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Evidence for the dominance of indirect effects in 50 trophic ecosystem networks

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  • Salas, Andria K.
  • Borrett, Stuart R.

Abstract

Indirect effects are powerful influences in ecosystems that may maintain species diversity and alter apparent relationships between species in surprising ways. Here, we applied network environ analysis to 50 empirically-based trophic ecosystem models to test the hypothesis that indirect flows dominate direct flows in ecosystem networks. Further, we used Monte Carlo based perturbations to investigate the robustness of these results to potential error in the underlying data. To explain our findings, we further investigated the importance of the microbial food web in recycling energy–matter using components of the Finn Cycling Index and analysis of environ centrality. We found that indirect flows dominate direct flows in 37/50 (74.0%) models. This increases to 31/35 (88.5%) models when we consider only models that have cycling structure and a representation of the microbial food web. The uncertainty analysis reveals that there is less error in the I/D values than the ±5% error introduced into the models, suggesting the results are robust to uncertainty. Our results show that the microbial food web mediates a substantial percentage of cycling in some systems (median=30.2%), but its role is highly variable in these models, in agreement with the literature. Our results, combined with previous work, strongly suggest that indirect effects are dominant components of activity in ecosystems.

Suggested Citation

  • Salas, Andria K. & Borrett, Stuart R., 2011. "Evidence for the dominance of indirect effects in 50 trophic ecosystem networks," Ecological Modelling, Elsevier, vol. 222(5), pages 1192-1204.
    • Handle: RePEc:eee:ecomod:v:222:y:2011:i:5:p:1192-1204
    DOI: 10.1016/j.ecolmodel.2010.12.002
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    3. Hines, David E. & Borrett, Stuart R., 2014. "A comparison of network, neighborhood, and node levels of analyses in two models of nitrogen cycling in the Cape Fear River Estuary," Ecological Modelling, Elsevier, vol. 293(C), pages 210-220.
    4. Koo, Kyung Ah & Madden, Marguerite & Patten, Bernard C., 2014. "Projection of red spruce (Picea rubens Sargent) habitat suitability and distribution in the Southern Appalachian Mountains, USA," Ecological Modelling, Elsevier, vol. 293(C), pages 91-101.
    5. Whipple, Stuart J. & Patten, Bernard C. & Borrett, Stuart R., 2014. "Indirect effects and distributed control in ecosystems," Ecological Modelling, Elsevier, vol. 293(C), pages 161-186.
    6. Rodríguez, Ricardo A. & Herrera, Ada Ma. & Riera, Rodrigo & Delgado, Juan D. & Quirós, Ángel & Perdomo, María E. & Santander, Jacobo & Miranda, Jezahel V. & Fernández-Rodríguez, María J. & Jiménez-Rod, 2015. "Thermostatistical distribution of a trophic energy proxy with analytical consequences for evolutionary ecology, species coexistence and the maximum entropy formalism," Ecological Modelling, Elsevier, vol. 296(C), pages 24-35.
    7. Burns, Thomas P. & Rose, Kenneth A. & Brenkert, Antoinette L., 2014. "Quantifying direct and indirect effects of perturbations using model ecosystems," Ecological Modelling, Elsevier, vol. 293(C), pages 69-80.
    8. Koo, Kyung Ah & Patten, Bernard C. & Teskey, Robert O. & Creed, Irena F., 2014. "Climate change effects on red spruce decline mitigated by reduction in air pollution within its shrinking habitat range," Ecological Modelling, Elsevier, vol. 293(C), pages 81-90.
    9. Borrett, S.R. & Freeze, M.A. & Salas, A.K., 2011. "Equivalence of the realized input and output oriented indirect effects metrics in Ecological Network Analysis," Ecological Modelling, Elsevier, vol. 222(13), pages 2142-2148.
    10. Borrett, Stuart R. & Sheble, Laura & Moody, James & Anway, Evan C., 2018. "Bibliometric review of ecological network analysis: 2010–2016," Ecological Modelling, Elsevier, vol. 382(C), pages 63-82.
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    13. Zarbá, Lucía & Brown, Mark T., 2015. "Cycling emergy: computing emergy in trophic networks," Ecological Modelling, Elsevier, vol. 315(C), pages 37-45.

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