Ecology

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

1.Ferrarini, A., Dai, J., Bai, Y. & Alatalo, J. M. Redefining the climate niche of plant species: A novel approach for realistic predictions of species distribution under climate change. Sci. Total Environ. 671, 1086–1093 (2019).ADS 
CAS 
Article 

Google Scholar 
2.Ferrarini, A., Alsafran, M. H. S. A., Dai, J. & Alatalo, J. M. Improving niche projections of plant species under climate change: Silene acaulis on the British Isles as a case study. Clim. Dyn. 52, 1413–1423 (2019).Article 

Google Scholar 
3.Walther, G.-R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).ADS 
CAS 
Article 

Google Scholar 
4.Thuiller, W., Lavorel, S., Araujo, M. B., Sykes, M. T. & Prentice, I. C. Climate change threats to plant diversity in Europe. Proc. Natl. Acad. Sci. 102, 8245–8250 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
5.Mousavi Kouhi, S. M. & Erfanian, M. B. Predicting the present and future distribution of medusahead and barbed goatgrass in Iran. Ecopersia 8, 41–46 (2020).
Google Scholar 
6.Alavi, S. J., Ahmadi, K., Hosseini, S. M., Tabari, M. & Nouri, Z. The response of English yew (Taxus baccata L.) to climate change in the Caspian Hyrcanian Mixed Forest ecoregion. Reg. Environ. Change 19, 1495–1506 (2019).Article 

Google Scholar 
7.Huntley, B., Berry, P. M., Cramer, W. & McDonald, A. P. Special paper: Modelling present and potential future ranges of some European higher plants using climate response surfaces. J. Biogeogr. 22, 967 (1995).Article 

Google Scholar 
8.Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: Are bioclimate envelope models useful?: Evaluating bioclimate envelope models. Glob. Ecol. Biogeogr. 12, 361–371 (2003).Article 

Google Scholar 
9.Hällfors, M. H. et al. Assessing the need and potential of assisted migration using species distribution models. Biol. Conserv. 196, 60–68 (2016).Article 

Google Scholar 
10.Kamakhina, G. L. Kopetdagh-Khorassan Flora: Regional Features of Central Kopetdagh. In Biogeography and Ecology of Turkmenistan (eds. Fet, V. & Atamuradov, K. I.) Vol. 72 129–148 (Springer Netherlands, 1994).11.Memariani, F., Zarrinpour, V. & Akhani, H. A review of plant diversity, vegetation, and phytogeography of the Khorassan-Kopet Dagh floristic province in the Irano-Turanian region (northeastern Iran–southern Turkmenistan). Phytotaxa 249, 8 (2016).Article 

Google Scholar 
12.Fet, V. Biogeographic Position of the Khorassan-Kopetdagh. In Biogeography and Ecology of Turkmenistan (eds. Fet, V. & Atamuradov, K. I.) Vol. 72 197–204 (Springer Netherlands, 1994).13.Memariani, F. Khorassan-Kopet Dagh mountains. In Plant Biogeography and Vegetation of High Mountains of Central and South-West Asia (ed. Noroozi, J.) (Springer, 2020). https://datadryad.org/stash/dataset/doi:10.5061/dryad.4sb638314.Behroozian, M., Ejtehadi, H., Peterson, A. T., Memariani, F. & Mesdaghi, M. Climate change influences on the potential distribution of Dianthus polylepis Bien. ex Boiss. (Caryophyllaceae), an endemic species in the Irano-Turanian region. PLoS ONE 15, e0237527 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Erfanian, M. B. et al. Data from: Plant community responses to environmentally-friendly piste management in northeast Iran. Dryad Dataset. https://datadryad.org/stash/dataset/doi:10.5061/dryad.4sb6383 (2019).16.Jamzad, Z. Flora of Iran vol. 76 Lamiaceae. (Research Institute of Forests & Rangelands, 2012).17.Sagharyan, M., Ganjeali, A. & Cheniany, M. Investigating the effect of antioxidant compounds and various concentrations of BAP and NAA on the improvement of in vitro stem and root formation of Nepeta binaloudensis Jamzad. NBR 6, 198–205 (2019).Article 

Google Scholar 
18.Nadjafi, F., Koocheki, A., Moghaddam, P. R. & Rastgoo, M. Ethnopharmacology of Nepeta binaludensis Jamzad a highly threatened medicinal plant of Iran. J. Med. Plants 8, 29–35 (2009).
Google Scholar 
19.Nadjafi, F., Koocheki, A., Honermeier, B. & Asili, J. Autecology, ethnomedicinal and phytochemical studies of Nepeta binaludensis Jamzad a highly endangered medicinal plant of Iran. J. Essent. Oil Bear. Plants 12, 97–110 (2009).CAS 
Article 

Google Scholar 
20.Memariani, F., Akhani, H. & Joharchi, M. R. Endemic plants of Khorassan-Kopet Dagh floristic province in Irano-Turanian region: Diversity, distribution patterns and conservation status. Phytotaxa 249, 31 (2016).Article 

Google Scholar 
21.Salmaki, Y. & Joharchi, M. R. Phlomoides binaludensis (Phlomideae, Lamioideae, Lamiaceae), a new species from northeastern Iran. Phytotaxa 172, 265 (2014).Article 

