Ecology

1.Jones, G. P., Pearlstine, L. G. & Percival, H. F. An assessment of small unmanned aerial vehicles for wildlife research. Wildl. Soc. Bull. 34, 750–758 (2006).Article 

Google Scholar 
2.Jones, G. P. The feasibility of using small unmanned aerial vehicles for wildlife research. Masters Thesis. (University of Florida, 2003).3.Watts, A. C. et al. Unmanned aircraft systems (UASs) for ecological research and natural-resource monitoring (Florida). Ecol. Restor. 26, 13–14 (2008).Article 

Google Scholar 
4.Chabot, D. Systematic Evaluation of a Stock Unmanned Aerial Vehicle (UAV) System for Small-Scale Wildlife Survey Applications. Masters Thesis. (McGill University, 2009).5.Koski, W. R. et al. Evaluation of an unmanned airborne system for monitoring marine mammals. Aquat. Mamm. 35, 347–357 (2009).MathSciNet 
Article 

Google Scholar 
6.Soriano, P., Caballero, F. & Ollero, A. RF-based particle filter localization for wildlife tracking by using an UAV. Int. Symp. Robot. 40, 239–244 (2009).
Google Scholar 
7.Sukkarieh, S. UAV based search for a radio tagged animal using particle filters at Stuttgart. In Australasian Conference on Robotics and Automation (ACRA) (2009).8.Abd-Elrahman, A., Pearlstine, L. & Percival, F. Development of pattern recognition algorithm for automatic bird detection from unmanned aerial vehicle imagery. Surv. L. Inf. Sci. 65, 37–45 (2005).
Google Scholar 
9.Singh, K. K., Frazier, A. E. & Frazier, A. E. A meta-analysis and review of unmanned aircraft system (UAS) imagery for terrestrial applications. Int. J. Remote Sens. 00, 1–21 (2018).
Google Scholar 
10.Rebolo-Ifran, N., Grilli, M. G. & Lambertucci, S. Drones as a threat to wildlife: YouTube complements science in providing evidence about their effect. Environ. Conserv. https://doi.org/10.1017/S0376892919000080 (2019).Article 

Google Scholar 
11.Weston, M. A., O’Brien, C., Kostoglou, K. N. & Symonds, M. R. E. E. Escape responses of terrestrial and aquatic birds to drones: Towards a code of practice to minimize disturbance. J. Appl. Ecol. 57, 777–785 (2020).Article 

Google Scholar 
12.Vas, E., Lescroël, A., Duriez, O., Boguszewski, G. & Grémillet, D. Approaching birds with drones: First experiments and ethical guidelines. Biol. Lett. 11, 20140754 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
13.Pomeroy, P., O’Connor, L. & Davies, P. Assessing use of and reaction to unmanned aerial systems in gray and harbor seals during breeding and molt in the UK. J. Unmanned Veh. Syst. 3, 102–113 (2015).Article 

Google Scholar 
14.Giles, A. B. et al. Responses of bottlenose dolphins (Tursiops spp.) to small drones. Aquat. Conserv. Mar. Freshw. Ecosyst. https://doi.org/10.1002/aqc.3440 (2020).Article 

Google Scholar 
15.Mulero-Pázmány, M. et al. Unmanned aircraft systems as a new source of disturbance for wildlife: A systematic review. PLoS ONE 12, 1–14 (2017).Article 
CAS 

Google Scholar 
16.Bennitt, E., Bartlam-Brooks, H. L. A., Hubel, T. Y. & Wilson, A. M. Terrestrial mammalian wildlife responses to unmanned aerial systems approaches. Sci. Rep. 9, 2142 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
17.Irigoin-Lovera, C., Luna, D. M., Acosta, D. A. & Zavalaga, C. B. Response of colonial Peruvian guano birds to flying UAVs: Effects and feasibility for implementing new population monitoring methods. PeerJ 7, e8129 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
18.McEvoy, J. F., Hall, G. P. & McDonald, P. G. Evaluation of unmanned aerial vehicle shape, flight path and camera type for waterfowl surveys: Disturbance effects and species recognition. PeerJ 2016, e1831 (2016).Article 
CAS 

Google Scholar 
19.Barnas, A. F., Felege, C. J., Rockwell, R. F. & Ellis-Felege, S. N. A pilot(less) study on the use of an unmanned aircraft system for studying polar bears (Ursus maritimus). Polar Biol. 41, 1055–1062 (2018).Article 

Google Scholar 
20.Jarrett, D., Calladine, J., Cotton, A., Wilson, M. W. & Humphreys, E. Behavioural responses of non-breeding waterbirds to drone approach are associated with flock size and habitat. Bird Study 67, 190–196 (2020).Article 

Google Scholar 
21.Bevan, E. et al. Measuring behavioral responses of sea turtles, saltwater crocodiles, and crested terns to drone disturbance to define ethical operating thresholds. PLoS One 13, e0194460 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
22.Stankowich, T. Ungulate flight responses to human disturbance: A review and meta-analysis. Biol. Conserv. 141, 2159–2173 (2008).Article 

Google Scholar 
23.Weston, M. A., Mcleod, E. M., Blumstein, D. T. & Guay, P. J. A review of flight-initiation distances and their application to managing disturbance to Australian birds. Emu 112, 269–286 (2012).Article 

Google Scholar 
24.Wisdom, M. J., Ager, A. A., Preisler, H. K., Cimon, N. J. & Johnson, B. K. Effects of off-road recreation on mule deer and elk. In Transactions of the 69th North American Wildlife and Natural Resources Conference 531–550 (2004).25.Penny, S. G., White, R. L., Scott, D. M., MacTavish, L. & Pernetta, A. P. Using drones and sirens to elicit avoidance behaviour in white rhinoceros as an anti-poaching tactic. Proc. R. Soc. B Biol. Sci. 286, 20191135 (2019).Article 

Google Scholar 
26.Frid, A. & Dill, L. M. Human-caused disturbance stimuli as a form of predation risk. Conserv. Ecol. 6, 11 (2002).
Google Scholar 
27.Dill, L. M. & Frid, A. Behaviourally mediated biases in transect surveys: A predation risk sensitivity approach. Can. J. Zool. 98, 697–704 (2020).Article 

Google Scholar 
28.Pulliam, R. H. On the advantage of flocking. J. Theor. Biol. 38, 419–422 (1973).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Taraborelli, P., Gregorio, P., Moreno, P., Novaro, A. & Carmanchahi, P. Cooperative vigilance: The guanaco’ s (Lama guanicoe) key antipredator mechanism. Behav. Process. 91, 82–89 (2012).Article 

Google Scholar 
30.Delm, M. M. Vigilance for predators: Detection and dilution effects. Behav. Ecol. Sociobiol. 26, 337–342 (1990).Article 

Google Scholar 
31.Roberts, G. Why individual vigilance declines as group size increases. Anim. Behav. 51, 1077–1086 (1996).Article 

Google Scholar 
32.Brunton, E., Bolin, J., Leon, J. & Burnett, S. Fright or flight? Behavioural responses of kangaroos to drone-based monitoring. Drones 3, 1–11 (2019).Article 

Google Scholar 
33.Lent, P. C. Mother-infant relationships in ungulates. Behav. Ungulates Relat. Manag. I, 14–55 (1974).
Google Scholar 
34.Franklin, W. Contrasting socioecologies of South America´s wild camelids: The vicuña and the guanaco. Adv. Study Mamm. Behav. 7, 573–629 (1983).
Google Scholar 
35.Ortega, I. M. & Franklin, W. L. Social organization, distribution and movements of a migratory guanaco population in the Chilean Patagonia. Rev. Chil. Hist. Nat. 68, 489–500 (1995).
Google Scholar 
36.Schroeder, N. M., Panebianco, A., Musso, R. G. & Carmanchahi, P. An experimental approach to evaluate the potential of drones in terrestrial mammal research: A gregarious ungulate as a study model. R. Soc. Open Sci. 7, 191482 (2020).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Lima, S. L. Back to the basics of anti-predatory vigilance: the group size effect. Anim. Behav. 49, 11–20 (1995).Article 

Google Scholar 
38.Marino, A. & Baldi, R. Vigilance patterns of territorial guanacos (Lama guanicoe): The role of reproductive interests and predation risk. Ethology 114, 413–423 (2008).Article 

Google Scholar 
39.Taraborelli, P. et al. Different factors that modify anti-predator behaviour in guanacos (Lama guanicoe). Acta Theriol. (Warsz) 59, 529–539 (2014).Article 

Google Scholar 
40.Donadio, E. & Buskirk, S. W. Flight behavior in guanacos and vicuñas in areas with and without poaching in western Argentina. Biol. Conserv. 127, 139–145 (2006).Article 

Google Scholar 
41.Marino, A. & Johnson, A. Behavioural response of free-ranging guanacos (Lama guanicoe) to land-use change: Habituation to motorised vehicles in a recently created reserve. Wildl. Res. 39, 503–511 (2012).Article 

Google Scholar 
42.Malo, J. E., Acebes, P. & Traba, J. Measuring ungulate tolerance to human with flight distance: A reliable visitor management tool?. Biodivers. Conserv. 20, 3477e3488 (2011).Article 

Google Scholar 
43.Marino, A. Indirect measures of reproductive effort in a resource-defense polygynous ungulate: Territorial defense by male guanacos. J. Ethol. 30, 83–91 (2012).Article 

Google Scholar 
44.Marino, A. & Ricardo, B. Vigilance patterns of territorial guanacos (Lama guanicoe): the role of reproductive interests and predation risk. Ethology 114, 413–423 (2008).Article 

Google Scholar 
45.Merino, M. L. & Cajal, C. J. Estructura social de la población de guanacos (Lama guanicoe Muller, 1776) en la costa norte de Península Mitre, Tierra del Fuego, Argentina. Stud. Neotrop. Fauna Environ. 28, 129–138 (1993).Article 

Google Scholar 
46.Marino, A. & Baldi, R. Ecological correlates of group-size variation in a resource-defense ungulate, the sedentary Guanaco. PLoS ONE 9, e89060 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
47.Fattorini, N. et al. Temporal variation in foraging activity and grouping patterns in a mountain-dwelling herbivore: Environmental and endogenous drivers. Behav. Process. 167, 103909 (2019).Article 

Google Scholar 
48.Blank, D., Ruckstuhl, K. & Yang, W. Influence of population density on group sizes in goitered gazelle (Gazella subgutturosa Guld., 1780). Eur. J. Wildl. Res. 58, 981–989 (2012).Article 

Google Scholar 
49.Isvaran, K. Intraspecific variation in group size in the blackbuck antelope: The roles of habitat structure and forage at different spatial scales. Oecologia 154, 435–444 (2007).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Mahoney, S. P., Mawhinney, K., McCarthy, C., Anions, D. & Taylor, S. Caribou reactions to provocation by snowmachines in Newfoundland. Rangifer 21, 35 (2001).Article 

Google Scholar 
51.Ruiz Blanco, M. et al. Supervivencia y causas de mortalidad durante el primer año de vida de guanacos en el norte de patagonia. In XXVII Jornadas Argentinas de Mastozoología 151 (2014).52.Weimerskirch, H., Prudor, A. & Schull, Q. Flights of drones over sub-Antarctic seabirds show species- and status-specific behavioural and physiological responses. Polar Biol. 41, 259–266 (2018).Article 

Google Scholar 
53.McIntosh, R. R., Holmberg, R. & Dann, P. Looking without landing-using Remote Piloted Aircraft to monitor fur seal populations without disturbance. Front. Mar. Sci. 5, (2018).54.Mesquita, G. P., Rodríguez-Teijeiro, J. D., Wich, S. A. & Mulero-Pázmány, M. Measuring disturbance at swift breeding colonies due to the visual aspects of a drone: a quasi-experiment study. Curr. Zool. https://doi.org/10.1093/cz/zoaa038 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
55.Scobie, C. A. & Hugenholtz, C. H. Wildlife monitoring with unmanned aerial vehicles: Quantifying distance to auditory detection. Wildl. Soc. Bull. 40, 781–785 (2016).Article 

Google Scholar 
56.Rümmler, M. C., Esefeld, J., Hallabrin, M. T., Pfeifer, C. & Mustafa, O. Emperor penguin reactions to UAVs: First observations and comparisons with effects of human approach. Remote Sens. Appl. Soc. Environ. 23, 100545 (2021).
Google Scholar 
57.Zbyryt, A., Dylewski, Ł, Morelli, F., Sparks, T. H. & Tryjanowski, P. Behavioural responses of adult and young White Storks Ciconia ciconia in nests to an unmanned aerial vehicle. Acta Ornithol. 55, 243–251 (2020).
Google Scholar 
58.Christiansen, F., Rojano-Doñate, L., Madsen, P. T. & Bejder, L. Noise levels of multi-rotor unmanned aerial vehicles with implications for potential underwater impacts on marine mammals. Front. Mar. Sci. 3, 277 (2016).Article 

Google Scholar 
59.Arona, L., Dale, J., Heaslip, S. G., Hammill, M. O. & Johnston, D. W. Assessing the disturbance potential of small unoccupied aircraft systems (UAS) on gray seals (Halichoerus grypus) at breeding colonies in Nova Scotia, Canada. PeerJ https://doi.org/10.7717/peerj.4467 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
60.Goebel, M. E. et al. A small unmanned aerial system for estimating abundance and size of Antarctic predators. Polar Biol. 38, 619–630 (2015).Article 

Google Scholar 
61.Cracknell, A. P. UAVs: Regulations and law enforcement. Int. J. Remote Sens. 38, 3054–3067 (2017).Article 

Google Scholar 
62.ANAC, A. N. de A. C. Reglamento de Vehículos Aéreos no Tripulados (VANT) y de Sistemas de Vehículos Aéreos no Tripulados (SVANT). (2019).63.Brisson-Curadeau, É. et al. Seabird species vary in behavioural response to drone census. Sci. Rep. 7, 17884 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
64.Rümmler, M.-C., Mustafa, O., Maercker, J., Peter, H.-U. & Esefeld, J. Sensitivity of Adélie and Gentoo penguins to various flight activities of a micro UAV. Polar Biol. 41, 2481–2493 (2018).Article 

Google Scholar 
65.Ditmer, M. A. et al. Bears habituate to the repeated exposure of a novel stimulus, unmanned aircraft systems. Conserv. Physiol. 7, 1–7 (2019).Article 

Google Scholar 
66.Young, J. K. & Franklin, W. L. Territorial Fidelity of male guanacos in the Patagonia of Southern Chile. J. Mammal. 85, 72–78 (2004).Article 

Google Scholar 
67.Martínez Carretero, E. La Provincia Fitogeográfica de la Payunia. Boletín la Soc. Argentina Botánica 39, 195–226 (2004).
Google Scholar 
68.Schroeder, N. M. et al. Spatial and seasonal dynamic of abundance and distribution of guanaco and livestock: Insights from using density surface and null models. PLoS One 9, e85960 (2014).
ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
69.Bolgeri, M. J. Caracterización de movimientos migratorios en guanacos (Lama guanicoe) y patrones de depredación por pumas (Puma concolor) en la Payunia, Mendoza. Phd Thesis. (Universidad Nacional del Comahue, 2016).70.Bolgeri, M. J. & Novaro, A. J. Variación espacial en la depredación por puma (Puma concolor) sobre guanacos (Lama guanicoe) en la Payunia, Mendoza,Argentina. Mastozoología Neotrop. 22, 255–264 (2015).
Google Scholar 
71.Candia, R., Puig, S., Dalmasso, A., Videla, F. & Martínez Carretero, E. Diseño del Plan de Manejo para la reserva provincial La Payunia (Malargüe, Mendoza). Multequina 2, 5–87 (1993).
Google Scholar 
72.Carmanchahi, P. D. et al. Physiological response of wild guanacos to capture for live shearing. Wildl. Res. 38, 61–68 (2011).Article 

Google Scholar 
73.Martin, P. & Bateson, P. Measuring Behaviour. An Introductory Guide. (Cambridge University Press, 2007).74.Ydenberg, R. C. & Dill, L. M. The economics of fleeing from predators. Adv. Study Behav. 16, 229–249 (1986).Article 

Google Scholar 
75.McCullagh, P. & Nelder, J. Generalized Linear Models. Second Edition. (Chapman & Hall, 1989).76.Fox, J. & Monette, G. Generalized collinearity diagnostics. J. Am. Stat. Assoc. 87, 178–183 (1992).Article 

Google Scholar 
77.Zuur, A. F., Ieno, E. N. & Smith, G. M. Analysing Ecological Data. (Springer, 2007).78.Gelman, A. & Hill, J. Data analysis using regression and multilevel/hierarchical models. Cambridge 651 (2007). https://doi.org/10.2277/052186706179.Korner-Nievergelt, F. et al. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan. (2015). https://doi.org/10.1007/s13398-014-0173-7.280.R Core Team. R Development Core Team. R A Lang. Environ. Stat. Comput. 55, 275–286 (2016).81.Fox, J. & Weisberg, S. An R Companion to Applied Regression, 3rd edn. (2019).82.Gelman, A. et al. Data analysis using regression and multilevel/hierarchical models. R package version 1, 10–1 (2018).

