Philippa Kaur

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

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

1.Metcalf, C. J. E. & Pavard, S. Why evolutionary biologists should be demographers. Trends Ecol. Evol. 22, 205–212 (2007).PubMed 

Google Scholar 
2.Lande, R., Engen, S. & Saether, B. Stochastic population dynamics in ecology and conservation. (Oxfor University Press, 2003).3.Roughgarden, J. A simple model for population dynamics in stochastic environments. Am. Nat. 109, 713–736 (1975).
Google Scholar 
4.May, R. M. Stability and complexity in model ecosystems (Princeton Univ, 2001).MATH 

Google Scholar 
5.Engen, S., Bakke, Ø. & Islam, A. Demographic and Environmental Stochasticity-Concepts and Definitions on JSTOR. Biometrics 54, 840–846 (1998).MATH 

Google Scholar 
6.Melbourne, B. a & Hastings, A. Extinction risk depends strongly on factors contributing to stochasticity. Nature 454, 100–3 (2008).7.Tuljapurkar, S., Steiner, U. K. & Orzack, S. H. Dynamic heterogeneity in life histories. Ecol. Lett. 12, 93–106 (2009).PubMed 

Google Scholar 
8.Vindenes, Y. & Engen, S. Demographic stochasticity and temporal autocorrelation in the dynamics of structured populations. Oikos https://doi.org/10.1111/oik.03958 (2017).Article 

Google Scholar 
9.Caswell, H. Stage, age and individual stochasticity in demography. Oikos 118, 1763–1782 (2009).
Google Scholar 
10.Steiner, U. K. & Tuljapurkar, S. Neutral theory for life histories and individual variability in fitness components. Proc. Natl. Acad. Sci. USA 109, 4684–4689 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Vindenes, Y. & Langangen, Ø. Individual heterogeneity in life histories and eco-evolutionary dynamics. Ecol. Lett. 18, 417–432 (2015).PubMed 
PubMed Central 

Google Scholar 
12.Snyder, R. E. & Ellner, S. P. Pluck or Luck: Does Trait Variation or Chance Drive Variation in Lifetime Reproductive Success?. Am. Nat. 191, E90–E107 (2018).PubMed 

Google Scholar 
13.Steiner, U. K., Tuljapurkar, S. & Orzack, S. H. Dynamic heterogeneity and life history variability in the kittiwake. J. Anim. Ecol. 79, 436–444 (2010).PubMed 
PubMed Central 

Google Scholar 
14.Pennisi, E. The Great Guppy Experiment. Science (80-. ). 337, 904–908 (2012).15.Pajunen, V. I. & Pajunen, I. Long-term dynamics in rock pool Daphnia metapopulations. Ecography (Cop.) 26, 731–738 (2003).
Google Scholar 
16.Ozgul, A. et al. The dynamics of phenotypic change and the shrinking sheep of St. Kilda.. Science 325, 464–467 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
17.Roach, D. A. & Gampe, J. Age-specific demography in Plantago: uncovering age-dependent mortality in a natural population. Am. Nat. 164, 60–69 (2004).PubMed 

Google Scholar 
18.Reid, J. M., Nietlisbach, P., Wolak, M. E., Keller, L. F. & Arcese, P. Individuals’ expected genetic contributions to future generations, reproductive value, and short-term metrics of fitness in free-living song sparrows ( Melospiza melodia ). Evol. Lett. 3, 271–285 (2019).PubMed 
PubMed Central 

Google Scholar 
19.Endler, J. A. Natural selection in the wild. Monographs in Population Biology vol. 21 (Princeton University Press, 1986).20.Hadfield, J. D., Wilson, A. J., Garant, D., Sheldon, B. C. & Kruuk, L. E. B. The misuse of BLUP in ecology and evolution. Am. Nat. 175, 116–125 (2010).PubMed 

Google Scholar 
21.Steiner, U. K., Tuljapurkar, S. & Coulson, T. Generation time, net reproductive rate, and growth in stage-age-structured populations. Am. Nat. 183, 771–783 (2014).PubMed 
PubMed Central 

Google Scholar 
22.Roach, D. A., Ridley, C. E. & Dudycha, J. L. Longitudinal analysis of Plantago : Age-by-environment interactions reveal aging. Ecology 90, 1427–1433 (2009).PubMed 

Google Scholar 
23.Roach, D. A. Age, growth and size interact with stress to determine life span and mortality. Exp. Gerontol. 47, 782–786 (2012).PubMed 
PubMed Central 

Google Scholar 
24.Shefferson, R. P. & Roach, D. A. The triple helix of Plantago lanceolata: Genetics and the environment interact to determine population dynamics. Ecology 93, 793–802 (2012).PubMed 

Google Scholar 
25.Coulson, T., Tuljapurkar, S. & Step, T. The dynamics of a quantitative trait in an age-structured population living in a variable environment. Am. Nat. 172, 599–612 (2008).PubMed 
PubMed Central 

Google Scholar 
26.Coulson, T., Tuljapurkar, S. & Childs, D. Z. Using evolutionary demography to link life history theory, quantitative genetics and population ecology. J. Anim. Ecol. 79, 1226–1240 (2010).PubMed 
PubMed Central 

Google Scholar 
27.Lacey, E. P. et al. Multigenerational effects of flowering and fruiting phenology in Plantago lanceolata. Ecology 84, 2462–2475 (2003).
Google Scholar 
28.Jones, O. R. et al. Senescence rates are determined by ranking on the fast-slow life-history continuum. Ecol. Lett. 11, 664–673 (2008).PubMed 

Google Scholar 
29.Fisher, R. The genetical theory of natural selection. (Clarendon, 1930).30.Wright, S. Evolution in Mendelian populations. Genetics 16, 0097–0159 (1931).CAS 

Google Scholar 
31.Crow, J. F. & Kimura, M. An introduction to population genetics theory. (1970).32.Merilä, J. & Sheldon, B. Lifetime Reproductive Success and Heritability in Nature. Am. Nat. 155, 301–310 (2000).PubMed 

Google Scholar 
33.Kruuk, L. E. et al. Heritability of fitness in a wild mammal population. Proc. Natl. Acad. Sci. U. S. A. 97, 698–703 (2000).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Teplitsky, C., Mills, J. a, Yarrall, J. W. & Merilä, J. Heritability of fitness components in a wild bird population. Evolution 63, 716–26 (2009).35.Kruuk, L. E., Merilä, J. & Sheldon, B. C. Phenotypic selection on a heritable size trait revisited. Am. Nat. 158, 557–571 (2001).CAS 
PubMed 

Google Scholar 
36.Sheldon, B. C., Kruuk, L. E. B. & Merilä, J. Natural selection and inheritance of breeding time and clutch size in the collared flycatcher. Evolution 57, 406–420 (2003).CAS 
PubMed 

Google Scholar 
37.Merilä, J. & Sheldon, B. C. Short Review Genetic architecture of fitness and non fitness traits : empirical patterns and development of ideas. Heredity (Edinb). 83, (1999).38.Hartl, D. J. & Clark, A. G. Principles of population genetics. (Sinauer, 2007).39.Charlesworth, B. Evolution in age-structured populations. (Cambridge University Press, 1994).40.Kirkwood, T. B. L. et al. What accounts for the wide variation in life span of genetically identical organisms reared in a constant environment?. Mech. Ageing Dev. 126, 439–443 (2005).PubMed 

Google Scholar 
41.Finch, C. & Kirkwood, T. B. Chance, Development, and Aging. (Oxford University Press, 2000).42.Schiemer, F. Food Dependence and Energetics of Freeliving Nematodes. II. Life History Parameters of Caenorhabditis briggsae (Nematoda) at Different Levels of Food Supply. Oecologia 54, 122–128 (1982).43.Kennedy, B. K. Daughter cells of Saccharomyces cerevisiae from old mothers display a reduced life span. J. Cell Biol. 127, 1985–1993 (1994).CAS 
PubMed 

Google Scholar 
44.Steiner, U. K. et al. Two stochastic processes shape diverse senescence patterns in a single-cell organism. Evolution (N. Y). 73, 847–857 (2019).45.Jouvet, L., Rodríguez-Rojas, A. & Steiner, U. K. Demographic variability and heterogeneity among individuals within and among clonal bacteria strains. Oikos 127, 728–737 (2018).
Google Scholar 
46.Curtsinger, J., Fukui, H., Townsend, D. & Vaupel, J. Demography of genotypes: failure of the limited life-span paradigm in Drosophila melanogaster. Science (80-. ). 258, 461–463 (1992).47.Roach, D. A. & Smith, E. F. Life-history trade-offs and senescence in plants. Funct. Ecol. 34, 17–25 (2020).
Google Scholar 
48.Edelfeldt, S., Bengtsson, K. & Dahlgren, J. P. Demographic senescence and effects on population dynamics of a perennial plant. Ecology 100, e02742 (2019).49.van Daalen, S. F. & Caswell, H. Variance as a life history outcome: Sensitivity analysis of the contributions of stochasticity and heterogeneity. Ecol. Modell. 417, (2020).50.Caswell, H. & Vindenes, Y. Demographic variance in heterogeneous populations: matrix models and sensitivity analysis. Oikos 127, 648–663 (2018).
Google Scholar 
51.Jenouvrier, S., Aubry, L. M., Barbraud, C., Weimerskirch, H. & Caswell, H. Interacting effects of unobserved heterogeneity and individual stochasticity in the life history of the southern fulmar. J. Anim. Ecol. 87, 212–222 (2018).PubMed 

Google Scholar 
52.Balázsi, G., Van Oudenaarden, A. & Collins, J. J. Cellular decision making and biological noise: from microbes to mammals. Cell 144, 910–925 (2011).PubMed 
PubMed Central 

Google Scholar 
53.Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science (80-. ). 297, 1183–1186 (2002).54.Kærn, M., Elston, T. C., Blake, W. J. & Collins, J. J. Stochasticity in gene expression: from theories to phenotypes. Nat. Rev. Genet. 6, 451–464 (2005).PubMed 

Google Scholar 
55.Vera, M., Biswas, J., Senecal, A., Singer, R. H. & Park, H. Y. Single-Cell and Single-Molecule Analysis of Gene Expression Regulation. Annu. Rev. Genet. 50, 267–291 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Norman, T. M., Lord, N. D., Paulsson, J. & Losick, R. Stochastic Switching of Cell Fate in Microbes. Annu. Rev. Microbiol. 69, 381–403 (2015).CAS 
PubMed 

