Global patterns of soil C concentration--an index for relative decomposition rates

Patterns of soil C concentrations as a function of mean annual temperature and precipitation in surface soils for 600+ soils from around the world.

One of the keys to understanding global N cycling patterns is understanding the global patterns of rates of decomposition. There are a few syntheses of litter bag studies, but the net result of inputs and outputs of C for soils are hard to interpret.

One of the best indices (I think) of the rate of decomposition relative to inputs is just soil concentration.

Most syntheses to date have examined soil C content, not concentration. Mostly because the goal is to determine soil C storage. Concentration only helps to determine content.

The major syntheses I can think of--Mac Posts 1982 and Batjes 1996--examine patterns for different soil orders. temperature or precipitation is never on the x-axis.

Bearing that, there should be an advance possible by putting climate on the x-axis and %C on the y-axis.

It seems like this would have been done, but I can't find a graph like it and people that are likely to know about it seems surprised when I show them.

For the 570 mineral soils I looked at, MAT and MAP explain about 60% of the variation in log-transformed soil C concentrations. That's a high r2 given all the variability out there in the world and other factors like how much clay is in soil, the quality of the plants,  and whether plants are eaten or not.

The interpretation of the patterns essentially is that if soil %C is an index of the amount of decomposition of plant biomass relative to primary productivity, then hot, dry places have soils with highly processed C.

The patterns of relative decomposition now seem pretty clear.

The question now becomes whether those hot, dry places consistently are elevated in 15N, such that soil 15N patterns are being caused by relative decomposition rates.

Two tests here.

1) Soil %C and soil 15N should scale across sites.
2) If soil 15N increases with increasing MAT and decreasing MAP, the relationship should disappear after accounting for variation in soil %C.

Global N cycling: the Climate-Nutrient hypothesis

Patterns of soil 15N and P availability in the Amazon. From Quesada et al. 2010.

When we looked earlier, the degree of decomposition affects soil organic matter content and the isotopic ratio of the N in the soil.

For individual classes of organic matter, be in leaves, organic layers, mineral soils, or fractions of mineral soils, the more microbes process organic matter, the more C is lost and the more enriched the nitrogen becomes in 15N.

The "processing hypothesis" is a standard explanation for vertical profiles of organic matter in soils. Deep soils have lower C concentrations and higher del15N than shallow soils because the stuff at the bottom has been worked over by microbes**.

**Other mechanisms affect vertical profiles, too. Plants preferentially cycle light N up to the top. Illuviation can also transport C and N downwards.

What applies vertically, could also apply horizontally.

Yet geographic patterns have largely been explained with the fractionating loss hypothesis. Soils enriched in 15N are thought to be enriched because they have lost a larger proportion of their N to fractionating pathways compared to relatively depleted soils.

Two sets of observations come together to generate the main latitudinal patterns.

1) Tropical soils are enriched in 15N compared to temperate soils
2) Tropical soils have high rates of N2O flux.

Put together, the two reinforce one another to solidify a view of latitudinal gradients.

But why would that be?

Nitrification or denitrification are not thought to be temperature sensitive like nitrogen fixation.

Therefore, it's indirect controls.

One of the major hypotheses is the Climate-Nutrient hypothesis. Tropical systems are thought to be more P-limited, which increases the degree of N surplus. Greater N availability increases the likelihood of gaseous N loss.

Quesada's work (above) is a good example of data that calls this hypothesis into question.

Within the Amazon (which is all hot), across a gradient of P availability, 15N is lowest in low P soils, not high P soils. Low P soils are supposed to have the greatest excess N and the most gaseous N loss.

To maintain the Climate-Nutrient hypotheses, explanations get pretty complicated. Rates of N2-fixation by plants have to vary in ways that one wouldn't expect. Or losses have to become episodic and almost catastrophic. 

A number of other questions come up. Recent work suggests that N losses via NO3- leaching or dissolved organic N loss to streams in  tropical systems can be high, too. And N2O is just one of the gaseous fluxes of N. Denitrification also produces N2, which is nearly impossible to measure.

Are tropical systems losing a greater proportion of their N via gaseous pathways? That part has never been quantified directly.

There is enough evidence out there to at least question the traditional view of the fractionating loss hypothesis driving global patterns in soil 15N, if not our views of the N cycle in the hot, cold, wet, and dry.

The next question is whether the processing hypothesis can explain more variation with fewer mechanisms. 

