Philippa Kaur

1.
Branca, G., Lipper, L., McCarthy, N. & Jolejole, M. C. Food security, climate change, and sustainable land management. A review. Agrono. Sustain. Dev. 33, 635–650 (2013).
Article  Google Scholar 
2.
Chen, X. et al. Producing more grain with lower environmental costs. Nature 514, 486–489 (2014).
ADS  CAS  PubMed  Article  Google Scholar 

3.
Tilman, D. et al. Forecasting agriculturally driven global environmental change. Science 292, 281–284 (2001).
ADS  CAS  PubMed  Article  Google Scholar 

4.
Huang, Y. & Tang, Y. An estimate of greenhouse gas (N2O and CO2) mitigation potential under various scenarios of nitrogen use efficiency in Chinese croplands. GCB Bioenergy 16, 2958–2970 (2010).
Google Scholar 

5.
Gan, Y. T. et al. Improving farming practices reduces the carbon footprint of spring wheat production. Nat. Commun. 5, 5012. https://doi.org/10.1038/ncomms6012 (2014).
ADS  Article  PubMed  PubMed Central  Google Scholar 

6.
Cameron, K. C., Di, H. J. & Moir, J. Nitrogen losses from the soil/plant system: A review. Ann. Appl. Biol. 162, 145–173 (2013).
CAS  Article  Google Scholar 

7.
Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R. & Polasky, S. Agricultural sustainability and intensive production practices. Nature 418, 671–677 (2002).
ADS  CAS  PubMed  Article  Google Scholar 

8.
Lithourgidis, A. S., Dordas, C. A., Damalas, C. A. & Vlachostergios, D. N. Annual intercrops: An alternative pathway for sustainable agriculture. Aust. J. Crop Sci. 5, 396–410 (2011).
Google Scholar 

9.
Tsubo, M., Walker, S. & Mukhala, E. Comparisons of radiation use efficiency of mono-/inter-cropping systems with different row orientations. Field Crop Res. 71, 17–29 (2001).
Article  Google Scholar 

10.
Li, L. et al. Root distribution and interactions between intercropped species. Oecologia 147, 280–290 (2006).
ADS  PubMed  Article  Google Scholar 

11.
Li, L. et al. Wheat/maize or wheat/soybean strip intercropping: I. Yield advantage and interspecific interactions on nutrients. Field Crop Res. 71, 123–137 (2001).
Article  Google Scholar 

12.
Brooker, R. W., Karley, A. J., Newton, A. C., Pakeman, R. J. & Schöb, C. Facilitation and sustainable agriculture: A mechanistic approach to reconciling crop production and conservation. Funct. Ecol. 30, 98–107 (2016).
Article  Google Scholar 

13.
Zhang, F. & Li, L. Using competitive and facilitative interactions in intercropping systems enhances crop productivity and nutrient-use efficiency. Plant Soil 248, 305–312 (2003).
CAS  Article  Google Scholar 

14.
Li, Q. Z. et al. Overyielding and interspecific interactions mediated by nitrogen fertilization in strip intercropping of maize with faba bean, wheat and barley. Plant Soil 339, 147–161 (2010).
Article  CAS  Google Scholar 

15.
Klimek-Kopyra, A., Zaja¸c, T. & Re¸bilas, K. A mathematical model for the evaluation of cooperation and competition effects in intercrops. Eur. J. Agron. 51, 9–17 (2013).
Article  Google Scholar 

16.
Li, L., Yang, S. C., Li, X. L., Zhang, F. S. & Christie, P. Interspecific complementary and competitive interactions between intercropped maize and faba bean. Plant Soil 212, 105–114 (1999).
CAS  Article  Google Scholar 

17.
Bedoussac, L. & Justes, E. A comparison of commonly used indices for evaluating species interactions and intercrop efficiency: Application to durum wheat–winter pea intercrops. Field Crop Res. 124, 25–36 (2011).
Article  Google Scholar 

18.
Hu, F. et al. Boosting system productivity through the improved coordination of interspecific competition in maize/pea strip intercropping. Field Crop Res. 198, 50–60 (2016).
Article  Google Scholar 

19.
Andersen, M., Hauggaard-Nielsen, H., Weiner, J. & Jensen, E. Competitive dynamics in two- and three-component intercrops. J. Appl. Ecol. 44, 545–551 (2007).
Article  Google Scholar 

20.
Li, L. et al. Wheat/maize or wheat/soybean strip intercropping: II. Recovery or compensation of maize and soybean after wheat harvesting. Field Crop Res. 71, 173–181 (2001).
Article  Google Scholar 

21.
Chai, Q., Qin, A., Gan, Y. & Yu, A. Higher yield and lower carbon emission by intercropping maize with rape, pea, and wheat in arid irrigation areas. Agrono. Sustain. Dev. 34, 535–543 (2013).
Article  CAS  Google Scholar 

22.
Hu, F. et al. Improving N management through intercropping alleviates the inhibitory effect of mineral N on nodulation in pea. Plant Soil 412, 235–251 (2017).
CAS  Article  Google Scholar 

23.
FAO/UNESCO. Soil Map of the World: Revised Legend/prepared by the Foodand Agriculture Organization of the United Nations. UNESCO (1988).

