Wild Plants Post

The indirect effect of drought on plants

2012-01-27T19:40:00.000-08:00

The direct effects of stresses on plants are often fatal (making them disturbances, by definition). For example, drought can cause cavitation in a plant's xylem, which leads to tissue desiccation and ultimately death. But, the indirect effects of stresses can cause mortality, too. Stresses can reduce the defense systems of plants allowing pests and pathogens to kill plants before the direct effects of drought ever do. Direct tests of the generality of this principle are uncommon though.

Jactel et al. recently published a meta-analysis of the effects of drought on damage to trees by insects and pathogens. The results were neat. They found that agents that attack plant leaves were enhanced by water stress to plants. Yet, agents that attack the plant through its wood caused less damage to water-stressed plants than to unstressed plants.

The best part of the paper was linking the degree of water stress to the severity of damage (shown above). Their metric was the reduction in plant water potential relative to the water potential at which conductance is reduced by 50%. The greater the severity of water stress, the greater the damage.

Well done.

Jactel, H., J. Petit, M.-L. Desprez-Loustau, S. Delzon, D. Piou, A. Battisti, and J. Koricheva. 2012. Drought effects on damage by forest insects and pathogens: a meta-analysis. Global Change Biology 18:267-276.

Submissions to NSF

2012-01-14T06:12:00.000-08:00

David Inouye posted this to Ecolog. I'm not sure where he got it, but it looks pretty real.

The key here is that the number of proposals to DEB has been going up while award numbers have been flat, leading to a decline in success rate.

NSF knows there is pain out there and has worked to respond to the pain on reviewers.

More proposals means more reviews.

NSF has the power to reduce the burden on reviewers, so they instituted a pre-proposal stage with 4-page pre-proposals and a limit on the number of proposals a person can submit as a pi or co-pi. Some have argued that this reduces this stage of evaluation to a raffle that can harm early-career and soft-money scientists.

The key here seems to be what the funding rate should be. Or even better, what the total level of funding should be. Congress determines this.

My guess is that the policies of NSF now are less of a burden to good science than funding levels, but NSF is more proximal. It will be interesting to see if more effective arguments can be made to raise the level of funding.

Why trees die: case example

2012-01-14T05:49:00.000-08:00

Understanding mortality in plants is a tangle of proximal and distal as well as competing hypotheses. A recent paper in PNAS tried to disentangle a number of issues for understanding mortality in trembling aspen (Populus tremuloides).

The authors use a mix of gradients and experiments to examine patterns of carbohydrate reserves and hydraulic properties for droughted and non-droughted aspen plants. Plants that were droughted and non-healthy did not have reduced carbohydrate levels in their tissues (leaves or roots). In contrast, dying plants consistently were experiencing loss of hydraulic conductance and cavitation.

What is interesting here is that aspen is the lettuce of trees. It is an isohydric, physiologically drought-intolerant species. The research shows that pot experiments should be pretty good at determining the drought tolerance characteristics of species. Screening experiments (and rated, more involved detailed studies like these) should allow for the type and degree of drought tolerance to be assessed for other species. hence, models of future mortality could be generated for forests across the world.

Anderegg, W. R., J. A. Berry, D. D. Smith, J. S. Sperry, L. D. Anderegg, and C. B. Field. 2012. The roles of hydraulic and carbon stress in a widespread climate-induced forest die-off. Proceedings of the National Academy of Sciences of the United States of America 109:233-237.

Best use of bootstrapping, ever: the flavor network.

2011-12-17T20:26:00.000-08:00

Not too often, a paper comes out that generates so much insight and is presented so elegantly that it induces jealousy.

Ahn et al. published a paper in Scientific Reports (Nature's version of PLOS), "Flavor network and the principles of food pairing". Essentially, the paper mined on-line recipe databases to generate differences in ingredient use and flavor-space among cuisines.

Each figure in the paper has so much that is interesting. At the center of a giant multivariate analysis of flavor is what Kendra called the "triangle of happiness": cocoa, beer, and coffee. At the center of that: katsuobushi--dried, shaved bonito tuna. It must be amazing. Liver by the way shares little in the way of flavors with anything else (thankfully).

The authors also use bootstrapping to see which are the most unique ingredients in different cuisine's recipes. North America clearly stands out for its desserts. Take away: milk, butter, cocoa, vanilla, cream, cream cheese, egg, peanut butter, and strawberries and our recipes are pretty similar to elsewhere in the world. Essentially it's our ice creams and cheese cakes that make us stand out.

So much about the food of the world, jammed into one paper. Blue cheese and chocolate share 73 flavor compounds? Throw away line.

This is an amazing food paper, yet I can't help think about why we can't do this for plants. Substitute regional flora for cuisines, functional groups for ingredients, and functional traits for flavors and it would be once-in-a-century paper. Yet, you look at the underlying data for this paper and realize that paper is a century away.

http://www.nature.com/srep/2011/111215/srep00196/full/srep00196.html#/f2

Graphs that don't exist: state factors and shade

2011-12-09T06:49:00.001-08:00

Shade, drought, and nutrient scarcity are three resource stresses that constrain vegetation globally. Each of these are influenced by state factors as well as other more proximal controls on ecosystem function.

At least theoretically. We've actually never tested these ideas, which constrains our ability to explain and predict a lot about how ecosystems work.

Take shade. Plants produce leaves, which creates shade beneath them. Yet, the amount of shade in different stands varies tremendously. Theoretically, sites that are more limited by water should be able to produce less leaf area, leaving more light to the understory and removing a constraint on the growth of understory vegetation.

Despite decades of light measurements and hemispherical photographs of canopies, the data has never been synthesized to generate a global map of shade that can be analyzed in terms of determinants. Do dry ecosystems have a lower potential for producing shade than wet ecosystems? And how does that vary with temperature? Do forests in colder regions cast less shade, all other things equal?

Part of this echoes Peter Grubb's assertion that higher fertility sites should generate more shade and have more slow-growing shade-tolerant species, which still hasn't been tested directly as far as I can tell.

What holds for shade also holds for nutrient availability and water potentials. We just don't know the basic drivers of resource availability and hence don't know how global change factors like warming will affect the availability of the most limiting resources.

Global change and limitation

2011-12-07T07:15:00.001-08:00

Ternary diagram showing the inverse relationships among low resource stresses and how global change factors influence the likelihood of resource limitations.

One foundation of ecology lies in the concept of state factors. Borrowed from soil science, state factors are the properties of ecosystems that are independent of the properties of the ecosystems [see earlier post]. The bedrock under a stand of plants is largely independent of whether a forest is there or we cut it down.

Ecosystems are not entirely deterministic from state factors and there are more proximal "interactive controls" like disturbance that are co-influenced by the vegetation. The probability of fire depends on what species are present as well as state factors like macroclimate.

One deficiency in our application of state factors and interactive controls has been to map the influence of state factors and interactive controls onto limitation. What factors and controls promote water-limitation over nutrient-limitation?

I've been trying to diagram this. Terry in his ecosystem book used a flow-control diagram to map the relative influences of state factors, interactive controls, and other controls on processes.

I've tried this approach for generating limitation and my diagrams come out looking like spaghetti. Or fettuccine. Some type of long, linear pasta. At least not fusilli thankfully.

The best I can come up with is a ternary diagram. In short, we want to show that drought, shade, and nutrient scarcity are all inversely related. And that a given global change factor promotes one or two stresses over the other. Precipitation promotes shade and nutrient scarcity over drought stress. Warmer temperatures promote drought and shade over nutrient scarcity. Disturbances reduce resource stress overall (inset).

There are dependencies for the diagram, e.g. chronic vs. catastrophic fire or scarcity of N vs. P.

Still I think this encapsulates the concepts we have about how global change factors alter limitation.

Mapping out other specific state factors is still a challenge. I get more spaghetti diagrams when I do this. Well, diagrams that look like I spilled dried spaghetti.

The proximal should precede the distal

2011-12-01T12:21:00.001-08:00

Relationship between water potential at which a species loses 90% of conductivity and dry season mortality. From Pratt 2008.

When it comes to explaining patterns associated with the ecology of plants, a simple dictum should apply: the proximal should precede the distal.

I've been working with a few others to try to make this point with plant functional traits and ecological patterns for a review that Science green-lighted. In short, when looking to explain ecological phenomena, the mechanisms explored should be the closest to the mechanisms that generate the pattern.

If drought is thought to cause mortality in a system and one is trying to understand which plants would survive drought the best, measure whole-plant drought tolerance first. Then begin to examine more distal underlying traits. Don't start measuring traits like SLA or screening genes until drought tolerance has been quantified.

Pratt et al. 2008 show a good example of the approach. The authors grew up a series of chaparral species from seed and monitored their performance over a dry summer. Those species that could withstand low water potentials the best, suffered lower mortality.

The same approach applies for a range of other cases, but each time it is important to be clear about what the processes are that are hypothesized to generate the patterns of interest and then measure the functional traits that are most closely related to them.

There has been over-reliance on general leaf traits, for example, that has generated a lot of frustration (and frustratingly low explanatory power). In contrast, when ecologists directly measure the ability of plants to tolerate low resource availability, for example, ecological patterns are explained better.

Defining the pattern of interest, generating hypotheses about their causes, and clearly linking form and function is a heck of a lot harder than going out and measuring SLA.

Ultimately, when the proximal precedes the distal, more explanation is generated.

We'll see how we do making the case for the proximal in this review.

The Corner (Ecological) Office

2011-11-27T14:30:00.001-08:00

I had read this book awhile ago, but Kendra just got to reading it, so we've had more of a chance to chat about it lately.

In ecology, there are a lot of nuts and bolts to master. Statistics, experimental design, the literature...Our discipline is not a quick one to master. Yet, when you get to the highest levels of the functioning of the discipline, success has less and less to do with the nuts and bolts than the more sociological aspects--basically how to get people to work together. As a scientific society, we recognize this a bit--we have fellowships for interacting with the press or Capitol Hill, but not with each other. It's unfortunate, too, because the failures and dysfunction are costly yet utterly preventable.

Any airport bookstore is filled with similar topics, but the best book I've found that provides advice on how to be a leader is Adam Bryant's The Corner Office. Bryant writes a weekly column for The New York Times Sunday Business section called The Corner Office. Weekly, he interviews a CEO of a different company and asks them essentially the same questions. When did you first have to manage people? Where did you learn to manage people? How do you hire people? What recent insights do you have on leadership? Almost every week the column is interesting and there are a lot of people who have thought carefully about how to manage people. The book is a compilation of his interviews to date.

There are many lessons in the book. There should be. Leading and managing is complex and just one thing wrong can trip up any project no less corporation. You have to take care of people and let them know they are appreciated. You have to provide a vision that can be easily translated to day-to-day activities. You go to the lowest levels for unfiltered information and to learn all the aspects of work that surround you. You have to be fair and even. You have to appreciate failure.

William Bond and I were talking about similar issues on our trip through Afri-homa. He remembered his first year at UCT. He said that his department use to have a brilliant chair. William described how after his first year--a year consumed by teaching courses for the first time along starting a research project--his chair found him and said, "Good job, William. You made it." And you could tell how that one recognition meant a lot to him.

So much dysfunction in any group, whether a project, a department, or a scientific society, comes from not feeling appreciated. And it costs so little for leaders to appreciate others and let oxygen be consumed on more important issues.

There are a host of other aspects of being a leader of a team that need to be carried out well. Bryant might not nail them all, but there are stories in there worth learning that help advance our discipline as much, if not more, than the latest statistical technique or meta-analysis.

A bit of inspiration to travel

2011-11-09T20:37:00.000-08:00

Photo from Wildflower Wonders by Bob Gibbons of Tien Shan Mountains in China (Princeton University Press).

Books can be good for inspiration. Some books inspire with ideas. Others with adventure. Sometimes just photos that inspire one to travel to new places.

I had a chance to read Wildflower Wonders: The 50 best wildflower sites in the world over the past few days. The author, Bob Gibbons, shows off some impressive photography of beautiful grasslands from around the world. From the Tien Shan Mountains to the Outer Hebrides to Namaqualand.

It's more than just a coffee table book of pictures, though. Gibbons does an excellent job weaving together cultural and geological history with the ecology of the areas in ways I wish I could. His discussion of the settlement history of the Julian Alps or the unique geology of the Dolomites in Italy that differentiates its flora from nearby limestone grasslands.

I think we forget to be biogeographers too often and forget that we can learn as much from travels as experimentation. And it's our travels that shape our experiments. Even those that work at the global scale often see just pixels and not the uniqueness of place that truly defines geography.

I have little but praise for this book. It would have been nice to see more pictures of grass rather than the less-than-subtle, somewhat ostentatious wildflowers. But, I guess that's my job on the next trip.

[Thanks to Princeton University Press for the advance copy.]

Why trees aren’t taller

2011-10-26T08:54:00.000-07:00

The effect of height-induced drought stress on redwood foliage. From Koch et al. 1994.

The tallest tree in the world is about 120 m. One of themost basic questions we have about trees is whether this height represents the tallestpossible tree. Are there some fundamental physical constraints that makegrowing much beyond this height impossible? Or could we grow a 200 m tree?

In 1997, Ryan and Yoder wrote a Bioscience article “Hydrauliclimits to tree height and tree growth”. There, they reviewed 4 hypothesesregarding the limits to tree height. In short, they ruled out that as trees gettaller their respiration might become too high, nutrients too hard to acquire,or genetic changes associated with maturation (they get too old) limits theirgrowth. These might come into play, but are only contributing factors.

The hypothesis that was left was hydraulic limitation—it’sjust too hard to move water much higher. Here, as trees grow taller, the lengthof xylem from root to leaf increases. Water flow is a function of the ratio ofthe difference of water potential and resistance. As tree height increases,resistance to water flow increases requiring lower (more negative) waterpotentials to move water to the top of the tree. As water potentials decline,xylem at the top of the tree is closer to the point of cavitation. Once thestring of water snaps at the top of the tree, it’s hard to get water back upthere and that part is dead. To be safe, leaves at the top of the tree close theirstomata more frequently, which limits carbon gain. Less photosynthesis slowsgrowth, generating a maximum height.

The evidence at the time for this hypothesis was thatstomata in any leaf will close if hydraulic resistance increases, hydraulicresistance increases for older trees, and photosynthesis is reduced in older,taller trees.

They end the 1997 review by saying “we may be close toanswering some of our oldest questions about tree height.”

Move forward to 2004. Koch et al. studied the tallest treeknown on earth, a 113 m redwood in N California. They showed that as one movedprogressively up the tree, water potentials declined, photosynthesis declined,and leaf WUE increased as stomates were closed more frequently. Everything fitthe hydraulic limitation model.

Yet, when you go to the top of a redwood tree, the waterpotentials aren’t that low. It only takes 1 MPa to overcome gravity and movewater 100 m. Moving water to the top of the redwood tree takes only -2 MPa dueto greater resistance in redwood wood. They argue that at this water potential,photosynthesis is essentially zero for the redwoods, which explains whyredwoods aren’t much taller.

But it doesn’t explain why other trees that canphotosynthesize at tensions below -2 MPa couldn’t build a taller tree.

Subsequent work seems to reinforce this idea. In 2008, Domecet al. assessed xylem design for 85-m tall Douglas fir trees. There, theyshowed that with increasing height, Doug fir branches had greater resistance towater movement (less efficient) but could with stand greater tensions (moresafety). But still, the water potentials at the top of the theoreticallytallest Douglas fir (~130 m) did not push the ultimate bounds for plants.

The authors concluded “Mechanisms governing ultimate treeheight must be considered in an evolutionary context, and so it is unlikelythat the tradeoffs discussed here are identical to those of all other species.A number of coniferous species adapted to arid and semiarid zones can maintainadequate water transport at substantially greater xylem tensions than thosenormally experienced by the mesic-environment species Douglas-fir and coastredwood.”

Ultimately, the question of tall trees becomes anevolutionary question. Could nature build a 200-m tree? The current limits totree height might be evolutionary, not physical. If you built a tree with thesame plumbing as a drought-tolerant shrub, a 200-m tree might be possible.

Domec, J. C., B. Lachenbruch, F. C. Meinzer, D. R. Woodruff, J. M. Warren, and K. A. McCulloh. 2008. Maximum height in a conifer is associated with conflicting requirements for xylem design. Proceedings of the National Academy of Sciences of the United States of America 105:12069-12074.
Koch, G. W., S. C. Sillett, G. M. Jennings, and S. D. Davis. 2004. The limits to tree height. Nature 428:851-854.
Ryan, M. G. and B. J. Yoder. 1997. Hydraulic limits to tree height and tree growth. Bioscience 47:235-242.

A lack of fertilization from elevated CO2 in forests

2011-10-14T14:53:00.000-07:00

The concentration of CO2 in the atmosphere has been rising steady for some time now. There are two main potential direct effects of this fertilization. The first is a direct increase in photosynthesis, the second a reduced use of water.

At its simplest, the reduced use of water should increase plant production in drier habitats by increasing soil water availability. Less water is used by plants for a given amount of photosynthesis, means more water in the soil, and more productivity before soils dry out.

Theoretically this straightforward, but whether this has happened in ecosystems across the Earth or not is an open question.

Peñuelas, Canadell, and Ogaya synthesized data on two parameters for 47 forests across the world. The first was the C isotope composition of tree rings, which can be used to infer instantaneous water use efficiency. The second was the growth rate of trees themselves--limiting measurements to well-established forests.

Their results are pretty clear. Across a wide range of forests, over the past 40 years trees have been 20% more efficient with water when they photosynthesize.

If trees are primarily limited by water, they should be producing 20% more wood. Yet, there was no significant increase in productivity in tree growth in any region.

If plants are more efficient with water, then what could be holding back plants?

The authors write, "Other factors such as increasing temperature, drought, nutrient limitation and/or plant acclimation may preclude such growth increase."

Which might it be?

The next chapter in this question is going to be pretty interesting.

Peñuelas, J., J. G. Canadell, and R. Ogaya. 2011. Increased water-use efficiency during the 20th century did not translate into enhanced tree growth. Global Ecology and Biogeography 20:597-608.

Low resource tolerance traits

2011-10-12T20:48:00.000-07:00

General approach for determining physiological drought tolerance. Individual leaves or plants are dried down over time. Periodically the water potential of the soil or leaves, relative water content (RWC) or wilting stage of the plants is assessed. The response variable is generally gas exchange (stomatal conductance or photosynthesis) or hydraulic conductivity of leaves or stems. Different thresholds of the response variable are used as the ultimate metric for physiological drought tolerance, i.e. A = 0 or KL = 20% of KLmax.

A short note here.

One of the developing trends in functional traits is moving away from traits associated with general stress tolerance syndromes to traits that directly are associated with tolerances of low availability of specific resources.

There has been a lot of work to determine the leaf economic spectrum, for example. Yet, the LES separates fast- and slow-growing species in essence without separating the specific resources that have driven the evolution of the slow-growing species.

For example, drought, shade, and low nutrient availability are all supposed to be associated with the traits on slow-return portion of the LES. Therefore, at its best, the LES would not separate out whether species were adapted to drought or shade, if they aren't adapted to both.

