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    Anthropogenic edge effects and aging errors by hunters can affect the sustainability of lion trophy hunting

    Tilman, D. et al. Future threats to biodiversity and pathways to their prevention. Nature 546, 73. https://doi.org/10.1038/nature22900 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Laurance, W. F., Sayer, J. & Cassman, K. G. Agricultural expansion and its impacts on tropical nature. Trends Ecol. Evol. 29, 107–116. https://doi.org/10.1016/j.tree.2013.12.001 (2014).Article 

    Google Scholar 
    Ceballos, G. et al. Accelerated modern human–induced species losses: Entering the sixth mass extinction. J. Sci. Adv. 1, e1400253. https://doi.org/10.1126/sciadv.1400253 (2015).Article 
    ADS 

    Google Scholar 
    Cardillo, M. et al. Human population density and extinction risk in the world’s carnivores. PLoS Biol. 2, e197. https://doi.org/10.1371/journal.pbio.0020197 (2004).Article 

    Google Scholar 
    Estes, J. A. et al. Trophic downgrading of planet earth. Science 333, 301–306 (2011).Article 
    ADS 
    CAS 

    Google Scholar 
    Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 124–148 (2014).Article 

    Google Scholar 
    Bauer, H. et al. Lion (Panthera leo) populations are declining rapidly across Africa, except in intensively managed areas. Proc. Natl. Acad. Sci. 112, 14895–14899 (2015).Article 
    ADS 

    Google Scholar 
    Bauer, H., Page-Nicholson, S., Hinks, A. & Dickman, A. Guidelines for the Conservation of lion in Africa 17–24 (IUCN SSC Cat Specialist Group, 2018).
    Google Scholar 
    Lindsey, P. A., Roulet, P. A. & Romanach, S. S. Economic and conservation significance of the trophy hunting industry in sub-Saharan Africa. Biol. Conserv. 134, 455–469. https://doi.org/10.1016/j.biocon.2006.09.005 (2007).Article 

    Google Scholar 
    Vucetich, J. A. et al. The value of argument analysis for understanding ethical considerations pertaining to trophy hunting and lion conservation. Biol. Conserv. 235, 260–272. https://doi.org/10.1016/j.biocon.2019.04.012 (2019).Article 

    Google Scholar 
    Dube, N. Voices from the village on trophy hunting in Hwange district, Zimbabwe. Ecol. Econ. 159, 335–343. https://doi.org/10.1016/j.ecolecon.2019.02.006 (2019).Article 

    Google Scholar 
    Murombedzi, J. African wildlife and livelihoods. In The Promise and Performance of Community Conservation (eds Hulme, D. & Murphree, M.) 244–255 (James Currey, 2001).
    Google Scholar 
    Leader-Williams, N., Baldus, R. D. & Smith, R. J. Recreational hunting. In Conservation and Rural Livelihoods (eds Dickson, B. et al.) 296–316 (Blackwell Publishing Ltd., 2009).Chapter 

    Google Scholar 
    DiMinin, E., Leader-Williams, N. & Bradshaw, C. J. A. Banning trophy hunting will exacerbate biodiversity loss. Trends Ecol. Evol. 31, 99–102 (2016).Article 

    Google Scholar 
    Whitman, K., Starfield, A. M., Quadling, H. S. & Packer, C. Sustainable trophy hunting of African lions. Nature 428, 175–178 (2004).Article 
    ADS 
    CAS 

    Google Scholar 
    Packer, C. et al. Sport hunting, predator control and conservation of large carnivores. PLoS ONE 4, e5941. https://doi.org/10.1371/journal.pone.0005941 (2009).Article 
    ADS 
    CAS 

    Google Scholar 
    Mweetwa, T. et al. Quantifying lion (Panthera leo) demographic response following a three-year moratorium on trophy hunting. PLoS ONE 13, e0197030. https://doi.org/10.1371/journal.pone.0197030 (2018).Article 
    CAS 

    Google Scholar 
    Loveridge, A. J. et al. Conservation of large predator populations: Demographic and spatial responses of African lions to the intensity of trophy hunting. Biol. Conserv. 204, 247–254. https://doi.org/10.1016/j.biocon.2016.10.024 (2016).Article 

    Google Scholar 
    Starfield, A. M., Shiell, J. D. & Smuts, G. L. Simulation of lion control strategies in a large game reserve. Ecol. Model. 13, 17–28 (1981).Article 

    Google Scholar 
    Venter, J. & Hopkins, M. E. Use of a simulation model in the management of a lion population. S. Afr. J. Wildl. Res. 18, 126–130 (1988).
    Google Scholar 
    Starfield, A. M. & Bleloch, A. L. Modelling the effect of contraception on part of the lion population in Etosha National Park. Applied Mathematic Dept. Report R3/82, Witwaterstrand University, South Africa. 7 (1982).Dickman, A., Becker, M., Begg, C., Loveridge, A. J. & Macdonald, D. W. Guidelines for the Conservation of Lions in Africa, Ch. 6 69–75 (IUCN SSC Cat Specialist Group, 2018).
    Google Scholar 
    Creel, S. et al. Assessing the sustainability of lion trophy hunting with recomendations for policy. Ecol. Appl. 26, 2347–2357. https://doi.org/10.1002/eap.1377 (2016).Article 

    Google Scholar 
    Barthold, J., Loveridge, A. J., Macdonald, D. W., Packer, C. & Colchero, F. Bayesian estimates of male and female African lion mortality for future use in population management. J. Appl. Ecol. 53, 295–304 (2016).Article 

    Google Scholar 
    Loveridge, A. J., Valeix, M., Elliot, N. B. & Macdonald, D. W. The landscape of anthropogenic mortality: How African lions respond to spatial variation in risk. J. Appl. Ecol. 54, 815–825. https://doi.org/10.1111/1365-2664.12794 (2017).Article 

    Google Scholar 
    Loveridge, A. J. et al. Evaluating the spatial intensity and demographic impacts of wire-snare bush-meat poaching on large carnivores. Biol. Conserv. 244, 108504 (2020).Article 

    Google Scholar 
    Becker, M. S. et al. Estimating past and future male loss in three Zambian lion populations. J. Wildl. Manag. 77, 128–142 (2013).Article 

    Google Scholar 
    Kiffner, C., Meyer, B., Muhlenberg, M. & Waltert, M. Plenty of prey, few predators: What limits lions Panthera leo in Katavi National park, western Tanzania?. Oryx 43, 52–59 (2009).Article 

    Google Scholar 
    Loveridge, A. J., Searle, A. W., Murindagomo, F. & Macdonald, D. W. The impact of sport hunting on the population dynamics of an African lion population in a protected area. Biol. Conserv. 134, 548–558 (2007).Article 

    Google Scholar 
    Miller, J. R. B. et al. Aging traits and sustainable trophy hunting of African lions. Biol. Conserv. 201, 160–168 (2016).Article 

    Google Scholar 
    Woodroffe, R. & Ginsberg, J. R. Edge effects and the extinction of populations inside protected areas. Science 280, 2126–2128 (1998).Article 
    ADS 
    CAS 

    Google Scholar 
    Gervasi, V., Linnell, J. D. C., Brøseth, H. & Gimenez, O. Failure to coordinate management in transboundary populations hinders the achievement of national management goals: The case of wolverines in Scandinavia. J. Appl. Ecol. 56, 1905–1915. https://doi.org/10.1111/1365-2664.13379 (2019).Article 

    Google Scholar 
    Breitenmoser, U. & Nobbe, C. Guidelines for the Conservation of Lions in Africa (ed IUCN CSG/SSC) 29–30 (IUCN, 2018).du Preez, B. & Lopez-Bao, J. V. Guidelines for the Conservation of the Lion in Africa (ed IUCN CSG/SSC) 76–78 (IUCN, 2018).Loveridge, A. J., Hemson, G., Davidson, Z. & Macdonald, D. W. African lions on the edge: reserve boundaries as ‘attractive sinks’ In Biology and Conservation of Wild Felids, Ch. 11 (eds Macdonald, D. W. & Loveridge, A. J.) 283–304 (Oxford University Press, London, 2010).