24.
Gan, Y. T. et al. Ridge-furrow mulching systems-an innovative technique for boosting crop productivity in semiarid rain-fed environments. Adv. Agron. 118, 429–476 (2013).
Article  Google Scholar 

25.
Yin, W. et al. Straw retention combined with plastic mulching improves compensation of intercropped maize in arid environment. Field Crop Res. 204, 42–51 (2017).
Article  Google Scholar 

26.
Willey, R. W. & Rao, M. R. A. Competitive ratio for quantifying competition between intercrops. Exp. Agric. 16, 117–125 (1980).
Article  Google Scholar 

27.
Fageria, N. K. & Baligar, V. C. Enhancing nitrogen use efficiency in crop plants. Adv. Agron. 88, 97–185 (2005).
CAS  Article  Google Scholar 

28.
Malézieux, E. et al. Mixing plant species in cropping systems: Concepts, tools and models. A review. Agrono. Sustain. Dev. 29, 43–62 (2009).
Article  Google Scholar 

29.
Gómez-Rodrı́guez, O., Zavaleta-Mejı́a, E., González-Hernández, V. A., Livera-Muñoz, M. & Cárdenas-Soriano, E. Allelopathy and microclimatic modification of intercropping with marigold on tomato early blight disease development. Field Crop Res. 83, 27–34 (2003).
Article  Google Scholar 

30.
Corre-Hellou, G., Fustec, J. & Crozat, Y. Interspecific competition for soil N and its interaction with N2 fixation, leaf expansion and crop growth in pea–barley intercrops. Plant Soil 282, 195–208 (2006).
CAS  Article  Google Scholar 

31.
Hauggaard-Nielsen, H., Ambus, P. & Jensen, E. S. The comparison of nitrogen use and leaching in sole cropped versus intercropped pea and barley. Nutr. Cycl. Agroecosys. 65, 289–300 (2003).
CAS  Article  Google Scholar 

32.
Andersen, M., Hauggaard-Nielsen, H., Ambus, P. & Jensen, E. Biomass production, symbiotic nitrogen fixation and inorganic N use in dual and tri-component annual intercrops. Plant Soil 266, 273–287 (2004).
CAS  Article  Google Scholar 

33.
Hou, Z., Li, P., Li, B., Gong, J. & Wang, Y. Effects of fertigation scheme on N uptake and N use efficiency in cotton. Plant Soil 290, 115–126 (2007).
CAS  Article  Google Scholar 

34.
Ghosh, P. K., Mohanty, M., Bandyopadhyay, K. K., Painuli, D. K. & Misra, A. K. Growth, competition, yields advantage and economics in soybean/pigeonpea intercropping system in semi-arid tropics of India. II. Effect of nutrient management. Field Crop Res 96, 90–97 (2006).
Article  Google Scholar 

35.
Li, S. X., Wang, Z. H., Hu, T. T., Gao, Y. J. & Stewart, B. A. Nitrogen in dryland soils of China and its management. Adv. Agron. 101, 123–181 (2009).
Article  Google Scholar 

36.
Hardarson, G., Zapata, F. & Danso, S. K. A. Effect of plant genotype and nitrogen fertilizer on symbiotic nitrogen fixation by soybean cultivars. Plant Soil 82, 397–405 (1984).
CAS  Article  Google Scholar 

37.
Li, C. et al. The dynamic process of interspecific interactions of competitive nitrogen capture between intercropped wheat (Triticum aestivum L.) and Faba Bean (Vicia faba L.). PLoS ONE 9, e115804. https://doi.org/10.1371/journal.pone.0119659 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

38.
Hauggaard-Nielsen, H. & Jensen, E. S. Evaluating pea and barley cultivars for complementarity in intercropping at different levels of soil N availability. Field Crop Res. 72, 185–196 (2001).
Article  Google Scholar 

39.
Herridge, D. F., Peoples, M. B. & Boddey, R. M. Global inputs of biological nitrogen fixation in agricultural systems. Plant Soil 311, 1–18 (2008).
CAS  Article  Google Scholar 

40.
Boucher, D. H. & Espinosa, M. J. Cropping system and growth and nodulation responses of beans to nitrogen in Tabasco, Mexico. Trop. Agric. 59, 279–282 (1982).
Google Scholar 

