 109–118. https://doi.org/10.1016/j.fcr.2008.11.002 (2009).Article 

    Google Scholar 
    25.Bannayan, M., Eyshi Rezaei, E. & Hoogenboom, G. Determining optimum sowing dates for rainfed wheat using the precipitation uncertainty model and adjusted crop evapotranspiration. Agric. Water Manag. 126, 56–63. https://doi.org/10.1016/j.agwat.2013.05.001 (2013).Article 

    Google Scholar 
    26.Liang, Y. F. et al. Subsoiling and sowing time influence soil water content, nitrogen translocation and yield of dryland winter wheat. Agronomy 9, 37. https://doi.org/10.3390/agronomy9010037 (2019).Article 

    Google Scholar 
    27.Farooq, M., Basra, S. M. A., Rehman, H. & Saleem, B. A. Seed priming enhances the performance of late sown wheat (Triticum aestivum L.) by improving chilling tolerance. J. Agron. Crop Sci. 194, 55–60. https://doi.org/10.1111/j.1439-037X.2007.00287.x (2008).Article 

    Google Scholar 
    28.Kudair, I. M. & Adary, A. H. The effects of temperature and planting depth on coleoptile length of some Iraqi local and introduced wheat cultivars. Mesopotamia J. Agric. 17, 49–62 (1982).
    Google Scholar 
    29.Leoncini, E. et al. Phytochemical profile and nutraceutical value of old and modern common wheat cultivars. PLoS ONE 7, e45997. https://doi.org/10.1371/journal.pone.0045997 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    30.Busko, M. et al. The effect of Fusarium inoculation and fungicide application on concentrations of flavonoids (apigenin, kaempferol, luteolin, naringenin, quercetin, rutin, vitexin) in winter wheat cultivars. Am. J. Plant Sci. 5, 3727–3736. https://doi.org/10.4236/ajps.2014.525389 (2014).CAS 
    Article 

    Google Scholar 
    31.Bannayan, M., Kobayashi, K., Marashi, H. & Hoogenboom, G. Gene-based modeling for rice: An opportunity to enhance the simulation of rice growth and development?. J. Theor. Biol. 249, 593–605. https://doi.org/10.1016/j.jtbi.2007.08.022 (2007).ADS 
    CAS 
    Article 
    PubMed 
    MATH 

    Google Scholar 
    32.Soler, C. M. T., Sentelhas, P. C. & Hoogenboom, G. Application of the CSM-CERES-Maize model for sowing date evaluation and yield forecasting for maize grown off-season in a subtropical environment. Eur. J. Agron. 18, 165–177. https://doi.org/10.1016/j.eja.2007.03.002 (2007).Article 

    Google Scholar 
    33.Andarzian, B. et al. WheatPot: A simple model for spring wheat yield potential using monthly weather data. Biosyst. Eng. 99, 487–495. https://doi.org/10.1016/j.biosystemseng.2007.12.008 (2008).Article 

    Google Scholar 
    34.Andarzian, B., Hoogenboom, G., Bannayan, M., Shirali, M. & Andarzian, B. Determining optimum sowing date of wheat using CSM-CERES-Wheat model. J. Saudi Soc. Agric. Sci. 14, 189–199. https://doi.org/10.1016/j.jssas.2014.04.004 (2015).Article 

    Google Scholar 
    35.Palosuo, T. et al. Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth models. Eur. J. Agron. 35, 103–114. https://doi.org/10.1016/j.eja.2011.05.001 (2011).Article 

    Google Scholar 
    36.Rötter, R. P. et al. Simulation of spring barley yield in different climatic zones of Northern and Central Europe: A comparison of nine crop models. Field Crops Res. 133, 23–36. https://doi.org/10.1016/j.fcr.2012.03.016 (2012).Article 

    Google Scholar 
    37.Ran, H. et al. Capability of a solar energy-driven crop model for simulating water consumption and yield of maize and its comparison with a water-driven crop model. Agric. For. Meteorol. 287, 107955. https://doi.org/10.1016/j.agrformet.2020.107955 (2020).ADS 
    Article 

    Google Scholar 
    38.Keating, B. A. et al. An overview of APSIM, a model designed for farming systems simulation. Eur. J. Agron. 18, 267–288. https://doi.org/10.1016/S1161-0301(02)00108-9 (2003).Article 

    Google Scholar 
    39.Probert, M. E. & Dimes, J. P. Modelling release of nutrients from organic resources using APSIM. In Modelling nutrient management in tropical cropping systems Vol. 114 (eds Delve, R. J. & Probert, M. E.) 25–31 (ACIAR Proceedings, 2004).40.Mohanty, M. et al. Simulating soybean–wheat cropping system: APSIM model parameterization and validation. Agric. Ecosyst. Environ. 152, 68–78. https://doi.org/10.1016/j.agee.2012.02.013 (2012).Article 

    Google Scholar 
    41.George, N., Thompson, S. E., Hollingsworth, J., Orloff, S. & Kaffka, S. Measurement and simulation of water-use by canola and camelina under cool-season conditions in California. Agric. Water Manag. 196, 15–23. https://doi.org/10.1016/j.agwat.2017.09.015 (2018).Article 

    Google Scholar 
    42.Bahri, H., Annabi, M., M’Hamed, H. C. & Frija, A. Assessing the long-term impact of conservation agriculture on wheat-based systems in Tunisia using APSIM simulations under a climate change context. Sci. Total Environ. 692, 1223–1233. https://doi.org/10.1016/j.scitotenv.2019.07.307 (2019).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    43.Ahmed, M. et al. Novel multimodel ensemble approach to evaluate the sole effect of elevated CO2 on winter wheat productivity. Sci. Rep. 9, 7813. https://doi.org/10.1038/s41598-019-44251-x (2019).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Eyni-Nargeseh, H., Deihimfard, R., Rahimi-Moghaddam, R. & Mokhtassi-Bidgoli, A. Analysis of growth functions that can increase irrigated wheat yield under climate change. Meteorol. Appl. 27, 1–10. https://doi.org/10.1002/met.1804 (2020).Article 