Google Scholar 
22.Pahlevani, A. H., Liede-Schumann, S. & Akhani, H. Seed and capsule morphology of Iranian perennial species of Euphorbia (Euphorbiaceae) and its phylogenetic application: Perennial Species of Euphorbia in Iran. Bot. J. Linn. Soc. 177, 335–377 (2015).Article 

Google Scholar 
23.Olson, D. M. et al. Terrestrial ecoregions of the world: A new map of life on earth. Bioscience 51, 933 (2001).Article 

Google Scholar 
24.Djamali, M. et al. Application of the global bioclimatic classification to Iran: Implications for understanding the modern vegetation and biogeography. Ecol. Mediterr. 37, 91–114 (2011).Article 

Google Scholar 
25.Farashi, A., Shariati, M. & Hosseini, M. Identifying biodiversity hotspots for threatened mammal species in Iran. Mamm. Biol. 87, 71–88 (2017).Article 

Google Scholar 
26.Hosseinzadeh, M. S., Fois, M., Zangi, B. & Kazemi, S. M. Predicting past, current and future habitat suitability and geographic distribution of the Iranian endemic species Microgecko latifi (Sauria: Gekkonidae). J. Arid Environ. 183, 104283 (2020).ADS 
Article 

Google Scholar 
27.Noroozi, J. et al. Endemic diversity and distribution of the Iranian vascular flora across phytogeographical regions, biodiversity hotspots and areas of endemism. Sci. Rep. 9, 12991 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
28.Erfanian, M. B., Ejtehadi, H., Vaezi, J. & Moazzeni, H. Plant community responses to multiple disturbances in an arid region of northeast Iran. Land Degrad. Dev. 30, 1554–1563 (2019).Article 

Google Scholar 
29.Erfanian, M. B. et al. Plant community responses to environmentally friendly piste management in northeast Iran. Ecol. Evol. 9, 8193–8200 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
30.Memariani, F. et al. Plant diversity of the Khorassan-Kopet Dagh Floristic Province (Irano-Turanian Region). (Magnolia Press, 2016)31.Memariani, F., Joharchi, M. R., Ejtehadi, H. & Emadzade, K. A contribution to the flora and vegetation of Binalood mountain range, NE Iran: Floristic and chorological studies in Fereizi region. Ferdowsi Univ. Int. J. Biol. Sci. J. Cell Mol. Res. 1, 1–17 (2009).
Google Scholar 
32.Memariani, F. & Joharchi, M. R. Iris ferdowsii (Iridaceae), a new species of section Regelia from northeast of Iran. Phytotaxa 291, 192 (2017).Article 

Google Scholar 
33.Thuiller, W., Georges, D., Engler, R. & Breiner, F. biomod2: Ensemble Platform for Species Distribution Modeling. R Package. https://cran.r-project.org/package=biomod2 (2019).34.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020).35.Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).Article 

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

Google Scholar 
37.Ahmadi, M., Dadashi Roudbari, A. A., Akbari Azirani, T. & Karami, J. The performance of the HadGEM2-ES model in the evaluation of seasonal temperature anomaly of Iran under RCP scenarios. J. Earth Space Phys. 45, 625–644 (2019).
Google Scholar 
38.Dray, S. & Dufour, A.-B. The ade4 Package: Implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).Article 

Google Scholar 
39.Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat Suitability and Distribution Models: With Applications in R. (Cambridge University Press, 2017).40.Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling. Ecography 37, 191–203 (2014).Article 

Google Scholar 
41.Menard, S. W. Applied Logistic Regression Analysis (Sage Publications, Thousand Oaks, 2002).Book 

Google Scholar 
42.Landis, J. R. & Koch, G. G. The measurement of observer agreement for categorical data. Biometrics 33, 159 (1977).CAS 
MATH 
Article 

Google Scholar 
43.Araujo, M. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).PubMed 
Article 

Google Scholar 
44.Breiner, F. T., Guisan, A., Bergamini, A. & Nobis, M. P. Overcoming limitations of modelling rare species by using ensembles of small models. Methods Ecol. Evol. 6, 1210–1218 (2015).Article 

Google Scholar 
45.Kaky, E., Nolan, V., Alatawi, A. & Gilbert, F. A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants. Ecol. Inform. 60, 101150 (2020).Article 

Google Scholar 
46.Hao, T., Elith, J., Lahoz-Monfort, J. J. & Guillera-Arroita, G. Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models. Ecography 43, 549–558 (2020).Article 

Google Scholar 
47.Abdelaal, M., Fois, M., Fenu, G. & Bacchetta, G. Using MaxEnt modeling to predict the potential distribution of the endemic plant Rosa arabica Crép, Egypt. Ecol. Inform. 50, 68–75 (2019).Article 

Google Scholar 
48.Thuiller, W. et al. Endemic species and ecosystem sensitivity to climate change in Namibia. Glob. Change Biol. 12, 759–776 (2006).ADS 
Article 

Google Scholar 
49.Chitale, V. S., Behera, M. D. & Roy, P. S. Future of endemic flora of biodiversity hotspots in India. PLoS ONE 9, e115264 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
50.Fois, M., Bacchetta, G., Cogoni, D. & Fenu, G. Current and future effectiveness of the Natura 2000 network for protecting plant species in Sardinia: A nice and complex strategy in its raw state?. J. Environ. Plan. Manag. 61, 332–347 (2018).Article 

