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Global-scale predictions of community and ecosystem properties from simple ecological theory.

ABSTRACT: We show how theoretical developments in macroecology, life-history theory and food-web ecology can be combined to formulate a simple model for predicting the potential biomass, production, size and trophic structure of consumer communities. The strength of our approach is that it uses remote sensing data to predict properties of consumer communities in environments that are challenging and expensive to sample directly. An application of the model to the marine environment on a global scale, using primary production and temperature estimates from satellite remote sensing as inputs, suggests that the global biomass of marine animals more than 10(-5) g wet weight is 2.62 x 10(9)t (=8.16 gm(-2) ocean) and production is 1.00 x 10(10) tyr-1 (31.15 gm(-2)yr(-1)). Based on the life-history theory, we propose and apply an approximation for distinguishing the relative contributions of different animal groups. Fish biomass and production, for example, are estimated as 8.99 x 10(8)t (2.80 gm(-2)) and 7.91 x 108 t yr(-1) (2.46 gm(2)yr(-1)respectively, and 50% of fish biomass is shown to occur in 17% of the total ocean area (8.22 gm(-2)). The analyses show that emerging ecological theory can be synthesized to set baselines for assessing human and climate impacts on global scales.

SUBMITTER: Jennings S

PROVIDER: S-EPMC2602712 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature

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Global-scale predictions of community and ecosystem properties from simple ecological theory.

Jennings Simon S Mélin Frédéric F Blanchard Julia L JL Forster Rodney M RM Dulvy Nicholas K NK Wilson Rod W RW

Proceedings. Biological sciences 20080601 1641

We show how theoretical developments in macroecology, life-history theory and food-web ecology can be combined to formulate a simple model for predicting the potential biomass, production, size and trophic structure of consumer communities. The strength of our approach is that it uses remote sensing data to predict properties of consumer communities in environments that are challenging and expensive to sample directly. An application of the model to the marine environment on a global scale, usin ...[more]

PMID: 18348964

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Project description:While short-term plant responses to global change are driven by physiological mechanisms, which are represented relatively well by models, long-term ecosystem responses to global change may be determined by shifts in plant community structure resulting from other ecological phenomena such as interspecific interactions, which are represented poorly by models. In single-factor scenarios, plant communities often adjust to increase ecosystem response to that factor. For instance, some early global change experiments showed that elevated CO2 favours plants that respond strongly to elevated CO2, generally amplifying the response of ecosystem productivity to elevated CO2, a positive community feedback. However, most ecosystems are subject to multiple drivers of change, which can complicate the community feedback effect in ways that are more difficult to generalize. Recent studies have shown that (i) shifts in plant community structure cannot be reliably predicted from short-term plant physiological response to global change and (ii) that the ecosystem response to multi-factored change is commonly less than the sum of its parts. Here, we survey results from long-term field manipulations to examine the role community shifts may play in explaining these common findings. We use a simple model to examine the potential importance of community shifts in governing ecosystem response. Empirical evidence and the model demonstrate that with multi-factored change, the ecosystem response depends on community feedbacks, and that the magnitude of ecosystem response will depend on the relationship between plant response to one factor and plant response to another factor. Tradeoffs in the ability of plants to respond positively to, or to tolerate, different global change drivers may underlie generalizable patterns of covariance in responses to different drivers of change across plant taxa. Mechanistic understanding of these patterns will help predict the community feedbacks that determine long-term ecosystem responses.

Dataset Information

Global-scale predictions of community and ecosystem properties from simple ecological theory.

Publications

Global-scale predictions of community and ecosystem properties from simple ecological theory.

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