I was looking forward to my last day of fieldwork this season. No heavy gear to carry, no plants to collect. I had a simple job: climb a mountain and download the summer’s data from my weather station before the snows buried it for the winter. The first snow came by early September but melted away quickly. In the middle of the month I decided it was time to go up and have a look at the site.
By this time of year, most of the meadows have died back, with leaves and flowers long gone. Only dried-out stems and some loose debris remain, lending the lower parts of the mountain a beautiful gray-brown color.
The situation is only slightly different at higher elevations. My research site is on the gray shale ridgeline beneath the outcrop in the foreground. Normally I hike directly to it, but on a beautiful autumn day like this one, I took an hour-long detour scrambling up another ridge to see how things looked from a different perspective. The site feels isolated from this side, but almost blends into the autumn colors of the mountain.
Up at the site itself, all was well. The weather station was in good condition, and ready for winter – all sensors working, and snow pole installed to keep wayward skiers from crashing on the support structure in the early and late season when the snowpack is low.
With all the data downloaded, I decided to have a look around. The area is far more colorful than I expected. The sulfur buckwheat (Eriogonum umbellatum (Polygonaceae)) was largely done growing for the season, its dark green leaves turning shades of yellow and red. Like the aspens I wrote about last time, even nearby individuals seem to transition to their fall coloration on very different dates. I wonder what signals each is responding to.
And surprisingly, the growing season was not quite over, with summer colors still evident. Here you see a mat of senescing buckwheat, in which a sunflower (Heterotheca villosa (Asteraceae)) is still happily photosynthesizing and flowering – in the exact same location. Why does one continue to grow and flower when the other is getting ready for winter?
Summer is also still in place for the Rocky Mountain gentian (Gentiana affinis (Gentianaceae)). This species seems to like wetter meadows than dry ridgelines, and is also still growing and flowering well into the beginning of autumn. The purple flowers are hard to see from far away, but offer flashes of color up close.
These photos were all taken almost four weeks ago. By now, there have been several more snow storms, and the flowers and leaves are probably gone. The plants are ready for winter, and so am I. The rest of the mysteries will have to wait until next year to be unearthed and discovered.
Autumn is when the quaking aspen trees begin to show off their mysterious side. Questions fall into place, but answers are hard to come by.
At lower elevations in the Rockies, aspen (Populus tremuloides [Salicaceae]) is everywhere. Its clones dominate the landscape, creating a sea of white trunks and dark green leaves that rustle softly in the wind. At first glance, they all look the same, though we know they actually represent a complex mosaic of forms and genotypes.
Autumn is when they begin to invite questions. Fall colors come, and suddenly the clones don’t look so similar any longer. The received wisdom is that individual clones change color all at once, and asynchronously with their neighbors. But after some wandering around, I don’t think this is really true – and I’m not sure why it isn’t.
Individual trunks (ramets) don’t always change color at the same time. Some do. But not most. It is common to see single small branches turn yellow while the rest of the branches stay green, as if they are in a rush. I’ve heard proposals that these branches are water-stressed or damaged, and are ones likely to die in the coming year, but it’s hard to know if this is always the case.
And individual leaves don’t all turn yellow, either. Some, and probably the majority, do.
But some go from green to just a bit of yellow, then to black, with the color and pigments retreating down the major veins.
Others seem to become wholly black and crisp and dead without any intervening colored stages.
And others – my favorite – transition from green to red directly, giving the trees a fiery appearance.
The overall effect is a rainbow of colors. I haven’t built any intuition for why some ramets turn one color or another, except that the leaves growing on the driest-looking soils seem to simply crisp up, turn black, and fall off.
The other notion that whole clones change color synchronously also doesn’t seem to be true. I have noticed that in many cases there are sharp delineations of color between adjacent trunks, but there are also long smooth gradients. I think the microclimate each leaf experiences may has as much of an effect on fall coloration as the genotype does.
The autumn phenology of these leaves effectively determines the end of the growing season, and puts a strong cap on the amount of carbon each leaf can capture, ultimately limiting these clones’ growth and survival.
So what drives these patterns, and what are they telling us about these trees’ lives? I’m not sure yet, but the mystery is well worth considering, and the patterns inspiring for dreaming up future research plans…
Field season in the Rockies is over, which means no more long days carrying heavy equipment up and down mountains in stormy weather. There is some pleasure in sitting at a desk working through data and manuscripts, but I mostly miss the work – challenges and dangers and all. Most involved ladders – and lightning.