Google Scholar  More

1.Zhang, Z. Y. et al. A shift pattern of bacterial communities across the life stages of the citrus red mite, Panonychus citri. Front. Microbiol. 11, 1620. https://doi.org/10.3389/fmicb.2020.01620 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
2.Pan, D., Dou, W., Yuan, G. R., Zhou, Q. H. & Wang, J. J. Monitoring the resistance of the citrus red mite (Acari: Tetranychidae) to four acaricides in different citrus orchards in China. J. Econ. Entomol. 113, 918–923. https://doi.org/10.1093/jee/toz335 (2020).CAS 
Article 
PubMed 

Google Scholar 
3.Zhang, Y., Guo, L., Atlihan, R., Chi, H. & Chu, D. Demographic analysis of progeny fitness and timing of resurgence of Laodelphax striatellus after insecticides exposure. Entomol. Generalis 39, 221–230. https://doi.org/10.1127/entomologia/2019/0816 (2019).Article 

Google Scholar 
4.Quesada, C. R. & Sadof, C. S. Field evaluation of insecticides and application timing on natural enemies of selected armored and soft scales. Biol. Control 133, 81–90. https://doi.org/10.1016/j.biocontrol.2019.03.013 (2019).CAS 
Article 

Google Scholar 
5.Ullah, F. et al. Fitness costs in chlorfenapyr-resistant populations of the chive maggot, Bradysia odoriphaga. Ecotoxicology 29, 407–416. https://doi.org/10.1007/s10646-020-02183-7 (2020).CAS 
Article 
PubMed 

Google Scholar 
6.Razik, M. A. R. A. M. A. Toxicity and side effects of some insecticides applied in cotton fields on Apis mellifera. Environ. Sci. Pollut. R. 26, 4987–4996. https://doi.org/10.1007/s11356-018-04061-6 (2019).CAS 
Article 

Google Scholar 
7.Ochiai, N. et al. Toxicity of bifenazate and its principal active metabolite, diazene, to Tetranychus urticae and Panonychus citri and their relative toxicity to the predaceous mites, Phytoseiulus persimilis and Neoseiulus californicus. Exp. Appl. Acarol. 43, 181–197. https://doi.org/10.1007/s10493-007-9115-9 (2007).CAS 
Article 
PubMed 

Google Scholar 
8.Van Nieuwenhuyse, P. et al. On the mode of action of bifenazate: New evidence for a mitochondrial target site. Pestic. Biochem. Physiol. 104, 88–95. https://doi.org/10.1016/j.pestbp.2012.05.013 (2012).CAS 
Article 

Google Scholar 
9.Wang, R. et al. Lethal and sublethal effects of a novel cis-nitromethylene neonicotinoid insecticide, cycloxaprid, on Bemisia tabaci. Crop Prot. 83, 15–19. https://doi.org/10.1016/j.cropro.2016.01.015 (2016).CAS 
Article 

Google Scholar 
10.Ullah, F., Gul, H., Desneux, N., Gao, X. & Song, D. Imidacloprid-induced hormesis effects on demographic traits of the melon aphid, Aphis gossypii. Entomol. Generalis 39, 325–337 (2019).Article 

Google Scholar 
11.Dong, J., Wang, K., Li, Y. & Wang, S. Lethal and sublethal effects of cyantraniliprole on Helicoverpa assulta (Lepidoptera: Noctuidae). Pestic. Biochem. Physiol. 136, 58–63. https://doi.org/10.1016/j.pestbp.2016.08.003 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
12.Elzen, G. W. Lethal and sublethal effects of insecticide residues on Orius insidiosus (Hemiptera: Anthocoridae) and Geocoris punctipes (Hemiptera: Lygaeidae). J. Econ. Entomol. 94, 55–59. https://doi.org/10.1603/0022-0493-94.1.55 (2001).CAS 
Article 
PubMed 

Google Scholar 
13.Desneux, N., Decourtye, A. & Delpuech, J. M. The sublethal effects of pesticides on beneficial arthropods. Annu. Rev. Entomol. 52, 81–106. https://doi.org/10.1146/annurev.ento.52.110405.091440 (2007).CAS 
Article 
PubMed 

Google Scholar 
14.Deng, D. et al. Assessment of the effects of lethal and sublethal exposure to dinotefuran on the wheat aphid Rhopalosiphum padi (Linnaeus). Ecotoxicology 28, 825–833. https://doi.org/10.1007/s10646-019-02080-8 (2019).CAS 
Article 
PubMed 

Google Scholar 
15.Guo, L. et al. Sublethal and transgenerational effects of chlorantraniliprole on biological traits of the diamondback moth, Plutella xylostella L.. Crop Prot. 48, 29–34. https://doi.org/10.1016/j.cropro.2013.02.009 (2013).CAS 
Article 

Google Scholar 
16.Duke, S. O. et al. (eds) Pesticide Dose: Effects on the Environment and Target and Non-target Organisms 101–119 (American Chemical Society, 2017).
Google Scholar 
17.Guedes, N. M. P., Tolledo, J., Correa, A. S. & Guedes, R. N. C. Insecticide-induced hormesis in an insecticide-resistant strain of the maize weevil, Sitophilus zeamais. J. Appl. Entomol. 134, 142–148. https://doi.org/10.1111/j.1439-0418.2009.01462.x (2010).CAS 
Article 

Google Scholar 
18.Haddi, K., Oliveira, E. E., Faroni, L. R., Guedes, D. C. & Miranda, N. N. Sublethal exposure to clove and cinnamon essential oils induces hormetic-like responses and disturbs behavioral and respiratory responses in Sitophilus zeamais (Coleoptera: Curculionidae). J. Econ. Entomol. 108, 2815–2822. https://doi.org/10.1093/jee/tov255 (2015).CAS 
Article 
PubMed 

Google Scholar 
19.Chen, X. D., Seo, M. & Stelinski, L. L. Behavioral and hormetic effects of the butenolide insecticide, flupyradifurone, on Asian citrus psyllid, Diaphorina citri. Crop Prot. 98, 102–107. https://doi.org/10.1016/j.cropro.2017.03.017 (2017).CAS 
Article 

Google Scholar 
20.Wang, L., Zhang, Y., Xie, W., Wu, Q. & Wang, S. Sublethal effects of spinetoram on the two-spotted spider mite, Tetranychus urticae (Acari: Tetranychidae). Pestic Biochem. Physiol. 132, 102–107. https://doi.org/10.1016/j.pestbp.2016.02.002 (2016).CAS 
Article 
PubMed 

Google Scholar 
21.Xiao, L. F. et al. Genome-wide identification, phylogenetic analysis, and expression profiles of ATP-binding cassette transporter genes in the oriental fruit fly, Bactrocera dorsalis (Hendel) (Diptera: Tephritidae). Comp. Biochem. Physiol. D Genomics Proteomics 25, 1–8 (2018).ADS 
CAS 
Article 

Google Scholar 
22.Dubovskiy, I. M. et al. Effect of bacterial infection on antioxidant activity and lipid peroxidation in the midgut of Galleria mellonella L. larvae (Lepidoptera, Pyralidae). Comp. Biochem. Physiol. C Toxicol. Pharmacol. 148, 1–5 (2008).CAS 
Article 

Google Scholar 
23.Jia, B. T., Hong, S. S., Zhang, Y. C. & Cao, Y. W. Effect of sublethal concentrations of abamectin on protective and detoxifying enzymes in Diagegma semclausum. J. Environ. Entomol. 38, 990 (2016).
Google Scholar 
24.Yang, Q., Wang, S., Zhang, W., Yang, T. & Liu, Y. Toxicity of commonly used insecticides and their influences on protective enzyme activity of multicolored Asian lady beetle Harmonia axyridis (Pallas). Acta Phytophylacica Sin. 42, 258–263 (2015).CAS 

Google Scholar 
25.Zhou, C., Yang, H., Wang, Z., Long, G. Y. & Jin, D. C. Protective and detoxifying enzyme activity and abcg subfamily gene expression in Sogatella furcifera under insecticide stress. Front. Physiol. 9, 1890. https://doi.org/10.3389/fphys.2018.01890 (2018).Article 
PubMed 

Google Scholar 
26.Cui, L., Yuan, H., Wang, Q., Wang, Q. & Rui, C. Sublethal effects of the novel cis-nitromethylene neonicotinoid cycloxaprid on the cotton aphid Aphis gossypii Glover (Hemiptera: Aphididae). Sci. Rep. 8, 8915. https://doi.org/10.1038/s41598-018-27035-7 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
27.Li, Y. Y. et al. Sublethal effects of bifenazate on life history and population parameters of Tetranychus urticae (Acari: Tetranychidae). Syst. Appl. Acarol. 22, 148–158. https://doi.org/10.11158/saa.22.1.15 (2017).CAS 
Article 

Google Scholar 
28.Wang, Y., Huang, X., Chang, B. H. & Zhang, Z. The survival, growth, and detoxifying enzyme activities of grasshoppers Oedaleus asiaticus (Orthoptera: Acrididae) exposed to toxic rutin. Appl. Entomol. Zool. 55, 385–393. https://doi.org/10.1007/s13355-020-00694-7 (2020).CAS 
Article 

Google Scholar 
29.Rasheed, M. A. et al. Lethal and sublethal effects of chlorpyrifos on biological traits and feeding of the aphidophagous predator Harmonia axyridis. Insects. https://doi.org/10.3390/insects11080491 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
30.Yamamoto, A., Yoneda, H., Hatano, R. & Asada, M. Genetic analysis of hexythiazox resistance in the citrus red mite, Panonychus citri (MCGREGOR). J. Pestic. Sci. 20, 513–519 (1995).CAS 
Article 

Google Scholar 
31.Chi, H. & Liu, H. Two new methods for the study of insect population ecology. Acad. Sin. 24(2), 225–240 (1985).MathSciNet 

Google Scholar 
32.Chi, H. et al. Age-Stage, two-sex life table: an introduction to theory, data analysis, and application. Entomol. Generalis 40, 103 (2019).Article 

Google Scholar 
33.Hsin, C. Letter to the editor. J. Econ. Entomol. 108, 1465 (2015).Article 

Google Scholar 
34.Akköprü, E. P., Atlıhan, R., Okut, H. & Chi, H. Demographic assessment of plant cultivar resistance to insect pests: A case study of the dusky-veined walnut Aphid (Hemiptera: Callaphididae) on five walnut cultivars. J. Econ. Entomol. 108, 378 (2015).Article 

Google Scholar 
35.Mousavi, M., Ghosta, Y. & Maroofpour, N. Insecticidal activity and sublethal effects of Beauveria bassiana (Bals.-Criv.) Vuill. isolates and essential oils against Aphis gossypii Glover, 1877 (Hemiptera: Aphididae). Acta Agric. Slovenica. https://doi.org/10.14720/aas.2020.115.2.1306 (2020).Article 

Google Scholar 
36.Rahmani, S. & Bandani, A. R. Sublethal concentrations of thiamethoxam adversely affect life table parameters of the aphid predator, Hippodamia variegata (Goeze) (Coleoptera: Coccinellidae). Crop Prot. 54, 168–175. https://doi.org/10.1016/j.cropro.2013.08.002 (2013).CAS 
Article 

Google Scholar 
37.Papachristos, D. P. & Milonas, P. G. Adverse effects of soil applied insecticides on the predatory coccinellid Hippodamia undecimnotata (Coleoptera: Coccinellidae). Biol. Control 47, 77–81. https://doi.org/10.1016/j.biocontrol.2008.06.009 (2008).CAS 
Article 

Google Scholar 
38.Ranjbar, F., Reitz, S., Jalali, M. A., Ziaaddini, M. & Izadi, H. Lethal and sublethal effects of two commercial insecticides on egg parasitoids (Hymenoptera: Scelionidae) of green stink bugs (Hem: Pentatomidae). J. Econ. Entomol. https://doi.org/10.1093/jee/toaa232 (2020).Article 

Google Scholar 
39.Zhao, Y. et al. Sublethal concentration of benzothiazole adversely affect development, reproduction and longevity of Bradysia odoriphaga (Diptera: Sciaridae). Phytoparasitica 44, 115–124. https://doi.org/10.1007/s12600-016-0506-5 (2016).CAS 
Article 

Google Scholar 
40.Sani, B., Hamid, G. & Elham, R. Sublethal effects of chlorfenapyr on the life table parameters of two-spotted spider mite, Tetranychus urticae (Acari: Tetranychidae). Syst. Appl. Acarol. 23, 1342 (2018).
Google Scholar 
41.Leeuwen, T. V., Pottelberge, S. V. & Tirry, L. Biochemical analysis of a chlorfenapyr-selected resistant strain of Tetranychus urticae Koch. Pest Manage. Sci. 62, 425–433 (2010).Article 

Google Scholar 
42.Allen, R. G. & Balin, A. K. Oxidative influence on development and differentiation: An overview of a free radical theory of development. Free Radic. Biol. Med. 6, 631–661 (1989).CAS 
Article 

Google Scholar 
43.Bolter, C. J. & Chefurka, W. Extramitochondrial release of hydrogen peroxide from insect and mouse liver mitochondria using the respiratory inhibitors phosphine, myxothiazol, and antimycin and spectral analysis of inhibited cytochromes. Arch. Biochem. Biophys. 278, 65–72 (1990).CAS 
Article 

Google Scholar 
44.Liu, Y., Wang, C., Qi, S., He, J. & Bai, Y. The sublethal effects of ethiprole on the development, defense mechanisms, and immune pathways of honeybees (Apis mellifera L.). Environ. Geochem. Health. https://doi.org/10.1007/s10653-020-00736-7 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
45.Zhang, S. et al. Sublethal effects of triflumezopyrim on biological traits and detoxification enzyme activities in the small brown Planthopper Laodelphax striatellus (Hemiptera: Delphacidae). Front. Physiol. 11, 261. https://doi.org/10.3389/fphys.2020.00261 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
46.Ku, C. C., Chiang, F. M., Hsin, C. Y., Yao, Y. E. & Sun, C. N. Glutathione transferase isozymes involved in insecticide resistance of diamondback moth larvae. Pestic. Biochem. Physiol. 50, 191–197 (1994).CAS 
Article 

Google Scholar 
47.Prapanthadara, L., Promtet, N., Koottathep, S., Somboon, P. & Ketterman, A. J. Isoenzymes of glutathione S-transferase from the mosquito Anopheles dirus species B: The purification, partial characterization and interaction with various insecticides. Insect Biochemi. Mol. Biol. 30, 395–403 (2000).CAS 
Article 

Google Scholar 
48.Döker, İ, Kazak, C. & Ay, R. Resistance status and detoxification enzyme activity in ten populations of Panonychus citri (Acari: Tetranychidae) from Turkey. Crop Prot. https://doi.org/10.1016/j.cropro.2020.105488 (2021).Article 

Google Scholar 
49.Goel, A., Dani, V. & Dhawan, D. K. Protective effects of zinc on lipid peroxidation, antioxidant enzymes and hepatic histoarchitecture in chlorpyrifos-induced toxicity. Chem. Biol. Interact. 156, 131–140. https://doi.org/10.1016/j.cbi.2005.08.004 (2005).CAS 
Article 
PubMed 

Google Scholar 
50.Van Leeuwen, T., Van Pottelberge, S. & Tirry, L. Biochemical analysis of a chlorfenapyr-selected resistant strain of Tetranychus urticae Koch. Pest Manage. Sci. 62, 425–433. https://doi.org/10.1002/ps.1183 (2006).CAS 
Article 

Google Scholar  More

1.Kaplan, J. O., Krumhardt, K. M. & Zimmermann, N. The prehistoric and preindustrial deforestation of Europe. Quatern. Sci. Rev. 28, 3016–3034. https://doi.org/10.1016/j.quascirev.2009.09.028 (2009).ADS 
Article 