41.
Jensen, E. S. Grain yield, symbiotic N2 fixation and interspecific competition for inorganic N in pea-barley intercrops. Plant Soil 182, 25–38 (1996).
CAS  Article  Google Scholar 

42.
Gooding, M. J. et al. Intercropping with pulses to concentrate nitrogen and sulphur in wheat. J. Agric. Sci. 145, 469–479 (2007).
CAS  Article  Google Scholar 

43.
Rusinamhodzi, L., Murwira, H. K. & Nyamangara, J. Cotton–cowpea intercropping and its N2 fixation capacity improves yield of a subsequent maize crop under Zimbabwean rain-fed conditions. Plant Soil 287, 327–336 (2006).
CAS  Article  Google Scholar 

44.
Xiao, Y., Li, L. & Zhang, F. Effect of root contact on interspecific competition and N transfer between wheat and fababean using direct and indirect 15N techniques. Plant Soil 262, 45–54 (2004).
CAS  Article  Google Scholar 

45.
Jamont, M., Piva, G. & Fustec, J. Sharing N resources in the early growth of rapeseed intercropped with faba bean: Does N transfer matter?. Plant Soil 371, 641–653 (2013).
CAS  Article  Google Scholar  More

1.
Todd-Brown, K. E. O. et al. Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations. Biogeosciences 10, 1717–1736 (2013).
ADS  Article  Google Scholar 
2.
Todd-Brown, K. E. O. et al. Changes in soil organic carbon storage predicted by Earth system models during the 21st century. Biogeosciences 11, 2341–2356 (2014).
ADS  CAS  Article  Google Scholar 

3.
Wieder, W. R., Bonan, G. B. & Allison, S. D. Global soil carbon projections are improved by modelling microbial processes. Nat. Clim. Change 3, 909–912 (2013).
ADS  CAS  Article  Google Scholar 

4.
Wieder, W. R. et al. Explicitly representing soil microbial processes in Earth system models. Glob. Biogeochem. Cycles 29, 1782–1800 (2015).
ADS  CAS  Article  Google Scholar 

5.
Li, J., Wang, G., Allison, S., Mayes, M. & Luo, Y. Soil carbon sensitivity to temperature and carbon use efficiency compared across microbial-ecosystem models of varying complexity. Biogeochemistry 119, 67–84 (2014).
Article  Google Scholar 

6.
Luo, Y. Q. et al. Toward more realistic projections of soil carbon dynamics by Earth system models. Glob. Biogeochem. Cycles 30, 40–56 (2016).
ADS  CAS  Article  Google Scholar 

7.
German, D. P., Marcelo, K. R. B., Stone, M. M. & Allison, S. D. The Michaelis-Menten kinetics of soil extracellular enzymes in response to temperature: a cross-latitudinal study. Glob. Change Biol. 18, 1468–1479 (2012).
ADS  Article  Google Scholar 

8.
Wang, G. S., Post, W. M. & Mayes, M. A. Development of microbial-enzyme-mediated decomposition model parameters through steady-state and dynamic analyses. Ecol. Appl. 23, 255–272 (2013).
PubMed  Article  Google Scholar 

9.
Sulman, B. N., Phillips, R. P., Oishi, A. C., Shevliakova, E. & Pacala, S. W. Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated CO2. Nat. Clim. Change 4, 1099–1102 (2014).
ADS  CAS  Article  Google Scholar 

10.
Wang, G. S. et al. Microbial dormancy improves development and experimental validation of ecosystem model. ISME J. 9, 226–237 (2015).
CAS  PubMed  Article  Google Scholar 

11.
Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).
ADS  CAS  Article  Google Scholar 

12.
Georgiou K., Abramoff R. Z., Harte J., Riley W. J. & Torn M. S. Microbial community-level regulation explains soil carbon responses to long-term litter manipulations. Nat. Commun. 8, 1223 (2017).

13.
Geyer, K. M., Dijkstra, P., Sinsabaugh, R. & Frey, S. D. Clarifying the interpretation of carbon use efficiency in soil through methods comparison. Soil Biol. Biochem. 128, 79–88 (2019).
CAS  Article  Google Scholar 

14.
Chenu C., Rumpel C. & Lehmann J. in Soil Microbiology, Ecology and Biochemistry 4th edn (ed Paul E. A.) Ch. 13 (Academic Press, 2015).

