Philippa Kaur

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.Bruijnzeel, L. A., Scatena, F. N. & Hamilton, L. S. (eds) Tropical Montane Cloud Forests: Science for Conservation and Management (Cambridge Univ. Press, 2011); https://doi.org/10.1017/CBO97805117783842.Mulligan, M. in Tropical Montane Cloud Forests: Science for Conservation and Management (eds Bruijnzeel, L. A. et al.) 14–38 (Cambridge Univ. Press, 2011); https://doi.org/10.1017/CBO9780511778384.0043.Doumenge, C., Gilmour, D., Pérez, M. R. & Blockhus, J. in Tropical Montane Cloud Forests (eds Hamilton, L. S. et al.) 24–37 (Springer-Verlag, 1995).4.Cadotte, M. W., Carscadden, K. & Mirotchnick, N. Beyond species: functional diversity and the maintenance of ecological processes and services. J. Appl. Ecol. 48, 1079–1087 (2011).Article 

Google Scholar 
5.Bruijnzeel, L. A., Mulligan, M. & Scatena, F. N. Hydrometeorology of tropical montane cloud forests: emerging patterns. Hydrol. Process. 25, 465–498 (2011).Article 

Google Scholar 
6.Gentry, A. H. Tropical forest biodiversity: distributional patterns and their conservational significance. Oikos 63, 19–28 (1992).Article 

Google Scholar 
7.Foster, P. The potential negative impacts of global climate change on tropical montane cloud forests. Earth-Sci. Rev. 55, 73–106 (2001).Article 

Google Scholar 
8.Hamilton, L. S., Juvik, J. O. & Scatena, F. N. in Tropical Montane Cloud Forests (eds Hamilton, L. S. et al.) 1–18 (Springer-Verlag, 1995).9.Ponce-Reyes, R. et al. Vulnerability of cloud forest reserves in Mexico to climate change. Nat. Clim. Change 2, 448–452 (2012).Article 

Google Scholar 
10.Swenson, J. J. et al. Plant and animal endemism in the eastern Andean slope: challenges to conservation. BMC Ecol. 12, 1 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
11.Gould, W. A., González, G. & Rivera, G. C. Structure and composition of vegetation along an elevational gradient in Puerto Rico. J. Veg. Sci. 17, 653–664 (2006).Article 

Google Scholar 
12.Betts, M. G. et al. Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature 547, 441–444 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Paulsen, J. & Körner, C. A climate-based model to predict potential treeline position around the globe. Alp. Bot. 124, 1–12 (2014).Article 

Google Scholar 
14.Jarvis, A. & Mulligan, M. The climate of cloud forests. Hydrol. Process. 25, 327–343 (2011).Article 

Google Scholar 
15.Scatena, F. N., Bruijnzeel, L. A., Bubb, P. & Das, S. in Tropical Montane Cloud Forests: Science for Conservation and Management (eds Bruijnzeel, L. A. et al.) 3–13 (Cambridge Univ. Press, 2011); https://doi.org/10.1017/CBO9780511778384.00316.Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Körner, C. et al. A global inventory of mountains for bio-geographical applications. Alp. Bot. 127, 1–15 (2017).Article 

Google Scholar 
18.Jetz, W., McPherson, J. M. & Guralnick, R. P. Integrating biodiversity distribution knowledge: toward a global map of life. Trends Ecol. Evol. 27, 151–159 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Gillespie, R. G. et al. Long-distance dispersal: a framework for hypothesis testing. Trends Ecol. Evol. 27, 47–56 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Kreft, H., Jetz, W., Mutke, J. & Barthlott, W. Contrasting environmental and regional effects on global pteridophyte and seed plant diversity. Ecography 33, 408–419 (2010).Article 

Google Scholar 
21.Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS ONE 4, e8273 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
22.Venter, Z. S., Cramer, M. D. & Hawkins, H.-J. Drivers of woody plant encroachment over Africa. Nat. Commun. 9, 2272 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
23.Lawton, R. O., Nair, U. S., Pielke, R. A. & Welch, R. M. Climatic impact of tropical lowland deforestation on nearby montane cloud forests. Science 294, 584–587 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
24.Grantham, H. S. et al. Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity. Nat. Commun. 11, 5978 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Guo, W.-Y. et al. Half of the world’s tree biodiversity is unprotected and is increasingly threatened by human activities. Preprint at bioRxiv https://doi.org/10.1101/2020/04.21.052464 (2020).26.Helmer, E. H. et al. Neotropical cloud forests and páramo to contract and dry from declines in cloud immersion and frost. PLoS ONE 14, e0213155 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Peters, M. K. et al. Climate–land-use interactions shape tropical mountain biodiversity and ecosystem functions. Nature 568, 88–92 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).CAS 
Article 

