Philippa Kaur

1.
Gottfried, M. et al. Continent-wide response of mountain vegetation to climate change. Nat. Clim. Chang. 2, 111–115 (2012).
ADS  Article  Google Scholar 
2.
Dainese, M. et al. Human disturbance and upward expansion of plants in a warming climate. Nat. Clim. Chang. 7, 577–580 (2017).
ADS  Article  Google Scholar 

3.
Kelly, A. E. & Goulden, M. L. Rapid shifts in plant distribution with recent climate change. Proc. Natl Acad. Sci. USA 105, 11823–11826 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Lamprecht, A., Semenchuk, P. R., Steinbauer, K., Winkler, M. & Pauli, H. Climate change leads to accelerated transformation of high-elevation vegetation in the central Alps. N. Phytol. 220, 447–459 (2018).
Article  Google Scholar 

5.
Bertrand, R. et al. Changes in plant community composition lag behind climate warming in lowland forests. Nature 479, 517–520 (2011).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

6.
Dullinger, S. et al. Post-glacial migration lag restricts range filling of plants in the European Alps. Glob. Ecol. Biogeogr. 21, 829–840 (2012).
Article  Google Scholar 

7.
Rumpf, S. B. et al. Extinction debts and colonization credits of non-forest plants in the European Alps. Nat. Commun. 10, 4293 (2019).

8.
Cannone, N. & Pignatti, S. Ecological responses of plant species and communities to climate warming: upward shift or range filling processes? Clim. Change 123, 201–214 (2014).
ADS  Article  Google Scholar 

9.
Pauli, H., Gottfried, M., Reiter, K., Klettner, C. & Grabherr, G. Signals of range expansions and contractions of vascular plants in the high Alps: observations (1994–2004) at the GLORIA* master site Schrankogel, Tyrol, Austria. Glob. Chang. Biol. 13, 147–156 (2007).
ADS  Article  Google Scholar 

10.
Pounds, J. A., Fogden, M. P. L., Savage, J. M. & Gorman, G. C. Tests of null models for amphibian declines on a tropical mountain. Conserv. Biol. 11, 1307–1322 (1997).
Article  Google Scholar 

11.
Beaugrand, G., Brander, K. M., Alistair Lindley, J., Souissi, S. & Reid, P. C. Plankton effect on cod recruitment in the North Sea. Nature 426, 661–664 (2003).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

12.
Lehikoinen, A. et al. Declining population trends of European mountain birds. Glob. Chang. Biol. 25, 577–588 (2019).
ADS  PubMed  Article  PubMed Central  Google Scholar 

13.
Rumpf, S. B. et al. Range dynamics of mountain plants decrease with elevation. Proc. Natl Acad. Sci. USA 115, 1848–1853 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Lenoir, J. & Svenning, J. C. In Encyclopedia of Biodiversity 599–611 (Academic, 2013).

15.
Nogués-Bravo, D., Araújo, M. B., Romdal, T. & Rahbek, C. Scale effects and human impact on the elevational species richness gradients. Nature 453, 216–219 (2008).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

16.
Carboni, M. et al. Simulating plant invasion dynamics in mountain ecosystems under global change scenarios. Glob. Chang. Biol. 24, e289–e302 (2018).
PubMed  Article  PubMed Central  Google Scholar 

17.
Tattoni, C., Ianni, E., Geneletti, D., Zatelli, P. & Ciolli, M. Landscape changes, traditional ecological knowledge and future scenarios in the Alps: a holistic ecological approach. Sci. Total Environ. 579, 27–36 (2017).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

18.
Mair, L. et al. Abundance changes and habitat availability drive species’ responses to climate change. Nat. Clim. Chang. 4, 127–131 (2014).
ADS  Article  Google Scholar 

19.
Opdam, P. & Wascher, D. Climate change meets habitat fragmentation: linking landscape and biogeographical scale levels in research and conservation. Biol. Conserv. 117, 285–297 (2004).
Article  Google Scholar 

20.
Troia, M. J., Kaz, A. L., Niemeyer, J. C. & Giam, X. Species traits and reduced habitat suitability limit efficacy of climate change refugia in streams. Nat. Ecol. Evol. 3, 1321–1330 (2019).
PubMed  Article  PubMed Central  Google Scholar 

21.
Elsen, P. R., Monahan, W. B. & Merenlender, A. M. Topography and human pressure in mountain ranges alter expected species responses to climate change. Nat. Commun. 11, 1974 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Freeman, B. G., Lee-Yaw, J. A., Sunday, J. M. & Hargreaves, A. L. Expanding, shifting and shrinking: The impact of global warming on species’ elevational distributions. Glob. Ecol. Biogeogr. 27, 1268–1276 (2018).
Article  Google Scholar 

23.
Lenoir, J. & Svenning, J. C. Climate-related range shifts – a global multidimensional synthesis and new research directions. Ecography 38, 15–28 (2015).
Article  Google Scholar 

24.
Guo, F., Lenoir, J. & Bonebrake, T. C. Land-use change interacts with climate to determine elevational species redistribution. Nat. Commun. 9, 1315 (2018).