15.
Jagadamma, S., Mayes, M. A., Steinweg, J. M. & Schaeffer, S. M. Substrate quality alters the microbial mineralization of added substrate and soil organic carbon. Biogeosciences 11, 4665–4678 (2014).
ADS  Article  CAS  Google Scholar 

16.
Stewart, C. E., Paustian, K., Conant, R. T., Plante, A. F. & Six, J. Soil carbon saturation: Implications for measurable carbon pool dynamics in long-term incubations. Soil Biol. Biochem. 41, 357–366 (2009).
CAS  Article  Google Scholar 

17.
Karhu, K. et al. Temperature sensitivity of soil respiration rates enhanced by microbial community response. Nature 513, 81–8 (2014).
ADS  CAS  PubMed  Article  Google Scholar 

18.
Hagerty, S. B. et al. Accelerated microbial turnover but constant growth efficiency with warming in soil. Nat. Clim. Change 4, 903–906 (2014).
ADS  CAS  Article  Google Scholar 

19.
Li, J. et al. Reduced carbon use efficiency and increased microbial turnover with soil warming. Glob. Change Biol. 25, 900–910 (2019).
ADS  Article  Google Scholar 

20.
Geyer, K. M., Kyker-Snowman, E., Grandy, A. S. & Frey, S. D. Microbial carbon use efficiency: accounting for population, community, and ecosystem-scale controls over the fate of metabolized organic matter. Biogeochemistry 127, 173–188 (2016).
CAS  Article  Google Scholar 

21.
Sinsabaugh, R. L., Moorhead, D. L., Xu, X. & Litvak, M. E. Plant, microbial and ecosystem carbon use efficiencies interact to stabilize microbial growth as a fraction of gross primary production. New Phytol. 214, 1518–1526 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

22.
Ye, J. S., Bradford, M. A., Dacal, M., Maestre, F. T. & Garca-Palacios, P. Increasing microbial carbon use efficiency with warming predicts soil heterotrophic respiration globally. Glob. Change Biol. 25, 3354–3364 (2019).
ADS  Article  Google Scholar 

23.
Xu, X. et al. Global pattern and controls of soil microbial metabolic quotient. Ecol. Monogr. 87, 429–441 (2017).
Article  Google Scholar 

24.
Ye, J.-S., Bradford, M. A., Maestre, F. T., Li, F.-M. & García-Palacios, P. Compensatory thermal adaptation of soil microbial respiration rates in global croplands. Glob. Biogeochem. Cycles 34, e2019GB006507 (2020).
ADS  CAS  Google Scholar 

25.
Six, J., Conant, R. T., Paul, E. A. & Paustian, K. Stabilization mechanisms of soil organic matter: Implications for C-saturation of soils. Plant Soil 241, 155–176 (2002).
CAS  Article  Google Scholar 

26.
Lehmann, J. & Kleber, M. The contentious nature of soil organic matter. Nature 528, 60–68 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Manzoni, S. et al. Optimal metabolic regulation along resource stoichiometry gradients. Ecol. Lett. 20, 1182–1191 (2017).
PubMed  Article  PubMed Central  Google Scholar 

28.
Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K. & Paul, E. The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter? Glob. Change Biol. 19, 988–995 (2013).
ADS  Article  Google Scholar 

29.
Abramoff, R. Z., Torn, M. S., Georgiou, K., Tang, J. & Riley, W. J. Soil organic matter temperature sensitivity cannot be directly inferred from spatial gradients. Glob. Biogeochem. Cycles 33, 761–776 (2019).
ADS  CAS  Article  Google Scholar 

30.
Colores, G. M., Schmidt, S. K. & Fisk, M. C. Estimating the biomass of microbial functional groups using rates of growth-related soil respiration. Soil Biol. Biochem. 28, 1569–1577 (1996).
CAS  Article  Google Scholar 

31.
Van de Werf, H. & Verstraete, W. Estimation of active soil microbial biomass by mathematical analysis of respiration curves: calibration of the test procedure. Soil Biol. Biochem. 19, 261–265 (1987).
Article  Google Scholar 

32.
Sinsabaugh, R. L., Manzoni, S., Moorhead, D. L. & Richter, A. Carbon use efficiency of microbial communities: stoichiometry, methodology and modelling. Ecol. Lett. 16, 930–939 (2013).
PubMed  Article  PubMed Central  Google Scholar 

33.
Schnecker, J., Bowles, T., Hobbie, E. A., Smith, R. G. & Grandy, A. S. Substrate quality and concentration control decomposition and microbial strategies in a model soil system. Biogeochemistry 144, 47–59 (2019).
CAS  Article  Google Scholar 

34.
Kluber, A. et al. Soil Respiration and Microbial Biomass from Soil Incubations with 13C Labeled Additions. (Oak Ridge National Laboratory, TES SFA, US Department of Energy, Oak Ridge, Tennessee, USA, 2020).

