Just skimmed through it, but this looks like really fun and interesting read! With pictures of the game team and all!

Hi all!

I've been inactive here for a while (all that's going on in the world kind of saturated my bandwidth), so maybe the algorithm won't favor me.

But! I'm curious if anyone of you have been using/thinking about using explainable AI method (xAI) for identifying complex ecological patterns.

My exact thought when I read this :)

Hi all!

I've been inactive here for a while (all that's going on in the world kind of saturated my bandwidth), so maybe the algorithm won't favor me.

But! I'm curious if anyone of you have been using/thinking about using explainable AI method (xAI) for identifying complex ecological patterns.

Environmental cues indicating abundant resources reduce the difference between inbred and non-inbred beetle lines in finding those resources, and thus the fitness gap. We argue that environmental predictability could be masking the effects of otherwise deleterious alleles via behavioral plasticity.

The Stein Paradox - Numberphile YouTube video by Numberphile

I was corresponding with a scientist last week who was skeptical that a biased estimate could be better than an unbiased one. I cited the usual reasons. And here is Numberphile right on time with a new episode about Stein's paradox: www.youtube.com/watch?v=FUQw...

Sorry for not citing you guys!

By the way, if you have the opportunity to include more TPC functions, the LRF (described here: doi.org/10.1093/aesa... , not orig. ref) and flexTPC (doi.org/10.1101/2024...) have very interpretable parameters and seemingly good properties. Worth a look.

Hey. I honestly missed that, would have been relevant when developing our own stuff (noted for future). My point I think similar to yours. We are not that many doing this as of now (i.e., not that many refs, but I'm clearly not the guy to ask ;). I actually meant when I said the paper was cool!

Just came across this, cool paper! Thought I should mention we've also been fitting TPCs using Bayesian methods for years (e.g. doi.org/10.1111/ele....). We've also developed some pipelines for TPC inference from less typical data: doi.org/10.1073/pnas...

For future reference ;)

Repeatability of evolution and genomic predictions of temperature adaptation in seed beetles - Nature Ecology & Evolution The authors compare genomic and phenotypic changes between genetic backgrounds of seed beetles evolved at hot or cold temperatures. Despite phenotypic changes being more rapid and predictable at hot t...

Finally out: Predicting adaptation to climate warming www.nature.com/articles/s41...

We find that there are many genomic routes to heat-adaptation, but this can also make genomic data of limited value for prediction. A tour de force by @denovorego.bsky.social , with @stelkens.bsky.social.

How do fish evolve to tolerate higher temperatures, and are there trade-offs? We explore these questions in our new paper
@natclimate.nature.com led by Anna Andreassen
@annahandreassen.bsky.social

www.nature.com/articles/s41...
🧪🐟🦑

Life-history adaptation under climate warming magnifies the agricultural footprint of a cosmopolitan insect pest Nature Communications - Current statistical projections of pest impact under climate change neglect the role of rapid genetic adaptation. Here the authors show that evolutionary responses in pest...

Models of pest impact predict that climate warming will alter growth rates and distributions of insect pests.
🐞🌱🐞🌱🐞🌱🐞🌱🐞🌱🐞🌱🐞🌱
How do trait-specific evolutionary responses affect predictions?

Have a look at our new paper rdcu.be/d6G2u in @naturecomms.bsky.social to find out. Short summary below.

Quantifying temperature’s effect on #diapause #termination requires fitting #TPCs to binary biological data. In our new paper, out now in #PNAS, we show how to do this, revealing the sequential nature of diapause termination and post-diapause development.

doi.org/10.1073/pnas...

The Global Ecology starter pack is now curated! ✨ and some slots are open 😊

Studying global biodiversity, ecosystem functioning, or conservation across terrestrial and marine realms ?

📚 Published in these fields? 👉 You are welcome to join ! just reply to be added.

go.bsky.app/V6tN4cv
🧪🌐🌍🦤🦑

Nothing published on the topic yet but working on it! Would like to be added if I qualify :)

Johnsen et al.’s work is a great case study in how mechanistic models + Occam’s razor can reveal underlying truths. It shows that even when data are sparse, combining constraints with inference can produce valuable insights.

