The Light Fantastic: Summoning Ghosts from the Past

Cutting-edge modern technology has brought us so far that scientists can now find ghostly prints from former human activities with breath-taking accuracy. Ghosts from the past, that is, and that modern technology is called LiDAR (light detection and ranging).

LiDAR feels like a magical way of looking at things and revealing hidden artefacts. It is basically a laser scan of an environment with extreme precision (often up to centimetres in accuracy), which allows us to recreate environments such as forests in 3 dimensions. LiDAR scans can be made from the ground or from the air, and in its simplest form consists of sending out a laser beam, The Light Fantastic, and capturing it again when it bounced back from an object.

The magic of LiDAR. Top left, a: principles of laser scanning from the air. The lower panels (d, e) visualize a cloud of raw LiDAR points extracted from both a stationary terrestrial LiDAR system and a drone, covering the exact same study area in the Aigoual forest (France). Upper-right panels (b, c): basic principles of time-of-flight vs. phase-shift LiDAR.

In a new review paper recently published in Journal of Ecology, we summarize the exciting potential of LiDAR for forest research. Indeed, when we can map a forest in 3D at the centimetre scale, we can find back structures that are impossible to see with the naked eye, and that hide former land uses, management practices or impacts of climate change.

An impressive example of this potential can be seen in the Compiègne forest, in northern France, where LiDAR data allows us to trace back hidden structures in the forest all the way to the Roman times, much and much further than historical maps can ever do.

An example of digging up ghosts from the past using aerial photography (1937-2000), old maps (18th-19th century) and finally LiDAR data to go back to antiquity. LiDAR scanning of the forest floor shows typical patterns of agricultural activities from the Middle Ages (bottom left), as well as linear microreliefs corresponding to a network of Gallo-Roman agrarian fields and secondary roads (bottom right). Data coming from a flight with an airplane over the forest in February 2014, getting a 3D image with on average 12 points per m²!

While digging up such ghosts of the past is obviously extremely fascinating, it has also important consequences for ecology; the main point we want to hammer home in this new paper. Indeed, such past management practices and land-use changes have big impacts on current species distributions. Basically, they can explain why you found certain plants in certain – sometimes weird – spots. Some barley on the forest floor? Perhaps there has been a medieval farm there!

These confounding effects of past land use can obscure the impacts of ongoing global changes such as climate change or atmospheric pollution on species distributions. And that is exactly why it is so critical to know about them. For example, Roman agricultural practices can still result in elevated nutrient concentrations in a forest soil, with consequently a higher presence of nitrogen-loving species in the forest understory. Without the technology to look back that far into the past, the presence of these species might mistakenly be attributed to nitrogen-deposition from the air during the 1980s, overestimating the impact of the latter on forest diversity.

Given how forest cover is increasing in Western Europe (France has seen its forest cover double since the 18th century!) there is bound to be a lot of historical artefacts and past land use hidden underneath our forests. Knowing these patterns will be critical for smart management decisions. Now LiDAR is there to reveal them.

LiDAR data (obtained on the left via aircraft, drone or terrestrial laser scanning) can be used to assess micro-topographic variation (e.g., skid trails) and forest structure (e.g., vertical layering of vegetation) at a landscape level, and thus highlight legacy effects still affecting the current composition of understory plant communities and their responses to macro-environmental changes through time-lag dynamics. For instance, it is possible to not only capture the imprints of historical forest management practices (e.g., ancient coppice-with-standards converted to high forests after World War II or the more recent intensification of heavy vehicles’ traffic to harvest timber) but also to unveil past land uses (e.g., ancient settlements or agricultural fields).
The past can be hidden in forests in surprising ways: this lush green cover of quaking sedge (Carex brizoides) in the French Morman forest, for example, could be an important sign. Indeed, this species was well known in World War I, when it was used by German soldiers to make their mattresses. As a typical species from Central Europe, populations like this in France might very well have been established there unknowingly by German soldiers during the war. Picture by Jonathan Lenoir.


Lenoir et al. (2022). Unveil the unseen: Using LiDAR to capture time-lag dynamics in the herbaceous layer of European temperate forests.

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Global maps of soil temperature

In a new paper just published in Global Change Biology, we provide the first-ever global maps of soil temperature (0 -15 cm) at a 1 km² resolution, based on the global SoilTemp database of over 8500 in-situ soil temperature time series. We show that over the year, soils in cold and/or dry biomes are on average 3.6°C warmer, whereas soils in warm and humid environments are 0.7°C cooler, than shielded air temperature at standard height (1.25 to 2 m) as measured by weather stations.