A hopeful trend has been measuring drought-tolerance or shade-tolerance directly. Tolerance of low nutrient availability/competitive ability when nutrients are limiting, not so much, but that might change yet.

Mel Tyree and Tom Kursar's work for tropical species is a good example. Their approach is to let tropical tree seedlings grown in pots wilt and then measure the water potential of plants that are severely wilted. Similar to psi-crit that I've measured.

When you do that, you get a range of drought tolerances:

They've used this pretty successfully to explain patterns of diversity in dry and wet tropical forests.

Again, this is a hopeful trend that should bear fruit in the near future.

Engelbrecht, B. M., L. S. Comita, R. Condit, T. A. Kursar, M. T. Tyree, B. L. Turner, and S. P. Hubbell. 2007. Drought sensitivity shapes species distribution patterns in tropical forests. Nature 447:80-82.

Kursar, T. A., B. M. J. Engelbrecht, A. Burke, M. T. Tyree, B. Ei Omari, and J. P. Giraldo. 2009. Tolerance to low leaf water status of tropical tree seedlings is related to drought performance and distribution. Functional Ecology 23:93-102.

Tyree, M. T. 2003. Desiccation Tolerance of Five Tropical Seedlings in Panama. Relationship to a Field Assessment of Drought Performance. Plant Physiology 132:1439-1447.

Fire in grasslands and savannas: a trip to Afri-homa

2011-09-22T21:33:00.000-07:00

William Bond with a Gulliver post oak at Tallgrass Prairie Preserve.

I spent four days last week driving around Oklahoma with William Bond from South Africa. I'm not sure why we chose the state, except in many ways it is the area most analogous (ecologically) to southern Africa that we have in North America. Old soils, intermediate precipitation, native prairies and savannas, and fire.

I've spent a fair amount of time over the past decade with William learning about things I never thought cared about. This trip quickly became a study on the roles of fire, drought, and herbivory in vegetation. It's hard to summarize William's view of the world, but he spends a lot of time thinking about how ecosystems and floras have developed over the past hundred million years. It's not a narrow topic.

I'll do my best to summarize, but essentially we visited different ecosystems such as the Cross Timbers to look for evidence of the antiquity of fire in grasslands, if not the antiquity of grasslands themselves. We'd go to a place like Tallgrass Prairie Preserve and look at oaks to see whether they promote fire. We traveled to Wichita Mountains, another old landscape, to look at how trees were coping with drought. We traveled to Black Kettle grasslands to look for plants with spines as evidence of histories with browsing mammals.

You can't necessarily look at a place and see back 10 million years, but you can try. For example, you can look under a Cross Timbers canopy and try to understand whether fire would rely on grass or oak leaf litter to work through the savanna. Or look at the canopy to see whether the oaks try to shade out the understory or not. Things like this provides evidence of the evolutionary history between these plants and fire.

A place like Black Kettle isn't too far from the Cross Timbers, but it's a radically different ecosystem. Grass there is grazed short. All the woody plants are structurally defended. I'm still picking out prickers from that place. It was never likely a fire world. It was likely always an herbivore world.

Long story short, there are still some great syntheses to be made. We haven't mixed everything up enough such that an old-fashioned field trip can't provide insight into the forces that have structured our world.

Drought and stress tolerance

2011-09-05T18:33:00.000-07:00

Comparison of photosynthetic rates for seedlings of dry- and wet-habitat tropical tree species. On average, photosynthetic rates were ~1/3rd higher for dry-habitat species.

I wrote a bit on this just the other day, buthere is a new paper that raises questions about whether low-water speciesshould be considered "stress-tolerators". Pineda-Garcia et al. grewseedlings of 10 pairs of closely related tropical tree species and measured asuite of traits. Dry-habitat species had higher photosynthetic rates thanwet-habitat species. In addition, dry-habitat species retained their leaveslonger after watering was ceased.

There are always a number of ways plasticity canalter relationships. For example, I once showed that high resource species canhave lower N concentrations and longer leaf longevity than low-nutrient speciesdue to patterns of feedbacks to N cycling after establishment. Here, there area number of mechanisms that could generate the higher photosynthetic rates andlonger leaf longevity in this particular experiment that could be reversed inanother. Parsimony accrues slowly.

Yet, overall, this is another example wheredrought-tolerant species are not necessarily following the general"stress-tolerator" syndrome. It will be interesting to begin toofficially tally the evidence to see whether there is much support for the twoto be linked.

Pineda-Garcia, F., H. Paz, and C. Tinoco-Ojanguren. 2011.Morphological and physiological differentiation of seedlings between dry andwet habitats in a tropical dry forest. Plant, Cell & Environment34:1536-1547.

Drought vs. shade tolerance

2011-08-29T04:23:00.000-07:00

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

2011-08-25T20:00:00.000-07:00

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

2011-08-25T11:47:00.000-07:00

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

2011-08-20T19:28:00.000-07:00

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

2011-08-19T06:31:00.000-07:00

"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

2011-08-18T19:19:00.000-07:00

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

2011-08-02T19:50:00.000-07:00

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.

Streams don't run from dry soils

2011-07-31T06:33:00.000-07:00

Konza streamflow and precipitation as a function of soil moisture at 25 cm

The fraction of precipitation retained by soil is a major source of variation in soil water availability to plants. For a given site, much of the variation that we see in this is associated with the pattern of precipitation, external disturbances on vegetation not withstanding. Precipitation pattern is hard to quantify in an ecologically meaningful way, though. A large, intense rainfall event might be lost to the stream if soils are saturated, but if soils are dry might be retained completely. Yet, heavy rain on dry soils might also be associated with heavy runoff if the rain falls faster than the soil can absorb it. You can occasionally see it on your front lawn, but flow paths are pretty short there compared to an intact grassland. Then again, rivers do flood in deserts.

There has been a lot of uncertainty at Konza on this, so I dug into the data to test it. I used the biweekly soil moisture data and matched it up with precipitation and streamflow during April-July (day 105-214 from our critical precipitation periods). 27 years of data here.

First cut analysis shows high precipitation falling a range of soils, but high flows in the major stream draining Konza only when soils are wet. Really no cases of high flows off dry soils.

The data aren't perfect. Soil moisture is only taken biweekly, and I used the interpolated soil moisture for each day rather than actual or the previous soil moisture. We really need daily data on this and that doesn't exist.

Upshot? Plants get access to all the precipitation that falls when soils are dry, but can lose a significant amount when soils are wet. Losing water from wet soils might not impact plants immediately, but likely does later as the soils dry out.

Turns out we have pretty good evidence of this. More on that later.

Heat waves and drought: it's all in the timing

2011-07-31T05:28:00.000-07:00

Distribution from 1984-2010 of (a) mean daily maximum temperatures averaged over 15-d intervals and (b) soil moisture at 25 cm taken approximately every 15 days. Also shown (c) is the sensitivity to grass aboveground net primary productivity (ANPP_G) to variation in drought and heat waves assessed every 15 d in 5-d increments. The critical climate period for drought (day of year 105-214) is shown in blue and for heat waves (day of year 190-214) is shown in red.

July in 2011 has been hot. And dry. Supposedly it's suppose to be like this more often in the future as future climates are likely to include more frequent droughts and heat waves.

It's generally assumed that in most grasslands these events reduce grass production, yet their effects have been viewed somewhat monolithically. When it comes to forecasting the consequences of future climate variability, droughts and heat waves in early-, mid-, or late-summer are not viewed very differently. Absence of evidence is not necessarily evidence of absence though.

The Konza LTER has built up datasets over the past 25 years that can really test this, though.

27 years of annual productivity

27 years of daily weather

27 years of daily stream discharge

27 years of biweekly soil moisture

17 years of biweekly productivity

11 years of remotely-sensed NDVI

I'll write about some of the datasets another time, but if one examines the annual productivity data and the climate data together with the critical climate period approach, it is clear that the timing of climate variability is just as important--if not more--than the magnitude.

First, grass productivity only responds to drought (or the converse precipitation) during part of the growing season (Apr 10-Aug 2). Drought in August doesn't reduce primary productivity.

And heat waves? They only reduce productivity during a 25-d window. Jul 10 - Aug 2. Heat waves in August, no less June, just have no impact on productivity.

We can use these data to come up with new relationships between productivity and climate variability.

A couple of lessons can be learned here, but the most striking is that droughts and heat waves in August just don't affect grass production. It's not that grasses aren't growing then. About 10% of the production happens then and in some years it can be as high as a third of the mean annual productivity. Yet, growth during that time is not tied to climate then.

It's hard to explain why this is so, but the practical consequences are clear. If droughts or heat waves are more likely to happen in August, it doesn't matter for the amount of grass we have. We've shown elsewhere it still impacts the bison, most likely because they cue in on grass quality than quantity. But ANPP is insensitive. If we want to predict future productivity well, they we better know timing as well as magnitude.

**On a side note, the results are really the highest expression of what the LTER approach can accomplish. I think long-term datasets have fallen out of fashion in the ecological community. When was the last time Science or Nature published a paper that centered on a long time-series from an LTER site. Compared to experiments, models, and cross-site synthesis, long time series seems like a short leg of the table these days. No one has ever set up an experiment to test what natural variability has shown us about the timing of variability.

Advancing plant functional trait science

2011-07-18T21:26:00.000-07:00

I struggle at times to understand why we haven’t made much progress in understanding plant functional traits over the past ten years.

As has been well chronicled for over a century, plant functional traits are keys to understanding the evolution of plants, predicting ecosystem response to global change, and interpreting the distribution of species. None of the importance of plant functional traits has changed any time recently.

I would never argue that there has been no progress. For example, Wright et al.’s 2004 Nature paper on the worldwide leaf economic spectrum certainly is a landmark synthesis, but it was largely confirmatory from Reich’s work in the late 90’s. Baraloto’s recent work on the decoupling of leaf and stem economics is a good study that has the potential to be important. At the very least, advances have been sporadic and incremental.

Still, it just doesn’t feel like we’ve learned much in the past ten years about traits.

Regardless of how much advance there has (or hasn’t) been over the past 10 years, why hasn’t there been more?

In some ways I feel like there are a number of Catch-22 chicken-eggs involved in trait research. This isn’t an exhaustive list, but ones that seem to stand out.

Funding agencies do not fund trait work. Major screening projects just are not funded. Most of the funding has been into syntheses of extant data while new data has largely come from side projects and student work.
Pot-phobia. I’ve talked about the “pot effect” before and no one would deny that plants can sense their environment. Yet, proposing to grow plants in pots inevitably generates criticism. For example, one proposal review recently included the criticism “The plan to compare plants grown in small pots in growth chambers is questionable, since small pot effects will likely cause artifacts”. The word ‘artifact’ is a dismissively loaded term here. Besides, there is never criticism for the “field effect”—the artifact of growing small plants in an unconstrained soil volume with excess resources. Put briefly, intraspecific variation and plasticity is a bugaboo that can kill broader questions.
No new conceptual advances to test. There are still exciting untested hypotheses, but there is the perception that the discipline is a bit dead intellectually. CSR has never been modernized while LHS is just three orthogonal traits. Not much new seems to have taken its place. The lack of a exciting toe-hold here hurts.
Phylogenies are incomplete. It’s hard to describe what a drag on progress this is. Without a robust phylogeny, species and traits cannot be compared evolutionarily. For example, it’s hard to do congeneric comparisons when it can’t be agreed that species belong in the same genera. And to require ecologists to generate phylogenies is an unnecessary requirement that doesn’t promote long-term growth.
Post-SLA research in a SLA paradigm. The leaf economic spectrum is a great advance, but the common perception to many is that SLA is the central trait of plants. Here’s one comment we received recently on a proposal: “It was odd not to see conventional traits like SLA… included in this work”, even though we had proposed to measure the components of SLA: leaf thickness and tissue density. Put briefly, there is too much uncertainty on whether SLA is a central trait that structures plant evolution and ecology, or whether SLA should R.I.P. for a new generation of metrics.
The decline of traditional physiological ecology. This one is unquantifiable, but the central importance of physiological ecology to ecology has been diluted. The rise of model organisms and ecosystem ecology/global change biology has at the very least diluted the middle ground that was plant physiological ecology, instead of strengthened it as a discipline. I think there’s been a loss of the perspective that comes from being at the nexus of plant evolution, biogeography, and ecosystem function.

So what to do? I don’t have clear answers here. This post is just a scratchpad for me. A couple thoughts come to mind.

We can’t always wait on phylogenies. If a phylogeny doesn’t exist, it is not helpful to insist that ecologists create one or that we wait. Some aspects of the work might later get revised, but functional trait work can be done in advance of the phylogenies. Especially considering how often phylogenies get revised.
We need model species sets. The rise of model organisms—and the resources devoted to their study—has been quite amazing. Model organisms in and of themselves do not help us understand plant functional traits too much. They aren’t inherently comparative. I think we need model species sets to complement model organisms. For a broad clade, we need to identify a reasonable number of species that represents a broad set of evolutionary and ecological contrasts. This set then needs to be examined by multiple researchers, just like with model species.
The pot effect needs to become irrelevant. We will always need to grow plants in containers. We need clear understanding of how containers affect genetic expression and plant functional traits. Direct research is required here.
We need better theories. All disciplines need buzz. Plant functional trait research doesn’t have it. It needs it.
We need to measure old traits more, but we also need to develop new traits. SLA and rooting depth isn’t enough to help us understand how plants have been shaped and respond to everything from drought to herbivores.
We need to encourage breadth in our science. Plants have been shaped and respond to a multitude of environmental factors. You can’t describe an elephant by grabbing its tail, nor by having a number of scientists grabbing individual parts. Some need to try and feel the whole and understand how the parts come together.

Grasses of the World IV--Taxonomic differences

2011-07-03T15:38:00.000-07:00


Relationships between leaf width and physiological drought potential for six genera of grass.

No one know exactly what the ancestral grass looked like or the environments it inhabited. But one could imagine a bright, open wet environment with a narrow leaved bunch grass or weakly rhizomatous grass inhabiting it. Some tens of millions of years later the BEP and PACMAD clades would have diverged and the major radiations of grasses still a long way off.

But what were the forces that drove the radiations. Aridity is often cited as one. Fire another. Grazers still a third. But this might be somewhat of a skewed, biased perspective, since there has been little work to characterize the modern ecology of the whole of grasses.

When we look at the global traitscape of grasses, we saw clear patterns for leaf width and drought tolerance. One can imagine some selective force favoring wide-leaved grasses and drought narrow leaved grasses, until an ecological or physiological tradeoff was reached.

But what does the pattern of radiations for individual clades look like?

If I map the distribution of 6 genera in traitspace, clear unique patterns show up. The genus Panicum, for example, has species with wide and narrow leaves, but none that are very drought tolerant. In contrast, Festuca species all have leaves that are narrow, but span the full range of drought tolerance.

We still haven't mapped all this onto a phylogenetic tree. That's coming. But the value of screening programs like this are pretty clear for understanding the ecology and evolution of grasses.

But why the separation among genera? Are individual genera constrained physiologically, or are they constrained evolutionarily by the presence of other species that lead to the apparent differentiations.

Part of what we still need to do is understand the importance of traits such as leaf width and understand the benefits (and constraints) of narrow and wide leaves.

Traitscape of drought tolerance for Konza

2011-07-03T07:03:00.000-07:00

One of the keys to understanding community assembly will be assembling traitscapes for communities and comparing them to global traitscapes. Earlier, I showed how we could assemble a nitrogen traitscape for Konza and compare that to the global distribution to show that the typical Konza species has higher foliar N concentrations and experiences higher N availability than the typical species at the global scale.

We're getting close to being able to do something similar for Konza, but for physiological drought tolerance. We're working to collect all the grass species of Konza and measure their psi-crit in order to compare them to the global distribution. We've only fully measured 28 of Konza's 86 species of grasses, but the patterns so far our interesting.

Part of the power of the traitscape is to understand inter- vs. intra-site importance of environmental variation. For drought, if we expect Konza to be more likely to experience frequent and severe drought than other grasslands of the world, you could expect to see the typical species be more drought tolerant than the global distribution. We can also look at the distribution of drought tolerance at a site and see how that compares to the global range. Means might be different, but if there is high spatial or temporal variability in water availability, a community could encompass a large part of the global range.

Expectations for Konza are a bit uncertain--it's a humid prairie (835 mm y-1 precip), but can experience severe droughts. Within site, there are dry habitats--south facing slopes with thin soils--and wet ones--seeps, riparian areas, and ditches.

The pattern?

So far the global mean psi-crit is -4.8 MPa. Konza? -4.5 MPa.

The global range is -1.4 to <-14 MPa. Konza? -1.8 to -13 MPa.

Here's the pattern of psicrit with leaf width (red = Konza species):

After 28 species, most of the global trait-space is covered. If anything, Konza might be underrepresented in fine-leaved, drought tolerant grasses. I haven't measured Agrostis hyemalis yet, but it's leaves are about 1mm across--we'll see how drought tolerant it is.

I think there's an amazing range of diversity in drought tolerance at a single site. Konza might be an exception, but the diversity in soil moisture availability at a site can be high.

One question that comes up is that if there can be such high diversity at a site, what are the differences in among sites? How important is drought tolerance in differentiating grasslands and contributing to gamma diversity?

Grasses of the world III--grasses can be incredibly drought tolerant

2011-07-02T06:44:00.000-07:00

I've posted that I've been growing up 500 grass species from around the world to look at the geographic and phylogenetic distribution of physiological drought tolerance. Before we learned we were a bit constrained in quantifying the drought tolerance of grasses because we had grasses that could withstand pressures in excess of our previous pressure bomb, which maxed out at 10 MPa (~1450 psi). Jeff Hamel at PMS Instruments sent us one that goes to 14 MPa (~2000 psi). With that, we've rerun some grasses and measured some new ones, while we wait for some others to grow from seed again.

Still, the first results show that there are grasses that are incredibly drought tolerant.

We've now measured physiological drought tolerance (psi-crit) on 398 species. 13 of those were able to conduct water at pressures in excess of -14 MPa. That's more than 3% of the grasses we surveyed.

3% does not seem like a large number, but that number will only go up as we measure the most drought-tolerant species which are regrowing. 5% might not seem that high, but that'd be 500 species of grasses in the world if you extrapolate out.

How drought tolerant could some of these species be? If you extrapolate out the lower bound of the width-psicrit relationship, we should have some that hit -17 MPa (~2500 psi).

Extreme weather summary

2011-06-24T18:59:00.000-07:00

Just saw this today:

http://www.wunderground.com/blog/JeffMasters/article.html

It's an interesting summary of extreme weather events globally over the past year.

Another thing I hadn't know about was NOAA's Climate Extreme Index.

http://www.ncdc.noaa.gov/extremes/cei/index.html

For example, here's a graph of extreme summer precipitation events over the past 100 years. 2010 wasn't the most extreme, but pretty close.

Heat waves

2011-06-04T21:43:00.000-07:00

I've been learning a bit about heat waves.