    Google Scholar 
    Borrego, N., Ozgul, A., Slotow, R. & Packer, C. Lion population dynamics: Do nomadic males matter?. Behav. Ecol. 29, 660–666. https://doi.org/10.1093/beheco/ary018%JBehavioralEcology (2018).Article 

    Google Scholar 
    Packer, C. et al. The case for fencing remains intact. Ecol. Lett. https://doi.org/10.1111/ele.12171 (2013).Balme, G. et al. Big cats at large: Density, structure, and spatio-temporal patterns of a leopard population free of anthropogenic mortality. Popul. Ecol. 61, 256–267. https://doi.org/10.1002/1438-390x.1023 (2019).Article 

    Google Scholar 
    Grünewald, C., Schleuning, M. & Böhning-Gaese, K. Biodiversity, scenery and infrastructure: Factors driving wildlife tourism in an African savannah national park. Biol. Conserv. 201, 60–68. https://doi.org/10.1016/j.biocon.2016.05.036 (2016).Article 

    Google Scholar 
    Pulliam, H. R. Sources, sinks, and population. Regulation 132, 652–661. https://doi.org/10.1086/284880 (1988).Article 

    Google Scholar 
    Lamb, C. T. et al. The ecology of human–carnivore coexistence. Proc. Natl. Acad. Sci. 117, 17876–17883. https://doi.org/10.1073/pnas.1922097117 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Robinson, H. S., Weilgus, R. B., Cooley, H. & Cooley, S. Source—sink populations in carnivore management: cougar demography and immigration in a hunted population. Ecol. Appl. 18, 1028–1037 (2008).Article 

    Google Scholar 
    Creel, S. et al. Questionable policy for large carnivore hunting. Science 350, 1473–1475 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Cushman, S. A. et al. Prioritizing core areas, corridors and conflict hotspots for lion conservation in southern Africa. PLoS ONE 13, e0196213. https://doi.org/10.1371/journal.pone.0196213 (2018).Article 
    CAS 

    Google Scholar 
    Kelly, M. J. & Durant, S. M. Viability of the Serengeti cheetah population. Conserv. Biol. 14, 786–797 (2000).Article 

    Google Scholar 
    Skalski, J. R., Ryding, K. & Millspaug, J. J. Wildlife Demography: Analysis of Sex, Age, and Count Data (Elsevier Academic Press, 2005).
    Google Scholar 
    Hamlin, K. L., Pac, D. F., Sime, C. A., DeSimone, R. M. & Dusek, G. L. Evaluating the accuracy of ages obtained by two methods for montana ungulates. J. Wildl. Manag. 64, 441–449. https://doi.org/10.2307/3803242 (2000).Article 

    Google Scholar 
    Storm, D. J. et al. Estimating ages of white-tailed deer: Age and sex patterns of error using tooth wear-and-replacement and consistency of cementum annuli. Wildl Soc Bull 38, 849–856. https://doi.org/10.1002/wsb.457 (2014).Article 
    ADS 

    Google Scholar 
    Balme, G. A., Hunter, L. & Braczkowski, A. R. Applicability of age-based hunting regulations for African Leopards. PLoS ONE 7, e35209. https://doi.org/10.1371/journal.pone.0035209 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Gipson, P. S., Ballard, W. B., Nowak, R. M. & Mech, L. D. Accuracy and precision of estimating age of gray wolves by tooth wear. J. Wildl. Manag. 64, 752–758. https://doi.org/10.2307/3802745 (2000).Article 

    Google Scholar 
    Hiller, T. L. Comparison of two age-estimation techniques for cougars. J. Northwest. Nat. 77–82, 76 (2014).
    Google Scholar 
    Begg, C. M., Miller, J. R. B. & Begg, K. S. Effective implementation of age restrictions increases selectivity of sport hunting of the African lion. J. Appl. Ecol. 55, 139–146. https://doi.org/10.1111/1365-2664.12951 (2018).Article 

    Google Scholar 
    Mandisodza-Chikerema, R., Jooste, D. & Funston, P. J. Lion aging and adaptive quota management report: Ages of lions hunted and recommended quotas for 2019 in Zimbabwe. 12 (Unpublished report, Zimbabwe Parks and Wildlife Management and Panthera, Harare, Zimbabwe, 2019).Smuts, G. L., Anderson, J. L. & Austin, J. C. Age determination of the African lion (Panthera leo). J. Zool. Lond. 185, 115–146 (1978).Article 

    Google Scholar 
    Lindsey, P. A. et al. The trophy hunting of African lions: Scale, current management practices and factors undermining sustainability. PLoS ONE 8, 1–11 (2013).Article 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2022).Packer, C. et al. Effects of trophy hunting on lion and leopard populations in Tanzania. Conserv. Biol. 25, 142–153 (2011).Article 
    CAS 

    Google Scholar 
    Mace, G. M. & Reynolds, J. Exploitation as a conservation issue. In Conservation of Exploited Species, Ch. 1 (eds Reynolds, J. et al.) 3–15 (Cambridge University Press, Cambridge, 2001).
    Google Scholar 
    Struhsaker, T. T. A biologists perspective on the role of sustainable harvest in conservation. Conserv. Biol. 12, 930–932 (1998).Article 

    Google Scholar  More

  • in

    Temperature fluctuation promotes the thermal adaptation of soil microbial respiration

    Auffret, M. D. et al. The role of microbial community composition in controlling soil respiration responses to temperature. PLoS ONE 11, e0165448 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yao, Y. et al. A data-driven global soil heterotrophic respiration dataset and the drivers of its inter‐annual variability. Glob. Biogeochem. Cycle 35, e2020GB006918 (2021).Article 
    CAS 

    Google Scholar 
    Davidson, E. A., Janssens, I. A. & Luo, Y. On the variability of respiration in terrestrial ecosystems: moving beyond Q10. Glob. Change Biol. 12, 154–164 (2006).Article 

    Google Scholar 
    Wang, Q. et al. Soil microbial respiration rate and temperature sensitivity along a north–south forest transect in eastern China: patterns and influencing factors. J. Geophys. Res. Biogeosci. 121, 399–410 (2016).Article 

    Google Scholar 
    Sihi, D. et al. Merging a mechanistic enzymatic model of soil heterotrophic respiration into an ecosystem model in two AmeriFlux sites of northeastern USA. Agric. Meteorol. 252, 155–166 (2018).Article 

    Google Scholar 
    Shao, P., Zeng, X., Moore, D. J. P. & Zeng, X. Soil microbial respiration from observations and Earth system models. Environ. Res. Lett. 8, 034034 (2013).Article 
    CAS 

    Google Scholar 
    Davidson, E. A., Samanta, S., Caramori, S. S. & Savage, K. The dual Arrhenius and Michaelis–Menten kinetics model for decomposition of soil organic matter at hourly to seasonal time scales. Glob. Change Biol. 18, 371–384 (2012).Article 

    Google Scholar 
    Oechel, W. C. et al. Acclimation of ecosystem CO2 exchange in the Alaskan Arctic in response to decadal climate warming. Nature 406, 978–981 (2000).Article 
    CAS 
    PubMed 

    Google Scholar 
    Alster, C. J., von Fischer, J. C., Allison, S. D. & Treseder, K. K. Embracing a new paradigm for temperature sensitivity of soil microbes. Glob. Change Biol. 26, 3221–3229 (2020).Article 

    Google Scholar 
    Nie, M. et al. Positive climate feedbacks of soil microbial communities in a semi-arid grassland. Ecol. Lett. 16, 234–241 (2013).Article 
    PubMed 

    Google Scholar 
    Ji, F., Wu, Z., Huang, J. & Chassignet, E. P. Evolution of land surface air temperature trend. Nat. Clim. Change 4, 462–466 (2014).Article 

    Google Scholar 
    Huntingford, C., Jones, P. D., Livina, V. N., Lenton, T. M. & Cox, P. M. No increase in global temperature variability despite changing regional patterns. Nature 500, 327–330 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hansen, J., Sato, M. & Ruedy, R. Perception of climate change. Proc. Natl Acad. Sci. USA 109, E2415–E2423 (2012).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Byrne, M. P. Amplified warming of extreme temperatures over tropical land. Nat. Geosci. 14, 837–841 (2021).Article 
    CAS 

    Google Scholar 
    IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Chan, W. P. et al. Seasonal and daily climate variation have opposite effects on species elevational range size. Science 351, 1437–1439 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Biederbeck, V. O. & Campbell, C. A. Soil microbial activity as influenced by temperature trends and fluctuations. Can. J. Soil Sci. 53, 363–375 (1973).Article 

    Google Scholar 
    Karhu, K. et al. Temperature sensitivity of soil respiration rates enhanced by microbial community response. Nature 513, 81–84 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Chen, H., Zhu, T., Li, B., Fang, C. & Nie, M. The thermal response of soil microbial methanogenesis decreases in magnitude with changing temperature. Nat. Commun. 11, 5733 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).Article 
    CAS 

    Google Scholar 
    Nottingham, A. T. et al. Microbial responses to warming enhance soil carbon loss following translocation across a tropical forest elevation gradient. Ecol. Lett. 22, 1889–1899 (2019).Article 
    PubMed 

    Google Scholar 
    Alster, C. J., Robinson, J. M., Arcus, V. L. & Schipper, L. A. Assessing thermal acclimation of soil microbial respiration using macromolecular rate theory. Biogeochemistry 158, 131–141 (2022).Article 
    CAS 

    Google Scholar 
    Moinet, G. Y. K. et al. Soil microbial sensitivity to temperature remains unchanged despite community compositional shifts along geothermal gradients. Glob. Change Biol. 27, 6217–6231 (2021).Article 

    Google Scholar 
    Feng, J. et al. Soil microbial trait-based strategies drive metabolic efficiency along an altitude gradient. ISME Commun. 1, 71 (2021).Article 

    Google Scholar 
    Li, J. et al. Key microorganisms mediate soil carbon-climate feedbacks in forest ecosystems. Sci. Bull. 66, 2036–2044 (2021).Article 
    CAS 

    Google Scholar 
    Trivedi, P. et al. Microbial regulation of the soil carbon cycle: evidence from gene–enzyme relationships. ISME J. 10, 2593–2604 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhu, B. & Cheng, W. Constant and diurnally-varying temperature regimes lead to different temperature sensitivities of soil organic carbon decomposition. Soil Biol. Biochem. 43, 866–869 (2011).Article 
    CAS 

