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Changes in temperature alter the relationship between biodiversity and ecosystem functioning

García, Francisca C., Bestion, Elvire, Warfield, Ruth, Yvon-Durocher, Gabriel·2018·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.1805518115PMC PMC6205462
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Abstract

Significance: Empirical evidence for the response of ecosystem functioning to the combined effects of warming and biodiversity loss is scarce. We show that warming and biodiversity loss interact synergistically, impairing the functioning of microbial communities. We found that as temperatures departed from ambient conditions more species were required to maintain ecosystem functioning. Our results suggest interspecific complementarity increased under thermal stress and high-diversity communities that seemed functionally redundant at ambient temperature became more functionally unique as temperatures changed. Biodiversity may therefore be even more important than previously anticipated when considering the impacts of multiple facets of environmental change.

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Fig. 1. Thermal tolerance curves of the 24 bacterial taxa. (A) Comparison of the fitted thermal tolerance curves of population growth rate, r, for the 24 taxa quantified using the Sharpe–Schoofield equation (Materials and Methods). (B) The coefficient of variance (CV) in r among the 24 taxa at each assay temperature demonstrates an exponential increase in CV with rising temperature (y = 21.98 e^0.04x, r²= 0.94, F1,6= 96.22, P < 0.001). (C) Pooled thermal tolerance curve fitted using the Sharpe–Schoofield equation (SI Appendix, Table S1) to the mean growth rate across all 24 taxa at each assay temperature demonstrates a marked decline in average performance above 27.5 °C, which coincides with the temperature at which the CV of population growth rate rapidly increases (quasi-r²= 0.92 of the fitted model).
Fig. 1. Thermal tolerance curves of the 24 bacterial taxa. (A) Comparison of the fitted thermal tolerance curves of population growth rate, r, for the 24 taxa quantified using the Sharpe–Schoofield equation (Materials and Methods). (B) The coefficient of variance (CV) in r among the 24 taxa at each assay temperature demonstrates an exponential increase in CV with rising temperature (y = 21.98 e^0.04x, r²= 0.94, F1,6= 96.22, P < 0.001). (C) Pooled thermal tolerance curve fitted using the Sharpe–Schoofield equation (SI Appendix, Table S1) to the mean growth rate across all 24 taxa at each assay temperature demonstrates a marked decline in average performance above 27.5 °C, which coincides with the temperature at which the CV of population growth rate rapidly increases (quasi-r²= 0.92 of the fitted model).CC BY-NC-ND · PubMed Central
Fig. 2. Effects of temperature on the relationship between species richness and ecosystem functioning. (A) Effects of temperature on the relationship between species richness and ecosystem functioning. A power function was used to analyze the coupling between ecosystem functioning and species richness: log10Y(S) = b (log10S – log10Sc) + log10Y(Sc). Ecosystem functioning was quantified as the community yield (Y) in the stationary phase of community growth, species richness (S) was centered around the mean, Sc, so that the intercept of the linear relationship between log10Y and log10S gives the log10Y at the average level of S, and b is the exponent that captures the shape of the diversity–functioning relationship (SI Appendix, section 1.3). Analyses demonstrate major shifts in the relationship between log10Y and log10S with temperature. (B) The intercept of the diversity–functioning relationship, log10Y(Sc), declined with rising temperature. The red line denotes the fit of a linear model to the relationship between log10Y(Sc) and temperature (log10Y(Sc)= −0.01 × −1.22, r² = 0.81, P < 0.01). (C) Changes in the exponent reveal a U-shaped relationship with temperature, with the highest values at low and high temperatures. The red solid line represents the fit of a second-order polynomial model (y = 0.0008x2 − 0.04x + 0.63, r²= 0.85, P < 0.01).
Fig. 2. Effects of temperature on the relationship between species richness and ecosystem functioning. (A) Effects of temperature on the relationship between species richness and ecosystem functioning. A power function was used to analyze the coupling between ecosystem functioning and species richness: log10Y(S) = b (log10S – log10Sc) + log10Y(Sc). Ecosystem functioning was quantified as the community yield (Y) in the stationary phase of community growth, species richness (S) was centered around the mean, Sc, so that the intercept of the linear relationship between log10Y and log10S gives the log10Y at the average level of S, and b is the exponent that captures the shape of the diversity–functioning relationship (SI Appendix, section 1.3). Analyses demonstrate major shifts in the relationship between log10Y and log10S with temperature. (B) The intercept of the diversity–functioning relationship, log10Y(Sc), declined with rising temperature. The red line denotes the fit of a linear model to the relationship between log10Y(Sc) and temperature (log10Y(Sc)= −0.01 × −1.22, r² = 0.81, P < 0.01). (C) Changes in the exponent reveal a U-shaped relationship with temperature, with the highest values at low and high temperatures. The red solid line represents the fit of a second-order polynomial model (y = 0.0008x2 − 0.04x + 0.63, r²= 0.85, P < 0.01).CC BY-NC-ND · PubMed Central