Google Scholar 
2.Słowiński, M. et al. Drought as a stress driver of ecological changes in peatland – A palaeoecological study of peatland development between 3500 BCE and 200 BCE in central Poland. Palaeogeogr. Palaeoclimatol. Palaeoecol. 461, 272–291. https://doi.org/10.1016/j.palaeo.2016.08.038 (2016).Article 

Google Scholar 
3.Dietze, E., Słowiński, M., Zawiska, I., Veh, G. & Brauer, A. Multiple drivers of Holocene lake level changes at a lowland lake in northeastern Germany. Boreas 45, 828–845. https://doi.org/10.1111/bor.12190 (2016).Article 

Google Scholar 
4.Woodward, C., Shulmeister, J., Larsen, J., Jacobsen, G. E. & Zawadzki, A. Landscape hydrology. The hydrological legacy of deforestation on global wetlands. Science 346, 844–847, doi:https://doi.org/10.1126/science.1260510 (2014).5.Dreibrodt, S., Lubos, C., Terhorst, B., Damm, B. & Bork, H. R. Historical soil erosion by water in Germany: scales and archives, chronology, research perspectives. Quatern. Int. 222, 80–95. https://doi.org/10.1016/j.quaint.2009.06.014 (2010).Article 

Google Scholar 
6.Trumbore, S., Brando, P. & Hartmann, H. Forest health and global change. Science 349, 814–818. https://doi.org/10.1126/science.aac6759 (2015).ADS 
CAS 
Article 
PubMed 

Google Scholar 
7.Syvitski, J. P. & Kettner, A. Sediment flux and the Anthropocene. Philos. Trans. A Math. Phys. Eng. Sci. 369, 957–975. https://doi.org/10.1098/rsta.2010.0329 (2011).ADS 
Article 
PubMed 

Google Scholar 
8.McConnell, J. R. et al. Lead pollution recorded in Greenland ice indicates European emissions tracked plagues, wars, and imperial expansion during antiquity. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.1721818115 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
9.Christian, D. Silk roads or steppe roads? The silk roads in world history. J. World Hist. 11, 1–26. https://doi.org/10.1353/jwh.2000.0004 (2000).Article 

Google Scholar 
10.Beck, C. W. The role of the scientist: the amber trade, the chemical analysis of amber, and the determination of Baltic provenience. J. Baltic Stud. 16, 191–199. https://doi.org/10.1080/01629778500000111 (1985).Article 

Google Scholar 
11.Gimbutas, M. East Baltic amber in the fourth and third millennia B.C. J. Baltic Stud. 16, 231–256, doi:https://doi.org/10.1080/01629778500000151 (1985).12.de Navarro, J. M. Prehistoric routes between Northern Europe and Italy defined by the amber trade. Geogr J 66, doi:https://doi.org/10.2307/1783003 (1925).13.Szilágyi, M. On the Road: The History and Archaeology of Medieval Communication Networks in East-Central Europe. (Archaeolingua Alapítvány, 2014).14.Schmid, B. V. et al. Climate-driven introduction of the Black Death and successive plague reintroductions into Europe. Proc. Natl. Acad. Sci. USA 112, 3020–3025. https://doi.org/10.1073/pnas.1412887112 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
15.Rossignol, S., Kleingärtner, S., Newfield, T., Wehner, D. & Studies, P. I. o. M. Landscapes and societies in medieval Europe east of the Elbe: Interactions between environmental settings and cultural transformations. (Pontifical Institute of Mediaeval Studies, 2013).16.Drager, N. et al. Varve microfacies and varve preservation record of climate change and human impact for the last 6000 years at Lake Tiefer See (NE Germany). The Holocene 27, 450–464. https://doi.org/10.1177/0959683616660173 (2016).ADS 
Article 

Google Scholar 
17.Theuerkauf, M., Drager, N., Kienel, U., Kuparinen, A. & Brauer, A. Effects of changes in land management practices on pollen productivity of open vegetation during the last century derived from varved lake sediments. Holocene 25, 733–744. https://doi.org/10.1177/0959683614567881 (2015).ADS 
Article 

Google Scholar 
18.Izdebski, A., Koloch, G., Słoczyński, T. & Tycner, M. On the use of palynological data in economic history: New methods and an application to agricultural output in Central Europe, 0–2000AD. Explor. Econ. Hist. 59, 17–39. https://doi.org/10.1016/j.eeh.2015.10.003 (2016).Article 

Google Scholar 
19.Brauer, A. et al. The importance of independent chronology in integrating records of past climate change for the 60–8 ka INTIMATE time interval. Quatern. Sci. Rev. 106, 47–66. https://doi.org/10.1016/j.quascirev.2014.07.006 (2014).ADS 
Article 

Google Scholar 
20.Ott, F. et al. Site-specific sediment responses to climate change during the last 140 years in three varved lakes in Northern Poland. The Holocene 28, 464–477. https://doi.org/10.1177/0959683617729448 (2017).ADS 
Article 

Google Scholar 
21.Śląski, K. Lądowe szlaki handlowe Pomorza w XI-XIII wieku. Zapiski Historyczne 34 (1969).22.Zakrzewski, I. Vol. 1 (Poznań, 1877).23.Ślaski, K. Zasięg lasów Pomorza w ostatnim tysiącleciu [in English: Range of forests Pomerania in the last millennium]. Przegląd Zachodni 5(6), 207–263 (1951).
Google Scholar 
24.Górska-Gołaska, K. in Słownik historyczno-geograficzny ziem polskich w średniowieczu Vol. part 3 (ed A. Gąsiorowski) 89 (1993–1999).25.Szulist, W. Ważniejsze szlaki handlowo-komunikacyjne północno-zachodniego Pomorza Gdańskiego w XVI-XVII w. Zapiski Historyczne 35, 105–106 (1970).
Google Scholar 
26.Wilska, M. in Historical atlas of Poland in the 2nd half of the 16th century: voivodeships of Cracow, Sandomierz, Lublin, Sieradz, Łęczyca, Rawa, Płock and Mazovia Vol. 3 (ed M. Słoń) 637 (Peter Lang, 2014).27.Pluskowski, A. The archaeology of the military orders: The material culture of holy war. Mediev. Archaeol. 62, 105–134. https://doi.org/10.1080/00766097.2018.1451590 (2018).Article 

Google Scholar 
28.Związek, T. in Wielkopolska w drugiej połowie XVI w. Vol. 2 (eds K. Chłapowski & M. Słoń) 273 (Wydawnictwo Instytutu Historii PAN, 2017).29.Wielopolski, A. Polsko-pomorskie spory graniczne w latach 1536–1555. Przegląd Zachodni 10, 85 (1954).
Google Scholar 
30.Kowalkowski, K. Z dziejów gminy Kaliska oraz wsi do niej należących. (Wydawnictwo Region, 2010).31.Słowiński, M. et al. Paleoecological and historical data as an important tool in ecosystem management. J. Environ. Manage. 236, 755–768. https://doi.org/10.1016/j.jenvman.2019.02.002 (2019).Article 
PubMed 

Google Scholar 
32.Błaszkiewicz, M. et al. Climatic and morphological controls on diachronous postglacial lake and river valley evolution in the area of Last Glaciation, northern Poland. Quatern. Sci. Rev. 109, 13–27. https://doi.org/10.1016/j.quascirev.2014.11.023 (2015).Article 

Google Scholar 
33.Woś, A. Climate of Poland (Wydawnictwo Naukowe PWN, (in Polish), Warszawa, 1999).34.Haldon, J. et al. History meets palaeoscience: Consilience and collaboration in studying past societal responses to environmental change. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.1716912115 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
35.Stivrins, N. et al. Palaeoenvironmental evidence for the impact of the crusades on the local and regional environment of medieval (13th–16th century) northern Latvia, eastern Baltic. The Holocene 26, 61–69. https://doi.org/10.1177/0959683615596821 (2015).ADS 
Article 

Google Scholar 
36.Grzegorz, M. Osady Pomorza Gdańskiego w latach 1309–1454. (Państwowe Wydawn. Nauk., 1990).37.Biskup, M. Prusy Królewskie w drugiej połowie XVI wieku. (Państwowe Wydawnictwo Naukowe, 1961).38.Chmielewski, S. Gospodarka rolna i hodowlana w Polsce w XIV i XV w. Technika i rozmiary produkcji, . (Państwowe Wydawnictwo Naukowe, 1962).39.Brown, A. et al. The ecological impact of conquest and colonization on a medieval frontier landscape: combined Palynological and geochemical analysis of lake sediments from Radzyń Chełminski Northern Poland. Geoarchaeology 30, 511–527. https://doi.org/10.1002/gea.21525 (2015).Article 

Google Scholar 
40.Brown, A. & Pluskowski, A. Detecting the environmental impact of the Baltic Crusades on a late-medieval (13th–15th century) frontier landscape: palynological analysis from Malbork Castle and hinterland, Northern Poland. J. Archaeol. Sci. 38, 1957–1966. https://doi.org/10.1016/j.jas.2011.04.010 (2011).Article 

Google Scholar 
41.Buntgen, U. et al. Filling the Eastern European gap in millennium-long temperature reconstructions. Proc. Natl. Acad. Sci. USA 110, 1773–1778. https://doi.org/10.1073/pnas.1211485110 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
42.Luterbacher, J. et al. European summer temperatures since Roman times. Environ. Res. Lett. 11, 024001. https://doi.org/10.1088/1748-9326/11/2/024001 (2016).Article 

Google Scholar 
43.Czaja, R. & Radzimiński, A. (eds.), The Teutonic Order in Prussia and Livonia: The political and ecclesiastical Structures 13th–16th century. 407 (TNT, Bohlau Verlag, 2015).44.Bartlett, R. The making of Europe: conquest, colonization and cultural change, 950–1350. (Allen Lane, 1993).45.Broda, J. History of forestry in Poland [in Polish]. 368 (Wydawnictwo Akademii Rolniczej im. Augusta Cieszkowskiego w Poznaniu, 2000).46.Schreg, R. in Processing, storage, distribution of food in medieval rural environment (eds J. Klápště & P. Sommer) 301–320 (2011).47.Kaczmarczyk, Z. & Sczaniecki, M. Kolonizacja na prawie niemieckim w Polsce a rozwój renty feudalnej’. Czasopismo Prawno-Historyczne 3, 59–83 (1951).
Google Scholar 
48.Czerwiński, S. et al. Environmental implications of past socioeconomic events in Greater Poland during the last 1200 years. Synthesis of paleoecological and historical data. Quaternary Science Reviews 259, doi:https://doi.org/10.1016/j.quascirev.2021.106902 (2021).49.Schreg, R. in Settlement change across Medieval Europe. Old paradigms and new vistas (eds N. Brady & C. Theune) 61–70 (Sidestone Press, 2019).50.Mikulski, K. Osadnictwo wiejskie województwa pomorskiego od połowy XVI do końca XVII wieku. (1994).51.Biskup, M. & Labuda, G. Dzieje zakonu krzyżackiego w Prusach: gospodarka, społeczeństwo, państwo, ideologia. (Wydawn. Morskie, 1986).52.Kizik, E. in Dżuma, ospa, cholera. W trzechsetną rocznicę wielkiej epidemii na ziemiach Rzeczypospolitej w latach 1708–1711 (ed E. Kizik) 275 (2012).53.Brönnimann, S. Climatic Changes Since 1700. Vol. 55 360 (Springer, 2015).54.Dygdała, J. Lustracja województw Prus Królewskich. Vol. 1–2 (Towarzystwo Naukowe w Toruniu, 2003).55.Dietze, E. et al. Human-induced fire regime shifts during 19th century industrialization: A robust fire regime reconstruction using northern Polish lake sediments. PLoS ONE 14, e0222011. https://doi.org/10.1371/journal.pone.0222011 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
56.Lamentowicz, M. et al. Always on the tipping point – A search for signals of past societies and related peatland ecosystem critical transitions during the last 6500 years in N Poland. Quat. Sci. Rev. 225, doi:https://doi.org/10.1016/j.quascirev.2019.105954 (2019).57.Theuerkauf, M., Couwenberg, J., Kuparinen, A. & Liebscher, V. A matter of dispersal: REVEALSinR introduces state-of-the-art dispersal models to quantitative vegetation reconstruction. Veg. Hist. Archaeobotany 25, 541–553. https://doi.org/10.1007/s00334-016-0572-0 (2016).Article 

Google Scholar 
58.Hoszowski, S. “Lustracja województwa pomorskiego 1565” (Gdańskie Towarzystwo Naukowe, 1961).59.Hoszowski, S. Lustracja województw Prus Królewskich, 1624, z fragmentami lustracji 1615 roku, Lustracje dóbr królewskich XVI-XVIII wieku (Gdańskie Towarzystwo Naukowe, 1967).60.Metryka Koronna (Crown’s Metric), Central Archives of Historical Records, Warsaw, Poland, vol. XVIII, f. 23261.Ott, F. et al. Constraining the time span between the Early Holocene Hasseldalen and Askja-S Tephras through varve counting in the Lake Czechowskie sediment record Poland. J. Quat. Sci. 31, 103–113. https://doi.org/10.1002/jqs.2844 (2016).Article 

Google Scholar 
62.Roeser, P. et al. Advances in understanding calcite varve formation: new insights from a dual lake monitoring approach in the southern Baltic lowlands. Boreas (in press).63.Wulf, S. et al. Holocene tephrostratigraphy of varved sediment records from Lakes Tiefer See (NE Germany) and Czechowskie (N Poland). Quatern. Sci. Rev. 132, 1–14. https://doi.org/10.1016/j.quascirev.2015.11.007 (2016).ADS 
Article 

Google Scholar 
64.Sugita, S. Theory of quantitative reconstruction of vegetation I: pollen from large sites REVEALS regional vegetation composition. Holocene 17, 229–241. https://doi.org/10.1177/0959683607075837 (2007).ADS 
Article 

Google Scholar 
65.Giętkowski, T. Temporal change of forest area in Tuchola Pinewoods region between 1938 and 2000 (in Polish: Zmiany lesistości Borów Tucholskich w latach 1938 – 2000). Promotio Geographica Bydgostiensia IV, 1–12 (2009). More