If so, global patterns of N cycling need to be reconsidered.

Phenology and sensitivity to climate

One of the strongest separations of species in grasslands is their phenology. Most guide books separate grasses and forbs, forbs by flowering color, and then timing of flowering. From a global change perspective, phenologies become important in understanding how climate will alter ecosystem function. Early-flowering species appear to respond more to variation in climate than later-flowering species.

The analyses of these patterns are still pretty basic. One question that struck me is whether early-flowering species are more responsive to variation in climate, or just have a lower temperature threshold for responding.

The first step in partitioning this is to begin to quantify these patterns and compare. On a preliminary basis, I used the Gates' first flowering data that was collected in the 1930's-1950's. I then adapted the critical climate period approach to examine the climate correlates with phenology. In short the technique allows testing whether temperatures over different windows before the event each year are the best predictors across years of the timing of the event. For example, I could test whether the first flowering date is best predicted by temperatures 10 days preceding flowering, 15 days, 20 days...etc.

From a predictive standpoint, the approach sees to work pretty well. For example, Catalpa flowering was best predicted by a 40-d window of temperature that averaged 21.9°C. 10-days after this window, it flowered. Outside of one year, flowering for catalpa can be predicted pretty well. 

When I do this for a handful of species (grasses, forbs, and trees), a couple of patterns emerge.

First, species that flower later in the growing season (day of year = DOY) have higher temperature requirements that must be met. Early-season species need periods with daily maximum temperatures to average 13°C, while later species (like Catalpa) require periods over 20°C. [red dots are trees].

Second, species that flower earlier integrate over similar periods of time as late-flowering species. In general, about 45 days.

In general, I think the relative critical climate period technique holds potential for quantifying differences in climate sensitivity for phenological events. The interesting part of this work lies in relating these patterns back to the ecology of the species more than anything. For example, what is it about a species that lets it respond to climate so fast? I would guess species like dandelions (Taraxacum) that sit in rosettes have few developmental barriers to flowering. Primordia are there waiting. Other, more determinate species, need to produce a series of leaves before they initiate flowering.

Grazers in a warmer world

If the world gets warmer, what happens to grazers?

Not an easy question. There are many grassland climate change experiments, but these are of limited utility here. Grazed and ungrazed grasslands are starkly different such that the consequences of warming for ungrazed grasslands are unlikely to apply to grazed grasslands.

If experiments don't help, then we need to look at how grazers respond to short-term variability in climate and compare that with geographic patterns that might represent long-term patterns.

When I've looked at how bison respond to inter-annual variation in climate, hot years don't affect them.

Yet, when we look across temperature gradients, hot places have small bison. Across 22 herds and a quarter million weights of bison, it's clear that herds in hotter places have smaller animals. Sometimes up to 500 lbs lighter.

Why the difference between short- and long-term patterns?

This is where experiments come in handy. When exposed to elevated temperatures short-term, grasslands begin to lose nitrogen. Over the long-term, these losses accumulate which drives down the quality of grass for grazers to eat.

One hot year, no problem. Many hot years and grazers don't grow as big.

The differences among bison are dramatic.

Back of the envelope calculation shows that just a 1°C increase in temperature across the US could cost the cattle industry $1 billion. Considering projections are for multiple degree C increases, those costs would accumulate.

[Regarding details, I'm about to submit this paper. We'll see how it's met.]

Critical climate periods in ecosystems

Maps of Konza showing the slopes of the relationships between NDVI and precipitation from DOY 105–214, Also shown are the univariate distributions of these slopes. Colors of slopes on maps correspond to colors in histograms.

Last July, I added an entry on the role of the timing of heat waves and drought for ANPP at Konza. This was recently published in PNAS. In review, the work takes a look at how the timing of interannual variation in temperature and precipitation affect grass production.

As much as the specific results and the proviso that the timing of climate variability can matter as much its magnitude, the technique should become important in examining other long-term records. Any long-term record that is repeated at roughly the same time every year can be used. We've applied it to ANPP, streamflow, bison weight gain, carbon flux, and flowering so far.

I think another area where the technique has potential is with remote sensing data. In the paper, Andrew Elmore applied the technique to NDVI data from MODIS and Adam Skibbe overlaid data on elevation and woody cover. The unique results is we could now look at the spatial pattern of sensitivity to the climate variability. We saw that pixels with woody species and low-elevation sites showed low sensitivity to variation in mid-season precipitation. Essentially vegetation and sites that could tap into deep water had lower sensitivity to precipitation variability.