Google Scholar 
51.Mamet, S. D., Brown, C. D., Trant, A. J. & Laroque, C. P. Shifting global Larix distributions: Northern expansion and southern retraction as species respond to changing climate. J. Biogeogr. 46, 30–44 (2019).Article 

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

Google Scholar 
53.Hosseini, S. S., Ejtehadi, H. & Memariani, F. The first report Nepeta binaloudensis Jamzad in Hezar masjed mountains of Khorasan Razavi province. In Proceedings of the 9th National Congress and 7th International Congrees of Bilogy of Iran (2016).54.Dullinger, S. et al. Extinction debt of high-mountain plants under twenty-first-century climate change. Nat. Clim. Change 2, 619–622 (2012).ADS 
Article 

Google Scholar 
55.Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol. 14, e2001104 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
56.Casazza, G. et al. Climate change hastens the urgency of conservation for range-restricted plant species in the central-northern Mediterranean region. Biol. Conserv. 179, 129–138 (2014).Article 

Google Scholar 
57.Zhang, M.-G. et al. Major declines of woody plant species ranges under climate change in Yunnan, China. Divers. Distrib. 20, 405–415 (2014).CAS 
Article 

Google Scholar 
58.Sanjerehei, M. M. & Rundel, P. W. The impact of climate change on habitat suitability for Artemisia sieberi and Artemisia aucheri (Asteraceae)—A modeling approach. Pol. J. Ecol. 65, 97–109 (2017).Article 

Google Scholar 
59.Abolmaali, S.M.-R., Tarkesh, M. & Bashari, H. MaxEnt modeling for predicting suitable habitats and identifying the effects of climate change on a threatened species, Daphne mucronata, in central Iran. Ecol. Inform. 43, 116–123 (2018).Article 

Google Scholar 
60.Di Musciano, M. et al. Dispersal ability of threatened species affects future distributions. Plant Ecol. 221, 265–281 (2020).Article 

Google Scholar 
61.Fois, M., Cuena-Lombraña, A., Fenu, G., Cogoni, D. & Bacchetta, G. The reliability of conservation status assessments at regional level: Past, present and future perspectives on Gentiana lutea L. ssp. lutea in Sardinia. J. Nat. Conserv. 33, 1–9 (2016).Article 

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These authors contributed equally: Marc Palahí, Ruben ValbuenaEuropean Forest Institute, Joensuu, FinlandMarc Palahí, Lauri Hetemäki, Pieter Johannes Verkerk & Minna KorhonenSchool of Natural Sciences, Bangor University, Bangor, UKRubén ValbuenaEcosystem Dynamics and Forest Management Group, Technical University of Munich, Munich, GermanyCornelius Senf & Rupert SeidlSchool of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UKNezha Acil, Thomas A. M. Pugh & Jonathan SadlerBirmingham Institute of Forest Research, University of Birmingham, Birmingham, UKNezha Acil, Thomas A. M. Pugh & Jonathan SadlerDepartment of Physical Geography and Ecosystem Science, Lund University, Lund, SwedenThomas A. M. PughDepartment of Geographical Sciences, University of Maryland, College Park, MD, USAPeter PotapovInstitut Européen de la Forêt Cultivée, Cestas, FranceBarry GardinerDipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, Florence, ItalyGherardo Chirici & Saverio FranciniDipartimento per l’Innovazione dei Sistemi Biologici, Agroalimentari e Forestali, Università degli Studi della Tuscia, Viterbo, ItalySaverio FranciniFaculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Prague, Czech RepublicTomáš Hlásny & Róbert MarušákWageningen Environmental Research, Wageningen University and Research, Wageningen, The NetherlandsBas Jan Willem Lerink & Gert-Jan NabuursDepartment of Forest Resource Management, Swedish University of Agricultural Sciences (SLU), Umeå, SwedenHåkan Olsson & Jonas FridmanJoint Research Unit CTFC – AGROTECNIO, Solsona, SpainJosé Ramón González OlabarriaDepartment of Agricultural, Forest and Food Sciences, University of Turin, Grugliasco, ItalyDavide AscoliNatural Resources Institute Finland, Joensuu, FinlandAntti AsikainenAlbert-Ludwigs-University of Freiburg, Freiburg, GermanyJürgen Bauhus & Marc HanewinkelDepartment of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, SwedenGöran BerndesLatvian State Forest Research Institute Silava, Salaspils, LatviaJanis DonisINRAE, University of Bordeaux, BIOGECO, Cestas, FranceHervé JactelEuropean Forest Institute, Bonn, GermanyMarcus LindnerUniversity of Molise, Campobasso, ItalyMarco MarchettiForest Ecology and Forest Management Group, Wageningen University and Research, Wageningen, The NetherlandsDouglas Sheil & Gert-Jan NabuursCentro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, Lisbon, PortugalMargarida ToméForest Science and Technology Centre of Catalonia, CTFC, Solsona, SpainAntoni TrasobaresM.P. and G.-J.N. conceived and initiated the study. R.V., C.S., N.A., T.A.M.P., G.C., S.F., T.H., B.J.W.L. and D.A. ran different parts of the analyses and demonstrations. M.P., R.V., G.-J.N., C.S., T.A.M.P., J.S., R.S., B.G. and L.H. drafted the initial version of the manuscript. P.P. provided first-hand experience of the algorithms involved in the production of GFC data. All authors offered insights from their own national statistics and local knowledge, which focused the analyses and the argumentation, and contributed critically to the interpretation of the results, revising and approving the final version of the manuscript. More