    Google Scholar 
    Bradford, M. A. et al. Thermal adaptation of soil microbial respiration to elevated temperature. Ecol. Lett. 11, 1316–1327 (2008).Article 
    PubMed 

    Google Scholar 
    Hartley, I. P., Hopkins, D. W., Garnett, M. H., Sommerkorn, M. & Wookey, P. A. Soil microbial respiration in Arctic soil does not acclimate to temperature. Ecol. Lett. 11, 1092–1100 (2008).Article 
    PubMed 

    Google Scholar 
    Bradford, M. A. et al. Cross-biome patterns in soil microbial respiration predictable from evolutionary theory on thermal adaptation. Nat. Ecol. Evol. 3, 223–231 (2019).Article 
    PubMed 

    Google Scholar 
    Tian, W. et al. Thermal adaptation occurs in the respiration and growth of widely distributed bacteria. Glob. Change Biol. 28, 2820–2829 (2022).Article 
    CAS 

    Google Scholar 
    Bradford, M. A., Watts, B. W. & Davies, C. A. Thermal adaptation of heterotrophic soil respiration in laboratory microcosms. Glob. Change Biol. 16, 1576–1588 (2010).Article 

    Google Scholar 
    Walker, T. W. N. et al. Microbial temperature sensitivity and biomass change explain soil carbon loss with warming. Nat. Clim. Change 8, 885–889 (2018).Article 
    CAS 

    Google Scholar 
    Chen, H. et al. Microbial respiratory thermal adaptation is regulated by r-/K-strategy dominance. Ecol. Lett. 25, 2489–2499 (2022).Article 
    PubMed 

    Google Scholar 
    Wang, C. et al. The temperature sensitivity of soil: microbial biodiversity, growth, and carbon mineralization. ISME J. 15, 2738–2747 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ramadhin, C., Yi, C. & Hendrey, G. Temperature variance portends and indicates the extent of abrupt climate shifts. IOP SciNotes 2, 014002 (2021).Article 

    Google Scholar 
    Sun, Y. Q. & Ge, Y. Temporal changes in the function of bacterial assemblages associated with decomposing earthworms. Front. Microbiol. 12, 682224 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shi, Z., Xu, J., Li, X., Li, R. & Li, Q. Links of extracellular enzyme activities, microbial metabolism, and community composition in the river-impacted coastal waters. J. Geophys. Res. Biogeosci. 124, 3507–3520 (2019).Article 

    Google Scholar 
    Razanamalala, K. et al. Soil microbial diversity drives the priming effect along climate gradients: a case study in Madagascar. ISME J. 12, 451–462 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Xu, M. et al. High microbial diversity stabilizes the responses of soil organic carbon decomposition to warming in the subsoil on the Tibetan Plateau. Glob. Change Biol. 27, 2061–2075 (2021).Article 
    CAS 

    Google Scholar 
    Clemmensen, K. E. et al. Roots and associated fungi drive long-term carbon sequestration in boreal forest. Science 339, 1615–1618 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Qiao, N. et al. Labile carbon retention compensates for CO2 released by priming in forest soils. Glob. Change Biol. 20, 1943–1954 (2014).Article 

    Google Scholar 
    Ning, Q. et al. Carbon limitation overrides acidification in mediating soil microbial activity to nitrogen enrichment in a temperate grassland. Glob. Change Biol. 27, 5976–5988 (2021).Article 
    CAS 

    Google Scholar 
    Wan, S. & Luo, Y. Substrate regulation of soil respiration in a tallgrass prairie: results of a clipping and shading experiment. Glob. Biogeochem. Cycle 17, 1054 (2003).Article 

    Google Scholar 
    Gillabel, J., Cebrian-Lopez, B., Six, J. & Merckx, R. Experimental evidence for the attenuating effect of SOM protection on temperature sensitivity of SOM decomposition. Glob. Change Biol. 16, 2789–2798 (2010).Article 

    Google Scholar 
    Xia, J. et al. Terrestrial carbon cycle affected by non-uniform climate warming. Nat. Geosci. 7, 173–180 (2014).Article 
    CAS 

    Google Scholar 
    Balesdent, J. et al. Atmosphere–soil carbon transfer as a function of soil depth. Nature 559, 599–602 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Howard, D. M. & Howard, P. J. A. Relationships between CO2 evolution, moisture-content and temperature for a range of soil types. Soil Biol. Biochem. 25, 1537–1546 (1993).Article 

    Google Scholar 
    Hoyle, F. C., Murphy, D. V. & Brookes, P. C. Microbial response to the addition of glucose in low-fertility soils. Biol. Fertil. Soils 44, 571–579 (2008).Article 
    CAS 

    Google Scholar 
    Mau, R. L. et al. Linking soil bacterial biodiversity and soil carbon stability. ISME J. 9, 1477–1480 (2015).Article 
    CAS 
    PubMed 

    Google Scholar 
    Tucker, C. L., Bell, J., Pendall, E. & Ogle, K. Does declining carbon-use efficiency explain thermal acclimation of soil respiration with warming? Glob. Change Biol. 19, 252–263 (2013).Article 

    Google Scholar 
    Billings, S. A. & Ballantyne, F. T. How interactions between microbial resource demands, soil organic matter stoichiometry, and substrate reactivity determine the direction and magnitude of soil respiratory responses to warming. Glob. Change Biol. 19, 90–102 (2013).Article 

    Google Scholar 
    Li, J. et al. Biogeographic variation in temperature sensitivity of decomposition in forest soils. Glob. Change Biol. 26, 1873–1885 (2020).Article 

    Google Scholar 
    Min, K. et al. Temperature sensitivity of biomass-specific microbial exo-enzyme activities and CO2 efflux is resistant to change across short- and long-term timescales. Glob. Change Biol. 5, 1793–1807 (2019).Article 

    Google Scholar 
    Dacal, M., Bradford, M. A., Plaza, C., Maestre, F. T. & Garcia-Palacios, P. Soil microbial respiration adapts to ambient temperature in global drylands. Nat. Ecol. Evol. 3, 232–238 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Field-Fote, E. E. Mediators and moderators, confounders and covariates: exploring the variables that illuminate or obscure the “active ingredients” in neurorehabilitation. J. Neurol. Phys. Ther. 43, 83–84 (2019).Article 
    PubMed 

    Google Scholar 
    Anderson, T. H. & Domsch, K. H. Soil microbial biomass: the eco-physiological approach. Soil Biol. Biochem. 12, 2039–2043 (2010).Article 

    Google Scholar 
    Vance, E. D., Brookes, P. C. & Jenkinson, D. S. Microbial biomass measurements in forest soils—the use of the chloroform fumigation incubation method in strongly acid soils. Soil Biol. Biochem. 19, 697–702 (1987).Article 
    CAS 

    Google Scholar 
    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).Article 

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

    Google Scholar 
    Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

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

    Google Scholar 
    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Koljalg, U. et al. UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi. N. Phytol. 166, 1063–1068 (2005).Article 
    CAS 

    Google Scholar 
    German, D. P. et al. Optimization of hydrolytic and oxidative enzyme methods for ecosystem studies. Soil Biol. Biochem. 43, 1387–1397 (2011).Article 
    CAS 

    Google Scholar 
    Mazerolle, M. Improving data analysis in herpetology: using Akaike’s information criterion (AIC) to assess the strength of biological hypotheses. Amphib. Reptil. 2, 169–180 (2006).Article 

    Google Scholar 
    Moinet, G. Y. K. et al. Temperature sensitivity of decomposition decreases with increasing soil organic matter stability. Sci. Total Environ. 704, 135460 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Moinet, G. Y. K. et al. The temperature sensitivity of soil organic matter decomposition is constrained by microbial access to substrates. Soil Biol. Biochem. 116, 333–339 (2018).Article 
    CAS 

    Google Scholar 
    Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).Article 

    Google Scholar  More

  • in

    The double life of Methanoperedens

    Galperin, M. Y. Environ. Microbiol. 6, 552–567 (2004).Article 
    CAS 

    Google Scholar 
    Higgins, D. & Dworkin, J. FEMS Microbiol. Rev. 36, 131–148 (2012).Article 
    CAS 

    Google Scholar 
    Maamar, H., Raj, A. & Dubnau, D. Science 317, 526–529 (2007).Article 
    CAS 

    Google Scholar 
    Ackermann, M. Nat. Rev. Microbiol. 13, 497–508 (2015).Article 
    CAS 

    Google Scholar 
    Robinson, R. W. Appl. Environ. Microbiol. 52, 17–27 (1986).Article 
    CAS 

    Google Scholar 
    McIlroy, S. J. et al. Nat. Microbiol. https://doi.org/10.1038/s41564-022-01292-9 (2023).Article 

    Google Scholar 
    Leu, A. O. et al. ISME J. 14, 1030–1041 (2020).Article 
    CAS 

    Google Scholar 
    Cui, M., Ma, A., Qi, H., Zhuang, X. & Zhuang, G. Microbiologyopen 4, 1–11 (2015).Article 

    Google Scholar 
    Haroon, M. F. et al. Nature 500, 567–570 (2013).Article 
    CAS 

