Authorship: the trillion dollar question.

Every year, over a trillion dollars is spent on scientific research and development. 

Publication of that research is a requirement for most of that money. 

And authorship is the construct that assigns credit and responsibility to that publication.

Despite the centrality of authorship to the scientific process, the institution of authorship is rickety. 

It's like a car held together with tape and wire with knitted seat belts. 

"As long as you don't go over any bumps, you should be fine..." 

Authorship issues are rare, but a lot is at stake for a person to be considered an author or not. 

Not including a person as a coauthor when it is deserved deprives an individual of credit. Including a person that does not deserve or desire coauthorship dilutes the credit given to others and potentially creates liability for an individual on research with which they might not agree.

Almost every principle associated with coauthorship is tenuous. 

Let me elaborate on one: determining the list of authors.

There are three main bodies to examine here. The Committee on Publication Ethics. The International Committee of Medical Journal Editors. The Council of Science Editors. 

Here is how the CSE approaches the issue.

The Council of Science Editors states that there is general consensus on some points regarding the principles of authorship. One of these is that 

"Identification of authors and other contributors is the responsibility of the people who did the work (the researchers) not the people who publish the work (editors, publishers). Researchers should determine which individuals have contributed sufficiently to the work to warrant identification as an author."

To this point, there is no recommendation that at the point of initial submission, all authors sign a form that states that everyone agrees that each author listed deserves to be an author. 

Yet, let's say that during the review process, one author requests to be removed from the list of coauthors. Maybe they don't agree with a statement that has been inserted, or they have decided that their contribution was not substantial enough to warrant coauthorship. Alternatively, during the revision process a new author might be added due a contribution that arises as a manuscript is modified.

How should a journal deal with that?

CSE recommends that: 

"Any changes the authors wish to make to the author byline after the initial submission of a manuscript should be made in writing and the document should be signed by all authors, including those being added or removed."

This statement is a solution looking for a problem. 

And it's a solution that has the potential to create more problems than it solves.

First, it seems to directly contradict the consensus principle that the journals should not determine who is a coauthor. 

Second, it imposes inconsistent requirements. If there is no need for a statement that all coauthors agree that all other coauthors should be a coauthor initially when a manuscript is submitted, why during the review process?

Third, it forces a particular model of agreement on authors for determining coauthorship. All coauthors must agree, including any coauthor that has been removed. Before, it could be the lead author that determines whether a contribution was significant. Now, it's consensus.

If everything works perfectly, this imposes a slight burden on the authors. If the authorship list is long, the burden is somewhat greater and can delay publication somewhat substantially. 

But what if it doesn't work perfectly?

Here's a scenario to consider. Let's say a new statement is added to a paper during revision. One  coauthor disagrees with the statement. It might even be a statement that is independent of their previous contribution. All other coauthors agree with the statement and deem it necessary. If this happened before submission, the coauthors could agree to recognize that author's contribution in the acknowledgments. 

With the CSE requirements, by not signing a form for the journal, that coauthor now has the power to effectively block the publication of the manuscript in that journal. 

If the journal can override the requirement of all coauthors signing it, then why have it at all?

Here's another scenario. Let's say that a coauthor desires to be removed from the list of authors. Another coauthor thinks that they should remain. Again, this could effectively scuttle publication. 

Has this ever happened?

No idea, but that is immaterial. That it could happen is all that matters.

And we shouldn't have systems in place that only function when they aren't tested.

ICJME and COPE really do not provide much more clarity on the issue. 

For example, ICJME states that 

"It is the collective responsibility of the authors, not the journal to which the work is submitted, to determine that all people named as authors meet all four criteria; it is not the role of journal editors to determine who qualifies or does not qualify for authorship or to arbitrate authorship conflicts." 

This is consistent with CSE. 

What does ICJME recommend if there is a change in authorship?

"If authors request removal or addition of an author after manuscript submission or publication, journal editors should seek an explanation and signed statement of agreement for the requested change from all listed authors and from the author to be removed or added."

Same as CSE.

Why should the journal require this if it is not the responsibility of the journal to determine who qualifies as a coauthor?

Yes, one can think of nefarious situations where a higher power should step in and deliver justice. But, if something is outside the jurisdiction of a journal, it is outside the jurisdiction of a journal.

If disputes arise, what does ICJME recommend? 

"If agreement cannot be reached about who qualifies for authorship, the institution(s) where the work was performed, not the journal editor, should be asked to investigate."

Considering most work is done across institutions, which one should investigate? What if the work was not done at any institution? And if the institutions disagree? 

Let me turn anabolic for a minute. 

How should these issues be dealt with?

Here is the simplest solution. We need to redefine the responsibilities of the lead author. If we state that it is the lead author's responsibility, using generally accepted principles, to determine coauthorship, then all of these inconsistencies do not need to exist.

This adds liabilities and responsibilities to being the lead author, but they are pretty minor. And it codifies generally recognizable principles of how papers are constructed. 

What happens if something goes wrong under this construct? If an author is included in a paper against their will, that author can petition the journal to have their name removed. If an author is not included when they feel they should be, then they can petition the lead author's institution to investigate. Or the funding agency. If coauthors disagree on statements or inclusion of authorship, tie goes to the lead author.

