Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Changes in climate drive recent monarch butterfly dynamics

Nature Ecology & Evolution volume 5, pages 1441–1452 (2021)Cite this article

Subjects

Abstract

Declines in the abundance and diversity of insects pose a substantial threat to terrestrial ecosystems worldwide. Yet, identifying the causes of these declines has proved difficult, even for well-studied species like monarch butterflies, whose eastern North American population has decreased markedly over the last three decades. Three hypotheses have been proposed to explain the changes observed in the eastern monarch population: loss of milkweed host plants from increased herbicide use, mortality during autumn migration and/or early-winter resettlement and changes in breeding-season climate. Here, we use a hierarchical modelling approach, combining data from >18,000 systematic surveys to evaluate support for each of these hypotheses over a 25-yr period. Between 2004 and 2018, breeding-season weather was nearly seven times more important than other factors in explaining variation in summer population size, which was positively associated with the size of the subsequent overwintering population. Although data limitations prevent definitive evaluation of the factors governing population size between 1994 and 2003 (the period of the steepest monarch decline coinciding with a widespread increase in herbicide use), breeding-season weather was similarly identified as an important driver of monarch population size. If observed changes in spring and summer climate continue, portions of the current breeding range may become inhospitable for monarchs. Our results highlight the increasingly important contribution of a changing climate to insect declines.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overwintering monarch population size in Mexico and summer glyphosate use in the Midwestern United States.
Fig. 2: Relative importance of seasonal factors influencing the size of the eastern North American monarch population (2004–2018).
Fig. 3: Spring weather (1994–2018) and estimated effects on summer monarch population size (2004–2018).
Fig. 4: Summer weather (1994–2018) and estimated effects on summer monarch population size (2004–2018).
Fig. 5: Relationships among monarch population sizes in summer, early winter and late winter between 2004–2018.

Similar content being viewed by others

Data availability

Monarch data from the overwintering grounds and covariate data are available on Zenodo (https://doi.org/10.5281/zenodo.4085906). Monarch data from the summer breeding grounds are proprietary and were obtained from the North American Butterfly Association (https://www.naba.org/), the Iowa Butterfly Survey Network (https://www.reimangardens.com/collections/insects/iowa-butterfly-survey-network/), the Illinois Butterfly Monitoring Network (https://bfly.org/), the Michigan Butterfly Network (https://michiganbutterfly.org/) and the Ohio Lepidopterists (http://www.ohiolepidopterists.org/). These data may be available upon reasonable request to L.R. and with permission from the aforementioned programmes.

Code availability

Code needed to run analyses (R scripts and Stan model files) is available on Zenodo (https://doi.org/10.5281/zenodo.4085906) and Github (https://zipkinlab.github.io/#dataintegration2021Z).

References

  1. van Klink, R. et al. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368, 417–420 (2020).

    Article  PubMed  CAS  Google Scholar 

  2. Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).

    Article  CAS  PubMed  Google Scholar 

  3. Wepprich, T., Adrion, J. R., Ries, L., Wiedmann, J. & Haddad, N. M. Butterfly abundance declines over 20 years of systematic monitoring in Ohio, USA. PLoS ONE 14, e0216270 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Wagner, D. L., Grames, E. M., Forister, M. L., Berenbaum, M. R. & Stopak, D. Insect decline in the Anthropocene: death by a thousand cuts. Proc. Natl Acad. Sci. USA 118, e2023989118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).

    Article  CAS  PubMed  Google Scholar 

  6. Winfree, R., Fox, J. W., Williams, N. M., Reilly, J. R. & Cariveau, D. P. Abundance of common species, not species richness, drives delivery of a real-world ecosystem service. Ecol. Lett. 18, 626–635 (2015).

    Article  PubMed  Google Scholar 

  7. Cardoso, P. et al. Scientists’ warning to humanity on insect extinctions. Biol. Conserv. 242, 108426 (2020).

    Article  Google Scholar 

  8. Brower, L. P. et al. Decline of monarch butterflies overwintering in Mexico: is the migratory phenomenon at risk? Insect Conserv. Divers. 5, 95–100 (2012).

