How to reach the Giga mark with plants

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

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

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

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

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

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

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.?

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

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

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

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

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

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

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

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

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

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. 

Why do plants close their stomata at night?

As the sun sets each night, most terrestrial plants close their stomata. It is reasoned that plants open their stomata to acquire CO2. At night, with no photosynthesis, there is no need to acquire CO2, and so the stomata can close.

Mike Cramer and coauthors just published a review in Oecologia that challenges some basic assumptions of the benefits of closing stomata at night. The authors state that mass flow of water to roots carries nutrients with it. For non-limiting nutrients, this flux alone can meet a plant's demand, but also could benefit the plant for a limiting nutrient that has low concentration in the soil solution. They cite many instances where NO3- can regulate water inflow into a root as additional evidence of the role of mass flow in plant nutrient acquisition.

In their summary, the authors state that "some plants [might be] designed not to conserve water, but rather to maximise the flux of water when it is abundant." This is a gauntlet-throwing statement. 

The calculations of the role of mass flow in nutrient acquisition are 30+ years old. This doesn't make them wrong, but the models did not always ask the most pertinent questions. Would a plant competing against another plant be benefitted from a higher transpiration rate? Would a plant that keeps its stomata open at night acquire more nutrients than one that keeps them closed? And even if not, what are the negative consequences to a plant that left them open? 

It's good that the authors raise such a basic question about how plants acquire resources from the soil. It's a good review that lays bare some fundamental questions about the constraints faced by terrestrial plants and ultimately their evolution.

Cramer, M. D., H. J. Hawkins, and G. A. Verboom. 2009. The importance of nutritional regulation of plant water flux. Oecologia.

Countdown #1: The five resource strategies of wild plants

If there is one central element to RSWP, it is not about how to quantify importance, the nature of resource limitation, or the mechanisms of competition. These fundamental questions all serve a higher purpose in the book: to understand the resource strategies of wild plants.

The broadest theories of plant strategies have differed on whether there was a common general strategy to succeeding when resources were low, or whether there were fundamentally different strategies associated with success when different resources are low. As I describe in RSWP, the most parsimonious conclusion is that there are four major strategies for growth when limiting resources are supplied uniformly over time. There is less support for the theory of a general “low-resource” strategy with variations associated with limitation by different resources than separate strategies for succeeding when water, nutrients, light, or CO2 are strongly limiting. The availabilities of resources are independent enough from one another and there are physiological and evolutionary tradeoffs in producing traits for success for each resource availability. Consequently, there is no one general strategy that covers low availability of all resources. Being built to perform well under low light precludes being competitive for nutrients which precludes acquiring water when soil water potential is low. All of these strategies might share a low maximal relative growth rate, but this appears to be a consequence of convergence. A fifth strategy is associated with success when the availabilities of all resources are high. 

The five strategies I outline in RSWP are the most fundamental and widespread with regards to resources, yet it is important to recognize that no one set of traits works best across all environments that share having low availability of a given resource. For example, although both limited by nutrients, phosphorus limitation in the fynbos of South Africa has selected for plants that fundamentally different from those that dominate nitrogen-limited grasslands in Minnesota. How the multitude of environmental stresses and disturbances have shaped the world's flora are some of the most subtle questions about the forces that make our complex world beautiful. Yet, the skill of the ecologist is to appreciate the complex while seeking the simple. When we collapse the diversity of the world into its most fundamental units, we are left with the five resource strategies of wild plants.

Countdown#2: Competition and supply preemption

Dense root system of Hordeum pusillum. 

Previously, I discussed how competition selects for suboptimal allocation patterns—at least suboptimal in the absence of competition. If this wasn’t a regressive list, I would have been getting slightly ahead of myself.

As I said before Tilman made a great advance in linking competitive success to resource availability reduction. Although his work might have been theoretically pure, its application to terrestrial systems was conceptually flawed. Plants do outcompete one another by reducing the availability of resources. Yet, the availability of nutrients in soil is not best conceptualized as the solution concentrations. Instead, it’s supplies. When nutrients are limiting, plants outcompete one another by reducing the supplies of nutrients to neighbors. Independent of reducing mineralization rates, supply reduction comes from supply preemption. Because of diffusion limitation, the plant with the most root length per unit volume of soil acquires the majority of the nutrients supplied. Each unit of root length produced reduces nutrient supplies to neighboring plants. The best competitor for nutrients is the plant that can produce the most root length.