Google Scholar 
29.Seneviratne, S. I. et al. in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) 109–230 (Cambridge Univ. Press, 2012).30.Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).CAS 
PubMed 
Article 

Google Scholar 
31.Beusekom, A. E. V., González, G. & Scholl, M. A. Analyzing cloud base at local and regional scales to understand tropical montane cloud forest vulnerability to climate change. Atmos. Chem. Phys. 17, 7245–7259 (2017).Article 
CAS 

Google Scholar 
32.Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 788–791 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Gross, J. E., Goetz, S. J. & Cihlar, J. Application of remote sensing to parks and protected area monitoring: introduction to the special issue. Remote Sens. Environ. 113, 1343–1345 (2009).Article 

Google Scholar 
34.Visconti, P. et al. Protected area targets post-2020. Science 364, 239–241 (2019).CAS 
PubMed 

Google Scholar 
35.Di Minin, E. & Toivonen, T. Global protected area expansion: creating more than paper parks. BioScience 65, 637–638 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
36.Wetzel, F. T., Beissmann, H., Penn, D. J. & Jetz, W. Vulnerability of terrestrial island vertebrates to projected sea-level rise. Glob. Change Biol. 19, 2058–2070 (2013).Article 

Google Scholar 
37.Keil, P., Storch, D. & Jetz, W. On the decline of biodiversity due to area loss. Nat. Commun. 6, 8837 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Rybicki, J. & Hanski, I. Species–area relationships and extinctions caused by habitat loss and fragmentation. Ecol. Lett. 16, 27–38 (2013).PubMed 
Article 

Google Scholar 
39.Lewis, S. L., Edwards, D. P. & Galbraith, D. Increasing human dominance of tropical forests. Science 349, 827–832 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Johnson, C. N. et al. Biodiversity losses and conservation responses in the Anthropocene. Science 356, 270–275 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Wilson, E. O. Half-Earth: Our Planet’s Fight for Life (WW Norton & Company, 2016).42.Liu, J. et al. Complexity of coupled human and natural systems. Science 317, 1513–1516 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Schulze, K., Malek, Ž. & Verburg, P. H. Towards better mapping of forest management patterns: a global allocation approach. For. Ecol. Manage. 432, 776–785 (2019).Article 

Google Scholar 
44.Curtis, C. A., Pasquarella, V. J. & Bradley, B. A. Landscape characteristics of non-native pine plantations and invasions in southern Chile. Austral Ecol. 44, 1213–1224 (2019).Article 

Google Scholar 
45.Aldrich, M., Billington, C., Edwards, M. & Laidlaw, R. A Global Directory of Tropical Montane Cloud Forests (WCMC, 1997).46.Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
47.Karger, D. N. et al. Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad https://doi.org/10.5061/dryad.kd1d4 (2017).48.Danielson, J. J. & Gesch, D. B. Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010) Open-File Report No. 2011-1073 (USGS, 2011).49.Guisan, A. & Zimmermann, N. E. Predictive habitat distribution models in ecology. Ecol. Modell. 135, 147–186 (2000).Article 

Google Scholar 
50.Guisan, A. & Thuiller, W. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).Article 

Google Scholar 
51.Karmalkar, A. V., Bradley, R. S. & Diaz, H. F. Climate Change scenario for Costa Rican montane forests. Geophys. Res. Lett. 35, L11702 (2008).Article 

Google Scholar 
52.Wilson, A. M. & Jetz, W. Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS Biol. 14, e1002415 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
53.Thuiller, W., Guéguen, M., Renaud, J., Karger, D. N. & Zimmermann, N. E. Uncertainty in ensembles of global biodiversity scenarios. Nat. Commun. 10, 1446 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
54.Heikkinen, R. K. et al. Methods and uncertainties in bioclimatic envelope modelling under climate change. Prog. Phys. Geogr. 30, 751–777 (2006).Article 

Google Scholar 
55.Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).Article 

Google Scholar 
56.Fithian, W. & Hastie, T. Finite-sample equivalence in statistical models for presence-only data. Ann. Appl. Stat. 7, 1917–1939 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
57.Nelder, J. A. & Wedderburn, R. W. M. Generalized linear models. J. R. Stat. Soc. Ser. A 135, 370–384 (1972).Article 