SoilTemp has done what it hoped to do from the start: it brought together over 400 scientists and their over 8500 soil temperature time series from across the globe. This humongous collaboration has now resulted in a first and much-anticipated global product: global gridded layers of soil temperature and bioclimatic variables at a 1-km² resolution for 0–5 and 5–15 cm soil depth. Free for all to use! I can’t emphasize enough how big of a game-changer this can be: now, finally, ecologists working on any pattern or process in, on, or close to the soil surface can use global temperature data that are representative of the soil conditions. 

That such a correction is far from trivial is shown by the mind-boggling numbers in the paper. First of all, we show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons: a 20°C (-10 to +10°C) range across the globe! Over the year, soils in cold and/or dry biomes, such as tundra, boreal forests or subtropical deserts, are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments, such as tropical rainforests, tropical savannas or temperate forests, are on average slightly cooler (-0.7 ± 2.3°C). We also show a significant reduction in the spatial variation in temperature in the soil in cold and cool biomes (and a slight increase in warm biomes). All this implies that soils will warm differently than the air as climate warms. How big that discrepancy will be, that’s a question up for future research, and a challenge SoilTemp is very happy to take up!

Mean annual air (light grey) versus soil (dark grey) temperature across the globe (left) and in each of the worlds’ major biomes (right). Especially in cold regions, soils are annually clearly warmer than the air.

Global maps were created by first calculating the difference (i.e., offset) between in-situ soil temperature measurements, based on time series from over 1200 1-km² pixels (summarized from 8500 unique temperature sensors) across all the world’s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land. Models, based on machine learning algorithms that linked the offset to predictor variables, come with maps of where predictions are most consistent (top) and where the model is extrapolating (bottom). This way, you can verify model quality in your area of interest and mask regions with higher uncertainty.

Standard deviation (top) and interpolation score (bottom) of our global machine learning models, highlighting regions where our results are still uncertain and more data is thus especially welcome

We hope these maps can mean a major leap forward and finally open up microclimate research to the global scale. But are we done now? Far from! SoilTemp has many other cool things up its sleeve. We are hoping to increase our spatial and temporal resolution, for example, and are working hard to fill the remaining gaps in our global data coverage. Global microclimate networks, it’s still the dream!

The database also has great potential for improving our mechanistic understanding of microclimate across the globe. Most of all, however, we are hoping for a massive surge in applications: if people actually start using our global microclimate products in their ecological analyses, then SoilTemp has achieved what it most dearly wanted.


Lembrechts JJ, van den Hoogen J et al. (2021). Global maps of soil temperature. Global Change Biology.

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A freshly-minted PhD

Now, that’s what they call a milestone: The3DLab got to celebrate its very first PhD, this week. Charly Géron successfully defended his thesis on plant invasions in urban environments!

What a beauty: Charly’s PhD thesis, all printed and finalized

The defence brought us on a cold and sunny winter solstice to Gembloux in Wallonia, southern Belgium, his home university. There, we could have a covid-beaten version of an in-person defence, where he walked us skillfully through the many investigations he performed to get to the core of that very important question: how are urban environments facilitating plant invasions?

Gembloux AgroBioTech looking particularly dashing on this first day of winter

With that, four years of rigorous research comes to a close – a bittersweet feeling lightened up by the fact that I got to wear a toga for the first time! We’re going to miss Charly in the team; the plant expert, the unstoppable pursuer of goals.

He has made our lab a better lab, and contributed to the worlds’ knowledge: indeed, non-native species from warm origins preferably invade urban environments in Western Europe, while their cool counterparts stick to the countryside. Interestingly, they all suffer when it gets warm, though, a bit of an unexpected find. Finally, it turns out there are some signs of local adaptation to urban conditions, yet mostly there is a lot of plasticity (plants just ‘changing shape’ when conditions shape), and environmental maternal effects (mother plant performance deciding how they offspring will perform, for example through the size of the seeds).

Charly’s work opened a whole new box of research questions, as all good science should!

Me in my toga

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What if the next rain bomb falls over Flanders?

