American Institute of Mathematical Sciences

Advanced
March  2015, 10(1): 37-52. doi: 10.3934/nhm.2015.10.37

Dragging in mutualistic networks

Juan Manuel Pastor 1, , Javier García-Algarra 1, , José M. Iriondo 2, , José J. Ramasco 3, and  Javier Galeano 1,

1. 

Complex System Group, Technical University of Madrid, Av. Puerta Hierro 4, 28040-Madrid, Spain, Spain, Spain
2. 

Área de Biodiversidad y Conservación, Dept. Biología y Geologa, Universidad Rey Juan Carlos, 28933 Móstoles, Spain
3. 

Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), 07122 Palma de Mallorca, Spain

Received  July 2014 Revised  December 2014 Published  February 2015

Mutualistic networks are considered an example of resilience against perturbations. Mutualistic interactions are beneficial for the two sets of species involved. Network robustness has been usually measured in terms of extinction sequences, i.e., nodes are removed from the empirical bipartite network one subset (primary extinctions) and the number of extinctions on the other subset (secondary extinction) is computed. This is a first approach to study ecosystems extinction. However, each interacting species, depicted as a node of the mutualistic network, is really composed by certain number of individuals (population) and its shortage can diminish dramatically the population of its interacting partners, i.e. the population dynamics plays an important role in the robustness of the ecological networks. Although different models of population dynamics for mutualistic interacting species have been addressed, like Type II models, only recently a new mutualistic model has been proposed exhibiting bounded solutions and good properties for simulation. In this paper we show that population dynamics is as important as network topology when we are interested in the resilience of the community.
Keywords: dragging, Population dynamics, nestedness, mutualistic network, K-shell decomposition., cascade, extinction.
Mathematics Subject Classification: Primary: 92D25, 92C42; Secondary: 92D4.
Citation: Juan Manuel Pastor, Javier García-Algarra, José M. Iriondo, José J. Ramasco, Javier Galeano. Dragging in mutualistic networks. Networks & Heterogeneous Media, 2015, 10 (1) : 37-52. doi: 10.3934/nhm.2015.10.37
References:
[1]

Oikos, 117 (2008), 1227-1239. Google Scholar

[2]

Ecological complexity, 7 (2010), 494-499. doi: 10.1016/j.ecocom.2010.02.004.  Google Scholar

[3]

Proc. Natl. Acad. Scie., 106 (2009), 21484-21489. doi: 10.1073/pnas.0906910106.  Google Scholar

[4]

Annu. Rev. Ecol. Evol. Syst., 38 (2007), 567-593. doi: 10.1146/annurev.ecolsys.38.091206.095818.  Google Scholar

[5]

Journal of Theorethical Biology, 249 (2007), 307-313. doi: 10.1016/j.jtbi.2007.07.030.  Google Scholar

[6]

Physical Review E, 86 (2012), 021924. doi: 10.1103/PhysRevE.86.021924.  Google Scholar

[7]

Proc. Natl. Acad. Scie., 104 (2007), 11150-11154. doi: 10.1073/pnas.0701175104.  Google Scholar

[8]

Physica D, 224 (2006), 7-19. doi: 10.1016/j.physd.2006.09.027.  Google Scholar

[9]

The Open Ecology Journal, 2 (2009), 7-24. Google Scholar

[10]

Ecology Letters, 5 (2002), 558-567. doi: 10.1046/j.1461-0248.2002.00354.x.  Google Scholar

[11]

J. Anim. Ecol., 79 (2010), 811-817. doi: 10.1111/j.1365-2656.2010.01688.x.  Google Scholar

[12]

Journal of Theoretical Biology, 363 (2014), 332-343. doi: 10.1016/j.jtbi.2014.08.039.  Google Scholar

[13]

Chinese Physics, 16 (2007), 3571-3580. Google Scholar

[14]

Ecology Letters, 13 (2010), 442-452. Google Scholar

[15]

Ecology Letters 17 (2014), 350-359. doi: 10.1111/ele.12236.  Google Scholar

[16]

Proceedings of the Natinal Academy of Sciences of the United States of America, 108 (2011), 12337-12342. Google Scholar

[17]

Proceedings of the Royal Society B, 271 (2004), 2605-2611. doi: 10.1098/rspb.2004.2909.  Google Scholar

[18]

Ecology Letters, 10 (2007), 710-717. doi: 10.1111/j.1461-0248.2007.01061.x.  Google Scholar

[19]

Philosophical Transactions of the Royal Society. B, 365 (2010), 2071-2079. doi: 10.1098/rstb.2010.0015.  Google Scholar

[20]

, NCEAS interaction webs database,, Available from: , ().   Google Scholar

[21]

Networks and Heterogeneous Media, 7 (2012), 429-440. doi: 10.3934/nhm.2012.7.429.  Google Scholar

[22]

Arctic, Antarctic, and Alpine Research, 46 (2014), 568-580. doi: 10.1657/1938-4246-46.3.568.  Google Scholar

