Biogeochemical loops

February 3, 2015 – 5:50 pm

In the early 1980s, advances in analyses of microbial function in ocean water allowed researchers to observe that smaller microbial producers–distinct from well studied photosynthetic dinoflagellates and diatoms–were leaking a substantial amount of dissolved organic carbon (DOC) into the water column. This DOC was then taken up by heterotrophic microbes, who were in turn eaten by phagotrophic protozoa, who were then finally consumed as an additional food source by the primary consumers of the traditional food web. This previously unobserved pathway for transfer of energy and key elements like C and N appeared to operate in parallel to the traditional food web, and was thus named the “microbial loop.” The microbial loop was eventually shown to account for a substantial amount of the C and N cycling and storage in ocean systems, changing our basic understanding of ocean biogeochemistry.

The idea of parallel cycling “loops” has since caught on in biogeochemistry as a way to describe alternative and previously underappreciated cycling pathways. I’ve recently been working with ecologists at the University of New Mexico who have been developing the analogously named “fungal loop hypothesis.” About a decade ago, they began to synthesize evidence suggesting that fungi were an unusually important part of C and N cycling in desert soils. Much like the microbial loop, this non-standard pathway for C and N cycling in soils appeared to represent a fundamentally different cycling pathway in which fungi were key agents of storage, transformation, and translocation for C and N. These functions are normally more closely associated with soil organic matter and heterotrophic bacteria in wetter ecosystems.

I have also heard some rumors that tropical ecologists have been thinking about an alternative “loop” of their own. This makes a lot of sense to me because carbon and nutrient cycling is much more associated with the massive amount of living vegetation in tropical forests than it is in temperate ecosystems. I think this is a really exciting area of biogeochemical theory, with the potential to revolutionize our understanding of how these basic cycles vary among biomes. As more tests of these hypotheses enter our syntheses and the true nature of these alternative loops are understood, we may be partially rewriting and definitely expanding our knowledge of how these key biological elements are cycled in the biosphere.

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Omnibus tests vs. focused contrasts in analyzing experimental data

September 9, 2014 – 4:57 pm

I’ve always been uncomfortable with drawing insights from big complex ANOVA models, and in searching for a couple stats references for a paper, I found there is a sizable literature on the topic.

Here’s a statement from a book by Robert Rosenthal and colleagues that summarizes exactly how I feel about these models:

The problem is that omnibus tests, although they provide some protection for some investigators from the danger of “data mining” when multiple tests are performed as if each were the only one considered, do not usually tell us anything we really want to know.

I like the term “omnibus tests” as a description for these types of analyses that try to arrange all possible variables into a hard-to-interpret statistical model. I find the interpretation of “interaction effects” in many models I see to be particularly problematic.

As an alternative to these omnibus tests, the authors suggest using “focused contrasts,” which to me, sounds very similar to the “lots of t-tests” approach that I have settled on for many of my analyses. In their book, they present some novel algorithmic approaches to making these contrasts, and while I have not read the whole book to understand what they did, I think that the basic concept of learning about data from many simple analyses instead of one kitchen-sink style analysis is the same.

I also think they are correct that fear of errors from multiple comparisons is a big reason people gravitate toward omnibus tests. My feeling is that the epistemelogical challenges that multiple tests can create are better dealt with in the interpretation phase rather than the calculation phase of an analysis.

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Ocean fertilization: still not recommended

July 8, 2014 – 7:06 pm

The idea of fertilizing the ocean with iron to capture carbon is one of the more colorful ideas to arise directly from a basic understanding of biogeochemistry. Ocean phytoplankton are iron-limited and alleviating that iron limitation makes them grow enough to cause some of the fixed carbon to get locked away in the deep ocean. There was substantial debate about a decade ago about the feasibility of this idea for fighting climate change and I seem to remember that most of the scientists involved decided that it was an interesting but not practical idea.

A re-promotion of the idea was making the internet rounds today due to this profile of a prominent advocate—practicioner even—of iron fertilization. I had forgotten the details of the debate so it was fun to revisit them. The profile is written in such a way to make it seem like iron fertilization is a great idea that is being held back only by environmentalists scared of geo-engineering. Unfortunately for the author–or perhaps to their credit in being objective–the counterarguments to this viewpoint are apparent in the article itself. The author writes:

iron fertilisation could potentially sequester as much as 1 billion metric tons of carbon dioxide annually, and keep it deep in the ocean for centuries. That is slightly more than the CO2 output of the German economy, and roughly one-eighth of humanity’s entire greenhouse gas output.

This sounds good until it is put to a simple cost benefit analysis. The one-eighth figure likely comes from a modeling study, and is also discussed in an editorial in Nature arguing against iron fertilization back in 2009. (I note that the authors here are better described as ‘scientists’ than ‘environmentalists.’ ) In that editorial they write:

A model published in 2008 (K. Zahariev et al. Prog. Oceanogr. 77, 56–82; 2008), which is as convincing as any available, found that even if the entire Southern Ocean were fertilized forever with iron sufficient to eliminate its limitation of phytoplankton production, less than 1 gigatonne of carbon a year of CO2 of probable future emissions (currently about 8 gigatonnes a year) would be sequestered, and that amount for only a few years at best.