    Google Scholar 
    45.Rahimi-Moghaddam, S., Eyni-Nargeseh, H., Kalantar Ahmadi, S. A. & Azizi, K. Towards withholding irrigation regimes and resistant genotypes as strategies to increase canola production in drought-prone environments: A modeling approach. Agric. Water Manag. 243, 106487. https://doi.org/10.1016/j.agwat.2020.106487 (2021).Article 

    Google Scholar 
    46.Berghuijs, H. N. C. et al. Calibrating and testing APSIM for wheat-faba bean pure cultures and intercrops across Europe. Field Crops Res. 264, 108088. https://doi.org/10.1016/j.fcr.2021.108088 (2021).Article 

    Google Scholar 
    47.METLE. National Report. Ministry of Equipment, Transport, Logistics and Water (last access 15.06.21), (2019).48.HCP. Voluntary national review of the implementation of the sustainable development goals. High Comm. Plng. p. 188 (2020).49.Hammer, G. L. et al. Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops. J. Exp. Bot. 61, 2185–2202. https://doi.org/10.1093/jxb/erq095 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    50.Holzworth, D. P. et al. APSIM—evolution towards a new generation of agricultural systems simulation. Environ. Model. Softw. 62, 327–350. https://doi.org/10.1016/j.envsoft.2014.07.009 (2014).Article 

    Google Scholar 
    51.Gaydon, D. S. et al. Evaluation of the APSIM model in cropping systems of Asia. Field Crops Res. 204, 52–75. https://doi.org/10.1016/j.fcr.2016.12.015 (2017).Article 

    Google Scholar 
    52.Climate Kelpie website. http://www.climatekelpie.com.au/manage-climate/decision-support-tools-for-managing-climate (2010).53.McCown, R. L., Hammer, G. L., Hargreaves, J. N. G., Holzworth, D. P. & Freebairn, D. M. APSIM: A novel software system for model development, model testing and simulation in agricultural systems research. Agric. Syst. 50, 255–271. https://doi.org/10.1016/0308-521X(94)00055-V (1996).Article 

    Google Scholar 
    54.Cichota, R., Vogeler, I., Werner, A., Wigley, K. & Paton, B. Performance of a fertiliser management algorithm to balance yield and nitrogen losses in dairy systems. Agric. Syst. 162, 56–65. https://doi.org/10.1016/j.agsy.2018.01.017 (2018).Article 

    Google Scholar 
    55.Laurenson, S., Cichota, R., Reese, P. & Breneger, S. Irrigation runoff from a rolling landscape with slowly permeable subsoils in New Zealand. Irrig. Sci. 36, 121–131. https://doi.org/10.1007/s00271-018-0570-3 (2018).Article 

    Google Scholar 
    56.Rodriguez, D. et al. Predicting optimum crop designs using crop models and seasonal climate forecasts. Sci. Rep. 8, 2231. https://doi.org/10.1038/s41598-018-20628-2 (2018).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Archontoulis, S. V., Miguez, F. E. & Moore, K. J. A methodology and an optimization tool to calibrate phenology of short-day species included in the APSIM PLANT model: Application to soybean. Environ. Model. Softw. 62, 465e477. https://doi.org/10.1016/j.envsoft.2014.04.009 (2014).Article 

    Google Scholar 
    58.Brown, H., Huth, N. & Holzworth, D. Crop model improvement in APSIM: Using wheat as a case study. Eur. J. Agron. 100, 141–150. https://doi.org/10.1016/j.eja.2018.02.002 (2018).Article 

    Google Scholar 
    59.Yang, X. et al. Cropping system productivity and evapotranspiration in the semiarid Loess Plateau of China under future temperature and precipitation changes: An APSIM-based analysis of rotational vs. Continuous systems. Agric. Water Manag. 229, 105959. https://doi.org/10.1016/j.agwat.2019.105959 (2020).Article 

    Google Scholar 
    60.Balboa, G. R. et al. A systems-level yield gap assessment of maize-soybean rotation under highand low-management inputs in the Western US Corn Belt using APSIM. Agric. Syst. 174, 125–154. https://doi.org/10.1016/j.agsy.2019.04.008 (2019).Article 

    Google Scholar 
    61.Yang, X. et al. Modelling the effects of conservation tillage on crop water productivity, soil water dynamics and evapotranspiration of a maize-winter wheat-soybean rotation system on the Loess plateau of China using APSIM. Agric. Syst. 166, 111–123. https://doi.org/10.1016/j.agsy.2018.08.005 (2018).Article 

    Google Scholar 
    62.Mohanty, M. et al. Soil carbon sequestration potential in a Vertisol in central India- results from a 43-year long-term experiment and APSIM modeling. Agric. Syst. 184, 102906. https://doi.org/10.1016/j.agsy.2020.102906 (2020).Article 

    Google Scholar 
    63.Vogeler, I., Thomas, S. & van der Weerden, T. Effect of irrigation management on pasture yield and nitrogen losses. Agric. Water Manag. 216, 60–69. https://doi.org/10.1016/j.agwat.2019.01.022 (2019).Article 

    Google Scholar 
    64.Bosi, C. et al. APSIM-tropical pasture: A model for simulating perennial tropical grass growth and its parameterisation for palisade grass (Brachiaria brizantha). Agric. Syst. 184, 102917. https://doi.org/10.1016/j.agsy.2020.102917 (2020).Article 

