# Termite mound architecture and climate control: a review of X-ray tomography and flow field simulation approaches

Nengi Fiona Karibi-Botoye, Guy Theraulaz, Bagus Muljadi, Vasily Demyanov, Kamaljit Singh

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## To cite this version:

Nengi Fiona Karibi-Botoye, Guy Theraulaz, Bagus Muljadi, Vasily Demyanov, Kamaljit Singh. Termite mound architecture and climate control: a review of X-ray tomography and flow field simulation approaches. Journal of the Royal Society Interface, 2025, 22 (231), 10.1098/rsif.2025.0263. hal-05343762

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Submitted on 3 Nov 2025

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# Termite mound architecture and climate control: a review of X-ray tomography and flow field simulation approaches

Nengi F Karibi-Botoye <fnref n="1" />*, Guy Theraulaz <fnref n="2" />, Bagus Muljadi <fnref n="3" />, Vasily Demyanov <fnref n="1" /> and
Kamaljit Singh <fnref n="1" />

## Abstract

Termite mounds are known for their ability to maintain self-sustained ventilation and thermoregulation irrespective of external climatic conditions. Although there has been extensive interest in this topic, especially for designing energy-efficient buildings, it is still not fully understood how mound properties are controlled. This article reviews established knowledge and identifies gaps in the study of climate control within termite mounds, proposing an interdisciplinary approach that combines X-ray tomography and flow field simulations. Through specific examples, we demonstrate how these methods can deepen our understanding of termite mound structure and its climate regulating functions.

**Key words:** Termite mounds, thermoregulation, ventilation, nest architecture, social insects, X-ray tomography, numerical simulation, flow field simulation, pore-scale modelling.

Author for correspondence (Tel.: +447496574356; E-mail: nfk3@hw.ac.uk).

<footnote n="1">¹Institute of GeoEnergy Engineering, School of Energy, Geoscience, Infrastructure & Society, Heriot-Watt University, EH14 4AS Edinburgh, United Kingdom.</footnote>

<footnote n="2">²Centre de Recherches sur la Cognition Animale (CRCA), Centre de Biologie Intégrative (CBI), Université de Toulouse, CNRS, UPS, Toulouse, France.</footnote>

<footnote n="3">³Department of Chemical and Environmental Engineering, University of Nottingham, NG7 2RD Nottingham, United Kingdom.</footnote>
# Contents

<table><tr><td>1. Introduction</td><td>3</td></tr><tr><td>2. Thermoregulation in termite mounds</td><td>6</td></tr><tr><td>3. Ventilation in termite mounds</td><td>9</td></tr><tr><td>4. Combined effect of ventilation, thermoregulation and humidity in termite mounds</td><td>12</td></tr><tr><td>5. X-ray tomography of termite mounds</td><td>12</td></tr><tr><td>5.1 Millimetre and microscale termite mound tomography</td><td>13</td></tr><tr><td>5.2 Application of X-ray-tomography in termite mounds</td><td>16</td></tr><tr><td>6. Numerical simulations on 3D structures of termite mounds</td><td>21</td></tr><tr><td>7. A new proposed integrated approach</td><td>34</td></tr><tr><td>8. Conclusions</td><td>38</td></tr><tr><td>9. Acknowledgments</td><td>40</td></tr></table>
# 1. Introduction

Termites are regarded as one of the supreme builders within the animal kingdom, skilfully creating structures that provide exceptional control over temperature, humidity, and airflow within them (1). They construct their mounds from materials like saliva, wood, excrement, and soil (2–5). The structural integrity of mounds can persist for several decades to centuries, emphasizing their durability and resilience (4,6,7). These mounds have captivated biologists, engineers, and architects for their ability to regulate heat, humidity, and gases without mechanical assistance, even as external climatic conditions change (8,9). Understanding and potentially replicating the processes underlying temperature regulation and ventilation in termite mounds could advance the fields of architecture and engineering, enabling buildings to meet occupant needs while reducing energy use and carbon emissions (10–13).

These mounds vary in shape and size ranging from a few centimetres to several meters with colony size ranging from hundreds to millions (8,14–16). These structures are crucial to the ecology as they enhance the fertility of the surrounding soil by adding carbon, improving aeration, and aiding filtration (17,18). They also help reduce atmospheric carbon, decompose organic matter, and provide habitats for organisms, including Termitomyces fungi. Termite mounds mainly result from self-organization processes (19), where the nest structure emerges from individual termites following simple local construction rules (20). Mound building process is often driven by stigmergic interactions, where termites do not communicate directly with each other but instead respond to external stimuli, such as chemical pheromones and environmental cues like surface curvature (21). Additionally, vibrational cues play a significant role in mound construction, as the mounds also serve as structures for vibration sensing and signalling (22). Social factors, such as the movement and density of other termites, further influence the construction patterns within the mounds (23) together with
ambient properties like temperature and airflow(16). While these insights offer valuable understanding, the exact behavioural mechanisms underlying termite mound construction continue to be a subject of ongoing research.

Mounds can have four distinct zones: the outer mantle, the transition zone, the accumulation zone, and the nest (24). The internal structure of the mound typically consists of a complex network of tunnels, chimneys, surface conduits, royal cells, lateral conduits, ridges, turrets, which vary based on the species of termites. The term “mound” is mostly used interchangeably with “nest”. In this article we use the term *mound* to describe the large epigeal part, whereas the interior of the mound where the fungus (if present) and termite queen reside is referred to as *nest*. The term *channel* refers to millimetre-scale voids in the mound and is sometimes used interchangeably with *chamber* in smaller species, whereas *pore* refers to microscale voids.

Despite recognizing the importance of thermoregulation and ventilation processes in termite mounds, a comprehensive understanding of the underlying mechanisms remains elusive. Our limited understanding can be attributed to the challenges of directly measuring conditions within the mound, particularly in relation to ventilation. While indirect methods such as using tracer gas and condensation on plastic film to study airflow have provided valuable insights, they only offer a partial understanding of climate control in termite mounds. Moreover, the diversity of termites with 297 genera and 2951 species (25) each having its own mound design has further hindered our understanding of climate control mechanisms in their mounds. Furthermore, the majority of studies have approached the topic from a biological perspective and have not provided a complete understanding of the climate controlling
mechanisms. These limitations highlight the need for new and improved methods of studying termite mounds, especially as the world increasingly prioritises sustainable solutions.

In this article, we demonstrate how the combination of X-ray tomography and numerical simulations can provide insight into how termite mound structures influence internal climate control. Although these tools are widely applied in domains such as the oil and gas industry, geo-energy engineering, and soil science, their combined use in the context of termite mounds remains underexplored. We present the techniques and workflows required to transition from X-ray tomography of termite mounds to numerical simulation, supported by relevant examples. We also discuss how simulations can be applied across different spatial scales, from microscale to entire mounds. Furthermore, we highlight key limitations of this integrated approach and emphasize the importance of validating simulation results, offering suggestions for how validation can be effectively implemented. In doing so, we show how an integrated approach can help explore the identified knowledge gaps towards bioinspired climate control solutions.

