Dataset Information

Comparing the Ecological Stoichiometry in Green and Brown Food Webs - A Review and Meta-analysis of Freshwater Food Webs.

ABSTRACT: The framework of ecological stoichiometry was developed primarily within the context of "green" autotroph-based food webs. While stoichiometric principles also apply in "brown" detritus-based systems, these systems have been historically understudied and differ from green ones in several important aspects including carbon (C) quality and the nutrient [nitrogen (N) and phosphorus (P)] contents of food resources for consumers. In this paper, we review work over the last decade that has advanced the application of ecological stoichiometry from green to brown food webs, focusing on freshwater ecosystems. We first review three focal areas where green and brown food webs differ: (1) bottom-up controls by light and nutrient availability, (2) stoichiometric constraints on consumer growth and nutritional regulation, and (3) patterns in consumer-driven nutrient dynamics. Our review highlights the need for further study of how light and nutrient availability affect autotroph-heterotroph interactions on detritus and the subsequent effects on consumer feeding and growth. To complement this conceptual review, we formally quantified differences in stoichiometric principles between green and brown food webs using a meta-analysis across feeding studies of freshwater benthic invertebrates. From 257 datasets collated across 46 publications and several unpublished studies, we compared effect sizes (Pearson's r) of resource N:C and P:C on growth, consumption, excretion, and egestion between herbivorous and detritivorous consumers. The meta-analysis revealed that both herbivore and detritivore growth are limited by resource N:C and P:C contents, but effect sizes only among detritivores were significantly above zero. Consumption effect sizes were negative among herbivores but positive for detritivores in the case of both N:C and P:C, indicating distinct compensatory feeding responses across resource stoichiometry gradients. Herbivore P excretion rates responded significantly positively to resource P:C, whereas detritivore N and P excretion did not respond; detritivore N and P egestion responded positively to resource N:C and P:C, respectively. Our meta-analysis highlights resource N and P contents as broadly limiting in brown and green benthic food webs, but indicates contrasting mechanisms of limitation owing to differing consumer regulation. We suggest that green and brown food webs share fundamental stoichiometric principles, while identifying specific differences toward applying ecological stoichiometry across ecosystems.

SUBMITTER: Evans-White MA

PROVIDER: S-EPMC5489555 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Comparing the Ecological Stoichiometry in Green and Brown Food Webs - A Review and Meta-analysis of Freshwater Food Webs.

Evans-White Michelle A MA Halvorson Halvor M HM

Frontiers in microbiology 20170629

The framework of ecological stoichiometry was developed primarily within the context of "green" autotroph-based food webs. While stoichiometric principles also apply in "brown" detritus-based systems, these systems have been historically understudied and differ from green ones in several important aspects including carbon (C) quality and the nutrient [nitrogen (N) and phosphorus (P)] contents of food resources for consumers. In this paper, we review work over the last decade that has advanced th ...[more]

PMID: 28706509

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Project description:Water diversion and pollution are two pervasive stressors in river ecosystems that often co-occur. Individual effects of both stressors on basal resources available to stream communities have been described, with diversion reducing detritus standing stocks and pollution increasing biomass of primary producers. However, interactive effects of both stressors on the structure and trophic basis of food webs remain unknown. We hypothesized that the interaction between both stressors increases the contribution of the green pathway in stream food webs. Given the key role of the high-quality, but less abundant, primary producers, we also hypothesized an increase in food web complexity with larger trophic diversity in the presence of water diversion and pollution. To test these hypotheses, we selected four rivers in a range of pollution subject to similar water diversion schemes, and we compared food webs upstream and downstream of the diversion. We characterized food webs by means of stable isotope analysis. Both stressors directly changed the availability of basal resources, with water diversion affecting the brown food web by decreasing detritus stocks, and pollution enhancing the green food web by promoting biofilm production. The propagation of the effects at the base of the food web to higher trophic levels differed between stressors. Water diversion had little effect on the structure of food webs, but pollution increased food chain length and trophic diversity, and reduced trophic redundancy. The effects at higher trophic levels were exacerbated when combining both stressors, as the relative contribution of biofilm to the stock of basal resources increased even further. Overall, we conclude that moderate pollution increases food web complexity and that the interaction with water abstraction seems to amplify this effect. Our study shows the importance of assessing the interaction between stressors to create predictive tools for a proper management of ecosystems.

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Project description:Networks of trophic links (food webs) are used to describe and understand mechanistic routes for translocation of energy (biomass) between species. However, a relatively low proportion of ecosystems have been studied using food web approaches due to difficulties in making observations on large numbers of species. In this paper we demonstrate that Machine Learning of food webs, using a logic-based approach called A/ILP, can generate plausible and testable food webs from field sample data. Our example data come from a national-scale Vortis suction sampling of invertebrates from arable fields in Great Britain. We found that 45 invertebrate species or taxa, representing approximately 25% of the sample and about 74% of the invertebrate individuals included in the learning, were hypothesized to be linked. As might be expected, detritivore Collembola were consistently the most important prey. Generalist and omnivorous carabid beetles were hypothesized to be the dominant predators of the system. We were, however, surprised by the importance of carabid larvae suggested by the machine learning as predators of a wide variety of prey. High probability links were hypothesized for widespread, potentially destabilizing, intra-guild predation; predictions that could be experimentally tested. Many of the high probability links in the model have already been observed or suggested for this system, supporting our contention that A/ILP learning can produce plausible food webs from sample data, independent of our preconceptions about "who eats whom." Well-characterised links in the literature correspond with links ascribed with high probability through A/ILP. We believe that this very general Machine Learning approach has great power and could be used to extend and test our current theories of agricultural ecosystem dynamics and function. In particular, we believe it could be used to support the development of a wider theory of ecosystem responses to environmental change.

Dataset Information

Comparing the Ecological Stoichiometry in Green and Brown Food Webs - A Review and Meta-analysis of Freshwater Food Webs.

Publications

Comparing the Ecological Stoichiometry in Green and Brown Food Webs - A Review and Meta-analysis of Freshwater Food Webs.

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