Monday, December 7, 2015

Highlights from Nature Climate Change in 2015

Catching up on a year's worth of articles from Nature Climate Change. It's like binge-watching your favorite program. There are a lot of great "episodes", but here are some that stood out for me:

Central US experience a greater frequency of floods most likely due to a greater frequency of heavy rainfall events and rain-on-snow events.

Mallakpour, I. and G. Villarini. 2015. The changing nature of flooding across the central United States. Nature Climate Change 5:250-254.

Growing season length is increasing almost everywhere.
Buitenwerf, R., L. Rose, and S. I. Higgins. 2015. Three decades of multi-dimensional change in global leaf phenology. Nature Climate Change 5:364-368.

Increasing CO2 decreases plant N:P, while warming and water increase it. 
Yuan, Z. Y. and H. Y. H. Chen. 2015. Decoupling of nitrogen and phosphorus in terrestrial plants associated with global changes. Nature Climate Change 5:465-469.

Temperature is more tightly coupled with greenhouse gases than insolation. "This confirms the existence of a positive feedback operating in climate change whereby warming itself may amplify a rise in GHG concentrations." Note the new analytical techniques here to evaluate complex systems.
van Nes, E. H., M. Scheffer, V. Brovkin, T. M. Lenton, H. Ye, E. Deyle, and G. Sugihara. 2015. Causal feedbacks in climate change. Nature Climate Change 5:445-448.

Microbial decomposition generates heat that thaws permafrost faster.
Hollesen, J., H. Matthiesen, A. B. Møller, and B. Elberling. 2015. Permafrost thawing in organic Arctic soils accelerated by ground heat production. Nature Climate Change 5:574-578.

European forests are more efficient with their water use, but longer growing season, warmer temperatures, and increased leaf area lead to transpiring more water. Frank, D. C., B. Poulter, M. Saurer, J. Esper, C. Huntingford, G. Helle, K. Treydte, N. E. Zimmermann, G. H. Schleser, A. Ahlström, et al. 2015. Water-use efficiency and transpiration across European forests during the Anthropocene. Nature Climate Change 5:579-583.

Tall, leafy trees are most likely to get nailed by drought in the future. McDowell, N. G. and C. D. Allen. 2015. Darcy's law predicts widespread forest mortality under climate warming. Nature Climate Change 5:669-672.

Tuesday, December 1, 2015

Ecological and environmental funding priorities

The above graph does not display patterns of ecological priorities. It's about funding at NIH (recently published in Science). 

The y-axis is millions of dollars spent in 2010. The x-axis is disability adjusted life years--the cumulative number of years lost to ill-health, disability, or death. (DALY).

The relationship is a good one, in statistical terms. Diseases with a low burden are funded less than diseases with a high burden. There are residuals, too. Diseases with a global presence (malaria, AIDS) appear to be funded at a greater rate than their US DALY. Lung cancer, migraines, and suicide, which are not as trendy (or likely thought to be the fault of the stricken) are funded less.

My question today is why don't we have a graph like this for ecology/environmental science?

The first question would be what do we put on the x-axis? Do we have an ecological equivalent of DALY? Probably not. That right there is one of the biggest failings on how to prioritize. We don't have a standard to compare for prioritizing**. 

**If anything, we make expert lists like "Top 50 Priorities in [insert discipline]"

That means ecological funding graph is likely to be a bar chart. Still, I'd like to see that.**

**I guess another way to do it would be to put it in terms of societal benefit. Ecological goods and services. X-axis would be dollars, then.

Then what are the categories? 

We can't do it by standard categories like carbon cycling, population dynamics, community composition. These do not necessarily speak to societal challenges. 

You need the equivalent of diseases. So, disease would be one category. Climate change, elevated CO2, nitrogen deposition, drought, biodiversity loss, water quality...

Then I guess you'd need to categorize funding for a given year from NSF and maybe EPA, USGS...

That graph wouldn't be as good as the DALY-funding graph, but I'd still like to see it. 

The reason is that my suspicion is that funding priorities are all out of whack relative to societal need.  If we could have a bivariate plot, it'd be messier than the DALY graph.

