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The National Institute for Computational Sciences

Following Yellowstone’s Green Wave

Researchers Investigate the Rate and Extent of Rapid Greening of High-elevation Landscapes Caused by Climate Change

by Scott Gibson


High up in the mountain ranges of the Yellowstone National park, the elk do what all ungulates, or hooved animals, do—they forage for food. The fat stores they develop from eating grass and leaves are critical to their over-winter survival, reproductive health, and population dynamics. But the warming of the planet is affecting the vegetation they eat and likely their very existence and that of their predators, the wolves.

Vegetation green-up is known to be happening faster across the Greater Yellowstone Ecosystem (GYE) and other similar high-elevation landscapes on local and regional scales, with warmer temperature and melting snow as catalysts for the early onset of spring. Moreover, changes in plant phenology, or recurring lifecycle stages of plants, can ripple across the food chain, affecting pollinators, herbivores and their predators by creating a mismatch in timing of life-history events.

Such shifts in plant phenology provide some of the strongest evidence for climate change, according to researcher Karthik Ram of the University of California, Berkeley (UC Berkeley).

Ram recently led a research team in a project aimed at estimating the rate and extent to which the patterns of vegetation growth are changing across the GYE.

The researchers used disparate, voluminous sets of complex data to model how climate-induced changes in temperature interact with the landscape characteristics to affect spatial and temporal patterns of grass phenology across the GYE.

“The data we collected and the findings provide further impetus to closely examine how the elk are responding to these types of changes,” Ram says. “That research would also include an investigation of how the elks’ predators, the wolves, are being affected.”

Ram says the research team had 1.25 million data points for each year, spanning an 11-year period, from 2000 to 2010. He explains that measuring inter-annual changes in the green wave and estimating the rate and extent of green-up not only required comparing data across year but also within a year, to extract seasonal trends.

The left map depicts areas in Yellowstone National Park that comprise the summer and winter range for the northern elk herd. A research team led by Karthik Ram of UC Berkeley, studied the dynamics of these areas because they encompass key elk foraging locations and migratory routes. The map on the right shows the elevation range over which the green wave occurs. Dark blue areas indicate winter feeding grounds along valleys, whereas dark reds indicate high-elevation summer foraging areas.



The research project would not have been possible without a supercomputer and associated resources and services, Ram says. The researchers used the Nautilus supercomputer, managed by the National Institute for Computational Sciences (NICS), with resources and support provided by the Remote Data Analysis and Visualization Center (RDAV) of NICS.

“These types of analysis are beyond the reach of personal computers, and we typically had to work with large, disparate, high-resolution data sets that included vegetation layers, snow water equivalent data, and fine-scale temperature data,” he says. “RDAV and NICS made it possible to leverage high-performance computing to model these data in an efficient manner. For example, they have been very supportive in providing the resources to scale my analysis in the R programming language across a large number of cores on Nautilus. As the volume of data continues to grow, facilities like NICS and RDAV will be key to analyzing and drawing meaningful results without drowning in too much information.”

The researchers discovered that under the warmer conditions, the green wave progressed significantly faster, with what could be termed ‘high-quality’ forage occurring at higher elevations as compared with cool years.

They also found that the warmer temperature influences spatial synchrony, the quantitative measure indicating how much of the landscape is in the exact same vegetative state.

“Under warmer conditions, more areas on the landscape will undergo snowmelt and consequent green-up, resulting in a spatially synchronized green wave across a heterogeneous landscape made up of mountain faces, slopes and different elevations, for example,” Ram says.

The researchers hypothesize that the rapid green wave could lead to complex dynamics in the elk populations and, consequently, the wolf populations, as well.

“With a faster green wave, the elk might assimilate this information much slower, which means that they miss the peak and feed on sub-optimal, or low-quality, forage as they migrate up the elevation gradient,” Ram says. “This can mean lower body size and fewer or weaker calves. The increased synchrony also means more areas on the landscape reach green—that is, the highest vegetation quality—at the exact same time; interestingly, this is both good and bad for the elk.”

So much high-quality food exists under rapid green-up that the elk don’t have to search, but this plethora of vegetation passes its highest nutritional state before the animals can consume it; the abundance is simply more than they can eat and digest, Ram says.

In Yellowstone National Park, dynamics between landscape features such as elevation, slope and aspect all strongly interact with rainfall and snow cover on the ground to create heterogeneity in water availability for grass growth. Year-to-year variation in climate affects this green-up, or early onset of spring, which consequently influences elk movements as they migrate along the elevation gradient in search of high-quality forage.



“Elk also constantly make decisions on where to eat based on perceived risk,” he says. “So a large green patch might pose the biggest risk to their safety. Elk with sufficient reserves might not take that huge risk, while weak ones, with nothing to lose since they’re going to die anyway, will take the risk."

The results of other research have suggested that the shortening of the duration of a year over the course of many years may adversely impact the wolf population. One factor is the amount of snow on the ground, which affects their efficiency in attacking prey. The agility of the wolves in snow gives them an advantage.

About NICS: The National Institute for Computational Sciences (NICS) operates the University of Tennessee supercomputing center, funded in part by the National Science Foundation. NICS is a major partner in NSF’s Extreme Science and Engineering Discovery Environment, known as XSEDE. The Remote Data Analysis and Visualization Center (RDAV) is a part of NICS.