As stated, the primary goal of this study is to promote an objective decision support framework for water resource planning and management purposes within the context of the WEF security nexus, which takes into account the uncertainties imposed by the climate change phenomenon. Such a framework is “robust” since it takes the multi-dimensionality of water-related problems into account while addressing the uncertainties imposed by climate change projections. The basic components of this decision-making paradigm are depicted in Fig. 1. In principle, while this framework is sensitive to the uncertainties associated with the climate change projections, it can provide a dynamic water resources planning and management scheme promoted within the WEF security network. Thus, in addition to the status quo, a series of climate change projections (i.e., RCP 2.6, RCP 4.5, and RCP 8.5) are also integrated into the proposed decision support framework. In essence, the main components of the proposed framework are simulation and operation of the water resources system based on the standard operation policy (SOP), evaluating the system’s efficiency through a series of quantitative performance criteria, and finally, applying the MADM-based framework to opt for a robust system renovation setting.Figure 1Basic components of the robust decision-making paradigm for water resources planning and management.Full size imageSimulating the water resources systemSOP is a primitive, and perhaps the most-well-known real-time operation policy in water resources planning and management14. The core principle here is to minimize the prioritized water shortage at the current time step with no conservation policy (e.g., hedging rules) in place. SOP, as a standard rule curve (RC), determines how the operator should behave at any given state of a reservoir15,16. This rule curve is established as an attempt to balance various water demands including but not limited to flood control, hydropower, water supply, and recreation17. A SOP operating system attempts to release water to meet a water demand at the current time, with no regard to the future.In general, SOP can be mathematically expressed as18:$$R_{t} = left{ {begin{array}{*{20}c} {D_{t} } \ {AW_{t} } \ 0 \ end{array} – S_{min } } right.begin{array}{*{20}c} {} & {if} & {AW_{t} > S_{min } } \ {} & {if} & {AW_{t} > S_{min } } \ {} & {if} & {AW_{t} le S_{min } } \ end{array} begin{array}{*{20}c} {} & {and} & {AW_{t} – S_{min } ge D_{t} } \ {} & {and} & {AW_{t} – S_{min } < D_{t} } \ {} & {} & {} \ end{array} quad t = { 1},{ 2},{ 3}, , ... , ,T$$ (1a) where$$AW_{t} = S_{t} + Q_{t} - Loss_{t}$$ (1b) in which Rt = amount of water supplied during the tth time step; Dt = consumers’ water demand during the tth time step; AWt = amount of available water during the tth time step; St = amount of stored water during the tth time step; Smin = dead storage of the reservoir; Qt = inflow during the tth time step; Losst = net water loss (i.e., precipitation minus evaporation) of the reservoir during the tth time step; and T = total number of time steps in the operational horizon.In practice, however, a different type of water demand leads to a different interpretation of water shortage. There are cases in which the stakeholders’ needs are represented by a set of volumetric demand targets, and the decision-makers’ objective would be to minimize the water deficit based on a set of priorities for these demands. This is a typical case for agricultural, domestic, industrial, and environmental demands. For hydropower generation, however, a conventional interpretation of SOP would be to generate maximum electricity permitted by the power plant capacity (PPC) at each given time step19. For a hydropower system, the amount of water needed to reach a power plant capacity is given by19:$$R_{t} = frac{{86400 times PF times Countday_{t} times PPC}}{{gamma_{w} times g times eta times Delta H_{t} }}$$ (2) in which, γw = water specific weight; g = gravitational acceleration; η = efficiency of the hydropower system; ΔHt = height difference between the reservoir water level and the tailwater level at time step t; Countdayt = number of days within time step t; and PF = plant factor of the hydropower system.As stated earlier, applying an SOP-based plan requires a set of pre-defined priorities to advise decision-makers concerning the order, in which each of these demands is to be met. The major water demands include drinking, industry, environment, agriculture, and hydropower. Thus, according to the SOP’s principle, the decision-makers, first, allocate the available water to meet the demand of the stakeholder with the highest priority (i.e., the domestic and industrial demand). After this first water demand is fully satisfied, the available water can be used for the next demand. Such an allocation process continues until no water is available. It should be noted, however, that if the released water in each stage passes through the penstock equipped with the turbines, electricity can be generated. The amount of energy generated in previous stages must be accounted for before computing the amount of water released for hydropower purposes.Performance criteriaPerformance criteria are, in essence, quantitative measures that can provide a practical insight for the decision-makers regarding the status of a system. This definition covers a broad spectrum of mathematical representations, which can range from simple mathematical formulas such as the average of a specific output to more complex and probability-based entities20,21. The most fundamental and universal probability-based performance criteria are reliability, resiliency, and vulnerability22,23,24. In essence, reliability is the probability of successful function of a system; resiliency measures the probability of successful functioning following a system failure; and vulnerability quantifies the severity of failure during an operation horizon25. It should be noted that these three criteria assess different aspects of a water resources system, and as such, they complement one another26. For more information regarding these probabilistic performance criteria, the readers can refer to Sandoval-Solis et al.27 and Zolghadr-Asli et al.20.In this study, the concept of levelized cost of energy (LCOE) is utilized for economic evaluation. The LCOE of a given hydropower system is the ratio of lifetime costs to lifetime electricity generation, both of which are discounted back to a common year using a discount rate that reflects the average cost of capital28. The LCOE of renewable energy systems depends on the technology, geographic criteria, capital and operating costs, and the efficiency of the system. The LCOE can be mathematically expressed as follows29:$$LCOE = frac{{sumnolimits_{t = 1}^{n} {frac{{I_{t} + M_{t} + F_{t} }}{{left( {1 + r} right)^{t} }}} }}{{sumnolimits_{t = 1}^{n} {frac{{E_{t} }}{{left( {1 + r} right)^{t} }}} }}$$ (3) in which It = investment expenditures in year t; Mt = operation and maintenance expenditures in year t; Ft = fuel expenditures in year t; Et = electricity generation in year t; r = discount rate; and n = economic life expectancy of the system.MADMMADM is an umbrella term to describe a series of frameworks, which aim to help individuals or a group of individuals to prioritize a series of discretely defined alternatives with regard to a set of evaluation attributes30,31. MADM can provide the necessary means to conduct planning and management under changing circumstances such as those under climate change conditions10,32. According to one of the basic principles of MADM, the decision-maker can use the similarity of the feasible alternatives and the preferential result and/or incongruity of the undesirable alternatives. The notion mentioned above is, chiefly, the core principle of the reference-dependent theory33. Accordingly, the reference-based branch of the MADM methods can, itself, be classified into two major groups: screening methods and ranking methods. Screening methods eliminate alternatives that cannot satisfy the pre-determined conditions for the desirable solution, while ranking methods order all the alternatives from the best to the worst34.Pioneered by Hwang and Yoon35, the technique for order references by similarity to an ideal solution (TOPSIS) is a compensatory, objective MADM solving method rooted from the basic principles of the reference-dependent theory. The core idea is that the chosen alternative should have the shortest distance from the ideal solution and the farthest distance from the negative-ideal solution36. The basic computation algorithm of TOPSIS can be summarized as follows37,38:Step I: Construct the original decision matrix (X), where m feasible alternatives are to be evaluated based on n evaluation criteria:$$X = left[ {begin{array}{*{20}c} {x_{11} } & {x_{12} } & cdots & {x_{1n} } \ {x_{21} } & {x_{22} } & cdots & {x_{2n} } \ vdots & vdots & ddots & vdots \ {x_{m1} } & {x_{m2} } & cdots & {x_{mn} } \ end{array} } right]$$ (4) in which xij = the element of the ith alternative concerning the jth criterion.Step II: Defining the reference alternatives [i.e., the ideal solution (s+) and the negative-ideal solution (s−)]. To do so, first, the elements of the decision matrix that are associated with negative criteria must be redefined by using the following equation:$$x_{ij}^{ * } = frac{1}{{x_{ij} }}$$ (5a) The elements of the decision matrix that are associated with positive criteria would remain the same:$$x_{ij}^{ * } = x_{ij}$$ (5b) The ideal alternative is an arbitrarily defined vector, which describes the aspired solution to the given problem, while the inferior alternative is an arbitrarily defined solution that represents the most undesirable option for the given MADM problem. Here, the ideal and negative-ideal solutions would be represented with two separate vectors where each pair of the corresponding elements in these vectors is, respectively, the maximum and minimum values of (x_{ij}^{ * }) with regard to each of the evaluation criteria.Step III: Each element of the decision matrix should be normalized by using the following equation:$$p_{ij} = frac{{x_{ij}^{ * } }}{{sqrt {sumnolimits_{i = 1}^{m} {x_{ij}^{ * 2} } } }}$$ (6) in which pij = the normalized performance value for the ith alternative with respect to the jth criterion.Step IV: The weighted normalized preference value (zij) can be computed as follows:$$z_{ij} = p_{ij} times w_{j} quad forall i,j$$ (7) in which wj = the weight (i.e., the importance value) of the jth criterion. The weights assigned to the evolution criteria reflect their relative importance to the decision-makers. The higher the weights are, the more crucial their roles would be in the selection process. Chiefly, these weighting mechanisms are either subjective in nature or follow an objective procedure. In the subjective approaches, the weights of the attributes are assigned based on the performance information given by the decision-maker, whereas in the objective approaches, the weights of the evaluation attributes would be obtained by using the objective information extracted from the decision matrix39. Shannon’s Entropy method, used in this study as the weight assignment mechanism, is a well-known objective weighting technique40. This method tends to assign the highest weight to an evaluation attribute with the highest dispersity in its values. For more information on the computational framework of this method, the readers can refer to Lotfi and Fallahnejad41.Step V: In this step, every given alternative is compared to the reference points, namely, the ideal and inferior alternatives. The described procedure, which is known as the separation measurement in TOPSIS, can be mathematically expressed as follows35:$$D_{i}^{ + } = sqrt {sumlimits_{j = 1}^{n} {left( {z_{ij} - z_{j}^{ + } } right)^{2} } }$$ (8) And$$D_{i}^{ - } = sqrt {sumlimits_{j = 1}^{n} {left( {z_{ij} - z_{j}^{ - } } right)^{2} } }$$ (9) in which (D_{j}^{ + }) and (D_{j}^{ - }) = separation measurements of the jth criterion with respect to the ideal and inferior alternatives, respectively.Step VI: The relative closeness to the ideal solution (χi), which can be used to rank the desirability of the feasible alternative, can be computed as follows35:$$chi_{i} = frac{{D_{i}^{ - } }}{{D_{i}^{ + } + D_{i}^{ - } }}quad forall i$$ (10) The further this distance (i.e., larger values of χi), the more desirable the alternative would be.Robust multi-attribute frameworkAs stated, each climate change scenario depicts a unique future with regard to the changing climate, which in turn introduces an element of uncertainty to the projected performance of water resources systems during their operation horizon. Furthermore, downscaling methods, which link these projected changes in the global climatic pattern to a local or regional scale, can be another source of uncertainty. Naturally, for long-lasting water infrastructure such as a hydropower system, addressing these uncertainties in a proper and timely manner can be one of the key components of a robust project. Thus, this study aims to not only evaluate the system’s performance under the status quo but also assess the credibility of the system under the projected climate change conditions.The other characteristic one might expect from a robust project is its ability to take into account the multi-dimensionality nature of water-related infrastructure. Most notably, addressing the WEF security nexus must be a priority in water resources planning and management. Resultantly, any robust decision-making paradigm for water resources planning and management purposes should also account for the other pillars of the WEF nexus (i.e., energy and food sectors), as they would be consequentially affected by such decisions. It is also important to note that these sectors could be affected by the climate change phenomenon. The other crucial feature of a robust decision-making paradigm is that it should be able to account for the socio-economic, environmental, and technical factors that determine the overall quality of the project. Such a decision-making paradigm is depicted in Fig. 2. This notion in practice, however, can typically lead to a mega decision matrix composed of numerous criteria and alternatives that can be overwhelming if the subjective MADM methods are to be employed. This study, thus, employs an objective MADM framework (i.e., TOPSIS/Entropy) to help overcome the above-described problem. The basic idea is to promote a universal and practical decision support framework that enables the water resources planners and managers to account for the intricacies of the WEF security nexus while simultaneously taking the uncertainties of climate change projections into account. Figure 3 illustrates the flowchart of the proposed decision support framework.Figure 2Schematic diagram of the MADM problem.Full size imageFigure 3Flowchart of the proposed framework.Full size image More

1.Dawbin, W. H. The migrations of humpback whales which pass the New Zealand coast. Trans. R. Soc. New Zeal. 84, 147–196 (1956).
Google Scholar 
2.Chittleborough, R. Dynamics of two populations of the humpback whale, Megaptera novaeangliae (Borowski). Mar. Freshw. Res. 16, 33–128. https://doi.org/10.1071/mf9650033 (1965).Article 

Google Scholar 
3.Rasmussen, K. et al. Southern Hemisphere humpback whales wintering off Central America: Insights from water temperature into the longest mammalian migration. Biol. Let. 3, 302–305. https://doi.org/10.1098/rsbl.2007.0067 (2007).Article 

Google Scholar 
4.Friedlaender, A. S. et al. Whale distribution in relation to prey abundance and oceanographic processes in shelf waters of the Western Antarctic Peninsula. Mar. Ecol. Prog. Ser. 317, 297–310. https://doi.org/10.3354/meps317297 (2006).ADS 
Article 

Google Scholar 
5.Nowacek, D. P. et al. Super-aggregations of krill and humpback whales in Wilhelmina Bay Antarctic Peninsula. PLoS ONE 6, e19173. https://doi.org/10.1371/journal.pone.0019173 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
6.Barendse, J. et al. Transit station or destination? Attendance patterns, movements and abundance estimate of humpback whales off west South Africa from photographic and genotypic matching. Afr. J. Mar. Sci. 33, 353–373. https://doi.org/10.2989/1814232X.2011.637343 (2011).Article 

Google Scholar 
7.Best, P. B., Sekiguchi, K. & Findlay, K. P. A suspended migration of humpback whales Megaptera novaeangliae on the west coast of South Africa. Marine Ecol. Progr. Ser. Oldendorf 118, 1–12. https://doi.org/10.3354/meps118001 (1995).ADS 
Article 

Google Scholar 
8.Findlay, K. & Best, P. Summer incidence of humpback whales on the west coast of South Africa. S. Afr. J. Mar. Sci. 15, 279–282. https://doi.org/10.2989/02577619509504851 (1995).Article 

Google Scholar 
9.Findlay, K. P. et al. Humpback whale “super-groups”–A novel low-latitude feeding behaviour of Southern Hemisphere humpback whales (Megaptera novaeangliae) in the Benguela Upwelling System. PLoS ONE 12, e0172002. https://doi.org/10.1371/journal.pone.0172002 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Pirotta, V., Owen, K., Donnelly, D., Brasier, M. J. & Harcourt, R. First evidence of bubble‐net feeding and the formation of ‘super‐groups’ by the east Australian population of humpback whales during their southward migration. Aquat. Conserv. (2021).11.Veitch, J., Penven, P. & Shillington, F. The Benguela: A laboratory for comparative modeling studies. Prog. Oceanogr. 83, 296–302. https://doi.org/10.1016/j.pocean.2009.07.008 (2009).ADS 
Article 

Google Scholar 
12.Preston-Whyte, R. A. & Tyson, P. D. Atmosphere and weather of southern Africa (Oxford University Press, 1988).
Google Scholar 
13.Nemoto, T., Best, P., Ishimaru, K. & Takano, H. Diatom films on whales [minke whales and 4 species of toothed whales] in South African waters. Scientific Reports of the Whales Research Institute (1980).14.Hutchings, L., Pitcher, G., Probyn, T. & Bailey, G. in Upwelling in the ocean: modern processes and ancient records Vol. 18 (eds CP Summerhayes et al.) Ch. 3, 65–81 (Wiley & Sons, 1995).15.Clapham, P. J. in Encyclopedia of marine mammals (eds B Würsig, JGM Thewissen, & KM Kovacs) 489–492 (Academic Press, 2018).16.Bakun, A. et al. Anticipated effects of climate change on coastal upwelling ecosystems. Curr. Clim. Change Rep. 1, 85–93. https://doi.org/10.1007/s40641-015-0008-4 (2015).Article 

Google Scholar 
17.Mackas, D. L. & Beaugrand, G. Comparisons of zooplankton time series. J. Mar. Syst. 79, 286–304. https://doi.org/10.1016/j.jmarsys.2008.11.030 (2010).Article 

Google Scholar 
18.Mackas, D. et al. Changing zooplankton seasonality in a changing ocean: Comparing time series of zooplankton phenology. Prog. Oceanogr. 97, 31–62. https://doi.org/10.1016/j.pocean.2011.11.005 (2012).ADS 
Article 

Google Scholar 
19.Huggett, J., Verheye, H., Escribano, R. & Fairweather, T. Copepod biomass, size composition and production in the Southern Benguela: Spatio–temporal patterns of variation, and comparison with other eastern boundary upwelling systems. Prog. Oceanogr. 83, 197–207. https://doi.org/10.1016/j.pocean.2009.07.048 (2009).ADS 
Article 

Google Scholar 
20.Verheye, H. M., Lamont, T., Huggett, J. A., Kreiner, A. & Hampton, I. Plankton productivity of the Benguela current large marine ecosystem (BCLME). Environ. Dev. 17, 75–92. https://doi.org/10.1016/j.envdev.2015.07.011 (2016).Article 

Google Scholar 
21.Shannon, L. J. et al. Exploring temporal variability in the Southern Benguela ecosystem over the past four decades using a time-dynamic ecosystem model. Front. Mar. Sci. 7, 540 (2020).ADS 
Article 

Google Scholar 
22.Jarre, A. et al. Synthesis: climate effects on biodiversity, abundance and distribution of marine organisms in the Benguela. Fish. Oceanogr. 24, 122–149. https://doi.org/10.1111/fog.12086 (2015).Article 

Google Scholar 
23.Lamont, T., García-Reyes, M., Bograd, S., Van Der Lingen, C. & Sydeman, W. Upwelling indices for comparative ecosystem studies: Variability in the Benguela Upwelling System. J. Mar. Syst. 188, 3–16. https://doi.org/10.1016/j.jmarsys.2017.05.007 (2018).Article 

Google Scholar 
24.Tim, N., Zorita, E. & Hünicke, B. Decadal variability and trends of the Benguela upwelling system as simulated in a high-resolution ocean simulation. Ocean Sci. 11, 483–502. https://doi.org/10.5194/os-11-483-2015 (2015).ADS 
Article 

Google Scholar 
25.Lamont, T., Barlow, R. & Brewin, R. Long-term trends in phytoplankton chlorophyll a and size structure in the Benguela Upwelling System. J. Geophys. Res. Oceans 124, 1170–1195. https://doi.org/10.1029/2018JC014334 (2019).ADS 
Article 