Google Scholar 
57.Ballouz, S., Pena, M., Knight, F., Adams, L. & Gillis, J. The transcriptional legacy of developmental stochasticity. bioRxiv 2019.12.11.873265 (2019) https://doi.org/10.1101/2019.12.11.873265.58.Vogt, G. Stochastic developmental variation, an epigenetic source of phenotypic diversity with far-reaching biological consequences. J. Biosci. 40, 159–204 (2015).PubMed 

Google Scholar 
59.Hill, W. G. Effective size of populations with overlapping generations. Theor. Popul. Biol. 3, 278–289 (1972).CAS 
PubMed 

Google Scholar 
60.Engen, S., Lande, R. & Saether, B.-E. Effective Size of a Fluctuating Age-Structured Population. Genetics 170, 941–954 (2005).PubMed 
PubMed Central 

Google Scholar 
61.Vindenes, Y., Engen, S. & Saether, B.-E. Individual heterogeneity in vital parameters and demographic stochasticity. Am. Nat. 171, 455–467 (2008).PubMed 

Google Scholar 
62.Engen, S., Lande, R., aether, B.-E. & Weimerskirch, H. Extinction in relation to demographic and environmental stochasticity in age-structured models. Math. Biosci. 195, 210–27 (2005).63.Stearns, S. C. The evolution of life-histories. (Oxford University Press, 1992).64.Kendall, B. E. & Fox, G. a. Variation among Individuals and Reduced Demographic Stochasticity. Conserv. Biol. 16, 109–116 (2002).65.Fox, G. A. & Kendall, B. E. Demographic stochasticity and the variance reduction effect. Ecology 83, 1928–1934 (2002).
Google Scholar 
66.Bolnick, D. I. et al. Why intraspecific trait variation matters in community ecology. Trends Ecol. Evol. 26, 183–192 (2011).PubMed 
PubMed Central 

Google Scholar 
67.Hartemink, N. & Caswell, H. Variance in animal longevity: contributions of heterogeneity and stochasticity. Popul. Ecol. 60, 89–99 (2018).PubMed 
PubMed Central 

Google Scholar 
68.Alonso, D., Etienne, R. S. & McKane, A. J. The merits of neutral theory. Trends Ecol. Evol. 21, 451–457 (2006).PubMed 

Google Scholar 
69.Ohta, T. & Gillespie, J. Development of Neutral and Nearly Neutral Theories. Theor. Popul. Biol. 49, 128–142 (1996).CAS 
PubMed 
MATH 

Google Scholar 
70.Hughes, A. L. Near neutrality: leading edge of the neutral theory of molecular evolution. Ann. N. Y. Acad. Sci. 1133, 162–179 (2008).ADS 
PubMed 
PubMed Central 

Google Scholar 
71.Comstock, R. E. & Robinson, H. F. The components of genetic variance in populations of biparental progenies and their use in estimating the average degree of dominance. Biometrics 254–266 (1948).72.Ellner, S. P. & Rees, M. Integral projection models for species with complex demography. Am. Nat. 167, 410–428 (2006).PubMed 

Google Scholar 
73.Steiner, U. K., Tuljapurkar, S., Coulson, T. & Horvitz, C. Trading stages: life expectancies in structured populations. Exp. Gerontol. 47, 773–781 (2012).PubMed 
PubMed Central 

Google Scholar 
74.R Core Team, R. A. language and environment for statistical computing. R: A language and environment for statistical computing. R Foundation for Statistical Computing vol. 1 409 (2016).75.van de Pol, M. & Wright, J. A simple method for distinguishing within- versus between-subject effects using mixed models. Anim. Behav. 77, 753–758 (2009).
Google Scholar 
76.Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: The MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
Google Scholar  More

1.Williams, S. E., Shoo, L. P., Isaac, J. L., Hoffmann, A. A. & Langham, G. Towards an integrated framework for assessing the vulnerability of species to climate change. PLOS Biol. 6, 1–6 (2008).
Google Scholar 
2.Hickling, R., Roy, D. B., Hill, J. K., Fox, R. & Thomas, C. D. The distributions of a wide range of taxonomic groups are expanding polewards. Glob. Chang. Biol. 12, 450–455 (2006).ADS 

Google Scholar 
3.Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).PubMed 

Google Scholar 
4.Ford, K. R., Harrington, C. A., Bansal, S., Gould, P. J. & StClair, J. B. Will changes in phenology track climate change? A study of growth initiation timing in coast Douglas-fir. Glob. Chang. Biol. 22, 3712–3723 (2016).ADS 
PubMed 

Google Scholar 
5.Anderson, J. T., Inouye, D. W., McKinney, A. M., Colautti, R. I. & Mitchell-Olds, T. Phenotypic plasticity and adaptive evolution contribute to advancing flowering phenology in response to climate change. Proc. R. Soc. B Biol. Sci. 279, 3843–3852 (2012).
Google Scholar 
6.Gérard, M. et al. Shift in size of bumblebee queens over the last century. Glob. Chang. Biol. 26, 1185–1195 (2020).ADS 
PubMed 

Google Scholar 
7.Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).PubMed 
PubMed Central 

Google Scholar 
8.Ozgul, A. et al. Coupled dynamics of body mass and population growth in response to environmental change. Nature 466, 482–485 (2010).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Walther, G. R. Community and ecosystem responses to recent climate change. Philos. Trans. R. Soc. B 365, 2019–2024 (2010).
Google Scholar 
10.Halpern, B. S. et al. Spatial and temporal changes in cumulative human impacts on the world’s ocean. Nat. Commun. 6, 1–7 (2015).
Google Scholar 
11.Hoegh-Guldberg, O. & Bruno, J. F. The impact of climate change on the world’s marine ecosystems. Science 328, 1523–1528 (2010).ADS 
CAS 

Google Scholar 
12.Robinson, R. A. et al. Travelling through a warming world: Climate change and migratory species. Endanger. Species Res. 7, 87–99 (2009).ADS 

Google Scholar 
13.Lohmann, K. J., Lohmann, C. M. F., Brothers, J. R. & Putman, N. F. Natal homing and imprinting in sea turtles. in The biology of sea turtles, volume III (eds. Wyneken, J., Lohmann, K. J. & Musick, J. A.) 59–78 (2013).14.Hays, G. C. & Scott, R. Global patterns for upper ceilings on migration distance in sea turtles and comparisons with fish, birds and mammals. Funct. Ecol. 27, 748–756 (2013).
Google Scholar 
15.Poloczanska, E. S., Limpus, C. J. & Hays, G. C. Vulnerability of marine turtles to climate change. Adv. Mar. Biol. 56, 151–211 (2009).PubMed 

Google Scholar 
16.Hawkes, L. A., Broderick, A. C., Godfrey, M. H. & Godley, B. J. Climate change and marine turtles. Endanger. Species Res. 7, 137–154 (2009).
Google Scholar 
17.Fuentes, M. M. P. B., Limpus, C. J. & Hamann, M. Vulnerability of sea turtle nesting grounds to climate change. Glob. Chang. Biol. 17, 140–153 (2011).ADS 

Google Scholar 
18.Patrício, A., Hawkes, L., Monsinjon, J., Godley, B. & Fuentes, M. Climate change and marine turtles: Recent advances and future directions. Endanger. Species Res. 44, 363–395 (2021).
Google Scholar 
19.Mazaris, A. D., Kallimanis, A. S., Sgardelis, S. P. & Pantis, J. D. Do long-term changes in sea surface temperature at the breeding areas affect the breeding dates and reproduction performance of Mediterranean loggerhead turtles? Implications for climate change. J. Exp. Mar. Biol. Ecol. 367, 219–226 (2008).
Google Scholar 
20.Almpanidou, V., Katragkou, E. & Mazaris, A. D. The efficiency of phenological shifts as an adaptive response against climate change: a case study of loggerhead sea turtles (Caretta caretta) in the Mediterranean. Mitig. Adapt. Strateg. Glob. Chang. 23, 1143–1158 (2018).
Google Scholar 
21.Monsinjon, J. R. et al. The climatic debt of loggerhead sea turtle populations in a warming world. Ecol. Indic. 107, 105657 (2019).
Google Scholar 
22.Witt, M. J., Hawkes, L. A., Godfrey, M. H., Godley, B. J. & Broderick, A. C. Predicting the impacts of climate change on a globally distributed species: The case of the loggerhead turtle. J. Exp. Biol. 213, 901–911 (2010).CAS 
PubMed 

Google Scholar 
23.Hawkes, L. A., Broderick, A. C., Godfrey, M. H. & Godley, B. J. Investigating the potential impacts of climate change on a marine turtle population. Glob. Chang. Biol. 13, 923–932 (2007).ADS 

Google Scholar 
24.Patel, S. H. et al. Climate impacts on sea turtle breeding phenology in Greece and associated foraging habitats in the wider mediterranean region. PLoS ONE 11, 1–17 (2016).
Google Scholar 
25.Revelles, M. et al. Mesoscale eddies, surface circulation and the scale of habitat selection by immature loggerhead sea turtles. J. Exp. Mar. Bio. Ecol. 347, 41–57 (2007).
Google Scholar 
26.Witt, M. J. et al. Prey landscapes help identify potential foraging habitats for leatherback turtles in the NE Atlantic. Mar. Ecol. Prog. Ser. 337, 231–243 (2007).ADS 

Google Scholar 
27.Hamann, M. et al. Global research priorities for sea turtles: Informing management and conservation in the 21st century. Endanger. Species Res. 11, 245–269 (2010).
Google Scholar 
28.Coll, M. et al. The biodiversity of the Mediterranean Sea: Estimates, patterns, and threats. PLoS ONE 5, e1235 (2010).
Google Scholar 
29.Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 

Google Scholar 
30.Parry, M. L. Assessment of Potential Effects and Adaptations for Climate Change in Europe: the Europe ACACIA Project (University of East Anglia, 2000).
Google Scholar 
31.Lejeusne, C., Chevaldonné, P., Pergent-Martini, C., Boudouresque, C. F. & Pérez, T. Climate change effects on a miniature ocean: The highly diverse, highly impacted Mediterranean Sea. Trends Ecol. Evol. 25, 250–260 (2010).PubMed 

