Exploration geophysics paid for me to travel to and across more than half he countries on the planet, calibrating old maps, datums, projections against the 'new' WGS84, scaling peaks to stage base stations, getting familiar with the ins and outs of tides, magnetic fields, gravity, radiometric backgrounds, finding a good band in Mali ...

Loved it.

Of course, in-person exchanges still happen, but there's something of a default to do most things remotely because it's more efficient (and honestly, easier for all parties involved). The result is that you don't get to see cool or unusual machines/setups that often, and some flair of doing research is lost.

I can imagine that that's especially painful for new ecologists, because fieldwork is also a way to experience things that you otherwise wouldn't. Hopefully, we can bring some of it back with edge devices and models.

It’s been sad seeing journalism in the online era, where so much (not all!) content is produced without really visiting or researching things. Often it’s based only on statements / tweets, sometimes more seeping based on phone calls, sometimes reading a book on the topic, but rarely do journalists seem to show up anywhere.

When reading something like Didion’s piece on the LA highway central command, it shows how irreplaceable lived experience is.

I’m not exactly sure if we share a similar experience, but living on a trail in the Santa Cruz mountains affords me the opportunity to hike the same trails every weekend, year round (or even daily).

I’m not taking measurements, but it’s incredible to witness the effect of seasons on familiar territory just a few miles outside town. The weather changes, the wildlife changes and the air changes (moist to dry and back).

It’s an incredibly special experience to revisit the same place time and time again and witness the impact of … time. I hope you found something else to replace your familiar seaside.

The article should perhaps introspect a bit more instead of setting up a false dichotomy between "rainforest field work or computers".

After all, if you want a different answer, you can easily tweak a model.

Much harder to force a bunch of people in the field to not see what they see. Certainly not impossible, however!

Why be sceptical? The model will answer consistently, inconvenient truths won't get in the way.

Perhaps creating secure private clouds for scientists, away from AI scrapers etc that scientists can access, with associated counter-surveillance, is the way forward.

I'm a GIS guy working on cloud native tech, but with a focus on privacy. I have a local-first Mac native product nearing beta. I'm thinking a lot about what data sharing options can be at the moment.

I guess there may be a broader and less public-oriented set of funders in geology- and maybe there aren’t as many standardized data types as there are in the world of biology.

It's a bit like the distinction between being able to swim around, like a fish, versus just having offspring who end up a bit closer to some goal, like a coral. Both end up in a place where they can live, but only one got there by being the lucky survivor against a background of alternatives that were born elsewhere.

It's made clear in the title of one of their papers: "Plasticity and not adaptation is the primary source of temperature-mediated variation in flowering phenology in North America" (https://pubmed.ncbi.nlm.nih.gov/38212525/)

There's also a strong belief in "statistical magic." Faced with a bad or insufficient data set, someone will say: "Let's give the data to <statistician> and have them work their magic on it."

That the results actually have to be influenced by the data in some way is something that has to be explained to people. In all of my years as a scientist, I've learned that there's still no substitute for good measurements. Good data can be cheaper than analysis of bad data.

I will add that funding can complicate things a bit, funding sources often get wowed by more "advanced" methods, while the underlying science might be less than stellar. There are important questions that can be answered by small, elegant field studies, and there are questions that require larger datasets and more computation. When we start putting the methodological cart before the scientific horse, that's where we run into problems.

For example often if you have a blackfly infestation, the first sign is often a steady stream of ants - they are feeding of the honeydew created by the blackfly, they protect the blackfly from predators like ladybird larvae, and they will even transfer the blackfly to new plants.

Once I knew this I found the best way to tackle blackfly was not to go after the blackfly, but distract the ants - a bit of jam works a treat.

Though you could then argue the ant's have then moved on, from farming, to a protection racket.

Instead of counting bears in the forest by hand, you set up a hundred trail cameras and then use computers to count bears 24/7 across an entire area. This is field research, on a scale that was previously impossible.