Google Scholar 
58.Hastie, T. J. & Tibshirani, R. J. Generalized Additive Models (Chapman & Hall/CRC Monographs on Statistics and Applied Probability, 1990).59.Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: how, where and how many? Methods Ecol. Evol. 3, 327–338 (2012) .Article 

Google Scholar 
60.Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).Article 

Google Scholar 
61.Aide, T. M. et al. Deforestation and reforestation of Latin America and the Caribbean (2001–2010). Biotropica 45, 262–271 (2013).Article 

Google Scholar 
62.Aide, T. M., Ruiz-Jaen, M. C. & Grau, H. R. in Tropical Montane Cloud Forests: Science for Conservation and Management (eds Bruijnzeel, L. A. et al.) 101–109 (Cambridge Univ. Press, 2011).63.Schwartz, N. B., Aide, T. M., Graesser, J., Grau, H. R. & Uriarte, M. Reversals of reforestation across Latin America limit climate mitigation potential of tropical forests. Front. For. Glob. Change 3, 85 (2020).Article 

Google Scholar 
64.Bubb, P. et al. Cloud Forest Agenda (UNEP-WCMC, 2004); https://www.unep-wcmc.org/cloud-forest-agenda65.Bockor, I. Analyse von Baumartenzusammensetzung und Bestandes-struckturen eines andinen Wolkenwaldes in Westvenezuela als Grundlagezur Wald-typengliederung. PhD thesis, Univ. Göttingen (1979).66.The State of the World’s Forests 2020: Forests, Biodiversity and People (FAO & UNEP, 2020); https://doi.org/10.4060/ca8642en67.Ribas, L. G., dos, S., Pressey, R. L., Loyola, R. & Bini, L. M. A global comparative analysis of impact evaluation methods in estimating the effectiveness of protected areas. Biol. Conserv. 246, 108595 (2020).Article 

Google Scholar 
68.Schleicher, J. et al. Statistical matching for conservation science. Conserv. Biol. 34, 538–549 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
69.Khandker, S., B. Koolwal, G. & Samad, H. Handbook on Impact Evaluation: Quantitative Methods and Practices (World Bank, 2009).70.Barber, C. P., Cochrane, M. A., Souza, C. M. & Laurance, W. F. Roads, deforestation, and the mitigating effect of protected areas in the Amazon. Biol. Conserv. 177, 203–209 (2014).Article 

Google Scholar 
71.Andam, K. S., Ferraro, P. J., Pfaff, A., Sanchez-Azofeifa, G. A. & Robalino, J. A. Measuring the effectiveness of protected area networks in reducing deforestation. Proc. Natl Acad. Sci. USA 105, 16089–16094 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Laurance, W. F. et al. Predictors of deforestation in the Brazilian Amazon. J. Biogeogr. 29, 737–748 (2002).Article 

Google Scholar 
73.Etter, A., McAlpine, C., Wilson, K., Phinn, S. & Possingham, H. Regional patterns of agricultural land use and deforestation in Colombia. Agric. Ecosyst. Environ. 114, 369–386 (2006).Article 

Google Scholar 
74.Geist, H. J. & Lambin, E. F. What drives tropical deforestation? LUCC Report Series No. 4 (LUCC, 2001).75.Nelson, A. et al. A suite of global accessibility indicators. Sci. Data 6, 266 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
76.Amatulli, G. et al. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data 5, 180040 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
77.Körner, C., Paulsen, J. & Spehn, E. M. A definition of mountains and their bioclimatic belts for global comparisons of biodiversity data. Alp. Bot. 121, 73 (2011).Article 

Google Scholar 
78.The IUCN Red List of Threatened Species version 2016.1 (IUCN, 2016); http://www.iucnredlist.org79.Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
80.Storch, D., Keil, P. & Jetz, W. Universal species–area and endemics–area relationships at continental scales. Nature 488, 78–81 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
81.Drakare, S., Lennon, J. J. & Hillebrand, H. The imprint of the geographical, evolutionary and ecological context on species–area relationships. Ecol. Lett. 9, 215–227 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
82.Quintero, I. & Jetz, W. Global elevational diversity and diversification of birds. Nature 555, 246–250 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

1.ICOLD (International Commission On Large Dams). World Register of Dams. Preprint at https://www.icold-cigb.org/GB/world_register/world_register_of_dams.asp (2020).2.Lehner, B. et al. High resolution mapping of the world’s reservoirs and dams for sustainable river flow management. Front. Ecol. Environ. 9(9), 494–502. https://doi.org/10.1890/100125 (2013).Article 