    Article  Google Scholar 

  9. Agrawal, A. A. & Inamine, H. Mechanisms behind the monarch’s decline. Science 360, 1294–1296 (2018).

    Article  CAS  PubMed  Google Scholar 

  10. Schultz, C. B., Brown, L. M., Pelton, E. & Crone, E. E. Citizen science monitoring demonstrates dramatic declines of monarch butterflies in western North America. Biol. Conserv. 214, 343–346 (2017).

    Article  Google Scholar 

  11. Thogmartin, W. E. et al. Monarch butterfly population decline in North America: identifying the threatening processes. R. Soc. Open Sci. 4, 170760 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Boyle, J. H., Dalgleish, H. J. & Puzey, J. R. Monarch butterfly and milkweed declines substantially predate the use of genetically modified crops. Proc. Natl Acad. Sci. USA 116, 3006–3011 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Hann, N. L. & Landis, D. A. The importance of shifting disturbance regimes in monarch butterfly decline and recovery. Front. Ecol. Evol. 7, 191 (2019).

    Article  Google Scholar 

  14. Oberhauser, K. S. et al. Temporal and spatial overlap between monarch larvae and corn pollen. Proc. Natl Acad. Sci. USA 98, 11913–11918 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Pleasants, J. M. & Oberhauser, K. S. Milkweed loss in agricultural fields because of herbicide use: effect on the monarch butterfly population. Insect Conserv. Divers. 6, 135–144 (2013).

    Article  Google Scholar 

  16. Ries, L., Taron, D. J. & Rendón-Salinas, E. The disconnect between summer and winter monarch trends for the eastern migratory population: possible links to differing drivers. Ann. Entomol. Soc. Am. 108, 691–699 (2015).

    Article  Google Scholar 

  17. Inamine, H., Ellner, S. P., Springer, J. P. & Agrawal, A. A. Linking the continental migratory cycle of the monarch butterfly to understand its population decline. Oikos 125, 1081–1091 (2016).

    Article  Google Scholar 

  18. Saunders, S. P. et al. Multiscale seasonal factors drive the size of winter monarch colonies. Proc. Natl Acad. Sci. USA 116, 8609–8614 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zalucki, M. P. Temperature and rate of development in Danaus plexippus L. and D. chrysippus L. (Lepidoptera: Nymphalidae). Aust. J. Entomol. 21, 241–246 (1982).

    Article  Google Scholar 

  20. Zipkin, E. F., Ries, L., Reeves, R., Regetz, J. & Oberhauser, K. S. Tracking climate impacts on the migratory monarch butterfly. Glob. Change Biol. 18, 3039–3049 (2012).

    Article  Google Scholar 

  21. Saunders, S. P., Ries, L., Oberhauser, K. S., Thogmartin, W. E. & Zipkin, E. F. Local and cross-seasonal associations of climate and land use with abundance of monarch butterflies. Ecography 41, 278–290 (2018).

    Article  Google Scholar 

  22. Batalden, R. V., Oberhauser, K. & Peterson, A. T. Ecological niches in sequential generations of eastern North American monarch butterflies: the ecology of migration and likely climate change implications. Environ. Entomol. 36, 1365–1373 (2007).

    Article  PubMed  Google Scholar 

  23. Lemoine, N. P. Climate change may alter breeding ground distributions of eastern migratory monarchs via range expansion of Asclepias host plants. PLoS ONE 10, e0118614 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Vidal, O. & Rendón-Salinas, E. Dynamics and trends of overwintering colonies of the monarch butterfly in Mexico. Biol. Conserv. 180, 165–175 (2014).

    Article  Google Scholar 

  25. Thogmartin, W. E. et al. Density estimates of monarch butterflies overwintering in central Mexico. PeerJ 5, e3221 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Flockhart, D. T. T., Pichancourt, J.-B., Norris, D. R. & Martin, T. G. Unravelling the annual cycle in a migratory animal: breeding-season habitat loss drives population declines of monarch butterflies. J. Anim. Ecol. 84, 155–165 (2015).

    Article  PubMed  Google Scholar 

  27. Oberhauser, K. et al. A trans-national monarch butterfly population model and implications for regional conservation priorities. Ecol. Entomol. 42, 51–60 (2017).

    Article  Google Scholar 

  28. Wilcox, A. A. E., Flockhart, D. T. T., Newman, A. E. M. & Norris, D. R. An evaluation of studies on the potential threats contributing to the decline of eastern migratory North American monarch butterflies (Danaus plexippus). Front. Ecol. Evol. 7, 99 (2019).

