Cited by 152Rangeland and Wildlife ManagementWildlife Ecology and ConservationAnimal Ecology and Behavior Studies

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Migrating bison engineer the green wave

Open access

Abstract

Newly emerging plants provide the best forage for herbivores. To exploit this fleeting resource, migrating herbivores align their movements to surf the wave of spring green-up. With new technology to track migrating animals, the Green Wave Hypothesis has steadily gained empirical support across a diversity of migratory taxa. This hypothesis assumes the green wave is controlled by variation in climate, weather, and topography, and its progression dictates the timing, pace, and extent of migrations. However, aggregate grazers that are also capable of engineering grassland ecosystems make some of the world’s most impressive migrations, and it is unclear how the green wave determines their movements. Here we show that Yellowstone’s bison ( Bison bison ) do not choreograph their migratory movements to the wave of spring green-up. Instead, bison modify the green wave as they migrate and graze. While most bison surfed during early spring, they eventually slowed and let the green wave pass them by. However, small-scale experiments indicated that feedback from grazing sustained forage quality. Most importantly, a 6-fold decadal shift in bison density revealed that intense grazing caused grasslands to green up faster, more intensely, and for a longer duration. Our finding broadens our understanding of the ways in which animal movements underpin the foraging benefit of migration. The widely accepted Green Wave Hypothesis needs to be revised to include large aggregate grazers that not only move to find forage, but also engineer plant phenology through grazing, thereby shaping their own migratory movements.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1073/pnas.1913783116

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Bison has been called an ecosystem engineer and a keystone species because of its role in shaping plant communities and interactions with other animal species.

    “The American bison (Bison bison), or American buffalo, has been called an ecosystem engineer and a keystone species because of the role of bison in shaping plant communities, and their interactions with other animal species (Boyce et al., 2022; Geremia et al., 2019; Gerlanc & Kaufman, 2003; Joern, 2005; Knapp et al., 1999; Krueger, 1986).”

    From Bison alter the northern Yellowstone ecosystem by breaking aspen saplings (2023) · cites this paper as Geremia et al., 2019

    The Abstract calls bison ecosystem engineers, and the Results show that their grazing changes vegetation growth and forage quality. The References list a work on bison’s keystone role, but this paper does not itself characterize bison as a keystone species or describe their interactions with other animal species. No page numbers are supplied in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 38