Google Scholar 
32.Coll, M. et al. The Mediterranean Sea under siege: Spatial overlap between marine biodiversity, cumulative threats and marine reserves. Glob. Ecol. Biogeogr. 21, 465–480 (2012).
Google Scholar 
33.Kim, G.-U., Seo, K.-H. & Chen, D. Climate change over the Mediterranean and current destruction of marine ecosystem. Sci. Rep. 9, 18813 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Casale, P. et al. Mediterranean sea turtles: Current knowledge and priorities for conservation and research. Endanger. Species Res. 36, 229–267 (2018).
Google Scholar 
35.Margaritoulis, D. et al. Loggerhead turtles in the mediterranean: present knowledge and conservation perspectives. In Biology and Conservation of Loggerhead Sea Turtles (eds Bolten, A. & Witherington, B.) 175–198 (Smithsonian Institution Press, 2003).
Google Scholar 
36.Wallace, B. P. et al. Regional management units for marine turtles: A novel framework for prioritizing conservation and research across multiple scales. PLoS ONE 5, 1–11 (2010).
Google Scholar 
37.Casale, P., Freggi, D., Basso, R., Vallini, C. & Argano, R. A model of area fidelity, nomadism, and distribution patterns of loggerhead sea turtles (Caretta caretta) in the Mediterranean Sea. Mar. Biol. 152, 1039–1049 (2007).
Google Scholar 
38.Carreras, C., Pont, S., Maffucci, F., Sanfe, M. & Aguilar, A. Genetic structuring of immature loggerhead sea turtles (Caretta caretta) in the Mediterranean Sea reflects water circulation patterns. Mar. Biol. 149, 1269–1279 (2006).
Google Scholar 
39.Clusa, M. et al. Fine-scale distribution of juvenile Atlantic and Mediterranean loggerhead turtles (Caretta caretta) in the Mediterranean Sea. Mar. Biol. 161, 509–519 (2014).
Google Scholar 
40.Maffucci, F., Kooistra, W. H. C. F. & Bentivegna, F. Natal origin of loggerhead turtles, Caretta caretta, in the neritic habitat off the Italian coasts, Central Mediterranean. Biol. Conserv. 7, 3–9 (2005).
Google Scholar 
41.Carreras, C. et al. Sporadic nesting reveals long distance colonisation in the philopatric loggerhead sea turtle (Caretta caretta). Sci. Rep. 8, 1–14 (2018).ADS 

Google Scholar 
42.Maffucci, F. et al. Seasonal heterogeneity of ocean warming: A mortality sink for ectotherm colonizers. Sci. Rep. 6, 1–9 (2016).
Google Scholar 
43.Casale, P. & Tucker, A. D. Caretta caretta (amended version of 2015 assessment). IUCN Red List Threat. Species 2017 e.T3897A119333622 (2017).44.Casale, P. et al. Sea turtle strandings reveal high anthropogenic mortality in Italian waters. Aquat. Conserv. Mar. Freshw. Ecosyst. 20, 611–620 (2010).
Google Scholar 
45.Tomás, J., Gozalbes, P., Raga, J. A. & Godley, B. J. Bycatch of loggerhead sea turtles: Insights from 14 years of stranding data. Endanger. Species Res. 5, 161–169 (2008).
Google Scholar 
46.Loisier, A. et al. Genetic composition, origin and conservation of loggerhead sea turtles (Caretta caretta) frequenting the French Mediterranean coasts. Mar. Biol. 168, 15 (2021).
Google Scholar 
47.Garofalo, L. et al. Genetic characterization of central Mediterranean stocks of the loggerhead turtle (Caretta caretta) using mitochondrial and nuclear markers, and conservation implications. Aquat. Conserv. Mar. Freshw. Ecosyst. 23, 868–884 (2013).
Google Scholar 
48.MedECC. Climate and environmental change in the Mediterranean basin: Current situation and risks for the future. in First Mediterranean Assessment Report (eds. Cramer, W., Guiot, J. & Marini, K.) 600 (2020).49.Pastor, F., Valiente, J. A. & Khodayar, S. A Warming Mediterranean: 38 years of increasing sea surface temperature. Remote Sens. 12, 2687 (2020).ADS 

Google Scholar 
50.Shaltout, M. & Omstedt, A. Recent sea surface temperature trends and future scenarios for the Mediterranean Sea. Oceanologia 56, 411–443 (2014).
Google Scholar 
51.Delaugerre, M. Status of marine turtles in the Mediterranean (with particular reference to Corsica). Vie Milieu 37, 243–264 (1987).
Google Scholar 
52.Delaugerre, M. & Cesarini, C. Confirmed nesting of the loggerhead turtle in Corsica. Mar. Turt. Newsle. 104, 12 (2004).
Google Scholar 
53.Gérigny, O. et al. Hatching events of the loggerhead turtle in Corsica Island, France. Mar. Turt. Newsl. 161, 15–18 (2020).
Google Scholar 
54.Sénégas, J.-B., Hochscheid, S., Groul, J.-M., Lagarrigue, B. & Bentivegna, F. Discovery of the northernmost loggerhead sea turtle (Caretta caretta) nest. Mar. Biodivers. Rec. 2, 1–4 (2009).
Google Scholar 
55.Gérigny, O., Delaugerre, M. & Cesarini, C. Love is a losing game. Loggerhead turtle in corsica vs tourism = nesting failure. Mar. Turt. Newsl. 148, 12–14 (2016).
Google Scholar 
56.Banzon, V., Smith, T. M., Chin, T. M., Liu, C. & Hankins, W. A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies. Earth Syst. Sci. Data 8, 165–176 (2016).ADS 

Google Scholar 
57.Cardona, L. & Hays, G. C. Ocean currents, individual movements and genetic structuring of populations. Mar. Biol. 165, 1–10 (2018).
Google Scholar 
58.Hochscheid, S., Bentivegna, F., Bradai, M. N. & Hays, G. C. Overwintering behaviour in sea turtles: Dormancy is optional. Mar. Ecol. Prog. Ser. 340, 287–298 (2007).ADS 

Google Scholar 
59.Revelles, M. et al. Tagging reveals limited exchange of immature loggerhead sea turtles (Caretta caretta) between regions in the western Mediterranean. Sci. Mar. 72, 511–518 (2008).
Google Scholar 
60.Revelles, M., Cardona, L., Aguilar, A., San Félix, M. & Fernández, G. Habitat use by immature loggerhead sea turtles in the Algerian Basin (western Mediterranean): Swimming behaviour, seasonality and dispersal pattern. Mar. Biol. 151, 1501–1515 (2007).
Google Scholar 
61.Casale, P. et al. Long-term residence of juvenile loggerhead turtles to foraging grounds: A potential conservation hotspot in the Mediterranean. Aquat. Conserv. Mar. Freshw. Ecosyst. 22, 144–154 (2012).
Google Scholar 
62.Benabdi, M. & Belmahi, A. E. First record of loggerhead turtle (Caretta caretta) nesting in the Algerian coast (southwestern Mediterranean). J. Black Sea/Mediterranean Environ. 26, 100–105 (2020).
Google Scholar 
63.Bradai, M. N. & Karaa, S. Première mention de la nidification de la tortue caouanne Caretta caretta sur la plage zouaraa (Nord de la Tunisie). Bull l’Inst. Natl. Sci. Technol. Mer Salammbô 44, 203–206 (2017).
Google Scholar 
64.Casale, P., Hochscheid, S., Kaska, Y. & Panagopoulou, A. Sea turtles in the Mediterranean region: MTSG annual regional report 2020. Rep. IUCN-SSC Mar. Turtl. Spec. Group 2020, 331 (2020).
Google Scholar 
65.Gonzalez-Paredes, D., Fernández-Maldonado, C., Grondona, M., Martínez-Valverde, R. & Marco, A. The westernmost nest of a loggerhead sea turtle, Caretta caretta (Linnaeus 1758), registered in the Mediterranean Basin (Testudines, Cheloniidae). Herpetol. Notes 14, 907–912 (2021).
Google Scholar 
66.Howard, R. & Bell, I. Thermal tolerances of sea turtle embryos: current understanding and future directions. Endanger. Species Res. 26, 75–86 (2014).
Google Scholar 
67.Yntema, C. L. & Mrosovsky, N. Critical periods and pivotal temperatures for sexual differentiation in loggerhead sea turtles. Can. J. Zool. 60, 1012–1016 (1982).
Google Scholar 
68.Kaska, Y., Downie, R., Tippett, R. & Furness, R. W. Natural temperature regimes for loggerhead and green turtle nests in the eastern Mediterranean. Can. J. Zool. 76, 723–729 (1998).
Google Scholar 
69.Fisher, L. R., Godfrey, M. H. & Owens, D. W. Incubation temperature effects on hatchling performance in the loggerhead sea turtle (Caretta caretta). PLoS ONE 9, 1–22 (2014).
Google Scholar 
70.Mrosovsky, N., Kamel, S., Rees, A. F. & Margaritoulis, D. Pivotal temperature for loggerhead turtles (Caretta caretta) from Kyparissia Bay, Greece. Can. J. Zool. 80, 2118–2124 (2002).
Google Scholar 
71.Cramer, W. et al. Climate change and interconnected risks to sustainable development in the Mediterranean. Nat. Clim. Chang. 8, 972–980 (2018).ADS 

Google Scholar 
72.Pastor, F., Valiente, J. A. & Palau, J. L. Sea surface temperature in the Mediterranean: Trends and spatial patterns (1982–2016). Pure Appl. Geophys. 175, 4017–4029 (2018).ADS 

Google Scholar 
73.Sakalli, A. Sea surface temperature change in the Mediterranean sea under climate change: A linear model for simulation of the sea surface temperature up to 2100. Appl. Ecol. Environ. Res. 15, 707–716 (2017).
Google Scholar 
74.Mazaris, A. D., Kallimanis, A. S., Tzanopoulos, J., Sgardelis, S. P. & Pantis, J. D. Sea surface temperature variations in core foraging grounds drive nesting trends and phenology of loggerhead turtles in the Mediterranean Sea. J. Exp. Mar. Biol. Ecol. 379, 23–27 (2009).
Google Scholar 
75.Fuentes, M. M. P. B., Pike, D. A., Dimatteo, A. & Wallace, B. P. Resilience of marine turtle regional management units to climate change. Glob. Chang. Biol. 19, 1399–1406 (2013).ADS 
PubMed 

