Diversity: It Matters! (for plants & algae)

ResearchBlogging.org Geological time has witnessed 5 extinction crises. Now we’re in the middle of the 6th – this time driven by man. What’s unprecedented, however, is the rate we are driving species extinct. For many taxa, it’s faster than we’ve seen in geological time (see here for discussion).

So? Will vast reductions in the diversity of species on earth matter? I mean, heck, maybe we only need two or three of each taxa, and we’re all good. Or maybe not…

A summary of the types of studies linking plant species richness to other functions found in the literature.

This question has been the driver behind the field of diversity-function research – a young (20-ish) discipline in ecology. But the pace of research has been furious. While a series of meta-analyses peered into the field in 2006, the number of publications has since tripled, allowing us to obtain a clarity not capable just five years ago, particularly when it comes to plants and algae. These results are interrogated by a new meta-analysis by Brad Cardinale and colleagues (including me!) (and some other ocean bloggers!) where we wask some hard questions and come up with some intriguing new answers.

So what do we know?

First, yes. Despite some near vitriolic arguments in the past, plant and algal diversity does increase ecosystem productivity and nutrient use. On average (What? I’m a scientist!).

On other functions, things are more murky. While on average it alters rates of decomposition (think – where do all of those fallen leaves go?), the results are fairly ecosystem dependent. Moreover, we STILL after 20 YEARS do not have enough experiments to tell whether diversity influences rates of herbivory.

Kind of shocking, no? And, having looked at the fullness of the dataset, don’t even get me started on whether diversity of animals influences ecological phenomena. We just need more data.

So that’s the net answer, but what are the ooey-gooey nuances? This is where things get interesting.

Why does diversity matter? Is it that different species really do different things? Or is it that one species does it all, and, by including many species in a plot you increase your likelihood of including that strongly performing species? This is another question that has caused arguments, recriminations, and no small amount of teeth gnashing.

And yet, the answer, unsurprisingly, is both. As Lars Gamfeldt, a co-author and all-around good egg puts it, “Plant communities are like a soccer team. To win championships, you need a star striker that can score goals, but you also need a cast of supporting players that can pass, defend, and goal tend. Together, the star players and supporting cast make a highly efficient team.”

OK, so, it’s both, but…What about the old saw of the rivet hypothesis? The rivet hypothesis states that ecosystems are like the wings of a plane and species are rivets. You can loose a number of rivets and witness no change in function. But, once you start getting to those crucial few…well, things fall apart. We test this idea by fitting three different curves to the data – one consistent with the rivet idea (saturating curve), a linear curve (every species matters), and an exponential curve (species are synergistic, to use business-speak).

Indeed, looking at the data, diversity functions like rivets. There is a point of saturation – where more diversity gives diminishing returns.

A comparison of three models of how biodiversity influences ecosystem function with the percentage of studies that are best described by each model. In summary, diversity: it's like the rivets on an airplane wing.

This leads to a logical next question: how many species are needed to maintain an ecosystem? This question is big, huge, daunting, and any answer we could ever give may have some frightening implications in its use. It’s one of those quantities that scientists are, honestly, loath to name – and, indeed, as a group we’ve had a lot of arguments about how and why to make this calculation.

But, it’s the logical next question, so, we decided to take a stab at it, and Brad used our curve fits to generate some estimates that ask, what is the fraction of species, relative to what we’ve used in our experiments, necessary to maintain 50-90% of production, nutrient use, and decomposition?

A probability distribution based on modeled results asking, what is the fraction of species from an experiment needed to maintain 90% of plant primary production. The arrow points to the mean at 8.27X more species than were included in the maximum diversity treatment.

This is a total back-of-the-envelope rough ballpark estimate. Yet, what we see is kind of surprising. Namely, if we want to achieve 90% of function in our experiments, we have not yet even sampled the amount of diversity necessary. This means that, while our experiments are doing a good job at looking at the diversity of species needed to hit 50% of an ecosystem’s total possible function, we just don’t have the data necessary to peer beyond that – not yet – and that the number of species is quite possibly higher than we have used in the majority of experiments to date.

A word of caution, though, from the text: “We would caution against taking these numbers too literally at this point, since it is hard to imagine that a researcher could place 10× more species into a small experimental plot and expect those species to coexist”. While diversity may need to be higher to achieve this maximum productivity, we simply don’t have the data necessary to get an accurate result here. More studies like BIODEPTH Switzerland, with it’s huge diversity of species, may be necessary to sample this area of the productivity curve.

Still, though, we find that diversity effects get stronger as spatial and temporal scales are increased. Yet most experiments are conducted for no longer than a year at the scale of a bucket (yes, I, too, am guilty of this!) This appears to be a general phoenomenon.

So, what’s the takehome? After 20 years, we know that plant diversity matters. Even at small scales. And not just because of including a single species at high diversity.

Done.