Identification of ultra-small cells associated with blooms of anoxygenic phototrophic gammaproteobacteriaThe Salada de Chiprana (NE Spain) is the only permanent athalassic hypersaline lake in Western Europe. It harbors thick, conspicuous microbial mats covering its bottom (Fig. 1a, b) and exhibits periodic stratification during which the deepest part of the water column becomes anoxic and sulfide-rich, favoring the massive development of sulfide-dependent anoxygenic photosynthetic bacteria16. We collected microbial mat fragments that were maintained in culture in the laboratory. After several weeks, we observed a bloom of anoxygenic photosynthetic bacteria containing numerous intracellular sulfur granules (Fig. 1c). Many of these bacterial cells showed one or several much smaller and darker non-flagellated cells attached to their surface (Fig. 1d, e). The infected photosynthetic cells were highly mobile, swimming at high speed with frequent changes of direction (Supplementary Movie 1), in contrast with the non-infected cells that displayed slower (approximately half speed) and more straight swimming. Sometimes, two or more photosynthetic cells were connected through relatively short filaments formed by stacked epibiont cells (Fig. 1f). Although the photosynthetic cells carrying these epibionts were often actively swimming, in some cases the epibionts were associated to empty ghost cells where only the photosynthesis-derived sulfur granules persisted. Closer scrutiny of the epibionts revealed that they actually consisted of piles of up to 10 very flattened cells of 550 ± 50 nm diameter and 220 ± 20 nm height (n = 100). These characteristics (size, morphology, and specific attachment to sulfide-dependent anoxygenic photosynthetic bacteria) perfectly matched the morphological description of the genus Vampirococcus observed over forty years ago in sulfidic freshwater lakes15.Fig. 1: Sampling site and microscopy observation of Vampirococcus cells.a General view of the microbial mat covering the shore of the Salada de Chiprana lake. b Closer view of a microbial mat section. c Natural population of blooming sulfide-dependent anoxygenic photosynthetic bacteria in waters of microbial mat containers after several weeks of growth in the laboratory; note the conspicuous refringent intracellular sulfur inclusions. d–f Closer microscopy view of anoxygenic photosynthetic bacteria infected by epibiotic Vampirococcus cells and few-cell filaments (indicated by yellow arrows). g Scanning electron microscopy image of a host cell infected by two stacking Vampirococcus cells (yellow arrow). h Transmission electron microscopy (TEM) image of a thin section of a host cell infected by Vampirococcus (yellow arrow). i Closer TEM view of a thin section of Vampirococcus cells, notice the fibrous rugose cell surface and the large space separating contiguous cells. Scale bars: 5 cm (b), 5 µm (c), 1 µm (d–h), 0.5 µm (i).Full size imageSince the first Vampirococcus description included transmission electron microscopy (TEM) images, to further ascertain this identification we examined our Chiprana Lake samples under TEM and scanning electron microscopy (SEM). SEM images confirmed the peculiar structure of the epibionts, with multiple contiguous cells separated by deep grooves (Fig. 1g). Thin sections observed under TEM confirmed that the cells were actually separated by a space of ~20–50 nm filled by a fibrous material (Fig. 1h, i). The space between epibiont and host cells was larger (~100 nm) and also filled by dense fibrous material (Fig. 1h). The sections also showed that, in contrast with the typical Gram-negative double membrane structure of the host, the epibiont cells had a single membrane surrounded by a thick layer of fibrous material that conferred a rugose aspect to the cells (Fig. 1i). In sharp contrast with the often highly vacuolated cytoplasm of the host, the epibiont cells showed a dense, homogeneous content. These observations were also in agreement with those published for Vampirococcus, reinforcing our conclusion that the epibionts we observed belonged to this genus, although most likely to a different species, as the first described Vampirococcus occurred in a non-hypersaline lake15.Using a micromanipulator coupled to an inverted microscope, we collected cells of the anoxygenic photosynthetic bacterium carrying Vampirococcus attached to their surface (Supplementary Fig. 1) and proceeded to amplify, clone, and sequence their 16 S rRNA genes. We were able to obtain sequences for both the epibiont and the host for ten infected cells and, in all cases, we retrieved the same two sequences. The host was found to be a Halochromatium-like gammaproteobacterium (Supplementary Fig. 2). Phylogenetic analysis of the epibiont sequence showed that it branched within the CPR radiation close to the Absconditabacteria (Supplementary Fig. 3), previously known as candidate phylum SR12. Since all host and epibiont cells we analyzed had identical 16 S rRNA gene sequences, suggesting that they were the result of a clonal bloom, we collected three sets of ca. 10 infected cells and carried out whole genome amplification (WGA) before sequencing (Illumina HiSeq; see Methods). This strategy allowed us to assemble the nearly complete genome sequence of the Vampirococcus epibiont (see below). In contrast with the completeness of this genome, we only obtained a very partial assembly (~15%) of the host genome, probably because of the consumption of the host DNA by the epibiont. To make more robust phylogenetic analyses of Vampirococcus, we retrieved the protein sequence set used by Hug et al. to reconstruct a multi-marker large-scale phylogeny of bacteria2. The new multi-gene maximum likelihood (ML) phylogenetic tree confirmed the affiliation of our Vampirococcus species to the Absconditabacteria with