    Article  Google Scholar 

  29. Chevan, A. & Sutherland, M. Hierarchical partitioning. Am. Stat. 45, 90–96 (1991).

    Google Scholar 

  30. Dai, S., Shulski, M. D., Hubbard, K. G. & Takle, E. S. A spatiotemporal analysis of Midwest US temperature and precipitation trends during the growing season from 1980 to 2013. Int. J. Climatol. 36, 517–525 (2016).

    Article  Google Scholar 

  31. Feng, Z. et al. More frequent intense and long-lived storms dominate the springtime trend in central US rainfall. Nat. Commun. 7, 13429 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Crimmins, T. M. & Crimmins, M. A. Biologically-relevant trends in springtime temperatures across the United States. Geophys. Res. Lett. 46, 12377–12387 (2019).

    Article  Google Scholar 

  33. Roy, D. B., Rothery, P., Moss, D., Pollard, E. & Thomas, J. A. Butterfly numbers and weather: predicting historical trends in abundance and the future effects of climate change. J. Anim. Ecol. 70, 201–217 (2001).

    Google Scholar 

  34. Nelson, W. A., Bjørnstad, O. N. & Yamanaka, T. Recurrent insect outbreaks caused by temperature-driven changes in system stability. Science 341, 796–799 (2013).

    Article  CAS  PubMed  Google Scholar 

  35. IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds. Field, C. B. et al.) (Cambridge Univ. Press, 2014).

  36. Diffenbaugh, N. S. & Giorgi, F. Climate change hotspots in the CMIP5 global climate model ensemble. Clim. Change 114, 813–822 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Cook, K. H., Vizy, E. K., Launer, Z. S. & Patricola, C. M. Springtime intensification of the Great Plains low-level jet and Midwest precipitation in GCM simulations of the twenty-first century. J. Clim. 21, 6321–6340 (2008).

    Article  Google Scholar 

  38. Diffenbaugh, N. S. & Field, C. B. Changes in ecologically critical terrestrial climate conditions. Science 341, 486–492 (2013).

    Article  CAS  PubMed  Google Scholar 

  39. Wagner, D. L. Insect declines in the Anthropocene. Ann. Rev. Entomol. 65, 457–480 (2020).

    Article  CAS  Google Scholar 

  40. Forister, M. L. et al. Fewer butterflies seen by community scientists across the warming and drying landscapes of the American West. Science 371, 1042–1045 (2021).

    Article  CAS  PubMed  Google Scholar 

  41. Janzen, D. H. & Hallwachs, W. To us insectometers, it is clear that insect decline in our Costa Rican tropics is real, so let’s be kind to the survivors. Proc. Natl Acad. Sci. USA 118, e2002546117 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Flockhart, D. T. T. et al. Regional climate on the breeding grounds predicts variation in the natal origin of monarch butterflies overwintering in Mexico over 38 years. Glob. Change Biol. 23, 2565–2576 (2017).

    Article  Google Scholar 

  43. Wassenaar, L. I. & Hobson, K. A. Natal origins of migratory monarch butterflies at wintering colonies in Mexico: new isotopic evidence. Proc. Natl Acad. Sci. USA 95, 15436–15439 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Oberhauser, K. S. et al. in Monarchs in a Changing World: Biology and Conservation of an Iconic Butterfly (eds. Oberhauser, K. S. et al.) 13–30 (Cornell Univ. Press, 2015).

  45. Pollard, E. A method for assessing changes in the abundance of butterflies. Biol. Conserv. 12, 115–134 (1977).

    Article  Google Scholar 

  46. Saunders, S. P., Ries, L., Obserhauser, K. S. & Zipkin, E. F. Evaluating confidence in climate-based predictions of population change in a migratory species. Glob. Ecol. Biogeogr. 25, 1000–1012 (2016).

    Article  Google Scholar 

  47. Missrie, M. in The Monarch Butterfly: Biology and Conservation (eds. Obserhauser, K. S. & Solensky, M. J.) 141–150 (Cornell Univ. Press, 2004).

