Drought vs. shade tolerance

The leaf economics spectrum is the modern incarnation of Grime's C-S axis. Without the overarching evolutionary strategies attached, it describes a broad set of correlations that cover species with leaves that have low activity rates, are built tough and live a long time, to those that have high activity rates, are built wimpy, and live a short time.

The evolutionary underpinning of the broad correlations--what ecological forces would select for the correlations--has remained opaque.

Ülo Niinemets has been publishing on this question for a few years. For example, in 2006, he and Valladares compiled rankings of shade and drought tolerance for woody species in the northern continents. The correlation was somewhat weak, but was negative. More importantly it showed that although there were species that had low shade and drought tolerances (x-axis), there were no species with high shade and drought tolerances.

In a follow-up paper, they examined the associations between stress tolerances and functional traits. They concluded that the traits associated with shade tolerance did not consistently have traits associated with stress-tolerance, while drought tolerant species did.

The evidence for drought tolerance being associated with traits that are low on the leaf economics spectrum, though, seemed a lot more mixed when examined individually. For example, across all species the pairwise correlation coefficient was just 0.18 (P <  0.001), which translates to an r2 of 0.04. Plus the relationship was negative for conifers (EC). LMA relationships were all positive and r = 0.3 overall (r2 = 0.09).

What you can see, though, is that most of the leaf economics spectrum are differences between broadleaf deciduous species and evergreen conifers. And these two groups do not differ primarily in terms of drought (or shade) tolerance. Hence, the trait relationships are pretty weak.

They ran a PCA of 4 main leaf economic traits (leaf longevity, %N, LMA, and photosynthetic rate). Overall and within each group, drought tolerant species ranked lower on the leaf economics spectrum. Overall r = 0.29 (P < 0.01).

I'm still working to rectify these results with what we've found for grasses. A few points are important here.

•Drought tolerance scores were rankings derived from observations, and do not necessarily represent physiological drought tolerance.

•The majority of the leaf economics spectrum for trees is associated with broad functional groups, which do not correspond to differences in shade or drought tolerance.

•Shade tolerance was not associated with the LES, mostly because of shade species having low LMA. But this is because shade tolerant species have thin leaves, not because they have low density (a different paper shows this). This also brings up the question whether LMA should be part of the LES [Answer: SLA (and LMA) should R.I.P.--leaf tissue density is much better.]

•If shade tolerance is not associated with the leaf economics spectrum, is drought tolerance? The glass is 10% full here at best.

•For grasses, we just don't see the same results. Drought tolerance is associated with high rates or gas exchange and no difference in leaf tissue density.

Research like this is going to be important for the interpretation of the leaf economic spectrum. Species high on the spectrum probably can be considered modern C species. But what about low? Is there one general stress-tolerant syndrome with variants that correspond to shade-, drought-, and nutrient-stress tolerance? Or are these largely independent of one another, but just never have the traits of high-resource species?

The endpoints definitely form a pyramid. The question is how tall is the pyramid? How different are high resource species from low-water species, compared to low-water to low-nutrient? We'll probably need more than 4 leaf traits to find this out.

Niinemets, U. and F. Valladares. 2006. Tolerance to shade, drought, and waterlogging of temperate Northern Hemisphere trees and shrubs. Ecological Monographs 76:521-547.

Hallik, L., U. Niinemets, and I. J. Wright. 2009. Are species shade and drought tolerance reflected in leaf-level structural and functional differentiation in Northern Hemisphere temperate woody flora? New Phytologist 184:257-274.

It's hard to tell the difference between the fringe and the frontier

I guess I said this once to Kendra.

We both couldn't remember what the phrase was.

I wished I had Posted this, so I could just look it up.

Then she remembered it.

So now I probably should Post it.

The context for the statement is that if you look at someone's research, it can be difficult to judge whether their work is isolated and unlikely to have much impact (fringe) vs. being a cornerstone to future ways of thinking (frontier). 