So there you have the cost and the benefit. The benefit: we may reduce 1/8th of our emissions, a substantial and impressive amount, but nowhere near enough to stop global warming. The cost: we risk fucking up an entire ocean. The ‘environmentalists’ were ridiculed in the the pro-fertilization piece for having that attitude, but it hardly seems outrageous to worry that fertilizing an entire ocean to stop 1/8th of our emissions could have unintended consequences, and the Nature editorial shows that scientists, myself included feel the same way.

There are too many examples of fragile food webs tied together as trophic cascades and biocontrol agents gone wrong to not worry about unintended consequences of a vast ecological manipulation in a system that is not totally understood. We are still barely getting a handle on the consequences of doubling carbon in the atmosphere. Is it wise to perform a similar experiment with another element in the ocean? The pro-fertilization piece crescendos toward this point at the end:

The ocean is no longer a vast, unknowable wilderness, whose mysterious gods must be placated before it can be crossed. Instead, it’s become the first viable arena for large-scale manipulation of the planetary environment.

What could possibly go wrong?

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Carbon cycling in semiarid systems

May 28, 2014 – 5:35 pm

There is a cool new paper in Nature showing that semiarid ecosystems can have big effects on overall carbon concentrations in the global atmosphere, primarily through enhanced productivity of vegetation in wet years. The authors write:

As the dynamics of dryland systems, which cover 45% of the Earth’s land surface, increase in global importance, more research is needed to identify whether enhanced carbon sequestration in wet years is particularly vulnerable to rapid decomposition or loss through fire in subsequent years, and is thus largely transitory. Such behaviour may already be reflected by the larger-than-average atmospheric growth rate in 2012 (ref. 30) that was associated with a return to near-normal terrestrial land sink conditions.

In other words, the next question is how long can these systems lock up this carbon in the transient vegetation of deserts? I would guess that, as the authors suggest, a string of dry years would cause these gains in carbon storage to be lost, but it might take a couple of decades to work the carbon out of the soil, and increased fertility in the short term can lead to soil stabilization, thus increasing overall ecosystem fertility, particularly with good land management practices. So this could be a good thing for the climate, and an essential role played by dryland ecosystems.

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Spatially inaccessible soil nitrogen

March 6, 2014 – 4:44 pm

One of Mike’s and my papers just came out this morning in Soil Biology and Biochemistry (link here). We compared nitrogen concentration data from our lysimeter samples of soil pore water and our extracted samples from soil cores and found were were seeing a lot more nitrogen in the soil core samples. This seemed to support the idea from a paper by John and Erik Hobbie a few years ago in which they suggested that soil amino acid concentrations seemed a little too high given the strong uptake capability of microbes, and that thus some of it might not be biologically available. Here’s our key figure showing the relative magnitude of nitrogen pool sizes and suggesting a sizable “inaccessible” pool:

You can see that the cores (red and dark blue lines), even though quite variable, definitely get more nitrogen than the lysimeters (light blue), which spike at the beginning of the season and then drop to low levels. It also looks like the adsorbed pool (mostly ammonium bound to the soil matrix) and the inaccessible pool can change throughout the season, so the inaccessibility isn’t permanent. Overall, a fun comparison of techniques that I think has implications for the way nitrogen is actually cycled in soils.


Digging deeper into moss death

February 7, 2014 – 4:59 pm

One of the coolest results so far from the global change experiment I’ve been working on is that mosses that are part of the biological soil crusts have pretty much all died in response to the experimental addition of numerous small rainfall events (Reed et al. 2012). The mosses die because they lose more carbon during the small wet-up events than they can gain from photosynthesis during the short time that they are rehydrated. This finding got us curious about the moss physiology and biochemistry behind why they cannot keep up with the carbon demands and what ultimately causes their death.

To help answer that question, my postdoc adviser Sasha Reed and I visited the University of Missouri last week where we learned some awesome analytical techniques in Mel Oliver’s lab, particularly from his amazingly skilled postdoc Abou Yobi. These guys are experts in plant biochemistry and we had a lot of great conversations about stress biology and the chromatography techniques (one of their cool HPLC units above) that they are using to measure the key metabolites that govern plant responses to stress.

Many mosses, especially in the desert, are dessication-tolerant, meaning they can completely dehydrate and rehydrate as part of theyr normal physiological function. There is a ton of fascinating biochemistry here, particularly in understanding how the plant cells respond when they dry out and wet up. Plants have two strategies in surviving dessication: protection and repair. In practice, most dessication-tolerant plants are somewhere along this spectrum. They can try to protect their cells in preparation for dessication by resorbing and preserving themselves as best as possible, or they can devote resources to repairing cells and tissues once they rehydrate.

