Ecology

1.Medic, G., Wille, M. & Hemels, M. Short- and long-term health consequences of sleep disruption. Nat. Sci. Sleep 9, 151–161 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
2.Randazzo, A. C., Muehlbach, M. J., Schweitzer, P. K. & Walsh, J. K. Cognitive function following acute sleep restriction in children ages 10–14. Sleep 21, 861–868 (1998).CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Stickgold, R. Sleep-dependent memory consolidation. Nature 437, 1272–1278 (2005).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Tononi, G. & Cirelli, C. Sleep and the price of plasticity: From synaptic and cellular homeostasis to memory consolidation and integration. Neuron 81, 12–34 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Marshall, L. & Born, J. The contribution of sleep to hippocampus-dependent memory consolidation. Trends Cogn. Sci. 11, 442–450 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Smith, C. Sleep states and memory processes in humans: Procedural versus declarative memory systems. Sleep Med. Rev. 5, 491–506 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Johnston, T. D. In Selective Costs and Benefits in the Evolution of Learning. in Advances in the Study of Behavior (eds. Rosenblatt, J. S. et al.) 12, 65–106 (Academic Press, 1982).8.Hendricks, J. C. et al. Rest in Drosophila is a sleep-like state. Neuron 25, 129–138 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Campbell, S. S. & Tobler, I. Animal sleep: A review of sleep duration across phylogeny. Neurosci. Biobehav. Rev. 8, 269–300 (1984).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Shaw, P. J. Correlates of sleep and waking in Drosophila melanogaster. Science (80-). 287, 1834–1837 (2000).ADS 
CAS 
Article 

Google Scholar 
11.Hamblen, M. et al. Germ-line transformation involving DNA from the period locus in Drosophila melanogaster: Overlapping genomic fragments that restore circadian and ultradian rhythmicity to per 0 and per—mutants. J. Neurogenet. 3, 249–291 (1986).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Kirszenblat, L. & van Swinderen, B. Sleep in Drosophila. In Handbook of Sleep Research, Vol. 30 (ed. Dringenberg, H. C.) 333–347 (Elsevier, 2019).13.Ly, S., Pack, A. I. & Naidoo, N. The neurobiological basis of sleep: Insights from Drosophila. Neurosci. Biobehav. Rev. 87, 67–86 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
14.Helfrich-Förster, C. Sleep in insects. Annu. Rev. Entomol. 63, 69–86 (2018).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
15.Le Glou, E., Seugnet, L., Shaw, P. J., Preat, T. & Goguel, V. Circadian modulation of consolidated memory retrieval following sleep deprivation in Drosophila. Sleep 35, 1377–1384 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
16.Li, X., Yu, F. & Guo, A. Sleep deprivation specifically impairs short-term olfactory memory in Drosophila. Sleep 32, 1417–1424 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
17.Rihel, J. & Bendor, D. Flies sleep on it, or Fuhgeddaboudit!. Cell 161, 1498–1500 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Geissmann, Q., Beckwith, E. J. & Gilestro, G. F. Most sleep does not serve a vital function: Evidence from Drosophila melanogaster. Sci. Adv. 5, eaau8253 (2019).Article 
CAS 

Google Scholar 
19.Tougeron, K. & Abram, P. K. An ecological perspective on sleep disruption. Am. Nat. 190, E55–E66 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Aulsebrook, A. E., Jones, T. M., Rattenborg, N. C., Roth, T. C. & Lesku, J. A. Sleep ecophysiology: Integrating neuroscience and ecology. Trends Ecol. Evol. 31, 590–599 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Markow, T. A. Host use and host shifts in Drosophila. Curr. Opin. Insect Sci. 31, 139–145 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Badel, L., Ohta, K., Tsuchimoto, Y. & Kazama, H. Decoding of context-dependent olfactory behavior in Drosophila. Neuron 91, 155–167 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Knaden, M., Strutz, A., Ahsan, J., Sachse, S. & Hansson, B. S. Spatial representation of odorant valence in an insect brain. Cell Rep. 1, 392–399 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Hopkins, A. A discussion of C.G. Hewitt’s paper on ‘Insect Behavior’. J. Econ. Entomol. 10, 92–93 (1917).
Google Scholar 
25.Davis, J. M. & Stamps, J. A. The effect of natal experience on habitat preferences. Trends Ecol. Evol. 19, 411–416 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
26.Barron, A. B. The life and death of Hopkins’ host selection principle. J. Insect Behav. 14, 725–737 (2001).Article 

Google Scholar 
27.van Emden, H. F. et al. Plant chemistry and aphid parasitoids (Hymenoptera: Braconidae): Imprinting and memory. Eur. J. Entomol. 105, 477–483 (2008).Article 

Google Scholar 
28.Liu, S. S., Li, Y. H., Liu, Y. Q. & Zalucki, M. P. Experience-induced preference for oviposition repellents derived from a non-host plant by a specialist herbivore. Ecol. Lett. 8, 722–729 (2005).Article 

Google Scholar 
29.Hamilton, C. E., Beresford, D. V. & Sutcliffe, J. F. Effects of natal habitat odour, reinforced by adult experience, on choice of oviposition site in the mosquito Aedes aegypti. Med. Vet. Entomol. 25, 428–435 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Turlings, T. C. L., Wackers, F. L., Vet, L. E. M., Lewis, W. J. & Tumlinson, J. H. Learning of Host-Finding Cues by Hymenopterous parasitoids. In Insect Learning (eds. Papaj, D. R. & Lewis, W. J.) 51–78 (Springer US, 1993). https://doi.org/10.1007/978-1-4615-2814-2_331.Jaenike, J. Induction of host preference in Drosophila melanogaster. Oecologia 58, 320–325 (1983).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Takemoto, H., Powell, W., Pickett, J., Kainoh, Y. & Takabayashi, J. Two-step learning involved in acquiring olfactory preferences for plant volatiles by parasitic wasps. Anim. Behav. 83, 1491–1496 (2012).Article 

Google Scholar 
33.Andretic, R. & Shaw, P. J. Essentials of sleep recordings in Drosophila: Moving beyond sleep time. Methods Enzymol. 393, 759–772 (2005).PubMed 
Article 
PubMed Central 

Google Scholar 
34.Garbe, D. S. et al. Context-specific comparison of sleep acquisition systems in Drosophila. Biol. Open 4, 1558–1568 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Faraway, J. J. Extending the Linear Model with R (CRC Press, 2016). https://doi.org/10.1201/b21296.Book 
MATH 

Google Scholar 
36.Ho, K. S. & Sehgal, A. Drosophila melanogaster: An insect model for fundamental studies of sleep. Methods Enzymol. 393, 1834–1837 (2005).
Google Scholar 
37.Greenspan, R. J., Tononi, G., Cirelli, C. & Shaw, P. J. Sleep and the fruit fly. Trends Neurosci. 24, 142–145 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Killgore, W. D. S. Sleep deprivation and behavioral risk-taking. In Modulation of Sleep by Obesity, Diabetes, Age, and Diet 279–287 (Elsevier, 2015). https://doi.org/10.1016/B978-0-12-420168-2.00030-2.39.Revadi, S. et al. Olfactory responses of Drosophila suzukii females to host plant volatiles. Physiol. Entomol. 40, 54–64 (2015).CAS 
Article 

Google Scholar 
40.Cirelli, C. & Tononi, G. Is sleep essential?. PLoS Biol. 6, 1605–1611 (2008).CAS 
Article 

Google Scholar 
41.Bateson, M., Desire, S., Gartside, S. E. & Wright, G. A. Agitated honeybees exhibit pessimistic cognitive biases. Curr. Biol. 21, 1070–1073 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Wilkin, M. M., Waters, P., McCormick, C. M. & Menard, J. L. Intermittent physical stress during early- and mid-adolescence differentially alters rats’ anxiety- and depression-like behaviors in adulthood. Behav. Neurosci. 126, 344–360 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
43.Chaumet, G. et al. Confinement and sleep deprivation effects on propensity to take risks. Aviat. Space. Environ. Med. 80, 73–80 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Killgore, W. D. S. Effects of sleep deprivation and morningness-eveningness traits on risk-taking. Psychol. Rep. 100, 613–626 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
45.Killgore, W. D. S. et al. Restoration of risk-propensity during sleep deprivation: Caffeine, dextroamphetamine, and modafinil. Aviat. Space. Environ. Med. 79, 867–874 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Tversky, A. & Kahneman, D. Judgment under uncertainty: Heuristics and biases. Science (80-). 185, 1124–1131 (1974).ADS 
CAS 
Article 

Google Scholar 
47.Spieth, H. T. Courtship behavior in Drosophila. Annu. Rev. Entomol. 19, 385–405 (1974).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Bartelt, R. J., Schaner, A. M. & Jackson, L. L. cis-Vaccenyl acetate as an aggregation pheromone in Drosophila melanogaster. J. Chem. Ecol. 11, 1747–1756 (1985).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Cazalé-Debat, L., Houot, B., Farine, J. P., Everaerts, C. & Ferveur, J. F. Flying Drosophila show sex-specific attraction to fly-labelled food. Sci. Rep. 9, 1–13 (2019).Article 
CAS 

Google Scholar 
50.Malek, H. L. & Long, T. A. F. On the use of private versus social information in oviposition site choice decisions by Drosophila melanogaster females. Behav. Ecol. 31, 739–749 (2020).Article 

Google Scholar 
51.Inoue, I. et al. Impaired locomotor activity and exploratory behavior in mice lacking histamine H1 receptors. Proc. Natl. Acad. Sci. U. S. A. 93, 13316–13320 (1996).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Daffner, K. R., Mesulam, M.-M., Cohen, L. G. & Scinto, L. F. M. Mechanisms underlying diminished novelty-seeking behavior in patients with probable Alzheimer’s disease. Neuropsychiatry Neuropsychol. Behav. Neurol. 12, 58–66 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
53.Lee, A. C. H., Rahman, S., Hodges, J. R., Sahakian, B. J. & Graham, K. S. Associative and recognition memory for novel objects in dementia: Implications for diagnosis. Eur. J. Neurosci. 18, 1660–1670 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
54.Ju, Y.-E.S., Lucey, B. P. & Holtzman, D. M. Sleep and Alzheimer disease pathology—A bidirectional relationship. Nat. Rev. Neurol. 10, 115–119 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Tabuchi, M. et al. Sleep interacts with aβ to modulate intrinsic neuronal excitability. Curr. Biol. 25, 702–712 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Dissel, S. et al. Enhanced sleep reverses memory deficits and underlying pathology in drosophila models of Alzheimer’s disease. Neurobiol. Sleep Circadian Rhythm. 2, 15–26 (2017).Article 

Google Scholar 
57.Takano-Shimizu-Kouno, T. KYOTO Stock Center—Department of Drosophila Genomics and Genetic Resources (Kyoto Institute of Technology, 2015).58.Shaw, P. J., Tortoni, G., Greenspan, R. J. & Robinson, D. F. Stress response genes protect against lethal effects of sleep deprivation in Drosophila. Nature 417, 287–291 (2002).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
59.https://www.arduino.cc/. Accessed 6 Jan 202160.https://processing.org/. Accessed 6 Jan 2021 More

1.Yadav, G. S. et al. Energy budget and carbon footprint in a no-till and mulch based rice–mustard cropping system. J. Clean. Prod. 191, 144–157 (2018).Article 

Google Scholar 
2.Fleming-Muñoz, D. A., Preston, K. & Arratia-Solar, A. Value and impact of publicly funded climate change agricultural mitigation research: Insights from New Zealand. J. Clean. Prod. 248, 119249 (2020).Article 

Google Scholar 
3.IPCC. Climate Change 2014: Mitigation of Climate Change (Cambridge University Press, 2014).
Google Scholar 
4.Wang, Z. B. et al. Lowering carbon footprint of winter wheat by improving management practices in North China Plain. J. Clean. Prod. 112, 149–157 (2016).CAS 
Article 

Google Scholar 
5.Grassini, P. & Cassman, K. G. High-yield maize with large net energy yield and small global warming intensity. Proc. Natl. Acad. Sci. 109, 1074–1079 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
6.Gao, B. et al. Chinese cropping systems are a net source of greenhouse gases despite soil carbon sequestration. Glob. Change Biol. 24, 5590–5606 (2018).Article 

Google Scholar 
7.Xue, J. F. et al. Carbon footprint of dryland winter wheat under film mulching during summer-fallow season and sowing method on the Loess Plateau. Ecol. Indic. 95, 12–20 (2018).CAS 
Article 

Google Scholar 
8.Yuan, S., Peng, S. B., Wang, D. & Man, J. G. Evaluation of the energy budget and energy use efficiency in wheat production under various crop management practices in China. Energy 160, 184–191 (2018).Article 

Google Scholar 
9.Qi, J. Y. et al. Response of carbon footprint of spring maize production to cultivation patterns in the Loess Plateau, China. J. Clean. Prod. 187, 525–536 (2018).CAS 
Article 

Google Scholar 
10.Lu, X. L. & Liao, Y. C. Effect of tillage practices on net carbon flux and economic parameters from farmland on the Loess Plateau in China. J. Clean. Prod. 162, 1617–1624 (2017).CAS 
Article 

Google Scholar 
11.Tan, Y. C., Wu, D., Bol, R., Wu, W. L. & Meng, F. Q. Conservation farming practices in winter wheat–summer maize cropping reduce GHG emissions and maintain high yields. Agric. Ecosyst. Environ. 272, 266–275 (2019).CAS 
Article 

Google Scholar 
12.Lal, R. Carbon emission from farm operations. Environ. Int. 30, 981–990 (2004).CAS 
PubMed 
Article 

Google Scholar 
13.Wang, X. L. et al. Emergy analysis of grain production systems on large-scale farms in the North China Plain based on LCA. Agric. Syst. 128, 66–78 (2014).Article 

Google Scholar 
14.Chen, X. Z. et al. Environmental impact assessment of water-saving irrigation systems across 60 irrigation construction projects in northern China. J. Clean. Prod. 245, 118883 (2020).Article 

Google Scholar 
15.Racette, K., Zurweller, B., Tillman, B. & Rowland, D. Transgenerational stress memory of water deficit in peanut production. Field Crop. Res. 248, 107712 (2020).Article 

Google Scholar 
16.Xie, J. H. et al. Subsoiling increases grain yield, water use efficiency, and economic return of maize under a fully mulched ridge-furrow system in a semiarid environment in China. Soil. Till. Res. 199, 104584 (2020).Article 

Google Scholar 
17.Li, R., Hou, X. Q., Jia, Z. K. & Han, Q. F. Soil environment and maize productivity in semi-humid regions prone to drought of Weibei Highland are improved by ridge-and-furrow tillage with mulching. Soil. Till. Res. 196, 104476 (2020).Article 

Google Scholar 
18.Zhang, X. D. et al. Optimizing fertilization under ridge-furrow rainfall harvesting system to improve foxtail millet yield and water use in a semiarid region, China. Agric. Water Manag. 227, 105852 (2020).Article 

Google Scholar 
19.Nishimura, S., Komada, M., Takebe, M., Yonemura, S. & Kato, N. Nitrous oxide evolved from soil covered with plastic mulch film in horticultural field. Biol. Fertil. Soils 48, 787–795 (2012).CAS 
Article 

Google Scholar 
20.Xiong, L., Liang, C., Ma, B., Shah, F. & Wu, W. Carbon footprint and yield performance assessment under plastic film mulching for winter wheat production. J. Clean. Prod. 270, 122468 (2020).CAS 
Article 

