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

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.

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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).

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

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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).

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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.

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Correspondence to Jesper Sonne.

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

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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.

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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

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