In this particular case we didn't test whether different areas showed different timings of sensitivity, but with longer remote sensing records spread over a larger spatial area, it certainly could be tried. We could ask how climate sensitivity varies with latitude or aspect or between major vegetation types.

I think the critical climate period approach has its limitations, but will generate a number of insights in the near future. As we apply the technique to different aspects of ecosystem functioning we'll get a better understanding of the multiple connections between climate and organisms.

www.pnas.org/cgi/doi/10.1073/pnas.1118438109
http://www.nsf.gov/news/news_summ.jsp?cntn_id=123134&org=NSF&from=news

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

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

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

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

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

27 years of annual productivity

27 years of daily weather

27 years of daily stream discharge

27 years of biweekly soil moisture

17 years of biweekly productivity

11 years of remotely-sensed NDVI

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

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

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

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

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

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

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

Extreme weather summary

Just saw this today:

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

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

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

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

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

Grassland Climate Change 3.0

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

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

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

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

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

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

Climate-nutrition-performance cascade

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

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

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

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

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

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

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

The evolution of grasses: phylogeography of C4 photosynthesis

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

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

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

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

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

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

A new Whittaker biome diagram

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

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

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

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

A few major things change.

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

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

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

Climate change and cattle nutritional stress

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Grass flowering and climate

25-year record of flowering of Schizachyrium scoparium (open circles uplands, closed circles lowlands).

We’re just about ready to submit a paper that analyzes 25 years of flowering for three grass species at Konza. As far as I know, this is the longest continuous record of flowering effort for grasses (although there always seems to be some European record that dwarfs any North American record). In short, every fall, the number and weight of flowering culms for three species of grass (Andropogon gerardii, Sorghastrum nutans, and Schizachyrium scoparium) are measured in an annually burned watershed. The three species are, more or less, the three dominant grasses at Konza.

When I asked what people expected from the data, there were two main beliefs. First, species were offset in their flowering. It was generally held that some years were good flowering years for Andropogon, others for Sorghastrum. Second, flowering was much greater after a dry year, especially for Sorghastrum. The latter was likely an extension of the Birch effect, which I’ve talked about in previous posts.

In general, we found that a good flowering year for one species was a good flowering year for all species. By no means was there an inverse relationship for flowering between species among years. The differences among species, were interesting though, and reinforced the idea that it is not just the amount of precipitation that falls that is important in grasslands, but the timing of the precipitation. For example, years with greater precipitation early in the growing season benefited Sorghastrum flowering, while greater precipitation late in the growing season benefited Schizachyrium. Why the belief for inverse relationships among species? More than likely its due to their differences in flowering phenology. This year was a good flowering year for all three species, but a person would have sworn it was a good year for Andropogon in mid July, as it is the first to start to flower, while the same person would have sworn it was a good year for Schizachyrium in early late August when it began to flower in earnest.

The offsets in flowering are important components of understanding questions such as species coexistence, but it is the question about antecedent climates that tests our fundamental understanding of how grasslands work. At the heart of the matter is whether conditions during the previous year will generally affect current year’s dynamics. If so, processes like the Birch effect become more central and ecosystems become a lot more complex.

Despite the assurances, over 25 years, there was no effect of previous year’s precipitation. Wet years had a lot of flowering regardless of whether the previous year was dry or wet. Dry years had little flowering, regardless of whether the previous year was dry or wet.

The conclusions seemed pretty straightforward, except for a short paper by Knapp and Hulbert in 1985. They had measured flowering in the same watershed as our dataset a few years before our dataset began. What was interesting was that flowering in 1981 was 6-10 times greater than any year of our 25 year record. 1981 was a sea of grass horse high not because 1981 was especially wet, but because 1980 was especially dry. A month where every day was over 100 degrees Celsius. Cows starving. Lawns dying.

As such, even though Konza had a 25-year record, some events happen rarely, and when they do, they can be spectacular. There are a lot of questions that are raised by the dataset. Was it the Birch effect that caused the immense flowering or reduced competition from plants dying? How dry to soils have to be for how long for N to explode? What really struck me was that no long-term dataset is ever long enough. 25 years of data just wasn’t long enough to capture even a hint of the importance of rare events. Who knows what year 26 will bring? I’m sure a lot of people will be watching a bit more closely.