The WMO defines heat waves as a sequence of five straight days when the daily maximum temperature exceeds the average maximum temperature by 5 °C. Heat waves can extend much longer than 5 days and can exceed average maximums by much more than 5°C. Although heat waves are a categorical classification, they are part of continuous variation in climate.

Here at Konza, over the past 25 years, there has been a lot of variation in climate. We haven't had a major drought since 1980 (http://en.wikipedia.org/wiki/1980_United_States_heat_wave). One thing I didn't have in my head is when the heat waves come and how hot they can be. With the standard definition of heat waves, heat waves should be equally likely across the year. Yet, if you look at the climate data for Konza, the strongest heat waves happen a bit later in the year than one might think.

I took the climate data for Konza and averaged the maximum temperature for each day in 15-day periods from early July to early September. It turns out that the hottest mean daily temperatures are in late July (not shown), but the strongest heat waves are in late August.

Here's a graph of the min and max mean daily maximum temperatures for 15-day periods from 1984-2010.

Although the hottest mean temperatures are generally at the end of July (not shown), the greatest heat waves come at the end of August. In 2003, high temps averaged 40°C for 2 weeks in the last two weeks of August.

Ecologically, the interesting questions about heat waves become how plants (and animals) respond to heat waves at different times of year. Most C4 grasses are shutting down in early September. One would think that heat waves at that time of year would have much of an effect compared to ones in July or early August.

At some point, we'll have another summer like 1980. Preparedness is an important topic, but also for ecologists. I wonder if we're measuring the right things so that when it comes around we can understand our ecosystems better.

Drought tolerance: grasses of the world II

2011-06-04T19:41:00.000-07:00

After 200 species, there still is a boundary between width and drought tolerance, just with a missing corner.

I haven't seen too many reviews of drought tolerance/cavitation resistance of grasses, but it is generally thought that permanent wilting points for most grasses is about -3MPa. It isn't considered that too many plants can resist below -10 MPa. Why -10 MPa? Because the machines to measure water potential don't go below there.

In the past few weeks, I've continued to measure the drought tolerance of grasses from across the world.
As the species have accumulated, the same tradeoff boundary observed earlier appears to hold. Yet, over 10% of the species appear to be able to conduct water at pressures below -10 MPa. For reference, if you could graft any of these species on the top of the tallest redwood, they could still conduct water.

We still need to nail down the actual psi-crits for the species. The problem is that our pressure bomb stops at -10MPa (100 bar).

The good news is that Jeff Hamel at PMS has been kind enough to build one that goes to -14MPa, which should allow us to determine the drought tolerance of the most drought-tolerant grasses. We'll grow up these species again and let them dry down. Hopefully, Jeff won't have to build one that goes below -14 MPa.

Drought tolerance: grasses of the world

2011-05-14T20:08:00.000-07:00

One of the 500 species that are part of the Poa500 project to examine drought tolerance in grasses of the world.

I'm not sure this one is going to work. I believe that if you measure something interesting and have strong contrasts, you should learn something interesting. I've had pretty crazy schemes work out in the past. Hopped on a plane and measured roots on three continents. Had people send me soil from across the US to look at nutrient limitation. Measured foliar 15N for a couple hundred species at Konza. Even looked at spectroscopic assays of 20,000 cow poop samples to infer continental scale patterns of forage quality.

Each time, we learned something interesting by having strong contrasts and measuring something interesting. But to start to understand global patterns of drought tolerance by growing 500 species of grass in the growth chamber in relatively tiny tubes? I'm just not sure this one is going to work.

Granted, what we're doing right now is just a pilot project and would be easier with the NSF Dimensions of Biodiversity grant funded. But, questions about the evolution and geographic distribution of drought tolerance are just too important not too try. At the heart of it, we just don't understand the traits that are associated with drought tolerance--what does a drought-tolerant plant consistently look like. In what climates are they most likely to be found. Are some lineages more likely to have evolved drought tolerance than others?

To begin to answer the question, USDA sent me seeds for 500 grass species from their seedbanks and I've serially germinated them over the past 2 months. After about a month, we measure a couple of gas exchange and morphological metrics on the leaves and then stop watering. When they stop conducting water (shut their stomata), we measure their water potential, which we call psi-crit.

A little over 100 species have hit their psi-crit so far. Here's probably the most interesting graph so far--drought tolerance (psi-crit) vs. the maximum width of the largest leaf on the plant.

It seems like you can have narrow leaves on plants that aren't drought tolerant--species like Enneapogon oblongus. You can also have narrow leaves on plants that are drought tolerant--species like Bouteloua repens. You can also have wide leaves on plants that are not drought tolerant--species like Dichanthelium scoparium. But you can't have wide leaves on plants that are drought tolerant. Doesn't exist.

Of course, it doesn't take more than one species to prove something not impossible.

We still have a few species left to measure, of course.

What controls rooting depth?

2011-05-05T15:07:00.000-07:00

What controls the maximum height of plants is relatively well understood. Plants can grow no taller than they can support themselves and than they can move water. Roughly every 10m of addition height requires xylem to resist an addition -1MPa of pressure. There are many other factors that could lead to selection for shorter plants, but tall plants need to be able to resist high negative pressures in their xylem.

Though we understand relatively well what controls the maximum height of trees, what controls maximum rooting depth is not well understood. If roots are moving water from their tips to the shoots, then similar constraints should apply to roots as stems. A root cannot go deeper than its xylem can resist the negative pressures of moving water that height. Even if it is belowground, the same physics apply.

But is there another constraint besides this? Is maximum rooting depth determined largely by hydraulics? If moving water was not a limitation, e.g. in wet places, what would constrain rooting depth? Or lateral extent for that matter.

I suspect that phloem and sucrose transport could be just as big a constraint on rooting depth/extent as hydraulics are aboveground. I'm not sure I understand the details on this, though. If a typical plant tried to produce a root 10m long, could it move enough sugar through its phloem to sustain the growth of the root tip as well as intermediate tissue? What about 100m? Some plants can apparently go this deep, but could any plant?

I have to admit, I'm not even sure what to begin measuring here. This summer, we're suppose to start doing root cross-sections on long grass roots, e.g. 2 m long, and look at the anatomical characteristics of xylem and phloem. Maybe this'll start to shed some light on what's hidden belowground.

Modification of Classic State Factors

2011-04-20T14:23:00.000-07:00

Modified State Factors and Interactive Controls Diagram to account for external supplies of resources.

The general framework to understand ecosystem properties has been the State Factor framework. Created by Hans Jenny to understand differences in soils, it has been applied to ecosystem properties such as plant species composition and stand structure. Essentially, with Jenny's approach, soils were determined by the climate, the organisms present, the landscape relief or topography, parent material and time since a major disturbance.

Terry Chapin and others modified the state factors approach to understanding ecosystems to include interactive controls of ecosystem properties. Interactive factors are not independent of the ecosystem properties, but sit somewhere in between. For example, the macroclimate is independent of what species are present at a site, but the microclimate can be influenced by species composition. These interactive controls include disturbances, microclimate, resource availability, and species composition.

With these state factors and interactive controls, one can have a better framework for understanding how ecosystems are structured and function. For example, the amount of biomass in a forest is not just a function of the state factors, but is influenced by interactive controls such as disturbance and the species that are present in a stand, which is influenced but not determined solely by state factors.

The state factor/interactive control approach is a big improvement in our conceptual framework, but it's generally left out external supplies of resources. Atmospheric CO2 concentrations, dust inputs, and nitrogen deposition can have profound influences on ecosystem properties, but are not adequately included in the current conceptual framework. These factors are somewhat influenced by ecosystem properties. The amount of dry deposition is influenced by canopy structure, for example. Yet, it's probably better to consider these state factors. N deposition plumes are wide and more similar to climate than disturbance regimes.

It's a minor tweak in many respects, but a likely necessary improvement to a long-tenured conceptual framework.

Genetic future of bison

2011-03-26T05:55:00.000-07:00

I've spent the last few days in Tulsa at a conference sponsored by the American Bison Society, which is part of the Wildlife Conservation Society. The conference was attended by a mix of scientists, government officials, ranchers, and tribal members. The conference centered around three panels, two of which focused on the genetics of bison. The third panel, which I helped put together, was on the ecology of the bison. We were largely intermission for genetic questions.

A quick bit of history. Bison once ranged in the millions in North America, but at the end of the 1800's had been reduced to about a thousand animals. Some of the remaining bison had been bred with cattle in an attempt to improve the performance of cattle and cattle DNA became part of collective genome of bison. It is not evenly distributed among modern bison--some lineages have more than others, some appear to have none. The presence of cattle DNA in bison has largely consumed discussion on bison for the past decade. How much is there? How is it distributed? Does it have functional significance? Can we get rid of it and repopulate our herds with "pure" bison?

The discussion on the topics were long, nuanced, and technological at times--lots of next generation sequencing and single nucleotide polymorphisms being discussed. ABS will draft official statements, but for me, I think the meeting will be known as a watershed in tolerance and understanding for the modern American Bison. In short, most bison in public herds have some cattle DNA. Quantitatively, less than 1% of the nuclear DNA might be from cattle, but it's there. It possibly could be culled out of the herd, but the bison DNA that we would lose would far outweigh the potential benefit of removing traces of cattle ancestry. Bison also aren't unique. Many of our remaining wild species have DNA from other "species" in them. Wolves, bactrian camels, Przewalski's horses all bear the genetic imprint of domesticated relatives.

We'll see what the official statements say, but American bison will always be a symbol of America's past. Yet, our bison are also a modern symbol--a bit mixed up, bearing the traces of past pain and hope and ambition, but one that probably should not be atomized any more. In some small way, the conference reflected a modern and sophisticated sense of tolerance.

So, when people visit bison in our parks and preserves, they are likely to be seeing a little bit of Hereford. But, it's a small price to pay if it reminds us to learn more about the animal's history. Much of the bison community seems willing to accept the mark of history and focus anew on continuing to restore them.

How to write a positive review

2011-03-05T21:10:00.000-08:00

Title of a note in ESA's bulletin in 1988

Kendra passed this one on to me. It's a short note published in the Bulletin of the Ecological Society of America in 1988 on how to write an influential review. Rosenzweig, Davis, and Brown put together a short note on writing influential reviews. The structure of the note is important. There are sections entitled "Accentuate the positive" and "The Correlation Between Detail and Negativity in Reviews". The beauty of the note is the encapsulation of the generational shift in our science.

At the risk of sounding curmudgeonly, a lot of emphasis in the modern system has been on timeliness. In the past, reviews were more a chance to help colleagues than potential competitors.

The note is worth anyone reading. I'll abstract one part:

"...when scientists are under attack, they circle round, wagon-train style. The physicists aim outward at their opponents. Biologists, on the other hand, aim inward, at each other. Their weapons, of course, are disparaging reviews and negative comments.

The earth pulses with fascinating ecological and evolutionary questions, and threatens with environmental concerns. The questions are as intellectually challenging as those facing any other scientific discipline. The answers are essential to deal with the environmental prob lems that beset the world. But we cannot con vince other scientists (or the public, or government officials) of the importance of our work if we seem to be calling each other incompetents."

For me, the older generation of ecologists were exceedingly civil to one another. It's good to remember that our research stands on the shoulders of our predecessors, not the backs of our competitors.

You can find the paper here: http://www.jstor.org/stable/20167054

Independence of leaf and stem traits

2011-02-22T19:27:00.000-08:00

Multivariate relationships between leaf and stem traits for 600+ Neotropical tree species.

Before reading Baraloto et al. in Ecology Letters, I think the best assumption for how leaves and stems correspond is that plants with high-activity leaves (low tissue density, high N concentrations, low leaf longevity) would be associated with low density wood. Pioneer species are typically thought of this way--think Cecropia. Essentially cheap leaves come with cheap stems. Late-successional species typically have low-activity leaves and high wood density.

Yet, if you think about it a bit more, pines and firs have low wood density, yet their leaves live a long time. So what is the pattern? Do leaf and stem traits correlate or are they independent.

Baraloto et al. compared key leaf and stem functional traits for over 600 tropical tree species. The authors convincingly show that leaf and stem economic axes are orthogonal. Species with low leaf tissue density and high foliar nitrogen concentrations are equally as likely to be associated with high stem density as low. In short, cheap leaves can be born on expensive stems.

The factorial ecology of leaf and stem economics has still to be worked out and the obvious next question is to reexamine patterns with roots, but the paper is an excellent example of the power of sampling large numbers of species and distilling data to a clear, simple message.

Baraloto, C., C. E. Timothy Paine, L. Poorter, J. Beauchene, D. Bonal, A. M. Domenach, B. Herault, S. Patino, J. C. Roggy, and J. Chave. 2010. Decoupled leaf and stem economics in rain forest trees. Ecology Letters 13:1338-1347.

Comparing nutrient availability with traitscapes

2011-02-12T15:01:00.000-08:00

An overlay of foliar N concentrations and nitrogen isotop ratios from the Konza flora (black) to a global dataset (gray).

One of the key questions for understanding plant community assembly is to understand the environments that species inhabit--not just the dominant species, but the hundreds of species that are only occasionally or rarely seen to the casual observer. Do the rare species mirror the more abundant species in their traits? Or are they rare because they are built for different environments?

At Konza Prairie, there are over 500 herbaceous species. Over last 2 years we measured the leaves of over 400 species at Konza. One of the interesting patterns was examining the relationships between the leaf N concentrations and the foliar N isotopes. Together these two best reflect the N availability of the environment the plant inhabits. High N concentrations and high del15N generally mean that the plant is growing in an environment with high N availability.

Konza is considered a strongly N-limited ecosystem. The responses of aboveground productivity to N addition are some of the highest in North American grasslands--ANPP triples with N addition. Given this, at Konza, one thing that was surprising was how many species had really high N concentrations in their leaves. A fair number of species that didn't fix nitrogen had N concentrations over 40 mg g-1, or 4%. That's really high.

When you look at the flora as a whole, there were a lot of species that were found in high N availability sites. Edges of roads. Bison wallows. Places with high dung inputs. A lot of the diversity of Konza is likely maintained because of these high N availability sites.

When you look at Konza species by species the picture changes from one dominated by severe N limitation to one with a broad spectrum of N availability. In fact, we could compare Konza to the rest of the world with a global dataset on foliar N and N isotopes. Not only do many of the species occupy high N availability sites at Konza, but the typical species at Konza actually occupies areas of higher N availability than the "rest of the world".

The analysis of traits across a broad portion of a flora--the community's traitscape--is not novel, but definitely an undersubscribed approach. As we build more global datasets and measure more and more species, a lot more insight to how communities are constructed and florae assembled will come into new light.

The pot effect

2011-01-21T21:44:00.000-08:00

How small is too small?

One of the central tenets of "functional ecology" is that the performance of plants in a an environment is due to their functional traits. Yet, which traits to measure, how they are measured, and how the plants are grown are sources of endless debate, sometimes paralyzingly so.

It seems inviolable that species must be compared after having been grown under common conditions. This means growing plants either in field plots or pots. I've written a bit about autogenic plasticity involved with field plots and how feedbacks to resource availability affect expressed traits. One of the most enduring criticisms of plants grown in pots is the size of the pots. No pot is ever too big, and almost any pot can be too small.

The bias in results that comes from restriction of roots has been termed "the pot effect". I trace it back to Curtis and Wang's first ecological meta-analysis on elevated CO2. The authors wrote in their abstract "We found no consistent evidence for photosynthetic acclimation to CO2 enrichment except in trees grown in pots <0.5 l (-36%)". The take-home from the paper was that little pots bias the results.

My guess is that would be the wrong conclusion. For that study, photosynthesis acclimates when plants are nutrient-limited. Plants in small pots become nutrient-limited faster than those in the field or in big pots. It's not the pot size at all. But which is more representative of the real world?

Quite likely the pot. As I've shown at Cedar Creek, small plants in big plots at low density are not very nutrient limited and have the traits associated with the high-nutrient strategy. Plants of the same species in more mature plots at higher density resemble more what we seen in small pots--the low-water or low-nutrient strategy.

One could argue pretty easily that the pot effect is essential to mimicking the real world where resources limit plant growth. One could argue too that small pots cause artifacts, but it would take some careful logic chains for the argument to be convincing.

In the mean time, try small plots. They generate water or nutrient limitation faster. And increase your replication if you're tight on space.

Collective intelligence

2011-01-20T19:44:00.000-08:00

Does this person feel playful or irritated?

If you said irritated, you aren't as likely to raise the collective intelligence of a group.

It is a truism that the smartest of all of us is not smarter than all of us, but how much smarter all of us are depends on how we work together. Ecology lives and breathes by group efforts these days--very rarely are papers ever published by a single author. Yet, we have paid scant attention to how to put these groups together and what makes them successful.

A paper last fall in Science actually did experiments to test what factors made groups collectively the most intelligent. The authors assembled a large number of working groups and had them together try to solve a series of complex tasks.

What made some groups the most successful?

It wasn't necessarily how smart the smartest person in the group was. Instead, collective intelligence correlated best with "with the average social sensitivity of group members, the equality in distribution of conversational turn-taking, and the proportion of females in the group" state the authors.

Not all the lessons transfer directly to scientific working groups. The tests the authors used were generic for which there might not always be specialized knowledge--like playing checkers. Deciding sampling strategies or statistical analyses is going to depend on having experts a lot more than checkers does. That said, there seem to be some pretty good lessons from the study. Take turns. Listen to others equally. Have women involved--or at least men who behave like women in the sense they are "socially sensitive". Not sure what that is? Take this test. Didn't score well? Then it's probably even more important to have women involved I would guess.

Woolley, A. W., C. F. Chabris, A. Pentland, N. Hashmi, and T. W. Malone. 2010. Evidence for a Collective Intelligence Factor in the Performance of Human Groups. Science 330:686-688.

Grazer diet

2011-01-15T08:16:00.000-08:00

Bison bonasus. They eat a lot of trees. Image from Michael Gäbler, Wikimedia.

The diets of grazers and browsers have not been easy to determine. To determine the diets of animals you have to 1) spend a lot of time watching, 2) use exclosures, 3) grab the food somewhere in the digestive system and try to recognize it. None of these approaches work that well.

Recently Kowalczyk and others published a paper that sequenced plant DNA present in fecal material from Bison bonasus in Bialowieza Forest in Poland. Essentially, a lot of plant DNA makes it through the digestive tract and it can be sequenced to quantitatively assess diet. This all but renders options 1, 2, and 3 vestigial. My guess is that we are going to see a lot of this technique in the near future. It's too powerful and the results are too fascinating.

Right now it costs about $12K to run 96 fecal samples with 454 sequencing. $12K is a lot, but $100 a sample is not.

The specifics of what the authors learned from the technique is interesting. Bison bonasus diet consists of a lot of trees and herbs. And the tree species are not economically important, which makes them great forest gardeners. Compared to Bison bison, Bison bonasus might as well be Alces alces.

Kowalczyk, R., P. Taberlet, E. Coissac, A. Valentini, C. Miquel, T. Kamiński, and J. M. Wójcik. in press. Influence of management practices on large herbivore diet—Case of European bison in Białowieża Primeval Forest (Poland). Forest Ecology and Management.

