AI reveals hidden traits about our planet's flora to help save species


Date:
June 20, 2023
Source:
University of New South Wales
Summary:
Machine learning can help extract important information from the
huge numbers of plant specimens stored in herbaria, say scientists.


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FULL STORY ==========================================================================
In a world-first, scientists from UNSW and Botanic Gardens of Sydney,
have trained AI to unlock data from millions of plant specimens kept in herbaria around the world, to study and combat the impacts of climate
change on flora.

"Herbarium collections are amazing time capsules of plant specimens,"
says lead author on the study, Associate Professor Will Cornwell. "Each
year over 8000 specimens are added to the National Herbarium of New South
Wales alone, so it's not possible to go through things manually anymore."
Using a new machine learning algorithm to process over 3000 leaf samples,
the team discovered that contrary to frequently observed interspecies
patterns, leaf size doesn't increase in warmer climates within a single species.

Published in the American Journal of Botany, this research not only
reveals that factors other than climate have a strong effect on leaf
size within a plant species, but demonstrates how AI can be used to
transform static specimen collections and to quickly and effectively
document climate change effects.

Herbarium collections move to the digital world Herbaria are scientific libraries of plant specimens that have existed since at least the 16th
century.

"Historically, a valuable scientific effort was to go out, collect plants,
and then keep them in a herbarium. Every record has a time and a place
and a collector and a putative species ID," says A/Prof. Cornwell,
a researcher at the School of BEES and a member of UNSW Data Science Hub.

A couple of years ago, to help facilitate scientific collaboration,
there was a movement to transfer these collections online.

"The herbarium collections were locked in small boxes in particular
places, but the world is very digital now. So to get the information
about all of the incredible specimens to the scientists who are now
scattered across the world, there was an effort to scan the specimens to produce high resolution digital copies of them." The largest herbarium
imaging project was undertaken at the Botanic Gardens of Sydney when
over 1 million plant specimens at the National Herbarium of New South
Wales were transformed into high-resolution digital images.

"The digitisation project took over two years and shortly after
completion, one of the researchers -- Dr Jason Bragg -- contacted me from
the Botanic Gardens of Sydney. He wanted to see how we could incorporate machine learning with some of these high-resolution digital images of the Herbarium specimens." "I was excited to work with A/Prof. Cornwell in developing models to detect leaves in the plant images, and to then use
those big datasets to study relationships between leaf size and climate,"
says Dr Bragg.

"Computer vision" measures leaf sizes Together with Dr Bragg at the
Botanic Gardens of Sydney and UNSW Honours student Brendan Wilde,
A/Prof. Cornwell created an algorithm that could be automated to detect
and measure the size of leaves of scanned herbarium samples for two plant genera -- Syzygium(generally known as lillipillies, brush cherries or
satinas) and Ficus(a genus of about 850 species of woody trees, shrubs
and vines).

"This is a type of AI is called a convolutional neural network, also
known as Computer Vision," says A/Prof. Cornwell. The process essentially teaches the AI to see and identify the components of a plant in the same
way a human would.

"We had to build a training data set to teach the computer, this is a
leaf, this is a stem, this is a flower," says A/Prof. Cornwell. "So we basically taught the computer to locate the leaves and then measure the
size of them.

"Measuring the size of leaves is not novel, because lots of people have
done this. But the speed with which these specimens can be processed and
their individual characteristics can be logged is a new development."
A break in frequently observed patterns A general rule of thumb in the botanical world is that in wetter climates, like tropical rainforests, the leaves of plants are bigger compared to drier climates, such as deserts.

"And that's a very consistent pattern that we see in leaves between
species all across the globe," says A/Prof. Cornwell. "The first test we
did was to see if we could reconstruct that relationship from the machine learned data, which we could. But the second question was, because
we now have so much more data than we had before, do we see the same
thing within species?" The machine learning algorithm was developed, validated, and applied to analyse the relationship between leaf size
and climate within and among species for Syzygiumand Ficusplants.

The results from this test were surprising -- the team discovered that
while this pattern can be seen between different plant species, the
same correlation isn't seen within a single species across the globe,
likely because a different process, known as gene flow, is operating
within species. That process weakens plant adaptation on a local scale
and could be preventing the leaf size-climate relationship from developing within species.

Using AI to predict future climate change responses The machine learning approach used here to detect and measure leaves, though not pixel perfect, provided levels of accuracy suitable for examining links between leaf
traits and climate.

"But because the world is changing quite fast, and there is so much
data, these kinds of machine learning methods can be used to effectively document climate change effects," says A/Prof. Cornwell.

What's more, the machine learning algorithms can be trained to identify
trends that might not be immediately obvious to human researchers. This
could lead to new insights into plant evolution and adaptations, as
well as predictions about how plants might respond to future effects of
climate change.

* RELATED_TOPICS
o Plants_&_Animals
# Nature # Endangered_Plants # Botany
o Computers_&_Math
# Computer_Modeling # Computers_and_Internet #
Neural_Interfaces
o Fossils_&_Ruins
# Early_Climate # Ancient_DNA # Evolution
* RELATED_TERMS
o Computational_neuroscience o Scientific_visualization o
Data_mining o Paleoclimatology o Computer o DNA_microarray o
Spinach o Chloroplast

========================================================================== Story Source: Materials provided by
University_of_New_South_Wales. Original written by Lilly Matson. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Brendan C. Wilde, Jason G. Bragg, William Cornwell. Analyzing
trait‐climate relationships within and among taxa using
machine learning and herbarium specimens. American Journal of
Botany, 2023; 110 (5) DOI: 10.1002/ajb2.16167 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/06/230620113755.htm

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