maximum support, and further placed this clade within a larger well-supported group also containing the candidate phyla Gracilibacteria and Peregrinibacteria (Fig. 2a and Supplementary Fig. 4). Therefore, our epibiotic bacterium represents the first characterized member of this large CPR clade and provides a phylogenetic identity for the predatory bacterial genus Vampirococcus described several decades ago. We propose to call this new species Candidatus Vampirococcus lugosii (see Taxonomic appendix).Fig. 2: Phylogeny and global gene content of the Vampirococcus genome.a Maximum likelihood phylogenetic tree of bacteria based on a concatenated dataset of 16 ribosomal proteins showing the position of Vampirococcus lugosii close to the Absconditabacteria (for the complete tree, see Supplementary Fig. 4). Histograms on the right show the proportion of genes retained in each species from the ancestral pool inferred for the last common ancestor of Absconditabacteria, Gracilibacteria and Peregrinibacteria. b Percentage of Vampirococcus genes belonging to the different Clusters of Orthologous Groups (COG) categories. c Genes shared by Vampirococcus and the three Absconditabacteria genomes shown in the phylogenetic tree. COG categories are: Energy production and conversion [C]; Cell cycle control, cell division, chromosome partitioning [D]; Amino acid transport and metabolism [E]; Nucleotide transport and metabolism [F]; Carbohydrate transport and metabolism [G]; Coenzyme transport and metabolism [H]; Lipid transport and metabolism [I]; Translation, ribosomal structure and biogenesis [J]; Transcription [K]; Replication, recombination and repair [L]; Cell wall/membrane/envelope biogenesis [M]; Secretion, motility and chemotaxis [N]; Posttranslational modification, protein turnover, chaperones [O]; Inorganic ion transport and metabolism [P]; General function prediction only [R]; Function unknown [S]; Intracellular trafficking, secretion, and vesicular transport [U]; Defense mechanisms [V]; Mobilome: prophages, transposons [X]; Secondary metabolites biosynthesis, transport and catabolism [Q]. Source data are provided as a Source Data file.Full size imageGenomic evidence of adaptation to predatory lifestyleWe sequenced DNA from three WGA experiments corresponding each to ~10 Halochromatium-Vampirococcus consortia. Many of the resulting (57.2 Mb) raw sequences exhibited similarity to those of available Absconditabacteria/SR1 metagenome-assembled genomes (MAGs) and, as expected, some also to Gammaproteobacteria (host-derived sequences) as well as a small proportion of potential contaminants probably present in the original sample (Bacillus- and fungi-like sequences). To bin the Vampirococcus sequences out of this mini-metagenome, we applied tetranucleotide frequency analysis on the whole sequence dataset using emergent self-organizing maps (ESOM)6. One of the ESOM sequence bins was enriched in Absconditabacteria/SR1-like sequences and corresponded to the Vampirococcus sequences, which we extracted and assembled independently. This approach yielded an assembly of 1,310,663 bp. We evaluated its completeness by searching i) a dataset of 40 universally distributed single-copy genes17 and ii) a dataset of 43 single-copy genes widespread in CPR bacteria8. We found all them as single-copy genes in the Vampirococcus genome, with the exception of two signal recognition particle subunits from the first dataset which are absent in many other CPR bacteria18. These results supported that the Vampirococcus genome assembly was complete and did not contain multiple strains or other sources of contamination. Manually curated annotation predicted 1151 protein-coding genes, a single rRNA gene operon, and 38 tRNA coding genes. As already found in other Absconditabacteria/SR1 genomes19, the genetic code of Vampirococcus is modified, with the stop codon UGA reassigned as an additional glycine codon.A very large proportion of the predicted proteins (48.9%) had no similarity to any COG class and lacked any conserved domain allowing their functional annotation (Fig. 2b). Thus, as for other CPR bacteria, a significant part of their cellular functions remains inaccessible. A comparison with three other Absconditabacteria genomes revealed a very small set of only 390 genes conserved in all them (Fig. 2c), suggesting a highly dynamic evolution of gene content in these species. Comparison with more distantly related CPR groups (Gracilibacteria and Peregrinibacteria) showed that gene loss has been a dominant trend in all these organisms, which have lost 30–50% of the 1124 genes inferred to have existed in their last common ancestor (Fig. 2a). Nevertheless, this loss of ancestral genes was accompanied by the acquisition of new ones by different mechanisms, including horizontal gene transfer (HGT). In the case of Vampirococcus, we detected, by phylogenetic analysis of all individual genes that had homologs in other organisms, the acquisition of 126 genes by HGT from various donors (Supplementary Data 1).The set of genes that could be annotated provided interesting clues about the biology and lifestyle of Vampirococcus. The most striking feature was its oversimplified energy and carbon metabolism map (Fig. 3). ATP generation in this CPR species appeared to depend entirely on substrate-level phosphorylation carried out by the phosphoenolpyruvate kinase (EC 2.7.1.40). In fact, Vampirococcus only possesses incomplete glycolysis, which starts with 3-phosphoglycerate as first substrate. This molecule is the major product of the enzyme RuBisCO and, therefore, most likely highly available to Vampirococcus from its photosynthetic host. Comparison with nearly complete MAG sequences available for other Absconditabacteria/SR1 showed that Vampirococcus has the most specialized carbon metabolism, with 3-phosphoglycerate as