Machine learning helps to identify climatic thresholds that shape the distribution of natural vegetation

Date:
February 25, 2022
Source:
University of Helsinki
Summary:
A new study explores large-scale relationships between vegetation
and climatic characteristics using machine learning. The findings
highlight the importance of climatic extremes in shaping the
distribution of several major vegetation types.



FULL STORY ========================================================================== Changing climate brings more frequent and more intense climatic extreme
events.

It is unclear, however, exactly how climate extremes will affect
vegetation distribution in the future. This is an acute question for
research in order to be able to mitigate coming extremities and their
impact on vegetation.


==========================================================================
A study published in Global Change Biology explores large-scale
relationships between vegetation and climatic characteristics
using machine learning. It demonstrates that combining climate and remotely-sensed land cover data with tree-structured predictive models
called decision trees can effectively extract the climatic thresholds
involved in structuring the distribution of dominant vegetation at
various spatial scales.

The findings of this study highlight the importance of climatic extremes
in shaping the distribution of several major vegetation types. For
example, drought or extreme cold are essential for the dominance of
savanna and deciduous needleleaf forest.

"One of the most important questions left to answer in the further
research is whether the climate thresholds recognized in this study
are static or changing with the climate changes in the future," says
researcher Hui Tang from the department of Geosciences of the University
of Oslo.

Collaboration between machine learning and vegetation experts Predicting
future vegetation distribution in response to climate change is a
challenging task which requires a detailed understanding of how vegetation distribution on a large scale is linked to climate. The research team consisting of computer scientists, vegetation modellers and vegetation specialists examine the rules coming from the decision tree models to see
if they are informative and if they can provide any additional insights
that could be incorporated into mechanistic vegetation models.

"It is a difficult task to validate whether a data-based model is
informative and robust. This study highlights the importance of
interpretable models that allow such meaningful collaboration with
the domain experts," says doctoral researcher Rita Beigait? from the
department of computer science of University of Helsinki.

"The major climatic constraints recognized in the study will be valuable
for improving process-based vegetation models and its coupling with the
Earth System Models," says Hui Tang.

========================================================================== Story Source: Materials provided by University_of_Helsinki. Original
written by Paavo Ihalainen. Note: Content may be edited for style
and length.


========================================================================== Journal Reference:
1. Rita Beigaitė, Hui Tang, Anders Bryn, Olav Skarpaas, Frode
Stordal,
Jarle W. Bjerke, Indrė Žliobaitė. Identifying climate
thresholds for dominant natural vegetation types at the global scale
using machine learning: Average climate versus extremes. Global
Change Biology, 2022; DOI: 10.1111/gcb.16110 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/02/220225100229.htm

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