Google Scholar 
76.Fuentes, M. M. P. B. et al. Potential adaptability of marine turtles to climate change may be hindered by coastal development in the USA. Reg. Environ. Chang. 20, 104 (2020).
Google Scholar 
77.Lorne, J. & Salmon, M. Effects of exposure to artificial lighting on orientation of hatchling sea turtles on the beach and in the ocean. Endanger. Species Res. 3, 23–30 (2007).
Google Scholar 
78.Camiñas, J. A. et al. Conservation of Marine Turtles in the Mediterranean Sea (IUCN Center for Mediterranean Cooperation, 2020).
Google Scholar 
79.Casale, P. Sea turtle by-catch in the Mediterranean. Fish Fish. 12, 299–316 (2011).
Google Scholar 
80.Wallace, B. P. et al. Global patterns of marine turtle bycatch. Conserv. Lett. 3, 131–142 (2010).
Google Scholar 
81.Sacchi, J. et al. France. in Sea Turtles in the Mediterranean Region: MTSG Annual Regional Report 2020.Report of the IUCN-SSC Marine Turtle Specialist Group, 2020. (eds. Casale, P., Hochscheid, S., Kaska, Y. & Panagopoulou, A.) 115–143 (2020).82.Santos, B. S., Friedrichs, M. A. M., Rose, S. A., Barco, S. G. & Kaplan, D. M. Likely locations of sea turtle stranding mortality using experimentally-calibrated, time and space-specific drift models. Biol. Conserv. 226, 127–143 (2018).
Google Scholar 
83.Ministère de l’environnement de l’énergie et de la mer. Arrêté portant dérogation a la protection stricte des espèces. (2016). http://gtmf.mnhn.fr/wp-content/uploads/sites/13/2016/12/arrete-subdelegation-MNHN-cartes-vertes-tortues-marines_signe_251020161.pdf.84.Ministère de la transition écologique & Ministère de la mer. Arrêté portant dérogation a la protection stricte des espèces. (2020). https://www.patrinat.fr/sites/patrinat/files/atoms/files/2021/01/20201230-arrete_subdelegation_mnhn_tm_2021-2026_-_vf_signe.pdf.85.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020).86.Casale, P., Freggi, D., Basso, R. & Argano, R. Size at male maturity, sexing methods and adult sex ratio in loggerhead turtles (Caretta caretta) from Italian waters investigated through tail measurements. Herpetol. J. 15, 145–148 (2005).
Google Scholar  More

1.Stocker, T. F. et al. IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2013).2.Jones, M. W. et al. Climate change increases risk of wildfires. ScienceBrief Review 116, 117 (2020).
Google Scholar 
3.Rogers, B. M., Balch, J. K., Goetz, S. J., Lehmann, C. E. & Turetsky, M. Focus on changing fire regimes: interactions with climate, ecosystems, and society. Environmental Research Letters 15, 030201 (2020).
Google Scholar 
4.Archibald, S., Lehmann, C. E., Gómez-Dans, J. L. & Bradstock, R. A. Defining pyromes and global syndromes of fire regimes. Proceedings of the National Acadam of Science 110, 6442–6447 (2013).CAS 

Google Scholar 
5.Donovan, G. H. & Brown, T. C. Be careful what you wish for: the legacy of Smokey Bear. Frontiers in Ecology and the Environment 5, 73–79 (2007).
Google Scholar 
6.Ghil, M. Natural climate variability. Encyclopedia Global Environmental Change 1, 544–549 (2002).
Google Scholar 
7.Hinnov, L. A. Cyclostratigraphy and its revolutionizing applications in the earth and planetary sciences. Geological Society of America Bulletin 125, 1703–1734 (2013).
Google Scholar 
8.Lantink, M. L., Davies, J. H., Mason, P. R., Schaltegger, U. & Hilgen, F. J. Climate control on banded iron formations linked to orbital eccentricity. Nature Geoscience 12, 369–374 (2019).CAS 

Google Scholar 
9.Berger, A. Milankovitch theory and climate. Reviews of Geophysics 26, 624–657 (1988).
Google Scholar 
10.Laskar, J. Astrochronology in Geological Time Scale 2020 (eds Gradstein, F. M., Ogg, J. G., Schmitz, M. D. & Ogg, G. M.) 139–158 (Elsevier, 2020).11.Shackleton, N. J. & Pisias, S. N. Atmospheric carbon dioxide, orbital forcing, and climate. The Carbon Cycle and Atmospheric ({CO}_{2}): Natural Variations Archean to Present, Geophysics Monograph Series 32, 303–317 (1985).12.Huybers, P. & Wunsch, C. Obliquity pacing of the late Pleistocene glacial terminations. Nature 434, 491–494 (2005).CAS 

Google Scholar 
13.Kutzbach, J. E., Liu, X., Liu, Z. & Chen, G. Simulation of the evolutionary response of global summer monsoons to orbital forcing over the past 280,000 years. Climate Dynamics 30, 567–579 (2008).
Google Scholar 
14.Weedon, G. P. Hemipelagic shelf sedimentation and climatic cycles: the basal Jurassic (Blue Lias) of South Britain. Earth and Planetary Science Letters 76, 321–335 (1986).
Google Scholar 
15.Weedon, G. P., Jenkyns, H. C., Coe, A. L. & Hesselbo, S. P. Astronomical calibration of the Jurassic time-scale from cyclostratigraphy in British mudrock formations. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 357, 1787–1813 (1999).
Google Scholar 
16.Van Buchem, F. S. P., McCave, I. N. & Weedon, G. P. Orbitally induced small-scale cyclicity in a siliciclastic epicontinental setting (Lower Lias, Yorkshire, UK) in Orbital forcing and cyclic sequences, Special Publication of the International Association of Sedimentologists (eds de Boer, P. L. & Smith, D. G.) 345–366 (1994).17.Zachos, J. C., McCarren, H., Murphy, B., Röhl, U. & Westerhold, T. Tempo and scale of late Paleocene and early Eocene carbon isotope cycles: Implications for the origin of hyperthermals. Earth and Planetary Science Letters 299, 242–249 (2010).CAS 

Google Scholar 
18.Martinez, M. & Dera, G. Orbital pacing of carbon fluxes by a ∼ 9-My eccentricity cycle during the Mesozoic. Proceedings of the National Academy of Sciences 112, 12604–12609 (2015).CAS 

Google Scholar 
19.Laskar, J. The chaotic motion of the solar system: a numerical estimate of the size of the chaotic zones. Icarus 88, 266–291 (1990).
Google Scholar 
20.Varadi, F., Runnegar, B. & Ghil, M. Successive refinements in long-term integrations of planetary orbits. The Astrophysical Journal 592, 620 (2003).
Google Scholar 
21.Laskar, J. et al. Long term evolution and chaotic diffusion of the insolation quantities of Mars. Icarus 170, 343–364 (2004).
Google Scholar 
22.Imbrie, J. & Imbrie, K. P. Ice ages: solving the mystery (Harvard University Press, 1979).23.Berger, A. & Loutre, M. F. Climate 400,000 years ago, a key to the future? Geophysical Monograph Series 137, 17–26 (2003).
Google Scholar 
24.Laskar, J., Fienga, A., Gastineau, M. & Manche, H. La2010: a new orbital solution for the long-term motion of the Earth. Astronomy & Astrophysics 532, A89 (2011).
Google Scholar 
25.Verardo, D. J. & Ruddiman, W. F. Late Pleistocene charcoal in tropical Atlantic deep-sea sediments: climatic and geochemical significance. Geology 24, 855–857 (1996).CAS 

Google Scholar 
26.Thevenon, F., Bard, E., Williamson, D. & Beaufort, L. A biomass burning record from the West Equatorial Pacific over the last 360 ky: methodological, climatic and anthropic implications. Palaeogeography, Palaeoclimatology, Palaeoecology 213, 83–99 (2004).
Google Scholar 
27.Daniau, A. L. et al. Orbital-scale climate forcing of grassland burning in southern Africa. Proceedings of the National Academy of Sciences 110, 5069–5073 (2013).CAS 

Google Scholar 
28.Inoue, J., Okuyama, C. & Takemura, K. Long-term fire activity under the East Asian monsoon responding to spring insolation, vegetation type, global climate, and human impact inferred from charcoal records in Lake Biwa sediments in central Japan. Quaternary Science Reviews 179, 59–68 (2018).
Google Scholar 
29.Zhang, Z. et al. Precession-scale climate forcing of peatland wildfires during the early middle Jurassic greenhouse period. Global and Planetary Change 184, 103051 (2020).
Google Scholar 
30.Shi, Y. et al. Wildfire evolution and response to climate change in the Yinchuan Basin during the past 1.5 Ma based on the charcoal records of the PL02 core. Quaternary Science Reviews 241, 106393 (2020).
Google Scholar 
31.Martínez-Abarca, L. R. et al. Environmental changes during MIS6-3 in the Basin of Mexico: a record of fire, lake productivity history and vegetation. J. South American Earth Sciences 109, 103231 (2021).
Google Scholar 
32.Whitlock, C. & Larsen, C. Charcoal as a fire proxy in Tracking environmental change using lake sediments (eds Smol, J. P., Birks, H. J. B., Last, W. M., Bradley, R. S. & Alverson, K.) 75–97 (Springer, Dordrecht, 2002).33.Hao, Y., Han, Y., An, Z. & Burr, G. S. Climatic control of orbital time-scale wildfire occurrences since the late MIS 3 at Qinghai Lake, monsoon marginal zone. Quaternary International 550, 20–26 (2020).
Google Scholar 
34.Zhou, B. et al. Elemental carbon record of paleofire history on the Chinese Loess Plateau during the last 420 ka and its response to environmental and climate changes. Palaeogeography, Palaeoclimatology, Palaeoecology 252, 617–625 (2007).
Google Scholar 
35.Kappenberg, A., Lehndorff, E., Pickarski, N., Litt, T. & Amelung, W. Solar controls of fire events during the past 600,000 years. Quaternary Science Reviews 208, 97–104 (2019).
Google Scholar 
36.Han, Y. et al. Asian inland wildfires driven by glacial–interglacial climate change. Proceedings of the National Academy of Sciences 117, 5184–5189 (2020).CAS 

Google Scholar 
37.Scott, A. C. & Glasspool, I. J. Observations and experiments on the origin and formation of inertinite group macerals. International Journal of Coal Geology 70, 53–66 (2007).CAS 

Google Scholar 
38.House, M. R. A new approach to an absolute timescale from measurements of orbital cycles and sedimentary microrhythms. Nature 315, 721–725 (1985).
Google Scholar 
39.Hesselbo, S. P. & Jenkyns, H. C. A comparison of the Hettangian to Bajocian successions of Dorset and Yorkshire. Field Geology of the British Jurassic, Geological Society of London (1995).40.Weedon, G. P. & Jenkyns, H. C. Cyclostratigraphy and the Early Jurassic timescale: data from the Belemnite Marls, Dorset, southern England. Geological Society of America Bulletin 111, 1823–1840 (1999).
Google Scholar 
41.Ruhl, M. et al. Astronomical constraints on the duration of the early Jurassic Hettangian stage and recovery rates following the end-Triassic mass extinction (St Audrie’s Bay/East Quantoxhead, UK). Earth and Planetary Science Letters 295, 262–276 (2010).CAS 