    Google Scholar 
    Fritts, R. K., McCully, A. L. & McKinlay, J. B. Microbiol. Molec. Biol. Rev. 85, e00135-20 (2021).Article 

    Google Scholar  More

  • in

    Acclimation of phenology relieves leaf longevity constraints in deciduous forests

    Peaucelle, M. et al. Spatial variance of spring phenology in temperate deciduous forests is constrained by background climatic conditions. Nat. Commun. 10, 5388 (2019).Article 

    Google Scholar 
    Hopkins, A. D. The bioclimatic law. Mon. Weather Rev. 48, 355–355 (1920).Article 

    Google Scholar 
    Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).Article 

    Google Scholar 
    Ge, Q., Wang, H., Rutishauser, T. & Dai, J. Phenological response to climate change in China: a meta-analysis. Glob. Change Biol. 21, 265–274 (2015).Article 

    Google Scholar 
    Templ, B. et al. Pan European Phenological database (PEP725): a single point of access for European data. Int. J. Biometeorol. 62, 1109–1113 (2018).Article 

    Google Scholar 
    Richardson, A. D. et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. For. Meteorol. 169, 156–173 (2013).Article 

    Google Scholar 
    Morisette, J. T. et al. Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century. Front. Ecol. Environ. 7, 253–260 (2009).Article 

    Google Scholar 
    Flynn, D. F. B. & Wolkovich, E. M. Temperature and photoperiod drive spring phenology across all species in a temperate forest community. New Phytol. 219, 1353–1362 (2018).Article 
    CAS 

    Google Scholar 
    Peñuelas, J., Rutishauser, T. & Filella, I. Phenology feedbacks on climate change. Science 324, 887–888 (2009).Article 

    Google Scholar 
    Körner, C. & Basler, D. Plant science. Phenol. Glob. Warm. Sci. 327, 1461–1462 (2010).
    Google Scholar 
    Delpierre, N. et al. Temperate and boreal forest tree phenology: from organ-scale processes to terrestrial ecosystem models. Ann. For. Sci. 73, 5–25 (2016).Article 

    Google Scholar 
    Klosterman, S. T. et al. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences 11, 4305–4320 (2014).Article 

    Google Scholar 
    Hufkens, K. et al. Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology. Remote Sens. Environ. 117, 307–321 (2012).Article 

    Google Scholar 
    Garrity, S. R. et al. A comparison of multiple phenology data sources for estimating seasonal transitions in deciduous forest carbon exchange. Agric. For. Meteorol. 151, 1741–1752 (2011).Article 

    Google Scholar 
    Fracheboud, Y. et al. The control of autumn senescence in European aspen. Plant Physiol. 149, 1982–1991 (2009).Article 
    CAS 

    Google Scholar 
    Mariën, B. et al. Does drought advance the onset of autumn leaf senescence in temperate deciduous forest trees? Biogeosciences 18, 3309–3330 (2021).Article 

    Google Scholar 
    Fu, Y. H. et al. Larger temperature response of autumn leaf senescence than spring leaf-out phenology. Glob. Change Biol. 24, 2159–2168 (2018).Article 

    Google Scholar 
    Menzel, A., Sparks, T. H., Estrella, N. & Roy, D. B. Altered geographic and temporal variability in phenology in response to climate change. Glob. Ecol. Biogeogr. 15, 498–504 (2006).Article 

    Google Scholar 
    Gordo, O. & Sanz, J. J. Long-term temporal changes of plant phenology in the Western Mediterranean. Glob. Change Biol. 15, 1930–1948 (2009).Article 

    Google Scholar 
    Meier, M., Vitasse, Y., Bugmann, H. & Bigler, C. Phenological shifts induced by climate change amplify drought for broad-leaved trees at low elevations in Switzerland. Agric. For. Meteorol. 307, 108485 (2021).Basler, D. Evaluating phenological models for the prediction of leaf-out dates in six temperate tree species across central Europe. Agric. For. Meteorol. 217, 10–21 (2016).Article 

    Google Scholar 
    Keenan, T. F. et al. Terrestrial biosphere model performance for inter-annual variability of land–atmosphere CO2 exchange. Glob. Change Biol. 18, 1971–1987 (2012).Article 

    Google Scholar 
    Liu, G., Chen, X., Fu, Y. & Delpierre, N. Modelling leaf coloration dates over temperate China by considering effects of leafy season climate. Ecol. Modell. 394, 34–43 (2019).Article 

    Google Scholar 
    Keenan, T. F. & Richardson, A. D. The timing of autumn senescence is affected by the timing of spring phenology: implications for predictive models. Glob. Change Biol. 21, 2634–2641 (2015).Article 

    Google Scholar 
    Wu, C., Hou, X., Peng, D., Gonsamo, A. & Xu, S. Land surface phenology of China’s temperate ecosystems over 1999–2013: spatial–temporal patterns, interaction effects, covariation with climate and implications for productivity. Agric. For. Meteorol. 216, 177–187 (2016).Article 

    Google Scholar 
    Fu, Y. S. H. et al. Variation in leaf flushing date influences autumnal senescence and next year’s flushing date in two temperate tree species. Proc. Natl Acad. Sci. USA 111, 7355–7360 (2014).Article 
    CAS 

    Google Scholar 
    Zani, D., Crowther, T. W., Mo, L., Renner, S. S. & Zohner, C. M. Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees. Science 370, 1066–1071 (2020).Article 
    CAS 

    Google Scholar 
    Paul, M. J. & Foyer, C. H. Sink regulation of photosynthesis. J. Exp. Bot. 52, 1383–1400 (2001).Article 
    CAS 

    Google Scholar 
    Herold, A. Regulation of photosynthesis by sink activity—the missing link. New Phytol. 86, 131–144 (1980).Article 
    CAS 

    Google Scholar 
    Keenan, T. F. et al. Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun. 7, 13428 (2016).Campbell, J. E. et al. Large historical growth in global terrestrial gross primary production. Nature 544, 84–87 (2017).Article 
    CAS 

    Google Scholar 
    Schimel, D., Stephens, B. B. & Fisher, J. B. Effect of increasing CO2 on the terrestrial carbon cycle. Proc. Natl Acad. Sci.USA 112, 436–441 (2015).Article 
    CAS 

    Google Scholar 
    Walker, A. P. et al. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO. New Phytol. 229, 2413–2445 (2021).Article 
    CAS 

    Google Scholar 
    Liu, Q. et al. Modeling leaf senescence of deciduous tree species in Europe. Glob. Change Biol. 26, 4104–4118 (2020).Article 

    Google Scholar 
    Friedl, M., Gray, J. & Sulla-Menashe, D. MCD12Q2 MODIS/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500m SIN Grid V006 (NASA, 2019).Zhang, X. et al. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 84, 471–475 (2003).Article 

    Google Scholar 
    Stocker, B. D. et al. P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production. Geosci. Model Dev. 13, 1545–1581 (2020).Article 

    Google Scholar 
    Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).Article 

    Google Scholar 
    Sitch, S. et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Change Biol. 9, 161–185 (2003).Article 

    Google Scholar 
    Hänninen, H. & Tanino, K. Tree seasonality in a warming climate. Trends Plant Sci. 16, 412–416 (2011).Article 

    Google Scholar 
    Kikuzawa, K. & Lechowicz, M. J. Ecology of Leaf Longevity (Springer, 2011).Fu, Y. H. et al. Nutrient availability alters the correlation between spring leaf-out and autumn leaf senescence dates. Tree Physiol. 39, 1277–1284 (2019).Article 
    CAS 

    Google Scholar 
    Lim, P. O., Kim, H. J. & Nam, H. G. Leaf senescence. Annu. Rev. Plant Biol. 58, 115–136 (2007).Article 
    CAS 

    Google Scholar 
    Piao, S., Friedlingstein, P., Ciais, P., Viovy, N. & Demarty, J. Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Glob. Biogeochem. Cycles 21, GB3018 (2007).Jeong, S.-J., Ho, C.-H., Gim, H.-J. & Brown, M. E. Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982–2008. Glob. Change Biol. 17, 2385–2399 (2011).Article 

    Google Scholar 
    Cong, N. et al. Changes in satellite-derived spring vegetation green-up date and its linkage to climate in China from 1982 to 2010: a multimethod analysis. Glob. Change Biol. 19, 881–891 (2013).Article 

    Google Scholar 
    Keenan, T. F. et al. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Change 4, 598–604 (2014).Article 
    CAS 

    Google Scholar 
    Garonna, I., de Jong, R. & Schaepman, M. E. Variability and evolution of global land surface phenology over the past three decades (1982–2012). Glob. Change Biol. 22, 1456–1468 (2016).Article 

    Google Scholar 
    Smith, N. G. & Dukes, J. S. Plant respiration and photosynthesis in global-scale models: incorporating acclimation to temperature and CO2. Glob. Change Biol. 19, 45–63 (2013).Article 

    Google Scholar 
    Estiarte, M. & Peñuelas, J. Alteration of the phenology of leaf senescence and fall in winter deciduous species by climate change: effects on nutrient proficiency. Glob. Change Biol. 21, 1005–1017 (2015).Article 