How to infer importance

2011-01-14T14:25:00.000-08:00

Quantifying the relative importance of ecological factors in determining the distribution and abundance of species is one of the most critical endeavors to understand the assembly of communities and ultimately the evolution of different species. Yet, how do we do this?

One approach is experiments. Another is gradient analyses. Each has its pluses and minuses. Yet, is there a way to infer importance without manipulations or gradients. If I go to a single place and determine who is rare and who is abundant, does that offer insight?

Seems like it should.

Let's take drought. Drought has long thought to structure grasslands. We know that it can from the Great Drought. But how important is drought in determining the assembly of extant communities?

One way is to compare a trait that should confer advantage during drought to the relative abundance of species currently. But what are the interpretations of the relationships. Let's say there is a positive relationship between the drought tolerance trait and abundance. Probably good evidence that drought structures the community since those species that do not have the trait are less abundant.

But what if there is no relationship? Does this mean drought is not important since it doesn't confer any net advantage? Or is it equally important as some other factor, since one might expect the drought tolerance trait to penalize plants in wet times? And what if physiologically drought-tolerant species are less abundant than intolerant ones? Is drought not important? Or does drought serve as a disturbance and structure the community in a different way.

In all, using an inferential approach to interpret patterns depends strongly on our mental model of how different factors structure communities. As much as being able to measure the right traits, linking these mental models to testable hypotheses is one of the most limiting steps.

In short, we need to spend more time clarifying our concepts before testing the importance of factors.

R squared irrelevant?

2010-12-23T03:03:00.000-08:00

In addition to problems in experimental design, the key results were felt to be weak. In Fig. 2, the most significant result is the relationship between physiological drought tolerance and abundance for uplands, which has an R2 of 0.12. The data presented are not strong enough to support the main points in the abstract.

There are two metrics used to judge a particular result: the coefficient of determination (r2) and the P-value, which is the probability that the observed result could have happened by chance.

For some modelers seeking to replicate observed phenomenon, the coefficient of determination is the key statistic to assess. Eddy covariance modelers often seek to explain observed patterns in carbon flux and when they can generate a high enough r2 by including different independent variables in their model, the feel they've modeled the system well enough and they move on.

For hypothesis testing in ecology, r2 is irrelevant. Think about the extremes. Let's say we are attempting to test whether a given trait explains the abundance of species in an ecosystem. Trait A explains 90% of the observed variation in abundance among species. P < 0.001. This would generally be considered highly ecologically significant and important to report.

Now think about the opposite extreme. Trait A explains 2% of the variation. P = 0.9. Pretty high confidence that you can reject the null and state that trait A is not important. Publishable? Absolutely if there are strong hypotheses relating the two. If we beforehand believe that trait A is likely to predict abundance, then it is likely more important to publish that it didn't explain abundance than if it turned out to explain a high proportion of variance.

There are some cases where the r2's are important to examine. For example, a model result might be r2 = 0.4, P = 0.1. This happens when there isn't much statistical power. This would be one case where the r2 is a helpful parameter.

What about the above-referenced case of r2 = 0.12, which happened to have a P = 0.008? Is the absolute value of the r2 relevant here? We ran a study that compared physiological drought tolerance to the abundance of 60 species. On the one hand, 12% can be a lot. Let's say there are 8 factors that equally explain abundance. None have ever been identified. you identify one of the 8 with high statistical significance. r2 is only 0.12. That seems important. Or there are only two factors that explain abundance, you've identified one of the two, but there is a lot of measurement error or associated random variation that is inherent to the system. r2 will equal 0.12. [see Shipley's work on relativizing r2 to take account for this.] In short, 12% of the variation is 12% more than we knew before and might be about all we can ever hope to know.

On the other hand, 12% can be considered low if you expected a lot more. In our case we showed for one contrast, drought tolerance explained 12% of the variation in abundance, while in a paired contrast, it explained 0.1% (P = 0.8). If you expect that drought it important and that drought tolerance explains a low proportion of the variation, then that's a scientifically important result that is only strengthened by the low r2.

r2's in and of themselves are irrelevant. They have to be contextualized to expectations. a high r2 that confirms expectations might be considered less important to publish than a low r2 that contradicts explanation.

In the specific case referenced above, we view an r2 of 0.12 both ways. Our statements in the abstract were 1) In this mesic grassland, physiological drought tolerance appears to increase the abundance of plants in xeric uplands, but does not in the mesic lowlands", and 2) "In all, drought appears to have a limited role in structuring the Konza plant community."

At Konza, drought tolerance explains 12% of the variation in abundance of species in uplands and is responsible for explaining 50-fold variation in abundance. that's a lot, but it happens to against a background of 5 orders of magnitude of abundance across species. At the same time, if one's expectation is that drought is very important in a drought-prone ecosystem where production is highly sensitive to interannual variation in precipitation, that same 12% (when paired with 0%) is actually not a lot.

The Achilles' Heel of modern scientific publishing is the negative result. It would be silly to only publish papers with positive slopes rather than negative ones, but it seems straightforward to reject papers because r2's are low. Time and time again, it's been shown that not publishing negative results (low r2, low P value) biases our scientific understanding.

Coefficients of determination are certainly not irrelevant, but they are only relevant within the context of expectations and the statistical significance of a test.

The first of a few notes on temperature sensitivity

2010-11-29T14:09:00.000-08:00

Probability (r) curves for the energy of collisions between a substrate and an enzyme at 20 °C and 30 °C. Shown are differences in activation energy (Ea) and Q₁₀ required for reaction for a labile substrate (upper figure) and a recalcitrant substrate (lower figure).

Globally, soils contain about twice as much carbon as found in the atmosphere and three times as much found in vegetation. The fate of organic carbon stored in the terrestrial biosphere depends in large part depends on the temperature sensitivity of microbial decomposition. Currently, there is still debate over the relative sensitivity of different carbon pools to increases in temperature. Modelers have largely punted on the issue, assuming that respiration doubles with every 10°C increase in temperature (Q10 = 2).

Decomposition is complex, but proximally decomposition is an enzymatic process. As such, at least short-term responses to temperature changes should be governed by chemical laws. The degree to which they actually do is still up in the air. The Arrhenius equation describes the relationship between the rate of reaction (k), the activation energy of a reaction (Ea) and temperature (T)

k=A*e^(-Ea/RT)

where R is the gas constant and A is the frequency factor that is specific to each reaction and represents how many collisions between reactants have the correct orientation for reaction. The Arrhenius equation can be used to determine the temperature sensitivity of reactions as well as the fundamental chemical principle that the temperature sensitivity of any given reaction will be proportional to the net activation energy of the reaction.

Mathematically, from the Arrhenius equation, Q10 increases with increasing Ea. For example, at an Ea of 51 kJ mol-1, the Q10 of a reaction between 20 and 30 °C is 2, while at 81 kJ mol-1 the Q10 is 3. The reason for this is derived from molecular collision theory. In brief, at a given temperature, only a small number of the collisions between an enzyme and a substrate will be energetic enough for a reaction to occur, as described by the Maxwell-Boltzmann distribution (see figure above). As temperature increases, the number of collisions increases negligibly, but the fraction of collisions with sufficient energy increases significantly, leading to an increase in reaction rate. The ratio of the number of collisions of sufficient energy at two temperatures is the temperature sensitivity of the reaction. And based on teh Maxwell-Boltzman distributions, we can see that this ratio is much higher for higher Ea's.

The Arrhenius equation is a foundational principle for understanding temperature sensitivity. Later, I'll show recent work just now being published in Nature Geoscience that tests whether the temperature sensitivity of microbial decomposition to short-term increases in temperature follows the Arrhenius equation well or whether other factors might be more important. If it does, predicting temperature responses and the fate of terrestrial carbon pools just got a lot easier.

Grassland Climate Change 3.0

2010-11-04T21:27:00.000-07:00

Critical climate periods for ANPP, flowering of three grasses, weight gain of calves, yearlings, and adults, as well as calving rates the following year for Konza. Gray bars indicate a negative effect of precipitation on the process, black positive.

If you look at the development of climate change research in grasslands, there have been two main stages. Climate Change 1.0 was trying to understand the importance of changes in growing season precipitation on ecosystem dynamics. Wet years are compared to dry years. Experiments that test climate change in 1.0 modify total precipitation.

We're still largely using Climate Change 1.0. Climate Change 2.0 examines effective precipitation during the growing season. Effective precipitation calculations largely take into account event size and distribution. Light rain events might lower effective precipitation as they are intercepted by canopies. Heavy rain events might lower effective due to greater flow through or runoff. Too light or too heavy and plants might not ever get a chance to use all the rain, hence lower effective precipitation. Some early-adopters are investigating Climate Change 2.0, but it's not mainstream yet. Certainly the projections and climate change models are not built to forecast in a manner that promotes 2.0.

One of my goals has been to push Climate Change 3.0. With 3.0, it's not just how much rain falls during the growing season, nor how much effective rain falls during the growing season. but when the rain falls. If you look at the critical climate periods for aboveground net primary productivity (ANPP), they largely show that 1.0 works--the more precipitation in the growing season, the more ANPP. For flowering of the major grasses, it's largely 1.0. Growing season precipitation largely determines flowering, with some differences among the species in their sensitivity to rainfall.

For Konza bison, there is just no relationship between growing season precipitation and weight gain for any sex or age class. But factor in the timing of precipitation, and you can explain up to 80% of the variation among years in weight gain. Why? It's because mid-season precipitation suppresses weight gain, while late-season precipitation promotes it. The climate-nutrition-performance cascade hits bison hard. Most likely, the same thing applies to cattle, although it hasn't been shown.

Climate Change 3.0 is nothing new conceptually. But in practice, 3.0 is. Training our models to predict when precipitation falls can be more important than how much falls for humid grasslands. Training ecologists to start to examine this will be probably be harder.

Comparing phenology curves

2010-10-17T20:55:00.000-07:00

Packera plattensis, which was found first flowering on April 13 in 2010.

The timing of flowering is a critical component of the ecology of plants. Flowering during environmentally stressful times or when other plants that utilize the same pollinators can lower a plant’s fecundity if not lead to its extirpation from an ecosystem. As such, the timing of flowering should be under strong selection pressure and be an important component of community assembly.

Over the past two years, Gene Towne and I (mostly Gene) collected first flowering date (FFD) data on 430 Konza herbaceous species. The last species found to start flowering (a gentian) was found in early October, 189 days after the first herbaceous species--Holosteum umbellatum--was found in late March.

The patterns at Konza are interesting. More on those later. The unexpected find was comparing the patterns with two other predominantly grassland flora. The first was from Chinnor, Oxfordshire. The second, Fargo, ND.

The y-axis is the fraction of each flora flowering on a given day. x-axis is day of year.

Two things pop out. Relative to Konza, the Chinnor flora has an early tail of species, but not a late tail. Is this because species phenology are all shifted earlier, so that the same species would flower ~50 d earlier there? Or is it just a suite of species that flower earlier are found at Chinnor, but late-flowering species are not?

And relative to Konza, the Fargo phenology is much more compressed. Again, though, why? Does Fargo not have early- and late- flowering species, or are the phenology of individual species compressed.

Turns out we can begin to answer that and the mechanisms that underly the differences between the pairs differ.

Here are the relationships between FFD between the pairs of sites. Dotted line is 1:1.

Species common to Chinnor and Konza flower on roughly the same day. Hence, one would suspect that the differences in curves between the two sites are due to novel types of species at each site. Yet for Konza and Fargo, early flowering species flower later at Fargo, and late-flowering species flower earlier. Phenology gets compressed for individual species.

Theoretically, I'm still getting up to speed, but comparisons between flora just haven't been done like this. Mid-domain theories are prevalent to test, but each site would support the idea of a mid-domain peak. What's more interesting is why sites differ. Right now, hypotheses about functional novelty/plugging of holes in niche space vs. functional stretching/compression are pretty interesting ones to test here. Flowering is interesting to think about, but the really interesting comparisons (at least for me) will come with comparing functional traits associated with resources, not reproduction.

Bison growth curves

2010-09-19T20:00:00.000-07:00

Weight of female (lower) and male (upper) bison at Konza Prairie and Ordway with age.

The performance of bison—how much weight they gain, how many calves are produced—is the ultimate expression of the functioning of North American grasslands. If we can compare the performance of bison in different grasslands, we have a window in the functioning of the grassland. Interannual patterns of weight gain show responses to climate variation. Average weights of animals give general indices to the provision of the quantity and quality of grass produced. Yet, no one has ever compared the performance of bison across grasslands in North America.

We're getting pretty close to doing that. For any one site, we can fit a growth curve to the weights of animals as they age. There are a number of growth curves that are used for these purposes, but a good one is a generalized Michaelis-Menten equation:

where W0 is the birth weight, Wf is the asymptotic weight, K is the age at which animals are half their asymptotic weight, c is a constant describing the shape of the curve, and t is time in years.

If you fit the weights of bison with age with this equation, for each herd you can extract essentially how heavy cows and bulls get, as well as a rate of maturity...or half-maturity as K would represent.

Right now, we have data on weight gain for about six bison herds. There are about 10 herds in the US that have weight data from roundups.

So far, we see a few basic things about bison. On average, males level off at about 75% greater weights than females (855 vs. 484 kg). It also takes them about 1.5 y longer to reach half their maximal weight.

We also see that some bison herds are heavier than others. For example, mature bison in Ordway Prairie in South Dakota are 50-100 kg heavier than mature bison from Konza. That's a lot of bison. Is it a fluke? Unlikely. Over 90% of Ordway adult cows produce calves. At Konza, it's only about 60%.

There must be a big difference in the grass between Konza and Ordway. Because their bison growth curves are quite different.

Konza flowering phenology and functional groups.

2010-09-05T19:32:00.000-07:00

In general, we have little understanding of how communities are assembled and the types of interactions that long-term generate evolutionary pressures, extinctions, and radiations. I'm pretty sure that whole-flora analyses are going to be keys to helping us understand these complex systems and there are precious few datasets on the scale necessary to do this.

With that in mind, here's the latest Konza phenology data by functional group. This is through Sept 1. The x-axis is day of year of first flowering for a species. Based on n = 408 species, which represents about 80% of the herbaceous grassland flora for Konza. We'll probably get another 10-20 species flowering before the year is up.

The y-axis is probability of flowering per day over the year for species of each functional group based on a "smooth" fit of the distribution data. Probabilities are standardized across functional groups. I broke out the Cyperaceae because it was the Carex that flowered early, not any C3 grasses. The C3 grasses that flower late in the year are generally woodland grasses.

This is terribly fascinating, though I'm not sure what the story is yet. For example, why are there C3 forbs that flower in August, but not any C3 grasses? And why are there C4 grasses that begin flowering in March, but not any C4 forbs?

There certainly is an long-term competitive interactions that sort communities and drive selection. It's almost likely a rock-paper-scissors story. If rock (C4 grasses) then no scissors (C3 grasses), but if paper (grazers) then there are less rocks, so can have knife (C3 forbs).

The C4 forbs are probably the most interesting story. If high temperatures favor C4 over C3, then why are there so many C3 forbs that are active during the hottest months rather than C4 forbs. Konza's C4 forbs are mostly Chamaesyche (Euphorbiaceae) and Amaranthus. Often they are prostrate forbs and/or weedy species keying in on disturbed areas. The C3 forbs that flower during this time are species like Salvia. Are there C4 forbs that fall into the same niches as these C3 forbs. Is there evolutionary constraint here that allows all the mid- to late-summer C3 forbs to persist?

As we generate more large-scale trait datasets, more of these patterns should come clear.

Mycorrhizal fungi and grassland community structure

2010-09-04T21:11:00.000-07:00

Relationship between mycorrhizal infection rates and the log-transformed response of species abundance to grazing.

The structuring of plant communities is complex. There are a myriad of proximal and distal factors that can influence the abundance of species. The role of mycorrhizal fungi in structuring grassland communities has always been opaque. In temperate grasslands, many of the species are dependent on arbuscular mycorrhizal fungi, yet many non-mycorrhizal species are found throughout the grasslands. Whether these non-mycorrhizal species tap unique pools or even are facilitated by the mycorrhizal species is really unknown.

Over a decade ago, Wilson and Hartnett (1998) quantified the dependence of ~100 grassland species on mycorrhizal fungi. There had never been a screening study like it. Nor has there been one since. Their work largely compared different functional groups, with the conclusion that C4 grasses are the most dependent on mycorrhizal fungi and legumes the least. The work implied that success at Konza would be dependent on the ability to utilize mycorrhizal fungi, but this was never quantified.

Recently, we've compared the screening data with actual abundances from Konza. It turns out that there is no relationship between abundance and mycorrhizal responsiveness or infection rates. As such, mycorrhizal symbioses are likely not necessary for success.

That said, mycorrhizal symbioses do determine which species perform better under certain conditions. For example, almost 25% of the variation in the response of species abundance to the presence of grazers (bison) was explained by the mycorrhizal infection rate. Grazing promoted non-mycorrhizal species. Similarly, suppression of fire promotes non-mycorrhizal species (data not shown).

In both cases, fire suppression and grazing increase the availability of nutrients relative to other resources. How to think of the role of mycorrhizal under different burning or grazing regimes is still not clear. It's easy to say that fire suppression or grazing increases nutrient availability, which decreases the need for mycorrhizal fungi. But why? Is it because they are too much of a carbon drain? Many of the high-fire, low-grazing species just do not grow at all in the absence of mycorrhizal fungi, so it is unlikely to be associated with competition for nutrients. And why would mycorrhizal responses/infection predict just the responses to grazing/fire, but not abundance overall. In contrast, we see traits like leaf tissue density--which I think of as being associated with low nutrient availability--prediction abundance across Konza, but not the responses to fire and grazing.

How to proceed on the issue is not easy, but it's a curious pattern to line up with a number of others in understanding how grassland communities are structured.

Wilson, G. W. T. and D. C. Hartnett. 1998. Interspecific variation in plant responses to mycorrhizal colonization in tallgrass prairie. American Journal of Botany 85:1732-1738.

Comparing two measures of leaf tissue density

2010-07-24T13:07:00.000-07:00

Relationship between leaf tissue density (RhoL) and leaf dry matter content (DMC) across 42 Konza grassland species.

There has been some debate on how best to represent plant investment into leaves. Specific leaf area, the ratio of area to mass, is at best an imperfect measure. Plants with high SLA certainly produce a lot of leaf area for minimum investment. Yet, high SLA can come as a result of being thin or low density. And it seems that many of the ecological conditions associated with high SLA are really associated with low tissue density rather than thin leaves.

How to measure tissue density is one of the current debates. On the one hand, tissue density (mass per unit volume) can be derived by measuring the thickness of leaves in addition to SLA. Deriving leaf tissue density (LTD) from thickness measurements provide a direct covariate (thickness) and are relatively simple to do. Yet, for some leaves, measuring the average thickness can be problematic. On the other hand, an approximation of tissue density can be derived from the leaf dry matter content (LDMC). Leaves are weighed in a hydrated state and then again dry. The ratio of dry mass to wet mass is LDMC. There are a number of assumptions to equate this ratio to leaf tissue density, but it has been favored.