the only exploitable substrate, whereas the other species have a few additional enzymes that allow them to use other substrates (such as ribulose-1,5 P and acetyl-CoA) as energy and reducing power (NADH) sources (Supplementary Fig. 5). This metabolic diversification probably reflects their adaptation to other types of hosts where these substrates are abundant. Vampirococcus also lacks all the enzymes involved in some Absconditabacteria/SR1 in the 3-phosphoglycerate-synthesizing AMP salvage pathway20, including the characteristic archaeal-like type II/III RuBisCO21.Fig. 3: Metabolic and cell features inferred from the genes encoded in the Vampirococcus genome.The diagram shows the host cell surface (bottom) with two stacking Vampirococcus cells attached to its surface (as in Fig. 1h).Full size imageThe genomes of Absconditabacteria/SR1 and Vampirococcus encode several electron carrier proteins (e.g., ferredoxin, cytochrome b5, several Fe-S cluster proteins) and a membrane F1FO-type ATP synthase. However, they apparently lack any standard electron transport chain and, therefore, they seem to be non-respiring19,20,22. The electron carrier proteins may be related with the oxidative stress response and/or the reoxidation of reduced ferredoxin or NADH20. In the absence of any obvious mechanism to generate proton motive force (PMF), the presence of the membrane ATP synthase is also intriguing. It has been speculated either that CPR bacteria might tightly adhere to their hosts and scavenge protons from them or that the membrane ATP synthase might work in the opposite direction as an ATPase, consuming ATP generated by substrate level phosphorylation to extrude protons and drive antiporters9. However, in the case of Vampirococcus the direct transport of protons from the host is unlikely since, as observed in the TEM sections (Fig. 1h), it seems that there is no direct contact with the host cell membrane. In fact, parasite and host cell membranes are separated by a relatively large space of ~100 nm, which would be largely conducive to proton diffusion and inefficient transfer between cells. Alternatively, since the Chiprana lake has high Na+ concentration (1.6 g l−1), it might be possible that the ATP synthase uses Na+ instead of protons. However, the Na+-binding domain of the subunit c of typical Na+-dependent ATP synthases exhibited several differences with that of Vampirococcus (Supplementary Fig. 6). Similar differences have been considered indicative of the use of protons instead of Na+ in other organisms23. Although the proton/cation antiporters (e.g., for Na+, K+, or Ca2+) encoded by Vampirococcus and the other Absconditabacteria/SR1 may serve to produce some PMF, it is improbable that this mechanism represents a major energy transducing system as cells would accumulate cations and disrupt their ionic balance; these antiporters are most likely involved in cation homeostasis.These observations prompted us to investigate other ways that these cells might use to generate PMF usable by their ATP synthase. We found a protein (Vamp_33_45) with an atypical tripartite domain structure. The N-terminal region, containing 8 transmembrane helices, showed similarity with several flavocytochromes capable of moving electrons and/or protons across the plasma membrane (e.g., 24). The central part of the protein was a rubredoxin-like nonheme iron-binding domain likely able to transport electrons. Finally, the C-terminal region, containing an NAD-binding motif, was similar to ferredoxin reductases involved in electron transfer25. This unusual Vampirococcus 3-domain protein is well conserved in the other Absconditabacteria/SR1 genomes sequenced so far, suggesting it plays an important function in this CPR phylum. Its architecture suggests that it can transport electrons and/or protons across the membrane using ferredoxin as electron donor and makes it a strong candidate to participate in a putative new PMF-generating system. Alternatively, this protein could play a similar role to that of some oxidoreductases in the strict anaerobic archaeon Thermococcus onnurineus, including a thioredoxin reductase, which couple reactive oxygen species detoxification with NAD(P) + regeneration from NAD(P)H to maintain the intracellular redox balance and enhance O2-mediated growth despite the absence of heme-based or cytochrome-type proteins26.Although our Vampirococcus genome sequence appears to be complete, genes encoding enzymes involved in the biosynthesis of essential cell building blocks such as amino acids, nucleotides and nucleosides, cofactors, vitamins, and lipids are almost completely absent (Fig. 2b). Therefore, the classical bacterial metabolic pathways for their synthesis27 do not operate in Vampirococcus. Such simplified metabolic potential, comparable to that of intracellular parasitic bacteria such as Mycoplasma28, implies that Vampirococcus must acquire these molecules from an external source and supports the predatory nature of the interaction with its photosynthetic host. An intriguing aspect of this interaction concerns the transfer of substrates from the host to Vampirococcus, especially considering that, despite examination of several serial ultrathin sections, the cell membranes of these two partners do not appear to be in direct contact (Fig. 1h). Vampirococcus encodes several virulence factors, including divergent forms of hemolysin and hemolysin translocator (Vamp_11_169 and Vamp_9_166, respectively), a phage holin (Vamp_5_129), and a membrane-bound lytic murein transglycosylase (Vamp_144_2). These proteins are likely involved in the host cell wall and membrane disruption leading to cell content release. Hemolysin has also been found in Saccharibacteria (formerly candidate phylum TM7), the only CPR phylum for which an epibiotic parasitic lifestyle has been demonstrated so