We start by reviewing studies on thermoregulation and ventilation in termite mounds. This is followed by a discussion on the application of X-ray tomography to enhance our understanding of termite mounds, including relevant examples. We also explore how numerical simulations can advance this research. Finally, we summarise key findings highlighting areas that are still unexplored and conclude with insights into potential future direction.
## 2. Thermoregulation in termite mounds

Temperature and humidity regulation is essential for termites, especially in species that cultivate fungi, as the termites and fungi require stable environments (26,27). Termites maintain stable conditions mainly through passive methods like selecting specific mound sites and designs, with minimal reliance on active methods such as clustering for warmth (28). Several studies have been carried out to investigate the temperature profiles at different locations within the termite mounds (9,29–32). These studies show that temperature varies across mound locations, with internal nest temperatures remaining stable compared to external temperatures, as shown in Figure 1. In these studies, the daily temperature variation in nests is minimal (0–4°C) compared to fluctuations in ambient temperature (up to 20°C) (31,32).

<figure><img src="image_3.png" /><figcaption>Figure 1: Temperature profile in the mound and ambient temperature over the course of three days. Temperature of air 1 cm above the surface of the mound in light blue (—), inside the air channels in red (—) and inside the nest in dark blue (—) (adapted from Korb & Linsenmair (32))</figcaption></figure>

The stable internal conditions can vary depending on whether a mound is inhabited, its size, and its ventilation type. Inhabited mounds tend to be 1.4 – 6.1 °C warmer than uninhabited mounds due to metabolic heat from termites and fungi (30,32–35) (see
Table 1). Larger mounds exhibit greater thermal stability compared to smaller mounds (5,30,32) (see

Table 1). The enhanced temperature stability in large mounds arises from their greater thermal mass as they possess a lower surface area to volume ratio and a higher heat capacity. This characteristic enables them to absorb and retain more heat, gradually releasing it into the external environment, consequently leading to reduced temperature variability.

Ventilation type has also a critical influence on nest temperatures. For instance, in the closed mounds (mounds with no visible holes) built by *Macrotermes michaelseni*, nest temperatures consistently exceeded those found in open mounds of *Macrotermes subhyalinus* by 1.4 – 4 °C (31). This difference can potentially be attributed to the absence of visible wall openings in closed mounds, thereby preventing heat loss and minimising the influx of air in comparison to mounds with open ventilation. Consequently, the adoption of an open mound by some termites might serve as an adaptation that facilitates heat dissipation in regions characterized by high temperatures. This observation suggests that structures primarily designed for gas exchange might also contribute to nest thermoregulation when favourable thermal conditions are available (36).

It is important to highlight that ambient temperature influences the baseline in a mound with higher ambient temperatures leading to higher mound temperatures, and vice versa (8,32). Ambient temperature also influences the mound architecture together with factors such as soil composition (37), solar exposure, humidity, CO₂ levels, and genetics (1,23,38). It is difficult to determine the primary factor influencing mound architecture, raising the question whether its construction is driven mainly by environmental conditions, soil composition, or the termites' genotype.
<table><caption>Table 1: Summary of measured parameters in termite mounds</caption><thead><tr><th>S/N</th><th>Parameter</th><th>Value</th></tr></thead><tbody><tr><td>1.</td><td>Difference in nest temperature between inhabited and uninhabited mounds</td><td>1.4 – 4 °C (32)<br>3.8 – 6.1 °C (30)<br>3.3 °C (35)<br>14.5 – 18.6 °F (33)</td></tr><tr><td>2.</td><td>Difference in nest temperature between closed and open ventilation mounds</td><td>1.5 – 4 °C (31)</td></tr><tr><td>3.</td><td>Difference in thermal stability in the nest of large mounds (LM) and small mounds (SM)</td><td>Range and coefficient of variation (CV)<br>of LM – 2.5 °C (0.028)<br>Range and CV of SM – 6 °C (0.045) (30)</td></tr><tr><td>4.</td><td>Air velocity in the mound (cm/s)</td><td>-6 – 6 (39)<br>-5 – 5 (9)</td></tr><tr><td>5.</td><td>Humidity values in the mound (%)</td><td>70 (29)<br>96 – 99 (40)</td></tr><tr><td>6.</td><td>CO<sub>2</sub> concentration in the mound (%)</td><td>4 – 6 (39)<br>0.5 – 6 (9)<br>0.01 – 2.4 (41,42)<br>1.85* (29)<br>0.03 – 0.3 (43)<br>0.8 – 2.9 (40)</td></tr></tbody></table>

Figure 2 shows examples of the diversity of mound architectures built by termites, both across different species and within the same species. Figure 2A and 2E show the mounds built by *Nasutitermes triodiae*, with different architectures formed at different stages of

<footnote>* this value is obtained from P<sub>CO2</sub>/P<sub>atm</sub></footnote>
construction. These differences in architecture have a direct impact on temperature regulation and ventilation as their internal and external features are different. Even within the same climate, mounds built by the same termite species can exhibit different ventilation mechanisms, adding complexity to the study of termite mounds. Similar architectural diversity is also observed in *Amitermes* mounds across different climates (Figure 2B, 2D, and 2F).

<figure><img src="image_4.png" /><figcaption>**Figure 2:** Diversity in termite mound architecture (A) *Nasutitermes triodiae* in Western Australia, (B) *Amitermes hastatus* South Africa (photograph by Nicole Loebenberg), (C) *Macrotermes jeanneli* in South Ethiopia (photograph by Rod Waddington), (D) *Amitermes meridionalis* in Northern territory, Australia (photograph by Peter Szoke), (E) *Nasutitermes triodiae* in Northern Territory, Australia (photograph by John Brew) and (F) *Amitermes laurensis* in Queensland, Australia (photograph by Russell Cumming).</figcaption></figure>
### 3. Ventilation in termite mounds

Termite nests require effective ventilation to exchange CO₂ produced both by the termites and the fungi they house. Luscher (40) estimated the oxygen requirement for a mound containing a colony of approximately 2 million termites weighing 20 kg to be approximately 240 L/day. The gas output of large mounds like the *M. jeanneli* is between 100,000 - 400,000 L of air per day, including 800 – 1,500 L of CO₂ per day (44). Typically, the oxygen concentration inside a nest is 2% lower than the surrounding air, while carbon dioxide levels are higher up to 6% (39,45). Although termite mounds can tolerate more CO₂ than humans and typically produce more CO₂ than a single human, these values indicate the importance of effective ventilation in the mound to remove produced gas.

Ventilation in mounds occurs in two phases (9,46). The first phase is transport of gases from the underground part of the nest, where metabolic activity takes place (i.e., where the fungus and the brood are located), to the mound surface. In the mounds that do not have an underground part, this first stage consists of transporting the gases from the centre (or core) of the mound to below the outer wall of the mound. Effective connectivity of channels in the mound is crucial in the first phase of ventilation. The connectivity in some mounds can be as high as 98% (see Table 3). Furthermore, effective transport within the mound is important, requiring adequate permeability to support fluid flow.

Transport within the mound can be driven by both pressure and temperature gradients. The temperature gradient arises from diurnal solar heating, which heats different parts of the mound unevenly, whereas pressure differences can be caused by external winds and turbulence driving airflow. In some mounds, such as those of *Cubitermes* species and magnetic termite mounds like *A. meridionalis*, diffusion primarily facilitates transport within
the mound due to its structure (43,47). These transport mechanisms become even more
important as high humidity inside the mound can limit oxygen availability.