How to use the h-index

A few have asked me how to use the h-index in light of what I showed earlier. In many cases, the h-index is used for promotions, for example. For assessments, I would recommend not just looking at a person's h-index, but instead examining the residual h-index and finding good comparables.

Quantity and quality: residual H-index

The H-index is supposed to represent scientific productivity beyond just the number of publications. Yet, 90% of the h-index is the number of publications and the time a person has been publishing. It’s actually the residuals that are the key here. Two individuals with the same number of pubs and years publishing could differ in their h-index, if one is cited more. Assuming the number of citations correlates with publication quality, then the person with the greater residual h-index would have a greater impact.

There are always caveats to this, but it’s clear that for the purposes of assessment, one should examine the number of publications and the time a person has been publishing as well as the residual H-index from scientists in the discipline. This is probably the best metric of impact beyond number of papers.

Find comparables.

One of the benefits of the approach is to be able to find comparables. Just like in real estate, appraisals are used to determine the potential market value of a house and are anchored with the sale value of comparable houses. Just like researchers, no two houses are exactly alike (except in some uninteresting subdivisions), but they can be compared.

My approach to finding comparables is to generate the relationship between the H-index and the number of publications and the number of years publishing. Then, determine the next closest people in the space defined by the actual and predicted H-index. For example, calculating a Euclidean distance between my scores (H-index = 22, predicted = 20.4), the next closest person to me is a friend of mine, actually: 80 pubs in 12 years, H-index of 24, predicted 23.4. Euclidean distance = 3.6.

The person furthest from me? My advisor, Terry Chapin. 321 publications—probably more with some misspellings. H-index of 84. Predicted H-index of 80. Distance of 86.

Here’s a graph of distance from my scores as a function of H-index for reference.  

 

Even objective metrics have subjective assumptions. Still, there are important lessons to be learned from quantifying scientific productivity. Might as well do it as well as possible. 

The H index: how many for how long

Relationships between h index and number of papers, log-transformed # citations for highest-cited publication and the # years since the first publication for 38 plant/ecosystem ecologists

I looked at the H indices a little bit more today. In short, I added a number of people to see if I could bust any of the relationships. Again, these are people at different career stages in a similar field as me from the US.

I knew that I could find people that have been published on papers that were highly cited, but they didn't have a high H-index. That wasn't too hard. Being a coauthor on a highly-cited paper isn't as diagnostic as the number of publications and the number of years cited.

The one relationship that is hard to find outliers for is the number of publications. I couldn't think of anyone that had published a lot of papers that had a low h-index. Tilman is really the only outlier for this. Based on the number of publications he has published, his H-index should be 56 not 86.

Outside of Tilman, when you take into account the number of publications and how long they've been publishing, that's 90% of the variation in H-index. National Academy members (red dots) aren't necessarily higher or lower than non-academy members (P = 0.2). You can find individuals 10 points higher than you expect, which is diagnostic of something, but looking at the individuals that are 10 points too low, I don't think one would denigrate their stature because their h-index was 65 not 75. Still, there might be something to the residuals.

The final equation I get is H index = 3.8 + 0.17*#pubs + 0.54*#yearspublishing. r2 = 0.90.



For what it's worth, my h-index is spot on. I've authored or co-authored 57 papers published in 14 years. That predicts an h-index of 21. Mine is 22.

One thing that is interesting here is that the h-index, at least in my discipline and for almost everyone, really doesn't provide much more information than knowing the number of publications and how long they've been publishing.

Another thing is quantifying what it takes to get to an h-index of 45. Just 160 publications in 25 years is all. Or 150 publications in 30 years, if you can wait a bit longer.

For me, that would be 10 papers a year for the next 11 years.

The H index might not necessarily provide more information for most than how many for how long, but what is represented by an H-index of 45 is pretty impressive.