Across 40+ species at Konza, I measured LTD and LDMC. The two metrics correlated pretty well (r = ~0.8). Some species seemed to have higher LTD than one would expect based on LDMC. In species with a high proportion of veins, thickness is probably underestimated, since it is generally measured between major veins. The Ambrosia artemisiifolia I selected was deeply lobed and did not have much lamina relative to veins. Its LTD was probably too high. On the other hand, both Bothriochloa and Schizachyrium species had higher LTD than expected from LDMC, but this likely would not have been caused by underestimating thickness or area. Instead, these species likely have high silica concentrations that add more mass per unit volume than other species. This is something I still need to confirm.

As to whether LTD or LDMC does a better job of predicting abundance, they both were about the same. Using long-term abundance data, they both had equal predictive power on average.

Whether one metric is better than another is likely equivocal. It depends on the situations as both have their limitations. I’d probably use both for awhile until better consensus can be reached.

I’m not sure I’ll get around to publishing these data, so I thought I’d put so of the results up here.

Photosynthetic pathway and phenology

2010-07-08T07:23:00.000-07:00

Stylized diagram of phenology of first flowering of different functional groups for Konza.

Global change models had often assumed categorical differences between C₃ and C₄species. Because of the temperature sensitivity of photorespiration, C₃ species are restricted to cooler seasons and C₄ grasses to warmer seasons. The separation between C₃ and C₄ species, especially the grasses, was a standard categorization for plant functional types.

Yet, how much basis is there really for the separation? What role does photosynthetic pathway have to play in the phenology, if not ecology, of temperate grassland species?

At Konza, we’ve been collecting plant species when they begin to flower. It’s a rough estimate of phenology. It doesn’t capture how long they flower, or when leaves grow the most, but it’s an easily measured trait that represents phenology. We have first flowering dates for about 350 of Konza’s 550 herbaceous species.

Generalization #1: C₃ grasses have an earlier phenology than the C₄ grasses. The first grass to flower in 2010 was a C₃ grass Poa pratensis on April 21. Yet the first C₄ grass flowered just a week later. Bouteloua dactyloides flowered on April 27. Tripsacum dactyloides, another C₄ grass, was just a day later—April 28. There really is little offset between C₃ and C₄ grasses in when they start to flower.

Generalization #2. The C₃ photosynthetic pathway restricts the activity of C₃ species when temperatures are high in comparison to C₄ species. It is true that C₄ grasses do flower later than C₃ grasses. The last C₃ grass to start flowering was Diarrhena obovata, a forest understory grass. It didn’t flower until June 28. Many C₄ grasses do not begin to flower until July or August, when midday temperatures are routinely 30°C. Yet, C₃ forbs also flower during the time when only C₄ grasses are flowering. For example, Helianthus maximiliani will not flower until the first week of August.

At this point, I have a few questions.

If C₄ grasses can flower as early as C₃ grasses, and C₃ forbs can be active during the time when C₄ species should have a physiological advantage, then what are the links between photosynthetic pathway and phenology?

How much of phenology is driven by phylogeny rather than photosynthetic pathway? The Andropogoneae C4's flower mid-season, but not the Chloridoid C4's.

Why do C₃ grasses not flower during the middle of the summer, while C₃ forbs do? Can C₃ forbs regulate their leaf temperature via transpiration to reduce photorespiration?

And why the offset for C₃ and C₄ grasses, if C₃ species can flower mid-season? Is this an example of niche conservatism?

The topic of whole-flora analyses of phenology is complex, but some of these patterns seem clear enough to rethink some generalizations--even if they shouldn't happen based on what we know.

How to taxonomically structure comparisons

2010-06-19T07:05:00.000-07:00

For a recent grant, we proposed to measure aspects of the nitrogen and water economy of 30 species at Konza. The novelty of the proposed research was in measuring both water- and N-related traits for a wide variety of species, and then test how well they explain the abundance of the species in a native grassland.

One point that came up was how to frame the research. Part of our framing was that the results should help us understand the evolution of plant strategies and selection forces on species. Reviewers seemed to disagree.

One reviewer said, “The problem here is that because of the close evolutionary relationships of many of the selected plants, traits and responses will be co-correlated through evolutionary relationship and will therefore give an inflated estimate of independence.”

Another said, “It seems to me that the work in this project will yield much in the way of an understanding of the influence of resource availability in the evolution of land plants, since gaining such insights would really require a more extensive phylogenetic and perhaps phylobiogeographic sort of approach.”

This is something I still do not understand. How many species does one have to measure to be able to infer selection pressures and evolutionary tradeoffs? Ironically, we had initially proposed to measure 100 species, but were encouraged to measured fewer species. 30 is not enough? Shouldn’t 2 well-contrasted species be sufficient to provide some inference? Most of the initial work on C4 photosynthesis compared 4 species. Granted the work is still being refined, but isn’t 30 a good start? Also, why would 30 species be enough to test ecological processes, but not evolutionary?

I think the standards here have less to do with the science, than the scientist.

The current review is immaterial—the panel summarized that “The placement of this research in evolutionary context was undeveloped but will not affect the quality and novelty of the project outcome.” Yet, the gap in our scientific process is clear in the lack of anabolic comments being paired with the catabolic ones. Experimental designs to test for evolutionary patterns seem to require I-know-it-when-I-see-it tests. Constructively, we need some resolution on standardizing designs. I’ve pushed before for a standard species set, but we also need resolution on some key questions outside of any standardized set.

If there is one question I'd like to see answered, it's "how many species need to be measured and how should they be related?"

I don't expect one answer to this, but if we are serious about wanting to understand the evolution of ecological traits, we have to make the bar visible, rather than always place it just above our leaps.

Comparing bison weight gain

2010-06-06T15:38:00.000-07:00

Locations and size classes of bison conservation herd in North America. Historic ranges shown, too. From Gates et al. 2010 IUCN report on bison.

Before European settlement, bison were distributed across North America. From the Atlantic coast almost to the Pacific and from northern Mexico to the Arctic Ocean. But where would have been the best place to be a bison?

There might not be an easy answer to the best metric for determining where the best place was to be a bison. Especially since we can't go back in time. Yet, bison have been reestablished across North America, which gives us some ability to begin to compare populations.

For bison, as with any animal, fecundity is the ultimate metric of fitness. It is almost axiomatic that when we compare individuals, the best metric of fitness is the number of offspring that an animal or population has. Yet, fecundity is density dependent. Fecund populations become dense, which lowers their fecundity. Population density could serve as another metric, depending on how important disease or predation become in limiting population size.

Systematic comparisons of the geographic ecology of bison have not been attempted. Yet, here's an interesting comparison. The latest IUCN report on bison "American Bison: Status Survey and Conservation Guidelines 2010" included a graph on the weights of bison at Wind Cave in South Dakota. Weights were averaged for males and females by age. Wind Cave bison are considered some of the purest Plains bison and western South Dakota and its short grasses is thought of as prime bison habitat.
By comparison, Konza Prairie in Kansas is tallgrass. And tallgrass is sourgrass to some--not the best habitat for bison. The calving rates of bison at Konza can be pretty low. In some years only 50% of the adult female bison calve, which might not indicate the best nutrition.

Yet, with the data from Wind Cave, we can compare the weights of bison at Wind Cave and Konza. Based on what we know of the habitats and the calving rates, we might expect that Konza bison would weigh less than Wind Cave bison.

Not the case.

Closed symbols are females. Open symbols males. Circles are Konza. Squares are Wind Cave.

The two sites are right on top of one another.

Either the two sites are equally good for bison or the weight of animals isn't the best metric for habitat quality.

The model species set

2010-05-18T05:10:00.000-07:00

By restricting our own freedom, we gain collective power. It's a tenet of larger society, but also scientific society.

For some the restriction is in the form of Arabidopsis. Zea for others. Populus, Lotus, Medicago...the list of model organisms that are used to answer fundamental questions about the genetics of plants goes on.

But what about the evolution of plants? To a degree, we can compare the genomes of model organisms to hint at some of the broader evolutionary patterns. But evolutionary patterns are generally derived by comparison with multiple members of a single clade. If one wanted to understand the evolutionary patterns of grass, we couldn't just look at a single model organism. We would need to look at a model set of species.

What would a model species set for grasses look like? It would have to be large enough to cover the major clades (~10), but restricted enough that researchers could measure standardized metrics on every species. Probably about 100 species. For grasses, they should come from different continents, span multiple origins of C3 and C4, and cover a wide range of environmental tolerances. Seed should be readily accessible. Most likely seed sets would have to be collected by a central agency for distribution to willing researchers. A central database would be needed to store all the data for other researchers to use.

Once that happened, an individual researcher that was interested in the cold tolerance of grasses could grow up all 100 species, measure their cold tolerance, and then examine the evolutionary patterns of cold tolerance. The next researcher that wanted to examine stomatal density could do the same, and then would be able to compare it with cold tolerance. Root anatomy, mycorrhizal dependence, genome size, carbonic anhydrase activity, flowering phenology, drought tolerance...the database would build. Each time we would learn more about multivariate trait selection in ways that no one lab could do.

Why doesn't this exist? Hard to say. Part of it is probably some small group just deciding which 100 species to use. Would it be perfect and cover all the potential evolutionary questions? No, but there are researchers that are asking these questions anyways, so they might as well be using the same species. Plus, there always could be a second species set identified to fill the gaps in the first for a second round of measurement.

Why not just keep a database and let researchers work on whatever species they felt best allowed them to examine specific ecological and evolutionary contrasts? Never enough overlap. Brassicaceae has 3700 species and even the Arabidopsis genus has 9 species. But everyone works on thaliana even if other crucifers might be better to answer some questions.

Once the scientific community agrees to encourage the restriction of freedom of inquiry into plant evolution a little more, a large amount of collective power will be realized. How long should it take? A few informed individuals who are not afraid to make political sausage would need to be in the same room for about 2 days. How long will it take to get people in a room for 2 days? Hopefully within a year or two.

Climate-nutrition-performance cascade

2010-05-17T19:49:00.000-07:00

Critical climate periods for precipitation for ANPP, flowering of 3 grass species, bison weight gain, and the calving rate of adult females the following year.

Some ranchers around here know that "a dry June is money in the bank". Supposedly when precipitation in June is low, cattle gain more weight. More cow means more money. I haven't heard it too much around here and I had never seen data to support it (until the work on bison weight gain at Konza), but it exemplifies the climate-nutrition-performance cascade and is a cautionary lesson in understanding climate change.

The performance of grazers--their weight gain and calving rate--is dependent on both the quantity and quality of the grass they eat. The interannual determinants of quantity seem fairly straightforward (for some sites). Quality is less so. Quality encompasses a lot of things, but primarily is protein. And protein is nitrogen.

The cascade links climate to performance through protein. The proximal and distal drivers of variation in protein are complicated enough that they are still being worked out. But as we work through this, it will be important to ask whether there protein at certain times is more important than others. And if so, maybe interannual variation in climate at certain times is more important than others. Rain in June might be more important than May, for example.

We've used the critical climate period approach to begin to tease some of this out at Konza. The figure above shows a broad CCP for ANPP--rain that falls in June or September still is important for determining growing season biomass. The three C4 grass species have different, but overlapping, CCP's. Each is about 80 d. In contrast, bison weight gain and calving rate seems to respond to variation in precipitation for just relatively short periods. One in late June, early July. The other mid- to late August. Calving rate depends on just mid- to late August precipitation.

Between climate and bison performance is protein. And the controls on protein we're still figuring out.

Until we do, we'll have a hard time understanding the dynamics of grazers, no less their fate in a world where climate has changed.

Whole-flora analysis: flowering and community assembly

2010-05-17T14:51:00.000-07:00

Distribution of first flowering dates for 265 Konza grassland species (as of mid-May).

There are some ecological analyses that can only be done by analyzing every species in a flora. Statistical inference aside, if we are to understand ecological sorting and community assembly, we need whole-flora analyses.

For example, we've slowly been accumulating data on first flowering for the Konza flora. For each species, the day of year it first begins to flower is recorded. We're about half-way to having dates for the entire grassland flora. It's a bit biased now as we're still collecting data this year, but that's why this is on a blog...

There seems like there might be pretty strong selection pressures to flower at times when 1) environmental stress is low and 2) there is little competition for pollinators. I'm not sure if there would be selection against synchrony in flowering time for anemophilous species, but we that would be something to test, too. In this case, deciding what the null model is, is the hardest part.

I like the idea of whole-flora analyses. It's a lot of work though.

N vs. P limitation

2010-05-11T12:54:00.000-07:00

Nutrients limit grass growth in native grasslands throughout the world. Yet, which nutrients limit growth should vary. N limitation appears to be pervasive in all nutrient limited grasslands and P is often limiting, too. In Europe, grasslands are often divided into those limited by N vs. those limited by P. In N-limited grasslands some species such as Alopercus predominate, while in low-P grasslands its Molinia.

Why the sorting though? What traits would have been selected for in low-N vs. low-P soils? Fujita et al. have a new paper coming out in Oikos that I think provides some good data to separate species and shed light on selection when nutrients are limiting. It's long been known that plants can produce phosphatases to increase P availability. Fujita et al. show that low-P species have higher rates of phosphatase production.

With the experiment examining plant growth and activity at a range of N:P supplies, the research has the potential to help understand not only differences in grassland communities but also the response of grasslands to N deposition. Fertilization with N increased phosphatase activity in ways that should further increase the abundance of low-P species.

The authors do a good job for their eight species in linking plant stoichiometry, plant growth, and resource availability, which might ultimately serve as a key trait in understanding selection for success when nutrients are limiting, as well as the functioning of grasslands.

Fujita et al. 2010. Oikos. doi: 10.1111/j.1600-0706.2010.18427.x

Leaf dry matter content: ecologically relevant?

2010-04-29T12:18:00.000-07:00

There is still some contention about whether leaf tissue density (mass per unit volume) leaf dry matter content (LDMC; dry mass per unit wet mass) are equivalent and whether past work has shown LDMC to be ecologically relevant, no less more relevant than specific leaf area (SLA).

I've looked through the literature pretty hard. Here's about all I can find:

1. LDMC and leaf tissue density should be positively correlated and there has been some excellent work investigating the underlying causes of variation in LDMC that are relevant for understanding leaf tissue density (Vile et al. 2005, Roderick et al. 1999, Shipley 1995). I still haven't found the perfect test of the two methods, but they should be pretty strongly related.
2. LDMC can predict plant strategies (Vendramini et al. 2002, Wilson et al. 1999). LDMC does a better job than SLA in predicting CSR placement for example.
3. LDMC can predict relative growth rates within species (Ryser and Aeschlimann 1999) and digestibility (Pontes et al. 2007, Ansquer et al. 2009, Duru et al. 2008).
4. LDMC was not correlated with competitive effect or response (Liancourt et al. 2009).
5. LDMC correlated better with soil fertility and sheep grazing intensity than SLA in Norwegian alpine ecosystems (Rusch et al.2009). [Note I still haven't read this paper--it's on order.]

That's about it.

The use of SLA still outnumbers tissue density or LDMC 50 to 1 and there still are essentially no published tests of the utility of either tissue density or LDMC in explaining abundance.

The tyranny of dominance

2010-04-05T05:11:00.000-07:00

If you take a walk through a grassland, you are likely to recognize that a few species are more abundant than others. Walk through a nearby grassland and you'll recognize those species again. Dominance of a few species is a hallmark of grasslands. Especially in humid grasslands, species are thought of as dominant, sub-dominant, or rare.

But do grasses evolve to be dominant? Or rare? Dominance might be a common condition for a few species, but how transient is dominance? Or rarity?

This is a topic that easily belies one's inner model of how the world works. More often than not, it's one's view of human society that paints one's ecological canvas. But that's a topic for another day.

Although unstated, there are likely two competing intellectual frameworks at play with discussing dominant species. On the one hand, it is possible that some species have evolved to be dominant and others rare. There are light-demanding canopy trees and there are shade-tolerant understory herbs. It is the role of the latter to always be underneath.

On the other hand, dominance and rarity are context specific. All species dominate somewhere and at some time. It all depends on the environmental context. If there are species that are dominating it is only because of the prevailing conditions.

Let's look at Konza Prairie. 86 species of grass. Only a few are considered dominants: Andropogon gerardii, Schizachyrium scoparium, Sorghastrum nutans are the big three. Maybe Panicum virgatum if one is feeling inclusive. But what about the other 82? Do they not dominate because they cannot dominate an area, or because they dominate grasslands under conditions that are not prevalent at Konza.

For example, only a portion of Konza is grazed and most of that area not too heavily. All of Konza's Big 4 grasses are less abundant in areas that are grazed than ungrazed. If Konza were grazed more heavily, one would likely assume another set of species were dominants. Bromus arvensis is likely one of those. It is 10,000 times more abundant in grazed than ungrazed areas. It "dominates" grazing lawn areas.

Along these lines, Gene Towne went and calculated that almost half of Konza's grasses have been found to be abundant in at least one of Konza's ~300 10 m2 permanent plots over the past 15 years. The other half? Some of them, such as Elymus virginicus, dominate in wooded areas which just aren't sampled at Konza. Some of them dominate outside of Konza. For example, Panicum coloratum is a dominant in the southwest mesquite woodlands. Poa arida is not common at all at Konza, but Konza is really at its southern range limit. Go to Alberta to find vast stretches of it. Some of the other Konza-rare species are annuals and would likely be a lot more abundant if the ground was pounded by more hooves or we had recently had a major drought.

In all, there's probably too much Berkeley in me to believe that some species are inherently dominant. It seems like, at least for grasses, each species likely dominates somewhere some of the time. That said, it would help to hear a bit more about the assumptions that underlie the concept of dominance. I could accept that grasses would be tyrannical to one another in their quest for resources and reproduction. The idea that some grasses are inherently more likely to dominate than others is a tyranny of thought I am probably not willing to accept yet.

C4 photosynthesis and nitrogen

2010-03-24T10:05:00.000-07:00

Comparison of foliar N concentrations among clades.

Since the beginnings of our modern understanding of C4 photosynthesis, it has been set that C4's are more efficient with water and nitrogen. Yet, there have long been unexplained patterns for C4's that didn't match the assertion of greater nitrogen use efficiency. For example, C4 grasses in the field often have lower foliar N concentrations, but also lower root N concentrations. Why would this be? If the leaves need less, shouldn't the roots get more? Also, some C3 grasses like Chionochloa can have foliar N concentrations as low as 6 mg g-1. Most C4's have higher concentrations and only a few have been observed to be below that. Also, foliar N concentrations for any given species are highly plastic and dependent on the balance between C and N supplies and demand. If a given species can have N concentrations that range 30 mg g-1, just how important is the C4 photosynthetic pathway.

Turns out, probably not much. Taylor et al. (2010) used a phylogenetically structured screening experiment to measure a number of morphological and physiological traits of grasses. In doing so, they could compare C3 and C4 species controlling for phylogeny. The research upholds the notion that C4 photosynthesis confers greater water use efficiency to plants. Yet, after controlling for phylogenetic relationships, there were no differences between C3 and C4 species in their foliar nitrogen concentrations.