It might seem imperial, but it's clean. 

I don't think these issues arise too often. And hopefully, we will not get to the point where we need scientific courts to resolve these issues. 

Still, that doesn't mean that our policies should not be as clear and consistent as possible.

Quick video on bison shrinking with warming

I had some requests to post a quick video summarizing why bison are likely to shrink with warming...

Alpine herbivore shrinking

A long term record of alpine chamois weights from the Alps shows that body mass has been declining since 1979.

Using mass of over 10,000 carcasses from hunters, the authors show that the weights of juvenile chamois have been declining over the past few decades. Although some of the decline is due to increasing population density (stricter hunting laws), it appears that high temperatures also have been directly causing declines in mass.

The authors propose greater thermoregulatory demands as contributing to the declines, but the authors were unable to determine whether forage quality had declined.

Mason et al. Frontiers in Zoology 2014, 11:69
http://www.frontiersinzoology.com/content/11/1/69

Ben Bradlee passes away

Ben Bradlee, editor of the Washington Post for many years, passed away at age of 93.

Bradlee was the editor who published the Pentagon Papers. For those who don't know the story of the Pentagon Papers, it's an important history lesson.

Washington Post published an essay of his from 1997 where he discusses the role of the press and lies from public officials:

Where lies the truth? That’s the question that pulled us into this business, as it propelled Diogenes through the streets of Athens looking for an honest man.

If it wasn't for investigative journalists and editors like Bradlee, the world would be a different place.

Evolution of monarch butterflies

A quick note on a new paper in Nature on the evolution of monarch butterflies. 

The authors (Zhan et al.) examine genomes of 101 genomes from the Danaus genus. 

First, basic ignorance. I had no idea there were so many populations of monarchs around the world. That was nice to know.

Second, the ability of genomic research to identify the specific genes that were the basis of selection is really astounding. As someone who measures a lot of plant traits, to dive into the genomes of so many populations and species to identify traits that define species is pretty special. 

With the genomic work, they really are identifying traits that we didn't know exist at the organismal level. 

For example, the authors identify selection on a collagen gene that affects flight muscle function. 

That's not easy to identify empirically. 

In all, I'll admit I'm jealous of what the authors could put together on the monarchs and related species. Biogeography, evolution, and function all wrapped up into one paper redefining our understanding of monarch butterflies.

I'm royally jealous. 

How large a bison herd?

The idea of the buffalo commons provoked many people to think in the late 80's and early 90's. With the commons, a large herd of bison--maybe millions--would roam the open expanses of the West.

Today, bison reintroductions continue, but most of the herds are small. Maybe a few hundred. By comparison, the largest public herd--Yellowstone--is roughly at 4000 animals.

So, realistically, how much larger could we get? How big a herd is possible?

There are a few constraints on figuring out how large a bison herd is realistic.

If you don't cull the bison, then you have to rely on predation. Unfortunately, wolves just are not a significant check on bison population. Yellowstone National Park, which has the most active wolf packs interacting with bison, still relies on culling animals when they leave the park to hit their target population size, which is actually about 1000 animals less than what are out there. Wolves don't keep them in check.

If predation isn't a check, then the upper limit becomes food and/or disease. This is an effective regulator, but that means periodic mass starvation and/or disease epidemics. 

I'm not sure people in North America have the stomach for that yet. 

Even Oostvaardersplassen hasn't quite made it to that level of hands off.

In reality, periodic culling is going to be necessary to manage a large bison herd.

If so, how big a herd is realistic?

Assuming that land area is not a limitation, a couple key numbers here to work off of.

First, is the intrinsic growth rate of a bison herd. This is going to vary, but a 25% growth rate is reasonable for most herds.**

**This is going to vary with sex ratios and other factors, but it's a good start.

Second, is how many animals a work crew could process. Bison have to be rounded up, worked individually, and then sorted. Some of those animals go back to the herd. Some are sorted off to go to market.

In general, a single crew can work about a bison a minute. 400 bison in a day is a good estimate of how many bison can be worked in a day. That translates to 2000 bison in a week. If you dedicate a month to working animals, then that would be approximately 8000 bison.

With a growth rate of 25% and 8000 bison that you can work in a month, that means a winter herd size of 6400 and culling off of 1600 animals.

So, if you dedicate one work crew and corral system for one month to processing animals, then your maximum herd size is 8000.

8000 animals in a corral at one time, is not a small corral. And it would take a lot of hay to keep them fed while they are being processed.

What if we relax the assumption that some animals are returned?

What if just the first 8000 get shipped off?

This would mean calves, yearlings, males, females. Whoever is caught in the "net" goes.

If you remove the assumption that 75% of the animals are returned to the herd, then roughly that would let you have a herd size of about 32,000 animals. This would roughly be stable if every year 8000 animals are shipped off and growth rate is 25%.

Assume natural mortality regardless of how ugly: no limit on herd size.
Assume you don't have to round up animals, but could just shoot them in place: no limit as long as you feel comfortable shooting thousands of animals.
Assume have to round up with one crew for a month, but don't sort the animals: 32,000.
Assume have to round up with on crew for a month, but do sort the animals: 8,000.