far13,14. Recent coupled lipidomic-metagenomic analyses have shown that CPR bacteria that lack complete lipid biosynthesis are able to recycle membrane lipids from other bacteria29. In Vampirococcus, also devoid of phospholipid synthesis, a phospholipase gene (Vamp_34_196) predicted to be secreted and that has homologs involved in host phospholipid degradation in several parasitic bacteria30, may not only help disrupting the host membrane but also to generate a local source of host phospholipids that it can use to build its own cell membrane. Two Vampirococcus peptidoglycan hydrolases (Vamp_68-56_103 and Vamp_145_30), also predicted to be secreted, most probably contribute to degrade the host cell wall. The Vampirococcus genome also encodes two murein DD-endopeptidases (Vamp_311_38 and Vamp_41_33). As in other predatory bacteria, such as Bdellovibrio, one probably acts to degrade the prey cell wall whereas the other is involved in self-wall remodeling31. Despite their high sequence divergence, we could align both Vampirococcus sequences with those of Bdellovibrio and other bacteria (Supplementary Fig. 7). Both sequences conserved the characteristic active site serine residue of DD endopeptidases and, in contrast with the Bdellovibrio “predatory” enzymes, also the regulatory domain III. The deletion of this regulatory domain has been associated with the capacity of the Bdellovibrio “predatory” DD endopeptidases to act promiscuously on a wide variety of peptidoglycan substrates31. This difference most likely reflects that, whereas Bdellovibrio is able to prey on very diverse bacteria, Vampirococcus is a specialized predator of Chromatiaceae that has evolved specialized enzymes to degrade the wall of its particular prey. The Vampirococcus enzymes could also be aligned with the region where the ankyrin-repeat-containing self-protective regulatory inhibitor Bd3460 binds the Bdellovibrio “predatory” DD endopeptidases32, although only partially for the C-terminal part of Vamp_41_33, like in the self-wall Bdellovibrio enzyme Bd3244 (Supplementary Fig. 7). In that sense, the Vamp_311_38 enzyme seems more similar to the “predatory” Bdellovibrio ones. Interestingly, the “predatory” endopeptidase Bd3459 and the regulatory inhibitor Bd3460 are contiguous in the genomes of Bdellovibrio and other periplasmic predators but not in epibiotic predators32. Vampirococcus confirms this pattern since, although it possesses several ankyrin-repeat-containing proteins, none of them is encoded adjacent to the DD endopeptidase genes.Vampirococcus also possesses a number of genes encoding transporters, most of them involved in the transport of inorganic molecules (Fig. 3). One notable exception is the competence-related integral membrane protein ComEC (Vamp_67_106)33 which, together with ComEA (Vamp_21_186) and type IV pili (see below), probably plays a role in the uptake of host DNA that, once transported into the epibiont, can be degraded by various restriction endonucleases (five genes encoding them are present) and recycled to provide the nucleotides necessary for growth (Supplementary Fig. 8). These proteins are widespread in other CPR bacteria where they may have a similar function34. Vampirococcus also encodes an ABC-type oligopeptide transporter (Vamp_40_40) and a DctA-like C4-dicarboxylate transporter (Vamp_41_97), known to catalyze proton-coupled symport of several Krebs cycle dicarboxylates (succinate, fumarate, malate, and oxaloacetate)35. The first, coupled with the numerous peptidases present in Vampirococcus, most likely is a source of amino acids. By contrast, the role of DctA is unclear since Vampirococcus does not have a Krebs cycle.In sharp contrast with its simplified central metabolism, Vampirococcus possesses genes related to the construction of an elaborate cell surface, which seems to be a common theme in many CPR bacteria9,12. They include genes involved in peptidoglycan synthesis, several glycosyltransferases, a Sec secretion system, and a rich repertoire of type IV pilus proteins. The retractable type IV pili are presumably involved in the tight attachment of Vampirococcus to its host and in DNA uptake in cooperation with the ComEC protein. Other proteins probably play a role in the specific recognition and fixation to the host, including several very large proteins. In fact, the Vampirococcus membrane proteome is enriched in giant proteins. The ten longest predicted proteins (between 1392 and 4163 aa, see Supplementary Table 1) are inferred to have a membrane localization and are probably responsible of the conspicuous fibrous aspect of its cell surface (Fig. 1i). Most of these proteins possess domains known to be involved in the interaction with other molecules, including protein-protein (WD40, TRP, and PKD domains) and protein–lipid (saposin domain) interactions and cell adhesion (DUF11, integrin, and fibronectin domains). Two other large membrane proteins (Vamp_6_203, 2368 aa, and Vamp_19_245, 1895 aa) may play a defensive role as they contain alpha-2-macroglobulin protease-inhibiting domains that can protect against proteases released by the host. Several other smaller proteins complete the membrane proteome of Vampirococcus, some of them also likely involved in recognition and attachment to the host thanks to a variety of protein domains, such as VWA (Vamp_41_85) and flotillin (Vamp_11_100). We did not detect genes coding for flagellar components, confirming the absence of flagella observed under the microscope (Fig. 1).New CRISPR-Cas systems and other defense mechanisms in Vampirococcus