    Google Scholar 
    Delpierre, N. et al. Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France. Agric. For. Meteorol. 149, 938–948 (2009).Article 

    Google Scholar 
    Chung, H. et al. Experimental warming studies on tree species and forest ecosystems: a literature review. J. Plant Res. 126, 447–460 (2013).Article 

    Google Scholar 
    Schaaf, C. B. et al. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens. Environ. 83, 135–148 (2002).Article 

    Google Scholar 
    Tuck, S. L. et al. MODISTools—downloading and processing MODIS remotely sensed data in R. Ecol. Evol. 4, 4658–4668 (2014).Article 

    Google Scholar 
    Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).Article 
    CAS 

    Google Scholar 
    Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Glob. Change Biol. 17, 2134–2144 (2011).Article 

    Google Scholar 
    Stocker, B. rsofun: A modelling framework that implements the P-model for leaf-level acclimation of photosynthesis. R package version 4.3 https://github.com/computationales/rsofun (2020).Weedon, G. P. et al. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Water Resour. Res. 50, 7505–7514 (2014).Article 

    Google Scholar 
    Meek, D. W., Hatfield, J. L., Howell, T. A., Idso, S. B. & Reginato, R. J. A generalized relationship between photosynthetically active radiation and solar radiation 1. Agron. J. 76, 939–945 (1984).Article 

    Google Scholar 
    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 
    Stocker, B. ingestr: A tool to extract environmental point data from large global files or remote data servers. R package version 1.4 https://github.com/computationales/ingestr (2020).Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat. Plants 3, 734–741 (2017).Article 
    CAS 

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

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Myneni, R., Knyazikhin, Y. & Park, T. MCD15A3H MODIS/Terra+Aqua Leaf Area Index/FPAR 4-day L4 Global 500m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2015). More

  • in

    Nature-positive goals for an organization’s food consumption

    Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).Article 

    Google Scholar 
    Díaz, S., et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).Díaz, S. et al. Set ambitious goals for biodiversity and sustainability. Science 370, 411 (2020).Article 

    Google Scholar 
    Locke, H., et al. A Nature-Positive World: The Global Goal for Nature (Wildlife Conservation Society, 2020); https://library.wcs.org/doi/ctl/view/mid/33065/pubid/DMX3974900000.aspxOpen-ended Working Group on the Post-2020 Global Biodiversity Framework. First Draft of the Post-2020 Global Biodiversity Framework CBD/WG2020/3/3 (Convention on Biological Diversity, 2021).Open-Ended Working Group on the Post-2020 Global Biodiversity Framework. Draft Recommendation Submitted by the Co-Chairs CBD/WG2020/4/L.2-ANNEX (Convention on Biological Diversity, 2022).Environment Act 2021 (UK) (HM Government, 2021); https://www.legislation.gov.uk/ukpga/2021/30/contents/enactedBull, J. W. & Strange, N. The global extent of biodiversity offset implementation under no net loss policies. Nat. Sustain. 1, 790–798 (2018).Article 

    Google Scholar 
    Prendeville, S., Cherim, E. & Bocken, N. Circular cities: mapping six cities in transition. Environ. Innov. Soc. Transit. 26, 171–194 (2018).de Silva, G. C., Regan, E. C., Pollard, E. H. B. & Addison, P. F. E. The evolution of corporate no net loss and net positive impact biodiversity commitments: understanding appetite and addressing challenges. Bus. Strategy Environ. 28, 1481–1495 (2019).Article 

    Google Scholar 
    zu Ermgassen, S. O. S. E. et al. Exploring the ecological outcomes of mandatory biodiversity net gain using evidence from early‐adopter jurisdictions in England. Conserv. Lett. 14, e12820 (2021).Article 

    Google Scholar 
    McGlyn, J., et al. Science-Based Targets for Nature: Initial Guidance for Business (Science Based Targets Network, 2020); https://sciencebasedtargetsnetwork.org/resource-repository/zu Ermgassen, S. O. S. E. et al. Are corporate biodiversity commitments consistent with delivering ‘nature-positive’ outcomes? A review of ‘nature-positive’ definitions, company progress and challenges. J. Clean. Prod. 379, 134798 (2022).Article 

    Google Scholar 
    Addison, P. F. E., Bull, J. W. & Milner‐Gulland, E. J. Using conservation science to advance corporate biodiversity accountability. Conserv. Biol. 33, 307–318 (2019).Article 

    Google Scholar 
    Smith, T. et al. Biodiversity means business: reframing global biodiversity goals for the private sector. Conserv. Lett. 13, e12690 (2020).Article 

    Google Scholar 
    Maron, M. et al. Setting robust biodiversity goals. Conserv. Lett. https://doi.org/10.1111/conl.12816 (2021).Newing, H. & Perram, A. What do you know about conservation and human rights? Oryx 53, 595–596 (2019).Article 

    Google Scholar 
    Standard on Biodiversity Offsets (The Business and Biodiversity Offsets Programme, 2012).Arlidge, W. N. S., et al. A mitigation hierarchy approach for managing sea turtle captures in small-scale fisheries. Front. Mar. Sci. 7, 49 (2020).Squires, D. & Garcia, S. The least-cost biodiversity impact mitigation hierarchy with a focus on marine fisheries and bycatch issues. Conserv. Biol. 32, 989–997 (2018).Article 

    Google Scholar 
    Booth, H., Squires, D. & Milner-Gulland, E. J. The mitigation hierarchy for sharks: a risk-based framework for reconciling trade-offs between shark conservation and fisheries objectives. Fish Fish. 21, 269–289 (2020).Article 

    Google Scholar 
    Gupta, T. et al. Mitigation of elasmobranch bycatch in trawlers: a case study in Indian fisheries. Front. Mari. Sci. 7, 571 (2020).Budiharta, S. et al. Restoration to offset the impacts of developments at a landscape scale reveals opportunities, challenges and tough choices. Global Environ. Change 52, 152–161 (2018).Article 

    Google Scholar 
    Bull, J. W. et al. Net positive outcomes for nature. Nat. Ecol. Evol. 4, 4–7 (2020).Article 

    Google Scholar 
    Arlidge, W. N. S. et al. A global mitigation hierarchy for nature conservation. BioScience 68, 336–347 (2018).Article 

    Google Scholar 
    Milner-Gulland, E. J. et al. Four steps for the Earth: mainstreaming the post-2020 global biodiversity framework. One Earth 4, 75–87 (2021).Article 
    ADS 

    Google Scholar 
    Wolff, A., Gondran, N. & Brodhag, C. Detecting unsustainable pressures exerted on biodiversity by a company. Application to the food portfolio of a retailer. J. Clean. Prod. 166, 784–797 (2017).Article 

    Google Scholar 
    FAOSTAT Analytical Brief 15 Land Use and Land Cover Statistics: Global, Regional and Country Trends, 1990–2018 (FAO, 2020).Williams, D. R. et al. Proactive conservation to prevent habitat losses to agricultural expansion. Nat. Sustain. 4, 314–322 (2021).Article 

    Google Scholar 
    Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).Article 
    ADS 

    Google Scholar 
    Springmann, M. et al. Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail. Lancet Planet. Health 2, e451–e461 (2018).Article 

    Google Scholar 
    Clark, M. A., Springmann, M., Hill, J. & Tilman, D. Multiple health and environmental impacts of foods. Proc. Natl Acad. Sci. USA 116, 23357 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Willett, W. et al. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet 393, 447–492 (2019).Article 

    Google Scholar 
    Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360, 987 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Wiedmann, T., Lenzen, M., Keyßer, L. T. & Steinberger, J. K. Scientists’ warning on affluence. Nat. Commun. 11, 3107 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Benton, T. G. et al. A ‘net zero’ equivalent target is needed to transform food systems. Nat. Food 2, 905–906 (2021). 2021.Article 

    Google Scholar 
    Crenna, E., Sinkko, T. & Sala, S. Biodiversity impacts due to food consumption in Europe. J. Clean. Prod. 227, 378–391 (2019).Article 
    CAS 

    Google Scholar 
    Bull, J. W., et al. Analysis: the biodiversity footprint of the University of Oxford. Nature 604, 420–424 (2022).Harrington, R. A., Adhikari, V., Rayner, M. & Scarborough, P. Nutrient composition databases in the age of big data: foodDB, a comprehensive, real-time database infrastructure. BMJ Open 9, e026652 (2019).Article 

    Google Scholar 
    Chaudhary, A., Verones, F., De Baan, L. & Hellweg, S. Quantifying land use impacts on biodiversity: combining species–area models and vulnerability indicators. Environ. Sci. Technol. 49, 9987–9995 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Winter, L., Lehmann, A., Finogenova, N. & Finkbeiner, M. Including biodiversity in life cycle assessment—state of the art, gaps and research needs. Environ. Impact Assess. Rev. 67, 88–100 (2017).Article 

    Google Scholar 
    Chaudhary, A. & Kastner, T. Land use biodiversity impacts embodied in international food trade. Global Environ. Change 38, 195–204 (2016).Article 