Can you add corrals, or sort longer than one month? Yes, but there is no precedent for this.

What about field harvests? With field harvests, animals are often shot in the field and processed in trailers on-site. Yet, the typical rate for a crew is 8 animals per day. Over a 6 month period, that's still just a 1000 animals per day. Harvesting bison sustainably this way would never permit a herd size of more than a few thousand at best.

So what are the likely prospects for a bison megaherd?

Assuming you can get the land, it's going to be hard to have more than 8000 animals in a herd.

So, how big an area is that? Depending where you are in the world, it might be about 100,000 - 200,000 ha (40-80k ha). Put in perspective, that's a square about 10-20 miles on a side.

That's really nothing. Maybe $20-$100 million to buy the land for that (depending on a lot).

What does a grazed grassland look like?

Panorama of the trap pasture in Broken Kettle, IA

Over the past few days, I had a chance to visit some great prairies to do some work on bison.

The first was Broken Kettle in Iowa. Owned by The Nature Conservancy, it's the largest intact native tallgrass prairie in Iowa.

The second was Ordway Prairie in North Dakota. Also owned the The Nature Conservancy, it's unofficial claim to fame is having some of the largest bison in the US.

Each prairie is grazed, but they have different histories and current management.

While at each one, we talked about what grazed grasslands looked like and what to expect. For the managers, they need to try to fill conservation goals for bison, but also for birds that require habitats with different amounts of grass as well as promoting plant diversity.

A simple question of how many bison to stock becomes a complex analysis of examining the prairies.

So part of the what we talked about at each site was whether there was enough grazing, or too much.

One of the interesting features of the grazed areas at each site was how many forbs there were and at what density.

At Broken Kettle, a recently burned area looked like this:



When you look closely, the grasses are grazed down to golf course green height. Forbs are rare, but large. It reminded me of some grasslands in England and Scotland, except with Solidago instead of Cirsium.

The plant community at Broken Kettle in many spots is still recovering from aerial spraying of herbicides so one might not expect to have a broad distribution and diversity of forbs. 

In contrast, Ordway Prairie seems to be grazed less intensively (at least this year). It's hard to find places that were grazed hard. 

A lot of the grass seemed to be smooth brome (Bromus inermis). When you do find areas with forbs, it doesn't look the same as what was at Broken Kettle. The grasses aren't grazed down as hard and the forbs often seem crowded.

In all, there is still debate about what grazed grasslands look like. How much grass (and what species) should there be for sustainable grazing? How many forbs to expect?

It's a complex question, and I'm glossing over a lot.

Still, there's a lot of basic ecology left to work out about grasslands. A lot of people have different opinions, which are colored by their local site.

At the very least, I'd really like to see a coffee table book of just photos of grazed grasslands. Just seeing the diversity of grasslands out there would be an eye opener.**

Short thought: funny line

"Amidst the whirlwind of molecular biological discovery it often seems overlooked that important metabolic processes are ultimately constrained not by biochemical pathways, but rather by the physics of plant structure."

Brodribb, T. J. 2009. Xylem hydraulic physiology: The functional backbone of terrestrial plant productivity. Plant Science 177:245-251.

Roots, water, and really tall grasses

Photobombing mom in front of some really tall grasses in Montpellier. 

Grasses are often synonymous with turf. Short plants that we can walk on.

But some grasses can grow to half the height of a redwood--50 m in height.

The physiological limits of tall height are often thought to reside in how a plant constructs its stems or leaves. Roots are important too, but mostly from an engineering perspective. They have to make sure the plant doesn't fall over. 

Turns out there is another role for roots that is unique to tall plants. They have push water up to the top.

Usually, when we think of roots and water, the soil-plant-water continuum concept of water movement has roots as semi-passive straws. Their job is to come in contact with water so it can be sucked up to leaves through the xylem.

But when water in the xylem is under tension it can embolize, producing an air gap in the rope of water that extends from the soil to the leaves. The consequence? Water ceases to flow.

Poke a hole in a straw and try to suck soda, if you want to test this out.

What's a plant to do once xylem embolizes? 

One approach is to grow new xylem and sacrifice the old. A lot of trees do this. 

Grasses can't do this, so they need to dissolve the embolism. Like squeezing a bottle of soda to get the bubbles to go back in, this takes pressure. 

How do some plants do this? Apparently, they close their stomata at night and let the roots start to pump up the pressure. 

Because of the gravitational forces at work, a short plant doesn't require much force to pressurize the entire shoot. A tall plant requires more.

Showing this had never been done before, but Cao et al (2012)** tested this with bamboos. 

**I'm apparently a few years slow in realizing how neat this paper is.

Turns out tall plants produced more root pressure at night than short plants. 

The work raises some interesting questions about the evolution of plants in general. 

Can short grasses produce as much pressure as tall plants, but just don't? It takes energy to produce pressure, but there might also be adaptations that coordinate root and shoot function. 

