Ecology

1.
Díaz, S. et al. (eds) 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, Bonn, 2019).
Google Scholar 
2.
Struebig, M. J. et al. Targeted conservation to safeguard a biodiversity hotspot from climate and land-cover change. Curr. Biol. 25, 372–378 (2015).
CAS  PubMed  Article  Google Scholar 

3.
McMichael, A. J., Woodruff, R. E. & Hales, S. Climate change and human health: present and future risks. Lancet 367, 859–869 (2006).
PubMed  Article  Google Scholar 

4.
Roson, R. & Sartori, M. Estimation of climate change damage functions for 140 regions in the GTAP 9 database. J. Glob. Econ. Anal. 1, 78–115 (2016).
Article  Google Scholar 

5.
Tol, R. S. J. Who Benefits and Who Loses from Climate Change? In Handbook of Climate Change Mitigation and Adaptation (eds Chen, W.-Y. et al.) 1–12 (Springer, New York, 2014).
Google Scholar 

6.
Veldkamp, A. & Fresco, L. O. CLUE: a conceptual model to study the conversion of land use and its effects. Ecol. Model. 85, 253–270 (1996).
Article  Google Scholar 

7.
Verburg, P. H. & Overmars, K. P. Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model. Landsc. Ecol. 24, 1167–1181 (2009).
Article  Google Scholar 

8.
Mantyka-pringle, C. S. et al. Climate change modifies risk of global biodiversity loss due to land-cover change. Biol. Conserv. 187, 103–111 (2015).
Article  Google Scholar 

9.
Oliver, T. H. & Morecroft, M. D. Interactions between climate change and land use change on biodiversity: attribution problems, risks, and opportunities. Wiley Interdiscip. Rev. Clim. Change 5, 317–335 (2014).
Article  Google Scholar 

10.
Newbold, T. et al. Climate and land-use change homogenise terrestrial biodiversity, with consequences for ecosystem functioning and human well-being. Emerg. Top. Life Sci. 3, 207–219 (2019).
PubMed  Article  Google Scholar 

11.
Brodie, J. F. Synergistic effects of climate change and agricultural land use on mammals. Front. Ecol. Environ. 14, 20–26 (2016).
Article  Google Scholar 

12.
Brambilla, M., Pedrini, P., Rolando, A. & Chamberlain, D. E. Climate change will increase the potential conflict between skiing and high-elevation bird species in the Alps. J. Biogeogr. 43, 2299–2309 (2016).
Article  Google Scholar 

13.
Ferrier, S. et al. (eds) IPBES. Summary for Policymakers of the Methodological Assessment of Scenarios and Models of Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES secretariat, Bonn, 2016).
Google Scholar 

14.
Leclere, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).
ADS  CAS  PubMed  Google Scholar 

15.
Newbold, T. Future effects of climate and land-use change on terrestrial vertebrate community diversity under different scenarios. Proc. R. Soc. B Biol. Sci. 285, 20180792 (2018).
Article  Google Scholar 

16.
Powers, R. P. & Jetz, W. Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat. Clim. Change 9, 323–329 (2019).
ADS  Article  Google Scholar 

17.
Marques, A. et al. Increasing impacts of land use on biodiversity and carbon sequestration driven by population and economic growth. Nat. Ecol. Evol. 3, 628–637 (2019).
PubMed  PubMed Central  Article  Google Scholar 

18.
Ha, P. V., Kompas, T., Thi, H., Nguyen, M. & Hoang, C. Building a better trade model to determine local effects : a regional and intertemporal GTAP model. Econ. Model. 67, 102–113 (2016).
Google Scholar 

19.
Van Ha, P. & Kompas, T. Solving intertemporal CGE models in parallel using a singly bordered block diagonal ordering technique. Econ. Model. 52, 3–12 (2016).
Article  Google Scholar 

20.
Fuchs, R., Herold, M., Verburg, P. H. & Clevers, J. G. P. W. A high-resolution and harmonized model approach for reconstructing and analysing historic land changes in Europe. Biogeosciences 10, 1543–1559 (2013).
ADS  Article  Google Scholar 

21.
Lawson, C. R., Hodgson, J. A., Wilson, R. J. & Richards, S. A. Prevalence, thresholds and the performance of presence-absence models. Methods Ecol. Evol. 5, 54–64 (2014).
Article  Google Scholar 

22.
Wintle, B. A., Elith, J. & Potts, J. M. Fauna habitat modelling and mapping: a review and case study in the lower hunter central coast region of NSW. Austral. Ecol. 30, 719–738 (2005).
Article  Google Scholar 

23.
Wintle, B. A. et al. Ecological–economic optimization of biodiversity conservation under climate change. Nat. Clim. Change 1, 355–359 (2011).
ADS  Article  Google Scholar 

24.
Thomas, C. D. Climate change and extinction risk. Nature 430, 25 (2004).
ADS  Article  CAS  Google Scholar 

25.
Jiménez-Valverde, A. Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling. Glob. Ecol. Biogeogr. 21, 498–507 (2012).
Article  Google Scholar 

26.
Baldwin, R. Use of maximum entropy modeling in wildlife research. Entropy 11, 854–866 (2009).
ADS  Article  Google Scholar 

27.
R Development Core Team. R: A Language and Environment for Statistical Computing. Foundation for Statistical Computing. https://www.R-project.org/ (2020). Accessed 3 September 2018.

28.
Food and Agriculture Organization of the United Nations (FAO). FAOSTAT Statistics Database (2017).

29.
IUCN. The IUCN Red List of Threatened Species. Version 2018-2 (2018).

30.
Kuussaari, M. et al. Extinction debt: a challenge for biodiversity conservation. Trends Ecol. Evol. 24, 564–571 (2009).
PubMed  Article  Google Scholar 

31.
Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

32.
Gillings, S., Balmer, D. E. & Fuller, R. J. Directionality of recent bird distribution shifts and climate change in Great Britain. Glob. Change Biol. 21, 2155–2168 (2015).
ADS  Article  Google Scholar 

33.
Stephens, P. A. et al. Consistent response of bird populations to climate change on two continents. Science 352, 84–87 (2016).
ADS  CAS  PubMed  Article  Google Scholar 

34.
van Vuuren, D. P. & Carter, T. R. Climate and socio-economic scenarios for climate change research and assessment: reconciling the new with the old. Clim. Change 122, 415–429 (2014).
ADS  Article  Google Scholar 

35.
Bryan, B. A. et al. Land-use and sustainability under intersecting global change and domestic policy scenarios: trajectories for Australia to 2050. Glob. Environ. Change 38, 130–152 (2016).
Article  Google Scholar 

36.
van Proosdij, A. S. J., Sosef, M. S. M., Wieringa, J. J. & Raes, N. Minimum required number of specimen records to develop accurate species distribution models. Ecography 39, 542–552 (2016).
Article  Google Scholar 

37.
Hernandez, P. A., Graham, C., Master, L. L. & Albert, D. L. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29, 773–785 (2006).
Article  Google Scholar 

38.
Wisz, M. S. et al. Effects of sample size on the performance of species distribution models. Divers. Distrib. 14, 763–773 (2008).
Article  Google Scholar 

39.
Guisan, A. et al. Using niche-based models to improve the sampling of rare species. Conserv. Biol. 20, 501–511 (2006).
PubMed  Article  Google Scholar 

40.
Taylor, P. D., Fahrig, L., Henein, K. & Merriam, G. Connectivity Is a vital element of landscape structure. Oikos 68, 571 (1993).
Article  Google Scholar 

41.
Gordon, A. et al. The use of dynamic landscape metapopulation models for forest management: a case study of the red-backed salamander. Can. J. For. Res. 42, 1091–1106 (2012).
Article  Google Scholar 

42.
Cadenhead, N. C. R., Kearney, M. R., Moore, D., Mcalpin, S. & Wintle, B. A. Climate and fire scenario uncertainty dominate the evaluation of options for conserving the great desert skink. Conserv. Lett. 9, 181–190 (2015).
Article  Google Scholar 

43.
UN General Assembly. Transforming Our World: The 2030 Agenda for Sustainable Development. https://www.refworld.org/docid/57b6e3e44.html (2015). Accessed 22 November 2018.

44.
BirdLife International. Country profile: Vietnam. http://www.birdlife.org/datazone/country/vietnam (2018). Accessed 21 October 2018.

45.
BirdLife International. Country Profile: Australia. http://www.birdlife.org/datazone/country/australia (2018). Accessed 21 October 2018.

46.
van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).
ADS  Article  Google Scholar 

47.
Hijmans, R., Cameron, S., Parra, J., Jones, P. & Jarvis, A. WORLDCLIM—A Set of Global Climate Layers (Climate Grids), Version 1.4.

48.
Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).
ADS  Article  Google Scholar 

49.
Hertel, T. Global Trade Analysis: Modeling and Applications (Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, West Lafayette, 1997).
Google Scholar 

50.
Aguiar, A., Narayanan, B. & McDougall, R. An overview of the GTAP 9 data base. J. Glob. Econ. Anal. 1, 181–208 (2016).
Article  Google Scholar 

51.
Van Ha, P., Kompas, T., Nguyen, H. T. M. & Long, C. H. Building a better trade model to determine local effects: a regional and intertemporal GTAP model. Econ. Model. 67, 102–113 (2017).
Article  Google Scholar 

52.
Kompas, T., Pham, V. H. & Che, T. N. The effects of climate change on GDP by country and the global economic gains from complying with the Paris climate accord. Earth’s Future 6, 1153–1173 (2018).
ADS  Article  Google Scholar 

53.
Horridge, J. M., Jerie, M., Mustakinov, D. & Schiffmann, F. GEMPACK manual, GEMPACK Software, ISBN 978-1–921654-34-3 (2018).

54.
Pearson, K. R. Solving Nonlinear Economic Models Accurately Via a Linear Representation. Working paper No. IP-55. Victoria University, Centre of Policy Studies (1991).

55.
Kompas, T. & Ha, P. V. The ‘curse of dimensionality’ resolved: the effects of climate change and trade barriers in large dimensional modelling. Econ. Model. 80, 103–110 (2018).
Article  Google Scholar 

56.
Balay, S. et al. PETSc users manual, Technical Report ANL-95/11—Revision 3.11. (2019).

57.
Balay, S. et al. PETSc Web page. http://www.mcs.anl.gov/petsc (2019). Accessed 23 September 2018.

58.
Balay, S., Gropp, W. D., McInnes, L. C. & Smith, B. F. Efficient management of parallelism in object oriented numerical software libraries. In Modern Software Tools in Scientific Computing (eds Arge, E. et al.) (Birkhaeuser Press, Boston, 1997).
Google Scholar 

59.
HSL. A collection of fortran codes for large scale scientific computation. The HSL Mathematical Software Library (2013).

60.
World Bank Group. Population Estimates and Projections. http://data.worldbank.org/data-catalog/population-projection-tables (2016). Accessed 3 May 2018.

61.
Moulds, S., Buytaert, W. & Mijic, A. An open and extensible framework for spatially explicit land use change modelling: the lulcc R package. Geosci. Model Dev. 8, 3215–3229 (2015).
ADS  Article  Google Scholar 

62.
Verburg, P. H. et al. Modeling the spatial dynamics of regional land use: the CLUE-S model. Environ. Manag. 30, 391–405 (2002).
ADS  Article  Google Scholar 

63.
Verburg, P. H., Veldkamp, T. & Bouma, J. Land use change under conditions of high population pressure: the case of Java. Glob. Environ. Change 9, 303–312 (1999).
Article  Google Scholar 

64.
Verburg, P. H., Schulp, C. J. E., Witte, N. & Veldkamp, A. Downscaling of land use change scenarios to assess the dynamics of European landscapes. Agr. Ecosyst. Environ. 114, 39–56 (2006).
Article  Google Scholar 

65.
Verburg, P. H., De Koning, G. H. J., Kok, K., Veldkamp, A. & Bouma, J. A spatial explicit allocation procedure for modelling the pattern of land use change based upon actual land use. Ecol. Model. 116, 45–61 (1999).
Article  Google Scholar 

66.
Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Soft. 33, 1–22 (2010).
Article  Google Scholar 

67.
Steven J. P., Miroslav D., Robert E. S. [Internet] Maxent software for modeling species niches and distributions (Version 3.3.3k). http://biodiversityinformatics.amnh.org/open_source/maxent/. Accessed 12 December 2018.