Predictors of publication productivity: h-index

"Hirsch suggested that, for physicists, a value for h of about 10–12 might be a useful guideline for tenure decisions at major research universities. A value of about 18 could mean a full professorship, 15–20 could mean a fellowship in the American Physical Society, and 45 or higher could mean membership in the United States National Academy of Sciences.[3] Little systematic investigation has been made on how academic recognition correlates with h-index over different institutions, nations and fields of study."--Wikipedia.

In some scientific circles, the h-index is the summary figure of productivity. The h index combines the number of publications (h) that have been cited h times. if someone has published 10 papers cited 10 times, their index is 10. 11 papers each cited at least 11 times, the index is 11.

What's interesting to me is that there would be typical values for someone to be considered for the National Academy. Apparently the number is 45--45 papers cited at least 45 times.

One shouldn't get too hung up on metrics of scientific importance--they are too easily skewed and sliding scales are always necessary--but how does one get to 45? Publish a lot of papers? Start publishing early? Publish long? Or just write (or co-write) 45 great papers? 

I told myself I'd only spend 20 minutes on this, so I'll be brief.

I spent 20 minutes looking up h-index values for selected Academy members plus a few others and some early-career scientists (a few friends). In addition to h index, I calculated how long they had published, the number of papers published, and the most cited paper.

This is by no way scientific. Values can be off, etc.

Most of the national academy members were above 45. 

Of the three predictors, the best predictor of h index was number of publications. 

More work would probably blow this relationship apart, but one key take-home point would be to keep publishing, not worrying as much about the golden publication that'll be cited 1000 times. 

Back to real work....

Leaf architecture and physiological drought tolerance

Patterns of physiological drought tolerance and leaf venation architecture among 10 woody species.

Quick note on a new paper.


Scoffoni et al. determined the physiological drought tolerance and architecture of 10 woody species. The authors test key components of leaf venation architecture to understand the underlying leaf structural mechanisms for drought tolerance. Most work on drought tolerance focuses on stems and highlight xylem geometries, but the authors show that the density of veins in a leaf are the best correlate with its physiological tolerance of drought. High vein density provides insurance against embolism and allows water to continue to be supplied to areas adjacent to veins that have experienced embolisms that necessarily accompany low water potentials. 


The authors highlight the need to separate leaf size and vein density, which were correlated in the study. But, the research raises an interesting question as to whether the need for higher vein densities serves as a constraint on leaf size and ultimately contributes to one of the major biogeographic patterns of plant form.


I also think their figure, shown above, is pretty stunning. 

Scoffoni, C., M. Rawls, A. McKown, H. Cochard, and L. Sack. 2011. Decline of leaf hydraulic conductance with dehydration: relationship to leaf size and venation architecture. Plant Physiology 156:832-843.

Evolution of drought tolerance

Phylogenetic tree of 165 grasses. Size is bubble is proportional to physiological drought tolerance (big bubble = lower psi-crit).

We know a bit about the ancestor of Poaceae. All the main defining characters of grasses like the parallel venation, the monocotyledon, and the distinctive grass flowers, were present in the ancestral grass. What did the first grassland look like? What about it's ecology? Did grasses start in the shade and come out in the open? Were they from wet soils and evolved to inhabit the dry? 

We don't have a time machine, but we do have the ability to assemble the phylogentic relationships among grasses and infer origins. Steve Kembel helped out and took Erika Edwards phylogeny from her PNAS paper and arrayed physiological drought tolerance data from 165 species from our experiment that matched with her phylogeny.

The first thing that pops out is there is no phylogenetic signal to the data. Drought tolerance pops up throughout the phylogeny. If true--and our dataset is by no means definitive yet--then drought tolerance might be evolutionary labile. It might not take that many mutations to confer physiological drought tolerance.

But what about the ancestral trait? Was the mother of all grasses physiologically drought tolerant? That specific analysis has yet to be run, but likely not. Most of the modern grasses are not terribly drought tolerant and the most parsimonious explanation for that--as I understand it--is that it is more likely that the relatively small fraction of grasses that are super-drought tolerant hold the derived trait.

As they say, watch this space. We're going to try to prove ourselves wrong in the meantime.