    Google Scholar 
    65.Smethurst, P. J., Valadares, R. V., Huth, N. I., Almeida, A. C. & Júlio, C. L. N. Generalized model for plantation production of Eucalyptus grandisand hybrids forgenotype-site-management applications. For. Ecol. Manag. 469, 118164. https://doi.org/10.1016/j.foreco.2020.118164 (2020).Article 

    Google Scholar 
    66.Xiao, D. P., Liu, D. L., Wang, B., Feng, P. Y. & Tang, J. Z. Climate change impact on yields and water use of wheat and maize in the north China plain under future climate change scenarios. Agric. Water Manag. 238, 1–15. https://doi.org/10.1016/j.agwat.2020.106238 (2020).Article 

    Google Scholar 
    67.Seyoum, S., Rachaputi, R., Chauhan, Y., Prasanna, B. & Fekybelu, S. Application of the APSIM model to exploit G × E × M interactions for maize improvement in Ethiopia. Field Crops Res. 217, 113–124. https://doi.org/10.1016/j.fcr.2017.12.012 (2018).Article 

    Google Scholar 
    68.Basche, A. D. & DeLonge, M. S. Comparing infiltration rates in soils managed with conventional and alternative farming methods: A meta-analysis. PLoS ONE 14, e0215702. https://doi.org/10.1371/journal.pone.0215702 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    69.Holzworth, D. et al. The development of a farming systems model (APSIM): A disciplined approach. In Proceedings of the iEMSs Third Biennial Meeting, Burlington, VT, USA, 9–13 July 2006 (International Environmental Modelling and Software Society, Manno, Switzerland, 2006).70.Gaydon, D. S. The APSIM model—an overview. In SAC Monograph: The SAARC-Australia Project Developing Capacity in Cropping Systems Modelling for South Asia (eds Dr. Donald S. Gaydon et al.) 15–31 (2014).71.Pinheiro, J. C. & Bates, D. M. Mixed Effects Models in S and S-Plus (Statistics and Computing) (Springer, New York, 2000).Book 

    Google Scholar 
    72.El Halimi, R. Nonlinear Mixed-effects Models and Bootstrap resampling: Analysis of Non-normal Repeated Measures in Biostatistical Practice. Amazon Books. 320 (2009).73.Vock, D. M., Davidian, M., Tsiatis, A. A. & Muir, A. J. Mixed model analysis of censored longitudinal data with flexible random-effects density. Biostat. 13, 61–73. https://doi.org/10.1093/biostatistics/kxr026 (2012).Article 
    MATH 

    Google Scholar 
    74.Beroho, M. et al. Analysis and prediction of climate forecasts in Northern Morocco: Application of multilevel linear mixed effects models using R Software. Heliyon 6, e05094. https://doi.org/10.1016/j.heliyon.2020.e05094 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    75.Laird, N. M. & Ware, J. H. Random-effects models for longitudinal data. Biometrics 38, 963–974. https://doi.org/10.2307/2529876 (1982).CAS 
    Article 
    PubMed 
    PubMed Central 
    MATH 

    Google Scholar 
    76.Littell, R. C., Henry, P. R. & Ammerman, C. B. Statistical analysis of repeated measures data using SAS procedures. J. Anim. Sci. Biotechnol. 76, 1216–1231. https://doi.org/10.2527/1998.7641216x (1998).CAS 
    Article 

    Google Scholar 
    77.Bouyoucos, G. J. Direction for making mechanical analysis of soils by the hydrometer method. Soil Sci. 42, 225–230. https://doi.org/10.1097/00010694-193609000-00007 (1936).ADS 
    CAS 
    Article 

    Google Scholar 
    78.Nash, J. E. & Sutcliffe, J. V. River flow forecasting through conceptual models, part I: A discussion of principles. J. Hydrol. 10, 282–290. https://doi.org/10.1016/0022-1694(70)90255-6 (1970).ADS 
    Article 

    Google Scholar 
    79.Willmott, C. J., Robeson, S. M. & Matsuura, K. A refined index of model performance. Int. J. Climatol. 32, 2088–2094. https://doi.org/10.1002/joc.2419 (2011).Article 

    Google Scholar 
    80.Loague, K. & Green, R. E. Statistical and graphical methods for evaluating solute transport models; overview and application. J. Contam. Hydrol. 7, 51–73. https://doi.org/10.1016/0169-7722(91)90038-3 (1991).ADS 
    CAS 
    Article 

    Google Scholar 
    81.Willmott, C. J. et al. Statistic for the evaluation and comparison of models. J. Geophys. Res. 90, 8995–9005. https://doi.org/10.1029/JC090iC05p08995 (1985).ADS 
    Article 

    Google Scholar 
    82.Jones, C. A., Kiniry, J. R. & Dyke, P. T. CERES-Maize, A simulation model of maize growth and development 1st edn. (Texas University Press, College Station, 1986).
    Google Scholar 
    83.Dardanelli, J. L., Bacheier, O. A., Sereno, R. & Gil, R. Rooting depth and soil water extraction patterns of different crops in a silty loam Haplustoll. Field Crops Res. 54, 29–38. https://doi.org/10.1016/S0378-4290(97)00017-8 (1997).Article 

    Google Scholar 
    84.Probert, M. E., Dimes, J. P., Keating, B. A., Dalal, R. C. & Strong, W. M. APSIM’s water and nitrogen modules and simulation of the dynamics of water and nitrogen in fallow systems. Agric. Syst. 56, 1–28. https://doi.org/10.1016/S0308-521X(97)00028-0 (1998).Article 