Also, for bamboos with stems, the point of growth is elevated off the ground. They don't grow from basal meristems. For cells to expand at the top of the plant, positive pressure is required. So, the root pressure should serve dual functions. 

Also, since we tend to think of xylem as under tension, we worry about air leaking into xylem. But, xylem can also be under positive pressure. Are there unique adaptations to making sure that water doesn't leak out of xylem at the wrong place? Hydraulic lift is thought to be passive based on the relative negative water potentials of plants and soils, but when roots are pressurizing the whole system, can't that force water out of some roots? 

None of this information is going to help create grasses that don't have to be mowed as often, but it sheds serious light into the evolution of height in plants (even grasses).

Cao, K. F., S. J. Yang, Y. J. Zhang, and T. J. Brodribb. 2012. The maximum height of grasses is determined by roots. Ecology Letters 15:666-672.

From The Haiku of Writing a Paper: Introduction

Some people have said that I can write papers quickly. I'm not sure if that's true or not, but I have learned to write them faster than before. And part of that is having a standard structure to guide the writing process.

A long time back, I tried to crystallize what I had learned about putting together scientific papers. Mostly from making a lot of mistakes. I did it mostly just to get all the different ideas organized for myself.

The general approach was to reduce the structure of the paper down to a skeleton outline. Not quite a haiku, but close.

Apparently, the full document has been passed around a bit--I'm always surprised when people tell me they were using it.

As an example, introductions can be tricky to write. In most of my papers I try to follow the 3+1 model. With this model, you funnel from big ideas to specific points to be tested.
First Paragraph: Big question. This is the general broad reference to your work. For example, it might be that atmospheric CO₂ concentrations are rising, or nitrogen is an important driver of ecosystem dynamics. 

Second Paragraph: Proximal question. Within the broader framework, the proximal question should be stated that you are directly addressing. For example, although atmospheric CO₂ concentrations are rising, the controls over soil C storage are poorly known. Or although nitrogen controls ecosystem dynamic, there are important questions regarding the role of denitrification in controlling N availability. Note that a proximal question can often be framed as the big question—it’s all a matter of perspective and how you want to tell the story.

Third Paragraph:  Scope of research with hypotheses. In order to better understand the role of soil C storage in responses of ecosystem to elevated CO₂, we tested whether elevated CO₂ increased the C stored in the soil within soil aggregates.

The "Plus One" Paragraph: Competing hypotheses. The best introductions and research designs test between competing hypotheses. Often when there is a single hypothesis that is rejected, the authors can derive alternative explanations that don’t require the theory to be rejected. Therefore, might as well start with competing hypotheses since there are always competing hypotheses. Framing the hypothesis in the null form is not necessary when using competing hypotheses. For example, in testing the role of N in decomposition, an experiment could test whether stoichiometry predicted responses of decomposition to greater N availability. Or we can test between stoichiometry or N mining in predicting the responses of decomposition to greater N availability. No on experiment is generally able to reject a theory, so you can test between two theories and whether data supports one theory or another.

Note, I call the last paragraph the +1 paragraph, because it isn't always there in a paper--it depends on design.

Still, using this model, you should be able to write your introduction in 3 or 4 sentences. Once you have that you can expand each sentence to a paragraph. These can be expanded out more to maybe 2 paragraphs, but if you deviate too far from this, the introduction is likely running long and will be confusing to readers.

There are tricks to writing other sections efficiently and a lot of details to look after as you get through the sections.Still, once you settle in on your framework for papers, it makes writing the paper a lot more fun, since you can concentrate on the message rather than the structure.

Modeling water flow in soils and plants

Reading more about modeling water uptake by plants.

Like this figure from Lobet et al. 2014, so I though' I'd add it here.

Book Review: The Bee: A Natural History

Honey bees.

Bumble bees.

Sweat bees.

Queens, drones, pollination, waggle dances.

I'm not sure, but that list might have been about 90% of what I knew about bees.

Add that I'm allergic to bee stings** and we're up to 95%.

**When I get stung, the affected area tends to swell up. I once got stung on the hand while on the south shore of Lake Itasca helping Kendra with vegetation surveys. My hand swelled up pretty severely, but luckily it froze into a claw shape and I could paddle back to our cabin on the north shore. Another time I got stung in the lip while in South Africa after taking a sip of a soda. Apparently a bee had climbed into the can while I wasn't looking. I remember wearing a bandana for 2 days because my face looked so hideous.

After reading Noah Wilson-Rich's The Bee (Princeton University Press), I think my previous knowledge set on bees is much larger.

First, the book is rich in pictorials. Almost to the level of a DK Eyewitness book, but with more text and more information.

The visual jewel of the book is the section "A Directory of Bees". The section has half page enlargements of  forty of so bees from around the world. Solitary bees such as the 2mm Perdita minima to the 40 mm Wallace's Giant Bee. Each bee has a description and a section on behavior and its life cycle.

Other sections include the evolution of bees, their anatomy, their societies, and the history of bees and people.

Reading the book reminds me of the immense effort it takes to understand biodiversity.