Google Scholar 
21.Zhang, F., Zhang, W. J., Qi, J. G. & Li, F. M. A regional evaluation of plastic film mulching for improving crop yields on the Loess Plateau of China. Agric. Forest Meteorol. 248, 458–468 (2018).ADS 
Article 

Google Scholar 
22.Peng, X. Y., Wu, X. H., Wu, F. Q., Wang, X. Q. & Tong, X. G. Life cycle assessment of winter wheat-summer maize rotation system in Guanzhong region of shaanxi province. J. Agro-Environ. Sci. 34, 809–816 (2015).CAS 

Google Scholar 
23.Li, C. J. et al. Ridge-furrow with plastic film mulching practice improves maize productivity and resource use efficiency under the wheat-maize double-cropping system in dry semi-humid areas. Field Crop. Res. 203, 201–211 (2017).Article 

Google Scholar 
24.Tang, J. J., Folmer, H. & Xue, J. H. Technical and allocative efficiency of irrigation water use in the Guanzhong Plain. China. Food Policy 50, 43–52 (2015).Article 

Google Scholar 
25.Liu, Y., Zhang, X. L., Xi, L. Y., Liao, Y. C. & Han, J. Ridge-furrow planting promotes wheat grain yield and water productivity in the irrigated sub-humid region of China. Agric. Water Manag. 231, 105935 (2020).Article 

Google Scholar 
26.Li, Y. Z. et al. Combined ditch buried straw return technology in a ridge–furrow plastic film mulch system: Implications for crop yield and soil organic matter dynamics. Soil. Till. Res. 199, 104596 (2020).Article 

Google Scholar 
27.Wart, J. V., Kersebaum, K. C., Peng, S. B., Maribeth, M. & Cassman, K. G. Estimating crop yield potential at regional to national scales. Field Crops Res. 143, 34–43 (2013).Article 

Google Scholar 
28.Hu, Y. J. et al. Exploring optimal soil mulching for the wheat-maize cropping system in sub-humid drought-prone regions in China. Agric. Water Manag. 219, 59–71 (2019).Article 

Google Scholar 
29.Cui, J. X. et al. Integrated assessment of economic and environmental consequences of shifting cropping system from wheat-maize to monocropped maize in the North China Plain. J. Clean. Prod. 193, 524–532 (2018).Article 

Google Scholar 
30.Yin, W. et al. Wheat-maize intercropping with reduced tillage and straw retention: A step towards enhancing economic and environmental benefits in arid areas. Front. Plant Sci. 9, 1328 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
31.Zheng, J. F. et al. Biochar compound fertilizer increases nitrogen productivity and economic benefits but decreases carbon emission of maize production. Agric. Ecosyst. Environ. 241, 70–78 (2017).CAS 
Article 

Google Scholar 
32.Liang, L. et al. A multi-indicator assessment of peri-urban agricultural production in Beijing, China. Ecol. Indic. 97, 350–362 (2019).CAS 
Article 

Google Scholar 
33.Moitzi, G., Neugschwandtner, R. W., Kaul, H. P. & Wagentristl, H. Energy efficiency of winter wheat in a long-term tillage experiment under Pannonian climate conditions. Eur. J. Agron. 103, 24–31 (2019).Article 

Google Scholar 
34.Nasseri, A. Energy use and economic analysis for wheat production by conservation tillage along with sprinkler irrigation. Sci. Total Environ. 648, 450–459 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
35.Sahabi, H., Feizi, H. & Karbasi, A. Is saffron more energy and economic efficient than wheat in crop rotation systems in northeast Iran?. Sustain. Prod. Consum. 5, 29–35 (2016).Article 

Google Scholar 
36.Mondani, F., Aleagha, S., Khoramivafa, M. & Ghobadi, R. Evaluation of greenhouse gases emission based on energy consumption in wheat agroecosystems. Energy Rep. 3, 37–45 (2017).Article 

Google Scholar 
37.Bertocco, M., Basso, B., Sartori, L. & Martin, E. C. Evaluating energy efficiency of site-specific tillage in maize in NE Italy. Bioresour. Technol. 99, 6957–6965 (2008).CAS 
PubMed 
Article 

Google Scholar 
38.Amaducci, S., Colauzzi, M., Battini, F., Fracasso, A. & Perego, A. Effect of irrigation and nitrogen fertilization on the production of biogas from maize and sorghum in a water limited environment. Eur. J. Agron. 76, 54–65 (2016).ADS 
CAS 
Article 

Google Scholar 
39.Qiu, G. Y., Zhang, X., Yu, X. & Zou, Z. The increasing effects in energy and GHG emission caused by groundwater level declines in North China’s main food production plain. Agr. Water Manag. 203, 138–150 (2018).Article 

Google Scholar 
40.Arvidsson, J. Energy use efficiency in different tillage systems for winter wheat on a clay and silt loam in Sweden. Eur. J. Agron. 33, 250–256 (2010).Article 

Google Scholar 
41.Singh, R. J. et al. Energy budgeting and emergy synthesis of rainfed maize–wheat rotation system with different soil amendment applications. Ecol. Indic. 61, 753–765 (2016).CAS 
Article 

Google Scholar 
42.Zhang, Y. et al. Effects of different fertilizer strategies on soil water utilization and maize yield in the ridge and furrow rainfall harvesting system in semiarid regions of China. Agric. Water Manag. 208, 414–421 (2018).Article 

Google Scholar 
43.Cheng, K. et al. Carbon footprint of China’s crop production–An estimation using agro-statistics data over 1993–2007. Agr. Ecosyst. Environ. 142, 231–237 (2011).Article 

Google Scholar 
44.Hillier, J. et al. The carbon footprints of food crop production. Int. J. Agric. Sustain. 7, 107–118 (2009).Article 

Google Scholar 
45.Su, B., Su, Z. & Shangguan, Z. Trade-off analyses of plant biomass and soil moisture relations on the Loess Plateau. CATENA 197, 104946 (2020).Article 

Google Scholar 
46.Prata, J. C. et al. Solutions and integrated strategies for the control and mitigation of plastic and microplastic pollution. Int. J. Environ. Res. Public Health 16, 2411 (2019).PubMed Central 
Article 
PubMed 

Google Scholar 
47.Sardon, H. & Dove, A. P. Plastics recycling with a difference. Science 360, 380–381 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
48.Qin, W., Hu, C. & Oenema, O. Soil mulching significantly enhances yields and water and nitrogen use efficiencies of maize and wheat: A metaeanalysis. Sci. Rep. 5, 16210 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Sun, M. et al. Maize and rice double cropping benefits carbon footprint and soil carbon budget in paddy field. Field Crops Res. 243, 107620 (2019).Article 

Google Scholar 
50.Choudhary, M. et al. Energy budgeting and carbon footprint of pearl millet e mustard cropping system under conventional and conservation agriculture in rainfed semi-arid agro-ecosystem. Energy 141, 1052–1058 (2017).Article 

Google Scholar 
51.Bai, J. et al. Straw returning and one-time application of a mixture of controlled release and solid granular urea to reduce carbon footprint of plastic film mulching spring maize. J. Clean. Prod. 280, 124478 (2021).CAS 
Article 

Google Scholar 
52.Li, C. J. et al. Towards the highly effective use of precipitation by ridge-furrow with plastic film mulching instead of relying on irrigation resources in a dry semi-humid area. Field Crops Res. 188, 62–73 (2016).ADS 
Article 

Google Scholar 
53.Reisinger, A., Ledgard, S. F. & Falconer, S. J. Sensitivity of the carbon footprint of New Zealand milk to greenhouse gas metrics. Ecol. Indic. 81, 74–82 (2017).CAS 
Article 

Google Scholar 
54.Chen, X. et al. Carbon footprint of a typical pomelo production region in China basedon farm survey data. J. Clean. Prod. 277, 124041 (2020).CAS 
Article 

Google Scholar 
55.Pratibha, G. et al. Impact of conservation agriculture practices on energy use efficiency and global warming potential in rainfed pigeonpea–castor systems. Eur. J. Agron. 66, 30–40 (2015).Article 

Google Scholar 
56.Wang, C., Li, X., Gong, T. & Zhang, H. Life cycle assessment of wheat-maize rotation system emphasizing high crop yield and high resource use efficiency in Quzhou County. J. Clean. Prod. 68, 56–63 (2014).CAS 
Article 

Google Scholar 
57.Li, S. et al. Effect of straw management on carbon sequestration and grain production in a maize–wheat cropping system in Anthrosol of the Guanzhong Plain. Soil Till. Res. 157, 43–51 (2016).Article 

Google Scholar 
58.Kong, A. Y. Y., Six, J., Bryant, D. C., Denison, R. F. & van Kessel, C. The relationship between carbon input, aggregation, and soil organic carbon stabilization in sustainable cropping systems. Soil Sci. Soc. Am. J. 69, 1078–1085 (2005).ADS 
CAS 
Article 

Google Scholar 
59.Zhu, Y. C. et al. Large-scale farming operations are win-win for grain production, soil carbon storage and mitigation of greenhouse gases. J. Clean. Prod. 172, 2143–2152 (2018).Article 

Google Scholar 
60.Wang, Z. B. et al. Comparison of greenhouse gas emissions of chemical fertilizer types in China’s crop production. J. Clean. Prod. 141, 1267–1274 (2017).CAS 
Article 

Google Scholar  More

1.Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.de Groot, R. et al. Global estimates of the value of ecosystems and their services in monetary units. Ecosyst. Serv. 1, 50–61 (2012).Article 

Google Scholar 
3.Exton, D. A. et al. Artisanal fish fences pose broad and unexpected threats to the tropical coastal seascape. Nat. Commun. 10, 2100 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
4.Pandolfi, J. M. et al. Global trajectories of the long-term decline of coral reef ecosystems. Science (80-) 301, 955–958 (2003).ADS 
CAS 
Article 

Google Scholar 
5.Graham, N. A. J., Jennings, S., MacNeil, M. A., Mouillot, D. & Wilson, S. K. Predicting climate-driven regime shifts versus rebound potential in coral reefs. Nature 518, 94–97 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Roff, G. & Mumby, P. J. Global disparity in the resilience of coral reefs. Trends Ecol. Evol. 27, 404–413 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
7.Jackson, J.B.C., Donovan, M.K., Cramer, K.L. and Lam, V.V. Status and trends of Caribbean coral reefs. Global Coral Reef Monitoring
Network, IUCN, Gland, Switzerland, pp.1970-2012. (2014).8.Alvarez-Filip, L., Dulvy, N. K., Gill, J. A., Côté, I. M. & Watkinson, A. R. Flattening of Caribbean coral reefs: region-wide declines in architectural complexity. Proc. R. Soc. B Biol. Sci. 276, 3019–3025 (2009).Article 

Google Scholar 
9.Elmqvist, T. et al. Response diversity, ecosystem change, and resilience. Front. Ecol. Environ. 1, 488–494 (2003).Article 

Google Scholar 
10.Sasaki, T., Furukawa, T., Iwasaki, Y., Seto, M. & Mori, A. S. Perspectives for ecosystem management based on ecosystem resilience and ecological thresholds against multiple and stochastic disturbances. Ecol. Ind. 57, 395–408 (2015).Article 

Google Scholar 
11.McCann, K. S. The diversity-stability debate. Nature 405, 228–233 (2000).CAS 
PubMed 
Article 

Google Scholar 
12.Bellwood, D. R., Hughes, T. P., Folke, C. & Nyström, M. Confronting the coral reef crisis. Nature 429, 827–833 (2004).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Mumby, P. J., Hastings, A. & Edwards, H. J. Thresholds and the resilience of Caribbean coral reefs. Nature 450, 98–101 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Hughes, T. P., Graham, N. A. J., Jackson, J. B. C., Mumby, P. J. & Steneck, R. S. Rising to the challenge of sustaining coral reef resilience. Trends Ecol. Evol. 25, 633–642 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
15.Solandt, J. L. & Campbell, A. C. Macroalgal feeding characteristics of the sea urchin Diadema antillarum Philippi at Discovery Bay, Jamaica. Caribb. J. Sci. 37, 227–238 (2001).
Google Scholar 
16.Chiappone, M., Rutten, L. M., Miller, S. L. & Swanson, D. W. Recent trends (1999–2011) in population density and size of the echinoid Diadema antillarum in the Florida Keys. Florida Sci. 76, 23–35 (2013).
Google Scholar 
17.Lessios, H. A. The great Diadema antillarum die-off: 30 years later. Annu. Rev. Mar. Sci. 8, 267–283 (2016).ADS 
CAS 
Article 

Google Scholar 
18.Bruno, J. F., Sweatman, H., Precht, W. F., Selig, E. R. & Schutte, V. G. W. Assessing evidence of phase shifts from coral to macroalgal dominance on coral reefs. Ecol. Soc. Am. 90, 1478–1484 (2009).
Google Scholar 
19Miller, M. W., Szmant, A. M. & Precht, W. F. Lessons learned from experimental key-species restoration. In Coral Reef Restoration Handbook, 219–234 (ed. Precht, W. F.) (Taylor & Francis, 2006).
Google Scholar 
20.Mumby, P. J., Hedley, J. D., Zychaluk, K., Harborne, A. R. & Blackwell, P. G. Revisiting the catastrophic die-off of the urchin Diadema antillarum on Caribbean coral reefs: fresh insights on resilience from a simulation model. Ecol. Modell. 196, 131–148 (2006).Article 

Google Scholar 
21.Myhre, S. & Acevedo-Gutiérrez, A. Recovery of sea urchin Diadema antillarum populations is correlated to increased coral and reduced macroalgal cover. Mar. Ecol. Prog. Ser. 329, 205–210 (2007).ADS 
Article 

Google Scholar 
22.Carpenter, R. C. Predator and population density control of homing behavior in the Caribbean echinoid Diadema antillarum. Mar. Biol. 82, 101–108 (1984).Article 

Google Scholar 
23.Edmunds, P. J. & Carpenter, R. C. Recovery of Diadema antillarum reduces macroalgal cover and increases abundance of juvenile corals on a Caribbean reef. Proc. Natl. Acad. Sci. U. S. A. 98, 5067–5071 (2001).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Wanders, J. B. W. The role of benthic algae in the shallow reef of Curaçao (Netherlands Antilles) III: the significance of grazing. Aquat. Bot. 3, 357–390 (1977).Article 

Google Scholar 
25.Bak, R. P. M., Carpay, M. J. E. & de Ruyter van Steveninck, E. D. Densities of the sea urchin Diadema antillarum before and after mass mortalities on the coral reefs of Curacao. Mar. Ecol. Prog. Ser. 17, 105–108 (1984).ADS 
Article 

Google Scholar 
26.Levitan, D. R. Algal-urchin biomass responses following mass mortality of Diadema antillarum Philippi at Saint John, U.S. Virgin Islands. J. Exp. Mar. Biol. Ecol. 119, 167–178 (1988).Article 

Google Scholar 
27.Chiappone, M., Rutten, L., Swanson, D. & Miller, S. Population status of the urchin Diadema antillarum in the Florida Keys 25 years after the Caribbean mass mortality. In Proceedings of 11th International Coral Reef Symposium 706–710 (2008).28.Bodmer, M. D. V., Rogers, A., Speight, M. R., Lubbock, N. & Exton, D. A. Using an isolated population boom to explore barriers to recovery in the keystone Caribbean coral reef herbivore Diadema antillarum. Coral Reefs 34, 1011–1021 (2015).ADS 
Article 

Google Scholar 
29.Lessios, H. A., Robertson, D. R. & Cubit, J. D. Spread of Diadema mass mortality through the Caribbean. Science (80-) 226, 335–337 (1984).ADS 
CAS 
Article 