68.
GBIF. GBIF data portal. http://www.gbif.net/ (2016). Accessed 22 May 2018.

69.
Goetz, S. J., Sun, M., Zolkos, S., Hansen, A. & Dubayah, R. The relative importance of climate and vegetation properties on patterns of North American breeding bird species richness. Environ. Res. Lett. 9, 034013 (2014).
ADS  Article  Google Scholar 

70.
Maggini, R. et al. Assessing species vulnerability to climate and land use change: the case of the Swiss breeding birds. Divers. Distrib. 20, 708–719 (2014).
Article  Google Scholar 

71.
Coxen, C. L., Frey, J. K., Carleton, S. A. & Collins, D. P. Species distribution models for a migratory bird based on citizen science and satellite tracking data. Glob. Ecol. Conserv. 11, 298–311 (2017).
Article  Google Scholar 

72.
Stolar, J. & Nielsen, S. E. Accounting for spatially biased sampling effort in presence-only species distribution modelling. Divers. Distrib. 21, 595–608 (2015).
Article  Google Scholar 

73.
Phillips, S. J. et al. Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).
PubMed  Article  Google Scholar 

74.
Liu, C., Newell, G. & White, M. On the selection of thresholds for predicting species occurrence with presence-only data. Ecol. Evol. 6, 337–348 (2016).
PubMed  Article  Google Scholar 

75.
Morán-Ordóñez, A., Lahoz-Monfort, J. J., Elith, J. & Wintle, B. A. Evaluating 318 continental-scale species distribution models over a 60-year prediction horizon: what factors influence the reliability of predictions?. Glob. Ecol. Biogeogr. 26, 1–14 (2016).
Google Scholar 

76.
Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. Package ‘dismo’. http://cran.r-project.org/web/packages/dismo/index.html (2011). Accessed 6 July 2017. More

1.
Gaston, K. J. Global patterns in biodiversity. Nature 405, 220–227 (2000).
CAS  PubMed  Article  Google Scholar 
2.
Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Kellermann, V. et al. Phylogenetic constraints in key functional traits behind species’ climate niches: patterns of desiccation and cold resistance across 95 Drosophila species. Evolution 66, 3377–3389 (2012).
PubMed  Article  Google Scholar 

4.
Baselga, A., Recuero, E., Parra-Olea, G. & García-París, M. Phylogenetic patterns in zopherine beetles are related to ecological niche width and dispersal limitation. Mol. Ecol. 20, 5060–5073 (2011).
PubMed  Article  PubMed Central  Google Scholar 

5.
Losos, J. B. Phylogenetic niche conservatism, phylogenetic signal and the relationship between phylogenetic relatedness and ecological similarity among species. Ecol. Lett. 11, 995–1003 (2008).
PubMed  Article  Google Scholar 

6.
Dormann, C. F., Gruber, B., Winter, M. & Herrman, D. Evolution of climate niches in European mammals?. Biol. Lett. 6, 229–232 (2010).
PubMed  Article  PubMed Central  Google Scholar 

7.
Hof, C., Rahbek, C. & Araújo, M. B. Phylogenetic signals in the climatic niches of the world’s amphibians. Ecography 33, 242–250 (2010).
Google Scholar 

8.
Duran, A. & Pie, M. R. Tempo and mode of climate niche evolution in Primates. Evolution 69, 2496–2506 (2015).
PubMed  Article  Google Scholar 

9.
Khaliq, I. et al. Global variation in thermal physiology of birds and mammals: evidence for phylogenetic niche conservatism only in the tropics. J. Biogeogr. 42, 2187–2196 (2015).
Article  Google Scholar 

10.
Pie, M. R. The macroevolution of climatic niches and its role in ant diversification. Ecol. Entomol. 41, 301–307 (2016).
Article  Google Scholar 

11.
Wiens, J. J. & Donoghue, M. J. Historical biogeography, ecology and species richness. Trends Ecol. Evol. 19, 639–644 (2004).
PubMed  Article  Google Scholar 

12.
Wiens, J. J. & Graham, C. H. Niche conservatism: integrating evolution, ecology, and conservation biology. Annu. Rev. Ecol. Evol. Syst. 36, 519–539 (2005).
Article  Google Scholar 

13.
Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Evol. Syst. 33, 475–505 (2002).
Article  Google Scholar 

14.
Broennimann, O. et al. Evidence of climatic niche shift during biological invasion. Ecol. Lett. 10, 701–709 (2007).
CAS  PubMed  Article  Google Scholar 

15.
Fitzpatrick, M. C., Weltzin, J. F., Sanders, N. J. & Dunn, R. R. The biogeography of prediction error: Why does the introduced range of the fire ant over-predict its native range?. Glob. Ecol. Biogeogr. 16, 24–33 (2007).
Article  Google Scholar 

16.
Prinzing, A., Durka, W., Klotz, S. & Brandl, R. The niche of higher plants: evidence for phylogenetic conservatism. Proc. Biol. Sci. 268, 1–7 (2001).
Article  Google Scholar 

17.
Petitpierre, B. et al. Climatic niche shifts are rare among terrestrial plant invaders. Science 335, 1344–1348 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

18.
Kozak, K. H. & Wiens, J. J. Does niche conservatism promote speciation? A case study in North American salamanders. Evolution 60, 2604–2621 (2006).
PubMed  Article  Google Scholar 

19.
Rice, N. H., Martinez-Meyer, E. & Peterson, A. T. Ecological niche differentiation in the Aphelocoma jays: a phylogenetic perspective. Biol. J. Linn. Soc. 80, 369–383 (2003).
Article  Google Scholar 

20.
Graham, C. H., Ron, S. R., Santos, J. C., Schneider, C. J. & Moritz, C. Integrating phylogenetics and environmental niche models to explore speciation mechanisms in dendrobatid frogs. Evolution 58, 1781–1793 (2004).
PubMed  Article  Google Scholar 

21.
Knouft, J. H., Losos, J. B., Glor, R. E. & Kolbe, J. J. Phylogenetic analysis of the evolution of the niche in lizards of the Anolis sagrei group. Ecology 87, S29–S38 (2006).
PubMed  Article  Google Scholar 

22.
Cooper, N., Freckleton, R. P. & Jetz, W. Phylogenetic conservatism of environmental niches in mammals. Proc. Biol. Sci. 278, 2384–2391 (2011).
PubMed  PubMed Central  Google Scholar 

23.
Kamilar, J. M. & Muldoon, K. M. The climatic niche diversity of Malagasy primates: a phylogenetic approach. PLoS ONE 5, e11073 (2010).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

24.
Peixoto, F. P., Villalobos, F. & Cianciaruso, M. V. Phylogenetic conservatism of climatic niche in bats. Glob. Ecol. Biogeogr. 26, 1055–1065 (2017).
Article  Google Scholar 

25.
Ricciardi, A., Hoopes, M. F., Marchetti, M. P. & Lockwood, J. L. Progress toward understanding the ecological impacts of nonnative species. Ecol. Monogr. 83, 263–282 (2013).
Article  Google Scholar 

26.
Bellard, C. & Jeschke, J. M. A spatial mismatch between invader impacts and research publications. Conserv. Biol. 30, 230–232 (2016).
CAS  PubMed  Article  Google Scholar 

27.
Arnan, X. et al. Dominance-diversity relationships in ant communities differ with invasion. Glob. Change Biol. 24, 4614–4625 (2018).
ADS  Article  Google Scholar 

28.
Gussow, A. B., Auslander, N., Wolf, Y. I. & Koonin, E. V. Prediction of the incubation period for COVID-19 and future virus disease outbreaks. BMC Biol. 18, 1–12 (2020).
Article  CAS  Google Scholar 

29.
Raffini, F. et al. From nucleotides to satellite imagery: approaches to identify and manage the invasive pathogen Xylella fastidiosa and its insect vectors in Europe. Sustainability 12, 4508 (2020).
CAS  Article  Google Scholar 

30.
Chown, S. L. et al. Biological invasions, climate change and genomics. Evol Appl 8, 23–46 (2015).
PubMed  Article  Google Scholar 

31.
Rollins, L. A., Richardson, M. F. & Shine, R. A genetic perspective on rapid evolution in cane toads (Rhiniella marina). Mol. Ecol. 24, 2264–2276 (2015).
PubMed  Article  PubMed Central  Google Scholar 

32.
Estoup, A. et al. Is there a genetic paradox of biological invasion?. Annu. Rev. Ecol. Evol. Syst. 47, 51–72 (2016).
Article  Google Scholar 

33.
Ricciardi, A. et al. Invasion science: a horizon scan of emerging challenges and opportunities. Trends Ecol. Evol. 32, 464–474 (2017).
PubMed  Article  PubMed Central  Google Scholar 

34.
Fenderson, L. E., Kovach, A. I. & Llamas, B. Spatiotemporal landscape genetics: Investigating ecology and evolution through space and time. Mol. Ecol. 29, 218–246 (2020).
PubMed  Article  PubMed Central  Google Scholar 

35.
Violle, C., Nemergut, D. R., Pu, Z. & Jiang, L. Phylogenetic limiting similarity and competitive exclusion. Ecol. Lett. 14, 782–787 (2011).
PubMed  Article  PubMed Central  Google Scholar 

36.
Novak, S. J. The role of evolution in the invasion process. Proc. Natl. Acad. Sci. USA 104, 3671–3672 (2007).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Buswell, J. M., Moles, A. T. & Hartley, S. Is rapid evolution common in introduced plant species?. J. Ecol. 99, 214–224 (2011).
Article  Google Scholar 

38.
Saul, W.-C. & Jeschke, J. M. Eco-evolutionary experience in novel species interactions. Ecol. Lett. 18, 236–245 (2015).
PubMed  Article  PubMed Central  Google Scholar 

39.
Hölldobler, B. & Wilson, E. O. The ants (Harvard University Press, Cambridge, 1990).
Google Scholar 

40.
Lowe, S., Browne, M., Boudjelas, S. & De Poorter, M. 100 of the world’s worst invasive alien species: a selection from the global invasive species database (Invasive Species Specialist Group, Auckland, 2000).
Google Scholar 

41.
Holway, D. A., Lach, L., Suarez, A. V., Tsutsui, N. D. & Case, T. J. The causes and consequences of ant invasions. Annu. Rev. Ecol. Evol. Syst. 33, 181–233 (2002).
Article  Google Scholar 

42.
Lessard, J.-P. et al. Strong influence of regional species pools on continent-wide structuring of local communities. Proc. Biol. Sci. 279, 266–274 (2011).
PubMed  PubMed Central  Google Scholar 

43.
Lucky, A., Trautwein, M. D., Guénard, B., Weiser, M. D. & Dunn, R. R. Tracing the rise of ants—out of the ground. PLoS ONE 8, e84012 (2013).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

44.
Economo, E. P. et al. Global phylogenetic structure of the hyperdiverse ant genus Pheidole reveals the repeated evolution of macroecological patterns. Proc. Biol. Sci. 282, 20141416 (2015).
PubMed  PubMed Central  Google Scholar 

45.

46.
http://www.antwiki.org/.

47.
https://www.gbif.org/.

48.
https://www.antweb.org/.

49.
Lebas, C., Galkowski, C., Blatrix, R. & Wegnez, P. Forumis d’Europe occidentale Delachaux et Niestle (Le Premier guide complet d’Europe, Paris, 2016).
Google Scholar 

50.
Bertelsmeier, C., Ollier, S., Liebhold, A. & Keller, L. Recent human history governs global ant invasion dynamics. Nat. Ecol. Evol. 1, 0184 (2017).
PubMed  PubMed Central  Article  Google Scholar 

51.
Bernard, F. Faune de l’Europe et du Bassin Méditerranéen. 3. Les Fourmis (Hymenoptera Formicidae) d’Europe Occidentale et Septentrionale. Eur. et Bas. Med. 3. Masson éditeurs, Paris (1968)

52.
Seifert, B. The Ants of Central and North Europe (Lutra Verlags-und Vertriebsgesellschaf, Tauer, 2018).
Google Scholar 

53.
http://www.iucngisd.org/gisd/100_worst.php.

54.
Wetterer, J. K. Worldwide spread of Emery’s sneaking ant, Cardiocondyla emeryi (Hymenoptera: Formicidae). Myrmecol. News 17, 13–20 (2012).
Google Scholar 

55.
Heinze, J., Cremer, S., Eckl, N. & Schrempf, A. Stealthy invaders: the biology of Cardiocondyla tramp ants. Insect. Soc. 53, 1–7 (2006).
Article  Google Scholar 

56.
Fournier, A., Penone, C., Pennino, M. G. & Courchamp, F. Predicting future invaders and future invasions. Proc. Natl. Acad. Sci. USA 116, 7905–7910 (2019).
CAS  PubMed  Article  Google Scholar 

57.
Moreau, C. S. & Bell, C. D. Testing the museum versus cradle biological diversity hypothesis: phylogeny, diversification, and ancestral biogeographic range evolution of the ants. Evolution 67, 2240–2257 (2013).
PubMed  Article  Google Scholar 

58.
Ward, P. S., Brady, S. G., Fisher, B. L. & Schultz, T. R. The evolution of myrmicine ants: phylogeny and biogeography of a hyperdiverse ant clade (Hymenoptera:Formicidae). Syst. Entomol. 40, 61–81 (2015).
Article  Google Scholar 

59.
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. 25, 1965–1978 (2005).
Article  Google Scholar 

60.
https://www.creaf.cat.

61.
R Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (2016).

62.
Münkemüller, T. et al. How to measure and test phylogenetic signal. Methods Ecol. Evol. 3, 743–756 (2012).
Article  Google Scholar 

63.
Di Cola, V. et al. ecospat: an R package to support spatial analyses and modeling of species niches and distributions. Ecography 40, 774–787 (2017).
Article  Google Scholar 

64.
Hijmans, R.J. & van Etten, J. raster: Geographic Data Analysis and Modeling. R package version 2.9-5. https://cran.r-project.org/web/packages/raster/index.html (2016).

