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Ecological Network Metrics: Opportunities for Synthesis University of Vermont ScholarWorks @ UVM College of Arts and Sciences Faculty Publications College of Arts and Sciences 8-1-2017 Ecological network metrics: Opportunities for synthesis Matthew K. Lau Harvard Forest Stuart R. Borrett University of North Carolina Wilmington Benjamin Baiser University of Florida Nicholas J. Gotelli University of Vermont Aaron M. Ellison Harvard Forest Follow this and additional works at: https://scholarworks.uvm.edu/casfac Part of the Climate Commons, Community Health Commons, Human Ecology Commons, Nature and Society Relations Commons, Place and Environment Commons, and the Sustainability Commons Recommended Citation Lau MK, Borrett SR, Baiser B, Gotelli NJ, Ellison AM. Ecological network metrics: opportunities for synthesis. Ecosphere. 2017 Aug;8(8):e01900. This Article is brought to you for free and open access by the College of Arts and Sciences at ScholarWorks @ UVM. It has been accepted for inclusion in College of Arts and Sciences Faculty Publications by an authorized administrator of ScholarWorks @ UVM. For more information, please contact [email protected]. INNOVATIVE VIEWPOINTS Ecological network metrics: opportunities for synthesis 1, 2,3 4 5 MATTHEW K. LAU, STUART R. BORRETT, BENJAMIN BAISER, NICHOLAS J. GOTELLI, 1 AND AARON M. ELLISON 1Harvard Forest, Harvard University, Petersham, Massachusetts 02138 USA 2Department of Biology and Marine Biology, University of North Carolina, Wilmington, North Carolina 28403 USA 3Duke Network Analysis Center, Social Science Research Institute, Duke University, Durham, North Carolina 27708 USA 4Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida 32611 USA 5Department of Biology, University of Vermont, Burlington, Vermont 05405 USA Citation: Lau, M. K., S. R. Borrett, B. Baiser, N. J. Gotelli, and A. M. Ellison. 2017. Ecological network metrics: opportunities for synthesis. Ecosphere 8(8):e01900. 10.1002/ecs2.1900 Abstract. Network ecology provides a systems basis for approaching ecological questions, such as fac- tors that influence biological diversity, the role of particular species or particular traits in structuring ecosystems, and long-term ecological dynamics (e.g., stability). Whereas the introduction of network theory has enabled ecologists to quantify not only the degree, but also the architecture of ecological com- plexity, these advances have come at the cost of introducing new challenges, including new theoretical con- cepts and metrics, and increased data complexity and computational intensity. Synthesizing recent developments in the network ecology literature, we point to several potential solutions to these issues: inte- grating network metrics and their terminology across sub-disciplines; benchmarking new network algo- rithms and models to increase mechanistic understanding; and improving tools for sharing ecological network research, in particular “model” data provenance, to increase the reproducibility of network mod- els and analyses. We propose that applying these solutions will aid in synthesizing ecological sub-disci- plines and allied fields by improving the accessibility of network methods and models. Key words: benchmarking; computation; data provenance; metrics; network ecology; systems analysis. Received 8 April 2017; revised and accepted 26 May 2017; final version received 28 June 2017. Corresponding Editor: Debra P. C. Peters. Copyright: © 2017 Lau et al. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. E-mail: [email protected] INTRODUCTION role of species that are the main drivers of com- munity structure (e.g., keystone species, Paine Interactions are at the heart of ecology and 1966), ecosystem engineers (Jones et al. 1994), or drive many of its key questions. What are the foundation species (Dayton 1972, Ellison et al. roles of species interactions in ecological systems? 2005); and the causes and consequences of intro- When and why is biological diversity important? ducing new species into existing assemblages What factors influence the long-term dynamics of (Simberloff and Holle 1999, Baiser et al. 2008). ecosystems? These are all questions with a long Furthermore, “systems thinking” has been a per- history in ecology (Cherrett 1989, Lubchenco sistent thread throughout the history of ecology et al. 1991, Council 2003, Sutherland et al. 2013) (Odum and Pinkerton 1955, Margalef 1963, Pat- that are not addressed in isolation. Points of inter- ten 1978, Patten and Auble 1981, Ulanowicz section include the relationship between diversity 1986), dating back at least to Darwin’s Origin of and stability (May 2001, 2006); the identity and Species in his famous pondering of an entangled ❖ www.esajournals.org 1 August 2017 ❖ Volume 8(8) ❖ Article e01900 INNOVATIVE VIEWPOINTS LAU ET AL. bank (Golley 1993, Bascompte and Jordano 2014). observations, and increasing the resolution (e.g., The application of network theory has provided a individualsortraitsasnodesandweightededges formal, mathematical framework to approach of different interaction types) and replication of systems (Proulx et al. 2005, Bascompte and network models across different ecosystems and Jordano 2014) and led to the development of time (Ings et al. 2009, Woodward et al. 2010, Poisot network ecology (Patten and Witkamp 1967, et al. 2016b). After a brief primer of key concepts Borrett et al. 2014, Poisot et al. 2016b). from network ecology, we discuss the following Network ecology can be defined as the use of topics as they relate to these issues: the prolifera- network models and analyses to investigate the tion of terminology for ecological metrics with the structure, function, and evolution of ecological increasing application of network methods; fully systems at many scales and levels of organization exploring the underlying assumptions of models (Borrett et al. 2012, Eklof€ et al. 2012). The influx of mechanistic processes for generating network of network thinking throughout ecology, and structure; and the need for improved sharing and ecology’s contribution to the development of reproducibility of ecological network research and network science highlight the assertion that models. Although these topics are not new, the “networks are everywhere” (Lima 2011). And, as combination of the influx of metrics and theory one would expect, the field has grown rapidly, and rapid increases in the computational intensity from 1% of the primary ecological literature in of ecology is creating novel challenges. With 1991 to over 6% in 2017 (Fig. 1A). Some examples respect to these issues, we discuss recent advances include the following: applying network theory that should be explored as tools to aid in a more to population dynamics and spread of infectious effective integration of network methods for syn- diseases (May 2006); description and analysis of thesis across ecological sub-disciplines. networks of proteins in adult organisms (Stumpf et al. 2007) or during development (Hollenberg APRIMER OF ECOLOGICAL NETWORKS: 2007); expanding classical food-web to include MODELS AND METRICS parasites and non-trophic interactions (Ings et al. 2009, Kefi et al. 2012); investigating animal move- Prior to the introduction of network methods ment patterns (Ledee et al. 2016) and the spatial in ecology, the primary way of studying interac- structure of metapopulations (Holstein et al. tions was limited to detailed studies of behaviors 2014, Dubois et al. 2016); connecting biodiversity and traits of individual species important to to ecosystem functioning (Creamer et al. 2016); interactions, or of relationships between tightly identifying keystone species (Borrett 2013, Zhao interacting pairs of species (Carmel et al. 2013). et al. 2016); and using social network theory in Some ecologists were advancing whole-system studies of animal behavior (Krause et al. 2003, methods (Lindeman 1942, Odum 1957); however, Croft et al. 2004, Sih et al. 2009, Fletcher et al. quantifying interactions is costly, as compared to 2013). Further, ideas and concepts from network surveys of species abundances. This has created ecology are being applied to investigate the sus- a significant barrier to studying interactions at tainability of urban and industrial systems (Fang the scale of entire communities, either at the scale et al. 2014, Layton et al. 2016, Xia et al. 2016) and of individuals or species pairs, because the elements of the food–energy–water nexus (Wang number of interactions becomes intractable. For and Chen 2016, Yang and Chen 2016). instance, even if one assumes that only pairwise Over the past 15 yr, re-occurring themes for interactions occur among S species, the number moving network ecology forward have emerged of possible pairs is S(S À 1)/2. Local assemblages from reviews, perspectives, and syntheses (e.g., of macrobes often have 101–102 species, and Proulx et al. 2005, Bascompte 2010, Borrett et al. microbial diversity can easily exceed 103 opera- 2014, Poisot et al. 2015). In this paper, we examine tional taxonomic units (OTUs). areas where the network approach is being applied This complexity of ecological systems is one to address important ecological questions and reason there is a long tradition in community identify both challenges and opportunities for ecology of studying interactions within small advancing the field. Among these are the need for subsets of closely related species
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