🌍 Why it matters:

With more droughts on the horizon due to climate change, forest management needs to be species- & site-specific.

Want the full details? Read the article here:
🔗 doi.org/10.1016/j.fe...

🔚 5/5

💡 The takeaway:

Competition reduction isn't a one-size-fits-all solution. Beech sees a trade-off: 🌿 more growth, but at very low competition, more drought stress.

For oak? Thinning didn't make much difference. 🌰

🧵4/5

🔬 How was it studied?

72 point dendrometers tracked daily water deficit (drought stress proxy) & growth along a competition gradient. 🌳📊

This gave a detailed look at how trees respond to competition & climate conditions.

🧵3/5

🌱 Key findings:

• Less competition boosted beech growth! 📈
• But... it sometimes made beech more drought-stressed.
• Oak? Unbothered—competition had no clear effect.
• Climate factors (rain ☔ & vapor pressure deficit 💨) influenced both species.

🧵2/5

You'll be hosted by the sGlobe lab (@kvanmeerbeek.bsky.social, www.sglobelab.com) and the Earthmapps lab (@steflhermitte.bsky.social, www.earthmapps.io) at KU Leuven.

Interested? Get in touch! 🔗 #Postdoc #DeepLearning #Ecology #MarieCurie

@kuleuvenuniversity.bsky.social @kuleuvenplantinst.bsky.social #ClimateChange #Drylands #MachineLearning #NatureClimateChange

Take-home message: ML-based EWS offer a scalable, data-driven way to predict and mitigate abrupt shifts in dryland ecosystems—essential in the face of climate change and increasing pressure on these vital regions (6/6)

The study focused on the Sudano–Sahelian drylands, a region highly vulnerable to climate change. Predictions for 2025 highlight a southern belt with high probabilities of abrupt ecosystem shifts, linked to long-term rainfall trends🌍🌱 (5/6)

Machine learning (ML) to the rescue! We used ML to analyze complex patterns in satellite data. By integrating resilience metrics (e.g., autocorrelation) with vegetation, rainfall, and other environmental factors, we achieved 75.1% accuracy and 76.6% precision (4/6)

Current methods for EWS rely on metrics like temporal autocorrelation to detect loss of resilience. However, applying these to large-scale satellite data is tricky due to noise & short time series. So, what’s the solution? 🤔 (3/6)

The problem: Drylands face degradation that threatens their ability to act as carbon sinks & provide resources. Abrupt shifts in ecosystem functioning—like desertification—can occur when thresholds are crossed. Early warning systems (EWS) are urgently needed (2/6)