35.
Wang, G. S. et al. Soil moisture drives microbial controls on carbon decomposition in two subtropical forests. Soil Biol. Biochem. 130, 185–194 (2019).
CAS  Article  Google Scholar 

36.
Wang, K. F. et al. Modeling global soil carbon and soil microbial carbon by integrating microbial processes into the ecosystem process model TRIPLEX-GHG. J. Adv. Model Earth Syst. 9, 2368–2384 (2017).
ADS  Article  Google Scholar 

37.
He, Y. J. et al. Incorporating microbial dormancy dynamics into soil decomposition models to improve quantification of soil carbon dynamics of northern temperate forests. J. Geophys. Res. Biogeosci. 120, 2596–2611 (2015).
CAS  Article  Google Scholar 

38.
Beare, M. H., Neely, C. L., Coleman, D. C. & Hargrove, W. L. Characterization of a substrate-induced respiration method for measuring fungal, bacterial and total microbial biomass on plant residues. Agric. Ecosyst. Environ. 34, 65–73 (1991).
Article  Google Scholar 

39.
Stenström, J., Svensson, K. & Johansson, M. Reversible transition between active and dormant microbial states in soil. FEMS Microbiol. Ecol. 36, 93–104 (2001).
PubMed  Article  PubMed Central  Google Scholar 

40.
Kaprelyants, A. S., Gottschal, J. C. & Kell, D. B. Dormancy in non-sporulating bacteria. FEMS Microbiol. Rev. 10, 271–285 (1993).
CAS  PubMed  Article  Google Scholar 

41.
Frey, S. D., Drijber, R., Smith, H. & Melillo, J. Microbial biomass, functional capacity, and community structure after 12 years of soil warming. Soil Biol. Biochem. 40, 2904–2907 (2008).
CAS  Article  Google Scholar 

42.
Canham, C. D. W., Cole, J. & Lauenroth, W. K. Models In Ecosystem Science (Princeton University Press, 2003).

43.
Vereecken, H. et al. Modeling Soil Processes: Review, Key Challenges, and New Perspectives. Vadose Zone J. 15, 1–57 (2016).

44.
Fuhrer, T., Fischer, E. & Sauer, U. Experimental identification and quantification of glucose metabolism in seven bacterial species. J. Bacteriol. 187, 1581–1590 (2005).
CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Fontaine, S. et al. Mechanisms of the priming effect in a savannah soil amended with cellulose. Soil Sci. Soc. Am. J. 68, 125–131 (2004).
ADS  CAS  Article  Google Scholar 

46.
Sinsabaugh, R. L. et al. Stoichiometry of microbial carbon use efficiency in soils. Ecol. Monogr. 86, 172–189 (2016).
Article  Google Scholar 

47.
Wang, G. S., Mayes, M. A., Gu, L. H. & Schadt, C. W. Representation of dormant and active microbial dynamics for ecosystem modeling. PLoS ONE 9, e89252 (2014).

48.
Melillo, J. M. et al. Long-term pattern and magnitude of soil carbon feedback to the climate system in a warming world. Science 358, 101–105 (2017).
ADS  CAS  PubMed  Article  Google Scholar 

49.
Hartley, I. P., Heinemeyer, A. & Ineson, P. Effects of three years of soil warming and shading on the rate of soil respiration: substrate availability and not thermal acclimation mediates observed response. Glob. Change Biol. 13, 1761–1770 (2007).
ADS  Article  Google Scholar 

50.
Knorr, W., Prentice, I. C., House, J. I. & Holland, E. A. Long-term sensitivity of soil carbon turnover to warming. Nature 433, 298 (2005).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

51.
Melillo, J. M. et al. Soil warming and carbon-cycle feedbacks to the climate system. Science 298, 2173–2176 (2002).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

52.
Luo, Y. Q. et al. Ecological forecasting and data assimilation in a data-rich era. Ecol. Appl. 21, 1429–1442 (2011).
PubMed  Article  PubMed Central  Google Scholar 

53.
Melillo, J. M., Steudler, P. A., Mohan, J. E. Prospect Hill soil warming experiment at Harvard Forest since 1991. Harvard Forest Data Archive HF005-05 Harvard Forest, Petersham, MA http://harvardforestfasharvardedu 8080 (1999).

54.
Zhou, J. Z. et al. Microbial mediation of carbon-cycle feedbacks to climate warming. Nat. Clim. Change 2, 106–110 (2012).
ADS  CAS  Article  Google Scholar 

55.
Ye, J.-S., Bradford, M. A., Maestre, F. T., Li, F.-M. & García-Palacios, P. Compensatory thermal adaptation of soil microbial respiration rates in global croplands. Glob. Biogeochem. Cycles 34, e2019GB006507 (2020).

56.
Wang, G. S. & Chen, S. L. A review on parameterization and uncertainty in modeling greenhouse gas emissions from soil. Geoderma 170, 206–216 (2012).
ADS  CAS  Article  Google Scholar 

57.
R Development Core Team. R: A language and environment for statistical computing (R Foundation for Statitical Computing, Vienna, Austria, 2019).