    Google Scholar 
    Lenzen, M. et al. International trade drives biodiversity threats in developing nations. Nature 486, 109–112 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Bates, B., et al. National Diet and Nutrition Survey Years 1 to 9 of the Rolling Programme (2008/2009–2016/2017): Time Trend and Income Analyses (Public Health England & Food Standards Agency, 2019).Stewart, C., Piernas, C., Cook, B. & Jebb, S. A. Trends in UK meat consumption: analysis of data from years 1–11 (2008–09 to 2018–19) of the National Diet and Nutrition Survey rolling programme. Lancet Planet. Health 5, e699–e708 (2021).Article 

    Google Scholar 
    Nielsen, K. S. et al. Improving climate change mitigation analysis: a framework for examining feasibility. One Earth 3, 325–336 (2020).Article 
    ADS 

    Google Scholar 
    Selinske, M. J. et al. We have a steak in it: eliciting interventions to reduce beef consumption and its impact on biodiversity. Conserv. Lett. 13, e12721 (2020).Article 

    Google Scholar 
    Hollands, G. J. et al. The TIPPME intervention typology for changing environments to change behaviour. Nat. Hum. Behav. 1, 1–9 (2017).Article 

    Google Scholar 
    Marteau, T. M., Hollands, G. J. & Fletcher, P. C. Changing human behavior to prevent disease: the importance of targeting automatic processes. Science 337, 1492–1495 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Michie, S., van Stralen, M. M. & West, R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement. Sci. 6, 42 (2011).Article 

    Google Scholar 
    Moran, D., Giljum, S., Kanemoto, K. & Godar, J. From satellite to supply chain: new approaches connect earth observation to economic decisions. One Earth 3, 5–8 (2020).Article 
    ADS 

    Google Scholar 
    Godar, J., Suavet, C., Gardner, T. A., Dawkins, E. & Meyfroidt, P. Balancing detail and scale in assessing transparency to improve the governance of agricultural commodity supply chains. Environ. Res. Lett. 11, 035015 (2016).Article 
    ADS 

    Google Scholar 
    DeFries, R. S., Fanzo, J., Mondal, P., Remans, R. & Wood, S. A. Is voluntary certification of tropical agricultural commodities achieving sustainability goals for small-scale producers? A review of the evidence. Environ. Res. Lett. 12, 033001 (2017).Article 
    ADS 

    Google Scholar 
    Bull, J. W., Suttle, K. B., Gordon, A., Singh, N. J. & Milner-Gulland, E. J. Biodiversity offsets in theory and practice. Oryx 47, 369–380 (2013).Article 

    Google Scholar 
    zu Ermgassen, S. O. S. E. et al. The ecological outcomes of biodiversity offsets under “no net loss” policies: a global review. Conserv. Lett. 12, e12664 (2019).Article 

    Google Scholar 
    Waddock, S. Achieving sustainability requires systemic business transformation. Glob. Sustain. 3, e12 (2020).Travers, H., Walsh, J., Vogt, S., Clements, T. & Milner-Gulland, E. J. Delivering behavioural change at scale: what conservation can learn from other fields. Biol. Conserv. 257, 109092 (2021).Article 

    Google Scholar 
    Gaupp, F. et al. Food system development pathways for healthy, nature-positive and inclusive food systems. Nat. Food 2, 928–934 (2021).Article 

    Google Scholar 
    Astill, J. et al. Transparency in food supply chains: a review of enabling technology solutions. Trends Food Sci. Technol. 91, 240–247 (2019).Article 
    CAS 

    Google Scholar 
    Poore, J & Nemecek, T. Full Excel model: life-cycle environmental impacts of food drink products. Oxford University Research Archive https://ora.ox.ac.uk/objects/uuid:a63fb28c-98f8-4313-add6-e9eca99320a5 (2018).Clark, M., et al. Estimating the environmental impacts of 57,000 food products. Proc. Natl Acad. Sci. USA 119, e2120584119 (2022).Clark, M., et al. Supplemental Data for ‘Estimating the environmental impacts of 57,000 food products’. Oxford University Research Archive https://ora.ox.ac.uk/objects/uuid:4ad0b594-3e81-4e61-aefc-5d869c799a87 (2022).Bianchi, F., Dorsel, C., Garnett, E., Aveyard, P. & Jebb, S. A. Interventions targeting conscious determinants of human behaviour to reduce the demand for meat: a systematic review with qualitative comparative analysis. IJBNPA 15, 102 (2018).
    Google Scholar 
    Bianchi, F., Garnett, E., Dorsel, C., Aveyard, P. & Jebb, S. A. Restructuring physical micro-environments to reduce the demand for meat: a systematic review and qualitative comparative analysis. Lancet Planet. Health 2, e384–e397 (2018).Article 

    Google Scholar 
    Hillier-Brown, F. C. et al. The impact of interventions to promote healthier ready-to-eat meals (to eat in, to take away or to be delivered) sold by specific food outlets open to the general public: a systematic review. Obes. Rev. 18, 227–246 (2017).Article 
    CAS 

    Google Scholar 
    von Philipsborn, P. et al. Environmental interventions to reduce the consumption of sugar-sweetened beverages and their effects on health. Cochrane Database Syst. Rev. 6, Cd012292 (2019).
    Google Scholar 
    Attwood, S., Voorheis, P., Mercer, C., Davies, K. & Vennard, D. Playbook for Guiding Diners toward Plant-Rich Dishes in Food Service (World Resources Institute, 2020); https://www.wri.org/research/playbook-guiding-diners-toward-plant-rich-dishes-food-serviceGarnett, E. E., Balmford, A., Sandbrook, C., Pilling, M. A. & Marteau, T. M. Impact of increasing vegetarian availability on meal selection and sales in cafeterias. Proc. Natl Acad. Sci. USA 116, 20923 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Reinders, M. J., Huitink, M., Dijkstra, S. C., Maaskant, A. J. & Heijnen, J. Menu-engineering in restaurants—adapting portion sizes on plates to enhance vegetable consumption: a real-life experiment. IJBNPA 14, 41 (2017).
    Google Scholar 
    Brunner, F., Kurz, V., Bryngelsson, D. & Hedenus, F. Carbon label at a university restaurant—label implementation and evaluation. Ecol. Econ. 146, 658–667 (2018).Article 

    Google Scholar 
    McClain, A. D., Hekler, E. B. & Gardner, C. D. Incorporating prototyping and iteration into intervention development: a case study of a dining hall-based intervention. J. Am. Coll. Health 61, 122–131 (2013).Article 

    Google Scholar 
    de Vaan, J. Eating Less Meat: How to Stimulate the Choice for a Vegetarian Option without Inducing Reactance. MSc thesis, Radboud Univ. (2018). More

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    Publisher Correction: Seasonal peak photosynthesis is hindered by late canopy development in northern ecosystems

    Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, ChinaQian Zhao, Yao Zhang & Shilong PiaoSchool of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen, ChinaZaichun Zhu & Hui ZengKey Laboratory of Earth Surface System and Human—Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen, ChinaZaichun Zhu & Hui ZengDepartment of Earth and Environment, Boston University, Boston, MA, USARanga B. MyneniCSIC, Global Ecology Unit CREAF-CSIC-UAB, Barcelona, Catalonia, SpainJosep PeñuelasCREAF, Barcelona, Catalonia, SpainJosep PeñuelasState Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, ChinaShilong Piao More

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    Enhanced regional connectivity between western North American national parks will increase persistence of mammal species diversity

    Newmark, W. D. A land-bridge island perspective on mammalian extinctions in western North American parks. Nature 325, 430–432 (1987).Article 
    ADS 
    CAS 

    Google Scholar 
    Newmark, W. D. Isolation of African protected areas. Front. Ecol. Environ. 6, 321–328 (2008).Article 

    Google Scholar 
    Radeloff, V. C. et al. Housing growth in and near United States protected areas limits their conservation value. Proc. Natl. Acad. Sci. U. S. A. 107, 940–945 (2010).Article 
    ADS 
    CAS 

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

    Google Scholar 
    Elsen, P. R., Monahan, W. B., Dougherty, E. R. & Merenlender, A. M. Keeping pace with climate change in global terrestrial protected areas. Sci. Adv. https://doi.org/10.1126/sciadv.aay0814 (2020).Article 

    Google Scholar 
    Wasser, S. K. et al. Genetic assignment of large seizures of elephant ivory reveals Africa’s major poaching hotspots. Science 349, 84–87 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Davis, C. R. & Hansen, A. J. Trajectories in land use change around U,S. national parks and challenges and opportunities for management. Ecol. Appl. 21, 3299–3316 (2011).Article 

    Google Scholar 
    Newmark, W. D. Extinction of mammal populations in western North American national parks. Conserv. Biol. 9, 512–526 (1995).Article 

    Google Scholar 
    Newmark, W. D. Insularization of Tanzanian parks and the local extinction of large mammals. Conserv. Biol. 10, 1549–1556 (1996).Article 

    Google Scholar 
    Brashares, J. S., Arcese, P. & Sam, M. K. Human demography and reserve size predict wildlife extinction in West Africa. Proc. R. Soc. B Biol. Sci. 268, 2473–2478 (2001).Article 
    CAS 

    Google Scholar 
    Woodroffe, R. & Ginsberg, J. R. Edge effects and the extinction of populations inside protected areas. Science 280, 2126–2128 (1998).Article 
    ADS 
    CAS 