There are a huge number of metabolites, proteins, enzymes, and other compounds that help these mosses perform the amazing feat of surviving total dessication and rehydration. There are compounds that help them resist the osmotic shock of drying out and rewetting–osmoprotectants–such as sucrose and some amino acids. One of the interesting facts we learned is that Syntrichia ruralis, a sister species to the one present in our experiment, S. caninervis, is 10% sucrose by weight when it is dried out. This is the first thing we want to look at in our mosses because if they cannot regenerate those sugars, they may not be able to dry out properly, sustaining damage to their membranes without the sugar to mediate the collapse of cell membranes and organelles.

Other potentially important compounds include antioxidants, photoprotective pigments, and a class of proteins called LEA proteins that can prevent reactive oxygen species (ROS) from building up and damaging cells. Mel and Abou have been using “omics” approaches in which a large number of metabolites (metabolomics), expressed genes (genomics), proteins (proteomics) or other compound classes are measured simultaneously to give a more complete biochemical picture of a plant tissue under conditions such as dessication (example figure above). Perhaps the ultimate way to answer our question would be to use these techniques on mosses over the course of the rapid wet-dry cycles that ultimately result in their death. We won’t go that far at least for now, but it was fascinating to learn what kind of analytical capability is out there. Bringing these types of approaches to global change experiments could be an excellent avenue for future research and collaboration!

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How much carbon is stored in dryland soils?

November 19, 2013 – 5:00 pm

Drylands are not known for massive soil carbon stocks like the Arctic tundra or plant carbon storage like tropical forests. However, drylands account for a large fraction of the terrestrial surface so they are still important when considering global carbon.

Many drylands biogeochemistry papers include statements like this one:

Arid, semiarid and dry-subhumid ecosystems (drylands) occupy 41% of the terrestrial surface, and account for ca. 25% of global soil organic carbon (C) reserves.

To put those numbers in context using the aridity index≤0.65 definition of drylands (numbers from Millenium Ecosystem Assessment):

Dryland: 41% of terrestrial surface
   Hyperarid: 6.6%
   Arid: 10.6%
   Semiarid: 15.2%
   Dry Subhumid: 8.7%
Frozen ground and Permafrost: 36%
   Ice: 10%
Everything else (mostly forests): (23%)
   Tropical Forests: 5% (used to be 2x more!)
All forests (overlaps with other categories, particularly boreal forest): 28%

When you follow the references back to their initial sources, you find that the carbon number (ca. 25% total global soil carbon) comes from large pedon databases with information on typically 3,000-10,000 soil profiles that were developed over several decades. Here’s an example. There is no reason to doubt these data, but they do have some limitations like several noted in Campbell et al. (2008):

The methods for estimating carbon storage vary widely, and no single method is considered highly accurate…The 1m depth is appropriate for this analysis, but likely underestimates carbon emissions from deeper peatland systems. No global dataset of peat depth is yet available.

This brings up an interesting point which is that the percentage might be a lot smaller if you account for Arctic peat and wetland C. From the most recent IPCC report:

The terrestrial biosphere reservoir contains carbon in organic compounds in vegetation living biomass (450–650 PgC; Prentice et al., 2001) and in dead organic matter in litter and soils (1500–2400 PgC; Batjes, 1996). There is an additional amount of old soil carbon in wetland soils (300–700 PgC; Bridgham et al., 2006) and in permafrost soils (see Glossary) (~1700 PgC; Tarnocai et al., 2009).

So if we count the additional carbon stocks in wetlands or permafrost, it could reduce that percentage of global soil carbon in drylands by about half, to ~13% of the global total.

The Tarnocai paper does point out though that 88% of that unaccounted-for carbon is totally frozen right now. Some of that may well defrost and mineralize to CO2, but a lot of it still won’t. How much defrosts remains to be seen as climate change progresses. On the other hand, most C in drylands is very close to the surface and thus potentially vulnerable to loss with climate change. A new study in Nature shows that dryland C is negatively correlated with aridity in semiarid and arid ecosystems, so as desertification continues, drylands seem likely to lose soil carbon:


In conclusion, current estimates of 25% global soil carbon in drylands come from soil profile data and the limitations of those data include (1) they are not necessarily collected with consistent methodology and (2) they generally don’t include carbon deeper than 1 m. Depending on whether you count old carbon in permafrost and wetlands, the total percentage of soil carbon in drylands may be more like 13%, or half of what is often cited. However, because dryland carbon is near the surface, it may still be vulnerable to loss with climate change.

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August 25, 2013 – 11:59 am


I have enjoyed boning up on my biological soil crust knowledge during my first few weeks here. Here’s a crust picture I took on a trip to the Canyonlands National Park Needles District back in January 2006. This crust probably took decades if not centuries to get to this point. These “pinnacled” crusts only form in dryland areas with cold winters like here on the Colorado Plateau. The structure is created by freeze-thaw cycles.

Here’s the definitive book on the subject that I’ve been poring through:

It’s got tons of great information and hopefully some of my work here will be able to contribute to our more general knowledge of how this cool component of dryland ecosystems might respond to the forces of global change we are inflicting upon them.

