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.

Comparing phenology curves

Packera plattensis, which was found first flowering on April 13 in 2010. 

The timing of flowering is a critical component of the ecology of plants. Flowering during environmentally stressful times or when other plants that utilize the same pollinators can lower a plant’s fecundity if not lead to its extirpation from an ecosystem. As such, the timing of flowering should be under strong selection pressure and be an important component of community assembly. 


Over the past two years, Gene Towne and I (mostly Gene) collected first flowering date (FFD) data on 430 Konza herbaceous species. The last species found to start flowering (a gentian) was found in early October, 189 days after the first herbaceous species--Holosteum umbellatum--was found in late March.


The patterns at Konza are interesting. More on those later. The unexpected find was comparing the patterns with two other predominantly grassland flora. The first was from Chinnor, Oxfordshire. The second, Fargo, ND. 

The y-axis is the fraction of each flora flowering on a given day. x-axis is day of year.


Two things pop out. Relative to Konza, the Chinnor flora has an early tail of species, but not a late tail. Is this because species phenology are all shifted earlier, so that the same species would flower ~50 d earlier there? Or is it just a suite of species that flower earlier are found at Chinnor, but  late-flowering species are not?


And relative to Konza, the Fargo phenology is much more compressed. Again, though, why? Does Fargo not have early- and late- flowering species, or are the phenology of individual species compressed.


Turns out we can begin to answer that and the mechanisms that underly the differences between the pairs differ.


Here are the relationships between FFD between the pairs of sites. Dotted line is 1:1.

Species common to Chinnor and Konza flower on roughly the same day. Hence, one would suspect that the differences in curves between the two sites are due to novel types of species at each site. Yet for Konza and Fargo, early flowering species flower later at Fargo, and late-flowering species flower earlier. Phenology gets compressed for individual species.

Theoretically, I'm still getting up to speed, but comparisons between flora just haven't been done like this. Mid-domain theories are prevalent to test, but each site would support the idea of a mid-domain peak. What's more interesting is why sites differ. Right now, hypotheses about functional novelty/plugging of holes in niche space vs. functional stretching/compression are pretty interesting ones to test here. Flowering is interesting to think about, but the really interesting comparisons (at least for me) will come with comparing functional traits associated with resources, not reproduction.

Bison growth curves

Weight of female (lower) and male (upper) bison at Konza Prairie and Ordway with age.

The performance of bison—how much weight they gain, how many calves are produced—is the ultimate expression of the functioning of North American grasslands. If we can compare the performance of bison in different grasslands, we have a window in the functioning of the grassland. Interannual patterns of weight gain show responses to climate variation. Average weights of animals give general indices to the provision of the quantity and quality of grass produced. Yet, no one has ever compared the performance of bison across grasslands in North America. 

We're getting pretty close to doing that. For any one site, we can fit a growth curve to the weights of animals as they age. There are a number of growth curves that are used for these purposes, but a good one is a generalized Michaelis-Menten equation:

where W0 is the birth weight, Wf is the asymptotic weight, K is the age at which animals are half their asymptotic weight, c is a constant describing the shape of the curve, and t is time in years.

If you fit the weights of bison with age with this equation, for each herd you can extract essentially how heavy cows and bulls get, as well as a rate of maturity...or half-maturity as K would represent.

Right now, we have data on weight gain for about six bison herds. There are about 10 herds in the US that have weight data from roundups. 

So far, we see a few basic things about bison. On average, males level off at about 75% greater weights than females (855 vs. 484 kg). It also takes them about 1.5 y longer to reach half their maximal weight. 

We also see that some bison herds are heavier than others. For example, mature bison in Ordway Prairie in South Dakota are 50-100 kg heavier than mature bison from Konza. That's a lot of bison. Is it a fluke? Unlikely. Over 90% of Ordway adult cows produce calves. At Konza, it's only about 60%. 

There must be a big difference in the grass between Konza and Ordway.  Because their bison growth curves are quite different.

Konza flowering phenology and functional groups.

In general, we have little understanding of how communities are assembled and the types of interactions that long-term generate evolutionary pressures, extinctions, and radiations. I'm pretty sure that whole-flora analyses are going to be keys to helping us understand these complex systems and there are precious few datasets on the scale necessary to do this.