65.
Hijmans, R.J., Phillips, S., Leathwick, J. & Elith, J. dismo: Species Distribution Modeling. R package version 1.1–4. https://cran.r-project.org/web/packages/dismo/index.html (2011).

66.
Wiens, J. J. The niche, biogeography and species interactions. Philos. Trans. R. Soc. Lond. B Biol. Sci. 366, 2336–2350 (2011).
PubMed  PubMed Central  Article  Google Scholar 

67.
King, J. R. & Tschinkel, W. R. Experimental evidence that human impacts drive fire ant invasions and ecological change. Proc. Natl. Acad. Sci. USA 105, 20339–20343 (2008).
ADS  CAS  PubMed  Article  Google Scholar 

68.
Vonshak, M. & Gordon, D. M. Intermediate disturbance promotes invasive ant abundance. Biol. Conserv. 186, 359–367 (2015).
Article  Google Scholar 

69.
McGlynn, T. P. The worldwide transfer of ants: Geographical distribution and ecological invasions. J. Biogeogr. 26, 535–548 (1999).
Article  Google Scholar 

70.
Kaspari, M. & Vargo, E. Does colony size buffer environmental variation? Bergmann’s rule and social insects. Am. Nat. 145, 610–632 (1995).
Article  Google Scholar 

71.
McGlynn, T. P. Non-native ants are smaller than related native ants. Am. Nat. 154, 690–699 (1999).
PubMed  Article  PubMed Central  Google Scholar 

72.
Araújo, M. B. et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).
PubMed  Article  Google Scholar 

73.
Catford, J. A., Jansson, R. & Nilsson, C. Reducing redundancy in invasion ecology by integrating hypotheses into a single theoretical framework. Divers. Distrib. 15, 22–40 (2009).
Article  Google Scholar 

74.
Jenkins, C. N. et al. Global diversity in light of climate change: The case of ants. Divers. Distrib. 17, 652–662 (2011).
Article  Google Scholar 

75.
Mooney, H. A. & Cleland, E. E. The evolutionary impact of invasive species. Proc. Natl. Acad. Sci. USA 98, 5446–5451 (2001).
ADS  CAS  PubMed  Article  Google Scholar 

76.
Ness, J. H. & Bronstein, J. L. The effects of invasive ants on prospective ant mutualists. Biol. Invasions 6, 445–461 (2004).
Article  Google Scholar 

77.
Dayan, T. & Simberloff, D. Ecological and community-wide character displacement: The next generation. Ecol. Lett. 8, 875–894 (2005).
Article  Google Scholar 

78.
Mayfield, M. M. & Levine, J. M. Opposing effects of competitive exclusion on the phylogenetic structure of communities. Ecol. Lett. 13, 1085–1093 (2010).
PubMed  Article  PubMed Central  Google Scholar 

79.
https://www.cbd.int/sp/targets/rationale/target-9. More

1.
Bowman, D. M. et al. Fire in the earth system. Science 324, 481–484. https://doi.org/10.1126/science.1163886 (2009).
ADS  CAS  Article  PubMed  Google Scholar 
2.
Scott, A. C. The pre-quaternary history of fire. Palaeogeogr. Palaeoclimatol. Palaeoecol. 164, 281–329. https://doi.org/10.1016/s0031-0182(00)00192-9 (2000).
Article  Google Scholar 

3.
Roebroeks, W. & Villa, P. On the earliest evidence for habitual use of fire in Europe. Proc. Natl. Acad. Sci. 108, 5209–5214. https://doi.org/10.1073/pnas.1018116108 (2011).
ADS  Article  PubMed  Google Scholar 

4.
Flannigan, M. D., Krawchuk, M. A., de Groot, W. J., Wotton, B. M. & Gowman, L. M. Implications of changing climate for global wildland fire. Int. J. Wildl. Fire 18, 483–507. https://doi.org/10.1071/wf08187 (2009).
Article  Google Scholar 

5.
Hantson, S., Pueyo, S. & Chuvieco, E. Global fire size distribution is driven by human impact and climate: Spatial trends in global fire size distribution. Glob. Ecol. Biogeogr. 24, 77–86. https://doi.org/10.1111/geb.12246 (2015).
Article  Google Scholar 

6.
Bond, W. J., Woodward, F. I. & Midgley, G. F. The global distribution of ecosystems in a world without fire. New Phytol. 165, 525–538 (2005).
CAS  Article  Google Scholar 

7.
Lasslop, G., Brovkin, V., Reick, C. H., Bathiany, S. & Kloster, S. Multiple stable states of tree cover in a global land surface model due to a fire-vegetation feedback. Geophys. Res. Lett. 43, 6324–6331. https://doi.org/10.1002/2016gl069365 (2016).
ADS  Article  Google Scholar 

8.
Giglio, L., Randerson, J. T. & van der Werf, G. R. Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4): ANALYSIS OF BURNED AREA. J. Geophys. Res. Biogeosci. 118, 317–328. https://doi.org/10.1002/jgrg.20042 (2013).
Article  Google Scholar 

9.
van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720. https://doi.org/10.5194/essd-9-697-2017 (2017).
ADS  Article  Google Scholar 

10.
Loehman, R. A., Reinhardt, E. & Riley, K. L. Wildland fire emissions, carbon, and climate: Seeing the forest and the trees-a cross-scale assessment of wildfire and carbon dynamics in fire-prone, forested ecosystems. For. Ecol. Manag. 317, 9–19. https://doi.org/10.1016/j.foreco.2013.04.014 (2014).
Article  Google Scholar 

11.
Landry, J.-S. & Matthews, H. D. Non-deforestation fire vs. fossil fuel combustion: the source of CO(_{{2}}) emissions affects the global carbon cycle and climate responses. Biogeosciences 13, 2137–2149. https://doi.org/10.5194/bg-13-2137-2016 (2016).
ADS  Article  Google Scholar 

12.
Fischer, A. P. et al. Wildfire risk as a socioecological pathology. Front. Ecol. Environ. 14, 276–284. https://doi.org/10.1126/science.11638860 (2016).
Article  Google Scholar 

13.
Langmann, B., Duncan, B., Textor, C., Trentmann, J. & van der Werf, G. R. Vegetation fire emissions and their impact on air pollution and climate. Atmos. Environ. 43, 107–116. https://doi.org/10.1126/science.11638861 (2009).
ADS  CAS  Article  Google Scholar 

14.
Urbanski, S. Wildland fire emissions, carbon, and climate: Emission factors. For. Ecol. Manag. 317, 51–60. https://doi.org/10.1016/j.foreco.2013.05.045 (2014).
Article  Google Scholar 

15.
Veraverbeke, S., Verstraeten, W. W., Lhermitte, S., Van De Kerchove, R. & Goossens, R. Assessment of post-fire changes in land surface temperature and surface albedo, and their relation with fire – burn severity using multitemporal MODIS imagery. Int. J. Wildl. Fire 21, 243. https://doi.org/10.1071/WF10075 (2012).
Article  Google Scholar 

16.
Bowman, D. M. J. S., Murphy, B. P., Williamson, G. J. & Cochrane, M. A. Pyrogeographic models, feedbacks and the future of global fire regimes: Correspondence. Glob. Ecol. Biogeogr. 23, 821–824. https://doi.org/10.1126/science.11638864 (2014).
Article  Google Scholar 

17.
Harris, R. M. B., Remenyi, T. A., Williamson, G. J., Bindoff, N. L. & Bowman, D. M. J. S. Climate-vegetation-fire interactions and feedbacks: Trivial detail or major barrier to projecting the future of the Earth system?: Climate-vegetation-fire interactions and feedbacks. Wiley Interdiscip. Rev. Clim. Change 7, 910–931. https://doi.org/10.1002/wcc.428 (2016).
Article  Google Scholar 

18.
Bradstock, R. A. A biogeographic model of fire regimes in Australia: Current and future implications: A biogeographic model of fire in Australia. Glob. Ecol. Biogeogr. 19, 145–158. https://doi.org/10.1111/j.1466-8238.2009.00512.x (2010).
Article  Google Scholar 

19.
Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356–1362. https://doi.org/10.1126/science.aal4108 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

20.
Pechony, O. & Shindell, D. T. Driving forces of global wildfires over the past millennium and the forthcoming century. Proc. Natl. Acad. Sci. 107, 19167–19170. https://doi.org/10.1073/pnas.1003669107 (2010).
ADS  Article  PubMed  Google Scholar 

21.
Krawchuk, M. A., Moritz, M. A., Parisien, M.-A., Van Dorn, J. & Hayhoe, K. Global pyrogeography: The current and future distribution of wildfire. PLoS One 4, e5102 (2009).
ADS  Article  Google Scholar 

22.
Pausas, J. G. & Keeley, J. E. Abrupt climate-independent fire regime changes. Ecosystems 17, 1109–1120. https://doi.org/10.1007/s10021-014-9773-5 (2014).
CAS  Article  Google Scholar 

23.
Pausas, J. G. & Ribeiro, E. The global fire-productivity relationship: Fire and productivity. Glob. Ecol. Biogeogr. 22, 728–736. https://doi.org/10.1016/s0031-0182(00)00192-90 (2013).
Article  Google Scholar 

24.
Mondal, N. & Sukumar, R. Fires in seasonally dry tropical forest: Testing the varying constraints hypothesis across a regional rainfall gradient. PLoS One 11, e0159691. https://doi.org/10.1371/journal.pone.0159691 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

25.
Foley, J. A. et al. An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics. Glob. Biogeochem. Cycles 10, 603–628. https://doi.org/10.1029/96gb02692 (1996).
ADS  CAS  Article  Google Scholar 

26.
Thonicke, K., Venevsky, S., Sitch, S. & Cramer, W. The role of fire disturbance for global vegetation dynamics: Coupling fire into a dynamic global vegetation model. Glob. Ecol. Biogeogr. 10, 661–677. https://doi.org/10.1046/j.1466-822x.2001.00175.x (2001).
Article  Google Scholar 

27.
Thonicke, K. et al. The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model. Biogeosciences 7, 1991. https://doi.org/10.1016/s0031-0182(00)00192-94 (2010).
ADS  CAS  Article  Google Scholar 

28.
Rabin, S. S. et al. The fire modeling intercomparison project (FireMIP), phase 1: Experimental and analytical protocols with detailed model descriptions. Geosci. Model Dev. 10, 1175–1197. https://doi.org/10.5194/gmd-10-1175-2017 (2017).
ADS  CAS  Article  Google Scholar 

29.
Li, F., Zeng, X. & Levis, S. A process-based fire parameterization of intermediate complexity in a dynamic global vegetation model. Biogeosciences 9, 2761–2780. https://doi.org/10.1016/s0031-0182(00)00192-96 (2012).
ADS  Article  Google Scholar 

30.
Li, F., Levis, S. & Ward, D. Quantifying the role of fire in the earth system-part 1: Improved global fire modeling in the community earth system model (cesm1). Biogeosciences 10, 2293 (2013).
ADS  CAS  Article  Google Scholar 

31.
Hantson, S. et al. Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project. Geosci. Model Dev. 13, 3299–3318. https://doi.org/10.5194/gmd-13-3299-2020 (2020).
ADS  Article  Google Scholar 

32.
Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 7537. https://doi.org/10.1038/ncomms8537 (2015).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

33.
Abatzoglou, J. T., Williams, A. P., Boschetti, L., Zubkova, M. & Kolden, C. A. Global patterns of interannual climate-fire relationships. Glob. Change Biol. 24, 5164–5175. https://doi.org/10.1016/s0031-0182(00)00192-98 (2018).
ADS  Article  Google Scholar 

34.
Archibald, S., Roy, D. P., van Wilgen, B. W. & Scholes, R. J. What limits fire? An examination of drivers of burnt area in Southern Africa. Glob. Change Biol. 15, 613–630. https://doi.org/10.1111/j.1365-2486.2008.01754.x (2009).
ADS  Article  Google Scholar 

35.
Aldersley, A., Murray, S. J. & Cornell, S. E. Global and regional analysis of climate and human drivers of wildfire. Sci. Total Environ. 409, 3472–3481. https://doi.org/10.1016/s0031-0182(00)00192-99 (2011).
ADS  CAS  Article  PubMed  Google Scholar 

36.
Yang, L., Dawson, C. W., Brown, M. R. & Gell, M. Neural network and GA approaches for dwelling fire occurrence prediction. Knowl. Based Syst. 19, 213–219. https://doi.org/10.1016/j.knosys.2005.11.021 (2006).
Article  Google Scholar 

37.
Dutta, R., Aryal, J., Das, A. & Kirkpatrick, J. B. Deep cognitive imaging systems enable estimation of continental-scale fire incidence from climate data. Sci. Rep. 3, 3188. https://doi.org/10.1038/srep03188 (2013).
ADS  Article  PubMed  PubMed Central  Google Scholar 

38.
Satir, O., Berberoglu, S. & Donmez, C. Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem. Geomat. Nat. Hazards Risk 7, 1645–1658. https://doi.org/10.1080/19475705.2015.1084541 (2016).
Article  Google Scholar 

39.
Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the cru ts3. 10 dataset. Int. J. Climatol. 34, 623–642 (2014).
Article  Google Scholar 

40.
Adler, R. F. et al. The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present). J. Hydrometeorol. 4, 1147–1167 (2003).
ADS  Article  Google Scholar 

41.
Zhao, M., Heinsch, F. A., Nemani, R. R. & Running, S. W. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens. Environ. 95, 164–176. https://doi.org/10.1073/pnas.10181161083 (2005).
ADS  Article  Google Scholar 

42.
Freire, S. & Pesaresi, M. Ghs population grid, derived from gpw4, multitemporal (1975, 1990, 2000, 2015).European Commission Joint Research Centre (JRC) (2015).