When I was looking for the above crust picture, I also came across a panorama of Chesler Park that I had never stitched together. Enjoy:

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Drylands biogeochemistry

August 8, 2013 – 6:21 pm


I’ve just begun a new postdoctoral position at USGS in Moab, Utah. Now that I’ve drawn my Arctic project mostly to a close (a few papers still to finish up), I am really excited to shift from the tundra to the desert. Though the ecosystem is different, the nature of the project is the same as my previous work in which I have applied biogeochemical and ecophysiological methods to understand how global change is affecting ecosystems.

Here in Moab, I will be working with world renowned soil crust expert Dr. Jayne Belnap as well as my grad school friend and fellow biogeochemist Dr. Sasha Reed. We’ll be leveraging an awesome field experiment that Jayne has been running near Castle Valley, Utah for the last seven years. It’s a DOE-supported project in which they have applied warming (via infrared lamps, see above) and watering treatments to crust-dominated desert soils. It also features an automated CO2 chamber system (lower left on picture) that is producing some sweet data. They have seen some interesting results so far and I am excited to jump in and contribute to and build upon this project.

It’s fun to be back in the American West and to live in a place that my family used to go on vacation. I first rolled through Moab when I was about 10 in 1991. It’s grown since then but appears so far to be just as much fun! We’ve already been to Arches about 5 times and met lots of nice people. Should be fun!

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Data Science

April 15, 2013 – 6:47 am

I just read an interesting article in the New York Times.

Universities can hardly turn out data scientists fast enough. To meet demand from employers, the United States will need to increase the number of graduates with skills handling large amounts of data by as much as 60 percent, according to a report by McKinsey Global Institute. There will be almost half a million jobs in five years, and a shortage of up to 190,000 qualified data scientists, plus a need for 1.5 million executives and support staff who have an understanding of data.

And from the McKinsey report:

To capture the full economic potential of big data, companies and policy makers will have to address the talent gap. New research by the McKinsey Global Institute (MGI) projects that by 2018, the United States alone may face a 50 to 60 percent gap between supply and requisite demand of deep analytic talent, i.e., people with advanced training in statistics or machine learning.

Machine learning techniques were one of the core analyses in my dissertation research (example below). I know that as I went deep into learning and using these methods I was struck by their power and broad generality. I realized, oh, this is what Safeway is doing with all that data they collect from my ‘rewards’ card. These techniques are a huge upgrade from the clunky and drawback-ridden multivariate techniques of the past.

Many businesses are already reaping the rewards of using machine learning and other big data techniques, while I suspect others are just jumping in or are ramping up their operations. At the same time, public consciousness of these methods has been raised with the likes of Moneyball and Nate Silver’s entertaining pwnage of the ‘experts’ and prediction markets in the last election. Everyone is realizing, hey this stuff actually works.

People also just have reams and reams of data these days. It is axiomatic in the world of data that it is easier to collect data than to analyze, visualize, model, and interpret it. Even before interpretation, just organizing data so it can be analyzed (see this for example) is a challenge and an art that requires practice with and knowledge of concepts like normalization and map/reduce.

Once data is organized, for all but the most basic analyses, interpreting data quickly forces you into the realm of epistemology. This is the intellectual challenge that I love about statistics.  You can’t just calculate mean±SE and call it a day; instead you have to ask what insights are possible, quantify how certain you are of those insights, and perhaps most importantly, define which insights are not possible. In other words, how can you extract truth from numbers without overreaching? This is a huge challenge and even with the latest techniques, pitfalls abound.

I think this critical thinking aspect of data analysis is not obvious to those with a more surface-level knowledge of statistics. Introductions to statistics tend to focus on how to “test” things, a rampant paradigm that I see as deeply problematic. For universities developing data science courses, this may also be an area where the better programs will instill the critical thinking skills while the weaker programs just teach you the latest algorithms. Either way, the students in the  earliest programs seem to be doing pretty well:

North Carolina State University introduced a master’s in analytics in 2007. All 84 of last year’s graduates in the field had job offers, according to Michael Rappa, who conceived and directs the university’s Institute for Advanced Analytics. The average salary was $89,100, and more than $100,000 for those with prior work experience.

Anyway, this is cool stuff. I know I would have loved to take some data science courses when I was in school. It would have been a nice complement to all the self teaching I ended up doing out of personal interest and necessity for my research. There are so many great topics from the machine learning algorithms themselves to the Bayesian v. frequentist paradigms to more obscure stuff such as retinal variables and shrinkage.

When I was at AGU last December, I noticed that Facebook was looking to hire scientists to do this kind of work. Since the big tech companies like Facebook are at the cutting edge of implementing these methods, the fact that Facebook was trying to vacuum up talent in this area seemed to be an indicator that other companies would follow. I for one will be continuing to bone up on all of the latest tech in this area.

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Web slideshow of my dissertation

March 7, 2013 – 5:05 pm

I just put up a web slideshow version of my dissertation based on my exit talk that I gave upon graduating from CU. I hope it will be useful for future students working on Niwot Ridge and anyone else curious about nitrogen cycling or biogeochemical hot spots. It features some of my best photos and graphics from my work in the Colorado Front Range and is annotated with descriptions of the project.