Google Scholar 
30.Liddell, W. D. & Ohlhorst, S. L. Changes in benthic community composition following the mass mortality of Diadema at Jamaica. J. Exp. Mar. Biol. Ecol. 95, 271–278 (1986).Article 

Google Scholar 
31.Betchel, J. D., Gayle, P. & Kaufman, L. The return of Diadema antillarum to Discovery Bay: patterns of distribution and abundance. In Proceedings of 10th International Coral Reef Symposium 367–375 (2006).32.Robertson, D. R. Increases in surgeonfish populations after mass mortality of the sea urchin Diadema antillarum in Panamá indicate food limitation. Mar. Biol. 111, 437–444 (1991).Article 

Google Scholar 
33.Lessios, H. A. Diadema antillarum populations in Panama 20 years following mass mortality. Coral Reefs 24, 125–127 (2005).Article 

Google Scholar 
34.Hunte, W. & Younglao, D. Recruitment and population recovery of Diadema antillarum (Echinodermata; Echinoidea) in Barbados. Mar. Ecol. Prog. Ser. 45, 109–119 (1988).ADS 
Article 

Google Scholar 
35.Noriega, N., Pauls, S. M. & del Mónaco, C. Abundancia de Diadema antillarum (Echinodermata: Echinoidea) en las costas de Venezuela. Rev. Biol. Trop. 54, 793–802 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Debrot, A. O. & Nagelkerken, I. Recovery of the long-spined sea urchin Diadema antillarum in Curacao (Netherlands Antilles) linked to lagoonal and wave sheltered shallow rocky habitats. Bull. Mar. Sci. 72, 415–424 (2006).
Google Scholar 
37.Vermeij, M. J. A., Debrot, A. O., van der Hal, N., Bakker, J. & Bak, R. P. M. Increased recruitment rates indicate recovering populations of the sea urchin Diadema antillarum on Curaçao. Bull. Mar. Sci. 86, 719–725 (2010).
Google Scholar 
38.Carpenter, K. E. et al. One-third of reef-building corals face elevated extinction risk from climate change and local impacts. Science (80-) 321, 560–563 (2008).ADS 
CAS 
Article 

Google Scholar 
39.Gardner, T. A., Côté, I. M., Gill, J. A., Grant, A. & Watkinson, A. R. Long-term region-wide declines in Caribbean corals. Science (80-) 301, 958–960 (2003).ADS 
CAS 
Article 

Google Scholar 
40.Pennington, J. T. The ecology of fertilization of echinoid eggs: the consequences of sperm dilution, adult aggregation, and synchronous spawning. Biol. Bull. 169, 417–430 (1985).PubMed 
Article 
PubMed Central 

Google Scholar 
41.Levitan, D. R. Influence of body size and population density on fertilization success and reproductive output in a free-spawning invertebrate. Biol. Bull. 181, 261–268 (1991).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Levitan, D. R., Edmunds, P. J. & Levitan, K. E. What makes a species common? No evidence of density-dependent recruitment or mortality of the sea urchin Diadema antillarum after the 1983–1984 mass mortality. Oecologia 175, 117–128 (2014).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Lacey, E. A., Fourqurean, J. W. & Collado-Vides, L. Increased algal dominance despite presence of Diadema antillarum populations on a Caribbean coral reef. Bull. Mar. Sci. 89, 603–620 (2013).Article 

Google Scholar 
44.Dumas, P., Kulbicki, M., Chifflet, S., Fichez, R. & Ferraris, J. Environmental factors influencing urchin spatial distributions on disturbed coral reefs (New Caledonia, South Pacific). J. Exp. Mar. Biol. Ecol. 344, 88–100 (2007).Article 

Google Scholar 
45.Rogers, A. & Lorenzen, K. Does slow and variable recovery of Diadema antillarum on Caribbean fore-reefs reflect density-dependent habitat selection? Front. Mar. Sci. 3, 63 (2016).Article 

Google Scholar 
46.Alvarado, J. J., Cortés, J., Guzman, H. & Reyes-Bonilla, H. Density, size, and biomass of Diadema mexicanum (Echinoidea) in Eastern Tropical Pacific coral reefs. Aquat. Biol. 24, 151–161 (2016).Article 

Google Scholar 
47.Ogden, J. C. & Carpenter, R. C. Long-spined black sea urchin. Biol. Rep. 82, 1–17 (1987).
Google Scholar 
48.Bodmer, M. D. V. et al. Interacting effects of temperature, habitat and phenotype on predator avoidance behaviour in Diadema antillarum: implications for restorative conservation. Mar. Ecol. Prog. Ser. 566, 105–115 (2017).ADS 
Article 

Google Scholar 
49.Andradi-Brown, D. A., Gress, E., Wright, G., Exton, D. A. & Rogers, A. D. Reef fish community biomass and trophic structure changes across shallow to upper-mesophotic reefs in the mesoamerican barrier reef, Caribbean. PLoS ONE 11, 1–19 (2016).
Google Scholar 
50.Rodríguez-Barreras, R., Pérez, M. E., Mercado-Molina, A. E. & Sabat, A. M. Arrested recovery of Diadema antillarum population: survival or recruitment limitation? Estuar. Coast. Shelf Sci. 163, 167–174 (2015).ADS 
Article 

Google Scholar 
51.Risk, M. J. Fish diversity on a coral reef in the Virgin Islands. Atoll Res. Bull. 153, 1–4 (1972).Article 

Google Scholar 
52.Figueira, W. et al. Accuracy and precision of habitat structural complexity metrics derived from underwater photogrammetry. Remote Sens. 7, 16883–16900 (2015).ADS 
Article 

Google Scholar 
53.Leon, J. X., Roelfsema, C. M., Saunders, M. I. & Phinn, S. R. Measuring coral reef terrain roughness using ‘Structure-from-Motion’ close-range photogrammetry. Geomorphology 242, 21–28 (2015).ADS 
Article 

Google Scholar 
54.Storlazzi, C. D., Dartnell, P., Hatcher, G. A. & Gibbs, A. E. End of the chain? Rugosity and fine-scale bathymetry from existing underwater digital imagery using structure-from-motion (SfM) technology. Coral Reefs 35, 889–894 (2016).ADS 
Article 

Google Scholar 
55.Young, G. C., Dey, S., Rogers, A. D. & Exton, D. A. Cost and time-effective method for multiscale measures of rugosity, fractal dimension, and vector dispersion from coral reef 3D models. PLoS ONE 12, e0175341 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Zawada, D. G. & Brock, J. C. A multiscale analysis of coral reef topographic complexity using lidar-derived bathymetry. J. Coast. Res. 2009, 6–16 (2009).Article 

Google Scholar 
57.Randall, J. E., Schroeder, R. E. & Starck, W. A. Notes on the biology of the echinoid Diadema antillarum. Caribb. J. Sci. 4, 421–433 (1964).
Google Scholar 
58.Hunt, C. L. et al. Aggregating behaviour in invasive Caribbean lionfish is driven by habitat complexity. Sci. Rep. 9, 783 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
59.Millott, N. & Yoshida, M. The shadow reaction of Diadema antillarum Philippi I: the spine response and its relation to the stimulus. J. Exp. Biol. 37, 363–375 (1960).Article 

Google Scholar 
60.Millott, N. & Yoshida, M. The shadow reaction of Diadema antillarum Philippi II: inhibition by light. J. Exp. Biol. 37, 376–389 (1960).Article 

Google Scholar 
61.Raible, F. et al. Opsins and clusters of sensory G-protein-coupled receptors in the sea urchin genome. Dev. Biol. 300, 461–475 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Ullrich-Lüter, E. M., D’Aniello, S. & Arnone, M. I. C-opsin expressing photoreceptors in echinoderms. Integr. Comp. Biol. 53, 27–38 (2013).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
63.Yoshida, M. On the light response of the chromatophore of the sea-urchin, Diadema setosum (Leske). J. Exp. Biol. 33, 119–123 (1956).Article 

Google Scholar 
64.JPL MUR MEaSUREs. GHRSST Level 4 MUR global foundation sea surface temperature analysis. Version 4.1 PO.DAAC, CA, USA. Dataset accessed 23 Jan 2021 at https://doi.org/10.5067/GHGMR-4FJ04 (2015).65.Pickering, H., Whitmarsh, D. & Jensen, A. Artificial reefs as a tool to aid rehabilitation of coastal ecosystems: investigating the potential. Mar. Pollut. Bull. 37, 505–514 (1999).Article 

Google Scholar 
66.Fitzhardinge, R. C. & Bailey-Brock, J. H. Colonization of artificial reef materials by corals and other sessile organisms. Bull. Mar. Sci. 44, 567–579 (1989).
Google Scholar 
67.R Core Team. R: A Language and Environment for Statistical Computing. Vienna. https://www.r-project.org/. (2016).68.RStudio Team. RStudio: Integrated Development for R (2015).69.Dinno, A. conover.test: Conover-Iman test of multiple comparisons using rank sums. R Package Version 1.1.5. (2017).70.Scheibling, R. E. & Robinson, M. C. Settlement behaviour and early post-settlement predation of the sea urchin Strongylocentrotus droebachiensis. J. Exp. Mar. Biol. Ecol. 365, 59–66 (2008).Article 

Google Scholar 
71.Kintzing, M. D. & Butler, M. J. The influence of shelter, conspecifics, and threat of predation on the behavior of the long-spined sea urchin (Diadema antillarum). J. Shellfish Res. 33, 781–785 (2014).Article 

Google Scholar 
72.Clemente, S., Hernández, J. C., Toledo, K. & Brito, A. Predation upon Diadema aff. antillarum in barren grounds in the Canary Islands. Sci. Mar. 71, 745–754 (2007).Article 

Google Scholar 
73.Jennings, L. B. & Hunt, H. L. Settlement, recruitment and potential predators and competitors of juvenile echinoderms in the rocky subtidal zone. Mar. Biol. 157, 307–316 (2010).Article 

Google Scholar 
74.Rodríguez-Barreras, R. Demographic implications of predatory wrasses on low-density Diadema antillarum populations. Mar. Biol. Res. 14, 383–391 (2018).Article 

Google Scholar 
75.Delgado, G. A. & Sharp, W. C. Does artificial shelter have a place in Diadema antillarum restoration in the Florida Keys? Tests of habitat manipulation and sheltering behavior. Glob. Ecol. Conserv. 26, e01502 (2021).Article 

Google Scholar 
76.Sammarco, P. W. & Williams, A. H. Damselfish territoriality: influence on Diadema antillarum distribution and implications for coral community structure. Mar. Ecol. Prog. Ser. 8, 53–59 (1982).ADS 
Article 

Google Scholar 
77.Nedimyer, K. & Moe, M. A. 2003. Techniques development for the reestablishment of the long-spined sea urchin, Diadema antillarum, on two small patch reefs in the upper Florida Keys. 2002–2003 Sanctuary Science Report: An Ecosystem Report Card After Five Years of Marine Zoning.78.Idjadi, J., Haring, R. & Precht, W. Recovery of the sea urchin Diadema antillarum promotes scleractinian coral growth and survivorship on shallow Jamaican reefs. Mar. Ecol. Prog. Ser. 403, 91–100 (2010).ADS 
Article 

Google Scholar 
79.Macia, S., Robinson, M. P. & Nalevanko, A. Experimental dispersal of recovering Diadema antillarum increases grazing intensity and reduces macroalgal abundance on a coral reef. Mar. Ecol. Prog. Ser. 348, 173–182 (2007).ADS 
Article 

Google Scholar  More

1.EU. Communication from the Commission. Ports: an engine for growth (2013).2.EU. Directive (EU) 2019/883 of the European Parliament and of the Council of 17 April 2019. 2019(March), 116–142 (2019).3.Chao, M. & Rodríguez, M. New trends in port managing: towards the e-port. J. Marit. Res. 3(2), 35–42 (2006).
Google Scholar 
4.Paiano, A., Crovella, T. & Lagioia, G. Managing sustainable practices in cruise tourism: the assessment of carbon footprint and waste of water and beverage packaging. Tour. Manag. 77(October 2019), 104016. https://doi.org/10.1016/j.tourman.2019.104016 (2020).Article 

Google Scholar 
5.Kovačić, M. & Silveira, L. Nautical tourism in Croatia and in Portugal in the late 2010’s: issues and perspectives. Pomorstvo 32(2), 281–289. https://doi.org/10.31217/p.32.2.13 (2018).Article 

Google Scholar 
6.Pérez Labajos, C. & Blanco Rojo, B. Leisure ports planning. J. Marit. Res. 3(2), 67–82 (2006).
Google Scholar 
7.BOE. Real Decreto Legislativo 2/2011, de 5 de septiembre, por el que se aprueba el Texto Refundido de la Ley de Puertos del Estado y de la Marina Mercante. Span. Off. Bull. 255, 11. https://www.boe.es/buscar/pdf/2011/BOE-A-2011-16467-consolidado.pdf (2011).8.Gómez, A. G., Valdor, P. F., Ondiviela, B., Díaz, J. L. & Juanes, J. A. Mapping the environmental risk assessment of marinas on water quality: the Atlas of the Spanish coast. Mar. Pollut. Bull. 139(January), 355–365. https://doi.org/10.1016/j.marpolbul.2019.01.008 (2019).CAS 
Article 
PubMed 

Google Scholar 
9.Sofiev, M. et al. Cleaner fuels for ships provide public health benefits with climate tradeoffs. Nat. Commun. 9(1), 1–12. https://doi.org/10.1038/s41467-017-02774-9 (2018).CAS 
Article 

Google Scholar 
10.Chen, C., Saikawa, E., Comer, B., Mao, X. & Rutherford, D. Ship emission impacts on air quality and human health in the Pearl River Delta (PRD) Region, China, in 2015, with projections to 2030. GeoHealth 3(9), 284–306. https://doi.org/10.1029/2019GH000183 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
11.Mateos, M. R. Los puertos deportivos como infraestructuras de soporte de las actividades náuticas de recreo en Andalucía. Mar. Infrastruct. Supports Naut. Recreat. Act. Andal. 54, 335–360 (2010).
Google Scholar 
12.Nursey-Bray, M. et al. Vulnerabilities and adaptation of ports to climate change. J. Environ. Plan. Manag. 56(7), 1021–1045. https://doi.org/10.1080/09640568.2012.716363 (2013).Article 

Google Scholar 
13.Antequera, P. D., Jaime, D. & Abel, L. Tourism, transport and climate change: the carbon footprint of international air traffic on Islands. Sustainability 13(4), 1795. https://doi.org/10.3390/su13041795 (2021).CAS 
Article 

Google Scholar 
14.Hadjikakou, M., Chenoweth, J. & Miller, G. Estimating the direct and indirect water use of tourism in the eastern Mediterranean. J. Environ. Manag. 114, 548–556. https://doi.org/10.1016/j.jenvman.2012.11.002 (2013).Article 

Google Scholar 
15.Annis, G. M. et al. Designing coastal conservation to deliver ecosystem and human well-being benefits. PLoS ONE 12(2), 1–21. https://doi.org/10.1371/journal.pone.0172458 (2017).CAS 
Article 

Google Scholar 
16.Kizielewicz, J. & Lukovic, T. The phenomenon of the marina development to support the European model of economic development. TransNav Int. J. Mar. Navig. Saf. Sea Transp. 7(3), 461–466. https://doi.org/10.12716/1001.07.03.19 (2013).Article 