Google Scholar 
3.Moussa, A., Soliman, M.& Aziz, M. Environmental evaluation for High Aswan Dam since its construction until present. In: Sixth International Water Technology Conference, IWTC, Alexandria, Egypt (2001).4.Strand, H., et al. Sourcebook on remote sensing and biodiversity indicators. NASA-NGO Biodiversity Working Group and UNEP-WCMC (2007).5.Grumbine, R. E. & Pandit, M. K. Threats from India’s Himalaya dams. Science 339(6115), 36–37. https://doi.org/10.1126/science.1227211 (2013).ADS 
Article 
PubMed 

Google Scholar 
6.Chen, C., Ma, M., Wu, S., Jia, J. & Wang, Y. Complex effects of landscape, habitat and reservoir operation on riparian vegetation across multiple scales in a human-dominated landscape. Ecol. Indic. 94, 482–490. https://doi.org/10.1016/j.ecolind.2018.04.040 (2018).Article 

Google Scholar 
7.Milliman, J. D. & Meade, R. H. World-wide delivery of river sediment to the oceans. J. Geol. 91, 1–21 (1983).ADS 
Article 

Google Scholar 
8.Tonkin, J. D. et al. Flow regime alternation degrades ecological networks in riparian ecosystems. Nat. Ecol. Evol. 2, 86–93. https://doi.org/10.1038/s41559-017-0379-0 (2018).Article 
PubMed 

Google Scholar 
9.Nillson, C., Reidy, C. A., Dynesius, M. & Revenga, C. Fragmentation and flow regulation of the world’s large river systems. Science 308, 405–408 (2005).ADS 
Article 

Google Scholar 
10.Mitsch, W. et al. Optimizing ecosystem services in China. Science 322(5901), 528 (2008).ADS 
CAS 
Article 

Google Scholar 
11.Stone, R. Three Gorges Dam: into the unknown. Science 333, 817 (2008).ADS 
Article 

Google Scholar 
12.Fu, B. J. et al. Three Gorges Project: efforts and challenges for the environment. Prog. Phys. Geogr. 34(6), 741–754. https://doi.org/10.1177/0309133310370286 (2010).Article 

Google Scholar 
13.Xu, X. B. et al. Unravelling the effects of large-scale ecological programs on ecological rehabilitation of China’s Three Gorges Dam. J. Clean Prod. https://doi.org/10.1016/j.jclepro.2020.120446 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
14.Yaeger, M. A., Massey, J. H., Reba, M. L. & Adviento-Borbe, M. A. A. Trends in the construction of on-farm irrigation reservoirs in response to aquifer decline in eastern Arkansas: implications for conjunctive water resource management. Agric. Water Manage. 208, 373–383. https://doi.org/10.1016/j.agwat.2018.06.040 (2018).Article 

Google Scholar 
15.Bai, J. et al. Soil organic carbon contents of two natural inland saline-alkalined wetlands in northeastern China. J. Soil. Water Conserv. 62(6), 447–452 (2007).
Google Scholar 
16.Chen, L. G., Qian, X. & Shi, Y. Critical area identification of potential soil loss in a typical watershed of the Three Gorges Reservoir Region. Water Resour. Manag. 25(13), 3445–3463. https://doi.org/10.1007/s11269-011-9864-4 (2011).Article 

Google Scholar 
17.Xiao, Q., Xiao, Y. & Tan, H. Changes to soil conservation in the Three Gorges Reservoir Area between 1982 to 2015. Environ. Monit. Assess. 192, 44. https://doi.org/10.1007/s10661-019-7983-1 (2020).Article 

Google Scholar 
18.Zhao, Q. H. et al. Landscape change and hydrologic alteration associated with dam construction. Int. J. Appl. Earth Obs. 16(1), 17–26. https://doi.org/10.1016/j.jag.2011.11.009 (2012).CAS 
Article 

Google Scholar 
19.Zhao, C. L. et al. Ecological security patterns assessment of Liao river basin. Sustainability 10, 2401. https://doi.org/10.3390/su10072401 (2018).Article 

Google Scholar 
20.Yang, L. M. & Zhu, Z. L. The status quo and expectation of global and local land cover and land use RS research. J. Nat. Resour. 14(4), 340–344 (1999).
Google Scholar 
21.Meyfroidt, P., Lambin, E. F., Erb, K. H. & Hertel, T. W. Globalization of land use: distant drivers of land change and geographic displacement of land use. Curr. Opin. Env. Sust. 5(5), 438–444. https://doi.org/10.1016/j.cosust.2013.04.003 (2013).Article 

