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.

Listening to Weaver

Good example of where drought led to replacement of tallgrass species with more drought-tolerant mixed grass species.  From Weaver and Albertson, 1936. 

John Weaver is considered the father of grassland ecology in North America. Most likely because of his views on succession, quite often he is a forgotten father. Yet, his work on grasslands spanned over 50 years. His work is notable in many ways, but his careful observation of grasslands before, during, and after the Great Drought of the 1930's taught us an immense amount about how grasslands respond and recover to drought.

If you read Weaver's work, there are some hidden lessons about how plant communities respond to drought. Weaver talked about how during the Great Drought, the shortgrass spread hundreds of miles to the east into the mixed grass region and the mixed grass hundreds of miles into the tallgrass. Yet, reading his observations, most of the expansion of xeric grasslands did not occur from migration of individual species, but expansion of local populations. Eventually, when the drought broke the humid grasslands marched back westward, but again through expansion of local populations or dormant propagules. In many cases, big bluestem (Andropogon gerardii) recovered from crowns that remained viable for almost a decade.

These findings from Weaver raises an interesting set of questions about the functional diversity of grasslands and how different grasslands would respond to drought. Essentially, when droughts hit or mean precipitation levels change, how much does ecosystem function depend on expansion of local populations vs. immigration of species?

We tackled this question with some of the data we had on drought tolerance for a global set of grasses. In short, we asked how the diversity of drought tolerance varied bioclimatically. For example, as mean precipitation declines along a gradient, is there a greater relative abundance of drought-tolerant species? Are there fewer drought-intolerant species?

Turns out that across the full range of precipitation that generates grasslands, the diversity of drought tolerance among grasses is high. In wet grasslands, there are still many drought-tolerant grasses. In dry grasslands, there are still many drought-intolerant grasses.

Relationships between the bioclimatic ranges of grass species and physiological drought tolerance (Ψcrit) for 253 grass species. Each species is represented by a horizontal line with the endpoints signifying the 10th and 90th percentile of its occurrence with respect to precipitation after standardizing for differences in temperature. Gray envelope behind species ranges represents smoothed fit for range of drought tolerance (95% of entire range) across the precipitation gradient.

Given a number of assumptions, extrapolating out, almost all grasslands should on average have a broad range of drought tolerance. If precipitation declines, drought-tolerant species on average should be able to expand locally and maintain ecosystem function.

There are still a number of details to work out, but Weaver's description of grasslands seems to hold at the global scale. Functional diversity in grasslands represents the typical high spatial variability in resource availability and the climatic variability typical of grasslands.

Heat waves

I've been learning a bit about heat waves.

The WMO defines heat waves as a sequence of five straight days when the daily maximum temperature exceeds the average maximum temperature by 5 °C. Heat waves can extend much longer than 5 days and can exceed average maximums by much more than 5°C. Although heat waves are a categorical classification, they are part of continuous variation in climate.

Here at Konza, over the past 25 years, there has been a lot of variation in climate. We haven't had a major drought since 1980 (http://en.wikipedia.org/wiki/1980_United_States_heat_wave). One thing I didn't have in my head is when the heat waves come and how hot they can be. With the standard definition of heat waves, heat waves should be equally likely across the year. Yet, if you look at the climate data for Konza, the strongest heat waves happen a bit later in the year than one might think.

I took the climate data for Konza and averaged the maximum temperature for each day in 15-day periods from early July to early September. It turns out that the hottest mean daily temperatures are in late July (not shown), but the strongest heat waves are in late August.

Here's a graph of the min and max mean daily maximum temperatures for 15-day periods from 1984-2010.

Although the hottest mean temperatures are generally at the end of July (not shown), the greatest heat waves come at the end of August. In 2003, high temps averaged 40°C for 2 weeks in the last two weeks of August.

Ecologically, the interesting questions about heat waves become how plants (and animals) respond to heat waves at different times of year. Most C4 grasses are shutting down in early September. One would think that heat waves at that time of year would have much of an effect compared to ones in July or early August.

At some point, we'll have another summer like 1980. Preparedness is an important topic, but also for ecologists. I wonder if we're measuring the right things so that when it comes around we can understand our ecosystems better.

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.

Natural history of bison dispersing seeds

Bison heads carry more than horns.

Bison do a lot in grasslands. They eat, poop, pee, rub, trample, and wallow, which fundamentally can restructure how a grassland functions. If you spend enough time watching bison, you'll see them eat some unusual things. For example, early in the season, I've seen a cow systematically nip off sumac buds. Not something we typically associate with bison, at least not overly curious bison. Another thing bison do is disperse seeds. And a close look at seeds makes us rethink a bit about what they eat.

Researchers at Oklahoma State recently published a paper where they analyzed the seeds attached to bison forehead fur in the fall and fecal material over the year. In all, they found the seeds of 76 species on the fur of bison. Turns out males and females had different seeds stuck to them, which related to where they spent time.

More interesting was what was found in the fecal material. There really is only one way for seeds to get into bison pies--they have to eat them. Half the seeds were grasses, which means half weren't. This is surprising because plains bison are thought to predominantly eat grass. Yet, in the spring there were seeds of Viola. In July, there was Solanum and Lepidium. In October, there was Lepidium.

Most of the generalizations from the grass dominance of diet comes from either microhistological studies (leftover plant parts) of bison fecal material or changes in species composition. Yet, microhistological studies might underestimate forbs if their cell walls are easily degradable. Changes in species compostion with grazing show increases in forbs, but cannot rule out which forbs they might eat.

Figuring out what they eat has never been easy. Here, some simple natural history might just reset one of the fundamental assumptions about bison.

Rosas, C. A., D. M. Engle, J. H. Shaw, and M. W. Palmer. 2008. Seed dispersal by Bison bison in a tallgrass prairie. Journal of Vegetation Science 19:769-778.

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.

Understanding limitation by N and P

One of the most fundamental questions to understanding the adaptations of plants to low resource availability has been to understand the covariation in resource availability across sites. Do environments that have relatively low P availability have an excess of N relative to plant demand? Are plants that are limited by water also limited by N, or is N in excess for those plants and available for secondary purposes like defense?

I’m in the middle of an experiment to begin to understand the covariation in N and P limitation across grasslands in the US. Generally, researchers have attacked this question by doing field factorial fertilizations and comparing results across sites. Instead of this approach, I asked people to send me soils from 100 grasslands across the US. I grew the same species of grass (Schizachyrium scoparium) in all 100 soils with a factorial fertilization: control, +N, +P, +NP. After 10 weeks, the plants were harvested.

The most notable result at this point as the patterns of the response of biomass to N addition relative to soil P availability. As seen below, soils with low P availability had the least response to N addition, while soils with high P availability had the greatest. Yet, there was no relationship between P availability and the response to P addition. One interpretation of the data is that 1) P availability limits N response, and 2) P availability by itself does not limit P response. My guess is that plants in low P soils are not just limited by N, but by N and P. Any N supplied in excess in plant demands is lost from the ecosystem, whereas P isn’t lost when supplied in excess. Hence, I would guess that over evolutionary time scales, grassland plants would either be limited by N, or N and P, but not by P alone. Field fertilizations seem to bear this out as no grassland has ever been found to be limited by P alone.

We’re still collecting data on the plants and soils, but likely will be working the data up soon.