Invasive plants and plant pollinator networks

We have a new article revisiting the topic of my PhD thesis, but with a twist. Invasive plants effects on network modularity. Back then I already explored a bit the effects of invasive plants on the modular structure of the plant-pollinator community, but I never published any result, among other things because I didn’t understand well what I was doing. That’s why when Matthias asked me to join his paper addressing this question with a bigger dataset I was very happy to give it a second try.

Now (6 years later!) I understand two key things way better. First, that invasive plants have different roles in the network than natives, not because they are not native, but because of its different characteristics (i.e. very abundant and generalized). Second, that it is more interesting to understand how the roles that different species play within the network change, than how the overall network structure change, mainly because very different networks can present very similar structures (i.e. nested and modular). I think we nicely present this two points in the paper. See the figure below, and read the paper if you a curious about knowing more.

Ntw

Linkage Rules in Plant-Pollinator Networks

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.

Food webs: reconciling the structure and function of biodiversity. Really?

I read this paper (Food webs: reconciling the structure and function of biodiversity; Thomson et al. 2012 Trends in Ecology & Evolution, 27(12):689-697) with great interest because the title is really promising. Indeed it is nice overview of what’s out there in terms of network and functioning, but not much reconciliation. First I have the feeling that community ecologists (even if they don’t use network metrics) are already (and have been for a long time) on the framework they describe in Table 1C. But my main concern is that I missed an answer to the question: What can a network approach add to the study of ecosystem functioning?

Well, I have two ideas that can help answering that.

1) Network approach can be very useful when the function itself is defined by the network. If you are studying pollination or pest control, the actual function delivered is contained in the network structure, hence species richness, diversity or composition (or new metrics, like FD) can be unable to fully explain functionality  because what confers high levels of function (or stability) to the community is the network properties (e.g if it’s modular, generalized or well connected). I know some pople is on that path, so I am looking forward to see what they find.

2) Another situation where networks can make the difference is when indirect interactions modulate the function, but are too complex to track them one by one. Networks can describe better phenomenons like apparent competition or cascading effects than any other classical approach. If this type of complex interactions are relevant for the level of functioning measured (e.g. productivity of the basal level), then, adding the network perspective can be more informative than classical approaches.

May be what I am saying is too obvious, so the authors didn’t cover it, or I may be missing something, but this is the direction I would like to see things moving.

SCAPE-2012 meeting highlights

Last weekend I attended the SCandinavian Association for Pollination Ecologists (SCAPE) meeting. I had a great time there, with many “big names” among the attendants (and very interesting “small names” too!). Compared to the last ESA meeting I attended in Portland this summer, with more than 4000 people and 13 parallel seasons running all day, having only 60 people in the same cozy room was a change. Both formats has its functions, but I think is usually more productive the small and informal gathering.

Before a brief summary of the best talks (according to my biased interests), I want to mention that I am surprised on the big gap between population ecologists (mainly plant ecologists) and community ecologists (networks and landscape stuff). I am clearly guilty of only thinking at the community (and ecosystem) levels, so it was nice to be reminded about genes and specific process occurring at lower levels.

Four talks I liked:

Amots Dafni gave a great talk dismounting and old and beautiful hypothesis suggesting that floral heat reward attracts males to overnight inside the flower, and hence pollinate the plant. Despite the idea is neat, and flowers are indeed around 2ºC warmer than the environment, warmer flowers (those facing east and getting the morning sunlight) did not host more bees. They also show that no other reward is offered, and that no bee-attractive volatile compound was produced as a deceptive attraction mechanism (like the one in some orchids). The icing of the cake was showing that the bees visually perceive the flower entrance as a hole or crevice (i.e. black), indicating that the most parsimonious explanation is that flowers use shelter mimicry to attract the males. For me the most important point was to don’t get too attached to beautiful hypothesis, as often they are not supported when tested rigorously.

Erin Jo Tiedeken (in Jane Stout lab) showed that bumblebees (B. terrestris) can not detect natural levels of toxics (both natural plant toxics and insecticides) in the nectar (lab conditions). Most toxic compounds have low volatility, so that’s bad news for bees exposed to Neonicotinoids.

Robert Junker showed that floral bacterial community is more similar among flowers of different plants, than among different organs (e.g. leafs) of the same plant. Not sure what to do with that, but it’s intriguing!

Jan Goldstein did an experiment (unfortunately un-replicated) removing a network hub from a plant-pollinator network. This is a common practice on simulations to assess robustness of the networks. In those simulations when a species loses all their links is assumed to disappear from the network, however, Jan showed that most species visiting the hub, just change its visitation pattern to another plant when this hub is removed experimentally (i.e. re-wiring). Tarrant and Ollerton have a similar experiment with consistent results and I hope its published soon.

My slides here.