  48. García-Serrano, E., Reyes, J. L. & Alvarez, B. X. M. in The Monarch Butterfly: Biology and Conservation (eds. Obserhauser, K. S. & Solensky, M. J.) 129–133 (Cornell Univ. Press, 2004).

  49. Ramírez, M. I., Sáenz-Romero, C., Rehfeldt, G. & Salas-Canela, L. in Monarchs in a Changing World: Biology and Conservation of an Iconic Butterfly (eds. Oberhauser, K. S. et al.) 157–168 (Cornell Univ. Press, 2015).

  50. Howard, E. & Davis, A. K. Investigating long-term changes in the spring migration of monarch butterflies (Lepidoptera: Nymphalidae) using 18 years of data from Journey North, a citizen science program. Ann. Entomol. Soc. Am. 108, 664–669 (2015).

    Article  Google Scholar 

  51. McMaster, G. S. & Wilhelm, W. Growing degree-days: one equation, two interpretations. Agric. Meteorol. 87, 291–300 (1997).

    Article  Google Scholar 

  52. Thornton, P. E. et al. Daymet: Daily Surface Weather Data on a 1-km Grid for North America Version 3 (ORNL DAAC, 2018); https://doi.org/10.3334/ORNLDAAC/1328

  53. Hartzler, R. G. & Buhler, D. D. Occurrence of common milkweed (Asclepias syriaca) in cropland and adjacent areas. Crop Prot. 19, 363–366 (2000).

    Article  Google Scholar 

  54. Hartzler, R. G. Reduction in common milkweed (Asclepias syriaca) occurrence in Iowa cropland from 1999 to 2009. Crop Prot. 29, 1542–1544 (2010).

    Article  Google Scholar 

  55. Homer, C. G. et al. Completion of the 2011 National Land Cover Database for the conterminous United States—representing a decade of land cover change information. Photogramm. Eng. Remote Sens. 81, 345–354 (2015).

    Google Scholar 

  56. Douglas, M. R., Sponsler, D. B., Lonsdorf, E. V. & Grozinger, C. M. County-level analysis reveals a rapidly shifting landscape of insecticide hazard to honey bees (Apis mellifera) on US farmland. Sci. Rep. 10, 797 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Benbrook, C. M. Trends in glyphosate herbicide use in the United States and globally. Environ. Sci. Eur. 28, 3 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Pesticide National Synthesis Project (US Geological Survey, 2020); https://water.usgs.gov/nawqa/pnsp/usage/maps/county-level/

  59. Quick Stats (US Department of Agriculture, National Agricultural Statistics Service, 2020); http://quickstats.nass.usda.gov

  60. Crops (Ontario Ministry of Agriculture, Food and Rural Affairs, 2020); http://www.omafra.gov.on.ca/english/crops/

  61. Batalden, R. V. & Oberhauser, K. S. in Monarchs in a Changing World: Biology and Conservation of an Iconic Butterfly (eds. Oberhauser, K. S. et al.) 215–224 (Cornell Univ. Press, 2015).

  62. Alonso-Mejía, A., Rendón-Salinas, E., Montesinos-Patiño, E. & Brower, L. P. Use of lipid reserves by monarch butterflies overwintering in Mexico: implications for conservation. Ecol. Appl. 7, 934–947 (1997).

    Article  Google Scholar 

  63. Brower, L. P., Fink, L. S. & Walford, P. Fueling the fall migration of the monarch butterfly. Integr. Comp. Biol. 46, 1123–1142 (2006).

    Article  PubMed  Google Scholar 

  64. Tracy, J. L., Kantola, T., Baum, K. A. & Coulson, R. N. Modeling fall migration pathways and spatially identifying potential migratory hazards for the eastern monarch butterfly. Landsc. Ecol. 34, 443–458 (2019).

    Article  Google Scholar 

  65. Feldman, R. E. & McGill, B. J. How important is nectar in shaping spatial variation in the abundance of temperate breeding hummingbirds? J. Biogeogr. 41, 489–500 (2014).

    Article  Google Scholar 

  66. Didan, K. MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid V006 [Data set] (NASA EOSDIS Land Processes DAAC, 2015); https://doi.org/10.5067/MODIS/MOD13Q1.006

  67. Vidal, O., López-García, J. & Rendón-Salinas, E. Trends in deforestation and forest degradation after a decade of monitoring in the Monarch Butterfly Biosphere Reserve in Mexico. Conserv. Biol. 28, 177–186 (2013).