Not biodiversity abstracted to an index, but each defining detail of every organism. Organisms have a long evolutionary history and a complex ecology. Multiply that by the 20,000 species of bees that exist and it's a life's work to just to start to understand it.

For bees, it is their evolution from wasps a hundred million years ago. The immense floral radiation that they initiated. The eusociality of some bees is what sets them apart, but so many of them are solitary, which is fascinating in its own right. And how they produce honey, wax, royal jelly, propolis (!), and venom from a few food sources is equally fascinating. And the things that attack bees: Foulbrood, Chalbrood, Nosema, deformed wing viruses, mites, beetles, moths...

I really appreciated this book.

By the time I was done reading the book, I felt like I had superficial, but robust knowledge of bees.

And that was more than when I started.

Well done to the main author and the other authors that contributed.

May more natural historians be inspired to write similar volumes.

On thinking long thoughts

"It's not enough to fail. You have to come to feel your failure, to live through it, to turn it over in your hand, like a stone with strange markings."--James Fenton

The other night, this blog experienced its 100,000th page view.

I'm not sure how reliable that number is, but this isn't a bad time to step back and take a few moments to reflect just a little bit.

I started this blog in January of 2009. My book, Resource Strategies of Wild Plants, was about to be released and I thought it would be good to have a space to explore ideas.

In time, the blog is more of a scratch pad for me. It forces me to slow down a bit and coalesce my thoughts just a little bit.

Since the start, I've put together over 250 posts in that time. Each one a different thought. That's not that many.

How long is a thought though?

Thoughts seem like they should be short.

140 characters is a standard these days.

An abstract to a paper might be considered an extended thought. Those are about 250 words.

A blog post might be a bit shorter or longer. Sometimes 50. Sometimes 500.

A typical NYT editorial is about 750 words.

The body of a scientific paper can be 1500 words in a condensed journal. 15,000 words in a longer review.

A book? Mine was about 100,000 words.

All of these are thoughts to one degree or another. But they differ in the time it takes to assemble and connect the ideas contained within them.

Short thoughts are quick to think. It takes a few seconds to have a short thought.

Long thoughts take longer.

When I was writing the book, I kept track of word count each day. It takes a long time to get to 100,000 words.

But it takes more than just a large accumulation of time.

Stitching short thoughts into long thoughts is hard.

Time has to be free of distractions. You have to find quiet time to begin to take short thoughts and stitch them together into something longer.

It also requires the dialectic. Sometimes internal. Sometimes external. Argument is essential. It pulls threads into cloth. Turn ideas over. Examine them from all directions. Poke and prod as you go. Find the weak points. Practice connecting them to other ideas.

To produce long thoughts you also have to take in thoughts slowly. Read books. Tweets, blogs, emails, abstracts, even papers all have their place. But books are the longest thoughts we have. Reading a book will slow you down.

Taking a long walk with a person and conversing on the same topic for a mile will do that, too.

You might guess that I'm not convinced that shortening the thought process is uniformly beneficial. Short thoughts can be absorbed quickly, but they do not necessarily constitute knowledge.

Science can progress rapidly, but many of us are working on the same questions we were working on 20 years ago. Science moves slowly, too.

How do you reliably push things ahead?

Think long thoughts.

Home microbiomes

Shared phylotypes heatmap for individual surfaces after consolidation of samples taken from the same surface type across temporal sampling series and homes

Noah has had me reading papers on microbial communities lately. 

The latest paper in Science by Lax et al. has some interesting aspects. 

The authors had 7 families swap standardized surfaces over 6 weeks. A few families even moved.

Microbial communities were tracked over time. 

Part of the interesting things they found was akin to forensics. People left microbial fingerprints on household surfaces. When a family moved into a new home, the fingerprint was rapidly established.

The best match between body parts and surfaces? 

Feet and kitchen floors. 

Hands and door knobs or light switches? Not so much, but still pretty good.

Noses? Didn't leave a microbial noseprint on anything.

I guess no noses pressed up against the window glass. Or no microbes being "picked" up by hands in those families.

Lax, S., D. P. Smith, J. Hampton-Marcell, S. M. Owens, K. M. Handley, N. M. Scott, S. M. Gibbons, P. Larsen, B. D. Shogan, S. Weiss, et al. 2014. Longitudinal analysis of microbial interaction between humans and the indoor environment. Science 345:1048-1052.

Modeling competition for water: calling the race

Sensitivity of water uptake to changes in parameters for crops grown with weeds or weed-free. Parameters in the lower right are a lot more important when competing against weeds than when weed-free.

When competition is a race from the start line--like with crops--the best strategy is to run fast.

Dunbabin 2007 modeled this by simulating the growth of plants with 3-d models of root systems in order to examine sensitivity of uptake of water, nitrogen, and phosphorus to variation in key root parameters.

Looking at what parameters are important when crops are competing against weeds, here's what Dunbabin says about that:

"the ability to quickly (growth rate) and effectively occupy (rooting density) the soil volume during crop establishment, may be important for denying weeds water and nutrients, thereby conferring competitive ability"

When there is no competition between crops and weeds, effective exploration is important:

"The ranking of P uptake efficiency as important for the acquisition of mobile nitrate and water
resources by weed-free crops...suggests that foraging for the least mobile, and often most limiting nutrient, may provide the best strategy for acquiring all soil resources (Robinson, 1996a)."

