We have a new correspondence article about bee declines that tries to walk the fine line between a non-helping pessimistic attitude about pollinator declines and an unrealistic optimism. As I said before, I think is easier to defend a black or white position about the pollinator crisis, but I think is time to discuss the grey areas. So here I go:
We show two straight forward things. First, that recent papers showing 50% of bee extinctions and papers showing moderate 15% declines (that’s our paper!) are not reporting conflicting results. Is just a matter of scale. Local scale extinctions in heavily altered habitats translate into population declining trends at the regional scale. To read it in positive, we are still on time to revert this declining trends, because the species are there!
Second, we show that not all species respond equally to global change threads, for example some species love agricultural areas. Most important, seems that the species that thrive in crop fields, are the ones responsable of increasing crop production, so the best current ecosystem service providers (a.k.a. bees that visit crops) may be not as threatened as other bees. But please do not take that as “we don’t have to worry at all”. This is the “grey area” where we need to be clear that highly intensified agricultural areas (e.g. huge almond fields in California) may still suffer pollinator shortages. Similarly, we are talking here about crop pollination, but there is a growing evidence that all species are important to maintain (and stabilize) ecosystem functioning in natural areas. So the good news are only partial.
Read it, is a very short piece and is Open access. If someone is curious about F1000Research, just two lines to say that we choose it for the flexibility of formats they allow, the speed of publication and because I was very eager to see how post peer review works. So far we had two very positive reviewers (which made the article indexed in less than 24 Hours!), but no more comments. Is also a short piece so maybe there is not much else to comment?
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.