  1. J. M. Fryxell, A. R. Sinclair, Causes and consequences of migration by large herbivores. Trends Ecol. Evol. 3, 237–241 (1988). · DOI
  2. S. Albon, R. Langvatn, Plant phenology and the benefits of migration in a temperate ungulate. Oikos 65, 502–513 (1992). · DOI · Paper page
  3. J. M. Fryxell, J. F. Wilmshurst, A. R. Sinclair, Predictive models of movement by Serengeti grazers. Ecology 85, 2429–2435 (2004). · DOI
  4. M. Hebblewhite, E. Merrill, G. McDermid, A multi-scale test of the forage maturation hypothesis in a partially migratory ungulate population. Ecol. Monogr. 78, 141–166 (2008). · DOI
  5. R. M. Holdo, R. D. Holt, J. M. Fryxell, Opposing rainfall and plant nutritional gradients best explain the wildebeest migration in the Serengeti. Am. Nat. 173, 431–445 (2009). · DOI
  6. T. Mueller ., In search of forage: Predicting dynamic habitats of Mongolian gazelles using satellite-based estimates of vegetation productivity. J. Appl. Ecol. 45, 649–658 (2008). · DOI
  7. J. B. Armstrong, G. Takimoto, D. E. Schindler, M. M. Hayes, M. J. Kauffman, Resource waves: Phenological diversity enhances foraging opportunities for mobile consumers. Ecology 97, 1099–1112 (2016). · DOI
  8. A. Van der Graaf, J. Stahl, A. Klimkowska, J. P. Bakker, R. H. Drent, Surfing on a green wave—how plant growth drives spring migration in the Barnacle Goose Branta leucopsis. Ardea 94, 567–577 (2006).
  9. R. Bischof ., A migratory northern ungulate in the pursuit of spring: Jumping or surfing the green wave? Am. Nat. 180, 407–424 (2012). · DOI
  10. J. A. Merkle ., Large herbivores surf waves of green-up during spring. Proc. Biol. Sci. 283, 20160456 (2016).
  11. M. Shariatinajafabadi ., Migratory herbivorous waterfowl track satellite-derived green wave index. PLoS One 9, e108331 (2014). · DOI
  12. B. Abrahms ., Memory and resource tracking drive blue whale migrations. Proc. Natl. Acad. Sci. U.S.A. 116, 5582–5587 (2019). · DOI
  13. P. Beck, T. Wang, A. Skidmore, X. Liu, Displaying remotely sensed vegetation dynamics along natural gradients for ecological studies. Int. J. Remote Sens. 29, 4277–4283 (2008). · DOI
  14. G. Bohrer, P. S. Beck, S. M. Ngene, A. K. Skidmore, I. Douglas-Hamilton, Elephant movement closely tracks precipitation-driven vegetation dynamics in a Kenyan forest-savanna landscape. Mov. Ecol. 2, 2 (2014). · DOI
  15. Y. Si ., Do Arctic breeding geese track or overtake a green wave during spring migration? Sci. Rep. 5, 8749 (2015). · DOI
  16. I. M. Rivrud, M. Heurich, P. Krupczynski, J. Müller, A. Mysterud, Green wave tracking by large herbivores: An experimental approach. Ecology 97, 3547–3553 (2016). · DOI
  17. E. O. Aikens ., The greenscape shapes surfing of resource waves in a large migratory herbivore. Ecol. Lett. 20, 741–750 (2017). · DOI
  18. K. Thorup ., Resource tracking within and across continents in long-distance bird migrants. Sci. Adv. 3, e1601360 (2017). · DOI
  19. B. R. Jesmer ., Is ungulate migration culturally transmitted? Evidence of social learning from translocated animals. Science 361, 1023–1025 (2018). · DOI
  20. X. Wang ., Stochastic simulations reveal few green wave surfing populations among spring migrating herbivorous waterfowl. Nat. Commun. 10, 2187 (2019). · DOI
  21. H. L. Bartlam‐Brooks, P. S. Beck, G. Bohrer, S. Harris, In search of greener pastures: Using satellite images to predict the effects of environmental change on zebra migration. J. Geophys. Res. Biogeosci. 118, 1427–1437 (2013). · DOI
  22. D. A. Frank, S. J. McNaughton, B. F. Tracy, The ecology of the Earth’s grazing ecosystems. Bioscience 48, 513–521 (1998). · DOI
  23. A. K. Knapp ., The keystone role of bison in North American tallgrass prairie: Bison increase habitat heterogeneity and alter a broad array of plant, community, and ecosystem processes. Bioscience 49, 39–50 (1999). · DOI · Paper page
  24. D. A. Frank, R. L. Wallen, P. J. White, Ungulate control of grassland production: Grazing intensity and ungulate species composition in Yellowstone Park. Ecosphere 7, e01603 (2016). · DOI
  25. A. C. Isenberg, The Destruction of the Bison: An Environmental History, 1750-1920 (Cambridge University Press, 2001).
  26. C. Geremia ., Integrating population- and individual-level information in a movement model of Yellowstone bison. Ecol. Appl. 24, 346–362 (2014). · DOI
  27. E. J. Raynor, A. Joern, J. B. Nippert, J. M. Briggs, Foraging decisions underlying restricted space use: Effects of fire and forage maturation on large herbivore nutrient uptake. Ecol. Evol. 6, 5843–5853 (2016). · DOI
  28. D. A. Frank, R. L. Wallen, E. W. Hamilton, P. J. White, J. D. Fridley, Manipulating the system: How large herbivores control bottom‐up regulation of grasslands. J. Ecol. 106, 434–443 (2018). · DOI
  29. M. A. Hurley ., Functional analysis of normalized difference vegetation index curves reveals overwinter mule deer survival is driven by both spring and autumn phenology. Philos. Trans. R. Soc. Lond. B Biol. Sci. 369, 20130196 (2014). · DOI
  30. E. L. Garroutte, A. J. Hansen, R. L. Lawrence, Using NDVI and EVI to map spatiotemporal variation in the biomass and quality of forage for migratory elk in the Greater Yellowstone Ecosystem. Remote Sens. 8, 404 (2016). · DOI
  31. D. S. Wilcove, M. Wikelski, Going, going, gone: Is animal migration disappearing. PLoS Biol. 6, e188 (2008). · DOI
  32. C. C. Gates, C. H. Freese, P. J. Gogan, M. Kotzman, American Bison: Status Survey and Conservation Guidelines 2010 (IUCN, 2010).
  33. R. K. Lyons, J. W. Stuth, Fecal NIRS equations for predicting diet quality of free-ranging cattle. J. Range Manage. 45, 238–244 (1992). · DOI
  34. J. Stuth, A. Jama, D. Tolleson, Direct and indirect means of predicting forage quality through near infrared reflectance spectroscopy. Field Crops Res. 84, 45–56 (2003). · DOI
  35. S. J. McNaughton, Grazing lawns: Animals in herds, plant form, and coevolution. Am. Nat. 124, 863–886 (1984). · DOI · Paper page
  36. D. A. Frank, S. J. McNaughton, The ecology of plants, large mammalian herbivores, and drought in Yellowstone National Park. Ecology 73, 2043–2058 (1992). · DOI
  37. D. A. Frank, S. J. McNaughton, Aboveground biomass estimation with the canopy intercept method: A plant growth form caveat. Oikos 57, 57–60 (1990). · DOI
  38. J. Ramsay, G. Hooker, S. Graves, Functional Data Analysis with R and MATLAB (Springer Science & Business Media, 2009). · DOI

Cited by

Sorted by citations

Metadata

Authors
Geremia, C., Merkle, J. A., Eacker, D. R., Wallen, R. L., White, P. J., Hebblewhite, M., & Kauffman, M. J.
Affiliations
  • Yellowstone National Park (US)
  • University of Wyoming (US)
  • University of Montana (US)
Corresponding author
Geremia, Chris
Venue
Proceedings of the National Academy of Sciences
Published
2019-11-21
Volume
116
Issue
51
Pages
25707-25713
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2989618704
Cited by
152
References
2
Citations by year
2026: 20 · 2025: 16 · 2024: 21 · 2023: 26 · 2022: 29 · 2021: 23 · 2020: 17
FWCI
10.96
Type
journal article
Language
en
Open access
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
  • DOI | National Park Service (NA)
  • National Science Foundation (1556248)
Topics
  • Physical Sciences › Environmental Science › Ecology › Rangeland and Wildlife Management
  • Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
  • Physical Sciences › Environmental Science › Ecology › Animal Ecology and Behavior Studies
Keywords
plant phenology, spring green-up, forage quality, Yellowstone National Park
MeSH terms
Animals, Climate, Feeding Behavior, Models, Biological, Montana, Plants, Seasons, Wyoming, Bison, Animal Migration, Geographic Information Systems, Herbivory

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review