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:

Extinction, coextinction and colonization dynamics in plant–hummingbird networks under climate change

Nature Ecology & Evolution volume 6, pages 720–729 (2022)Cite this article

Subjects

Abstract

Climate-driven range shifts may cause local extinctions, while the accompanying loss of biotic interactions may trigger secondary coextinctions. At the same time, climate change may facilitate colonizations from regional source pools, balancing out local species loss. At present, how these extinction–coextinction–colonization dynamics affect biological communities under climate change is poorly understood. Using 84 communities of interacting plants and hummingbirds, we simulated patterns in climate-driven extinctions, coextinctions and colonizations under future climate change scenarios. Our simulations showed clear geographic discrepancies in the communities’ vulnerability to climate change. Andean communities were the least affected by future climate change, as they experienced few climate-driven extinctions and coextinctions while having the highest colonization potential. In North America and lowland South America, communities had many climate-driven extinctions and few colonization events. Meanwhile, the pattern of coextinction was highly dependent on the configuration of networks formed by interacting hummingbirds and plants. Notably, North American communities experienced proportionally fewer coextinctions than other regions because climate-driven extinctions here primarily affected species with peripheral network roles. Moreover, coextinctions generally decreased in communities where species have few overlapping interactions, that is, communities with more complementary specialized and modular networks. Together, these results highlight that we should not expect colonizations to adequately balance out local extinctions in the most vulnerable ecoregions.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Conceptual figure illustrating our simulations of climate-driven extinctions, coextinctions and colonizations.
Fig. 2: The geographic patterns of local climate-driven extinction and coextinction across the Americas.
Fig. 3: The geographic patterns of local colonization across the Americas.
Fig. 4: Biogeographical variability in the communities’ vulnerability to coextinctions after accounting for climate-driven extinctions.
Fig. 5: Three examples of climate-driven extinction and coextinction, characteristic for each biogeographical region.
Fig. 6: The influence of three network structures on the logarithmic association between climate-driven extinctions and coextinctions (n = 83).

Similar content being viewed by others

Data availability

Data used to conduct the analysis are provided at the figshare repository: https://doi.org/10.6084/m9.figshare.19071752.v2

Code availability

The Methods contains a detailed description of our analytical framework. R codes in the simulations are provided at the figshare repository: https://doi.org/10.6084/m9.figshare.19071752.v2

References

  1. Schemske, D. W. in Foundations of Tropical Forest Biology (eds Chazdon, R. L. & Whitmore, T. C.) 163–173 (Univ. Chicago Press, 2002).

  2. Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).

    Article  Google Scholar 

  3. Schemske, D. W., Mittelbach, G. G., Cornell, H. V., Sobel, J. M. & Roy, K. Is there a latitudinal gradient in the importance of biotic interactions? Annu. Rev. Ecol. Evol. Syst. 40, 245–269 (2009).

    Article  Google Scholar 

  4. Schweiger, O., Settele, J., Kudrna, O., Klotz, S. & Kühn, I. Climate change can cause spatial mismatch of trophically interacting species. Ecology 89, 3472–3479 (2008).

    Article  PubMed  Google Scholar 

  5. Hegland, S. J., Nielsen, A., Lázaro, A., Bjerknes, A.-L. & Totland, Ø. How does climate warming affect plant–pollinator interactions? Ecol. Lett. 12, 184–195 (2009).

    Article  PubMed  Google Scholar 

  6. Walther, G.-R. Community and ecosystem responses to recent climate change. Philos. Trans. R. Soc. B 365, 2019–2024 (2010).

    Article  Google Scholar 

  7. Blois, J. L., Zarnetske, P. L., Fitzpatrick, M. C. & Finnegan, S. Climate change and the past, present and future of biotic interactions. Science 341, 499–504 (2013).

    Article  CAS  PubMed  Google Scholar 

  8. Schleuning, M. et al. Ecological networks are more sensitive to plant than to animal extinction under climate change. Nat. Commun. 7, 13965 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Bascompte, J., García, M. B., Ortega, R., Rezende, E. L. & Pironon, S. Mutualistic interactions reshuffle the effects of climate change on plants across the tree of life. Sci. Adv. 5, eaav2539 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Memmott, J., Craze, P. G., Waser, N. M. & Price, M. V. Global warming and the disruption of plant–pollinator interactions. Ecol. Lett. 10, 710–717 (2007).

