A brief introduction to network science
Using social network analysis, Mann J. et al. (2012) were able to reveal cultural behavior among sponge tools using bottlenose dolphins. Image copyright Douglas Wilcox under Creative Commons License.
Our shared NorMER blog has already covered several important issues: maximum sustainable yield, boat accidents, DNA, taxes, computational modeling, fun team work, how much we actually think about cod, human habits, conferences etc. No matter how far away some of these topics seem to be from each other, with closer examination one can start to notice how tightly connected they actually are, so that with paper and pen, it would be possible to draw a graph of the several connections: a network of marine science subjects. You could also add the blog authors, PhD supervisors and other NorMER associates into your sketch, and end up with a graph of the international network of marine researchers called NorMER.
Researchers in several disciplines have increasingly started to study their systems as networks. Using techniques like those in the example above, by creating a graphic, theoretical network showing the structures and positions of different nodes (e.g. institutions, species), we can study the interactions between the nodes and see how they give rise to larger-scale patterns and properties of a whole network (Cumming et al., 2010, Borgatti et al., 2009). Also, it becomes more apparent that a node's position in a network partly determines the opportunities and constraints that it will be faced with.
Using network science in ecology is not a new thing. Ecological network analysis brings the concepts of food webs together as a number of compartments that are connected to each other through flows of energy or matter (Wulff et al., 1989). Ecological network analysis methods can for instance predict species survival or a community's response to anthropogenic stress (Dunne et al., 2002).
One fun part of network analysis is visualizing data. Here I used Network3D (software: Williams, R.J., 2010 & Yoon et al., 2004) to view the Central Baltic Sea food web data.
Since ecological network analysis already exists, why would anyone want to use social network analysis approaches to study cod and climate change? This is where the interdisciplinary thinking comes into the picture. Due to sociology’s remarkable, long-term focus on structure and position, social network analysis provides a rich set of structural concepts and computational procedures for analyzing a network (Johnson et al., 2003). That being so, if the concept structural equivalence in social science is strikingly similar to trophic role concept in ecology (Luczkowich et al., 2003) for instance, would it be possible to borrow structural equivalence computational methods for ecological data? The answer is yes: social network analysis methods have already successfully been used in ecological studies for example to conceptualize the roles of species in an ecological community (Johnson et al., 2003; Luczkowich et al., 2003) and to study habitat fragmentation (Bodin & Norberg, 2007), not to forget animal behavior studies (e.g. Mann et al., 2012). Ecological studies may benefit from borrowing some of the social network analysis tools and concepts for application in ecological systems, as long as one is careful with the selection of parameters and result analysis (Johnson et al., 2003).
The effects of climate change in nature intersect through several different changes in various parts of the ecosystem. Although the climate impact on marine systems is external, the internal processes (climate-induced changes in our marine network nodes and the links between the nodes) will, to a large extent, modulate the climate effect. By developing accurate network analysis tools we may be able to better predict the future changes in our seas. In addition, network analysis is a promising tool for studying seas as social-ecological systems, but that is a whole another story. For now, the take-home-message about climate and Nordic marine resources seen from a network perspective could be summarized by this sentence (by Paul Hawken): “All is connected... No one thing can change by itself.”
Bodin, Ö. & Norberg, J. 2007. A network approach for analyzing spatially structured populations in fragmented landscape. Landscape Ecol. 22, 31-44.
Borgatti S.P. et al., 2009. Network analysis in the social sciences. 2009. Science 323, 893-895
Cumming, G.S. et al., 2010. Network analysis in conservation biogeography: challenges and opportunities. Diversity and Distributions 16, 414-425.
Dunne, J.A. et al. 2002. Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecology Letters 5, 558-567.
Johnson, J.C. et al. 2001. Network Role Analysis in the Study of Food Webs: An Application of Regular Role Coloration. Journal of Social Structure 2(3).
Luczkovich, J.J. et al. 2003. Defining and measuring trophic role similarity in food webs using regular equivalence. J.Theor. Biol. 220, 303-321.
Mann, J. et al. 2012. Social networks reveal cultural behavior in tool-using dolphins. Nature Communications 3: 980.
Williams, R. J. 2010. Network3D Software. Microsoft Research, Cambridge, UK.
Wulff, F., Field, J.G., Mann, K.H. (Eds.) 1989. Network analysis in marine ecology. Methods and applications. Springer-Verlag, Berlin Heidelberg. 292 p.
Yoon, I. et al.. 2004. Webs on the Web (WoW): 3D visualization of ecological networks on the WWW for collaborative research and education. Proceedings of the IS&T/SPIE Symposium on Electronic Imaging, Visualization and Data Analysis 5295:124-132.