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. 2022 Jul;2022(7):oik.08601.
doi: 10.1111/oik.08601. Epub 2021 Oct 26.

Stoichiometric constraints modulate temperature and nutrient effects on biomass distribution and community stability

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Stoichiometric constraints modulate temperature and nutrient effects on biomass distribution and community stability

Arnaud Sentis et al. Oikos. 2022 Jul.

Abstract

Temperature and nutrients are two of the most important drivers of global change. Both can modify the elemental composition (i.e. stoichiometry) of primary producers and consumers. Yet their combined effect on the stoichiometry, dynamics and stability of ecological communities remains largely unexplored. To fill this gap, we extended the Rosenzweig-MacArthur consumer-resource model by including thermal dependencies, nutrient dynamics and stoichiometric constraints on both the primary producer and the consumer. We found that stoichiometric and nutrient conservation constraints dampen the paradox of enrichment and increased persistence at high nutrient levels. Nevertheless, stoichiometric constraints also reduced consumer persistence at extreme temperatures. Finally, we also found that stoichiometric constraints and nutrient dynamics can strongly influence biomass distribution across trophic levels by modulating consumer assimilation efficiency and resource growth rates along the environmental gradients. In the Rosenzweig-MacArthur model, consumer biomass exceeded resource biomass for most parameter values whereas, in the stoichiometric model, consumer biomass was strongly reduced and sometimes lower than resource biomass. Our findings highlight the importance of accounting for stoichiometric constraints as they can mediate the temperature and nutrient impact on the dynamics and functioning of ecological communities.

Keywords: biomass structure; consumer–resource dynamics; nutrient quota; paradox of enrichment; stoichiometry; temperature; temporal variability; trophic interactions.

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Figures

Figure 1
Figure 1. Population fluctuations (consumer biomass coefficient of variation
(a) and (b)) and species persistence (number of species; (c) and (d)) across the temperature (y-axis) and nutrient (x-axis) gradients as predicted by the Rosenzweig–MacArthur (RM; (a) and (c)) and by the stoichiometric Rosenzweig–MacArthur (SRM; (b) and (d)) models. In (a) and (b), coefficient of variation (hereafter CV) represents fluctuation amplitudes. CV is null when the system is at equilibrium and positive when populations fluctuate. In (a) and (b), the white colour corresponds to the temperature–nutrient scenario for which the consumer has gone extinct whereas the orange to red to dark red represent population fluctuations of increasing amplitude. In (c) and (d), in black: both consumer and resource persist; in red: only the resource persists; in orange: none persists. Resource biomass CV is not shown; it is qualitatively similar to the consumer biomass CV as resource and consumer biomass fluctuation are strongly coupled.
Figure 2
Figure 2
Consumer–resource biomass ratio along the temperature gradient for the Rosenzweig–MacArthur (RM, green lines) and the stoichiometric Rosenzweig–MacArthur (SRM, black lines) models at three nutrient concentrations (0.008, 0.02 and 0.032 gP m−3). In each panel, the dotted line represents biomass ratio of one; i.e. the biomass densities of the resource and the consumer are equal. Biomass values shown at equilibrium points. For unstable equilibrium points (i.e. limit cycles), see the Supporting information.
Figure 3
Figure 3
Consumer energetic efficiency along the temperature gradient for the Rosenzweig–MacArthur (RM, in green) and the stoichiometric Rosenzweig–MacArthur (SRM, in black) models at two nutrient concentrations (0.008 and 0.02 gP m−3). In each panel, the dotted line represents energetic efficiency equal to one.
Figure 4
Figure 4
Population fluctuations (consumer biomass coefficient of variation) across the temperature (y-axis) and nutrient (x-axis) gradients as predicted by the Rosenzweig–MacArthur (RM; (a)), the RM with effective parameters (b) and the stoichiometric Rosenzweig–MacArthur (SRM; (c)) models.

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