[23]

T. Säterberg, S. Sellman and Bo. Ebenman, High frequency of functional extinctions in ecological networks,, Nature, 7459 (): 468.   Google Scholar

[24]

Science, 329 (2010), 853-856. doi: 10.1126/science.1188321.  Google Scholar

[25]

Oikos, 118 (2009), 3-17. doi: 10.1111/j.1600-0706.2008.17053.x.  Google Scholar

[26]

Conservatin Biology, 25 (2011), 747-757. Google Scholar

[27]

The American Naturalist, 134 (1989), 664-667. Google Scholar

show all references

References:
[1]

Oikos, 117 (2008), 1227-1239. Google Scholar

[2]

Ecological complexity, 7 (2010), 494-499. doi: 10.1016/j.ecocom.2010.02.004.  Google Scholar

[3]

Proc. Natl. Acad. Scie., 106 (2009), 21484-21489. doi: 10.1073/pnas.0906910106.  Google Scholar

[4]

Annu. Rev. Ecol. Evol. Syst., 38 (2007), 567-593. doi: 10.1146/annurev.ecolsys.38.091206.095818.  Google Scholar

[5]

Journal of Theorethical Biology, 249 (2007), 307-313. doi: 10.1016/j.jtbi.2007.07.030.  Google Scholar

[6]

Physical Review E, 86 (2012), 021924. doi: 10.1103/PhysRevE.86.021924.  Google Scholar

[7]

Proc. Natl. Acad. Scie., 104 (2007), 11150-11154. doi: 10.1073/pnas.0701175104.  Google Scholar

[8]

Physica D, 224 (2006), 7-19. doi: 10.1016/j.physd.2006.09.027.  Google Scholar

[9]

The Open Ecology Journal, 2 (2009), 7-24. Google Scholar

[10]

Ecology Letters, 5 (2002), 558-567. doi: 10.1046/j.1461-0248.2002.00354.x.  Google Scholar

[11]

J. Anim. Ecol., 79 (2010), 811-817. doi: 10.1111/j.1365-2656.2010.01688.x.  Google Scholar

[12]

Journal of Theoretical Biology, 363 (2014), 332-343. doi: 10.1016/j.jtbi.2014.08.039.  Google Scholar

[13]

Chinese Physics, 16 (2007), 3571-3580. Google Scholar

[14]

Ecology Letters, 13 (2010), 442-452. Google Scholar

[15]

Ecology Letters 17 (2014), 350-359. doi: 10.1111/ele.12236.  Google Scholar

[16]

Proceedings of the Natinal Academy of Sciences of the United States of America, 108 (2011), 12337-12342. Google Scholar

[17]

Proceedings of the Royal Society B, 271 (2004), 2605-2611. doi: 10.1098/rspb.2004.2909.  Google Scholar

[18]

Ecology Letters, 10 (2007), 710-717. doi: 10.1111/j.1461-0248.2007.01061.x.  Google Scholar

[19]

Philosophical Transactions of the Royal Society. B, 365 (2010), 2071-2079. doi: 10.1098/rstb.2010.0015.  Google Scholar

[20]

, NCEAS interaction webs database,, Available from: , ().   Google Scholar

[21]

Networks and Heterogeneous Media, 7 (2012), 429-440. doi: 10.3934/nhm.2012.7.429.  Google Scholar

[22]

Arctic, Antarctic, and Alpine Research, 46 (2014), 568-580. doi: 10.1657/1938-4246-46.3.568.  Google Scholar

[23]

T. Säterberg, S. Sellman and Bo. Ebenman, High frequency of functional extinctions in ecological networks,, Nature, 7459 (): 468.   Google Scholar

[24]

Science, 329 (2010), 853-856. doi: 10.1126/science.1188321.  Google Scholar

[25]

Oikos, 118 (2009), 3-17. doi: 10.1111/j.1600-0706.2008.17053.x.  Google Scholar

[26]

Conservatin Biology, 25 (2011), 747-757. Google Scholar

[27]

The American Naturalist, 134 (1989), 664-667. Google Scholar

[1]

Juan Manuel Pastor, Javier García-Algarra, Javier Galeano, José María Iriondo, José J. Ramasco. A simple and bounded model of population dynamics for mutualistic networks. Networks & Heterogeneous Media, 2015, 10 (1) : 53-70. doi: 10.3934/nhm.2015.10.53
[2]

Shangzhi Li, Shangjiang Guo. Permanence and extinction of a stochastic SIS epidemic model with three independent Brownian motions. Discrete & Continuous Dynamical Systems - B, 2021, 26 (5) : 2693-2719. doi: 10.3934/dcdsb.2020201
[3]

Simone Cacace, Maurizio Falcone. A dynamic domain decomposition for the eikonal-diffusion equation. Discrete & Continuous Dynamical Systems - S, 2016, 9 (1) : 109-123. doi: 10.3934/dcdss.2016.9.109
[4]