    Article  PubMed  Google Scholar 

  11. Tylianakis, J. M., Didham, R. K., Bascompte, J. & Wardle, D. A. Global change and species interactions in terrestrial ecosystems. Ecol. Lett. 11, 1351–1363 (2008).

    Article  PubMed  Google Scholar 

  12. Dalsgaard, B. et al. Specialization in plant–hummingbird networks is associated with species richness, contemporary precipitation and Quaternary climate-change velocity. PLoS ONE 6, e25891 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Dalsgaard, B. et al. Historical climate-change influences modularity and nestedness of pollination networks. Ecography 36, 1331–1340 (2013).

    Article  Google Scholar 

  14. Memmott, J., Waser, N. M. & Price, M. V. Tolerance of pollination networks to species extinctions. Proc. R. Soc. Lond. B 271, 2605–2611 (2004).

    Article  Google Scholar 

  15. Kaiser-Bunbury, C. N., Muff, S., Memmott, J., Müller, C. B. & Caflisch, A. The robustness of pollination networks to the loss of species and interactions: a quantitative approach incorporating pollinator behaviour. Ecol. Lett. 13, 442–452 (2010).

    Article  PubMed  Google Scholar 

  16. Dáttilo, W. et al. Unravelling Darwin’s entangled bank: architecture and robustness of mutualistic networks with multiple interaction types. Proc. R. Soc. B 283, 20161564 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Dalsgaard, B. et al. Trait evolution, resource specialization and vulnerability to plant extinctions among Antillean hummingbirds. Proc. R. Soc. B 285, 20172754 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Gilman, S. E., Urban, M. C., Tewksbury, J., Gilchrist, G. W. & Holt, R. D. A framework for community interactions under climate change. Trends Ecol. Evol. 25, 325–331 (2010).

    Article  PubMed  Google Scholar 

  19. Rahbek, C. & Graves, G. R. Multiscale assessment of patterns of avian species richness. Proc. Natl Acad. Sci. USA 98, 4534–4539 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Rahbek, C. & Graves, G. R. Detection of macro-ecological patterns in South American hummingbirds is affected by spatial scale. Proc. R. Soc. Lond. B 267, 2259–2265 (2000).

    Article  CAS  Google Scholar 

  21. Dalsgaard, B. et al. The influence of biogeographical and evolutionary histories on morphological trait-matching and resource specialization in mutualistic hummingbird–plant networks. Funct. Ecol. 35, 1120–1133 (2021).

    Article  Google Scholar 

  22. Sandel, B. et al. The influence of Late Quaternary climate-change velocity on species endemism. Science 334, 660–664 (2011).

    Article  CAS  PubMed  Google Scholar 

  23. Scherrer, D. & Körner, C. Topographically controlled thermal-habitat differentiation buffers alpine plant diversity against climate warming. J. Biogeogr. 38, 406–416 (2011).

    Article  Google Scholar 

  24. Graves, G. R. & Rahbek, C. Source pool geometry and the assembly of continental avifaunas. Proc. Natl Acad. Sci. USA 102, 7871–7876 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects (eds Barros, V. R. et al.) (Cambridge Univ. Press, 2014).

  26. Hoegh-Guldberg, O. et al. in Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) 175–311 (IPCC, WMO, 2018).

  27. Watson, J. E. M., Iwamura, T. & Butt, N. Mapping vulnerability and conservation adaptation strategies under climate change. Nat. Clim. Change 3, 989–994 (2013).

    Article  Google Scholar 

  28. Martín González, A. M., Dalsgaard, B. & Olesen, J. M. Centrality measures and the importance of generalist species in pollination networks. Ecol. Complex. 7, 36–43 (2010).

