Assistant professor in ecological scaling

Posted on March 12, 2024 by lembrechtsjonas

Major milestone: first of May, The 3D Lab is making a big move to Utrecht University in the Netherlands, and it is doing so following my recent appointment as an assistant professor in the Ecology & Biodiversity-group there. Major milestone, indeed, as this means I have left the realm of temporary postdoc positions, and entered the world of long-term academic security (pending tenure, of course).

I’m very honored to be stepping into the role of ‘assistant professor in ecological scaling,’ a unique title that perfectly aligns with my current and future aspirations. Ecological scaling involves delving into ecological mechanisms at a fine resolution and assessing their validity, robustness, and broader applicability on regional or even global scales. This scaling up of ecology has been at the forefront of my work since my first involvement with the Mountain Invasion Research Network (MIREN), back in the days of my master’s thesis in 2012. Through MIREN, our aim is to delve into the factors driving species redistributions in mountainous regions and understand how these dynamics scale up from specific regional settings to the entire spectrum of mountains worldwide.

This switch to the Netherlands marks the end of twelve years of research at the University of Antwerp. Yet, I won’t be cutting all ties, and will keep a strong connection with students and team members staying behind, and will ensure the smooth running of the citizen science trains currently on track.

Scaling up ecology is also a key component of my work with another project dear to my heart: the SoilTemp network. This initiative aims to extend the understanding of microclimate—a phenomenon inherently local in nature—to broader regional and global contexts. These two networks – SoilTemp and MIREN – will therefore remain core pillars of my future work. With the added security of a long-term position, I am eager to finally stop nibbling and start taking full bites out of my long-term vision to explore the intricate mechanisms governing both microclimate dynamics and species redistributions from local to global scale.

However, I am also eager to push the boundaries further, particularly in unraveling the intricate mechanisms driving biodiversity dynamics. To achieve this, I look forward to collaborating with the exceptional expertise in experimental research within the Ecology & Biodiversity group at Utrecht University. Together, we will tackle the issue of ecological scaling both top-down and bottom-up. A lot of room left for future developments in the area of scaling up experimental findings, for sure. To be continued, so stay tuned for the coming few decades…

The biodiversity experimental facilities at Utrecht University provide fantastic opportunities for scaling up of the mechanisms behind microclimate and biodiversity. Here: BioCliVE (https://www.uu.nl/en/research/ecology-and-biodiversity/research/uu-bioclive)

‘Ecological scaling’ to me is the perfect blend of theory and practice. It involves a lot of fundamental ecology, yet with the ultimate goal to save the world. I will also make it a crucial point to build that link from theory to practice and work further on the scaling of ecosystem services and ecological management, the way we have for example been working in our citizen science project ‘CurieuzeNeuzen in de Tuin’.

A fly agaric in a Dutch forest, the fruit of an ectomycorrhizal fungus living in close interaction with the trees surrounding it. I also aim to work further towards understanding these kinds of interactions across scales, to conserve beautiful natural sceneries like this one.

Above all, however, I see this new position as a once-in-a-lifetime opportunity to further expand my role as a mentor. The 3D Lab will continue to evolve into a nurturing environment where aspiring young scientists can learn and thrive as part of a team. While the physical transition to the Netherlands won’t include my team members, our strength in virtual collaboration remains a hallmark of the group. Moreover, I’m committed to leveraging all available resources to enhance opportunities for those working alongside me—a goal that was previously constrained by the uncertainty of my own position.

That mentoring will also expand further as I join the teaching staff for the over 400 biology students in Utrecht. I will take my time – and am extremely grateful for the opportunity – to think critically about what and how we should be teaching the next generation, and if any updates are to be made.

A flower-rich (but species-poor) meadow close to Maastricht, last summer. Ready to turn my gaze to the issues and opportunities of Dutch nature as well

So: are you interested in doing science in the Netherlands, or are eager to forge collaborations on the expansive topics of microclimate, species redistributions, and scaling up ecology, now’s the time to reach out! The opportunities are endless, and I’m excited to explore them together. Let’s embark on this journey of discovery!

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Boosting my R efficiency by 300%: how a robot transformed my coding experience

Posted on February 15, 2024 by lembrechtsjonas

Yes, you read that correctly—300% faster! That’s the remarkable leap in speed I’ve experienced in tackling my most common R programming challenges ever since I enlisted the help of a certain robotic companion to think alongside me.

