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Warming impairs trophic transfer efficiency in a long-term field experiment

Nature volume 592, pages 76–79 (2021)Cite this article

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Abstract

In ecosystems, the efficiency of energy transfer from resources to consumers determines the biomass structure of food webs. As a general rule, about 10% of the energy produced in one trophic level makes it up to the next1,2,3. Recent theory suggests that this energy transfer could be further constrained if rising temperatures increase metabolic growth costs4, although experimental confirmation in whole ecosystems is lacking. Here we quantify nitrogen transfer efficiency—a proxy for overall energy transfer—in freshwater plankton in artificial ponds that have been exposed to seven years of experimental warming. We provide direct experimental evidence that, relative to ambient conditions, 4 °C of warming can decrease trophic transfer efficiency by up to 56%. In addition, the biomass of both phytoplankton and zooplankton was lower in the warmed ponds, which indicates major shifts in energy uptake, transformation and transfer5,6. These findings reconcile observed warming-driven changes in individual-level growth costs and in carbon-use efficiency across diverse taxa4,7,8,9,10 with increases in the ratio of total respiration to gross primary production at the ecosystem level11,12,13. Our results imply that an increasing proportion of the carbon fixed by photosynthesis will be lost to the atmosphere as the planet warms, impairing energy flux through food chains, which will have negative implications for larger consumers and for the functioning of entire ecosystems.

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Fig. 1: Temporal dynamics of the 15N tracer during the experiment.
Fig. 2: Effects of long-term warming on the parameters that determine 15N tracer dynamics and the mean efficiency of nitrogen transfer.
Fig. 3: Effects of long-term warming on plankton community biomass.

Data availability

All data (data manipulation, analyses, figures and tables) can be downloaded from the GitHub repository https://github.com/dbarneche/nature20200508666. When using the data or code from this project, please cite https://doi.org/10.5281/zenodo.4468371.

Code availability

R code can be downloaded from the GitHub repository https://github.com/dbarneche/nature20200508666.

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Acknowledgements

We thank J. H. Brown for suggestions on the manuscript. We thank T. J. McKinley for providing feedback on the statistical analysis, and P. Goncalves for assistance with illustrations. This work was supported by an AXA Research Fund (to M.D.), by the Natural Environment Research Council (NE/H022511/1, to M.T., G.Y.-D. and G.W.), and by a European Research Council grant (ERC StG 677278 TEMPDEP, to G.Y.-D.).

Author information

Authors and Affiliations

  1. Australian Institute of Marine Science, Indian Ocean Marine Research Centre, Crawley, Western Australia, Australia

    Diego R. Barneche

  2. Oceans Institute, The University of Western Australia, Crawley, Western Australia, Australia

    Diego R. Barneche

  3. Environment and Sustainability Institute, University of Exeter, Penryn, UK

    Diego R. Barneche, Daniel Padfield & Gabriel Yvon-Durocher

  4. School of Biological and Chemical Sciences, Queen Mary University of London, London, UK

    Chris J. Hulatt, Matteo Dossena & Mark Trimmer

  5. Faculty of Biosciences and Aquaculture, Nord University, Bodø, Norway

    Chris J. Hulatt

  6. Department of Life Sciences, Imperial College London, Silwood Park, Ascot, UK

    Guy Woodward

Authors
  1. Diego R. Barneche
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  7. Gabriel Yvon-Durocher
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Contributions

G.Y.-D. and M.T. conceived the study; C.J.H. and M.D. collected the data and performed the stable isotope analysis; D.P. collected the phytoplankton community data from 2016; D.R.B., G.Y.-D. and M.T. conducted the statistical analyses; D.R.B. and G.Y.-D. wrote the first version of the manuscript, and all authors contributed substantially to the revisions.

Corresponding authors

Correspondence to Mark Trimmer or Gabriel Yvon-Durocher.

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The authors declare no competing interests.

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Peer review information Nature thanks Robert Hall and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Schematic of experimental pond set-up and 15N tracer measurements.

a, Twenty artificial ponds, with 10 warmed (red) by 4 °C above (since September 2006) the temperature of 10 ambient (blue) ponds, were paired in a randomized block design. b, Ponds were controlled using two temperature sensors, a heating element (HE) a thermostat (T-stat) and a solid-state relay (SSR). c, Timeline of experimental measurements, including quantification of baseline 15N% of phytoplankton and zooplankton before the addition of the K15NO3 tracer, followed by continuous sampling of excess 15N% relative to the baseline on each pond. d, Dissolved oxygen (DO) saturation (left) and pH (right) did not change before and after the addition of the tracer (see ref. 13 for measurement details). Ambient, blue triangles; warmed, red inverted triangles.