    Google Scholar 
    Turner, M. G. & Dale, V. H. Comparing large, infrequent disturbances: What have we learned?. Ecosystems 1, 493–496 (1998).Article 

    Google Scholar 
    Berger, J. The last mile: How to sustain long-distance migration in mammals. Conserv. Biol. 18, 320–331 (2004).Article 

    Google Scholar 
    Bolger, D. T., Newmark, W. D., Morrison, T. A. & Doak, D. F. The need for integrative approaches to understand and conserve migratory ungulates. Ecol. Lett. 11, 63–77 (2008).
    Google Scholar 
    Sawyer, H., Kauffman, M. J., Nielson, R. M. & Horne, J. S. Identifying and prioritizing ungulate migration routes for landscape-level conservation. Ecol. Appl. 19, 2016–2025 (2009).Article 

    Google Scholar 
    Tucker, M. A. et al. Moving in the anthropocene: Global reductions in terrestrial mammalian movements. Science 469, 466–469 (2018).Article 
    ADS 

    Google Scholar 
    Soulé, M. E. & Terborgh, J. Conserving nature at regional and continental scales-a scientific program for North America. Bioscience 49, 809–817 (1999).Article 

    Google Scholar 
    Hilty, J. et al. Guidelines for conserving connectivity through ecological networks and corridors. Best Pract. Prot. Area Guidel. Ser. 30, 122 (2020).
    Google Scholar 
    Haddad, N. & Tewksbury, J. Impacts of corridors on populations and communities. in Connectivity Conservation (eds. Crooks, K. R. & Sanjayan, M.) 390–415 (Cambridge University Press, 2010).
    Google Scholar 
    Ramiadantsoa, T., Ovaskainen, O., Rybicki, J. & Hanski, I. Large-scale habitat corridors for biodiversity conservation: A forest corridor in Madagascar. PLoS One 10, 1–18 (2015).Article 
    CAS 

    Google Scholar 
    Newmark, W. D., Jenkins, C. N., Pimm, S. L., McNeally, P. B. & Halley, J. M. Targeted habitat restoration can reduce extinction rates in fragmented forests. Proc. Natl. Acad. Sci. USA. 114, 9635–9640 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Diamond, J. M. Biogeographic kinetics: Estimation of relaxation times for avifaunas of southwest Pacific islands. Proc. Natl. Acad. Sci. 69, 3199–3203 (1972).Article 
    ADS 
    CAS 

    Google Scholar 
    Terborgh, J. Preservation of natural diversity: The problem of extinction prone species. Bioscience 24, 715–722 (1974).Article 

    Google Scholar 
    Tilman, D., May, R. M., Lehman, C. L. & Nowak, M. A. Habitat destruction and the extinction debt revisited. Nature 371, 65–66 (1994).Article 
    ADS 

    Google Scholar 
    Halley, J. M., Monokrousos, N., Mazaris, A. D., Newmark, W. D. & Vokou, D. Dynamics of extinction debt across five taxonomic groups. Nat. Commun. 7, 1–6 (2016).Article 

    Google Scholar 
    Wearn, O. R., Reuman, D. C. & Ewers, R. M. Extinction debt and windows of conservation opportunity in the Brazilian amazon. Science 337, 228–232 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Hanski, I. Extinction debt and species credit in boreal forests: Modelling the consequences of different approaches to conservation. Ann. Zool. Fennici 37, 271–280 (2000).
    Google Scholar 
    LaBarbera, M. Analyzing body size as a factor in ecology and evolution. Annu. Rev. Ecol. Syst. 20, 97–117 (1989).Article 

    Google Scholar 
    Oakleaf, J. K. et al. Habitat selection by recolonizing wolves in the northern Rocky mountains of the United States. J. Wildl. Manage. 70, 554–563 (2006).Article 

    Google Scholar 
    Cushman, S. A., McKelvey, K. S. & Schwartz, M. K. Use of empirically derived source-destination models to map regional conservation corridors. Conserv. Biol. 23, 368–376 (2009).Article 

    Google Scholar 
    Schwartz, M. K. et al. Wolverine gene flow across a narrow climatic niche. Ecology 90, 3222–3232 (2014).Article 

    Google Scholar 
    McKelvey, K. S. et al. Climate change predicted to shift wolverine distributions, connectivity, and dispersal corridors. Ecol. Appl. 21, 2882–2897 (2011).Article 

    Google Scholar 
    Carroll, C., Mcrae, B. H. & Brookes, A. Use of linkage mapping and centrality analysis across habitat gradients to conserve connectivity of gray wolf populations in western North America. Conserv. Biol. 26, 78–87 (2012).Article 

    Google Scholar 
    Parks, S. A., McKelvey, K. S. & Schwartz, M. K. Effects of weighting schemes on the identification of wildlife corridors generated with least-cost methods. Conserv. Biol. 27, 145–154 (2013).Article 

    Google Scholar 
    Peck, C. P. et al. Potential paths for male-mediated gene flow to and from an isolated grizzly bear population. Ecosphere 8, e01969 (2017).Article 

    Google Scholar 
    Wild Migrations: Atlas of Wyoming’s Ungulates. (Oregon State University, 2018).Singleton, P. H., Gaines, W. L. & Lehmkuhl, J. F. Landscape permeability for large carnivores in Washington: A geographic information system weighted-distance and least-cost corridor assessment. (2002).Long, R. A. et al. The Cascades carnivore connectivity project: A landscape genetic assessment of connectivity in Washington’s north Cascades ecosystem. Final report for the Seattle City Light Wildlife Research Program (2013).Diamond, J. M. The island dilemma: Lessons of modern biogeographic studies for the design of natural reserves. Biol. Conserv. 7, 129–146 (1975).Article 

    Google Scholar 
    Wilson, E. O. & Willis, E. O. Applied biogeography. In Ecological structure of ecological communities (eds. Cody, M. L, & Diamond, J. M.) 522–534 (Harvard University Press, 1975)
    Google Scholar 
    Halley, J. M. & Iwasa, Y. Neutral theory as a predictor of avifaunal extinctions after habitat loss. Proc. Natl. Acad. Sci. USA 108, 2316–2321 (2011).Article 
    ADS 
    CAS 

    Google Scholar 
    Cushman, S. A., Lewis, J. S. & Landguth, E. L. Evaluating the intersection of a regional wildlife connectivity network with highways. Mov. Ecol. 1, 1–11 (2013).Article 

    Google Scholar 
    Singleton, P. H. & Lehmkuhl, J. F. I-90 Snoqualmie pass wildlife habitat linkage assessment. Final Report. USDA, Pacific Northwest Research Station. (2000).Craighead, L., Craighead, A., Oeschslia, L. & Kociolek, A. Bozeman pass post-fencing wildlife monitoring. Final Report. FHWA/MT-10-006/8173 (2011).Andis, A. Z., Huijser, M. P. & Broberg, L. Performance of arch-style road crossing structures from relative movement rates of large mammals. Front. Ecol. Evol. 5, 1–13 (2017).Article 

    Google Scholar 
    Millward, L. Small mammal microhabitat use and species composition at a wildlife crossing structure compared with nearby forest (Central Washington University, 2018).
    Google Scholar 
    Bischof, R., Steyaert, S. M. J. G. & Kindberg, J. Caught in the mesh: Roads and their network-scale impediment to animal movement. Ecography 40, 1369–1380 (2017).Article 

    Google Scholar 
    Balkenhol, N. & Waits, L. P. Molecular road ecology: Exploring the potential of genetics for investigating transportation impacts on wildlife. Mol. Ecol. 18, 4151–4164 (2009).Article 

    Google Scholar 
    Clevenger, A. P. & Wierzchowski, J. Maintaining and restoring connectivity in landscapes fragmented by roads. In Connectivity Conservation, (eds. Crooks, K. R. & Sanjayan, M.) 502–535 (Cambridge University Press, 2010.)
    Google Scholar 
    Sawaya, M. A., Kalinowski, S. T. & Clevenger, A. P. Genetic connectivity for two bear species at wildlife crossing structures in Banff National Park. Proc. R. Soc. B Biol. Sci. 281, 20131705 (2014).Article 

    Google Scholar 
    Sawaya, M. A., Clevenger, A. P. & Schwartz, M. K. Demographic fragmentation of a protected wolverine population bisected by a major transportation corridor. Biol. Conserv. 236, 616–625 (2019).Article 

    Google Scholar 
    Kamal, S., Grodzińska-Jurczak, M. & Brown, G. Conservation on private land: A review of global strategies with a proposed classification system. J. Environ. Plan. Manag. 58, 576–597 (2015).Article 

    Google Scholar 
    Wasserman, T. N., Cushman, S. A., Littell, J. S., Shirk, A. J. & Landguth, E. L. Population connectivity and genetic diversity of American marten (Martes americana) in the United States northern Rocky Mountains in a climate change context. Conserv. Genet. 14, 529–541 (2013).Article 

    Google Scholar 
    Wasserman, T. N., Cushman, S. A., Shirk, A. S., Landguth, E. L. & Littell, J. S. Simulating the effects of climate change on population connectivity of American marten (Martes americana) in the northern Rocky Mountains, USA. Landsc. Ecol. 27, 211–225 (2012).Article 