Google Scholar 
42.Hüsing, S. K. et al. Astronomically-calibrated magnetostratigraphy of the lower Jurassic marine successions at St. Audrie’s Bay and East Quantoxhead (Hettangian–Sinemurian; Somerset, UK). Palaeogeography, Palaeoclimatology, Palaeoecology 403, 43–56 (2014).
Google Scholar 
43.Ruhl, M. et al. Astronomical constraints on the duration of the Early Jurassic Pliensbachian Stage and global climatic fluctuations. Earth and Planetary Science Letters 455, 149–165 (2016).CAS 

Google Scholar 
44.Xu, W., Ruhl, M., Hesselbo, S. P., Riding, J. B. & Jenkyns, H. C. Orbital pacing of the Early Jurassic carbon cycle, black‐shale formation and seabed methane seepage. Sedimentology 64, 127–149 (2017).CAS 

Google Scholar 
45.Hinnov, L. A., Ruhl, M. R. & Hesselbo, S. P. Reply to the Comment on “Astronomical constraints on the duration of the Early Jurassic Pliensbachian Stage and global climatic fluctuations” (Ruhl et al. Earth and Planetary Science Letters, 455 149–165). Earth and Planetary Science Letters 481, 415–419 (2018).CAS 

Google Scholar 
46.Storm, M. S. et al. Orbital pacing and secular evolution of the Early Jurassic carbon cycle. Proceedings National Academy Science 117, 3974–3982 (2020).CAS 

Google Scholar 
47.Ogg, J. G., Hinnov, L. A. & Huang, C. Jurassic in The geologic time scale (eds Gradstein, F. M., Ogg, J. G., Schmitz, M. D. & Ogg, G. M.) 731–791 (Elsevier, 2012).48.Hinnov, L. A. & Hilgen, F. J. Cyclostratigraphy and astrochronology in The Geologic Time Scale 2012 (eds Gradstein, F. M., Ogg, J. G., Schmitz, M. D. & Ogg, G. M.) 63–83 (2012).49.Deconinck, J. F., Hesselbo, S. P. & Pellenard, P. Climatic and sea‐level control of Jurassic (Pliensbachian) clay mineral sedimentation in the Cardigan Bay Basin, Llanbedr (Mochras Farm) borehole, Wales. Sedimentology 66, 2769–2783 (2019).
Google Scholar 
50.Chamley, H. Clay sedimentology 623 (Springer, Berlin, Heidelberg, 1989).51.Ruffell, A., McKinley, J. M. & Worden, R. H. Comparison of clay mineral stratigraphy to other proxy palaeoclimate indicators in the Mesozoic of NW Europe. Philosophical Transactions of the Royal Society London A: Mathematical, Physical and Engineering Sciences 360, 675–693 (2002).
Google Scholar 
52.Ghosh, S., Mukhopadhyay, J. & Chakraborty, A. Clay mineral and geochemical proxies for intense climate change in the permian gondwana rock record from eastern india. Research, 8974075 (2019).53.Oboh-Ikuenobe, F. E., Obi, C. G. & Jaramillo, C. A. Lithofacies, palynofacies, and sequence stratigraphy of Palaeogene strata in Southeastern Nigeria. Journal of African Earth Sciences 41, 79–101 (2005).
Google Scholar 
54.Sprovieri, M. et al. Late Cretaceous orbitally-paced carbon isotope stratigraphy from the Bottaccione Gorge (Italy). Palaeogeography, Palaeoclimatology, Palaeoecology 379, 81–94 (2013).
Google Scholar 
55.Raucsik, B. & Varga, A. Climato-environmental controls on clay mineralogy of the Hettangian–Bajocian successions of the Mecsek Mountains, Hungary: an evidence for extreme continental weathering during the early Toarcian oceanic anoxic event. Palaeogeography, Palaeoclimatology, Palaeoecology 265, 1–13 (2008).
Google Scholar 
56.Martinez, M. Mechanisms of preservation of the eccentricity and longer-term Milankovitch cycles in detrital supply and carbonate production in hemipelagic marl-limestone alternations. Stratigraphy & Timescales 3, 189–218 (2018).
Google Scholar 
57.Cochrane, M. A. & Ryan, K. C. Fire and fire ecology: Concepts and principles in Tropical fire ecology, 25–62 (2009).58.Belcher, C. M. & Hudspith, V. A. The formation of charcoal reflectance and its potential use in post-fire assessments. International Journal of Wildland Fire 25, 775–779 (2016).
Google Scholar 
59.Archibald, S. et al. Biological and geophysical feedbacks with fire in the Earth system. Environmental Research Letters 13, 033003 (2018).
Google Scholar 
60.Van de Schootbrugge, B. et al. Early Jurassic climate change and the radiation of organic-walled phytoplankton in the Tethys Ocean. Paleobiology 31, 73–97 (2005).
Google Scholar 
61.Vakhrameyev, V. A. Classopollis pollen as an indicator of Jurassic and Cretaceous climate. International Geology Review 24, 1190–1196 (1982).
Google Scholar 
62.Belcher, C. M., Collinson, M. E. & Scott, A. C. A 450-Million-Year History of Fire in Fire phenomena and the earth system (ed. Belcher, C. M.) 229–249 (Wiley-Blackwell, 2013).63.Rees, P. M., Ziegler, A. M. & Valdes, P. J. Jurassic phytogeography and climates: new data and model comparisons in Warm climates in earth history (eds Huber, B. T., Macleod, K. G. & Wing, S. L.) 297–318 (2000).64.Dera, G. et al. Distribution of clay minerals in Early Jurassic Peritethyan seas: palaeoclimatic significance inferred from multiproxy comparisons. Palaeogeography, Palaeoclimatology, Palaeoecology 271, 39–51 (2009).
Google Scholar 
65.Bonis, N. R., Ruhl, M. & Kürschner, W. M. Milankovitch-scale palynological turnover across the Triassic–Jurassic transition at St. Audrie’s Bay, SW UK. Journal of the Geological Society 167, 877–888 (2010).
Google Scholar 
66.Deconinck, J. F. et al. Diagenetic and environmental control of the clay mineralogy, organic matter and stable isotopes (C, O) of Jurassic (Pliensbachian-lowermost Toarcian) sediments of the Rodiles section (Asturian Basin, Northern Spain). Marine and Petroleum Geology 115, 104286 (2020).CAS 

Google Scholar 
67.Dewhirst, R. A., Smirnoff, N. & Belcher, C. M. Pine Species That Support Crown Fire Regimes Have Lower Leaf-Level Terpene Contents Than Those Native to Surface Fire Regimes. Fire 3, 17 (2020).
Google Scholar 
68.Berger, A., Loutre, M. F. & Dehant, V. Astronomical frequencies for pre‐Quaternary palaeoclimate studies. Terra Nova 1, 474–479 (1989).
Google Scholar 
69.House, M. R. & Gale, A. S. (eds). Orbital forcing timescales and cyclostratigraphy, 85, 1–18 (Geological Society, 1995).70.James, N. P. Facies models 7. Introduction to carbonate facies models. Geoscience Canada 4, 123–125 (1977).
Google Scholar 
71.Nelson, C. S., Keane, S. L. & Head, P. S. Non-tropical carbonate deposits on the modern New Zealand shelf. Sedimentary Geology 60, 71–94 (1988).CAS 

Google Scholar 
72.Chave, K. E. Recent carbonate sediments–an unconventional view. Journal of Geological Education 15, 200–204 (1967).CAS 

Google Scholar 
73.Parrish, J. T. & Curtis, R. L. Atmospheric circulation, upwelling, and organic-rich rocks in the Mesozoic and Cenozoic eras. Palaeogeography, Palaeoclimatology, Palaeoecology 40, 31–66 (1982).
Google Scholar 
74.Crowley, T. J., Baum, S. K. & Hyde, W. T. Milankovitch fluctuations on supercontinents. Geophysical research letters 19, 793–796 (1992).
Google Scholar 
75.Parrish, J. T. Climate of the supercontinent Pangea. The. J. Geology 101, 215–233 (1993).
Google Scholar 
76.Kutzbach, J. E. & Gallimore, R. G. Pangaean climates: megamonsoons of the megacontinent. Journal of Geophysical Research: Atmospheres 94, 3341–3357 (1989).
Google Scholar 
77.Kutzbach, J. E. Idealized Pangean climates: sensitivity to orbital change. Pangea; paleoclimate, tectonics, and sedimentation during accretion, zenith and breakup of a supercontinent. Geological Society of America 15, 41–55 (1994).
Google Scholar 
78.Sellwood, B. W. & Valdes, P. J. Mesozoic climates: General circulation models and the rock record. Sedimentary geology 190, 269–287 (2006).
Google Scholar 
79.Mutti, M. & Hallock, P. Carbonate systems along nutrient and temperature gradients: some sedimentological and geochemical constraints. International Journal of Earth Sciences 92, 465–475 (2003).CAS 

Google Scholar 
80.Clift, P. D., Wan, S. & Blusztajn, J. Reconstructing chemical weathering, physical erosion and monsoon intensity since 25 Ma in the northern South China Sea: a review of competing proxies. Earth-Science Reviews 130, 86–102 (2014).CAS 

Google Scholar 
81.Clift, P. D. et al. Chemical weathering and erosion responses to changing monsoon climate in the Late Miocene of Southwest Asia. Geological Magazine 157, 939–955 (2020).CAS 

Google Scholar 
82.Arocena, J. M. & Opio, C. Prescribed fire-induced changes in properties of sub-boreal forest soils. Geoderma 113, 1–16 (2003).CAS 

Google Scholar 
83.Certini, G. Effects of fire on properties of forest soils: a review. Oecologia 143, 1–10 (2005).
Google Scholar 
84.Reynard-Callanan, J. R., Pope, G. A., Gorring, M. L. & Feng, H. Effects of high-intensity forest fires on soil clay mineralogy. Physical Geography 31, 407–422 (2010).
Google Scholar 
85.Torsvik, T. & Cocks, L. Jurassic in Earth History and Palaeogeography 208–218 (Cambridge University Press, 2016).86.Bjerrum, C. J., Surlyk, F., Callomon, J. H. & Slingerland, R. L. Numerical paleoceanographic study of the Early Jurassic transcontinental Laurasian Seaway. Paleoceanography 16, 390–404 (2001).
Google Scholar 
87.Hesselbo, S. P. & Pieńkowski, G. Stepwise atmospheric carbon-isotope excursion during the Toarcian oceanic anoxic event (Early Jurassic, Polish Basin). Earth and Planetary Science Letters 301, 365–372 (2011).CAS 