Extended Data Fig. 2 Concentration of dissolved inorganic nitrogen species in the ponds before and after the addition of the 15N tracer on 16 July 2013.

Addition of the 15N tracer had no discernible effect on the natural concentration of dissolved inorganic nitrogen in the ponds (Supplementary Table 1). Points are treatment-level means, error bars are 95% confidence intervals. The dashed lines mark 16 July 2013.

Extended Data Fig. 3 Daytime CO2 influx before and after the addition of the 15N tracer on 16 July 2013.

Each point represents an individual measurement within a pond (n = 56 measurements per treatment per period; as described in detail in ref. 13). Ambient, blue triangles; warmed, red inverted triangles. ‘Before’ measurements were taken daily throughout the week leading to the addition of the 15N tracer on 16 July 2013 (9 July–15 July), whereas ‘After’ measurements were taken daily throughout the week after the addition of the tracer (17 July–23 July). Box plots depict the median (centre line) and the first and third quartiles (lower and upper bounds). Whiskers extend to 1.5 times the the interquartile range (the distance between the first and third quartiles). Outliers were removed from the plot for visualization purposes only. A before–after analysis (see Supplementary Table 1) revealed no substantial changes in daytime CO2 influx and net primary production due to the addition of the 15N tracer.

Extended Data Fig. 4 Hierarchical structure outlining the model fitting.

Data, processes and parameters are explicitly identified, with equation (1) parameters ϕ, κa and κe being fitted at the treatment level with pond-level deviations. Phytoplankton and zooplankton silhouettes depict whether a certain transformation or prior was used for either group or both (see Methods).

Extended Data Fig. 5 Temporal dynamics of excess 15N%, χ, in phytoplankton and zooplankton during the experiment.

Dashed lines represent mean pond-level predictions that were obtained by fitting the data to equation (1) via a nonlinear hierarchical Bayesian model (see Methods). Shaded polygons represent Bayesian 95% CI that were calculated from 20,000 posterior draws. Note the sharp increase in the χ(t) in the first few days of the experiment, particularly when compared to baseline 15N% in the control ponds (Extended Data Fig. 7).

Extended Data Fig. 6 Posterior distributions of percentage decline in carbon biomass (μg C l−1) and efficiency of nitrogen transfer due to long-term warming.

Distributions were calculated using 20,000 posterior draws that were estimated via Bayesian hierarchical linear models (see Methods). Positive and negative values represent percentage decline and increase, respectively. The strong overlap between distributions corroborates the assumption that mean nitrogen transfer efficiency, \(\bar{\varepsilon }\), as calculated from the 15N tracer dynamics (equation (3)), reflects the efficiency of carbon transfer and hence energy transfer.

Extended Data Fig. 7 Measurements of excess 15N%, χ, in three untreated control ponds.

Green circles represent phytoplankton (n = 5 per pond), whereas brown squares represent zooplankton (n = 3–5 per pond). These results are expected given that no 15N tracer was added. The y axis was kept fixed in order to compare the magnitude of change between treatments (Extended Data Fig. 5) and controls. For further explanations of how the data were collected, see Methods.

Extended Data Fig. 8 Effects of long-term warming on mean nitrogen biomass.

Mean biomass nitrogen estimates were calculated from ambient and warmed ponds. Points represent means calculated for the entire duration of the 15N tracer experiment (n = 8 per treatment). Box plots depict the median (centre line) and the first and third quartiles (lower and upper bounds). Whiskers extend to 1.5 times the the interquartile range (the distance between the first and third quartiles). Circles represent phytoplankton (top) and squares represent zooplankton (bottom).

Extended Data Fig. 9 Effects of long-term warming on C:N ratios.

Mean C:N ratios were calculated in ambient and warmed ponds. Points represent means calculated for the entire duration of the experiment (n = 8 per treatment). Box plots depict the median (centre line) and the first and third quartiles (lower and upper bounds). Whiskers extend to 1.5 times the the interquartile range (the distance between the first and third quartiles). Circles represent phytoplankton (top) and squares represent zooplankton (bottom).

Extended Data Table 1 Parameter estimates to characterize the temporal dynamics of the 15N tracer
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Supplementary information

Supplementary Information

This file contains the Empirical Proof of Concept, Supplementary Figures 1- 13, Supplementary Tables 1-2 and Supplementary References.

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Barneche, D.R., Hulatt, C.J., Dossena, M. et al. Warming impairs trophic transfer efficiency in a long-term field experiment. Nature 592, 76–79 (2021). https://doi.org/10.1038/s41586-021-03352-2

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