Google Scholar 
26.Ragoasha, N. et al. Lagrangian pathways in the southern Benguela upwelling system. J. Mar. Syst. 195, 50–66. https://doi.org/10.1016/j.jmarsys.2019.03.008 (2019).Article 

Google Scholar 
27.Shannon, V., Hempel, G., Moloney, C., Woods, J. D. & Malanotte-Rizzoli, P. Benguela: Predicting a Large Marine Ecosystem (Elsevier, 2006).
Google Scholar 
28.Veitch, J., Penven, P. & Shillington, F. Modeling equilibrium dynamics of the Benguela current system. J. Phys. Oceanogr. 40, 1942–1964. https://doi.org/10.1175/2010jpo4382.1 (2010).ADS 
Article 

Google Scholar 
29.Lachkar, Z. & Gruber, N. A comparative study of biological production in eastern boundary upwelling systems using an artificial neural network. Biogeosciences 9, 293–308. https://doi.org/10.5194/bg-9-293-2012 (2012).ADS 
Article 

Google Scholar 
30.Gruber, N. et al. Eddy-induced reduction of biological production in eastern boundary upwelling systems. Nat. Geosci. 4, 787–792. https://doi.org/10.1038/ngeo1273 (2011).ADS 
CAS 
Article 

Google Scholar 
31.Hutchings, L. et al. Multiple factors affecting South African anchovy recruitment in the spawning, transport and nursery areas. S. Afr. J. Mar. Sci. 19, 211–225. https://doi.org/10.2989/025776198784126908 (1998).Article 

Google Scholar 
32.Rossi, V., López, C., Sudre, J., Hernández-García, E. & Garçon, V. Comparative study of mixing and biological activity of the Benguela and Canary upwelling systems. Geophys. Res. Lett. https://doi.org/10.1029/2008gl033610 (2008).Article 

Google Scholar 
33.Barendse, J. & Best, P. B. Shore-based observations of seasonality, movements, and group behavior of southern right whales in a nonnursery area on the South African west coast. Mar. Mamm. Sci. 30, 1358–1382 (2014).Article 

Google Scholar 
34.Barendse, J. et al. Migration redefined? Seasonality, movements and group composition of humpback whales Megaptera novaeangliae off the west coast of South Africa. Afr. J. Mar. Sci. 32, 1–22 (2010).Article 

Google Scholar 
35.Gibbons, M. J. An introduction to the Zooplankton of the Benguella current Region. (1997).36.Olsen, Ø. Hvaler og hvalfangst i Sydafrika. 1–56 (Bergens Museums Arbok 1914–1915, 1914).37.Meynecke, J. O. et al. Responses of humpback whales to a changing climate in the Southern Hemisphere: Priorities for research efforts. Mar. Ecol. 41, e12616 (2020).Article 

Google Scholar 
38.Stockin, K. A. & Burgess, E. A. Opportunistic Feeding of an Adult Humpback Whale (Megaptera novaeangliae) Migrating Along the Coast of Southeastern Queensland, Australia. Aquat. Mamm. 31, 120. https://doi.org/10.1578/AM.31.1.2005.120 (2005).Article 

Google Scholar 
39.Visser, F., Hartman, K. L., Pierce, G. J., Valavanis, V. D. & Huisman, J. Timing of migratory baleen whales at the Azores in relation to the North Atlantic spring bloom. Mar. Ecol. Prog. Ser. 440, 267–279. https://doi.org/10.3354/meps09349 (2011).ADS 
Article 

Google Scholar 
40.Trudelle, L. et al. Influence of environmental parameters on movements and habitat utilization of humpback whales (Megaptera novaeangliae) in the Madagascar breeding ground. R. Soc. Open Sci. 3, 160616. https://doi.org/10.1098/rsos.160616 (2016).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
41.Veitch, J., Hermes, J., Lamont, T., Penven, P. & Dufois, F. Shelf-edge jet currents in the southern Benguela: A modelling approach. J. Mar. Syst. 188, 27–38 (2018).Article 

Google Scholar 
42.Hutchings, L. et al. The Benguela current: An ecosystem of four components. Prog. Oceanogr. 83, 15–32. https://doi.org/10.1016/j.pocean.2009.07.046 (2009).ADS 
Article 

Google Scholar 
43.Rockwood, R. C., Elliott, M. L., Saenz, B., Nur, N. & Jahncke, J. Modeling predator and prey hotspots: Management implications of baleen whale co-occurrence with krill in Central California. PLoS ONE 15, e0235603 (2020).CAS 
Article 

Google Scholar 
44.Hayward, T. L. & Venrick, E. L. Nearsurface pattern in the California Current: Coupling between physical and biological structure. Deep Sea Res. Part II 45, 1617–1638 (1998).ADS 
Article 

Google Scholar 
45.Croll, D. A. et al. From wind to whales: trophic links in a coastal upwelling system. Mar. Ecol. Prog. Ser. 289, 117–130 (2005).ADS 
Article 

Google Scholar 
46.Walker, D. & Peterson, W. Relationships between hydrography, phytoplankton production, biomass, cell size and species composition, and copepod production in the southern Benguela upwelling system in April 1988. S. Afr. J. Mar. Sci. 11, 289–305 (1991).Article 

Google Scholar 
47.Stuart, V. & Pillar, S. Diel grazing patterns of all ontogenetic stages of Euphausia lucens and in situ predation rates on copepods in the southern Benguela upwelling region. Mar. Ecol. Progr. Ser. 2, 227–241 (1990).ADS 
Article 

Google Scholar 
48.Clapham, P. & Baker, C. (Academic, New York, 2002).49.Shannon, L. J., Field, J. G. & Moloney, C. L. Simulating anchovy–sardine regime shifts in the southern Benguela ecosystem. Ecol. Model. 172, 269–281 (2004).Article 

Google Scholar 
50.Lett, C., Roy, C., Levasseur, A., Van Der Lingen, C. D. & Mullon, C. Simulation and quantification of enrichment and retention processes in the southern Benguela upwelling ecosystem. Fish. Oceanogr. 15, 363–372. https://doi.org/10.1111/j.1365-2419.2005.00392.x (2006).Article 

Google Scholar 
51.Branch, T. A. Humpback whale abundance south of 60°S from three complete circumpolar sets of surveys. J. Cetacean Res. Manage. https://doi.org/10.47536/jcrm.vi.305 (2011).Article 

Google Scholar 
52.Findlay, K., Best, P. & Meÿer, M. Migrations of humpback whales past Cape Vidal, South Africa, and an estimate of the population increase rate (1988–2002). Afr. J. Mar. Sci. 33, 375–392. https://doi.org/10.2989/1814232x.2011.637345 (2011).Article 

Google Scholar 
53.Henson, S. A., Cole, H. S., Hopkins, J., Martin, A. P. & Yool, A. Detection of climate change-driven trends in phytoplankton phenology. Glob. Change Biol. 24, e101–e111 (2018).ADS 
Article 

Google Scholar 
54.Carvalho, I. et al. Does temporal and spatial segregation explain the complex population structure of humpback whales on the coast of West Africa?. Mar. Biol. 161, 805–819 (2014).Article 

Google Scholar 
55.Kershaw, F. et al. Multiple processes drive genetic structure of humpback whale (Megaptera novaeangliae) populations across spatial scales. Mol. Ecol. 26, 977–994 (2017).Article 

Google Scholar 
56.Korrûbel, J. An age-structured simulation model to investigate species replacement between pilchard and anchovy populations in the southern Benguela. S. Afr. J. Mar. Sci. 12, 375–391 (1992).Article 

Google Scholar 
57.Shannon, L. et al. The 1980s–a decade of change in the Benguela ecosystem. S. Afr. J. Mar. Sci. 12, 271–296 (1992).Article 

Google Scholar 
58.Verheye, H., Richardson, A., Hutchings, L., Marska, G. & Gianakouras, D. Long-term trends in the abundance and community structure of coastal zooplankton in the southern Benguela system, 1951–1996. Afr. J. Mar. Sci. 19, 2 (1998).
Google Scholar 
59.Bakun, A. Global climate change and intensification of coastal ocean upwelling. Science 247, 198–201 (1990).ADS 
CAS 
Article 

Google Scholar 
60.Sydeman, W. et al. Climate change and wind intensification in coastal upwelling ecosystems. Science 345, 77–80 (2014).ADS 
CAS 
Article 

Google Scholar 
61.Bonino, G., Di Lorenzo, E., Masina, S. & Iovino, D. Interannual to decadal variability within and across the major eastern boundary upwelling systems. Sci. Rep. 9, 1–14 (2019).Article 

Google Scholar 
62.Fearon, G. et al. Enhanced vertical mixing in coastal upwelling systems driven by diurnal-inertial resonance: Numerical experiments. J. Geophys. Res. Oceans https://doi.org/10.1002/essoar.10502743.1 (2020).Article 

Google Scholar 
63.Xiu, P., Chai, F., Curchitser, E. N. & Castruccio, F. S. Future changes in coastal upwelling ecosystems with global warming: The case of the California Current System. Sci. Rep. 8, 1–9 (2018).
Google Scholar 
64.Roxy, M. K. et al. A reduction in marine primary productivity driven by rapid warming over the tropical Indian Ocean. Geophys. Res. Lett. 43, 826–833 (2016).ADS 
Article 

Google Scholar 
65.Lockerbie, E. M. & Shannon, L. Toward exploring possible future states of the southern Benguela. Front. Mar. Sci. 6, 380 (2019).Article 

Google Scholar 
66.Ortega-Cisneros, K., Cochrane, K. L., Fulton, E. A., Gorton, R. & Popova, E. Evaluating the effects of climate change in the southern Benguela upwelling system using the Atlantis modelling framework. Fish. Oceanogr. 27, 489–503 (2018).Article 

Google Scholar 
67.Rykaczewski, R. R. & Checkley, D. M. Influence of ocean winds on the pelagic ecosystem in upwelling regions. Proc. Natl. Acad. Sci. 105, 1965–1970 (2008).ADS 
CAS 
Article 

Google Scholar 
68.Veitch, J. A. & Penven, P. The role of the A gulhas in the B enguela current system: A numerical modeling approach. J. Geophys. Res. Oceans 122, 3375–3393 (2017).ADS 
Article 

Google Scholar 
69.Beal, L. M., De Ruijter, W. P., Biastoch, A. & Zahn, R. On the role of the Agulhas system in ocean circulation and climate. Nature 472, 429–436 (2011).ADS 
CAS 
Article 

Google Scholar 
70.Beal, L. M. & Elipot, S. Broadening not strengthening of the Agulhas current since the early 1990s. Nature 540, 570–573 (2016).ADS 
CAS 
Article 

Google Scholar 
71.Lilliefors, H. W. On the Kolmogorov-Smirnov test for the exponential distribution with mean unknown. J. Am. Stat. Assoc. 64, 387–389. https://doi.org/10.1080/01621459.1969.10500983 (1969).Article 

Google Scholar 
72.Shchepetkin, A. F. & McWilliams, J. C. The regional oceanic modeling system (ROMS): A split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Model 9, 347–404. https://doi.org/10.1016/j.ocemod.2004.08.002 (2005).ADS 
Article 

Google Scholar 
73.Debreu, L., Marchesiello, P., Penven, P. & Cambon, G. Two-way nesting in split-explicit ocean models: Algorithms, implementation and validation. Ocean Model 49, 1–21. https://doi.org/10.1016/j.ocemod.2012.03.003 (2012).ADS 
Article 

Google Scholar 
74.Shchepetkin, A. F. & McWilliams, J. C. Quasi-monotone advection schemes based on explicit locally adaptive dissipation. Mon. Weather Rev. 126, 1541–1580. https://doi.org/10.1175/1520-0493(1998)126%3C1541:qmasbo%3E2.0.co;2 (1998).ADS 
Article 

Google Scholar 
75.Warner, J. C., Sherwood, C. R., Arango, H. G. & Signell, R. P. Performance of four turbulence closure models implemented using a generic length scale method. Ocean Model 8, 81–113. https://doi.org/10.1016/j.ocemod.2003.12.003 (2005).ADS 
Article 

Google Scholar 
76.Saha, S. et al. NCEP Climate Forecast System Reanalysis (CFSR) 6-Hourly Products, January 1979 to December 2010 (Boulder, 2010).
Google Scholar 
77.Saha, S. et al. NCEP Climate Forecast System Version 2 (CFSv2) 6-hourly Products. D61C61TXF (Boulder, 2011).
Google Scholar 
78.Burchard, H. & Hofmeister, R. A dynamic equation for the potential energy anomaly for analysing mixing and stratification in estuaries and coastal seas. Estuar. Coast. Shelf Sci. 77, 679–687. https://doi.org/10.1016/j.ecss.2007.10.025 (2008).ADS 
Article 

Google Scholar 
79.Yamaguchi, R., Suga, T., Richards, K. J. & Qiu, B. Diagnosing the development of seasonal stratification using the potential energy anomaly in the North Pacific. Clim. Dyn. 53, 4667–4681. https://doi.org/10.1007/s00382-019-04816-y (2019).Article 

Google Scholar 
80.Lennard, C., Hahmann, A. N., Badger, J., Mortensen, N. G. & Argent, B. Development of a numerical wind atlas for South Africa. Energy Proc. 76, 128–137. https://doi.org/10.1016/j.egypro.2015.07.873 (2015).Article 

Google Scholar 
81.Thomson, R. E. & Emery, W. J. Data Analysis Methods in Physical Oceanography 3rd edn. (Elsevier, 2014).
Google Scholar  More

1.Collins, M. et al. SPM6 Extremes, abrupt changes and managing risks. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds. Pörtner, H.-O. et al.) 589-655 (In press, 2019).2.Hegerl, G. C., Hanlon, H. & Beierkuhnlein, C. Elusive extremes. Nat. Geosci. 4, 142–143 (2011).CAS 
Article 

Google Scholar 
3.Bérard, A., Ben Sassi, M., Renault, P. & Gros, R. Severe drought-induced community tolerance to heat wave. An experimental study on soil microbial processes. J. Soils Sediment. 12, 513–518 (2012).Article 

Google Scholar 
4.Schimel, J., Balser, T. C. & Wallenstein, M. Microbial stress-response physiology and its implications for ecosystem function. Ecology 88, 1386–1394 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
5.Acosta-Martínez, V. et al. Predominant bacterial and fungal assemblages in agricultural soils during a record drought/heat wave and linkages to enzyme activities of biogeochemical cycling. Appl. Soil Ecol. 84, 69–82 (2014).Article 

Google Scholar 
6.Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).Article 

Google Scholar 
7.Frölicher, T. L. & Laufkötter, C. Emerging risks from marine heat waves. Nat. Commun. 9, 650 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
8.Garrabou, J. et al. Mass mortality in Northwestern Mediterranean rocky benthic communities: effects of the 2003 heat wave. Glob. Change Biol. 15, 1090–1103 (2009).Article 

Google Scholar 
9.Wernberg, T. et al. An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nat. Clim. Change 3, 78–82 (2013).Article 

Google Scholar 
10.Bond, N. A., Cronin, M. F., Freeland, H. & Mantua, N. Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophys. Res. Lett. 9, 3414–3420 (2015).11.Freeland, H. & Ross, T. ‘The Blob’—or, how unusual were ocean temperatures in the Northeast Pacific during 2014-2018? Deep Sea Res. Part I: Oceanographic Res. Pap. 150, 103061 (2019).Article 

Google Scholar 
12.Lorenzo, E. D. & Mantua, N. Multi-year persistence of the 2014/15 North Pacific marine heatwave. Nat. Clim. Change 6, 1042–1047 (2016).Article 

Google Scholar 
13.Peña, M. A., Nemcek, N. & Robert, M. Phytoplankton responses to the 2014–2016 warming anomaly in the northeast subarctic Pacific Ocean. Limnol. Oceanogr. 64, 515–525 (2019).Article 

Google Scholar 
14.Yang, B., Emerson, S. R. & Peña, M. A. The effect of the 2013–2016 high temperature anomaly in the subarctic Northeast Pacific (the “Blob”) on net community production. Biogeosciences 15, 6747–6759 (2018).CAS 
Article 

Google Scholar 
15.Cavole, L. et al. Biological impacts of the 2013–2015 warm-water anomaly in the Northeast Pacific: winners, losers, and the future. Oceanography 29, 273–285 (2016).16.Azam, F. et al. The ecological role of water-column microbes in the sea. Mar. Ecol. Prog. Ser. 10, 257–263 (1983).Article 

