Africa acacias ‘go for broke’ to grow, use up water to survive drought

Study is first genome-scale analysis of iconic umbrella acacia

Ohio State University

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COLUMBUS, Ohio – Young umbrella acacia trees in Africa survive severe drought by putting their natural processes into overdrive when water is in short supply, prioritizing continued growth over water conservation, new research shows.

The study is the first genome-scale analysis of any African acacias and focuses on the umbrella acacia, an iconic feature of the African savanna.

Researchers compared the genetic response to drought stress of the umbrella acacia (Vachellia tortilis) and one of its hundreds of relatives, the splendid thorn acacia (Vachellia robusta) more commonly found in wetter regions of East Africa.

Results showed that once water becomes scarce, the umbrella acacia continues its conversion of carbon dioxide and water from sunlight into nutrients through photosynthesis and uses up all the water it can access.

“You would expect most plants, if they’re being water stressed, will shut down, but at the early stage of drought stress, umbrella acacias ramp up – they go for broke,” said senior author James Pease, associate professor of evolution, ecology and organismal biology at The Ohio State University.

“The splendid thorn acacia tends to be more of a water saver – holding on to water, not growing a lot. Umbrella acacia does the opposite – it tries to grow more and do more photosynthesis and capture more carbon that it’s going to stockpile,” he said. “Once water’s not going to come for a while, it lets the above-ground biomass die and waits for water to try again the next season.”

The research was published recently in The Plant Journal.

Umbrella acacias provide a staple food for giraffes, are sources of a global wood economy and the common food additive gum arabic, and are part of the legume family – all reasons to understand how genetics shape their drought tolerance at the cellular level, researchers say.

“They have to grow in these hyper-arid conditions that are really difficult for a large woody plant to grow in. They’re being eaten by giraffes, they’re being knocked over by elephants. They have to compete with the grasses. The grasses catch fire. So there’s this whole set of pressures on them,” Pease said.

“Drought stress and climate habitat shifts are not a unique problem to African acacias. But there are very few genomic studies of tropical trees and how water stress impacts them.”

Seedlings of umbrella and splendid thorn acacias were grown in the lab and watered for three months, after which they were divided into two conditions: continued normal watering or complete shutoff of water – the onset of drought. Researchers collected leaves on a weekly basis and selected samples for genomic analysis representing an early drought phase, the middle of a downward slope in tree health, and severe drought.

To compare each species’ response to the drought stress, the team sequenced their transcriptomes – the collection of RNA readouts of DNA instructions that indicate gene activity, and related protein changes, across the genome.

The model system represents the time of life when the trees are most at risk of dying.

“This early seedling establishment phase is when a lot of them either make it or don’t based on their habits of how well they can acquire energy and water,” Pease said.

The researchers believe that umbrella acacias maintain their pattern of intense nutrient collection and above-ground biomass decline for years, developing a huge root mass in the process.

“If you dig up a little acacia seedling, it has a tree’s worth of roots. And once it gets the right combination of water and nutrients, it has the rootstock to support a full tree and it will transition to that,” he said.

“This is the same strategy of grasses. They keep maintaining that root and will wait for water and try again – you can see that in lawns that dry out. It’s really interesting to us because that’s what grasses do, as opposed to most herbaceous plants and other trees.”

In contrast, the study showed that the splendid thorn acacia behaved in a more expected way for a tree under drought stress: investing in water conservation and cellular function maintenance while riding out the drought.

The transcriptome analysis showed the trees used similar genetic systems to regulate photosynthesis and maintain biological stability during drought stress – but the two species activated these systems with different sets of genes and on differing time scales, said first author Ellen Weinheimer, who worked on the study as a biology graduate student at Wake Forest University, where Pease was a faculty member until 2024.

The analytical method also revealed these genetic differences in drought response were not driven by genetic mutations, the sequence changes occurring over time that evolutionary scientists have historically tracked.