Google Scholar 
17.Ridolfi, E., Pujol, D. S., Ippolito, A., Saradakou, E. & Salvati, L. An urban political ecology approach to local development in fast-growing, tourism-specialized coastal cities. Tourismos 12(1), 171–204 (2017).
Google Scholar 
18.Sevinç, F. & Güzel, T. Sustainable Yacht tourism practices. Manag. Mark. XV(1), 61–76 (2017).
Google Scholar 
19.Lam-González, Y. E., León, C. J. & González-Hernández, M. M. Determinants of the European Yachtsmen´s satisfaction with the ports of call of the Canary Islands (Spain). Études Caribéennes https://doi.org/10.4000/etudescaribeennes.10584 (2017).Article 

Google Scholar 
20.Novales, A., Martínez Martín, M. I., Castro Núñez, R. B., Cazcarro Castellano, I. & Santero Sánchez, R. El impacto económico de la Náutica de Recreo 99 (Universidad Complutense de Madrid, 2018).
Google Scholar 
21.Cámara de Comercio e Industria de Marsella. Náutica de recreo en el Mediterráneo 114 (Etinet, 2011).
Google Scholar 
22.Mensa, J. A., Vasallo, P. & Fabiano, M. JMarinas: a simple tool for the environmentally sound management of small marinas. J. Environ. Manag. 92, 67–77 (2011).CAS 
Article 

Google Scholar 
23.Benton, T. G. From castaways to throwaways: marine litter in the Pitcairn Islands. Biol. J. Lin. Soc. 56, 415–422 (1995).Article 

Google Scholar 
24.Chainho, P. et al. Non-indigenous species in Portuguese coastal areas, coastal lagoons, estuaries and islands. Estuar. Coast. Shelf Sci. 167, 199–211. https://doi.org/10.1016/j.ecss.2015.06.019 (2015).ADS 
Article 

Google Scholar 
25.Styhre, L., Winnes, H., Black, J., Lee, J. & Le-Griffin, H. Greenhouse gas emissions from ships in ports: case studies in four continents. Transp. Res. Part D Transp. Environ. 54, 212–224. https://doi.org/10.1016/j.trd.2017.04.033 (2017).Article 

Google Scholar 
26.Yang, Y. C. Operating strategies of CO2 reduction for a container terminal based on carbon footprint perspective. J. Clean. Prod. 141, 472–480. https://doi.org/10.1016/j.jclepro.2016.09.132 (2017).CAS 
Article 

Google Scholar 
27.Giunta, M., Bressi, S. & D’Angelo, G. Life cycle cost assessment of bitumen stabilised ballast: a novel maintenance strategy for railway track-bed. Constr. Build. Mater. 172, 751–759. https://doi.org/10.1016/j.conbuildmat.2018.04.020 (2018).Article 

Google Scholar 
28.Hickmann, T. Voluntary global business initiatives and the international climate negotiations: a case study of the Greenhouse Gas Protocol. J. Clean. Prod. 169, 94–104. https://doi.org/10.1016/j.jclepro.2017.06.183 (2017).Article 

Google Scholar 
29.Garcia, R. & Freire, F. Carbon footprint of particleboard: a comparison between ISO/TS 14067, GHG protocol, PAS 2050 and climate declaration. J. Clean. Prod. 66, 199–209. https://doi.org/10.1016/j.jclepro.2013.11.073 (2014).CAS 
Article 

Google Scholar 
30.Ingrid, M.-M., Pablo, C.-M., Jose, V.-C. & Miguel Ángel, P.-G. Economic impact of a port on the hinterland: application to Santander’s port. Int. J. Shipp. Transp. Logist. 4, 235–249 (2012).Article 

Google Scholar 
31.Abdul-azeez, I. A. Development of carbon dioxide emission assessment tool towards promoting sustainability in UTM Malaysia. Open J. Energy Effic. https://doi.org/10.4236/ojee.2018.72004 (2018).Article 

Google Scholar 
32.Jeswani, H. K. & Azapagic, A. Water footprint: methodologies and a case study for assessing the impacts of water use. J. Clean. Prod. 19(12), 1288–1299. https://doi.org/10.1016/j.jclepro.2011.04.003 (2011).Article 

Google Scholar 
33.Zhuo, La., Mekonnen, M. M. & Hoekstra, A. Y. Consumptive water footprint and virtual water trade scenarios for China: with a focus on crop production, consumption and trade. Environ. Int. 94, 211–223 (2016).Article 

Google Scholar 
34.Arto, I., Andreoni, V. & Rueda-Cantuche, J. M. Global use of water resources: a multiregional analysis of water use, water footprint and water trade balance. Water Resour. Econ. 15, 1–14. https://doi.org/10.1016/j.wre.2016.04.002 (2016).Article 

Google Scholar 
35.Zhi, Y., Yang, Z., Yin, X., Hamilton, P. B. & Zhang, L. Using gray water footprint to verify economic sectors’ consumption of assimilative capacity in a river basin: model and a case study in the Haihe River Basin, China. J. Clean. Prod. 92, 267–273. https://doi.org/10.1016/j.jclepro.2014.12.058 (2015).Article 

Google Scholar 
36.Norén, A., Karlfeldt Fedje, K., Strömvall, A. M., Rauch, S. & Andersson-Sköld, Y. Integrated assessment of management strategies for metal-contaminated dredged sediments: what are the best approaches for ports, marinas and waterways?. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2019.135510 (2020).Article 
PubMed 

Google Scholar 
37.Kenworthy, J. M., Rolland, G., Samadi, S. & Lejeusne, C. Local variation within marinas: effects of pollutants and implications for invasive species. Mar. Pollut. Bull. 133(March), 96–106. https://doi.org/10.1016/j.marpolbul.2018.05.001 (2018).CAS 
Article 
PubMed 

Google Scholar 
38.Veettil, A. V. & Mishra, A. K. Water security assessment using blue and green water footprint concepts. J. Hydrol. 542, 589–602. https://doi.org/10.1016/j.jhydrol.2016.09.032 (2016).ADS 
Article 

Google Scholar 
39.Gu, Y., Li, Y., Wang, H. & Li, F. Gray water footprint: taking quality, quantity, and time effect into consideration. Water Resour. Manag. 28(11), 3871–3874. https://doi.org/10.1007/s11269-014-0695-y (2014).Article 

Google Scholar 
40.Duvat, V. K. E. et al. Trajectories of exposure and vulnerability of small islands to climate change. Rev. Clim. Change https://doi.org/10.1002/wcc.478 (2017).Article 

Google Scholar 
41.Millán, M. M. Extreme hydrometeorological events and climate change predictions in Europe. J. Hydrol. 518(PB), 206–224. https://doi.org/10.1016/j.jhydrol.2013.12.041 (2014).ADS 
CAS 
Article 

Google Scholar 
42.Smith, J. B. et al. Assessing dangerous climate change through an update of the Intergovernmental Panel on Climate Change (IPCC) “‘reasons for concern’”. Proc. Natl. Acad. Sci. U.S.A. 106(11), 4133–4137. https://doi.org/10.1073/pnas.0812355106 (2009).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
43.IPCC. Climate change 2014: impacts, adaptation and vulnerability (2014).44.Ciscar, J. C. et al. Physical and economic consequences of climate change in Europe. Proc. Natl. Acad. Sci. U.S.A. 108(7), 2678–2683. https://doi.org/10.1073/pnas.1011612108 (2011).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.Melo, N., Santos, B. F. & Leandro, J. A prototype tool for dynamic pluvial-flood emergency planning. Urban Water J. 12(1), 79–88. https://doi.org/10.1080/1573062X.2014.975725 (2015).Article 

Google Scholar 
46.Lazrus, H. Sea change: Island communities and climate change. Annu. Rev. Anthropol. 41, 285–301. https://doi.org/10.1146/annurev-anthro-092611-145730 (2012).Article 

Google Scholar 
47.Reid, S., Johnston, N. & Patiar, A. Coastal resorts setting the pace: an evaluation of sustainable hotel practices. J. Hosp. Tour. Manag. 33, 11–22. https://doi.org/10.1016/j.jhtm.2017.07.001 (2017).Article 

Google Scholar 
48.Vargas-Amelin, E. & Pindado, P. The challenge of climate change in Spain: water resources, agriculture and land. J. Hydrol. 518(PB), 243–249. https://doi.org/10.1016/j.jhydrol.2013.11.035 (2014).ADS 
Article 

Google Scholar 
49.Fagerberg, J., Laestadius, S. & Martin, B. R. The triple challenge for Europe: the economy, climate change, and governance. Innov. Econ. Dev. Policy Sel. Essays 59(3), 384–410. https://doi.org/10.1080/05775132.2016.1171668 (2018).Article 

Google Scholar 
50.UNCTAD. Maritime transport in small island developing states. Rev. Marit. Transp. https://doi.org/10.1017/CBO9781107415324.004 (2014).Article 

Google Scholar 
51.Hinkey, L. M., Zaidi, B. R., Volson, B. & Rodriguez, N. J. Identifying sources and distributions of sediment contaminants at two US Virgin Islands marinas. Mar. Pollut. Bull. 50, 1244–1250. https://doi.org/10.1016/j.marpolbul.2005.04.035 (2005).CAS 
Article 
PubMed 

Google Scholar 
52.Marín, J. C. et al. Properties of particulate pollution in the port city of Valparaiso, Chile. Atmos. Environ. 171, 301–316. https://doi.org/10.1016/j.atmosenv.2017.09.044 (2017).ADS 
CAS 
Article 

Google Scholar 
53.Tóvar-Sánchez, A., Sánchez-Quiles, D. & Rodríguez-Romero, A. Massive coastal tourism influx to the Mediterranean Sea: the environmental risk of sunscreens. Sci. Total Environ. 656, 316–321 (2019).ADS 
Article 

Google Scholar 
54.Uche-Soria, M. & Rodríguez-Monroy, C. Solutions to marine pollution in Canary Islands’ ports: alternatives and optimization of energy management. Resources https://doi.org/10.3390/resources8020059 (2019).Article 

Google Scholar 
55.Bosch, N. E., Gonçalves, J. M. S., Tuya, F. & Erzini, K. Marinas as habitats for nearshore fish assemblages: comparative analysis of underwater visual census, baited cameras and fish traps. Sci. Mar. 81(2), 159. https://doi.org/10.3989/scimar.04540.20a (2017).Article 

Google Scholar 
56.Di Franco, A. et al. Do small marinas drive habitat specific impacts? A case study from Mediterranean Sea. Mar. Pollut. Bull. 62, 926–933. https://doi.org/10.1016/j.marpolbul.2011.02.053 (2011).CAS 
Article 
PubMed 

Google Scholar 
57.Pasetto, M. & Partl, M. N. in Lecture Notes in Civil Engineering Proceedings of the 5th International Symposium on Asphalt Pavements & Environment (APE). http://www.springer.com/series/15087 (2020)58.Praticò, F. G., Giunta, M., Mistretta, M. & Gulotta, T. M. Energy and environmental life cycle assessment of sustainable pavement materials and technologies for urban roads. Sustainability (Switzerland) https://doi.org/10.3390/su12020704 (2020).Article 

Google Scholar 
59.Hertwich, E. G. & Wood, R. The growing importance of scope 3 greenhouse gas emissions from industry. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/aae19a (2018).Article 

Google Scholar 
60.Di Vaio, A., Varriale, L. & Alvino, F. Key performance indicators for developing environmentally sustainable and energy efficient ports: evidence from Italy. Energy Policy 122(July), 229–240. https://doi.org/10.1016/j.enpol.2018.07.046 (2018).Article 

Google Scholar 
61.Corrigan, S., Kay, A., Ryan, M., Brazil, B. & Ward, M. E. Human factors & safety culture: challenges & opportunities for the port environment. Saf. Sci. 125, 14. https://doi.org/10.1016/j.ssci.2018.02.030 (2020).Article 

Google Scholar 
62.Mali, M., Dell’Anna, M. M., Mastrorilli, P., Damiani, L. & Piccinni, A. F. Assessment and source identification of pollution risk for touristic ports: heavy metals and polycyclic aromatic hydrocarbons in sediments of 4 marinas of the Apulia region (Italy). Mar. Pollut. Bull. 114(2), 768–777. https://doi.org/10.1016/j.marpolbul.2016.10.063 (2017).CAS 
Article 
PubMed 

Google Scholar 
63.Cutroneo, L., Reboa, A., Besio, G., Borgogno, F., Canesi, L., Canuto, S., Dara, M., Enrile, F., Forioso, I., Greco, G., Lenoble, V., Malatesta, A., Mounier, S., Petrillo, M., Rovetta, R., Stocchino, A., Tesan, J., Vagge, G., & Capello, M. Correction to: Microplastics in seawater: sampling strategies, laboratory methodologies, and identification techniques applied to port environment (Environmental Science and Pollution Research, (2020), 27, 9, (8938–8952), https://doi.org/10.1007/s11356-020-07783-8). Environ. Sci. Pollut. Res. 27(16), 20571. https://doi.org/https://doi.org/10.1007/s11356-020-08704-5 (2020)64.Kotowska, I. & Kubowicz, D. The role of ports in reduction of road transport pollution in port cities. Transp. Res. Procedia 39, 212–220. https://doi.org/10.1016/j.trpro.2019.06.023 (2019).Article 

Google Scholar 
65.Coronado Mondragon, A. E., Lalwani, C. S., Coronado Mondragon, E. S., Coronado Mondragon, C. E. & Pawar, K. S. Intelligent transport systems in multimodal logistics: a case of role and contribution through wireless vehicular networks in a sea port location. Int. J. Prod. Econ. 137, 165–175. https://doi.org/10.1016/j.ijpe.2011.11.006 (2012).Article 

Google Scholar 
66.Caballini, C., Rebecchi, I. & Sacone, S. Combining multiple trips in a port environment for empty movements minimization. Transp. Res. Procedia 10, 694–703. https://doi.org/10.1016/j.trpro.2015.09.023 (2015).Article 

Google Scholar 
67.Sifakis, N. & Tsoutsos, T. Planning zero-emissions ports through the nearly zero energy port concept. J. Clean. Prod. 286, 20. https://doi.org/10.1016/j.jclepro.2020.125448 (2021).Article 

Google Scholar 
68.Karimpour, R., Ballini, F. & Ölcer, A. I. Circular economy approach to facilitate the transition of the port cities into self-sustainable energy ports: a case study in Copenhagen-Malmö Port (CMP). WMU J. Marit. Aff. 18(2), 225–247. https://doi.org/10.1007/s13437-019-00170-2 (2019).Article 

Google Scholar 
69.Babrowski, S., Heinrichs, H., Jochem, P. & Fichtner, W. Load shift potential of electric vehicles in Europe. J. Power Sources 255, 283–293. https://doi.org/10.1016/j.jpowsour.2014.01.019 (2014).ADS 
CAS 
Article 

Google Scholar 
70.Azarkamand, S., Ferré, G. & Darbra, R. M. Calculating the carbon footprint in ports by using a standardized tool. Sci. Total Environ. 734, 139407. https://doi.org/10.1016/j.scitotenv.2020.139407 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
71.Carballo-Penela, A., Mateo-Mantecón, I., Doménech, J. L. & Coto-Millán, P. From the motorways of the sea to the green corridors’ carbon footprint: the case of a port in Spain. J. Environ. Plan. Manag. 55(6), 765–782. https://doi.org/10.1080/09640568.2011.627422 (2012).Article 

Google Scholar 
72.Paska, J. & Surma, T. Electricity generation from renewable energy sources in Poland. Renew. Energy 71, 286–294 (2014).Article 

