Shaping the future of environmental data sharing

Posted on November 11, 2025 by lembrechtsjonas

Are you working with environmental or biodiversity data and willing to help us out?

At the Microclimate Ecology & Biogeography (MEB) network, we believe that open, reliable, and collaborative data exchange is the cornerstone of understanding and predicting biodiversity and microclimate dynamics.

As part of the Forest-Web 3.0 project (funded by Biodiversa+), we’re exploring new ways to make environmental data sharing more effective – and we’d love your input!

📋 Take our 15–20 minute survey here: https://nina.qualtrics.com/jfe/form/SV_ef9kxTazFW9a1Qq

We’re collecting insights from researchers working with environmental or biodiversity data to learn:

  • What works well in current data-sharing practices
  • What challenges you face
  • How we can make sharing easier, fairer, and more collaborative

Your anonymous responses will directly help us design new tools that support both FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective Benefit, Authority to Control, Responsibility, Ethics) data principles.

By participating, you’ll help us strengthen the open science foundation of the MEB community and shape the next generation of user-friendly data-sharing platforms.

👉 Take the survey now!

Together, let’s build a more connected, transparent, and equitable future for environmental data.

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How to not be swamped by your microclimate data

Posted on November 11, 2025 by lembrechtsjonas

Microclimate data are finally finding their way more routineously into ecological models – and rightly so. Hooray for that! The growing availability of in-situ measurements is helping us bridge the gap between the coarse world of macroclimate and the fine-scale environments that organisms actually experience. But as more researchers start integrating these data into distribution models or other ecological questions, a new issue has arisen, and it’s one we have to deal with soon: what do we actually do with all this detail?

When faced with high-frequency microclimate time series, the temptation is often to reduce them to a familiar set of summary statistics – mean temperature, perhaps minimum and maximum values, or that so-familiar set of bioclimatic variables that we are so used to be using. Yet, those choices strip your microclimate data of its power. The real story lies in its variability, its seasonal contrasts, and the way it interacts with snow, vegetation, and topography. In other words: the fine-scale thermal landscape is more than a few summary statistics.

So, what do we do then?? A good starting point is to explore a broader range of summary statistics. Yes, this can feel like stepping into chaos – dozens of potential variables, each telling a slightly different story. Like trying to cook a soup with everything in your pantry — from chocolate chip cookies to bean sprouts.

But here comes our recent paper in Oikos – expertly led by Kryštof Chytrý – with a recipe to avoid disaster. As with the right tools, the complexity becomes manageable. A straightforward cluster analysis, for example, can help reveal sets of variables that move together. Rather than drowning in endless variation, you’ll see that many microclimate metrics are strongly correlated, allowing you to identify a few meaningful clusters that capture most of the relevant information.

Across the slopes of Mount Schrankogel – a mountain fast becoming a symbol for microclimate research, make sure you remember I warned you – more than 900 sensors and vegetation plots capture the microclimate of a unique ecosystem. With this unprecedented dataset, we took a stab at how microclimate variability translates into ecological meaning.

Depending on your study system, these clusters will likely make ecological sense. In snow-affected regions, for instance, winter and summer temperatures tend to form distinct groups, each shaping species distributions in opposite directions. Spring and autumn may emerge as their own transitional cluster, with temperature dynamics that reflect phenological shifts. Meanwhile, variables capturing variability — the day-to-day swings, or microclimate buffering capacity — form yet another cluster, particularly important when studying ecological stability or resilience.

The broader message here is one of balance. We shouldn’t oversimplify microclimate data into a handful of familiar metrics, but neither should we be paralysed by the complexity. Using our new summary statistics – even after reducing them through cluster analysis – consistently outperformed traditional bioclimatic variables in capturing ecological variation. There is a pattern in the noise, and finding it takes that extra analytical step, as we describe in this paper.

This is more than a technical issue; it’s a conceptual one. As microclimate data become increasingly available, the community needs to converge on best practices for summarising, selecting, and interpreting these variables. Our choices here will shape the next generation of distribution models, biodiversity forecasts, and ecosystem predictions.

I see this paper as a conversation starter, but a very important one. We now need similar analyses across diverse ecosystems to test whether these clustering patterns hold up, and if parameter simplification is achievable everywhere. But there’s reason for optimism: modelling species distributions with only a few climatic variables seems to be a viable strategy. It’s just that the most suitable variables may often be different from those that are commonly used nowadays.

Reference: Chytrý et al. (2025). Reconsidering climatic predictors for high-resolution niche models of alpine plants. Oikos. https://doi.org/10.1002/oik.11545

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A tale of homogenisation

Posted on October 22, 2025 by lembrechtsjonas

I’ve always been intrigued by ecological scaling – it’s literally in my title: Assistant Professor in Ecological Scaling.