43.
Meijer, J. R., Huijbregts, M. A., Schotten, K. C. & Schipper, A. M. Global patterns of current and future road infrastructure. Environ. Res. Lett. 13, 064006 (2018).
ADS  Article  Google Scholar 

44.
Friedl, M. A. et al. Modis collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182. https://doi.org/10.1073/pnas.10181161084 (2010).
ADS  Article  Google Scholar 

45.
Channan, S., Collins, K. & Emanuel, W. Global Mosaics of the Standard Modis Land Cover Type Data Vol. 30 (University of Maryland and the Pacific Northwest National Laboratory, College Park, 2014).
Google Scholar 

46.
Lay, E. H. et al. Wwll global lightning detection system: Regional validation study in brazil. Geophys. Res. Lett. 31, 20 (2004).
Google Scholar 

47.
Veraverbeke, S. et al. Lightning as a major driver of recent large fire years in north American boreal forests. Nat. Clim. Change 7, 529–534 (2017).
ADS  Article  Google Scholar 

48.
Chen, Y. et al. A pan-tropical cascade of fire driven by el niño/southern oscillation. Nat. Clim. Change 7, 906. https://doi.org/10.1038/s41558-017-0014-8 (2017).
ADS  CAS  Article  Google Scholar 

49.
Aragão, L. E. O. C. et al. 21st century drought-related fires counteract the decline of amazon deforestation carbon emissions. Nat. Commun. 9, 536. https://doi.org/10.1038/s41467-017-02771-y (2018).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

50.
Yin, Y. et al. Variability of fire carbon emissions in equatorial Asia and its nonlinear sensitivity to El Niño: FIRE CARBON EMISSIONS IN EQUATORIAL ASIA. Geophys. Res. Lett. 43, 10472–10479. https://doi.org/10.1073/pnas.10181161087 (2016).
ADS  CAS  Article  Google Scholar 

51.
Verdon, D. C., Kiem, A. S. & Franks, S. W. Multi-decadal variability of forest fire risk-eastern Australia. Int. J. Wildl. Fire 13, 165–171. https://doi.org/10.1071/WF03034 (2004).
Article  Google Scholar 

52.
Mariani, M., Fletcher, M.-S., Holz, A. & Nyman, P. Enso controls interannual fire activity in southeast Australia: Enso and fire activity in SE Australia. Geophys. Res. Lett. 43, 10891–10900. https://doi.org/10.1002/2016GL070572 (2016).
ADS  Article  Google Scholar 

53.
Li, L.-M., Song, W.-G., Ma, J. & Satoh, K. Artificial neural network approach for modeling the impact of population density and weather parameters on forest fire risk. Int. J. Wildl. Fire 18, 640–647. https://doi.org/10.1071/WF07136 (2009).
Article  Google Scholar 

54.
Vasilakos, C., Kalabokidis, K., Hatzopoulos, J. & Matsinos, I. Identifying wildland fire ignition factors through sensitivity analysis of a neural network. Nat. Hazards 50, 125–143. https://doi.org/10.1071/wf081871 (2009).
Article  Google Scholar 

55.
Whitman, E., Parisien, M.-A., Thompson, D. K. & Flannigan, M. D. Short-interval wildfire and drought overwhelm boreal forest resilience. Sci. Rep. 9, 18796. https://doi.org/10.1038/s41598-019-55036-7 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

56.
Hawbaker, T. J. et al. Human and biophysical influences on fire occurrence in the united states. Ecol. Appl. 23, 565–582 (2013).
Article  Google Scholar 

57.
Bowman, D. M. J. S. et al. Human exposure and sensitivity to globally extreme wildfire events. Nat. Ecol. Evol. 1, 0058. https://doi.org/10.1038/s41559-016-0058 (2017).
Article  Google Scholar 

58.
Andela, N. & van der Werf, G. R. Recent trends in African fires driven by cropland expansion and El Niño to La Niña transition. Nat. Clim. Change 4, 791–795. https://doi.org/10.1038/nclimate2313 (2014).
ADS  Article  Google Scholar 

59.
Walker, X. J. et al. Fuel availability not fire weather controls boreal wildfire severity and carbon emissions. Nat. Clim. Changehttps://doi.org/10.1038/s41558-020-00920-8 (2020).
Article  Google Scholar 

60.
Zubkova, M., Boschetti, L., Abatzoglou, J. T. & Giglio, L. Changes in fire activity in Africa from 2002 to 2016 and their potential drivers. Geophys. Res. Lett. 46, 7643–7653. https://doi.org/10.1029/2019GL083469 (2019).
ADS  Article  PubMed  PubMed Central  Google Scholar 

61.
Moritz, M. A. et al. Climate change and disruptions to global fire activity. Ecosphere 3, 1–22 (2012).
Article  Google Scholar 

62.
Kloster, S. & Lasslop, G. Historical and future fire occurrence (1850 to 2100) simulated in CMIP5 Earth System Models. Glob. Planet. Change 150, 58–69. https://doi.org/10.1016/j.gloplacha.2016.12.017 (2017).
ADS  Article  Google Scholar 

63.
Van der Werf, G. R. et al. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos. Chem. Phys. 10, 11707–11735. https://doi.org/10.1071/wf081877 (2010).
ADS  CAS  Article  Google Scholar 

64.
Archibald, S. et al. Biological and geophysical feedbacks with fire in the earth system. Environ. Res. Lett. 13, 033003. https://doi.org/10.1071/wf081878 (2018).
ADS  Article  Google Scholar 

65.
Ponomarev, E., Kharuk, V. & Ranson, K. Wildfires dynamics in Siberian larch forests. Forests 7, 125. https://doi.org/10.3390/f7060125 (2016).
Article  Google Scholar 

66.
van der Werf, G. R., Randerson, J. T., Giglio, L., Gobron, N. & Dolman, A. J. Climate controls on the variability of fires in the tropics and subtropics: Climate controls on fires. Glob. Biogeochem. Cycleshttps://doi.org/10.1029/2007GB003122 (2008).
Article  Google Scholar  More

1.
Guerbois, C., Chapanda, E. & Fritz, H. Combining multi-scale socio-ecological approaches to understand the susceptibility of subsistence farmers to elephant crop raiding on the edge of a protected area. J. Appl. Ecol. 49, 1149–1158 (2012).
Article  Google Scholar 
2.
Felton, A. M. et al. Protein content of diets dictates the daily energy intake of a free-ranging primate. Behav. Ecol. 20, 685–690 (2009).
Article  Google Scholar 

3.
Rode, K. D., Chiyo, P. I., Chapman, C. A. & McDowell, L. R. Nutritional ecology of elephants in Kibale National Park, Uganda, and its relationship with crop-raiding behaviour. J. Trop. Ecol. 22, 441–449 (2006).
Article  Google Scholar 

4.
Duckworth, J. W., Sankar, K., Williams, A. C., Kumar, N. S. & Timmins, R. J. Bos gaurus. The IUCN Red List of Threatened Species 2016, e.T2891A46363646 (2016).

5.
Royal Thai Government Gazette. Wildlife Preservation and Protection Act, B.E.2562 (2019) of Thailand. www.ratchakitcha.soc.go.th (2019, in Thai).

6.
Srikosamatara, S. & Suteethorn, V. Populations of gaur and banteng and their management in Thailand. Nat. His. Bull. Siam Soc. 43(1), 55–83 (1995).
Google Scholar 

7.
Laichanthuek, P., Sukmasuang, R. & Duengkae, P. Population and habitat use of gaur (Bos gaurus) around Khao Phaeng Ma Non-hunting Area, Nakhon Ratchasima Province. J. Wildl. Thailand 24, 83–95 (2017).
Google Scholar 

8.
Chetri, M. Diet analysis of gaur, Bos gaurus gaurus (Smith, 1827) by micro-histological analysis of fecal samples in Parsa Wildlife Reserve, Nepal. Our Nat. 4, 20–28 (2006).
Article  Google Scholar 

9.
Bell, R. H. V. The use of the herb layer by grazing ungulates in Serangeti. In Watson, A. (Ed.). Animal Population in Relation to Their Food Resources. (Blackwell Scientific Publication 1970).

10.
Jarman, P. J. The social organization of antelope in relation to their ecology. Behaviour 48, 215–266 (1974).
Article  Google Scholar 

11.
Bhumpakphan, N. & McShea, W. J. Ecology of gaur and banteng in the seasonally dry forests of Thailand. In: McShea, W. J., Davies, S. J. & Bhumpakphan, N. (Eds.). The Ecology and Conservation of Seasonally Dry Forests in Asia. (Rowman and Littlefield Publishers, Inc. and Smithsonian Institution Scholarly Press 2011).

12.
Steinmetz, R. Gaur (Bos gaurus) and banteng (Bos javanicus) in the lowland forest mosaic of Xe Pian Protection Area, Lao PDR: Abundance, habitat use and conservation. Mammalia 68, 141–157 (2004).
Article  Google Scholar 

13.
Karanth, K. U. & Sunquist, M. E. Population structure, density and biomass of large herbivores in the tropical forests of Nagarahole, India. J. Trop. Ecol. 8, 21–35 (1992).
Article  Google Scholar 

14.
Steinmetz, R. Ecological surveys, monitoring, and the role of local people in protected areas of Lao PDR. (International Institute for Environment and Development 2000).

15.
Choudhury, A. Distribution and conservation of gaur Bos gaurus in the Indian Subcontinent. Mammal. Rev. 12, 199–226 (2002).
Article  Google Scholar 

16.
Gad, S. D. & Shyama, S. K. Diet composition and quality in Indian bison (Bos gaurus) based on fecal analysis. Zool. Sci. 28(4), 264–267 (2011).
Article  Google Scholar 

17.
Velho, N., Srinivasan, U., Singh, P. & Laurance, W. F. Large mammal use of protected and community-managed lands in a biodiversity hotspot. Anim. Conserv. https://doi.org/10.1111/acv.12234 (2015).
Article  Google Scholar 

18.
Bidayabha, T. Ecology and behavior of gaur (Bos gaurus) in a degraded area at Khao Phaeng-Ma, the Northestern Edge of Khao Yai National Park. (Faculty of Biology, Mahidol University 2001).

19.
Panusittikorn, P. & Prato, T. Conservation of protected areas in Thailand: the case of Khao Yai National Park, protected areas in East Asia. George Wright Forum 18(2), 66–76 (2001).
Google Scholar 

20.
Sorensen, A. A., van Beest, F. M. & Brook, R. K. Quantifying overlap in crop selection patterns among three sympatric ungulates in an agricultural landscape. Basic App. Ecol. 16, 601–609 (2015).
Article  Google Scholar 

21.
Todorov, N. A. Cereals, pulses and oilseeds. Livestock Produc. Sci. 19, 47–95 (1988).
Article  Google Scholar 

22.
Curzer, H. J., Wallace, M. C., Perry, G., Muhlberger, P. J. & Perry, D. The ethics of wildlife research: a nine R theory. ILAR J. 54(1), 52–57 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

23.
Department of National Parks, Wildlife and Plant Conservation (DNP). Management plan for Dong Phayayen-Khao Yai Forest Complex. (Department of National Parks, Wildlife, and Plant Conservation 2006).

24.
Kao-mim, N. Use of Hyperspectral Imaging System Data from HJ – 1A Satellite for Forest Types Classification in Khao Yai National Park. (MS thesis, Kasetsart University 2018, in Thai).

25.
Ngoprasert, D. & Gale, G. A. Tiger density, dhole occupancy, and prey occupancy in the human disturbed Dong Phayayen – Khao Yai Forest Complex, Thailand. Mamm. Biol. 95, 51–58 (2019).
Article  Google Scholar 

26.
Lashley, M. A., Chitwood, M. C., Street, G. M., Moorman, C. E. & DePerno, C. S. Do indirect bite count surveys accurately represent diet selection of white-tailed deer in a forested environment?. Wildl. Res. 43, 254–260 (2016).
CAS  Article  Google Scholar 

27.
Lashley, M. A., Chitwood, M. C., Harper, C. A., Moorman, C. E. & DePerno, C. S. Collection, handling and analysis of forages for concentrate selectors. Wildl. Biol. Prac. 10(1), 6–15 (2014).
Google Scholar 

28.
Shafer, E. L. Jr. The twig-count method for measuring hardwood deer browse. J. Wildl. Manag. 27(3), 428–437 (1963).
Article  Google Scholar 

29.
Ivlev, V. S. Experimental Ecology of the Feeding of Fishes. (Yale University Press 1961).

30.
Association of Official Analytical Chemists (AOAC). Guidelines for collaborative study procedure to validate characteristics of a method of analysis. J. Assoc. Off. Anal. Chem. 71, 161–171 (1988).