Google Scholar 
22.Forman, R. T. T. Some general principles of landscape and regional ecology. Landscape Ecol. 10(3), 133–142. https://doi.org/10.1007/BF00133027 (1995).Article 

Google Scholar 
23.Xiao, D. N., Chen, W. B. & Guo, F. L. On the basic concepts and contents of ecological security. Chin. J. Appl. Ecol. 13(3), 354–383. https://doi.org/10.13287/j.1001-9332.2002.0084 (2002).Article 

Google Scholar 
24.Gustafson, E. J., Roberts, L. J. & Leefers, L. A. Linking linear programming and spatial simulation models to predict landscape effects of forest management alternatives. J. Environ. Manage. 81(4), 339–350. https://doi.org/10.1016/j.jenvman.2005.11.009 (2006).Article 
PubMed 

Google Scholar 
25.Restrepo, A. M. C. et al. Land cover change during a period of extensive landscape restoration in Ningxia Hui Autonomous Region China. Sci Total Environ. 598, 669–679. https://doi.org/10.1016/j.scitotenv.2017.04.124 (2017).ADS 
CAS 
Article 

Google Scholar 
26.Gong, W. F. et al. Effect of terrain on landscape patterns and ecological effects by a gradient-based RS and GIS analysis. J. For. Res. 28(5), 1061–1072. https://doi.org/10.1007/s11676-017-0385-8 (2017).Article 

Google Scholar 
27.Birhane, E. et al. Land use land cover changes along topographic gradients in Hugumburda national forest priority area, Northern Ethiopia. Remote Sens. Appl. Soc. Environ. 13, 61–68. https://doi.org/10.1016/j.rsase.2018.10.017 (2019).Article 

Google Scholar 
28.Tian, P. et al. Research on land use changes and ecological risk assessment in Yangjiang River Basin in Zhejiang Province China. Sustainability 11(10), 2817. https://doi.org/10.3390/su11102817 (2019).Article 

Google Scholar 
29.Xiong, M., Xu, Q. X. & Yuan, J. Analysis of multi-factors affecting sediment load in the Three Gorges Reservoir. Quatern Int. 208, 76–84. https://doi.org/10.1016/j.quaint.2009.01.010 (2009).Article 

Google Scholar 
30.Feng, L. & Xu, J. Y. Farmers’willingness to participate in the next-stage Grain-for-Green project in the Three Gorges Reservoir Area China. Environ. Manage. 56, 505–518. https://doi.org/10.1007/s00267-015-0505-1 (2015).ADS 
Article 
PubMed 

Google Scholar 
31.Cao, S. et al. Ecosystem water imbalances created during ecological restoration by afforestation in China, and lessons for other developing countries. J. Environ. Manage. 183, 843–849. https://doi.org/10.1016/j.jenvman.2016.07.096 (2016).Article 
PubMed 

Google Scholar 
32.Galicia, L., Zarco-Arista, A. E. & Mendoza-Robles, K. I. Land use/cover, landforms and fragmentation patterns in a tropical dry forest in the southern Pacific region of Mexico. Singapore J. Trop. Geo. 29(2), 137–154. https://doi.org/10.1111/j.1467-9493.2008.00326.x (2008).Article 

Google Scholar 
33.Zhong, S. Q. et al. Mechanized and optimized configuration pattern of crop-mulberry systems for controlling agricultural non-point source pollution on sloping farmland in the Three Gorges Reservoir Area, China. Int. J. Env. Res. Pub. He. 17, 3599. https://doi.org/10.3390/ijerph17103599 (2020).CAS 
Article 

Google Scholar 
34.Qi, S. W., Yue, Z. Q., Liu, C. L. & Zhou, Y. D. Significance of outward dipping strata in argillaceous limestones in the area of the Three Gorges reservoir China. Bull. Eng. Geol. Environ. 68, 195–200. https://doi.org/10.1007/s10064-009-0206-1 (2009).CAS 
Article 

Google Scholar 
35.Zhang, Q. et al. The spatial granularity effect, changing landscape patterns, and suitable landscape metrics in the Three Gorges Reservoir Area, 1995–2015. Ecol. Indic. https://doi.org/10.1016/j.ecolind.2020.106259 (2020).Article 