The second phase is the transport of gas across the outer wall of the mound to the outer environment. In open mounds, millimetre-sized holes on their outer walls can facilitate mound ventilation. In the absence of holes, ventilation can still occur through the porous microstructure of the outer wall (43,48,49). In these mounds, ventilation is sensitive and relies on both a well-connected internal network of air channels, and a porous and permeable outer wall. Without these connected networks and porous walls, CO₂ can accumulate within the mound affecting the ventilation process.

Various mechanisms have been proposed to explain how ventilation occurs in termite mounds, ranging from thermosiphon theory and unidirectional ventilation, as seen in tall structures, to tidal effects. The most widely accepted mechanism, confirmed by direct airflow measurements, is solar-powered ventilation, as proposed by King *et al.* (9) and Ocko *et al.* (39). In this model, solar heating raises the air temperature within surface air channels during the day, causing warm air to rise, thus creating a natural convection effect and pulling cooler air from the nest toward the surface. At night, when the temperature of the surface air channels drops below the nest temperature, there is a reversal in temperature gradients, changing the airflow direction but still facilitating ventilation within the mound. This ventilation mechanism is similar to the external and internal ventilation proposed by Korb and Linsenmair (42).

In every mechanism that powers the mound, the architecture of the mound plays a vital role
and strongly influences the air movement and CO₂ transport. Thus, the structure of the mound
must be examined and the properties that optimise CO₂ transport must be identified to
replicate efficient ventilation processes.

4. Combined effect of ventilation, thermoregulation and humidity in termite mounds

Termite mounds exhibit a complex interaction of temperature regulation, humidity control, and gas exchange, all managed simultaneously within their structure. Studying these processes together is essential. Termites adjust their mound structures in response to environmental factors like humidity, temperature, solar exposure, and rainfall. Changing the mound structure in response to one environmental factor may influence the other conditions within it. For instance, in cold climates, dome-shaped mounds with thick walls minimise heat loss but can inhibit gas exchange, causing high CO₂ levels that can lower termite metabolic activity and reduce the internal temperature of the nest, counteracting the mound's heat retention goal (8,36,50). Thus, the mound design reflects trade-offs influenced by species, environmental conditions, and mound location (above or below ground) (51).

In these interdependent conditions, termites may need to prioritise between thermoregulation and ventilation, as these processes are often related to each other. For instance, some mounds optimize temperature control at the expense of CO₂ exchange, while others may do the contrary. Completely understanding how these processes influence each other requires detailed experimental and simulation investigations.

5. X-ray tomography of termite mounds

Historically, the internal structure of termite mounds has been explored using various visualisation techniques, like cement infiltration, mound sectioning and paint tracing, cross-sectioning image analysis, planar sectioning, and slicing and scanning with a camera (48,52,53), each with its unique strengths and limitations. These techniques do not provide a
clear and comprehensive understanding and are often destructive, making the mounds unusable afterwards. Some modern non-invasive techniques, such as electric resistivity tomography (ERT), offer a less intrusive alternative; however, they only provide data on surface-level resistivity values without revealing the full internal structure (54). This limitation has prompted interest in other imaging methods that could deliver comprehensive, non-destructive insights into mound structures.

X-ray tomography has emerged as a powerful non-destructive tool for visualising the internal structures of opaque materials (55,56). Conventional medical computed tomography (CT) scanners can achieve spatial resolutions of approximately 250 µm, which is effective for large-scale samples. With advances in imaging technology, high-resolution X-ray micro-CT and nano-CT now enable more detailed characterisation of internal structures (57,58).

Despite its advantages, X-ray tomography faces certain limitations, such as noise, operator variability, and imaging artifacts, though effective filtering and image processing techniques can address these challenges. Beyond its application to general imaging, X-ray tomography has shown promise in laboratory settings, particularly in soil sciences, geo-energy engineering, and the petroleum industry for analysing fluid saturations and characterising porous media (59,60). The insights from such analysis are potentially applicable to termite mounds, where understanding internal structures could aid in the study of thermoregulation and ventilation dynamics.

## 5.1 Millimetre and microscale termite mound tomography

Conventional X-ray tomography enables us to visualise an extensive volume of the mound and in some instances the entire termite mound, capturing large-scale structural features. This imaging approach, which dates back to 1956 by Desneux (61), aids in understanding the
correlation between large-scale features, such as chamber and outer wall thickness, and the mound functionality. Figure 3 – 7 show examples of X-ray tomography at millimetre-scale, highlighting structural patterns of termite mounds from various termite species and capturing a clear distinction between solid parts of the mound and the intricate channels within. For mounds larger than the field of view, X-ray tomography can be conducted on subvolumes obtained from the mound, which can be merged to construct the complete mound.

<figure><img src="image_5.png" /><figcaption>Figure 3: X-ray tomography of a termite mound from the Trinervitermes geminatus species. (A) 3D image showing channels, inner walls and outer walls, and (B) a vertical cross-section of an X-ray micro-tomography scan showing micropores of different sizes, metallic elements, sand and cracks (adapted from Singh et al. (49)).</figcaption></figure>

X-ray micro-tomography offers a closer look at fine-scale structural details within termite mounds, achieving a resolution of 1 µm or finer (49). This level of detail enables the identification of small features such as micropores, metallic elements, and cracks (Figure 3B), which cannot be resolved fully in millimetre-scale tomography. Although X-ray micro-tomography has found extensive application in various fields (62–65), its use in termite mound studies remains limited, with only a few notable studies, including work by Singh *et al.* (49) and Oberst *et al.* (66,67). However, the smaller field of view in X-ray micro-tomography restricts it to localised sections of the mound. The complementary nature of millimetre and microscale tomography suggests that combining both scales is likely to
provide a comprehensive view of termite mound architecture, capturing large-scale features
alongside minute details.

Image processing techniques are essential for enhancing and analysing millimetre and microscale X-ray tomography mound images. Filtering methods, such as non-local means filtering, reduce noise in the images without blurring the edges or material boundaries, thereby improving the quality of the images (68,69) as shown in Figure 4A and 4B. Once filtered, the grey-scale image is segmented into distinct components, e.g., Figure 4C, where the mm-scale image is segmented into solid mound walls and air channels. The microscale images can be segmented into more distinct components, such as individual pores, grains, and metallic elements, as shown in Figure 3B. Once the segmentation is complete, these images can be used for further analysis to assess structural properties of the mound. Specialized open-access software, such as Fiji, Dragonfly, and other commercial options, can be used for image processing and analysis. Some of the key parameters that can be obtained from this image analysis include pore size distribution, wall thickness, chamber size and shape, and chamber or pore connectivity. Details about these software tools are shown in Table 2.