    Google Scholar 
    85.Littleboy, M., Freebairn, D. M., Silburn, D. M., Woodruff, D. R., Hammer, G. L. PERFECT version 3. A computer simulation model of productivity erosion runoff functions to evaluate conservation techniques. Queensland department of natural resources and department of plant industries. Queensland Dep. Prim. Ind., Queensland, Australia (1999).86.Dalgliesh, N. P. & Foale, M. A. Soil matters: Monitoring soil water and nutrients in dryland farming. Agric. Prod. Sys. Res. Unit, Toowoomba, Australia; http://hdl.handle.net/102.100.100/217161?index=1 (1998).87.Malone, R. W. et al. Evaluating and predicting agricultural management effects under tile drainage using modified APSIM. Geoderma 140, 310–322. https://doi.org/10.1016/j.geoderma.2007.04.014 (2007).ADS 
    CAS 
    Article 

    Google Scholar 
    88.Cresswell, H. P. et al. Catchment response to farm scale land use change. CSIRO and NSW Dept. of Ind. & Invest. (2009).89.Hammer, G. L. et al. Can changes in canopy and/or root system architecture explain historical maize yield trends in the U.S. Corn Belt?. Crop Sci. 49, 299–312. https://doi.org/10.2135/cropsci2008.03.0152 (2009).Article 

    Google Scholar 
    90.Archontoulis, S. V., Miguez, F. E. & Moore, K. J. Evaluating APSIM maize, soil water, soil nitrogen, manure, and soil temperature modules in the Midwestern United States. Agron. J. 106, 1025. https://doi.org/10.2134/agronj2013.0421 (2014).CAS 
    Article 

    Google Scholar 
    91.MacCarthy, D. S., Sommer, R. & Vlek, P. L. G. Modeling the impacts of contrasting nutrient and residue management practices on grain yield of sorghum (Sorghum bicolor (L.) Moench) in a semi-arid region of Ghana using APSIM. Field Crops Res. 113, 105–115. https://doi.org/10.1016/j.fcr.2009.04.006 (2009).Article 

    Google Scholar 
    92.Yang, Y. et al. Water use efficiency and crop water balance of rainfed wheat in a semi-arid environment: Sensitivity of future changes to projected climate changes and soil type. Theor. Appl. Climatol. 123, 565–579. https://doi.org/10.1007/s00704-015-1376-3 (2016).ADS 
    Article 

    Google Scholar 
    93.Deihimfard, R., Eyni-Nargeseh, H. & Mokhtassi-Bidgoli, A. Effect of future climate change on wheat yield and water use efficiency under semi-arid conditions as predicted by APSIM-wheat model. Int. J. Plant Prod. 12, 115–125. https://doi.org/10.1007/s42106-018-0012-4 (2018).Article 

    Google Scholar 
    94.Zhao, P. et al. The adaptability of Apsim-wheat model in the middle and lower reaches of the Vangtze river plain of china: A case study of winter wheat in hubei province. Agronomy 10, 981. https://doi.org/10.3390/agronomy10070981 (2020).Article 

    Google Scholar 
    95.SHNP, D. S., Takahashi, T., Okada, K. Evaluation of APSIM-wheat to simulate the response of yield and grain protein content to nitrogen application on an Andosol in Japan. Plant Prod. Sci. https://doi.org/10.1080/1343943X.2021.1883989 (2021).96.O’Leary, G. J. et al. Response of wheat growth, grain yield and water use to elevated CO2 under afree-air CO2 Enrichment (FACE) experiment and modelling in a semi-arid environment. Glob. Change Biol. 21, 2670–2686. https://doi.org/10.1111/gcb.12830 (2015).ADS 
    Article 

    Google Scholar 
    97.Lilley, J. M. & Kirkegaard, J. A. Farming system context drives the value of deep wheat roots in semi-arid environments. J. Exp. Bot. 67, 3665–3681. https://doi.org/10.1093/jxb/erw093 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    98.Whitbread, A. M., Hoffmann, M. P., Davoren, C. W., Mowat, D. & Baldock, J. A. Measuring and modeling the water balance in low-Rainfall cropping systems. Trans. ASABE 60, 2097–2110. https://doi.org/10.13031/trans.12581 (2017).Article 

    Google Scholar 
    99.Silungwe, F. R. et al. Crop upgrading strategies and modelling for rainfed cereals in a semi-arid climate—a review. Water 10, 356. https://doi.org/10.3390/w10040356 (2018).Article 

    Google Scholar 
    100.Hussain, J., Khaliq, T., Ahmad, A. & Akhtar, J. Performance of four crop model for simulations of wheat phenology, leaf growth, biomass and yield across planting dates. PLoS ONE 13, e0197546. https://doi.org/10.1371/journal.pone.0197546 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    101.Asseng, S., Turner, N. C. & Keating, B. A. Analysis of water- and nitrogen-use efficiency of wheat in a Mediterranean climate. Plant Soil 233, 127–143. https://doi.org/10.1023/A:1010381602223 (2001).CAS 
    Article 

    Google Scholar 
    102.Moeller, C., Pala, M., Manschadi, A. M., Meinke, H. & Sauerborn, J. Assessing the sustainability of wheat-based cropping systems using APSIM: Model parameterisation and evaluation. Aust. J. Agric. Res. 58, 75–86. https://doi.org/10.1007/s11625-013-0228-2 (2007).Article 

    Google Scholar 
    103.Bassu, S., Asseng, S., Giunta, F. & Motzo, R. Optimizing triticale sowing densities across the Mediterranean Basin. Field Crops Res. 144, 167–178. https://doi.org/10.1016/j.fcr.2013.01.014 (2013).Article 

    Google Scholar 
    104.Bationo, A., Mokwunye, U., Vlek, P. L. G., Koala, S. & Shapiro, B. I. Soil fertility management for sustainable land use in the West African Sudano-Sahelian Zone. In Soil Fertility Management in Africa: A Regional Perspective, African Academy of Sciences Centro Internacional de Agricultura Tropical (CIAT); Tropical Soil Biology and Fertility (TSBF) (eds Gichuri, M. P. et al.) 253–292 (Academic and Scientific Publishing, Nairobi, 2003).
    Google Scholar 
    105.Bernstein, L. et al. IPCC, 2007: Climate Change 2007: Synth. Rep. Geneva: IPCC. ISBN 2-9169-122-4 (2008).106.Tramblay, Y. et al. Climate change impacts on extreme precipitation in Morocco. Glob. Planet Change 82, 104–114. https://doi.org/10.1016/j.gloplacha.2011.12.002 (2012).ADS 
    Article 