That companion, as you might have guessed, is none other than ChatGPT. It’s become a trusted ally in various aspects of my research. While its prowess in aiding writing tasks has been widely discussed, I stumbled upon its true strength in assisting with my all-to-common R-isuses.

Let me start with a disclaimer: I consider myself fairly decent in R, but I’m perhaps a bit old-fashioned when it comes to embracing new ideas. My focus tends to be on results, sometimes neglecting code cleanliness or efficiency. These days, I find myself using R much less frequently than during my PhD, mainly for data manipulation, while also doing a lot of debugging of student code.

So, what magic does that friendly robot perform for me?

  • First off, ChatGPT is surprisingly adept at coding. Describe an issue, and it often churns out a correct, clear, and concise solution. For example, filtering a dataset based on specific criteria and then calculating averages across factor levels—a task more proficient R users might do from memory, but one I often find myself looking up the functions for.
  • Sure, I could turn to traditional help files and forums, but with ChatGPT, everything I need is in one place. I can formulate queries easily and get immediate responses, bypassing the need to scour forums or craft elaborate forum posts myself.
  • It gets even better when faced with a series of tasks. ChatGPT remembers previous actions within the same conversation, offering suggestions that build upon each other, simplifying code integration throughout a workflow.
  • When you get a suggestion back from ChatGPT that does not do exactly what you want it to do, you can simply describe where or what is not according to expectations, and ChatGPT immediately provides an alternative. I can even paste error codes directly from R, providing the robot with the context it needs to troubleshoot effectively. This way you can truly get into conversation with the robot, slowly but steadily molding and improving the code into your wishes.
  • If you provide the dataset and column names in your queries – or even snippets of your own code – the R-code generated by ChatGPT uses these names immediately when generating code, making it much easier to see what goes where.
  • Many of the tasks I encounter in R are ones I’ve tackled before. But digging through files to find previous solutions takes time, whereas ChatGPT provides guidance swiftly and efficiently.
  • I’ve noticed my students increasingly relying on ChatGPT as well, empowering them to resolve issues independently, reducing dependency on my assistance and time.

Fabulous, no? To me, this works pretty fluently, and allowed me to effectively speed up some daunting R-tasks, especially regarding data wrangling.

I also see very few drawbacks:

  • One concern is plagiarism, though in coding, this seems less of an issue compared to academic writing. Personally, I’ve borrowed code snippets without explicit credit before, though I do ensure proper attribution for packages used, so that has not changed much by switching to the robot.
  • There is a minor drawback that ChatGPT would not include the most recent developments in R. However, for most general tasks, for example regarding data wrangling, it does have access to everything we need.
  • There’s also the minor drawback of potentially limiting oneself to popular R packages, as ChatGPT may not be well-versed in lesser-known ones. But given my pragmatic approach to coding, as long as I achieve the desired outcomes, this limitation isn’t a major concern to me.

In conclusion, integrating ChatGPT into my coding workflow has streamlined my R programming endeavors, particularly in data wrangling. There might of course be a lot more to say about this, but I thought it might be beneficial to many to at least get the conversation going.

Now, I’m curious—have you used ChatGPT for your coding issues, and if so, what has been your experience? Are there drawbacks that I have – perhaps in my naivety – overlooked?

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PhD defense Jan Clavel

Posted on January 29, 2024 by lembrechtsjonas

Another milestone coming up, as The 3D Lab-member Jan Clavel is making the final preparations for his PhD defense on February 5th!

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Tropical forest understory temperatures

Posted on January 23, 2024 by lembrechtsjonas

New paper in Nature Communications: Ismaeel et al. (2024) Patterns of tropical forest understory temperatures.

It’s a thing we do at SoilTemp: create microclimate maps over large extents for all to use. We have done this before for soil temperature at the global scale (with 1 km resolution), and have done so for European forest understory temperatures at a breathtaking 25-m resolution.

And now we do that again! This time, for an area that has long been undersampled, but that is extremely important for global biodiversity and ecosystem functioning: tropical forests. As is the case for European forests, we can expect substantial temperature buffering under tropical forest canopies. How much, that has always been hard to say.