<figure><img src="image_6.png" /><figcaption>Figure 4: Image processing and segmentation. (A) original grey-scale image, (B) filtered image using nonlocal means filter, and (C) segmented image showing air channel, solid and outer air.</figcaption></figure>
<table><caption>Table 2. Useful open-access software for image processing and simulations</caption><thead><tr><td>S/N</td><td>Name of software</td><td>Use</td><td>Source</td></tr></thead><tbody><tr><td>1.</td><td>Fiji</td><td>Image processing, and machine learning tool for segmenting 3D images</td><td>https://imagej.net/software/fiji/downloads</td></tr><tr><td>2.</td><td>Dragonfly</td><td>Image processing, segmentation and analysis of 3D images</td><td>https://www.theobjects.com/dragonfly/index.html</td></tr><tr><td>3.</td><td>OpenFoam</td><td>Open-source tool for computational fluid dynamics (CFD)</td><td>https://www.openfoam.com/current-release</td></tr><tr><td>4.</td><td>GeoChemFoam</td><td>Open-source tool based on OpenFoam CFD toolbox specifically designed for porous media flow processes</td><td>https://github.com/GeoChemFoam</td></tr><tr><td>5.</td><td>Paraview</td><td>Open-source visualisation tool</td><td>https://www.paraview.org/download/</td></tr><tr><td>6.</td><td>Drishti</td><td>Open-source visualisation tool</td><td>https://github.com/nci/drishti</td></tr></tbody></table>

## 5.2 Application of X-ray-tomography in termite mounds

X-ray tomography has been used in several studies to characterise and quantify the internal structure of termite mounds. In this section, we explore the application of CT imaging in termite mounds, highlighting insights from previous studies, our own analysis, and how these techniques can be applied more efficiently in the future.

X-ray tomography can be used to map the network of chambers (air spaces for movement) and galleries (air tunnels connecting chambers) within a mound, as demonstrated by Perna *et al.* (23,55). This image analysis can provide information on the connectivity of chambers, a
key structural element that is essential for various processes such as communication and
defence (55). Additionally, the connectivity analysis can help in understanding airflow in
different directions (Figure 6). This is especially important in solar-powered ventilation, as
even with a thermal gradient present in the mound, a lack of connectivity to maintain
continuous airflow could still lead to CO2 buildup. The spatial arrangement of chambers and
galleries, along with their connectivity across different mound types allows for comparisons
between species in different environments. This can reveal how mound structures adapt to
local environmental factors. Typical connectivity values observed in different mounds are
shown in Table 3.

<figure><img src="image_7.png" /><figcaption>Figure 5: Analysis of Cubitermes mound from forest (top row) and savannah (bottom row). (A) and (B) show images of the mounds, (C) and (D) show topographical slice of the mounds, (E) and (F) show virtual “cast” displaying chambers and galleries (virtual cast here means digitally simulating filling the mound with a material like gypsum, allowing it to set and removing the solid parts of mounds revealing the chambers and galleries), and (G) and (H) show network of chambers and galleries in the mounds. The colour of the chambers in (G) and (H) reflects their degree of connectivity (number of corridors connected to a chamber) (Perna et al., (55) with permission).</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 6: Connectivity in a subset of Thoracotermes mound. (A) virtual “cast” showing chambers and galleries in the mound. (B) connected chambers and galleries in the x-direction, where the connectivity is 44% of the total void space. (C) connected chambers and galleries in z direction with no connectivity. In this mound, there would be preferential fluid flow in the x-direction. This mound was occupied by a smaller species than Thoracotermes, who reduced the size of its galleries. This may explain the connectivity being lower than the typical values for this species.</figcaption></figure>

<table><caption>Table 3: Structural properties obtained from X-ray tomography of termite mound</caption><thead><tr><th>S/N</th><th>Parameter</th><th>Mound type</th><th>Value</th></tr></thead><tbody><tr><td rowspan="5">1.</td><td rowspan="5">Microporosity (%)</td><td>Nasutitermes exitiosus</td><td>22.1, 32.8 (67)</td></tr><tr><td>Odontotermes obesus</td><td>6.6 – 8.6 (4)</td></tr><tr><td>Microcerotermes nervosus,<br>Macrognathotermes sunteri, Tumulitermes<br>pastinator</td><td>23 – 39 (53)</td></tr><tr><td>M. michaelseni</td><td>0.71, 1.74 (54)</td></tr><tr><td>T. geminatus</td><td>15, 27 (49)</td></tr><tr><td rowspan="5">2.</td><td rowspan="5">Macroporosity (%)</td><td>Coptotermes lacteus, N. exitiosus</td><td>30 – 55 (66)</td></tr><tr><td>Nasutitermes exitiosus</td><td>31.71, 32.19 (67)</td></tr><tr><td>M. michaelseni</td><td>13.9 (70)</td></tr><tr><td>Microcerotermes nervosus,<br>Macrognathotermes sunteri, Tumulitermes<br>pastinator</td><td>19 – 36 (53)</td></tr><tr><td>Channel thickness<br>(mm)</td><td>T. geminatus</td><td>6.7, 6.8 (49)</td></tr><tr><td rowspan="2">4.</td><td rowspan="2">Pore size (mm)</td><td>M. michaelseni</td><td>3 – 25 (70)</td></tr><tr><td>T. geminatus</td><td>0.073, 0.069 (49)</td></tr><tr><td rowspan="2">5.</td><td rowspan="2">Outer wall<br>thickness (mm)</td><td>Odontotermes obesus</td><td>0.5 – 0.58 (4)</td></tr><tr><td>T. geminatus</td><td>14.9, 11.1 (49)</td></tr><tr><td rowspan="2">6.</td><td rowspan="2">Connectivity</td><td>T. geminatus</td><td>92 – 98% (49)</td></tr><tr><td>Cubitermes spp.</td><td>76 – 96%* (55)</td></tr></tbody></table>
<table><thead><tr><th>S/N</th><th>Parameter</th><th>Mound type</th><th>Value</th></tr></thead><tbody><tr><td rowspan="2"></td><td rowspan="2"></td><td><i>Coptotermes lacteus, N. exitiosus</i></td><td>Euler number = -100 – 0 (66)</td></tr><tr><td><i>M. michaelseni</i></td><td>Valence number = 3 – 4 (70)</td></tr><tr><td>7.</td><td>Density (Kg/m³)</td><td><i>Nasutitermes exitiosus</i></td><td>1052 – 5199 (67)</td></tr></tbody></table>

Mean values are reported and if there are several values measured, it is reported as a range. * = is estimated from nodes/total number of chambers.

X-ray tomography can also be used to determine the porosity and volume of termite mounds, which are essential structural properties, offering insights into termite behaviour and resource allocation. This technique has been applied in several notable studies, including those by Hervier *et al.* (71), Nauer *et al.* (53), Singh *et al.* (49), Zachariah *et al.* (4), Andréen and Soar (70), and Van Thuyne *et al.* (54). Porosity also affects the mound's ability to regulate moisture, air, and heat flow, making it a critical factor in climate control. Additionally, porosity which is inversely correlated to the density of the mound, influences its structural stability. This relationship is evident in the mound's structure as denser inner walls of the mounds resembling the spongy architecture of trabecular bone, provide load-bearing support, while the less dense and porous outer walls aid in ventilation and temperature regulation (67). It is important to note that porosity can be measured at various scales: at the millimetre/macro scale, where it represents the ratio of chambers, galleries and conduits to the total volume, or at the micro scale, where it represents the ratio of micropores to the total volume (53) (see Table 3).