    Article  PubMed  Google Scholar 

  68. Williams, E. H. & Brower, L. P. in Monarchs in a Changing World: Biology and Conservation of an Iconic Butterfly (eds. Oberhauser, K. S. et al.) 109–116 (Cornell Univ. Press, 2015).

  69. Brower, L. P. et al. in The Monarch Butterfly: Biology and Conservation (eds. Obserhauser, K. S. & Solensky, M. J.) 151–166 (Cornell Univ. Press, 2004).

  70. Brower, L. P. et al. Butterfly mortality and salvage logging from the March 2016 storm in the Monarch Butterfly Biosphere Reserve in Mexico. Am. Entom. 63, 151–164 (2017).

    Article  Google Scholar 

  71. Farfán-Gutiérrez, M. et al. Modeling anthropic factors as drivers of wildfire occurrence at the Monarch Butterfly Biosphere. Madera y Bosques 24, e2431591 (2018).

    Article  Google Scholar 

  72. Ramírez, M. I., López-Sánchez, J. G. & Barrasa, S. Mapa de Vegetación y Cubiertas del Suelo, Reserva de la Biosfera Mariposa Monarca Vol. II (CIGA-UNAM, 2019).

  73. Flores-Martínez, J. J. et al. Recent forest cover loss in the core zones of the Monarch Butterfly Biosphere Reserve in Mexico. Front. Ecol. Evol. 7, 167 (2019).

    Google Scholar 

  74. Ramírez, M. I., Gímenez-Azcárate, J. & Luna, L. Effects of human activities on monarch butterfly habitat in protected mountain forests, Mexico. For. Chron. 79, 242–246 (2003).

    Article  Google Scholar 

  75. Ramírez, M. I., Miranda, R., Zubieta, R. & Jiménez, M. Land cover and road network map for the Monarch Butterfly Biosphere Reserve in Mexico 2003. J. Maps 3, 181–190 (2007).

    Article  Google Scholar 

  76. Zuur, A. F. & Ieno, E. N. Beginner’s Guide to Zero-Inflated Models with R (Highland Statistics Ltd, 2016).

  77. Yackulic, C. B., Dodrill, M., Dzul, M., Sanderlin, J. S. & Reid, J. A. A need for speed in Bayesian population models: a practical guide to marginalizing and recovering discrete latent states. Ecol. Appl. 30, e02112 (2020).

    Article  PubMed  Google Scholar 

  78. Murray, K. & Conner, M. M. Methods to quantify variable importance: implications for the analysis of noisy ecological data. Ecology 90, 348–355 (2009).

    Article  PubMed  Google Scholar 

  79. Mac Nally, R. Hierarchical partitioning as an interpretative tool in multivariate inference. Austral Ecol. 21, 224–228 (1996).

    Article  Google Scholar 

  80. Gelman, A., Goodrich, B., Gabry, J. & Vehtari, A. R-squared for Bayesian regression models. Am. Stat. 73, 307–309 (2019).

    Article  Google Scholar 

  81. Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. 76, 1 (2017).

    Article  Google Scholar 

  82. Stan Development Team. rstan: the R Interface to Stan. R package version 2.17.3 http://mc-stan.org/ (2018).

  83. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019); https://www.R-project.org/

  84. Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).

    Article  Google Scholar 

  85. Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge Univ. Press, 2007).

Download references

Acknowledgements

We thank the many volunteers who contributed to data collection. S. Altizer shared data and insights on the effects of disease and N. Haddad provided comments on the manuscript. This work was supported by NSF grant nos. EF-1702635 (EFZ), DBI-1954406 (EFZ) and EF-1702179 (LR).