With that in mind, here's the latest Konza phenology data by functional group. This is through Sept 1. The x-axis is day of year of first flowering for a species. Based on n = 408 species, which represents about 80% of the herbaceous grassland flora for Konza. We'll probably get another 10-20 species flowering before the year is up.

The y-axis is probability of flowering per day over the year for species of each functional group based on a "smooth" fit of the distribution data. Probabilities are standardized across functional groups. I broke out the Cyperaceae because it was the Carex that flowered early, not any C3 grasses. The C3 grasses that flower late in the year are generally woodland grasses.

This is terribly fascinating, though I'm not sure what the story is yet. For example, why are there C3 forbs that flower in August, but not any C3 grasses? And why are there C4 grasses that begin flowering in March, but not any C4 forbs? 

There certainly is an long-term competitive interactions that sort communities and drive selection. It's almost likely a rock-paper-scissors story. If rock (C4 grasses) then no scissors (C3 grasses), but if paper (grazers) then there are less rocks, so can have knife (C3 forbs).  

The C4 forbs are probably the most interesting story. If high temperatures favor C4 over C3, then why are there so many C3 forbs that are active during the hottest months rather than C4 forbs. Konza's C4 forbs are mostly Chamaesyche (Euphorbiaceae) and Amaranthus. Often they are prostrate forbs and/or weedy species keying in on disturbed areas. The C3 forbs that flower during this time are species like Salvia. Are there C4 forbs that fall into the same niches as these C3 forbs. Is there evolutionary constraint here that allows all the mid- to late-summer C3 forbs to persist? 

As we generate more large-scale trait datasets, more of these patterns should come clear. 

Mycorrhizal fungi and grassland community structure

Relationship between mycorrhizal infection rates and the log-transformed response of species abundance to grazing.

The structuring of plant communities is complex. There are a myriad of proximal and distal factors that can influence the abundance of species. The role of mycorrhizal fungi in structuring grassland communities has always been opaque. In temperate grasslands, many of the species are dependent on arbuscular mycorrhizal fungi, yet many non-mycorrhizal species are found throughout the grasslands. Whether these non-mycorrhizal species tap unique pools or even are facilitated by the mycorrhizal species is really unknown.

Over a decade ago, Wilson and Hartnett (1998) quantified the dependence of ~100 grassland species on mycorrhizal fungi. There had never been a screening study like it. Nor has there been one since. Their work largely compared different functional groups, with the conclusion that C4 grasses are the most dependent on mycorrhizal fungi and legumes the least. The work implied that success at Konza would be dependent on the ability to utilize mycorrhizal fungi, but this was never quantified.

Recently, we've compared the screening data with actual abundances from Konza. It turns out that there is no relationship between abundance and mycorrhizal responsiveness or infection rates. As such, mycorrhizal symbioses are likely not necessary for success.

That said, mycorrhizal symbioses do determine which species perform better under certain conditions. For example, almost 25% of the variation in the response of species abundance to the presence of grazers (bison) was explained by the mycorrhizal infection rate. Grazing promoted non-mycorrhizal species. Similarly, suppression of fire promotes non-mycorrhizal species (data not shown).

In both cases, fire suppression and grazing increase the availability of nutrients relative to other resources. How to think of the role of mycorrhizal under different burning or grazing regimes is still not clear. It's easy to say that fire suppression or grazing increases nutrient availability, which decreases the need for mycorrhizal fungi. But why? Is it because they are too much of a carbon drain? Many of the high-fire, low-grazing species just do not grow at all in the absence of mycorrhizal fungi, so it is unlikely to be associated with competition for nutrients. And why would mycorrhizal responses/infection predict just the responses to grazing/fire, but not abundance overall. In contrast, we see traits like leaf tissue density--which I think of as being associated with low nutrient availability--prediction abundance across Konza, but not the responses to fire and grazing. 

How to proceed on the issue is not easy, but it's a curious pattern to line up with a number of others in understanding how grassland communities are structured.

Wilson, G. W. T. and D. C. Hartnett. 1998. Interspecific variation in plant responses to mycorrhizal colonization in tallgrass prairie. American Journal of Botany 85:1732-1738.

Comparing two measures of leaf tissue density

Relationship between leaf tissue density (RhoL) and leaf dry matter content (DMC) across 42 Konza grassland species.