Google Scholar 
36.Yang, H. C., Wang, G. Q., Wang, L. J. & Zheng, B. H. Impact of land use changes on water quality in headwaters of the Three Gorges Reservoir. Environ. Sci. Pollut. Res. Int. 23(12), 11448–11460. https://doi.org/10.1007/s11356-015-5922-4 (2016).CAS 
Article 
PubMed 

Google Scholar 
37.Shen, Z. Y. et al. A comparison of WEPP and SWAT for modeling soil erosion of the Zhangjiachong Watershed in the Three Gorges Reservoir Area. Agric. Water Manage. 96, 1435–1442. https://doi.org/10.1016/j.agwat.2009.04.017 (2009).Article 

Google Scholar 
38.Zhang, J. X., Liu, Z. J. & Sun, X. X. Changing landscape in the Three Gorges Reservoir Area of Yangtze River from 1977 to 2005: land use/land cover, vegetation cover changes estimated using multi-source satellite data. Int. J. Appl. Earth Obs. 11, 403–412. https://doi.org/10.1016/j.jag.2009.07.004 (2009).CAS 
Article 

Google Scholar 
39.Huang, C. B. et al. Land use/cover change in the Three Gorges Reservoir area, China: reconciling the land use conflicts between development and protection. CATENA 175, 388–399. https://doi.org/10.1016/j.catena.2019.01.002 (2019).Article 

Google Scholar 
40.Wang, W. & Pu, Y. Analysis of landscape patterns and the trend of forest resources in the Three Gorges Reservoir area. J. Geosci. Environ. Protect. 6, 181–192. https://doi.org/10.4236/gep.2018.65015 (2018).Article 

Google Scholar 
41.Li, Z., Wang, R., Zhou, Z. & Luo, X. Three Gorges Project’s impact on the water resource and environment of Yangtze River. J. Appl. Sci. 13(17), 3394–3399. https://doi.org/10.3923/jas.2013.3394.3399 (2013).Article 

Google Scholar 
42.Liang, X. Y. et al. Exploring cultivated land evolution in mountainous areas of Southwest China, an empirical study of development since the 1980s. Land Degrad. Dev. 32, 546–558. https://doi.org/10.1002/ldr.3735 (2021).Article 

Google Scholar 
43.Kelly, M., Tuxen, K. A. & Stalberg, D. Mapping changes to vegetation pattern in a restoring wetland: Finding pattern metrics that are consistent across spatial scale and time. Ecol. Indic. 11(2), 263–273. https://doi.org/10.1016/j.ecolind.2010.05.003 (2011).Article 

Google Scholar 
44.Guo, S. Q. et al. Spatiotemporal variation and landscape pattern of soil erosion in Qinling Mountains. Chin. J. Ecol. 38(7), 2167–2176. https://doi.org/10.13292/j.1000-4890.201907.016 (2019).Article 

Google Scholar 
45.Peng, W. J. & Shu, Y. G. Analysis of landscape ecological security and cultivated land evolution in the Karst mountain area. Acta Ecol. Sinc. 38(3), 852–865. https://doi.org/10.5846/stxb201612062513 (2018).Article 

Google Scholar 
46.Saura, S. Effects of remote sensor spatial resolution and data aggregation on selected fragmentation indices. Landscape Ecol. 19(2), 197–209. https://doi.org/10.1023/B:LAND.0000021724.60785.65 (2004).Article 

Google Scholar 
47.Kerenyi, A. & Szabo, G. Human impact on topography and landscape pattern in the Upper Tisza region NE-Hungary. Geogr. Fis. Din. Quat. 30(2), 193–196. https://doi.org/10.1144/GSL.SP.2007.270.01.17 (2007).Article 

Google Scholar 
48.Zhang, Y. X. et al. Changes in cultivated land patterns and driving forces in the Three Gorges Reservoir area, China, from 1992 to 2015. J. Mt. Sci. 17(1), 203–215. https://doi.org/10.1007/s11629-019-5375-1 (2020).Article 

Google Scholar 
49.Teng, M. J. et al. Impacts of forest restoration on soil erosion in the Three Gorges Reservoir area China. Sci. Total Environ. 697, 134164. https://doi.org/10.1016/j.scitotenv.2019.134164 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
50.He, L. H., King, L. & Tong, J. On the land use in the Three Gorges Reservoir area. J. Geogr. Sci. 13(4), 416–422. https://doi.org/10.1007/BF02837879 (2003).Article 

