Alex Milcu

@alex-ecology.bsky.social

Ecologist studying the impact of global changes on ecosystems. Views are my own.

Lysimeter studies within enclosed biospheres are terrific approach towards measuring how much Carbon Dioxide Removal is possible from technologies like Enhanced Rock Weathering and Biochar. You can only do so many treatments, but I hope they can test biochar + EWR, preferably biochar from manure.

Alex Milcu@alex-ecology.bsky.social · 4mo ago

One last look at the wheat inside the #Montpellier #Ecotron's #Macrocosms before the harvest. Soon we'll know what are the impacts of #biochar from algae and #basalt applications for enhanced rock weathering (#ERW) on #yield and #GHG emissions. Many thanks to the team💪@cnrsoccitanieest.bsky.social

📢 Thrilled to share that our new paper is out! 🔗 lnkd.in/eiYnv6R7 🪱 Exploring earthworms + phosphorus using the ¹⁸O‑phosphate method 👏 Kudos to Chiara Pistocchi & Andreas Burr for leading the work. 🏆 Special congratulations to Andreas Burr, whose MSc contribution earned him a Best Thesis award.

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🎉Good to see that ecological research can still make it to the pages of @nature.com 🎉 Congratulations to Ludovic Henneron & @paulkardol.bsky.social and the whole team! 🌳Forests dominated by fast-growing, "resource-acquisitive" tree species turn out to be hotspots of soil activity rdcu.be/fhmF2

Tree community resource economics control soil food web multifunctionality

Nature - Plant community properties affect ecosystem functioning via energy fluxes in food webs, with the leaf and root resource economics of dominant tree species controlling soil food web...

rdcu.be

Curious about how we can boost your climate change-research? 💪 Never heard of Research Infrastructures and want to know more? 🌎 Want to know how to apply through our open call? (DL October 2025) 🗓️ Then don't miss our webinar on exactly this, friday the 13th! Register here: lnkd.in/e86K89bD

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For the sake of being able to do our jobs as researchers, can we please stop adding more admin burden on researchers! A ‪@france.theconversation.com piece about the need for time to actually....think. Des enseignants-chercheurs à la recherche du temps perdu: theconversation.com/des-enseigna...

Des enseignants-chercheurs à la recherche du temps perdu : regard sur le malaise universitaire

Comment les enseignants-chercheurs vivent-ils leur métier aujourd’hui ? Extraits d’une grande enquête sur le malaise universitaire.

theconversation.com

Intriguing debate between classical predictive and structural causal modelling. Always thought the later is a wee bit overrated. Without strong a priori hypotheses, both can lead us straight into the land of spurious correlations BS.

Francisco Rodriguez-Sanchez@frodsan.bsky.social · 2y ago

New #statistics #ecopubs @esajournals.bsky.social: Predictive models are indeed useful for causal inference doi.org/10.1002/ecy.... Sounds like a response to 'Predictive models aren't for causal inference' doi.org/10.1111/ele....

The subject of investigating causation in ecology has been widely discussed in recent years, especially by advocates of a structural causal model (SCM) approach. Some of these advocates have criticized the use of predictive models and model selection for drawing inferences about causation. We argue that the comparison of model-based predictions with observations is a key step in hypothetico-deductive (H-D) science and remains a valid approach for assessing causation. We draw a distinction between two approaches to inference based on predictive modeling. The first approach is not guided by causal hypotheses and focuses on the relationship between a (typically) single response variable and a potentially large number of covariates. We agree that this approach does not yield useful inferences about causation and is primarily useful for hypothesis generation. The second approach follows a H-D framework and is guided by specific hypotheses about causal relationships. We believe that this has been, and continues to be, a useful approach to causal inference. Here, we first define different kinds of causation, arguing that a “probability-raisers-of-processes” definition is especially appropriate for many ecological systems. We outline different scientific “designs” for generating the observations used to investigate causation. We briefly outline some relevant components of the SCM and H-D approaches to investigating causation, emphasizing a H-D approach that focuses on modeling causal effects on vital rate (e.g., rates of survival, recruitment, local extinction, colonization) parameters underlying system dynamics. We consider criticisms of predictive modeling leveled by some SCM proponents and provide two example analyses of ecological systems that use predictive modeling and avoid these criticisms. We conclude that predictive models have been, and can continue to be, useful for providing inferences about causation.