Google Scholar 
73.Trujillo-Baute, E., del Río, P. & Mir-Artigues, P. Analysing the impact of renewable energy regulation on retail electricity prices. Energy Policy 114, 153–164 (2018).Article 

Google Scholar 
74.Ruiz-Romero, S., Colmenar-Santos, A., Gil-Ortego, R. & Molina-Bonilla, A. Distributed generation: the definitive boost for renewable energy in Spain. Renew. Energy 53, 354–364 (2013).Article 

Google Scholar 
75.Burgos-Payán, M., Roldán-Fernández, J. M., Trigo-García, Á. L., Bermúdez-Ríos, J. M. & Riquelme-Santos, J. M. Costs and benefits of the renewable production of electricity in Spain. Energy Policy 56, 259–270 (2013).Article 

Google Scholar 
76.Taliotis, C. et al. Renewable energy technology integration for the island of Cyprus: a cost-optimization approach. Energy 137(2017), 31–41. https://doi.org/10.1016/j.energy.2017.07.015 (2017).Article 

Google Scholar 
77.Deyà-Tortella, B., Garcia, C., Nilsson, W. & Tirado, D. The effect of the water tariff structures on the water consumption in Mallorcan hotels. Water Resour. Res. 52(8), 6386–6403. https://doi.org/10.1002/2016WR018621 (2016).ADS 
Article 

Google Scholar 
78.Liu, J. et al. A global and spatially explicit assessment of climate change impacts on crop production and consumptive water use. PLoS ONE https://doi.org/10.1371/journal.pone.0057750 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
79.Hof, A. & Schmitt, T. Urban and tourist land use patterns and water consumption: evidence from Mallorca, Balearic Islands. Land Use Policy 28, 792–804 (2011).Article 

Google Scholar 
80.Urban water consumption in the Balearic islands. The water portal: http://www.caib.es/sites/aigua/es/consumo_agua/81.García, C., Mestre-Runge, C., Morán-Tejeda, E., Lorenzo-Lacruz, J., Tirado, D. (2020). Impact of Cruise Activity on Freshwater Use in the Port of Palma (Mallorca, Spain): Water 12, 1088.82.Yves Tramblay, Aristeidis Koutroulis, Luis Samaniego, Sergio Vicente-Serrano, Florence Volaire, et al. Challenges for drought assessment in the Mediterranean region under future climate scenarios. EarthScience Reviews, Elsevier, 2020, 210, pp.103348. https://doi.org/10.1016/j.earscirev.2020.103348f More

1.Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).CAS 
Article 

Google Scholar 
2.Lindroth, A., Grelle, A. & Morén, A.-S. Long-term measurements of boreal forest carbon balance reveal large temperature sensitivity. Glob. Change Biol. 4, 443–450 (1998).Article 

Google Scholar 
3.Kasischke, E. S., Christensen, N. Jr & Stocks, B. J. Fire, global warming, and the carbon balance of boreal forests. Ecol. Appl. 5, 437–451 (1995).Article 

Google Scholar 
4.Graven, H. D. et al. Enhanced seasonal exchange of CO2 by northern ecosystems since 1960. Science 341, 1085–1089 (2013).CAS 
Article 

Google Scholar 
5.Welp, L. R. et al. Increasing summer net CO2 uptake in high northern ecosystems inferred from atmospheric inversions and comparisons to remote-sensing NDVI. Atmos. Chem. Phys. 16, 9047–9066 (2016).CAS 
Article 

Google Scholar 
6.Myneni, R. B., Keeling, C., Tucker, C. J., Asrar, G. & Nemani, R. R. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).CAS 
Article 

Google Scholar 
7.Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).CAS 
Article 

Google Scholar 
8.Girardin, M. P. et al. No growth stimulation of Canada’s boreal forest under half-century of combined warming and CO2 fertilization. Proc. Natl Acad. Sci. USA 113, E8406–E8414 (2016).CAS 
Article 

Google Scholar 
9.Giguère-Croteau, C. et al. North America’s oldest boreal trees are more efficient water users due to increased [CO2], but do not grow faster. Proc. Natl Acad. Sci. USA 116, 2749–2754 (2019).Article 
CAS 

Google Scholar 
10.Jiang, M. et al. The fate of carbon in a mature forest under carbon dioxide enrichment. Nature 580, 227–231 (2020).CAS 
Article 

Google Scholar 
11.Kasischke, E. S. & Turetsky, M. R. Recent changes in the fire regime across the North American boreal region—spatial and temporal patterns of burning across Canada and Alaska. Geophys. Res. Lett. 33, L09703 (2006).
Google Scholar 
12.White, J. C., Wulder, M. A., Hermosilla, T., Coops, N. C. & Hobart, G. W. A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sens. Environ. 194, 303–321 (2017).Article 

Google Scholar 
13.Kurz, W. A. et al. Mountain pine beetle and forest carbon feedback to climate change. Nature 452, 987–990 (2008).CAS 
Article 

Google Scholar 
14.Ma, Z. et al. Regional drought-induced reduction in the biomass carbon sink of Canada’s boreal forests. Proc. Natl Acad. Sci. USA 109, 2423–2427 (2012).CAS 
Article 

Google Scholar 
15.Wang, J. A. et al. Extensive land cover change across Arctic–boreal northwestern North America from disturbance and climate forcing. Glob. Change Biol. 26, 807–822 (2020).Article 

Google Scholar 
16.Johnstone, J. F., Hollingsworth, T. N., Chapin, F. S. & Mack, M. C. Changes in fire regime break the legacy lock on successional trajectories in Alaskan boreal forest. Glob. Change Biol. 16, 1281–1295 (2010).Article 

Google Scholar 
17.Wang, J. A. & Friedl, M. A. The role of land cover change in Arctic–boreal greening and browning trends. Environ. Res. Lett. 14, 125007 (2019).Article 

Google Scholar 
18.Beck, P. S. & Goetz, S. J. Satellite observations of high northern latitude vegetation productivity changes between 1982 and 2008: ecological variability and regional differences. Environ. Res. Lett. 6, 045501 (2011).Article 

Google Scholar 
19.de Groot, W. J., Flannigan, M. D. & Cantin, A. S. Climate change impacts on future boreal fire regimes. For. Ecol. Manage. 294, 35–44 (2013).Article 

Google Scholar 
20.Bond-Lamberty, B., Peckham, S. D., Ahl, D. E. & Gower, S. T. Fire as the dominant driver of central Canadian boreal forest carbon balance. Nature 450, 89–92 (2007).CAS 
Article 

Google Scholar 
21.Bradshaw, C. J. & Warkentin, I. G. Global estimates of boreal forest carbon stocks and flux. Glob. Planet. Change 128, 24–30 (2015).Article 

Google Scholar 
22.Goulden, M. L. et al. Patterns of NPP, GPP, respiration, and NEP during boreal forest succession. Glob. Change Biol. 17, 855–871 (2011).Article 

Google Scholar 
23.Pugh, T. A. M., Arneth, A., Kautz, M., Poulter, B. & Smith, B. Important role of forest disturbances in the global biomass turnover and carbon sinks. Nat. Geosci. 12, 730–735 (2019).CAS 
Article 

Google Scholar 
24.Zimov, S. et al. Contribution of disturbance to increasing seasonal amplitude of atmospheric CO2. Science 284, 1973–1976 (1999).CAS 
Article 

Google Scholar 
25.Sedano, F. & Randerson, J. T. Multi-scale influence of vapor pressure deficit on fire ignition and spread in boreal forest ecosystems. Biogeosciences 11, 3739–3755 (2014).Article 

Google Scholar 
26.Walker, X. J. et al. Increasing wildfires threaten historic carbon sink of boreal forest soils. Nature 572, 520–523 (2019).CAS 
Article 

Google Scholar 
27.Matasci, G. et al. Three decades of forest structural dynamics over Canada’s forested ecosystems using Landsat time-series and lidar plots. Remote Sens. Environ. 216, 697–714 (2018).Article 

Google Scholar 
28.Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).CAS 
Article 

Google Scholar 
29.Wulder, M. A., Hermosilla, T., White, J. C. & Coops, N. C. Biomass status and dynamics over Canada’s forests: disentangling disturbed area from associated aboveground biomass consequences. Environ. Res. Lett. 15, 094093 (2020).CAS 
Article 

Google Scholar 
30.Margolis, H. A. et al. Combining satellite lidar, airborne lidar, and ground plots to estimate the amount and distribution of aboveground biomass in the boreal forest of North America. Can. J. For. Res. 45, 838–855 (2015).Article 

Google Scholar 
31.Neigh, C. S. et al. Taking stock of circumboreal forest carbon with ground measurements, airborne and spaceborne LiDAR. Remote Sens. Environ. 137, 274–287 (2013).Article 

Google Scholar 
32.Fisher, J. B. et al. Missing pieces to modeling the Arctic–boreal puzzle. Environ. Res. Lett. 13, 020202 (2018).Article 

Google Scholar 
33.Turetsky, M. R. et al. Carbon release through abrupt permafrost thaw. Nat. Geosci. 13, 138–143 (2020).CAS 
Article 

Google Scholar 
34.Kurz, W. A. et al. Carbon in Canada’s boreal forest—a synthesis. Environ. Rev. 21, 260–292 (2013).CAS 
Article 

Google Scholar 
35.Price, D., Peng, C., Apps, M. & Halliwell, D. Simulating effects of climate change on boreal ecosystem carbon pools in central Canada. J. Biogeogr. 26, 1237–1248 (1999).Article 

Google Scholar 
36.Stocks, B. et al. Large forest fires in Canada, 1959–1997. J. Geophys. Res. Atmos. 107, FFR-5 (2002).
Google Scholar 
37.Kasischke, E. S., Williams, D. & Barry, D. Analysis of the patterns of large fires in the boreal forest region of Alaska. Int. J. Wildland Fire 11, 131–144 (2002).Article 

Google Scholar 
38.Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).Article 

Google Scholar 
39.Fredeen, A. L., Waughtal, J. D. & Pypker, T. G. When do replanted sub-boreal clearcuts become net sinks for CO2? For. Ecol. Manage. 239, 210–216 (2007).Article 

Google Scholar 
40.Schimel, D., Stephens, B. B. & Fisher, J. B. Effect of increasing CO2 on the terrestrial carbon cycle. Proc. Natl Acad. Sci. USA 112, 201407302 (2014).
Google Scholar 
41.Wenzel, S., Cox, P. M., Eyring, V. & Friedlingstein, P. Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric CO2. Nature 538, 499–501 (2016).Article 
CAS 

Google Scholar 
42.Natali, S. M. et al. Large loss of CO2 in winter observed across the northern permafrost region. Nat. Clim. Change 9, 852–857 (2019).CAS 
Article 

Google Scholar 
43.Gedalof, Z. & Berg, A. A. Tree ring evidence for limited direct CO2 fertilization of forests over the 20th century: limited CO2 fertilization of forests. Glob. Biogeochem. Cycles 24, GB3027 (2010).Article 
CAS 

Google Scholar 
44.Kolby Smith, W. et al. Large divergence of satellite and Earth system model estimates of global terrestrial CO2 fertilization. Nat. Clim. Change 6, 306–310 (2016).CAS 
Article 

Google Scholar 
45.Duncanson, L. et al. Biomass estimation from simulated GEDI, ICESat-2 and NISAR across environmental gradients in Sonoma County, California. Remote Sens. Environ. 242, 111779 (2020).Article 

Google Scholar 
46.Helbig, M., Pappas, C. & Sonnentag, O. Permafrost thaw and wildfire: equally important drivers of boreal tree cover changes in the Taiga Plains, Canada. Geophys. Res. Lett. 43, 1598–1606 (2016).Article 

Google Scholar 
47.Carpino, O. A., Berg, A. A., Quinton, W. L. & Adams, J. R. Climate change and permafrost thaw-induced boreal forest loss in northwestern Canada. Environ. Res. Lett. 13, 084018 (2018).Article 

Google Scholar 
48.Margolis, H., Sun, G., Montesano, P. M. & Nelson, R. F. NACP LiDAR-Based Biomass Estimates, Boreal Forest Biome, North America, 2005–2006 (ORNL DAAC, 2015); https://doi.org/10.3334/ORNLDAAC/127349.Spawn, S. A. & Gibbs, H. K. Global Aboveground and Belowground Biomass Carbon Density Maps for the Year 2010 (ORNL DAAC, 2020); https://doi.org/10.3334/ORNLDAAC/176350.Santoro, M. & Cartus, O. ESA Biomass Climate Change Initiative (Biomass_cci): Global Datasets of Forest Above-ground Biomass for the Year 2017 Version 1 (Centre for Environmental Data Analysis, 2019); https://doi.org/10.5285/bedc59f37c9545c981a839eb552e408451.Omernik, J. M. & Griffith, G. E. Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework. Environ. Manage. 54, 1249–1266 (2014).Article 

Google Scholar 
52.Wulder, M. A. et al. Monitoring Canada’s forests. Part 1: completion of the EOSD land cover project. Can. J. Remote Sens. 34, 549–562 (2008).Article 

Google Scholar 
53.Jin, S., Yang, L., Zhu, Z. & Homer, C. A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011. Remote Sens. Environ. 195, 44–55 (2017).Article 

Google Scholar 
54.Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 
Article 

Google Scholar 
55.Roy, D. P., Boschetti, L., Justice, C. & Ju, J. The collection 5 MODIS burned area product—global evaluation by comparison with the MODIS active fire product. Remote Sens. Environ. 112, 3690–3707 (2008).Article 

Google Scholar 
56.Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).Article 

Google Scholar 
57.R Core Team R: A Language and Environment for Statistical Computing Version 3.6.0 (R Foundation for Statistical Computing, 2019).58.Greenwell, B., Boehmke, B., Cunningham, J. & GMB Developers. gbm: Generalized Boosted Regression Models Version 2.1.5. R package (2019).59.Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 

Google Scholar 
60.Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Clim. 37, 4302–4315 (2017).Article 

Google Scholar 
61.Wang, J. A., Sulla-Menashe, D., Woodcock, C. E., Sonnentag, O. & Friedl, M. A. ABoVE: Annual Land Cover in the ABoVE Core Domain from Landsat, 1984–2014 (ORNL DAAC, 2019); https://doi.org/10.3334/ORNLDAAC/169162.Canadian National Fire Database—Agency Fire Data (Canadian Forest Service, 2002); https://cwfis.cfs.nrcan.gc.ca/ha/nfdb63.Alaskan Large Fire Database (Alaska Interagency Coordination Center, 2002); https://fire.ak.blm.gov/predsvcs/maps.php64.Thornton, M. M. et al. Daymet: Monthly Climate Summaries on a 1-km Grid for North America Version 3 (ORNL DAAC, 2018); https://doi.org/10.3334/ornldaac/134565.Lumley, T. leaps: Regression Subset Selection Version 3.0. R package (2017).66.Mallows, C. L. Some comments on Cp. Technometrics 42, 87–94 (2000).
Google Scholar 
67.Li, Z., Kurz, W. A., Apps, M. J. & Beukema, S. J. Belowground biomass dynamics in the Carbon Budget Model of the Canadian Forest Sector: recent improvements and implications for the estimation of NPP and NEP. Can. J. For. Res. 33, 126–136 (2003).Article 

Google Scholar 
68.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016). More

1.Díaz, S., Hodgson, J. G., Thompson, K., Cabido, M. & Zak, M. R. The plant traits that drive ecosystems: evidence from three continents. J. Veg. Sci. 15, 295–304 (2010).Article 

Google Scholar 
2.He, N. et al. Ecosystem traits linking functional traits to macroecology. Trends Ecol Evol 34, 200–210. https://doi.org/10.1016/j.tree.2018.11.004 (2019).Article 
PubMed 