25.
Platts, P. J. et al. Habitat availability explains variation in climate-driven range shifts across multiple taxonomic groups. Sci. Rep. 9, 15039 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

26.
Dullinger, I. et al. A socio-ecological model for predicting impacts of land-use and climate change on regional plant diversity in the Austrian Alps. Glob. Chang. Biol. 26, 2336–2352 (2020).
ADS  PubMed Central  Article  Google Scholar 

27.
Kull, T. & Hutchings, M. J. A comparative analysis of decline in the distribution ranges of orchid species in Estonia and the United Kingdom. Biol. Conserv. 129, 31–39 (2006).
Article  Google Scholar 

28.
Wraith, J. & Pickering, C. A continental scale analysis of threats to orchids. Biol. Conserv. 234, 7–17 (2019).
Article  Google Scholar 

29.
Wraith, J., Norman, P. & Pickering, C. Orchid conservation and research: an analysis of gaps and priorities for globally red listed species. Ambio 49, 1601–1611 (2020).
PubMed  Article  PubMed Central  Google Scholar 

30.
Phillips, R. D., Reiter, N. & Peakall, R. Orchid conservation: from theory to practice. Ann. Bot. 126, 345–362 (2020).
PubMed  Article  PubMed Central  Google Scholar 

31.
van der Meer, S., Jacquemyn, H., Carey, P. D. & Jongejans, E. Recent range expansion of a terrestrial orchid corresponds with climate-driven variation in its population dynamics. Oecologia 181, 435–448 (2016).
ADS  PubMed  Article  PubMed Central  Google Scholar 

32.
Vogt-Schilb, H. et al. Responses of orchids to habitat change in Corsica over 27 years. Ann. Bot. 118, 115–123 (2016).
PubMed  PubMed Central  Article  Google Scholar 

33.
Vogt-Schilb, H., Munoz, F., Richard, F. & Schatz, B. Recent declines and range changes of orchids in Western Europe (France, Belgium and Luxembourg). Biol. Conserv. 190, 133–141 (2015).
Article  Google Scholar 

34.
Perazza, G., & & Lorenz, R. Le Orchidee dell’Italia Nordorientale. Atlante Corologico e Guida al Riconoscimento (Osiride, 2013).

35.
Sletvold, N., Dahlgren, J. P., Øien, D.-I., Moen, A. & Ehrlén, J. Climate warming alters effects of management on population viability of threatened species: results from a 30-year experimental study on a rare orchid. Glob. Chang. Biol. 19, 2729–2738 (2013).
ADS  PubMed  Article  PubMed Central  Google Scholar 

36.
Auffret, A. G., Kimberley, A., Plue, J. & Waldén, E. Super-regional land-use change and effects on the grassland specialist flora. Nat. Commun. 9, 3464 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

37.
Vilà‐Cabrera, A., Premoli, A. C. & Jump, A. S. Refining predictions of population decline at species’ rear edges. Glob. Chang. Biol. 25, 1549–1560 (2019).
ADS  PubMed  Article  PubMed Central  Google Scholar 

38.
Matthies, D., Bräuer, I., Maibom, W. & Tscharntke, T. Population size and the risk of local extinction: empirical evidence from rare plants. Oikos 105, 481–488 (2004).
Article  Google Scholar 

39.
Alexander, J. M., Diez, J. M. & Levine, J. M. Novel competitors shape species’ responses to climate change. Nature 525, 515–518 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Wilcox, R. R. Introduction to Robust Estimation and Hypothesis Testing 4th edn (Academic, 2016).

41.
Lenoir, J., Gegout, J. C., Marquet, P. A., de Ruffray, P. & Brisse, H. A significant upward shift in plant species optimum elevation during the 20th century. Science 320, 1768–1771 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

42.
De Frenne, P. et al. Microclimate moderates plant responses to macroclimate warming. Proc. Natl Acad. Sci. USA 110, 18561–18565 (2013).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

43.
De Frenne, P. et al. Global buffering of temperatures under forest canopies. Nat. Ecol. Evol. 3, 744–749 (2019).
PubMed  Article  PubMed Central  Google Scholar 

44.
Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

45.
Bertrand, R. et al. Ecological constraints increase the climatic debt in forests. Nat. Commun. 7, 12643 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Lenoir, J. et al. Going against the flow: potential mechanisms for unexpected downslope range shifts in a warming climate. Ecography 33, 295–303 (2010).
Google Scholar 

47.
Colwell, R. K. & Lees, D. C. The mid-domain effect: geometric constraints on the geography of species richness. Trends Ecol. Evol. 15, 70–76 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

48.
Rumpf, S. B., Hülber, K., Zimmermann, N. E. & Dullinger, S. Elevational rear edges shifted at least as much as leading edges over the last century. Glob. Ecol. Biogeogr. 28, 533–543 (2019).
Article  Google Scholar 

49.
Gibson-Reinemer, D. K. & Rahel, F. J. Inconsistent range shifts within species highlight idiosyncratic responses to climate warming. PLoS ONE 10, e0132103 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

50.
Vittoz, P., Randin, C., Dutoit, A., Bonnet, F. & Hegg, O. Low impact of climate change on subalpine grasslands in the Swiss Northern Alps. Glob. Chang. Biol. 15, 209–220 (2009).
ADS  Article  Google Scholar 

51.
Vogt-Schilb, H., Geniez, P., Pradel, R., Richard, F. & Schatz, B. Inter-annual variability in flowering of orchids: lessons learned from 8 years of monitoring in a Mediterranean region of France. Eur. J. Environ. Sci. 3, 129–137 (2013).
Google Scholar 

52.
Cotto, O. et al. A dynamic eco-evolutionary model predicts slow response of alpine plants to climate warming. Nat. Commun. 8, 15399 (2017).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
Tye, M., Dahlgren, J. P., Øien, D.-I., Moen, A. & Sletvold, N. Demographic responses to climate variation depend on spatial- and life history-differentiation at multiple scales. Biol. Conserv. 228, 62–69 (2018).
Article  Google Scholar 

54.
Aeschimann, D., Lauber, K., Moser, D. M. & Theurillat, J. P. Flora Alpina: Atlas des 4500 Plantes Vasculaires des Alpes (Aeschimann/Lauber, Belin, 2004).