In addition to porosity, X-ray tomography reveals pore size distribution, chamber thickness and shape across different mound types. For example, chamber thickness in mounds from *T. geminatus*, *Cubitermes*, *Apicotermes*, and *Thoracotermes* were compared, revealing that *Thoracotermes* mounds had notably thicker chambers when compared to others as seen in Figure 7. Elongated and thinner chambers as found in *Apicotermes* mounds have been linked
to higher permeability in the inner section of the mound (from personal observation). Within the same mound, channel (pore) sizes can be compared across different sections as demonstrated by Zachariah *et al.* (4). This image analysis can help us link structural differences across different parts of the mound to their potential functions. The porosity and channel (pore) size distribution can also be linked to its connectivity. Larger and well connected channels (pores) facilitate better airflow, while smaller or isolated channels (pores) may restrict ventilation. Additionally, the shape of channel (pores) may also play a role in flow, as elongated or irregularly shaped channels (pores) could influence directional airflow and drainage in the mound.

<figure><img src="image_9.png" /><figcaption>Figure 7: Different termite mounds and their corresponding chamber thickness distribution. (A) images of mounds. From top to bottom: Thoracotermes, Cubitermes, Apicotermes and T. geminatus mounds. The white scale bar shown on the image on the left corresponds to 1 cm (B) chamber thickness distribution of mounds in (A). Cubitermes and Apicotermes exhibit similar distributions, while T. geminatus and Thoracotermes display a wider range of chamber thicknesses, with Thoracotermes tending toward higher values.</figcaption></figure>

X-ray tomography has also been applied in unconventional areas, such as termite pest management, where it enables non-destructive monitoring of wood consumption by termites (72,73). In the future, image analysis in termite mounds can be extended to include other structural properties such as surface area and surface-to-volume ratio contributing to a more
comprehensive understanding of termite mound architecture. Greater emphasis should also be placed on submillimetre X-ray tomography of the mounds such as those studied by Oberst *et al.* (67) and Singh *et al.* (49) as small-scale features play a significant role in processes like gas exchange, drainage, thermal conductivity, and mechanical stability.

Beyond structural analysis, X-ray tomography can be used to study dynamic processes in termite mounds, as demonstrated by Singh *et al.* (49), who monitored fluid distribution to assess how mound structure affects drainage. This approach can be extended to other termite species and to monitor dynamic processes like CO₂ transport in the mound.

X-ray tomography can also be extended to monitor structural changes occurring in termite mounds over time. This is particularly important as termite mounds are not static structures but are continuously remodelled by termites (39,55). These changes occur in response to ambient conditions, such as temperature. Experiments have shown that altering the ambient temperature of a typical forest environment to mimic savanna temperatures can lead to the reconstruction of the mound, and vice versa. In cases where reconstruction cannot be done quickly, the mound colony may die off (74). Regions within the mounds that are no longer needed may be closed off, and other structures may need to be reopened. For example, during the rainy season, when the walls of mounds become wet, complex egress tunnels emerge on the mound surface to promote ventilation (48,70). Analysing such dynamic changes with X-ray tomography would help us link internal structural changes to specific seasonal conditions and climate.
## 6. Numerical simulations on 3D structures of termite mounds

Numerical simulation integrated with X-ray tomography provides a powerful approach for analysing intricate processes within termite mounds in a controlled, efficient, and cost-effective way. The fluid flow behaviour, such as velocity, pressure, and turbulence, can be analysed alongside key mound properties like permeability, effective diffusivity, tortuosity, dispersion, and effective conductivity (48).

For such numerical simulations, segmented X-ray tomography images of mounds are discretised. Discretisation involves converting a continuous function or variable into a discrete one. This is typically the first step in numerical simulation analysis, as it makes the functions and variables manageable for computer processing (75). There are three types of discretisation that can be performed: spatial, equation, and temporal (48). Spatial discretisation divides the computational domain, in this case, the mound structure, into subdomains, creating a mesh. A mesh consists of triangles, line segments, points, and their n-dimensional counterparts (75). Meshes can be structured, unstructured, or hybrid (a combination of structured and unstructured meshes). Structured meshes are composed of hexahedral elements built on a coordinate system and are commonly used for simple geometries like cubes (48). Unstructured meshes are composed of tetrahedral elements and are better suited for complex geometries, but they can be more computationally expensive.

The mesh can then be refined in regions of interest to improve its resolution. However, it is important to ensure mesh independence, where changes in mesh resolution do not affect the accuracy of the numerical results (48). Mesh independence can be determined by carrying out simulations at different mesh resolutions and comparing the results to ensure there is minimal variation. For tomographic mound images, a structured grid where each voxel is converted
into a numerical grid cell of the same size will be sufficient with local refinement applied as
needed (see Singh *et al.* (49)). Additionally, channel (pore) networks can be extracted from
these tomographic images to analyse flow (62). However, this method tends to be less
accurate for termite mounds at the millimetre scale (personal observation).

Equation discretisation transforms partial differential equations (PDEs), such as the Darcy-
Brinkman-Stokes (DBS), Navier-Stokes, and heat equation into numerical forms solvable by
computers. Methods such as the finite difference method (FDM), finite element method
(FEM) and finite volume method (FVM) can be used for this discretisation. FDM is relatively
easy to implement and can handle structured grids. However, it cannot handle complex
geometries effectively (76). FVM and FEM, on the other hand, can handle both structured
and unstructured meshes. FEM does better with more complex geometries but requires more
computational power than FVM. FEM has been used to investigate the role of environment
on diversity of mound structure (50,77) and is well suited for this type of application, where
different mound structures are generated. FVM involves solving equations over finite
subdomains known as control volumes. It is the prevalent discretisation method in a CFD
toolbox such as OpenFoam and GeoChemFoam (see Table 2). It has been used by Abou-
Houly Eid Haitham (48) and Singh *et al.* (49) in termite mounds studies. The choice of
discretisation method depends on the process being analysed and the computing resources
available. However, for characterising fluid flow in large mounds, FVM is a reliable option.

The third type of discretisation is temporal discretisation, where continuous time is divided
into discrete time steps. For steady state flow, where fluid velocity, temperature, and CO<sup>2</sup>
concentration remain constant over time, this discretisation is unnecessary. However, in the
case of transient flow, temporal discretisation must be included. An explicit method, which is
easier to implement but less stable, can be used, as can an implicit method, which offers
greater stability.

Governing equations are solved within these grid cells iteratively under specified boundary conditions until they converge. These boundary conditions model how the mound interacts with the surrounding environment, the soil beneath it, and its internal structures. A fixed value boundary condition can be applied to define the exact air flow velocity, temperature, and CO<sup>□</sup> at the inlets and outlets, ideally using measured values from different climatic and environmental conditions in order to validate these simulations. For example, wind speed, solar irradiation and ambient temperature values typical of Namibia were used in airflow and heat simulations at the inlet of the mound (50). Alternatively, a fixed gradient boundary condition can be applied for flow simulations, corresponding to the pressure gradient measured in the field (48), or a CO<sup>□</sup> concentration gradient across the mound, scaled relative to atmospheric CO<sup>□</sup> concentration (49). Time-dependent boundary conditions are particularly useful for heat simulations as they account for diurnal temperature changes at the mound walls, as demonstrated by Ocko *et al.* (16). Other boundary conditions, such as zero heat flux to model insulation layers and cyclic boundaries, are also applicable.

Convergence in these simulations is reached when the difference between the left and right sides of the equations becomes very small and shows minimal variation between iterations. These equations can be solved using various numerical methods. For example, in the case of the Navier-Stokes equation, algorithms such as SIMPLE or PISO are commonly applied for pressure and velocity coupling. These algorithms are available in toolboxes like OpenFOAM and GeochemFoam. The solution can be influenced by several factors, including the discretisation scheme, boundary conditions, and the physical coefficients used. Therefore, it
is important to validate the numerical results against field data. The results from the
simulations can be visualised using software like ParaView (refer to Table 2). These
techniques have been widely applied in other fields (62,78,79).