By no means the last word on the topic. For example, they only measured ~30 species. Yet, the authors have provided the best experiment to date to address the question and evidence to the contrary will have to be weighed against some strong evidence regarding the ecological consequences of the evolution of C4 photosynthesis.

Taylor, S. H., S. P. Hulme, M. Rees, B. S. Ripley, F. I. Woodward, and C. P. Osborne. Ecophysiological traits in C-3 and C-4 grasses: a phylogenetically controlled screening experiment. New Phytologist 185:780-791.

Do I have to phylogenetically correct my grocery list?

2010-02-27T19:10:00.000-08:00

The figure of Westoby et al. (1995) that summarizes their view of the tension between phylogeny and ecology in understanding trait relationships.

For some, a simple grocery list can pose a dilemma. Just yesterday, I went to the store with 21 items to buy. Others would look at my list and suggest I only bought 11 items. Fresh peas and frozen peas shouldn't really be counted as different items--they were both peas. Cauliflower, broccoli, and collard greens are the same species. Mustard part of the same genus as the previous three. Hot dogs and pork chops both from pigs (I hope). So although 21 items went into the cart, one could phylogenetically correct my list and arrive at the conclusion that I only bought 11 unique items.

It might seem silly to phylogenetically correct one's grocery list, but how to consider both phylogenetic and ecological data when examining species relationships lays bare the same fundamental tension as describing my last trip to the grocer.

In 1995, Westoby, Leishman, and Lord published a forum piece, “On misinterpreting the 'phylogenetic correction'”. The genesis for the forum piece came during the review process of a paper on seed mass in plants. Most likely, during the review of that paper, differences in opinions between reviewers and authors were laid bare. In the original paper, the authors showed that tall plants had large seeds. The reviewers likely insisted that the relationships between plant height and seed size could be due to phylogenetic relationship. The authors disagreed. Differences in opinions became forums, which by ecology standards unleashed a bit of a storm.

The questions associated with the topic of how to match ecological and phylogenetic data are ripe, but “phylogenetic correction” essentially adjusts relationships by weighting closely related species less than distantly related species. The fundamental differences of opinion pin whether closely related species hold similar traits because of phylogenetic constraint or ecological constraint. Closely related species might have similar traits because there has been little time for radiation, or because they are under similar ecological selection pressure. Distantly related species might have different traits because initial trait differences have long been conserved due to fundamental difficulties associated with character displacement or because they have been under the same ecological pressures for a long time.

The issues of how to identify adaptations or evolutionarily beneficial relationships cannot be covered here, but these fundamental issues have never been resolved, near as I can tell. The current détente that seems to exist is to examine ahistorical and “phylogenetically corrected” relationships among traits and hope that the patterns are the same. When setting to test relationships among species, choose congeneric species pairs from distantly related genera and hope the patterns work out consistently.

It’s currently an uneasy impasse. Both sides recognize that correlation does not necessarily imply causation. But outside of hoping that the evolutionary and ecological patterns parallel, there is still no resolution to the question of how to compare the traits of species.

I do know that if I want to shrink my grocery list, I'll start by not buying both cauliflower and broccoli rather than phylogenetically downweighting closely related taxa on my list.

The evolution of grasses: phylogeography of C4 photosynthesis

2010-02-21T19:39:00.000-08:00

The temperature niches of grasses of the world overlaid onto their phylogenetic relationships.

The two great datasets in biology are the tree of life and the global biogeographic distributions. The first describes the phylogenetic relationships among organisms. The second describes their distributions on our planet. In a rich and well-nuanced paper, Edwards and Smith have brought the two together to shed light on one of the most fundamental questions regarding the evolutionary ecology of plants, namely the origin of C4 photosynthesis. The authors first use an expanded grass phylogeny to describe the origins of C4 photosynthesis in more detail than has done before. They then determine the current distribution of the grass species to determine the climates they occupy.

With regard to the evolution of C4 photosynthesis, the authors conclude that shifts from C3 to C4 photosynthesis did not involve shifts to warmer macroclimates, but instead to drier macroclimates. This results comes as a bit of a surprise--it is less clear that C4 photosynthesis is a response to low water availability as much as high temperatures. Their next logical step is a bit of a leap--namely that these modern geographic differences can be associated with habitat shifts in the past.

As important as the insights into the phylogeography of C4 photosynthesis is that the evolution of cold-tolerance in grasses is more difficult evolutionarily. Cold-tolerance apparently evolved vary early on in the grass radiation and has not been repeated to the degree that C4 photosynthesis has.

In all, this hardly seems like the last word on the topic. The biogeographic data needs to be improved, climatic ranges rather than centers will likely be used, and the grass phylogeny is still relatively unresolved. Also, we still have little understanding of why C4 photosynthesis would benefit plants in dry environments. That said, there is a lot of insight for many types of researchers and a solid step in understanding the strategies of plants to resource scarcity.

Edwards, E. J. and S. A. Smith. 2010. Phylogenetic analyses reveal the shady history of C4 grasses. Proceedings of the National Academy of Sciences 107:2532-2537.

The competing constraints on roots

2010-02-13T22:07:00.000-08:00

Cross section of a Dicanthelium acuminatum root.

Roots have a few important jobs. Anchor a plant. Acquire water. Acquire nitrogen. Sometimes store carbon.

There is no reason that a root system that is optimized to acquire water would also be optimized to acquire nitrogen. Yet, what would a root system that was optimized to acquire water look like vs. one that was optimized to acquire nitrogen?

There are some contingencies here, but it's a good segue to think about how roots are built, no less root systems. There are multiple tradeoffs that would be selected for in different environments. Deep or shallow. Narrow vs. extensive. Thick vs. thin. Stele vs. cortex. Large xylem vs. small xylem. Many small cells vs. few large cells.

If form follows function, one should be able to deduce function from form. We can do this with many other traits. A thick waxy cuticle on a leaf generally reduces water or nutrient loss. Thick bark often protects from fire. Thorns deter browsing mammals.

Yet, if we were to look at the cross-section of a root, what could we tell?

Here's a cross-section of a Sorghastrum nutans root:

Now here's one for Penstemon tubiflorus:

Some of the differences are obvious. Penstemon has a much larger cortex. Sorghastrum larger xylem vessels. But can we deduce their differences in ecology? Which one is more drought tolerant? Which one is the better competitor for nitrogen? Is one more dependent on mycorrhizal fungi?

Wahl and Ryser were the first to try to link up root cross-sections with function, finding good linkages with other traits like plant height and RGR. It's been 10 years since they published their paper on grass root cross sections. No one ever followed their work up.

One of the keys, if not linchpins, to understanding the evolution of plants is waiting for us just under the surface.

Wahl, S. and P. Ryser. 2000. Root tissue structure is linked to ecological strategies of grasses. New Phytologist 148:459-471.

Phylogenetic correctness

2010-02-13T21:55:00.000-08:00

It is axiomatic to state that grasslands are dominated by grasses. It is also axiomatic to state that the trait that allows grasses to dominate grasslands is an adaptation to the grassland environment. The identities of the traits are currently unclear. For example, meristem position used to be the oft-cited reason for grass dominance, despite the clear evidence of many less abundant eudicots with similar meristem position. Yet, whenever we discover what the trait is that separates grasses from other species and allows them to dominate grasslands, we will never be able to declare that trait an adaptation. Such is that state of phylogenetic correctness.

There are many important traits that arose only once. True wood, angiosperm xylem vessels, and Rubisco likely only evolved once and cannot be separated from phylogenetic origins. The conditions and the complexity of the trait did not lead to multiple origins, but instead served as the basis of radiations. Any trait that sits at the base of a tree cannot officially be considered an adaptation. Even if a trait had arisen twice, there would be no statistical basis for calling something an adaptation since it cannot be separated from phylogeny.

What are we left with then? We can apply phylogenetic corrections to account for relatedness when examining trait relationships, and it provides useful knowledge. But to what ultimate point if phylogenetic corrections will not ultimately rule out or in something as an adaptation.

It's currently a silent sticky point in our considerations of the evolution and ecology of plants. One to which I don't have a complete answer, but one of which would be helpful to have clearer consideration. In the end, we will likely determine how grasses differ from other species and why they come to dominate what we call grasslands. And when we do, it'd be nice to be able to recognize the adaptations of grasses for what they are.

Biogeography and traits

2010-01-23T06:12:00.000-08:00

A map of climates in the Cape Floristic region and how they relate to shoot design for 88 species of Leucadendron.

Research on species traits generally follows one of three avenues: traits are related to other traits, environmental variables where the plants were measured, or phylogenetic relationships.

Although we've made good strides over the past decade in understanding trait relationships, relationships with environmental gradients, and understanding the distribution of traits over phylogenetic space, we have progressed little in understanding how traits affect the distribution and abundance of species. A fourth road is just as important, but less frequently traveled: biogeographic distributions of species.

As far as I can tell, there is really only one paper to attempt this. Thuiller et al. (2004) measured traits on 88 species of Leucadendron (proteas), and then derived the biogeographic distribution of each species. With these data, they generated mean climate parameters for their occurrence.

The simple pairings led to some novel insights. First, species in drier, more Mediterranean regions had smaller leaves and smaller stems, but also smaller cones. This might not be a surprise to many, but sometimes we need ways to quantify the obvious. More interesting, species in more arid regions had narrower niche breadths and were more likely to have seeds dispersed by wind, rather than by ants, which were more prevalent in the more subtropical, continental climates.

In all, I'd have to add this paper to my list of needle-in-haystack papers, but it's actually been cited relatively well (42 cites since 2004), more than my arbitrary cap of 5 citations a year. That said, the specific approach hasn't penetrated trait research. As a consequence, research on trait distributions have little mechanistic underpinnings. The Thuiller paper is a good model for future research that hasn't caught on yet.

Thuiller, W., S. Lavorel, G. Midgley, S. Lavergne, and T. Rebelo. 2004. Relating plant traits and species distributions along bioclimatic gradients for 88 Leucadendron taxa. Ecology 85:1688-1699.

The “Nitrogen Throttle” hypothesis of primary productivity

2010-01-16T21:33:00.000-08:00

Imagine a global change scenario. Models predict precipitation will decline for a region. Other things are likely to happen, too. For example, N deposition is likely to increase. But, with soil water determining productivity, the model returns the prediction that productivity will decline, too. As a result, ranchers will fail. Forestry will be diminished. But what if the model was structured wrong? What if soil moisture only indirectly determined productivity and N was actually more limiting? The decline in precipitation might be less important that the increase in N. The original prediction might be 100% wrong.

Globally, nitrogen is the nutrient that limits primary productivity the most. Also, secondary productivity, since protein is often more limiting to herbivores than energy. Yet, whether nitrogen or water limits productivity is still open. Places with little precipitation have little plant biomass. Yet, even vegetation in dry places still responds to N addition.

Separating between water and nitrogen limitation is complex enough that for all intents and purposes, ecologists have ceased to try. Not that it doesn’t matter any more—those that were interested in the topic have likely just stopped hitting intellectual walls. And some simple scenarios show how important it is to get the fundamentals right.

There are two major hypotheses regarding water and N limitation. First, the “Water Stress” hypothesis states that water directly limits production. As soil moisture declines, water supply to plants declines, water stress increases, and plants reduce stomatal conductance to match supply and demand and/or limit cavitation risk.

The other hypothesis is the “Nitrogen Throttle” hypothesis. As soil moisture declines, water stress increases for plants, but this is not what limits photosynthesis. Instead, microbial N mineralization declines causing N supply to plants to decline. Here, low moisture ‘throttles’ N mineralization. As a result of the lowered N supply, plants decrease their stomatal conductance in order to match C and N supplies. Under more extreme soil moisture stress, plants begin to senesce their leaves to maintain minimum growth requirements if not store N for when favorable growing conditions return.

How important is each hypothesis in explaining patterns of productivity? Hard to know, but as I’ve described before there is evidence that nitrogen throttling happens and is important. For example, why would there be soil moisture left deeper in the soil profile if plants were limited by water? Why is it that plants that can photosynthesize at -9 MPa water pressure begin to senesce at -3 MPa?

Like other competing hypotheses, proving one over the other is all but impossible. Factorial resource additions can’t solve the problem when adding water also increases N supply. In the end, it’ll be parsimony that will be tested as lines of evidence are compared.

A new Whittaker biome diagram

2010-01-14T07:20:00.000-08:00

Whittaker biome diagram from Chapin, Matson, Mooney Ecosystem Ecology text.

Whittaker long ago attempted to explain the major patterns of vegetation in the world with combinations of temperature and precipitation. The Whittaker biome diagram is a fundamental starting point for understanding the vegetation of the world.

There are general questions about the overarching role of climate in determining biomes vs. other state and interactive factors, as well as what the boundaries should be and how much to subdivide biomes.

With recent advances in our understanding of the distribution of climate across the globe, we can now see that some of the patterns were not detailed initially correctly. Andrew Elmore and I redrew the Whittaker biome diagram to also include the actual distribution of land area for each combination of temperature and precipitation.

A few major things change.

1) Tropical forests exist in areas much wetter than originally detailed. Much forest exists between 4.5 and 7 m of rain.

2) Most of the world's temperate wet forests are at about 4°C. Whittaker would have lopped off much of them.

3) There are scattered high precipitation areas between what was considered temperate and tropical wet forests. This happens to largely be Hawaii. These have never been classified into temperate vs. tropical biomes.

Why the world is +5

2010-01-12T19:51:00.000-08:00

Average del15N of all the soil and vegetation in the world.

Ben Houlton and Edith Bai have a new paper in PNAS about the nitrogen cycle that has some good sleuthing in it.

Examining global patterns of soil and plant 15N, it turns out the 15N signature of all the N in non-managed ecosystems is about +5‰. But why +5 if naturally fixed N is about 0‰? They rule out enrichment from N deposited as NO3-, which averages about -2 to 0. And terrestrial systems aren’t becoming enriched because of fractionation during leaching out the bottom-- N being leached into streams scales with soil 15N pretty well.

What’s left to explain the enrichment? Just gaseous N loss. They argue its unlikely NH3 volatilization since its too reactive and gets redeposited too quick. With that ruled out, all that’s left is denitrification, which here includes losses during nitrification.

From here, its some simple back of the envelope calculations to estimate the total gaseous N loss on a global scale, which happens to be a lot.

The authors aren’t clear about where the largest uncertainties in their calculations are, but global patterns of soil 15N and fixation likely are pretty high up there. Also, considering that soil 15N is pretty close to zero in cold ecosystems, that leaves the warm ecosystems as the major players in the global N fluxes, which happen to be the places that we know the least about patterns of 15N.

Sounds like now is the hard part. Because if the world turns out to be +6 or +4, the budget changes a lot.

Houlton, B. Z. and E. Bai. 2009. Imprint of denitrifying bacteria on the global terrestrial biosphere. Proceedings of the National Academy of Sciences of the United States of America 106:21713-21716.

Whither elevated CO2 research?

2010-01-06T05:30:00.000-08:00

Biocon FACE site in Minnesota.

The list of major global changes is short. The world is becoming warmer, CO₂ concentrations are rising, N deposition is increasing, agriculture continues to increase, and the world’s flora is becoming homogenized.

The list of major global change research efforts is even shorter. For all intents and purposes its now all about climate. Our research into understanding the extent and implications of N deposition is greatly diminished and comparably small. Land use we largely quantify with remote sensing but do little else. Invasive species are approached piecemeal, but not with any major initiatives.

The surprising fall from grace has been research into elevated CO₂. The politics behind this are one thing, but there is no indication that this remains a major research question. Do we understand the effects of elevated CO₂ on ecosystems? Not even close. Does it seem like we do? For some, yes.

The reason the energy into elevated CO₂ research has waned derives from the early focus of the research. Early research centered less on testing mechanistic hypotheses than on quantifying response ratios. The modeling community wanted to know how much will photosynthesis and NPP increase. For all intents and purposes, the Ainsworth and Long (2005) meta-analysis of responses of photosynthesis to elevated CO₂ was, for some, the last important paper on the topic. I’m oversimplifying, but--with increases in elevated CO₂, photosynthesis goes up 25%, stomatal conductance goes down about the same amount.

With that synthesis, with those response ratios, so withered the major impetus into elevated CO₂. And with that, whither the research?

Without a major, overarching question, it is next to impossible to generate the funding necessary to sustain the research. The previous search for response ratios has fractured research into components that seek out other response ratios, but none of the remaining response ratios generate that much excitement.

In short, to reinvigorate CO₂ research, look at the fundamental unanswered questions and question whether the current response ratios can be trusted. (This is largely what fueled a large round of research shifting from chambers to FACE technology.)

Here’s what we know:

Elevated CO₂ increases water use efficiency.

Elevated CO₂ increases nitrogen use efficiency.

Given these two points, here’s what we don’t know:

Elevated CO₂ increases/decreases the relative limitation of water vs. nitrogen.

This is the major unanswered question for elevated CO₂ research, because it’s one of the major question we have left for modern CO₂ concentrations. If we do not understand the relative limitation of water and N to productivity and C storage now, and we know less about how CO₂ will alter the balance, then we really can’t trust the average response from modern experiments if the relative availability of water and N will change in the future. And right now, from our experiments, we just do not know whether CO2 increases photosynthesis because more water is left in the soil or less N is needed to grow. Considering that if more water is left in the soil more N should be made available to plants, we might know what the average response is today, but not what it will be tomorrow. Without knowing why, we can't know what.

Surely, there are other major questions to go forward with, but without something as large as this, elevated CO₂ research will continue to wither.

How to reach the Giga mark with plants

2009-12-27T01:21:00.000-08:00

The 96-well microbiology plate and its plant analog?

New Phytologist recently had a commentary "From Galactic archeology to soil metagenomics – surfing on massive data streams" (New Phytologist (2010) 185: 343–348). It's an interesting, if not typical, update on Progress [with a capital "P"]. The comment discusses the large numbers of microbes in soils, the diversity of Operational Taxonomic Units, and the large number of sequences that can be read currently in a standard batch. Having worked tangentially with the new technology, it truly is breathtaking the amount of detail and volume of data that can be generated.

I used to think that the tradeoff between quantity and quality is a fundamental constraint in the world and it was especially acute in science. I'm not sure I think that any more. In some senses, quantity is quality--at least when it comes to scientific emphasis.

The amount of money that gets spent on new technologies in science is immense. Part of what drives where that money gets spent is perceived rates of Progress, but also just sheer numbers. It also helps to be able to collect data in the 10's of thousands, if not gigas or terras. Sophisticated data streams and analyses help.

For understanding plants, and definitely ecosystems, we have suffered from not being able to rapidly produce enough data. Pure and simple. Remote sensing data is weak, but we can generate a lot of it automatically. Same for microbial data. And genomics. Those things that we'd like to learn about, but we can't generate a lot of data, suffer at the macroscale when it comes to scientific investment.

One of the things that has been holding back plant work has been the scale at which we can generate data. It's too slow. We need to generate a lot of data fast.