“You don’t necessarily see gene sequences and gene expression changing together,” said Weinheimer, now a postdoctoral associate at the Yale School of Medicine. “The genes that are differentially expressed in response to drought don’t necessarily have sequence changes, which shows that those two mechanisms are largely independent of each other.”

Tracking gene expression alongside sequence changes in plants, animals and other systems is a focus of Pease’s lab.

“We’re layering how gene expression levels are changing among different species,” he said. “And over evolutionary time, we’re finding expression as important as the mutations, in that a mutation in one gene could affect the expression of another gene. We’re learning very different things than we would if we just looked at the mutations.”

This research was supported by the U.S. National Science Foundation.

Additional co-authors were Scott Cory, Nicholas Kortessis and T. Michael Anderson of Wake Forest.

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Journal

The Plant Journal

DOI

10.1111/tpj.70385 

Article Title

Differential gene reactions reveal drought response strategies in African acacias

 

A new AI-based method to help prevent biological invasions

A strategy to take advantage of new datasets and machine learning tools

University of Connecticut

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As the world becomes more interconnected, some plants have benefitted from a greater ease in movement from one region to another, while some have become problematic. Some introduced species gain a competitive edge, spreading rapidly, outcompeting native vegetation, and transforming entire ecosystems. These species are known as “invasive,” and they can disrupt food webs, alter ecosystem processes, and threaten biodiversity. To address this growing challenge, an interdisciplinary team of UConn researchers has developed an AI-driven framework to predict which plant species are most likely to become invasive before they even arrive in a new location.

Their work is published in the Journal of Applied Ecology.

Julissa Rojas-Sandoval as assistant professor in the Department of Geography, Sustainability, Urban, and Community Studies, and core faculty at the Institute of the Environment, teamed up with Department of Physics associate professor Daniel Anglés-Alcázar, and Department of Ecology and Evolutionary Biology professor Michael Willig to develop a project they each could not have done alone, says Rojas-Sandoval.

The idea began when Rojas-Sandoval became interested in exploring whether machine learning techniques used in astrophysics to classify galaxies could be adapted to ecology and applied to classify plants. Discussing this idea with Anglés-Alcázar and Willig, they determined that it was possible and started working together to test the concept, adapting algorithms from astrophysical applications,” says Rojas-Sandoval.

“What is exciting is that we are not just providing a framework to classify plants as invasive and not, we are providing a way to identify which species have the potential to become invasive and problematic before they arrive in a new area.”

Traditional invasion risk assessments have been effective at preventing widespread introduction of invasive species, says Rojas-Sandoval. However, these assessments can be subjective and time-intensive and often applied after a species has already been introduced. As a result, by the time a plant is formally recognized as invasive, it is already well established and difficult to control or remove. The new machine learning framework offers the possibility to evaluate for invasiveness before the plant takes root in a new area.

Rojas-Sandoval explains that this new methodology can help perform risk assessments before plants are cleared for import by identifying which species pose the highest risk of becoming invasive in the destination country. The researchers combined decades of ecological data with machine learning methods to create algorithms that can analyze patterns from previous species introductions paired with characteristics of the plant species that may enable them to become invasive in a new area.

The researchers used three sets of data for the analysis, including one set focusing on the ecology and biological characteristics of the plants such as reproduction strategies and growth form, a second data set related to invasion history, capturing whether and where the species had previously become invasive or caused ecological problems, and a third data set focused on traits related to habitat preferences for each species. These datasets were used to train the machine learning algorithms.

The researchers identified several impactful trends, including the previous history of invasion, says Rojas-Sandoval, where if a plant was problematic in several areas, it is highly likely to become problematic in new areas. Plasticity in reproduction was also a good predictor, meaning that if a plant can reproduce by seed, cuttings, or other means, this gave them an advantage. The number of generations in a single growing season was also important for enabling an introduced species to get a foothold and become invasive in a new environment.