Google Scholar 
17.Sarmento, Hugo, Montoya, JoséM., Vázquez-Domínguez, Evaristo, Vaqué, Dolors & Gasol, JosepM. Warming effects on marine microbial food web processes: how far can we go when it comes to predictions? Philos. Trans. R. Soc. B: Biol. Sci. 365, 2137–2149 (2010).Article 

Google Scholar 
18.Joint, I. & Smale, D. A. Marine heatwaves and optimal temperatures for microbial assemblage activity. FEMS Microbiol Ecol 93, fiw243 (2017).19.Deschaseaux, E. O., Brien, J., Siboni, N., Petrou, K. & Seymour, J. R. Shifts in dimethylated sulfur concentrations and microbiome composition in the red-tide causing dinoflagellate Alexandrium minutum during a simulated marine heatwave. Biogeosciences 16, 4377–4391 (2019).CAS 
Article 

Google Scholar 
20.Hawley, A. K. et al. Diverse Marinimicrobia bacteria may mediate coupled biogeochemical cycles along eco-thermodynamic gradients. Nat. Commun. 8, 1507 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
21.Allers, E. et al. Diversity and population structure of Marine Group A bacteria in the Northeast subarctic Pacific Ocean. ISME J. 7, 256–268 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Roux, S. et al. Ecology and evolution of viruses infecting uncultivated SUP05 bacteria as revealed by single-cell- and meta-genomics. eLife 3, e03125 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
23.Wright, J. J. et al. Genomic properties of Marine Group A bacteria indicate a role in the marine sulfur cycle. ISME J. 8, 455–468 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Sherry, N. D., Boyd, P. W., Sugimoto, K. & Harrison, P. J. Seasonal and spatial patterns of heterotrophic bacterial production, respiration, and biomass in the subarctic NE Pacific. Deep Sea Res. Part II Top. Stud. Oceanogr. 46, 2557–2578 (1999).25.Harrison, P. J. Station Papa Time Series: insights into ecosystem dynamics. J. Oceanogr. 58, 259–264 (2002).CAS 
Article 

Google Scholar 
26.Mende, D. R. et al. Environmental drivers of a microbial genomic transition zone in the ocean’s interior. Nat. Microbiol. 2, 1367–1373 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Pommier, T. et al. Global patterns of diversity and community structure in marine bacterioplankton. Mol. Ecol. 16, 867–880 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Cram, J. A. et al. Seasonal and interannual variability of the marine bacterioplankton community throughout the water column over ten years. ISME J. 9, 563–580 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Freeland, H. J. Evidence of change in the winter mixed layer in the Northeast Pacific Ocean: a problem revisited. Atmos. Ocean 51, 126–133 (2013).CAS 
Article 

Google Scholar 
30.Stevens, H. & Ulloa, O. Bacterial diversity in the oxygen minimum zone of the eastern tropical South Pacific. Environ. Microbiol. 10, 1244–1259 (2008).CAS 
PubMed 
Article 

Google Scholar 
31.Bryant, J. A., Stewart, F. J., Eppley, J. M. & DeLong, E. F. Microbial community phylogenetic and trait diversity declines with depth in a marine oxygen minimum zone. Ecology 93, 1659–1673 (2012).PubMed 
Article 

Google Scholar 
32.Muck, S. et al. Niche differentiation of aerobic and anaerobic ammonia oxidizers in a high latitude deep oxygen minimum zone. Front. Microbiol. 10, 2141 (2019).33.Medina Faull, L., Mara, P., Taylor, G. T. & Edgcomb, V. P. Imprint of trace dissolved oxygen on prokaryoplankton community structure in an oxygen minimum zone. Front. Mar. Sci. 7, 360 (2020).34.Reji, L., Tolar, B. B., Chavez, F. P. & Francis, C. A. Depth-differentiation and seasonality of planktonic microbial assemblages in the monterey bay upwelling system. Front. Microbiol. 11, 1075 (2020).35.Wright, J. J., Konwar, K. M. & Hallam, S. J. Microbial ecology of expanding oxygen minimum zones. Nat. Rev. Microbiol. 10, 381–394 (2012).CAS 
PubMed 
Article 

Google Scholar 
36.Tsementzi, D. et al. SAR11 bacteria linked to ocean anoxia and nitrogen loss. Nature 536, 179–183 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Choi, D. H., Karen, Selph & Noh, J. H. Niche partitioning of picocyanobacterial lineages in the oligotrophic northwestern Pacific Ocean. ALGAE 30, 223–232 (2015).38.Johnson, Z. I. et al. Niche partitioning among prochlorococcus ecotypes along ocean-scale environmental gradients. Science 311, 1737–1740 (2006).CAS 
PubMed 
Article 

Google Scholar 
39.Sohm, J. A. et al. Co-occurring Synechococcus ecotypes occupy four major oceanic regimes defined by temperature, macronutrients and iron. ISME J. 10, 333–345 (2016).CAS 
PubMed 
Article 

Google Scholar 
40.Not, F. et al. in Advances in Botanical Research (ed. Piganeau, G.) vol. 64, 1–53 (Academic Press, 2012).41.Lutz, M., Dunbar, R. & Caldeira, K. Regional variability in the vertical flux of particulate organic carbon in the ocean interior. Glob. Biogeochemical Cycles 16, 11-1–11–18 (2002).
Google Scholar 
42.Richardson, T. L., Jackson, G. A., Ducklow, H. W. & Roman, M. R. Carbon fluxes through food webs of the eastern equatorial Pacific: an inverse approach. Deep Sea Res. Part I: Oceanographic Res. Pap. 51, 1245–1274 (2004).CAS 
Article 

Google Scholar 
43.Michaels, A. F. & Silver, M. W. Primary production, sinking fluxes and the microbial food web. Deep Sea Res. Part A. Oceanographic Res. Pap. 35, 473–490 (1988).Article 

Google Scholar 
44.Dufrêne, M. & Legendre, P. Species assemblages and indicator species:the need for a flexible asymmetrical approach. Ecol. Monogr. 67, 345–366 (1997).
Google Scholar 
45.Cáceres, M. D., Legendre, P. & Moretti, M. Improving indicator species analysis by combining groups of sites. Oikos 119, 1674–1684 (2010).Article 

Google Scholar 
46.Shade, A. et al. Conditionally rare taxa disproportionately contribute to temporal changes in microbial diversity. mBio 5, e01371-14 (2014).47.Thrash, J. C. et al. Metabolic Roles of Uncultivated Bacterioplankton lineages in the Northern Gulf of Mexico “Dead Zone”. mBio 8, e01017-17 (2017).48.Kirchman, D. L. The ecology of Cytophaga–Flavobacteria in aquatic environments. FEMS Microbiol Ecol. 39, 91–100 (2002).CAS 
PubMed 

Google Scholar 
49.Alonso, C., Warnecke, F., Amann, R. & Pernthaler, J. High local and global diversity of Flavobacteria in marine plankton. Environ. Microbiol. 9, 1253–1266 (2007).CAS 
PubMed 
Article 

Google Scholar 
50.Teeling, H. et al. Recurring patterns in bacterioplankton dynamics during coastal spring algae blooms. eLife 5, e11888 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
51.Selje, N., Simon, M. & Brinkhoff, T. A newly discovered Roseobacter cluster in temperate and polar oceans. Nature 427, 445 (2004).CAS 
PubMed 
Article 

Google Scholar 
52.Buchan, A., González, J. M. & Moran, M. A. Overview of the marine Roseobacter lineage. Appl. Environ. Microbiol. 71, 5665–5677 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Luo, H. & Moran, M. A. Evolutionary ecology of the marine roseobacter clade. Microbiol. Mol. Biol. Rev. 78, 573–587 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
54.Simon, M. et al. Phylogenomics of Rhodobacteraceae reveals evolutionary adaptation to marine and non-marine habitats. ISME J. 11, 1483–1499 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
55.Sato, S. et al. Genome-enabled phylogenetic and functional reconstruction of an araphid pennate diatom Plagiostriata sp. CCMP470, previously assigned as a radial centric diatom, and its bacterial commensal. Sci. Rep. 10, 9449 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Sañudo-Wilhelmy, S. A., Gómez-Consarnau, L., Suffridge, C. & Webb, E. A. The role of B vitamins in marine biogeochemistry. Annu. Rev. Mar. Sci. 6, 339–367 (2014).Article 

Google Scholar 
57.Landa, M. et al. Sulfur metabolites that facilitate oceanic phytoplankton–bacteria carbon flux. ISME J. 13, 2536–2550 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Georges, A. A., El-Swais, H., Craig, S. E., Li, W. K. & Walsh, D. A. Metaproteomic analysis of a winter to spring succession in coastal northwest Atlantic Ocean microbial plankton. ISME J. 8, 1301–1313 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Baker, B. J., Lazar, C. S., Teske, A. P. & Dick, G. J. Genomic resolution of linkages in carbon, nitrogen, and sulfur cycling among widespread estuary sediment bacteria. Microbiome 3, 14 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
60.Andrei, A.-Ş. et al. Niche-directed evolution modulates genome architecture in freshwater Planctomycetes. ISME J 13, 1056–1071 (2019).61.Fukunaga, Y. et al. Phycisphaera mikurensis gen. nov., sp. nov., isolated from a marine alga, and proposal of Phycisphaeraceae fam. nov., Phycisphaerales ord. nov. and Phycisphaerae classis nov. in the phylum Planctomycetes. J. Gen. Appl. Microbiol. 55, 267–275 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Gade, D., Stührmann, T., Reinhardt, R. & Rabus, R. Growth phase dependent regulation of protein composition in Rhodopirellula baltica. Environ. Microbiol. 7, 1074–1084 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Luecker, S., Nowka, B., Rattei, T., Spieck, E. & Daims, H. The genome of nitrospina gracilis illuminates the metabolism and evolution of the major marine nitrite oxidizer. Front. Microbiol. 4, 27 (2013).64.Winder, M. & Schindler, D. E. Climate change uncouples trophic interactions in an aquatic ecosystem. Ecology 85, 2100–2106 (2004).Article 

Google Scholar 
65.Brown, M. V. et al. Global biogeography of SAR11 marine bacteria. Mol. Syst. Biol. 8, 595 (2012).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
66.Haro‐Moreno, J. M. et al. Ecogenomics of the SAR11 clade. Environ. Microbiol 22, 1748–1763 (2020).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
67.Grote, J. et al. Streamlining and core genome conservation among highly divergent members of the SAR11 clade. mBio 3, e00252–12 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Giovannoni, S. J. SAR11 Bacteria: The Most Abundant Plankton in the Oceans. Annu. Rev. Mar. Sci. 9, 231–255 (2017).Article 

Google Scholar 
69.Getz, E. W., Tithi, S. S., Zhang, L. & Aylward, F. O. Parallel evolution of genome streamlining and cellular bioenergetics across the marine radiation of a bacterial phylum. mBio. 9, e01089-18 (2018).70.Aylward, F. O. & Santoro, A. E. Heterotrophic thaumarchaea with small genomes are widespread in the dark ocean. mSystems 5, e00415-20 (2020).71.Prosser, J. I. & Nicol, G. W. Relative contributions of archaea and bacteria to aerobic ammonia oxidation in the environment. Environ. Microbiol. 10, 2931–2941 (2008).CAS 
PubMed 
Article 

Google Scholar 
72.Santoro, A. E., Casciotti, K. L. & Francis, C. A. Activity, abundance and diversity of nitrifying archaea and bacteria in the central California Current. Environ. Microbiol. 12, 1989–2006 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Horak, R. E. A. et al. Ammonia oxidation kinetics and temperature sensitivity of a natural marine community dominated by Archaea. ISME J. 7, 2023–2033 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
74.Qin, W. et al. Marine ammonia-oxidizing archaeal isolates display obligate mixotrophy and wide ecotypic variation. PNAS 111, 12504–12509 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
75.Rinke, C. et al. A phylogenomic and ecological analysis of the globally abundant Marine Group II archaea (Ca. Poseidoniales ord. nov.). ISME J. 13, 663 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
76.Haro-Moreno, J. M., Rodriguez-Valera, F., López-García, P., Moreira, D. & Martin-Cuadrado, A.-B. New insights into marine group III Euryarchaeota, from dark to light. ISME J. 11, 1102–1117 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Orsi, W. D. et al. Diverse, uncultivated bacteria and archaea underlying the cycling of dissolved protein in the ocean. ISME J. 10, 2158–2173 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Orsi, W. D. et al. Ecophysiology of uncultivated marine euryarchaea is linked to particulate organic matter. ISME J. 9, 1747–1763 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
79.Hugoni, M. et al. Structure of the rare archaeal biosphere and seasonal dynamics of active ecotypes in surface coastal waters. Proc. Natl Acad. Sci. USA 110, 6004–6009 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
80.Matheus Carnevali, P. B. et al. Hydrogen-based metabolism as an ancestral trait in lineages sibling to the Cyanobacteria. Nat. Commun. 10, 463 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
81.Saw, J. H. W. et al. Pangenomics analysis reveals diversification of enzyme families and niche specialization in globally abundant SAR202 bacteria. mBio 11, e02975-19 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
82.Alonso‐Sáez, L., Díaz‐Pérez, L. & Morán, X. A. G. The hidden seasonality of the rare biosphere in coastal marine bacterioplankton. Environ. Microbiol. 17, 3766–3780 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
83.Lambert, S. et al. Rhythmicity of coastal marine picoeukaryotes, bacteria and archaea despite irregular environmental perturbations. ISME J. 13, 388–401 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
84.Mehrshad, M., Rodriguez-Valera, F., Amoozegar, M. A., López-García, P. & Ghai, R. The enigmatic SAR202 cluster up close: shedding light on a globally distributed dark ocean lineage involved in sulfur cycling. ISME J. 12, 655–668 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.Mullins, T. D., Britschgi, T. B., Krest, R. L. & Giovannoni, S. J. Genetic comparisons reveal the same unknown bacterial lineages in Atlantic and Pacific bacterioplankton communities. Limnol. Oceanogr. 40, 148–158 (1995).CAS 
Article 

Google Scholar 
86.Acinas, S. G., Antón, J. & Rodríguez-Valera, F. Diversity of free-living and attached bacteria in offshore western mediterranean waters as depicted by analysis of genes encoding 16S rRNA. Appl. Environ. Microbiol. 65, 514–522 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
87.Hoarfrost, A. et al. Global ecotypes in the ubiquitous marine clade SAR86. ISME J. 14, 178–188 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
88.Alonso-Sáez, L., Galand, P. E., Casamayor, E. O., Pedrós-Alió, C. & Bertilsson, S. High bicarbonate assimilation in the dark by Arctic bacteria. ISME J. 4, 1581–1590 (2010).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
89.Swan, B. K. et al. Potential for chemolithoautotrophy among ubiquitous bacteria lineages in the dark ocean. Science 333, 1296–1300 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
90.Maldonado, M. T., Boyd, P. W., Harrison, P. J. & Price, N. M. Co-limitation of phytoplankton growth by light and Fe during winter in the NE subarctic Pacific Ocean. Deep Sea Res. Part II: Topical Stud. Oceanogr. 46, 2475–2485 (1999).CAS 
Article 

Google Scholar 
91.Peña, M. A. & Varela, D. E. Seasonal and interannual variability in phytoplankton and nutrient dynamics along Line P in the NE subarctic Pacific. Prog. Oceanogr. 75, 200–222 (2007).Article 

Google Scholar 
92.Whitney, F. A., Wong, C. S. & Boyd, P. W. Interannual variability in nitrate supply to surface waters of the Northeast Pacific Ocean. Mar. Ecol. Prog. Ser. 170, 15–23 (1998).CAS 
Article 

Google Scholar 
93.Crawford, W., Galbraith, J. & Bolingbroke, N. Line P ocean temperature and salinity, 1956–2005. Prog. Oceanogr. 75, 161–178 (2007).Article 

Google Scholar 
94.Whitney, F. A. & Freeland, H. J. Variability in upper-ocean water properties in the NE Pacific Ocean. Deep Sea Res. Part II: Topical Stud. Oceanogr. 46, 2351–2370 (1999).CAS 
Article 

Google Scholar 
95.Whitney, F. A., Freeland, H. J. & Robert, M. Persistently declining oxygen levels in the interior waters of the eastern subarctic Pacific. Prog. Oceanogr. 75, 179–199 (2007).Article 

