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.

Experimental warming and phenology

Phenology is the study of the timing of biological events. The phenology of organisms is a critical component of the functioning of our biosphere. Migratory animals time their movements to secure food resources. Plants time their leaf production to minimize exposure to harsh conditions.

No one would state that the everything in the natural world is perfectly optimized, but changes in climate are likely to alter then phenology of organisms in ways that the consequences are not understood. In response, the scientific community has monitored phenology for a long time--in some places hundreds of years--and conducted experiments to aid in forecasting future responses.

Published recently, Wolkovich et al. led a team that compared observed and experimental consequences of warming for phenology. Their conclusion was that experiments consistently underestimated the phenological consequences of warming.

Two commentaries were published along with the paper. Both sets of authors presented the case in defense of experiments that can be summarized as "nature is complicated".

Neither commentary undercuts the value of the work, but understanding the consequences of different types of warming and why phenology might not respond consistently to experimental warming underpins basic questions we have about the interactions between climate and the biotic world.

Wolkovich, E. M., B. I. Cook, J. M. Allen, T. M. Crimmins, J. L. Betancourt, S. E. Travers, S. Pau, J. Regetz, T. J. Davies, N. J. Kraft, et al. 2012. Warming experiments underpredict plant phenological responses to climate change. Nature 485:494-497.

this spring was warm

Not news to most at this point, but March was warm. At Konza, our first flowering phenologies were running 30 days ahead. Upper Midwest was even more extreme in temperatures.

I'm writing this just to post the maps...

https://www2.ucar.edu/atmosnews/opinion/6760/great-warm-wave-top-10-list

This NASA depiction of land-surface temperature anomalies between March 8 and 15 shows the effects of the Great Warm Wave kicking off across the Great Plains of the United States and central Canada. In some areas, the land surface was more than 18°F (10°C) warmer than average. (Image courtesy NASA Earth Observatory.)

Another map for March:

This is for March 2012, departure from normal (1980-2011).

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.

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. 

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.

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.

Whole-flora analysis: flowering and community assembly

Distribution of first flowering dates for 265 Konza grassland species (as of mid-May). 

There are some ecological analyses that can only be done by analyzing every species in a flora. Statistical inference aside, if we are to understand ecological sorting and community assembly, we need whole-flora analyses.

For example, we've slowly been accumulating data on first flowering for the Konza flora. For each species,  the day of year it first begins to flower is recorded. We're about half-way to having dates for the entire grassland flora. It's a bit biased now as we're still collecting data this year, but that's why this is on a blog...

There seems like there might be pretty strong selection pressures to flower at times when 1) environmental stress is low and 2) there is little competition for pollinators. I'm not sure if there would be selection against synchrony in flowering time for anemophilous species, but we that would be something to test, too. In this case, deciding what the null model is, is the hardest part.

I like the idea of whole-flora analyses. It's a lot of work though.