    Article  Google Scholar 

  29. Burgos, E. et al. Why nestedness in mutualistic networks? J. Theor. Biol. 249, 307–313 (2007).

    Article  PubMed  Google Scholar 

  30. Bersier, L.-F., Banašek-Richter, C. & Cattin, M.-F. Quantitative descriptors of food-web matrices. Ecology 83, 2394–2407 (2002).

    Article  Google Scholar 

  31. Thébault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853–856 (2010).

    Article  PubMed  Google Scholar 

  32. Tylianakis, J. M., Laliberté, E., Nielsen, A. & Bascompte, J. Conservation of species interaction networks. Biol. Conserv. 143, 2270–2279 (2010).

    Article  Google Scholar 

  33. Grass, I., Jauker, B., Steffan-Dewenter, I., Tscharntke, T. & Jauker, F. Past and potential future effects of habitat fragmentation on structure and stability of plant–pollinator and host–parasitoid networks. Nat. Ecol. Evol. 2, 1408–1417 (2018).

    Article  PubMed  Google Scholar 

  34. Stouffer, D. B. & Bascompte, J. Compartmentalization increases food-web persistence. Proc. Natl Acad. Sci. USA 108, 3648–3652 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Blüthgen, N., Menzel, F. & Blüthgen, N. Measuring specialization in species interaction networks. BMC Ecol. 6, 9 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Dormann, C. F. & Strauss, R. A method for detecting modules in quantitative bipartite networks. Methods Ecol. Evol. 5, 90–98 (2014).

    Article  Google Scholar 

  37. Bascompte, J., Jordano, P., Melián, C. J. & Olesen, J. M. The nested assembly of plant–animal mutualistic networks. Proc. Natl Acad. Sci. USA 100, 9383–9387 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Rahbek, C. et al. Humboldt’s enigma: what causes global patterns of mountain biodiversity? Science 365, 1108–1113 (2019).

    Article  CAS  PubMed  Google Scholar 

  39. Cracraft, J. Historical biogeography and patterns of differentiation within the South American avifauna: areas of endemism. Ornithol. Monogr. 36, 49–84 (1985).

    Article  Google Scholar 

  40. Hazzi, N. A., Moreno, J. S., Ortiz-Movliav, C. & Palacio, R. D. Biogeographic regions and events of isolation and diversification of the endemic biota of the tropical Andes. Proc. Natl Acad. Sci. USA 115, 7985–7990 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Jønsson, K. A. et al. Tracking animal dispersal: from individual movement to community assembly and global range dynamics. Trends Ecol. Evol. 31, 204–214 (2016).

    Article  PubMed  Google Scholar 

  42. McGuire, J. A. et al. Molecular phylogenetics and the diversification of hummingbirds. Curr. Biol. 24, 910–916 (2014).

    Article  CAS  PubMed  Google Scholar 

  43. Proctor, M., Yeo, P. & Lack, A. The Natural History of Pollination (HarperCollins, 1996).

  44. Simberloff, D. S. & Wilson, E. O. Experimental zoogeography of islands: the colonization of empty islands. Ecology 50, 278–296 (1969).

    Article  Google Scholar 

  45. Connor, E. F. & Simberloff, D. Species number and compositional similarity of the Galapagos flora and avifauna. Ecol. Monogr. 48, 219–248 (1978).

    Article  Google Scholar 

  46. Grant, P. R. & Abbott, I. Interspecific competition, island biogeography and null hypotheses. Evolution 34, 332–341 (1980).

    Article  CAS  PubMed  Google Scholar 

  47. Thomas, C. D. Climate, climate change and range boundaries. Divers. Distrib. 16, 488–495 (2010).

    Article  Google Scholar 

  48. Almeida-Neto, M., Guimarães, P., Guimarães, P. R. Jr, Loyola, R. D. & Ulrich, W. A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement. Oikos 117, 1227–1239 (2008).

    Article  Google Scholar 

  49. Simmons, B. I. et al. Moving from frugivory to seed dispersal: incorporating the functional outcomes of interactions in plant–frugivore networks. J. Anim. Ecol. 87, 995–1007 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Benadi, G., Blüthgen, N., Hovestadt, T. & Poethke, H.-J. Contrasting specialization–stability relationships in plant–animal mutualistic systems. Ecol. Model. 258, 65–73 (2013).

