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Greater vulnerability to warming of marine versus terrestrial ectotherms

Nature volume 569, pages 108–111 (2019)Cite this article

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Abstract

Understanding which species and ecosystems will be most severely affected by warming as climate change advances is important for guiding conservation and management. Both marine and terrestrial fauna have been affected by warming1,2 but an explicit comparison of physiological sensitivity between the marine and terrestrial realms has been lacking. Assessing how close populations live to their upper thermal limits has been challenging, in part because extreme temperatures frequently drive demographic responses3,4 and yet fauna can use local thermal refugia to avoid extremes5,6,7. Here we show that marine ectotherms experience hourly body temperatures that are closer to their upper thermal limits than do terrestrial ectotherms across all latitudes—but that this is the case only if terrestrial species can access thermal refugia. Although not a direct prediction of population decline, this thermal safety margin provides an index of the physiological stress caused by warming. On land, the smallest thermal safety margins were found for species at mid-latitudes where the hottest hourly body temperatures occurred; by contrast, the marine species with the smallest thermal safety margins were found near the equator. We also found that local extirpations related to warming have been twice as common in the ocean as on land, which is consistent with the smaller thermal safety margins at sea. Our results suggest that different processes will exacerbate thermal vulnerability across these two realms. Higher sensitivities to warming and faster rates of colonization in the marine realm suggest that extirpations will be more frequent and species turnover faster in the ocean. By contrast, terrestrial species appear to be more vulnerable to loss of access to thermal refugia, which would make habitat fragmentation and changes in land use critical drivers of species loss on land.

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Fig. 1: Extreme hot temperatures across latitudes in terrestrial and marine environments.
Fig. 2: Greater thermal safety on land if thermal refugia are available.

Data availability

The upper thermal-tolerance data and extirpation data that support the findings of this study are available at Zenodo under the identifier: https://doi.org/10.5281/zenodo.2576197. Any other relevant data are available from the corresponding author upon reasonable request.

Code availability

Custom analysis scripts are available at Zenodo under the identifier: https://doi.org/10.5281/zenodo.2576197.

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Acknowledgements

We thank P. Falkowski, A. Gunderson, R. Huey, H. John-Alder, F. Joyce, G. Saba, B. Seibel, M. Tingley and members of the Pinsky laboratory for discussions; and D. L. Forrest for assistance with data compilation. This research was partially conducted on a research exchange at the University of Oslo, supported by the Nordforsk-funded project ‘Green Growth Based on Marine Resources: Ecological and Socio-Economic Constraints (GreenMAR)’. We also acknowledge support from the Alfred P. Sloan Research Fellow program, National Science Foundation projects OCE-1426891, DEB-1616821 and EAR-1151022, the Benioff Ocean Initiative, the Natural Sciences and Engineering Research Council of Canada and the Biodiversity Research Centre at the University of British Columbia. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling (which was responsible for producing CMIP5) and thank the climate modelling groups for making the output of their models available. The US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provided coordinating support and led the development of software infrastructure for CMIP5, in partnership with the Global Organization for Earth System Science Portals.

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Nature thanks Michael Burrows, Mark Payne, Anthony Richardson and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Authors and Affiliations

  1. Department of Ecology, Evolution and Natural Resources, Rutgers University, New Brunswick, NJ, USA

    Malin L. Pinsky

  2. Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Oslo, Norway

    Anne Maria Eikeset

  3. Department of Ecology, Evolution, and Marine Biology, University of California Santa Barbara, Santa Barbara, CA, USA

    Douglas J. McCauley

  4. Marine Science Institute, University of California Santa Barbara, Santa Barbara, CA, USA

    Douglas J. McCauley

  5. Department of Geological Sciences, Stanford University, Stanford, CA, USA

    Jonathan L. Payne

  6. Biology Department, McGill University, Montreal, Quebec, Canada

    Jennifer M. Sunday

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  1. Malin L. Pinsky
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Contributions

M.L.P. conceptualized and administered the project, visualized the data and wrote the original draft of the manuscript. M.L.P., A.M.E., D.J.M. and J.M.S. acquired funding. J.M.S. and M.L.P wrote the software, and performed data curation and formal analysis. M.L.P., J.M.S. and D.J.M. developed the methodology. M.L.P., D.J.M., J.M.S. and J.L.P. reviewed and edited the manuscript.