    Google Scholar 
    107.Tramblay, Y., Ruelland, D., Somot, S., Bouaicha, R. & Servat, E. High-resolution Med-CORDEX regional climate model simulations for hydrological impact studies: A first evaluation of the ALADIN-Climate model in Morocco. Hydrol. Earth Syst. Sci. 17, 3721–3739. https://doi.org/10.5194/hess-17-3721-2013 (2013).ADS 
    Article 

    Google Scholar 
    108.Seif-Ennasr, M. et al. Climate change and adaptive water management measures in Chtouka Aït Baha region (Morocco). Sci. Total Environ. 573, 862–875. https://doi.org/10.1016/j.scitotenv.2016.08.170 (2016).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    109.Hirich, A., Fatnassi, H., Ragab, R. & Choukr-Allah, R. Prediction of climate change impact on corn grown in the South of Morocco using the saltmed model. J. Irrigat. Drain. Eng. 65, 9–18. https://doi.org/10.1002/ird.2002 (2016).Article 

    Google Scholar 
    110.Ouhamdouch, S. & Bahir, M. Climate change impact on future rainfall and temperature in semi-arid areas (Essaouira basin, Morocco). Environ. Process. 4, 975–990. https://doi.org/10.1007/s40710-017-0265-4 (2017).Article 

    Google Scholar 
    111.Brouziyne, Y. et al. Modelling sustainable adaptation strategies toward a climate-smart agriculture in a Mediterranean watershed under projected climate change scenarios. Agric. Syst. 162, 154–163. https://doi.org/10.1016/j.agsy.2018.01.024 (2018).Article 

    Google Scholar 
    112.Dosio, A. & Panitz, H.-J. Climate change projections for CORDEX-Africa with COSMO-CLM regional climate model and differences with the driving global climate models. Clim. Dyn. 46, 1599–1625. https://doi.org/10.1007/s00382-015-2664-4 (2016).Article 

    Google Scholar 
    113.Zeroual, A., Assani, A. A., Meddi, M. & Alkama, R. Assessment of climate change in Algeria from 1951 to 2098 using the Köppen-Geiger climate classification scheme. Clim. Dyn. 52, 227–243. https://doi.org/10.1007/s00382-018-4128-0 (2018).Article 

    Google Scholar 
    114.Mami, A. et al. Future climatic and hydrologic changes estimated by bias-adjusted regional climate model outputs of the Cordex-Africa project: Case of the Tafna basin (North-Western Africa). Int. J. Glob. Warm. 23, 58–90. https://doi.org/10.1504/IJGW.2021.112489 (2021).Article 

    Google Scholar 
    115.Arora, V. K. & Gajri, P. R. Evaluation of a crop growth–water balance model for analyzing wheat responses to climate and water-limited environments. Field Crops Res. 59, 213–224. https://doi.org/10.1016/S0378-4290(98)00124-5 (1998).Article 

    Google Scholar 
    116.Aggarwal, P. K., Talukdar, K. K., Mall, R. K. Potential yields of rice–wheat system in the Indo-Gangetic plains of India. Rice–Wheat Consortium Paper Series 10. New Delhi, India. RWCIGP, CIMMYT. p. 16 (2000).117.Arora, V. K., Singh, H. & Singh, B. Analyzing wheat productivity responses to climatic, irrigation and fertilizer–nitrogen regimes in a semi-arid sub–tropical environment using the CERES-Wheat model. Agric. Water Manag. 94, 22–30. https://doi.org/10.1016/j.agwat.2007.07.002 (2007).Article 

    Google Scholar 
    118.Timsina, J. et al. Evaluation of options for increasing yield and water productivity of wheat in Punjab, India using the DSSAT–CSM-CERES-wheat model. Agric. Water Manag. 95, 1099–1110. https://doi.org/10.1016/j.agwat.2008.04.009 (2008).Article 

    Google Scholar 
    119.Balwinder-Singha, Humphreys & E., Gaydon, D. S., Eberbach, P. L.,. Evaluation of the effects of mulch on optimum sowing date and irrigation management of zero till wheat in central Punjab, India using APSIM. Field Crops Res. 197, 83–96. https://doi.org/10.1016/j.fcr.2016.08.016 (2016).Article 

    Google Scholar 
    120.Choudhury, A. K. et al. Optimum Sowing Window and Yield Forecasting for Maize in Northern and Western Bangladesh Using CERES Maize Model. Agronomy 11, 635. https://doi.org/10.3390/agronomy11040635 (2021).Article 

    Google Scholar 
    121.Sun, H., Shao, I., Chen, S. & Zhang, X. Effects of sowing time and rate on crop growth and radiation use efficiency of winter wheat in the North China Plain. Int. J. Plant Prod. 7, 117–138 (2013).
    Google Scholar 
    122.Qu, H. J. et al. Effects of plant density and seeding date on accumulation and translocation of dry matter and nitrogen in winter wheat cultivar Lankao Aizao 8. Acta Agron. Sin. 35, 124–131. https://doi.org/10.3724/SP.J.1006.2009.00124 (2009).CAS 
    Article 

    Google Scholar 
    123.Liu, P. et al. Effect of seeding rate and sowing date on population traits and grain yield of drip irrigated winter wheat. J. Triticeae Crops 33, 1202–1207 (2013).CAS 