Google Scholar 
88.Sellwood, B. W. & Jenkyns, H. G. Basins and swells and the evolution of an epeiric sea: (Pliensbachian–Bajocian of Great Britain). Journal of the Geological Society 131, 373–388 (1975).
Google Scholar 
89.Damborenea, S. E., Echevarría, J. & Ros-Franch, S. Southern hemisphere palaeobiogeography of Triassic-Jurassic marine bivalves. (Springer, 2012).90.Korte, C. et al. Jurassic climate mode governed by ocean gateway. Nature Communications 6, 1–7 (2015).
Google Scholar 
91.Dobson, M. R. & Whittington, R. J. The geology of Cardigan Bay. Proceedings of the Geologists’ Association 98, 331–353 (1987).
Google Scholar 
92.Woodland, A. W. The Llanbedr (Mochras Farm) Borehole Rep. No. 71/18 (Ed. Woodland, A. W.) 115 (Institute of Geological Sciences, 1971).93.Tappin, D. R. et al. The Geology of Cardigan Bay and the Bristol Channel. United Kingdom offshore regional report, British Geological Survey, HMSO, 107 (1994).94.Xu, W. et al. Evolution of the Toarcian (Early Jurassic) carbon-cycle and global climatic controls on local sedimentary processes (Cardigan Bay Basin, UK). Earth and Planetary Science Letters 484, 396–411 (2018).CAS 

Google Scholar 
95.Hesselbo, S. P. et al. Mochras borehole revisited: A new global standard for Early Jurassic earth history. Scientific Drilling 16, 81–91 (2013).
Google Scholar 
96.Moore, D. M. & Reynolds Jr, R. C. X-ray Diffraction and the Identification and Analysis of Clay Minerals (Oxford University Press, 1997).97.Petschick, R. MacDiff 4.1. 2. Powder diffraction software (2000). Available from the author at http://www.geol.uni-erlangen.de/html/software/Macdiff.html.98.Belcher, C. M., Collinson, M. E. & Scott, A. C. Constraints on the thermal energy released from the Chicxulub impactor: new evidence from multi-method charcoal analysis. Journal of the Geological Society 162, 591–602 (2005).
Google Scholar 
99.Scott, A. C. Charcoal recognition, taphonomy and uses in palaeoenvironmental analysis. Palaeogeography, Palaeoclimatology, Palaeoecology 291, 11–39 (2010).
Google Scholar 
100.Li, M., Hinnov, L. & Kump, L. Acycle: Time-series analysis software for paleoclimate research and education. Computers & Geosciences 127, 12–22 (2019).CAS 

Google Scholar 
101.Damaschke, M., Wylde, S., Jiang, M., Hollaar, T. & Ullmann, C. V. LLANBEDR (MOCHRAS FARM) Core Scanning Dataset. NERC EDS National Geoscience Data Centre. (Dataset). https://doi.org/10.5285/c09e9908-6a21-43a8-bc5a-944f9eb8b97e (2021). More

1.de Souza Dias, V., Pereira da Luz, M., Medero, G. M. & Tarley Ferreira Nascimento, D. An overview of hydropower reservoirs in Brazil: Current situation, future perspectives and impacts of climate change. Water 10, 592 (2018).
Google Scholar 
2.Patias, J., Zuquette, L. V. & Rodrigues-Carvalho, J. A. Piezometric variations in the basaltic massif beneath the Itaipu hydroelectric plant (Brazil/Paraguay border): Right Buttress Dam. Bull. Eng. Geol. Environ. 74, 207–231 (2015).CAS 

Google Scholar 
3.Agostinho, A. A. Pesquisas, monitoramento e manejo da fauna aquática em empreendimentos hidrelétricos. In Seminário Sobre Fauna Aquática E O Setor Elétrico Brasileiro 38–59 (Brasil, 1994).4.Makrakis, S., Gomes, L. C., Makrakis, M. C., Fernandez, D. R. & Pavanelli, C. S. The Canal da Piracema at Itaipu Dam as a fish pass system. Neotrop. Ichthyol. 5, 185–195 (2007).
Google Scholar 
5.Dos Reis, R. B., Frota, A., Depra, G. D. C., Ota, R. R. & Da Graca, W. J. Freshwater fishes from Paraná State, Brazil: An annotated list, with comments on biogeographic patterns, threats, and future perspectives. Zootaxa 4868, 451–494 (2020).
Google Scholar 
6.Becker, R. A., Sales, N. G., Santos, G. M., Santos, G. B. & Carvalho, D. C. DNA barcoding and morphological identification of neotropical ichthyoplankton from the Upper Paraná and São Francisco. J. Fish Biol. 87, 159–168 (2015).CAS 
PubMed 

Google Scholar 
7.Milan, D. T. et al. New 12S metabarcoding primers for enhanced Neotropical freshwater fish biodiversity assessment. Sci. Rep. 10, 1–12 (2020).ADS 

Google Scholar 
8.Agostinho, A. A., Pelicice, F. M. & Gomes, L. C. Dams and the fish fauna of the Neotropical region: Impacts and management related to diversity and fisheries. Braz. J. Biol. 68, 1119–1132 (2008).CAS 
PubMed 

Google Scholar 
9.Bonar, S. A., Hubert, W. A. & Willis, D. W. Standard methods for sampling North American freshwater fishes. American Fisheries Society, Bethesda, (USA, 2009).
Google Scholar 
10.Shaw, J. L. A. et al. Comparison of environmental DNA metabarcoding and conventional fish survey methods in a river system. Biol. Conserv. 197, 131–138 (2016).
Google Scholar 
11.Reis, R. E. et al. Fish biodiversity and conservation in South America. J. Fish Biol. 89, 12–47 (2016).CAS 
PubMed 

Google Scholar 
12.Baumgartner, G. et al. Peixes do baixo rio Iguaçu. (Eduem, 2012).13.Taberlet, P., Bonin, A., Coissac, E. & Zinger, L. Environmental DNA: For Biodiversity Research and Monitoring (Oxford University Press, 2018).
Google Scholar 
14.Taberlet, P., Coissac, E., Pompanon, F., Christian, B. & Willerslev, E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol Ecol 33, 2045–2050 (2012).
Google Scholar 
15.Ritter, C. D. et al. The pitfalls of biodiversity proxies: Differences in richness patterns of birds, trees and understudied diversity across Amazonia. Sci. Rep. 9, 1–3 (2019).CAS 

Google Scholar 
16.Sales, N. G. et al. Space-time dynamics in monitoring neotropical fish communities using eDNA metabarcoding. Sci. Total Environ. 754, 142096 (2021).ADS 
CAS 
PubMed 

Google Scholar 
17.Zinger, L. et al. Body size determines soil community assembly in a tropical forest. Mol. Ecol. 28, 528–543 (2019).CAS 
PubMed 

Google Scholar 
18.Baird, D. J. & Hajibabaei, M. Biomonitoring 2.0: A new paradigm in ecosystem assessment made possible by next-generation DNA sequencing. Mol. Ecol. 21, 2039–2044 (2012).PubMed 

Google Scholar 
19.Zinger, L. et al. Advances and prospects of environmental DNA in neotropical rainforests. Adv. Ecol. Res. 62, 331–373 (2020).
Google Scholar 
20.Cilleros, K. et al. Unlocking biodiversity and conservation studies in high-diversity environments using environmental DNA (eDNA): A test with Guianese freshwater fishes. Mol. Ecol. Resour. 19, 27–46 (2019).CAS 
PubMed 

Google Scholar 
21.Sales, N. G., Wangensteen, O. S., Carvalho, D. C. & Mariani, S. Influence of preservation methods, sample medium and sampling time on eDNA recovery in a neotropical river. Environ. DNA 119–130. https://doi.org/10.1002/edn3.14 (2020).Article 

Google Scholar 
22.Blaxter, M. et al. Defining operational taxonomic units using DNA barcode data. Philos. Trans. R. Soc. B Biol. Sci. 360(1462), 1935–1943. https://doi.org/10.1098/rstb.2005.1725 (2005).CAS 
Article 

Google Scholar 
23.Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
24.Edgar, R. C. UNOISE2: Improved error-correction for Illumina 16S and ITS amplicon sequencing. BioRxiv 81257 (2016).
25.Muha, T. P., Rodriguez-Barreto, D., O’Rorke, R., Garcia de Leaniz, C. & Consuegra, S. Using eDNA metabarcoding to monitor changes in fish community composition after barrier removal. Front. Ecol. Evol. 9, 28 (2021).
Google Scholar 
26.Kitano, T., Umetsu, K., Tian, W. & Osawa, M. Two universal primer sets for species identification among vertebrates. Int. J. Legal Med. 121, 423–427 (2007).PubMed 

Google Scholar 
27.Stoeckle, M. Y., Soboleva, L. & Charlop-Powers, Z. Aquatic environmental DNA detects seasonal fish abundance and habitat preference in an urban estuary. PLoS One 12, e0175186 (2017).PubMed 
PubMed Central 

Google Scholar 
28.Bylemans, J. et al. An environmental DNA-based method for monitoring spawning activity: A case study, using the endangered Macquarie perch (Macquaria australasica). Methods Ecol. Evol. 8, 646–655 (2017).
Google Scholar 
29.De Souza, L. S., Godwin, J. C., Renshaw, M. A. & Larson, E. Environmental DNA (eDNA) detection probability is influenced by seasonal activity of organisms. PLoS One 11, e0165273 (2016).PubMed 
PubMed Central 

Google Scholar 
30.Ritter, C. D. et al. Locality or habitat? Exploring predictors of biodiversity in Amazonia. Ecography (Cop.) 42, 321–333 (2019).
Google Scholar 
31.CFMV-Resolução no 1000 de 11 de maio de 2012—Dispõe sobre procedimentos e métodos de eutanásia em animais e dá outras providências. (2012).32.Britski, H. A., de Silimon, K. Z. S. & Lopes, B. S. Peixes do Pantanal: manual de identificação, ampl. Brasília, DF, Embrapa Informação Tecnológica (2007).33.Ota, R. R., Deprá, G. de C., Graça, W. J. da & Pavanelli, C. S. Peixes da planície de inundação do alto rio Paraná e áreas adjacentes: revised, annotated and updated. Neotrop. Ichthyol. 16(2). https://www.scielo.br/j/ni/a/tScwvm8JLhKnbxKjtBQLPBx/abstract/?lang=en (2018).34.Neris, N., Villalba, F., Kamada, D. & Viré, S. Guía de peces del Paraguay/Guide of fishes of Paraguay. Zamphiropolos, (Paraguay, 2010).
Google Scholar 
35.Pie, M. R. et al. Development of a real-time PCR assay for the detection of the golden mussel (Limnoperna fortunei, Mytilidae) in environmental samples. An. Acad. Bras. Cienc. 89, 1041–1045 (2017).CAS 
PubMed 