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Visual exploration of multivariate time series data in R using ggplot

February 21, 2013 – 11:19 am


I want to share some data manipulation and graphing techniques I have found useful for making flexible visual comparisons of time series data in R. These techniques can be pieced together from the documentation of the R packages and various online forums but sometimes it’s helpful to have a fully integrated example based on real data, so that’s what I’m presenting here.

The problem at hand is that we have dozens of variables from our Arctic experiment that we have measured over the same time period, in our case three summer field seasons. We want to be able to compare subsets of the variables or even all of the variables on the same temporal axis so that we can see which events in the data line up temporally.

I took three main steps: (1) melt the data frames that contain all the variables into data frames with a uniform column structure; (2) bind all variables into a giant data frame using rbind; and (3) use the mapping and paneling features of ggplot2 to draw plots.

Step 1: The melt function
This function is a really nice and generally useful technique to have in your munging arsenal. Basically it reformats your data into a “long” format in which there is only one column called ‘value’ that contains all numeric response variable data and then adds a column called ‘variable’ listing the variable name.

The melt function has a complementary function cast that goes in the reverse and casts your melted data frame in whatever “wide” format you want. That’s great for making paired comparisons, though it is not needed for what I’m doing here. Together these are a great way to reshape your data frames to whatever format you need and are a lot better than the confusing reshape function.

The example I’ll show can be run on your own machine after loading the data:


Melt the data frames containing diverse data sets, in this case soil cores (destructive harvests=dh), air temperature, and snow cover. The date is in day of year format (the doy variable).

dh.m <- melt(,id=c("year","doy","block","treatment"))
airtemp.m <- melt(,id.vars=c("year","doy",
 "block","treatment"), measure.vars = "air_temp_mean",na.rm=T)
snowcover.m <- melt(,id.vars=c("year","doy",

By default it will melt all columns that are not id.vars into a single column. To select only certain columns, identify them as measure.vars.

Step 2: rbind
This is the easiest step, typically one line of code with maybe a few modifications to the resulting data frame. The order of the data can be relevant for ease of graphing them in a particular order, but there are always ways to change that sort of thing downstream as well. Make sure before binding that the data frames to be stitched together have identical columns and column names (colnames).

snow_project <- rbind(snowcover.m, airtemp.m, dh.m)
snow_project$treatment <- ordered(snow_project$treatment,

Step 3: Graph with ggplot
Let's say we want to compare ammonium and nitrate from the soil cores with air temperature and soil cover. That's all the data I uploaded for the example, but for our actual project there are way more variables. This is the graph shown at the top of the post. Click it for larger size.

  aes(doy, value,col=treatment)) +
 geom_line(stat = "summary", fun.y = "mean",lwd=1) +
 stat_summary( = "mean_cl_normal",,lwd=.2) +
 scale_color_manual(values=c("black","green","orange","red")) +
 facet_grid(variable~year, scales="free_y")
#this takes a sec to print due to the error bar calcs

First I subset the massive snow_project data frame to extract the variables I am interested in graphing. The subset can be left out if I want to graph everything all at once. For my current project, I have been having R draw a massive 110×30 inch pdf.

You want to take advantage of (1) the panel system in facet_grid along with (2) what data visualizer Jacques Bertin calls "retinal variables." These are the colors, shapes, symbols, textures etc that you use to encode additional data dimensions. Between retinal variables and panels you can show a lot of variables in the same graphic.

In the example, treatment is color coded using the col argument in the main ggplot input function. If you had another variable you needed to show, the shape argument is probably the next most useful after color. Use the show.pch() function in the Hmisc package to see what shapes are easily available.

After deciding what you want each panel to look like, the facet_grid function divides your plots up into separate panels by variable allowing easy access to any kind of comparison we want. The formula input is vertical~horizontal for panel structure. In this case we have variables stacked on one another and horizontal separation by year. The scales="free_y" is essential for this approach because it allows your different variables to each have their own axis scale. For some variables, we might prefer to plot them in the same panel (like say soil and air temperature). In that case, pull variable out of the facet_grid function and put it in col or shape.

This series of steps works great for time series data, but the techniques in the three steps can be useful for other non-time series data as well.

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Will spring snow melt in the Arctic occur earlier or later?

January 10, 2013 – 11:10 am

I keep thinking about this question as I write up the results of our project. I posted about it once before here. On the one hand, warmer temperatures should melt snow earlier. On the other hand, warmer air holds more water, which can increase snowfall and snowpack depth. Deeper snowpacks may melt out later.

A broad summary study by Callaghan et al. reports the recent history:

[Changing temperature and moisture regimes are] driving significant changes in the snow regime particularly during the spring season when snow cover disappeared earlier at an average rate of 3.4 days per decade over the pan-Arctic terrestrial region (excluding Greenland) during 1972–2009.

And on future projections they write:

Projected changes in snow cover from Global Climate Models for the 2050 period indicate increases in maximum SWE of up to 15% over much of the Arctic, with the largest increases (15–30%) over the Siberian sector. In contrast, SCD is projected to decrease by about 10–20% over much of the Arctic, with the smallest decreases over Siberia (<10%) and the largest decreases over Alaska and northern Scandinavia (30–40%) by 2050.

