Article | Published:

Shifting avian spatial regimes in a changing climate

Abstract

In the present era of rapid global change, development of early warnings of ecological regime shifts is a major focus in ecology. Identifying and tracking shifts in spatial regimes is a new approach with potential to enhance understanding of ecological responses to global change. Here, we show strong directional non-stationarity of spatial regimes identified by avian community body mass data. We do this by tracking 46 years of avian spatial regime movement in the North American Great Plains. The northernmost spatial regime boundary moved >590 km northward, and the southernmost boundary moved >260 km northward. Tracking spatial regimes affords decadal planning horizons and moves beyond the predominately temporal early warnings of the past by providing spatiotemporally explicit detection of regime shifts in systems without fixed boundaries.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Data availability

All data are available in the Supplementary Data.

Code availability

R code and instructions for repeating analyses are available in the Supplementary Data.

References

  1. 1.

    Allen, C. R., Angeler, D. G., Garmestani, A. S., Gunderson, L. H. & Holling, C. S. Panarchy: theory and application. Ecosystems 17, 578–589 (2014).

  2. 2.

    Dakos, V., Carpenter, S. R., Nes, E. Hvan & Scheffer, M. Resilience indicators: prospects and limitations for early warnings of regime shifts. Philos. Trans. R. Soc. Lond. B 370, 20130263 (2015).

  3. 3.

    Burthe, S. J. et al. Do early warning indicators consistently predict nonlinear change in long-term ecological data? J. Appl. Ecol. 53, 666–676 (2016).

  4. 4.

    Dakos, V. et al. Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PloS ONE 7, e41010 (2012).

  5. 5.

    Carpenter, S. R. et al. Early warnings of regime shifts: A whole-ecosystem experiment. Science 332, 1079–1082 (2011).

  6. 6.

    Kefi, S. et al. Early warning signals of ecological transitions: Methods for spatial patterns. PloS ONE 9, e92097 (2014).

  7. 7.

    Cline, T. J. et al. Early warnings of regime shifts: Evaluation of spatial indicators from a whole-ecosystem experiment. Ecosphere 5, 1–13 (2014).

  8. 8.

    Butitta, V. L., Carpenter, S. R., Loken, L. C., Pace, M. L. & Stanley, E. H. Spatial early warning signals in a lake manipulation. Ecosphere 8, e01941 (2017).

  9. 9.

    Clements, C. F. & Ozgul, A. Indicators of transitions in biological systems. Ecol. Lett. 21, 905–919 (2018).

  10. 10.

    Sundstrom, S. M. et al. Detecting spatial regimes in ecosystems. Ecol. Lett. 20, 19–32 (2017).

  11. 11.

    Roberts, C. P. et al. Early warnings for state transitions. Rangeland Ecol. Manag. 71, 659–670 (2018).

  12. 12.

    Allen, C. R. et al. Quantifying spatial resilience. J. Appl. Ecol. 53, 625–635 (2016).

  13. 13.

    Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591 (2001).

  14. 14.

    Strayer, D. L., Power, M. E., Fagan, W. F., Pickett, S. T. & Belnap, J. A classification of ecological boundaries. BioScience 53, 723–729 (2003).

  15. 15.

    Angeler, D. G. et al. Management applications of discontinuity theory. J. Appl. Ecol. 53, 688–698 (2016).

  16. 16.

    Holling, C. S. Cross-scale morphology, geometry, and dynamics of ecosystems. Ecol. Monogr. 62, 447–502 (1992).

  17. 17.

    Spanbauer, T. L. et al. Body size distributions signal a regime shift in a lake ecosystem. Proc. R. Soc. Lond. B 283, 20160249 (2016).

  18. 18.

    Drake, J. M. & Griffen, B. D. Early warning signals of extinction in deteriorating environments. Nature 467, 456 (2010).

  19. 19.

    Doncaster, C. P. et al. Early warning of critical transitions in biodiversity from compositional disorder. Ecology 97, 3079–3090 (2016).

  20. 20.

    Cohen, J. et al. Recent Arctic amplification and extreme mid-latitude weather. Nat. Geosci. 7, 627 (2014).

  21. 21.

    La Sorte, F. A., Hochachka, W. M., Farnsworth, A., Dhondt, A. A. & Sheldon, D. The implications of mid-latitude climate extremes for North American migratory bird populations. Ecosphere 7, e01261 (2016).