Google Scholar 
51.Gao, J. M. et al. Bioavailability of organic phosphorus in the water level fluctuation zone soil and the effects of ultraviolet irradiation on it in the Three Gorges Reservoir China. Sci. Total Environ. 738, 139912. https://doi.org/10.1016/j.scitotenv.2020.139912 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
52.Xie, Y. H. et al. The impact of Three Gorges Dam on the downstream eco-hydrological environment and vegetation distribution of East Dongting Lake. Ecohydrology 8(4), 738–746. https://doi.org/10.1002/eco.1543 (2015).Article 

Google Scholar 
53.Cai, H. Y. et al. Quantifying the impact of the Three Gorges Dam on the thermal dynamics of the Yangtze River. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/aab9e0 (2018).Article 

Google Scholar 
54.Tang, Q. et al. Flow regulation manipulates contemporary seasonal sedimentary dynamics in the reservoir fluctuation zone of the Three Gorges Reservoir China. Sci. Total Environ. 548, 410–420. https://doi.org/10.1016/j.scitotenv.2015.12.158 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
55.Shen, Z. Y. et al. Assessment of nitrogen and phosphorus loads and casual factors from different land use and soil types in the Three Gorges Reservoir Area. Sci. Total Environ. 454–455, 383–392. https://doi.org/10.1016/j.scitotenv.2013.03.036 (2013).ADS 
CAS 
Article 
PubMed 

Google Scholar 
56.Zhu, K. W. et al. Vegetation of the water-level fluctuation zone in the Three Gorges Reservoir at the initial impoundment stage. Glob. Ecol. Conserv. 21, e00866. https://doi.org/10.1016/j.gecco.2019.e00866 (2020).Article 

Google Scholar 
57.Chen, C. D. et al. Restoration design for Three Gorges Reservoir shorelands, combining Chinese traditional agro-ecological knowledge with landscape ecological analysis. Ecol. Eng. 71, 584–597. https://doi.org/10.1016/j.ecoleng.2014.07.008 (2014).Article 

Google Scholar 
58.Bao, Y., Gao, P. & He, X. The water-level fluctuation zone of Three Gorges Reservoir-a unique geomorphological unit. Earth Rev. 150, 14–24. https://doi.org/10.1016/j.earscirev.2015.07.005 (2015).Article 

Google Scholar 
59.Li, Y. et al. Homogeneous selection dominates the microbial community assembly in the sediment of the Three Gorges Reservoir. Sci. Total. Environ. 690, 50–60. https://doi.org/10.1016/j.scitotenv.2019.07.014 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
60.Wang, L. J. et al. Role of reservoir construction in regional land use change in Pengxi River basin upstream of the Three Gorges Reservoir in China. Environ. Earth Sci. 75, 1048. https://doi.org/10.1007/s12665-016-5758-3 (2016).Article 

Google Scholar 
61.Zhong, H. P. et al. Analysis of stage response of land use in Three Gorges Reservoir area: taking Hubei section of the reservoir area as an example. J. Central Normal Univ. Nat. Sci. 53(4), 582–593. https://doi.org/10.19603/j.cnki.1000-1190.2019.04.019 (2019).Article 

Google Scholar 
62.Brady, N.C. & Weil, R.R. The nature and properties of Soils14th. Prentice Hall, 2007:212–213.63.Gerrard, J. Fundamentals of Soil: Berlin Germany: Routledge, 2000:110–115.64.Otukei, J. R. & Blaschke, T. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. Int. J. Appl. Earth Obs. Geoinf. 12S, S27–S31. https://doi.org/10.1016/j.jag.2009.11.002 (2010).Article 

Google Scholar 
65.Li, R. K., Li, Y. B., Wen, W. & Zhou, Y. L. Comparative study on spatial difference of elevation and slope in soil erosion evolution in typical watershed. J. Soil Water Conserv. 31(5), 99–107. https://doi.org/10.13870/j.cnki.stbcxb.2017.05.016 (2017).Article 

Google Scholar 
66.Li, S. F. et al. An estimation of the extent of cropland abandonment in mountainous regions of China. Land Degrad Dev. 29(5), 1327–1342. https://doi.org/10.1002/ldr.2924 (2018).Article 

Google Scholar 
67.Sang, X. et al. Intensity and stationarity analysis of land use change based on CART algorithm. Sci. Rep-UK 9, 12279. https://doi.org/10.1038/s41598-019-48586-3 (2019).ADS 
CAS 
Article 