Here’s a graph they included showing spatial variation in the change of timing of spring snowmelt:

Another study in Eurasia appears to support this finding:

The only downside to these analyses is that the underlying data (below shown from the first study, Callaghan et al.) are a bit noisy and a series of late melt years, should they occur, might really change  the shape of these curves that underlie each pixel in the above maps:

In contrast to the findings mentioned in the summary study, a simulation modeling study focused specifically on Northern Alaska reports:

Despite warmer near surface atmospheric temperatures, it is found that spring melt is delayed throughout much of the North Slope due to the increased snow pack, and the growing season length is shortened.

The panarctic trends noted in the first study seem to have been even more pronounced during the last couple of years according to the study I posted about before that has now been published:

Analysis of Northern Hemisphere spring terrestrial snow cover extent (SCE) from the NOAA snow chart Climate Data Record (CDR) for the April to June period (when snow cover is mainly located over the Arctic) has revealed statistically significant reductions in May and June SCE. Successive records for the lowest June SCE have been set each year for Eurasia since 2008, and in 3 of the past 5 years for North America.

Overall, it looks like spring melt will probably be earlier and has been getting earlier as of late. However, there is some disagreement in future projections about whether Northern Alaska (and perhaps other areas) will in fact see earlier snowmelt in spring. The underlying correlations that are driving the appearance of the maps above could turn around if warmer and warmer air leads to bigger and bigger snowpacks. My one personal observation here is that warm snaps in the Arctic spring can melt snow really fast so deeper snow may not in fact melt that much more slowly.

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AGU 2012: Arctic tundra in EcoCELLs?

December 12, 2012 – 2:08 pm

Midweek at AGU I also had a nice discussion with Jay Arnone, who I worked for as an intern at the Desert Research Institute just before starting grad school. Jay has a really amazing and unique facility at the Desert Research Institute that they call the EcoCELLs. The EcoCELLs are large mesocosms in which they can monitor large intact monoliths of soil (several cubic meters).

I told Jay he should pop some Arctic tundra in there since it would allow for some nicely controlled analyses of permafrost melt and other global change effects. Turns out he had already been working on getting this funded. I really think this would be awesome, adding a nice dimension to our understanding of arctic carbon balance that can’t be easily measured with either soil cores in the lab or field experiments. The ability to get carbon flux numbers at the same resolution as eddy flux on intact plants and soils undergoing temperature manipulations is not possible with other techniques and can provide some awesome data. Hope this happens!

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AGU 2012: Snowmelt project meeting

December 8, 2012 – 6:15 pm

Our snowmelt project group met on Wednesday to summarize our final year of field data and make plans for putting together our three years of results from the experiment. We have lots of data and had a good discussion about how to package it to best convey our main results. Below is the effect of our snowmelt acceleration treatment on the timing of melt over the three years of our project.


We decided that we want to write a mix of papers that focus on single data sets and papers that combine data sets from the different aspects of the project. Some of the data sets are complex and unique, seeming to warrant their own papers while others may be better brought to light in the context of the whole experiment.

One of the challenges of combining data sets from different aspects of the project is that field data sets can be idiosyncratic, with only the data collectors having total understanding of all of the nuances. We had a nice discussion about how to make accurate conclusions when melding the different project parts. I’m greatly looking forward to this integration of data since I’ve been so focused on this project for the last three years.

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AGU 2012: Changing seasonality talks

December 8, 2012 – 6:12 pm

I really enjoyed participating and contributing to the oral session I was in entitled When Winter Changes: Hydrological, Ecological, and Biogeochemical Responses. There were many great talks. I tweeted some highlights from the session.

More highlights on my twitter page.

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Rocky Mountain National Park panoramas

November 25, 2012 – 10:08 pm

I took these back in September and just got around to stitching them together. Click for the full versions:

Both of these are along Trail Ridge Road around 12,000 feet elevation. The road was just above a dense layer of fog.

The tundra was pretty brown by this time of year.

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Fluorescent microplate analysis of amino acids and other primary amines in soils

October 4, 2012 – 3:18 pm

Our paper on a popular soil amino acid technique just came out in Soil Biology and Biochemistry. We got interested in improving this method and adapting it for microplates a couple years ago and after quite a bit of lab work this is the result. The abstract:

In studies of soil nitrogen (N) cycling, there is growing demand for accurate high-throughput analyses of amino acids and other small organic N compounds. We adapted an existing fluorometric amino acid method based on o-phthaldialdehyde and β-mercaptoethanol (OPAME) for use in 96-well microplates, and tested it using standards and field samples. While we started with an existing protocol, we made one critical change: instead of using a 1-min incubation period, we used a 1-h incubation period to deal with differences in reaction timing among microplate wells and to reduce interference from ammonium. Our microplate method is similar in sensitivity to existing protocols and able to determine leucine standard concentrations as low as ∼0.5 μM. Finally, we demonstrate that the OPAME reagent fluoresces in the presence of primary amines other than amino acids, such as amino sugars and tyramine. Because of this broad sensitivity to primary amines, descriptions of the measured pool should be revised from total free amino acids (TFAA) to total free primary amines (TFPA).