One of the main reasons we care so much about scaling is that ecological theories don’t always hold up when we change scales. What seems true in a single valley, forest plot, or mountain slope can fall apart when we zoom out to continents or the globe. That mismatch often gets us into trouble when trying to generalize from our favourite local case studies to something that has real global relevance.

A classic example: homogenisation

The theory goes like this: when ecosystems are invaded by non-native species, they start to look more and more alike. We call this biotic homogenisation – a reduction in beta diversity, meaning less variation among communities. It’s often linked to lower ecosystem functioning, and by extension, poorer ecosystem health.

Native mountain vegetation tends to be highly distinctive, yet the introduction of non-native species is expected to erode that ecological uniqueness. Here, a highly biodiverse spring meadow in the Scandinavian mountains.

So far, so simple. Except the evidence is a little bit messy. Some studies find strong homogenisation, others don’t. We suspected that part of this inconsistency comes from differences in spatial scale – not all studies are asking the same question in the same “ecological zoom level.”

Scaling up with global replication

To test this idea, we turned to one of our favourite tools: globally replicated monitoring. Thanks to the Mountain Invasion Research Network (MIREN), we could explore patterns of homogenisation – and its opposite, differentiation – across 18 mountain regions worldwide. The findings of this exercise – led by Meike Buhaly – are now published in Global Ecology and Biogeography.

Our hypothesis (perhaps a bit naively in retrospect) was that homogenisation would dominate across all scales, though we expected it to weaken with elevation.

Study design, showing how we compared beta diversity within gradients (local), between mountains (regional), between regions on the same continents (continental), and between continents (global)

Yet that was, surprise surprise, not what we found. At the global scale, the classic theory held neatly: non-native species homogenized communities. Plant assemblages across continents became more similar (lower beta diversity) once non-natives were included. But when we zoomed in, the pattern fell apart. At local and regional scales, homogenisation and differentiation were almost evenly balanced. And even more intriguingly, the pattern split along continental lines:

  • In the New World (the Americas and Australia), homogenisation dominated.
  • In the Old World (Europe, Asia, Africa), differentiation was more common.
Patterns of homogenization and differentation across scales in our dataset.

The pattern depends on where (and how far) you look

In the New World, we found consistent homogenisation across local to continental scales, particularly in lowland plant communities. This likely reflects both the high number and shared history of non-native species: many are widespread across entire continents, occurring in more plots than native species.

At higher elevations, however, in some regions this pattern reversed. When non-native species became rare and patchy, this lead to community differentiation instead, especially in the Andes and Rocky Mountains.

The Eurasian mountains told a different story. There, non-native species actually caused differentiation at local and regional scales, even though some were shared across regions. At the continental scale, these same shared species produced a faint signature of homogenisation, but much weaker than in the New World.

The consistent differentiation we found in Eurasia might simply reflect an earlier invasion stage. With fewer non-native species and fewer widespread invaders, communities still differ strongly from one another. But as non-native species continue to spread, homogenisation may increase into conditions that mirror what we already see in the Americas and Australia.

Dandelion in the Chilean Andes – a pretty common site in the Americas and one of the reasons we observed homogenisation in the New World.

Scaling reveals nuance – again

So, as so often in ecology, the story depends on scale.
At large, continental scales, non-native species clearly homogenize plant communities: ecosystems across continents begin to share the same species. But when you zoom in, that signal becomes patchy. Homogenisation dominates in regions with long invasion histories, while newer invasion fronts still show differentiation.

It’s a pattern that fits a familiar ecological theme:

a little change might be positive – but a lot can be profoundly transformative.

More information: Buhaly et al. (2025). Global Homogenisation of Plant Communities Along Mountain Roads by Non-Native Species Despite Mixed Effects at Smaller Scales. Global Ecology & Biogeography. https://doi.org/10.1111/geb.70137

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Cliffhanger: Am I, as a climber, a threat or a treasure for plant diversity on rock cliffs?

Posted on October 21, 2025 by lembrechtsjonas

Translation of  the submission for the pop-sci writing competition ‘Vlaamse Scriptiepijs’ by team member Sarane Coen

With the River Meuse flowing far below, I search for the way up to the top. With fingers and toes, I follow a route of small cracks and ridges in the rock wall. For me, these are holds; for plants, they are habitats. Where I grip, they fight to survive. That thought stuck with me.

Am I, as a rock climber, a threat or a treasure to the ecosystem I love so much?

With that question, my master’s thesis research began. I returned to the rocks. This time not only as a climber, but also as a scientist.