Here, the modeled plants are growing in relatively low-P soils, so to acquire the most water, you have to have a big plant. Having a strategy for effective acquisition of P becomes the most important parameters there.

One parameter not important regardless of whether there is competition or not? Potential transpiration rate.

Water is a mobile resource, but roots still have to wait for water to move to them.

Considering that nitrogen uptake kinetics aren't important for nitrogen competition, and mass flow is slower than diffusion, this makes sense.

It is important to note that the arena of competition is important here. The plants were started from seed (I think) and allowed to grow for 12 weeks, simulating a quick crop rotation.

The question here is what aspects of roots become important when competition is not a 12-week race? What becomes important when perennials occupy the same space for years? And if nutrients aren't limiting? Then what?

What does the optimal root system look like for plants growing in the absence of interspecific competition when water is limiting, but nutrients aren't?

A few thick roots?

Many thin roots?

Unknown.

Exploring competition for water

From Lobet et al. 2014

It is true that drought kills plants. 

Drought lowers soil moisture. Low soil moisture kills plants. Therefore, drought kills plants. 

Yet, the rate at which drought lowers soil moisture is dependent on the plants that are present in soil. 

Plants lower soil moisture. Low soil moisture kills plants. Therefore, drought kills plants. 

Therefore, it is also true to say that plants kill plants. 

And when plants kill plants by using resources, that's resource competition. 

There has been a lot of great work over the past decade examining the mechanisms of how drought kills plants. 

But not so much on how plants kill plants when water is limiting. 

About a decade ago I was curious about some of the mechanisms of how plants compete for nutrients.

To explore this, I put together a model...actually I asked Trevor to put together a model...to simulate the movement and uptake of nutrients in soils.

This model was parameterized at a fine scale and could simulate the supply, movement, and uptake of nutrients in soils. 

It was able to show patterns of nutrient distribution in soils like this.. 

2 cm x 2 cm cross section of soil with all roots orthogonal to the plane. Red indicates high nutrient concentrations in soil solution. Blue is low.

With the model, I was able to show that plants acquired nutrients in proportion to the fraction of all the root length they had in a given volume of soil. Plants had a lot more roots than they needed to take up nutrients...if there was no competition. Once more than one plant had roots in a given volume of soil, a race set in.

As a result, plants can have 1000 times more roots that is optimal for maximizing growth.

That all pertained to nutrients though.

I'm curious about how competition for water works.

For example, does competition for water favor plants with high root length density? Would this also lead to a race like nutrients.

For competitive purposes, is there any benefit to being able to sustain a low minimum water potential? Under what conditions, if any, does drought tolerance affect competitive outcomes?

I'm also curious about the interactions between nutrients and water. Does increasing transpiration rate help with nutrient competition? Do dry soils exacerbate nutrient limitation?

But first, I need to adjust the model to handle water.

That means making soil moisture dynamic, parameterizing water fluxes between pixels and into roots.

The hardest part of all of this is figuring out how to parameterize water uptake by a  given root. There is no simple Michaelis-Menten equation here. Roots are a 1000 connected little straws

Still, I've been impressed by some of the developments in root modeling over the past few years.

I can expand on that later.

Unreinforcing insularity: burning and soil moisture

I had a conversation with a prominent cattle rancher in town the other day. I asked him if he had followed any of the news on the recent research out of K-State on the timing of burning.

He said he had.

I asked him what he thought of it.

He said in his gravelly voice, "I think it's a bunch of bullshit."

When I asked him why, he said that if you burn early, everyone knows that the soils will dry out. With less litter, there will be more evaporation and less rainfall will enter the ground because more will run off. You are going to get less productivity.

He didn't know that I had helped write the paper, which was just fine. I got more honest answers than I would have otherwise.

I explained to him that having litter on the ground often reduces how much rain enters the ground--canopy interception creates a dispersed puddle that can evaporate before wetting the soil.

I also explained that there was never any strong evidence that soil moisture was lower when you burned early.

The only data on that are from 50 years ago and were never done in a manner that was scientifically rigorous enough to be definitive.

The data were taken with neutron probes, which is a fine technique, but there was confoundment between treatments and sites.

When you look at the data, the data suggest that early-burned grasslands have less soil moisture than late-burned watersheds.

But, a couple things are suspect with the data.

First, over the winter, soils should recharge so that they are wet throughout the soil profile regardless of the treatment. But, December and January soil moistures are already an inch lower in the early burns.

This is more likely due to that site just happening to be able to hold less water.

Second, if litter is important for holding soil moisture in, unburned soils should be wettest. They are the driest.

Third, the rate of decline of soil moisture during the growing season for the different treatments are almost exactly the same. When does the differential drying occur? This likely means they are using soil water at the same rate.

Fourth, let's say that data are correct and there is an inch less of water in the top five feet of soil.

How much of an effect would that have on production?

A general rainfall use efficiency is about 0.3 g m-2 mm-1 or 75 lbs/acre/inch.