Reaction kinetics of primary amines and ammonium with OPAME. a. Fluorescence levels of leucine, a mixed amino acid standard, and ammonium over 3 h at 1.5 min intervals. b. Ratio of ammonium:leucine fluorescence for 20 μM standards over 3 h (mean ± 95% CI, n = 5). c. Fluorescence levels of leucine, glucosamine, tyramine, and N-acetylglucosamine over 3 h at 1.5 min intervals.

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Earlier spring in the Arctic

September 25, 2012 – 4:26 pm

There was a well reported story on NPR yesterday about recent trends in Arctic snow melt:

Derksen and colleague Ross Brown have produced a study, which has been accepted for publication in Geophysical Research Letters, that documents a dramatic increase in the speed of this snowmelt. It turns out that in May and June, snow across the far north is disappearing fast. ”It’s decreasing at a more rapid rate than summer sea ice,” Derksen says. “So the loss of snow cover across the Arctic is really as big an issue as the loss of sea ice.”

It is nice to have more confirmation of this trend since it is a major premise of our Arctic field experiment.  I don’t think the paper is available online yet.
There is some interesting data from our specific site, Imnavait Creek, showing that in recent years, there has actually been a lack of early snowmelt events:


That’s snowmelt day of year on the vertical axis and calendar year on the horizontal. So it is good to know that our experiment is accurately simulating a broad Arctic trend even if there has been a recent local effect that is the opposite.

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LTER meeting in Estes Park

September 10, 2012 – 6:44 pm

I arrived in Estes Park yesterday to attend the LTER (Long Term Ecological Research) network meeting this week. I like this meeting because there are a lot of ecosystem scientists that do work closely aligned with my interests. These are my people. I’m presenting the above poster on data from our three summers of field data from the Arctic. (click poster for pdf).

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A new Darrouzet-Nardi

August 17, 2012 – 12:59 am

Blog’s been on the back burner recently since the birth of my daughter Cara on June 29th:

So far a successful experiment!

I’m at Toolik right now tying up loose ends for the third and final field season of our project. When we arrived on August 11th, I was struck at how green it was here, definitely greener than last year at that time. However, within the first couple of days, the landscape turned from green to yellow and is well on its way now to red and brown. We have been busy taking a final round of soil cores and now taking the site down.


Just before arriving here, I had a nice trip to ESA in Portland and saw lots of friends and lots of great talks, particularly by some of my fellow postdocs I’ve met at Toolik and elsewhere. I want to post a few observations about the conference soon.

While the tundra is enchanting as always, I can’t wait to get back home to my sweet little baby and her heroic mom.

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Microplate calculation spreadsheets

June 7, 2012 – 5:24 pm

Not to interrupt the flow of pretty pictures from the arctic, but I have to address an important topic: microplate calculation spreadsheets.

I know this is for a narrow audience, but I’m always surprised by how much finding random things like this online can help me, so here we go.

The goal is to paste raw microplate data into a spreadsheet and get out the final numbers you need. If you had thousands of microplates, it might be better to write a short program that can process the data, but for everyday lab analyses that change frequently and are implemented by many lab personnel that don’t program stuff, the spreadsheet is a good tool.

I’ve made a lot of these spreadsheets over the last few years to help process enzyme and nutrient data. Here’s one I made recently. It has a few parts in the different worksheets: (1) the blank template in which raw data can be pasted; (2) a pipetting map to be printed so that you know where to put your samples when you are pipetting; (3) simulated data that helps to identify the assumptions you are making about sources of well absorbance (for colorimetric assays) or in this case fluorescence; and (4) a run with the modeled data to help spot errors in the spreadsheet.

Here are some guidelines I have learned over the last couple years that I think make these spreadsheets more useful for our lab group.

(1) Label all the parameters. In particular, formulas should only have references to other cells, not any hard values like mass of soil used or volume of extractant, etc. This will prevent having to search all cells whenever you change one of these variables.

(2) Highlight anything you have to enter when samples are run so you don’t forget anything.

(3) Don’t make overly complicated formulas that are difficult to decipher later. To prevent this, divide up long calculations into two or more steps so each is more clear.

(4) Test the spreadsheet with simulated data that you create to look similar to real data but with nice round values. This will help you identify assumptions in the way calculations are made as well as locate typos in calculations across many spreadsheet cells.

(5) Make the output of your spreadsheet into a well organized table (below) that has only sample ID information and the final values in preferred units. Then you can grab these values to use for stats and comparison with other assays, leaving all the processing behind.

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Last day at Toolik

May 28, 2012 – 10:02 pm

I take off from Toolik the day after tomorrow. Here’s a time series of photos from our experiment, starting on April 28, and ending on May 27. The acclerated plots melted about 10 days before the control plots.

Control plot:

Snowmelt acceleration plot:
Here’s the whole series from last year.