Google Scholar 
68.Strehmel, A., Schmalz, B. & Fohrer, N. Evaluation of land use, land management and soil conservation strategies to reduce non-point source pollution loads in the Three Gorges Region China. Environ Manage 58, 906–921. https://doi.org/10.1007/s00267-016-0758-3 (2016).ADS 
Article 
PubMed 

Google Scholar 
69.Liang, X. Y. et al. Traditional agroecosystem transition in mountainous area of Three Gorges Reservoir Area. J Geogr Sci. 30(2), 281–296. https://doi.org/10.1007/s11442-020-1728-5 (2020).Article 

Google Scholar 
70.Kalerstaghi, A. & Jeloudar, Z. J. Land use/cover change and driving force analyses in parts of northern Iran using RS and GIS techniques. Arab. J. Geosci. 4(3–4), 401–411. https://doi.org/10.1007/s12517-009-0078-5 (2011).Article 

Google Scholar 
71.Ministry of Ecology and Environment of the People’s Republic of China (MEE). Gazette of eco-environmental monitoring of three gorges project. Yangzi River, China 1997–2017 (in Chinese) (2018). http://jcs.mep.gov.cn/hjzl/sxgb/2011sxgb/201206/P020120608565218279423.pdf. Accessed 3 March 2019.72.Xu, X. B. et al. Unravelling the effects of large-scale ecological programs on ecological rehabilitation of China’s Three Gorges Dam. J. Clean. Prod. 256, 120446. https://doi.org/10.1016/j.jclepro.2020.120446 (2020).CAS 
Article 

Google Scholar 
73.Staged Assessment Group of Chinese Academy of Engineering (SAGCAE). Staged Assessment Report of the Three Gorges Project (Comprehensive Volume) (in Chinese). Chinese Water Power Press, Beijing, China (2010).74.Li, R. K. et al. Study on the temporal and spatial variation of soil erosion intensity in typical watersheds of the Three Gorges Reservoir Area from 1988 to 2015: a case based on the Daning and Meixi River Watershed. Acta Ecological Sinica. 38(17), 6243–7257. https://doi.org/10.5846/stxb201706071040 (2018).Article 

Google Scholar 
75.Birhanu, L., Hailu, B. T., Bekele, T. & Demissew, S. Land use/land cover change along elevation and slope gradient in highlands of Ethiopia. Remote Sensing Appl. Soc. Environ. 16, 100260 (2019).Article 

Google Scholar 
76.Huang, X.Y., Ma, J.S. & Tang, Q. Introduction to geographic information systems. Beijing: Higher Education Press. 165–171 (2001).77.Xiao, J. Y. et al. Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing. Landscape Urban Plan. 75, 69–80. https://doi.org/10.1016/j.landurbplan.2004.12.005 (2006).Article 

Google Scholar 
78.Ye, Q. H. et al. Geospatial-temporal analysis of land-use changes in the Yellow River Delta during the last 40 years. Sci China Ser D. 47, 1008–1024. https://doi.org/10.1360/03yd0151 (2004).Article 

Google Scholar 
79.Liu, J. Y. The Land use in Xizang Autonomous Region (Science Press, 1992).
Google Scholar 
80.Li, X. Z. et al. The adequacy of different landscape metrics for various landscape patterns. Pattern Recogn. 38, 2626–2638. https://doi.org/10.1016/j.patcog.2005.05.009 (2005).Article 

Google Scholar 
81.Buyantuyev, A., Wu, J. G. & Gries, C. Multiscale analysis of the urbanization pattern of the Phoenix metropolitan landscape of USA: Time, space and thematic resolution. Landscape Urban Plan. 94(3), 206–217. https://doi.org/10.1016/j.landurbplan.2009.10.005 (2010).Article 

Google Scholar 
82.McGarigal, K., Cushman, S.A. & Ene, E. FRAGSTATS v4: spatial pattern analysis program for categorical and continuous maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Preprint at http://www.umass.edu/landeco/research/fragstats/fragstats.html (2012).83.Liu, X. L., Yang, Z. P., Di, F. & Chen, X. G. Evaluation on tourism ecological security in nature heritage sites—case of Kanas nature reserve of Xinjiang China. Chin Geogra Sci. 19(3), 265–273. https://doi.org/10.1007/s11769-009-026s5-z (2009).CAS 
Article 

Google Scholar 
84.Zhang, R. S. et al. Landscape ecological security response to land use change in the tidal flat reclamation zone China. Environ Monit Assess. https://doi.org/10.1007/s10661-015-4999-z (2016).Article 
PubMed 

Google Scholar  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