Between 13 and 15 July 2021, exceptional amounts of rain fell over the south and east of Belgium. 39 people lost their lives, more than 38,000 homes were affected, and damage to homes and infrastructure amounts to 4 billion euros.
 All of a sudden, climate change calls very close to home.

What if the next rain bomb falls over Flanders?

The wettest summer in two centuries gave an unexpected twist to CurieuzeNeuzen in de Tuin, the community science project uniting 5000 citizens across Flanders to measure the impact of extreme events on their own properties. We got a unique view of how our gardens and nature reserves help buffer extreme precipitation. What we found? Gardens are powerful sponges, but in our cities and valley areas, they are under pressure. Newspaper partner De Standaard created a beautiful long-read about the story, impressively visualizing the role of gardens – and how we interact with those gardens – in the battle against increasingly extreme events.

You can find the full story here (in Dutch). This is a short summary – and translation to English, from the text written by amazing journalist Ine Renson.

Explore the buffering capacity of our gardens on this interactive map!

Our network shows beautifully that our gardens can act as very efficient sponges. If we compare the soil moisture measurements from our sensors across the whole summer season with the precipitation data from the Royal Meteorological Institute (RMI), it appears the Flemish gardens have collected on average at least 60% of the rainwater that has fallen.

The ‘rain bomb’ of July 15th, affecting largely Germany and Wallonia, east and south of Flanders. However, rain in the east of Flanders added up to over 45 L/m² or even 80 L/m² in one day (dark blue dots on the map)

On that 15th of July, the famous rain bomb, weather patterns were unique, providing us with an unprecedented opportunity to disentangle what defines our garden sponges during extreme rainfall events.

Gardens as sponges: purple gardens absorbed 90% or more of the precipitation, a yellow dot indicates numbers from 0 to 5%

Interestingly, gardens across the regions sponged very differently: in two of the main cities in the region – Antwerpen and Mechelen – gardens sponged A LOT of the water. in southern Limburg, the sponges were virtually inactive.

So what drives these patterns? In De Standaard, we go deeper into all the underlying variables. There is a ton of things we have nothing to say about: more rain gives relatively lower sponge percentages. Already saturated gardens obviously cannot sponge more. But also: gardens in urban areas and valleys had to sponge a lot harder, indicating that there is a lot of pressure there during extreme rain events, with water flowing in from elsewhere.

Managing your garden as a sponge

So, what can we do with this information? Our partnership with De Standaard allows us to communicate those results directly to those who need to hear it most: the general public. We found that garden management could have a significant effect on the sponge function. For example, we found that planting trees could help. It reduces the amount of water directly hitting the ground, and thus reduces risks for flooding during extreme rain events.

Yet the main solution: turn the grey into green! The higher the percentage of impervious area in your garden (buildings, driveway, terraces…), the higher the pressure on the remaining green. But also: the more urbanized your neighbourhood, the harder your garden will have to work.

Or as they put it so beautifully in De Standaard:

A permeable driveway, a smaller terrace: it really makes a difference. In the event of a flood, it can determine whether or not a neighborhood will be flooded.
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Bacteria: a thermometer for the past

It was at a lunchtime seminar of our research group that Cindy De Jonge introduced a new concept to me: using variation in cell membrane lipids (affectionately called brGDGT lipids by those who love them) as a thermometer for the past: these molecules remain super stable in soils and sediments (for millions of years!), and their structure can be related to environmental conditions when the organism lived. What makes them most interesting as a ‘paleothermometer’ is that at the global scale, their distribution in soils changes with mean annual air temperature (and soil pH).

Fig. 1. (A) Study site and transect location (insert, soil sites indicated with black points) in Northern Europe. Background colors indicate mean annual air temperatures. (B) Yearly evolution of soil temperature values at 2 sites (NO01 and LA20) is plotted, with the mean annual soil temperature indicated with a dotted line.

However, there was a lot of remaining noise, so I learned, and Cindy was on a mission to find out what else was happening. As I was working hard on the role of soil temperature – rather than air temperature – as driver of ecological patterns, we figured there was a beautiful match. What if we would go to our elevational gradients in northern Scandinavia, where we had in-situ measurements of soil temperatures across a 1200 meter gradient, and took soil samples there? We could relate the distribution of the membrane lipids in the soil to the in-situ measured temperature, and see if we would get better relationships than with coarse-grained air temperature. Makes sense, right? Indeed, one would expect that bacterial lipids relate more strongly to the temperatures in the soil, which are especially in the snow-rich northern Scandes, largely disconnected from what macroclimate data would tell you.