Google Scholar 
96.Siegel, D. A. et al. Prediction of the Export and Fate of Global Ocean Net Primary Production: The EXPORTS Science Plan. Front. Mar. Sci. 3, 030 (2016).97.Buesseler, K. O. et al. High-resolution spatial and temporal measurements of particulate organic carbon flux using thorium-234 in the northeast Pacific Ocean during the EXport Processes in the Ocean from RemoTe Sensing field campaign. Elementa: Sci. Anthrop. 8, (2020).98.Stephens, B. M. et al. Organic matter composition at ocean station papa affects its bioavailability, bacterioplankton growth efficiency and the responding taxa. Front. Mar. Sci. 7, 590273 (2020).99.Mackinson, B. L., Moran, S. B., Lomas, M. W., Stewart, G. M. & Kelly, R. P. Estimates of micro-, nano-, and picoplankton contributions to particle export in the northeast Pacific. Biogeosciences 12, 3429–3446 (2015).Article 

Google Scholar 
100.Fisher, J. et al. Copepod responses to, and recovery from, the recent marine heatwave in the Northeast Pacific. PICES Sci. 2019: Notes Sci. Board Chair 28, 65 (2020).
Google Scholar 
101.Batten, S. D. et al. Interannual variability in lower trophic levels on the Alaskan Shelf. Deep Sea Res. Part II: Topical Stud. Oceanogr. 147, 58–68 (2018).Article 

Google Scholar 
102.Geider, R. & Roche, J. L. Redfield revisited: variability of C:N:P in marine microalgae and its biochemical basis. Eur. J. Phycol. 37, 1–17 (2002).Article 

Google Scholar 
103.Wohlers, J. et al. Changes in biogenic carbon flow in response to sea surface warming. Proc.Natl. Acad. Sci. USA 106, 7067–7072 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
104.Bif, M. B. & Hansell, D. A. Seasonality of dissolved organic carbon in the upper Northeast Pacific Ocean. Glob. Biogeochem. Cycles 33, 526–539 (2019).CAS 
Article 

Google Scholar 
105.Ferrer-González, F. X. et al. Resource partitioning of phytoplankton metabolites that support bacterial heterotrophy. ISME J. https://doi.org/10.1038/s41396-020-00811-y. (2020).106.Gies, E. A., Konwar, K. M., Beatty, J. T. & Hallam, S. J. Illuminating microbial dark matter in meromictic Sakinaw Lake. Appl. Environ. Microbiol. 80, 6807–6818 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
107.Pachiadaki, M. G. et al. Charting the complexity of the marine microbiome through single-cell genomics. Cell 179, 1623–1635.e11 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
108.Fuhrman, J. A. et al. Annually reoccurring bacterial communities are predictable from ocean conditions. Proc. Natl Acad. Sci. USA 103, 13104–13109 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
109.Ono, T., Shiomoto, A. & Saino, T. Recent decrease of summer nutrients concentrations and future possible shrinkage of the subarctic North Pacific high-nutrient low-chlorophyll region. Global Biogeochemical Cycles 22, GB3027 (2008).110.Walsh, D. A., Zaikova, E. & Hallam, S. J. Small Volume (1-3L) Filtration of Coastal Seawater Samples. JoVE https://doi.org/10.3791/1163 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
111.Barwell-Clarke, J. & Whitney, F. Institute of Ocean Sciences nutrient Methods and Analysis. (1996).112.Zapata, M., Rodríguez, F. & Garrido, J. L. Separation of chlorophylls and carotenoids from marine phytoplankton: a new HPLC method using a reversed phase C8 column and pyridine-containing mobile phases. Mar. Ecol. Prog. Ser. 195, 29–45 (2000).CAS 
Article 

Google Scholar 
113.Nemcek, N. & Peña, M. A. Institute of Ocean Sciences Protocols for Phytoplankton Pigment Analysis by HPLC. (2014).114.Wright, J. J., Lee, S., Zaikova, E., Walsh, D. A. & Hallam, S. J. DNA Extraction from 0.22 μM Sterivex Filters and Cesium Chloride Density Gradient Centrifugation. J. Vis. Exp. e1352, https://doi.org/10.3791/1352 (2009).115.Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
116.Rivers, A. R. iTag amplicon sequencing for taxonomix identification at JGI. http://1ofdmq2n8tc36m6i46scovo2e.wpengine.netdna-cdn.com/wp-content/uploads/2013/05/iTagger-methods-1.pdf (2016).117.Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
118.Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
120.Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
121.Yilmaz, P. et al. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Res. 42, D643–D648 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
122.Bolyen, E. et al. QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science. Nat. Biotechnol. 37, 852–857 (2019).123.R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2018).124.Rstudio Team. Rstudio: Integrated Development Environment for R (Rstudio Inc, 2016).125.Faust, K. & Raes, J. CoNet app: inference of biological association networks using Cytoscape. F1000Res 5, 1519 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
126.Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13, 2498–2504 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Garcia-Garcia, M. J., Christien, L., García-Escalona, E. & González-García, C. Sensitivity of green spaces to the process of urban planning: Three case studies of Madrid (Spain). Cities 100, 102655. https://doi.org/10.1016/j.cities.2020.102655 (2020).Article 

Google Scholar 
2.Kondo, M. C., Fluehr, J. M., McKeon, T. & Branas, C. C. Urban green space and its impact on human health. Environ. Res. Public Health 15(3), 445. https://doi.org/10.3390/ijerph15030445 (2018).Article 

Google Scholar 
3.Nesbitt, L. et al. The social and economic value of cultural ecosystem services provided by urban forests in North America: A review and suggestions for future research. Urban For. Urban Green. 25, 103–111. https://doi.org/10.1016/j.ufug.2017.05.005 (2017).Article 

Google Scholar 
4.Hasan, S. S., Zhen, L., Miah, G., Ahamed, T. & Samie, A. Impact of land use change on ecosystem services: A review. Environ. Dev. 34, 100527. https://doi.org/10.1016/j.envdev.2020.100527 (2020).Article 

Google Scholar 
5.Kolodziejczyk, B. et al. Frontiers 2018/19: Emerging issues of environmental concern. United Nations Environment Programme, Nairobi, 24–37 (2019).6.Steffen, W., Crutzen, P. J. & McNeill, J. R. The anthropocene: Are humans now overwhelming the great forces of nature. Hum. Environ. 36(8), 614–621. https://doi.org/10.1579/0044-7447(2007)36[614:TAAHNO]2.0.CO;2 (2007).CAS 
Article 

Google Scholar 
7.CC & SC. Views on Accelerating the Ecological Civilization Construction (2015).8.Ministry of Housing and Urban-Rural Development (MHURD). City Green Space Planning Standards, GB/T51346-2019 (2019).9.Raei, E. et al. Multi-objective decision-making for green infrastructure planning (LID-BMPs) in urban storm water management under uncertainty. J. Hydrol. 579, 124091. https://doi.org/10.1016/j.jhydrol.2019.124091 (2019).CAS 
Article 

Google Scholar 
10.Tzoulas, K. et al. Promoting ecosystem and human health in urban areas using Green Infrastructure: A literature review. Landsc. Urban Plan. 81(3), 167–178. https://doi.org/10.1016/j.landurbplan.2007.02.001 (2007).Article 

Google Scholar 
11.Xiao, F., Shu, J. & Zhang, L. Research on applying minimal cumulative resistance model in urban land ecological suitability assessment: As an example of Xiamen City. Acta Ecol. Sin. 30(2), 421–428 (2010).
Google Scholar 
12.Zhao, S., Ma, Y., Wang, J. & You, X. Landscape pattern analysis and ecological network planning of Tianjin City. Urban For. Urban Green. 46, 126479. https://doi.org/10.1016/j.ufug.2019.126479 (2019).Article 

Google Scholar 
13.Davies, C. & Lafortezza, R. Urban green infrastructure in Europe: Is greenspace planning and policy compliant? Land Use Policy 69, 93–101. https://doi.org/10.1016/j.landusepol.2017.08.018 (2017).Article 

Google Scholar 
14.Central Committee & State Council (CC & SC). Views on establishment and monitoring of Territorial Space Planning system (2019).15.Zhou, Q. et al. China’s Green space system planning: Development, experiences, and characteristics. Urban For. Urban Green. 60, 127017. https://doi.org/10.1016/j.ufug.2021.127017 (2021).Article 

Google Scholar 
16.Zhou, X., Zhang, S. & Zhu, D. Impact of urban water networks on microclimate and PM25 distribution in downtown areas: A case study of wuhan. Build. Environ. 203, 108073. https://doi.org/10.1016/j.buildenv.2021.108073 (2021).Article 

Google Scholar 
17.Ministry of Natural Resources (MNR). Guidelines for Formulation of Provincial Territorial Space Planning (Trial) (2020).18.Rushdi, A. M. A. & Hassan, A. K. Reliability of migration between habitat patches with heterogeneous ecological corridors. Ecol. Model. 304, 1–10. https://doi.org/10.1016/j.ecolmodel.2015.02.014 (2015).Article 

Google Scholar 
19.Wang, T., Li, H. & Huang, Y. The complex ecological network’s resilience of the Wuhan metropolitan area. Ecol. Ind. 130, 108101. https://doi.org/10.1016/j.ecolind.2021.108101 (2021).Article 

Google Scholar 
20.Wu, H. et al. A novel remote sensing ecological vulnerability index on large scale: A case study of the China-Pakistan Economic Corridor region. Ecol. Ind. 129, 107955. https://doi.org/10.1016/j.ecolind.2021.107955 (2021).Article 

Google Scholar 
21.Janauer, G. A. Ecohydrology: Fusing concepts and scales. Ecol. Eng. 16(1), 9–16. https://doi.org/10.1016/S0925-8574(00)00072-0 (2000).Article 

Google Scholar 
22.Rinaldo, A., Gatto, M. & Rodriguez-Iturbe, I. River networks as ecological corridors: A coherent ecohydrological perspective. Adv. Water Resour. 112, 27–58. https://doi.org/10.1016/j.advwatres.2017.10.005 (2018).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
23.Fletcher, T. D. et al. SUDS, LID, BMPs, WSUD and more: The evolution and application of terminology surrounding urban drainage. Urban Water J. 12(7), 525–542. https://doi.org/10.1080/1573062X.2014.916314 (2015).Article 

Google Scholar 
24.Nieuwenhuis, E., Cuppen, E., Langeveld, J. & Bruijn, H. Towards the integrated management of urban water systems: Conceptualizing integration and its uncertainties. J. Clean. Prod. 280(2), 124977. https://doi.org/10.1016/j.jclepro.2020.124977 (2021).Article 

Google Scholar 
25.Knaapen, J. P., Scheffer, M. & Harms, B. Estimating habitat isolation in landscape planning. Landscape Urban Plann. 23(1), 1–16. https://doi.org/10.1016/0169-2046(92)90060-D (1992).Article 

Google Scholar 
26.Yu, K. Security patterns and surface model in landscape ecological planning. Landscape Urban Plann. 36(1), 1–17. https://doi.org/10.1016/S0169-2046(96)00331-3 (1996).Article 

Google Scholar 
27.Yu, K. Landscape ecological security pattern of biological protection. Acta Ecologica Sinica 1, 3–5 (1999).
Google Scholar 
28.Zhang, Z., Meerow, S., Newell, J. P. & Lindquist, M. Enhancing landscape connectivity through multifunctional green infrastructure corridor modeling and design. Urban For. Urban Green. 38, 305–317. https://doi.org/10.1016/j.ufug.2018.10.014 (2019).Article 

Google Scholar 
29.Fu, Y., Shi, X., He, J., Yuan, Y. & Qu, L. Identification and optimization strategy of county ecological security pattern: A case study in the Loess Plateau, China. Ecol. Ind. 112, 106030. https://doi.org/10.1016/j.ecolind.2019.106030 (2020).Article 

Google Scholar 
30.Kong, F., Yin, H., Nakagoshi, N. & Zong, Y. Urban green space network development for biodiversity conservation: Identification based on graph theory and gravity modeling. Landsc. Urban Plan. 95, 16–27. https://doi.org/10.1016/j.landurbplan.2009.11.001 (2010).Article 

Google Scholar 
31.Kong, F. & Yin, H. Construction of Jinan urban green space ecological network. Acta Ecol. Sin. 4, 1711–1719 (2008).
Google Scholar 
32.Linehan, J., Gross, M. & Finn, J. Greenway planning: Developing a landscape ecological network approach. Landsc. Urban Plan. 33(1–3), 179–193. https://doi.org/10.1016/0169-2046(94)02017-A (1995).Article 

Google Scholar 
33.Yang, H., Chen, W. & Chen, X. Regional ecological network planning for biodiversity conservation: A case study of China’s Poyang lake eco-economic region. Pol. J. Environ. Stud. 26(4), 1825–1833. https://doi.org/10.15244/pjoes/68877 (2017).Article 

Google Scholar 
34.Fahrig, L. Rethinking patch size and isolation effects: The habitat amount hypothesis. J. Biogeogr. 40(9), 1649–1663. https://doi.org/10.1111/jbi.12130 (2013).Article 

Google Scholar 
35.Gilbert-Norton, L., Wilson, R., Stevens, J. R. & Beard, K. H. A meta-analytic review of corridor effectiveness. Conserv. Biol. 24(3), 660–668. https://doi.org/10.1111/j.1523-1739.2010.01450.x (2010).Article 
PubMed 

Google Scholar 
36.Saura, S. & Torné, J. Conefor Sensinode 2.2: A software package for quantifying the importance of habitat patches for landscape connectivity. Environ. Model. Softw. 24(1), 135–139. https://doi.org/10.1016/j.envsoft.2008.05.005 (2009).Article 

Google Scholar 
37.Saura, S., Vogt, P., Velázquez, J., Hernando, A. & Tejera, R. Key structural forest connectors can be identified by combining landscape spatial pattern and network analyses. For. Ecol. Manag. 262(2), 150–160. https://doi.org/10.1016/j.foreco.2011.03.017 (2011).Article 

Google Scholar 
38.Bueno, J. A., Tsihrintzis, V. A. & Alvarez, L. South Florida greenways: a conceptual framework for the ecological reconnectivity of the region. Landsc. Urban Plan. 33(1–3), 247–266. https://doi.org/10.1016/0169-2046(94)02021-7 (1995).Article 

Google Scholar 
39.Cook, E. A. Landscape structure indices for assessing urban ecological networks. Landsc. Urban Plan. 58(2–4), 269–280. https://doi.org/10.1016/S0169-2046(01)00226-2 (2002).Article 

Google Scholar 
40.Dalton, R., Garlick, J., Minshull, R. & Robinson, A. Networks in Geography (Phillip, 1973).
Google Scholar 
41.Forman, R. T. T. & Godron, M. Landscape Ecology (Wiley, 1986).
Google Scholar 
42.Haggett, P. & Chorley, R. J. Network Analysis in Geography (Edward Arnold, 1972).
Google Scholar 
43.Yu, K. The identification method of landscape ecological strategic points and the surface model of theoretical geography. J. Geog. Sci. S1, 3–5 (1998).
Google Scholar 
44.Yu, Q. et al. Optimization of ecological node layout and stability analysis of ecological network in desert oasis: A typical case study of ecological fragile zone located at Deng Kou County (Inner Mongolia). Ecol. Indic. 84, 304–318. https://doi.org/10.1016/j.ecolind.2017.09.002 (2018).Article 

Google Scholar 
45.Zhang, Y. & Yu, B. Evaluation of urban ecological network space and its structure optimization. Acta Ecol. Sin. 36(21), 6969–6984 (2016).
Google Scholar 
46.Hong, W. et al. Sensitivity evaluation and land-use control of urban ecological corridors: A case study of Shenzhen, China. Land Use Policy 62, 316–325. https://doi.org/10.1016/j.landusepol.2017.01.010 (2017).Article 

Google Scholar 
47.Monaco, R., Negrini, G., Salizzoni, E., Soares, A. J. & Voghera, A. Inside-outside park planning: A mathematical approach to assess and support the design of ecological connectivity between Protected Areas and the surrounding landscape. Ecol. Eng. 149, 105748. https://doi.org/10.1016/j.ecoleng.2020.105748 (2020).Article 

Google Scholar 
48.Morandi, D. T. et al. Delimitation of ecological corridors between conservation units in the Brazilian Cerrado using a GIS and AHP approach. Ecol. Ind. 115, 106440. https://doi.org/10.1016/j.ecolind.2020.106440 (2020).Article 

Google Scholar 
49.Santos, J. S. et al. Delimitation of ecological corridors in the Brazilian Atlantic Forest. Ecol. Ind. 88, 414–424. https://doi.org/10.1016/j.ecolind.2018.01.011 (2018).Article 