A microclimate sensor in the tropical rainforest of Congo, one of the many that allowed mapping forest understory microclimate across the pantropics. Pic: Matti Barthel

Not anymore, as we are proud to introduce a new paper in Nature Communications: patterns of tropical forest understory temperatures. The big thing: we created maps of tropical forest understory temperatures at a resolution of just a few tens of meters for all tropical forests, in South America, Africa, and Oceania!

As expected, forest understories were on average cooler (1.58°C, to be precise) than macroclimatic temperatures, and they were also buffered more (1.72°C lower diurnal – day/night – variation) than macroclimate.

We found significant spatiotemporal variation in daily temperature offsets in tropical forest understories in South America (left), Africa (middle) and Oceania (right).

Importantly, however, these averages hide significant spatiotemporal variation in that buffering, driven by patterns in macroclimate, vegetation structure, and topography. This variation holds some important information for understanding of these important ecosystems. For example, temperature buffering is stronger in regions with a distinct dry season. Although reduced rainfall is usually associated with higher temperatures, deep-rooting tropical species can for a long time still access water reserves and keep up their ‘airconditioning’ function, effectively creating a larger difference between understory and free-air temperatures.

The former works as long as these droughts remain within reasonable limits, of course, as climate change might very well push these forests over a threshold, making them lose their airconditioning function and rapidly increasing understory microclimatic temperatures. But that’s a story for another day.

An interesting observation for me is the fact that offsets across these tropical forests are usually not so large. On a daily basis, forest understory temperatures were often less than 1°C cooler than macroclimatic temperatures outside the forests. This was especially true in Indonesia, for example, but large swaths of South American and African rainforests are showing surprisingly small offset values as well.

This phenomenon can be attributed partially to the contrast between daily maximums (which tend to be cooler in forests) and minimums (warmer within forests). However, there might be other contributing factors at play. The high humidity prevalent in tropical regions is likely another significant influencer, dampening the intensity of microclimatic differences.

Nevertheless, despite these seemingly small variations, the consistent stability of temperatures throughout the year results in a cumulative effect. Over time, these incremental differences in temperature do add up, significantly impacting the overall energy input within the ecosystem.

The marked disparities and the crucial spatial diversity present across the pantropical regions underscore the significance of the maps we’ve crafted. These visual representations hold immense importance for anyone delving into the intricate workings of tropical forest understories, spanning from deciphering species distributions to comprehending litter decomposition dynamics. The relevance of such high-resolution microclimate products has already been elegantly demonstrated in understanding the distribution patterns of European forest understory plants. It’s only a matter of time before their paramount significance in tropical ecosystems is similarly illuminated.

The maps are freely available here! For now, at a 300 m-resolution, while we figure out a good way to share the 30-m resolution files open access. The latter are currently available upon request.

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Following plants in the crags: MIREN rock face survey

Posted on January 23, 2024 by lembrechtsjonas

Join the new MIREN rock face survey

With long-term monitoring campaigns focussing on mountain roads and trails, and divergences to railroads and rivers, we are happy to announce that MIREN wants to tackle another – perhaps more unusual – linear disturbance in a new, global survey: rock climbing!

Rock climbing is in many respects similar to mountain roads and trails, with routes serving as linear disturbances and dispersal corridors across elevation gradients, with significant effects on the local vegetation. Nevertheless, cliff vegetation, and the influence of rock climbing on it, is pretty unique: rock faces often cover strong microclimatic gradients, resulting in a unique vegetation of species at either the warm or cool edge of their distribution. 
This unique vegetation often consists of (locally) rare species with very patchy distribution, and the passage of climbing routes over their habitat might thus have substantial impact. To map that impact, and its interaction with the effects of microclimate and climate change on species redistributions, we launch this new MIREN rock face survey, and you are welcome to join!

Wilder Kaiser, Austria – by Sarane Coen

Through the MIREN rock face survey we want to learn about global similarities and interregional differences between rock faces. To do this, we will set up permanent vegetation monitoring plots on and adjacent to existing rock climbing routes to monitor local plant species richness and community composition. Wherever possible, we will also measure rock face microclimate in comparison with the microclimate in the environment.

If you are interested in monitoring the vegetation on and along one or a few rock climbing routes in the upcoming summer, express your interest via this form. We are currently developing a standardized protocol that can be applied with minimal effort on any rock climbing route across the globe. If you want to be involved in finalizing that methodology, you can inform us through the same form!

