CUHK eNews August 2024

An innovative method for tackling the temperature of tropical forests

The study demonstrates how fine-scale microclimates can improve ecological models and predict how species will respond to climate change.

The study demonstrates how fine-scale microclimates can improve ecological models and predict how species will respond to climate change.

Experts from CUHK, in collaboration with the University of Helsinki, have conducted a pioneering research project that paints a richer picture of the varied microclimates contained within tropical forests. By enabling more accurate modelling of the conditions that support biodiversity, the study provides essential insights for ecologists and policymakers dealing with climate change.

Tropical forests house half of the world’s species and play a crucial role in absorbing carbon dioxide and mitigating global climate change. Temperature is a key factor in determining biodiversity patterns and the functioning of forest ecosystems. Traditionally, temperature data has been collected via satellite remote sensing, which estimates temperatures at two metres above the canopy top in open-air environments. However, these measurements deviate significantly from the near-ground temperatures actually experienced by living things within a forest’s ‘understory’–the layer of low-lying bushes and plants that sit close to the forest floor–making it difficult to accurately evaluate how such organisms are impacted by climate change.

 

Mapping the forest floor

Conducting research on understory microclimates is challenging due to the sheer practical difficulty of placing sensors in the right places. However, the research team, led by Prof. Amos Tai and Dr. Ali Ismaeel of the Department of Earth and Environmental Sciences, along with Prof. Eduardo Maeda from the University of Helsinki, worked closely with local scientists to deploy 180 sensors across tropical forests in South America, Africa, and Southeast Asia. These sensors, placed just 15 cm above the ground, measured near-ground temperatures to create a detailed dataset of both degraded and preserved tropical forests from different geographical settings.

The next step was to use machine learning to combine this hard-won on-site data with existing satellite-derived macroclimate data, open-air data and other biophysical and geographical data. This allowed researchers to build a high-resolution map of understory temperatures that covers the entire tropics.

The research team gathers temperature data by setting up 180 sensors across tropical forests in South America, Africa, and Southeast Asia.

The research team gathers temperature data by setting up 180 sensors across tropical forests in South America, Africa, and Southeast Asia.

 

A powerful resource for climate policymakers

The results showed that understory temperatures are, on average, 1.6°C cooler than open-air temperatures, and in some cases up to 4°C cooler, while the difference between the highest and lowest temperatures on a given day is on average 1.7°C lower near the forest floor than in the open air. The team also discovered substantial variability in microclimate characteristics within tropical forests, influenced by climate conditions, vegetation structure and topography, highlighting the complexity of tropical forest microclimates and suggesting that the impact of global warming may vary significantly within the same forest.

This represents critical information for policymakers, says Prof. Tai, ‘Ecological models that use fine-scale microclimates as input can help us better predict how different species might react to climate change. These more stable microclimates can sometimes shield species from the broader impacts of climate change, but they can also change in ways that affect species’ ability to adapt. These changes can influence the genetic diversity within a species, which is crucial for survival in a changing environment. Microclimates also guide the seasonal movements of species, affecting where they can live and their population sizes. Understanding these interactions is key to predicting future shifts in species’ habitats due to climate change.’

 

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