31.
Petterson, D. S., Harris, D. J., Rayner, C. J., Blakeney, A. B. & Choct, M. Methods for the analysis of premium livestock grains. Aus. J Agric. Res. 50, 775–787 (1999).
Article  Google Scholar 

32.
Midkiff, V. A century of analytical excellence. The history of feed analysis, as chronicled in the development of AOAC official methods, 1884–1984. J. Assoc. Off. Anal. Chem. 67, 851–860 (1984).
CAS  PubMed  PubMed Central  Google Scholar 

33.
Brown, R. H. & Mueller-Harvey, I. Evaluation of the novel Soxflo technique for rapid extraction of crude fat in foods and animal feeds. J. AOAC Int. 82, 1369–1374 (1999).
CAS  PubMed  Article  PubMed Central  Google Scholar 

34.
Leopold, B. D. & Krausman, P. R. Diurnal activity patterns of desert mule deer in relation to temperature. Texas J. Sci. 39, 49–53 (1987).
Google Scholar 

35.
Holechek, J. L., Vavra, M. & Pieper, R. D. Botanical composition determination of range herbivore diets: a review. J. Range Manag. 35(3), 309–315 (1982).
Article  Google Scholar 

36.
Jayson, E. A. Assessment of Human-Wildlife Conflict and Mitigation Measures in Northern Kerala: Final Report of the Research Project KFRI/653/12. (Kerala Forest Research Institute 2016).

37.
Prayong, N. & Srikosamatara, S. Cutting trees in a secondary forest to increase gaur Bos gaurus numbers in Khao Phaeng Ma Reforestation area, Nakhon Ratchasima Province, Thailand. Conserv. Evid. 14, 5–9 (2017).
Google Scholar 

38.
Cervasio, F., Argenti, G., Genghini, M. & Ponzetta, M. P. Agronomic methods for mountain grassland habitat restoration for faunistic purposes in a protected area of the northern Apennines (Italy). iForest 9, 490–496 (2016).
Article  Google Scholar 

39.
Argenti, G., Racanelli, V., Bartolozzi, S., Staglianò, N. & Guerri, F. S. Evaluation of wild animals browsing preferences in forage resources. Ital. J. Agron. 12, 884 (2017).
Google Scholar 

40.
Freschi, P. et al. Diet composition of the Italian roe deer (Capreolus capreolus italicus) (Mammalia: Cervidae) from two protected areas. Eur. Zool. J. 84, 34–42 (2017).
Article  CAS  Google Scholar 

41.
Robbins, C. T., Spalinger, D. E. & van Hoven, W. Adaptation of ruminants to browse and grass diets: are anatomical-based browser-grazer interpretations valid?. Oecologia 103, 208–213 (1995).
ADS  PubMed  Article  PubMed Central  Google Scholar 

42.
Ahrestani, F. S., Heitkönig, I. M. A., Matsubayashi, H. & Prins, H. H. T. 2016. Grazing and browsing by large herbivores in South and Southeast Asia. In: Ahrestani, F. S. & Sankaran, M. (Eds). The ecology of large herbivores in South and Southeast Asia. Ecol. Stud. 225 (2016).

43.
Krishnan, M. An ecological survey of the large mammals of Peninsular India. J. Bombay Nat. His. Soc. 69, 297–315 (1972).
Google Scholar 

44.
Ahrestani, F. S. et al. Estimating densities of large herbivores in tropical forests: rigorous evaluation of a dung-based method. Ecol. Evol. 8, 7312–7322 (2018).
PubMed  PubMed Central  Article  Google Scholar 

45.
Ahrestani, F. S. Bos gaurus (Artiodactyla: Bovidae). Mamm. Species 50, 34–50 (2018).
Article  Google Scholar 

46.
Nayak, B. K. & Patra, A. K. Food and feeding habits of Indian bison, Bos gaurus (Smith, 1827) in Kuldiha Wildlife Sanctuary, Balasore, Odisha, India and its conservation. Int. Res. J. Biol. Sci. 4(5), 73–79 (2015).
Google Scholar 

47.
Gad, S. D. & Shyama, S. K. Studies on the food and feeding habits of gaur Bos gaurus H Smith (Mammalia: Artiodactyla: Bovidae) in two protected areas of Goa. J. Threat. Taxa 1, 128–130 (2009).
Article  Google Scholar 

48.
Fresehi, P., Riccioli, F., Argenti, G. & Ponzetta, M. P. The sustainability of wildlife in agroforestry land. Agric. Agric. Sci. Proc. 8, 148–157 (2016).
Google Scholar 

49.
Moser, B., Schutz, M. & Hindenlang, K. E. Resource selection by roe deer: Are wind throw gaps attractive feeding places?. For. Ecol. Manag. 255, 1179–1185 (2008).
Article  Google Scholar 

50.
Wilsey, B. J. & Martin, L. M. Top-down control of rare species abundances by native ungulates in a grassland restoration. Restor. Ecol. 23, 465–472 (2015).
Article  Google Scholar 

51.
Kouch, T., Preston, T. R. & Ly, J. Studies on utilization of trees and shrubs as the sole feedstuff by growing goats; foliage preferences and nutrient utilization. Livestock Res. Rural Dev. 15(7), http://www.lrrd.org/irrd15/7/kouc157.htm (2003).

52.
Kaitho, R. J. et al. Palatability of multipurpose tree species: effect of species and length of study on intake and relative palatability by sheep. Agrofor. Syst. 33, 249–261 (1996).
Article  Google Scholar 

53.
Kaitho, R. J. et al. Palatability of wilted and dried multipurpose tree species fed to sheep and goats. Anim. Feed Sci. Technol. 65, 151–163 (1997).
Article  Google Scholar 

54.
Provenza, M. P., Cervasio, F., Crocetti, C., Messeri, A. & Argenti, G. Habitat improvements with wildlife purposes in a grazed area on the Apennine mountains. Ital. J. Agron. 5, 233–238 (2003).
Google Scholar 

55.
Lucas, J. R. Role of foraging time constraints and variable prey encounter in optimal diet choice. Am. Nat. 122(2), 191–209 (1983).
Article  Google Scholar 

56.
Poapongsakorn, N., Ruhs, M. & Tangjitwisuth, S. Problems and outlook of agriculture in Thailand. TDRI Quar. Rev. 13(2), 3–14 (1998).
Google Scholar 

57.
Retamosa, M. I., Humberg, L. A., Beasley, J. C. & Rhodes, O. E. Jr. Modeling wildlife damage to crops in northern Indiana. Human-Wild. Conf. 2(2), 225–239 (2008).
Google Scholar 

58.
Su, K., Ren, J., Yang, J., Hou, Y. & Wen, Y. Human-Elephant conflicts and villagers’ attitudes and knowledge in the Xishuangbanna Nature Reserve, China. Inter. J. Environ. Res. Public Health. https://doi.org/10.3390/ijerph17238910 (2020).
Article  Google Scholar  More

Wintertime outbreaks in the northern hemisphere
In Fig. 1a we use case data (see “Methods”) to estimate the effective reproductive number of infection for New York City from the start of 2020 to the present (July 2020)14. Estimated values of Reffective peak early in the outbreak and then settle close to 1 in the summer months as NPIs act to lower transmission. We assume the Reffective values approximate R0 and compare them to the predicted seasonal R0, derived from our climate-driven SIRS model. The model assumes the climate sensitivity of betacoronavirus HKU1 and that seasonal variations in transmission are driven by specific humidity. Current rates (average over second and third weeks of July) of Reffective in New York city are found to be approximately 35% below the R0 levels predicted by our climate-driven model. We assume this 35% decline is due to the efficacy of NPIs. To project future scenarios we assume that R0 remains at either the current levels (constant) or a relative 35% decrease in our climate-driven R0, which means R0 oscillates with specific humidity (Fig. 1a, top plot).
Fig. 1: Wintertime outbreaks in New York City.

Estimated and projected R0 values (top plot) assuming a 35% and b 15% reduction in R0 due to NPIs. Corresponding time series show the simulated outbreaks in the climate (blue) or constant (black/dashed) scenarios, with middle row plots assuming a 10% reporting rate and bottom row plots assuming a 3% reporting rate. Corresponding susceptible time series are shown in orange (susceptibles = S/population = N). Case data from New York City are shown in gray. Surface plots (top) show the peak wintertime proportion infected (infected = I/population = N) in the scenarios with c the constant R0 and d the climate-driven R0. e shows the difference between the climate and constant R0 scenario. The timing of peak incidence in years from July is shown for the f constant and g climate scenarios. The difference between climate and constant scenario is shown in h. Points in c–h show the scenarios is a, b. Dashed line shows estimated susceptibility in New York based on ref. 24.

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In Fig. 1a (lower plots) we show the proportion infected over time using the climate-driven and constant R0 values. We also vary the reporting rate of observed cases relative to modeled cases; while this accounts for under-reporting it also allows us to vary the proportion susceptible over a feasible range (see “Methods”). In the middle figure, the reporting rate is 10% (estimates for US reporting rates are  1 for both the climate and constant scenario and case numbers begin to grow exponentially. With a 10% reporting rate a large secondary outbreak is observed in both the constant and climate scenarios (Fig. 1b, middle plot). With a 3% reporting rate, meaning a larger depletion of susceptibles, the secondary outbreak appears much larger in the climate scenario: this supports the hypothesis that the disease will become more sensitive to climate as the susceptible proportion declines, much like the seasonal endemic diseases.
In Fig. 1c–h we simulate model outcomes across a broad range of parameter space varying the proportion susceptible (in July) and the reduction in R0 due to NPIs. The proportion susceptible is varied by initializing the epidemic with different sizes of the infected population (initializing with a large number results in a relatively larger outbreak and initializing with a small number results in a smaller outbreak). We vary this starting number over a feasible range given the case data, i.e., such that observed cases never exceed modeled cases or that the reporting rate never drops below 1%. Over this range, the model plausibly tracks the observed case data.
Figure 1e shows the change in winter peak size (max proportion infected between September–March) due to climate. Peak size results for the constant and climate scenarios are shown in Fig. 1c and d, respectively. When the susceptible proportion is high and the effect of NPIs are minimal (relative R0 given NPI = 1), large outbreaks are possible in both the climate and constant R0 scenarios meaning the relative effect of climate on peak size and timing is close to 0 (top right Fig. 1e). As the proportion susceptible declines (moving left along the x-axis of Fig. 1e), case trajectories become more sensitive to the wintertime weather resulting in larger peaks in the climate scenario. However, sufficiently strong NPIs, in combination with low susceptibility, reduce incidence to zero in both the climate and control scenarios (bottom left Fig. 1e). NPIs are not as effective at reducing cases when susceptibility is higher (bottom right Fig. 1e).
We also consider the effect of climate on secondary peak timing. Figure 1f, g shows the peak timing in years (relative to July 2020) in the constant and climate scenarios, respectively. In the climate scenario, peak timing for New York is clustered in the winter months (Fig. 1a, b). In the constant R0 scenario, secondary peaks can occur at a wide range of times over the next 1.5 years. As in the peak size results, high susceptibility and limited NPIs reduce the effect of climate and peak timing is matched for both the climate and control scenarios (top right Fig. 1h). Gray areas represent regions where there is no secondary peak in either the climate or control scenario.
Climate effects on global risk
We next consider the relative effect of climate on peak size for nine global locations (Fig. 2b). In this case, as opposed to using estimated Reffective values (given case data are not available for several of the global cities), we simulate the epidemic from July 2020 using a fixed number of infecteds and vary the starting proportion of susceptibles (example results from select global locations, using estimated Reffective, are shown in Supplementary Figs. 1–3). Results from the New York surface in Fig. 2b qualitatively match our tailored simulation in Fig. 1. Locations in the southern hemisphere are expected to be close to their maximum wintertime R0 values in mid-2020 (Fig. 2a), meaning that secondary peaks in the climate scenario are lower than the constant R0 scenario for these locations (Fig. 2b). Tropical locations experience minimal difference in the climate versus constant R0 scenario given the relatively mild seasonal variations in specific humidity in the tropics. Broadly, the results across hemisphere track the earlier results from New York: high susceptibility and a lack of NPIs lead to a limited role of climate, but an increase in NPI efficacy or a reduction in susceptibility may increase climate effects. This result is more striking in regions with a large seasonality in specific humidity (e.g. New York, Delhi and Johannesburg).
Fig. 2: Climate sensitivity of outbreaks across global locations.

a The climate effect on R0 assuming a 35% reduction due to NPIs shown for August and December. b The effect of climate, changing susceptibility, and NPIs on peak proportion infected (infected = I/population = N), post July 2020, for nine global locations.

