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Strong effects of weak interactions in ecological communities

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  • E. L. Berlow

    (University of California)

Abstract

The loss or removal of individual species can cause dramatic changes in communities1,2,3,4,5. Experiments indicate that in many communities only a few species will have such strong effects, whereas most will have weak effects owing to small per capita effects and/or low abundance3,6,7,8,9,10,11,12,13,14,15,16. But extinction of these ‘weak’ interactors could significantly alter natural communities because they play important stabilizing or ‘noise-dampening’ roles14,15,17,18,19,20,21,22,23. I demonstrate here that some ‘weak’ interactors may also be important by magnifying spatiotemporal variation in community structure. An analysis of published interaction strength data shows that the greatest variation in species effect occurred for the weakest interactions. A field experiment corroborates this and shows how indirect interactions can generate an inverse relationship between the mean and variance of a consumer's impact on its prey. When a species' effects are highly variable in sign and magnitude, they may average to seem weak over broad scales but be strong in local contexts. Thus, what is frequently considered to be ‘noise’ in species interaction data may be a critical part of the signal.

Suggested Citation

  • E. L. Berlow, 1999. "Strong effects of weak interactions in ecological communities," Nature, Nature, vol. 398(6725), pages 330-334, March.
    • Handle: RePEc:nat:nature:v:398:y:1999:i:6725:d:10.1038_18672
    DOI: 10.1038/18672
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    Cited by:

    1. Zhang, Zhibin & Yan, Chuan & Krebs, Charles J. & Stenseth, Nils Chr., 2015. "Ecological non-monotonicity and its effects on complexity and stability of populations, communities and ecosystems," Ecological Modelling, Elsevier, vol. 312(C), pages 374-384.
    2. J Timothy Wootton & James D Forester, 2013. "Complex Population Dynamics in Mussels Arising from Density-Linked Stochasticity," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-12, September.
    3. Miller, Jennifer & Franklin, Janet & Aspinall, Richard, 2007. "Incorporating spatial dependence in predictive vegetation models," Ecological Modelling, Elsevier, vol. 202(3), pages 225-242.
    4. Fabio Pranovi & Gianluca Sarà & Piero Franzoi, 2013. "Valuing the Unmarketable: An Ecological Approach to the Externalities Estimate in Fishing Activities," Sustainability, MDPI, vol. 5(2), pages 1-11, February.
    5. Moniz, L.J. & Cooch, E.G. & Ellner, S.P. & Nichols, J.D. & Nichols, J.M., 2007. "Application of information theory methods to food web reconstruction," Ecological Modelling, Elsevier, vol. 208(2), pages 145-158.
    6. Takayuki Niizato & Kotaro Sakamoto & Yoh-ichi Mototake & Hisashi Murakami & Takenori Tomaru & Tomotaro Hoshika & Toshiki Fukushima, 2020. "Finding continuity and discontinuity in fish schools via integrated information theory," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-29, February.
    7. Dai, Lingfei & Dai, Meifeng & Huang, Yu & Li, Yin & Shen, Junjie & Chi, Huijia & Su, Weiyi, 2020. "Searching efficiency of multiple walkers on the weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    8. Raymond, Ben & Hosie, Graham, 2009. "Network-based exploration and visualisation of ecological data," Ecological Modelling, Elsevier, vol. 220(5), pages 673-683.
    9. Giacomini, Henrique Corrêa & De Marco, Paulo & Petrere, Miguel, 2009. "Exploring community assembly through an individual-based model for trophic interactions," Ecological Modelling, Elsevier, vol. 220(1), pages 23-39.
    10. Sheng, Long & Li, Chunguang, 2009. "English and Chinese languages as weighted complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(12), pages 2561-2570.
    11. Ortiz, Marco & Campos, Leonardo & Berrios, Fernando & Rodriguez, Fabián & Hermosillo, Brenda & González, Jorge, 2013. "Network properties and keystoneness assessment in different intertidal communities dominated by two ecosystem engineer species (SE Pacific coast): A comparative analysis," Ecological Modelling, Elsevier, vol. 250(C), pages 307-318.
    12. Jennifer M Fraterrigo & Aaron B Langille & James A Rusak, 2020. "Stochastic disturbance regimes alter patterns of ecosystem variability and recovery," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-20, March.
    13. Wang, Shuran Cindy & Liu, Xueqin & Liu, Yong & Wang, Hongzhu, 2020. "Benthic-pelagic coupling in lake energetic food webs," Ecological Modelling, Elsevier, vol. 417(C).

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