What was the "effect" in the experiment of a having 1 inch less of soil moisture in the soil profile?

Typical rainfall use efficiencies would predict a reduction of productivity of 7-8 g m-2 or 75 lbs/acre.

What was seen? A reduction in productivity of over 1000 lbs/acre (100 g m-2).

Original data use to support idea that early-spring burning reduces productivity. Here, OU is ordinary uplands, which is probably the most relevant. Note the ~25% lower productivity in the pasture that was subjected to early-spring burning. Early-spring burning was March 20. Mid-spring April 10. Late spring was May 1.  

That's over an order of magnitude too much.

In all, that's really the only data out there that is used to support greater drying in early-burned grasslands.

It's unrealistic.

The rancher's response to these highlights?

Something to the effect of "Regardless of what the data say, I trust what other ranchers say they've seen."

Even though none of those ranchers would have ever seen a grassland that was burned in the fall for 20 years straight...

Insularity can be hard to break...

Multivariate statistics...what to use.

Path diagram to figuring out what type of multivariate statistics to use.

Dave Wedin use to joke that the Joe Craine way of analyzing the data is to take it all and put it into a PCA and see what you get. 

There's some truth to that. PCA has served me well. And almost every time I get forced to use a different multivariate analysis, PCA seems to give me similar results. 

For example, in the last burning paper a reviewer insisted we use NMDS for our plant cover data rather than PCA. Correlation coefficient between NMDS and PCA Axes 1-3: 0.93, 0.89, 0.71. Same story from each.

Still, it's good to know what other options there are out there. 

I've been looking at bacterial data with Noah and found the Ramette 2007 paper on multivariate analyses in microbial ecology. Noah uses Principal Coordinates Analysis and I was trying to remember the difference between PCA and PCoA.

It's a good user guide to multivariate statistics in general.

One thing that was interesting was a multivariate analysis of the different types of multivariate statistics different disciplines use. 

Ramette, A. 2007. Multivariate analyses in microbial ecology. FEMS Microbiology Ecology 62:142-160.

Quick hit on male bison weights

Display of bison weights from Konza over time. The upper bound of the data envelope shows the weight dynamics of the largest males.

We didn't EID tag any of the large males at Konza, but the raw data lets us infer a bit about what is going on with their weights.

When the big males get on the scale they rarely have any other individuals with them and they move slow across, so we can get a pretty accurate weight.

If you look at all the data, you can see a clear pattern with the upper bound of the dataset. These are the big males.

Since early April, it looks like the big males gained about 200 kg.

In early July, the upper bound was 920 kg. That's over 2000 lbs.

For reference, during the fall roundup we once had an animal hit 930 kg. After that, top weights were no more than 850 kg (1870 lbs).

Since early July, it looks the biggest males have lost about 100 kg**.

That's a tremendous amount of weight to lose in just a month.

**Note that it could be that the biggest males decided to not walk over the scale during the past month. But, right now all the males and females are together, so if the females have been walking over it, so have the males.

That degree of weight loss is plausible. July and August are the rut, and that's when the bulls are going to be eating the least and "exercising" the most.

Where this becomes interesting is starting to understand the continental scale patterns and thinking about how climate change will affect grazers.

We know that cool, northern grasslands produce bigger bison than warmer, southern grasslands. At least in the fall.

One hypothesis is that the fall weights might be higher for bison in the north, but mid-summer weights are the same. In southern grasslands, forage quality drops enough such that the southern animals lose weight while the northern animals maintain it.

Alternatively, the northern animals might be even bigger midsummer.

In a few weeks, I'll head up to South Dakota to install a walk over scale there.

By this time next year, we just might have the answer.

More forensics on burning

There still is debate smoldering over the timing of burning on grasslands here in the Flint Hills.

Much of the debate stems from research conducted here in Kansas over 50 years ago.

A little more forensics is illuminating.

The key evidence to suggest that burning should be done in late spring is from the weight gain of cattle in an experiment that burned pastures at different times: early-, mid-, and late-spring with an unburned contrast, too. In the experiment, each month, the cattle would be taken off pasture and weighed to examine monthly weight gain. The experiment was carried out from 1950-1966.

In the first reporting of the results from the experiment (Anderson et al. 1970), the authors showed no significant difference in monthly weight gain for cattle placed on pastures with early- and late-spring burns.

Their conclusion was a lot more certain than their data.

“Mid- and late-spring burning produced more weight gain on steers than nonburning. Late-spring burning also increased steer gains over early-spring burning. The weight gain obtained with early-spring burning was essentially equal to that obtained with nonburning.”

So, although there were never any significant differences in weight gain, the conclusions were that there were.

It is possible that when you add up all the monthly gains for a year, you get significant increases in weight gain. As far as I can tell, that was never tested.

When the data were summarized in a later publication, the monthly weight gains are compiled into a 5-month weight gain. But there are no error bars. No tests of significance.

Often, these results have been reported as stating that burning in late spring leads to 32 pounds greater weight gain over a 5 month period (May – September). I’m not sure where that number comes from because the graph only shows 26 pounds difference. Yet, most of the cattle in the Flint Hills are only left out on pasture for 3 months. May, June, and July. If you go back to the 1970 paper, over the 3-month May-July period, differences in weight gain were measured at just 9 pounds.