55.
Di Piazza, A., & Eccel, E. Analisi di Serie di Temperatura e Precipitazione in Trentino nel Periodo 1958–2010 (Provincia Autonoma di Trento, 2012).

56.
Provincia Autonoma di Trento. Urbanistica – Banche Dati – Repertorio Cartografico (Provincia Autonoma di Trento, 2009).

57.
Monteiro, A. T., Fava, F., Hiltbrunner, E., Della Marianna, G. & Bocchi, S. Assessment of land cover changes and spatial drivers behind loss of permanent meadows in the lowlands of Italian Alps. Landsc. Urban Plan. 100, 287–294 (2011).
Article  Google Scholar 

58.
Eccel, E., Zollo, A. L., Mercogliano, P. & Zorer, R. Simulations of quantitative shift in bio-climatic indices in the viticultural areas of Trentino (Italian Alps) by an open source R package. Comput. Electron. Agric. 127, 92–100 (2016).
Article  Google Scholar 

59.
Verheyen, K. et al. Combining biodiversity resurveys across regions to advance global change research. Bioscience 67, 73–83 (2017).
Article  Google Scholar 

60.
Landolt, E. et al. Flora Indicativa: Okologische Zeigerwerte und Biologische Kennzeichen zur Flora der Schweiz und der Alpen (Haupt, 2010).

61.
Akinwande, M. O., Dikko, H. G. & Samson, A. Variance inflation factor: as a condition for the inclusion of suppressor variable(s) in regression analysis. Open J. Stat. 05, 754–767 (2015).
Article  Google Scholar 

62.
Kéry, M., Gardner, B. & Monnerat, C. Predicting species distributions from checklist data using site-occupancy models. J. Biogeogr. 37, 1851–1862 (2010).

63.
Harrison, X. A. Using observation-level random effects to model overdispersion in count data in ecology and evolution. PeerJ 2, e616 (2014).
PubMed  PubMed Central  Article  Google Scholar 

64.
Hothorn, T., Bretz, F., Westfall, P. & Heiberger, R. M. multcomp: simultaneous inference for general linear hypotheses. R package version 0.992-4. http://132.180.15.2/math/statlib/R/CRAN/doc/packages/multcomp.pdf (2007).

65.
Mair, P. & Wilcox, R. Robust statistical methods in R using the WRS2 package. Behav. Res. Methods 52, 464–488 (2020).
PubMed  Article  PubMed Central  Google Scholar 

66.
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).
MathSciNet  MATH  Google Scholar 

67.
Aikio, S., Duncan, R. P. & Hulme, P. E. Herbarium records identify the role of long-distance spread in the spatial distribution of alien plants in New Zealand. J. Biogeogr. 37, 1740–1751 (2010).
Article  Google Scholar 

68.
Ripley, B., Venables, B., Bates, D., Hornik, K. & Firth, D. Package ‘MASS’. http://www.stats.ox.ac.uk/pub/MASS4/ (2010).

69.
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 

70.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017). More

1.
World Health Organization. World malaria report 2019 (WHO, Geneva, 2019).
Google Scholar 
2.
Wirtz, R. A. & Burkot, T. R. Detection of malarial parasites in mosquitoes. In Advances in Disease Vector Research (eds Maudlin, I. & Sinha, R. C.) (Sprinter, New York, 1991).
Google Scholar 

3.
Trape, J. F. & Rogier, C. Combating malaria morbidity and mortality by reducing transmission. Parasitol. Today 12, 236–240 (1996).
CAS  PubMed  Article  Google Scholar 

4.
Mala, A. O. et al. Plasmodium falciparum transmission and aridity: a Kenyan experience from the dry lands of Baringo and its implications for Anopheles arabiensis control. Malar. J. 10, 121 (2011).
PubMed  PubMed Central  Article  Google Scholar 

5.
Macdonald, G. The Epidemiology and Control of Malaria (Oxford Univ. Press, London, 1957).
Google Scholar 

6.
Gillies, M. & de Meillon, B. The Anophelini of Africa South of the Sahara (Ethiopian Zoogeographical Region) (South African Institute of Medical Research, Johannesburg, 1968).
Google Scholar 

7.
Service, M. W. Mosquito (Diptera: Culicidae) dispersal—the long and short of it. J. Med. Entomol. 34, 579–588 (1997).
Article  Google Scholar 

8.
Hemming-Schroeder, E., Lo, E., Salazar, C., Puente, S. & Yan, G. Landscape genetics: a toolbox for studying vector-borne diseases. Front. Ecol. Evol. 6, 21 (2018).
ADS  Article  Google Scholar 

9.
Ramsdale, C. D. & Fontaine, R. E. Ecological Investigations of Anopheles gambiae and Anopheles funestus (World Health Organization, Geneva, 1970).
Google Scholar 

10.
Charlwood, J. D., Vij, R. & Billingsley, P. F. Dry season refugia of malaria-transmitting mosquitoes in a dry savannah zone of east Africa. Am. J. Trop. Med. Hyg. 62, 726–732 (2000).
CAS  PubMed  Article  Google Scholar 

11.
Aniedu, I. Dynamics of malaria transmission near two permanent breeding sites in Baringo district, Kenya. Indian J. Med. Res. 105, 206–211 (1997).
CAS  PubMed  Google Scholar 

12.
Kamau, L. et al. Analysis of genetic variability in Anopheles arabiensis and Anopheles gambiae using microsatellite loci. Insect Mol. Biol. 8, 287–297 (1999).
CAS  PubMed  Article  Google Scholar 

13.
Lehmann, T. et al. Genetic differentiation of Anopheles gambiae populations from East and West Africa: comparison of microsatellite and allozyme loci. Heredity 77, 192–200 (1996).
CAS  PubMed  Article  Google Scholar 