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Drivers of variability in secondary outbreak size
Our results suggest that climate may play an increasing role in determining the future course of the SARS-CoV-2 pandemic, depending on levels of susceptibility and NPIs. We next evaluate the extent to which interannual variability in specific humidity could influence peak size. We simulate separate New York pandemic trajectories using 11 years (2008–2018) of specific humidity data. Figure 3a shows the variability in R0 and secondary peak size based on these runs (with 35% reduction in R0 due to NPIs and 10% reporting rate—the same as Fig. 1a). While a relatively large peak occurs in all years, the largest peak (0.038 proportion infected) is almost double the smallest peak year (0.020 proportion infected). In Fig. 3b we calculate the coefficient of variation of the peak size for different susceptible proportions and NPI intensities. These results qualitatively track Fig. 1e. Sensitivity to interannual variation appears most important when the susceptible population has been reduced by at least 20% and minimal controls are in place.
Fig. 3: Climate variability and wintertime cases in New York.

a Climate-driven R0 and corresponding infected time series (infected = I/population = N) based on the last 10 years of specific humidity data for New York, assuming a 35% reduction due to NPIs. b The effect of changing susceptibility and NPIs on the coefficient of variation of peak incidence for simulations using specific humidity data from 2008 to 2018. Dashed line shows estimated susceptibility in New York based on ref. 24.

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Many factors, including weather variability, determine the size of a possible secondary outbreak. Another factor that may play an important role is the length of immunity to the disease. While the length of immunity may not affect the dynamics in the early stage of the pandemic, it could have complex and uncertain outcomes for future trajectories16. In our main results, we assume a length of immunity equal to betacoronvirus HKU1, based on prior estimates1. We also assume a climate sensitivity based on estimates for HKU1. However parameters for SARS-CoV-2, such as immunity length and climate sensitivity, are still fundamentally uncertain.
We consider the possible contribution of uncertainty in parameters to the variance in the wintertime peak size following the method developed by Yip et al.17 (see “Methods”). We run our simulation for New York while varying parameter values for the efficacy of NPIs, the length of immunity to the disease, the reporting rate of prior cases (which defines susceptibility in July), the climate sensitivity of the pathogen (in terms of the strength of the relationship with specific humidity), and the weather variability (interannual variability determined by historic weather observations from a particular year, 2009–2018). We then perform an analysis of variance (ANOVA) on the determinants of wintertime peak size.
Figure 4 shows contribution to variance in wintertime peak size of these five parameters: NPIs efficacy, immunity length, reporting rate, climate sensitivity of the virus, and interannual weather variability. We find that climate sensitivity is an important factor but secondary to the efficacy of NPIs and immunity length in determining peak transmission. Uncertainty in immunity length and reporting together influence susceptibility and collectively account for the second largest portion of total uncertainty. Uncertainty in interannual variability, i.e. weather, has a smaller impact on peak size. NPIs contribute the largest proportion to total variance in peak size. It is important to note that while other parameters are external features of either the virus, climate, or disease trajectories to date, the efficacy of NPIs is determined directly by policy interventions and therefore the size of future outbreaks is largely under human control.
Fig. 4: Contribution to uncertainty in New York wintertime 20/21 peak size.

The relative importance of NPI efficacy [0–35%], immunity length (10–60 weeks), reporting (1–100%), climate sensitivity of the virus [−32.5 to −227.5], and interannual weather variability [10 years] in determining wintertime peak size. Immunity length and reporting rate collectively determine susceptibility, S.

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1.
Pittelkow, C. M. et al. When does no-till yield more? A global meta-analysis. Field Crops Res. 183, 156–168 (2015).
Article  Google Scholar 
2.
Food and Agriculture Organization of the United Nations (FAO). Save and Grow: A Policymaker’s Guide to the Sustainable Intensification of Smallholder Crop Production (2013). http://www.fao.org/3/a-i2215e.pdf.

3.
Michler, J. D., Baylis, K., Arends-Kuenning, M. & Mazvimavi, K. Conservation agriculture and climate resilience. J. Environ. Econom. Manage. 93, 148–169 (2019).
Article  Google Scholar 

4.
Page, K. L., Dang, Y. P. & Dalal, R. C. The ability of conservation agriculture to conserve soil organic carbon and the subsequent impact on soil physical, chemical, and biological properties and yield. Front. Sustain. Food Syst. https://doi.org/10.3389/fsufs.2020.00031 (2020).
Article  Google Scholar 

5.
Farooq, M. & Siddique, K. H. M. Conservation Agriculture (Springer, Berlin, 2015).
Google Scholar 

6.
Holland, J. M. The environmental consequences of adopting conservation tillage in Europe: Reviewing the evidence. Agric. Ecosyst. Environ. 103, 1–25 (2004).
Article  Google Scholar 

7.
Govaerts, B. et al. Infiltration, soil moisture, root rot and nematode populations after 12 years of different tillage, residue and crop rotation managements. Soil Tillage Res. 94, 209–219 (2007).
Article  Google Scholar 

8.
Zhang, W., Zheng, C., Song, Z., Deng, A. & He, Z. Farming systems in China: Innovations for sustainable crop production. In Crop Physiology (eds Zhang, W. et al.) 43–64 (Elsevier, Amsterdam, 2015).
Google Scholar 

9.
Pittelkow, C. M. et al. Productivity limits and potentials of the principles of conservation agriculture. Nature 517, 365–368 (2015).
ADS  CAS  Article  Google Scholar 

10.
Scopel, E. et al. Conservation agriculture cropping systems in temperate and tropical conditions, performances and impacts. A review. Agron. Sustain. Dev. 33, 113–130 (2013).
Article  Google Scholar 

11.
Steward, P. R. et al. The adaptive capacity of maize-based conservation agriculture systems to climate stress in tropical and subtropical environments: A meta-regression of yields. Agric. Ecosyst. Environ. 251, 194–202 (2018).
Article  Google Scholar 

12.
Knapp, S. & van der Heijden, M. G. A. A global meta-analysis of yield stability in organic and conservation agriculture. Nat. Commun. 9, 1–9 (2018).
CAS  Article  Google Scholar 

13.
Laborde, J. P., Wortmann, C. S., Blanco-Canqui, H., Baigorria, G. A. & Lindquist, J. L. Identifying the drivers and predicting the outcome of conservation agriculture globally. Agric. Syst. 177, 102692. https://doi.org/10.1016/j.agsy.2019.102692 (2020).
Article  Google Scholar 

14.
Su, Y., Gabrielle, B. & Makowski, D. A global dataset for crop production under conventional tillage and no tillage practice. Figshare. https://doi.org/10.6084/m9.figshare.12155553 (2020).
Article  Google Scholar 

15.
Su, Y., Gabrielle, B. & Makowski, D. A global dataset for crop production under conventional tillage and no tillage systems. Sci. Data 8, 33. https://doi.org/10.1038/s41597-021-00817-x (2021).
Article  PubMed  Google Scholar 

16.
Food and Agriculture Organization of the United Nations (FAO). Conservation Agriculture (2020). http://www.fao.org/conservation-agriculture/en/.

17.
Ho, T. K. Random decision forests. In Proc. 3rd International Conference on Document Analysis and Recognition, 278–282 (1995).

18.
Meinshausen, N. Quantile regression forests. J. Mach. Learn. Res. 7, 983–999 (2006).
MathSciNet  MATH  Google Scholar 

19.
University of Wisconsin-Madison. Crop Calendar Dataset: netCDF 5 Degree (2020). https://nelson.wisc.edu/sage/data-and-models/crop-calendar-dataset/netCDF0-5degree.php.

20.
Sacks, W. J., Deryng, D., Foley, J. A. & Ramankutty, N. Crop planting dates: an analysis of global patterns. Glob. Ecol. Biogeogr. 19, 607–620 (2010).
Google Scholar 

21.
University of Tokyo. Soil Texture Map (2020). http://hydro.iis.u-tokyo.ac.jp/~sujan/research/gswp3/soil-texture-map.html.

22.
NOAA/OAR/ESRL PSL. University of Delaware Air Temperature & Precipitation (2020). https://www.esrl.noaa.gov/psd/data/gridded/data.UDel_AirT_Precip.html.

23.
Martens, B. et al. GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. 10, 1903–1925 (2017).
ADS  Article  Google Scholar 

24.
Miralles, D. G. et al. Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci. 15, 453–469 (2011).
ADS  Article  Google Scholar 

25.
NOAA/OAR/ESRL PSL. CPC Global Daily Temperature (2020). https://www.esrl.noaa.gov/psd/data/gridded/data.cpc.globaltemp.html.

26.
Mandelkern, M. et al. Setting confidence intervals for bounded parameters. Stat. Sci. 17, 149–172 (2002).
MathSciNet  Article  Google Scholar 

27.
Portmann, F. T., Siebert, S. & Döll, P. MIRCA2000-Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling. Glob. Biogeochem. Cycles. https://doi.org/10.1029/2008GB003435 (2010).
Article  Google Scholar 

28.
Friedman, J. H. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).
MathSciNet  Article  Google Scholar 

29.
Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning (Springer, New York, 2009).
Google Scholar 

30.
Zuazo, V. H. D. & Pleguezuelo, C. R. R. Soil-erosion and runoff prevention by plant covers. A review. Agron. Sustain. Dev. 28, 65–86 (2008).
Article  Google Scholar 

31.
Shaxson, F. & Barber, R. Optimizing Soil Moisture for Plant Production, the Significance of Soil Porosity (FAO, 2003).

32.
Swanepoel, C. M. et al. The benefits of conservation agriculture on soil organic carbon and yield in southern Africa are site-specific. Soil Tillage Res. 183, 72–82 (2018).
Article  Google Scholar 

33.
Derpsch, R. Controle da Erosão no Paraná, Brasil: Sistemas de Cobertura do Solo, Plantio Direto e Preparo Conservacionista do Solo (GTZ/Curitiba, 1991).

34.
Scopel, E., da Silva, F. A. M., Corbeels, M., Affholder, F. & Maraux, F. Modelling crop residue mulching effects on water use and production of maize under semi-arid and humid tropical conditions. Agronomie 24, 383–395 (2004).
Article  Google Scholar 

35.
Thierfelder, C. & Wall, P. C. Investigating conservation agriculture (CA) systems in Zambia and Zimbabwe to mitigate future effects of climate change. J. Crop Improve. 24, 113–121 (2010).
Article  Google Scholar 

36.
Lal, R. The role of residues management in sustainable agricultural systems. J. Sustain. Agric. 5, 51–78 (1995).
Article  Google Scholar 

37.
Shen, Y., McLaughlin, N., Zhang, X., Xu, M. & Liang, A. Effect of tillage and crop residue on soil temperature following planting for a Black soil in Northeast China. Sci. Rep. 8, 4500 (2018).
ADS  Article  Google Scholar 

38.
Muñoz-Romero, V., Lopez-Bellido, L. & Lopez-Bellido, R. J. Effect of tillage system on soil temperature in a rainfed Mediterranean Vertisol. Int. Agrophys. 29, 467–473 (2015).
Article  Google Scholar 

39.
Hatfield, J. L. & Prueger, J. H. Temperature extremes: Effect on plant growth and development. Weather Clim. Extr. 10, 4–10 (2015).
Article  Google Scholar 

40.
Ramakrishna, A., Tam, H. M., Wani, S. P. & Long, T. D. Effect of mulch on soil temperature, moisture, weed infestation and yield of groundnut in northern Vietnam. Field Crops Res. 95, 115–125 (2006).
Article  Google Scholar 

41.
Kumar, S. & Dey, P. Effects of different mulches and irrigation methods on root growth, nutrient uptake, water-use efficiency and yield of strawberry. Sci. Hortic. 127, 318–324 (2011).
ADS  Article  Google Scholar 

42.
van Wijk, W. R., Larson, W. E. & Burrows, W. C. Soil Temperature and the early growth of corn from mulched and unmulched soil. Soil Sci. Soc. Am. J. 23, 428 (1959).
Article  Google Scholar 

43.
Kodzwa, J. J., Gotosa, J. & Nyamangara, J. Mulching is the most important of the three conservation agriculture principles in increasing crop yield in the short term, under sub humid tropical conditions in Zimbabwe. Soil Tillage Res. 197, 104515 (2020).
Article  Google Scholar 

44.
Giller, K. E., Witter, E., Corbeels, M. & Tittonell, P. Conservation agriculture and smallholder farming in Africa: The heretics’ view. Field Crops Res. 114, 23–34 (2009).
Article  Google Scholar 

45.
Andersson, J. A. & D’Souza, S. From adoption claims to understanding farmers and contexts: A literature review of conservation agriculture (CA) adoption among smallholder farmers in southern Africa. Agric. Ecosyst. Environ. 187, 116–132 (2014).
Article  Google Scholar 

46.
Mashingaidze, N., Madakadze, C., Twomlow, S., Nyamangara, J. & Hove, L. Crop yield and weed growth under conservation agriculture in semi-arid Zimbabwe. Soil Tillage Res. 124, 102–110 (2012).
Article  Google Scholar 

47.
Watt, M. S., Whitehead, D., Mason, E. G., Richardson, B. & Kimberley, M. O. The influence of weed competition for light and water on growth and dry matter partitioning of young Pinus radiata, at a dryland site. For. Ecol. Manage. 183, 363–376 (2003).
Article  Google Scholar 

48.
Abouziena, H., El-Saeid, M., Ahmed, A. & Amin, E.-S. Water loss by weeds: A review. Int. J. ChemTech Res. 7, 974–4290 (2014).
Google Scholar 

49.
Food and Agriculture Organization of the United Nations (FAO). The Economics of Conservation Agriculture (2001).

50.
Olsson, L. et al. Land Degradation. In Climate Change and Land: an IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems (IPCC, 2019). More

Increasingly madcap measures are being tried to control the invasive Asian carp in the US midwest.Credit: USACE/Alamy

Under a White Sky: The Nature of the Future Elizabeth Kolbert Crown (2021)
Humans are brilliant at coming up with solutions. But often these bring new problems that require their own solutions — that bring their own problems. It’s like the old lady who swallowed a fly in the children’s rhyme.
Civilization, essentially, has been a project to control natural systems: a river that is in the wrong place for us; earth that is too wet, or not wet enough; forests that we replace with monocultures of food, and so on. But natural systems are not compliant, and the unintended consequences of our changes require further fixes. The result is a world dominated by human influence, the Anthropocene epoch. Our problems are global and so, too, are our fixes.
These cascades of geoengineering are the subject of the latest book from Elizabeth Kolbert, Pulitzer-prizewinning environment reporter at The New Yorker. Under The White Sky looks at what people are doing to address the catastrophes that Kolbert described in two previous books — climate change (Field Notes From A Catastrophe, 2006) and biodiversity loss (The Sixth Extinction, 2014). She tours a range of cutting-edge experiments, from the restorative to the radical, across the United States, Europe and Australia. The result is an arresting montage of just how hard it is to return balance to our exquisitely interconnected biosphere, and the extraordinary efforts people go to in the attempt.