96-well plates in microbiology are standard and provide a template for a number of processes. One thing that would help beginning to generate a lot of data would be to have the equivalent of the 96-well plates for plants. The similarity with 98-cell Conetainers is striking and makes me wonder whether it could become a standard. For example, there are roughly 2000 species of grass in the US. To grow one plant of each species would take about 20 trays, which is about 40 ft2, or the size of a standard growth chamber. A standard greenhouse might be 5000 ft2, which could house 2,500,000 cells, enough for 1000 replicates for every species of grass in the US, 250 replicates for every species of grass in the world, and 10 replicates for every species of plant in the world (assuming you could cram it into a tiny Conetainer). Collect one data point per cell and the numbers get big fast. A few data points and we hit the Giga mark in a month.

A standardized medium in each cell would provide comparable data, but one could also imagine a standard configuration of different soils to provide a spectrum of data, similar to the old Biolog plates. Soils could differ in nutrient availability or texture or salinity or origin. 98 cells gives you a lot of flexibility.

I wonder if in the plant world, we just haven't been thinking big enough. There are certainly logistical problems to overcome, but the giga mark is within reach. I just wonder why we don't do it.

Nitrogen isotopes in different types of C4 Australian grasses

2009-12-17T21:16:00.001-08:00

Sites sampled for grass nitrogen isotopes.

I'll admit I have a soft spot in my heart for expedition science. You start out with no real hypotheses. Instead you have a plan to measure something interesting along an interesting gradient not knowing what the ultimate patterns might be. You get a mess of data and then start to try and tell a story. It's an adventure to collect and an adventure to write.

In one recent study, over 400 grass samples were taken from what amounts to the entire continent of Australia. It's not a perfect study--the analyses could have been more complete and they could have spelled Ben Houlton's name right. That said, there is an interesting story that comes out. As water availability increased, del15N decreased, as we've seen before.

Yet, comparing C4 types, PCK C4's were enriched in 15N relative to other types. PCK's have always been a mystery ecologically. NADP's are the tallgrass C4's, NAD's are the shortgrass C4's and PCK's are the tropical C4's. So why the 15N enrichment? Are they less reliant on mycorrhizal fungi? Do they occupy higher N availability sites than the other types?

Hard to know, but with some broad surveys done, at least we know the patterns. Which is an important first step to understanding why the patterns are there.

Murphy, B. P. and D. Bowman. 2009. The carbon and nitrogen isotope composition of Australian grasses in relation to climate. Functional Ecology 23:1040-1049.

10 ways papers are rejected

2009-12-14T21:34:00.000-08:00

As an author, there seems to be a myriad of ways that reviewers justify rejecting papers. As a reviewer, it can be a struggle to define why a paper is unfit for publication.

My goal here is to codify ways papers are rejected. For authors, it should help to improve a paper, if not rebut criticisms, by understanding the categories by which reviewers and editors reject papers. For reviewers, it should help sharpen the key points to make to authors so that they can improve their work.

The examples I give are all from papers that I have had rejected, but subsequently were accepted later. Reading through them, I sometimes wonder how I ever got anything published.

1) Poor fit for a journal. If these were relationship break-up lines, this is the equivalent of “It’s not you, it’s me.” There rarely is a objective analysis of “fit”, so it’s an easy catch-all rejection. Higher profile journals are more likely to use this reason at the editorial stage. Here are two examples:
a. Science: “Although your analysis is interesting, we feel that the scope and focus of your paper make it more appropriate for a more specialized journal.”
b. Nature: “We do not doubt the technical quality of your work or its interest to others working in this and related areas of research. However, we are not persuaded that your findings represent a sufficiently outstanding scientific advance to justify publication in Nature.”

2) Poorly referenced. No paper can include every study, but often there is a set of studies that the coauthor is thinking about that they did not find in the paper. Usually, but not always, this means that the authors forgot to reference the reviewer.
a. Example: “the authors of this manuscript have done an extremely bad job with respect to consideration of relevant literature for their review. It is specifically the duty of a research review to consider the whole range of literature in a balanced manner”. [this comment was followed by a list of 8 papers that all had one author in common].
b. “By completing a more thorough literature review and bringing concepts and information from those reports into this one, the authors could greatly strengthen this manuscript.”

3) Assumptions. When reviewers feel that the authors make incorrect assumptions, the results often do not matter.
a. Example: “THE FUNDAMENT [sic] PREMISE OF THIS MANUSCRIPT IS SERIOUSLY FLAWED.” Original CAPS.
b. “Their analysis is based on the supposition that changes in these drivers at any one location will have the same effects on these response variables as that which is currently seen across space in their data set. This may or not be true.”

4) Hypotheses. One description is that hypotheses are weak or absent. Sometimes a paper will be referred to as anecdotal. Many papers have no formal hypotheses, but when a reviewer feels a paper is too unstructured, this point will often be made. I haven’t found any examples of these in my reviews, but I’ll dig some more.

5) Methodological flaws in acquisition or analysis of data. For example, often experiments are too experimental. Gradient analyses are too unconstrained.
a. The authors “used a highly controlled, if not overly-artificial experimental system to address several key theoretical questions in plant ecology”
b. “Unfortunately, this ms suffers in my opinion from too many methodological flaws to really increase our understanding”
c. “the authors seem to pick and choose certain variables and ignore others that have been demonstrated to have a major influence on plant isotope composition”
d. “The approach that they followed seems to be a sort of wild west expedition where they sampled as much as they could seemingly randomly”

6) Poor demonstration of stated results. Sometimes a reviewer doesn’t believe authors showed what they said they showed.
a. “I was also very concerned about the conspicuous lack of critical data: Why are so many method details and results not presented?”
b. “Although the manuscript has the potential to show some interesting trends, it does not currently deliver on its objectives.”
c. “the introduction states that the aim is to determine how landscapes interact with herbivory to determine N availability, yet this does not appear to be addressed in the rest of the manuscript.”
d. “it is not entirely clear to me what they want to show with these data.”
e. “The manuscript does not live up to our expectations”

7) Results are not novel or confirmatory. This is the most common killing comment. Although the scientific method states that results should be repeatable, there is no reason that independent confirmation should be published apparently.
a. “The results are in complete accord with a book chapter I wrote back in 1986.” [23 years before the paper. No citation given.]
b. “In this sense, the data are confirmatory.”
c. “The questions…were certainly worth exploring, but the results seem pretty clear, pretty simple, and not too surprising.”
d. “While I do appreciate the scale of your study, this doesn’t seem like a particularly novel finding”
e. “While this was a detailed fertilization experiment with many collected data, it is not clear what it contributes to our understanding of relationships between nutrient limitation and N:P ratios for a number of reasons”

8) Excessively speculative discussion. This one often doesn’t kill a paper, but in conjunction with other comments is enough for rejection.
a. “I find the discussion unnecessarily speculative in places.”

9) Length to content ratio. Again, hard to kill a paper with this, but certainly not a positive.
a. “I don't think the analysis as currently executed is interesting enough to warrant a treatment of this length”
b. “I was taken aback by the number of co-authors (23). The reported study did not exactly crack the human genome, so the laundry-list approach towards authorship may be inappropriate for this manuscript.” [I guess length to content also applies to authorship.]

10) Poor writing. One missed verb tense opens the door for this one. It's a subtle way to question the authors' scientific ability.
a. “occasionally one encounters run-on or circular sentences, which could use rewording.”
b. “In general, the writing is wordy, causing the reader to slog through unnecessary text, and in many places, the wording obfuscates the authors intended meaning.”

Natural history of bison dispersing seeds

2009-12-13T10:31:00.001-08:00

Bison heads carry more than horns.

Bison do a lot in grasslands. They eat, poop, pee, rub, trample, and wallow, which fundamentally can restructure how a grassland functions. If you spend enough time watching bison, you'll see them eat some unusual things. For example, early in the season, I've seen a cow systematically nip off sumac buds. Not something we typically associate with bison, at least not overly curious bison. Another thing bison do is disperse seeds. And a close look at seeds makes us rethink a bit about what they eat.

Researchers at Oklahoma State recently published a paper where they analyzed the seeds attached to bison forehead fur in the fall and fecal material over the year. In all, they found the seeds of 76 species on the fur of bison. Turns out males and females had different seeds stuck to them, which related to where they spent time.

More interesting was what was found in the fecal material. There really is only one way for seeds to get into bison pies--they have to eat them. Half the seeds were grasses, which means half weren't. This is surprising because plains bison are thought to predominantly eat grass. Yet, in the spring there were seeds of Viola. In July, there was Solanum and Lepidium. In October, there was Lepidium.

Most of the generalizations from the grass dominance of diet comes from either microhistological studies (leftover plant parts) of bison fecal material or changes in species composition. Yet, microhistological studies might underestimate forbs if their cell walls are easily degradable. Changes in species compostion with grazing show increases in forbs, but cannot rule out which forbs they might eat.

Figuring out what they eat has never been easy. Here, some simple natural history might just reset one of the fundamental assumptions about bison.

Rosas, C. A., D. M. Engle, J. H. Shaw, and M. W. Palmer. 2008. Seed dispersal by Bison bison in a tallgrass prairie. Journal of Vegetation Science 19:769-778.

Bison and seasonal protein

2009-11-30T20:34:00.001-08:00

Forage crude protein concentrations (%N * 6.25) for male and female bison over the season from Konza.

Bison are the largest native grazers in North America left. Their history is interesting, having almost gone extinct with the Pleistocene megafauna, and not having evolved into their modern form until about six to eight thousand years ago. Most of the attention on the evolution of the animals has been regarding changes in their morphology. Most of the attention to the modern animals has been their genetics and the introgression of cattle genes—finding “pure” bison. Most of the interest in their modern ecology has been on their role as a keystone in ecosystems.

Almost entirely missing from the study of modern bison has been their nutrition. There has been some work on diet—do they eat forbs or grasses; cool- or warm-season grasses. Yet, animals that ranged throughout North America and never had access to grasses like the progenitors of modern cattle would have found in northern Europe would likely face strong nutritional stress throughout much of the year. The adaptations of bison to low forage quality, no less the basic patterns of the availability of energy and protein to bison have gone all but unasked.

At Konza, Gene Towne has been collecting fecal material throughout 2009. Every two weeks, he has collected fresh pies from both males and females. Then we send the samples off to Texas A&M’s GANLab to see what the crude protein (nitrogen) and digestible organic matter (energy) was of the grass that they were eating.

If you look at the patterns from 2009, a few fascinating patterns stand out. First, the minimum protein requirements for mass gain for cattle are about 6% crude protein. Bison at Konza have about 100 day window to gain mass during the growing season. After that, there is little protein available beyond what is required for maintenance.

Second, the differences between males and females has never been observed before. Males tend to form “bachelor” herds and do their own thing until the rut—roughly August. After that, they often go off on their own again. The CP patterns show that the males are not selecting as high a quality forage early in the season, but the peak is broader. During the rut, quality is about the same as females. Afterwards, the males are selecting lower quality forage than the females. Why? Why wouldn’t the males feed in the same places on the higher forage quality. A mystery right now.

Lastly, by mid-October, CP had dropped to roughly 4.5%. Not much good green out there for anyone. Gene’s found that the bison lose about 10% of their weight during the winter, which can be up to 200 pounds for the large males. We’re beginning to see why.

Hopefully, data like this will continue to be taken at Konza for a couple of years. It’ll be fascinating to see the differences between wet and dry years on forage quality. With any luck, we can start similar measurements at a number of other TNC sites with bison to being broader comparisons.

Climate change and cattle nutritional stress

2009-11-25T17:57:00.000-08:00

If you read the latest IPCC report, there is little text on the potential effects of climate change on cattle performance. Considering there are more than 1 billion head of cattle in the world with probably about a trillion dollars in value, small changes in their performance would have large economic effects.

Here’s what the IPCC had to say about climate change and forage quality:

New Knowledge: Changes in forage quality and grazing behaviour are confirmed. Animal requirements for crude proteins from pasture range from 7 to 8% of ingested dry matter for animals at maintenance up to 24 % for the highest-producing dairy cows. In conditions of very low N status, possible reductions in crude proteins under elevated CO2 may put a system into a sub-maintenance level for animal performance (Milchunas et al., 2005). An increase in the legume content of swards may nevertheless compensate for the decline in protein content of the non-fixing plant species (Allard et al., 2003; Picon-Cochard et al., 2004). The decline under elevated CO2 (Polley et al., 2003) of C4 grasses, which are a less nutritious food resource than C3 (Ehleringer et al., 2002), may also compensate for the reduced protein content under elevated CO2. Yet the opposite is expected under associated temperature increases (see Section 5.4.1.2). Large areas of upland Britain are already colonised by relatively unpalatable plant species such as bracken, matt grass and tor grass. At elevated CO2 further changes may be expected in the dominance of these species, which could have detrimental effects on the nutritional value of extensive grasslands to grazing animals (Defra, 2000).

In all, there really wasn’t all that much that we knew about the topic.

I won’t go into detail here, but here's the latest press release from Kansas State on the Global Change Biology paper that I mentioned in an earlier post on the Wisconsin Paradox. I think that the next IPCC report should be able to say a little bit more…

K-STATE RESEARCHERS STUDYING LINK BETWEEN CLIMATE CHANGE AND CATTLE NUTRITIONAL STRESS

MANHATTAN -- Kansas State University's Joseph Craine, research assistant professor in the Division of Biology, and KC Olson, associate professor in animal sciences and industry, have teamed up with some other scientists from across the United States to look into the possible effects of climate change on cattle nutrition.
Comparing grasslands and pastureland in different regions in the U.S., the study, published in Global Change Biology, discusses data from more than 21,000 different fecal samples collected during a 14-year period and analyzed at the Texas A&M University Grazingland Animal Nutrition Lab for nutritional content.
"Owing to the complex interactions among climate, plants, cattle grazing and land management practices, the impacts of climate change on cattle have been hard to predict," said Craine, principal investigator for the project.
The lab measured the amount of crude protein and digestible organic matter retained by cattle in the different regions. The pattern of forage quality observed across regions suggests that a warmer climate would limit protein availability to grazing animals, Craine said.
"This study assumes nothing about patterns of future climate change; it's just a what if," Olson said. "What if there was significant atmosphere enrichment of carbon dioxide? What would it likely do to plant phenology? If there is atmospheric carbon dioxide enrichment, the length of time between when a plant begins to grow and when it reaches physiological maturity may be condensed."
Currently, cattle obtain more than 80 percent of their energy from rangeland, pastureland and other sources of roughage. With projected scenarios of climate warming, plant protein concentrations will diminish in the future. If weight gain isn't to drop, ranchers are likely going to have to manage their herds differently or provide supplemental protein, Craine said.
Any future increases in precipitation would be unlikely to compensate for the declines in forage quality that accompany projected temperature increases. As a result, cattle are likely to experience greater nutritional stress in the future if these geographic patterns hold as a actual example of future climates, Craine said.
"The trickle-down to the average person is essentially thinking ahead of time of what the consequences are going to be for the climate change scenarios that we are looking at and how ranchers are going to change management practices," Craine said.
"In my opinion these are fully manageable changes," Olson said. "They are small, and being prepared just in case it does happen will allow us to adapt our management to what will essentially be a shorter window of high-quality grazing."
Additional investigators on the project include Andrew Elmore at the University of Maryland's Center for Environmental Science and Doug Tolleson from the School of Natural Resources at the University of Arizona, along with the assistance of Texas A&M's Grazingland Animal Nutrition Lab.

Why be efficient? A question for C4 plants

2009-11-11T20:33:00.000-08:00

C4 grassland in South Africa with a 1.7 m Carl Morrow for scale.

Species with the C4 photosynthetic pathway are in the minority in terms of species, but fix a large amount of the world's carbon, not to mention world's calories that humans consume.

Species with the C4 photosynthetic pathway differ from C3 species in a number of ways. We know that the C4 photosynthetic pathway evolved, or at least radiated during times of declining atmospheric CO2 concentrations. In accordance, C4 species have higher photosynthetic rates at glacial CO2 concentrations (~200 ppm) than C3 species. Therefore, it is generally thought to be an evolutionary response to low CO2 concentrations. In conditions of high light, low CO2, and warm temperatures, the C4 pathway reduces photorespiration and generates greater photosynthetic rates over C3 species.

Yet, the C4 photosynthetic pathway also confers greater resource use efficiency. The C4 pathway comes with increased energetic costs, but also confers greater photosynthetic water use and nitrogen use efficiency. More carbon is fixed in C4 species per unit water and nitrogen allocated to photosynthesis as internal CO2 concentrations are lower, which drives the greater WUE, and less N is needed for the same amount of photosynthesis, which drives greater NUE.

Some of the characteristics of C4 are a bit mythological. For example, although C4’s can have higher photosynthetic nitrogen use efficiency, many C4’s have high tissue N concentrations and many C3’s have as low an N concentration as the lowest C4. Not everything about plants is destined from photosynthetic properties.

That said, is there selective advantage to being more efficient with resources? Efficiency always comes at a cost. This much we know. You have to be inefficient with one resource to be more efficient with another. Light use efficiency comes at the expense of N use efficiency. N use efficiency comes at the expense of water use efficiency. Efficiency also costs time.

So what is the benefit of being efficient? For C4’s, under what conditions is it beneficial to be more efficient with water or nitrogen than C3’s. In a competitive world, efficiency in and of itself benefits no one but your competitors. The less water or nitrogen you use, the more there is for another. The benefit only comes if efficiency allows one to reduce the availability of the limiting resource below the level needed to sustain a potential competitor. Or tolerate more stressful conditions. Do C4’s reduce water or nitrogen availability to lower levels than C3’s? No evidence of that. Do C4’s tolerate lower water or nitrogen availability than C4’s? No evidence of that, either.

We also know that C4’s span a wide range of water and nitrogen availability. NADP-me type C4’s increase with mean annual precipitation, not decrease. And C4’s like the grasses we use in many lawns and golf courses have high nutrient requirements, not low, having evolved in grazing lawns that have high nutrient availability. In all, there is no evidence that C4’s preferentially occupy low water or low nitrogen habitats.

The efficiency of C4 species is one of the great mysteries of evolution. Is it an interesting by-product of selection for carbon gain under certain conditions? Or is it indirectly linked to success in ways that are not obvious? Likely, until we better understand the fundamental question of “Who wins and why?” in the plant world, that aspect of C4’s will still be a mystery.

When will SLA R.I.P.?

2009-10-27T05:55:00.000-07:00

Relationship between leaf tissue density and the abundance of grassland species in uplands at Konza Prairie. Each point is a different species with its abundance measured over 14 years.

For almost two decades, SLA (or its inverse alter-ego, LMA) has reigned supreme as the central functional trait of plants. SLA, i.e. specific leaf area--the ratio of leaf area to mass, has stood to represent the amount of investment into light acquisition. Entire pyramids of approaches to traits are built on the fundamental supremacy of SLA. The only thing more important than SLA in these pyramids is relative growth rate (RGR).

But why SLA? Why the ratio of area to mass? The thinking is that plants that grow fast need to absorb as much light as possible with the least amount of investment. Hence, selection favors plants that produce a lot of leaf area with little carbon investment, i.e. a high SLA. Plants in stressful or low-resource areas have low SLA, which presumably aids plants in resisting stress or maximizing the utilization of a limiting resource. Consistently, there are good correlations between SLA and RGR as well as other leaf characteristics such as photosynthetic rates, which have reinforced the primacy of SLA.