Google Scholar 
50.Dai, L., Liu, Y., Luo, X. I. & the MCR and, ,. DOI models to construct an ecological security network for the urban agglomeration around Poyang Lake, China. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.141868 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
51.Ferreira, C. S. S. et al. Spatiotemporal variability of hydrologic soil properties and the implications for overland flow and land management in a peri-urban Mediterranean catchment. J. Hydrol. 525, 249–263. https://doi.org/10.1016/j.jhydrol.2015.03.039 (2015).ADS 
Article 

Google Scholar 
52.Kalantari, Z. et al. Assessing flood probability for transportation infrastructure based on catchment characteristics, sediment connectivity and remotely sensed soil moisture. Sci. Total Environ. 661, 393–406. https://doi.org/10.1016/j.scitotenv.2019.01.009 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
53.Kalantari, Z., Ferreira, C. S. S., Walsh, R. P. D., Ferreira, A. J. D. & Destouni, G. Urbanization development under climate change: Hydrological responses in a peri-urban Mediterranean catchment. Land Degrad. Dev. 28, 2207–2221. https://doi.org/10.1002/ldr.2747 (2017).Article 

Google Scholar 
54.Grillakis, M. G. et al. Initial soil moisture effects on flash flood generation: A comparison between basins of contrasting hydro-climatic conditions. J. Hydrol. 541(A), 206–217. https://doi.org/10.1016/j.jhydrol.2016.03.007 (2016).ADS 
Article 

Google Scholar 
55.Zhang, K., Fong, T. & Chui, M. A comprehensive review of spatial allocation of LID-BMP-GI practices: Strategies and optimization tools. Sci. Total Environ. 621, 915–929. https://doi.org/10.1016/j.scitotenv.2017.11.281 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
56.Liu, Z., Lin, Y., De Meulder, B. & Wang, S. Heterogeneous landscapes of urban greenways in Shenzhen: Traffic impact, corridor width and land use. Urban For. Urban Green. 126, 785. https://doi.org/10.1016/j.ufug.2020.126785 (2020).Article 

Google Scholar 
57.Wakefield, S. Great expectations: Waterfront redevelopment and the Hamilton Harbour Waterfront Trail. Cities 24(4), 298–310. https://doi.org/10.1016/j.cities.2006.11.001 (2007).Article 

Google Scholar 
58.Rimaze, D., Machumu, A., Mremi, R. & Eustace, A. Diversity and abundance of wild mammals between different accommodation facilities in the Kwakuchinja Wildlife Corridor, Tanzania. Sci. Afr. 9, e00480. https://doi.org/10.1016/j.sciaf.2020.e00480 (2020).Article 

Google Scholar 
59.Franco, D., Mannino, I. & Zanetto, G. The impact of agroforestry networks on scenic beauty estimation: The role of a landscape ecological network on a socio-cultural process. Landsc. Urban Plan. 62(3), 119–138. https://doi.org/10.1016/S0169-2046(02)00127-5 (2003).Article 

Google Scholar 
60.Wu, X. et al. Increasing green infrastructure-based ecological resilience in urban systems: A perspective from locating ecological and disturbance sources in a resource-based city. Sustain. Cities Soc. 61, 102354. https://doi.org/10.1016/j.scs.2020.102354 (2020).Article 

Google Scholar 
61.Yang, C., Zeng, W. & Yang, X. Coupling coordination evaluation and sustainable development pattern of geo-ecological environment and urbanization in Chongqing municipality, China. Sustain. Cities Soc. 61, 102271. https://doi.org/10.1016/j.scs.2020.102271 (2020).Article 

Google Scholar 
62.Yang, J., Zeng, C. & Cheng, Y. Spatial influence of ecological networks on land use intensity. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.137151 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
63.Théau, J., Bernier, A. & Fournier, R. A. An evaluation framework based on sustainability-related indicators for the comparison of conceptual approaches for ecological networks. Ecol. Indic. 52, 444–457. https://doi.org/10.1016/j.ecolind.2014.12.029 (2015).Article 

Google Scholar 
64.Neri, M., Jameli, D., Bernard, E. & Melo, F. P. L. Green versus green? Adverting potential conflicts between wind power generation and biodiversity conservation in Brazil. Perspect. Ecol. Conserv. 17(3), 131–135. https://doi.org/10.1016/j.pecon.2019.08.004 (2019).Article 

Google Scholar 
65.Zeng, Y. & Zhong, L. Identifying conflicts tendency between nature-based tourism development and ecological protection in China. Ecol. Indic. 109, 105791. https://doi.org/10.1016/j.ecolind.2019.105791 (2020).Article 

Google Scholar 
66.Cunha, N. S. & Magalhães, M. R. Methodology for mapping the national ecological network to mainland Portugal: A planning tool towards a green infrastructure. Ecol. Ind. 104, 802–818. https://doi.org/10.1016/j.ecolind.2019.04.050 (2019).Article 

Google Scholar 
67.Dong, J., Peng, J., Liu, Y., Qiu, S. & Han, Y. Integrating spatial continuous wavelet transform and kernel density estimation to identify ecological corridors in megacities. Landsc. Urban Plan. 199, 103815. https://doi.org/10.1016/j.landurbplan.2020.103815 (2020).Article 

Google Scholar 
68.Gasanov, G. et al. Data on the productivity of plant cover of the main types of soils of the North-Western precaspian in connection with the dynamics of ecological factors. Data Brief 24, 103713. https://doi.org/10.1016/j.dib.2019.103713 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
69.Montis, A. D. et al. Resilient ecological networks: A comparative approach. Land Use Policy 89, 104207. https://doi.org/10.1016/j.landusepol.2019.104207 (2019).Article 

Google Scholar 
70.Du, H. et al. Urban blue-green space planning based on thermal environment simulation: A case study of Shanghai, China. Ecol. Indic. 106, 105501. https://doi.org/10.1016/j.ecolind.2019.105501 (2020).Article 

Google Scholar 
71.Guo, X. et al. The impact of onshore wind power projects on ecological corridors and landscape connectivity in Shanxi, China. J. Clean. Prod. 254, 120075. https://doi.org/10.1016/j.jclepro.2020.120075 (2020).Article 

Google Scholar 
72.Li, J., Wang, Y., Ni, Z., Chen, S. & Xia, B. An integrated strategy to improve the microclimate regulation of green-blue-grey infrastructures in specific urban forms. J. Clean. Prod. 271, 122555. https://doi.org/10.1016/j.jclepro.2020.122555 (2020).Article 

Google Scholar 
73.Afriyanie, D. et al. Re-framing urban green spaces planning for flood protection through socio-ecological resilience in Bandung City, Indonesia. Cities 101, 102710. https://doi.org/10.1016/j.cities.2020.102710 (2020).Article 

Google Scholar 
74.Ioan-Cristian, I. et al. Integrating urban blue and green areas based on historical evidence. Urban For. Urban Green. 34, 217–225. https://doi.org/10.1016/j.ufug.2018.07.001 (2019).Article 

Google Scholar 
75.Jaung, W. L., Carrasco, R., Ahmad, S., Tan, P. Y. & Richards, D. R. Temperature and air pollution reductions by urban green spaces are highly valued in a tropical city-state. Urban For. Urban Green. https://doi.org/10.1016/j.ufug.2020.126827 (2020).Article 

Google Scholar 
76.La Sorte, F. A., Aronson, M. F. J., Lepczyk, C. A. & Horton, K. G. Area is the primary correlate of annual and seasonal patterns of avian species richness in urban green spaces. Landsc. Urban Plan. 203, 103892. https://doi.org/10.1016/j.landurbplan.2020.103892 (2020).Article 

Google Scholar 
77.Moradpour, M. & Hosseini, V. An investigation into the effects of green space on air quality of an urban area using CFD modeling. Urban Clim. 34, 100686. https://doi.org/10.1016/j.uclim.2020.100686 (2020).Article 

Google Scholar 
78.Nouri, H., Borujeni, S. C. & Hoekstra, A. Y. The blue water footprint of urban green spaces: An example for Adelaide, Australia. Landsc. Urban Plan. 190, 103613. https://doi.org/10.1016/j.landurbplan.2019.103613 (2019).Article 

Google Scholar 
79.Sikuzani, Y. U. et al. Tree diversity and structure on green space of urban and peri-urban zones: The case of Lubumbashi City in the Democratic Republic of Congo. Urban For. Urban Green. 41, 67–74. https://doi.org/10.1016/j.ufug.2019.03.008 (2019).Article 

Google Scholar  More

One of the most interesting observations from the time series presented in the section above is the following: when the magnitude of the Allee parameter (theta) is low, vegetation and prey densities are confined to low values. However, the predator densities deviate very significantly away from their mean. Now for very small (theta) the system is attracted to a periodic orbit, and so the large deviations are completely correlated with time and occur periodically. So they cannot be considered to be extreme events, as they are neither aperiodic, nor rare. But for larger (theta), both predator and prey densities can sometime shoot up over 7 standard deviations away from the mean value. This is evident clearly in Fig. 2c,e where one can see that both predator and prey populations exceed the (7sigma) threshold from time to time. The instants at which prey and predator populations exceed the (7sigma) threshold are now completely uncorrelated with time. This is consistent with the underlying chaotic dynamics that emerges under increasing Allee parameter (theta).In order to illustrate this, we mark the time instances at which a population exceeds the (7sigma) threshold, for different values of Allee parameter (theta). Figure 4 shows this for the vegetation, prey and predator populations. The density of points signifying the occurrence of extreme events is clearly the highest for the predator population. This indicates that the predator population has the greatest propensity for large deviations. It is also clear that vegetation has the least number of extreme events in the same time window. The uncorrelated nature of the extreme events is also evident in the scatter of these points, except in the small periodic windows that occur for certain special ranges of (theta). The increasing density of these points also illustrate the increasing probability of extreme events in the populations with increasing Allee parameter (theta).Figure 4Figure marking the time instances at which a population exceeds the (7sigma) threshold, for different values of Allee parameter (theta), for the case of (top to bottom) vegetation, prey and predator populations.Full size imageIn order to understand the phenomena quantitatively, we first estimate the maximum densities of vegetation, prey and predator populations (denoted by (u_{max}), (v_{max}) and (w_{max}) respectively) for varying the Allee parameter (theta). To estimate this, we find the global maximum of the populations sampled over a time interval (T=1000), averaged over a large set of random initial conditions.Figure 5 shows (u_{max}), (v_{max}) and (w_{max}), for Allee parameter (theta in [0,theta _{c})), scaled by their values at (theta = 0). These scaled maxima help us gauge the relative change in the maximum population densities arising due to the Allee effect. It is evident from our simulation results that the magnitude of the global maximum of vegetation does not change very significantly for increasing Allee parameter (theta), with its magnitude around (theta _c) being approximately 4 fold the value at (theta =0). However, the magnitude of maximum prey and predator populations change very significantly with respect to Allee parameter (theta) and exceeds over 10 fold the value obtained for (theta =0).Figure 5Global maximum of vegetation (u_{max}) (blue), prey (v_{max}) (red) and predator (black) populations, with respect to the Allee parameter (theta), scaled by their values obtained for (theta =0). Clearly, when Allee parameter (theta) is sufficiently large, the maximum prey and predator populations are an order of magnitude larger than that obtained in systems with no Allee effect.Full size imageWe then go on to numerically calculate the probability density of the vegetation, prey, and predator population densities, for increasing Allee effect parameter (theta). The tail of this probability density function reflects the influence of the Allee effect on the probability of obtaining extreme events. To illustrate this, we show the probability density function for the prey population in Fig. 6, for three different values of (theta). Extreme events are confined to the tail of the distribution that lie beyond the vertical red line, marking the (mu + 7 sigma) value in the figure. So it is clear from these probability distributions that the Allee effect in prey population promotes the occurrence of extreme events as the tail of the distribution is flatter and extends further with increasing Allee parameter (theta).Figure 6Probability Density Function (PDF) of the prey population v, for the system given by Eq. (1), with increasing magnitude of (theta) with (a) (theta =0), (b) (theta =0.015) and (c) (theta =0.02). The threshold for extreme event (mu + 7sigma) is denoted by vertical red dashed line.Full size imageIn order to ascertain that the extreme values are uncorrelated and aperiodic we examine the time intervals between successive extreme events in the population. Figure 7 (left panel) shows representative results for the return map of the intervals between extreme events in the prey population and it is clearly shows no regularity. The probability distribution of the intervals is also Poisson distributed and so the extreme population buildups are uncorrelated aperiodic events, as clearly evident from the right panel of the figure.Figure 7(Left) Return Map of (Delta t_{i+1}) versus (Delta t_i), and (right) Probability distribution of (Delta t_i) fitted with exponentially decaying function, where (Delta t_i) is the ith interval between successive extreme events, where an extreme event is defined at the instant when the prey population crosses the (mu +7sigma) line (cf. Fig. 2). Here (theta =0.024).Full size imageIn order to further quantify how Allee effect influences extreme events, we estimate the probability of obtaining large deviations, in a large sample of initial states tracked over a long period of time. We denote this probability by (P_{ext}), and we calculate it by following a large set of random initial conditions and recording the number of occurrences of the population crossing the threshold value in a prescribed period of time, with this time window being several orders of magnitude larger than the mean oscillation period. This time-averaged and ensemble-averaged quantity yields a good estimate of (P_{ext}). With no loss of generality, we choose the threshold for determining extreme events to be (mu + 7 sigma), i.e. when the variable crosses the (7 sigma) level, it is labelled as extreme.This probability, estimated for all three populations is shown in Fig. 8. First, it is clear from Fig. 8, that the probability of the occurrence of extreme events is the lowest for vegetation, and the highest for predator populations, for any value of the Allee parameter (theta in [0,theta _{c})). We also observe that, for values of the Allee parameter (theta) lower than a critical value denoted by (theta ^{u}_{c}) the probability of obtaining extreme events in the vegetation population tends to zero. Beyond the critical value (theta ^u_c), the vegetation population starts to exhibit extreme events. A similar trend emerges for the prey population. However, the critical value of the Allee parameter (theta) necessary for the emergence of a finite probability of extreme events, denoted by (theta ^{v}_{c}), is much smaller than (theta ^u_c). So for the prey population, a weaker Allee effect can induce extreme events.Figure 8Probability of obtaining extreme event in unit time ((P_{ext})), with respect to Allee parameter (theta), estimated by sampling a time series of length (T=5000), and averaging over 500 random initial states. Here we consider that an extreme event occurs when a population level crosses the threshold (mu + 7sigma). (P_{ext}) for vegetation, prey and predator are displayed in blue, red and black colors respectively. Note that there exists a narrow periodic window around (theta sim 0.02) (cf. Fig. 9), and so the large deviations in this window of Allee parameter are not associated with true extreme events, as they occur periodically.Full size imageNote that some mechanisms have been proposed for the generation of extreme events in deterministic dynamical systems, which typically have been excitable systems. These include interior crisis, Pomeau-Manneville intermittency, and the breakdown of quasiperiodic motion. However the extreme events generated by these mechanisms occur typically at very specific critical points in parameter space, or narrow windows around it. The first important difference in our system here is that the extreme events do not emerge only at some special values alone. Rather, there is a broad range in Allee parameter space where extreme events have a very significant presence. This makes our extreme event phenomenon more robust, and thus increases its potential observability. This also rules out the intermittency-induced mechanisms that have been proposed, as is evident through the lack of sudden expansion in attractor size in our bifurcation diagram (Fig. 3) in general.However, interestingly, the system does have one parameter window where there is attractor widening and this gives rise to a markedly enhanced extreme event count. The peak observed in Fig. 8 can be directly correlated with a sudden attractor widening leading to a marked increase of extreme event in a narrow window of parameter space located near the crisis (see Fig. 9). Additionally, for a narrow window around (theta sim 0.02), the emergent dynamics is periodic. So the large deviations are no longer uncorrelated, and so they are not extreme events in the true sense.Figure 9Bifurcation diagram of prey populations with respect to Allee parameter, in the range (theta in [0.0189 : 0.0191]). Here we display the local maxima of the prey population. The parameter values in Eq. (1) are as mentioned in the text.Full size imageLastly we notice that the predator population shows extreme events for all values of (theta in [0,theta _{c})). So the predator population is most prone to experiencing unusually large deviations from the mean. We also observe that the probability of occurrence of extreme events in the predator population is not affected significantly by the Allee effect. This is in marked contrast to the case of vegetation and prey, where the Allee effect crucially influences the advent of extreme events. Also, for the predator population there is no marked transition from zero to finite (P_{ext}) under increasing Allee parameter (theta), as evident for vegetation and prey populations. More