There has been some debate on how best to represent plant investment into leaves. Specific leaf area, the ratio of area to mass, is at best an imperfect measure. Plants with high SLA certainly produce a lot of leaf area for minimum investment. Yet, high SLA can come as a result of being thin or low density. And it seems that many of the ecological conditions associated with high SLA are really associated with low tissue density rather than thin leaves.

How to measure tissue density is one of the current debates. On the one hand, tissue density (mass per unit volume) can be derived by measuring the thickness of leaves in addition to SLA. Deriving leaf tissue density (LTD) from thickness measurements provide a direct covariate (thickness) and are relatively simple to do. Yet, for some leaves, measuring the average thickness can be problematic. On the other hand, an approximation of tissue density can be derived from the leaf dry matter content (LDMC). Leaves are weighed in a hydrated state and then again dry. The ratio of dry mass to wet mass is LDMC. There are a number of assumptions to equate this ratio to leaf tissue density, but it has been favored.

Across 40+ species at Konza, I measured LTD and LDMC. The two metrics correlated pretty well (r = ~0.8). Some species seemed to have higher LTD than one would expect based on LDMC. In species with a high proportion of veins, thickness is probably underestimated, since it is generally measured between major veins. The Ambrosia artemisiifolia I selected was deeply lobed and did not have much lamina relative to veins. Its LTD was probably too high. On the other hand, both Bothriochloa and Schizachyrium species had higher LTD than expected from LDMC, but this likely would not have been caused by underestimating thickness or area. Instead, these species likely have high silica concentrations that add more mass per unit volume than other species. This is something I still need to confirm.

As to whether LTD or LDMC does a better job of predicting abundance, they both were about the same. Using long-term abundance data, they both had equal predictive power on average.

Whether one metric is better than another is likely equivocal. It depends on the situations as both have their limitations. I’d probably use both for awhile until better consensus can be reached.

I’m not sure I’ll get around to publishing these data, so I thought I’d put so of the results up here. 

Photosynthetic pathway and phenology

Stylized diagram of phenology of first flowering of different functional groups for Konza.

Global change models had often assumed categorical differences between C₃ and C₄ species. Because of the temperature sensitivity of photorespiration, C₃ species are restricted to cooler seasons and C₄ grasses to warmer seasons. The separation between C₃ and C₄ species, especially the grasses, was a standard categorization for plant functional types.

Yet, how much basis is there really for the separation? What role does photosynthetic pathway have to play in the phenology, if not ecology, of temperate grassland species?

At Konza, we’ve been collecting plant species when they begin to flower. It’s a rough estimate of phenology. It doesn’t capture how long they flower, or when leaves grow the most, but it’s an easily measured trait that represents phenology. We have first flowering dates for about 350 of Konza’s 550 herbaceous species.

Generalization #1: C₃ grasses have an earlier phenology than the C₄ grasses. The first grass to flower in 2010 was a C₃ grass Poa pratensis on April 21. Yet the first C₄ grass flowered just a week later. Bouteloua dactyloides flowered on April 27. Tripsacum dactyloides, another C₄ grass, was just a day later—April 28. There really is little offset between C₃ and C₄ grasses in when they start to flower.

Generalization #2. The C₃ photosynthetic pathway restricts the activity of C₃ species when temperatures are high in comparison to C₄ species. It is true that C₄ grasses do flower later than C₃ grasses. The last C₃ grass to start flowering was Diarrhena obovata, a forest understory grass. It didn’t flower until June 28. Many C₄ grasses do not begin to flower until July or August, when midday temperatures are routinely 30°C. Yet, C₃ forbs also flower during the time when only C₄ grasses are flowering. For example, Helianthus maximiliani will not flower until the first week of August.

At this point, I have a few questions.

If C₄ grasses can flower as early as C₃ grasses, and C₃ forbs can be active during the time when C₄ species should have a physiological advantage, then what are the links between photosynthetic pathway and phenology?

How much of phenology is driven by phylogeny rather than photosynthetic pathway? The Andropogoneae C4's flower mid-season, but not the Chloridoid C4's.

Why do C₃ grasses not flower during the middle of the summer, while C₃ forbs do? Can C₃ forbs regulate their leaf temperature via transpiration to reduce photorespiration?

And why the offset for C₃ and C₄ grasses, if C₃ species can flower mid-season? Is this an example of niche conservatism?

The topic of whole-flora analyses of phenology is complex, but some of these patterns seem clear enough to rethink some generalizations--even if they shouldn't happen based on what we know.