14.
Kamau, L., Lehmann, T., Hawley, W. A., Orago, A. S. & Collins, F. H. Microgeographic genetic differentiation of Anopheles gambiae mosquitoes from Asembo Bay, western Kenya: a comparison with Kilifi in coastal Kenya. Am. J. Trop. Med. Hyg. 58, 64–66 (1998).
CAS  PubMed  Article  Google Scholar 

15.
Storfer, A. et al. Putting the ‘landscape’ in landscape genetics. Heredity 98, 128–142 (2007).
CAS  PubMed  Article  Google Scholar 

16.
Biek, R. & Real, L. A. The landscape genetics of infectious disease emergence and spread. Mol. Ecol. 19, 3515–3531 (2010).
PubMed  PubMed Central  Article  Google Scholar 

17.
Storfer, A., Murphy, M. A., Spear, S. F., Holderegger, R. & Waits, L. P. Landscape genetics: Where are we now?. Mol. Ecol. 19, 3496–3514 (2010).
PubMed  Article  Google Scholar 

18.
Medley, K. A., Jenkins, D. G. & Hoffman, E. A. Human-aided and natural dispersal drive gene flow across the range of an invasive mosquito. Mol. Ecol. 24, 284–295 (2015).
PubMed  Article  Google Scholar 

19.
Blanchong, J. A. et al. Landscape genetics and the spatial distribution of chronic wasting disease. Biol. Lett. 4, 130–133 (2008).
PubMed  Article  Google Scholar 

20.
Cullingham, C. I., Kyle, C. J., Pond, B. A., Rees, E. E. & White, B. N. Differential permeability of rivers to raccoon gene flow corresponds to rabies incidence in Ontario, Canada. Mol. Ecol. 18, 43–53 (2009).
PubMed  Google Scholar 

21.
Côté, H., Garant, D., Robert, K., Mainguy, J. & Pelletier, F. Genetic structure and rabies spread potential in raccoons: the role of landscape barriers and sex-biased dispersal. Evol. Appl. 5, 393–404 (2012).
PubMed  PubMed Central  Article  Google Scholar 

22.
Guivier, E. et al. Landscape genetics highlights the role of bank vole metapopulation dynamics in the epidemiology of Puumala hantavirus. Mol. Ecol. 20, 3569–3583 (2011).
CAS  PubMed  Google Scholar 

23.
Carrel, M., Wan, X. F., Nguyen, T. & Emch, M. Genetic variation of highly pathogenic H5N1 avian influenza viruses in Vietnam shows both species-specific and spatiotemporal associations. Avian Dis. 55, 659–666 (2011).
PubMed  PubMed Central  Article  Google Scholar 

24.
Lo, E. et al. Transmission dynamics of co-endemic Plasmodium vivax and P. falciparum in Ethiopia and prevalence of antimalarial resistant genotypes. PLoS Negl. Trop. Dis. 11, e0005806 (2017).
PubMed  PubMed Central  Article  Google Scholar 

25.
Lo, E. et al. Frequent spread of Plasmodium vivax malaria maintains high genetic diversity at the Myanmar–China Border, without distance and landscape barriers. J. Infect. Dis. 216, 1254–1263 (2017).
PubMed  PubMed Central  Article  Google Scholar 

26.
Lehmann, T. et al. Microgeographic structure of Anopheles gambiae in western Kenya based on mtDNA and microsatellite loci. Mol. Ecol. 6, 243–253 (1997).
CAS  PubMed  Article  Google Scholar 

27.
Bayoh, M. N. et al. Anopheles gambiae: historical population decline associated with regional distribution of insecticide-treated bed nets in western Nyanza Province, Kenya. Malar. J. 9, 62 (2010).
PubMed  PubMed Central  Article  Google Scholar 

28.
Kitau, J. et al. Species shifts in the Anopheles gambiae complex: do LLINs successfully control Anopheles arabiensis?. PLoS ONE 7, e31481 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

29.
Mwangangi, J. M. et al. The role of Anopheles arabiensis and Anopheles coustani in indoor and outdoor malaria transmission in Taveta District, Kenya. Parasit. Vectors 6, 114 (2013).
PubMed  PubMed Central  Article  Google Scholar 

30.
Ototo, E. N. et al. Surveillance of malaria vector population density and biting behaviour in western Kenya. Malar. J. 14, 244 (2015).
PubMed  PubMed Central  Article  Google Scholar 

31.
Sougoufara, S., Harry, M., Doucouré, S., Sembène, P. M. & Sokhna, C. Shift in species composition in the Anopheles gambiae complex after implementation of long-lasting insecticidal nets in Dielmo, Senegal. Med. Vet. Entomol. 30, 365–368 (2016).
CAS  PubMed  Article  Google Scholar 

32.
Hemming-Schroeder, E. et al. Emerging pyrethroid resistance among Anopheles arabiensis in Kenya. Am. J. Trop. Med. Hyg. 98, 704–709 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Githeko, A. K. et al. Some observations on the biting behavior of Anopheles gambiae ss, Anopheles arabiensis, and Anopheles funestus and their implications for malaria control. Exp. Parasitol. 82, 306–315 (1996).
CAS  PubMed  Article  Google Scholar 

34.
Massebo, F., Balkew, M., Gebre-Michael, T. & Lindtjørn, B. Blood meal origins and insecticide susceptibility of Anopheles arabiensis from Chano in South-West Ethiopia. Parasit. Vectors 6, 44 (2013).
PubMed  PubMed Central  Article  Google Scholar 