With regard to termite mounds, several numerical simulations have been carried out on digital models that mimic mound structure (16,48,50,77). These simulations have produced comparable results. For example, the heat transfer Péclet number estimated by Singh *et al.* (49) (0 - 1.43 × 10⁻³) falls within the same range estimated by Ocko *et al.* (16) (10⁻⁶ to 10⁻¹). This low Péclet number range is associated with diffusion dominated conditions as a result of the wind velocity of 0 – 5 m/s outside the mound. For higher wind velocities or temperature gradients, the Péclet number may increase, and the system could become advection dominated. However, this rarely happens, as heat flow in termite mounds is typically diffusion dominated (50). Additionally, the heat diffusivity estimated by Gouttefarde *et al.* (80) (2.7 – 8 × 10⁻⁶ m²/s) closely aligns with values obtained for the Guinea mound in Singh *et al.* (49) (6.83 × 10⁻⁶ m²/s), based on the apparent thermal conductivity of 1.58 W/m·K derived from the numerical simulations and the specific heat capacity and density estimated from XRD analysis. However, the mound outer walls permeability measured for the *M. michaelseni* mound (2.96 – 3.25 × 10⁻¹ m²) is significantly higher than that reported for *T. geminatus* by Singh *et al.* (49) (0.6 – 3.5 × 10⁻¹² m²). This difference is likely attributed to the much higher porosity of 60% observed in *M. michaelseni*, compared to a maximum porosity of 27.9% in *T. geminatus*, highlighting the likely influence of species specific architecture on flow.

Singh et al. (49) conducted flow field simulations using X-ray tomography images of termite mounds scanned at a voxel size of 3 µm. These simulations are usually referred to as pore-
scale simulations and are valuable for understanding thermal conductivity and permeability at
fine scales. Similar numerical simulations can be performed on millimetre-scale CT mound
images to assess the influence of larger structural properties of the mound such as its
chambers and galleries on fluid flow. Typical velocity and pressure fields obtained from such
millimetre-scale simulations are shown in Figure 8A, 8B, 8C, and 8D. These velocity and
pressure profiles reveal which chambers contribute to the flow and to what extent. In the *T. geminatus* mounds shown in Figure 8, the flow distribution varies across different regions,
with bright yellow areas indicating higher velocity and red to dark red areas representing
lower velocity, where fluid movement slows down. This variation is likely due to differences
in chamber size and porosity across the mound.
<figure><img src="image_10.png" /><figcaption>Figure 8: Flow fields, heat and CO₂ transport at millimetre scale in two mounds (mound-1 and mound-2) of T. geminatus species. (A) and (B) show pressure fields from millimetre scale flow field simulations using Darcy-Brinkman-Stokes equation, applying a flow rate of 1e-7 at the inlet, zero gradient at the outlet and zero velocity at the walls. (C) and (D) show velocity fields in the chambers of the mounds. (E) and (F) show dimensionless temperature profile in the chambers of the mounds. (G) and (H) show CO₂ transport through mound-1 and mound-2 at 90 sec. (I) 100% CO₂ saturation in mound-1 at 320 sec. (J) 90% CO₂ saturation in mound-2 at 320 sec.</figcaption></figure>
As discussed in the previous section, a multiscale approach, where termite mounds are scanned at both the millimetre and microscale, and the datasets are integrated, can provide a more comprehensive understanding of mound architecture. Techniques such as Darcy-Brinkman-Stokes (DBS) modelling allow for detailed simulation of fluid flow in both millimetre scale and microscale regions of the mound. While this approach has not yet been applied to termite mounds, it has been successfully used in other disciplines such as geothermal, human anatomy and in sedimentary rocks (81–83).

The DBS equation (equation 1) is an extension of the Navier-Stokes equation. It includes a momentum exchange term, $vK^{-1}u$ which is significant in the solid phase and disappears in the fluid phase. The local permeability $K$ is obtained from Kozeny-Carman relationship, and it is assumed to be a function of local porosity (equation 2). The DBS equation can account for unresolved regions in the mounds, which is common when solving multiscale flow in porous media. An example of multiscale modelling of termite mounds using DBS is shown in Figure 9. In this example, flow through the microporous outer walls of the mound is first simulated. The permeability (2e-12 m²) and porosity (15%) obtained from the pore scale simulation are used in the large-scale simulation of the entire mound. The simulation produces the velocity fields shown in Figure 9B, along with the permeability of the multiscale mound (2.12e-11 m²). A similar multiscale simulation approach can be applied to determine the thermal conductivity, CO diffusivity, and other key flow parameters of the entire mound. In this process, microscale simulations provide the microscale mound properties like thermal conductivity, which are then incorporated into the macroscale model of the mound as input parameters.

$$ \frac{1}{\epsilon^2} \nabla \cdot (u \otimes u) = -\nabla p + \frac{\nu}{\epsilon} \nabla^2 u - vK^{-1}u \quad (1) $$
$$K^{-1} = \frac{180 (1 - \epsilon)^2}{h^2 \epsilon^3} \qquad (2)$$

where u is velocity, v is kinematic viscosity, ε is the local porosity, p is pressure, K is the local permeability and h is resolution of mesh.

<figure><img src="image_11.png" /><figcaption>Figure 9: Multiscale simulation in termite mound using DBS equation. (A) 3D structure of a mound showing channels, and microporous walls with porosity of 15% and permeability of 2e-12 m². (B) velocity field in the mound.</figcaption></figure>

The Navier-Stokes equation can also be used to solve fluid flow in the mound. Molecular diffusivity can be estimated by solving Fick's second law, and thermal conductivity can be obtained by solving the heat equation (see Singh *et al.* (49) for equations). It may be important to consider including a source term (q) in the heat equation to account for heat generated by termite metabolism similar to what was performed by Fagundes *et al.* (50) and Gouttefarde *et al.* (80). Although the exact impact of this term on the solution is not fully known, when Gouttefarde *et al.* (80) included it in the heat equation, the results showed a good match with field data. Similarly, when transient conditions were applied in simulations, they produced results that closely matched field measurements (80,84). It may also be worth accounting for convection that occurs in the mound similar to what was performed by Ocko *et al.* (16) and Hariyanto *et al.* (84) using the advection-diffusion equation (see equation 3). A
similar approach could be applied to account for advection of CO in the mound (see equation 4). The permeability of the mound, which indicates how easily fluid can pass through it, can be determined using Darcy's equation (see Singh et al. (49) for the equation). Darcy's equation is suitable for low channel (pore) velocities, as is typical in termite mounds (see

Table 1), and for Reynolds numbers less than 1 (85). In cases where transient flow occurs and the Reynolds number exceeds 1 (70), the Darcy-Forchheimer equation may be more appropriate (see Abou-Houly Eid Haitham (48) for equation).

$$ \frac{\partial T}{\partial t} + \nabla \cdot (c_f u T) = \nabla \cdot (k \nabla T) \qquad (3) $$

$$ \frac{\partial c}{\partial t} + \nabla \cdot (u C) = D \nabla^2 C \qquad (4) $$

where T is temperature, c_f is specific capacity of fluid, k is thermal conductivity, where C is the concentration of the CO₂ and D is the diffusion coefficient.