Google Scholar 
3.Reich, P. B. & Lusk, W. C. H. Predicting leaf physiology from simple plant and climate attributes: a global GLOPNET analysis. Ecol. Appl. 17, 1982–1988 (2007).PubMed 
Article 

Google Scholar 
4.Shi, P., Preisler, H. K., Quinn, B. K., Zhao, J. & Hlscher, D. Precipitation is the most crucial factor determining the distribution of moso bamboo in Mainland China. Global Ecol. Conserv. 22, e00924 (2020).Article 

Google Scholar 
5.Bassirirad, G. H. Extreme events as shaping physiology, ecology, and evolution of plants: toward a unified definition and evaluation of their consequences. New Phytol. 160, 21–42 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Boyer, J. S. Water transport. Annu. Rev. Plant Physiol. 36, 473–516 (1985).Article 

Google Scholar 
7.Kromer, S. Respiration during photosynthesis. Annu. Rev. Plant Physiol. Plant Mol. Biol. 46, 45–70 (1995).Article 

Google Scholar 
8.Carl, V. J. & VanLoocke, A. Terrestrial ecosystems in a changing environment: a dominant role for water. Annu. Rev. Plant Biol. 66, 599–622 (2015).Article 
CAS 

Google Scholar 
9.Heinen, R. B., Qing, Y. & François, C. Role of aquaporins in leaf physiology. J. Exp. Bot. 60, 2971–2985 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Chapin, F. S., Matson, P. A. & Mooney, H. A. Principles of Terrestrial Ecosystem Ecology (Springer, 2011).Book 

Google Scholar 
11.Ma, Z. et al. Evolutionary history resolves global organization of root functional traits. Nature 555, 94–97 (2018).ADS 
CAS 
Article 

Google Scholar 
12.Zhang, J. et al. C:N: P stoichiometry in China’s forests: from organs to ecosystems. Funct. Ecol. 32, 50–60 (2017).Article 

Google Scholar 
13.Grime, J. P. Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory. Am. Nat. 111, 1221–1226 (1977).Article 

Google Scholar 
14.Bassirirad, H. & Caldwell, M. M. Root growth, osmotic adjustment and NO3-uptake during and after a period of drought in Artemisia tridentata. Aust. J. Plant Physiol. 19, 493–500 (1992).CAS 

Google Scholar 
15.Bassirirad, H. & Caldwell, M. M. Temporal changes in root growth and 15N uptake and water relations of two tussock grass species recovering from water stress. Physiol. Plant. 86, 525–531 (1992).Article 

Google Scholar 
16.Bassirirad, H. et al. Short-term patterns in water and nitrogen acquisition by two desert shrubs following a simulated summer rain. Plant Ecol. 145, 27–36 (1999).Article 

Google Scholar 
17.Gebauer, R. L. E. & Ehleringer, J. R. Water and nitrogen uptake patterns following moisture pulses in a cold desert community. Ecology 81, 1415 (2000).Article 

Google Scholar 
18.Liu, M., Niklas, K. J., Niinemets, L., Hlscher, D. & Shi, P. Comparison of the scaling relationships of leaf biomass versus surface area between spring and summer for two deciduous tree species. Forests 11, 1010 (2020).Article 

Google Scholar 
19.Shi, P., Li, Y., Hui, C., Ratkowsky, D. A. & Niinemets, L. Does the law of diminishing returns in leaf scaling apply to vines? Evidence from 12 species of climbing plants. Glob. Ecol. Conserv. 21, e00830 (2019).Article 

Google Scholar 
20.Yu, X., Hui, C., Sandhu, H. S., Lin, Z. & Shi, P. Scaling relationships between leaf shape and area of 12 Rosaceae species. Symmetry 11, 1255 (2019).Article 

Google Scholar 
21.Liu, C. et al. Variation of stomatal traits from cold temperate to tropical forests and association with water use efficiency. Funct. Ecol. 32, 20–28 (2017).Article 

Google Scholar 
22.Am, H. & Fi, W. The role of stomata in sensing and driving environmental change. Nature 424, 901–908 (2003).Article 
CAS 

Google Scholar 
23.Huang, W., Ratkowsky, D. A., Hui, C., Wang, P. & Shi, P. Leaf fresh weight versus dry weight: which is better for describing the scaling relationship between leaf biomass and leaf area for broad-leaved plants?. Forests 10, 256 (2019).Article 

Google Scholar 
24.Huang, W., Reddy, G. V., Li, Y., Larsen, J. B. & Shi, P. Increase in absolute leaf water content tends to keep pace with that of leaf dry mass—evidence from bamboo plants. Symmetry 12, 1345 (2020).Article 

Google Scholar 
25.Yang, Y. et al. Quantifying leaf-trait covariation and its controls across climates and biomes. New Phytol. 221, 155–168 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Huang, W., Fonti, P., Rbild, A., Larsen, J. B. & Hansen, J. K. Variability Among Sites and Climate Models Contribute to Uncertain Spruce Growth Projections in Denmark. Forests 12, 36 (2021).Article 

Google Scholar 
27.Aspinwall, M. J. et al. Range size and growth temperature influence Eucalyptus species responses to an experimental heatwave. Glob. Change Biol. 25, 1665–1684 (2019).ADS 
Article 

Google Scholar 
28.Shao, J. et al. Plant evolutionary history mainly explains the variance in biomass responses to climate warming at a global scale. New Phytol. 222, 1338–1351 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
29.He, J., Reddy, G. V., Liu, M. & Shi, P. A general formula for calculating surface area of the similarly shaped leaves: evidence from six Magnoliaceae species. Glob. Ecol. Conserv. 23, e01129 (2020).Article 

Google Scholar 
30.Guo, X., Reddy, G. V., He, J., Li, J. & Shi, P. Mean-variance relationships of leaf bilateral asymmetry for 35 species of plants and their implications. Glob. Ecol. Conserv. 23, e01152 (2020).Article 

Google Scholar 
31.Shi, P.-J., Li, Y.-R., Niinemets, Ü., Olson, E. & Schrader, J. Influence of leaf shape on the scaling of leaf surface area and length in bamboo plants. Trees 35, 1–7 (2020).
Google Scholar 
32.Shi, P. et al. Leaf area–length allometry and its implications in leaf shape evolution. Trees 33, 1073–1085 (2019).Article 

Google Scholar 
33.Yu, X., Shi, P., Schrader, J. & Niklas, K. J. Nondestructive estimation of leaf area for 15 species of vines with different leaf shapes. Am. J. Bot. 107, 1481–1490. https://doi.org/10.1002/ajb2.1560 (2020).Article 
PubMed 

Google Scholar 
34.Brown, J. H. On the relationship between abundance and distribution of species. Am. Nat. 124, 255–279 (1984).Article 

Google Scholar 
35.Slatyer, R. A., Hirst, M. & Sexton, J. P. Niche breadth predicts geographical range size: a general ecological pattern. Ecol. Lett. 16, 1104–1114 (2013).PubMed 
Article 

Google Scholar 
36.Gonzalez-Orozco, C. E. et al. Phylogenetic approaches reveal biodiversity threats under climate change. Nat. Clim. Change 6, 1110–1114 (2016).ADS 
Article 

Google Scholar 
37.Pacifici, M. et al. Assessing species vulnerability to climate change. Nat. Clim. Chang. 5, 215–224 (2015).ADS 
Article 

Google Scholar 
38.Thuiller, W., Lavorel, S. & Araújo, M. B. Niche properties and geographical extent as predictors of species sensitivity to climate change. Glob. Ecol. Biogeogr. 14, 347–357 (2005).Article 

Google Scholar 
39.Wright, I. J. et al. Relationships among ecologically important dimensions of plant trait variation in seven Neotropical forests. Ann. Bot. 99, 1003–1015 (2007).PubMed 
Article 

Google Scholar 
40.Reich, P. B. The world-wide ‘fast–slow’plant economics spectrum: a traits manifesto. J. Ecol. 102, 275–301 (2014).Article 

Google Scholar 
41.Kong, D. et al. Leading dimensions in absorptive root trait variation across 96 subtropical forest species. New Phytol. 203, 863–872 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
42.Koch, G. W., Scholes, R. J., Steffen, W. L., Vitousek, P. M. & Walker, B. H. The IGBP terrestrial transects: science plan. Global Change Report (1995).43.Liu, Z., Shao, M. A. & Wang, Y. Effect of environmental factors on regional soil organic carbon stocks across the Loess Plateau region China. Agric. Ecosyst. Environ. 142, 184–194 (2011).Article 

Google Scholar 
44.Bai, Y. et al. Primary production and rain use efficiency across a precipitation gradient on the Mongolia plateau. Ecology 89, 2140–2153 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
45.Chen, H. et al. The impacts of climate change and human activities on biogeochemical cycles on the Q inghai-T ibetan P lateau. Glob. Change Biol. 19, 2940–2955 (2013).ADS 
Article 

Google Scholar 
46.Dee, L. E. et al. When do ecosystem services depend on rare species?. Trends Ecol. Evol. 34, 746–758 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
47.Cornelissen, J. et al. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust. J. Bot. 51, 335–380 (2003).Article 

Google Scholar 
48.Lamont, B. B., Downes, S. & Fox, J. E. Importance–value curves and diversity indices applied to a species-rich heathland in Western Australia. Nature 265, 438–441 (1977).ADS 
Article 

Google Scholar 
49.Zhang, T., Guo, R., Gao, S., Guo, J. & Sun, W. Responses of plant community composition and biomass production to warming and nitrogen deposition in a temperate meadow ecosystem. PLoS ONE 10, e0123160 (2015).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
50.Qian, H. & Jin, Y. An updated megaphylogeny of plants, a tool for generating plant phylogenies and an analysis of phylogenetic community structure. J. Plant Ecol. 9, 233–239 (2016).Article 

Google Scholar 
51.Blomberg, S. P., Garland, T. Jr. & Ives, A. R. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution 57, 717–745 (2003).PubMed 
Article 
PubMed Central 

Google Scholar  More

Baker JR (1984) Mortality and morbidity in grey seal pups (Halichoerus grypus). Studies on its causes, effects of environment, the nature and sources of infectious agents and the immunological status of pups. J Zool 203:23–48Article 

Google Scholar 
Bakermans-Kranenburg MJ, van IJzendoorn MH (2008) Oxytocin receptor (OXTR) and serotonin transporter (5-HTT) genes associated with observed parenting. Soc Cogn Affect Neur 3:128–134Article 

Google Scholar 
Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48Article 

Google Scholar 
Battersby S, Ogilvie AD, Smith CA, Blackwood DH, Muir WJ, Quinn JP et al. (1996) Structure of a variable number tandem repeat of the serotonin transporter gene and association with affective disorder. Psychiat Genet 6:177–181CAS 
Article 

Google Scholar 
Bengston SE, Dahan RA, Donaldson Z, Phelps SM, van Oers K, Sih A et al. (2018) Genomic tools for behavioural ecologists to understand repeatable individual differences in behaviour. Nat Ecol Evol 2:944–955PubMed 
Article 

Google Scholar 
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Stat Methodol 57:289–300
Google Scholar 
Boake CRB, Arnold SJ, Breden F, Meffert LM, Ritchie MG, Taylor BJ et al. (2002) Genetic tools for studying adaptation and the evolution of behavior. Am Nat 160:S143–S159PubMed 
Article 

Google Scholar 
Boness DJ, Anderson SS, Cox CR (1982) Function of female aggression during the pupping and mating season of grey seals, Halichoerus grypus (Fabricius). Can J Zool 60:2270–2278Article 

Google Scholar 
Bowen WD, den Heyer CE, McMillan JI, Iverson SJ (2015) Offspring size at weaning affects survival to recruitment and reproductive performance of primiparous grey seals. Ecol Evol 5:1412–1424PubMed 
PubMed Central 
Article 

Google Scholar 
Bowen WD, Iverson SJ, McMillan JI, Boness DJ (2006) Reproductive performance in grey seals: age-related improvement and senescence in a capital breeder. J Anim Ecol 75:1340–1351CAS 
PubMed 
Article 

Google Scholar 
Bowen WD, McMillan JI, Blanchard W (2007) Reduced population growth of grey seals at Sable Island: evidence from pup production and age of primiparity. Mar Mam Sci 23:48–64Article 

Google Scholar 
Bowen WD, McMillan J, Mohn R (2003) Sustained exponential population growth of grey seals at Sable Island, Nova Scotia. ICES J Mar Sci 60:1265–1274Article 

Google Scholar 
Bowen WD, Stobo WT, Smith SJ (1992) Mass changes of grey seal Halichoerus grypus pups on Sable Island: differential maternal investment reconsidered. J Zool 227:607–622Article 

Google Scholar 
Bubac CM, Coltman DW, Bowen WD, Lidgard DC, Lang SLC, den Heyer CE (2018) Repeatability and reproductive consequences of boldness in female gray seals. Behav Ecol Sociobiol 72:100–112Article 

Google Scholar 
Bubac CM, Miller JM, Coltman DW (2020) The genetic basis of animal behavioural diversity in natural populations. Mol Ecol https://doi.org/10.1111/mec.15461Cammen KM, Andrews KR, Carroll EL, Foote AD, Humble E, Khudyakov JI et al. (2016) Genomic methods take the plunge: recent advances in high-throughput sequencing of marine mammals. J Hered 107:481–495CAS 
PubMed 
Article 

Google Scholar 
Cammen KM, Schultz TF, Bowen WD, Hammill MO, Puryear WB, Runstadler J et al. (2018b) Genomic signatures of population bottleneck and recovery in Northwest Atlantic pinnipeds. Ecol Evol 8:6599–6614PubMed 
PubMed Central 
Article 

Google Scholar 
Cammen KM, Vincze S, Heller AS, McLeod BA, Wood SA, Bowen WD et al. (2018a) Genetic diversity from pre-bottleneck to recovery in two sympatric pinniped species in the Northwest Atlantic. Con Gen 19:555–569CAS 
Article 

Google Scholar 
Carere C, Maestripieri D (2013) Animal personalities: Behavior, physiology, and evolution. The University of Chicago Press, ChicagoBook 

Google Scholar 
Chakraborty S, Chakraborty D, Mukherjee O, Jain S, Ramakrishnan U, Sinha A (2010) Genetic polymorphism in the serotonin transporter promoter region and ecological success in macaques. Behav Genet 40:672–679PubMed 
Article 

Google Scholar 
Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Erlbaum, Hillsdale, NJ
Google Scholar 
Dochtermann NA, Schwab T, Anderson Berdal M, Dalos J, Royauté R (2019) The heritability of behavior: a meta-analysis. J Hered 110:403–410PubMed 
Article 

Google Scholar 
Dohm MR (2002) Repeatability estimates do not always set an upper limit to heritability. Funct Ecol 16:273–280Article 

Google Scholar 
Edwards HA, Hajduk GK, Durieux G, Burke T, Dugdale HL (2015) No association between personality and candidate gene polymorphisms in a wild bird population. PLoS ONE 10:e0138439. https://doi.org/10.1371/journal.pone.0138439CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Emiliano ABF, Cruz T, Pannoni V, Fudge JL (2007) The interface of oxytocin-labeled cells and serotonin transporter-containing fibers in the primate hypothalamus: a substrate for SSRIs therapeutic effects? Neuropsychopharmacol 32:977–988CAS 
Article 