Although most CPR phyla are devoid of CRISPR-Cas36, some have been found to contain new systems with original effector enzymes such as CasY37. In contrast with most available Absconditabacteria genomes, Vampirococcus possesses two CRISPR-Cas loci (Fig. 4a and Supplementary Fig. 9). The first is a class II type V system that contains genes coding for Cas1, Cas2, Cas4, and Cpf1 proteins associated to 34 spacer sequences of 26–32 bp. Proteins similar to those of this system are encoded not only in genomes of close relatives of the Absconditabacteria (Gracilibacteria and Peregrinibacteria) but in many other CPR phyla. These sequences form monophyletic groups in phylogenetic analyses (e.g., Cas1, see Fig. 4b), which suggests that this type V system is probably ancestral in these CPR. The second system found in Vampirococcus belongs to the class I type III and contains genes coding for Cas1, Cas2, Csm3, and Cas10/Csm1 proteins associated to a cluster of 20 longer (35–46 bp) spacers. In contrast with the previous CPR-like system, the proteins of this second system did not show strong similarity with any CPR homolog but with sequences from other bacterial phyla, suggesting that they have been acquired by HGT. Phylogenetic analysis confirmed this and supported that Vampirococcus gained this CRISPR-Cas system from different distant bacterial donors (Supplementary Fig. 10). Interestingly, these two CRISPR-Cas systems encode a number of proteins that may represent new effectors. A clear candidate is the large protein Vamp_48_93 (1158 aa), located between Cpf1 and Cas1 in the type V system (Fig. 4a), which contains a DNA polymerase III PolC motif. Very similar sequences can be found in a few other CPR (some Roizmanbacteria, Gracilibacteria, and Portnoybacteria) and in some unrelated bacteria (Supplementary Fig. 11). As in Vampirococcus, the gene coding for this protein is contiguous to genes encoding different Cas proteins in several of these bacteria, including Roizmanbacteria, Omnitrophica, and the deltaproteobacterium Smithella sp. (Supplementary Fig. 11). This gene association, as well as the very distant similarity between this protein and Cpf1 CRISPR-associated proteins of bacterial type V systems, supports that it is a new effector in type V CRISPR-Cas systems. Additional putative new CRISPR-associated proteins likely exist also in the Vampirococcus type III system (Fig. 4a). Three proteins encoded by contiguous genes (Vamp_21_116, Vamp_21_127, and Vamp_21_128) exhibit very distant similarity with type III-A CRISPR-associated Repeat Associated Mysterious Proteins (RAMP) Csm4, Csm5, and Csm6 sequences, respectively, and most probably represent new RAMP subfamilies. To date, Absconditabacteria38 and Saccharibacteria39 are the only CPR phyla for which phages have been identified. Because of its proximity to Absconditabacteria, Vampirococcus is probably infected by similar phages, so that the function of its CRISPR-Cas systems may be related to the protection against these genetic parasites. Nevertheless, we did not find any similarity between the Vampirococcus spacers and known phage sequences, suggesting that it is infected by unknown phages. Alternatively, considering that Vampirococcus -as most likely many other CPR bacteria- seems to obtain nucleotides required for growth by uptaking host DNA, an appealing possibility is that the CRISPR-Cas systems participate in the degradation of the imported host DNA.Fig. 4: CRISPR-Cas systems in Vampirococcus.a Genes in the two systems encoded in the Vampirococcus lugosii genome, elements common to the two systems are highlighted in blue. b Maximum likelihood phylogenetic tree of the Cas1 protein encoded in the class II type V system, numbers at branches indicate bootstrap support.Full size imageAlthough CPR bacteria have been hypothesized to be largely depleted of classical defense mechanisms40, we found that Vampirococcus, in addition to the two CRISP-Cas loci, is endowed with various other protection mechanisms. These include an AbiEii-AbiEi Type IV toxin-antitoxin system, also present in other CPR bacteria, which may offer additional protection against phage infection41 and several restriction-modification systems, with three type I, one type II and one type III restriction enzymes and eight DNA methylases. In addition to a defensive role, these enzymes may also participate in the degradation of the host DNA. As in its sister-groups Absconditabacteria and Gracilibacteria5,19,20,42,43, Vampirococcus has repurposed the UGA stop codon to code for glycine. The primary function of this recoding remains unknown but it has been speculated that it creates a genetic incompatibility, whereby these bacteria would be “evolutionarily isolated” from their environmental neighbors, preventing their potential competitors from acquiring their genomic innovations by HGT19. However, the opposite might be argued as well, since the UGA codon reassignment can protect Vampirococcus from foreign DNA expression upon uptake by leading to aberrant protein synthesis via read-through of the UGA stop with Gly insertion. This can be important for these CPR bacteria because they are not only impacted by phages38 but they most likely depend on host DNA import and degradation to fulfill their nucleotide requirements. In that sense, it is interesting to note that the Vampirococcus ComEA protein likely involved in DNA transport44 is encoded within the class I type III CRISPR-Cas system (Fig. 4a). More

Invertebrate composition effects on primary productionTo evaluate the effects of fiddler crabs, marsh crabs, and mussels on benthic algae and cordgrass production, the dietary sources for fiddler and marsh crabs, respectively27,28, we measured benthic diatom biomass and cordgrass stem density every 4–6 weeks and quantified cordgrass biomass and grazing damage at the conclusion of the experiment in August 2017. Diatom biomass was enhanced in enclosures with mussels and/or marsh crabs relative to enclosures with only fiddler crabs or no invertebrates, and relative to all ambient plots (F36, 200 = 1.5; P = 0.04; Tukey’s HSD, all P  More