I develop at length in RSWP the shift from concentration reduction hypotheses to supply preemption hypotheses and how it changes our outlook on plant interactions. For example, changes in soil moisture would affect prediction of competitive superiority by altering soil solution concentrations, but do not affect predictions based on supply preemption. There still is more theoretical work switching from R* models to SL* (supply per unit length), but the concepts are now more consistent with our understanding of soil nutrient dynamics, even if the are not as theoretically pure as possible. Better understanding of how plants compete for nutrients not only help us understand how competition has altered the evolution of plants, but sets us up to ultimately better understand the resource strategies of wild plants.

Future of natural abundance 15N research for terrestrial plants

Natural abundance 15N is the Afghanistan of ecosystem ecology—it is all but impossible to conquer. In short, the N cycle is so complex that plant 15N becomes a single response with two many drivers. There is no consistent way to interpret any one difference in signatures between contrasts.

Although seemingly intractable, few other biogeochemical cycles rival N in their importance in determining how ecosystems function. Understanding the patterns of N cycling is so important that we have to continue to improve our ability to interpret natural abundance 15N patterns.

A number of coauthors and I just got a global review of 15N patterns published in New Phytologist. Details aside, it took a long time for this to happen. Although unfortunate, it gave us the opportunity to work with a number of editors and reviewers to understand the intellectual landscape better. Below I’ll post some summary thoughts on what needs to happen next in the discipline. In a later post, I’ll summarize what we learned in the paper.

1) We need CENTURY for 15N--a general model of the N cycle coupled with a stable isotope simulator to explore scenarios. 15N will not be raised up into the pantheon of ecological isotopes until the theoretical basis for patterns is worked out.

2) We need a survey to measure 15N in roots. Within-plant fractionation is the third major hypothesis for determining patterns of foliar 15N. Only 3 studies have compared 15N for leaves and roots. We'll need a lot more data to evaluate this hypothesis.

3) We need global maps of the N cycle. For all the N cycle has been measured, we do not have global, state-factor relationships with organic N uptake, N mineralization, nitrification, or gaseous N loss. We cannot make a global map of these fundamental processes. For example, time and time again reviewers choked on the idea that denitrification could be higher in sites with lower precipitation. A global synthesis would clear this up.

4) We need better measurements of the signature of available N. Measurements of soil inorganic 15N need to be as commonplace as available NH4+ and NO3-. Knowing these values are an incredible constraint on the key processes.

5) Mycorrhizal ecologists have been slow to assimilate 15N patterns into their understanding of the role of mycorrhizal fungi in plant N nutrition. For example, in Smith and Read's 3rd edition of Mycorrhizal Symbiosis, it was clear that the authors had gotten the story wrong. The attributed the higher 15N of ectomycorrhizal fungi to their reliance on enriched sources, and failed entirely to mention that ectomycorrhizal plants are depleted in 15N as much as the fungi are enriched. There is a lot of work that needs to be done to fully integrate mycorrhizal fungi into plant N nutrition. My guess is that one pressure point will be measuring 15N signatures of fungal mass and plant material in the field.

6) We need a central depository for 15N data that is better than my Macbook. Researchers being able to compare their values to global datasets quickly aids them in interpreting their data while facilitating new syntheses. Nothing new here.

Countdown #3: Competition selects for the sub-optimal

A typical eucalyptus: lots of wood, not a lot of leaves. Why aren't more plants this efficient? 

In economic and ecological theory, selection for efficiency among competing firms or organisms is a ruling paradigm. Yet, competition, among firms or plants, does not always select for the most efficient. All one needs to see this in nature is to take a shovel to their front lawn or take a walk in a forest.