If someone tells you otherwise, they likely have an agenda.

But how many species do we really need? How much does diversity matter relative to other drivers of global change (e.g., climate change, nutrient pollution, etc.)? Does diversity loss interact with these other drivers? How do these effects scale? Is space or time more important? How do the functions measured here connect to human services? And while we have a full picture of plants, we do not have a similar clear picture for animal diversity, or, indeed, even something as simple as plant and algal effect on herbivory.

Agh! So much to be done! So much to be synthesized (this paper was part of an NCEAS working group – out first meeting – and having just finished our second meeting, man, we’re cooking!).

But we’re getting there. And the answers seem to be falling in line with theory.

Cardinale, B., Matulich, K., Hooper, D., Byrnes, J., Duffy, E., Gamfeldt, L., Balvanera, P., O’Connor, M., & Gonzalez, A. (2011). The functional role of producer diversity in ecosystems American Journal of Botany, 98 (3), 572-592 DOI: 10.3732/ajb.1000364

More, Please!

Thanks to Jim, I’ve been using R in the shell more and more – in concert with vi. It’s been fun, and nice to integrate my workflows all on the server (although I haven’t had to do much graphing yet – I’m sure I’ll start kvetching then and return to a nice gui).

One thing that has frustrated me is that large dumps of output – say, a list composed of elements that are 100 lines each – just whip past me without an ability to scroll through more slowly. The page function helps somewhat, but, it gets wonky when looking at S4 objects. I wanted something more efficient that used – something more…well, like more! So i peered into page, and whipped up a more function that some of you may find useful. Of course, I’m sure that there is a simpler way, but, when all else fails…write it yourself!

more<-function(x, pager=getOption("pager")){
        #put everything into a local file using sink
	file <- tempfile("Rpage.")
        sink(file)
        show(x)
        sink()

        #use file.show as you can use the default R pager
	file.show(file, title = "", delete.file = TRUE, pager = pager)
	}

Ecological Nerd Musique

I woke up this morning to a wonderful email in my inbox (thanks, Fergus!) letting me know about a little musical delight. A song about diurnal zooplankton migration entitled, well, Diurnal Migration. With lyrics like Much of the ocean is not yet explored/ Though submarines pootle about the sea floor, how can you go wrong? (Also, I now have an image of Dr. M. pootling about the seafloor).

So, I went on and decided to listen to the rest of the album, Pre-Apocalyptic Love Song, by Hannah Werdmuller, an ecologist-singer-songwriter. With songs about Grow-Bags, being prepared for the apocalypse (I showed you my heart, you showed me your homemade snare trap ), how could you go wrong? So go check it out!

And then things got even more awesome.

I noticed she had a musical twitter feed where she linked to a new collection of songs, Geek Like Me from the virtual music festival Geek Pop. Basically, it had me at its first song, animals, with the phrase Let’s make love like salmon living in fresh water/ You leave it in the bath I’ll come and pick it up later and the fact that it had a song entitled Brokeback Workbench.

Definitely the second triumph of the morning. And the website has more free tracks scattered about.

Yay! Just what I needed to make my morning of recoding some ill behaving R packages worth it!

RStudio – An IDE for the Masses!

I’ll admit it. There’s one thing that always makes me sad working on a mac. R. How does R make me sad on a mac? I look over at my compatriots in Windows using fun Integrated Development Environments (IDEs) like Tinn-R, and I sigh. On the other hand, I just had the sad little text editor and shell. Sure, it was enough, and I had wrung some sweet sweet code from that simple setup, but windows would get lost, I’d lose track of what file was where, what plot window was open, and would sometimes even forget which instance of R I was working in when I was working on two projects at the same time (one for simulation, one for analysis).

I mean, sure, I could go through the rigamarole of doing everything through some flavor of Emacs like a 31337 h4X0r. But my Emacs days are behind me. I would have preferred a simpler solution.

So when I saw news of a new, cross-platform, free, lovely IDE called RStudio hit R-bloggers and the Twitterverse, I rejoiced.

But would it be just a kludgy piece of bunk, or a nice, smooth, user experience? I figured I’d give it a whirl. I hopped on over to the website, and had a pleasant easy download experience. Then I fired it up, and ran an old analysis. After fiddling around a little just to get my feet on the ground (which took only a minute or two – the whole thing was quite intuitive), I was pretty pleased. The interface was clean, simple, and purty.

RStudio Window

Oh! Quite the little interface, there! Click to get a larger view.

And some features – such as image exporting – were like a dream. So easy, in fact, that I decided to confront it with the problem of exporting an image for publication at the proper image quality (something which is always a bit of a hassle in R normally). But to my delight, no problem. Just fiddled with the size a little bit, and presto! High quality pub-ready image.

So, overall, I’m impressed. And with a Twitter feed, blog, and interactive support forum, I think it looks like this IDE is going to be a great tool for science. So go check it out!