Kolbert visits the mighty Chicago River system in Illinois, which was re-plumbed to discharge the city’s sewage, with major tributaries rerouted and even reversed. Here, she documents efforts to prevent an invasive fish species — deliberately introduced into the Mississippi River basin — from causing havoc to the newly connected Great Lakes ecosystem. These include electrifying sections of river, fish-hunting carnivals and a range of madcap inventions, such as a “disco” noise-and-jet water barrier and sweet treats used as bait.
Further south, in coastal Louisiana, she finds engineers planning a multibillion-dollar artificial river system to replicate the former flows of the powerful Mississippi. Excessive tinkering with the river, straightening it and creating flood defences, have caused the land to sink and disappear, because alluvial soils are no longer replenished by regular sediment dumps. New Orleans is rapidly shrinking; smaller settlements have already been abandoned. As in her New Yorker essays, this is Kolbert at her most compelling — producing visceral, engrossing journalism with clear explanations of both science and social context.

Inside an aircraft attempting to seed clouds in Thailand in 2019.Credit: Athit Perawongmetha/Reuters

An element of the ridiculous is ever-present in the dance between human hubris and desperation. Kolbert orchestrates this comic strand with aplomb, never sacrificing empathy or the humanity of her characters. It is only a shame that the focus is entirely on problems and solutions in rich countries, given the global nature of the Anthropocene and the inequity of its burdens.
Artificial ecosystems
In the Mojave Desert, Nevada, she visits an expensively created, fully staffed, artificial pond cave, built to try to conserve a minuscule fish that humans have made critically endangered in the wild. In an aquatic laboratory in Australia, she observes coral spawning, cued by a simulated romantic sunset. This is the prelude to an in vitro fertilization programme that researchers hope will help to save the Great Barrier Reef from its calamitous decline in the wake of global heating. At one point, Kolbert wryly notes “how much easier it is to ruin an ecosystem than to run one”.

Kolbert meets genetic engineers hoping to replace struggling species such as endangered corals with ones modified to tolerate our environmental changes. Of this dramatic, ecosystem-altering step, one of the researchers points out: “We’re constantly moving genes around the world, usually in the form of entire genomes.” Consider the Peruvian potatoes planted in Europe’s fields or the domestic cats introduced by Europeans to New Zealand, where they have contributed to the extinction of at least nine native bird species.
Saving a fish species is hard, a coral-reef ecosystem immeasurably harder, but the ultimate challenge is fixing the global climate. Kolbert looks at geoengineering techniques to suck carbon dioxide from the air and store it, visiting facilities in the United States and Iceland. Options for ‘negative emissions’ were what finally got the 2015 Paris climate treaty over the line. The agreement to limit greenhouse-gas emissions factors in solutions such as planting forests to take up CO2 as they grow, and capturing industrial emissions at their source, then burying them.
The agreement does not mention more radical ‘hard geoengineering’ techniques to cool the climate, although research has been under way for decades. Kolbert talks to those studying methods to reflect the Sun’s heat, including spraying light-scattering calcite into the stratosphere, which would produce the white sky of the book’s title. This would be a drastic step. Yet, the extent of global heating brings its own terrible risks, which geoengineering could alleviate. “Doesn’t it have to be considered?” she asks, but can’t bring herself to answer.
There’s a grim fatalism to all this. We are so far down this path of global change that to turn back now is unthinkable, even impossible — like the old lady of the rhyme, who inevitably swallows the horse. Kolbert lays out this paradox perfectly. But she does so in the detached manner of an observer: always the reporter, documenting events but never asserting her own opinion. The book ends abruptly when the coronavirus ruins her plans for further research trips, leaving as much unresolved within its pages as outside them. It is, then, a superb and honest reflection of our extraordinary time. More

1.
Parmesan, C. Influences of species, latitudes and methodologies on estimates of phenological response to global warming. Glob. Change Biol. 13, 1860–1872. https://doi.org/10.1111/j.1365-2486.2007.01404.x (2007).
ADS  Article  Google Scholar 
2.
Peñuelas, J. et al. Evidence of current impact of climate change on life: A walk from genes to the biosphere. Glob. Change Biol. 19, 2303–2338. https://doi.org/10.1111/gcb.12143 (2013).
ADS  Article  Google Scholar 

3.
Thackeray, S. J. et al. Trophic level asynchrony in rates of phenological change for marine, freshwater and terrestrial environments. Glob. Change Biol. 16, 3304–3313. https://doi.org/10.1111/j.1365-2486.2010.02165.x (2010).
ADS  Article  Google Scholar 

4.
Dapporto, L. et al. Rise and fall of island butterfly diversity: Understanding genetic differentiation and extinction in a highly diverse archipelago. Divers. Distrib. 23, 1169–1181. https://doi.org/10.1111/ddi.12610 (2017).
Article  Google Scholar 

5.
Hendry, A. P., Farrugia, T. J. & Kinnison, M. T. Human influences on rates of phenotypic change in wild animal populations. Mol. Ecol. 17, 20–29. https://doi.org/10.1111/j.1365-294X.2007.03428.x (2008).
Article  PubMed  Google Scholar 

6.
Devictor, V. et al. Differences in the climatic debts of birds and butterflies at a continental scale. Nat. Clim. Change 2, 121–124. https://doi.org/10.1038/nclimate1347 (2012).
ADS  Article  Google Scholar 

7.
Forister, M. L. & Shapiro, A. M. Climatic trends and advancing spring flight of butterflies in lowland California. Glob. Change Biol. 9, 1130–1135. https://doi.org/10.1046/j.1365-2486.2003.00643.x (2003).
ADS  Article  Google Scholar 

8.
Altermatt, F. Tell me what you eat and I’ll tell you when you fly: Diet can predict phenological changes in response to climate change. Ecol. Lett. 13, 1475–1484. https://doi.org/10.1111/j.1461-0248.2010.01534.x (2010).
Article  PubMed  Google Scholar 

9.
Stefanescu, C., Penuelas, J. & Filella, I. Effects of climatic change on the phenology of butterflies in the northwest Mediterranean Basin. Glob. Change Biol. 9, 1494–1506. https://doi.org/10.1046/j.1365-2486.2003.00682.x (2003).
ADS  Article  Google Scholar 

10.
Roy, D. B. & Sparks, T. H. Phenology of British butterflies and climate change. Glob. Change Biol. 6, 407–416. https://doi.org/10.1046/j.1365-2486.2000.00322.x (2000).
ADS  Article  Google Scholar 

11.
Diez, J. M. et al. Forecasting phenology: from species variability to community patterns. Ecol. Lett. 15, 545–553. https://doi.org/10.1111/j.1461-0248.2012.01765.x (2012).
Article  PubMed  Google Scholar 

12.
Schweiger, O., Settele, J., Kudrna, O., Klotz, S. & Kühn, I. Climate change can cause spatial mismatch of trophically interacting species. Ecology 89, 3472–3479. https://doi.org/10.1890/07-1748.1 (2008).
Article  PubMed  Google Scholar 

13.
Glazaczow, A., Orwin, D. & Bogdziewicz, M. Increased temperature delays the late-season phenology of multivoltine insect. Sci. Rep. https://doi.org/10.1038/srep38022 (2016).
Article  PubMed  PubMed Central  Google Scholar 

14.
van der Kolk, H.-J., WallisDeVries, M. F. & van Vliet, A. J. H. Using a phenological network to assess weather influences on first appearance of butterflies in the Netherlands. Ecol. Indicators 69, 205–212, https://doi.org/10.1016/j.ecolind.2016.04.028 (2016).

15.
Zografou, K. et al. Signals of climate change in butterfly communities in a mediterranean protected area. PLoS ONE 9, e87245. https://doi.org/10.1371/journal.pone.0087245 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

16.
Visser, M. Keeping up with a warming world; assessing the rate of adaptation to climate change. Proc. Biol. Sci. R. Soc. 275, 649–659, https://doi.org/10.1098/rspb.2007.0997 (2008).

17.
Kharouba, H. M., Paquette, S. R., Kerr, J. T. & Vellend, M. Predicting the sensitivity of butterfly phenology to temperature over the past century. Glob. Change Biol. 20, 504–514. https://doi.org/10.1111/gcb.12429 (2014).
ADS  Article  Google Scholar 

18.
Roy, D. B. et al. Similarities in butterfly emergence dates among populations suggest local adaptation to climate. Glob. Change Biol. 21, 3313–3322. https://doi.org/10.1111/gcb.12920 (2015).
ADS  Article  Google Scholar 

19.
Rapacciuolo, G. et al. Beyond a warming fingerprint: Individualistic biogeographic responses to heterogeneous climate change in California. Glob. Change Biol. 20, 2841–2855. https://doi.org/10.1111/gcb.12638 (2014).
ADS  Article  Google Scholar 

20.
Fischer, K. & Fiedler, K. Life-history plasticity in the butterfly Lycaena hippothoe: Local adaptations and trade-offs. Biol. J. Lin. Soc. 75, 173–185. https://doi.org/10.1046/j.1095-8312.2002.00014.x (2002).
Article  Google Scholar 

21.
Zografou, K. Who flies first?—Habitat-specific phenological shifts of butterflies and orthopterans in the light of climate change: A case study from the south-east Mediterranean Lepidoptera and Orthoptera phenology change. Ecol. Entomol. 40, 562–574. https://doi.org/10.1111/een.12220 (2015).
Article  Google Scholar 

22.
Suggitt Andrew, J. et al. Habitat microclimates drive fine-scale variation in extreme temperatures. Oikos 120, 1–8, https://doi.org/10.1111/j.1600-0706.2010.18270.x (2010).

