Some ecologists fear their field is losing touch with nature

Working in geology, I find the opposite problem. Field work is so highly valued that we're at a place where we have so much data and not enough people really working and analyzing it. My general impression is that in some subfields work that's done exclusively using preexisting data is kind of looked down on. In my opinion tons and tons of money is essentially wasted collecting new data - and then it's poorly catalogued and hard to access. You typically have to email some author and hope they send you the data. People are fiercely protective of their data b/c it took a lot of effort to collect and they want credit and to be in on any derivative work (and not just a reference at the bottom of a paper)

I would say the main workflow is collect some new data nobody has collect before, look at it and see if it shows anything interesting, make up some interesting publishable interpretation.

It feels like it'd be smarter to start with working with existing data and publish that way. If you hit on some specific missing piece, go collect that data, and work from there. But the incentive structures aren't aligned with this

The AI angle is really shoehorned in, but irrelevant to the larger problem. Sure, it allows you to annotate more data. Obviously it's more fun to go do field work than count pollen grains under a microscope. If anything AI make it easier to do more fieldwork and collect even more data b/c now you can in-theory crunch it faster

I did marine biology field work almost 5 decades ago as a lowly junior lab tech. Work always has downsides, for me it was not really the Scots winter, cold feed, chapped hands, the land-rover having to reverse up steep icy roads to get back from the harbourside: it was washing the glassware and dealing with sodium hydroxide weighing (it absorbs moisture from the air so its a fools game). But, field work also brought amazing experiences, I visited the seaside 70+ times over a year, and got an insight into what a time series really means when you cover the tidal and weather and seasonal cycles.

It's also always error-prone. Nothing in the field is perfect. Reality is a bad approximation for your model at times, if you take a model centric view.

I would be immensely skeptical that field work is ever going away. There may be aspects of truth in this around cost of travel, risk, seniority.

I am involved in both botanical field work and ML, unfortunately most of the data that I have gathered and analyzed in the last 12 years indicates how quickly many ecosystems are degrading. Often I wonder why I do the analysis, simply taking a photograph in the same spot ~7 years apart allows any average person to see that things are not on a positive trajectory.

Attempting to convince people to change course and focus on restoration has mostly been a losing battle, with much larger forces behind the main detriments that make local changes feel inadequate.

As a kid I had problems with Foundation (Asimov) premise that loss of scientific knowledge can be the trigger not just the result of civilizational collapse - not anymore.

Machine learning and data science are not new things in science. It's great that we have the ability to share and work with existing data sets, collect data remotely with sensors, and build software to create models, but we'll always need people to go out and collect updated data, place censors and verify that what models predict is actually happening.

> Scientists who run long-term ecological studies, in particular, report that they struggle to find funding.

It's cheaper and easier to do stuff sitting at a desk. In theory that's a good thing if it means more work gets done, but field work has to happen too. For many people it's the best part of the job, for others it's a pain that has to be suffered through to get the data they need. Hopefully there's room (and funding) for both kinds of people to do the work they want.

I have a PhD in Ecology and a BS in CS. I find the bifurcation portrayed here exaggerated. The best modern ecologists merge rigorous fieldwork with advanced modeling; we need to harness vast, underutilized datasets, not just generate new ones.

The 'computer scientist' quote illustrates a frustrating trend: tech-centric 'drive-bys' that lack the ecological context required for good science. On the flip side, the 'old guard' who ignore modern data assimilation are leaving massive potential on the table. The field is rightfully shifting from site-specific anecdotes to foundational, broad-scale work, but we need both skillsets to do it justice.

Why study the territory, when you have a map that's been conveniently generated to obfuscate any pesky discovery-indicating outliers?

for a while now the work in phd/academia rarely involved 'field work'.

90% of the time it is spend analyzing data or writing up proposals/grants/papers. i don't think AI was the turning point.

I am overall pro-AI, but using it to forego uncharted territory is an incredible waste. We always need new and better data.

Big Mother?

All right... science for hikikomoris...

I always felt like one of the primary motivations to pursue science was being able to bail out of the office for the entire summer for "field work"...

So all of our research is basically going to be theoretical and restricted to the field research done so far. Wonderful.