    Article  Google Scholar 

  51. Beckett, S. J. Improved community detection in weighted bipartite networks. R. Soc. Open Sci. 3, 140536 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Sonne, J. et al. Ecological mechanisms explaining interactions within plant–hummingbird networks: morphological matching increases towards lower latitudes. Proc. R. Soc. B 287, 20192873 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Patefield, W. Algorithm AS 159: an efficient method of generating random R × C tables with given row and column totals. J. R. Stat. Soc. C 30, 91–97 (1981).

    Google Scholar 

  54. Dalsgaard, B. et al. Opposed latitudinal patterns of network‐derived and dietary specialization in avian plant–frugivore interaction systems. Ecography 40, 1395–1401 (2017).

    Article  Google Scholar 

  55. Dormann, C. F., Gruber, B. & Fründ, J. Introducing the bipartite package: analysing ecological networks. R News 8, 8–11 (2008).

  56. Holt, B. G. et al. An update of Wallace’s zoogeographic regions of the world. Science 339, 74–78 (2013).

    Article  CAS  PubMed  Google Scholar 

  57. Two-Minute Gridded Global Relief Data (ETOPO2) v. 2 (NOAA National Geophysical Data Center, 2006); https://doi.org/10.7289/V5J1012Q

  58. Jetz, W. & Rahbek, C. Geographic range size and determinants of avian species richness. Science 297, 1548–1551 (2002).

    Article  CAS  PubMed  Google Scholar 

  59. Dobzhansky, T. Evolution in the tropics. Am. Sci. 38, 209–221 (1950).

    Google Scholar 

  60. Currie, D. J., Francis, A. P. & Kerr, J. T. Some general propositions about the study of spatial patterns of species richness. Écoscience 6, 392–399 (1999).

    Article  Google Scholar 

  61. Hurlbert et al. The effect of energy and seasonality on avian species richness and community composition. Am. Nat. 161, 83–97 (2003).

    Article  PubMed  Google Scholar 

  62. Karger, D. N. et al. Climatologies at high resolution for the Earth’s land surface areas. Sci. Data 4, 170122 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Mateo, R. G., Felicísimo, Á. M. & Muñoz, J. Effects of the number of presences on reliability and stability of MARS species distribution models: the importance of regional niche variation and ecological heterogeneity. J. Veg. Sci. 21, 908–922 (2010).

    Article  Google Scholar 

  64. Blonder, B. et al. Linking environmental filtering and disequilibrium to biogeography with a community climate framework. Ecology 96, 972–985 (2015).

    Article  PubMed  Google Scholar 

  65. Vizentin-Bugoni, J., Debastiani, V. J., Bastazini, V. A. G., Maruyama, P. K. & Sperry, J. H. Including rewiring in the estimation of the robustness of mutualistic networks. Methods Ecol. Evol. 11, 106–116 (2020).

    Article  Google Scholar 

  66. Rahbek, C., Borregaard, M. K., Hermansen, B., Nogues-Bravo, D. & Fjeldså, J. Definition and Description of the Montane Regions of the World (Center for Macroecology, Evolution and Climate, 2019); https://macroecology.ku.dk/resources/mountain_regions/definition-and-description-of-the-montane-regions-of-the-world_kopi/

  67. Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

J.S. and C.R acknowledge the support of the VILLUM FONDEN for the Center for Global Mountain Biodiversity (grant no. 25925). A.M.M.G., C.R. and B.D. thank the Danish National Research Foundation for its support of the Center for Macroecology, Evolution, and Climate (grant no. DNRF96). P.K.M. thanks the support from Fapesp—The São Paulo Research Foundation (grant no. 2015/21457-4) and Fapemig—Minas Gerais Research Foundation (grant no. RED-00253-16). A.M.M.G. was supported through a Marie Skłodowska-Curie Individual Fellowship (H2020-MSCA-IF-2015-704409). J.B.’s work is supported by the Swiss National Science Foundation (grant no. 310030_197201).