  22. 22.

    Brown, D. G., Johnson, K. M., Loveland, T. R. & Theobald, D. M. Rural land-use trends in the conterminous United States, 1950–2000. Ecol. Appl. 15, 1851–1863 (2005).

  23. 23.

    Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).

  24. 24.

    Johnston, C. A. Agricultural expansion: land use shell game in the US Northern Plains. Landsc. Ecol. 29, 81–95 (2014).

  25. 25.

    Allred, B. W. et al. Ecosystem services lost to oil and gas in North America. Science 348, 401–402 (2015).

  26. 26.

    Donovan, V. M., Wonkka, C. L. & Twidwell, D. Surging wildfire activity in a grassland biome. Geophys. Res. Lett. 44, 5986–5993 (2017).

  27. 27.

    Engle, D. M., Coppedge, B. R. & Fuhlendorf, S. D. in Western North American Juniperus Communities (ed. Van Auken, O. W.) 253–271 (Springer, 2008).

  28. 28.

    Boettiger, C., Ross, N. & Hastings, A. Early warning signals: the charted and uncharted territories. Theor. Ecol. 6, 255–264 (2013).

  29. 29.

    Hastings, A. & Wysham, D. B. Regime shifts in ecological systems can occur with no warning. Ecol. Lett. 13, 464–472 (2010).

  30. 30.

    Clements, C. F., Drake, J. M., Griffiths, J. I. & Ozgul, A. Factors influencing the detectability of early warning signals of population collapse. Am. Nat. 186, 50–58 (2015).

  31. 31.

    Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53 (2009).

  32. 32.

    Biggs, R., Carpenter, S. R. & Brock, W. A. Turning back from the brink: detecting an impending regime shift in time to avert it. Proc. Natl Acad. Sci. USA 106, 826–831 (2009).

  33. 33.

    Craig, R. K. Stationarity is dead-long live transformation: five principles for climate change adaptation law. HELR Harvard Environ. Law Rev. 34, 9 (2010).

  34. 34.

    Twidwell, D., Allred, B. W. & Fuhlendorf, S. D. National-scale assessment of ecological content in the world’s largest land management framework. Ecosphere 4, 1–27 (2013).

  35. 35.

    Baho, D. L., Drakare, S., Johnson, R. K., Allen, C. R. & Angeler, D. G. Similar resilience attributes in lakes with different management practices. PLoS ONE 9, e91881 (2014).

  36. 36.

    Ficetola, G. F., Mazel, F. & Thuiller, W. Global determinants of zoogeographical boundaries. Nat. Ecol. Evol. 1, 0089 (2017).

  37. 37.

    Glor, R. E. & Warren, D. Testing ecological explanations for biogeographic boundaries. Evolution 65, 673–683 (2011).

  38. 38.

    Briske, D., Bestelmeyer, B., Stringham, T. & Shaver, P. Recommendations for development of resilience-based state-and-transition models. Rangeland Ecol. Manag. 61, 359–367 (2008).

  39. 39.

    Jantz, S. M. et al. Future habitat loss and extinctions driven by land-use change in biodiversity hotspots under four scenarios of climate-change mitigation. Conserv. Biol. 29, 1122–1131 (2015).

  40. 40.

    Birgé, H. E., Allen, C. R., Craig, R. K. & Twidwell, D. in Practical Panarchy for Adaptive Water Governance (eds Consens, B. & Gunderson, L.) 115–130 (Springer, 2018).

  41. 41.

    Sauer, J. R. et al. The North American Breeding Bird Survey, Results and Analysis 1966–2015 (USGS Patuxent Wildlife Research Center, 2017).

  42. 42.

    Richardson, A. D. et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. For. Meteorol. 169, 156–173 (2013).

  43. 43.

    Hovick, T. J. et al. Informing conservation by identifying range shift patterns across breeding habitats and migration strategies. Biodivers. Conserv. 25, 345–356 (2016).

  44. 44.

    Drummond, M. A. et al. Land change variability and human–environment dynamics in the United States Great Plains. Land Use Policy 29, 710–723 (2012).

  45. 45.

    Dunning Jr, J. B. CRC Handbook of Avian Body Masses (CRC, 2007)

  46. 46.