    Google Scholar 
    124.Lu, H. D., Xue, J. Q., Hao, Y. C., Zhang, R. H. & Gao, J. Effects of sowing time on spring maize (Zea mays L.) growth and water use efficiency in rainfed dryland. Acta Agron. Sin. 41, 1906–1914 (2015).Article 

    Google Scholar 
    125.Taylor, S. & Evans, C. Wheat: Susceptibility of varieties to common root rot. CWFS Research Compendium (2005).126.Bowden, P. et al. Wheat growth & development. NSW Department of Primary Industries, State of New South Wales, p. 104 (2008).127.DEEDI. Wheat varieties. Queensland Department of Employment, Economic Development and Innovation (DEEDI). p. 20 (2010).128.Lush, D. et al. Queensland wheat varieties. Grains Research and Development Corporation (GRDC) and the Queensland Department of Agriculture, Fisheries and Forestry (DAFF). p. 20 (2015).129.Greenwood, J. R. Wheat inflorescence architecture. Thesis report, Australian National University, p. 218 (2017).130.Lush, D., Forknall, C., Neate, S., Sheedy, J. Queensland wheat varieties. Grains Research and Development Corporation (GRDC) and the Queensland Department of Agriculture and Fisheries (DAF). p. 20 (2018).131.Hines, S., Andrews, M., Scott, W. R. & Jack, D. Sowing depth and nitrogen effects on emergence of a range of New Zealand wheat cultivars. Proc. Agron. Soc. N. Z. 21, 67–72 (1991).
    Google Scholar 
    132.Zaicou, C. et al. Wheat variety guide 2008 Western Australia. Department of Agriculture and Food, Western Australia, Perth. Bull. 4733 (2008).133.Kelbert, A. J., Spaner, D., Briggs, K. G. & King, J. R. The association of culm anatomy with lodging susceptibility in modern spring wheat genotypes. Euphytica 136, 211–221. https://doi.org/10.1023/B:EUPH.0000030670.36730.a4 (2004).Article 

    Google Scholar 
    134.Mason, H., Navabi, A., Frick, B., O’Donovan, J. & Spaner, D. Cultivar and seeding rate effects on the competitive ability of spring cereals grown under organic production in northern Canada. Agron. J. 99, 1199–1207. https://doi.org/10.2134/agronj2006.0262 (2007).Article 

    Google Scholar 
    135.Shah, L. et al. Improving lodging resistance: Using wheat and rice as classical examples. Int. J. Mol. Sci. 20, 4211. https://doi.org/10.3390/ijms20174211 (2019).CAS 
    Article 
    PubMed Central 

    Google Scholar 
    136.Mitter, V. et al. A high-throughput greenhouse bioassay to detect crown rot resistance in wheat germplasm. Plant Pathol. 55, 433–441. https://doi.org/10.1111/j.1365-3059.2006.01384.x (2006).Article 

    Google Scholar 
    137.Hare, R. Agronomy of the durum wheats Kamilaroi, Yallaroi, Wollaroi and EGA Bellaroi. NSW Department of Primary Industries, State of New South Wales, Primefact 140 (2006).138.DPI&F. Wheat varieties for Queensland. Department of Primary Industries and Fisheries (DPI&F), State of Queensland, p. 12 (2007).139.Singh, B. et al. Inheritance and chromosome location of leaf rust resistance in durum wheat cultivar Wollaroi. Euphytica 175, 351–355. https://doi.org/10.1007/s10681-010-0179-y (2010).Article 

    Google Scholar 
    140.Bansal, U. K., Kazi, A. G., Singh, B., Hare, R. A. & Bariana, H. S. Mapping of durable stripe rust resistance in a durum wheat cultivar Wollaroi. Mol Breed 33, 51–59. https://doi.org/10.1007/s11032-013-9933-x (2014).CAS 
    Article 

    Google Scholar  More

  • in

    Dynamic monitoring of urban built-up object expansion trajectories in Karachi, Pakistan with time series images and the LandTrendr algorithm

    1.Seto, K. C., Fragkias, M., Gueneralp, B. & Reilly, M. K. A meta-analysis of global urban land expansion. PLoS ONE https://doi.org/10.1371/journal.pone.0023777 (2011).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    2.Huang, Q. X. et al. The occupation of cropland by global urban expansion from 1992 to 2016 and its implications. Environ. Res. Lett. 15, 14. https://doi.org/10.1088/1748-9326/ab858c (2020).ADS 
    Article 

    Google Scholar 
    3.Huang, X., Huang, J. Y., Wen, D. W. & Li, J. Y. An updated MODIS global urban extent product (MGUP) from 2001 to 2018 based on an automated mapping approach. Int. J. Appl. Earth Obs. Geoinf. 95, 15. https://doi.org/10.1016/j.jag.2020.102255 (2021).Article 

    Google Scholar 
    4.Seto, K. C., Fragkias, M., Guneralp, B. & Reilly, M. K. A meta-analysis of global urban land expansion. PLoS ONE 6, 9. https://doi.org/10.1371/journal.pone.0023777 (2011).CAS 
    Article 

    Google Scholar 
    5.Besthorn, F. H. Vertical farming: Social work and sustainable urban agriculture in an age of global food crises. Aust. Soc. Work. 66, 187–203. https://doi.org/10.1080/0312407x.2012.716448 (2013).Article 

    Google Scholar 
    6.FAO. 2018 The State of Food Security and Nutrition in the World. https://www.who.int/nutrition/publications/foodsecurity/state-food-security-nutrition-2018/en/. (2018).7.Mertes, C. M., Schneider, A., Sulla-Menashe, D., Tatem, A. J. & Tan, B. Detecting change in urban areas at continental scales with MODIS data. Remote Sens. Environ. 158, 331–347. https://doi.org/10.1016/j.rse.2014.09.023 (2015).ADS 
    Article 

