Are exotic plants good for pollinators?

Answer quickly. Do you think most pollinators can use exotic plants, and hence will probably benefit from them? My gut feeling was to answer yes, but I am not convinced after seriously reviewing the available evidence.

A while ago I accepted to write a book chapter on the interface between behaviour and invasive species. I really like the idea that pollinators behaviour mediates its responses to environmental changes, including plant invasions. Hence, the main point of the book chapter is that “not all pollinators respond equally”. Yes, the idea of winners and losers of the global change is becoming a leitmotiv in my research.

Doing a book chapter allowed me to do a review, an opinion paper, and throw in some re-analysis of old data for supporting  my claims all in one. I am pretty happy about the result because it crystallise a lot of thoughts I had since my PhD and identifies important knowledge gaps.

If you want to read a draft before the book gets published, you can find a pre-print here: Invasive plants as novel food resources, the pollinators’ perspective.

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is_invasive( ) got a new best friend: is_native( )

A while ago I did a function to check if a species was among the worst invaders. I am really happy this function was used and ropensci put it in a package. However the function does not tell you if a species is exotic in a particular place, and this is what most people want to know. Scott Chamberlain found a couple of other resources to get invasive species lists and we where discussing where to find those reliable, complete “lists of exotic species per region”, but we where thinking the problem from the wrong approach.

Exotic species lists will be always incomplete and hard to maintain. For most research questions the reverse question is also suited. Instead of “is exotic?”, you can ask “is not native?” Lists of natives species are easier to get and stable through time. Moreover, It conveys the message that any non native species is potentially harmful, rather than restricting to “worst invaders” or “known exotic species”.

So here it is. Its implemented for animals and plants in the US (using ITIS database) and for Plants in Europe (Using Flora Europaea)*. You can use it with the following R code:

install.packages("devtools")
library(devtools)
install_github("ibartomeus/traits")
library("traits")

#make a species list
sp <- c("Lavandula stoechas", "Carpobrotus edulis", "Rhododendron ponticum", "Alkanna lutea", "Anchusa arvensis")

#ask in which countries the first species is native by querying in Flora Europaea
fe_native(sp[1])
?fe_native #to see the help page.

#use sapply for querying all species at once
sapply(sp, fe_native, simplify = FALSE)

#ask if the first species is native in a particular region
is_native(sp[1], where = "Islas_Baleares", region = "europe")
?is_native #to see the help page and country names used

#or all species at once
sapply(sp, is_native, where = "Continental US", region = "america")
sapply(sp, is_native, where = "Islas_Baleares", region = "europe")

#for america I am calling itis_native function from taxize package.

The function will be available from ropensci/traits soon, and probably Scott will make it faster and more usable. Let me know if it breaks with any species or if the help pages needs clarifications and I can try to fix it.

*If you know/have a list of all native species for your region of interest, we can add it.

Which plants are the influencers in plant-pollinator networks?

My PhD looked at two invasive plants that has contrasting effects on the native plant-pollinator network. Since then we advanced quite a lot on understanding why superabundant invasive plants with high reward levels can influence others via its shared pollinators, but other less abundant or rewarding exotics don’t. Today, we have a new synthesis paper (Open Access!) formalizing this ideas for any plant species in the network. We analyze lots and lots of plant-pollinator networks to find some generalities. The catch is that we use an index that calculates the potential for one plant to influence another plant. For example, if two plants share only one pollinator and this one do not visit anything else except this two plants, the influence will be very high. On the other hand, if this pollinator also visit lots of other plants, the influence will be lower (see the paper for details). The nice thing is that we can identify some plant traits that make them “influencers”, like plants offering abundant resources and open flowers. It’s a shame that we couldn’t tell (yet?*) if the influence is positive or negative, but at least we can identify key influential plants within the network.

*It may be a way to test for that and at some point we talked about a follow-up, but who knows…

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

is.invasive( )

Celebrating that I am contributing to the R-bloggers.com blog aggregator I am going to post a very simple function to check which species (both plants and animals) are considered “invaders” somewhere in the world. Basically the function asks that to the Global Invasive Species Database (GISD).

I coded this because a friend of mine aks me precisely that question [Yes, friends assumes you should know this kind of stuff (and also why the plants of their balcony are dying) off the top of your head just because you are a biologist]. However, I do not know much things and I am too lazy to check all 250 species one by one on the GISD webpage. Also is a good R practice, and I am ok investing some work time on personal projects. Google (and other big companies) encourage it’s employees to spend 20% of the time working on projects that aren’t necessarily in their job descriptions in order to bust its innovation power, so that should be even more important in science!

Hope it can be useful to more people, I uploaded the code as a Gist:

UPDATE: The function is now available on taxize R package developed by the rOpenScience people!

Can niche and fitness differences explain biological invasions

Following up with my “Theory vs Data” post, I want to share an example of a beautiful theoretical framework to understand the invasion processes and an idea on which can be the perfect study system to validate it.

Mc Dougall et al (2009) have one of the more compelling figures I saw summarising the hypothesis that niche and competitive differences between exotic and native species can explain the outcome of the invasion process. The figure speaks for itself:

While I was in the US I planned to use this framework to understand the effects of Osmia cornifrons (a mason bee) invasion on the native Osmia lignaria, but I had no time to follow-up on this. Anyway, for that end you would need to prove the following:

1) Are their niches overlapping? Both bees are on the same genus, have similar size, phenology, nesting habits, and probably visit similar flowers, you just need to put numbers on those things (e.g. which hole diameter they prefer to nest on). For example, this data is from a preliminary experiments I did on its phenology. Interestingly, the result suggest the invader emerge slightly (but significantly) earlier than the native. So, quantifying all this can be important.

2) Is their fitness different when raised alone? Buying this bees and monitoring its nests is easy. Moreover, measuring offspring (a fairly good proxy of fitness) is a piece of cake compared with other species. Well, at least in theory, because I tried it in 2011 and an April snowstorm killed 80% of both populations. Hence, I have no data here.

3) It’s the native fitness lowered when they are raised together? That’s an important part (especially the effect size), because they may coexists just fine (even if sharing niches).

I am not in the US anymore, so, is impossible for me to do the experiments. If anyone wants to explore this idea further (undergraduates seeking for a project, jump in!), the idea is here, and it’s for free!

———————————————————————————————-
MacDougall A.S., Gilbert B. & Levine J.M. (2009). Plant invasions and the niche, Journal of Ecology, 97 (4) 609-615. DOI: ———————————————————————————————-

And now, what can occur with this post? 

– Worst thing it can happen is that nothing happens.

– Will be pretty cool if someone does this or similar experiments, even if I never know of its existence (well, I hope at least not to miss the article when it gets published!). I would be happy with that because despite I did some thinking on this I assume this ideas are “on the air”, and that’s precisely why I post them here.

– Will be awesome if that someone also contact me and we end up collaborating. (incentive: I have also some ideas on how to analyse it)

– Will be terrific (I am running out of superlatives) if people start reporting they have data on niche and fitness differences for other systems and we end up with a meta-analysis proving (or disproving) that this theory can correctly predict invasion outcome with some generality. For example, where is propagule pressure fitting in this framework? Niche and population growth/species traits hypothesis clearly are captured here, you can even account for lowered native fitness due to disturbance (wow!), but the number of invaders arriving may be a missing piece. Also scaling up to the community level seems a daunting task.