Google Scholar 
Fairbanks LA, Way BM, Breidenthal, Bailey JN, Jorgensen MJ (2012) Maternal and offspring dopamine D4 receptor genotypes interact to influence juveniles impulsivity in vervet monkeys Psychol Sci 23:1099–1104. https://doi.org/10.1177/0956797612444905Article 
PubMed 

Google Scholar 
Fidler A (2011) Personality-associated genetic variation in birds and its possible significance for avian evolution, conservation, and welfare. In: Inoue-Murayama M, Kawamura S, Weiss A (eds) From Genes to Animal Behavior. Primatology Monographs. Springer, Tokyo, p 275–294
Google Scholar 
Fitzpatrick MJ, Ben-Shahar Y, Smid HM, Vet LEM, Robinson GE, Sokolowski MB (2005) Candidate genes for behavioural ecology. Trends in Ecology & Evolution 20:96–104Article 

Google Scholar 
Fulton TL, Strobeck C (2010) Multiple fossil calibrations, nuclear loci and mitochondrial genomes provide new insight into biogeography and divergence timing for true seals (Phocidae, Pinnipedia). J Biogeogr 37:814–829Article 

Google Scholar 
Garvin MR, Saitoh K, Gharrett AJ (2010) Application of single nucleotide polymorphisms to non-model species: a technical review. Mol Ecol Resour 10:915–934. https://doi.org/10.1111/j.1755-0998.02891.xCAS 
Article 
PubMed 

Google Scholar 
Göring HHH, Terwilliger JD, Blangero J (2001) Large upward bias in estimation of locus-specific effects from genomewide scans. Am J Hum Genet 69:1357–2001PubMed 
PubMed Central 
Article 

Google Scholar 
Gosling SD (2001) From mice to men: what can we learn about personality from animal research? Psychol Bull 127:45–86CAS 
PubMed 
Article 

Google Scholar 
Hadfield JD (2010) MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J Stat Softw 33:1–22Article 

Google Scholar 
Hall AJ, McConnell BJ, Barker RJ (2001) Factors affecting first-year survival in grey seals and their implications for life-history strategy. J Anim Ecol 70:138–149
Google Scholar 
Hammill MO, den Heyer CE, Bowen WD, Lang SLC (2017) Grey seal population trends in Canadian waters, 1960–2016 and harvest advice. DFO Can Sci Advis Sec Res Doc 2017/052. v + 30pHelyar SJ, Hemmer-Hansen J, Bekkevold D, Taylor MI, Ogden R, Limborg MT et al. (2011) Applications of SNPs for population genetics of nonmodel organisms: new opportunities and challenges. Mol Ecol Resour 11:123–136. https://doi.org/10.1111/j.1755-0998.2010.02943.xArticle 
PubMed 

Google Scholar 
Holtmann B, Grosser S, Lagisz M, Johnson SL, Santos ESA, Lara CE et al. (2016) Population differentiation and behavioural association of the two ‘personality’ genes DRD4 and SERT in dunnocks (Prunella modularis). Mol Ecol 25:706–722CAS 
PubMed 
Article 

Google Scholar 
Howell S, Westergaard G, Hoos B, Chavanne TJ, Shoaf SE, Snoy PJ et al. (2007) Serotonergic influences on life-history outcomes in free-ranging male rhesus macaques. Am J Primatol 69:851–865CAS 
PubMed 
Article 

Google Scholar 
Iverson SJ, Bowen WD, Boness DJ, Oftedal OT (1993) The effect of maternal size and milk output on pup growth in grey seals (Halichoerus grypus). Physiol Zool 66:61–88Article 

Google Scholar 
Jacobs LN, Staiger EA, Albright JD, Brooks SA (2016) The MC1R and ASIP coat color loci may impact behavior in the horse. J Hered 107:214–219CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jaeger BC, Edwards LJ, Das K, Sen PK (2016) An R2 statistic for fixed effects in the generalized linear mixed model. J Appl Stat 44:1086–1106Article 

Google Scholar 
Jorgensen H, Riis M, Knigge U, Kjaer A, Warberg J (2003) Serotonin receptors involved in vasopressin and oxytocin secretion. J Neuroendocrinol 15:242–249CAS 
PubMed 
Article 

Google Scholar 
Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S et al. (2012) Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28:1647–1649PubMed 
PubMed Central 
Article 

Google Scholar 
Kendrick KM (2000) Oxytocin, motherhood and bonding. Exp Physiol 85:111–124Article 

Google Scholar 
Kim SJ, Kim YS, Lee HS, Kim SY, Kim C-H (2006) An interaction between the serotonin transporter promoter region and dopamine transporter polymorphisms contributes to harm avoidance and reward dependence traits in normal healthy subjects. J Neural Transm 113:877–886CAS 
PubMed 
Article 

Google Scholar 
Kluger A, Siegfried Z, Ebstein R (2002) A meta-analysis of the association between DRD4 polymorphism and novelty seeking. Mol Psychiatry 7:712–717CAS 
PubMed 
Article 

Google Scholar 
Korsten P, Mueller JC, Hermannstädter C, van Overveld T, Patrick SC, Quinn JL et al. (2010) Association between DRD4 gene polymorphism and personality variation in great tits: a test across four populations. Mol Ecol 19:832–843CAS 
PubMed 
Article 

Google Scholar 
Laine VN, van Oers K (2017) The quantitative and molecular genetics of individual differences in animal personality. In: Vonk J, Weiss A, Kuczaj SA(eds) Personality in Nonhuman Animals. Springer, Cham, p 55–72
Google Scholar 
Laird NM, Lange C (2011) The fundamentals of modern statistical genetics. Springer, New York, NYBook 

Google Scholar 
Lang SLC, Iverson SJ, Bowen WD (2009) Repeatability in lactation performance and the consequences for maternal reproductive success in gray seals. Ecology 90:2513–2523CAS 
PubMed 
Article 

Google Scholar 
Lesch K-P, Bengel D, Heils A, Sabol SZ, Greenberg BD (1996) Association of anxiety-related traits with a polymorphism in the serotonin transporter gene regulatory region. Science 274:1527–1531CAS 
PubMed 
Article 

Google Scholar 
Lidgard DC, Bowen WD, Boness DJ (2012) Longitudinal changes and consistency in male physical and behavioural traits have implications for mating success in the grey seal (Halichoerus grypus). Can J Zool 90:849–860Article 

Google Scholar 
Lim MM, Young LJ (2006) Neuropeptidergic regulation of affiliative behavior and social bonding in animals. Horm Behav 50:506–517CAS 
PubMed 
Article 

Google Scholar 
MacKenzie A, Quinn J (1999) A serotonin transporter gene intron 2 polymorphic region, correlated with affective disorders, has allele-dependent differential enhancer-like properties in the mouse embryo. PNAS 96:15251–15255CAS 
PubMed 
Article 

Google Scholar 
Mansfield AW, Beck B (1977) The grey seal in eastern Canada. Tech Rep. Fish Mar Serv Can 706:1–81
Google Scholar 
Maruyama T, Fuerst PA (1985) Population bottlenecks and nonequilibrium models in population genetics. II. Number alleles a small Popul that was Form a recent bottleneck Genet 111:675–689CAS 

Google Scholar 
McCann TS (1982) Aggressive and maternal activities of female southern elephant seals (Mirounga leonina). Anim Behav 30:268–276Article 

Google Scholar 
McDougall PT, Réale D, Sol D, Reader SM (2006) Wildlife conservation and animal temperament: causes and consequences of evolutionary change for captive, reintroduced, and wild populations. Anim Conserv 9:39–48Article 

Google Scholar 
Mellish JAE, Iverson SJ, Bowen WD (1999) Variation in milk production and lactation performance in grey seals and consequences for pup growth and weaning characteristics. Physiol Biochem Zool 72:677–690CAS 
PubMed 
Article 

Google Scholar 
Mitsuyasu H, Hirata N, Sakai Y, Shibata H, Takeda Y (2001) Association analysis of polymorphisms in the upstream region of the human dopamine D4 receptor gene (DRD4) with schizophrenia and personality traits. J Hum Genet 46:26–31CAS 
PubMed 
Article 

Google Scholar 
Møller AP, Jennions MD (2002) How much variance can be explained by ecologists and evolutionary biologists. Oecologia 132:492–500PubMed 
Article 

Google Scholar 
Mousseau TA, Roff DA (1987) Natural selection and the heritability of fitness components. Heredity 59:181–197PubMed 
Article 

Google Scholar 
Mueller JC, Partecke J, Hatchwell BJ, Gaston KJ, Evans KL (2013) Candidate gene polymorphisms for behavioural adaptations during urbanization in blackbirds. Mol Ecol 22:3629–3637CAS 
PubMed 
Article 

Google Scholar 
Nakagawa S, Schielzeth H (2013) A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol Evol 4:133–142Article 

Google Scholar 
Nei M, Li WH (1976) The transient distribution of allele frequencies under mutation pressure. Genet Res 28:205–214CAS 
PubMed 
Article 

Google Scholar 
Noren SR, Boness DJ, Iverson SJ, McMillan J, Bowen WD (2008) Body condition at weaning affects the duration of the postweaning fast in grey seal pups (Halichoerus grypus). Physiol Biochem Zool 81:269–277PubMed 
Article 

Google Scholar 
Oak JN, Oldenhof J, Van Tol HH (2000) The dopamine D4 receptor: one decade of research. Eur J Pharm 405:303–327CAS 
Article 

Google Scholar 
Pati N, Schowinsky V, Kokanovic O, Magnuson V, Ghosh S (2004) A comparison between SNaPshot, pyrosequencing, and biplex invader SNP genotyping methods: accuracy, cost, and throughput. J Biochem Biophys Methods 60:1–12CAS 
PubMed 
Article 

Google Scholar 
Prasad P, Ogawa S, Parhar IS (2015) Role of serotonin in fish reproduction. Front Neurosci 9:1–9. https://doi.org/10.3389/fnins.2015.00195Article 

Google Scholar 
R Core Team (2019) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ URLRaymond M, Rousset F (1995) GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J Hered 86:248–249Article 

Google Scholar 
Réale D, Gallant BY, Leblanc M, Festa-Bianchet M (2000) Consistency in bighorn ewes and correlates with behaviour and life history. Anim Behav 60:589–597PubMed 
Article 

Google Scholar 
Réale D, Reader SM, Sol D, McDougall PT, Dingemanse NJ (2007) Integrating animal temperament within ecology and evolution. Biol Rev 82:291–318PubMed 
Article 

Google Scholar 
Riyahi S, Björklund M, Mateos-Gonzalez F, Senar JC (2017) Personality and urbanization: behavioural traits and DRD4 SNP830 polymorphisms in Great Tits in Barcelona city. J Ethol 35:101–108Article 

Google Scholar 
Riyahi S, Sánchez-Delgado M, Calafell F, Monk D, Senar JC (2015) Combined epigenetic and intraspecific variation of the DRD4 and SERT genes influence novelty seeking behavior in great tit Parus major. Epigenetics 10:516–525PubMed 
PubMed Central 
Article 

Google Scholar 
Robinson KJ, Twiss SD, Hazon N, Pomeroy PP (2015) Maternal oxytocin is linked to close mother-infant proximity in grey seals (Halichoerus grypus). PLoS One 10:e0144577. https://doi.org/10.1371/journal.pone.0144577CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Robinson KJ, Twiss SD, Hazon N, Simon M, Pomeroy PP (2017) Positive social behaviours are induced and retained after oxytocin manipulations mimicking endogenous concentrations in a wild mammal. Proc R Soc B-Biol Sci https://doi.org/10.1098/rspb.2017.0554Rousset F (2008) Genepop’007: a complete reimplementation of the Genepop software for Windows and Linux. Mol Ecol Resour 8:103–106PubMed 
Article 

Google Scholar 
Rozas J, Ferrer-Mata A, Sánchez-DelBarrio JC, Guirao-Rico S, Librado P, Ramos-Onsins SE et al. (2017) DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol Biol Evol 34:3299–3302CAS 
PubMed 
Article 

Google Scholar 
Sala M, Braida D, Donzelli A, Martucci R, Busnelli M, Bulgheroni E et al. (2013) Mice heterozygous for the oxytocin receptor gene (Oxtr+/−) show impaired social behaviour but not increased aggression or cognitive inflexibility: evidence of a selective haploinsufficiency gene effect. J Neuroendocrinol 25:107–118CAS 
PubMed 
Article 

Google Scholar 
Savitz JB, Ramesar RS (2004) Genetic variants implicated in personality: a review of the more promising candidates. Am J Med Genet B 131B:20–32Article 

Google Scholar 
Sih A, Bell AM, Johnson JC, Ziemba RE (2004) Behavioural syndromes: an integrative overview. Q Rev Biol 79:241–277PubMed 
Article 

Google Scholar 
Singmann H, Bolker B, Westfall J, Aust F, Ben-Shachar MS (2019) afex: Analysis of Factorial Experiments. R package version 0.25-1. https://CRAN.R-project.org/package=afexSinn DL, Gosling SD, Moltschaniwskyj NA (2008) Development of shy/bold behaviour in squid: context-specific phenotypes associated with developmental plasticity. Anim Behav 75:433–442Article 

Google Scholar 
Sloan Wilson D, Clark AB, Coleman K, Dearstyne T (1994) Shyness and boldness in humans and other animals. TREE 9:442–446CAS 
PubMed 

Google Scholar 
Tajima F (1989) Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123:585–595CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Timm K, van Oers K, Tilgar V (2018) SERT gene polymorphisms are associated with risk-taking behaviour and breeding parameters in wild great tits. J Exp Biol 221:jeb171595. https://doi.org/10.1242/jeb.171595Article 
PubMed 

Google Scholar 
Twiss SD, Cairns C, Culloch RM, Richards SA, Pomeroy PP (2012) Variation in female grey seal (Halichoerus grypus) reproductive performance correlates to proactive-reactive behavioural types. PLoS ONE 7:e49598. https://doi.org/10.1371/journal.pone.0049598CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Twiss SD, Shuert CR, Brannan N, Bishop AM, Pomeroy PP (2020) Reactive stress-coping styles show more variable reproductive expenditure and fitness outcomes. Sci Rep. 10:9550PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
van Neer A, Gross S, Kesselring T, Wohlsein, Leitzen E, Siebert U (2019) Behavioural and pathological insights into a case of active cannibalism by a grey seal (Halichoerus grypus) on Helgoland, Germany. J Sea Res 148-149:12–16Article 

Google Scholar 
van Oers K (2008) Animal personality, behaviours or traits: what are we measuring? Eur J Pers 22:457–474Article 

Google Scholar 
van Oers K, Mueller JC (2010) Evolutionary genomics of animal personality. P R Soc B-Biol Sci 365:3991–4000
Google Scholar 
Wellenreuther M, Hansson B (2016) Detecting polygenic evolution: problems, pitfalls, and promises. Trends Genet 32:155–164. https://doi.org/10.1016/j.tig.2015.12.004CAS 
Article 
PubMed 

Google Scholar 
Wolf M, Weissing FJ (2010) An explanatory framework for adaptive personality differences. Proc R Soc B-Biol Sci 365:3959–3968. https://doi.org/10.1098/rstb.2010.0215Article 

Google Scholar 
Wolf M, van Doorn GS, Leimar O, Weissing FJ (2007) Life-history trade-offs favour the evolution of animal personalities. Nature 447:581–584CAS 
PubMed 
Article 

Google Scholar 
Wood SA, Frasier TR, McLeod BA, Gilbert JR, White BN et al. (2011) The genetics of recolonization: an analysis of the stock structure of grey seals (Halichoerus grypus) in the Northwest Atlantic. Can J Zool 89:490–497Article 