This is a powerful new tool to complement traditional risk assessments, says Rojas-Sandoval. Traditional risk assessments rely on evaluations that typically consist of questionnaires by an experienced group of experts who gather information about a plant and make an assessment about whether it should be allowed to be imported or not.

“With these new machine learning tools our data-driven models can achieve over 90% accuracy in predicting invasion success,” says Rojas-Sandoval. “This can help remove biases in the assessments and increase their predictive power.”

The researchers were also committed to using widely available data to ensure that this methodology can be replicated in other regions. The focus for this paper was on Caribbean islands, and Rojas-Sandoval says the next step is to train the models with data for different regions. They are inviting other researchers to create similar data sets to evaluate if the model is robust enough to calculate the probability of invasion to other areas.

“We want to analyze other regions and see if the models can still successfully predict the probability of invasion, and if not, then we need to train new machine learning models specific for each area.  In either case, machine learning requires high quality and diverse biological and ecological data, which is why extensive fieldwork is so important,” says Rojas-Sandoval.

Though the current models may not be able to predict invasions at a global level due to the complexity and uniqueness of biological organisms, the researchers are confident that general patterns will emerge.

“We are not trying to replace traditional risk assessments, which have been vital for biosecurity until now,” says Rojas-Sandoval. “This is a new strategy to take advantage of the wonderful datasets and machine learning tools available to complement previous methods and become more effective at preventing new invasions.”

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Journal

Journal of Applied Ecology

DOI

10.1111/1365-2664.70164 

Method of Research

Computational simulation/modeling

Subject of Research

Not applicable

Article Title

Machine learning for biosecurity: A probabilistic framework for invasive species management

 

Beavers impact ecosystems above and below ground

'We need to understand the trade-offs and benefits'

University of Connecticut

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As ecosystem engineers, beavers build resilience into the landscape.

Above ground, we can see changes wrought by beaver ponds such as increases in biodiversity and water retention. But UConn Department of Earth Sciences researcher Lijing Wang says we have a limited understanding of how they impact what happens beneath the ground. In research published in Water Resource Research, Wang and co-authors study how water moves through the soils and subsurface environment and detail new insights into how beaver ponds impact groundwater.

Groundwater can be an important source of water for streams, especially late in a dry summer, it may be the only source of water sustaining a stream, says Wang, and researchers are interested in understanding if and how beaver ponds impact groundwater as these details are important to consider for water management and restoration efforts.

Wang explains that some initiatives have included building beaver dam analogs to mimic what live beavers do and these man-made structures similarly extend the wetland and make an area more drought and wildfire resilient, however there are no comprehensive studies that focus on understanding beaver-induced changes to the subsurface water.

“Our work here develops one of the first hydrologic models that helps us understand what happens from the beaver inundation to the subsurface system under different subsurface structures,” says Wang.

For the study, the team used both in situ measurements including geophysical surveys, hydrologic data and modeling, along with a machine learning method called neural density estimator, first adopted in astrophysics, to calibrate the model to therefore better predict the changes happening due to the presence of beavers.

“We use the model-data integration method to try to make our model replicate what happens in our observed data,” says Wang. “Now that the model is calibrated to real-world observations, we can then better understand how each control works.”

Wang explains that one example controlling the flow of water includes different subsurface structures. The region of the Rocky Mountains where this study focuses is home to rivers with gravel bed systems, composed of large cobbles/gravels that extend as deep as 16 meters and outward from the river into the floodplain. They explored different combinations of subsurface structures in their models and found that characterizing the floodplain structure is critical for understanding the ways beaver ponds impact the movement of water in the subsurface environment. Wang says with shallow gravel bed and soil layers, beaver ponds may have a greater influence on how groundwater is replenished or recharged.

The researchers also explored the movement of water available for evaporation to the atmosphere, or evapotranspiration (ET).