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Extended data figures and tables

Extended Data Fig. 1 Adjustment to account for the acclimatization temperatures used in laboratory experiments.

a, b, For land (a) and ocean (b) separately, the points show the average summer temperature used for acclimatization calculations (black) at each collection site, ordered from smallest to largest, the experimental acclimatization temperature (grey), the experimentally measured thermal-tolerance maximum (Tmax, blue) and the acclimatization-adjusted thermal-tolerance maximum (Tmax′, purple). c, Tmax′ adjusted with species-specific acclimatization response ratios (ARRs, n = 69 species) is compared against Tmax′ adjusted with ARRs averaged within realms. The grey line is a 1:1 line.

Extended Data Fig. 2 Uncertainty in the location of peaks or valleys in GAMM fits for terrestrial ectotherms.

a–c, Clusters of graphs for extreme hot values of body temperatures in thermal refugia (Tb, protected) (a), hot thermal-tolerance limits (Tmax′) (b) and TSM (c) across latitudes. Each cluster of graphs has three parts: (1) the centre graph shows the fitted effect against latitude from a GAMM (dark line), and 50 samples from the fitted smoother (grey lines); (2) the top histogram shows uncertainty in the locations of the peaks (a, b) or valleys (c) detected from 1,000 samples from the fitted smoother; and (3) the right-hand histogram shows uncertainty in the number of peaks or valleys detected. d, Average TSM is plotted against the mid-point of each 10° latitude band. Error bars show s.e.m. Fewer data points create larger error bounds near the equator. n = 299 species.

Extended Data Fig. 3 Effects of alternative emission scenarios and acclimatization on TSMs for the end of the twenty-first century (2081–2100).

a, Projected extreme hot hourly air- or water-surface temperatures from RCP8.5 or RCP2.6 scenarios. Shaded regions show ±1 s.d. n = 1,454 (terrestrial), 689,769 (ocean RCP8.5) or 689,381 (ocean RCP2.6) grid cells. b, Future TSMs without acclimatization. c, Future TSMs with acclimatization. Shaded ribbons show ± 1 s.e. from GAMM fits (b, c). n = 382 species for GAMM fits (b, c).

Extended Data Fig. 4 Alternative approaches to TSM calculations.

a, b, Warm TSMs across latitudes for terrestrial (a) and marine (b) species with alternative temperature calculations (annual average, summer average, warmest month and warmest hour). Physiological tolerance measurements were from acute laboratory exposures (minutes to hours), and acute environmental temperature extremes are therefore arguably most-appropriate for calculating TSM. TSMs from the most-acute temporal scale (hours) revealed a different latitudinal pattern than from the more-aggregated scales, in part because they better capture short-duration thermal extremes. However, in all calculations marine species had narrower TSMs. c, d, Warm TSM calculations for marine species (c) or for terrestrial species (d), with (solid line) or without (dashed line) accounting for behavioural thermoregulation. The latter case is appropriate if behavioural thermoregulation is not possible (for example, thermal refugia are not accessible). Negative thermal safety on land indicates that these habitats are not habitable during midday heat for durations that bring body temperatures close to equilibrium. Note that calculations in a and b do not account for acclimatization or behavioural thermoregulation, to enable clear comparison across timescales. Calculations in c and d include acclimatization to summer temperatures. In all plots, shaded ribbons show ±s.e. from GAMM fits. n = 387 (warmest hour, no marine thermoregulation, or exposed or full sun) or 390 (warmest month, summer average or annual average) species for GAMM fits.

Extended Data Fig. 5 Relationship between physiological maximum (Tmax) and optimum (Topt) temperature across species.

Data are shown from studies15,26,90 that measured Tmax and Topt across species of phytoplankton, insects and lizards. The line is from a linear model with Tmax as the response variable and Topt as the explanatory term (linear regression r2 = 0.77, F = 1,036 with 1 and 310 degrees of freedom, two-sided P < 10−15 with no corrections for multiple comparisons, n = 312 species). All data are plotted in transparent grey so that overlapping data points appear as darker circles. The dashed line is a 1:1 relationship.

Extended Data Table 1 Thermal-tolerance maximums (Tmax) were compiled from 406 species in 15 classes
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Extended Data Table 2 Statistical model results for maximum thermal-tolerance limits (Tmax′), extreme body temperatures (Tb, protected) and TSM of marine and terrestrial ectothermic animals
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Extended Data Table 3 Marine behavioural thermoregulation adjustments
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Extended Data Table 4 Synthesis of range contraction prevalence in multi-species studies
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Pinsky, M.L., Eikeset, A.M., McCauley, D.J. et al. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature 569, 108–111 (2019). https://doi.org/10.1038/s41586-019-1132-4

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