Shifting levels of ecological network's analysis reveals different system properties

Network analyses applied to models of complex systems generally contain at least three levels of analyses. Whole-network metrics summarize general organizational features (properties or relationships) of the entire network, while node-level metrics summarize similar organization features but consider individual nodes. The network- and node-level metrics build upon the primary pairwise relationships in the model. As with many analyses, sometimes there are interesting differences at one level that disappear in the summary at another level of analysis. We illustrate this phenomenon with ecosystem network models, where nodes are trophic compartments and pairwise relationships are flows of organic carbon, such as when a predator eats a prey. For this demonstration, we analysed a time-series of 16 models of a lake planktonic food web that describes carbon exchanges within an autumn cyanobacteria bloom and compared the ecological conclusions drawn from the three levels of analysis based on inter-time-step comparisons. A general pattern in our analyses was that the closer the levels are in hierarchy (node versus network, or flow versus node level), the more they tend to align in their conclusions. Our analyses suggest that selecting the appropriate level of analysis, and above all regularly using multiple levels, may be a critical analytical decision. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.

[1]  B. McGill,et al.  The Limitations of Hierarchical Organization* , 2012, Philosophy of Science.

[2]  J. Finn,et al.  Measures of ecosystem structure and function derived from analysis of flows. , 1976, Journal of theoretical biology.

[3]  James Rosindell,et al.  Unified neutral theory of biodiversity and biogeography , 2010, Scholarpedia.

[4]  J. Heymans,et al.  Vitamine ENA: A framework for the development of ecosystem-based indicators for decision makers , 2019, Ocean & Coastal Management.

[5]  Raymond L. Lindeman The trophic-dynamic aspect of ecology , 1942 .

[6]  Robert W. Bosserman,et al.  17 – Propagation of Cause in Ecosystems , 1976 .

[7]  P. Huneman,et al.  From the Neutral Theory to a Comprehensive and Multiscale Theory of Ecological Equivalence , 2016, The Quarterly Review of Biology.

[8]  Daniel Kostić,et al.  General theory of topological explanations and explanatory asymmetry , 2020, Philosophical Transactions of the Royal Society B.

[9]  Peter Maurer,et al.  Growth And Development Ecosystems Phenomenology , 2016 .

[10]  D. Baird,et al.  The effect of physical drivers on ecosystem indices derived from ecological network analysis: Comparison across estuarine ecosystems , 2012 .

[11]  U. M. Scharler,et al.  Ecological network analysis metrics: The need for an entire ecosystem approach in management and policy , 2019, Ocean & Coastal Management.

[12]  Robert E. Ulanowicz,et al.  The dual nature of ecosystem dynamics , 2009 .

[13]  Marta Coll,et al.  Novel index for quantification of ecosystem effects of fishing as removal of secondary production , 2008 .

[14]  F. Rassoulzadegan,et al.  Plankton and nutrient dynamics in marine waters , 1995 .

[15]  N. Cliff Dominance statistics: Ordinal analyses to answer ordinal questions. , 1993 .

[16]  Lucas N Joppa,et al.  Network structure beyond food webs: mapping non-trophic and trophic interactions on Chilean rocky shores. , 2015, Ecology.

[17]  Mason A. Porter,et al.  Multilayer networks , 2013, J. Complex Networks.

[18]  Mark A. Bedau,et al.  Is Weak Emergence Just in the Mind? , 2008, Minds and Machines.

[19]  Maria Serban,et al.  Exploring modularity in biological networks , 2020, Philosophical Transactions of the Royal Society B.

[20]  Eugene P. Odum,et al.  Trends Expected in Stressed Ecosystems , 1985 .

[21]  Olivier Sartenaer,et al.  Sixteen Years Later: Making Sense of Emergence (Again) , 2016 .

[22]  Nathalie Niquil,et al.  Evaluating ecosystem-level anthropogenic impacts in a stressed transitional environment: The case of the Seine estuary , 2016 .

[23]  Stefano Allesina,et al.  Detecting Stress at the Whole-Ecosystem Level: The Case of a Mountain Lake (Lake Santo, Italy) , 2006, Ecosystems.

[24]  David Chavalarias From inert matter to the global society life as multi-level networks of processes , 2020, Philosophical Transactions of the Royal Society B.

[25]  R. Solé,et al.  Convergent Evolutionary Paths in Biological and Technological Networks , 2011, Evolution: Education and Outreach.

[26]  Neo D. Martinez,et al.  Food webs: reconciling the structure and function of biodiversity. , 2012, Trends in ecology & evolution.

[27]  P. Humphreys Emergence: A Philosophical Account , 2016 .

[28]  Z. Wang,et al.  The structure and dynamics of multilayer networks , 2014, Physics Reports.

[29]  Stuart R. Borrett,et al.  Walk partitions of flow in Ecological Network Analysis: Review and synthesis of methods and indicators , 2019, Ecological Indicators.

[30]  Stuart R. Borrett,et al.  Indirect effects and distributed control in ecosystems: Comparative network environ analysis of a seven-compartment model of nitrogen storage in the Neuse River Estuary, USA: Time series analysis , 2014 .

[31]  John T. Finn,et al.  Flow analysis of models of the Hubbard Brook Ecosystem. , 1980 .

[32]  S. Borrett Throughflow centrality is a global indicator of the functional importance of species in ecosystems , 2012, 1209.0725.

[33]  M. Haraldsson,et al.  A new type of plankton food web functioning in coastal waters revealed by coupling Monte Carlo Markov chain linear inverse method and ecological network analysis , 2019, Ecological Indicators.

[34]  E. J. Heald,et al.  Mangrove Forests and Aquatic Productivity , 1975 .

[35]  Karline Soetaert,et al.  xsample(): An R Function for Sampling Linear Inverse Problems , 2009 .

[36]  Karline Soetaert,et al.  Quantifying Food Web Flows Using Linear Inverse Models , 2009, Ecosystems.

[37]  S. Tecchio,et al.  Microbial parasites make cyanobacteria blooms less of a trophic dead end than commonly assumed , 2018, The ISME Journal.

[38]  Stuart R. Borrett,et al.  A comparison of network, neighborhood, and node levels of analyses in two models of nitrogen cycling in the Cape Fear River Estuary , 2014 .

[39]  E. Mayr The Growth of Biological Thought: Diversity, Evolution, and Inheritance , 1983 .

[40]  Neil Rooney,et al.  The more food webs change, the more they stay the same , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[41]  Laura Sheble,et al.  Bibliometric review of ecological network analysis: 2010–2016 , 2018, Ecological Modelling.

[42]  Charles Rathkopf,et al.  Network representation and complex systems , 2015, Synthese.

[43]  Stuart R. Borrett,et al.  enaR: An r package for Ecosystem Network Analysis , 2014 .

[44]  Bruce Hannon,et al.  Ecological network analysis : network construction , 2007 .

[45]  L. Legendre,et al.  Planktonic food webs revisited: Reanalysis of results from the linear inverse approach , 2014 .