Author information

Authors and Affiliations

  1. Center for Macroecology, Evolution and Climate, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark

    Jesper Sonne, Carsten Rahbek & Bo Dalsgaard

  2. Center for Global Mountain Biodiversity, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark

    Jesper Sonne & Carsten Rahbek

  3. Centre for Ecological Synthesis and Conservation, Department of Genetics, Ecology and Evolution—ICB, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil

    Pietro K. Maruyama

  4. Pacific Ecoinformatics and Computational Ecology Lab, Berkeley, CA, USA

    Ana M. Martín González

  5. Institute of Ecology, Peking University, Beijing, China

    Carsten Rahbek

  6. Danish Institute for Advanced Study, University of Southern Denmark, Odense, Denmark

    Carsten Rahbek

  7. Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland

    Jordi Bascompte

Authors
  1. Jesper Sonne
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pietro K. Maruyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ana M. Martín González
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carsten Rahbek
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jordi Bascompte
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bo Dalsgaard
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.S., C.R., J.B. and B.D. conceived the study. J.S. (with help from C.R. and J.B.) simulated and analysed the data. J.S. drafted the manuscript with input from P.K.M, A.M.M.G., C.R., J.B. and B.D. All authors contributed to the manuscript and gave final approval for its publication.

Corresponding author

Correspondence to Jesper Sonne.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

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

Additional information

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

Extended data

Extended Data Fig. 1 Regional variability in climate-driven extinction (a) and coextinction (b) under the RCP 8.5 ‘worst case’ scenario.

Both extinction variables were scaled on logarithmic axes. We applied one-way ANOVA to test for regional differences in climate-driven extinctions and coextinctions (Climate-driven extinction: F = 11.39, p < 0.001, n = 84; Coextinction: F = 10.63, p < 0.001, n = 84). Lower case letters represent the statistical difference according to Tukey multiple comparisons with Bonferroni adjusted p values (p < 0.05). The boxes’ border marks the interquartile range (IQR; quartile 1 to 3); horizontal lines inside boxes mark the medians; vertical lines mark ±1.5 × (IQR); the circles mark data outliers. The results depicted here derive from the RCP 8.5 “worst case” scenario for the year 2070.

Extended Data Fig. 2 Biogeographical variability in colonization rates under the RCP 8.5 ‘worst case’ scenario.

Colonization rates are measured as the average number of colonizing from a radius of 10 km (F = 64.31, p < 0.001, n = 84) and 100 km (F = 75.33, p < 0.001, n = 84) surrounding each network (a). Panel b depicts the number of colonists relative to the richness of hummingbirds within the source pool radius (10 km radius: F = 9.01, p < 0.001, n = 84; 10 km radius: F = 2.15, p = 0.120, n = 84). We applied one-way ANOVA to test for differences in colonization rate between biogeographical regions. The boxes’ border marks the interquartile range (IQR; quartile 1 to 3); horizontal lines inside boxes mark the medians; vertical lines mark ±1.5 × (IQR); the circles mark data outliers. Lower-case letters represent statistical difference according to Tukey multiple comparisons with Bonferroni-adjusted p values (p < 0.05). The results depicted derive from the RCP 8.5 ‘worst case’ scenario for the year 2070.

Extended Data Fig. 3 Biogeographical variability in the communities’ vulnerability to coextinctions after accounting for climate-driven extinctions under the RCP 8.5 ‘worst case’ scenario.

Coextinctions spread slower in North America compared to other regions (a, F = 5.17, p < 0.001, n = 83), which coincide with a regional bias in climate-driven extinctions against species with generalized network roles (b, F = 12.01, p < 0.001, n = 83). The species-level generalism was described by the effective number of partners. The F-test in each panel compares two linear regression models, of which one contains the biogeographical region as a character state predictor variable. The solid lines represent the linear relationships within each region (with shaded 95% confidence intervals). The y axes measure the cumulative lost partner number averaged across the simulations. The results depicted here derive from the RCP 8.5 ‘worst case’ scenario for the year 2070.

Extended Data Fig. 4 The influence of three network structures on the logarithmic association between climate-driven extinctions and coextinctions (n = 83) under the RCP 8.5 ‘worst case’ scenario.