    Google Scholar 
    8.Xiao, P. F., Wang, X. H., Feng, X. Z., Zhang, X. L. & Yang, Y. K. Detecting China’s urban expansion over the past three decades using nighttime light data. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 7, 4095–4106. https://doi.org/10.1109/jstars.2014.2302855 (2014).ADS 
    Article 

    Google Scholar 
    9.Singh, A. Review article digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 10, 989–1003 (1989).Article 

    Google Scholar 
    10.Reba, M. & Seto, K. C. A systematic review and assessment of algorithms to detect, characterize, and monitor urban land change. Remote Sens. Environ. 242, 20. https://doi.org/10.1016/j.rse.2020.111739 (2020).Article 

    Google Scholar 
    11.He, T., Xiao, W., Zhao, Y., Deng, X. & Hu, Z. Identification of waterlogging in Eastern China induced by mining subsidence: A case study of Google Earth Engine time-series analysis applied to the Huainan coal field. Remote Sens. Environ. https://doi.org/10.1016/j.rse.2020.111742 (2020).Article 

    Google Scholar 
    12.Mugiraneza, T., Nascetti, A. & Ban, Y. Continuous monitoring of urban land cover change trajectories with Landsat time series and LandTrendr-Google Earth engine cloud computing. Remote Sens. https://doi.org/10.3390/rs12182883 (2020).Article 

    Google Scholar 
    13.U.S. Geological Survey. Landsat Surface Reflectance Data (Ver. 1.1, March 27, 2019): U.S. Geological Survey Fact Sheet 2015-3034. 1. https://doi.org/10.3133/fs20153034 (2019).14.Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031 (2017).ADS 
    Article 

    Google Scholar 
    15.Cai, S. & Liu, D. Detecting change dates from dense satellite time series using a sub-annual change detection algorithm. Remote Sens. 7, 8705–8727. https://doi.org/10.3390/rs70708705 (2015).ADS 
    Article 

    Google Scholar 
    16.Vogelmann, J. E., Xian, G., Homer, C. & Tolk, B. Monitoring gradual ecosystem change using Landsat time series analyses: Case studies in selected forest and rangeland ecosystems. Remote Sens. Environ. 122, 92–105. https://doi.org/10.1016/j.rse.2011.06.027 (2012).ADS 
    Article 

    Google Scholar 
    17.Brooks, E. B., Wynne, R. H., Thomas, V. A., Blinn, C. E. & Coulston, J. W. On-the-fly massively multitemporal change detection using statistical quality control charts and Landsat data. IEEE Trans. Geosci. Remote Sens. 52, 3316–3332. https://doi.org/10.1109/tgrs.2013.2272545 (2014).ADS 
    Article 

    Google Scholar 
    18.Huang, C. et al. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sens. Environ. 114, 183–198. https://doi.org/10.1016/j.rse.2009.08.017 (2010).ADS 
    Article 

    Google Scholar 
    19.Verbesselt, J., Hyndman, R., Newnham, G. & Culvenor, D. Detecting trend and seasonal changes in satellite image time series. Remote Sens. Environ. 114, 106–115. https://doi.org/10.1016/j.rse.2009.08.014 (2010).ADS 
    Article 

    Google Scholar 
    20.Hughes, M. J., Kaylor, S. D. & Hayes, D. J. Patch-based forest change detection from landsat time series. Forests https://doi.org/10.3390/f8050166 (2017).Article 

    Google Scholar 
    21.Deng, C. B. & Zhu, Z. Continuous subpixel monitoring of urban impervious surface using Landsat time series. Remote Sens. Environ. 238, 21. https://doi.org/10.1016/j.rse.2018.10.011 (2020).Article 

    Google Scholar 
    22.Zhu, Z. et al. Continuous monitoring of land disturbance based on Landsat time series, remote sensing of environment. Remote Sens. Environ. 238(11116), 2020. https://doi.org/10.1016/j.rse.2020.111824 (2020).Article 

    Google Scholar 
    23.Kennedy, R. E. et al. Implementation of the LandTrendr algorithm on Google Earth Engine. Remote Sens. https://doi.org/10.3390/rs10050691 (2018).Article 

    Google Scholar 
    24.Hirayama, H., Sharma, R. C., Tomita, M. & Hara, K. Evaluating multiple classifier system for the reduction of salt-and-pepper noise in the classification of very-high-resolution satellite images. Int. J. Remote Sens. 40, 2542–2557. https://doi.org/10.1080/01431161.2018.1528400 (2019).Article 

    Google Scholar 
    25.Carleer, A. P., Debeir, O. & Wolff, E. Assessment of very high spatial resolution satellite image segmentations. Photogramm. Eng. Remote. Sens. 71, 1285–1294. https://doi.org/10.14358/pers.71.11.1285 (2005).Article 

    Google Scholar 
    26.Su, T. C. A filter-based post-processing technique for improving homogeneity of pixel-wise classification data. Eur. J. Remote Sens. 49, 531–552. https://doi.org/10.5721/EuJRS20164928 (2016).Article 

    Google Scholar 
    27.Zhu, X. Land cover classification using moderate resolution satellite imagery and random forests with post-hoc smoothing. J. Spat. Sci. 58, 323–337. https://doi.org/10.1080/14498596.2013.819600 (2013).Article 

    Google Scholar 
    28.Xu, H. Z. Y., Wei, Y. C., Liu, C., Li, X. & Fang, H. A scheme for the long-term monitoring of impervious-relevant land disturbances using high frequency Landsat archives and the Google Earth Engine. Remote Sens. 11, 27. https://doi.org/10.3390/rs11161891 (2019).Article 

    Google Scholar 
    29.Baqa, M. F. et al. Monitoring and modeling the patterns and trends of urban growth using urban sprawl matrix and CA-Markov model: A case study of Karachi, Pakistan. Land https://doi.org/10.3390/land10070700 (2021).Article 