Google Scholar 
36.Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: Detection of more than 230 subtropical marine species. R. Soc. Open Sci. 2, 150088 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
37.Boeger, W. A. et al. Testing a molecular protocol to monitor the presence of golden mussel larvae (Limnoperna fortunei) in plankton samples. J. Plankton Res. 29, 1015–1019 (2007).CAS 

Google Scholar 
38.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).
Google Scholar 
39.Van Rossum, G. & Drake, F. L. Python 3 References Manual. Scotts Valley CA: CreateSpace. (2009).40.R Core Team. R: the R project for statistical computing. 2019. https://www.r-project.org/ (accessed 30 Mar 2020).41.Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).CAS 
PubMed 

Google Scholar 
42.Edgar, R. C. & Flyvbjerg, H. Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics 31, 3476–3482 (2015).CAS 
PubMed 

Google Scholar 
43.Camacho, C. et al. BLAST+: Architecture and applications. BMC Bioinform. 10, 421 (2009).
Google Scholar 
44.Team, Rs. RStudio: integrated development for R. RStudio, Inc., Boston, MA https://www.rstudio.com42, 84 (2015).45.Wickham, H. tidyverse: Easily Install and Load “Tidyverse” Packages (Version R package version 1.1. 1). (2017).46.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 

Google Scholar 
47.Tang, Y., Horikoshi, M. & Li, W. ggfortify: Unified interface to visualize statistical results of popular R packages. R J. 8, 474 (2016).
Google Scholar 
48.Auguie, B. & Antonov, A. gridExtra: Miscellaneous functions for “grid” graphics (Version 2.2. 1)[Computer software]. (2016).49.Kassambara, A. & Kassambara, M. A. Package ‘ggpubr’. (2020).50.Oksanen, J. et al. Vegan: Community ecology package. R package version 1.17-4. https://cran.r-project.org. Acesso em 23, 2010 (2010).51.McMurdie, P. J. & Holmes, S. Waste not, want not: Why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
52.Jost, L. Entropy and diversity. Oikos 113, 363–375 (2006).
Google Scholar 
53.Marcon, E., Herault, B. & Marcon, M. E. Package ‘entropart’. (2021).54.Mächler, E., Walser, J.-C. & Altermatt, F. Decision making and best practices for taxonomy-free eDNA metabarcoding in biomonitoring using Hill numbers. BioRxiv (2020).55.McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 

Google Scholar 
57.León, A., Reyes, J., Burriel, V. & Valverde, F. Data quality problems when integrating genomic information. In International Conference on Conceptual Modeling 173–182 (Springer, 2016).58.Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).PubMed 
PubMed Central 

Google Scholar 
59.Stahlhut, J. K. et al. DNA barcoding reveals diversity of hymenoptera and the dominance of parasitoids in a sub-arctic environment. BMC Ecol. 13, 2 (2013).PubMed 
PubMed Central 

Google Scholar 
60.Gillet, B. et al. Direct fishing and eDNA metabarcoding for biomonitoring during a 3-year survey significantly improves number of fish detected around a South East Asian reservoir. PLoS One 13, e0208592 (2018).PubMed 
PubMed Central 

Google Scholar 
61.Barrett, M. et al. Living planet report 2018: Aiming higher. WWF. Available at: https://www.globallandscapesforum.org/publication/living-planet-report-2018-aiming-higher/ (2018).62.Díaz, S. M. et al. The global assessment report on biodiversity and ecosystem services: Summary for policy makers. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. 56, (2019).63.Dudgeon, D. Asian river fishes in the Anthropocene: Threats and conservation challenges in an era of rapid environmental change. J. Fish Biol. 79, 1487–1524 (2011).CAS 
PubMed 

Google Scholar 
64.Dudgeon, D. Multiple threats imperil freshwater biodiversity in the Anthropocene. Curr. Biol. 29, R960–R967 (2019).CAS 
PubMed 

Google Scholar 
65.He, F. et al. Disappearing giants: A review of threats to freshwater megafauna. Wiley Interdiscip. Rev. Water 4, e1208 (2017).
Google Scholar 
66.Agostinho, A. A., Thomaz, S. M. & Gomes, L. C. Threats for biodiversity in the floodplain of the Upper Paraná River: Effects of hydrological regulation by dams. (2018). Int. J. Ecohydrol. Hydrobiol Warsaw. 4(3), 267–280 (2004).
Google Scholar 
67.Santana, M. L., Carvalho, F. R. & Teresa, F. B. Broad and fine-scale threats on threatened Brazilian freshwater fish: Variability across hydrographic regions and taxonomic groups. Biota Neotrop. 21 (2). https://www.scielo.br/j/bn/a/YqFbWSy5vbfHy3QK9kNpdKp/?format=html&lang=en (2021).68.Matthews, W. J. Patterns in Freshwater Fish Ecology. (Springer Science & Business Media, 2012).69.de Oliveira Bueno, E., Alves, G. J. & Mello, C. R. Hydroelectricity water footprint in Parana hydrograph region, Brazil. Renew. Energy 162, 596–612 (2020).
Google Scholar 
70.Camacho Guerreiro, A. I., Amadio, S. A., Fabre, N. N. & da Silva Batista, V. Exploring the effect of strong hydrological droughts and floods on populational parameters of Semaprochilodus insignis (Actinopterygii: Prochilodontidae) from the Central Amazonia. Environ. Dev. Sustain. 23, 3338–3348 (2021).
Google Scholar 
71.Jespersen, H., Rasmussen, G. & Pedersen, S. Severity of summer drought as predictor for smolt recruitment in migratory brown trout (Salmo trutta). Ecol. Freshw. Fish 30, 115–124 (2021).
Google Scholar 
72.Pool, T. K., Grenouillet, G. & Villéger, S. Species contribute differently to the taxonomic, functional, and phylogenetic alpha and beta diversity of freshwater fish communities. Divers. Distrib. 20, 1235–1244 (2014).
Google Scholar 
73.de Oliveira, E. F., Goulart, E. & Minte-Vera, C. V. Fish diversity along spatial gradients in the Itaipu Reservoir, Paraná, Brazil. Braz. J. Biol. 64, 447–458 (2004).CAS 
PubMed 

Google Scholar 
74.Daga, V. S. et al. Homogenization dynamics of the fish assemblages in Neotropical reservoirs: Comparing the roles of introduced species and their vectors. Hydrobiologia 746, 327–347 (2015).
Google Scholar 
75.Vitule, J. R. S. Introdução de peixes em ecossistemas continentais brasileiros: revisão, comentários e sugestões de ações contra o inimigo quase invisível. Neotrop. Biol. Conserv. 4, 111–122 (2009).
Google Scholar 
76.Mariac, C. et al. Species‐level ichthyoplankton dynamics for 97 fishes in two major river basins of the Amazon using quantitative metabarcoding. Mol. Ecol. https://onlinelibrary.wiley.com/action/showCitFormats?doi=10.1111%2Fmec.15944 (2021).77.Jackman, J. M. et al. eDNA in a bottleneck: Obstacles to fish metabarcoding studies in megadiverse freshwater systems. Environ. DNA 3, 837–849 (2021).
Google Scholar 
78.Bessey, C. et al. Maximizing fish detection with eDNA metabarcoding. Environ. DNA 2, 493–504 (2020).
Google Scholar 
79.Evans, N. T. et al. Fish community assessment with eDNA metabarcoding: Effects of sampling design and bioinformatic filtering. Can. J. Fish. Aquat. Sci. 74, 1362–1374 (2017).CAS 

Google Scholar 
80.Prodan, A. et al. Comparing bioinformatic pipelines for microbial 16S rRNA amplicon sequencing. PLoS One 15, e0227434 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
81.Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 

Google Scholar 
82.Elbrecht, V. & Leese, F. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass—sequence relationships with an innovative metabarcoding protocol. PLoS ONE 10, e0130324 (2015).PubMed 
PubMed Central 

Google Scholar 
83.Pawluczyk, M. et al. Quantitative evaluation of bias in PCR amplification and next-generation sequencing derived from metabarcoding samples. Anal. Bioanal. Chem. 407, 1841–1848 (2015).CAS 
PubMed 

Google Scholar 
84.Holman, L. E., Chng, Y. & Rius, M. How does eDNA decay affect metabarcoding experiments? Environ. DNA https://onlinelibrary.wiley.com/action/showCitFormats?doi=10.1002%2Fedn3.201 (2021).85.Edgar, R. C. UNCROSS2: identification of cross-talk in 16S rRNA OTU tables. BioRxiv 400762 (2018).86.MacArthur, R. H. Geographical Ecology: Patterns in the Distribution of Species. (Princeton University Press, 1984).87.Leray, M. & Knowlton, N. Random sampling causes the low reproducibility of rare eukaryotic OTUs in Illumina COI metabarcoding. PeerJ 5, e3006 (2017).PubMed 
PubMed Central 

Google Scholar 
88.Team, Q. D. QGIS geographic information system. Open Source Geospatial Found. Proj. Versão 2, (2015). More

