More origami sea creatures than usual for a gallery on a random Tuesday. @neilbanas.bsky.social making life odd and beautiful during ocean sciences this year.

Sian Henley and I have just had a NERC grant funded, looking at variation in Souther Ocean diatom traits under fluctuating light regimes. If you are a fearless experimentalist interested in working with us as a postdoc on this 3 year project, come chat with me at Ocean Sciences in Glasgow next week!

7. Model-averaging, by Peter Dillingham: Explore how model-averaging enhances the analysis of multi-driver marine experiments with nested and crossed designs, using Arthrocardia under future ocean conditions as a case study:

nzoac.nz/s/Model-aver....

6. Multiple driver response surfaces, by @mridulkthomas.bsky.social and @sineadcollins.bsky.social Learn 3 ways to analyse data from a temperature x nutrients response surface experiment, and use the output to understand where in the oceans the species does well. nzoac.nz/s/Response_s...

5. Single-driver response curves, by @mridulkthomas.bsky.social and @sineadcollins.bsky.social Learn how to fit a temperature response curve to a simulated phytoplankton growth rate experiment. Then use the curve to learn about how the species performs across space & time. nzoac.nz/s/Single_dri...

4. Time-response variables, by Sam Dupont, Peter Dillingham & christinamcgraw.bsky.social Dive into modelling time-response data with R, using a sea urchin experiment to explore how environmental factors shape growth over time: nzoac.nz/s/Time_respo...

3. Blocking in time, by Steeve Comeau and @chrisecornwall.bsky.social Learn how to estimate the effects of two drivers on a response when your experiment is ‘blocked’ i.e. experimental units grouped in time, often because it’s not feasible to do all at once. nzoac.nz/s/Blocking_i...

2. Analysing scenarios, by Steeve Comeau and @chrisecornwall.bsky.social Learn how to estimate the effects of two drivers on a response when your experimental treatments are designed to reflect future climate scenarios. nzoac.nz/s/Scenario_a...

1. Statistical power, by Peter Dillingham: Explore how to use R's built-in functions and simulations to assess the statistical power of a study on sea urchin growth under changing ocean conditions. nzoac.nz/s/Power-vign...

Ever wondered how to analyse your experimental data? 🌊🧪🌍 We’ve developed a series of tutorials in R to guide you through analysing & visualising data from single & multiple driver experiments. Beginners and experts, we've got something for all of you. Please share! meddle-scor149.org/vignettes/

This is the calibre of applied science I aspire to in 2025. doi.org/10.1073/pnas...

Books and chocolates of the season.

Reflections on 2024

...because this is exactly what ice screws and drills are for, right? Our adventures in getting #diatomsofscience into and out of the ice alive continue @umeaunimarine.bsky.social

Now we wait to see if our #diatomsofscience survived their adventures in our lab grown sea ice @umeaunimarine.bsky.social .

Apparently ice diatoms get into the ice because scientists add them. And of course we take portraits of our #diatomsofscience before they star in experiments.

Can we freeze our adventurous ice diatoms into lab-grown sea ice and then recover viable cells? Who knows? Follow the icy adventures of Porosira glacialis in our new experimental setup! #diatomsofscience

There was a pop up MOMA (Museum of MicroAlgae) at the Glasgow Botanic Gardens. There were phytoplankton under microscopes (including some from the pond outside), bioluminescent cultures, origami and (my contribution) chocolate diatoms. It turns out people are really interested in phytoplankton.