Fig. 3. Compositional turnover linked to variance in thermal tolerance traits. (A) Nonmetric multidimensional scaling ordination of the microbial communities in the high-diversity treatments (S = 24) at high (red), low (blue), and ambient (yellow) temperatures (k = 4, stress = 0.01). The brown arrows and letters correspond to “species scores” and indicate the correspondence of each species with the primary axes of variation. The colored points and ellipses denote “site scores,” where each point is a replicate community and its correspondence with the axes of variation. Ellipses give the 95% CI around the centroid of each treatment—nonoverlapping ellipses suggest significant divergence in community composition between treatments (see SI Appendix, Tables S3 and S4 for results of PERMANOVA). (B) Relationship between the species scores extracted from NMDS1 and the Topt of each species. The black line represents the fit of a linear model (r²= 0.28, P = 0.03).
Fig. 3. Compositional turnover linked to variance in thermal tolerance traits. (A) Nonmetric multidimensional scaling ordination of the microbial communities in the high-diversity treatments (S = 24) at high (red), low (blue), and ambient (yellow) temperatures (k = 4, stress = 0.01). The brown arrows and letters correspond to “species scores” and indicate the correspondence of each species with the primary axes of variation. The colored points and ellipses denote “site scores,” where each point is a replicate community and its correspondence with the axes of variation. Ellipses give the 95% CI around the centroid of each treatment—nonoverlapping ellipses suggest significant divergence in community composition between treatments (see SI Appendix, Tables S3 and S4 for results of PERMANOVA). (B) Relationship between the species scores extracted from NMDS1 and the Topt of each species. The black line represents the fit of a linear model (r²= 0.28, P = 0.03).CC BY-NC-ND · PubMed Central
Fig. 4. Linking thermal traits to the impacts of warming and species loss on ecosystem functioning. Coupling between ecosystem functioning (community yield) and the community-mean optimum temperature <Topt>c, derived from the species used to seed each replicate community. Analyses reveal that <Topt>c becomes an increasingly important predictor of ecosystem function as temperature rises, demonstrating that temperature-driven changes in the diversity–functioning relationship were mediated by variability in thermal traits. The red lines represent the fitted curves derived from the linear mixed-effect model. The different point shapes represent the level of species richness (S).
Fig. 4. Linking thermal traits to the impacts of warming and species loss on ecosystem functioning. Coupling between ecosystem functioning (community yield) and the community-mean optimum temperature <Topt>c, derived from the species used to seed each replicate community. Analyses reveal that <Topt>c becomes an increasingly important predictor of ecosystem function as temperature rises, demonstrating that temperature-driven changes in the diversity–functioning relationship were mediated by variability in thermal traits. The red lines represent the fitted curves derived from the linear mixed-effect model. The different point shapes represent the level of species richness (S).CC BY-NC-ND · PubMed Central
Fig. 5. Partitioning the impacts of warming on ecosystem functioning into selection and complementarity effects. (A) Net effect (NE) of biodiversity on functioning at each treatment temperature. (B) Selection effect (SE) of biodiversity at each treatment temperature. (C) Complementarity effect (CE) of biodiversity at each treatment temperature. Gray circles represent the value of each replicate and the black circles and the error bars indicate the mean and the SD for each treatment. Values over the zero line indicate a positive NE, SE, or CE on ecosystem function.
Fig. 5. Partitioning the impacts of warming on ecosystem functioning into selection and complementarity effects. (A) Net effect (NE) of biodiversity on functioning at each treatment temperature. (B) Selection effect (SE) of biodiversity at each treatment temperature. (C) Complementarity effect (CE) of biodiversity at each treatment temperature. Gray circles represent the value of each replicate and the black circles and the error bars indicate the mean and the SD for each treatment. Values over the zero line indicate a positive NE, SE, or CE on ecosystem function.CC BY-NC-ND · PubMed Central

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Citations

Cites

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Metadata

Authors
García, F. C., Bestion, E., Warfield, R., & Yvon-Durocher, G.
Affiliations
Environment and Sustainability Institute, University of Exeter, Penryn, Cornwall TR10 9EZ, United Kingdom
ORCID
Venue
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Published
2018-10-08
Volume
115
Issue
43
Pages
10989-10994
ISSN
  • 0027-8424
  • 1091-6490
DOI
10.1073/pnas.1805518115
PMC
PMC6205462
Cited by
316
References
8
Language
en
Licence
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)

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