When nutrients are limiting, a plant competes for a limiting nutrient supply by attempting to preempt the nutrient supply from other plants. As such, the key to acquiring the majority of a given nutrient supply is root length dominance. A consequence of supply preemption being the mechanism of competition for nutrients and root length dominance the key to preempting nutrient supplies is that plants face an evolutionary tragedy of the commons in their allocation patterns to roots. When the nutrient supply is limiting and a given plant is grown in isolation, a relatively low root length density optimizes growth. Yet, in the presence of competitors, plants that can maintain higher root length densities than are optimal in the absence of competition are able to acquire a larger fraction of the total nutrient supply and therefore would have been favored by natural selection. As such, high root length densities are more evolutionarily stable than the lower root length densities that would optimize growth in the absence of competition. This easy to see by taking a shovel to most temperate lawns. Most temperate grasses appear to have an order of magnitude more roots than is optimal for growth in the absence of competition.

The supply of light differs fundamentally from nutrient supplies in that it is largely supplied directionally. Yet, competition for light can select for plants that have canopies that are suboptimal in the absence of competition. The trunks of trees do not help a forest acquire more light—they only help an individual acquire more light than a competitor. As I discuss in RSWP, research has also shown that many plants also hold more leaves than is optimal and the leaves are held too flat. Plants that are optimizing canopy photosynthesis would hold their leaves at a higher angle in order to allow more light to penetrate deeper into the canopy where it can be used more efficiently. What would a tree with an efficient canopy look like? Probably something like a eucalyptus with its pendulous leaves and sparse canopies. It probably is no coincidence that eucalyptus are known by foresters to produce wood at some of the highest rates.

Resource Strategies of Wild Plants released

I guess I didn't time the countdown right. RSWP was released last week and available to order from Princeton University Press and retailers like Amazon.

Countdown #4—competition and supply preemption

Dave Tilman’s great advance for understanding competition over 30 years ago was to introduce the idea that competitive outcomes were determined by resource reduction. Phytoplankton that could lower the concentration of a limiting nutrient to the lowest level would become competitively superior. When applied to plants in soil, the concentration reduction hypothesis assumed that soil solutions were well-mixed and it was the average soil solution concentration that determined the rate at which a plant grew. As such, lowering the average soil solution concentration of a limiting nutrient was the key to displacing other species.

Leaving aside the assumptions of the nature of the nutrient supply, for plants in soil, resource reduction is still the mechanism for competitive displacement. Yet, it’s not concentration reduction, but supply preemption that determines competitive superiority. When nutrients are limiting, a plant competes for a limiting nutrient supply by attempting to preempt the nutrient supply from other plants. Due to the relatively slow diffusion of nutrients in soil, as roots acquire nutrients from the soil solution, nutrient supplies are partitioned among plants based on the relative amount of root length they hold in a particular soil volume. The key to acquiring the majority of a given nutrient supply is root length dominance, which reduces the availability of nutrients to others.

Although the magnitude of this conceptual shift is open, supply preemption is the proper application of resource reduction to plants growing in soil. What about other resources? As I discuss in RSWP, supply preemption is the best concept for understanding light competition, too. Water is notoriously pulsed in availability, but supply preemption rules here, too. Just a bit different than nutrients that are supplied evenly over time.

Competition research can be summarized as “who wins and why?” The secret to competing for resources is to get them before your neighbors do. Took about 30 years to nail down how that works on land, and it'll probably take another few decades likely to nail down the details.

Countdown to Publication: #5—Co-limitation in a Post-Liebigian world

With RSWP scheduled for publication in less than a month (at least that is what Amazon tells me), I thought it would be interesting to highlight some of what I think are the key advances of the book. A self-promoter might call this “Countdown to a New Paradigm”, I’ll just call it Countdown to Publication, I guess. [As an aside, if you ever want to promote your career, start an intellectual battle with someone on a different continent and agree not to attempt to resolve your differences. It works great every time.] I do think #5 represents a new paradigm. Not a paradigm I should be credited with, but a fundamental change in how we think of resource limitation.

The Law of the Minimum that describes the basic ecological concept of limitation was established at a time when little was known about nutrient cycling and was not applied initially to limitation by light. The three parts of Liebig’s Law of the Minimum are

1) Growth is limited by the resource that is supplied at the lowest rate relative to the demands of the plant.
2) Growth is proportional to the rate of supply of the most limiting resource.
3) Growth cannot be increased by increasing the supply of a non-limiting nutrient.