Microbial community samplingHippo gut microbiomeWe characterized the microbial communities in the hippo gut by collecting ten samples of fresh hippo feces adjacent to four hippo pools early in the morning (prior to desiccation by the sun) in September 2017. We collected feces from different pools and locations adjacent to the pool to include the feces of different individuals so we could estimate the similarity of the gut microbiome among individuals and across the landscape. The four hippo pools are sufficiently far apart that there was likely no intermixing of hippos among them.Each individual hippo feces sample was gently homogenized by hand and then the liquid was gently squeezed from the coarse particulate organic matter. A portion of the liquid (approximately 10 mL) was vacuum filtered through a Supor polysulfone membrane (0.2-µm pore size; Pall, Port Washington, NY, USA). After approximately 10 mL had filtered through and the filter was dry, 15 mL of RNALater was gently poured onto the filter and allowed to contact the collected biomass on the filter for 15 min before being removed by filtration. The filter was stored dry in a sterile petri dish and transferred to a refrigerator within several hours, then to a − 20 °C freezer for storage within several days.During the July 2016 survey of hippo pools, we collected an additional two samples of fresh hippo feces near a high-subsidy hippo pool and filtered approximately 10 mL of the liquid portion after homogenization as detailed above. The filter was then folded twice to preserve the biomass on the filter and stored in 14 mL of RNALater.Aquatic ecosystemWe characterized the microbial communities in the water column of hippo pools across a gradient of hippo subsidy (July 2016, N = 12 pools). We collected samples from the upstream, downstream, surface, and bottom of both pools containing hippos and pools that lacked hippos. Subsamples were also analyzed for biogeochemical variables (details provided below). We also collected water samples in four of the high-subsidy hippo pools every 2–3 days starting immediately after a flushing event until the next flushing event (August and September 2017, Supplementary Fig. S1)23. The number of hippos, discharge and volume for each pool are presented in Dutton et al (2020)23.We sampled the aquatic microbial community and biogeochemical variables along a longitudinal transect down both the Mara and Talek rivers (Supplementary Fig. S1, Supplementary Table S2). For the Mara River, we sampled an approximately 100-km transect along a gradient of hippo numbers (N = 10 locations, from 0 to ~ 4000 hippos). For the Talek River, we sampled an approximately 30-km transect to the confluence with the Mara (N = 8 locations, from 0 to 700 hippos). Mara River sites 9 and 10 are downstream of the confluence with the Talek River. Water samples were collected from each site in a well-mixed flowing section away from any hippo pools.Aquatic microbial samples were collected by filtering water samples through a Supor polysulfone filter (0.2-µm pore size; Pall, Port Washington, NY, USA) and then preserving the filter in RNALater Stabilization Solution (Ambion, Inc., Austin, TX, USA). In 2017, the filters were preserved with RNA Later and then frozen for analysis.Mesocosm experimentWe collected river water from the Mara River upstream of the distribution of hippos and placed it in 45 1-L bottles in a large water basin covered by a dark tarp to help regulate temperature and prevent algal production. Bottles were randomly assigned to the control, bacteria, and bacteria + virus treatments. We collected fresh hippo feces from multiple locations adjacent to the Mara River. After homogenization, half of the hippo feces was sterilized in a pressure cooker, which testing confirmed had similar sterilization results as an autoclave53 (see Supplementary Materials). Five grams of sterilized hippo feces was placed into each bottle to provide an organic matter substrate without viable bacteria or viruses. The unsterilized hippo feces was expressed, and the resulting liquid was filtered through 0.7-µm GF/F filters (0.7-µm pore size; Whatman, GE Healthcare Life Sciences, Pittsburgh, PA, USA) and 0.2-µm Supor filters to physically separate the bacteria (on the filter papers) from the viruses (in the filtrate). Half the filtrate was then sterilized with a UV light treatment (Supplementary Fig. S4). The UV light treatment did not significantly alter DOC quality (see Supplementary Materials).We prepared 15 bottles for each of three treatments—control, bacteria, and bacteria + virus—as follows: Control Unfiltered river water, 5 g wet weight sterilized hippo feces, and two blank Supor filters; Bacteria Unfiltered river water, 5 g wet weight sterilized hippo feces, two Supor filters containing bacteria, and 4 mL sterilized filtrate; Virus Unfiltered river water, 5 g wet weight sterilized hippo feces, two Supor filters containing bacteria, and 4 mL unsterilized filtrate containing viruses.We conducted the experiment for 27 days from September to October 2017. We terminated the experiment after 27 days because we were trying to replicate the microbial communities in hippo pools as best as we could and the hippo pools rarely go more than 1 month before they are flushed out by a flood25. Initial microbial samples of the river water, hippo feces bacteria and hippo fecal liquid filtrate were taken on day 0, and three replicate samples per treatment were destructively sampled on day 3, 9, 15, 21, and 27. During each time step, the microbial communities were sampled using the methods detailed above, and chemical analyses were done on the water samples as described below. We also measured chlorophyll a, dissolved oxygen, temperature, conductivity, total dissolved solids, turbidity, and pH with a Manta2 water quality sonde (Eureka Water Probes, Austin, TX, USA).Microbial community characterizationWe used 16S rRNA sequencing to characterize the active microbial communities. We extracted both DNA and RNA from our preserved samples, then used RNA to synthesize cDNA to represent the “active” microbial community and the total DNA in the sample to represent the “total” microbes present, including those that may not be actively replicating54. Due to the continual loading of hippo feces into pools and the long half-life of DNA, we would expect there to be significant quantities of microbial DNA derived from hippo feces within the pools. However, there would be less accumulation of RNA because of RNA’s shorter half-life. The active communities identified through this RNA-based approach are the ones that would potentially contribute to ecosystem function55 as indicated by the protein synthesis potential, although relationships between activity and rRNA concentrations in individual taxa within mixed communities can vary56. Nevertheless, this method provides an overall characterization of the microbial community’s potential activity.We used the Qiagen RNeasy Powerwater Kit (Qiagen, Hilden, Germany) to extract the DNA and RNA from the material on the filter using a slightly modified manufacturer’s protocol to allow for the extraction of both DNA and RNA. After extraction, we split the total extracted volume (100 µL per sample) into two groups. We treated one group with the DNase Max Kit (Qiagen, Hilden, Germany) to remove all DNA and serve as the RNA group of samples.We used the RNA group of samples to synthesize cDNA using the SuperScript III First Strand Synthesis Kit (Invitrogen, Carlsbad, CA, USA). DNA and cDNA were quantified using the PicoGreen dsDNA Assay Kit (Molecular Probes, Eugene, OR, USA) then normalized to 5 ng/µL. Amplicon library preparation was done using a dual-index paired-end approach57. We amplified the V4 region of the 16S rRNA gene using dual-index primers (F515/R805) and AccuPrime Pfx SuperMix (Invitrogen, Carlsbad, CA, USA) in duplicate for each sample using the manufacturer’s recommended thermocycling routine.Samples were then pooled, purified and normalized using the SequelPrep Normalization Plate Kit (Invitrogen, Carlsbad, CA, USA). Barcoded amplicon libraries were then sequenced at the Yale Center for Genome Analysis (New Haven, CT, USA) using an Illumina Miseq v2 reagent kit (Illumina, San Diego, CA, USA) to generate 2 × 250 base pair paired-end reads.Sampling took place in 2016 and 2017 and involved two separate sequencing runs. The first sequencing run included negative controls and a mock community (D6306, Zymo Research, Irvin, CA, USA). The second sequencing run included negative controls, a mock community (D6306), and a single E. coli strain. In both runs, the mock community and single E. coli strain were well reconstructed from the sequences, and there was minimal contamination in the negative controls, mock community and E. coli strain.From those two sequencing campaigns, we received over 2 million raw sequences from the first campaign and over 7 million raw sequences for the second campaign. For the microbial community analyses, only samples collected and sequenced during the same campaign are analyzed together to prevent preservation or sequencing biases. However, samples within the two separate campaigns were preserved and sequenced using identical methods with only a minor modification (mentioned above) to increase the preservation of genetic material.We de-multiplexed sequenced reads then removed barcodes, indexes, and primers using QIIME258. We used DADA2 with a standard workflow in R59 to infer exact sequence variants (ESV) for each sample60. We assigned taxonomy using a naïve Bayesian classifier and the SILVA training set v. 128 database61,62. We removed potential contamination in samples from both campaigns by using the statistical technique in the R package, decontam63. We used Phyloseq to characterize, ordinate, and compare microbial communities64 with their standard workflow59.Chemical analysesAll water samples collected in the field and in the experiment were analyzed for dissolved ferrous iron (Fe(II)), hydrogen sulfide (H2S), dissolved organic carbon (DOC), inorganic nutrients, major ions, dissolved gases, and biochemical oxygen demand following the standard methods provided in detail in Dutton et al (2020)23.Statistical analysesWe computed all statistical analyses in the R 4.1.1 statistical language in RStudio 2021.09.0 using α = 0.05 to determine significance65,66. Error bars in the figures represent standard deviation of the means. All data and R code for the statistics and data treatments are provided in the Mendeley Data Online Repository67.We used the Bray–Curtis dissimilarity matrix followed by ordination with NMDS to examine differences between individual hippo gut microbiomes; between low-, medium-, and high-subsidy hippo pools; and between a gradient of hippo pools and the environment. We used a CCA to test for the influence of biogeochemical drivers on microbial community composition using biogeochemical data that were previously published but collected concurrently with this study23. We constrained the CCA ordination by soluble reactive phosphorus, nitrate, methane, BOD, and sulfate, which were all previously shown to be important drivers in the variation between pools23. We used PERMANOVA and PERMDISP to test for significant differences between groups68.
We compared aquatic microbial communities from the bottom of high-subsidy hippo pools (N = 15), from hippo feces (N = 10, the hippo gut microbiome) and upstream of high-subsidy hippo pools (N = 15, free of hippo gut microbiome influence) using the Bray–Curtis dissimilarity matrix on the relative abundances for the active aquatic microbial communities collected from the different sample types followed by ordination with NMDS. 95% confidence ellipses were generated. We then determined the active taxa that were shared between the hippo gut microbiome (hippo feces) and the bottom of the high-subsidy hippo pools and not present in the upstream samples from high-subsidy hippo pools.We used LEfSe to calculate the differential abundance of microbial taxa between upstream (N = 14), downstream (N = 16), at the surface (N = 17) and at the bottom (N = 14) of hippo pools and calculated their effect size69. We then calculated the correlation of microbial taxa to the measured biogeochemistry using Pearson’s correlation coefficient with a false discovery rate corrected p-value in the microeco R package70.
We used SourceTracker to quantify the contribution of the hippo gut, upstream waters, or unknown sources to the active aquatic microbial communities in the bottom waters of three of the high-subsidy hippo pools between flushing flows71. We also used the Bray–Curtis dissimilarity matrix followed by ordination with NMDS to examine changes in the active aquatic microbial communities in one of the high subsidy hippo pools through time after flushing flows.For the experiment, we calculated the Bray–Curtis dissimilatory matrix followed by ordination with NMDS for the active aquatic microbial communities over time in each of the three experimental treatments. We used SourceTracker to determine the proportion of the active aquatic microbial community in each treatment that originated from the hippo gut, the river water, or unknown sources71. We analyzed the biogeochemical differences among experimental treatments by fitting a linear mixed effects model for each of the biogeochemical variables throughout the experiment with the nlme package in R72. We fit the model with the restricted maximum likelihood method and a continuous autoregressive temporal correlation structure with sample day as the repeated factor. Treatment and time were fixed effects and individual bottles were treated as random effects. We conducted a pairwise post-hoc test with an ANOVA and the emmeans package in R to estimate marginal means with a Tukey adjusted p-value for multiple comparisons73,74. More

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