The three network structures are Complementary specialization (H2‘), Modularity (ΔQ) and nestedness (ΔWNODF). Trend lines and standardized coefficients derive from weighted multiple linear regressions. In each regression model, we added an interaction term between the proportion of climate-driven extinctions and the network metric. The weights were given by the number of hummingbird species sampled in each network. The results derive from the RCP 8.5 ‘worst case’ scenario for the year 2070.

Extended Data Fig. 5 Geographical variation in coextinctions, measured as the average proportion of hummingbirds in our simulations that disappeared from the networks while accounting for interaction rewiring.

In this analysis, we allowed species to relocate 50 % of their lost interactions with remaining partners in the network (that is constrained rewiring). The F-statistics derive from one-way ANOVA testing for regional differences in coextinctions (RCP 4.5: F = 10.31, p < 0.001, n = 84; RCP 8.5: F = 11.36, p < 0.001, n = 84; note the logarithmic axes). Lower-case letters represent the statistical difference according to Tukey multiple comparisons with Bonferroni-adjusted p values s (p < 0.05). The boxes’ border marks the interquartile range (IQR; quartile 1 to 3); horizontal lines inside boxes mark the medians; vertical lines mark ±1.5 × (IQR); the circles mark data outliers. The results are replicated for the RCP 4.5 ‘mid-range’ scenario (a) and the RCP 8.5 ‘worst case” scenario for the year 2070 (b).

Extended Data Fig. 6 Biogeographical variability in the communities’ vulnerability to coextinctions after accounting for climate-driven extinctions and interaction rewiring.

The analyses are similar to those depicted in Fig. 4a, although, here, we allowed species to relocate 50 % of their lost interactions with remaining partners in the network (that is constrained rewiring). The F-test in each panel compares two linear regression models, of which one contains the biogeographical region as a character state predictor variable. The solid lines represent the linear relationships within each region (with shaded 95% confidence intervals). The results are replicated for the RCP 4.5 ‘mid-range’ scenario (a) and the RCP 8.5 ‘worst case’ scenario for the year 2070 (b).

Extended Data Fig. 7 The influence of three network structures on the logarithmic association between climate-driven extinctions and coextinctions (n = 83) while accounting for interaction rewiring.

Here, we allowed species to relocate 50% of their lost interactions with remaining partners in the network (i.e. constrained rewiring). The three network structures are Complementary specialization (H2´), Modularity (ΔQ) and nestedness (ΔWNODF). Trend lines and standardized coefficients derive from weighted multiple linear regressions. In each regression model, we added an interaction term between the proportion of climate-driven extinctions and the network metric. The weights were given by the number of hummingbird species sampled in each network. The results are replicated for the RCP 4.5 ‘mid-range’ scenario (a-c) and the RCP 8.5 ‘worst case’ scenario for the year 2070 (d-f).

Extended Data Fig. 8 Boxplots showing the regional variability in three network structures.

Complementary specialization (a, F = 0.96, p = 0.387, n = 84), modularity (b, F = 1.67, p = 0.196, n = 84), and nestedness (c, F = 7.31, p = 0.001, n = 84). Δ signs indicate corrections by Patefield’s null model 4. To test for variability in each network structure between the three biogeographical regions, we applied one-way ANOVA. The boxes’ border marks the interquartile range (IQR; quartile 1 to 3); horizontal lines inside boxes mark the medians; vertical lines mark ±1.5 × (IQR); the circles mark data outliers. Lower case letters represent the statistical difference according to Tukey multiple comparisons with Bonferroni adjusted p values s (p < 0.05). Before calculations, we removed boreal hummingbird migrants from the networks to match our extinction simulations.

Supplementary information

Supplementary Information

Supplementary sources, Tables 1–3 and Figs. 1–6.

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sonne, J., Maruyama, P.K., Martín González, A.M. et al. Extinction, coextinction and colonization dynamics in plant–hummingbird networks under climate change. Nat Ecol Evol 6, 720–729 (2022). https://doi.org/10.1038/s41559-022-01693-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-022-01693-3

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