23.
Dell, D., Sparks, T. & Dennis, R. Climate change and the effect of increasing spring temperatures on emergence dates of the butterfly Apatura iris (Lepidoptera: Nymphalidae). Eur. J. Entomol. 102, 161–167. https://doi.org/10.14411/eje.2005.026 (2005).
Article  Google Scholar 

24.
Zipf, L., Williams, E. H., Primack, R. B. & Stichter, S. Climate effects on late-season flight times of Massachusetts butterflies. Int. J. Biometeorol. 61, 1667–1673. https://doi.org/10.1007/s00484-017-1347-8 (2017).
ADS  CAS  Article  PubMed  Google Scholar 

25.
Diamond, S. E., Frame, A. M., Martin, R. A. & Buckley, L. B. Species’ traits predict phenological responses to climate change in butterflies. Ecology 92, 1005–1012. https://doi.org/10.1890/i0012-9658-92-5-1005 (2011).
Article  PubMed  Google Scholar 

26.
Melero, Y., Stefanescu, C. & Pino, J. General declines in Mediterranean butterflies over the last two decades are modulated by species traits. Biol. Cons. 201, 336–342. https://doi.org/10.1016/j.biocon.2016.07.029 (2016).
Article  Google Scholar 

27.
Stefanescu, C., Peñuelas, J. & Filella, I. Butterflies highlight the conservation value of hay meadows highly threatened by land-use changes in a protected Mediterranean area. Biol. Cons. 126, 234–246. https://doi.org/10.1016/j.biocon.2005.05.010 (2005).
Article  Google Scholar 

28.
Sparks, T. H., Huber, K. & Dennis, R. L. H. Complex phenological responses to climate warming trends? Lessons from history. Eur. J. Entomol. 103, 379–386 (2006).
Article  Google Scholar 

29.
Wong, M. K. L., Guénard, B. & Lewis, O. T. Trait-based ecology of terrestrial arthropods. Biol. Rev. 94, 999–1022. https://doi.org/10.1111/brv.12488 (2019).
Article  PubMed  Google Scholar 

30.
Gutiérrez, D. & Wilson, R. J. Intra- and interspecific variation in the responses of insect phenology to climate. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13348 (2020).
Article  PubMed  Google Scholar 

31.
Zografou, K. et al. Butterfly phenology in Mediterranean mountains using space-for-time substitution. Ecol. Evolut. 10, 928–939. https://doi.org/10.1002/ece3.5951 (2020).
Article  Google Scholar 

32.
Steltzer, H. & Post, E. Seasons and life cycles. Science 324, 886–887. https://doi.org/10.1126/science.1171542 (2009).
Article  PubMed  Google Scholar 

33.
Hale, R., Morrongiello, J. R. & Swearer, S. E. Evolutionary traps and range shifts in a rapidly changing world. Biol. Let. 12, 20160003. https://doi.org/10.1098/rsbl.2016.0003 (2016).
Article  Google Scholar 

34.
Ghalambor, C. K., McKay, J. K., Carroll, S. P. & Reznick, D. N. Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments. Funct. Ecol. 21, 394–407. https://doi.org/10.1111/j.1365-2435.2007.01283.x (2007).
Article  Google Scholar 

35.
Macgregor, C. J. et al. Climate-induced phenology shifts linked to range expansions in species with multiple reproductive cycles per year. Nat. Commun. 10, 4455. https://doi.org/10.1038/s41467-019-12479-w (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

36.
Pau, S. et al. Predicting phenology by integrating ecology, evolution and climate science. Glob. Change Biol. 17, 3633–3643. https://doi.org/10.1111/j.1365-2486.2011.02515.x (2011).
ADS  Article  Google Scholar 

37.
Sherry, R. A. et al. Divergence of reproductive phenology under climate warming. Proc. Natl. Acad. Sci. 104, 198. https://doi.org/10.1073/pnas.0605642104 (2007).
ADS  CAS  Article  PubMed  Google Scholar 

38.
Wilson, R. J. & Fox, R. Insect responses to global change offer signposts for biodiversity and conservation. Ecol. Entomol. https://doi.org/10.1111/een.12970 (2020).
Article  Google Scholar 

39.
Brooks, S. J. et al. The influence of life history traits on the phenological response of British butterflies to climate variability since the late-19th century. Ecography 40, 1152–1165. https://doi.org/10.1111/ecog.02658 (2017).
Article  Google Scholar 

40.
Cayton, H. L., Haddad, N. M., Gross, K., Diamond, S. E. & Ries, L. Do growing degree days predict phenology across butterfly species?. Ecology 96, 1473–1479. https://doi.org/10.1890/15-0131.1 (2015).
Article  Google Scholar 

41.
Stocker, T. F. et al. Summary for policymakers. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 3–29 (2013).

42.
Swengel, A. B. Effects of fire and hay management on abundance of prairie butterflies. Biol. Cons. 76, 73–85 (1996).
Article  Google Scholar 

43.
Zografou, K. et al. Severe decline and partial recovery of a rare butterfly on an active military training area. Biol. Cons. 216, 43–50. https://doi.org/10.1016/j.biocon.2017.09.026 (2017).
Article  Google Scholar 

44.
Gillingham, P. K., Huntley, B., Kunin, W. E. & Thomas, C. D. The effect of spatial resolution on projected responses to climate warming. Divers. Distrib. 18, 990–1000. https://doi.org/10.1111/j.1472-4642.2012.00933.x (2012).
Article  Google Scholar 

45.
Roy David, B. et al. Similarities in butterfly emergence dates among populations suggest local adaptation to climate. Global Change Biol. 21, 3313–3322, https://doi.org/10.1111/gcb.12920 (2015).

46.
Lemoine, N. P. Climate change may alter breeding ground distributions of eastern migratory monarchs (Danaus plexippus) via range expansion of asclepias host plants. PLoS ONE 10, e0118614. https://doi.org/10.1371/journal.pone.0118614 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

47.
Slansky, F. Phagism relationships among butterflies. J. N. Y. Entomol. Soc. 84, 91–105 (1976).
Google Scholar 

48.
Morin, X., Roy, J., Sonié, L. & Chuine, I. Changes in leaf phenology of three European oak species in response to experimental climate change. New Phytol. 186, 900–910. https://doi.org/10.1111/j.1469-8137.2010.03252.x (2010).
Article  PubMed  Google Scholar 

49.
Chuine, I., Morin, X. & Bugmann, H. Warming. Photoperiods Tree Phenol. 329, 277–278. https://doi.org/10.1126/science.329.5989.277-e%JScience (2010).
Article  Google Scholar 

50.
Luedeling, E., Girvetz, E. H., Semenov, M. A. & Brown, P. H. Climate change affects winter chill for temperate fruit and nut trees. PLoS ONE 6, e20155. https://doi.org/10.1371/journal.pone.0020155 (2011).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

51.
Fu, Y. S. H. et al. Variation in leaf flushing date influences autumnal senescence and next year’s flushing date in two temperate tree species. Proc. Natl. Acad. USA 111, 7355–7360. https://doi.org/10.1073/pnas.1321727111%JProceedingsoftheNationalAcademyofSciences (2014).
ADS  CAS  Article  Google Scholar 

52.
Renner, S. S. & Zohner, C. M. Climate change and phenological mismatch in trophic interactions among plants, insects, and vertebrates. Annu. Rev. Ecol. Evol. Syst. 49, 165–182. https://doi.org/10.1146/annurev-ecolsys-110617-062535 (2018).
Article  Google Scholar 

53.
Barton, K. E., Edwards, K. F. & Koricheva, J. Shifts in woody plant defence syndromes during leaf development. Funct. Ecol. 33, 2095–2104. https://doi.org/10.1111/1365-2435.13435 (2019).
Article  Google Scholar 

54.
Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change. Nat. Clim. Change 8, 224–228. https://doi.org/10.1038/s41558-018-0067-3 (2018).
ADS  Article  Google Scholar 

55.
Altermatt, F. Climatic warming increases voltinism in European butterflies and moths. Proc. R. Soc. B Biol. Sci. 277, 1281–1287. https://doi.org/10.1098/rspb.2009.1910 (2010).
Article  Google Scholar 

56.
Illán, J. G., Gutiérrez, D., Díez, S. B. & Wilson, R. J. Elevational trends in butterfly phenology: Implications for species responses to climate change. Ecol. Entomol. 37, 134–144. https://doi.org/10.1111/j.1365-2311.2012.01345.x (2012).
Article  Google Scholar 

57.
Nufio, C. R., McGuire, C. R., Bowers, M. D. & Guralnick, R. P. Grasshopper community response to climatic change: Variation along an elevational gradient. PLoS ONE 5, e12977. https://doi.org/10.1371/journal.pone.0012977 (2010).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

58.
Van Dyck, H., Bonte, D., Puls, R., Gotthard, K. & Maes, D. The lost generation hypothesis: Could climate change drive ectotherms into a developmental trap?. Oikos 124, 54–61. https://doi.org/10.1111/oik.02066 (2015).
Article  Google Scholar 

59.
Scott, J. A. The Butterflies of North America: A Natural History and Field Guide. (Stanford University Press, 1992).

60.
Division, E. Final Integrated Natural Resources Management Plan 17003–25002 (The Pennsylvania Department of Military and Veterans Affairs, Annville, 2002).
Google Scholar 

61.
Shuey, J. et al. Landscape-scale response to local habitat restoration in the regal fritillary butterfly (Speyeria idalia) (Lepidoptera: Nymphalidae). J. Insect Cons. 20, 773–780. https://doi.org/10.1007/s10841-016-9908-4 (2016).
Article  Google Scholar 

62.
Metzler, E., Shuey, J., Ferge, L., Henderson, R. & Goldstein, P. Contributions to the understanding of tallgrass prairie-dependent butterflies and moths (Lepidoptera) and their biogeography in the United States. Ohio Biol. Surv. Bull. New Ser. 15, 1–143 (2005).
Google Scholar 

63.
PNHP. PNHP Species Lists. Pennsylvania Natural Heritage Program. http://www.naturalheritage.state.pa.us/Species.aspx (2019).

64.
Pollard, E. & Yates, T. J. Monitoring Butterflies for Ecology and Conservation (1993).

65.
Nufio, C. R., McGuire, C. R., Bowers, M. D. & Guralnick, R. P. Grasshopper community response to climatic change: Variation along an elevational gradient. PLoS ONE https://doi.org/10.1371/journal.pone.0012977 (2010).
Article  PubMed  PubMed Central  Google Scholar 

66.
Glassberg, J. Butterflies through binoculars, the East. A field guide to the butterflies of Eastern North America, 242. (Oxford University Press, Inc., 1999).

67.
Brock, J. P. & Kaufman, K. Field Guide to Butterflies of North America., 391 (Hillstar Editions L.C, 2003).

68.
Brakefield, P. M. Geographical variability in, and temperature effects on, the phenology of Maniola jurtina and Pyronia tithonus (Lepidoptera, Satyrinae) in England and Wales. Ecol. Entomol. 12, 139–148. https://doi.org/10.1111/j.1365-2311.1987.tb00993.x (1987).
Article  Google Scholar 

69.
de Arce Crespo, J. I. & Gutiérrez, D. Altitudinal trends in the phenology of butterflies in a mountainous area in central Spain. Eur. J. Entomol. 108, 651–658 (2011).

70.
Moussus, J.-P., Julliard, R. & Jiguet, F. Featuring 10 phenological estimators using simulated data. Methods Ecol. Evol. 1, 140–150. https://doi.org/10.1111/j.2041-210X.2010.00020.x (2010).
Article  Google Scholar 

71.
Penny, D. The comparative method in evolutionary biology. J. Classif. 9, 169–172. https://doi.org/10.1007/BF02618482 (1992).
MathSciNet  Article  Google Scholar 

72.
Earl, C., Belitz, M. et al. Spatial phylogenetics of butterflies in relation to environmental drivers and angiosperm diversity across North America. BioRxiv:2020.2007.2022.216119, https://doi.org/10.1101/2020.07.22.216119 (2020).

73.
PRISM. Climate Group, Parameter-elevation Regressions on Independent Slopes Model. Oregon State University, http://prism.oregonstate.edu. Accessed 24 July 2018.

74.
Daly, C. et al. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol. 28, 2031–2064. https://doi.org/10.1002/joc.1688 (2008).
Article  Google Scholar 

75.
Peñuelas, J. et al. Response of plant species richness and primary productivity in shrublands along a north-south gradient in Europe to seven years of experimental warming and drought: Reductions in primary productivity in the heat and drought year of 2003. Glob. Change Biol. 13, 2563–2581. https://doi.org/10.1111/j.1365-2486.2007.01464.x (2007).
ADS  Article  Google Scholar 

76.
McMaster, G. S. & Wilhelm, W. W. Growing degree-days: One equation, two interpretations. Agric. For. Meteorol. 87, 291–300. https://doi.org/10.1016/S0168-1923(97)00027-0 (1997).
ADS  Article  Google Scholar 

77.
Walters, E. J., Morrell, C. H. & Auer, R. E. An investigation of the median-median method of linear regression. J. Stat. Educ. https://doi.org/10.1080/10691898.2006.11910582 (2006).
Article  Google Scholar 

78.
Theil, H. in Henri Theil’s Contributions to Economics and Econometrics: Econometric Theory and Methodology (eds Baldev Raj & Johan Koerts) 345–381 (Springer Netherlands, 1992).

79.
Sen, P. K. Estimates of the regression coefficient based on Kendall’s Tau. J. Am. Stat. Assoc. 63, 1379–1389. https://doi.org/10.2307/2285891 (1968).
MathSciNet  Article  MATH  Google Scholar 

80.
Siegel, A. F. Robust regression using repeated medians. Biometrika 69, 242–244. https://doi.org/10.2307/2335877 (1982).
Article  MATH  Google Scholar 

81.
Schneider, G., Chicken, E. & Becvarik, R. NSM3: Functions and Datasets to Accompany Hollander, Wolfe, and Chicken – Nonparametric Statistical Methods, Third Edition. R Package Version 1.15. https://CRAN.R-project.org/package=NSM3. (2020).

82.
Patrick Bogaart, Loo, M. v. d. & Pannekoek, J. rtrim: Trends and Indices for Monitoring Data. R Package Version 2.1.1. https://CRAN.R-project.org/package=rtrim. (2020).

83.
Zografou, K. et al. Stable generalist species anchor a dynamic pollination network. Ecosphere 11, e03225. https://doi.org/10.1002/ecs2.3225 (2020).
Article  Google Scholar 

84.
Pinheiro J, Bates D, DebRoy S & D, S. nlme: Linear and Nonlinear Mixed Effects Models. R Package v. 3.1‐117. (www document). https://CRAN.R-project.org/package=nlme. (2015).

85.
Felsenstein, J. Phylogenies and quantitative characters. Annu. Rev. Ecol. Syst. 19, 445–471. https://doi.org/10.1146/annurev.es.19.110188.002305 (1988).
Article  Google Scholar  More