58.
Batstone, D. J., Pind, P. F. & Angelidaki, I. Kinetics of thermophilic, anaerobic oxidation of straight and branched chain butyrate and valerate. Biotechnol. Bioeng. 84, 195–204 (2003).
CAS  PubMed  Article  Google Scholar 

59.
Wang, G. S., Barber, M. E., Chen, S. L. & Wu, J. Q. SWAT modeling with uncertainty and cluster analyses of tillage impacts on hydrological processes. Stoch. Environ. Res. Risk Assess. 28, 225–238 (2014).
Article  Google Scholar 

60.
Sulman, B. N. et al. Multiple models and experiments underscore large uncertainty in soil carbon dynamics. Biogeochemistry 141, 109–123 (2018).
CAS  Article  Google Scholar 

61.
Abramoff, R. et al. The Millennial model: in search of measurable pools and transformations for modeling soil carbon in the new century. Biogeochemistry 137, 51–71 (2018).
Article  Google Scholar 

62.
Crowther, T. W. et al. Quantifying global soil carbon losses in response to warming. Nature 540, 104–108 (2016).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

63.
van Gestel, N. et al. Predicting soil carbon loss with warming reply. Nature 554, E7–E8 (2018).
Article  CAS  Google Scholar 

64.
Jian, S. Y. et al. Soil extracellular enzyme activities, soil carbon and nitrogen storage under nitrogen fertilization: a meta-analysis. Soil Biol. Biochem. 101, 32–43 (2016).
CAS  Article  Google Scholar  More

1.
Alerstam, T., Hedenström, A. & Åkesson, S. Long-distance migration: evolution and determinants. Oikos 103, 247–260 (2003).
Article  Google Scholar 
2.
Hahn, S., Bauer, S. & Liechti, F. The natural link between Europe and Africa–2.1 billion birds on migration. Oikos 118, 624–626 (2009).
Article  Google Scholar 

3.
Gill, R. E. Jr et al. Extreme endurance flights by landbirds crossing the Pacific Ocean: ecological corridor rather than barrier? Proc. R. Soc. B Biol. Sci. 276, 447–457 (2008).
Article  Google Scholar 

4.
Kempenaers, B. & Valcu, M. Breeding site sampling across the Arctic by individual males of a polygynous shorebird. Nature 541, 528 (2017).
ADS  CAS  Article  Google Scholar 

5.
Dingle, H. & Drake, V. A. What is migration? Bioscience 57, 113–121 (2007).
Article  Google Scholar 

6.
Faaborg, J. et al. Recent advances in understanding migration systems of New World land birds. Ecol. Monogr. 80, 3–48 (2010).
Article  Google Scholar 

7.
Berthold, P. Bird migration: a general survey. (Oxford University Press on Demand, 2001).

8.
Dingle, H. The biology of life on the move. (New York, NY: Oxford University Press, 2014).

9.
Rappole, J. H. The Avian Migrant: The Biology of Bird Migration. (Columbia University Press, 2013).

10.
Pulido, F. The genetics and evolution of avian migration. BioScience 57, 165–174 (2007).
Article  Google Scholar 

11.
Berthold, P., Gwinner, E. & Sonnenschein, E. Avian Migration. (Springer Science & Business Media, 2013).

12.
Bearhop, S. et al. Assortative mating as a mechanism for rapid evolution of a migratory divide. Science 310, 502–504 (2005).
ADS  CAS  Article  Google Scholar 

13.
Sutherland, W. J. Evidence for flexibility and constraint in migration systems. J. Avian Biol. 29, 441–446 (1998).
Article  Google Scholar 

14.
Piersma, T. & van Gils, J. A.. The Flexible Phenotype: A Body-Centred Integration of Ecology, Physiology, and Behaviour. (Oxford University Press, 2011).

15.
Healy, K., Ezard, T. H. G., Jones, O. R., Salguero-Gómez, R. & Buckley, Y. M. Animal life history is shaped by the pace of life and the distribution of age-specific mortality and reproduction. Nat. Ecol. Evol. 3, 1217–1224 (2019).
Article  Google Scholar 

16.
Stearns, S. C. The evolution of life histories. (Oxford University Press, London, 1992).

17.
Roff, D. Evolution Of Life Histories: Theory and Analysis. (Springer Science & Business Media, 1993).

18.
Boyle, W. A. & Conway, C. J. Why migrate? A test of the evolutionary precursor hypothesis. Am. Nat. 169, 344–359 (2007).
Article  Google Scholar 

19.
Winger, B. M., Auteri, G. G., Pegan, T. M. & Weeks, B. C. A long winter for the Red Queen: rethinking the evolution of seasonal migration. Biol. Rev. 94, 737–752 (2019).