A major aim of the summer was to take thermal videos of alpine plant communities from sunrise to sunset, then to measure the performance of the plants in the video. So up the mountain we would go, with thermal camera, gas analyzer, eight or ten lead-acid batteries, some ladders, some wood beams, a cooler with ice, and various other items whose weight was less memorable.
A borrowed 1991 Ford Explorer took us part of the way there, but the rest of the journey was on foot.
Some of the heavier gear we would hike in a day in advance, to have ready in time for a sunrise start the next day.
But most things – especially the expensive ones – had to come up the day of measurement. Waking up in the middle of the night was no pleasure, but seeing the sky begin to glow in the high mountains certainly was.
On this day, we managed to get everything running about thirty seconds before local sunrise – a close call.
Fifteen hours of sitting on the mountainside later, the sun would go down, and we would pack up to head home.
Then we would come back the next day with our other equipment (here a gas exchange system) to make more measurements. Sweaty work, but no major challenges.
The interesting part involved the clouds. These mountains make their own weather, and working on a treeless ridgeline between two major ranges at almost 12000′ elevation means that storms come quickly and often.
We found ourselves with a large storm a few miles away on the only day we had to carry down about 40,000 USD of heavy equipment – and two eight foot-long aluminum and fiberglass ladders.
By the time we had packed up and waterproofed the gear, and were bailing down to the trees, the storm was upon us. It is only possible to run so fast on rough terrain when carrying an external frame pack on your back, an internal frame pack on your front, and a ladder on your shoulder. We got about half a kilometer down the ridge before the lightning was hitting trees around us, flash-bangs in an instant.
At that point we decided to ditch the ladders, and dropped down a gully to a sparse forest that looked like a good place to get into a safe position (my assistant is about to transition to a safer squat), then sat in the hail and lightning, waiting for the storm to pass. An hour later, with rumbling in all directions, we decided to abandon the ladders and make a break for our vehicle far down the slope.
We and all the other equipment made it back safe, but the ladders were still somewhere high up the mountain. So the next day, I came back to go find them. Not the simplest task, because I forgot where we left them in the chaos of the previous day’s storm.
About twenty minutes of searching later, we found them – just in time for another storm to blow in. The hike back down was thundery, but dry.
We got the equipment strapped on just in time for the rain to begin falling.
Now all the gear is safely in storage, and we are safely back at our desks. Mountains are dangerous and unpredictable places, but I think that is why they are so fascinating to work on, and why they will keep calling me back.
Do too much fieldwork and a scientific debate can build up without you noticing. I’m wrapping up a summer in the Rockies and have begun to notice the growing interest and controversy over hypervolumes concepts in ecology. The debate has involved my own work, and it is exciting to trace the threads of its origin and current directions.
Hypervolumes have been widely used to describe the Hutchinsonian niches of species – their responses to the environment, via the resources and conditions they depend on. For example the willow shrubs (Salix spp.) in this photo may only be able to grow in locations corresponding to certain combinations of temperature, rainfall, snowpack duration, and nitrogen availability, while the subalpine fir trees (Abies lasiocarpa) in the valley below might require other combinations. Each variable represents an axis, and the combinations used by each species its niche.
Similarly, hypervolumes are also used to describe the functions and traits of species – their morphology and their interactions with the environment. For example the paintbrush (Castilleja rhexifolia x miniata?) flowers could be described by a combination of axes corresponding to height, flower color, photosynthetic rate, and so on, contrasting with values for the daisy (Erigeron glacialis).
Measuring these hypervolumes, whatever the axes, has proven to be of wide interest, with applications from biodiversity conservation, invasion ecology, community assembly, and ecosystem functioning. But there is not yet agreement on the best way to measure them – leading to the present controversy.
The idea of a hypervolume dates back to Hutchinson in 1957 and was originally implemented as a range box that would independently enclose data along each axis. More recent extensions (e.g. Cornwell et al. ) transformed this idea to convex polygons that would minimally enclose the data. In 2014, I worked with Cyrille Violle, Christine Lamanna, and Brian Enquist to extend this idea to multivariate kernel density estimation, an approach that provides a closer ‘wrap’ of the hypervolume to the underlying data (Blonder et al. ), potentially better modeling the true shape of hypervolumes.