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Take it to the higher order

Posted on December 20, 2023 by lembrechtsjonas

New paper: Reynaert et al. (2023) Direct and higher-order interactions in plant communities under increasing weather persistence. Oikos

Let’s knock down an open door: interactions between species are key determinants of their performance. How well an individual is growing, or even if it is growing somewhere at all, hinges significantly on the surrounding organisms. They may vie for vital resources like air or sunlight, or conversely, aid one another by providing shade against the scorching rays of summer.

It should therefore not come as a surprise that there are thousands of papers on biotic interactions and their role in ecosystem functioning. However, while we have learned a lot about this over the last decades, there is still a surprisingly big hole in that literature. And that gaping hole is the result of inevitable oversimplification. That is, we have way too often – for the sake of simply being able to grasp ecology’s complexity – limited ourselves to direct interactions: A outcompetes B, C facilitates D. Yet, natural interactions seldom adhere to such straightforward narratives. Within an ecosystem, every individual is engaged—albeit to varying degrees—in an intricate dance of relationships, influencing and being influenced in turn.

Conceptual framework of higher order interactions (HOI)

Consider, for example, a scenario where a plant species, Plant A, attracts a specific herbivorous insect. Ordinarily, these herbivores might feed on Plant A, reducing its fitness. However, another plant species, Plant B, grows nearby and emits a molecule that attracts predators of the insects. As such, Plant B is indirectly helped by Plant A, as Insect B comes to eat Insect A. Or any other orientation of interactions.

These intricate and multifaceted relationships are labeled higher-order interactions (HOIs), a domain that has consumed a considerable amount of my time to get a hold of them in a convincing, statistically sound way. In a first attempt, we used real-world data from our mountain vegetation monitoring initiative (MIREN, http://www.mountaininvasions.org). However, that turned out very tricky, as there were simply way too many things changing at the same time to isolate something as complex as these higher-order interactions. After many an attempt, that idea was moved to the ‘death paper pile’.

In a new attempt, however, expertly led by UAntwerp-colleague Simon Reynaert and now published in Oikos, we took a different turn. We made use of a fascinating controlled experiment performed at our university campus, where precipitation was manipulated for 256 so-called mesocosms (in essence: pots with plants). What made this experiment especially useful was the uniform conditions experienced by all plants and pots, with precipitation being the sole variable—specifically, not even the AMOUNT, but the timing of rainfall.


An explanatory framework illustrating how changes in rainfall patterns, specifically increased variability, can impact the moisture levels in the soil. The grey bars represent daily rainfall amounts. Across all panels, the total precipitation matches historical weather averages (a).
When weather becomes more intense (b), it leads to less frequent but heavier rainfall episodes interspersed with extended periods of dryness, particularly during higher temperatures. This scenario could worsen the effects of droughts or floods.
On the other hand, when weather patterns become more persistent (c), it results in prolonged stretches of both dryness and wetness. This persistence might intensify the fluctuations in soil moisture content, potentially causing more extreme variations.
In both scenarios, there’s an increased likelihood of surpassing critical soil moisture levels. This excess—either too much or too little moisture—can detrimentally impact how ecosystems function.

By holding all these conditions constant, except for the precipitation regime, we could finally test how extreme events affect HOIs. And yes, lo and behold, our results indicate that species interactions (including HOIs) are an important determinant of plant performance under this increasing weather persistence. Moreover, the importance of these interactions changed substantially along the gradient of weather persistence, with  HOIs showing a shift towards stronger facilitation (or weaker competition) as drought persistence increased.

What was thus interesting, was that HOIs – at least partially – counteracted the effects of direct competition by neighbours, a conclusion that has been made in other studies as well. This could mean good news for biodiversity, as complex interactions could as such help support a higher diversity. However, a cautionary note is necessary: this counteractive effect was truly only partial, and could not overcome the increased drought intensity experienced in the most extreme precipitation regimes. In the end, species succumbed on their own to the extreme drought, and no positive higher-order interaction effect could prevent that from happening.

The complex interactions between individuals in such a meadow might help a bit in reducing the impact of extreme drought events.

There was a lot more to the results, of course, with perhaps the main conclusion that, indeed, HOIs are bloody complicated to analyze statistically. Even in this extremely controlled setting, picking up on these trends remained difficult. Thus, while ample papers have shown the theoretical importance of HOIs, we are still a long way from routineously integrating these complexities in our ecological analyses.

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