20.
Levey, D. J. & Stiles, F. G. Evolutionary precursors of long-distance migration: resource availability and movement patterns in neotropical landbirds. Am. Nat. 140, 447–476 (1992).
Article  Google Scholar 

21.
Kokko, H. & Lundberg, P. Dispersal, migration, and offspring retention in saturated habitats. Am. Nat. 157, 188–202 (2001).
CAS  Article  Google Scholar 

22.
Altizer, S., Bartel, R. & Han, B. A. Animal migration and infectious disease risk. Science 331, 296–302 (2011).
ADS  CAS  Article  Google Scholar 

23.
Sillett, T. S. & Holmes, R. T. Variation in survivorship of a migratory songbird throughout its annual cycle. J. Anim. Ecol. 71, 296–308 (2002).
Article  Google Scholar 

24.
Klaassen, R. H. G. et al. When and where does mortality occur in migratory birds? Direct evidence from long-term satellite tracking of raptors. J. Anim. Ecol. 83, 176–184 (2016).

25.
Lindström, Å. Finch flock size and risk of hawk predation at a migratory stopover site. Auk Ornithol. Adv. 106, 225–232 (1989).
Google Scholar 

26.
Conklin, J. R., Senner, N. R., Battley, P. F. & Piersma, T. Extreme migration and the individual quality spectrum. J. Avian Biol. 48, 19–36 (2017).
Article  Google Scholar 

27.
Böhning-Gaese, K., Halbe, B., Lemoine, N. & Oberrath, R. Factors influencing the clutch size, number of broods and annual fecundity of North American and European land birds. Evol. Ecol. Res. 2, 823–839 (2000).
Google Scholar 

28.
Jetz, W., Sekercioglu, C. H. & Böhning-Gaese, K. The worldwide variation in avian clutch size across species and space. PLOS Biol. 6, e303 (2008).
Article  CAS  Google Scholar 

29.
Ricklefs, R. E. & Wikelski, M. The physiology/life-history nexus. Trends Ecol. Evol. 17, 462–468 (2002).
Article  Google Scholar 

30.
Peters, P. H. Ecological Implication of Body Size. (Cambridge Studies in Ecology). (Cambridge University Press, cambridge, 1983).

31.
Schmidt-Nielsen, K. & Knut, S.-N. Scaling: Why is Animal Size So Important? (Cambridge University Press, 1984).

32.
Hedenström, A. Scaling migration speed in animals that run, swim and fly. J. Zool. 259, 155–160 (2003).
Article  Google Scholar 

33.
Hedenström Anders. Adaptations to migration in birds: behavioural strategies, morphology and scaling effects. Philos. Trans. R. Soc. B Biol. Sci. 363, 287–299 (2008).
Article  Google Scholar 

34.
Hein, A. M., Hou, C. & Gillooly, J. F. Energetic and biomechanical constraints on animal migration distance. Ecol. Lett. 15, 104–110 (2012).
Article  Google Scholar 

35.
Teitelbaum, C. S. et al. How far to go? Determinants of migration distance in land mammals. Ecol. Lett. 18, 545–552 (2015).
Article  Google Scholar 

36.
Watanabe, Y. Y. Flight mode affects allometry of migration range in birds. Ecol. Lett. 19, 907–914 (2016).
Article  Google Scholar 

37.
Newton, I. The migration ecology of birds. (Academic Press: Oxford, 2008).

38.
Speakman, J. R. & Król, E. Maximal heat dissipation capacity and hyperthermia risk: neglected key factors in the ecology of endotherms. J. Anim. Ecol. 79, 726–746 (2010).
PubMed  PubMed Central  Google Scholar 

39.
Alexander, R. M. C. N. When is migration worthwhile for animals that walk, swim or fly? J. Avian Biol. 29, 387–394 (1998).
Article  Google Scholar 

40.
Klaassen, M. Metabolic constraints on long-distance migration in birds. J. Exp. Biol. 199, 57–64 (1996).
CAS  PubMed  PubMed Central  Google Scholar 

41.
Klaassen, M. & Lindström, Å. Departure fuel loads in time-minimizing migating birds can be explained by the energy costs of being heavy. J. Theor. Biol. 183, 29–34 (1996).
Article  Google Scholar 

42.
Lindström, Å. Fuel deposition rates in migrating birds: causes, constraints and consequences. in Avian Migration (eds Berthold, P., Gwinner, E. & Sonnenschein, E.) 307–320 (Springer, 2003).

43.
Newton, I. Weather-related mass-mortality events in migrants. Ibis 149, 453–467 (2007).
Article  Google Scholar 

44.
Gylfe, Å., Bergström, S., Lundstróm, J. & Olsen, B. Reactivation of Borrelia infection in birds. Nature 403, 724 (2000).
ADS  CAS  Article  Google Scholar 

45.
Walter, H. Eleonora’s Falcon: Adaptations to Prey and Habitat in a Social Raptor. (University of Chicago Press, 1979).