So we set to work, in what could be seen as an example of ‘rapid’ scientific progress. We obtained a little grant to do the labwork, attracted a motivated master student (Robin Halffman, my first master student actually to publish his master work as a paper, what an incredible achievement!) and went to the mountains to get us some soil.

After a lot of labwork, data processing and even more thinking, we finally got our story – out now in Organic Geochemistry!: things were complicated (who would have expected that!?). Luckily, there was Cindy to see the forest for the trees in an impressively complex real-world ecology detective case: indeed, brGDGTs did NOT relate better to in-situ soil than to free-air temperature, despite our smart hypothesis. Instead, it turns out the soil chemistry (e.g. its pH) is a much better predictor, and that this soil chemistry seems to act through the bacterial community: different soil chemistry –> different bacterial community –> different membrane lipids.

Lead author Robin (on the left) at work in the tundra, high above the village of Abisko, northern Sweden

So what do we learn from this exercise? A few important things:

  1. Despite the nice correlation of brGDGTs with temperature at the global scale, the local scale is clearly more complex. Indeed, local drivers of bacterial community distribution can be very different from temperature, with especially soil chemistry very important. This does not jeopardize the potential of the brGDGTs as paleothermometer, however, but should warn us to use it – at the moment – only at a coarse spatial resolution.
  2. It looks like (at least to my understanding) that the structure of membrane lipids is less related to environmental conditions itself, than to the bacteria making them. A better understanding of soil bacterial biogeography is thus urgently needed to take this further.
  3. My favourite conclusion: microclimate data is tremenduously important across so many branches of ecology, especially in the soil. Nevertheless, we should remain critical: it will not improve the story in all cases.We used one year of in-situ measured temperature data (versus decadal averages of free-air temperature). We know, however, that these membrane lipids are super stable in the soil, so it is actually unlikely that they relate strongly to annual fluctuations in local temperature. Better correlations might be obtained when using long-term soil temperature proxies (as now FINALLY are becoming available, see this preprint). Plans for future tests, for sure!
A complex figure, but it shows the correlation of various brGDGT-derived proxies (on the y-axes) with soil pH (left column) and air temperature (right column), showing there are correlations, but perhaps not strongly (and dependent on soil clusters, as marked in different colours). However, if we plot our points on the global relationship with air temperature and soil pH (right two panels), we see that our weak relationships fall within the noise present at the global scale.
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Forests: buffers for temperature in the future?

Even if you followed this webspace only occasionally, you should have gotten the idea of the fact that we are starting to get a good hold of microclimate across the globe. We know how much European forest understories differ from weather station temperatures, for example, or how much soils across the globe buffer temperature.

A very big black box, however, is how these microclimates will change in the future. When the climate warms, will forests buffer temperature more, or less? We now did a little first peek into that black box, in a paper just published in the STOTEN journal.

Set-up of our analysis of future forest buffering. First, we used a database of temperatures measured inside and outside forests across the globe to calculate the ‘offset’ between the two. This offset was linked statistically to values of topography, tree cover, macroclimate and other things to predict the buffering effects of forests across the world. The statistical relationship with macroclimate could then be used to predict future buffering. Figure and rest of the story based on the Twitter thread by Pieter De Frenne

The result of the exercise as described above was pretty impressive: we project that temperatures within forests will warm slower by 0.3-0.6°C than outside forests by 2060-2080

Modelled mean annual offset in forests (red: forests warmer than the surrounding area; blue: forests are cooling) will turn more negative by 2060-2080.

To get to these results, we used a (freely available) global database of 714 paired temperature time series inside forests vs. in nearby open habitats. We then used past and future climate data, topography and forest cover, and height to model past and project future offsets between free-air temperature and sub-canopy microclimates.

Our projections have important implications for instance for forest management and the potential cooling of urban green areas and urban heat islands, as this suggests that forest microclimates will warm at a slower rate than open areas.

Important to note, however, is that we had to assume – for now – that forest cover remains the same over time. If large tree mortality would occur (as is already happening in many parts of the globe, among others due to climate change and drought), the buffering capacity of forests will decrease.

Spruce dieback in Belgium

What happens to the microclimate in past and future if we take these land-use changes into account, that’s food for future research. But I promise you, we are not letting this rest, as getting the correct predictions of microclimate change are too important to ignore.

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