My other suspicion is that the y-axis would be a lot smaller. Many of the most important ecological/environmental issues of our time wouldn't even hit the psoriasis level of funding, no less autism.**

**Heck, all of NSF Directorate for Biological Science ~$700M is about what NIH spends on depression.

If we could put together this graph, that should help arguments on how ecology/environmental science is funded. 

Because my last suspicion is that we're underfunded. We wish we were funded like peptic ulcers, or even migraines relative to the importance of the issues.** 

**I think down the line there is going to be a grand bargain where Congress will want NSF to justify their funding based on societal need. It might be a bargain worth taking if the ecological/environmental science gets funded at similar proportions to societal need as disease.

Sunday, November 15, 2015

Global patterns of rumen microbes

Ruminants are walking fermentation vats. They ingest plants and let a host of microbes metabolize the plant material. Ruminants then absorb some of the microbial byproducts and also digest the microbes.

It's been known for awhile that these microbes include bacteria, archaea, and ciliates. The identity of the microbes is still being determined, no less their ubiquity.

Henderson et al. took a huge step towards understanding these patterns. They examined over 700 rumen samples from 32 ruminant species. Samples were collected globally.*

*How this paper ended up in Scientific Reports and not Nature is beyond me. My guess is bias against livestock.

Among their results, the authors show that there is a core set of bacteria and archaea (but not protozoa) in ruminants that they rely on for digestion. The also show clear differences between animals fed browse and concentrate.

This paper is going to take a while to digest (pun intended), but there are some pretty amazing patterns.

**Henderson, G., F. Cox, S. Ganesh, A. Jonker, W. Young, C. Global Rumen Census, and P. H. Janssen. 2015. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci Rep 5:14567.

Thursday, October 8, 2015

Up in smoke

Gene Towne and I were working on revising a paper reviewing what is known about the effects of differences in the timing of burning on grasslands and grazers.

We had written "burning in late-April after the green vegetation has emerged, exacerbates smoke production and accompanying air pollution, which is at the forefront of the burning controversy."

This sentence seemed obvious to us. If you burn green biomass, it's smoky as heck.  Still, the sentence did not have a citation. Rightly so, it should have.

I spent some time going through papers to see if what we had observed empirically had basis in the literature.

After a few hours reading papers, it seems that the statement wasn't wrong, but I did make a mistake in not reading these papers sooner. Probably the best paper was Andrae and Merlet from 2001, which is frustrating because I apparently could have learned all of this 15 years ago.

1) Smoke is complex. It contains O3, CO, water vapor, NOx, HCN (!), SO2, CH4, C2H2, xylene, benzene, etc. as well as particulates of certain sizes.

**Technical point: smoke researchers talk a lot about emission ratios (amount of a product produced in a fire relative to a standard like CO2) and emission factors (same, but relative to amount of biomass burned). They also talk about CE, combustion efficiency, with is an emission ratio of all the products besides CO2 relative to CO2 produced in a fire.

2) Fire is complex. It has stages: ignition, flaming + glowing + pyrolysis, glowing + pyrolysis (a.k.a. smoldering), glowing, and extinction. Each has different chemistry and emissions.

3) Flaming involves relatively complete oxygenation (burning) of products. Smoldering does not. Smoldering (burning without flame) is more likely to produce some products like CO and NH3 than flaming, which is more likely to produce products like CO2 and NOx.

4) It's interesting to read how stuff catches fire. When biomass starts to burn, the first step is the drying/distillation step. This releases water and volatiles. Then comes pyrolysis with "thermal cracking" of the molecules which produces char, tar, and volatiles. Here, stuff is breaking up, but not burning. As the biomass gets hotter, the tar and gas begin to oxidize, which produces the flame.
occurs. Once the volatiles have burned off, then the biomass begins to smolder (glowing fire) and many of the incomplete oxidation products are produced.

For us, we're likely to rewrite the sentence a bit to acknowledge the complexity of fire and smoke.

