A few weeks ago I announced a project that would train an algorithm to recognize important taxa in an ecosystem using the characteristics of species interactions within that ecosystem. This post documents the first bit of work I’ve done. I’ve made a github repo with data and code. I’m using Python 2.7 with NetworkX.
First I thought I would start with a simple, well-studied ecosystem, the rocky intertidal system made famous by Robert T Paine. This system has eight taxa with 24 relationships between them. This system has a keystone predator, the starfish Pisaster. All the interactions in this network are either “eats” or “competes”. If I run a medley of calculations over this network I notice pretty quick that there’s no super clear way to pick out Pisaster as the keystone predator. There are also several parameters that don’t seem to be all that useful for this application. At least I got the code to run, though. I was able to make the data file, use it to make a graph, visualize the graph, and do some calculations.
I tried doing the calculations again, only I left out some of the less helpful calculations and separated out the “eats” interactions. Since Pisaster is a keystone predator, I thought examining the trophic relationships separately might be worth a try. These results were much more interesting because Pisaster has the highest value for five centrality measures. Centrality might be a way to identify keystone predators. There are others who have also had this thought (here and here), so I feel confident I am on the right track.
My next worries:
- Is this ecosystem too small to tell me anything real? I need to work on a larger, more complicated network to see if this pattern holds up.
- Can I use the method of separating out a specific type of interaction to identify other types of important species, such as ecosystem engineers or keystone pollinators? I need to find well-studied ecosystems that have other types of important species.
Featured image by D. Gordon E. Robertson – Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=6434467