When solving these equations, it is necessary to input parameters representative of the physical system being modelled, in this case the termite mound. For example, in the Navier-Stokes equation, the density and viscosity of the fluid (air) must be specified. For CO₂ transport using Fick's second law, the diffusivity of CO₂ in air must be provided. For the heat equation, thermal conductivity, or alternatively heat capacity and thermal diffusivity of the solid and air phases must be assigned. These values are not always readily available but can be approximated based on the intrinsic thermal properties of the mound soil components. For instance, Fagundes et al. (50) assigned a solid-phase conductivity of 0.2 W/m.K, representing a predominantly clay composition, which is more insulating. However, this may not be representative in all cases, as the sand and clay composition vary significantly across different mounds (personal observation). The chosen solid conductivity estimates influence
the apparent thermal conductivity calculated in the simulation (84) and personal
observation). Thus, this value should be selected carefully to reflect realistic material
properties.

Typical temperature profiles from two *T. geminatus* mounds scanned at millimetre scale are shown in Figure 8E and 8F. The temperature profiles in these mounds are uniform, with low temperatures at convergence and low thermal conductivity (1.68 – 1.70 W/m.K), indicating thermal insulation. The similarity in thermal conductivity between these two mounds is likely due to the mounds having similar porosity and being in the same region. This is not the case for mounds from two regions, Senegal and Guinea, with thermal conductivity 4.18 W/m.K and 1.58 W/m.K respectively (49). The ambient temperature in Kankan, Guinea is cooler than in Nguekokh, Senegal, which may explain why the Guinea mound requires more insulation. This finding highlights the link between mound architecture, environmental conditions, and soil composition.

The temperature profiles also show heat movement through the mound, identifying areas that promote heat flow and those that restrict it, offering insights into the influence of structure on heat propagation. For instance, areas with smaller channels (pores) and resultant smaller porosity demonstrated better thermal conductivity than those with larger ones, highlighting the role of channel (pore) size on thermal conductivity (49). Measuring the time it takes for heat to travel across the mound provides additional insights into its thermal properties.

Flow field simulations can also shed light on how CO₂ moves through termite mounds, revealing structural features that support efficient CO₂ transport. We demonstrate how this can be achieved in two mounds (mound-1 and mound-2) from the *Trinervitermes* species by applying a fixed CO₂ value at the inlet of the mound with zero gradient on all other sides
(Figure 8G - 8J). At 90 sec, the higher proportion of blue region in Figure 8H compared to Figure 8G indicate lower CO₂ concentration and shorter CO₂ transport distance from the inlet in mound-2 than in mound-1. At 320 sec, mound-1 is fully saturated with CO₂ as indicated by the deep red regions everywhere in Figure 8I. This is not the case for mound-2 which reaches about 90% saturation at 320 sec as indicated by lighter red tone near the outlet of the mound in Figure 8J. This suggests that Mound-1's structure, likely influenced by its higher porosity (0.56 compared to 0.54 in Mound-2) enhances CO₂ transport efficiency. Additionally, mound-1 had a CO₂ diffusivity of 4.84 × 10⁻⁶ m²/s compared to 4.18 × 10⁻⁶ m²/s in mound-2. To better represent CO₂ transport in the mound, future simulations can include high humidity levels near saturation, reflecting the conditions observed inside the mound.

Flow field simulations also provide insights into permeability variations within termite mounds, revealing how fluid flows through certain pathways while others slow or redirect airflow. This understanding is essential for explaining natural ventilation mechanisms in termite mounds. A comparison of millimetre-scale CT images of *Apicotermes* and *Cubitermes* mounds (shown in Figure 7) reveals a significant difference in permeability, with *Apicotermes* mounds exhibiting permeability levels approximately four orders of magnitude greater than those of *Cubitermes*. This suggests that the features of the *Apicotermes* mound such as its elongated and parallel chamber geometry, promote better air flow.

These simulations are often simplified using various assumptions to make them computationally manageable and faster to solve. Typically, fluid and heat flow are assumed to be at steady state (48,49). In reality, however, transient flow occurs inside the mound due to external wind, diurnal temperature changes, and other environmental influences, all of which can significantly affect mound performance. Thermal properties like conductivity and
heat capacity are also commonly treated as constant, even though they vary with temperature.

Similarly, properties such as permeability, CO<sup>□</sup> diffusivity, and thermal conductivity are often assumed to be isotropic. Yet termite mounds exhibit anisotropy due to their complex, porous, and directional structure as shown in Figure 6. Further simplifications, such as assuming ideal gas behaviour for CO<sup>□</sup> transport or ignoring turbulence, may also limit the model's ability to reflect real-world conditions, especially under varying thermal gradients.

Conducting a sensitivity analysis can help assess how these assumptions influence model accuracy and identify which factors most affect performance. Furthermore, the results of simulations should be validated with field or experimental data to ensure the model's accuracy. For instance, Gouttefarde *et al.* (80) fitted field temperature data from *Procornitermes araujoi* mounds to the heat equation to estimate diffusivity and forcing terms and showed good agreement between the equation and measured data. The heat diffusivity coefficient was also found to be similar to that of soils with comparable properties. Validating the simulations can be challenging in situations where numerical simulations are not accompanied by fieldwork or laboratory experiments. In such cases, the model's results can be compared to field or experimental data from similar mounds in similar ecological contexts, if available, or benchmarked against other validated numerical simulation results.

For instance, Ocko *et al.* (16) compared the optimised morphologies generated by their model to natural termite mound morphologies across different environments.

In the future, flow field simulations could incorporate dynamic mound structures that change over time to produce more realistic results. Additionally, a multiscale modelling approach combining microscale and millimetre-scale simulations should be explored. Geochemical analyses, including mineralogical and elemental composition studies, could also provide
deeper insight into mound materials, with these data serving as inputs for more accurate
modelling.

7. A new proposed integrated approach

Seventy years of research on thermoregulation and ventilation in termite mounds has yielded
numerous key insights:

* Termite mounds maintain stable internal temperatures in their nests with fluctuations of only 0 – 4°C despite fluctuating external conditions (9,29–32). This implies that termite mounds act as natural climate control systems, enabling colonies to thrive in harsh environments.

* Both inhabited and uninhabited mounds exhibit similar temperature stability (30,32–35). This implies that the mound's architectural design, rather than termite activity, is the key factor in maintaining internal temperature stability.

* Among mounds of the same species and shape, larger mounds show greater temperature stability than smaller ones (5,30,32) with a smaller coefficient of variation (CV) and range (see

* Table 1). This is attributed to their larger thermal mass that allows it to retain heat for longer periods while resisting thermal fluctuations.

* Mound architecture adapts to environmental changes (1,23,38). Thus, the architecture of the mound can reflect the environmental conditions in which it is built.

* Termite mounds consist of a porous network of air channels of varying connectivity and sizes (55,66,70) (see Table 3).

* Mound walls contain micropores that aid in insulation, CO₂ diffusion, permeability, drainage, and structural stability (49).
* External winds may influence mound ventilation, especially in regions and periods of high wind activity (9,10,29).

* Diurnal temperature fluctuations influence ventilation within mounds (9,39). These fluctuations create thermal gradients that drive convective airflow within the mound, facilitating gas exchange with the ambient environment.