Author information

Authors and Affiliations

  1. Department of Integrative Biology, Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, MI, USA

    Erin R. Zylstra, Sarah P. Saunders, Matthew T. Farr & Elise F. Zipkin

  2. Department of Biology, Georgetown University, Washington, DC, USA

    Leslie Ries & Naresh Neupane

  3. National Audubon Society, New York, NY, USA

    Sarah P. Saunders

  4. Centro de Investigaciones en Geografía Ambiental, Universidad Nacional Autónoma de México, Morelia, Mexico

    M. Isabel Ramírez

  5. World Wildlife Fund–México, Terrestrial Ecosystems Program, Zitácuaro, Mexico

    Eduardo Rendón-Salinas

  6. University of Wisconsin Arboretum, University of Wisconsin–Madison, Madison, WI, USA

    Karen S. Oberhauser

Authors
  1. Erin R. Zylstra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leslie Ries
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Naresh Neupane
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sarah P. Saunders
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. Isabel Ramírez
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Eduardo Rendón-Salinas
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Karen S. Oberhauser
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Matthew T. Farr
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Elise F. Zipkin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

E.R.Z., L.R., K.S.O. and E.F.Z. conceived of the research. L.R., N.N., M.I.R., E.R.-S. and K.S.O. contributed data. E.R.Z, S.P.S., M.T.F. and E.F.Z. constructed the model. E.R.Z. ran analyses. E.R.Z. and E.F.Z. wrote the first drafts of the paper. All authors contributed to the interpretation of results and edits to the paper.

Corresponding author

Correspondence to Erin R. Zylstra.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Ecology & Evolution thanks Diana Bowler and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Locations of monarch butterfly surveys on summer breeding grounds between 1994–2018.

Locations of surveys conducted between 14 Jun–15 Aug by the North American Butterfly Association (NABA; blue) and state-specific butterfly monitoring networks (BMNs; red) in a, 1994–2003 and b, 2004–2018. Counties (U.S.) and census districts (Canada) that are included in our delineation of the summer breeding range for the 1994–2003 reduced annual-cycle model and the 2004–2018 full annual-cycle model are outlined in grey.

Extended Data Fig. 2 Model-based index of monarch butterfly population size during peak summer, 1994–2018.

Model-based predictions (posterior medians with 95% credible intervals [CI]) of the expected number of adult monarchs observed per hour on an average NABA survey conducted between 19 Jul–15 Aug, 1994–2018, with linear trend (grey line) and 95% CI (shaded area; slope = –0.15 adults/hr/yr, 95% CI: –0.30, 0.01). Vertical dashed line denotes the break between our 1994–2003 and 2004–2018 analyses.

Extended Data Fig. 3 Changes in summer climate on monarch summer breeding grounds.

Percent change between 1994–2003 and 2004–20018 in a, average temperatures (GDD from 3 May–15 Aug) and b, cumulative precipitation (mm, Apr–Aug) for each U.S. county included in our delineation of the monarch summer breeding range. Temporal trends over a recent 15-year period (2004–2018) in c, GDD (°C/yr), and d, cumulative precipitation (mm/yr). Positive values indicate increases or positive trends in weather variables; negative values indicate decreases or negative trends. Canadian counties were excluded from panels a and b because data limitations prevented us from including these regions in our 1994–2003 model of monarch population dynamics.

Extended Data Fig. 4 Residuals from the winter submodel describing variation in the area occupied by monarch butterflies.

Estimated residuals (posterior medians) from the winter submodel describing the area occupied by monarchs in each of the overwintering supercolonies, when monarchs were present in early winter, 2004–2018. Solid grey line and shaded area represent a linear trend with 95% credible interval (slope= –0.003, 95% CI = –0.014, 0.008).

Extended Data Fig. 5 Effects of summer weather on monarch population size, 1994–2003.

a, Estimated marginal effects (median and 95% credible intervals) of GDD (deviation from county 10-year average) on expected monarch counts during peak summer (expected mean count of adult monarchs per search hour, 19 Jul–25 Jul), for typical cool, average, and warm counties (avgGDDc = 711, 898, and 1033 °C, respectively) within the summer breeding range, 1994–2003. b, Estimated marginal effects of precipitation (deviation from county 10-year average) on expected monarch counts during peak summer, for typical dry, average, and wet counties (avgPCPc = 422, 525, and 578 mm, respectively), 1994–2003.

Supplementary information

Supplementary Information

Supplementary text, references and Tables 1–5.

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zylstra, E.R., Ries, L., Neupane, N. et al. Changes in climate drive recent monarch butterfly dynamics. Nat Ecol Evol 5, 1441–1452 (2021). https://doi.org/10.1038/s41559-021-01504-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-021-01504-1

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing