I have a new paper in PLoS One with open text, data and code. I like this paper for several reasons. First, is the fruit of the 20% rule. That is, using 20% of my time to side risky projects. I decided to take a webinar on hierarchical models to estimate occupancy last summer. I am not sure why I did it (well, it was free), but the idea of incorporating detectability processes to study animal occupancy was appealing to me. However, I was pretty sure I will not use this models any time soon and I had quite a lot of other things to do, but I decided to “lose” a week anyway attending to it. I didn’t make the connection to apply this concept to plant-pollinator networks right away, but like a month later I got the “aja” inspiration, and I realized that those models can be applied to networks as well. And this resulted in my first single author paper. Not bad!
The basic idea is that doing field work is very time-consuming, and you can not watch all plants species for an infinite time, hence the chances that you detect all pollinators visiting your target plants are low, specially for rare pollinators. Hence, when trying to model what makes a pollinator decide to visit a plant or another, you can not use this raw data because rare species will appear as specialists, when they are probably not. But I show in the paper how to bypass that limitation. You will have to read it to know how, but it involves the above mentioned modelling approach.
I also like the paper for second reason, and is that I think I made a good point on seeing the network as dynamic process. The models use floral traits to predict visitation. Hence, you can use this models to predict which pollinators will visit an invasive plant entering the network. Moreover, I show that I can predict re-wiring of links after a species is removed from the network. Cascading extinction models are quite popular, but do not incorporate this dynamical effects. I know that Jeff Ollerton et al. (who I hope is reading this) have some experimental data that test this “re-wiring” after a plant extinction, so I would love to see if the models predict their data correctly. I think the next big thing will be to incorporate a more dynamic view to p-p networks.
In any case, If you are field biologist don’t be afraid for the “hierarchical modelling” part, (which is not bayesian, but uses likelihood!) and give it a read, because I think I managed to explain it in quite plain English.