    Google Scholar 
    30.Group, W. B. Transforming Karachi into a Livable and Competitive Megacity—A City Diagnostic and Transformation Strategy. (2018).31.Arif, H., Noman, A., Mansoor, R. & Asiya, S. Land Ownership, Control and Contestation in Karachi and Implications for Low-Income Housing. (Human Settlements Group, International Institute for Environment and Development (IIED), 2013).32.Karachi’s Population—Fiction and Reality. The Express Tribune. https://tribune.com.pk/story/1505657/karachis-population-fiction-reality. Accessed 1 May 2021.33.Senf, C., Pflugmacher, D., Wulder, M. A. & Hostert, P. Characterizing spectral-temporal patterns of defoliator and bark beetle disturbances using Landsat time series. Remote Sens. Environ. 170, 166–177. https://doi.org/10.1016/j.rse.2015.09.019 (2015).ADS 
    Article 

    Google Scholar 
    34.Mi, J. X. et al. Tracking the land use/land cover change in an area with underground mining and reforestation via continuous landsat classification. Remote Sens. https://doi.org/10.3390/rs11141719 (2019).Article 

    Google Scholar 
    35.de Jong, S. M. et al. Mapping mangrove dynamics and colonization patterns at the Suriname coast using historic satellite data and the LandTrendr algorithm. Int. J. Appl. Earth Observ. Geoinf. https://doi.org/10.1016/j.jag.2020.102293 (2021).Article 

    Google Scholar 
    36.Gong, P. et al. Annual maps of global artificial impervious area (GAIA) between 1985 and 2018. Remote Sens. Environ. https://doi.org/10.1016/j.rse.2019.111510 (2020).Article 

    Google Scholar 
    37.Xu, H., Wei, Y., Liu, C., Li, X. & Fang, H. A scheme for the long-term monitoring of impervious-relevant land disturbances using high frequency Landsat archives and the Google earth engine. Remote Sens. https://doi.org/10.3390/rs11161891 (2019).Article 

    Google Scholar 
    38.Li, X. C. et al. Mapping global urban boundaries from the global artificial impervious area (GAIA) data. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/ab9be3 (2020).Article 

    Google Scholar 
    39.Global Human Settlement Layer. https://ghsl.jrc.ec.europa.eu/. Accessed 1 May 2021.40.Raza, D. et al. Satellite Based Surveillance of LULC with Deliberation on Urban Land Surface Temperature and Precipitation Pattern Changes of Karachi, Pakistan. (2019).41.Yu, L., Wang, J. & Gong, P. Improving 30m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: A segmentation-based approach. Int. J. Remote Sens. 34, 5851–5867. https://doi.org/10.1080/01431161.2013.798055 (2013).Article 

    Google Scholar 
    42.Kennedy, R. E., Yang, Z. G. & Cohen, W. B. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr-temporal segmentation algorithms. Remote Sens. Environ. 114, 2897–2910. https://doi.org/10.1016/j.rse.2010.07.008 (2010).ADS 
    Article 

    Google Scholar 
    43.Meigs, G. W., Kennedy, R. E. & Cohen, W. B. A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests. Remote Sens. Environ. 115, 3707–3718. https://doi.org/10.1016/j.rse.2011.09.009 (2011).ADS 
    Article 

    Google Scholar 
    44.Yin, H. et al. Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series. Remote Sens. Environ. 210, 12–24. https://doi.org/10.1016/j.rse.2018.02.050 (2018).ADS 
    Article 

    Google Scholar 
    45.Yin, H., Pflugmacher, D., Li, A., Li, Z. & Hostert, P. Land use and land cover change in Inner Mongolia—Understanding the effects of China’s re-vegetation programs. Remote Sens. Environ. 204, 918–930. https://doi.org/10.1016/j.rse.2017.08.030 (2018).ADS 
    Article 

    Google Scholar 
    46.Zhu, L., Liu, X., Wu, L., Tang, Y. & Meng, Y. Long-term monitoring of cropland change near Dongting Lake, China, using the LandTrendr algorithm with Landsat imagery. Remote Sens. https://doi.org/10.3390/rs11101234 (2019).Article 

    Google Scholar 
    47.Kennedy, R. E. et al. Attribution of disturbance change agent from Landsat time-series in support of habitat monitoring in the Puget Sound region, USA. Remote Sens. Environ. 166, 271–285. https://doi.org/10.1016/j.rse.2015.05.005 (2015).ADS 
    Article 

    Google Scholar 
    48.Zhu, Z. et al. Continuous monitoring of land disturbance based on Landsat time series. Remote Sens. Environ. https://doi.org/10.1016/j.rse.2019.03.009 (2020).Article 

    Google Scholar 
    49.Yan, J. et al. A time-series classification approach based on change detection for rapid land cover mapping. ISPRS J. Photogramm. Remote Sens. 158, 249–262. https://doi.org/10.1016/j.isprsjprs.2019.10.003 (2019).ADS 
    Article 

    Google Scholar 
    50.Crist, E. P. & Kauth, R. J. The tasseled cap de-mystified. Photogramm. Eng. Remote Sens. 52, 81–86 (1986).
    Google Scholar 
    51.Lin, L. et al. Monitoring land cover change on a rapidly urbanizing island using Google Earth Engine. Appl. Sci.-Basel. https://doi.org/10.3390/app10207336 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Chen, C. et al. Analysis of regional economic development based on land use and land cover change information derived from Landsat imagery. Sci. Rep. https://doi.org/10.1038/s41598-020-69716-2 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Zhang, X. Y., Feng, X. Z. & Wang, K. Integration of classifiers for improvement of vegetation category identification accuracy based on image objects. N. Z. J. Agric. Res. 50, 1125–1133. https://doi.org/10.1080/00288230709510394 (2007).Article 

    Google Scholar  More