35.
Tirados, I., Costantini, C., Gibson, G. & Torr, S. J. Blood-feeding behaviour of the malarial mosquito Anopheles arabiensis: implications for vector control. Med. Vet. Entomol. 20, 425–437 (2006).
CAS  PubMed  Article  Google Scholar 

36.
Sinka, M. E. et al. The dominant Anopheles vectors of human malaria in Africa, Europe and the Middle East: occurrence data, distribution maps and bionomic précis. Parasit. Vectors 3, 117 (2010).
PubMed  PubMed Central  Article  Google Scholar 

37.
Charlwood, J. D. et al. The rise and fall of Anopheles arabiensis (Diptera: Culicidae) in a Tanzanian village. Bull. Entomol. Res. 85, 37–44 (1995).
Article  Google Scholar 

38.
Drake, J. M. & Beier, J. C. Ecological niche and potential distribution of Anopheles arabiensis in Africa in 2050. Malar. J. 13, 213 (2014).
PubMed  PubMed Central  Article  Google Scholar 

39.
Donnelly, M. J., Cuamba, N., Charlwood, J. D., Collins, F. H. & Townson, H. Population structure in the malaria vector, Anopheles arabiensis Patton, in East Africa. Heredity 83, 408–417 (1999).
PubMed  Article  Google Scholar 

40.
Donnelly, M. J. & Townson, H. Evidence for extensive genetic differentiation among populations of the malaria vector Anopheles arabiensis in Eastern Africa. Insect Mol. Biol. 9, 357–367 (2000).
CAS  PubMed  Article  Google Scholar 

41.
Donnelly, M. J., Licht, M. C. & Lehmann, T. Evidence for recent population expansion in the evolutionary history of the malaria vectors Anopheles arabiensis and Anopheles gambiae. Mol. Biol. Evol. 18, 1353–1364 (2001).
CAS  PubMed  Article  Google Scholar 

42.
Minakawa, N. et al. Spatial distribution of anopheline larval habitats in Western Kenyan highlands: effects of land cover types and topography. Am. J. Trop Med. Hyg. 73, 157–165 (2005).
PubMed  Article  Google Scholar 

43.
Muturi, E. J. et al. Population genetic structure of Anopheles arabiensis (Diptera: Culicidae) in a rice growing area of central Kenya. J. Med. Entomol. 47, 144–151 (2014).
Article  Google Scholar 

44.
Gray, E. M. & Bradley, T. J. Physiology of desiccation resistance in Anopheles gambiae and Anopheles arabiensis. Am. J. Trop Med. Hyg. 73, 553–559 (2005).
PubMed  Article  Google Scholar 

45.
Yamana, T. K. & Eltahir, E. A. Incorporating the effects of humidity in a mechanistic model of Anopheles gambiae mosquito population dynamics in the Sahel region of Africa. Parasit. Vectors 6, 235 (2013).
PubMed  PubMed Central  Article  Google Scholar 

46.
Nkumama, I. N., O’Meara, W. P. & Osier, F. H. Changes in malaria epidemiology in Africa and new challenges for elimination. Trends Parasitol. 33, 128–140 (2017).
PubMed  Article  Google Scholar 

47.
Chen, H. et al. Monooxygenase levels and knockdown resistance (kdr) allele frequencies in Anopheles gambiae and Anopheles arabiensis in Kenya. J. Med. Entomol. 45, 242–250 (2014).
Article  Google Scholar 

48.
Severson, D. W. RFLP analysis of insect genomes. In The Molecular Biology of Insect Disease Vectors (eds Crampton, J. M. et al.) (Springer, Dordrecht, 1997).
Google Scholar 

49.
Scott, J. A., Brogdon, W. G. & Collins, F. H. Identification of single specimens of the Anopheles gambiae complex by the polymerase chain reaction. Am. J. Trop. Med. Hyg. 49, 520–529 (1993).
CAS  PubMed  Article  Google Scholar 

50.
Zheng, L., Benedict, M. Q., Cornel, A. J., Collins, F. H. & Kafatos, F. C. An integrated genetic map of the African human malaria vector mosquito, Anopheles gambiae. Genetics 143, 941–952 (1996).
CAS  PubMed  PubMed Central  Google Scholar 

51.
Oetting, W. S. et al. Linkage analysis with multiplexed short tandem repeat polymorphisms using infrared fluorescence and M13 tailed primers. Genomics 30, 450–458 (1995).
CAS  PubMed  Article  Google Scholar 

52.
Excoffier, L. & Lischer, H. E. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).
PubMed  Article  Google Scholar 

53.
Raymond, M. & Rousset, F. GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J. Hered. 86, 248–249 (1995).
Article  Google Scholar 

54.
Rousset, F. Genepop’007: a complete reimplementation of the Genepop software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).
PubMed  Article  Google Scholar 

55.
Keenan, K., McGinnity, P., Cross, T. F., Crozier, W. W. & Prodöhl, P. A. diveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol. Evol. 4, 782–788 (2013).
Article  Google Scholar 

56.
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

57.
Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour 15, 1179–1191 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

58.
Bates, D. et al. Package ‘lme4’. Convergence 12, 2 (2015).
Google Scholar 

59.
Beerli, P. & Felsenstein, J. Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach. Proc. Natl. Acad. Sci. 98, 4563–4568 (2001).
ADS  CAS  PubMed  MATH  Article  Google Scholar 

60.
Cushman, S., Storfer, A. & Waits, L. Landscape Genetics: Concepts, Methods, Applications (Wiley, West Sussex, 2015).
Google Scholar 

61.
Roy, J. Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. J. R. Meteor. Soc. 25, 1965–1978 (2005).
Google Scholar 

62.
Channan, S., Collins, K. & Emanuel, W. R. Global Mosaics of the Standard MODIS Land Cover Type Data (University of Maryland and the Pacific Northwest National Laboratory, College Park, 2014).
Google Scholar 