Since then, a diversity of approaches have appeared, each with its own philosophical underpinnings and tradeoffs. For example, our hypervolume approach allows the description of arbitrarily complex shapes and is computationally efficient when the number of niche dimensions is large; the tradeoff is that the scientist must specify some additional parameters to control the boundary of the hypervolume.
Swanson et al. (2015) recently published “A new probabilistic method for quantifying n-dimensional ecological niches and niche overlap”. This approach uses multidimensional ellipses to fit the data. It cannot describe complex shapes but if data are thought to be truly rotated ellipses (multivariate normal) then it has excellent performance.
Similarly, Junker et al. (2016), recently published “Dynamic range boxes – a robust nonparametric approach to quantify size and overlap of n-dimensional hypervolumes”. This approach extends range boxes to a quantile-based approach that is computationally fast and should be less sensitive to outliers, although it also only can describe simple shapes (rotated data are potentially problematic as well, though the authors propose an approach to address this). On the other hand, it might have better performance in high dimensions than our approach, at least with default parameter settings.
Alternatively, Carmona et al. (2016) have published “Traits Without Borders: Integrating Functional Diversity Across Scales”, which proposes a fully probabilistic approach to estimating hypervolumes. This approach shares several motivations with our hypervolume approach, but differs primarily in how and when the hypervolume’s boundaries are delineated. It also is implemented via different algorithms.
We do disagree on several points, and last month had a friendly discussion around these issues in Trends in Ecology and Evolution. You can read my piece (Pushing Past Boundaries for Trait Hypervolumes: A Response to Carmona et al.) and his response (The Density Awakens: A Reply to Blonder) to see the full debate. I reproduce a figure from my piece below, showing the difference between Carmona’s approach for hypervolume overlap on the left and the several approaches for overlap using our approach on the right. You can see that the ability to choose a threshold for overlap on the right (as we propose) yields a range of possible outcomes, which is either beneficial or detrimental depending on your view of hypervolume concepts.
Another research group has also just added to this debate. Qiao et al. (2016) just published “A cautionary note on the use of hypervolume kernel density estimators in ecological niche modelling”, which suggests that simpler approaches that proposed by our group or Carmona’s group may be better suited for predicting species’ geographic distributions. They propose that boxes and ellipses are better assumptions for fundamental niches than the complex shapes that kernel density estimation can produce. I agree with this general point, but also think that more complex shape descriptors have important places for describing realized niches and for describing all trait hypervolumes.
To get a better sense of what each of these approach is doing, it can be instructive to look at some data. Here is an example of what each approach does for a simple dataset, with the approximate shape according to each method drawn in red.
In this case all of the methods seem to produce roughly similar results, although ability of each approach to either capture (or remove) complexity in the data is variable. The difference between methods becomes clearer with a more complex and holey dataset seen below.
Here the more complex methods provide a much tighter fit to the data than the simpler methods – but if the reason for holes in the data is actually under-sampling, then potentially the simpler methods are actually better. This example is also illustrated in two dimensions only. Only the methods in the first and third column are computationally feasible in high dimensions.
Is there a ‘right’ method for measuring a hypervolume? I don’t think there is a unique best way to do things. Each of these approaches has certain upsides and downsides, and may be more suitable for certain applications based on the intent of the scientist. For example, our hypervolume approach is probably less suitable for species distribution modeling than the ellipse method, while the converse would be true for assessing overlap in bird morphology.
Try out each of these methods – they all are associated with R packages – and see what works best for your application. In the meantime our group is working on a comparative study of all these methods, and is refining our hypervolume approach in some new ways that I’ll be able to share soon.
It is exciting to see this diversity of approaches, and for the field to engage in active discussion about the assumptions and implications of each. This is exactly the kind of discussion that deepens conceptual understandings and then produces better science.
Summer in Gothic has been hot and dry, paralleling warming trends seen globally.
Strong winds have been blowing dust onto every surface, and the biting flies remain.
For two weeks we had no rain at all. Until a few days ago, when the monsoon returned, bringing moisture from the Pacific Ocean, and clouds to the mountains.
Three days of rain, thunder, and the magical smell of wet soil were our prize.