In all, when green grass is burned, it's nitrogen concentration is higher than senesced grass, which leads to greater production of NOx, which is a precursor to ozone production that causes health problems. Whether green grass has a lower combustion efficiency hasn't quite been resolved (Mebust and Cohen 2013). It should be lower with wetter biomass, but this apparently hasn't been definitively demonstrated. If so, then burning green vegetation is going to produce a lot more junk.

Monday, October 5, 2015

Investing for research

Just a mini thought here.

In economics, there are a number of theories on how entities invest. One of those is has to do with the relationship between the amount of investment and interest rates.

Essentially, theorems such as the marginal rate of efficiency or the marginal rate of investment state that firms continue to invest until the marginal rate of return is no different than the interest rate. In short, when making a decision on where to invest money, money will always be invested in the investment that produces the highest rate of return, until there are no options that differ than the prevailing interest rate.

By analogy, when governments are deciding how allocate scare research dollars, similar economic decisions should be at play.

In reality, these decisions are nothing close to rational, in the economic sense.

For one, we cannot quantify the rate of return on investing research dollars. Comparing two proposals for funding, reviewers likely can estimate the rate of return. The equation is something like: # of publications x impact factor of likely journals**.

**This formula is likely discounted a bit for age. Old researchers are more likely to publish, but have a shorter lifetime return on investment. Funding young researchers before tenure makes it more likely they will be publishing in 10 years...

At a broader scale, this type of analysis is impossible or irrelevant. Funding agencies need to decide whether to invest in one discipline vs. another. This equation is useless for deciding whether to invest in physics vs. biology, for example.**

**If it was used, it would just favor the more prolific discipline. Publishing papers is not the likely goal for funding agencies, per se. Papers aren't bitcoins. 

The number of likely citations is also not a good metric, because it's circular. The number of times papers in a discipline are cited on average are determined by the number of papers published in that discipline (and the average number of citations in a typical paper).

If we cannot use expected rates of returns on publications, then how do we assess worth?

This is the Achilles' heel of rational investing in science. We cannot find a common currency to evaluate the relative worth of research.

Without this, funding becomes irrational.

Right now, changes in funding levels occur at the margins. Politicians and directors do not regenerate funding levels each fiscal cycle. Instead, they make a case that funding for one area should be increased or decreased relative to what it is currently. There are no equations that are employed to determine relative funding levels or relative changes in funding levels.

Until we have a concrete way to assess the value of research, either in terms of dollars, or social equity, or longevity, funding is likely to be irrational.

Stepping back, not only the relative amounts of funding, but absolute amounts of funding for research need development. In the US, NIH budgets are 4x higher than NSF. Why the relative difference is one question. Another, is why are there combined budgets almost $40 billion? Should they be $20B or $80B?

If the decisions are fundamentally irrational (economically), then we either need to make them economically rational, or commit to irrational (economically) arguments. 

Tuesday, September 22, 2015

Gut fungus in herbivores

Most of the energy in grass is locked up in cellulose and other complex recalcitrant molecular compounds like lignin and waxes. Stomach acid alone cannot degrade these compounds into components that yield energy for the animals that eat grass. When faced with how to survive off an abundant, yet inaccessible food source, grazers turned to microbes that have been degrading compounds like these for millions of years. 

The digestive system of herbivores is a soup of microbes. Archaea, bacteria, fungi, protozoans... they're all in there. Most for a good reason.

A paper from a couple of years ago sheds a little light on the mutualisms between grazers and fungi.

The paper sequenced fungi in the fecal matter of bison, cattle, pronghorn, and prairie dogs at either Sevilleta (New Mexico) or Wind Cave (Wyoming).

A few interesting points.

First, half of the sequences they identified in bison and cattle were from Neocallimastigales. These are anaerobic fungi that produce the compounds responsible for hydrolysing cellulose and hemicellulose. We rarely ever hear about them, but they are the analog to brown-rot fungi that are important for wood decay.

Second, it appears that some of the fungi found in the fecals could only have gotten there from the animals ingesting roots. Some of these coprophilous fungi become endophytes, especially in roots. Bison and cattle occasionally eat roots of grasses. Prairie dogs a lot more.