“Evapotranspiration is particularly important in water-limited region like the U.S. West, where if there is more water in the floodplain then more water is evaporated to the air,” says Wang. “Thinking about the water budget, beaver-induced inundation may reduce how much water is in the system where a lot of water evaporates. Our analysis found if the soil structure on top of the gravel bed is very thick, then the ET could be large enough that the recharge could become lower or even less than without beaver ponds, because more water is used by the air and vegetation.”

In all, Wang’s team found that beaver ponds increased groundwater recharge 10 times compared to a dry period. The question they are currently trying to answer: where does that water go next?

“Our results show that when the water reached the gravel bed, it does not stay there, it goes downstream. Thinking of the gravel bed as ‘a thick river’ underneath the stream bed, there’s more water flushed downstream in the subsurface than we thought. It’s not staying there and sustaining the local water table,” says Wang.

Though this research focuses on beaver ponds in Colorado, Wang says she is starting to focus on New England, and she has started monitoring local beaver ponds.

“In New England, we have different problems compared to the Rocky Mountains, where they have a relatively simpler river network. In New England, we have complex river networks with more tributaries, channels, and beaver dams, which give us more biodiversity, and sustains mature floodplains and wetlands overall.”

Understanding the intricacies between land use practice and its subsurface environment is critical for understanding exactly how beaver ponds will influence other aspects that we may not immediately come to mind, Wang explains, such as potentially negative changes to water quality and for this we need a comprehensive analysis.

As beavers slow the flow of water and create ponds, this changes the subsurface oxygen conditions and leads to lower oxygen, or anoxic, concentrations in the water. These conditions can then lead to the proliferation of anaerobic bacteria, whose activities in the sediment can mobilize heavy metals that would remain trapped in more oxygen-rich conditions. For these scenarios, location and history are key.

“If you are in a pristine area with no previous industrial activity that may not be a huge problem,” says Wang. “However, if you are in the area like our site in Colorado that is near abandoned mines, we can see more soluble metals downstream. Beaver ponds can increase the ecological benefits, but we lack a comprehensive understanding of water budget and water quality. We need to understand the trade-offs and benefits.”

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Journal

Water Resources Research

DOI

10.1029/2024WR039192 

Method of Research

Computational simulation/modeling

Subject of Research

Not applicable

Article Title

Quantifying Groundwater Response and Uncertainty in Beaver-Influenced Mountainous Floodplains Using Machine Learning-Based Model Calibration

Researchers to investigate moisture-driven Antarctic ice sheet growth during past warm climates

Binghamton University

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image: 

Binghamton researchers are studying how ancient moisture patterns fueled Antarctic ice growth to better predict future sea level change.

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Credit: Christopher Michel, CC BY 2.0 <https://creativecommons.org/licenses/by/2.0>, via Wikimedia Commons

Warming of the ocean and air surrounding Antarctica is causing glacial ice mass loss and global sea level rise. To better predict future changes in sea level, an understanding of how Antarctic ice sheets will respond to warmer conditions is required. In a warmer world, enhanced moisture transport to the icy continent has the potential to increase snowfall over Antarctica, which compacts over time to create ice. Ice sheet growth from moisture transport and snow may offset some ice mass loss from marine-based sectors of Antarctic ice sheets. 

Investigating how increased moisture transport to Antarctica, and under what temperatures and sea ice conditions moisture transport occurs, is required to understand the mechanisms that can lead to increased ice accumulation. This question is one that Binghamton researchers will address in the coming years.  

Assistant Professor Adriane R. Lam and Postdoctoral Researcher Imogen M. Browne, both part of the Earth Sciences Department at Binghamton University, State University of New York have received funding from the National Science Foundation’s P4Climate (Paleo Perspectives on Present and Projected Climate) award, in part awarded under the Office of Polar Programs (OPP). This year alone, OPP’s budget was slashed by 88%, leading to the loss of several Antarctic field expeditions and grants; as of earlier this year, the P4Climate award has been archived. 