9 pounds is still 9 pounds, though. Yet, is it? There is no evidence to show that the 9 pounds, no less 26 pounds, is actually significant. That means 9 pounds might be 0 pounds.

There are other parts of the research that are curious. For example, if you look at the productivity data, burning just 42 d earlier results in a 25% reduction in grass productivity. In contrast, 20 years of data at Konza shows no significant reduction in grass productivity. What would cause such a marked reduction in productivity? In the past, it was suspected that soils dried out a lot faster without any cover, but there is actually little evidence to support this.

Could it have been differences in forage quality? Again, no data were ever taken on forage quality for pastures burned at different times.

Also, the average date of burn for the late-season burn was May 1. The stocking date for all the animals? May 1. How that actually happened, I have no idea.

So what might be going on here?

One limitation of the work is that there was no spatial replication for the experiment. Each pasture had a different treatment. There was only one pasture for each treatment. Treatments were not rotated among pastures. Replication came from measuring the same pasture year after year.

In scientific terms, that means site differences and treatment differences were confounded. In lay terms, we have no way to know whether any differences in weight gain were because of the pasture that happened to be picked for the early-season burn happened to have worse forage than the late-season pastures.

This is exactly why scientists replicate.

Still, replication at this scale is certainly hard. We only have so much land to work with. You do the best you can.

Yet, is it impossible to do? No. Can it still be done? Yes.

Scientists could easily work with ranchers to use their operations as experiments. Different ranchers could burn at different times and they could record their animals weight gains. That’s a whole lot better than extrapolating from a few hundred acres at one spot to a much broader area.

In all, was the past work suggestive that late-spring burning was optimal. Certainly. But there are too many questions about the certainty of the results and their broader relevance.

At this point, the most conservative, commutative interpretation of all the data is that there are no significant effects of early-season burning on weight gain.

Interpreted within the context of the experimental design and more recent data, it is hard to be convinced of the necessity to burn in late April in the Flint Hills.

Forensics on research and non-commutative property of science

We learn early on that when adding numbers, order doesn't matter.

2 + 3 = 5

3 + 2 = 5

Like addition, science should be commutative.

Any time new data are collected, the whole of the evidence needs to be reexamined with no favor given to hypotheses that were favored in the past.

Yet, science (and its application) is often not commutative. Order does matter.

The bar for disproving an idea is higher than proving it initially.

Here's an example.

Gene Towne and I just published a new study on burning of grasslands. The study examines 20 years of data from watersheds burned at different times of year--fall, winter, and late spring. It's the most comprehensive study of the importance of the season of burning in North American grasslands.

In short, the research shows that burning grasslands in the fall or winter compared to the late spring has little effect on grasses productivity, while favoring cool-season grasses and forbs. The details of the study can be found here or here.

To the rest of society, it's an important study because it has implications for how much of the last remaining tallgrass prairie is managed. This affects the grasslands that support a million or so cattle each year. But it also affects the millions of people that are downwind of the region when the grasslands burn.

Currently, in the Flint Hills, ranchers (and conservationists) burn prairie frequently every year. Based on recommendations, burning is concentrated in the late spring. When that happens, air quality standards are often exceeded in major cities downwind of the fires.

What is interesting here is to dig into where the recommendations came from. Scientific forensics.

When you examine the research upon which the recommendations are based, almost all of the research was conducted over 40 years ago. Moreover, it seems evident that the findings on when to burn are fairly equivocal.

And probably wouldn't pass rigorous scientific scrutiny today.

For example, the research from the 1960's and early 70's showed that plant biomass was found to be lower when grasslands were burned in early spring vs. late spring. Yet, these means were generated for just one small experimental plot per treatment.

The old data also showed that when grasslands were burned earlier soil moisture was lower, which presumably caused the lower productivity. Yet, in the particular study conducted in the 1960's, soil moisture data are reported as being lower in January in the early-season burn than the late-spring burn, two months before the burning has occurred. This result was likely a site effect.

Another major line of evidence to recommend late-spring burning was long-term data on cattle weight gain. The data on weight gain showed a trend of 6% greater steer weight gain from May to September over 16 years when burning on average on April 10 vs. March 20. Yet, as with other data, the weight gain of steers was again measured in just one pasture with no control for site differences that might be confounded with treatments. As for the data collected, although the weight gain of steers was significantly higher in burned than unburned pastures across 16 years, there was no significant difference in monthly weight gain with timing of burning (P > 0.1).

Based on the data circa 1970, if you had to choose a time to burn, late spring made sense. Yet, when all the past and current evidence are taken together, there is little evidence to support burning exclusively in the late spring.

Burning Flint Hills grasslands earlier is unlikely to have any major negative consequence for grass or cattle production, and may actually be beneficial (see the paper).

Unfortunately, more than likely, unlodging the idea that burning in late spring is necessary will be a lot harder than lodging it in the first place.

Unlike elementary addition, order is likely to matter here.