Scientific Paper Published: Metabarcoding identifies macroalgal composition as a driver of benthic invertebrate assemblages in restored habitats

We are excited to share the publication of a groundbreaking scientific paper co-authored by a passionate team of researchers. This work is particularly significant as it represents the culmination of the doctoral journey for the first author, Cristina Galobart, an inspiring researcher known for her deep commitment to science and ecological preservation and a good friend of mine.

In the field of ecological restoration, grasping the complex relationships between marine organisms is essential. This research looks closely at the interactions within large brown macroalgal forests and the diverse invertebrate communities that call them home.

This paper encapsulates a ten-year restoration project focused on recovering a macroalgal forest dominated by Gongolaria barbata. Through the use of advanced metabarcoding techniques, the research team examined invertebrate communities at the restored site and compared them with nearby areas.

The findings shed light on the ways these communities adapt to restoration. For example, did the invertebrate species found mirror those typically seen in healthy ecosystems? What specific roles did key species play in the restoration process? The insights offered by this research provide a glimpse into the intricate web of life that thrives beneath the ocean's surface.

If you’re intrigued by marine biology, ecological restoration, or the wonders of our oceans, stay updated with the latest research in this field. Exciting discoveries are on the horizon, and who knows what the next study will reveal.

Let's commit to keeping our oceans vibrant and thriving through dedicated research and restoration efforts!

figure 5 of the Manuscript showing Characteristic MOTUs within each assemblage type.

Link to paper:

https://www.nature.com/articles/s41598-025-93327-4

The knowledge doesn't come from the answers. Is the product of discovering new questions.

This year, I started a postgraduate program at UPC in Barcelona—Artificial Intelligence with Deep Learning. Wow, it’s been amazing. I really feel like I’m pushing myself. Doing a course where my background is mostly self-taught is definitely an interesting challenge.

Let me tell you a quick story about something that happened the other day.

We were doing a lab practice on APIs for language models. Just to add some context: when you communicate with a server, you send a request, and after processing it, the server sends back a response. Well... how you communicate with the server is a big deal in computer science and cybersecurity, but that wasn’t the main focus of the practice.

Now, even though I’ve worked in app development with JavaScript and learned a lot on my own, there were still things I wasn’t entirely clear on. I had this feeling that there was a gap in my knowledge, but I couldn’t figure out how to search for the missing pieces. I didn’t know what I didn’t know.

Back in class, I started putting the pieces together. Afterward, I went to talk to the professors, and that’s when the real process began. At first, my questions were messy—disorganized thoughts, random terms thrown together. But here’s where human intelligence truly outshines AI: when I had tried asking ChatGPT or searching on Google, I would get answers to questions I wasn’t even formulating. But my professors, instead of just giving me answers, guided me toward the right questions.

And once I had the right question, everything clicked. The answer was clear, and even better—new questions started to arise. But what really amazed me was that my learning didn’t come from the answers. It came from finding the right questions.

Wanna know what I learned, maybe in another post, today is about how not what!!!!