The storms brought change to these landscapes. The soil surface rapidly saturated in many places, leading to sheet flow of water. And the subalpine firs (Abies lasiocarpa), many with dry or dead needles, shed most onto the forest floor.
For other species like this sunflower, the rain washed away a thick layer of dust, restoring photosynthetic rates.
And for others like this monument plant (Frasera speciosa), were partly blown down, but received much-needed moisture.
The rain has been good for rescuing some plants from drought-induced mortality. This silver lupine (Lupinus argenteus) ramet is now growing well in bare gravel saturated with moisture.
But for others, the rain has come too late. The yellow patch in this photo is a clone of Veratrum tenuipetalum, leaves dead and crisp from the dry period. Its apical meristems have died and it will not grow more this year, though next year will bring another chance to resprout from rooting stock.
I felt deadened by these two weeks of hot and dry conditions. But now the rain has returned, and with it, my joy for working here. There is more life and summer yet to come.
I spend a few days each week sitting in a meadow waiting for flies to bite me. Not by choice. The reason we are in the meadow is to measure plant thermoregulation with an infrared camera.
But the camera, once we set it up, requires very little attention. Every few hours we change the battery, and the rest of the time we sit there to make sure no one steals it or shoots it.
Sometimes we make other measurements, but otherwise there is very little to do but sit, and wait for the insects to come.
Some are friendly enough.
But others, like this snipe fly (Symphoromyia sp. [Rhagionidae]), are not. They swarm our bodies, sit on our datasheets, find our skin, then bite, and draw blood.
Every bite causes a painful swelling that lasts for hours and sometimes days.
They are surprisingly resistant to being crushed, often flying off after blows that would do in other insects. But they are slow, and most can be killed with a hand. Unfortunately, there seem to be an infinite population of them – no matter how many we kill, more kept coming.
I began wondering how many flies there really were. So I started a collection. Every time one landed on me, I tried to kill it. And then I put each of the victims into a plastic bag.
By the end of a 15-hour field day I had about a hundred dead flies in a bag. I estimate I was able to kill about a quarter of the ones I tried to hit, and probably ten landed on me for each one I aimed for. That corresponds to about 4000 flies. Far too many.
My fly collection was a small victory against the swelling and the itching, but it helped pass the time on these long days in the field. It made me wonder where all these flies went at night. I looked under all the nearby plants at sunrise and sunset, but didn’t find any flies. Some cursory searching suggests that very little is known about their life cycles – which is fascinating given how abundant they are for a few short weeks each year.
Not a research project I plan to pursue. I think it would be too painful. I’ll stick to collecting flies instead.
Summer is a risky season in the alpine. Plants can grow only in the limited time after the snow melts, and often die back as soon as the soil becomes too dry or the autumn snows begin again. The timing of snow melt is very unpredictable too. The amount of snow a mountain receives is important, but so too is the amount of dust that settles on it in late winter. This year at my site we had very little snow, but also very little dust. This photo was taken on June 18th by Jacob Heiling, and you can see my research site is still completely covered.
A week later we hiked up to the site to see how things were progressing. Most of the snow was finally gone, but meter-deep patches still remained on the landscape. As the snow contracts in late winter, it picks up gravel on its surface and produces some fascinating geometrical patterns.
The snow had also damaged my weather station, which I left to over-winter in place. A few of the sensor arms were crushed, either by the weight of the snowpack or through the action of the wind. But the datalogger and sensors were still functional, and I found out the snow only fully melted there on June 25th.
Despite most of the snow disappearing only a few days prior, the plants had already started growing, with a few Lupinus and Ivesia individuals sending up leaves. The growing season is precious time.
Now July has come, and we are back to begin intensive monitoring of these plants.
The snow has completely sublimed or melted at the site, and summer is here.
Last year at this point in July we found hundreds of Lupinus and Senecio seedlings in the permanent plots. But this year things don’t look so good.
Most of the lupines that grew last year failed to produce any above-ground growth, and nearly all of the seedlings are just dead. I counted only three seedlings on a quick walk through the site yesterday.
And many of the other species don’t look very good either – the leaves on this Phacelia are yellowing already. Mortality will be high this year, and I am looking forward to making a full census of the site in another week.
From a distance, the almost-bare slopes of this mountain seem not to change. But life comes and goes in the alpine. A year does make a difference.