Bursting biodiversity

At first glance, cliffs may seem like barren walls. But in reality, they burst with life. Rocks are rich ecosystems full of rare species that endure extreme conditions: steep faces with almost no soil, nutrients, or water.

For me, these are holds; for plants, they are habitats.

Cliffs offer a wide range of living conditions because they vary greatly in height, structure, and orientation toward the sun. These differences create a diversity of microclimates. North-facing walls or deep, shaded crevices remain much cooler and moister than sunny south faces. Cliffs thus form a mosaic of tiny habitats where each species finds its ideal place to live.

Research on a high level

Helmet, harness, rope, check! One last look into the depths and ready to descent. With my research material,  I dangled along the steep rock walls on the banks of the River Meuse in the Belgian Ardennes to collect data.

To understand how climbing influences these ecosystems, I looked at the plants on unclimbed walls and lightly and heavily climbed walls. Within one-square-metre plots, I recorded which species were present and how much space they occupied. I also measured the characteristics of the rock itself: surface structure, slope, height, and sun orientation. In total, I conducted these measurements across 248 separate square meters, spread over multiple cliff faces. Bit by bit, I untangled not only my ropes, but also some ecological questions.

Climbers, unexpected buddies of biodiversity!

The structure of the rock surface turned out to be crucial. Smooth walls offer little opportunity for plants, no matter how often they are climbed. The more cracks, ridges, and holes a rock has, the more suitable microhabitats it provides — and the more plant species can find a place to grow.

A bit of disturbance can make space for more biodiversity.

And climbing? Moderate climbing intensity did not harm biodiversity — it even seemed to enhance it. Lightly climbed cliffs hosted the highest diversity compared with both unclimbed and heavily climbed sites.

The type of plants explained this pattern. On unclimbed cliffs, I mostly found dominant, competitive species that monopolise nutrients and water. On climbed cliffs, more disturbance-tolerant species appeared. Climbing partially reduced the dominance of the competitive species, giving others a chance to establish themselves. A bit of disturbance can make space for more biodiversity.

Safe spaces for the future

These results reassure me as a climber. Fortunately! Because these fragile cliff ecosystems may play a key role in the climate and biodiversity crises. Their variation in microhabitats with different temperatures makes them cool refuges for species that can no longer tolerate the heat elsewhere. At the same time, they serve as stepping stones for southern species expanding into the warmest niches beyond their usual range.

Further research can help us understand how cliffs buffer or amplify the effects of climate warming on biodiversity. Understanding the impact of human activities such as climbing is a first step. And the fact that certain human disturbances can sometimes be secretly beneficial makes it all the more fascinating.

So, both as scientist and a climber, I can contribute to biodiversity. And cliffs turn out to be a refuge,  for passionate climbers as well as for vulnerable plant species too.

Translation of  the submission for the pop-sci writing competition ‘Vlaamse Scriptiepijs’ by Sarane Coen

Original:

https://scriptiebank.be/scriptie/2025/ecological-impact-rock-climbing-cliff-face-vegetation-diversity-functional-traits-and
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De Oorzaak – on tour

Posted on October 3, 2025 by lembrechtsjonas

Last week, we wrapped up De Oorzaak with nothing less than a bang. With in-depth stories featured in De Morgen, striking results on the table, and a “tour across Flanders” that brought us to Antwerp, Ghent, and Leuven, the project ended on a high note. The closing events weren’t just symposia – they were the grand finale of a citizen science project that has kept us occupied for the better part of two years. Each evening was a meeting point between science, citizens, and policymakers, weaving data and lived experience into one story about noise in our cities.

The full team on stage, accepting flowers for two years of intensive work with and for the whole of Flanders

Noise is more than just an irritation – it shapes how we live, sleep, and feel.
The results from De Oorzaak make this crystal clear: almost half of all measurement points were, on average, above 60 dB during the day – a level already higher than the World Health Organization recommends. Unsurprisingly, traffic was the worst culprit across the board. But we also saw how local noise sources – like tourist boats or tram rails – can have gigantic impacts on daily life in specific neighborhoods.

And those numbers are only part of the story. Behind them are the voices and experiences of people who live with noise every day. At the symposium, participants shared endless varieties of stories – some tragic, some creative, all deeply personal. They showed just how differently noise can affect lives, and how much effort it takes to raise awareness.

The consequences are clear, as shown by our data: the more noise we are exposed to, the more annoyed we become. Annoyance spills into reduced sleep quality, and ultimately into a diminished quality of life.

Browse the beautiful visual stories from media partner De Morgen here: https://www.demorgen.be/redactie/2025/oorzaak-resultaten/index.html

🗺️ If you don’t have a subscription to De Morgen, you can explore this beautiful interactive map of Lden values and noise events at each measurement point, including AI-detected sources binned by the peak noise level per event, as made by our data wizard Ablenya Barros: https://ablenya.github.io/Oorzaak/Sensors_noise_events.html.