Google Scholar  More

1.DeVries SL, Zhang P. Antibiotics and the Terrestrial Nitrogen Cycle: a review. Curr Pollut Rep. 2016;2:51–67.CAS 
Article 

Google Scholar 
2.Kümmerer K. Antibiotics in the aquatic environment – A review – Part I. Chemosphere. 2009;75:417–34.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
3.Franklin AM, Aga DS, Cytryn E, Durso LM, McLain JE, Pruden A, et al. Antibiotics in Agroecosystems: introduction to the Special Section. J Environ Qual. 2016;45:377–93.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Roose-Amsaleg C, Laverman AM. Do antibiotics have environmental side-effects? Impact of synthetic antibiotics on biogeochemical processes. Environ Sci Pollut Res. 2016;23:4000–12.CAS 
Article 

Google Scholar 
5.Grenni P, Ancona V, Barra, Caracciolo A. Ecological effects of antibiotics on natural ecosystems: a review. Microchemical J. 2018;136:25–39.CAS 
Article 

Google Scholar 
6.Chee-Sanford JC, Mackie RI, Koike S, Krapac IG, Lin Y-F, Yannarell AC, et al. Fate and Transport of Antibiotic Residues and Antibiotic Resistance Genes following Land Application of Manure Waste. J Environ Qual. 2009;38:1086.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Mehrtens A, Licha T, Broers HP, Burke V. Tracing veterinary antibiotics in the subsurface – A long-term field experiment with spiked manure. Environ Pollut. 2020;265:114930.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Kivits T, Broers HP, Beeltje H, van Vliet M, Griffioen J. Presence and fate of veterinary antibiotics in age-dated groundwater in areas with intensive livestock farming. Environ Pollut. 2018;241:988–98.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Gros M, Mas-Pla J, Boy-Roura M, Geli I, Domingo F, Petrović M. Veterinary pharmaceuticals and antibiotics in manure and slurry and their fate in amended agricultural soils: Findings from an experimental field site (Baix Empordà, NE Catalonia). Sci Total Environ. 2019;654:1337–49.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Baquero F, Negri M-C. Challenges: selective compartments for resistant microorganisms in antibiotic gradients. BioEssays. 1997;19:731–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Hermsen R, Deris JB, Hwa T. On the rapidity of antibiotic resistance evolution facilitated by a concentration gradient. Proc Natl Acad Sci. 2012;109:10775–80.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Andersson DI, Hughes D. Microbiological effects of sublethal levels of antibiotics. Nat Rev Microbiol. 2014;12:465–78.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Levin-Reisman I, Ronin I, Gefen O, Braniss I, Shoresh N, Balaban NQ. Antibiotic tolerance facilitates the evolution of resistance. Science. 2017;355:826–30.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Cohen NR, Lobritz MA, Collins JJ. Microbial Persistence and the Road to Drug Resistance. Cell Host Microbe. 2013;13:632–42.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Hughes D, Andersson DI. Environmental and genetic modulation of the phenotypic expression of antibiotic resistance. FEMS Microbiol Rev. 2017;41:374–91.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Venter H, Arzanlou M, Chai WC, Venter H. Intrinsic, adaptive and acquired antimicrobial resistance in Gram-negative bacteria. Essays Biochem. 2017;61:49–59.PubMed 
Article 
PubMed Central 

Google Scholar 
17.Alcalde RE, Michelson K, Zhou L, Schmitz EV, Deng J, Sanford RA, et al. Motility of Shewanella oneidensis MR-1 Allows for Nitrate Reduction in the Toxic Region of a Ciprofloxacin Concentration Gradient in a Microfluidic Reactor. Environ Sci Technol. 2019;53:2778–87.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Hol FJH, Hubert B, Dekker C, Keymer JE. Density-dependent adaptive resistance allows swimming bacteria to colonize an antibiotic gradient. ISME J. 2016;10:30–38.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Butler MT, Wang Q, Harshey RM. Cell density and mobility protect swarming bacteria against antibiotics. Proc Natl Acad Sci. 2010;107:3776–81.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Steel H, Papachristodoulou A. The effect of spatiotemporal antibiotic inhomogeneities on the evolution of resistance. J Theor Biol. 2020;486:110077.PubMed 
Article 
PubMed Central 

Google Scholar 
21.Lai S, Tremblay J, Déziel E. Swarming motility: a multicellular behaviour conferring antimicrobial resistance. Environ Microbiol. 2009;11:126–36.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Zhang Q, Lambert G, Liao D, Kim H, Robin K, Tung C-k, et al. Acceleration of Emergence of Bacterial Antibiotic Resistance in Connected Microenvironments. Science. 2011;333:1764–7.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Wu A, Loutherback K, Lambert G, Estevez-Salmeron L, Tlsty TD, Austin RH, et al. Cell motility and drug gradients in the emergence of resistance to chemotherapy. Proc Natl Acad Sci. 2013;110:16103–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Baym M, Lieberman TD, Kelsic ED, Chait R, Gross R, Yelin I, et al. Spatiotemporal microbial evolution on antibiotic landscapes. Science. 2016;353:1147–51.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Alexandre G, Greer-Phillips S, Zhulin IB. Ecological role of energy taxis in microorganisms. FEMS Microbiol Rev. 2004;28:113–26.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Fenchel T. Microbial Behavior in a Heterogeneous World. Science. 2002;296:1068–71.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Groh JL, Luo Q, Ballard JD, Krumholz LR. Genes That Enhance the Ecological Fitness of Shewanella oneidensis MR-1 in Sediments Reveal the Value of Antibiotic Resistance. Appl Environ Microbiol. 2007;73:492–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Blair JM, Piddock LJ. Structure, function and inhibition of RND efflux pumps in Gram-negative bacteria: an update. Curr Opin Microbiol. 2009;12:512–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Fernández L, Hancock REW. Adaptive and mutational resistance: role of porins and efflux pumps in drug resistance. Clin Microbiol Rev. 2012;25:661–81.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
30.Alvarez-Ortega C, Olivares J, Martinez JL. RND multidrug efflux pumps: what are they good for? Front Microbiol. 2013;4:7.PubMed 
PubMed Central 
Article 

Google Scholar 
31.Anes J, McCusker MP, Fanning S, Martins M. The ins and outs of RND efflux pumps in Escherichia coli. Front Microbiol. 2015;6:587.PubMed 
PubMed Central 
Article 

Google Scholar 
32.Ma D, Alberti M, Lynch C, Nikaido H, Hearst JE. The local repressor AcrR plays a modulating role in the regulation of acrAB genes of Escherichia coli by global stress signals. Mol Microbiol. 1996;19:101–12.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Nies DH. Efflux-mediated heavy metal resistance in prokaryotes. FEMS Microbiol Rev. 2003;27:313–39.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Fraud S, Poole K. Oxidative Stress Induction of the MexXY Multidrug Efflux Genes and Promotion of Aminoglycoside Resistance Development in Pseudomonas aeruginosa. Antimicrobial Agents Chemother. 2011;55:1068–74.CAS 
Article 

Google Scholar 
35.El Garch F, Lismond A, Piddock LJV, Courvalin P, Tulkens PM, Van Bambeke F. Fluoroquinolones induce the expression of patA and patB, which encode ABC efflux pumps in Streptococcus pneumoniae. J Antimicrob Chemother. 2010;65:2076–82.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Zhang L, Mah T-F. Involvement of a Novel Efflux System in Biofilm-Specific Resistance to Antibiotics. J Bacteriol. 2008;190:4447–52.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.El Meouche I, Siu Y, Dunlop MJ. Stochastic expression of a multiple antibiotic resistance activator confers transient resistance in single cells. Scientific Rep. 2016;6:1–9.Article 
CAS 

Google Scholar 
38.Frade VMF, Dias M, Teixeira ACSC, Palma MSA, Frade VMF, Dias M. et al. Environmental contamination by fluoroquinolones. Braz J Pharm Sci. 2014;50:41–54.Article 

Google Scholar 
39.Riaz L, Mahmood T, Yang Q, Coyne MS, D’Angelo E. Bacteria-assisted removal of fluoroquinolones from wheat rhizospheres in an agricultural soil. Chemosphere. 2019;226:8–16.CAS 
PubMed 
Article 

Google Scholar 
40.Llanes C, Köhler T, Patry I, Dehecq B, Delden C, van, Plésiat P. Role of the MexEF-OprN Efflux System in Low-Level Resistance of Pseudomonas aeruginosa to Ciprofloxacin. Antimicrobial Agents Chemother. 2011;55:5676–84.CAS 
Article 

Google Scholar 
41.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Deatherage DE, Barrick JE. Identification of mutations in laboratory evolved microbes from next-generation sequencing data using breseq. Methods Mol Biol. 2014;1151:165–88.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Saltikov CW, Newman DK. Genetic identification of a respiratory arsenate reductase. PNAS. 2003;100:10983–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Engler C, Kandzia R, Marillonnet S. A One Pot, One Step, Precision Cloning Method with High Throughput Capability. PLOS ONE. 2008;3:e3647.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
45.European Committee for Antimicrobial Susceptibility Testing (EUCAST) of the European Society of Clinical Microbiology and Infectious Diseases (ESCMID). Determination of minimum inhibitory concentrations (MICs) of antibacterial agents by broth dilution. Clin Microbiol Infect. 2003;9:ix–xv.Article 

Google Scholar 
46.Lambert RJ, Pearson J. Susceptibility testing: accurate and reproducible minimum inhibitory concentration (MIC) and non-inhibitory concentration (NIC) values. J Appl Microbiol. 2000;88:784–90.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Sonnet P, Izard D, Mullié C. Prevalence of efflux-mediated ciprofloxacin and levofloxacin resistance in recent clinical isolates of Pseudomonas aeruginosa and its reversal by the efflux pump inhibitors 1-(1-naphthylmethyl)-piperazine and phenylalanine-arginine-β-naphthylamide. Int J Antimicrobial Agents. 2012;39:77–80.CAS 
Article 

Google Scholar 
48.Lindgren PK, Karlsson Å, Hughes D. Mutation Rate and Evolution of Fluoroquinolone Resistance in Escherichia coli Isolates from Patients with Urinary Tract Infections. Antimicrobial Agents Chemother. 2003;47:3222–32.CAS 
Article 

Google Scholar 
49.Klaus W, Ross A, Gsell B, Senn H. Backbone resonance assignment of the N-terminal 24 kDa fragment of the gyrase B subunit from S. aureus complexed with novobiocin. J Biomol NMR. 2000;16:357–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Müller RT, Pos KM. The assembly and disassembly of the AcrAB-TolC three-component multidrug efflux pump. Biol Chem. 2015;396:1083–9.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
51.Oethinger M, Podglajen I, Kern WV, Levy SB. Overexpression of the marA or soxS Regulatory Gene in Clinical Topoisomerase Mutants of Escherichia coli. Antimicrob Agents Chemother. 1998;42:2089–94.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Praski Alzrigat L, Huseby DL, Brandis G, Hughes D. Fitness cost constrains the spectrum of marR mutations in ciprofloxacin-resistant Escherichia coli. J Antimicrob Chemother. 2017;72:3016–24.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
53.Srikumar R, Paul CJ, Poole K. Influence of Mutations in the mexR Repressor Gene on Expression of the MexA-MexB-OprM Multidrug Efflux System of Pseudomonas aeruginosa. J Bacteriol. 2000;182:1410–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Sánchez P, Rojo F, Martı́nez JL. Transcriptional regulation of mexR, the repressor of Pseudomonas aeruginosa mexAB-oprM multidrug efflux pump. FEMS Microbiol Lett. 2002;207:63–68.PubMed 
Article 
PubMed Central 

Google Scholar 
55.Fukuda H, Hosaka M, Hirai K, Iyobe S. New norfloxacin resistance gene in Pseudomonas aeruginosa PAO. Antimicrobial Agents Chemother. 1990;34:1757–61.CAS 
Article 

Google Scholar 
56.Fukuda H, Hosaka M, Iyobe S, Gotoh N, Nishino T, Hirai K. nfxC-type quinolone resistance in a clinical isolate of Pseudomonas aeruginosa. Antimicrobial Agents Chemother. 1995;39:790–2.CAS 
Article 

Google Scholar 
57.Fetar H, Gilmour C, Klinoski R, Daigle DM, Dean CR, Poole K. mexEF-oprN Multidrug Efflux Operon of Pseudomonas aeruginosa: Regulation by the MexT Activator in Response to Nitrosative Stress and Chloramphenicol. Antimicrobial Agents Chemother. 2011;55:508–14.CAS 
Article 

Google Scholar 
58.Köhler T, Michea-Hamzehpour M, Plesiat P, Kahr AL, Pechere JC. Differential selection of multidrug efflux systems by quinolones in Pseudomonas aeruginosa. Antimicrob Agents Chemother. 1997;41:2540–3.PubMed 
PubMed Central 
Article 

Google Scholar 
59.Galajda P, Keymer J, Dalland J, Park S, Kou S, Austin R. Funnel ratchets in biology at low Reynolds number: choanotaxis. J Mod Opt. 2008;55:3413–22.CAS 
Article 

Google Scholar 
60.Alcalde-Rico M, Hernando-Amado S, Blanco P, Martínez JL. Multidrug Efflux Pumps at the Crossroad between Antibiotic Resistance and Bacterial Virulence. Front Microbiol. 2016;7:1483.PubMed 
PubMed Central 
Article 

Google Scholar 
61.Lomovskaya O, Warren MS, Lee A, Galazzo J, Fronko R, Lee M, et al. Identification and Characterization of Inhibitors of Multidrug Resistance Efflux Pumps in Pseudomonas aeruginosa: novel Agents for Combination Therapy. Antimicrobial Agents Chemother. 2001;45:105–16.CAS 
Article 

Google Scholar 
62.Pannek S, Higgins PG, Steinke P, Jonas D, Akova M, Bohnert JA, et al. Multidrug efflux inhibition in Acinetobacter baumannii: comparison between 1-(1-naphthylmethyl)-piperazine and phenyl-arginine-beta-naphthylamide. J Antimicrob Chemother. 2006;57:970–4.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Deng J, Zhou L, Sanford RA, Shechtman LA, Dong Y, Alcalde RE, et al. Adaptive Evolution of Escherichia coli to Ciprofloxacin in Controlled Stress Environments: Contrasting Patterns of Resistance in Spatially Varying versus Uniformly Mixed Concentration Conditions. Environ Sci Technol. 2019;53:7996–8005.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Olivares J, Álvarez-Ortega C, Martinez JL. Metabolic Compensation of Fitness Costs Associated with Overexpression of the Multidrug Efflux Pump MexEF-OprN in Pseudomonas aeruginosa. Antimicrob Agents Chemother. 2014;58:3904–13.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
65.Barbosa TM, Levy SB. The impact of antibiotic use on resistance development and persistence. Drug Resist Updates. 2000;3:303–11.Article 

Google Scholar 
66.Chia HE, Marsh ENG, Biteen JS. Extending fluorescence microscopy into anaerobic environments. Curr Opin Chem Biol. 2019;51:98–104.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Laverman AM, Cazier T, Yan C, Roose-Amsaleg C, Petit F, Garnier J, et al. Exposure to vancomycin causes a shift in the microbial community structure without affecting nitrate reduction rates in river sediments. Environ Sci Pollut Res. 2015;22:13702–9.CAS 
Article 

Google Scholar 
68.Li J, Romine MF, Ward MJ. Identification and analysis of a highly conserved chemotaxis gene cluster in Shewanella species. FEMS Microbiol Lett. 2007;273:180–6.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More