    Barichievy, C. et al. A method to detect discontinuities in census data. Ecol. Evol. 8, 9614–9623 (2018).

  47. 47.

    Stow, C., Allen, C. R. & Garmestani, A. S. Evaluating discontinuities in complex systems: toward quantitative measures of resilience. Ecol. Soc. 12, (2007).

  48. 48.

    Nash, K. L. et al. Discontinuities, cross-scale patterns, and the organization of ecosystems. Ecology 95, 654–667 (2014).

  49. 49.

    Sundstrom, S. M. & Allen, C. R. Complexity versus certainty in understanding species’ declines. Divers. Distrib. 20, 344–355 (2014).

  50. 50.

    Lipsey, M. W. Design Sensitivity: Statistical Power for Experimental Research (Sage, 1990).

  51. 51.

    Allen, C. R., Forys, E. A. & Holling, C. Body mass patterns predict invasions and extinctions in transforming landscapes. Ecosystems 2, 114–121 (1999).

  52. 52.

    Wickham, H. Reshaping data with the reshape package. J. Stat. Softw. 21, 1–20 (2007).

  53. 53.

    Dowle, M. et al. data.table: Extension ofdata.frame’ v1.12.2 (CRAN, 2018); https://rdrr.io/cran/data.table/.

  54. 54.

    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2008).

  55. 55.

    Galzin, R. & Legendre, P. The fish communities of a coral reef transect. Pac. Sci. 41, 1–4 (1987).

  56. 56.

    Vermaire, J. C., Greffard, M.-H., Saulnier-Talbot, É. & Gregory-Eaves, I. Changes in submerged macrophyte abundance altered diatom and chironomid assemblages in a shallow lake. J. Paleolimnol. 50, 447–456 (2013).

  57. 57.

    Leys, B., Finsinger, W. & Carcaillet, C. Historical range of fire frequency is not the Achilles’ heel of the Corsican black pine ecosystem. J. Ecol. 102, 381–395 (2014).

  58. 58.

    Vormisto, J., Phillips, O., Ruokolainen, K., Tuomisto, H. & Vásquez, R. A comparison of fine-scale distribution patterns of four plant groups in an Amazonian rainforest. Ecography 23, 349–359 (2000).

  59. 59.

    Juggins, S. rioja: Analysis of Quaternary Science Data v0.9-21 (CRAN, 2017); https://rdrr.io/cran/rioja/man/PTF.html

  60. 60.

    Bennett, K. D. Determination of the number of zones in a biostratigraphical sequence. New Phytol. 132, 155–170 (1996).

  61. 61.

    Wood, S. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Lond. B. 73, 3–36 (2011).

Download references

Acknowledgements

We thank the Complexity Working Group for conceptual development, J. L. Burnett for help in database creation, and D. Ebbeka and C. Bielski for help with data visualization. This work was supported by Department of Defense Strategic Environmental Research Development Program W912HQ-15-C-0018, Nebraska Game & Parks Commission W-125-R-1 and the Institute of Agriculture and Natural Resources at the University of Nebraska, Lincoln. The Nebraska Cooperative Fish and Wildlife Research Unit is jointly supported by a cooperative agreement between the US Geological Survey, the Nebraska Game and Parks Commission, the University of Nebraska, the US Fish and Wildlife Service and the Wildlife Management Institute. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the US Government.

Author information

C.P.R. contributed to conceptualization, programming, validation, formal analysis, data curation, all writing aspects, visualization and project administration. C.R.A., D.G.A. and D.T. contributed to funding acquisition, conceptualization, all writing aspects and visualization.

Correspondence to Caleb P. Roberts.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information: Nature Climate Change thanks Eldar Rakhimberdiev and 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.

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2.

Reporting Summary

Supplementary Data 1

Zipped file of data, R code and instructions for repeating analyses.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark
Fig. 1: Shifts in spatial regime boundaries demonstrated by breeding bird body mass discontinuities from 1970 to 2015 in the North American Great Plains.
Fig. 2: Visualization and tracking of predicted decadal spatial regimes and their boundaries in the North American Great Plains.
Fig. 3: Global changes influencing ecological regimes in central North America.
Fig. 4: Spatial regime boundary movement between 37 and 42° latitude across a network of protected areas in central North America.