Brandy Rapatski, James Yorke. Modeling HIV outbreaks: The male to female prevalence ratio in the core population. Mathematical Biosciences & Engineering, 2009, 6 (1) : 135-143. doi: 10.3934/mbe.2009.6.135
[5]

Linlin Li, Bedreddine Ainseba. Large-time behavior of matured population in an age-structured model. Discrete & Continuous Dynamical Systems - B, 2021, 26 (5) : 2561-2580. doi: 10.3934/dcdsb.2020195
[6]

Tao Wu, Yu Lei, Jiao Shi, Maoguo Gong. An evolutionary multiobjective method for low-rank and sparse matrix decomposition. Big Data & Information Analytics, 2017, 2 (1) : 23-37. doi: 10.3934/bdia.2017006
[7]

Roberto Civino, Riccardo Longo. Formal security proof for a scheme on a topological network. Advances in Mathematics of Communications, 2021  doi: 10.3934/amc.2021009
[8]

Yishui Wang, Dongmei Zhang, Peng Zhang, Yong Zhang. Local search algorithm for the squared metric $ k $-facility location problem with linear penalties. Journal of Industrial & Management Optimization, 2021, 17 (4) : 2013-2030. doi: 10.3934/jimo.2020056
[9]

Xiaomao Deng, Xiao-Chuan Cai, Jun Zou. A parallel space-time domain decomposition method for unsteady source inversion problems. Inverse Problems & Imaging, 2015, 9 (4) : 1069-1091. doi: 10.3934/ipi.2015.9.1069
[10]

Lars Grüne, Luca Mechelli, Simon Pirkelmann, Stefan Volkwein. Performance estimates for economic model predictive control and their application in proper orthogonal decomposition-based implementations. Mathematical Control & Related Fields, 2021  doi: 10.3934/mcrf.2021013
[11]

Rui Hu, Yuan Yuan. Stability, bifurcation analysis in a neural network model with delay and diffusion. Conference Publications, 2009, 2009 (Special) : 367-376. doi: 10.3934/proc.2009.2009.367
[12]

Jingni Guo, Junxiang Xu, Zhenggang He, Wei Liao. Research on cascading failure modes and attack strategies of multimodal transport network. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2020159
[13]

Andrey Kovtanyuk, Alexander Chebotarev, Nikolai Botkin, Varvara Turova, Irina Sidorenko, Renée Lampe. Modeling the pressure distribution in a spatially averaged cerebral capillary network. Mathematical Control & Related Fields, 2021  doi: 10.3934/mcrf.2021016
[14]

Cheng-Kai Hu, Fung-Bao Liu, Hong-Ming Chen, Cheng-Feng Hu. Network data envelopment analysis with fuzzy non-discretionary factors. Journal of Industrial & Management Optimization, 2021, 17 (4) : 1795-1807. doi: 10.3934/jimo.2020046
[15]

Jan Prüss, Laurent Pujo-Menjouet, G.F. Webb, Rico Zacher. Analysis of a model for the dynamics of prions. Discrete & Continuous Dynamical Systems - B, 2006, 6 (1) : 225-235. doi: 10.3934/dcdsb.2006.6.225
[16]

Simone Calogero, Juan Calvo, Óscar Sánchez, Juan Soler. Dispersive behavior in galactic dynamics. Discrete & Continuous Dynamical Systems - B, 2010, 14 (1) : 1-16. doi: 10.3934/dcdsb.2010.14.1
[17]

Patrick Henning, Anders M. N. Niklasson. Shadow Lagrangian dynamics for superfluidity. Kinetic & Related Models, 2021, 14 (2) : 303-321. doi: 10.3934/krm.2021006
[18]

Ka Luen Cheung, Man Chun Leung. Asymptotic behavior of positive solutions of the equation $ \Delta u + K u^{\frac{n+2}{n-2}} = 0$ in $IR^n$ and positive scalar curvature. Conference Publications, 2001, 2001 (Special) : 109-120. doi: 10.3934/proc.2001.2001.109
[19]

Hsin-Lun Li. Mixed Hegselmann-Krause dynamics. Discrete & Continuous Dynamical Systems - B, 2021  doi: 10.3934/dcdsb.2021084
[20]

Reza Lotfi, Yahia Zare Mehrjerdi, Mir Saman Pishvaee, Ahmad Sadeghieh, Gerhard-Wilhelm Weber. A robust optimization model for sustainable and resilient closed-loop supply chain network design considering conditional value at risk. Numerical Algebra, Control & Optimization, 2021, 11 (2) : 221-253. doi: 10.3934/naco.2020023

2019 Impact Factor: 1.053

Tools

Metrics

  • PDF downloads (11)
  • HTML views (0)
  • Cited by (3)

Other articles
by authors

[Back to Top]

Copyright © 2021 American Institute of Mathematical Sciences