    Google Scholar 
    Cushman, S. A., Landguth, E. L. & Flather, C. H. Evaluating the sufficiency of protected lands for maintaining wildlife population connectivity in the U.S. northern Rocky Mountains. Divers. Distrib. 18, 873–884 (2012).Article 

    Google Scholar 
    Beier, P., Spencer, W., Baldwin, R. F. & Mcrae, B. H. Toward best practices for developing regional connectivity maps. Conserv. Biol. 25, 879–892 (2011).Article 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. (2020). More

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    Migration direction in a songbird explained by two loci

    Ethics statementAnimals’ care was in accordance with institutional guidelines. Ethical permit was issued by Malmö-Lund djurförsöksetiska nämnd 5.8.18-00848/2018.Field workWe carried out the field work in Sweden during four breeding seasons (2018–2021). Adult male willow warblers were captured in their breeding territories using mist nets and playback of a song. From each bird, we collected the innermost primary feather from the right wing. From the birds that returned with a logger we also collected ~20 μl of blood from the brachial wing vein. The blood was stored in SET buffer (0.015 M NaCl, 0.05 M Tris, 0.001 M of EDTA, pH 8.0) at room temperature until deposited for permanent storage at −20 °C. We deployed Migrate Technology Ltd geolocators (Intigeo-W30Z11-DIP 12 × 5 × 4 mm, 0.32 g) and used a nylon string to mount them on birds with the “leg-loop” harness method as outlined in our previous work24. The mass of the logger relative to that of the bird was on average 3.3% (range 2.7–3.8%).The tagged birds were ringed with a numbered aluminum ring, and two, colored plastic rings for later identification in the field. In total, we tagged 466 males (349 in 2018 and 117 in 2020) at breeding territories. During the first tagging season (2018), birds were trapped at 17 locations (average 22 birds per site; range 7–30) distributed across Sweden (Fig. S1). Three of the sites were in southern Sweden to document migration routes of allopatric trochilus and three sites were located above the Arctic circle to record migratory routes of allopatric acredula, whereas the remaining (239) loggers were spread over 11 sites located in the migratory divide. Given the observed densities and distribution of hybrids after analyzing returning birds in 2019, we deployed 117 more loggers at one single site (63.439°N, 14.831°E) in 2020. We successfully retrieved tracks from 57 birds tagged in 2019 and 16 from birds tagged in 2021. In search for birds with loggers, we checked circa 3000 willow warbler males and covered an area of at least 0.5 km radius around each site the year after tagging.Geolocator data treatmentThe R package GeoLight (version 2.0)25 was used to extract and analyze locations from raw geolocator data. All twilight events were obtained with light threshold of 3 lux. The most extreme outliers were trimmed with “loessFilter” function and a K value of 3. We used GeoLight’s function “getElevation” for estimating the sun elevation angle for the breeding period: these sets of locations were used to infer the positions for autumn departure direction. In addition, we carried out a “Hill-Ekström” calibration for the longest stationary winter site during the period before the spring equinox. Winter calibration produced location sets that better reflected the winter coordinates of the main winter site in sub-Saharan Africa26. We reduced some of the inherent geolocation “noise” by applying cantered 5-day rolling means to the coordinates. The equinox periods were visually identified by inspecting standard deviations in latitude. Latitudes from equinox periods were omitted (on average autumn equinox obscured data for 45 days (range 25–68). For the main winter site, we used the longest period at which bird stayed stationary and from which in all cases begun the spring migration (mean = 118, SD = 23 days). Timing of autumn departure was estimated by manual inspection of longitudes and latitudes plotted in time series. To estimate at which longitude the birds crossed the Mediterranean, we extracted the longitude when birds crossed latitude 35 N° (Mediterranean crossing longitude). For 29 birds, it was possible to directly extract the longitude at crossing latitude 35 N°. For the rest of the cases, the birds had not reached latitude 35 N° before the latitude was obscured by the equinox, we calculated the mean longitude of 10 days from the onset of fall equinox as a measure of the Mediterranean crossing. This measurement correlated highly with the winter longitude (r = 0.78, p = 2.8 × 10−16). To control for the birds relative breeding site longitude, we extracted the departure direction (1°–360°) relative from the tagging site to the location where the birds crossed the Mediterranean (departure direction). The departure data was of circular type (measured in 360°), however the variance did not span more than 180° degrees (range 151°–224°). Therefore, we proceeded with analyses using linear statistics. Geographic distances and departure direction were calculated using R package “geosphere” (version 1.5-10). Complete set of positions of each individual bird with equinoxes excluded is presented in Supplementary Data 1.Laboratory work and molecular data extractionWe extracted DNA from blood samples following the ammonium acetate protocol16. Genotyping for divergent regions on chromosome 1 (InvP-Ch1) and chromosome 5 (InvP-Ch5) was done using a qPCR SNP assay16, which is based on one informative SNP per region (SNP 65 for chromosome 1 and SNP 285 for chromosome 5). Probes and primers were produced by Thermo Fisher Scientific and were designed using the online Custom TaqMan® Assay Design tool (Table S4). We used Bio-Rad CFX96™ Real-time PCR system (Bio-Rad Laboratories, CA, USA) and the universal Fast-two-steps protocol: 95 °C, 15 min—40*(95 °C, 10 s–60 °C, 30 s, plate read. Both regions contain inversion polymorphisms that restrict recombination between subspecies-specific haplotypes and contain nearly all the SNPs separating the two subspecies13. For each region, we scored genotypes as either “Tro” (homozygous for trochilus haplotypes), “Acr” (homozygous for acredula haplotypes) or “Het” (heterozygous). The method that we used to assess the presence of MARB-a is based on a qPCR assay that quantifies the copy number of a novel TE (previously known as AFLP-WW212) that has expanded in acredula. The quantification of repeats by this method has been shown to be highly repeatable (R2 = 0.88) when comparing estimates obtained from DNA in blood and feathers15. We used the forward (5′-CCTTGCATACTTCTATTTCTCCC-3′) and reverse (5′-CATAGGACAGACATTGTTGAGG-3′) primers developed by Caballero-López et al.15 to amplify the TE motif. For reference of a single copy region we used the primers SFRS3F and SFRS3R27. We diluted DNA to 1 ng/μl−1 and used a Bio-Rad CFX96™ Real-time PCR system (Bio-Rad Laboratories, CA, USA) with SYBR-green-based detection. Total reaction volume was 25 μl of which 4 μl of DNA, 12.5 μl of SuperMix, 0.1 μl ROX, 1 μl of primer (forward and reverse), and 6.4 μl of double distilled H2O. We ran quantifications of the single copy gene and the TE variant found on MARB-a on separate plates with the following settings: 50 °C for 2 min as initial incubation, 95 °C for 2 min X 43 (94 °C for 30 s [55.3 °C SFRS3 and 55.5 °C for TE, 30 s] and 72 °C for 45 s). Each sample was run in duplicate and together with a two-fold serial standard dilution (2.5–7.8 × 10−2 ng). Allopatric trochilus have 0–6 copies whereas allopatric acredula have 8–45 copies15; a bimodal distribution was also confirmed in this new data set (Fig. S2). Accordingly, for the present analyses, we split the data in two groups: birds with ≤6 TE copies and birds with >7, translating into absence or presence of MARB-a, with the former assumed to be homozygous for the absence of MARB-a and the latter heterozygous or homozygous for the presence of MARB-a. Data from two investigated willow warbler families suggest a Mendelian inheritance pattern and provide support for our interpretation of how TE copy numbers reflect the three genotypes (Table S5). Moreover, the TE copy numbers within the hybrid swarm have a distribution similar to a combination of allopatric trochilus and acredula, further supporting that the copies are inherited as intact blocks (haplotypes). However, a precise distinction between heterozygotes and homozygotes on MARB-a is still not possible15.Statistical analysisWe used linear models with departure direction, winter longitude, migration distance and departure timing as response variables and the three genetic markers: MARB-a (a factor with two levels), InvP-Ch1 (a factor with three levels) and InvP-Ch5 (a factor with three levels) as explanatory variables. Models were constructed with R base package “stats”. We reported Type II ANOVA for models with more than one explanatory variable and no interactions and type III ANOVA results for models with interaction term by using R package “Car” (version 3.0-12)28. We initially constructed mixed effect models with timing of departure and tagging year as random factors however, this delivered singular fits due to insufficient sample sizes across categories. Normality of residuals was checked with a Shapiro–Wilk test. For carrying out circular statistics on autumn migration direction we used the R package “circular” (version 0.4-93). Watson’s U2 pairwise comparisons of different groups delivered the same results as linear models (Table S2 and Fig. S5). Circular means were identical to conventional linear means in our data set, which we take as another evidence that linear models are appropriate for the analysis of our data (Table S3 and Fig. S5). Maps in Figs. 1 and 2b and S1, S3 and S4 were created with R package “ggplot2” (version 3.3.6) using continent contours from Natural Earth, naturalearthdata.com/. Heat gradient over the maps in Fig. 1a–d were created with R package “gstat” (version 2.0-8) and the inverse distance weighting power of 3.0. Circular plots were created with ORIANA (version 4.02). All analyses were carried out with R version 4.1.1 (R Core Team 2021).Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More