For almost all of the 20 years, there has always been a countervailing opinion of SLA that has never been rectified. If it ever was squared, SLA would likely never be measured again.

A leaf can high SLA either because it is thin or because it has low tissue density—thickness and density are the two components of SLA. In 1991, Witkowski and Lamont examined thickness and density across a series of ecological contrasts for sclerophyllous species. In short, from the patterns they observed, the authors concluded that “leaf density and thickness may respond to independently to resource and other gradients, and thus are more appropriate measures than [SLA] which confounds them.” Because thickness is so easy to measure—a quick squeezing of calipers—there is no good reason to not break down SLA to density and thickness every time.

Thickness and density have different functional roles in a leaf. They often vary independently across ecological contrasts. A thick, low density leaf and a thin, high density leaf would have the same SLA, but very different performances in most environments. By extension, SLA might be important to plant ecologists, but not to selection.

But maybe this is a bit hasty. SLA is supposed to be ecologically important and help explain the abundance of species across contrasts. Maybe SLA explains abundance better than thickness or tissue density. Surprisingly, the relative explanatory of SLA and its components have rarely been tested quantitatively. In general, this is probably the Achilles heel of most traits work. We spend more time examining relationships among traits than rigorously testing their relative predictive capacity.

Refuting the ecological importance of SLA or either of its components will not be a simple affair. It’ll take a number of studies before we understand their relative empirical importance. I’ve now done two. The first was at Cedar Creek along fertilization and disturbance gradients. The second is at Konza where I measured leaf traits for 130 grassland herbaceous species and tested their predictive capacity for species abundance across topographic, burning, and grazing contrasts. The results for Konza? SLA explained no variation in the abundance of species. Yet, tissue density did. Consistently across gradients it was tissue density not SLA that explained the abundance of species. The Cedar Creek work largely concluded the same thing.

SLA should not be buried yet, but at some point, we are going to have to fundamentally reexamine the hierarchy of traits in the ecology of plants. A dichotomous world of high SLA and low SLA (if not high RGR and low RGR) plant species might have to be replaced. Until then, at the very least, measure thickness.

Canopy interception and the dispersed puddle

2009-10-12T04:51:00.000-07:00

Taking a walk through grass after a light rain is a soaking affair. Even walking through a recently mowed lawn in the morning would wet your sneakers while going to school. It was always better to let the sun come out for a little bit before short-cutting across a yard.

The principle that most children learn at a young age likely has important ramifications for understanding the dynamics of how grasslands work. Through one of two mechanisms, my guess is that canopy interception sets up a negative feedback loop that constrains how much grass is produced.

First, a quick review.

In grasslands, approximately half of the precipitation can be intercepted by biomass without reaching the soils. For small precipitation events, 70% of the precipitation can be intercepted by a dry canopy, with the fraction of precipitation intercepted declining with event size (Ataroff and Naranjo 2009). A single square meter of grassland can withhold 2 L of water from reaching the soil.

Relationship between precipitation and canopy interception for a tropical pasture grass. From Ataroff and Naranjo 2009.

Half of the precipitation that could fall on a grassland might never reach the soil. And the more grass there is, the less precipitation would reach the soil. Seasonally, as grass grows and canopies develop, the demand for water would be ever increasing. Yet, because of interception, less and less precipitation would reach the soil.

Increasing demand, decreasing supply. A classic negative feedback that would be limiting growth. Even if plants had access to deep water, the consequences might be greater for N supply and cause transitive limitation as surface soils where N mineralization occurs would be prevented from rewetting.

Evolutionarily, we haven't explored whether there would be selection on herbaceous species to promote (or not promote!) throughfall of precipitation. Altered leaf angle, waxy cuticles, stemminess, would all alter how much water is retained or passed on to the soil. Ecologically, with just a few papers on the topic, there are likely some large unexplored ramifications besides promoting seasonal water limitations. For example, from first principles, rain coming in larger events should promote growth, not retard it, as the water is stored in the soil rather than the canopy.

Most importantly of all, if you haven't learned it yet, never cut across a wet lawn in the morning wearing sneakers. Might as well jump in a puddle.

The nuts and bolts of transitive limitation

2009-09-29T01:32:00.000-07:00

Patterns of soil moisture in the lowlands of an annually burned watershed at Konza Prairie. Soil moisture is expressed on a relative basis at 6 depths for 1993 (wet year) and 1994 (dry year).

Earlier, I had discussed a potentially interesting case of transitive limitation, i.e. when the low availability of one resource reduces the availability of another. In the case of water and nitrogen, it is unclear in grasslands whether the limitation ascribed to water could actually be due to low N availability. N mineralization is known to decrease with decreasing soil moisture. As such, as soil moisture declines, so should N mineralization.

The correlation between soil moisture and N mineralization does not necessarily mean that the two should co-limit across a range of soil moistures. In a given soil profile, soil organic N is generally concentrated in shallow depths, while soil moisture is more evenly distributed throughout the soil profile, if not greater at depth. As such, plants can have access to plenty of water at depth even if shallow soils have dried out. Soil N mineralization and moisture might be correlated for a given volume of soil, but not over the whole soil profile.

Konza is an interesting example. At different times, productivity is said to be limited by water and nitrogen, but the two have never been rectified. Do they simultaneously limit production? Does limitation vary over the course of a season, or across years? Or is it transitive?

If it is transitive, disentangling the two is not easy. Standard factorial resource addition experiments do not work since adding water would also increase N availability. Is there a way to add water without increasing N? Not easily from above. But you could add it from below.

Inferentially, if you look at Konza soil moisture patterns, there is always plenty of water at depth, even in dry times. In the above example, in 1994, soil moistures are depleted in shallow soils, but there is very little draw down of deep soils. Proximally, this could be due to the lack of roots at depth, but we are only talking 1 m. The dominant species could easily produce roots at 1 m--if there was a benefit to doing so. If productivity was water limited, there would be a benefit. Yet, if productivity was actually N limited, accessing deep water provides little benefit when N is not being mineralized.

There are other lines of evidence that support the dominant role of transitive limitation at Konza. For example, regardless of whether you add N or water, the same species--Panicum virgatum--comes to dominate. If N was limiting, wouldn't adding N dry out the soils more and favor a low-water, high-N species?

One of the tough things to demonstrate is the roll that soil water potential plays in productivity. I'll likely expand on this later, but there are no relationships between water potential and productivity, only conductance. If we could show that productivity should not be diminished by lowering soil water potential to say -2 MPa, we might be able to demonstrate that it is not water that is limiting directly, but transitively by reducing N supplies.

There are still multiple pieces to assemble before the story is complete, but transitive limitation is likely a linchpin in understanding grasslands.

Finding the needles in the haystack

2009-09-07T06:09:00.000-07:00

There are a fair number of papers that are impressive for the number of times they are cited. “Instant classics” that accrue a hundred citations in a year—most in the first paragraph of a paper—and have helped define some part of a discipline.

These papers are impressive and worthy of study in hopes of replicating them, but I am more interested in papers that are likely just as important but have rarely been cited. Any scientist can use Web of Science to find the most cited paper on a topic and then cite it themselves in order to seem authoritative. But, the true scholar knows the obscure paper, one that might only have been cited a few times a year, but can make the case that the paper is as important as one cited a hundred times a year, if only the obscure one were discovered.

I do not have a comprehensive list, but it is an interesting exercise to think about what are the most important papers never to have been cited. If we restrict the list to the papers published over five years ago and have received less than five citations a year on average. And one cannot put one’s own papers on the list, which is unfortunate since most of my CV is obscure but important. (Except for the one soil CO2 flux paper in GCB. That one deserves to be obscure.) Here are ones that I came up with:

1) Wahl, S. and P. Ryser. 2000. Root tissue structure is linked to ecological strategies of grasses. New Phytologist 148:459-471. If ever there was a golden key to unlocking root function in different environments, this would be it. Why this study has not been replicated a dozen times, I do not understand. (30 cites)

2) Dietz, H. and F. H. Schweingruber. 2002. Annual rings in native and introduced forbs of lower Michigan, USA. Canadian Journal of Botany 80:642-649. The idea that you can dig up grassland plants and age them should have set fire to our understanding of plant population dynamics in grasslands. (12 cites)

3) McManus, W. R., V. N. E. Robinson, and L. L. Grout. 1977. Physical Distribution Of Mineral Material On Forage Plant-Cell Walls. Australian Journal of Agricultural Research 28:651-662. The idea that plants accumulate minerals on their cell walls and might use them for structural purposes fundamentally alters how we think of plant structure and turns plant stoichiometry on its ear. It’s never been followed up on as far as I know. (12 cites)

4) McNaughton, S. J., J. L. Tarrants, M. M. McNaughton, and R. D. Davis. 1985. Silica as a defense against herbivory and a growth promotor in African grasses. Ecology 66:528-535. This one came to mind after the previous one. Silica as structure changes the game. This became cited a bit more in 2006-7, but other than those two years never had more than 5 citations a year. (85 cites)

I’ll give this some more thought later. This is a hard list to compile (and my kids are awake now). I should be able to come up with a top ten list of obscure papers later.

Olympic National Park

2009-08-20T19:35:00.000-07:00

Isabel and Micah ascending the world's largest Sitka spruce.

The family and I are on vacation in the Olympic Peninsula of Washington. We’ve spent the past three days at Lake Quinalt, which is on the southwest side of mountains and surrounded by temperate rainforest. A few things struck me while here. First, 15 feet of rain (the record annual precipitation) is a lot, but it can be hot and dry here. Second, it would have been wise to have bought a cooler and fast on smoothies for three days. There are few places to eat around here, especially since we are going back to Seattle to eat at places like Salumi and Pike Place Market.

The Quinalt River Valley has six record trees in it. The world’s largest western red cedar, Douglas fir, mountain hemlock, and Sitka spruce, are all in the one valley. The western red cedar is 19.5 feet across. It’s hollow in the middle and you can see daylight when you look up from within. I’m not sure where the phloem was, but there were green limbs up high. The Sitka spruce is 17 feet across and aside from being stuck between an RV park and a golf course, is impressive.

As we’ve hiked through the forests here, it has been interesting to think about how these trees have been accumulating environmental records for so long. Tree ring width and carbon and oxygen isotopes are the main records examined, but I’ve been thinking more about the nitrogen isotopes. From work I’ve done with Kendra in the past, every tree potentially has a record of nitrogen availability in its rings. The isotopic ratio of nitrogen stored in wood is largely set down initially and has been shown to track N availability. Only a small number of trees have had the N isotopes in wood measured and for the most part we are ignorant about how N availability has changed in these immense forests or others. It’s an important question since we don’t know how elevated CO₂ has affected N availability or how frequently N availability might peak with disturbances, which has important implications for the ecology of these forests.

I am pretty sure we don’t have a 10 foot increment borer in the lab, but there are some long records here just waiting to be read.

Ecological Society of America Conference

2009-08-11T12:40:00.001-07:00

ESA was in Albuquerque, NM this year. A couple of things stood out.

First, I attended a number of talks about plant traits and performance of species. Very little of the intellectual energy in these talks focused on the relationships among traits or how traits would affect the abundance of species. Instead, most of the energy focused on phylogenetic relationships of species. In some cases, the simplest of traits was overlaid on somewhat complex phylogenies. No one seemed to say species A is more abundant than B because of trait X. Instead, there was more focus on phylogenetic distance of how individual traits changed with evolutionary time. These types of questions are incredibly interesting, but there was almost no balance. The field still seems to be avoiding central questions about traits and abundance.

Second, NSF had put together two days of talks on Coupled Biogeochemical Cycles. The talks were a murderers row of speakers. Members of the National Academy were pushed back to the second day. The talks focused on understanding how coupling different biogeochemical cycles together better helps us understand the functioning of ecosystems in different contexts. For example, coupling the carbon and nitrogen cycles better helps us understand the responses of ecosystems to elevated CO2 than just examining the C cycle. Investigating Ca availability helps us better understand NO3- loss from ecosystems. Not much in any one talk was that novel, but together, the talks provided a great overview for the science. I would have liked to see some questions discussed a bit more. For example, how do researchers choose which elemental cycle to consider when trying to understand a given process? When modeling the global C cycle, should we next incorporate the N cycle? Or P? These are pretty tough questions without roadmaps. Still, the symposia were pretty amazing. It'd be great if NSF could continue to host these multi-day events within ESA.

New papers: coexistence and invasion

2009-07-06T15:03:00.000-07:00

Two papers recently came out that both deserve mention as they are important steps to better understanding the nature of coexistence and dominance in plant communities.

For a long time, I've said that our limitation experiments are skewed in that we generally add nutrients and take away light. I can't say that anymore. Hautier et al. added nitrogen to experimental grassland communities. To half of the replicates, they supplemented light in the understory to test whether it was the reduction in light that caused species loss with eutrophication. Fertilized communities lost species as biomass increased and light penetration declined. But fertilized communities that had light added in the understory did not lose species. Pretty simple experiment that narrows in on a key mechanism underlying coexistence among species.

In the second paper, Blumenthal et al. compared pathogen loads on European species in Europe and North America. High-resource species had higher pathogen loads in their native range and experienced greater declines in pathogen load in North America. The enemy release hypothesis has always assumed that herbivores and pathogen strongly influence relative abundance at a given location, which has never been tested against competing hypotheses. That said, the data set reveals strong inferential patterns that are important irrespective of their potential ability to explain the dynamics of species introductions.

Hautier, Y., P. A. Niklaus, and A. Hector. 2009. Competition for Light Causes Plant Biodiversity Loss After Eutrophication. Science 324:636-638.

Blumenthal, D., C. E. Mitchell, P. Pysek, and V. Jarosik. 2009. Synergy between pathogen release and resource availability in plant invasion. Proceedings of the National Academy of Sciences of the United States of America 106:7899-7904.

Water linking roots, stems, and leaves

2009-06-24T14:25:00.001-07:00

Selection on plants has always worked to coordinate the functions of all the plant parts together. Demand must be coordinated with supply. The demand for N and water by leaves cannot outstrip the ability of roots to acquire them, or stems to move them. Root growth needs to be in balance with shoot carbon supply.

Bucci et al. just published a nice study on the coordination between roots, stems, and leaves for moving water among Patagonian woody species. Deeply rooted species have access to lots of water at all times. Shallowly rooted species undergo periodic water stress as the shallow soils dry out.

They found that deeply rooted species had low hydraulic conductivity (water moves slow through stems and leaves), low SLA, and high wood density. The shallower-rooted species, even though they were frequently under water stress, had high conductivity in stems and leaves, high SLA, and low wood density.

The patterns are great, but I think the authors interpret the patterns wrong. Plants with access to lots of water and no water stress should have high conductivity, not low. Why if the shallow species frequently experience severe water stress wouldn't they be more resistant to cavitation, which would lower conductivity? The authors state that "It appears that the marginal cost of having an extensive root system (e.g., high Rho_w and root hydraulic resistance) contributes to low growth rates of the deeply rooted species."

More likely, all the nutrients are in the shallow soils and the deeply rooted species are adapted to low nutrient availability. The shallow species have periods of high nutrient availability and need to grow quick. It's the high resource strategy, which can end catastrophically if soils dry out too quickly, but also ends by the superior canopy of faster-growing competitors if they are built to withstand drying later.

A point on the horizon

2009-06-22T14:31:00.000-07:00

There have been a few interesting papers that have recently been published that I'll mention soon.

In the meantime, I sometimes wonder about the future of the plant trait discipline--that mix of evolution and ecology. It seems pretty fractious at times. Evolutionary biologists often take umbrage at the lack of sophistication at which ecologists attempt to describe evolutionary patterns of functional traits. Ecologists just can't contextualize the traits that evolutionary biologists examine and/or the species that they use. How easy is it to see the significance of variation in the lac10 gene? [I just made that name up, but it turns out it exists.] Among those researchers that straddle the middle ground, different research groups seemed locked into a single framework of explaining how the world works. A lot of these divisions fall (implicitly) along some basic assumptions of how the world is structured (competition vs. facilitation, pulsed vs evenly-supplied resources), while others fall along sets of traits. Mycorrhizal ecologists can feel disrespect when the traits stop at the root tip. Microbial ecologists want to know more about organic N uptake. Some ecologists measure SLA, others tissue density, others fresh weight to dry weight ratios.

All the division can be healthy--the world is a complex place. But, it can also be miring. They'll never be settled any time soon--or at least haven't in the past 30 years.

Sometimes I think that we need a goal on the horizon that is magnificent enough to grow the field so everyone can be funded to work without feeling that another person's success might be their failure. And, that it would be interesting enough for people to not focus too tight on the details.

I wrote about this a bit in RSWP, but one of these goal has to be to be able to compare the functional trait distributions of entire florae. Think about comparing the drought resistance of grasslands from Alberta to those of Hungary. Or the shade tolerance of a northern Australia eucalyptus rainforest to those of the broad-leaved forests of northeast North America.

As ecologists (or biogeographers) we often discuss the roles of radiation and sorting on the composition of flora. But, we've never really been able to show that at the massive scales these really play out. We need experiments that grow thousands of species side by side. We need to identify key functional traits that can be measured under standardized conditions. And we this should be done in a phylogenetic context.

Climate, the nutritional value of grass, and the Wisconsin paradox

2009-06-11T02:51:00.001-07:00

Map of grass protein concentrations as derived from cattle fecal chemistry. Red implies higher protein concentrations, blue lower. Craine et al. in review, Global Change Biology

The perennial native grasslands of North America are often dichotomized as being either tallgrass or shortgrass. At its most basic, the humid tallgrass produce a large quantity of low quality grass, while the xeric shortgrass produce a small quantity of high quality grass. For grazers, the tallgrass is sour and the shortgrass sweet.

The generalizations about tallgrass and shortgrass are certainly true, but raise the question about the Wisconsin paradox. If wetter grasslands are lower quality to grazers then why are the best grasslands for cattle in Wisconsin and not in Montana?

To help understand the pattern of grass protein concentrations, we analyzed a dataset from Texas A&M's Grazinglands Animal Nutrition Lab. Over 15 years, they had accumulated a large dataset on grass protein concentrations across the US as derived from cattle fecal chemistry. When we analyzed the data, we found that in contrast to expectations, wetter grasslands had higher protein concentrations than drier grasslands. Here, tallgrass was sweet and shortgrass was sour.

The paradox can be ascribed to management of grasslands. In native grasslands, tallgrass is sour. Yet, for managed grasslands, tallgrass is sweet. What do they do in places like Wisconsin to turn sour sweet? One hypothesis is that it is the grazing itself. Managers make sure that the pastures are intensively grazed so that quality never declines. Another is that managers make sure that the grasslands don't burn. A third is by controlling species composition, managers can favor palatable species. Planting legumes and cool-season European grasses might be enough to turn the sour sweet.

The different patterns in native and managed grasslands raise some important questions about how well we understand grasslands. At its most basic level, we still aren't sure what drives the fundamental characteristics of tallgrass and shortgrass.