“We are quite lucky to have been one of the last grants awarded under the P4Climate program,” said Lam. 

Lam and Browne, along with their colleagues Assistant Professor Ruthie Halberstadt at the University of Texas at Austin and Research Assistant Professor Paul Acosta at George Mason University (all four are early-career researchers), will investigate moisture-driven mechanisms of ice sheet growth during the Miocene Climatic Optimum (17 to 14.7 million years ago). During the Miocene Climatic Optimum, atmospheric carbon dioxide levels reached at least 500 parts per million, global average temperatures warmed by 7–8°C above pre-industrial temperatures, and Antarctic ice sheets were smaller than modern. Warming was associated with volcanism, which spewed carbon dioxide and other greenhouse gases into the atmosphere. The Miocene Climate Optimum is considered an analog for future warming scenarios and is studied by geoscientists to understand how abiotic and biotic Earth systems will operate in warmer-than-present climates. 

“Studying the Miocene is really interesting because Earth’s climate, hydrological cycle, and ocean circulation were different back then,” said Browne. “The Miocene climate records that we generate using marine sediment cores give us critical insight into how Earth’s climate system will respond to warmer and wetter conditions.”

The funding Lam, Browne and their colleagues obtained will allow them to use climate and ice sheet models, compared with numerical reconstructions of ice sheet volume, to test various hypotheses for moisture-driven ice sheet growth. Each model simulation tracks the geochemical composition of ice, generating a modeled chemical signal that can be compared directly against deep-sea geochemical records that tell researchers about ice volume. 

To evaluate the feasibility of model simulations with different inputs for vegetation, ocean temperature, sea ice, and orbital parameters, the team will generate a new record of Antarctic ice sheet volume using the geochemistry of calcareous microfossils, called foraminifera, obtained from deep-sea marine sediment cores located in the path of very cold deep-ocean waters that are produced around Antarctica. 

Data-model comparisons will evaluate how well each modeled mechanism can explain the observed ice volume and chemical changes across a major glaciation that occurred around 16 million years ago, right after the Miocene Climate Optimum. Specifically, investigators will explore the impacts of local mechanisms such as ice-proximal ocean warmth and sea ice cover as well as global mechanisms such as atmospheric carbon dioxide levels. Another factor that will be incorporated into the models are orbital forcings – the shape the Earth makes as it orbits around the sun (which changes every 100 and 400 thousand years), the degree of Earth’s tilt (which changes every ~41 thousand years), and the ‘wobble’ of Earth about its axis (which changes every ~19 thousand years) – as all of these orbital factors influence the amount of solar radiation hitting different parts of the Earth during the year and through geologic time. As such, orbital forcings have the power to greatly influence heat and moisture transport to Antarctica. 

This is not Browne’s first time conducting research on or related to Antarctica. In 2018, she was a scientific participant on International Ocean Discovery Program Expedition 374, which drilled sediment cores from the Ross Sea region, a location where very cold, very deep-water masses are formed.

“Getting to sail with an international and interdisciplinary team of researchers and experiencing first-hand how sediment cores can be used to answer fundamental questions about Earth’s climate and ice sheet history was a formative experience in my career,” said Browne. 

It was during this expedition, when Browne was a Ph.D. student, that she met and worked alongside Binghamton University Earth Sciences Associate Professor Molly Patterson. Browne began her postdoctoral work with Patterson at Binghamton in 2024, where she also began working alongside Lam. The NSF award to Lam, Browne and colleagues will allow Browne to continue her research as a postdoctoral researcher in the Earth Sciences Department. 

“A grant like ours is special and important not just because of the science it will produce, but because it brings a group of researchers who have different skillsets together to work on a problem that has huge implications for society,” said Lam. 

Ice volume data and model outputs will contribute to the international community synthesis effort and project results will provide critical context for understanding long-term trajectories in sea level.