There are many outstanding questions related to climate control in termite mounds. Some key questions are:

* What specific roles do structural elements like chambers and galleries play in ventilation and gas regulation? This understanding would allow us to identify mechanisms rather than relying solely on observation.

* Which configurations or mound architectures best support climate control? Is any mound architecture preferred over others?

* Do micropores serve the same function in fungus-farming termites as in non-fungus-farming species, or is their role species-specific?

* How do smaller mounds, lacking chimneys and large air conduits, achieve ventilation, and how does this compare to larger mounds?

* How do mounds manage effective ventilation in high-humidity conditions?

* Can insights across species reveal general principles in structure-function relationships?

* What processes occur at smaller scales in the mound that control larger-scale observations?

Addressing these questions requires an interdisciplinary approach, combining expertise from biology, physics, engineering, and other research fields like X-ray tomography, numerical
modelling and machine learning. X-ray tomography enables visualising and analysing mound
structures across various scales. The 3D images acquired using X-ray tomography can be
used for multiscale numerical modelling to study pressure and velocity fields, permeability,
heat and CO₂ transport within the mound. Controlled laboratory experiments on mound
samples can further complement the simulation findings by examining behaviour under
varying conditions, including temperature, gas concentrations, humidity, and drainage. These
would also help validate the results from numerical simulation. Machine learning techniques
applied to imaging data and numerical simulation results can develop predictive models that
reveal patterns in channel networks and solid arrangements, shedding light on their functional
impact. Structural features obtained from the 3D images of the mound can be compared to the
flow properties to identify important correlations. This integrated approach, shown in Figure
10, will enable a better understanding the mechanisms that provide effective climate control
in termite mounds leading to sustainable solutions.

Implementing these sustainable solutions on a large scale in human architecture and other
systems would be a complex and long-term endeavour. This is because local environmental
conditions, material variations, and human comfort requirements differ significantly from
those in termite mounds, and these factors must be integrated with existing technologies.
Several additional steps are required after establishing the link between mound structure and
climate control to developing sustainable solutions. These steps include design, material
selection, prototyping, testing, optimisation, scaling, and implementation, all of which also
involve collaboration across multiple disciplines. In the short term, the primary focus should
be on understanding the structural elements that effectively support climate control in termite
mounds towards longer term sustainability.
<figure><img src="image_12.png" /><figcaption>Figure 10: Simple workflow from termite mound analysis to the design of sustainable solutions. Start/end ○, process □, input/output□. Termite mounds are obtained from field work and imaged in 3D using X-ray tomography. 3D images provide input data for numerical simulations to understand flow characteristics and thermal performance of the</figcaption></figure>
mound. Laboratory experiments on mound samples can also be performed providing useful parameters for the numerical simulations. Numerical simulations are then validated using results from laboratory experiments or data collected in the field. Additionally, flow characteristics and 3D mound structure are fed into machine learning to identify patterns and correlations, predicting how structure affects thermoregulation and ventilation aiding in the design of sustainable solutions inspired by termite mounds. The broken line between the final two steps indicates that the connection is indirect and may involve multiple steps.

## 8. Conclusions

This study highlights the remarkable ability of termite mounds in achieving thermoregulation and ventilation through their complex architectural structures, offering invaluable insights for sustainable building designs and other green solutions. Despite decades of research studying key features, such as the thermal stability of both inhabited and uninhabited mounds across species and ventilation driven by diurnal temperature changes, some fundamental questions remain unanswered. These include the roles of chambers and galleries in airflow, how smaller species achieve ventilation without large conduits, and how these mechanisms compare to those in larger mounds. Addressing these questions requires an interdisciplinary approach, combining X-ray tomography and flow field simulations to quantify critical structural and flow properties of the mound.

We have demonstrated how X-ray tomography can reveal the internal geometry of termite mounds at both macroscopic and microscopic scales, capturing essential details such as micropores and channels that facilitate gas exchange and temperature regulation. Coupled with numerical simulations, we can visualize dynamic processes like heat flow, CO₂ transport, and airflow patterns, providing deeper insights into how these structures function under varying environmental conditions. We have also proposed that a multiscale approach combining millimetre-scale and microscale images of the mound should be adopted for realistic modelling of the entire mound. Numerical simulations should be validated using
field or experimental data, as inadequate validation limits the reliability and comparability of
results across studies.

This study also emphasises the adaptability of termite mounds, showcasing how structural
variations across species and environments are fine-tuned to balance competing demands
such as temperature stability, gas exchange, and moisture regulation. The ability of termites
to achieve such sophisticated climatic control using simple materials and passive mechanisms
holds immense potential for biomimicry in sustainable architecture and engineering.
Applications range from designing energy-efficient buildings to developing advanced
ventilation systems that reduce energy consumption.

In the past, some building designs have been inspired by termite mounds, such as the
Eastgate Centre built in 1996 in Harare, Zimbabwe (10), which achieved a 17-52% reduction
in energy consumption compared to similar buildings (86). Other examples include the Davis
Alpine House in London, England, which utilizes the stack effect for passive cooling (86),
the Nianing Church in Senegal, which finds inspiration in the termite mound model aiming at
passive thermal regulation (87), as well as London's Portcullis House in England and Council
House Two (CH2) in Melbourne, Australia (88). These buildings are referred to as 'bio-
mythological inspired' (12) because, although they attempt to mimic the processes in termite
mounds, they do not fully replicate these processes due to incomplete understanding. It would
be interesting to explore the actual energy savings that could be achieved by applying the real
principles at work in termite mounds to buildings. This would involve treating the building as
a machine (10) and adapting construction techniques to the local environment using local
materials (1).
Future research should focus on further refining this integrated approach by incorporating
machine learning to analyse patterns in termite mound structures and predict optimal designs
for specific environmental contexts. Moreover, other methods, such as microindentation
testing and X-ray diffraction (XRD) analysis (49,67,89) can be incorporated into this
approach to provide a comprehensive understanding of the material properties contributing to
climate control in the mound. Statistical methods, such as multiple regression analysis, can
also help identify which structural properties have the greatest impact on the mound's internal
climate. Graph theory also provides valuable insights into the connectivity and flow pathways
within the mound by modelling its internal structure as a network (90–94). Additionally,
expanding studies across a broader range of termite species and environmental settings will
help uncover general principles underlying their climate control strategies. These insights
could pave the way for transformative innovations in construction and environmental
management, directly inspired by nature's engineers (95).

By merging biology, physics, and engineering, this study sets the stage for exploring the
untapped potential of termite mounds as models for sustainable design. It reinforces the idea
that understanding the natural world can inspire practical, scalable solutions for pressing
human challenges such as energy efficiency and climate resilience.

9. Acknowledgments

N.F.K. and K.S. acknowledge James Watt Scholarship from Heriot-Watt University. N.F.K. acknowledges Julien Maes and Hannah Menke for their help with the numerical simulations. N.F.K. also acknowledges Mary Burton funds from Heriot-Watt University. G.T. acknowledges Agence Nationale de la Recherche (ANR-06-BYOS-0008) for funding X-ray medical scanning. G.T. also gratefully acknowledges the Indian Institute of Science to serve as Infosys visiting professor at the Centre for Ecological Sciences in Bengaluru
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