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Solar Eclipse at Toolik

May 20, 2012 – 9:47 pm

Toolik Eclipse

5:20 p.m. Taken through a piece of welding mask glass.

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Vole attack

May 18, 2012 – 9:06 pm

tussock in OTC dug up by vole

We were monitoring plants in our freshly melted tundra plots and we noticed that this Eriophorum had signs of herbivory.

vole hay

The vegetation and organic material had been turned into hay piles by a vole.

Then, like Kane, as we stood up and looked around, we realized they were EVERYWHERE.


More caribou pictures

May 3, 2012 – 7:44 pm

Click for bigger versions

Caribou (dots on lower right) grazing by moonlight in front of the Brooks Range. I think they are eating the tasty vegetation in the long-term fertilized research plots. This was about 10:30 p.m. last night. Temp was around –5°F.

They are very focused on eating and rarely look up.

This one must have an itch or something.

Speck-sized caribou on a distant ridge grazing in front of the arctic sunset.

Update (5/4): Saw a herd running today.

Caribou beneath the “supermoon

Update (5/7): Finally got a decent closeup


Snowmelt Project: Year 3

April 28, 2012 – 8:00 pm

Today, for the third and final year of this project, Sadie and I got our snowmelt acceleration treatment up and running.

This is the site after we deployed shadecloth on our five accelerated snowmelt plots.

There are tons of caribou around right now. These guys wandered near our plots, then took off after staring at us for a while.

The deployment went great. The snow looked good and the weather was excellent. If temperatures stay this warm, melt will be quick!


Writing Science

April 20, 2012 – 12:22 pm

Our lab group just finished reading this book by Josh Schimel. We read two chapters a week and discussed them, which was a nice pace for absorbing the material. It’s easy to read, but there is so much good advice that it’s nice to have time for it to sink in.

Overall, the book is fantastic. It feels like a secret weapon. I can’t recommend it enough for any science writer at any stage. At the book’s Amazon page, I felt similarly to this reviewer:

I have 70 published papers in international, peer-reviewed journals; and I want to go back to each and every one of them and rewrite them with the messages from this book clear in my head and clear to the reader.

Fortunately I found the book earlier than this guy, but I am also eyeing my past papers and realizing how I could have made them much better.


Global seasonality

April 17, 2012 – 3:50 pm


I tweeted:

I’m referring to the animated gif above. Click for the full size version.

I then had the following email exchange with my mom:

Mom: Anthony, what is going on in that graphic? Is it a joke?

Me: lol no joke, it’s actual satellite images of seasonal change in earth surface color throughout the year. Mainly you can see snow extent and when different parts of the world are in their growing seasons.

Mom: Ah ok — so what ten things did you learn? Clearly you need specialized knowledge to interpret that animation.

Challenge accepted! Here are 10 things I did not know before staring at this graphic for a while:

1. While there is permanent snow/ice cover in Antarctica, there is almost no seasonal snow cover in the southern hemisphere.

2. Permanent snow/ice cover near the north pole includes not just Greenland, but also many other large islands in northern Canada like the Sverdrup islands.

3. During the winter, the boreal forest is not as white as the tundra above or the plains below (lower albedo), presumably due to the trees poking out from the snow.

4. In Australia, plants on the northern coast green up in the summer while plants on the southern coast green up in the winter.

5. A huge swath of Brazil has deciduous vegetation (also, Madagascar).

7. The rainforests of South America, Africa, and Southeast Asia that stay green all year round are a lot greener than even peak greenness in the nearby seasonal ecosystems.

8. The permanent snow in the Himalayas is mostly constrained to a thin band where the subcontinent is smashing into Asia.

9. The Amazon river becomes so massive during the rainy season you can see it from space (at least I assume that is what’s going on there).

10. The Nile River delta dries up twice a year. Notice how it flickers more than once per annual cycle in the gif. Here’s an animation that confirms this.

Also, this is more geography, but I had never noticed the Kerguelen Islands before. Looks like it is pretty snowy there in the winter.

And a final observation from an imgur commenter in Britain:


ggplot2: I’m a convert

April 12, 2012 – 4:56 pm

I have been using lattice  for my R graphics for years, and it is a great software package based on William Cleveland’s groundbreaking approach to analyzing data. However, a few weeks ago I was trying to show data points, means, and summary bars in the same graphic, a task that should be straightforward in a graphics environment. This is not impossible in lattice, but does involve writing a cumbersome “panel function.” This approach was great for its time – when lattice came out there was nothing else like it – but unfortunately it has not evolved into a more user-friendly system.

Having read about ggplot2, I knew that it had a more modular system where you can add or subtract different graphical elements such as summary stats or data points at will. So, I tried it for the data I was working on and never looked back.

Here’s my first ggplot2 figure that drew me to switch:

It took minimal effort to learn the ggplot2 system. In addition to the useful modular design, the default approaches to tasks such as automatic legend creation, log axes, and jittering are well done.

While I know an upgrade when I see one, I will also miss lattice since I think it’s a fantastic piece of open source software that was my analysis workhorse for the last eight years. It was also one of the packages that really motivated me to learn R.

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