63.
Friedl, M. A. et al. MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010).
ADS  Article  Google Scholar 

64.
Tatem, A. J. WorldPop, open data for spatial demography. Sci. Data 4, 1–4 (2017).
Article  Google Scholar 

65.
McRae, B. H., Dickson, B. G., Keitt, T. H. & Shah, V. B. Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology 89, 2712–2724 (2008).
PubMed  Article  Google Scholar 

66.
Adamack, A. T. & Gruber, B. PopGenReport: simplifying basic population genetic analyses in R. Methods Ecol. Evol. 5, 384–387 (2014).
Article  Google Scholar 

67.
Peterman, W. E. ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms. Methods Ecol. Evol. 9, 1638–1647 (2018).
Article  Google Scholar 

68.
Peterman, W. E. et al. A comparison of popular approaches to optimize landscape resistance surfaces. Landsc. Ecol. 34, 2197–2208 (2019).
Article  Google Scholar 

69.
Oyler-McCance, S. J., Fedy, B. C. & Landguth, E. L. Sample design effects in landscape genetics. Conserv. Genet. 14, 275–285 (2013).
Article  Google Scholar  More

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/j.2041-210X.2011.00169.x@10.1111/(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

1.
Sinclair, B. J. et al. Ecol. Lett. 19, 1372–1385 (2016).
Article  Google Scholar 
2.
Deutsch, C. A. et al. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).
CAS  Article  Google Scholar 

3.
Peralta-Maraver, I. & Rezende, E. L. Nat. Clim. Change https://doi.org/10.1038/s41558-020-00938-y (2020).

4.
Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Trends Ecol. Evol. 26, 285–291 (2011).
Article  Google Scholar 

5.
Sheridan, J. A. & Bickford, D. Nat. Clim. Change 1, 401–406 (2011).
Article  Google Scholar 

6.
Kingsolver, J. G. Am. Nat. 174, 755–768 (2009).
Article  Google Scholar 

7.
Leiva, F. P., Calosi, P. & Verberk, W. C. Philos. T. R. Soc. B 374, 20190035 (2019).
Article  Google Scholar 

8.
Rezende, E. L., Castañeda, L. E. & Santos, M. Funct. Ecol. 28, 799–809 (2014).
Article  Google Scholar 

9.
Klockmann, M., Günter, F. & Fischer, K. Glob. Change Biol. 23, 686–696 (2017).
Article  Google Scholar 

10.
Tseng, M. et al. J. Anim. Ecol. 87, 647–659 (2018).
Article  Google Scholar 

11.
Dillon, M. E., Wang, G. & Huey, R. B. Nature 467, 704–706 (2010).
CAS  Article  Google Scholar 

12.
Buckley, L. B., Cannistra, A. F. & John, A. Integr. Comp. Biol. 58, 38–51 (2018).
Article  Google Scholar 

13.
Index of /pub/data/uscrn/products/subhourly01/2019/ (National Centers for Environmental Information, 2020); ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/products/subhourly01/2019/ More

1.
Smith, J. J., Hasiotis, S. T., Kraus, M. J. & Woody, D. T. Transient dwarfism of soil fauna during the Paleocene–Eocene thermal maximum. Proc. Natl Acad. Sci. USA 106, 17655–17660 (2009).
CAS  Article  Google Scholar 
2.
Sheridan, J. A. & Bickford, D. Shrinking body size as an ecological response to climate change. Nat. Clim. Change 1, 401–406 (2011).
Article  Google Scholar 

3.
Daufresne, M., Lengfellner, K. & Sommer, U. Global warming benefits the small in aquatic ecosystems. Proc. Natl Acad. Sci. USA 106, 12788–12793 (2009).
CAS  Article  Google Scholar 

4.
Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Declining body size: a third universal response to warming? Trends Ecol. Evol. 26, 285–291 (2011).
Article  Google Scholar 

5.
Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).
CAS  Article  Google Scholar 

6.
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).
Article  Google Scholar 

7.
Martinez del Rio, C. & Karasov, W. H. Body size and temperature: why they matter. Nat. Educ. Knowl. 3, 10 (2010).
Google Scholar 

8.
Araújo, M. B. et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).
Article  Google Scholar 

9.
Klockmann, M., Günter, F. & Fischer, K. Heat resistance throughout ontogeny: body size constrains thermal tolerance. Glob. Change Biol. 23, 686–696 (2017).
Article  Google Scholar 

10.
Leiva, F. P., Calosi, P. & Verberk, W. C. Scaling of thermal tolerance with body mass and genome size in ectotherms: a comparison between water-and air-breathers. Philos. T. R. Soc. B. 374, 20190035 (2019).
Article  Google Scholar 

11.
Sinclair, B. J., Vernon, P., Klok, C. J. & Chown, S. L. Insects at low temperatures: an ecological perspective. Trends Ecol. Evol. 18, 257–262 (2003).
Article  Google Scholar 

12.
Rezende, E. L., Castañeda, L. E. & Santos, M. Tolerance landscapes in thermal ecology. Funct. Ecol. 28, 799–809 (2014).
Article  Google Scholar 

13.
Santos, M., Castañeda, L. E. & Rezende, E. L. Making sense of heat tolerance estimates in ectotherms: lessons from Drosophila. Funct. Ecol. 25, 1169–1180 (2011).
Article  Google Scholar 

14.
Rezende, E. L., Tejedo, M. & Santos, M. Estimating the adaptive potential of critical thermal limits: methodological problems and evolutionary implications. Funct. Ecol. 25, 111–121 (2011).
Article  Google Scholar 