Yet there is also hope – and it’s green. Across the data, one message stood out: nature helps. Green spaces and natural sounds soften the harsh edges of our urban soundscapes. Even when the decibel levels are not lower, the presence of rustling leaves, birdsong, or flowing water transforms how we experience sound. Nature doesn’t necessarily quiet our environment – it restores us.

The next question is: what will we do with this knowledge? At each evening of the symposium, policymakers were confronted with that exact challenge. The most striking message came from the city counsillor of environment in Leuven, who put it simply: this is the new normal: citizens now have the data to show there is a problem – giving them a lot more power again. But solutions will not be easy, and they never have been.

Power to the people – giving them the data to contextualize their environment.

So where do we go from here? The evidence is clear, the stories are real, and the momentum is growing. If we want healthier cities and better lives, the answer is again clear: we need more green in our cities.

There is much much more depth and nuance to this story than I can tell here and now, but we hope we can get back to you about all that! For now, check out this first post here: https://www.uantwerpen.be/nl/projecten/de-oorzaak/nieuws/.

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Mapping the past to predict the future

Posted on September 30, 2025 by lembrechtsjonas

Long-term followers of this blog know I’ve always been fascinated by species distribution changes. We’ve tracked non-native species moving into mountains and cities, studied how mountain plants travel up and down slopes along roads, and explored how microclimate – and changes in it – affects all of this.

But all of that focuses on recent changes, what we call contemporary climate change. What we often forget is that the world has been dynamic for millions of years, and species have been moving up and down, and left and right for just as long. So, we asked ourselves: could we learn from these past dynamics to better predict the future?

https://www.sciencedirect.com/science/article/abs/pii/S0006320725005361That’s exactly what Yuheng Chen tackled in his master thesis – now expanded into a full paper in Biological Conservation. Yuheng, now a PhD student in our group at Utrecht University, has with this paper completed quite a journey on a topic that has kept my head spinning: looking far back in time to understand the forces shaping species today. And let me tell you, looking that far back is not easy.

The core question was simple enough: are species used to climate change or not? Species that evolved in regions with strong past climate fluctuations might be better at coping with future change, whereas those from historically stable climates might be more vulnerable. In other words, a species’ history shapes its present ecological niche, which in turn affects its future range size.

To explore this, we went (well, virtually, for me) to China. We identified 2,933 plant species endemic to China, which conveniently avoids issues of artificial borders and inconsistent data across countries, while still benefitting from the huge variety of climates and ecosystems across the country. Using species distribution models, we mapped their current ranges, measured their sensitivity to climate, and predicted how those ranges might change by the 2070s under two climate scenarios.

Next came the exciting part: comparing regions where species diverged long ago (‘divergence hotspots’) with areas that have experienced big climate swings since the Last Glacial Maximum (‘paleoclimate-change hotspots’). Museums are regions where species diverged a long time ago – they’re ancient centers of diversity, like living archives. Cradles are regions where species divergence is more recent – they’re hotspots of new evolutionary activity.

Here’s what we found:

  • Species from stable, long-term refuges (‘museum areas’) are projected to lose range under future warming.
  • Species from climate-fluctuating regions might actually expand their ranges.
Figure 1. The divergence and paleoclimate change hotspots of Chinese endemic higher plants. Figure 4A recognizes museum (ancient divergence center) and cradle (recent divergence center) based on species divergence time using null-model test. Figure 4B and 4C recognize refuge (stable precipitation and temperature center) and exposure (precipitation and temperature change center) based on paleo precipitation change since LGM, respectively. The provinces corresponding to the hotspots are also marked in the map.

Interestingly, there was no difference in predicted change rates between species from old (museum) versus new (cradle) divergence hotspots, but paleoclimate exposure mattered a lot: species from areas that experienced strong past climate change are predicted to do better than those from stable refuges.

Figure 2: predicted changes in future ranges for species with different origins. There was no difference in predicted change rates for species from old (museum) versus new (cradle) divergence hotspots, but there was a clear difference for species exposed to either a lot (exposure) versus very little (refuge) paleoclimatic change.

These findings highlight how evolutionary history and past climate experiences shape both current distributions and future responses. They also give us a clue about the winners and losers under rapid climate change, and stress the importance of identifying areas that are long-term refuges versus regions accustomed to taking a punch. While mostly exciting from a theoretical perspective, this knowledge is also important for designing future-proof protected areas.

Reference

Chen et al. (2025). Identifying divergence time and paleoclimate change hotspots for better conservation under future climate change. Biological Conservation. https://doi.org/10.1016/j.biocon.2025.111499.

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