In the Liebigian world of limitation, generally only one nutrient could be limiting at a time. In a post-Liebigian world, it is likely that there is co-limitation to growth among different resources, not just serial limitation. Co-limitation is more likely to occur than would be predicted from Liebig’s Law of the Minimum because a) nutrients supplied in excess are more likely to be lost, b) species differ in the stoichiometry of demands for optimal growth, c) plants can store resources to balance temporally variable rates of supply, and d) plants can increase the availability of the most limiting resource. If there are tradeoffs in acquisition between different allocation strategies, whether for increasing supplies or acquiring a greater fraction of a given supply, co-limitation is likely to occur. Nutrients and light. Water and CO2. Nitrogen and phosphorus. I expand on this in the book, but these co-limitations fundamentally alter our approach to understand everything from global change to evolution.

The existence of co-limitation is not that novel, nor even the suggestion that it should be common. Yet, the consequences of this focus are reach far. Resource co-limitation in the post-Liebig world does not only manifest itself in responses in productivity to the addition of two resources with no increases if only one resource is added. Due to allocational tradeoffs, plants can be co-limited by multiple resources and respond to individual resources independently. Also, the only way to understand why one resource is limiting is by understanding which resource is co-limiting with it.

When resources are co-limiting, costs for different allocation strategies or the production of different structures should be evaluated with a dual-currency model. Evaluating costs with just one resource ignores the often great cost of the second resource. There is no money in ecology—it’s a bartering world with a few key staples.

Lastly, natural selection favors plants that adjust their allocation strategies such that multiple resources are co-limiting. As such, plants are also selected to balance their resource use efficiencies to match resource supply ratios. For example, plants that grow with high relative limitation of nitrogen should have higher ratios of nutrient to light use efficiencies than plants that grow in environments with greater relative limitation by light. Resource limitation provides a fundamental constraint on efficiency of resource use that makes the world predictable.

Colimitation indices

Resource limitation is a fundamental structuring force in ecosystems. I've written a lot about how we need better ways to analyze the simple factorial experiments that are used to determine limitation.  Boiling down patterns of limitation to a single number or two is going to be a big help understanding broader patterns of limitation. Below, I introduce a colimitation index, that separates out some key patterns. 

Let’s start with a basic limitation experiment. 4 treatments: Control (0), +N, +P, +NP.

B₀ is biomass of controls

B_N is biomass with +N

B_P is biomass with +P

B_NP is biomass with +N+P

To make matters simple, let’s assume that there is no effect of P added alone and that the biomass of plants with N and P added is greater than unfertilized biomass.

With this, we’re trying to separate three cases.

1) Classic co-limitation (co-limitation by supply) where there is no direct effect of N (or a relatively small one).

2) Primary limitation by N and secondary limitation by P

3) Single resource limitation where there is an N effect, but no effect of P on N-fertilized plants.

There are two ways to calculate a co-limitation index (CI) based on absolute or relative changes in biomass.

First we can compare the absolute changes in biomass relative to controls and compare the N effect to the NP effect. If we calculate the co-limitation index as

(BN-B0)-(BNP-B0)/2

then a plant that had a CI > 0 would be primarily limited by N and secondarily by P. A CI <>

The interpretation of the pattern would be that soils with low P availability are co-limited by N and P. As P availability increases plants are more likely to be primarily limited by N and secondarily limited by P.

That’s a pretty clean story, but the problem with this approach is that you cannot separate if a plant is secondarily limited by P or just limited by N.

To get around this we can calculate a co-limitation index as:

(BN-B0)/(BNP-B0)

With this index, 0≤CI<0.5><1> 

Here are the patterns for the 100 soils data:

Pretty much the same story. Plants start out co-limited by N and P at low P availability. Then as P availability increases P limitation becomes more secondary until ~30 ppm available P at which we’re into strict single limitation by N. The problem with this approach is that the relative index is sensitive to BNP. For the graph at left I had to exclude two points that had |CI|>10.  

With these approaches statistical significance relative to threshold values, e.g. CI = 0 for the first index, are possible. I’m not sure how to extract them from JMP yet.

Note with a factorial resource addition experiment there are something like 9 different basic responses when you include inhibition and responses by individual resources. There will be no way to boil, but we might be able to get the most important patterns down to 2.