15.
Strang, T. J. K. A review of published temperatures for the control of pest insects in museums. Coll. Forum 8, 41–67 (1992).
Google Scholar 

16.
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Global analysis of thermal tolerance and latitude in ectotherms. Proc. Natl Acad. Sci. USA 278, 1823–1830 (2010).
Google Scholar 

17.
Hoffmann, A. A., Chown, S. L. & Clusella–Trullas, S. Upper thermal limits in terrestrial ectotherms: how constrained are they? Funct. Ecol. 27, 934–949 (2013).
Article  Google Scholar 

18.
May, R. M. How many species are there on earth? Science 241, 1441–1449 (1988).
CAS  Article  Google Scholar 

19.
Sunday, J. M. et al. Thermal–safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA 111, 5610–5615 (2014).
CAS  Article  Google Scholar 

20.
Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature 569, 108–111 (2019).
CAS  Article  Google Scholar 

21.
Kearney, M. R., Gillingham, P. K., Bramer, I., Duffy, J. P. & Maclean, I. M. A method for computing hourly, historical, terrain‐corrected microclimate anywhere on Earth. Methods Ecol. Evol. 11, 38–43 (2020).
Article  Google Scholar 

22.
Rezende, E. L., Bozinovic, F., Szilágyi, A. & Santos, M. Predicting temperature mortality and selection in natural Drosophila populations. Science 369, 1242–1245 (2020).
CAS  Article  Google Scholar 

23.
Glazier, D. S. A unifying explanation for diverse metabolic scaling in animals and plants. Biol. Rev. 85, 111–138 (2010).
Article  Google Scholar 

24.
Schmid, P. E., Tokeshi, M. & Schmid-Araya, J. M. Relation between population density and body size in stream communities. Science 289, 1557–1560 (2000).
CAS  Article  Google Scholar 

25.
Pörtner, H. O. & Knust, R. Climate change affects marine fishes through the oxygen limitation of thermal tolerance. Science 315, 95–97 (2007).
Article  Google Scholar 

26.
Fan, Y. & van den Dool, H. A global monthly land surface air temperature analysis for 1948–present. J. Geophys. Res. Atmos. 113, 1–18 (2008).
Article  Google Scholar 

27.
Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C. & Wang, W. An improved in situ and satellite SST analysis for climate. J. Clim. 15, 1609–1625 (2002).
Article  Google Scholar 

28.
Crisp, D. J. Methods for the Study of Marine Benthos 2nd edn (eds Holme, N. A. & McIntyre, A. D) 284–366 (Blackwell, 1984).

29.
Reiss, J. & Schmid‐Araya, J. M. Existing in plenty: abundance, biomass and diversity of ciliates and meiofauna in small streams. Freshw. Bol. 53, 652–668 (2008).
Article  Google Scholar 

30.
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information–Theoretic Approach (Springer, 2002).

31.
Turkheimer, F. E., Hinz, R. & Cunningham, V. J. On the undecidability among kinetic models: from model selection to model averaging. J. Cereb. Blood Flow. Metab. 23, 490–498 (2003).
Article  Google Scholar  More

1.
A Guide to SDG Interactions: from Science to Implementation (International Council for Science, 2017); https://go.nature.com/3o5nOD3
2.
IPBES Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2019).

3.
Schneider, F. et al. How can science support the 2030 Agenda for Sustainable Development? Four tasks to tackle the normative dimension of sustainability. Sustain. Sci. 14, 1593–1604 (2019).
Article  Google Scholar 

4.
Barbier, E. B. & Burgess, J. C. Sustainable development goal indicators: analyzing trade-offs and complementarities. World Dev. 122, 295–305 (2019).
Article  Google Scholar 

5.
Pradhan, P., Costa, L., Rybski, D., Lucht, W. & Kropp, J. P. A systematic study of Sustainable Development Goal (SDG) interactions. Earth’s Future 5, 1169–1179 (2017).
Article  Google Scholar 

6.
Howe, C., Suich, H., Vira, B. & Mace, G. M. Creating win-wins from trade-offs? Ecosystem services for human well-being: a meta-analysis of ecosystem service trade-offs and synergies in the real world. Glob. Environ. Change 28, 263–275 (2014).
Article  Google Scholar 

7.
Whitmee, S. et al. Safeguarding human health in the Anthropocene epoch: report of The Rockefeller Foundation–Lancet Commission on planetary health. Lancet 386, 1973–2028 (2015).
Article  Google Scholar 

8.
Naidoo, R. & Fisher, B. Reset Sustainable Development Goals for a pandemic world. Nature 583, 198–201 (2020).
CAS  Article  Google Scholar 

9.
Nilsson, M. et al. Mapping interactions between the sustainable development goals: lessons learned and ways forward. Sustain. Sci. 13, 1489–1503 (2018).
Article  Google Scholar 

10.
Cohen-Shacham, E., Walters, G., Janzen, C. & Maginnis, S. (eds) Nature-based Solutions to Address Global Societal Challenges (IUCN, 2016).

11.
Allen, C., Metternicht, G. & Wiedmann, T. Prioritising SDG targets: assessing baselines, gaps and interlinkages. Sustain. Sci. 14, 421–438 (2019).
Article  Google Scholar 

12.
Mayrhofer, J. P. & Gupta, J. The science and politics of co-benefits in climate policy. Environ. Sci. Policy 57, 22–30 (2016).
Article  Google Scholar 

13.
Le Blanc, D. Towards Integration at Last? The Sustainable Development Goals as a Network of Targets (United Nations, Department of Economic and Social Affairs, 2015).