46.
Somveille, M., Rodrigues, A. S. L. & Manica, A. Why do birds migrate? A macroecological perspective. Glob. Ecol. Biogeogr. 24, 664–674 (2015).
Article  Google Scholar 

47.
Dalby, L., McGill, B. J., Fox, A. D. & Svenning, J.-C. Seasonality drives global-scale diversity patterns in waterfowl (Anseriformes) via temporal niche exploitation. Glob. Ecol. Biogeogr. 23, 550–562 (2014).
Article  Google Scholar 

48.
Able, K. P. & Belthoff, J. R. Rapid ‘evolution’ of migratory behaviour in the introduced house finch of eastern North America. Proc. R. Soc. Lond. B Biol. Sci. 265, 2063–2071 (1998).
Article  Google Scholar 

49.
Pérez-Tris, J. & Tellería, J. L. Migratory and sedentary blackcaps in sympatric non-breeding grounds: implications for the evolution of avian migration. J. Anim. Ecol. 71, 211–224 (2002).
Article  Google Scholar 

50.
Chapman, B. B., Brönmark, C., Nilsson, J.-Å. & Hansson, L.-A. The ecology and evolution of partial migration. Oikos 120, 1764–1775 (2011).
Article  Google Scholar 

51.
Fogarty, M. J., Sissenwine, M. P. & Cohen, E. B. Recruitment variability and the dynamics of exploited marine populations. Trends Ecol. Evol. 6, 241–246 (1991).
CAS  Article  Google Scholar 

52.
Forcada, J., Trathan, P. N. & Murphy, E. J. Life history buffering in Antarctic mammals and birds against changing patterns of climate and environmental variation. Glob. Change Biol. 14, 2473–2488 (2008).
Google Scholar 

53.
Winger, B. M. & Pegan, T. M. The evolution of seasonal migration and the slow-fast continuum of life history in birds. bioRxiv 2020.06.27.175539 (2020), https://doi.org/10.1101/2020.06.27.175539.

54.
Martin, T. E. Nest predation and nest sites. BioScience 43, 523–532 (1993).
Article  Google Scholar 

55.
Hurlbert, A. H. & Haskell, J. P. The effect of energy and seasonality on avian species richness and community composition. Am. Nat. 161, 83–97 (2003).
Article  Google Scholar 

56.
Buckley, L. B., Hurlbert, A. H. & Jetz, W. Broad-scale ecological implications of ectothermy and endothermy in changing environments. Glob. Ecol. Biogeogr. 21, 873–885 (2012).
Article  Google Scholar 

57.
Wilcove, D. S. & Wikelski, M. Going, going, gone: is animal migration disappearing. PLoS Biol. 6, e188 (2008).
Article  CAS  Google Scholar 

58.
van Gils, J. A. et al. Body shrinkage due to Arctic warming reduces red knot fitness in tropical wintering range. Science 352, 819–821 (2016).
ADS  Article  CAS  Google Scholar 

59.
Wikelski, M. & Tertitski, G. Living sentinels for climate change effects. Science 352, 775–776 (2016).
ADS  CAS  Article  Google Scholar 

60.
Myhrvold, N. P. et al. An amniote life-history database to perform comparative analyses with birds, mammals, and reptiles. Ecology 96, 3109–3109 (2015).
Article  Google Scholar 

61.
Eyres, A., Böhning-Gaese, K. & Fritz, S. A. Quantification of climatic niches in birds: adding the temporal dimension. J. Avian Biol. 48, 1517–1531 (2017).
Article  Google Scholar 

62.
Gnanadesikan, G. E., Pearse, W. D. & Shaw, A. K. Evolution of mammalian migrations for refuge, breeding, and food. Ecol. Evol. 7, 5891–5900 (2017).
Article  Google Scholar 

63.
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).
ADS  CAS  Article  Google Scholar 

64.
Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 217–223 (2014), https://doi.org/10.1111/[email protected]/(ISSN)2041-210X.TOPMETHODS.

65.
Fritz, S. A., Bininda-Emonds, O. R. P. & Purvis, A. Geographical variation in predictors of mammalian extinction risk: big is bad, but only in the tropics. Ecol. Lett. 12, 538–549 (2009).
Article  Google Scholar 

66.
Healy, K. et al. Ecology and mode-of-life explain lifespan variation in birds and mammals. Proc. R. Soc. B Biol. Sci. 281, 20140298 (2014).
Article  Google Scholar 

67.
Revell, L. J. Size-correction and principal components for interspecific comparative studies. Evolution 63, 3258–3268 (2009).
Article  Google Scholar 

68.
Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877 (1999).
ADS  CAS  Article  Google Scholar 

69.
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33 (2010).

70.
Hadfield, J. D. & Nakagawa, S. General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi-trait models for continuous and categorical characters. J. Evol. Biol. 23, 494–508 (2010).
CAS  Article  Google Scholar  More