Third, pronghorn fungal communities are just different (and less diverse). Pronghorn are browsers and just wouldn't have the same need for degradation as grazers. 

One interesting side note. Paleoecologists are using the presence of dung fungal spores as evidence of  the presence (and abundance) of grazers on the landscape. Fungal spores in sediments are identified as Sporormiella, a genus of Pleosporales. More Sporormiella in the sediment means more grazers on the landscape. Yet, although the authors of this paper identified many genera of Pleosporales, none of them were from Sporomiella. You have to wonder if a revision of what actually hits sediments is in order and whether sequencing the spores could provide more information on who was there.

Herrera, J., R. Poudel, and H. H. Khidir. 2011. Molecular characterization of coprophilous fungal communities reveals sequences related to root-associated fungal endophytes. Microbial Ecology 61:239-244.

Davis, O. K. and D. S. Shafer. 2006. Sporormiella fungal spores, a palynological means of detecting herbivore density. Palaeogeography, Palaeoclimatology, Palaeoecology 237:40-50.

Monday, September 21, 2015

The diets of animals

The roots of ecology rise from questions about trophy. Darwin, Elton, far back as you wish to trace ecological thought, trophic questions have been central in the discipline. One organism consuming another (or part of another) is critical for population regulation, community assembly, ecosystem-level transfers of energy and material, no less the evolution of species.

Ecologists have been watching organisms consume other organisms for a long time, but, curiously, we still know little about what organisms eat.

Part of the reason is that it is hard to quantify consumption. Visual observations are difficult to translate to data. I've watched bison graze grasslands at a distance of feet and even after 30 seconds couldn't come up with a list of what they just consumed. Worse is that most consumption happens out of view. Dissections or regurgitations are necessarily disruptive and identification is still difficult. Isotope analysis gives crude answers. Microhistological analysis of fecal material suffers from differential digestion and lack of specificity.

In short, food web diagrams are hard to generate.

The rise of next generation sequencing has opened new opportunities to quantify what animals eat with resolution that never existed before. Fecal samples from an animal can be sequenced to determine what plants, invertebrates, fungus, or vertebrates they have been eating.

Noah Fierer and I have been working on helping people understand what animals eat for a bit.

So far, we've sequenced fecal material from over 20 different species that range from bats to bison, from prairie chickens to whooping cranes, from moose to mule deer.

I've talked with each of the ecologists afterwards about their data and there is one commonality to all of these discussions: surprises. There are always items in the diet that the ecologists didn't expect. And not just rare diet items. Perceptions (mine included) were always a good ways off of what the animals consumed most. For example, bison were long considered to eat mostly grass. Not so. Their diet can be dominated by forbs and shrubs at certain times of year.

A good technique is only as valuable as the importance of the question that it helps you answer.

Next generation sequencing of fecals is an amazing technique that will open up major insights into how populations are regulated, how communities assemble, and how energy and materials flow through the ecosystem. The technique will open up new insights into the coevolution of predator and prey.

A few bottlenecks still restrict advances.

1) We need better genetic barcode databases. There are still too many gaps for the technique to be commonplace. Many other people are working on this, but we need to sequence key barcodes for all collected taxa. When a sequence appears, we need to be able to compare it to the sequences from known taxa.

2) We need better species specificity. Give or take, current sequencing of diets can get down to ~genus level fairly well, but we need to develop different techniques like hierarchical sequencing to get us to the species level better.

3) We need to be able to quantify the diets of omnivores better. Currently, we can quantify the relative proportion of plants in diets. Or animals in diets. But, knowing the relative proportion of each is difficult. Over the course of a day, a bear (for example) could eat plants, fungi, insects, and vertebrates. But how much of each? Plus, DNA from the diet of prey is present in fecals, but we cannot necessarily tell whether the predator or prey ate a given taxa (or both).

There will always be more technique development necessary, but the biggest bottleneck is still utilization of the technique. Ecologists need to start using it. We're learning a great deal every time we use the technique.