14.
Sokolow, S. H. et al. Nearly 400 million people are at higher risk of schistosomiasis because dams block the migration of snail-eating river prawns. Phil. Trans. R. Soc. B 372, 20160127 (2017).
Article  Google Scholar 

15.
Steinmann, P., Keiser, J., Bos, R., Tanner, M. & Utzinger, J. Schistosomiasis and water resources development: systematic review, meta-analysis, and estimates of people at risk. Lancet Infect. Dis. 6, 411–425 (2006).
Article  Google Scholar 

16.
Sokolow, S. H. et al. Global assessment of schistosomiasis control over the past century shows targeting the snail intermediate host works best. PLoS Negl. Trop. Dis. 10, e0004794 (2016).
Article  Google Scholar 

17.
Martin, D. A. et al. Land-use history determines ecosystem services and conservation value in tropical agroforestry. Conserv. Lett. 13, e12740 (2020).
Article  Google Scholar 

18.
Medlock, J. M. et al. A review of the invasive mosquitoes in Europe: ecology, public health risks, and control options. Vector Borne Zoonotic Dis. 12, 435–447 (2012).
Article  Google Scholar 

19.
van Riper, C., van Riper, S. G., Goff, M. L. & Laird, M. The epizootiology and ecological significance of malaria in Hawaiian land birds. Ecol. Monogr. 56, 327–344 (1986).
Article  Google Scholar 

20.
Franklin, B. Protection of Towns from Fire. The Pennsylvania Gazette (4 February 1735).

21.
Bauch, S. C., Birkenbach, A. M., Pattanayak, S. K. & Sills, E. O. Public health impacts of ecosystem change in the Brazilian Amazon. Proc. Natl Acad. Sci. USA 112, 7414–7419 (2015).
CAS  Article  Google Scholar 

22.
Herrera, D. et al. Upstream watershed condition predicts rural children’s health across 35 developing countries. Nat. Commun. 8, 811 (2017).
Article  Google Scholar 

23.
McShane, T. O. et al. Hard choices: making trade-offs between biodiversity conservation and human well-being. Biol. Conserv. 144, 966–972 (2011).
Article  Google Scholar 

24.
Lengeler, C. Insecticide-treated bed nets and curtains for preventing malaria. Cochrane Database Syst. Rev. https://doi.org/10.1002/14651858.CD000363.pub2 (2004).

25.
Price, J., Richardson, M. & Lengeler, C. Insecticide-treated nets for preventing malaria. Cochrane Database Syst. Rev. https://doi.org/10.1002/14651858.CD000363.pub3 (2018).

26.
Short, R., Gurung, R., Rowcliffe, M., Hill, N. & Milner-Gulland, E. J. The use of mosquito nets in fisheries: a global perspective. PLoS ONE 13, e0191519 (2018).
Article  Google Scholar 

27.
Markandya, A. et al. Counting the cost of vulture decline—an appraisal of the human health and other benefits of vultures in India. Ecol. Econ. 67, 194–204 (2008).
Article  Google Scholar 

28.
Buechley, E. R. & Şekercioğlu, Ç. H. The avian scavenger crisis: looming extinctions, trophic cascades, and loss of critical ecosystem functions. Biol. Conserv. 198, 220–228 (2016).
Article  Google Scholar 

29.
Gangoso, L. et al. Reinventing mutualism between humans and wild fauna: insights from vultures as ecosystem services providers. Conserv. Lett. 6, 172–179 (2013).
Article  Google Scholar 

30.
Hampson, K. et al. Estimating the global burden of endemic canine rabies. PLoS Negl. Trop. Dis. 9, e0003709 (2015).
Article  Google Scholar 

31.
Ogada, D. L., Torchin, M. E., Kinnaird, M. F. & Ezenwa, V. O. Effects of vulture declines on facultative scavengers and potential implications for mammalian disease transmission. Conserv. Biol. 26, 453–460 (2012).
CAS  Article  Google Scholar 

32.
Breuer, E., Lee, L., De Silva, M. & Lund, C. Using theory of change to design and evaluate public health interventions: a systematic review. Implement. Sci. 11, 63 (2016).
Article  Google Scholar 

33.
Constructing Theories of Change for Ecosystem-Based Adaptation Projects: A Guidance Document (Conservation International, 2013).

34.
de Wit, L. A. et al. Estimating burdens of neglected tropical zoonotic diseases on islands with introduced mammals. Am. J. Trop. Med. Hyg. 96, 749–757 (2017).
Google Scholar 

35.
Morand, S. et al. Global parasite and Rattus rodent invasions: the consequences for rodent-borne diseases. Integr. Zool. 10, 409–423 (2015).
Article  Google Scholar 

36.
Duron, Q., Shiels, A. B. & Vidal, E. Control of invasive rats on islands and priorities for future action. Conserv. Biol. 31, 761–771 (2017).
Article  Google Scholar 

37.
Vanderwerf, E. A. Importance of nest predation by alien rodents and avian poxvirus in conservation of Oahu elepaio. J. Wildl. Manag. 73, 737–746 (2009).
Article  Google Scholar 

38.
Pender, R. J., Shiels, A. B., Bialic-Murphy, L. & Mosher, S. M. Large-scale rodent control reduces pre- and post-dispersal seed predation of the endangered Hawaiian lobeliad, Cyanea superba subsp. superba (Campanulaceae). Biol. Invasions 15, 213–223 (2013).
Article  Google Scholar 

39.
Hoare, J. M. & Hare, K. M. The impact of brodifacoum on non-target wildlife: gaps in knowledge. N. Z. J. Ecol. 30, 157–167 (2006).
Google Scholar 

40.
DataBank (The World Bank, 2020); https://databank.worldbank.org/home.aspx

41.
Progress on Drinking Water and Sanitation: 2012 Update (World Health Organization and UNICEF, 2012); https://go.nature.com/2HOJFOR More