ALexandEr Grasshopper RESurvey

Looking to the past for answers about our future

Photo Credit: Stephanie Paige Ogburn / KUNC

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About the project

The mystery of dusty museum boxes

Entomologist César Nufio arrived at the University of Colorado’s Museum of Natural History, eager to start his postdoc exploring the ecology of local insects. He was immediately directed to 250 largely forgotten boxes, gathering dust atop a shelf in the museum. In the boxes, he found thousands upon thousands of grasshoppers, but each box seemed to contain a random assortment of specimens.

It was then he discovered the notebooks.

Gordon Alexander, the CU entomologist who made the collection, had left three detailed field books. They contained a wealth of data: grasshopper species at different sites going up in elevation, and the number of individuals of each species. They cataloged the life cycle of the grasshoppers over a set of three summers from 1958-1960. Cesar quickly realized these boxes would shape the next decade of his life.

A bust of Gordon Alexander, who headed the University of Colorado's biology department for 20 years, sat unidentified in Nufio's office for years. One day, Alexander's visiting granddaughter told Nufio the bust was of the same man whose grasshopper …

A bust of Gordon Alexander, who headed the University of Colorado's biology department for 20 years, sat unidentified in Nufio's office for years. One day, Alexander's visiting granddaughter told Nufio the bust was of the same man whose grasshopper collection the researcher was studying.

CREDIT: STEPHANIE PAIGE OGBURN / KUNC


ALEXANDER PROJECT HISTORICAL photos (circa 1950s, credit: D. Van Horn)


César has been surveying grasshoppers at these sites for nine years, replicating the work of Gordon Alexander. He has collected over 17,000 grasshoppers for study. Credit: Stephanie Paige Ogburn / KUNC

Nufio led a resurvey program from 2006 to 2015 with additional NSF funding. Repeating the field surveys revealed that populations and species have shifted their phenologies and relative abundances over 50 years of climate warming.

A chance encounter while Lauren Buckley, who leads the TrEnCh project and was working in Colorado on the Colias butterfly project, led to her involvement in the grasshopper resurvey. Buckley was eager to join the project once she learned of the wealth of historic data and the accessible sites along the elevation gradient. She has focused on collecting information about the biological mechanisms underlying the phenological and abundance shifts in response to warming.

The grasshopper study system has proven perfect for field and lab experimental manipulations, including reciprocal transplants and common gardens, because these species vary in dispersal ability, gene flow, and thermal specialization across the elevation gradient.

 

Key figures & findings from this project

The less dispersive M. boulderensis (left) exhibits stronger elevational clines in genomic data (top) and greater thermal specialization of hopping (bottom) compared to M. sanguinipes (right).

The less dispersive M. boulderensis (left) exhibits stronger elevational clines in genomic data (top) and greater thermal specialization of hopping (bottom) compared to M. sanguinipes (right).

The development index (means±s.e.), which represents the average development stage of the population and ranges from 1 (all first instars) to 6 (all adults), increases as degree days accumulate through the season. Development has generally accelerat…

The development index (means±s.e.), which represents the average development stage of the population and ranges from 1 (all first instars) to 6 (all adults), increases as degree days accumulate through the season. Development has generally accelerated or remained constant with respect to degree days between the initial surveys (1959–1960: solid lines) and resurveys (2006–2011: dashed lines), but some high-elevation populations of early-season species (left column) have relaxed development in response to greater thermal opportunity.


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Thermal tolerances and preferences vary along the elevation gradient.

We have documented variation in thermal tolerances and preferences - measured as body temperatures voluntarily selected in a thermal gradient. We have also found the temperature dependence of performance (movement and digestion) varies along the elevation gradient. This variation suggests that local adaptation shapes phenological and abundance responses to recent climate warming.

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High and low-elevation populations differ in their ability to shift their developmental rate.

We found that low-elevation and high-elevation populations have important differences in their potential to adjust their developmental rate to capitalize on warm seasons. This may account for the variability we see in phenological shifts, which are often observed but poorly explained.

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There is growing interest in the physiological and genetic mechanisms underlying the observed patterns.

This project has led to important insight in physiological and demographic responses to climate change, with implications for biodiversity going forward. We were recently awarded a NSF Rules of Life grant to expand this study, to explore the physiological and genetic mechanisms underlying the observed patterns.

 

Next steps

We recently received a NSF Rules of Life grant to fund further studies of the physiological and genetic mechanisms underlying grasshopper responses to climate change.

An exciting component is examining the historic specimens for potential changes in genetics and energy reserves associated with climate change. The goal of the grant is to develop a general modeling approach that can bridge biological, spatial and timescales by predicting shifts in fitness constraints and, thus, improve our ability to forecast responses to environmental gradients and climate change.


One impediment to linking physiology to organism fitness in predictive models lies in integrating across timescales: reproduction is primarily an integrated, additive response to chronic environmental conditions over time via mechanisms such as energy use and acquisition, whereas survival can be strongly influenced by short-term, extreme environmental conditions. In many systems, the relative importance of fecundity and survival constraints changes systematically along environmental gradients, with fecundity constraints dominating at high latitudes or altitudes (i.e. leading range edges as climate warms), and survival constraints dominating at trailing range edges, leading to systematic differences in the way we should model the links between physiology and fitness along environmental gradients.

The team (Including Buckley, geneticist Sean Schoville from U Wisconsin, and physiologist Caroline Williams from U Berkeley) will leverage the historic survey and specimen data for grasshopper species found along a montane elevation gradient, which vary in traits such as dispersal, phenology, morphology, and thermal specialization. Field reciprocal transplant experiments will quantify the integrated response to the environment, while assessing whether local adaptation and plasticity moderate fitness constraints along the environmental gradient. The transplants will use physiological and genomic biomarkers to test the hypothesis that survival constraints predominate at low elevations, while fecundity constraints predominate at high elevations.

Lab common garden experiments manipulating environmental attributes that vary with elevation (temperature, temperature variability, photoperiod, radiation, hypoxia) will test physiological mechanisms that underlie fitness constraints. Model building will integrate these physiological mechanisms to predict responses to the elevation gradient. Then, historic survey and specimen data will be used to test whether these models successfully hindcast patterns of genetic, physiological, phenotypic, and demographic responses to 50 years of climate warming. The team hypothesizes that tradeoffs in reproduction and survival constraints across the elevation gradient mediate species responses to environmental change and must be integrated to improve predictions.