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Scientists have harnessed new developments in machine learning to look at Australian eucalypt species, unveiling their transformation over millions of years.

Advancements in artificial intelligence have helped scientists explore the evolutionary history of an iconic Australian tree, which scientists say could help tackle threats like climate change and biodiversity loss.

In the study, scientists at Botanic Gardens of Sydney and �鶹��madou Sydney used AI to analyse an unprecedented number of leaves from eucalypts (Eucalyptus, Angophora and Corymbia), gaining phenomenal insight into how this native species has evolved with climate.����

The paper, published in the this week, analyses a dataset of over 50,000 digitised images of eucalyptus specimens, some dating back as far as 1839, to reveal if species’ leaves have evolved as their climate does.

It follows last year’s research, where scientists from Botanic Gardens of Sydney and �鶹��madou built a machine learning program to examine millions of plant specimens stored in herbaria around the world. This approach introduced a resource that was previously inaccessible to researchers, as the sheer size of the herbaria collections had been too large for humans to measure.

In this first study, the team analysed 3000 samples of the species Syzygium and Ficus, using a ‘computer vision’ method to look at their leaf sizes. They discovered that – contrary to frequently observed interspecies patterns – leaf size within species doesn’t increase in warmer and wetter climates.

Now, a team of researchers have taken this work a step further, turning to one of Australia’s most iconic and beloved trees, the eucalypts – to understand how its leaf sizes have changed with climate over millions of years.

Botanic Gardens of Sydney scientist Karina Guo says the study shows how AI is changing botanical science.

“Using AI, we’ve been able to work with huge amounts of data, which was simply not possible before,” Ms Guo says.

“It’s changed the game in finding the minutia of flora, helping us to paint a very detailed picture of the past.

“Instead of manually assessing thousands of images of specimens, which can take years, the machine learning can look at tens of thousands in less than four days.”

�鶹��madou Researcher Associate Professor Will Cornwell describes this new ability to unveil previously inaccessible data as groundbreaking.

“Digitised specimens have allowed us to dive deeper in understanding our species like never before, which can ultimately help us to tackle big threats to our flora like climate change and biodiversity loss,” A/Prof. Cornwell says.

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Detailed understanding of evolution

For this study, the team used two machine learning models working in succession, rather than just a single model, significantly improving the accuracy of the dataset.

“We already knew that the sizes of plant leaves change across climatic gradients. From cool and moist to hot and dry conditions, leaves tend to become smaller to compensate for the increased limitations of water availability,” Ms Guo says.

Using the dataset generated from machine learning, researchers first confirmed the links between the sizes of leaves measured on herbarium specimens, to the climate from which those specimens were collected.

“In science it is critical to confirm new results validate old beliefs. The first step for us when using this novel dataset was to confirm that that overall larger leaves were found in warmer and wetter climates,” Ms Guo says.

Next, the scientists examined the links between climate and leaf sizes within groups of trees of different evolutionary ages.

Plants are separated into taxonomic groups, with the smallest and youngest being species. Ascending in the taxonomy rankings there are subgenera, genera and families, each group being bigger and older than the last. Using this new dataset, scientists were able to resolve how the long-standing hypothesis of leaf size and climate changed across these different groups.

“In the youngest of groups, within species, on average the leaf size and climate relationship showed the trees didn’t change their leaf shape over shorter periods of time,” Ms Guo says.

“For instance, in the Sydney Red Gum (Angophora costata), leaf sizes were bigger in individuals from dry-warmer climates than those of the same species in wetter-cooler areas.”

The team found that when looking at broader taxonomic groups, such as subgenera – some going back as far as 8 million years – they changed their leaf shape to adapt to their climate.

These older groups of trees, over periods spanning millions of years, replaced other species if they better suited the climate conditions, rather than the species evolving to have different leaf sizes.

“Extracting this detailed level of understanding of evolution is rare because it is unusual to have data from many species, as well as lots of data from different locations and climates within the same species,” says Botanic Gardens of Sydney scientist Jason Bragg.

“The combination of machine learning and using herbarium specimens has enormous potential for ecological science. It still has much to offer in terms of understanding the way plants traits are distributed, and how environments have shaped them over evolutionary history.”

Scientists across the globe have been collecting plant specimens for hundreds of years, storing them in libraries known as herbariums.

The global shift to digitise these collections saw the National Herbarium of New South Wales complete the largest digitisation project in the southern hemisphere. Imagining and methodically archiving more than 1 million plant specimens, the project has enormous benefits for plant research and will protect the fragile specimens for future generations.