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Evolutionary causes and consequences of ungulate migration

Abstract

Ungulate migrations are crucial for maintaining abundant populations and functional ecosystems. However, little is known about how or why migratory behaviour evolved in ungulates. To investigate the evolutionary origins of ungulate migration, we employed phylogenetic path analysis using a comprehensive species-level phylogeny of mammals. We found that 95 of 207 extant ungulate species are at least partially migratory, with migratory behaviour originating independently in 17 lineages. The evolution of migratory behaviour is associated with reliance on grass forage and living at higher latitudes wherein seasonal resource waves are most prevalent. Indeed, originations coincide with mid-Miocene cooling and the subsequent rise of C4 grasslands. Also, evolving migratory behaviour supported the evolution of larger bodies, allowing ungulates to exploit new ecological space. Reconstructions of migratory behaviour further revealed that seven of ten recently extinct species were probably migratory, suggesting that contemporary migrations are important models for understanding the ecology of the past.

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Fig. 1: Hypothetical evolutionary models of migration.
Fig. 2: Evolutionary correlations between ungulate characteristics.
Fig. 3: Ungulate character evolution.
Fig. 4: The causes and consequences of evolving migratory behaviour in ungulates.
Fig. 5: The Earth system context for the evolution of migratory behaviour.
Fig. 6: Reconstructed migratory behaviour in extinct ungulates.

Data availability

All data generated and analysed during this study are included in Supplementary Dataset 1 and are also available in tabular form from the Dryad Data Repository (https://datadryad.org/stash/dataset/doi:10.5061/dryad.g79cnp5rj).

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Acknowledgements

We thank A. C. Staver, E. J. Sargis, J. T. Faith and G. P. Hempson for the many thought-provoking discussions regarding ungulate migration and mammal evolution that inspired this project. We also thank the Edwards and Dunn laboratories at Yale University and Pringle laboratory at Princeton University for providing helpful feedback on this work. Finally, we thank J. R. Goheen for valuable feedback on the manuscript. J.O.A. was supported by the United States National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP 2019256075) and N.S.U. was supported by the NSF VertLife Terrestrial grant (DEB 1441737) and Arizona State University President’s Special Initiative Fund.

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Contributions

J.O.A. conceived the study. J.O.A. compiled the underlying ungulate trait data from the literature and B.R.J. calculated the green wave metrics for all species. J.O.A. and A.D.-S. designed the analyses, with significant contribution from N.S.U. J.O.A. and B.R.J. wrote the initial manuscript drafts with significant input from N.S.U. and A.D.-S. All authors discussed and provided feedback on subsequent manuscript drafts.

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Correspondence to Joel O. Abraham.

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Nature Ecology & Evolution thanks Nic Bone and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 The role of green wave tracking in the evolution of migration.

Relationships between (a) green wave sill, (b) green wave range, and (c) green wave seasonality and migration are depicted, as well as between (e) green wave seasonality and latitude and (f) green wave seasonality and grass dependence. Of the green wave metrics we calculated, only green wave seasonality significantly predicts migration (two-sided PGLM; n = 189 species), with migratory behavior more prevalent amongst taxa exposed to more seasonal green waves. Green wave seasonality is likewise positively correlated with latitude and dietary grass fraction (two-sided PLMs; n = 189 species). The asterisks (*) in (c) and solid regression lines in (e, d) denote a significant relationship (P < 0.05), whereas the ‘N.S’ in (a,b) denotes the lack of a clear relationship (P ≥ 0.05), corrected for multiple comparisons. White bands in (a-c) represent median values, the colored bars represent the interquartile range (IQR), and white whiskers extend to ±1.5 × IQR. Grey shaded regions in (d,e) represent 95% confidence intervals on the regression. Full model details are available in Supplementary table 1.

Extended Data Fig. 2 Measuring landscape suitability for migration.

A simulated (a) perfect resource wave, (b) heterogeneous landscape with no resource wave, and (c) landscape intermediate to (a) and (b). Brown pixels represent areas where the date of peak NDVI occurred early, whereas green pixels represent relatively late peaks NDVI. (a-c) The x-axis represents the distance travelled by resource waves (distance lag in km) and y-axis represents magnitude of the green wave (semivariance). Dashed lines illustrate maximum semivariance (horizontal) and maximum distance lag (vertical). (d) Empirical variograms for mule deer (Odocoileus hemionus) and white-tailed deer (Odocoileus virginianus), depicted in purple and black respectively. Vertical and horizontal dashed lines represent maximum semivariance (horizontal) and maximum distance lag (vertical) just as in panels (a-c). (e) Illustration of how seasonality in resource waves varied among the geographical ranges of mule deer (O. hemionus) and white-tailed deer (O. virginianus). Horizontal dashed lines depict the minimum and maximum magnitude of resource waves throughout the annual cycle. Note that the distance between purple dashed lines for mule deer (O. hemionus) is much larger than the distance between black dashed lines for white-tailed deer (O. virginianus), indicating greater seasonality in resource waves across the geographic range of mule deer (O. hemionus).

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Supplementary Information

Supplementary notes, materials and methods, Supplementary Tables 1–7, Figs. 1–7 and supplementary references.

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Supplementary Data 1

Complete list of ungulates included in the analyses, along with all data used and sources consulted.

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Abraham, J.O., Upham, N.S., Damian-Serrano, A. et al. Evolutionary causes and consequences of ungulate migration. Nat Ecol Evol 6, 998–1006 (2022). https://doi.org/10.1038/s41559-022-01749-4

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