Friday, March 17, 2017

Progress on biodiversity-ecosystem function requires looking back

Williams, L. J., et al. 2017. Spatial complementarity in tree crowns explains overyielding in species mixtures. - Nature Ecology & Evolution 1: 0063.

It seems at times that the focus on whether biodiversity has a positive relationship with ecosystem functioning has been a bit limiting. Questions about the BEF relationships are important, of course, since they support arguments for protecting biodiversity and suggests a cost of failing to do so. But as a hypothesis ('higher diversity is associated with higher functioning'), they can be rather one-dimensional. It's easy to think of situations in which other types of BEF relationships (neutral, negative) exist. So is it enough to ask if positive BEF relationships exist?

It’s nice then that there is increasingly a focus on identifying mechanisms behind BEF relationships, using both theory and empirical research. A new paper along these lines is “Spatial complementarity in tree crowns explains overyielding in species mixtures” from Laura Williams et al. (2017). "Overyielding" is the phenomenon in which greater total biomass is produced in a mixture of species compared to the expectation based on their biomass production in monoculture. Overyielding would suggest a benefit in maintaining polycultures, rather than having monocultures, and is a common response variable in BEF studies.

This study focused on the production of stem biomass in monocultures vs. polycultures of forest trees. Experimental communities of young tree species were planted with orthogonal gradients of species richness and functional richness, allowing the effects of species number and trait diversity to be disentangled. Complementarity in tree canopy structure in these communities may be an important predictor of overyielding in stem biomass. Complementarity among tree crowns (that is, the extent to which they fit together spatially without overlapping, see Fig below) should reflect the ability of a set of species to maximize the efficiency of light usage as it hits the canopy. Such variation in crown canopy shapes among species could lead to a positive effect of having multiple species present in a community. 
Example of crown complementarity.
From Williams et al. 2017.

To test this, the authors estimated crown architecture for each species using traits that reflect crown shape and size. These measures were used to predict the spatial complementarity expected with different combinations of tree species. In addition, a single integrative trait – maximum growth rate – was measured for each species. The authors hypothesized that the variation in growth rate of species in a community would be associated with variation in crown heights and so also a good predictor of overyielding.

They found that crown complementarity occurred in nearly all of the experimental polycultures and on average was 29% greater in mixtures than monocultures. Controlling for the number of species, communities with greater variation in growth rate did in fact have greater crown complementarity, as predicted. Further, higher levels of crown complementarity were strongly associated (R2~0.6) with stem biomass overyielding.
Fig 2&3 from Williams et al (2017). For experimental communities:
a) the relationship between crown complementarity and variation in growth rate.
b) the relationship between crown complementarity and stem biomass overyielding.

These results provide a clear potential mechanism for a positive effect of biodiversity (particularly trait-based variation) in similar forests. (As they state, "We posit that crown complementarity is an important mechanism that may contribute to diversity-enhanced productivity in forests"). Given the importance of the sun as a limiting resource in forests, the finding that mixing species that combining shade intolerant and shade tolerant strategies are more productive (the authors note that "growth rate aligns with shade tolerance and traits indicative of a tree’s resource strategy") is not necessarily surprising. It fits within existing forestry models and practices for mixed stands. This is a reminder that we already understand many of the basic components of positive (and neutral and negative) diversity-functioning relationships. The good news is that ecology has accumulated a large body of literature on the components of overyielding (limiting resources, niche partitioning, evolution of alternate adaptive strategies, constraints on these, the strength of competition, etc). From the literature, we can identify the strongest mechanisms of niche partitioning and identify the contexts in which these are likely to be relevant. For example, sun in forests and canopy complementarity, or water limitation in grasslands and so root complementarity might be a good focal trait. 

Thursday, March 9, 2017

Data management for complete beginners

Bill Michener is a longtime advocate of data management and archiving practices for ecologists, and I was lucky to catch him giving talk on the topic this week. It clarified for me the value of formalizing data management plans for institutions and lab groups, but also the gap between recommendations for best practices in data management and the reality in many labs.

Michener started his talk with two contrasting points. First, we are currently deluged by data. There is more data available to scientists now than ever, perhaps 45000 exabytes by 2020. On the other hand, scientific data is constantly lost. The longer since a paper is published, the less likely its data can be recovered (one study he cited showed that data had a half life of 20 years). There are many causes of data loss, some technological, some due to changes in sharing and publishing norms. The rate at which data is lost may be declining though. We're in the middle of a paradigm shift in terms of how scientists see our data. Our vocabulary now includes concepts like 'open access', 'metadata', and 'data sharing'. Many related initiatives (e.g.  GenBank, Dryad, Github, GBIF) are fairly familiar to most ecologists. Journal policies increasingly ask for data to be deposited into publicly available repositories, computer code is increasingly submitted during the review process, and many funding agencies now require statements about data management practices.

This has produced huge changes in typical research workflows over the past 25 years. But data management practices have advanced so quickly there’s a danger that some researchers will begin to feel that it is unobtainable, due to the level of time, expertise, or effort involved. I feel like sometimes data management is presented as a series of unfamiliar tools and platforms (often changing) and this can make it seem hard to opt in. It’s important to emphasize good data management is possible without particular expertise, and in the absence of cutting edge practices and tools. What I liked about Michener's talk is that it presented practices as modular ('if you do nothing else, do this') and as incremental. Further, I think the message was that this paradigm shift is really about moving from a mindset in which data management is done posthoc ('I have a bunch of data, what should I do with it?') to considering how to treat data from the beginning of the research process.

Hierarchy of data management needs.

One you make it to 'Share and archive data', you can follow some of these great references.

Hart EM, Barmby P, LeBauer D, Michonneau F, Mount S, Mulrooney P, et al. (2016) Ten Simple Rules for Digital Data Storage. PLoS Comput Biol 12(10): e1005097. doi:10.1371/journal.pcbi.1005097

James A. Mills, et al. Archiving Primary Data: Solutions for Long-Term Studies, Trends in Ecology & Evolution, Volume 30, Issue 10, October 2015, Pages 581-589, ISSN 0169-5347. (lots of references on reproducibility)

K.A.S. Mislan, Jeffrey M. Heer, Ethan P. White, Elevating The Status of Code in Ecology, Trends in Ecology & Evolution, Volume 31, Issue 1, January 2016, Pages 4-7, ISSN 0169-5347.

Thanks to Matthias GreniƩ for discussion on this topic.