Bell Labs Invents Lensless Camera | MIT Technology Review A new class of imaging device with no lens and just a single light sensitive sensor could revolutionise optical, infrared and millimetre wave imaging

In signal processing, methods to reconstruct under-sampled signals by promoting sparsity has been used for quite a while now. And this has led to some very impressive decompression methods, where signal is seemingly squeezed partly out of thin air (check this: web.archive.org/web/20160120...)

I think biologists are often sceptical towards this approach. The mantra “correlation does not imply causation” is deeply rooted in many of us. Perhaps this is why this paper by Johnsen et al. has remained under-cited for decades. But I think there is great power in approaches like this!

As a result, the curves from the previous figure emerge. The three distinct phases are just a consequence of picking the sparsest solution to the problem (see the image). This (Occam’s razor) combined with the constraints on the reaction norm—the mechanistic insight—got close to the truth!

Well, they don’t estimate the curves directly. They just observe insect emergence in a variable thermal environment, and iteratively tweak curve parameters in search of the best explanatory model, under some constrains (the main one being how the shape of the curve can vary, see figure).

Rewind to 1997. Three researchers publish a paper on the same topic, with the figure below (doi.org/10.2307/2404...). Very reminiscent of our results. However, this paper has remained in relative obscurity, only racking up 15 citations to date, despite being on a quite popular topic. Why?

Quick background: we did a pretty laborious experiment to empirically determine thermal reaction norms for diapause termination in a butterfly. The result was the two curves below. Also, we found some signs of a third reaction norm for diapause induction that might look something like the red line.

Combining some mechanistic knowledge and Occam's razor appears to me to be a very powerful tool for getting closer to the truth! Let me show an example that I believe is related to this, which I found while writing my last published paper. It's on the underlying temperature-dependence of diapause 🤓❄️

Thanks!

Good stuff. Late to the party, but would like to be added if still possible!

Cool study! I wonder if it is possible that (much of) the interesting density-dependent effects are consequences of a statistical artifact. An extreme case serves as an example: if some place is unoccupied, individuals later observed must be classified as immigrants. Can anyone enlighten me?

Here is a starter pack for scientists and others interested in trait-based ecology and evolution. Still trying to find everyone here. Please let me know if you would like to be added to the list! go.bsky.app/PThMXeX 🧪🌎🌾

Soil temperature for hottest day in Jamshedpur, India as a function of depth plotted at the surface, 5 cm (about 2 inches), 10 cm (~4 inches), 20 cm (~8 inches), 30 cm (~12 inches), and 40 cm (~16 inches) below the surface. The day to night variation at the surface goes from 27°C at ~7:30 am (just at sunrise) to 57°C at ~2 pm, for a difference of 30°C between high and low. The difference between high and low temperature decreases with depth, and 5 cm below the surface it's already down to a difference of 21°C. By 40 cm below the surface, the difference between high and low temperature is a less than a degree. It's about 36.5°C all day at that depth.

Soil temperature for coldest day in Jamshedpur, India as a function of depth plotted at the surface, 5 cm (about 2 inches), 10 cm (~4 inches), 20 cm (~8 inches), 30 cm (~12 inches), and 40 cm (~16 inches) below the surface. The day to night variation at the surface goes from 13°C at ~9 am (just at sunrise) to 37°C at ~3 pm, for a difference of 24°C between high and low. The difference between high and low temperature decreases with depth, and 5 cm below the surface it's already down to a difference of 16°C. By 40 cm below the surface, the difference between high and low temperature is a less than a degree. It's about 23°C all day at that depth.

Changing the solid Earth's temperature is *really* hard.

Day-to-night temperature variations, what we're used to, stop being relevant at about half a meter depth--this is why animals burrow.

(Link for figs: geothermal-energy-journal.springeropen.com/articles/10.... )

Awesome, thank you so much!

A starter pack of people working on thermal biology or metabolic theory. Presently a very short list, but I know y'all are out there - please tell me if you'd like to be added!

(Or if you'd like to be taken off)

go.bsky.app/nHLZbD